Proceedings of the 27th International Symposium on Advancement of Construction Management and Real Estate (Lecture Notes in Operations Research) [1st ed. 2023] 9789819936250, 9789819936267, 981993625X

Table of contents :
Contents
An Integrated Visualization Framework to Enhance Human–Robot Collaboration in Facility Management
1 Introduction
2 Related Work
2.1 Robots in Facility Management
2.2 Visualization Technology in Facility Management
3 Visualization Framework of HRC in Facility Management
3.1 Integrated HRC Framework Overview
3.2 Remote Monitoring Module (RMM)
3.3 Onsite Collaboration Module (OCM)
4 Experimentation and Result
4.1 RMM Demonstration
4.2 OCM Demonstration
4.3 Discussion
5 Conclusion
References
Developing a Robotic System for Construction Truck Crane
1 Introduction
2 Literature Review
3 System Architecture
4 Implementation of System
4.1 Kinematics Analysis of the Truck Crane
4.2 Hardware Layer
4.3 Control Layer
4.4 Server Layer
4.5 Application Layer
5 Experiment and Discussion
6 Conclusion
References
COVID-19 Impact on the Implicit Value of Open Space in High Density Cities: Evidence from the Hong Kong Housing Market
1 Introduction
2 Research Design
2.1 Development of Hypotheses
2.2 Model Specifications
3 Data Sources
4 Results
4.1 Baseline Results
4.2 DID Model Results
4.3 Heterogeneous Effects
4.4 Robustness Checks
5 Conclusion and Discussion
References
Digital Twin Technology for Improving Safety Management in Construction
1 Introduction
2 Overview of Digital Twin Technology
2.1 Concepts, History, Functions and General Applications
2.2 Potential Application of Digital Twin Technology to Improve Safety Management
2.3 Challenges of Applying Digital Twin Technologies in Construction Safety Management
3 Conceptual Digital Twin Model for Human-Machine-Environment Safety Monitoring
3.1 Five-Dimensional Digital Twin Model
3.2 Model Operation Mechanism
4 Technical Elements
4.1 Cyber-Physical Fusion Modeling
4.2 Data Storage and Management
4.3 Model Evolution and Improvement
4.4 Deep Learning
4.5 Unusual State Diagnosis and Trend Prediction
4.6 Data Mining
4.7 Digital Twin for the Collaboration of Human-Machine-Environment
5 Design and Exploration of Application Scenarios
5.1 Abnormal State and Condition Warning and Predictive Maintenance
5.2 Unsafe Behavior Detection
5.3 Case Study
6 Conclusion
References
Interview Methods in Construction and Demolition Research: Based on Case Study and Recommended Best Practices
1 Introduction
2 Literature Review
2.1 The Introduction of Interview Methods
2.2 Current Implementation of Interview
2.3 The Factors Influencing the Interview Implementation
3 The Decision Framework
3.1 The Introduction of Decision Framework
3.2 An Illustrative Example in C&D Waste Management Research
4 Discussion
4.1 Clarification of the Relationship Among Participants
4.2 Time Management
4.3 Selection of the Most Appropriate Interview Method
5 Conclusions
References
Application of High-Rise Building Fire Rescue Based on BIM and GIS
1 Introduction
2 Difficulties During Fire Rescue of High-Rise Building
3 Application Analysis of BIM and GIS
3.1 Application of GIS in Fire Protection Field
3.2 Research on BIM in the Field of Fire Protection
3.3 Comparison of BIM and GIS Data Information
3.4 Relative Research on the Combination of BIM and GIS in China
4 Fire Command Flow Chart of BIM-GIS Applied in Building
5 Conclusion
References
Analysis of Flow and Stock of Sand and Gravel in Shenzhen Buildings and Associated Environmental Impact
1 Introduction
2 Calculation Method and Data Inventory
2.1 Flow and Stock Accounting Model
2.2 Flow and Stock Calculation Method
2.3 Embodied Carbon Emission Calculation
2.4 Data Inventory
3 Results and Discussion
3.1 Sand and Gravel Flow Analysis
3.2 Sand and Gravel Stock Analysis
3.3 Embodied Carbon Emission
4 Conclusions
References
Developing Virtual Labs for Engineering Education: Lessons from Leveling Experiment
1 Introduction
2 Virtual Lab Development
3 Implication and Validation
4 Results
5 Discussion
6 Conclusions
7 Funding Statement
References
Insights into the Resource Utilization Behavior of Reclaimed Asphalt Pavement Based on Theory of Planned Behavior from Different Stakeholders’ Perspective
1 Introduction
2 Literature Review
2.1 Relation Behavior Research on RAP Management
2.2 Theory of Planned Behavior
2.3 Research Gap and Innovation in this Study
3 Hypothesis and Theoretical Model Development
3.1 Hypothesis Development
3.2 Theoretical Model Development
4 Research Design
4.1 Questionnaire Design
4.2 Data Collection
4.3 Data Analysis Process
5 Results and Discussion
5.1 Descriptive Statistics
5.2 Reliability and Validity Tests
5.3 Confirmatory Factor Analysis
5.4 Hypothesis Testing Analysis
5.5 Multi-group Structural Equation Model Analysis
6 Conclusion
References
Image Quality Assessment for Construction E-inspection: A Case Study
1 Introduction
2 IQA Metrics
2.1 Image Attributes
2.2 NIMA
3 Case Study
3.1 Project Background
3.2 Model and Data Acquisition
3.3 Designed Workflow
4 Discussion
4.1 Effectiveness Validation
4.2 Image Attributes vs NIMA
5 Conclusion
References
Housing Choice Willingness of Urban Residents: The Interaction of Tenure Choice, Space Choice, and Time Choice
1 Introduction
2 Theoretical Analysis of Residents’ Housing Choice Willingness
2.1 Housing Consumption Perception and Housing Choice Willingness
2.2 Housing Choice Motivation and Housing Choice Willingness
2.3 Interactive Relationships in Housing Choice
2.4 Path Analysis of Influencing Factors on Housing Choice Willingness
3 Sample Selection and Data Collection
4 Results
4.1 Effect of Choice Motivation and Consumption Perception on Choice Willingness
4.2 Effect of Three-Dimensional Interactions of Housing Choice Motivation on Perceived Value
4.3 Effect of Three-dimensional Interactions of Housing Choice on Perceived Risk
5 Discussion and Conclusions
5.1 From the Perspective of Consumption Perception on House Choice Willingness
5.2 From the Perspective of House Choice Motivation on Residents’ Perception
5.3 From the Perspective of Interaction Relationship on Residents’ Perception
References
A 10-Year Review of the Semantic Web Technology Applications in Building Energy Reductions
1 Introduction and Background
2 SWT Applications in Building Energy Reductions
2.1 Semantic Web Technologies
2.2 Reducing Energy Loads in Designs
2.3 Applying Renewable Energy
2.4 Using Efficient Building Systems
3 Analysis and Discussion
3.1 Benefits of SWT in Building Energy Reductions
3.2 Research Gaps and the Practical Challenges
3.3 Future Work on SWT Applications in Building Energy Reductions
4 Conclusion
References
An Empirical Analysis of Key Factors of Construction and Demolition Waste Management Using the DEMATEL Approach
1 Introduction
2 Literature Review
3 Research Method
3.1 Research Approach
3.2 DEMATEL as a Data Analysis Method
4 Results and Analyses
5 Discussions and Implications
6 Conclusion
References
How Can Robot Replacement Be Achieved? – Technology Development Direction for Automatic Construction Robot
1 Introduction
2 Literature Review
2.1 Technology Mining Method
2.2 Technology Foresight Method
3 Methodology
3.1 Pre-preparation
3.2 Extracting and Merging Patent Information
3.3 Patent Similarity Model
3.4 Development Direction of ACR Replacement
4 Results
4.1 Pre-preparation
4.2 Extracting and Merging Patent Information
4.3 Patent Similarity Model Based on SAO Structure
4.4 Development Direction of ACR Replacement
5 Discussion and Conclusion
References
A Study of Factors Influencing Community Health Transformation in The Post-epidemic Era
1 Introduction
2 Research Methodology
2.1 Setting Variables and Research Model
2.2 Data Collection
2.3 fsQCA
3 Analysis of Results
3.1 Single Condition Variable Necessity Analysis
3.2 Condition Combination Analysis
4 Discussion
5 Conclusion
References
Research on the Influencing Factors of the Transformation of Migrant Workers into Industrial Workers in China’s Construction Industry
1 Introduction
2 Research Methods
2.1 Literature Research Method
2.2 Expert Interview Method
2.3 Questionnaire Survey Method
2.4 Factor Analysis Method
3 Identification of Influencing Factors
3.1 Literature Research Method to Identify the Influencing Factors
3.2 Expert Interview Method to Determine the Influencing Factors
4 Study Data Collection and Processing
4.1 Questionnaire Survey Collection of Study Data
4.2 Reliability Test and Suitability Test
4.3 Extract Master Factors and Key Factors
4.4 Analysis of the Results
5 Conclusion and Recommendations
5.1 Strengthen Enterprise Training and Production Mode Reform
5.2 Promote the Innovation and Development of the Construction Industry System
5.3 Perfect the National Policy, Improve the Industry Environment
References
Improving Safety Compliance of Construction Workers: The Role of Safety Communication, Management Commitment to Safety, and Perceived Ease of Use
1 Introduction
2 Background and Hypothesis Development
2.1 Deep and Surface Safety Compliance
2.2 Theoretical Framework
2.3 Hypothesis Development
3 Method
4 Results
4.1 Reliability and Validity
4.2 Hypothesis Testing
5 Discussion
6 Conclusion
References
GRA-Fuzzy-Based Green Urban Planning Scheme Decision-Making
1 Introduction
2 Construction of Evaluation Index System
3 Construction of Urban Green Planning Scheme Evaluation Model Based on GRA-Fuzzy
3.1 Indicator Assignment
3.2 Quantification of Indicators Based on Fuzzy Comprehensive Evaluation
3.3 Gray Correlation Analysis
4 Analysis of Calculation Cases
4.1 Determining Weights Based on Shapley-Valued Non-additive Measures
4.2 Quantification of Indicators Based on Fuzzy Comprehensive Evaluation
4.3 Gray Correlation Analysis
5 Conclusion
References
Path Analysis of Regional Carbon Lock-in and Unlocking from a Qualitative Comparative Perspective
1 Introduction
2 Theoretical Framework
2.1 Influence Mechanism of Carbon Lock-in
2.2 Identification of the Carbon Lock-in Stage
3 Method and Data
3.1 Variables and Data
3.2 Data Processing
3.3 Robustness Tests
4 Results
4.1 Carbon Lock-In Trends
4.2 The Impact Pathway of High and Low Carbon
5 Conclusion and Implications
References
The Influence of Real Estate Investment on Economic Development: From New Production Element Perspective
1 Introduction
2 Theoretical Framework
3 Methodology
3.1 Model Establishment
3.2 Variables and Data
4 Results and Discussion
4.1 Correlation Test
4.2 The Influence of the Investment in Real Estate on Economic Development at National Level
4.3 Regional Heterogeneity Analysis
4.4 Temporal Heterogeneity Analysis
5 Conclusion
References
A Study of the Relationship Between Psychological Capital and Unsafe Behavior of Construction Workers
1 Introduction
2 Theoretical Basis and Research Hypothesis
2.1 Psychological Capital
2.2 Safety Attitude
2.3 Unsafe Behavior
2.4 Theoretical Mechanisms and Research Hypothesis
3 Research Design and Methodology
3.1 Scale Design and Data Collection
3.2 Scale Reliability Test
4 Results and Discussion
4.1 Fitting of the Model
4.2 Modification of the Model
4.3 Analysis of the Relationship Between Variables
4.4 Discussion of Results
5 Implications
5.1 Theoretical Implications
5.2 Practical Implications
6 Conclusion
References
BIM-Enabled Design for Hospital Retrofit in China: A Case Study
1 Introduction
2 Challenges of Hospital Retrofit
3 BIM-Enabled Design in Hospitals
4 Case Study: Retrofit of Beijing Ditan Hospital
4.1 Case Descriptions
4.2 BIM-Enabled Concept Design for Hospital Retrofit
4.3 BIM-Enabled Building Systems Design for Hospital Retrofit
5 Discussion and Conclusion
References
Holistic Analysis of the Influencing Factors of Construction 4.0 Technology Implementation in the Construction Industry: A Twin Sustainable and Digital Transition Perspective
1 Introduction
2 Research Background
2.1 Twin Sustainable and Digital Transitions in the Construction Industry
2.2 Construction 4.0 and Sustainability
3 Research Methods
3.1 Identification of Relevant Papers
3.2 Identification of the Influencing Factors of C4.0TechIm
3.3 Simplified Analysis
4 Results and Discussions
5 Conclusion
References
Status Quo of Construction and Demolition Waste Management in Guangdong-Hong Kong-Macao Greater Bay Area Based on Desktop Survey
1 Introduction
2 Methodology
2.1 Research Contents and Methods
2.2 Data Collection and Sources
3 Results and Discussion
3.1 C&D Waste Generation and Treatment Quantities in GBA
3.2 C&D Waste Treatment and Disposal Methods in GBA
3.3 Status Quo of C&D Waste Treatment and Disposal Facilities in GBA
3.4 Promulgation of C&D Waste Management Policies and Norms in GBA
4 Conclusions
References
Urban Resilience: A Systematic Review
1 Introduction
2 Roadmap and Materials
3 Origins and Evolution of “Resilience”
4 Connotation and Composition of Urban Resilience
5 Framework of Assessing Urban Resilience
6 Prospects and Trends Analysis
References
Research on Cement Price Fluctuation Prediction Based on EEMD-ARIMA
1 Introduction
2 Research Idea on Prediction of Price Fluctuation
3 Research Methods About Price Forecasting
3.1 Ensemble Empirical Mode Decomposition Method
3.2 ARIMA(P,d,q)
3.3 RBF Neural Network Prediction
4 Empirical Research on Cement Price Fluctuation Forecasting
4.1 EEMD Decomposition Process and Results
4.2 Price Fluctuation Analysis
4.3 Comparison of Price Fluctuation Forecasting Methods
5 Conclusion
References
Construction Equipment Maintenance Supervision: An i-Core and Blockchain-Enabled Conceptual Framework
1 Introduction
2 Background
2.1 Issues in the Supervision of Construction Equipment Maintenance
2.2 i-Core Potentials
2.3 Blockchain Potentials
3 Methodology
4 Proposed Framework
5 Case Study
5.1 Implementation
5.2 An Illustrative Example
6 Discussion
7 Conclusions
References
Optimizing Efficiency of Energy-Saving Service Industry Based on SE-SBM Model
1 Introduction
2 Literature Review
2.1 Energy-Saving Service Industry (ESI)
2.2 Optimal Scale Measurement in DEA Methods
3 Methods
3.1 Indicator Selection
3.2 Data Collection
3.3 Establish the SBM and SE-SBM Model
4 Results and Discussion
4.1 Indicator Selection and Data Collection
4.2 Results of ESI Efficiency Measurement
4.3 Determine the Optimal Scale of ESCO
4.4 Policy Suggestions
5 Conclusion
6. References
Automatic Classification of Remote Sensing Images of Landfill Sites Based on Deep Learning
1 Introduce
2 Literature Review
2.1 Waste Landfill
2.2 Remote Sensing Technology in Landfills
2.3 Deep Learning in Landfill
3 Method
3.1 Image Collection
3.2 Data Labeling
3.3 Model Building
3.4 Model Training
3.5 Model Evaluation
4 Results and Discussion
4.1 Preliminary Experimental Results
4.2 Model Improvement Results
5 Conclusions
References
RFID-BIM-Enabled Reuse of “Fangcang Shelter Hospitals” Modular Components During the Post-Covid-19 Era
1 Introduction
2 Literature Review
2.1 Modular Construction and Fangcang Shelter Hospital
2.2 BIM and RFID Technologies
3 Methodology
4 Proposed Solutions
5 Conclusions and Limitations
References
How Does Leadership Style Affect Safety? A Mixed-Methods Investigation for the Influence of Superiors’ Varying Leadership Style on the Stress and Safety of Construction Workers
1 Introduction
2 Literature
2.1 Stress and Safety Behavior
2.2 Leadership Style
3 Conception Model
4 Research Method
4.1 Questionnaire Survey
4.2 Agent-Based Modeling
5 Result
5.1 Descriptive Statistics, Factor Analysis and Reliability Analysis
5.2 Correlation Analysis
5.3 Regression
5.4 Results of ABM Simulation
6 Discussion
7 Conclusion
References
Applications of Artificial Intelligence Enabled Systems in Buildings for Optimised Sustainability Performance
1 Introduction
2 Artificial Intelligence
2.1 AI-Enabled Systems for Sustainable Buildings and Cities
3 Research Methods
4 Result and Analysis
4.1 Discussion
5 Conclusion
5.1 Limitations
References
Coupling Coordination Development of Urban Resilience in Yangtze River Delta
1 Introduction
2 Urban Resilience
2.1 Development of Urban Resilience
2.2 Evaluation Dimension of Urban Resilience
2.3 Coupling Coordination of the Subsystems
3 Method
3.1 Research Area
3.2 Indicators
3.3 Research Model
3.4 Evaluation Criteria
4 Analysis and Results
4.1 Time Evolution of Coupling Coordination Degree
4.2 Spatial Distribution of Coupling Coordination Degree
5 Conclusions and Recommendations
References
Sewer Sediment Inspection Based on Multisensor Fusion Considering Sewage Flow
1 Introduction
2 Methodology
3 Case Study
3.1 Implementation Details
3.2 Analysis of Results
4 Conclusions
References
Promoting the Competency of Construction Management Postgraduates: A Literature Review
1 Introduction
2 Literature Review
2.1 Abilities
2.2 Quality
2.3 Knowledge Areas
3 Conclusion
References
Edge Computing-Based Real-Time Blind Spot Monitoring System for Tower Cranes in Construction
1 Introduction
2 Related Works
2.1 Real-Time Safety Assistance Systems
2.2 Object Detection Algorithm
3 Method
3.1 YF-BSM System
3.2 Structure of the Blind Spot Monitoring System
3.3 Region of Interest and Alerts
3.4 Algorithm Structure
3.5 Dataset Introduction
3.6 Classifier Modification
4 Experience
4.1 Experimental Setup
4.2 Experiment Result
4.3 Efficiency
5 Conclusions
References
Research on Carbon Emission of Residential Buildings
1 Introduction
2 Materials and Methods
2.1 Lighting Calculation
2.2 Carbon Emission Calculation
2.3 Optimization Principle
3 The Case Study
3.1 Project Introduction
3.2 Analysis of Lighting Simulation Results of Original Lighting Design
3.3 The Optimization Design
4 Discussion
5 Conclusions
References
Correlation for Project Decision Making Process Between Green Building Proposal Evaluation and Life Cycle Costing Applications
1 Introduction
2 Enhance Office Building Functionality by Using Green Building Standards
3 Comparison of LOTUS with Other Green Building Rating tools in the Industry
4 The Development of Green Building Evaluation System in Vietnam
5 Discussion and Conclusion
References
Exploring the Spatial-Temporal Evolution Characteristics of Urban Eco-efficiency: A Case Study of 276 Chinese Cities
1 Introduction
2 Literature Review
3 Materials and Methods
3.1 The Super-SBM Model
3.2 Indicator System and Research Data
4 Results and Discussion
4.1 The Overall Analysis of the EE
4.2 Dynamic Analysis of the EE
4.3 Regional Comparative Analysis on the EE
5 Conclusions and Policy Implications
5.1 Conclusions
5.2 Policy Implications
References
Spatiotemporal Evolution the GTFP of the Construction Industry--Empirical Analysis Based on the Yangtze River Economic Belt
1 Introduction
2 Literature Review
3 Research Methods and Data Sources
3.1 Research Methods
3.2 Data Sources
4 Analysis of the Temporal and Spatial Evolution of the GTFP of the Construction Industry
4.1 Variable Selection and Data Description
4.2 Analysis of the Spatio-Temporal Differences in GTFP
5 Conclusions
References
Federated Learning Based Collaboration Framework of Data Sharing for Intelligent Design of Residential Buildings
1 Introduction
2 Literature Review
2.1 Intelligent Design
2.2 Federated Learning
2.3 Cooperative Game Theory
3 The Proposed Data Sharing Framework
3.1 Client Layer
3.2 Trading Layer
3.3 Recording Layer
4 Modelling of Data Sharing Problem for Intelligent Building Design
4.1 Definitions and Notations
4.2 Collaboration Matching Mechanism
4.3 Clients Behaviours Analysis
5 Case Study on Intelligent Design of Residential Buildings
5.1 Dataset of Floor Plan of Residential Buildings
5.2 Experiments and Results
6 Discussion
7 Conclusions
References
Design for Manufacture and Assembly (DfMA) Communication Network and the Impact of COVID-19
1 Introduction
2 Literature Review
2.1 Communication Network
2.2 DfMA and IPD in the New Normal
3 Research Methods
3.1 Data Collection
3.2 Data Analysis
3.3 Data Visualization
4 Results
5 DfMA Communication Network
6 Discussion
7 Conclusion
References
The Impact of Internationalization on Corporate Social Responsibility: Evidence from Chinese Listed Construction Companies
1 Introduction
2 Literature Review and Hypothesis
2.1 Construction Corporate Social Responsibility
2.2 Internationalization of Construction Companies
2.3 The Relationship Between Internationalization and Corporate Social Responsibility
3 Research Design
3.1 Data and Sample Collection
3.2 Variables Measurement
4 Results
4.1 Descriptive Statistical Analysis
4.2 Empirical Analysis and Results
5 Discussion
6 Conclusion
References
A Simulation-Driven Data Collection Method of External Wall by Integrating UAV and AR
1 Introduction
2 Literature Review
2.1 Data Collection of Structure Surfaces
2.2 Application of AR
2.3 UAV Intelligent Control
3 Methodology
3.1 Construction of AR Terminal
3.2 Control of UAV Terminal
3.3 Terminal Communication
4 Case Study
5 Conclusions
References
A Systematic Review of Quantitative Measurement Methods for Accessibility of Urban Infrastructure
1 Introduction
2 Review Methodology
2.1 Search Strategy of Measure Methods
2.2 Literature Results
3 Overview of the Selected Accessibility Measures
4 Discussion
4.1 Discussion on the Spatial Externality of Accessibility Approach
4.2 Discussion on the Static and Dynamic Aspects of Accessibility Methods
4.3 Discussion on the Parameters Used in the Accessibility Method
4.4 Discussion on the Object of Research on Accessibility Methods
5 Conclusion
References
Urban Renewal Planning Strategies Guided by Public Values
1 Introduction
2 The Development Process of Urban Renewal
3 The Public Value Attribute Model of Urban Renewal Planning
4 Implementation Path of Urban Renewal Based on Public Value
4.1 High-Quality Urban Public Space Construction
4.2 An Efficient Collaborative Model for Implementing Public Value
5 Conclusion
References
Research on the Similarity of Highway Construction Projects Based on EWM-GRA
1 Introduction
2 Research Methods
2.1 Selection of Attributes
2.2 The Dimensionless Data
2.3 Case Representation
2.4 Case Retrieval
3 Case Study
4 Conclusion
References
Understanding the Role of Housing in Family Reunion: Evidence from Rural-Urban Migrant Families in China
1 Introduction
2 Literature Review
2.1 Studies on the Factors Influencing Migration
2.2 Introduction of Family Migration Theory
3 Theoretical Analysis
3.1 The Todaro Model
3.2 Staged and Dynamic Family Migration Decision Mechanism
4 Data and Methodology
4.1 Variables and Empirical Model
4.2 Data
5 Empirical Results
5.1 Basic Regression Results
5.2 Robustness Test
6 Endogenous Discussions
6.1 IVtobit Estimates of “Housing Cost”
6.2 CMP Estimates of “Housing Ownership”
7 Conclusions and Policy Implications
References
The Characteristics of Land Use Around Rail Transit Stations in Tianjin, China
1 Introduction
2 Literature Review
3 Data and Methods
4 Empirical Results
4.1 Spatial Characteristics of Land Use Around the Stations
4.2 Model Results of Land Use Diversity
5 Conclusions and Suggestions
References
Evolution Analysis Model and Catastrophe Theory Explanation of Shrinking Cities
1 Introduction
2 Related Research on Shrinking Cities
2.1 Motivation Mechanism
2.2 Quantitative Measure
2.3 Evolution Trend
2.4 Evaluation of Literature
3 Research Model of Shrinking Cities
3.1 Construction of the Evaluation Index System for the Comprehensive Development Index of Shrinking Cities
3.2 Ranking of the Importance of Indicators
3.3 Mutation Model Construction
4 Case-Based Interpretation of Urban Shrinkage Catastrophe Theory
4.1 Case 1: Ordos City
4.2 Case 2: Taizhou City
5 Conclusions
Appendix
References
Battery Storage Analysis for Residential Solar Photovoltaic Systems
1 Introduction
2 Description of Grid-Connected Solar PV Homes with Battery Storage
2.1 Design of PV-Battery System
2.2 PV Generation and Smart Meter Data Acquisition from a Case Study House
3 The Effect of Using a Battery
4 Payback Period of Applying a Battery
5 Conclusion
References
Making Decisions for Urban Regeneration: A Bibliometric Analysis and Critical Review
1 Introduction
2 Methodology
2.1 Data Search
2.2 Literature Review Method
3 Bibliometric Analysis
3.1 Yearly Trend
3.2 Research Topics
4 Critical Review
4.1 Decision Objectives
4.2 Decision Content
4.3 Decision Makers
4.4 Decision Methods
4.5 Decision Results
5 Discussion and Research Agenda
6 Conclusion
References
A Scientometric Review on Real Estate Investment Trusts: Towards a New Asset-Information-Capital Framework
1 Introduction
2 Methodology
3 Scientometric Analysis Results
3.1 Popular Publications
3.2 Active Authors, Affiliations and Countries
3.3 Keywords and Themes
4 AIC Framework and Future Directions
4.1 More Attention on Social Performance of REITs
4.2 Asset Choice with Environment-Oriented Configuration
4.3 The Match Between Governance and Regulation of REITs
5 Conclusion
References
The Spatial Relationship Between Rail Transit Network and Population and Employment Density in Tianjin, China
1 Introduction
2 Literature Review
2.1 Impacts of Rail Transit on Residential Development
2.2 Impacts of Rail Transit on Spatial Distribution of Employment
3 Data and Methodology
3.1 Study Area
3.2 Data
3.3 Methodology
4 Empirical Results
4.1 Coupling Relationship Between Rail Transit and Jobs-Housing Distribution in Tianjin City
4.2 Influencing Factors on the Distribution of Population and Employment Around Rail Transit Stations
5 Conclusions and Implications
References
Model Development to Link Cultural Intelligence and Individual Work Performance: Mediator and Moderator Considerations
1 Introduction
2 Literature Review
2.1 Cultural Intelligence (CQ)
2.2 Organizational Culture (OC)
2.3 Cultural Adjustment (CA)
2.4 Perceived Organizational Support (POS)
2.5 Individual Work Performance (IWP)
3 Research Framework
4 Discussions
References
Understanding Causes and Resolutions of Construction Disputes: A Case Study
1 Introduction
2 Literature Review
2.1 Definition of Construction Disputes
2.2 Causes of Construction Disputes
2.3 Construction Dispute Resolutions
3 Research Method
3.1 Questionnaire Design
3.2 Data Collection
3.3 Interviews
4 Results and Discussion
4.1 Reliability and Validity Tests
4.2 Main Causes of Construction Disputes
4.3 Differences of Stakeholders’ Perceptions on the Causes of Construction Disputes
4.4 The Most Frequently Used Dispute Resolution Method
5 Conclusion
References
Research on the Impact of Market Sentiment on the Second-Hand Housing Market
1 Introduction
2 Research Design
2.1 Select Variable Indicators
2.2 Construct the Market Sentiment Index
3 Empirical Research
3.1 Data Collection and Processing
3.2 Data Stationarity Test
3.3 Empirical Research Between Market Sentiment and Second-Hand Housing Market
4 Conclusion
References
Research on Emergency Decision Making Considering Decision-Maker Peference Based on Improved Regret Theory—A Case Study of Covid-19
1 Quotes
2 Asking Questions
3 Method, Principle and Calculation Steps
3.1 Method and Principle
3.2 Calculation Steps
4 Case Studies
5 Conclusion
References
Integrating BIM and Quality Standards for Highway Construction Inspection
1 Introduction
2 Background and Related Studies
3 Methodology – Integrating BIM and Quality Standards
3.1 Analysis of the Semantic Structure in Quality Standards
3.2 Integration of Quality Standards with BIM
4 Design of a User Form Gor Highway Construction Inspection
5 Summary and Conclusions
References
Influence of Spatial Ability on Virtual Annotation Response in Construction Equipment Teleoperation
1 Introduction
2 Related Work
2.1 Virtual Annotation (VA)
2.2 SPA in Teleoperation
3 Experiment
3.1 SPA Test Questionnaire
3.2 Virtual Annotation Design
3.3 Teleoperation Experiment Design
3.4 Experimental Conditions
3.5 Participants
3.6 VA Response Assessment Indices and Research Hypothesis
4 Results
4.1 Descriptive Statistics of SPA Scores
4.2 Descriptive Statistics and t-test of Correct Response Time for High and Low SPA Score Group
4.3 Correct Response Rate
4.4 Number of Transferred Balls
5 Data Analysis and Interpretation
6 Conclusion
References
Risk Assessment of Rail Transit Equipment Failure Disaster Chain Based on the Complex Network
1 Introduction
2 Statistical Analysis of Rail Transit Equipment Failure Disaster Accidents
3 Construction of Rail Transit Equipment Fault Disaster Chain Network
4 Risk Assessment of Rail Transit Equipment Failure Complex Network
4.1 Node Importance
4.2 Edge Vulnerability
4.3 Disaster Chain Network Risk Assessment
5 Summary
References
Transmission Strength Evaluation of Metro Safety Risks: An Integrated Study of Causal and Coupling Relationship
1 Introduction
2 Literature Review
3 Safety Risk Transmission
4 Research Methods
4.1 Research Design
4.2 Framework of the Method
5 Data Analysis
5.1 Data Collection
5.2 Developing the Direct Relation Matrices
5.3 Data Processing and Output
6 Discussion
7 Conclusion
References
Conceptualizing Key Performance Indicators for Building Critical Infrastructure Resilience Through Public-Private Partnership
1 Introduction
2 PPP in CIR
3 Research Methodology
3.1 Identification
3.2 Screening and Analyzing Target Papers
4 Key Performance Resilience Indicators in PPP in CIR
4.1 System Performance
4.2 Equipment KPRI
4.3 Human Resource
4.4 Social
4.5 Record Keeping
4.6 Implementing Key Performance Resilience Indicators
5 Conclusion
References
Decoration and Renovation Waste Recycling Intention of Homeowners: A Perceived Value Perspective
1 Introduction
2 Conceptual Framework and Research Hypotheses
2.1 DRW Recycling Intention
2.2 Environmental Values
2.3 Perceived Value
3 Methods
3.1 Data Collection
3.2 Measurement Development
4 Data Analysis and Results
4.1 Measurement Model
4.2 Structural Model
4.3 Mediation Analysis
5 Discussion
5.1 Comparison with Existing Literature
5.2 Implications
6 Conclusions
References
The Influence of 2D/3D Urban Spatial Form Indicators on Surface Urban Heat Island Based on Spatial Regression Models: A Case Study of Hangzhou, China
1 Introduction
2 Data and Study Area
2.1 Study Area
2.2 Data Collection
3 Methods
3.1 Calculation of SUHII
3.2 Spatial Autocorrelation
3.3 Indicators of 2D/3D Urban Spatial Form
3.4 Spatial Regression Models
4 Results
4.1 LST Inversion in the Central City of Hangzhou
4.2 Spatial Distribution Characteristic of SUHII
4.3 Results of the Spatial Regressions
5 Conclusion
References
Application Analysis of Existing Industrial Robots in Precast Concrete Component Factory
1 Introduction
2 Literature Review
2.1 Overseas Research Status
2.2 Domestic Research Status
3 Research Methods
4 Robots in Component Production
4.1 Data Acquisition
4.2 Sorting Out the Production Process
4.3 Robotic Summary of the Production Process
4.4 Application of Existing Industrial Robots
5 Conclusions
References
Gauging the Knowledge Development of Innovations in Mega-infrastructure Projects
1 Introduction
2 Background
3 Research Methods
3.1 Scientometric Approach
3.2 Systematic Analysis
4 Data Collection
4.1 Bibliometric Search
4.2 Manual Screening
5 Result and Discussion
5.1 Descriptive Analysis
5.2 Results of Scientometric Analysis
5.3 Systematic Analysis and Findings
6 Conclusion
References
The Analysis of Urban Expansion Based on Space Syntax: A Case Study of the Main Urban Area of Hangzhou, China
1 Introduction
2 Study Area and Data
2.1 Study Area
2.2 Data Collection
3 Methodology
3.1 Space Syntax
3.2 Spatial Autocorrelation
3.3 Space Syntax Expand Intensity Index
4 Results and Discussion
4.1 Basic Information on the Expansion of the Road Network
4.2 Integration Change and Spatial Aggregation Characteristics
4.3 Urban Expansion Analysis Based on SS-EII
5 Conclusion
References
An Evolutionary Game Analysis of Organizational Relational Behavior in Megaprojects Considering the Reciprocal Preference
1 Introduction
2 Establishment of the Game Model
2.1 Applicability of Evolutionary Game Theory
2.2 Game Assumptions and Description
3 Model Analysis
3.1 Establish Replication Dynamic Equation
3.2 Stability Analysis of the Evolutionary Game
4 Numerical Simulation
4.1 Base Simulation
4.2 The Effect of Initial Willingness on Evolution Results
4.3 The Effect of Reciprocal Preference on Evolutionary Results
4.4 The Evolutionary Result in Different Megaproject Scenarios
5 Conclusions and Suggestions
References
Research on Evaluation of Construction Workers’ Job Satisfaction Based on Improved AHP-FCE Method
1 Introduction
2 Research Status
3 Improved Modeling Steps for AHP-FCE
3.1 Construct an Evaluation Index Set
3.2 Establish a Satisfaction Rating Scale
3.3 Make a Single Factor Judgment to Construct a Membership Matrix
3.4 Use the Improved AHP Method to Judge the Weight
4 Empirical Analysis
4.1 Model Building of AHP
4.2 Description of the Indicator
4.3 Construction of Judgment Matrix and Single-Layer Weight Calculation
4.4 Calculation of the Composite Weight of Each Layer Element to the Target Layer
4.5 Determination of Evaluation Criteria Set
4.6 Fuzzy Comprehensive Evaluation of Criterion Level
4.7 Fuzzy Comprehensive Evaluation of Target Layer
4.8 Analysis of Evaluation Results
5 Conclusions and Suggestions
References
Modularization Considerations for Modular Integrated Construction in Hong Kong: A Case Study
1 Introduction
2 Literature Review
2.1 Module, Modularity, and Modularization
2.2 MiC in Hong Kong
2.3 MiC Building Design
3 Method
4 Case Study
4.1 Case Description
4.2 Modularization of MiC Design
5 Conclusion
References
Complexity Management of Emergency Projects from the Perspective of Complex Adaptive Systems Theory—The Case of the National Exhibition and Convention Center (Shanghai)
1 Introduction
2 Literature Review
2.1 The Complexity of EPs
2.2 The Management of EPs Complexity
3 Methodology
3.1 Case Study of the NECC (Shanghai) Reconstruction Project
3.2 Theoretical Basis
3.3 Data Collection
3.4 Data Analysis
4 Empirical Findings and Discussions
4.1 The Complexity Framework for EPs Construction Period
4.2 The Critical Adaptive Behaviors for EPs Construction Period Complexity
5 Conclusion
References
The Application Status and Outlook of CGE Model in the Construction Sector Under the Dual-Carbon Target
1 Introduction
2 Applications of the CGE Model
2.1 Construction Business License
2.2 Applications in the Field of Carbon Trading
3 Application and Prospect of CGE in the Construction Field
3.1 Application in the Field of Building Environmental Impact Assessment
3.2 Difficulties in the Field of Environmental Impact Assessment of Buildings
3.3 Outlooks in the Field of Environmental Impact Assessment of Buildings
4 Conclusion
References
ISM-MICMAC Model-Based Construction Risk Evaluation for Green Retrofit Project of Public Buildings
1 Introduction
2 ISM-MICMAC Integrated Modeling Approach
3 Risk Indicator System Development
4 ISM-MICMAC Model Development
4.1 Risk ISM Model
4.2 MICMAC Analysis
5 Discussion
6 Conclusion
References
Multiscale Evaluation of the Cooling Effect of Greenspace in Urban Environments
1 Introduction
2 Methodology
2.1 Study Area
2.2 Retrieval of Land Surface Temperature from Satellite Imageries
2.3 Microclimatic Modeling with ENVI-Met
2.4 Identification of Greenspace Characteristics and Cooling Effect Indicators
3 Result Analysis and Discussion
3.1 Land Surface Temperature of Greenspace
3.2 The Cooling Effect of Street-level Greenspace
3.3 The Cooling Effect of Green Infrastructure
4 Conclusion
References
Blockchain-Based Decentralized Reputation Framework: Understanding the Residents’ Satisfaction About Living House with Trustworthiness Consideration
1 Introduction
2 Blockchain-Based Decentralized Reputation Framework for Residence Satisfaction Evaluation
2.1 A Residence Satisfaction Evaluation Model
2.2 Blockchain-Based Residence Satisfaction Evaluation Management
3 Evaluation of Blockchain-Based Decentralized Reputation Framework
4 Discussion and Conclusion
References
Research on Job Stressors and Mental Health of Construction Practitioners in China
1 Introduction
2 Literature Review
2.1 Job Stressors of Construction Practitioners
2.2 Mental Health Status of Construction Practitioners
3 Research Methods
3.1 Questionnaire Design
3.2 Data Analysis Methods
4 Data Analysis Results
4.1 Demographic Information Analysis
4.2 Questionnaire Reliability Test
4.3 Factor Analysis of Job Stressors
4.4 Descriptive Analysis of Mental Health Status
4.5 Correlation Analysis Between Job Stressors and Mental Health Status
5 Conclusion
References
Heterogeneous Local Policy Responses to Housing Market Regulation: An Interpretive Framework and Evidence from 177 Chinese Cities
1 Introduction
2 Methodology
2.1 Theoretical Framework
2.2 Policy Intensity
2.3 Models and Variables
2.4 Data
3 Results and Discussion
4 Conclusions and Policy Implications
References
A Double Deep Q-Network-Enabled Two-Layer Adaptive Work Package Scheduling Approach
1 Introduction
2 Literature Review
2.1 The Multi-project Scheduling Problem
2.2 DRL in Scheduling
3 Research Method
3.1 MWPSP Transformation
3.2 The Upper Layer DDQN
3.3 The Lower Layer Heuristic Algorithm
4 Experiment
4.1 Data Briefing
4.2 Result Analysis
5 Discussion
6 Conclusion
References
Research on the Differences of Job Stressors Among Construction Project Managers in China
1 Introduction
2 Literature Review
2.1 Job Task Stressor
2.2 Organizational Stressor
2.3 Individual Stressor
2.4 Career Development Stressor
3 Research Methodology
3.1 Questionnaire Design
3.2 Data Collection
4 Statistical Analysis
4.1 Reliability Tests
4.2 Exploratory Factor Analysis of Job Stressors
4.3 Difference Analysis of Job Stressors for Different Types of Project Managers
5 Conclusion
References
Research on the Influencing Factors of Concrete Waste Production in the Whole Process
1 Introduction
2 Research Methods
2.1 Literature Research Method
2.2 Expert Interview Method
2.3 Factor Analysis Method
3 Identification of Influencing Factors
3.1 Literature Research to Identify Influencing Factors
3.2 Determining the Influencing Factors Through Expert Interviews
4 Questionnaire Distribution and Data Processing
4.1 Questionnaire Distribution
4.2 Data Processing
5 Discussion
6 Recommendations
7 Conclusion
References
Measurement of Carbon Emission Rebound Effect of Construction Industry Based on Technological Progress
1 Introduction
2 Action Paths and Hypotheses
3 Rebound Effect Measure
3.1 Data Sources
3.2 Model Construction
4 Conclusion and Enlightenment
4.1 Conclusion
4.2 Enlightenment
References
Accelerating Urban Green Economic Growth Through Government Guidance Funds: Case Study of a National Green Finance Pilot City
1 Introduction
2 Government Guidance Fund as the Propeller of Green Economy Startups
3 Case Study of Quzhou Government Guidance Fund
3.1 Case Description
3.2 Critical Analysis
4 Conclusions and Implications
References
Analysis on the Critical Node of the Chengdu-Chongqing Economic Circle Based on the Expressway Network
1 Introduction
2 Research Methods
2.1 Complex Networks
2.2 Critic Weighting Method
3 Calculation Results of the Chengdu-Chongqing Economic Zone
4 Conclusion
References
Security Assessment for Indoor Spaces: A Framework Based on 3D Space Syntax and BIM
1 Introduction
2 Background of Space Syntax
3 Literature Review
3.1 Application of Space Syntax
3.2 Space and Security
3.3 Space Security Assessment
4 Methodology
4.1 Parameters of 3D Space Syntax
4.2 Modeling
4.3 Results and Discussion
5 Conclusion and Recommendations for Future Studies
References
A Bibliometric Analysis of Smart Tourism City Research
1 Introduction
2 Data Sources and Research Method
2.1 Data Sources
2.2 Research Method
3 Research Trends of Smart Tourism Cities
3.1 Analysis of Literature Growth Trends
3.2 Analysis of Research Hotspots
3.3 Analysis of Changes in Research Themes
3.4 Country and Region Analysis of Literature Publication
3.5 Research Literature Analysis of the Six Elements of Tourism Smart Construction in Smart Tourism Cities
4 Conclusion
References
Risk Analysis for Green Renovation Project of Public Buildings Based on EWGM-FMEA
1 Introduction
2 Research Methods
2.1 Identification of Risk Factors
2.2 Risk Analysis Model
3 Empirical Study
3.1 Background Information
3.2 Results of Risk Factor Weight Assessment
3.3 Results of EWGMRPN Evaluation
4 Discussions
4.1 Potential Causes of Risk
4.2 Risk Response Measures and Recommendations
5 Conclusions
References
Automated Detection for the Reserved Rebars of Bridge Pile Caps Based on Point Cloud Data and BIM
1 Introduction
2 Literature Review
2.1 Object Recognition
2.2 Rebar Inspection
3 Methodology
3.1 Overview
3.2 Data Preprocessing
3.3 Rebar Extraction
3.4 Position and Dimension Estimation
3.5 Spacing Estimation
4 Experiment Results
4.1 Validation Experiments
4.2 Preliminary Validation of the Rebar Extraction Algorithm
4.3 Experiment Results
4.4 Accuracy Analysis
5 Conclusions
References
Research on Quality of Prefabricated Construction Components Based on MIV-BP Neural Network Optimization Algorithm
1 Introduction
2 Theoretical Basis and Research Methods
2.1 Characteristics of Prefabricated Construction
2.2 PC Components
2.3 Algorithm Concept
3 Establishment of Model
4 Conclusion
References
Construction Management in the Post Covid Era: Towards Improving Construction Productivity in Developing Countries - Example from Nigeria
1 Introduction
2 Factors Affecting Construction Productivity
2.1 Environmental Factors
2.2 Economic Factors
2.3 Organizational Factors
2.4 Communication Factors
2.5 Workforce Factors
3 Critical Factors Affecting Construction Productivity in Nigeria
4 Conclusion
References
Inverse Generative Design: A Guideline
1 Introduction
2 Forward Generative Design
3 Inverse Generative Design
3.1 The Functionalism Culture
3.2 Implementing Inverse Generative Design
4 Case Study of the to Kwa Wan Urban Renewal Project
4.1 Background
4.2 Functions
4.3 Results of Generating Design Layouts
5 Conclusion
References
Can Construction Enterprises Adopt Digital Transformation Behavior? A Dynamic Game Perspective
1 Introduction
2 Literature Review
3 Evolutionary Game Analysis of the Digital Transformation of Construction Enterprises
3.1 Participants’ Behavior Strategies
3.2 Model Assumption
3.3 Payment Matrix of the Tripartite Game
4 System Dynamics Simulation
4.1 Establish a System Dynamics Model
4.2 Initial Simulation Analysis
4.3 Simulation of Main Parameters
4.4 Countermeasures and Suggestions Based on Simulation Analysis
5 Research Conclusion
References
Automated LiDAR Scan Planning of 3D Indoor Space Based on BIM and an Improved GA
1 Background
2 Related Work
2.1 BIM Component Information Extraction
2.2 Generation of Optimal Scanning Plan
3 Methodology
3.1 Data Extraction and Preprocessing
3.2 Generating of Optimal Scanning Site
3.3 Adjustment of Optimal Scanning Scheme
4 Case Study
5 Conclusions
References
Exploring the Capabilities Required for Construction Expatriates Functioning Effectively in Unfamiliar Technical Context
1 Introduction
2 Literature Review
2.1 Technical Standards
2.2 Professional Competence and Work Adjustment
3 Methodology
3.1 Selection of Participants
3.2 Interview the Participants
3.3 Criteria of Consensus
4 Results
5 Discussions
5.1 Adaptable Facet
5.2 Knowledgeable Facet
5.3 Motivational Facet
6 Conclusion and Limitation
References
Applications of 4D Point Clouds (4DPC) in Digital Twin Construction: A SWOT Analysis
1 Introduction
2 Strength
3 Weaknesses
4 Opportunities
5 Threats
6 Summary
References
Optimization of Housing Retrofit Policies: A Perspective of Homeowners’ Motivations
1 Introduction
2 Overview of China’ Building EER Policies
3 Methodology
3.1 Literature Review
3.2 Data Collection and Analysis
4 Results and Discussion
4.1 Homeowners’ EER Motivations
4.2 Implications for EER Policy
5 Conclusion
References
China’s International Engineering Risks in the Post-pandemic Age Based on Network Theory
1 Introduction
2 Methodology and Data
2.1 Risk Factors Identification for CIE
3 CIE Risk Network Model Establishment
3.1 Define the Correlations between Risk Factors
3.2 Establish CIE Risk Network Model
4 Analysis of CIE Risk Network Model
4.1 Network Overall Parameters
4.2 Network Local Parameters
4.3 Identification of Critical Risks
5 Analysis of Critical Risks
5.1 Critical Initial Risks
5.2 Critical Transmission Risks
5.3 Critical Endpoint Risks
6 Conclusion
References
Life Cycle Carbon Emission Assessment of Prefabricated Buildings: A Case Study in Nantong, China
1 Introduction
2 Life Cycle Carbon Emission Assessment System of Prefabricated Buildings
2.1 Purpose and Scope
2.2 Inventory Analysis
2.3 Impact Assessment and Interpretation
3 Carbon Emission Calculation Model of Prefabricated Buildings in the Whole Life Cycle
3.1 Carbon Emission Calculation Formula of the Production and Processing Stage
3.2 Carbon Emission Calculation Formula of the Transportation Stage
3.3 Carbon Emission Calculation Formula of the On-Site Building Stage
3.4 Carbon Emission Calculation Formula of the Operation Stage
3.5 Carbon Emission Calculation Formula of the Abandonment Stage
4 Case Study
4.1 Basic Data
4.2 Carbon Emission Factors
4.3 Scope and Inventory Analysis of Prefabricated Building A
4.4 Impact Assessment and Interpretation of Prefabricated Building A
5 Conclusions
References
A Bibliometric Review of the Carbon Emissions and Machine Learning Research in the Post-COVID-19 Era
1 Introduction
2 Methodology
2.1 Literature Retrieval
2.2 Bibliometric Methods
3 Bibliometric Analysis
3.1 Post-COVID-19 Era
3.2 Carbon Emission
3.3 Machine Learning
4 Conclusion
References
Job Stress of Chinese Construction Project Management Personnel in Project Overall Implementation Process
1 Introduction
2 Project Overall Implementation Process and Job Stress
2.1 Project Overall Implementation Process
2.2 Job Stress of Construction Project Management Personnel at Different Stages
2.3 Job Stressors of Construction Project Management Personnel
3 Methodology
3.1 Data Collection
3.2 Reliability Test
3.3 Analysis of Job Stress Change in the Project Overall Implementation Process
3.4 The Difference Analysis of the Job Stressors Between the Two Types of Management Personnel
4 Conclusion
References
How has COVID-19 Pandemic Influenced the Quality Assurance of Cross-Border Construction Logistics and Supply Chain? A Conceptual Analysis Based on Ishikawa Diagram
1 Introduction
2 Literature Review
2.1 Quality Assurance (QA) of Cross-Border Construction Logistics and Supply Chain (Cb-CLSC)
2.2 Coronavirus (COVID-19) Pandemic Era
3 Research Method
3.1 Desk Literature Review
3.2 Ishikawa Diagram
4 Findings and Discussion
4.1 Proposed Conceptual Framework Based on the Ishikawa Diagram
4.2 Toward Positive Influence
5 Contribution of the Study
6 Conclusions and Recommendations
References
Study on the Law of Flue Gas and Temperature Propagation at Different Fire Locations in Subway Stations
1 Introduction
2 Principle and Simulation Parameter Design
2.1 Principle and scene design
2.2 Simulation principle of PyroSim
2.3 Meshing
2.4 Analog Parameter Setting
3 Case study
3.1 Modeling
3.2 Setting of Fire Location
3.3 Setting of Measuring Device
4 Analysis Results
4.1 Temperature Distribution Law
4.2 Distribution Law of CO
4.3 Growth Law of Temperature and CO Concentration at The Most Unfavorable Position
5 Conclusion
References
Inclusion of Durability of Recycled Aggregate Concrete in Life Cycle Assessment (LCA)
1 Introduction
2 LCA of Concrete Concerning Durability Parameters
3 Research Methodology
3.1 Goal and Scope
3.2 Life Cycle Inventory and Impact Assessment
4 Results and Discussions
4.1 Environmental Impacts of Concrete under Different FUs
4.2 Importance of Inclusion of Durability of Concrete in LCA
5 Conclusion
References
Industrialized Construction Firms and Digitally-Enabled Product Platforms: An International Case Study
1 Introduction
2 PERSPECTIVE: Three Platform Elements and Customer Requirements
2.1 Digitally-Enabled Kit of Parts
2.2 Digitally-Enabled Interface
2.3 Digitally-Enabled Design Rule for Future Products
2.4 Customer Requirement in Products Platforms
3 Research Methods
3.1 Research Setting
3.2 Methods
4 Findings
4.1 Three Types of Platforming Strategies
4.2 Summary of Findings
5 Discussion
5.1 Towards Digitally-Enabled Product Platforms: Three Strategies
6 Conclusions
6.1 Digitally-Enabled Product Platform: Three Strategies Depending on Customer Requirement Certainty Across Multiple Market Segments
6.2 Implications to Policy and Practice
References
Estimating Embodied Carbon Reduction in Modular High-Rise Residential Buildings Through Low Carbon Concrete
1 Introduction
2 Methodology
2.1 Building’s Carbon Inventory Analysis for the EC Assessment
2.2 Case Study
2.3 Reducing EC Through Low Carbon Concrete Solutions
3 Results and Discussions
3.1 Results of the EC Emissions of the Case Buildings
3.2 EC Mitigations Using Low Carbon Concrete Solutions
4 Conclusions
References
Machine Learning Approach to Examine the Influence of the Community Environment on the Quality of Life of the Elderly
1 Introduction
2 Literature Review
2.1 Quality of Life
2.2 Community Environment
2.3 Support Vector Regression
3 Conceptual Model
4 Research Methodology
4.1 Questionnaire Survey and Sample
4.2 Statistical Analyses
4.3 Support Vector Regression
5 Results
5.1 Reliability Test
5.2 Correlation Analysis
5.3 Support Vector Regression
6 Discussion
6.1 Space Management and the QoL of the Elderly
6.2 Building Services and the QoL of the Elderly
6.3 Supporting Facilities and the QoL of the Elderly
7 Recommendations
8 Conclusions
References
Assess the Reusability Potential of Building Products in an Early Design Stage
1 Introduction
2 Literature Review
2.1 Reusability in the Built Environment
2.2 Influence Factors
3 Reusability Assessment Tool
3.1 Design Science Approach
3.2 Expert Interview Resulted in 19 Factors
3.3 Expert Panel Resulted in 7 Factors and Their Weights
3.4 The Assessment Tool
4 Case Study
4.1 Traditional Product vs. Circular Product
4.2 Product Scenarios and Sensitivity
5 Conclusion and Discussion
References
Risk Assessments with Probabilistic Linguistic Information for Green Building Projects - The Case of Vietnam
1 Introduction
2 Literature Review
3 Methodology for the Study
4 Case Study: The Case of Vietnam
5 Conclusions
References
Mechanism and Collaborative Governance of Public Participation in Urban Renewal Project
1 Introduction
2 Previous Research
2.1 Urban Renewal
2.2 Public Participation
3 Mechanism of Public Participation in Urban Renewal Project
3.1 Theoretical Model and Research Hypothesis
3.2 Observed Variables Selection
4 Empirical Analysis
4.1 Distribution and Collection of Questionnaires
4.2 Data Quality Analysis
4.3 Confirmatory Factor Analysis
4.4 Hypothetical Path Analysis
4.5 Results
5 Collaborative Governance Mechanism for Urban Renewal Project
5.1 Mechanism Design Based on Collaborative Governance
5.2 Government
5.3 Public
5.4 Social Organization
6 Conclusion
References
The Influence of Institutional Regulation on Megaproject Social Responsibility: The Moderating Effect of Political Connection
1 Introduction
2 Theoretical Foundation and Hypotheses Development
2.1 MSR
2.2 Institutional Regulation
2.3 Moderation Effect of Political Connection
3 Method
3.1 Questionnaire Design and Validation
3.2 Measurement
3.3 Sample and Data Collection
4 Analysis and Results
4.1 Validity and Reliability Test
4.2 Hypothesis Testing
5 Discussion
5.1 Effects of Institutional Completeness on MSR
5.2 Effects of Regulatory Normality on MSR
5.3 Effects of Institutional Constraint on MSR
5.4 Effects of Policy Incentive on MSR
5.5 Moderating Effects of Political Connection
6 Conclusion and Implications
References
Quality Control in Modular Construction Manufacturing During COVID-19: Process and Management Standardization
1 Introduction
2 Quality Control in Modular Construction Manufacturing
3 Data Collection
4 Data Analyses and Findings
4.1 In-Factory Production of Modular Construction
4.2 QC Performance Evaluation in the Factory
5 Discussion and Conclusion
References
Factors Influencing the Promotion of Green Building Materials: Perspective of Multiple Stakeholders
1 Introduction
2 Methodology
2.1 Identification of Influencing Factors
2.2 Analysis of the Influencing Factors by DEMATEL
2.3 Analysis of the Interaction Mechanism by ISM
3 Results
3.1 Causality and Centrality Analysis
3.2 Hierarchical Model
4 Discussions and Implications
4.1 Discussions
4.2 Implications
5 Conclusions
Appendix A
References
Examining the Use of BIM-Based Digital Twins in Construction: Analysis of Key Themes to Achieve a Sustainable Built Environment
1 Introduction
2 Materials and Methods
2.1 Stage 1
2.2 Stage 2
2.3 Stage 3
2.4 Stage 4
3 Discussion
4 Conclusion
References
Investigating the Competency of Project Managers in the Chinese Construction Industry: A Case Study
1 Introduction
2 Literature Review
2.1 Definition of Professional Competence
2.2 Development of Professional Competence Index System for Project Managers
3 Questionnaire Survey and Statistical Analysis
3.1 Questionnaire Design
3.2 Data Collection
3.3 Data Analysis
3.4 Analysis on the Difference of Professional Competence Index of Project Managers
3.5 IPA Analysis
4 Conclusion
References
Study on Collaborative Development Planning of Airport and City
1 Introduction to Urban and Airport Development
2 Description of Urban and Airport Development Conflicts
2.1 Noise Impact Analysis
2.2 Analysis of Flight Delay Factors During Airport Operation
2.3 Study on the Strategy of Conflict Allocation Between City and Airport Development
3 A Study on Conflict Management Strategies for Urban and Airport Development
3.1 Noise Contradiction Allocation Strategy Between City and Airport Development in Airport Site Selection Stage
3.2 Analysis of Allocation Strategies for Urban and Airport Development Affected by Air Traffic Operation Efficiency
4 Strategy for Urban and Airport Development During Airport Operation
5 Conclusions
References
Research on the Causes of Safety Accidents in Super High-Rise Buildings—Empirical Analysis Based on Bivariate Probit Model
1 Introduction
2 Literature Review and Hypothesis
3 Model Construction and Resolution
3.1 Variable Selection
3.2 Research Design
3.3 Bivariate Probit Regression
3.4 Model Solving
3.5 Marginal Effect
4 Results Analyses
4.1 Statistical Analyses
4.2 Model Estimation Analyses
4.3 Model Marginal Effects Analysis
4.4 Robustness Analysis
5 Conclusion
References
Research on the Carbon Emission Prediction of Chongqing Transportation Industry Based on Scenario Analysis
1 Introduction
2 Methods and Data
2.1 Carbon Emission Calculation Method
2.2 Construction of Carbon Emission Prediction Model
2.3 Data Sources
3 Case Analysis
3.1 Calculation of Carbon Emissions
3.2 Analysis of Influencing Factors of Carbon Emissions
4 Scenario Analysis
4.1 Scenario Setting
4.2 Scenario Prediction and Analysis
5 Conclusion
References
Life Cycle Application and Optimization of BIM+VR in Hospital Buildings
1 Introduction
2 Possible Application Innovation of BIM+VR Technology in the Whole Life-Cycle of Projects
3 Case Background and BIM+VR Application Optimization
4 Discussion and Conclusion
References
In-Depth Understanding of Construction Robot Research a Bibliometric Analysis
1 Introduction
2 Research Method
2.1 Data Collection
2.2 Analysis Tools Selection and Techniques
3 Results
3.1 Trend of Research on Construction Robot
3.2 Authorship Analysis
3.3 Region Activity Analysis
3.4 Co-citation Analysis
3.5 Keywords Co-occurrence Analysis
4 Discussion
5 Conclusions
References
Domain Ontology Development Methodology for Construction Contract
1 Introduction
2 Related Works
2.1 Domain Ontology in Construction
2.2 Ontology Development Methodology
3 Methods
3.1 Specification
3.2 Competency Questions
3.3 Conceptualization
3.4 Formalization
3.5 Implementation
3.6 Evaluation
4 Case Illustration
4.1 Data Preparation
4.2 Data Formalization
4.3 Results
5 Conclusions and Discussion
References
Carbon Emission Reduction Indicators in Green Building Evaluation System Based on Meta-analysis
1 Introduction
2 Literature Review
3 Research Methods and Data Analysis
3.1 Meta-analysis: Literature Search and Screen
3.2 Results of Data Analysis
4 Discussion
5 Conclusions
References
A Comparative Analysis of Green Construction Material Certification Systems
1 Introduction
2 Background
2.1 Environmental Labeling and Certification System Structure
2.2 Green Construction Material Selection
2.3 GCMs-Related Requirements in Green Building Standards
3 Methodology
4 Results
4.1 Comparison of Requirement Categories in GCMC Systems
4.2 Comparison of Requirements for Ready-Mixed Concrete in GCMC Systems
4.3 Conclusions
References
Game Engine-Based Synthetic Dataset Generation of Entities on Construction Site
1 Introduction
2 Related Works
2.1 Synthetic Dataset with 2D Annotation
2.2 Synthetic Dataset with 3D Annotation
3 Methodology
3.1 Generation of Dataset with 2D Bounding Box
3.2 Generation of Dataset with 3D Bounding Box
4 Experiment
4.1 Dataset Preparing
4.2 Model Training
4.3 Result and Discussions
5 Conclusions
References
Effects of Inter-organizational Activities on Construction Project Resilience in the Context of COVID-19 Pandemic
1 Introduction
2 Theoretical Background
3 Research Methodology
3.1 Data Collection
3.2 Measures
4 Result
4.1 Descriptive Statistics
4.2 Convergent Validity Analysis
4.3 Discriminant Validity Analysis
4.4 Model Fit Analysis
4.5 Structural Model Evaluation
5 Discussion and Contribution
6 Conclusion
References
Conceptualizing Community Participation in the Context of Megaprojects-Induced Internal Displacement
1 Introduction
2 Background
2.1 Historical Context
2.2 Community Participation
3 Research Methodology
4 Determinants and Model Development
4.1 Determinants of Community Participation
4.2 Conceptual Model Development
5 Conclusion
References
Real-Time Detection and Tracking of Defects in Building Based on Augmented Reality and Computer Vision
1 Introduction
2 Methodology
2.1 YOLOv5-Deepsort Model
2.2 Evaluation of Model Performance
2.3 Server-Client (S/C) Architecture
3 Experiments and Results
3.1 Data Processing
3.2 Results
4 Conclusions
References
Precise Urban Green Volume-Enabled Building and Environment Simulation: Sub-meter Voxel Modeling of Airborne and Hand-Held 3D Scans of Urban Trees
1 Introduction
2 Methodology
3 Preliminary Experiment
3.1 Study Area and Settings
3.2 Results
4 Discussion and Future Work
5 Conclusion
References
Exploring Anti-rumor Behaviors in Mega Projects on Sina Weibo: A Text Mining Analysis
1 Introduction
2 Literature Review
2.1 Social Impacts of MPs
2.2 Online Rumors and Anti-rumors
2.3 Echo Chamber Effect on Social Media
3 Methodology
3.1 Data Collection and Preprocessing
3.2 Content Analysis
3.3 Anti-rumor Network Analysis
4 Results
4.1 Framework of Online Anti-rumors
4.2 Anti-rumor Strategy Network
5 Discussion
6 Conclusion
References
Spatial Characteristics Analysis of COVID-19 in Guangdong and Suggestions for Community Prevention
1 Introduction
2 Literature Review
2.1 Community Epidemic Prevention
2.2 The Spatial Distribution Characteristics of Infectious Diseases
3 Research Design
3.1 Research Strategy and Data Sources
3.2 Research Methods
4 Results and Analysis
4.1 Development and Distribution of COVID-19 in Guangdong
4.2 Spatial Agglomeration Characteristics of COVID-19 in Communities
4.3 Spatial Distribution Characteristics of POIs and Traffic Points
4.4 Community Prevention and Control Advice
5 Conclusion and Discussion
References
Public Evaluation of the Effects of River Restoration Projects on Social Benefits
1 Introduction
2 Methodology
2.1 Case Sites
2.2 Data Collection and Analysis
3 Results
3.1 Socio-demographic Information of the Respondents
3.2 Public Evaluation of the CS of the Projects
3.3 Visiting Patterns and Feelings
4 Conclusion
References
Secure Version Management of BIM Using Blockchain and Smart Contract Cluster
1 Introduction
2 Research Methodology
3 Blockchain-Enabled CDE Framework
3.1 Identification of Versioning Activities
3.2 BECDE Framework
3.3 Development of SCC
4 Validation and Evaluation
4.1 Validation in Design Example
4.2 Security Valuation
5 Conclusion
References
Facilitating Integration in Complex Projects: A Case Study
1 Introduction and Background
2 Methodology
3 Results and Findings
3.1 Recognition of Integration Needs
3.2 Taskforce: Design Formal Integrative Mechanisms
3.3 Integrated Digital Delivery: Make Integration as Routines
4 Concluding Remarks
References
Construction Industry Job Image Analysis Among Job-Seekers Based on Social Media Perspective
1 Introduction
2 Literature Review
2.1 Social Media Usage in the Construction Industry
2.2 Social Media Influence on Mindset Changing
2.3 Job Satisfaction and Job Determinant Index (JDI)
3 Methodology
4 Data Acquisition and Pre-Processing
5 Data Analysis
5.1 Key Themes of Research on Remote Work in Relation to the COVID-19 Pandemic
6 Discussion
7 Conclusion
8 Limitations and Future Research
References
Critical Risks Associated with Blockchain Adoption in China’s Construction Supply Chain
1 Introduction
2 Model Development
2.1 Theoretical Foundation
2.2 Conceptual Model
3 Risk Analysis
4 Results and Discussion
5 Conclusions and Recommendations
References
Predicting the Extended Life Cycle Energy Consumption of Building Based on Deep Learning LSTM Model
1 Introduction
2 Literature Review
3 Methodology
3.1 Life Cycle Energy Boundary of Buildings
3.2 LSTM Model Analysis
4 Results and Discussion
4.1 Life Cycle Energy Consumption from 2005 to 2019
4.2 Life Cycle Energy Consumption from 2020 to 2029
5 Conclusion
References
Research on Constraints and Countermeasures for the Development of New Energy Vehicles in China
1 Introduction
2 Methodology
3 Results
3.1 AHP Method Results: Establishment of Weights for EV Restricting Factors and Sub-factors
3.2 Multi-level Fuzzy Comprehensive Evaluation: Calculate the Score Result
4 Discussions and Conclusion
References
Analysing Impacts of Landfill Charge on Recycling Rate Based on a System Dynamics System Model
1 Introduction
2 Research Method
2.1 Model Description
2.2 Casual Loop Diagram and Stock Flow Diagram
2.3 Data Collection
2.4 Model Testing
3 Simulation Results
3.1 Scenario Design
3.2 Recycling Percentage of C&D Waste
4 Discussion
5 Conclusion
References
Overcoming the Effect of Young Workers’ Rebellious Psychology on Unsafe Behavior in Construction
1 Introduction and Research Aim
2 Unsafe Behavior and Influencing Factors
2.1 Types of Unsafe Behavior
2.2 Influencing Factors
2.3 Factor Analysis
3 Young Workers’ Rebellious Psychology
3.1 Concept of Rebellious Psychology
3.2 Causes of Rebellious Psychology
3.3 Consequences of Rebellious Psychology
4 Theoretical Explanation of Rebellious Psychology
4.1 From Equity Theory Perspective
4.2 From Social Psychology Perspective
5 Suggestions for Dealing with Rebellious Psychology to Improve Safety Performance
5.1 Understand Young Workers’ Needs
5.2 Improve Organizational Fairness
5.3 Enhance Young Workers’ Safety Training and Learning
5.4 Implement Psychological Intervention Measures
5.5 Intervene with Technology-Enabled Measures
5.6 Forster a Strong Safety Culture and Environment
6 Summary and Future Research
6.1 Summary
6.2 Future Research
References
Socio-economic Drivers of Energy Consumption: Evidence from Three Urban Agglomerations in the Yangtze River Economic Belt
1 Introduction
2 Literature Review
3 Methodology
4 Data Source
5 Results and Discussion
5.1 Analysis of Decomposition Results in Urban Agglomerations
5.2 Analysis of Decomposition Results in Cities
6 Conclusion
References
The Evolution of Employment Spatial Structure in Shenzhen Based on Mobile Phone Signaling Data
1 Introduction
2 Urban Spatial Structure
3 Data and Methodology
3.1 Study Area
3.2 Data Sources
3.3 Methodology
4 The Evolution of Employment Spatial Structure of Shenzhen
4.1 Characteristics of Employment Distribution in Shenzhen
4.2 The Evolution of Employment Spatial Structure of Shenzhen
4.3 A Comparison with the Urban Spatial Structure in Shenzhen Master Plan
5 Conclusions and Discussions
References
Author Index

Citation preview

Lecture Notes in Operations Research

Jing Li · Weisheng Lu · Yi Peng ·   Hongping Yuan · Daikun Wang Editors

Proceedings of the 27th International Symposium on Advancement of Construction Management and Real Estate

Lecture Notes in Operations Research Editorial Board Members Ana Paula Barbosa-Povoa, University of Lisbon, Lisboa, Portugal Adiel Teixeira de Almeida , Federal University of Pernambuco, Recife, Brazil Noah Gans, The Wharton School, University of Pennsylvania, Philadelphia, USA Jatinder N. D. Gupta, University of Alabama in Huntsville, Huntsville, USA Gregory R. Heim, Mays Business School, Texas A&M University, College Station, USA Guowei Hua, Beijing Jiaotong University, Beijing, China Alf Kimms, University of Duisburg-Essen, Duisburg, Germany Xiang Li, Beijing University of Chemical Technology, Beijing, China Hatem Masri, University of Bahrain, Sakhir, Bahrain Stefan Nickel, Karlsruhe Institute of Technology, Karlsruhe, Germany Robin Qiu, Pennsylvania State University, Malvern, USA Ravi Shankar, Indian Institute of Technology, New Delhi, India Roman Slowi´nski, Pozna´n University of Technology, Poznan, Poland Christopher S. Tang, Anderson School, University of California Los Angeles, Los Angeles, USA Yuzhe Wu, Zhejiang University, Hangzhou, China Joe Zhu, Foisie Business School, Worcester Polytechnic Institute, Worcester, USA Constantin Zopounidis, Technical University of Crete, Chania, Greece

Lecture Notes in Operations Research is an interdisciplinary book series which provides a platform for the cutting-edge research and developments in both operations research and operations management field. The purview of this series is global, encompassing all nations and areas of the world. It comprises for instance, mathematical optimization, mathematical modeling, statistical analysis, queueing theory and other stochastic-process models, Markov decision processes, econometric methods, data envelopment analysis, decision analysis, supply chain management, transportation logistics, process design, operations strategy, facilities planning, production planning and inventory control. LNOR publishes edited conference proceedings, contributed volumes that present firsthand information on the latest research results and pioneering innovations as well as new perspectives on classical fields. The target audience of LNOR consists of students, researchers as well as industry professionals.

Jing Li · Weisheng Lu · Yi Peng · Hongping Yuan · Daikun Wang Editors

Proceedings of the 27th International Symposium on Advancement of Construction Management and Real Estate

Editors Jing Li Department of Geography and Resource Management The Chinese University of Hong Kong Hong Kong, China Yi Peng Department of Emergency Management Nanjing University Nanjing, China

Weisheng Lu Department of Real Estate and Construction University of Hong Kong Hong Kong, Hong Kong Hongping Yuan School of Management Guangzhou University Guangzhou, Guangdong, China

Daikun Wang Department of Geography and Resource Management The Chinese University of Hong Kong Hong Kong, China

ISSN 2731-040X ISSN 2731-0418 (electronic) Lecture Notes in Operations Research ISBN 978-981-99-3625-0 ISBN 978-981-99-3626-7 (eBook) https://doi.org/10.1007/978-981-99-3626-7 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Contents

An Integrated Visualization Framework to Enhance Human–Robot Collaboration in Facility Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yonglin Fu, Junjie Chen, Yipeng Pan, and Weisheng Lu Developing a Robotic System for Construction Truck Crane . . . . . . . . . . . . . . . . Xiao Lin, Songchun Chen, Hongling Guo, and Ziyang Guo

1

11

COVID-19 Impact on the Implicit Value of Open Space in High Density Cities: Evidence from the Hong Kong Housing Market . . . . . . . . . . . . . . . . . . . . . Ruiyang Wang and Shuai Shi

24

Digital Twin Technology for Improving Safety Management in Construction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Patrick X. W. Zou and Songling Ma

40

Interview Methods in Construction and Demolition Research: Based on Case Study and Recommended Best Practices . . . . . . . . . . . . . . . . . . . . . . . . . . Zhikun Ding, Xinrui Wang, Jian Zuo, Patrick X. W. Zou, and Lili Yuan

57

Application of High-Rise Building Fire Rescue Based on BIM and GIS . . . . . . Dongmei Huangfu, Lihui Rong, and Guanglan Wei

74

Analysis of Flow and Stock of Sand and Gravel in Shenzhen Buildings and Associated Environmental Impact . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yao Zhou, Feng He, Jian Liu, Jing Bai, and Huabo Duan

85

Developing Virtual Labs for Engineering Education: Lessons from Leveling Experiment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Baoquan Cheng, Hao Su, Dahao Cheng, and Xiaowei Luo

96

Insights into the Resource Utilization Behavior of Reclaimed Asphalt Pavement Based on Theory of Planned Behavior from Different Stakeholders’ Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dan Chong, Yihao Huang, and Hongyang Li Image Quality Assessment for Construction E-inspection: A Case Study . . . . . Zhiming Dong, Weisheng Lu, and Junjie Chen

104

125

vi

Contents

Housing Choice Willingness of Urban Residents: The Interaction of Tenure Choice, Space Choice, and Time Choice . . . . . . . . . . . . . . . . . . . . . . . . Xiaodong Yang, Huili Li, and Jiayu Yao

134

A 10-Year Review of the Semantic Web Technology Applications in Building Energy Reductions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xiaoyue Yi, Llewellyn Tang, Mengtian Yin, and Haotian Li

150

An Empirical Analysis of Key Factors of Construction and Demolition Waste Management Using the DEMATEL Approach . . . . . . . . . . . . . . . . . . . . . . Wei Bin, Hongping Yuan, and Xiaozhi Ma

164

How Can Robot Replacement Be Achieved? – Technology Development Direction for Automatic Construction Robot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xinyao Ma, Chao Mao, Xiao Li, and Chengke Wu

173

A Study of Factors Influencing Community Health Transformation in The Post-epidemic Era . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lu Liu, HongLin Lu, and Xun Zhang

189

Research on the Influencing Factors of the Transformation of Migrant Workers into Industrial Workers in China’s Construction Industry . . . . . . . . . . . Zhiyu Huang, Ye Liu, Qili Li, Hongxia Li, and Yanling Ruan

199

Improving Safety Compliance of Construction Workers: The Role of Safety Communication, Management Commitment to Safety, and Perceived Ease of Use . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Diya Yan and Xianbo Zhao GRA-Fuzzy-Based Green Urban Planning Scheme Decision-Making . . . . . . . . Zhenjun Nie, Chenghao Zhou, and Jihuan Zhuo

213

225

Path Analysis of Regional Carbon Lock-in and Unlocking from a Qualitative Comparative Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yang Chen, Tianxin Lai, Jingke Hong, and Yue Teng

235

The Influence of Real Estate Investment on Economic Development: From New Production Element Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ben Pang, Rui Liu, and Jingfeng Yuan

253

A Study of the Relationship Between Psychological Capital and Unsafe Behavior of Construction Workers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Wenmin Gao, Xiaoli Yan, and Hongyu Chen

265

Contents

BIM-Enabled Design for Hospital Retrofit in China: A Case Study . . . . . . . . . . Yue Xu, Tan Tan, Jinying Xu, Ke Chen, and Qi Zheng Holistic Analysis of the Influencing Factors of Construction 4.0 Technology Implementation in the Construction Industry: A Twin Sustainable and Digital Transition Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . Qian Zhang and Chang Liu Status Quo of Construction and Demolition Waste Management in Guangdong-Hong Kong-Macao Greater Bay Area Based on Desktop Survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Run Chen, Huanyu Wu, Qiaoqiao Yong, and Bo Yu Urban Resilience: A Systematic Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jiayu Li, Liyin Shen, Shiju Liao, and Meiyue Sang Research on Cement Price Fluctuation Prediction Based on EEMD-ARIMA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fan Yao, Hui Zeng, Tongfei Liu, and Yuwei Wu Construction Equipment Maintenance Supervision: An i-Core and Blockchain-Enabled Conceptual Framework . . . . . . . . . . . . . . . . . . . . . . . . . . Liupengfei Wu, Weisheng Lu, Lang Zheng, Jinfeng Lou, and Wenjun Gao

vii

280

291

300

313

324

340

Optimizing Efficiency of Energy-Saving Service Industry Based on SE-SBM Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hui He, Albert P. C. Chan, and Qinghua He

353

Automatic Classification of Remote Sensing Images of Landfill Sites Based on Deep Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jiayuan Wang, Qiaoqiao Yong, Huanyu Wu, and Run Chen

366

RFID-BIM-Enabled Reuse of “Fangcang Shelter Hospitals” Modular Components During the Post-Covid-19 Era . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Wenjun Gao, Weisheng Lu, and Liupengfei Wu

379

How Does Leadership Style Affect Safety? A Mixed-Methods Investigation for the Influence of Superiors’ Varying Leadership Style on the Stress and Safety of Construction Workers . . . . . . . . . . . . . . . . . . . . . . . . . Lin Mei, Qi Liang, and Yuanyuan Qiu Applications of Artificial Intelligence Enabled Systems in Buildings for Optimised Sustainability Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cheng Siew Goh and Hey Yee Wang

389

405

viii

Contents

Coupling Coordination Development of Urban Resilience in Yangtze River Delta . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Beibei Zhang and Dongyue Zhan

417

Sewer Sediment Inspection Based on Multisensor Fusion Considering Sewage Flow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chen Li, Ke Chen, Hanlin Li, Yixiao Shao, and Hanbin Luo

431

Promoting the Competency of Construction Management Postgraduates: A Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mingsen Dai, Shang Zhang, Qianqian Xu, and Hao Zhou

440

Edge Computing-Based Real-Time Blind Spot Monitoring System for Tower Cranes in Construction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xinqi Liu and Wei Pan

452

Research on Carbon Emission of Residential Buildings . . . . . . . . . . . . . . . . . . . . Yanmin Li, Lei Yuan, Xiaoqiang Gong, Feiping Zeng, and Zengwen Bu

466

Correlation for Project Decision Making Process Between Green Building Proposal Evaluation and Life Cycle Costing Applications . . . . . . . . . . Cuong N. N. Tran, Nhung T. T. Nguyen, and Vivian W. Y. Tam

475

Exploring the Spatial-Temporal Evolution Characteristics of Urban Eco-efficiency: A Case Study of 276 Chinese Cities . . . . . . . . . . . . . . . . . . . . . . . Xi Cai, Yu Zhang, Mengxue Li, Liudan Jiao, and Xiaosen Huo

484

Spatiotemporal Evolution the GTFP of the Construction Industry--Empirical Analysis Based on the Yangtze River Economic Belt . . . . . Hongsheng Kan, Yuanyuan Dong, Bin Hu, Jingxiao Yu, Yajun Chen, and Jinxian Zhao Federated Learning Based Collaboration Framework of Data Sharing for Intelligent Design of Residential Buildings . . . . . . . . . . . . . . . . . . . . . . . . . . . . Qiqi Zhang and Wei Pan Design for Manufacture and Assembly (DfMA) Communication Network and the Impact of COVID-19 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Vikrom Laovisutthichai, Weisheng Lu, K. L. Tam, and Stephen Siu Yu Lau The Impact of Internationalization on Corporate Social Responsibility: Evidence from Chinese Listed Construction Companies . . . . . . . . . . . . . . . . . . . . Meiyue Sang, Lingyu Zhang, and Jiayu Li

501

516

533

547

Contents

ix

A Simulation-Driven Data Collection Method of External Wall by Integrating UAV and AR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dianwei Song, Yi Tan, Penglu Chen, and Shenghan Li

561

A Systematic Review of Quantitative Measurement Methods for Accessibility of Urban Infrastructure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Gunjun Li, Zhongwei Xiong, and Yanqiu Song

574

Urban Renewal Planning Strategies Guided by Public Values . . . . . . . . . . . . . . . Xu Yu

593

Research on the Similarity of Highway Construction Projects Based on EWM-GRA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bo Yu, Liudan Jiao, Yu Zhang, and Xiaosen Huo

602

Understanding the Role of Housing in Family Reunion: Evidence from Rural-Urban Migrant Families in China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jun Qiu and Ping Lv

615

The Characteristics of Land Use Around Rail Transit Stations in Tianjin, China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yifei Wu, Junhong Zhou, and Yani Lai

633

Evolution Analysis Model and Catastrophe Theory Explanation of Shrinking Cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xiaoteng Ma, Donghan Meng, and Guijun Li

649

Battery Storage Analysis for Residential Solar Photovoltaic Systems . . . . . . . . . Zheng Wang, Mark B. Luther, Peter Horan, Jane Matthews, and Chunlu Liu

669

Making Decisions for Urban Regeneration: A Bibliometric Analysis and Critical Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yan Liu, Yi Yang, and Haotian Zhang

679

A Scientometric Review on Real Estate Investment Trusts: Towards a New Asset-Information-Capital Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Huicang Wu, Hui Sun, Qian Tian, and Yingzi Liang

695

The Spatial Relationship Between Rail Transit Network and Population and Employment Density in Tianjin, China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Junhong Zhou and Yani Lai

706

x

Contents

Model Development to Link Cultural Intelligence and Individual Work Performance: Mediator and Moderator Considerations . . . . . . . . . . . . . . . . . . . . . Djoen San Santoso, Jungang Luo, Hecai Song, and Miao Li

720

Understanding Causes and Resolutions of Construction Disputes: A Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Haijun Gu, Shang Zhang, and Qianqian Wang

727

Research on the Impact of Market Sentiment on the Second-Hand Housing Market . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Deheng Zeng, Jinyu Wang, and Yan Shan

739

Research on Emergency Decision Making Considering Decision-Maker Peference Based on Improved Regret Theory—A Case Study of Covid-19 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yang Su and Sun Taibao

749

Integrating BIM and Quality Standards for Highway Construction Inspection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xin Xu, Kaiwen Chen, Jingwen Zhou, Jiawei Chen, and Xin He

765

Influence of Spatial Ability on Virtual Annotation Response in Construction Equipment Teleoperation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xiaomeng Li, Jiamin Fan, and Xing Su

776

Risk Assessment of Rail Transit Equipment Failure Disaster Chain Based on the Complex Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xingyu Chang, Liudan Jiao, Xiaosen Huo, and Yu Zhang

786

Transmission Strength Evaluation of Metro Safety Risks: An Integrated Study of Causal and Coupling Relationship . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Wenhua Zeng, Na Xu, Keyi Di, Wencheng Zhao, and Bo Zhang

801

Conceptualizing Key Performance Indicators for Building Critical Infrastructure Resilience Through Public-Private Partnership . . . . . . . . . . . . . . . Godslove Ampratwum, Robert Osei-Kyei, and Vivian W. Y. Tam

811

Decoration and Renovation Waste Recycling Intention of Homeowners: A Perceived Value Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xinping Wen, Zhikun Ding, and Chunbao Yuan

823

The Influence of 2D/3D Urban Spatial Form Indicators on Surface Urban Heat Island Based on Spatial Regression Models: A Case Study of Hangzhou, China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Haotian Chen and Sheng Zheng

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Contents

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Application Analysis of Existing Industrial Robots in Precast Concrete Component Factory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jianqiu Bao, Huanyu Wu, Yongqi Liu, Yuang Huang, and Yongning Niu

856

Gauging the Knowledge Development of Innovations in Mega-infrastructure Projects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Long Li, Shuqi Wang, Haiying Luan, and Shengxi Zhang

867

The Analysis of Urban Expansion Based on Space Syntax: A Case Study of the Main Urban Area of Hangzhou, China . . . . . . . . . . . . . . . . . . . . . . . . Yukuan Huang and Sheng Zheng

885

An Evolutionary Game Analysis of Organizational Relational Behavior in Megaprojects Considering the Reciprocal Preference . . . . . . . . . . . . . . . . . . . . Chunxi Luo, Xian Zheng, and Chunlin Wu

898

Research on Evaluation of Construction Workers’ Job Satisfaction Based on Improved AHP-FCE Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Peng Peng, Weishu Zhao, Xinran Deng, Bao Guo, and Weidong Wu

911

Modularization Considerations for Modular Integrated Construction in Hong Kong: A Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jinfeng Lou, Weisheng Lu, Liupengfei Wu, and Frank Ato Ghansah

927

Complexity Management of Emergency Projects from the Perspective of Complex Adaptive Systems Theory—The Case of the National Exhibition and Convention Center (Shanghai) . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zhiwei Chen, Xian Zheng, Ju Bai, and Tao Huo

938

The Application Status and Outlook of CGE Model in the Construction Sector Under the Dual-Carbon Target . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Weina Zhu, Jiannan Jiang, Boyang Liu, and Chengshuang Sun

951

ISM-MICMAC Model-Based Construction Risk Evaluation for Green Retrofit Project of Public Buildings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shengnan Li, Xiaosen Huo, and Liudan Jiao

960

Multiscale Evaluation of the Cooling Effect of Greenspace in Urban Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jia Siqi and Wang Yuhong

975

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Contents

Blockchain-Based Decentralized Reputation Framework: Understanding the Residents’ Satisfaction About Living House with Trustworthiness Consideration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xing Pan, Botao Zhong, Luoxin Shen, Jun Tian, Xueyan Zhong, and Xiaowei Hu Research on Job Stressors and Mental Health of Construction Practitioners in China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Qianqian Xu, Shang Zhang, Lilin Zhao, Mingsen Dai, Haoxiang Li, and Haijun Gu

988

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Heterogeneous Local Policy Responses to Housing Market Regulation: An Interpretive Framework and Evidence from 177 Chinese Cities . . . . . . . . . . 1011 Yuesong Zhang, Shuhai Zhang, Wei Jing, and Dapeng Xiu A Double Deep Q-Network-Enabled Two-Layer Adaptive Work Package Scheduling Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1027 Yaning Zhang, Xiao Li, Chengke Wu, and Zhi Chen Research on the Differences of Job Stressors Among Construction Project Managers in China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1042 Haoran Xu, Shang Zhang, and Qiqing Zhong Research on the Influencing Factors of Concrete Waste Production in the Whole Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1055 Zhi-yu Huang, Qi-li Li, Ye Liu, Yan Li, and Rui Liu Measurement of Carbon Emission Rebound Effect of Construction Industry Based on Technological Progress . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1069 Li Wen and Xiaoli Yan Accelerating Urban Green Economic Growth Through Government Guidance Funds: Case Study of a National Green Finance Pilot City . . . . . . . . . 1084 Yanan Xue, Hongdi Wang, and Qing Yang Analysis on the Critical Node of the Chengdu-Chongqing Economic Circle Based on the Expressway Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1097 Xinyu Zhang, Liudan Jiao, Liu Wu, and Ya Wu Security Assessment for Indoor Spaces: A Framework Based on 3D Space Syntax and BIM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1109 Hui Deng, Dongyang An, Yiwen Xu, and Yichuan Deng A Bibliometric Analysis of Smart Tourism City Research . . . . . . . . . . . . . . . . . . 1123 Hongman He, Wenyu Ye, and Shuang Feng

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Risk Analysis for Green Renovation Project of Public Buildings Based on EWGM-FMEA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1134 Tong Hao, Xiaosen Huo, and Liudan Jiao Automated Detection for the Reserved Rebars of Bridge Pile Caps Based on Point Cloud Data and BIM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1147 Limei Chen, Shenghan Li, and Yi Tan Research on Quality of Prefabricated Construction Components Based on MIV-BP Neural Network Optimization Algorithm . . . . . . . . . . . . . . . . . . . . . . 1163 Shu Wen, Qingyi Yu, Shuo Li, and Zhenchao Guo Construction Management in the Post Covid Era: Towards Improving Construction Productivity in Developing Countries - Example from Nigeria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1176 Muhammad Nasir Ibrahim, Ahsan Nawaz, Xing Su, and Abubakar Sadiq Ibrahim Inverse Generative Design: A Guideline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1186 Ziyu Peng, Weisheng Lu, Xu Tang, and Chris Webster Can Construction Enterprises Adopt Digital Transformation Behavior? A Dynamic Game Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1198 Long Li, Ziwei Yi, Tengteng Wang, and Haiying Luan Automated LiDAR Scan Planning of 3D Indoor Space Based on BIM and an Improved GA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1214 Yuzhe Chen, Yi Tan, and Shenghan Li Exploring the Capabilities Required for Construction Expatriates Functioning Effectively in Unfamiliar Technical Context . . . . . . . . . . . . . . . . . . . 1222 Jungang Luo, Rui Zhang, Deliang Wang, and Djoen San Santoso Applications of 4D Point Clouds (4DPC) in Digital Twin Construction: A SWOT Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1231 Dong Liang and Fan Xue Optimization of Housing Retrofit Policies: A Perspective of Homeowners’ Motivations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1239 Zhuo Xu, Xin Hu, and Guo Liu China’s International Engineering Risks in the Post-pandemic Age Based on Network Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1248 Lijia Shao, Zhaoqian Liao, and Lin Yang

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Life Cycle Carbon Emission Assessment of Prefabricated Buildings: A Case Study in Nantong, China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1262 Qiwen Chen and Peng Mao A Bibliometric Review of the Carbon Emissions and Machine Learning Research in the Post-COVID-19 Era . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1278 Peiyi Liao and Dan Chong Job Stress of Chinese Construction Project Management Personnel in Project Overall Implementation Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1291 Zihao Wang, Shang Zhang, and Yi Zhu How has COVID-19 Pandemic Influenced the Quality Assurance of Cross-Border Construction Logistics and Supply Chain? A Conceptual Analysis Based on Ishikawa Diagram . . . . . . . . . . . . . . . . . . . . . . . 1301 Frank Ato Ghansah, Weisheng Lu, Benjamin Kwaku Ababio, and Jinfeng Lou Study on the Law of Flue Gas and Temperature Propagation at Different Fire Locations in Subway Stations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1315 Xinyu Wang, Liudan Jiao, Xiaosen Huo, and Ya Wu Inclusion of Durability of Recycled Aggregate Concrete in Life Cycle Assessment (LCA) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1328 Weiqi Xing, Vivian W. Y. Tam, Khoa N. Le, Jian Li Hao, and Jun Wang Industrialized Construction Firms and Digitally-Enabled Product Platforms: An International Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1337 Shanjing Zhou Estimating Embodied Carbon Reduction in Modular High-Rise Residential Buildings Through Low Carbon Concrete . . . . . . . . . . . . . . . . . . . . . . 1357 Siwei Chen, Yang Zhang, Yue Teng, Chi Sun Poon, and Wei Pan Machine Learning Approach to Examine the Influence of the Community Environment on the Quality of Life of the Elderly . . . . . . . . . . . . . . . . . . . . . . . . . 1370 Qi Liang, Yang Zhou, and Qin Li Assess the Reusability Potential of Building Products in an Early Design Stage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1382 Qi Han and Nick Kentie Risk Assessments with Probabilistic Linguistic Information for Green Building Projects - The Case of Vietnam . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1396 Lina Wang and Daniel W. M. Chan

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Mechanism and Collaborative Governance of Public Participation in Urban Renewal Project . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1405 Hao Liu and Beibei Zhang The Influence of Institutional Regulation on Megaproject Social Responsibility: The Moderating Effect of Political Connection . . . . . . . . . . . . . . 1419 Delei Yang, Jiawen Li, Qinghua He, Jun Zhu, and Kexin Dong Quality Control in Modular Construction Manufacturing During COVID-19: Process and Management Standardization . . . . . . . . . . . . . . . . . . . . . 1437 Zhongze Yang and Weisheng Lu Factors Influencing the Promotion of Green Building Materials: Perspective of Multiple Stakeholders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1448 Guanying Huang, Dezhi Li, and S. Thomas Ng Examining the Use of BIM-Based Digital Twins in Construction: Analysis of Key Themes to Achieve a Sustainable Built Environment . . . . . . . . 1462 Karoline Figueiredo, Vivian W. Y. Tam, and Assed Haddad Investigating the Competency of Project Managers in the Chinese Construction Industry: A Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1475 Haoyu Wang, Shang Zhang, and Chen Wang Study on Collaborative Development Planning of Airport and City . . . . . . . . . . 1485 Guangtao Zhang Research on the Causes of Safety Accidents in Super High-Rise Buildings—Empirical Analysis Based on Bivariate Probit Model . . . . . . . . . . . . 1499 Bing Zhang and Qian Lu Research on the Carbon Emission Prediction of Chongqing Transportation Industry Based on Scenario Analysis . . . . . . . . . . . . . . . . . . . . . . . 1522 Ying Liu, Liudan Jiao, Ya Wu, and Liu Wu Life Cycle Application and Optimization of BIM+VR in Hospital Buildings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1538 Yuyang Liu, Rong Leng, Lan Luo, and Qiushi Bo In-Depth Understanding of Construction Robot Research a Bibliometric Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1553 Yongqi Liu, Huanyu Wu, Yuang Huang, and Jianqiu Bao Domain Ontology Development Methodology for Construction Contract . . . . . 1566 Saika Wong, Jianxiong Yang, Chunmo Zheng, and Xing Su

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Carbon Emission Reduction Indicators in Green Building Evaluation System Based on Meta-analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1576 Xinru Qu and Xiaojing Zhao A Comparative Analysis of Green Construction Material Certification Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1585 Jindao Chen Game Engine-Based Synthetic Dataset Generation of Entities on Construction Site . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1602 Shenghan Li, Yaolin Zhang, and Yi Tan Effects of Inter-organizational Activities on Construction Project Resilience in the Context of COVID-19 Pandemic . . . . . . . . . . . . . . . . . . . . . . . . . 1615 Kangda Wan, Liyue Tan, Shiyu Bian, and Wenxin Shen Conceptualizing Community Participation in the Context of Megaprojects-Induced Internal Displacement . . . . . . . . . . . . . . . . . . . . . . . . . . 1627 Shuang Zhang, Jamie Mackee, Michael Sing, and Liyaning Maggie Tang Real-Time Detection and Tracking of Defects in Building Based on Augmented Reality and Computer Vision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1638 Wenyu xu, Yi Tan, and Shenghan Li Precise Urban Green Volume-Enabled Building and Environment Simulation: Sub-meter Voxel Modeling of Airborne and Hand-Held 3D Scans of Urban Trees . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1651 Qianyun Zhou, Jiajia Wang, Bin Chen, and Fan Xue Exploring Anti-rumor Behaviors in Mega Projects on Sina Weibo: A Text Mining Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1660 Chen Shen and Xiangyu Li Spatial Characteristics Analysis of COVID-19 in Guangdong and Suggestions for Community Prevention . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1673 Fan Wu, Yuxuan Li, Junjie Ma, Zilin Chen, and Weijia Luo Public Evaluation of the Effects of River Restoration Projects on Social Benefits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1682 Yang Chen, Yuhong Wang, and Charissa Chi Yan Leung

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Secure Version Management of BIM Using Blockchain and Smart Contract Cluster . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1692 Xingyu Tao, Moumita Das, Yuhan Liu, Peter Kok-Yiu Wong, Xingbo Gong, and Jack C. P. Cheng Facilitating Integration in Complex Projects: A Case Study . . . . . . . . . . . . . . . . . 1701 Yinbo Li, Cheryl Shu-Fang Chi, and Yilong Han Construction Industry Job Image Analysis Among Job-Seekers Based on Social Media Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1710 Angela Palaco and Xing Su Critical Risks Associated with Blockchain Adoption in China’s Construction Supply Chain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1723 Xiaoyue Lv, Zhaoqian Liao, and Lin Yang Predicting the Extended Life Cycle Energy Consumption of Building Based on Deep Learning LSTM Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1737 Lei Liu, Vivian W. Y. Tam, Khoa N. Le, and Laura Almeida Research on Constraints and Countermeasures for the Development of New Energy Vehicles in China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1747 Ziwei Chen and Liyin Shen Analysing Impacts of Landfill Charge on Recycling Rate Based on a System Dynamics System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1758 Mingxue Ma, Vivian W. Y. Tam, Khoa N. Le, and Robert Osei-Kyei Overcoming the Effect of Young Workers’ Rebellious Psychology on Unsafe Behavior in Construction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1767 Patrick X. W. Zou and Ruili Wang Socio-economic Drivers of Energy Consumption: Evidence from Three Urban Agglomerations in the Yangtze River Economic Belt . . . . . . . . . . . . . . . . 1783 Mengxue Li, Yu Zhang, Xi Cai, Liudan Jiao, and Xiaosen Huo The Evolution of Employment Spatial Structure in Shenzhen Based on Mobile Phone Signaling Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1797 Chunmei Chen and Yani Lai Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1811

An Integrated Visualization Framework to Enhance Human–Robot Collaboration in Facility Management Yonglin Fu, Junjie Chen(B) , Yipeng Pan, and Weisheng Lu Department of Real Estate and Construction, University of Hong Kong, Hong Kong SAR, China [email protected]

Abstract. The global pandemic has sparked the popularity of robots in facility management tasks (FMTs) such as floor cleaning and disinfection. This trend brings many task scenarios where humans and robots need to cooperate with each other. Effective human–robot collaboration (HRC) relies on precise communication of the robots’ intentions (e.g., to move to a position, or to grasp an object), so that their human counterparts can adapt their behaviors/actions accordingly. However, little has been known on how this can be done in FMTs. Visualization technologies have potential to enhance HRC by communicating the robot intention in a visualized manner. This research aims to develop a framework that integrates the latest visualization technologies, e.g., building information modelling (BIM) and augmented reality (AR), to enhance HRC in facility management. The framework includes two complementary modules: (a) a remote monitoring module (RMM) that can remotely transmit and visualize robot information in a Web-based BIM to inform decision-making, and (b) an onsite collaboration module (OCM) that augments human co-workers with real-time robot intention to allow effective cooperation. Experiments were conducted to validate the proposed framework in typical FMTs. Results show that the integrated visualization framework can intuitively and unambiguously convey robots’ intentions to their human counterparts, significantly improving the performance of HRC. Future research is suggested to complement the framework with a reverse mechanism to effectively convey human intentions to robots. Keywords: facility management tasks · human–robot collaboration · visualization

1 Introduction The rapid spread of COVID-19 pandemic has posed tremendous challenges to the world. Society is taking action to stop the further spread of this highly contagious virus. From a technical perspective, robots are suggested to hold significant potential in the fight against pandemics, as they are immune to infection and can be easily disinfected [1, 2]. This has significantly accelerated automation and robotization in all industries [3]. In the built environment sector, building owners and facility managers are turning to © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 1–10, 2023. https://doi.org/10.1007/978-981-99-3626-7_1

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new digital technologies, like robotics and artificial intelligence (AI), as evidenced by the increasing number of facility management tasks (FMTs) being performed by robots instead, such as floor cleaning and disinfection [4]. By utilizing robots to assist with repetitive physical tasks, human workers can focus on higher-value tasks, including flexible and professional tasks alongside the robot [5], or remote supervision of the robot [6]. In other words, humans can potentially transition their roles to co-workers or high-level managers of robots. To enable humans and robots to collaborate more efficiently and safely, many efforts have been focused on research and implementation of human–robot collaboration (HRC) systems [7–9]. Effective HRC is highly reliant on precise communication of the robots’ intentions. For example, human counterparts should understand the current motion of the robots, and predict robots’ upcoming movement, so that humans can adjust their actions and plans accordingly. This implies robots must have a means of expressing their intent, such as their work plans and next movement. However, little has been known on how this can be done in FMTs. Visualization technologies have the potential to improve HRC by enabling humans to understand the robot’s intention in a visual manner. In the past years, visualization technologies combined with the latest smart technologies, e.g., building information modelling (BIM) and augmented reality (AR), have significantly changed facility management. Liu and Issa utilize BIM and geographic information systems (GIS) to detect and map pipe network information in order to visualize the facility management (FM) process of pipelines both inside and outside of buildings [10]. Baek et al. [11] proposed an AR method for facility management that uses image-based indoor localization to present location-specific data. Moreover, some studies have combined these new technologies to pave the way for the deployment of facility management robots. For example, to achieve better indoor localization, Chen et al. used BIM as a reference to correct and fine-tune the rough camera pose estimated by photogrammetry [12]. However, there is still a lack of using visualization technologies to improve HRC in FM. As the growing number of FMTs require collaboration between humans and robots, there is an urgent need to visualize the robot’s intentions in a form that is easily understood by humans. This research aims to develop an integrated visualization framework with BIM and AR for this need, mainly involving two complementary modules, the remote monitoring module (RMM) and the onsite collaboration module (OCM) respectively. For remote monitoring, the visualization of robot intent in RMM enables managers to understand the location, status, and work of the robot at the moment. This may allow for higher reliability and better schedule control, thereby improving the performance of facility robots to optimize building operations. Besides, FM co-workers can utilize visualization technologies through OCM to communicate the robot’s intention onsite, so as to change human action and behavior accordingly and keep safety. By integrating the edge-cutting visualization technologies, this framework extends traditional HRC in shared workspaces, to jointly facilitate human–robot interoperability from both remote monitoring and on-site collaboration. Humans and robots will better collaborate with each to accomplish FMTs through this integrated visualization framework.

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2 Related Work 2.1 Robots in Facility Management By integrating AI and advanced sensors, robots become a perfect solution for a wide range of FMTs [13]. The cleaning robot is a common case in our daily life since they are perfect for automated cleaning with great precision in the dynamic built environment and providing proven cleanliness [14]. Besides, some robots are used for inspection work such as pipe monitoring [15]. Furthermore, with the COVID-19 pandemic, there is a surge in the adoption of robots. For example, indoor disinfection has become a usual task during the post-Covid era, resulting in the development and adoption of disinfection robots, which can avoid the exposure risk to humans and enhance the disinfection quality [4]. While autonomous robots can work onsite independently in most cases, it is necessary to consider a larger team that consists of robots working autonomously onsite and humans who will monitor or work with robots. Understanding the intentions of team members is key to effective collaboration [16]. In human–robot teams, poor communication of robot intents may degrade safety and task performance. Robot engineers may know the details of how the robot will make decisions while non-training users may not understand the robot. Thus, some studies have made it easier for non-specialists to understand robot intent by visualization. Coovert et al. [17] extended the ability of mobile robots to communicate intended actions to humans through visual arrow projections and simplified maps. Schaefer et al. [18] developed a display interface to communicate the intent of intelligent agents so as to engender trust in humanagent teams. However, most of studies address a single scenario, such as displaying robot intent from a remote location or projecting robot intent in a shared workspace. Fewer research pays attention to integrating them into one implementation framework. FMTs are diverse, requiring robots to be able to perform tasks both independently and work with humans in a shared workspace. Therefore, it is important to develop an integrated HRC framework that can serve both remote and field ends. 2.2 Visualization Technology in Facility Management Visualization is important since it helps humans intuitively understand and process data more easily. Conventionally, the meaning of the data is presented to the user in a twodimensional format through static visualization techniques (e.g., Gantt charts, and bar charts). Such static visualization techniques are insufficient for presenting real-time sensor data in an interactive manner. To address this limitation, several studies have explored the development of alternative visualization methods that can effectively and intuitively present real-time data during FM operation. Chang et al. [19] developed a visualization platform to integrate Internet of Things (IoT) data with BIM for decision support in FM. Natephra and Motamedi [20] combined the live environment data captured by multiple sensors with the BIM model and visualized them in an AR environment. Some efforts are paid to combine these visualization technologies into robotized indoor localization. For example, Chen et al. proposed an indoor localization method for BIM based on 3D style-transferred BIM [21] and further compared these generated images with real-world images to estimate the camera pose [12]. Visualization technology has shown power in

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FM operation, however, its application in HRC has received little attention. By using the state-of-the-art visualization techniques, humans have the potential to access the intentions of robots more intuitively and thus work together. Even though there are some visualization interfaces for robot developers to test and evaluate their robots, it is tough for those FM workers without special training to understand and communicate robot intentions. In this context, we aim to develop an intuitive visualization framework for FM operation that allows humans and robots to better collaborate on FMTs.

3 Visualization Framework of HRC in Facility Management 3.1 Integrated HRC Framework Overview As shown in Fig. 1, an implementation framework is proposed to support the collaboration between humans and robots in FM. The proposed framework consists of three processes, i.e., task assignment, task execution, and information archive.

Fig. 1. Integrated HRC visualization framework

(1) Task assignment In human–robot teams, it’s common for there to be an organizational structure that extends beyond just single human–robot interaction. This structure typically involves a hierarchical system with a small number of managers at the top, and a larger group of agents, including robots and their potential co-workers, at the bottom. The human managers are responsible for controlling the broad goals and assigning and prioritizing tasks according to the needs of the context. In this structure, FM managers first make a task definition (e.g., space scope, task content), and determine whether robots need to work with human collaboration. FM Team members then execute their own tasks.

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(2) Task execution As shown in Fig. 1, FM team members can easily communicate with each other benefiting from the robot’s intent visualization. Two complementary modules are developed in this visualization framework, the remote monitoring module (RMM) and the onsite collaboration module (OCM) respectively. During the task execution process, FM managers can monitor the robots through the developed RMM, where robot remotely report their intent to FM managers, such as their status and captured videos of actual working area. Besides, their supervisors may know the working progress of robots from RMM. FM managers are truly involved in the robot’s work by monitoring the ongoing processes and intervening when problems occur. Once some unexpected situations are discovered through RMM, FM managers are able to inform FM co-workers to check and operate onsite, where OCM can also be helpful. OCM allows FM co-workers to obtain the robots’ intent when they collaborate with robots in a shared space. The basic information of the robot such as the robot’s ID and work content will be presented to humans. Also, the next movement of robots, which implies the dangerous area that the robot is likely to reach, could be visualized through OCM for safety concerns. FM co-workers and robots can therefore collaborate smoothly onsite using OCM. The details of both two modules are described in the next. Note that RMM and OCM can be operated independently. In many cases, tasks (e.g., floor cleaning) can be executed by autonomous robots smoothly without humans’ cooperation onsite, so FM managers just need to monitor robots’ status and intent from RMM. On the other hand, the information obtained from RMM is not necessary during the operation of OCM. (3) Information archive The proposed visualization framework provides more access to record the necessary information during the task execution process, which will be used for backup check and future review. The information to be stored varies depending on the type of FMTs, including but not limited to the work content, action trajectory, task duration, etc. 3.2 Remote Monitoring Module (RMM) The further FM managers are from the robot, both physically and operationally, the more important it is to have transparency in communication and proper trust. Therefore, humans expect to monitor them in real time although autonomous robots are allowed to perform tasks on their own. To meet this need, RMM is developed to access relevant information in an understandable manner from robots to humans remotely. Human managers who are interested in both the status and progress of the FM robots in working conditions will have remote oversight of the robots through RMM. By using this visualization system, FM managers in the control center can analyze the data sent by the robot in real time. RMM is mainly used to deal with two situations: responding to unexpected situations and controlling task schedules. (1) Responding to unexpected situations: It is hard to guarantee that a robot will complete its task 100% successfully, and there will inevitably be some unexpected situations. For example, the robot may appear in a position outside the planned range. In this

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situation, the abnormal robot needs to be called off quickly and managers should immediately send the necessary information to FM co-workers. (2) Controlling task schedules: It is common that many robots are working at the same time. FM managers can monitor the work progress through RMM, so as to change actions or modify plans due to changes in goal priorities. For example, managers can schedule spare robots to catch up with progress. 3.3 Onsite Collaboration Module (OCM) In many cases, robots should collaborate with human workers and share their workspace. For example, the disinfection robot is unable to disinfect hard-to-reach surfaces such as drawers, and therefore, requires human assistance to pull open the drawers. In human– human cooperation, it is generally straightforward to anticipate the intent of the other person based on previous experience. However, when collaborating with robots, it can be difficult or impossible to discern their intentions. This can lead to misunderstandings and safety hazards in human–robot teams. To address this issue, it’s crucial to enable robots to communicate their intentions to their human co-workers. By incorporating AR technology in the proposed OCM, robots can project information and intentions onto the real environment, allowing human colleagues to stay informed about the work area’s safety, the next robot manipulation task’s location, and the upcoming subtasks. This will improve the confidence and efficiency of human–robot collaboration.

4 Experimentation and Result In the following, we presented the developed RMM and OCM interface respectively and discussed the benefit and limitation of proposed framework. 4.1 RMM Demonstration In more generalized situations, FM managers are non-specialized users. Therefore, it is important to develop a newer-friendly interface for monitoring robots, where robots may report their intent to human managers intuitively. BIM is integrated into RMM as it provides a user-friendly 3D visualization interface for humans. We designed the RMM as a web application for its feature that enables users to access data from anywhere via a personal computer or smartphone. Figure 2 shows the real-time monitoring process using RMM. As shown in Fig. 2, the status of robot can be visualized in the web page. Compared with 2D map, 3D is more intuitive to human managers. The video is captured through the camera that installed in front of the robot and the pose is obtained from their LADAR and IMU data. By combining the robot’s real-time information with the existing BIM model, FM managers can easily understand the intent of robots. Besides, robots may communicate with humans and convey any problems they encounter, as well as suggest an appropriate course of action. The developed RMM makes robots more reliable for FMTs by visualizing the robot’s intentions to human managers.

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Fig. 2. Demonstration of RMM

4.2 OCM Demonstration The OCM requires co-workers to hold AR devices. Compared to AR devices based on handheld mobile devices such as tablets, head-mounted AR devices can help co-workers release their hands and thus gain more flexibility. We developed an AR-based method in Unity 3D (U3D), where a virtual robot described by Unified Robot Description Format (URDF) was connected to the real UR5e robot with a OnRobot-RG2 gripper through Robot Operating System (ROS). Microsoft Hololens2, one kind of HMD, is adopted for providing human co-workers with additional information about robots in an AR environment. Figure 3 shows the implementation of OCM. In a head-mounted display (HMD), the virtual robot should first be calibrated with the real robot, and the robot’s next action will be shown in advance through the virtual robot. This can be a useful aid for co-workers to understand the intent of the robot for better collaboration. In addition, taskrelated information and the scope of work are also be presented in the AR environment. With the help of OCM, co-workers can quickly build trust with the robot, get familiar with the task, and get better task performance. 4.3 Discussion A good visualization framework has many benefits. It provides a good means of promoting trust in human–robot teams by communicating robot intent in a visualization manner. Comprehending the intent of a robot within a collaborative framework facilitates the selection of appropriate responses and actions based on the expected behavior of the robot. This enhances overall team performance by reducing communication and reaction times, as well as minimizing the occurrence of errors. Good visualization reduces the workload of human team members from a cognitive and time perspective, leaving humans with more time to complete other tasks.

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Fig. 3. Demonstration of OCM

The proposed visualization framework to support communication robots still faces some limitations. First, this visualization framework is limited to one-way intent transfer from robot to human; however, it is also important for robots to understand human intentions so that they can make behavioral adjustments on their own. Another limitation may arise from the given information in the visualization interface. There are many different types of collaboration situations to consider when adopting robots in FMTs. The information that needs to be visualized will vary for different FMTs, so only some of the basic intent of robots has been developed in this paper. In the future, the proposed framework can be easily extended to any specific FMTs.

5 Conclusion With more intelligent robots integrating into FMTs, it is necessary to consider how humans can well collaborate with robots. The key challenge in HRC is to overcome the problem of mutual understanding of intents between humans and robots. This study proposed an integrated framework by introducing the latest visualization technologies, including BIM and AR, to facilitate communication between humans and robots. The framework indicates the process of adopting robots in FMTs, which includes two key modules: RMM and OCM. Combining with the existing BIM model, RMM may help FM managers exactly know the robots’ status and intents, which results in high reliability and better task performance. Besides, benefiting from OCM, FM co-workers can enhance the confidence of collaboration with robots and assist the robot in both physical action and decision-making. The main contribution of this study is developing an integrated HRC framework in FM by visualizing robots’ intent both remotely from the site and on-site. The framework presented in this paper will be validated through user studies ultimately. The next work is suggested to complement the framework with a reverse mechanism

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to effectively convey human intentions to robots, where the bi-directional process of intention recognition may form a better human–robot team in the FM operation.

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19. Chang, K.M., Dzeng, R.J., Wu, Y.J.: An automated IoT visualization BIM platform for decision support in facilities management. Applied Sciences 8(7), 1086 (2018) 20. Natephra, W., Motamedi, A.: Live data visualization of IoT sensors using augmented reality (AR) and BIM. In: 36th International Symposium on Automation and Robotics in Construction (ISARC 2019) (2019, May) 21. Chen, J., Li, S., Liu, D., Lu, W.: Indoor camera pose estimation via style-transfer 3D models. Comp.-Aided Civil and Infrastr. Eng. 37(3), 335–353 (2022)

Developing a Robotic System for Construction Truck Crane Xiao Lin, Songchun Chen, Hongling Guo(B) , and Ziyang Guo Department of Construction Management, Tsinghua University, Beijing, China [email protected]

Abstract. Automation and robot have been strong roles in improving productivity and safety of the construction industry. While a quantity of research and practice are conducted on new-type construction robots, there is a lack of robotization for traditional construction equipment. This research presents a robotic truck crane system (RTCS), which provides traditional truck cranes with automatic operation abilities. It is a system consisting of real hardware and modular-designed software, providing monitoring, path planning and automatic execution functions. An experiment was carried out on a scaled prototype to verify the feasibility and efficiency of the system. The result shows satisfactory operational accuracy, path planning capability, and accessibility of the system. Keywords: construction · robotic system · automation · truck crane

1 Introduction Truck crane hoisting operation is a general but essential operation on construction sites, which supplies a convenient solution for lifting cargos thanks to its mobility. However, it is troubled by low autonomy, requiring experienced manual guidance and driving. In recent years, some efforts have been made to increase the level of automation (LoA) of cranes, but most of them are limited in automated decisions and planning, typical examples are the research on lifting path algorithms [1–4] and automated collision checking [5, 6]. However, accurately executing the planning or adopting the suggestions provided by computers in time is challenging for workers in a real construction environment. Consequently, there is an urgent need for more automated and intelligent lifting equipment to address the issues. Robotics is believed as an impressive technology to increase productivity in the construction field. Relative research and applications in the construction industry have been available since the 1980s, and construction robots have made significant progress in automation for single tasks (e.g., welding, bricklaying, and painting) over the past four decades. However, researchers pay less attention to construction robots for lifting equipment and few to mobile crane robots. Rosenfeld et al. claimed that the automation of cranes would benefit the productivity of the industry in the early 1980s [7], however the absence of automated lifting equipment is still a shortcoming that hinders the efficiency and safety improvement of construction automation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 11–23, 2023. https://doi.org/10.1007/978-981-99-3626-7_2

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This research aims to develop a robotic truck crane system (RTCS) that provides traditional truck cranes with automatic operation abilities. The rest of this paper is structured as follows. First, the literature review is made in Sect. 2, and then the architecture of the proposed system is presented in Sect. 3. Further, the implementation of the system is described in Sect. 4. Additionally, an experiment was conducted, and its result is discussed in Sect. 5. Finally, this research is concluded in Sect. 6.

2 Literature Review According to the Oxford dictionary definition, a robot is a machine capable of automatically carrying out a complex series of actions, especially one programmable by a computer. Correspondingly, robotic cranes should be capable of automatically taking actions involved in hoisting tasks, such as monitoring, path planning and execution. A more rigorous automation strategy for cranes should be founded on the comprehension of current research and practice based on the different LoA. Sheridan et al. classified the LoA into ten levels [8], from the extreme of completely manual to fully autonomy. To further investigate the LoA, Parasuraman et al. proposed that the automation can be further decomposed into four aspects: information acquisition, information analysis, decision and action [9]. Similarly, the automation for cranes can be investigated in the above aspects. Among the relative researches, automated information acquisition represents the earliest endeavor of enhancing the LoA of cranes. For instance, Lee et al. utilized a wireless controller with a display linked to a tower crane camera to allow the operator to observe without being in the driver cab [10]. Another approach is to provide the operator with supplemental information about cranes. For example, Price et al. developed a crane monitoring system (CMS) to inform the operator of the crane load, rotation angle and lifting height [11]; Zhang et al. used ultra wideband (UWB) technology to visualize the position of the hook relative to the building [12]. In general, automation related to information acquisition has been well established and has considerable application in practice. Automated information analysis and decisions have recently been a focus of cranerelated research. They are expected to lower the requirement of human experience, enhancing safety and productivity. Collision checking is a familiar form of automated information analysis based on predicting the movement of machinery. For instance, Tak et al. have developed a simulation system with spatio-temporal site analysis functions to predict potential conflicts [13]. Likewisely, Dutta et al. developed a decision support system (DSS) to stop the crane before a collision occurs [14]. Regarding automated decisions, lifting path planning represents the most advanced part of relative research. Many algorithms are designed and developed to support finding a feasible lifting path [1–4], but it is worth noting that most are only tested in simulated environments without being applied on a real crane. While automation in the rest aspects is explored adequately, only a few studies have devoted to how to enhance the automation of action implementation of the crane, the lack of which has become a major barrier to developing robotic cranes. Among the limited relevant studies, most of them are also based on simulation environments, seldom

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involving real hardware. For example, Andonovski et al. developed an autonomic tower crane robot system with a simulated crane robot [16]. Another example is that Chen et al. used a 6-DOF robotic arm in a simulation environment to simulate the motion of a mobile crane [17]. The simulation systems in these studies generally do not concern the actual hardware of cranes. Therefore, the performance of the systems implied in this studies might decrease when they are applied to the real scenes. In summary, current robotic crane-related research seldom considers automated action implementation ability. Thus, RTCS is proposed in this research to fill the research gap. It addresses automated action implementation while realizing automated information acquisition, information analysis, and decision as predecessor functions.

3 System Architecture RTCS consists of four layers: hardware layer, control layer, service layer, and application layer, as shown in Fig. 1. The lowest level, hardware layer, contains sensors and actuators. Encoders, IMUs, and cameras are main sensors responsible for sensing the working conditions and surrounding environment of the crane in real-time. Actuators consist of motors, relays and switches, which control the movement of the crane by signals received. Data from sensors and signals for actuators are transmitted through hardware interface, which is provided by hardware layer as a bridge to communicate with the upper layer. The control layer is designed for reading sensing data and sending control signals. The control layer processes sensing data from the hardware-specific form into standard form, the ROS msg [18]. Consequently, the system promises to update new features more conveniently with a unified data form. Meanwhile, the control layer is responsible for interpreting the command messages delivered by the service layer into individual commands and then sending them to each actuator. The service layer is built on top of the control layer to accept service requests, the latter comprise inquiring for robot information and commanding, mainly raised by applications. For debugging and testing use, the layer also accepts input from users directly. Functioning as a server, the server layer maintains the information of the crane and supplies a standardized service interface to the upper applications, hence reducing the coupling of the system. The application layer consists of applications interacting with users frequently. Users are encouraged to customize applications regarding to pratical requirements, such as monitoring, advising and planning. Rviz, a visualization tool merged with ROS, is operated as the monitoring application for RTCS. It provides users with critical information about the robot system and a straightforward 3D visualization. For lifting path planning, an open-sourced motion planning library, MoveIt, supplies an interactive interface for planning lifting path and sending movement commands. Apart from automatic controlling, a remote control application is furthermore developed for testing and emergency operation.

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Fig. 1. The system architecture of RTCS

4 Implementation of System This section describes how to apply the above system architecture to a physical truck crane. For safety and economy considerations, a scaled model of a truck crane with a ratio of 1:6 is used instead of a real one. Despite its size, the model has a similar structure and mechanism to a real truck crane. Therefore, the implementation method illustrated can be migrated to a real truck crane theoretically. To deploy the system, the kinematics of the truck crane is first analyzed. Then, the details of the four system levels are described. 4.1 Kinematics Analysis of the Truck Crane With appropriate simplification, the crane can be treated as a robotic arm combining rigid joints and links. More specifically, the rotation, deformation and swing of the rope are excluded from consideration. Thus, the wire rope is deemed a joint capable of parallel telescoping. As a result, without considering the moving wheel, the crane would be simplified to consist of 5 joints: rotate joint (rotary), raise joint (rotary), telescope joint (prismatic), pulley joint (rotary), and lift joint (prismatic), and the hook is viewed as the end effector, as illustrated in Fig. 2a. These joints are linked by six links: base, driver cab, the main boom, the boom with extension, pully, and the hook. There are two primary differences between a real truck crane and the simplified model: a real truck crane uses a

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hydraulic rod to raise the main boom, but it is treated as if the boom were driven directly by the raise joint in the model. Another refers to the boom with extension that consists of multiple sections, but it is treated as one joined section after simplifying. These simplifications help describe the configuration of the crane more concisely by reducing the number of joints in the model. Figure 2b presents the correspondence between the simplified model and the physical crane.

Fig. 2. The simplified model of the truck crane

According to the kinematics model, the Denavit-Hartenberg (DH) parameter is measured according to the crane model, as shown in Table 1. Where, the offset, joint angle, length and twist between parent and child links are represented by di, θ i, ai and αi, respectively. Table 1. Denavit-Hartenberg (DH) parameter of the crane model Joint

Parent

Child

d/m

θ/degree

a/m

α/degree

Rotate joint

Chassis

Driver cab

0.27

θ0

0.13

0

Raise joint

Driver cab

Main boom

0.22

θ1

0.15

-90

Telescope joint

Main boom

Boom with extension

d2

-90

0.06

90

Pulley joint

Boom with extension

Pulley

1.45

θ3

0.06

90

Lift joint

Pulley

Hook

d4

90

0

-90

Accordingly, the position of the end effector can be acquired as Eq. (1): xend = T01 T12 T23 T34 T45 xbase

(1)

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i where, xbase Is the position of the base link, Ti−1 is transform matrix of the ith joint, expressed as Eq. (2):

⎤ cos θi − cos αi sin θi sin αi sin θi ai cos θi ⎢ sin θi cos αi cos θi − sin αi cos θi ai sin θi ⎥ ⎥ =⎢ ⎣ 0 sin αi cos αi di ⎦ 0 0 0 1 ⎡

i Ti−1

(2)

4.2 Hardware Layer 4.2.1 Actuator The actuators for cranes and general robots vary significantly due to several differences in their working conditions. First, cranes operate in a much harsher environment, frequently accompanied by vibration and dust. Second, the workload of cranes is much heavier than typical industrial robotic arms. For example, the biggest robotic arm of ABB, IRB8700, has a maximum loading capacity of 800 kg. However, it is standard for cranes to lift cargo of tons. Therefore, severe working conditions and requirements propel the crane to adopt actuators with simpler mechanisms and higher robustness. Most cranes employ motors without servo functions, which is common in most industrial robots. For this reason, the crane model used in this research also equips non-servo motors. More specifically, the crane has four motors that control the slewing, telescoping, raising of the boom and lifting of the hook respectively, and relay and switches are used to control the power and running directions of the motors. 4.2.2 Sensor The sensors are applied in a non-intrusive method without changing the original mechanical and electrical structure of the crane. The kinematic analysis shows that the configuration of the crane can be presented by the states of five joints, namely, the configuration is a five-dimensional vector, shown as Eq. (3).  T q = θrotate , θraise , dextend , θpulley , dlift

(3)

where, θrotate , θraise , dextend , dlift are controlled by motors. However, θpulley is not constrained since the pulley at the end of the boom is powerless. Luckily, it inverses with θraise since the wire rope is considered perpendicular to the horizontal plane due to gravity. Thereby, four sensors are sufficient to obtain the configuration.

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There have been matured studies exploring how to monitor the working condition of cranes. Regarding dextend and dlift , available distance sensor includes encoders [11], laser [19], and UWB [5]. In particular, encoders are the preferred choice because of their low cost and high accuracy. Meanwhile, the inertial measurement unit (IMU) is used to measure the θrotate and θraise . While the IMU is fixed on the boom with its X-axis pointing to the front, the measured yaw angle and the pitch angle will be referred to θraise and θrotate , respectively. The specific installation location and method for sensors are presented in Fig. 3.

Fig. 3. Sensors installed on the crane

4.3 Control Layer The control layer of RTCS is responsible for reading data from sensors and controlling actuators. It provides drivers to parse sensor signals into readable data, then reconstructs the data structure into standard ROS msg. Besides, it manages actuator controllers that control motors according to the movement command given by the upper layer. Actuator controllers are designed to access data from sensors and listen to movement commands continuously. It functions through incessant loops: as a start, it reads data from sensors to obtain the current state of the joints; then, it updates the command given by the upper layer for each actuator; finally, it decides whether to close or open the motor according to the current joint state and movement command. Although the motors equipped by the crane are much simpler than servo motors, the control strategy is rather more complicated. Servo motors applied to the industrial robots allow quantitative control of joint position by transmitting PWM signals with specific duty cycle. However, a non-servo motor can only switch between different directions. In order to control the joints precisely, a servo function needs to be supplied by the actuator controller. Therefore, the following control strategy is designed to support the servo function, as expressed by pseudocode:

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Algorithm 1. Control strategy for the motors.

Read the joint state Update the movement command If no new movement command is received If no mevment command is running For each joint Keep the motor closed If a movement command is executing For each joint Compare the current joint state to the joint command If reached Close the motor Else Keep the motor running Else if a new joint command is received For each joint Compare the current joint state to the joint command If reached Keep the motor closed Else Run the motor Loop

4.4 Server Layer The server layer provides a unified interface to inquire about the system information and send movement commands. It is responsible for integrating the sensor data into structured state information and updating the latter on a public node. Nodes play the roles of maintaining data and processing access requests. For instance, the pose of the crane is maintained by a specific node. By subscribing to this node, a 5-dimensional vector representing the configuration of the crane is returned as required. Thus, the system is low coupling with high scalability due to information is maintained by seperated nodes rather than a specific application. Simultaneously, the server layer is also in charge of commanding controllers. It has a server node that accepts the request for a new configuration of the crane, parsing the request into commands for each controller. It is worth noticing that the server node only cares about whether the requests meet the format requirements of the interface, regardless of the type of requester. Hence, RTCS supports a toggleable control mode from manual inputting to automatic running. 4.5 Application Layer 4.5.1 Monitoring Rviz is a 3D visualizer for displaying sensor data and state information of the robot system. In RTCS, Rviz is used as a monitoring application to assists the operator in monitoring the crane’s operational status in real time. The 3D model is synchronized

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to the physical crane according to real-time sensor data in Rviz, shown in Fig. 4a. To reflect the state of the crane presicely, the 3D model of the crane is constructed based on the drawing in Solidwork, as presented in Fig. 4b. As described, the visual monitoring interface provides users with an intuitive method to observe the operational status of the crane, which benefits the accessibility of the system.

Fig. 4. The monitoring interface and the 3D model of the crane

4.5.2 Path Planning and Executing MoveIt is integrated with the system to support lift path planning and executing. It functions as an upper application to generate movement commands and send them to the server node. As a matured platform, MoveIt consists open motion planning library (OMPL), flexible collision library (FCL), kinematics and dynamics library (KDL) and a highly interoperable interface for movement planning. MoveIt has been applied widely in industrial robots, but this research might be the first attempt to use MoveIt on a truck crane for lift path planning. By specifying the initial and terminal positions of the hook, as well as the shape and position of the surrounding obstacle, MoveIt automatically decodes a collision-free path based on various built-in algorithms. First, it calculates the initial and terminal configuration according to the positions of the hook by inverse kinematics. Then, collision checking and path planning algorithms cooperate to generate a series of waypoints between the configurations. Finally, the waypoints will be sent to the server node to control motors, guiding the crane to reach the terminal. 4.5.3 Remote Controlling For testing and necessary manual intervention, a remote control application is established. The remote controlling of the crane support three kinds of input methods: via a keyboard, a joystick, or a visual interface. These control methods only vary in input tools but follow the same requirement of the server interface as described in the last section, consquently they are essentially the same regarding the control result.

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5 Experiment and Discussion In order to evaluate the performance of RTCS, a case experiment was conducted in the laboratory environment. The experiment simulated lifting cargo from the ground to the top floor of a building. We assumed that the following information was known: the position of crane, cargo and building; the shape of cargo and building. To clarify, the building model is a cylinder with a radius of 0.3 m and a height of 1 m, and the cargo is a cuboid of 0.1 m*0.1 m*0.2 m. Magnets were fixed on the cargo for connecting to the iron hook automatically. The 3D model of the environment was added to the planner application in advance, as shown in Fig. 5.

Fig. 5. The layout of the simulated task for the experiment

The task consists of two steps: first, hooking the cargo; second, lifting the cargo to the target position. For the first stage, the crane moved its hook above the cargo from its initial position to hook the cargo. With the help of MoveIt, a collision-freed feasible path consisting of waypoints was planned by Rapidly-exploring Random Trees*(RRT*), as illustrated in Fig. 6a. Afterward, the path was executed by the crane. Due to measurement errors and execution errors, the hook did not reach the estimated position precisely, with a deviation of 5 cm, as shown in Fig. 6b. Despite this error, the magnet successfully connected the cargo to the hook. Similarly, the second stage is composed of path planning and execution. Nevertheless, additional factors made the operation more challenging. Since the cargo was attached to the hook, its collision volume should be considered, increasing the complexity of the collision checking and taking more time for RRT* to find a collision-freed path, as shown in Fig. 7a. Worse still, longer travel distances increased the cumulative error. Compared with the first stage, the operating error of the second stage was significantly greater, reaching 15 cm, as presented in Fig. 7bc. Nevertheless, the result proved that RTCS is capable for lifting task with its error in a reasonable margin. Regarding the experimental results, several discussions are demonstrated as follows. The accuracy of RTCS is affected by errors from three aspects: (1) measurement errors

Developing a Robotic System for Construction Truck Crane

21

Fig. 6. The planning and executing result in the first step

Fig. 7. The planning and executing result in the second step

of the machine, cargo and environment; (2) executing errors of the actuator; (3) sensor errors. While RTCS displays acceptable mistakes, errors may scales in real scenes, consequently results in exponentially increased deviation. Moreover, RRT* and analogous algorithms for universal robots may not be suitable for the crane with particular kinematics and task-specific requirements. The fact is that some paths planned in experiments are not acceptable due to safety considerations despite being collision-free. Therefore, a new path planning method should be designed to support safe and efficient lifting.

6 Conclusion The main goal of this research is to enhance the intelligence of the truck crane by robotic methodologies. RTCS, a robotic system for truck crane is developed to improve the autonomy of the truck crane. It is a modular designed system based on ROS, permitting high scalability, rapid implementation and iteration of functionalities. To better understand how to implement robotic technologies onto the truck crane, the kinematic and the actuating characteristic of the truck crane are analyzed. Based on this, an automatic control strategy for the crane is designed. Further, the system provides several applications to assist the operation, including a monitoring application, a lift path planner and a remote controller. In order to test the performance of RTCS, a scaled experiment was conducted, where RTCS was required to perform a typical hoisting task. In the experiment, RTCS functions as expected, presenting satisfactory accuracy in monitoring and execution.

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The result highlighted the potential usefulness of a robotic system for lifting equipment similar to the truck crane. Nevertheless, non-negligible challenges still exist in applying RTCS to the practical construction environment. Firstly, it is crucial to conduct full-size experiments to examine the applicability of RTCS on actual cranes. Secondly, a more practical lift path planning method should be developed rather than universal algorithms. In addition, dynamic perception is urgently required to improve the security of the automatic lifting. Currently, the path planning function is based on a static environmental model, but dynamic obstacles are common and of great concern in the real construction scene. Besides, more functionalities are encouraged, such as integration with industrial foundation class (IFC), anti-swing controlling and safety inspection. In summary, future research will focus on filling the gap between the RTCS prototype and the real crane, increasing the system’s intelligence, and adding features to suit practical needs.

References 1. Cai, P., Cai, Y., Chandrasekaran, I., Zheng, J.: Parallel genetic algorithm based automatic path planning for crane lifting in complex environments. Automation in Construction 62, 133–147 (2016) 2. Kayhani, N., Taghaddos, H., Mousaei, A., Behzadipour, S., Hermann, U.: Heavy mobile crane lift path planning in congested modular industrial plants using a robotics approach. Automation in Construction 122, 103508 (2021) 3. Wu, D., Sun, Y., Wang, X., Wang, X.: An improve RRT algorithm for crane path planning. Int. J. Robo. Automa. 31(2), 84–92 (2016) 4. Zhou, Y., Zhang, E., Guo, H., Fang, Y., Li, H.: Lifting path planning of mobile cranes based on an improved RRT algorithm. Adv. Eng. Info. 50, 101376 (2021) 5. Hwang, S.: Ultra-wide band technology experiments for real-time prevention of tower crane collisions. Automation in Construction 22, 545–553 (2012) 6. Ren, W., Wu, Z.: Real-Time Anticollision System for Mobile Cranes during Lift Operations. J. Comp. Civil Eng. 29(6), 04014100 (2015) 7. Rosenfeld, Y.: Automation of existing cranes: From concept to prototype. Automation in Construction 4(2), 125–138 (1995) 8. Sheridan, T.B., Verplank, W.L.: Human and Computer Control of Undersea Teleoperators: Defense Technical Information Center (1978) 9. Parasuraman, R., Sheridan, T.B., Wickens, C.D.: A model for types and levels of human interaction with automation. IEEE Tran. Sys. Man, and Cybernetics - Part A: Sys. Hum. 30(3), 286–297 (2000) 10. Lee, U.-K., Kang, K.-I., Kim, G.-H., Cho, H.-H.: Improving tower crane productivity using wireless technology. Comp.-Aid. Civil and Infrastr. Eng. 21(8), 594–604 (2006) 11. Price, L.C., Chen, J., Park, J., Cho, Y.K.: Multisensor-driven real-time crane monitoring system for blind lift operations: Lessons learned from a case study. Automation in Construction 124, 103552 (2021) 12. Zhang, C., Hammad, A., AlBahnassi, H.: Path Re-Planning of Cranes Using Real-Time Location System. In: 26th International Symposium on Automation and Robotics in Construction. Austin, TX, USA (2009) 13. Tak, A.N., Taghaddos, H., Mousaei, A., Bolourani, A., Hermann, U.: BIM-based 4D mobile crane simulation and onsite operation management. Automation in Construction 128, 103766 (2021)

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14. Dutta, S., Cai, Y., Huang, L., Zheng, J.: Automatic re-planning of lifting paths for robotized tower cranes in dynamic BIM environments. Automation in Construction 110, 102998 (2020) 15. Hu, S., Fang, Y., Guo, H.: A practicality and safety-oriented approach for path planning in crane lifts. Automation in Construction 127, 103695 (2021) 16. Andonovski, B., Jianqiang, L., Jeyaraj, S., Quan, A.Z., Yonggao, X., Tech, A.W.: Towards a Development of Robotics Tower Crane System, 2020 16th International Conference on Control, Automation, Robotics and Vision (ICARCV), pp. 345–350 (2020) 17. Chen, H., Fang, Y., Sun, N.: An adaptive tracking control method with swing suppression for 4-DOF tower crane systems. Mechan. Sys. Sig. Proc. 123, 426–442 (2019) 18. msg—ROS Wiki. http://wiki.ros.org/msg, latest access: 30/3/2022. 19. Lee, G., et al.: A laser-technology-based lifting-path tracking system for a robotic tower crane. Automation in Construction 18(7), 865–874 (2009)

COVID-19 Impact on the Implicit Value of Open Space in High Density Cities: Evidence from the Hong Kong Housing Market Ruiyang Wang and Shuai Shi(B) Department of Real Estate and Construction, University of Hong Kong, Hong Kong, China [email protected], [email protected]

Abstract. Open spaces such as parks and gardens provide a variety of ecosystem services that enhance human physical and mental well-being. Previous studies have extensively investigated the homebuyers’ willingness to pay for the utility of open space. However, few studies have investigated the price elasticity of open space to exogenous shock such as the COVID-19 pandemic. Due to massive social distancing and travel restrictions, open space within walkable distance is hypothesized to be appreciated under the pandemic, especially in high density cities like Hong Kong. Does this shock a one-off incident or create a lasting effect on price gradient? This article addresses this question by employing a multi-level difference-indifference (DID) model based on property transaction data from 2019 to 2021 in the Hong Kong housing market, aiming to unravel the dynamic relationship between open space and residential property price during the COVID-19 pandemic. The results show that: (1) the price gap between proximate and distant properties from open space was widened, steepening the price gradient; (2) the premium effect of open space was time-varying and mainly appreciated in the later stage of the pandemic; (3) low-to-medium-wealth communities showed higher elasticity of open space to the COVID-19 pandemic than high-wealth communities; (4) the utility of open space is more appreciated in highly-infected communities. This article contributes to the juxtaposition of price elasticity, exogenous shock, and urban environments. The concerns of open space inequity and housing affordability are also raised, which calls for the policy coordination for housing, public health, and urban planning. Keywords: COVID-19 · Open space · Housing price · Hong Kong

1 Introduction Unprecedented rapid urbanization in the last century has witnessed massive rural-tourban migrations worldwide, making cities the primary habitat of human beings. As people move from rural settings to urban circumstances, the natural environment is no longer readily available and becomes a luxury for urban dwellers [1]. Open spaces, serving as essential venues for recreation and activity, become a vital part of the built environment and are closely related to the amenities and well-being of urban residents. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 24–39, 2023. https://doi.org/10.1007/978-981-99-3626-7_3

COVID-19 Impact on the Implicit Value of Open Space

25

The term ‘open space’ encompasses a range of land uses, including parks, gardens, playgrounds, promenades, and setting-out areas. As a publicly accessible place with a green landscape ambiance, open space has long been valued for providing numerous environmental, health, and social benefits to the general public, particular for highdensity cities with a large population and scant living spaces, making people more willing to pay a premium to live in a neighborhood with close proximity to public open spaces [2, 3]. However, due to the non-commodity nature of open space, its value is often difficult to assess. In recent years, several methods have been developed and widely implemented to quantify the implicit value of open space, including the stated preference approach (e.g., contingent valuation method) and the revealed preference approach (e.g., hedonic pricing method) [4–6]. In high-density cities like Hong Kong with high population density but limited natural spaces, such scarce open spaces push their hedonic value to a higher level. Several studies conducted in Hong Kong confirmed that the accessibility and visibility of open spaces have a significant value-added effect on nearby house prices [7–9]. At the end of 2019, the COVID-19 pandemic broke out and spread swiftly all over the world, hitting the world profoundly and affecting people’s lives. Numerous measures, such as social distancing, lockdown, mandatory quarantine, border restrictions, and work-from-home orders, have been initiated by governments to prevent virus transmission. As residents are forced to spend most of their time at home and are generally unable to travel long distances or visit public commercial premises as usual, the importance of neighborhood open spaces within walkable distance has been highlighted [10]. Against this background, the relationship between open space proximity and housing price is likely to be altered by COVID-19 because open spaces have become almost the only and the best choice for citizens to engage in outdoor activities, which significantly contributes to people’s physical and mental health [11, 12]. A number of recent investigations revealed that urban residents have increased their frequency of visits to nearby open spaces during COVID-19 to maintain essential daily exercise and activities in a relatively safe outdoor environment[13, 14]. Under the far-reaching impact of the epidemic, the neighborhood built environment plays an increasingly significant role in improving immune defense, maintaining emotional resilience, and promoting physical and mental well-being [15]. Therefore, an in-depth understanding and accurate assessment of open spaces provide essential support for urban planning and policies in the post-pandemic era. Studies on the COVID-19 impact on housing markets fall into the research stream on the effects of external shocks on housing markets. Numerous studies have investigated the holistic effect of COVID-19 on local housing price dynamics in various places [16– 18]. However, limited studies have explored the COVID-19 impact on the implicit value of particular hedonic attributes [19]. In terms of urban open spaces, most of the existing literature focused on the use and perception of urban open space during COVID-19, but very few studies have analyzed the change in its implicit value [20]. Facing these gaps, this study aims to shed light on the time-varying effects of environmental amenities on housing markets and homebuyer behaviors in the context of the pandemic. The findings of this study could also enhance the precision of residential property valuation and provide

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conducive suggestions for urban planning and residential developments in the postpandemic era. The objectives of this study are threefold: (1) to assess the implicit value of open space and its value-added effect on nearby housing prices; (2) to investigate the COVID-19 impact on the implicit value of open space and to quantify the timevarying relationship between open space proximity and housing price; (3) to explore the heterogeneity of the value-added effect of open space proximity among different housing wealth levels under the pandemic, and to probe the difference in the perceived value of open space between infected and uninfected communities.

2 Research Design 2.1 Development of Hypotheses As mentioned above, urban open space plays an irreplaceable role in the urban environment and public health, especially in a high-density and land-hungry city like Hong Kong. Urban open spaces provide individuals with a communal and essential place for recreation and activity in an expansive outdoor circumstance, which is always highly valued by the local citizens [21]. During the Covid-19 pandemic, strict social distancing measures are believed to induce widespread negative emotions and psychological disorders such as fear, anxiety, stress, fatigue, and depression among urban residents [22]. Besides, urban quarantine policies result in residents being restricted from entering public spaces and making physical contact with others. This reduction in social connection and interaction led to a decline in people’s health, well-being, and quality of life [23]. In light of this, multiple studies highlighted the increasing use of public open spaces during COVID-19 restrictions [13, 14]. Urban dwellers generally have a constant and steady demand for outdoor activities, mainly for physical exercise, mental relaxation, and daily exposure to nature. However, due to the temporal closure or restricted access to office buildings, commercial centers, and sports facilities, urban open spaces such as parks and gardens have become the primary choice for residents to participate in necessary outdoor activities [20]. Therefore, the importance of spending time in open space is more apparent than ever before, amplifying residents’ preference for open space proximity. Therefore, because of the increasing demand for adjacent open space, it is reasonable to hypothesize that the impact of open space on housing prices will also be magnified under the pandemic: H1: Proximity to open space has a positive impact on housing prices. H2: Proximity to open space has a stronger positive impact on housing prices during the pandemic. Furthermore, the effect of open space is found to be heterogeneous among residents with diverse socioeconomic backgrounds [24]. Although wealthy homebuyers of highend residential properties may have a higher appreciation of landscape amenities, they can more easily access larger living spaces and private gardens with stronger purchasing power. However, other citizens may not be able to afford larger living spaces due to limited wealth capacity and can only pursue public open spaces, thereby paying a higher price for accessing those places [25]. This privilege of the wealthy has been further

COVID-19 Impact on the Implicit Value of Open Space

27

exaggerated during the ongoing pandemic. Higher-income groups enjoy better living conditions in private spaces and tend to avoid exposure to unfamiliar risky groups in public, leading to deeper privilege and housing gentrification [26]. At the same time, in order to alleviate the physical and mental stress brought by social distancing under the pandemic, ordinary citizens with smaller living spaces will show a more pronounced increase in their preference and willingness to pay for public open spaces. Therefore, we propose that: H3: Proximity to open space has more significant positive impacts on housing prices for low-to-medium-wealth properties than for high-wealth properties during the pandemic. In addition, housing estates with infected cases are usually considered to have a higher risk of infection due to the potential risk of aerosol transmission through ventilation and drainage systems [27]. Residents living in these infected housing estates are reluctant to use or stay in the community to avoid additional risk of infection. In addition, as activityfriendly environments, open spaces have mitigating effects on the negative impact of the pandemic and are beneficial to human health and well-being because of their therapeutic potential in maintaining physical functional capacities, improving immune defense, and increasing emotional resilience [15]. Thereupon, residents in infected communities will have a higher demand for outdoor public open spaces: H4: Proximity to open space has more significant positive impacts on housing prices for infected communities than uninfected communities during the pandemic.

2.2 Model Specifications The empirical design of this study relies on a multi-level difference-in-difference (DID) model. Our baseline model refers to the hedonic price model, which decomposes housing price into a set of observable hedonic attributes of properties. The underlying assumption of the traditional hedonic price model based on ordinary least square (OLS) is that the residuals should be independent and uncorrelated with each other. However, in the context of Hong Kong, plenty of residential buildings are nested in a so-called housing estate. A housing estate is a cluster of buildings built together as a single development with similar designs and shared public facilities. This means housing units in the same estate are likely to have unobservable common attributes, violating the OLS error term assumption. To solve this problem, a multi-level modeling approach is used to acquire unbiased estimates for spatially nested data [19]. The multi-level hedonic model can be written as:  βk Xk + δ + u + ε (1) lnP = α0 + β1 lnDOpenSpace + k

where lnP is the natural logarithm of the housing transaction price; α0 is the constant; lnDOpenSpace is the natural logarithm of the distance to the closest open space; α1 is the coefficient of interest; Xk is the k-th property attributes (all in natural logarithm form except for dummy variables); βk is the corresponding coefficients of Xk ; δ is a set of calendar-year-month dummies capturing year-month fixed effects; u is the random

28

R. Wang and S. Shi

intercept term of the housing estate and follows the normal distribution with mean of zero; and ε is the error term. The difference-in-difference (DID) model is a typical and useful approach to evaluate the effects of policy implementation or exogenous shock (e.g., COVID-19). The traditional DID model divides observations into treatment and control groups. The treatment group is directly exposed to policy or exogenous shock, while the control group is not, thus providing a counterfactual scenario. For our study, it is difficult to divide samples into treatment and control groups and decide if they are affected by open space. To tackle this problem, we introduce a continuous treatment variable instead of the traditional binary variable into the DID model. The multi-level continuous DID model can be written as:  βk Xk + δ + u + ε lnP = α0 + α1 lnDOpenSpace × Post + β1 lnDOpenSpace + k

(2) where Post is a binary variable that equals one for transactions after February 2020 (the outbreak of COVID-19) and zero otherwise; α1 is the coefficient of the interaction term; and other terms are same as above. In this equation, α1 is the coefficient of primary interest and is expected to be significantly negative, indicating that COVID-19 further increases the housing price near open spaces. Moreover, to examine how the impact of COVID-19 on the implicit value of open space changes over time, a DID model with time-varying effect is further introduced as follows: lnP = α0 + α1 lnDOpenSpace × Wave1 + α2 lnDOpenSpace × Wave2 +α3 lnDOpenSpace × Wave3 + α4 lnDOpenSpace × Wave4 (3)  +α5 lnDOpenSpace × Clear + β1 lnDOpenSpace + βk Xk + δ + u + ε k

where Wave1, Wave2, Wave3, Wave4, and Clear are five dummy variables representing the frits (Feb. 2019), second (March to June 2020), third (July to Oct. 2020), and fourth (Nov. 2020 to April 2021) waves as well as the clearance interval (May to Dec. 2021) of the COVID-19 pandemic in Hong Kong; α1 , α2 , α3 , α4 , α5 are the coefficients of interest.

3 Data Sources The empirical analysis of this study relies on a complied dataset incorporating five major sources: housing transaction records, open space data, property attributes, location and neighborhood attributes, and COVID-19 infections. (1) The housing transaction records in Hong Kong from January 2019 to December 2021 are extracted from the Economic Property Research Centre (EPRC) database. After excluding the detached or semi-detached houses and first-hand transactions, a total of 115,081 transaction records are included in our data set for further analysis.

COVID-19 Impact on the Implicit Value of Open Space

29

(2) Urban parks and gardens were extracted from the land use dataset of OpenStreetMap and defined as open spaces in our study. Hong Kong has many scattered and sporadic open spaces (e.g., pocket parks and playgrounds) spread across the urban fabric. However, many of these small spaces with outdoor fitness facilities or playgrounds are closed during the pandemic. Therefore, according to the definition in the Hong Kong Planning Standards and Guidelines, we only select the District Open Space with a minimum size of 1 ha for analysis. The Euclidean distance between housing property and the nearest district open space is employed as the measurement of open space proximity. (3) The property attributes of housing estates are collected from Midland Realty. Additionally, we also obtained the public housing estates list from the Housing Authority and incorporated it as a dummy control variable. (4) To measure the location of housing properties, the travel distance to the city center is calculated by Google Distance Matrix. Moreover, the distance to the closest MTR station and the number of bus stops within 200 m are used to capture transportation accessibility. The number of primary schools within 500 m and the distance to the nearest hospital are used to reflect neighborhood amenities of residential properties. The georeferenced datasets of neighborhood amenities are obtained from Hong Kong Geodata Store. (5) The list of buildings with infected cases is collected from the COVID-19 Thematic Website of the HKSAR Government. The infection rate of each housing estate is calculated by dividing the number of infected cases by the total number of units in the housing estate. We incorporated the infection rate into our model since some studies demonstrated that infection rates may also affect housing prices [16, 17]. The definitions and descriptive statistics of variables are listed in Table 1.

Table 1. Definitions and descriptive statistics of variables. Variable

Description

Mean

St. D.

Min

Max

Unit

Level*

6.57

0.03

450

million HK$

11196

m

level 1

Dependent variable Price

Property 8.18 transaction price

Independent variables DOpenSpace

Distance to the nearest district open space (≥ 1 ha)

441

511

1

SOpenSpace

Area of the nearest district open space (≥ 1 ha)

51888

53922

10009 537184

m2

level 1

Age

Building age

23.39

13.26

0

year

level 1

64

(continued)

30

R. Wang and S. Shi Table 1. (continued)

Variable

Description

Mean

St. D.

Min

Max

Unit

Level*

Floor

Floor

17.62

12.45

0

81

–

level 1

Size

Property size in saleable area

536

224

93

4664

ft2

level 1

South

Dummy variable, 1 for south facing housing property, and 0 otherwise

32.91% –

0

1

–

level 1

Bedroom

Number of 1.80 bedrooms in the housing property

0

5

–

level 1

Complex

Dummy 83.83% – variable, 1 for complex housing estate, and 0 otherwise

0

1

–

level 2

Public

Dummy variable, 1 for public housing estate, and 0 otherwise

15.55% –

0

1

–

level 2

Swimming

Dummy variable, 1 for housing estate with swimming pool, and 0 otherwise

62.09% –

0

1

–

level 2

Clubhouse

Dummy 54.60% – variable, 1 for housing estate with clubhouse, and 0 otherwise

0

1

–

level 2

1.11

(continued)

COVID-19 Impact on the Implicit Value of Open Space

31

Table 1. (continued) Variable

Description

Mean

St. D.

Min

Max

Unit

Level*

DCityCenter

Travel distance to the nearest city center (i.e., Central or Tsim Sha Tsui)

15.18

10.16

0.02

55.80

km

level 1

DMTR

Distance to the nearest MTR station

714

846

3

12599

m

level 1

Bus

Number of bus stops within 200 m

3.43

2.45

0

16

–

level 1

School

Number of 2.95 primary schools within 500 m

2.18

0

16

–

level 1

DHospital

Distance to the nearest hospital

1181

29

10516

m

level 1

InfectRate

Infection rate of 0.23% the housing estate

21.43% –

level 2

Post

Dummy 73.24% – variable, 1 for a property transacted after the outbreak of COVID-19 (Feb. 2020), and 0 otherwise

0

1

–

level 1

Wave.1

Dummy variable, 1 for a property transacted during the first wave (Feb. 2020), and 0 otherwise

0

1

–

level 1

1484

1.41%

0.54% 0

–

(continued)

32

R. Wang and S. Shi Table 1. (continued)

Variable

Description

Wave.2

Mean

St. D.

Min

Max

Unit

Level*

Dummy 12.25% – variable, 1 for a property transacted during the second wave (Mar. 2020 – June 2020), and 0 otherwise

0

1

–

level 1

Wave.3

Dummy 10.95% – variable, 1 for a property transacted during the third wave (July 2020 – Oct. 2020), and 0 otherwise

0

1

–

level 1

Wave.4

Dummy 20.25% – variable, 1 for a property transacted during the fourth wave (Nov. 2020 – April 2021), and 0 otherwise

0

1

–

level 1

Wave.Clear

Dummy variable, 1 for a property transacted during the clearance (May 2021 – Dec. 2021), and 0 otherwise

0

1

–

level 1

28.38% –

* Note: level 1 and 2 represent property level and housing estate level respectively.

4 Results 4.1 Baseline Results Two multi-level hedonic price models (Model 1 and 2) are estimated to decipher the relationship between open space proximity with housing prices before (Jan 2019 to Jan 2020) and during COVID-19 (Feb 2020 to Dec 2021). As expected, the results of

(0.002) (0.003) (0.008) (0.001) (0.006) (0.002) (0.002) (0.011) (0.017) (0.014) (0.015) (0.009) (0.003) (0.001) (0.001) (0.005)

(0.067)

-0.0039* 0.016*** -0.132*** 0.037*** 0.862*** 0.010*** 0.009*** 0.045*** -0.297*** 0.061*** 0.029* -0.261*** -0.014*** -0.004*** 0.001 -0.038***

-2.748*** Yes 0.039*** 15664*** 0.612 10603 46975***

(0.001) (0.002) (0.003) (0.001) (0.003) (0.001) (0.001) (0.011) (0.018) (0.012) (0.012) (0.008) (0.002) (0.000) (0.001) (0.004) (0.315) (0.044)

-0.0084*** 0.012*** -0.143*** 0.043*** 0.861*** 0.010*** 0.006*** 0.078*** -0.347*** 0.073*** 0.025** -0.234*** -0.004* -0.005*** -0.000 -0.034*** -1.196*** -2.741*** Yes 0.058*** 62161*** 0.766 44684 163732*** (0.002)

Std. Err.

Coef.

M2: during-Covid (Feb 20 – Dec 21) N = 84,288

-0.0053*** 0.011*** -0.143*** 0.041*** 0.856*** 0.010*** 0.006*** 0.067*** -0.348*** 0.074*** 0.024** -0.230*** -0.006*** -0.005*** -0.000 -0.031*** -1.043*** -2.746*** Yes 0.058*** 80905*** 0.746 56647 193148*** (0.002)

(0.001) (0.002) (0.003) (0.001) (0.003) (0.001) (0.001) (0.011) (0.018) (0.011) (0.012) (0.008) (0.002) (0.000) (0.001) (0.004) (0.301) (0.040)

Coef. Std. Err. -0.0021** (0.001)

M3: DID (Jan 19 – Dec 21) N = 115,081

(0.003) (0.001) (0.001) (0.001) (0.001) (0.001) (0.002) (0.003) (0.001) (0.003) (0.001) (0.001) (0.011) (0.018) (0.011) (0.012) (0.008) (0.002) (0.000) (0.001) (0.004) (0.301) (0.040)

0.0008 -0.0006 -0.0005 -0.0030*** -0.0031*** -0.0053*** 0.011*** -0.143*** 0.041*** 0.856*** 0.010*** 0.006*** 0.067*** -0.348*** 0.074*** 0.024** -0.230*** -0.006*** -0.005*** -0.000 -0.031*** -1.042*** -2.747*** Yes 0.058*** 80910*** 0.746 56651 193166*** (0.002)

Std. Err.

Coef.

M4: DID with time-varying effect (Jan 19 – Dec 21) N = 115,081

-0.0155*** 0.004 -0.140*** 0.056*** 1.103*** 0.012*** -0.000 -0.052*** 0.113 0.029** -0.075*** -0.144*** -0.014*** -0.005*** -0.000 -0.029*** -0.800 -3.990*** Yes 0.066*** 24116*** 0.881 15825 97055***

Coef. -0.0015

(0.003)

(0.002) (0.004) (0.004) (0.001) (0.005) (0.002) (0.002) (0.018) (0.091) (0.013) (0.018) (0.012) (0.002) (0.001) (0.001) (0.007) (0.997) (0.079)

Std. Err. (0.001)

M5: DID for high wealth group (Jan 19 – Dec 21) N = 19,092

Yes 0.032*** 52204*** 0.636 50627 68252***

-0.0022 0.006*** -0.110*** 0.036*** 0.644*** 0.006*** 0.013*** 0.064*** -0.248*** 0.016 0.055*** -0.191*** -0.015*** -0.002*** -0.001 -0.022*** -1.013*** -1.658*** (0.001)

(0.001) (0.002) (0.003) (0.001) (0.004) (0.001) (0.001) (0.010) (0.014) (0.012) (0.011) (0.007) (0.002) (0.000) (0.001) (0.003) (0.243) (0.041)

Coef. Std. Err. -0.0031*** (0.001)

(0.001) (0.002) (0.003) (0.001) (0.004) (0.001) (0.001) (0.019) (0.022) (0.014) (0.016) (0.013) (0.002) (0.000) (0.001) (0.005) -2.747*** (0.051) Yes 0.053*** (0.003) 50541*** 0.710 37211 118047***

-0.0047*** 0.008*** -0.145*** 0.040*** 0.835*** 0.012*** 0.007*** 0.091*** -0.314*** 0.071*** 0.032** -0.220*** -0.009*** -0.004*** -0.002** -0.013***

Coef. Std. Err. -0.0028*** (0.001)

M6: DID for M7: DID for low-to-medium infected group wealth group (Jan 19 – Dec 21) (Jan 19 – Dec 21) N = 79,310 N = 95,989

Notes: * significant at 10% level; ** significant at 5% level; *** significant at 1% level; standard errors in parentheses; coefficients of interest are shown in bold.

LR test chibar2 ICC log likelihood Wald chi2

(0.001)

Std. Err.

Coef.

Variables

Constant Calendar-year-month dummies

M1: pre-Covid (Jan 19 – Jan 20) N = 30,793

Models

Table 2. Regression results.

Yes 0.058*** 29708*** 0.799 20520 81745***

-2.965***

-0.0158*** 0.022*** -0.135*** 0.043*** 0.890*** 0.004** 0.009*** 0.068*** -0.389*** 0.081*** 0.028 -0.220*** 0.013** -0.006*** 0.005*** -0.061***

Coef. 0.0021

(0.002)

(0.078)

(0.004) (0.003) (0.008) (0.001) (0.005) (0.002) (0.002) (0.014) (0.034) (0.019) (0.021) (0.009) (0.005) (0.001) (0.002) (0.006)

Std. Err. (0.002)

M8: DID for uninfected group (Jan 19 – Dec 21) N = 35,771

COVID-19 Impact on the Implicit Value of Open Space 33

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Models 1 and 2 (shown in Table 2) confirm a negative relationship between housing price and distance to the nearest open space. This finding confirms our Hypothesis 1 and is consistent with the former studies in Hong Kong [8, 9]. As expected in Hypothesis 2, COVID-19 notably enhanced the value-added effect of open space proximity on housing prices, which is indicated by the observation that the absolute value of the coefficient of lnDOpenSpace is much higher in Model 2 than in Model 1 (0.0084 versus 0.0039). Additionally, the area of the closest open space also positively contributes to nearby housing prices, indicating that large areas of open space are more favored by residents. The performance of the control variables is generally in line with expectations. First, for property-level attributes, the floor level, property size, south-facing orientation, and the number of bedrooms are all positively associated with housing prices, whereas the building age is negatively related. Second, for housing estate-level attributes, complex housing estates positively affect housing prices since they provide better facilities and services than single housing estates. As expected, public housing prices are lower than private housing, as the living conditions of public housing may not be satisfactory as private properties. Additionally, community facilities such as swimming pools and clubhouses positively contribute to housing prices. Third, for location and neighborhood attributes, proximity to the city center, MTR stations, and hospitals positively correlates with housing prices. Unexpectedly, the number of nearby bus stops has a negative impact on housing prices. A possible reason is that bus stops are often concentrated in crowded and noisy old towns, which may reduce people’s quality of life. Another thing is that the number of primary schools does not significantly affect house prices, which may be due to the fact that schools are evenly distributed according to school catchment zones in Hong Kong. Last, the COVID-19 infection rate is negatively associated with housing prices, which is consistent with the findings of other related studies [16, 17]. People generally tend to avoid potential risks, which may reduce the perceived value of these risky properties. In terms of the random effect at the housing estate level, the variances of u (level 2 errors), Var(Estate), , are significant in the baseline models with high intraclass correlation coefficients (ICC) of 0.612 and 0.766, respectively. This finding demonstrates the effectiveness of our multi-level modeling approach with random intercepts for housing estates. 4.2 DID Model Results Two multi-level difference-in-difference (DID) models are employed to assess the implicit value change of open spaces. Model 3 scrutinizes the dynamic relationship between open space proximity and housing prices, while Model 4 further incorporates a time-varying effect to probe the variation between different waves. As stated in Hypothesis 2, COVID-19 notably enhances the value-added effect of open space proximity on housing prices, which is confirmed by the results of the DID models. Specifically, in Model 3, the moderating effect of COVID-19 indicated by the coefficient of lnDOpenSpace×Post is – 0.0021. This demonstrates that the effect of lnDOpenSpace has increased from – 0.0053 to – 0.0074 (= – 0.0053 – 0.0021) under the pandemic. In other words, the price gradient near open space is steepened, reflecting residents’ growing demand and preference for open space. In this case, properties closer to open space enjoy a higher premium than before.

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Furthermore, people’s mentality and behavior may also change with the epidemic situation and control policies. Therefore, a multi-level DID model with time-varying effects is employed to capture this change. Figure 1 depicts the coefficients of interest estimated by Model 4. The results reveal that the moderating effect of COVID-19 is significant only during the fourth wave (Nov. 2020 to April 2021) and the clearance period (May 2021 to Dec. 2021). In the early stages of Covid-10 (i.e., the first wave to the third wave), people’s preference for green space did not increase significantly. This may be attributed to people’s initial concerns and fears about the pandemic, causing them to avoid frequent visits to public places. However, under the increasingly strict social distancing and quarantine policy, people’s daily activities are severely restricted, prompting a growing demand and preference for neighborhood open space. As a result, the housing price premiums stemming from open space proximity have increased in the later stages of COVID-19.

Fig. 1. The time-varying effect estimated from the multi-level DID model (Model 4). The coefficients of lnDOpenSpace × Wave are plotted in the figure. The solid line indicates significant results, while the dotted line indicates insignificant results. The shaded area represents a 95% confidence interval. *** Significant at 1% level.

4.3 Heterogeneous Effects We further explore the heterogenous impact of COVID-19 on the implicit value of open space among different wealth groups. According to the Hong Kong Mortgage Insurance Program, we define properties worth more than HK$ 10 million as high-wealth properties, otherwise as low-to-medium wealth properties. Models 5 and 6 estimate the COVID-19 impact on the implicit value at two wealth levels. According to estimations, open space proximity has not significantly affected price premiums for high-wealth properties during COVID-19. However, this premium has significantly increased for lowto-medium-wealth properties. This confirms our Hypothesis 3, stating that households

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with lower wealth tend to rely more on public open spaces for essential outdoor activities. Due to their relatively limited private living space, which cannot satisfy the needs for daily physical activity, they are prone to place more emphasis on public open spaces. This is consistent with other research findings that low-income groups need to pay a higher value for accessing urban public spaces [25]. Moreover, we further investigate the heterogeneity caused by COVID-19 infections. Models 7 and 8 estimate the relationship between open space proximity and housing prices for infected and uninfected, respectively. The results indicate that the value-added effect of open space proximity has been enhanced for infected communities but not uninfected ones, confirming our Hypothesis 4. As mentioned above, residents living in infected communities are reluctant to make additional connections within the community to avoid additional infection risks. Thus, residents in the infected communities tend to increase their visits to adjacent public open spaces to pursue a relatively safe environment in large outdoor circumstances. Open space can also provide restorative features in maintaining physical and mental health to mitigate the negative impacts brought by the pandemic [11, 15]. In this scenario, the implicit value of open space is more likely to appreciate. 4.4 Robustness Checks To verify the effectiveness of the empirical findings, we performed two robustness checks. First, an alternative variable setting is introduced using the housing unit rate (i.e., the unit price per square feet) as the dependent variable to reduce the confounding effect of property size on housing price and quality. Second, an alternative model specification incorporating housing estate fixed effect and calendar-year-month fixed effect is used to absorb the time-invariant characteristics of housing estates and the temporal changes in the housing market. No significant differences are found in these alternative settings. Detailed results are not presented due to the word limit but are available upon reasonable request.

5 Conclusion and Discussion The relationship between open space and housing prices has been widely investigated in various contexts. However, such a relationship has not been further investigated in the era of the COVID-19 pandemic. This study examines the COVID-19 impact on the implicit value of open space in terms of its relationship with neighborhood housing prices. A multi-level difference-in-difference (DID) hedonic model has been employed based on property transaction data from 2019 to 2021 in the Hong Kong housing market. The findings of this study elucidate the existence of a premium for open space proximity to housing prices, and this premium has been further boosted under the COVID-19 pandemic. In other words, the elasticity of the distance to open spaces increased during the pandemic. Due to restrictions on long-distance international travel and closures of local public commercial premises, people’s preference for open space has been inspired during the pandemic. This observation is consistent with other studies on the use and perception of open space, which stated that residents have increased their visits to nearby

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urban parks [13, 14]. Under the pandemic, people are facing a crucial trade-off that public greenspaces could be therapeutic for physical and mental health but also could increase their exposure to potential infections [28]. However, the health benefits of visiting natural landscapes may outweigh potential risks when appropriate visitor management measures are implemented (e.g., wearing masks and crowd control) [29]. Open space is proven effective in mitigating the pandemic, stimulating physical activities, releasing stress, and promoting emotional resilience and spiritual well-being [30, 31]. These benefits have boosted the implicit value of open space under the pandemic and are reflected in housing prices. Further time-varying study shows that the value-added effect of open space proximity occurs mainly in the later stages of the pandemic (i.e., after the fourth wave). This captures the changing mindset of citizens at different times of the pandemic and their growing demand for outdoor open spaces. Meanwhile, our findings also corroborate the heterogeneity of the amenity effect of open spaces. The value-added effect of open space is more prominent for low-to-medium wealth properties but not high-wealth properties. This is to say that low-income citizens have to pay a higher value for access to public open space, while the wealthy can easily acquire private greenspace to meet their needs for outdoor activities [25]. This suggests that the pandemic has exacerbated inequality and exclusion in the use of public open spaces. Related literature also well documented that greenspaces are unevenly distributed in urban areas, thereby increasing social and environmental inequalities [32]. Moreover, Hong Kong residents’ high willingness to pay for open space also reflects the scarcity of open space in Hong Kong. The cramped private living conditions are likely to push people towards public open space, which can be considered an extension of domestic space [21]. Some studies also proposed that the current open space supply and planning standards are outdated and unable to satisfy the actual needs of the public [33, 34]. Therefore, more space should be allocated to high-density urban areas to meet the outdoor exercise and recreation demands of local residents. This study is not immune from limitations. First, a placebo test is typically required for DID model. Generally, scholars will assume another fictitious external shock (e.g., supposing that the COVID-19 outbroke one year before) and re-examine the DID model to eliminate periodic or accidental factors [19]. This work will be completed later. Second, this study only adopts the simple measurement of open space proximity based on Euclidean distance. Other measurements can be further introduced using more advanced technologies (e.g., remote sensing or BIM) to capture the accessibility and visibility of various green spaces. In addition, more cross-regional comparative studies of cities with different geographic and social contexts are needed.

References 1. Gong, P., et al.: Urbanisation and health in China. The Lancet 379(9818), 843–852 (2012) 2. Chiesura, A.: The role of urban parks for the sustainable city. Landsc. Urban Plan. 68(1), 129–138 (2004) 3. Xue, F., Gou, Z., Lau, S.S.Y.: Green open space in high-dense Asian cities: Site configurations, microclimates and users’ perceptions. Sustain. Cities Soc. 34, 114–125 (2017) 4. Jim, C.Y., Chen, W.Y.: Recreation–amenity use and contingent valuation of urban greenspaces in Guangzhou. China. Landscape and Urban Planning 75(1–2), 81–96 (2006)

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5. Jim, C.Y., Chen, W.Y.: Impacts of urban environmental elements on residential housing prices in Guangzhou (China). Landsc. Urban Plan. 78(4), 422–434 (2006) 6. Brander, L.M., Koetse, M.J.: The value of urban open space: Meta-analyses of contingent valuation and hedonic pricing results. J. Environ. Manage. 92(10), 2763–2773 (2011) 7. Jim, C.Y., Chen, W.Y.: Value of scenic views: Hedonic assessment of private housing in Hong Kong. Landsc. Urban Plan. 91(4), 226–234 (2009) 8. Jim, C.Y., Chen, W.Y.: External effects of neighbourhood parks and landscape elements on high-rise residential value. Land Use Policy 27(2), 662–670 (2010) 9. Hui, E.C.M., Zhong, J.W., Yu, K.H.: The impact of landscape views and storey levels on property prices. Landsc. Urban Plan. 105(1–2), 86–93 (2012) 10. Mouratidis, K., Yiannakou, A.: COVID-19 and urban planning: Built environment, health, and well-being in Greek cities before and during the pandemic. Cities 121, 103491 (2022) 11. Slater, S.J., Christiana, R.W., Gustat, J.: Recommendations for Keeping Parks and Green Space Accessible for Mental and Physical Health During COVID-19 and Other Pandemics. Prev. Chronic Dis. 17, 200204 (2020) 12. Johnson, T.F., Hordley, L.A., Greenwell, M.P., Evans, L.C.: Associations between COVID-19 transmission rates, park use, and landscape structure. Sci. Total Environ. 789, 148123 (2021) 13. Ugolini, F., et al.: Effects of the COVID-19 pandemic on the use and perceptions of urban green space: An international exploratory study. Urban Forestry & Urban Greening 56, 126888 (2020) 14. Geng, D. (Christina), Innes, J., Wu, W., Wang, G.: Impacts of COVID-19 pandemic on urban park visitation: a global analysis.Journal of Forestry Research 32(2), 553–567 (2021) 15. Lai, K.Y., Webster, C., Kumari, S., Sarkar, C.: The nature of cities and the Covid-19 pandemic. Current Opinion in Environmental Sustainability 46, 27–31 (2020) 16. Qian, X., Qiu, S., Zhang, G.: The impact of COVID-19 on housing price: Evidence from China. Financ. Res. Lett. 43, 101944 (2021) 17. Liu, Y., Tang, Y.: Epidemic shocks and housing price responses: Evidence from China’s urban residential communities. Reg. Sci. Urban Econ. 89, 103695 (2021) 18. D’Lima, W., Lopez, L.A., Pradhan, A.: COVID-19 and housing market effects: Evidence from U.S. shutdown orders. Real Estate Economics 1540–6229.12368 (2022) 19. Yang, L., Liang, Y., He, B., Lu, Y., Gou, Z.: COVID-19 effects on property markets: The pandemic decreases the implicit price of metro accessibility. Tunn. Undergr. Space Technol. 125, 104528 (2022) 20. Day, B.H.: The Value of Greenspace Under Pandemic Lockdown. Environ. Resource Econ. 76(4), 1161–1185 (2020) 21. Lo, A.Y., Jim, C.Y.: Willingness of residents to pay and motives for conservation of urban green spaces in the compact city of Hong Kong. Urban Forestry & Urban Greening 9(2), 113–120 (2010) 22. Fofana, N.K., Latif, F., Sarfraz, S., Bilal, Bashir, M.F., Komal, B.: Fear and agony of the pandemic leading to stress and mental illness: An emerging crisis in the novel coronavirus (COVID-19) outbreak.Psychiatry Research 291, 113230 (2020) 23. Ka´zmierczak, A.: The contribution of local parks to neighbourhood social ties. Landsc. Urban Plan. 109(1), 31–44 (2013) 24. Li, X., Chen, W.Y., Hu, F.Z.Y., Cho, F.H.T.: Homebuyers’ heterogeneous preferences for urban green–blue spaces: A spatial multilevel autoregressive analysis. Landsc. Urban Plan. 216, 104250 (2021) 25. Xiao, Y., Lu, Y., Guo, Y., Yuan, Y.: Estimating the willingness to pay for green space services in Shanghai: Implications for social equity in urban China. Urban Forestry & Urban Greening 26, 95–103 (2017) 26. Cole, H.V.S., et al.: The COVID-19 pandemic: Power and privilege, gentrification, and urban environmental justice in the global north. Cities & Health 5(sup1), S71–S75 (2021)

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27. Wang, Q., et al.: Aerosol transmission of SARS-CoV-2 due to the chimney effect in two high-rise housing drainage stacks. J. Hazard. Mater. 421, 126799 (2022) 28. Dass, S., O’Brien, D.T., Ristea, A.: Strategies and inequities in balancing recreation and COVID exposure when visiting green spaces. Environment and Planning B: Urban Analytics and City Science 239980832211146 (2022) 29. Ma, A.T.H., Lam, T.W.L., Cheung, L.T.O., Fok, L.: Protected areas as a space for pandemic disease adaptation: A case of COVID-19 in Hong Kong. Landsc. Urban Plan. 207, 103994 (2021) 30. Yang, L., et al.: Neighbourhood green space, perceived stress and sleep quality in an urban population. Urban Forestry & Urban Greening 54, 126763 (2020) 31. Yang, Y., Lu, Y., Yang, L., Gou, Z., Liu, Y.: Urban greenery cushions the decrease in leisuretime physical activity during the COVID-19 pandemic: A natural experimental study. Urban Forestry & Urban Greening 62, 127136 (2021) 32. Tang, B.: Is the distribution of public open space in Hong Kong equitable, why not? Landsc. Urban Plan. 161, 80–89 (2017) 33. Tang, B., Wong, S.: A longitudinal study of open space zoning and development in Hong Kong. Landsc. Urban Plan. 87(4), 258–268 (2008) 34. Lai, L.W.C., et al.: Property rights & the perceived health contribution of public open space in Hong Kong. Land Use Policy 107, 105496 (2021)

Digital Twin Technology for Improving Safety Management in Construction Patrick X. W. Zou1,2 and Songling Ma1(B) 1 School of Economics and Management, Chang’an University, Xi’an, China

[email protected], [email protected] 2 Engineering Research Center of Highway Infrastructure Digitalization, Ministry of Education,

Chang’an University, Xi’an, China

Abstract. The application of digital twin technology (DT) in construction is becoming more and more widespread, and within this, construction safety management has always been an important part that cannot be ignored. The development of digital twin technology provides new possibilities on how to improve construction safety management using DT. This paper develops a conceptual human-machine-environment safety detection and control system based on identifying potential applications of digital twin technology focuses on unsafe behavior, unsafe machine/equipment condition and unsafe environment as the main subjects. This system includes a five-dimensional model of the digital twin technology, including cyber-physical modeling, data storage and management, perspective simulation, deep learning, unusual state diagnosis and trend prediction. In addition, key technologies, such as data mining and simulation, and twin collaboration for human-machine-environment co-integration, have also been designed. In addition, the case of Qinling Tunnel digital twin technology application is studied. The research provides several threads for the integration of digital twin technology and construction safety and several areas for future research. The research makes contribution to the field by applying emerging information and communication technology-based management of construction safety. Keywords: Digital twin · Construction safety · Safety management · Unsafe behavior monitoring · Unsafe environment · Human-machine-environment

1 Introduction With the rapid development of the construction industry, and the rapid iteration of construction technology updates, efficiency has been the primary goal of the pursuit of construction operations, and the maintenance of safety is lost. It is important to increase attention to construction safety, making safety as the efficiency of the backing. Thus, effective supervision of construction sites, timely warning and curb the occurrence of unsafe behavior, unsafe equipment or machine state or unsafe working environment, effectively ensure that construction activities are stable and efficient to move forward.

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 40–56, 2023. https://doi.org/10.1007/978-981-99-3626-7_4

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Safety is people-oriented, but according to the statistics, unsafe human behavior is a main cause of accidents, and workers and operators are directly exposed to the construction sites, in close contact with machinery and equipment and the environment. The dynamic and complex nature of the construction site makes the traditional manual monitoring and management methods which are often in a passive position, unable to take early warning and braking measures, and the resulting information also leads to the situation of information fragmentation, hindering the real-time interaction between physical and cyber objects, unable to provide decision makers with timely and comprehensive decision data support. In relation to construction safety research, there have been four stages as shown in Fig. 1 [1] and it is entering the fourth stage of technology-enabled safety management. In other words, the current research focused has shifted to technology-enabled safety management. With the emergence of new technologies and the gradual deepening of research, this paper expands on the progress of construction safety research and practice.

Fig. 1. Development of construction safety research and practice (based on Zou and Sunindijo 2015)

As shown in Fig. 1, in the technology empowerment stage of construction safety management, advanced technologies such as digital twin technology and BIM technology have become very promising tools to improve construction safety management and help construction sites realize information and intelligent control. Digital twin is a technology that is based on the accurate reduction of physical entities or systems to build virtual representation models, ensuring the consistency between physical objects and virtual models through the interaction of virtual and real environments, and providing detection, diagnosis, and prediction for physical entities with the help of data, models, and analysis technologies [2]. The effective combination of DT and safety management can give a full play to the comparative advantages of this cutting-edge technology in simulation and data fusion, realize the accurate sensing and early warning of the realtime safety states of man-machine-environment. However, the theory and practice of DT are still in the primary development stage, and how DT can play its unique advantages in construction safety management is still unclear and fragmented. Currently the main problem is how to integrating the capabilities of DT technology to improve safety from human-machine-environment perspective. The aim of this research is to develop a conceptual human-machine-environment safety detection and control system, identify the key technologies of the system. The functions of system include the identification and early warning of unsafe human behavior, unsafe machine movement and unsafe working environment at construction sites. It

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can also help stakeholders to understand the application of DT from the perspective of human-machine-environment, and provide a new thinking perspective and conceptual path for how DT can improve construction safety management. To achieve the above-mentioned research aim, the research first constructed a comprehensive five-dimensional model of the DT from the human-machine-environment perspectives, then identified the technical architecture and key technical elements for constructing the DT, and finally, the relevant concepts of this study are deepened through case study.

2 Overview of Digital Twin Technology 2.1 Concepts, History, Functions and General Applications 2.1.1 Concepts and Development History DT can achieve a profound interaction between the physical and digital components through the continual and dynamic data exchange between the physical and digital environments, which provides real-time monitoring, updating, simulating, analyzing, controlling, predicting, and optimizing [3]. DT is a relatively new technology and the key milestone development history is shown in Fig. 2.

Fig. 2. Historical development of digital twin technologies

2.1.2 Functions and Technologies DT may perform one or more of the following functions: From the perspective of whole life cycle management, DT can realize the life cycle management of entities [4, 5]; in the design phase, DT enables designers to visualize complex systems, helping to clarify complex relationships and facilitating the evaluation and verification of solutions [6]; in

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the operation stage, for product production, DT realizes the optimal allocation of human, financial and material resources and process optimization by simulating, verifying and confirming the process plan and production plan [7]; in the maintenance phase, DT can help diagnose the current situation and generate the best maintenance plan by simulating specific scenarios [8]. There are a number of technologies that work together to form a complete DT, as listed in Table 1. Table 1. Key Technologies and functions of Digital Twin Authors

Key Technologies and Functions of Digital Twin

Qi, Q. et al. [9] (2019)

1. Enabling technologies for cognizing and controlling the physical world 2. Enabling technologies for digital twin modeling 3. Enabling technologies for digital twin data management 4. Enabling technologies for digital twin services enabling technologies for connections

Li, G. [10] (2020)

1. Software architecture technology to support the system 2. Full set of data management and security technologies 3. Dynamic modeling and model-driven technology 4. Efficient computing and targeted service technology 5. Immersive experience technology for the integration of reality and imagination

Yang, G. et al. [2] (2021)

1. How to build a high-precision virtual model • Accurate construction and fusion of multidimensional virtual models • Autonomous updating of virtual models • Virtual model accuracy evaluation 2. How to ensure the accuracy of interaction data • Multi-source heterogeneous data fusion • Data semantic consistency assurance

Sharma, A. et al. [11] (2022) 1. High-fidelity 2-way synchronization 2. The compatibility of DT with existing software being used in a production lifecycle 3. Cybersecurity concerns, IoT security, cross industrial partners security 4. As DT requires interoperability among various components, real-time tools, formulating a joint optimization problem, and big data resources, putting these together can be time-consuming for industry, and may lead to unwanted distractions

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2.1.3 General Applications The selection of DT technologies has different narrative perspectives in different research areas for different problem-solving. In the field of complex industrial systems and complex equipment, Liu et al. (2018) summarized that the key technologies of DT are multi-domain multi-scale fusion modeling, data-driven and physical model fusion state assessment, data acquisition and transmission, life cycle data management, VR presentation, high-performance computing, and other key technologies to provide constructive advice and development ideas for the coupling development of industrial intelligence and DT [12]. In the manufacturing field, Wu et al. (2021) stated the key technologies of DT mainly include multidimensional modeling and simulation technology, virtual reality technology, data analysis, and processing technology, and platform construction and processing technology [13]. From the perspective of the aerospace field, Mandolla et al. (2019) mentioned the key technologies that need to add a secure interconnection technology to ensure that the twin data cannot be tampered with but can be traced [14]. Tao et al. (2022) proposed a DT interaction guideline on how DT interaction can understand and realize the application service requirements of simulation, control, prediction, and optimization, and built a DT interaction theoretical system based on “perceptioncommunication-physical and virtual mapping-data and model coupling-fusion”, and analyzed five key DT technology interactions, namely information perception, connection and communication, virtual-real mapping, digital-model linkage, and interaction fusion [15]. DT has also been applied to various scenarios in different fields to seek pioneering developments to answer practical questions. For example, in the field of industrial manufacturing, Tao et al. (2017) put forward the concept of the DT workshop, and on this basis, discussed the theory and implementation method of interaction and integration between the physical world and virtual information world of workshop based on twin data of workshop [16]. Armendia (2019) combines machine dynamics, control loops and other models to design and optimize machine tools with DT models [17]. In the fields of mining and oil exploitation, Ma et al. (2020) put forward an intelligent mine operation mechanism based on DT [18]. Zhang, Ge, and Li (2020) put forward a new idea of intelligent mine construction based on “DT + 5G”, built a DT mine model, and based on this, designed a DT intelligent mining integration scheme [19]. It can be seen from the above discussions that DT has been comprehensively applied in many industries, but the basic expression of DT is to realize the digital virtualization of physical entities and to choose the best scheme to avoid risks through model construction and simulation. In a word, the core of the realization path of the coupling development of digital technology in various fields is the ability of accurate mirroring, panoramic observation, dynamic evolution, virtual-real interaction and modular integration based on the whole life cycle of products, to realize all-around support and guidance from R&D, manufacturing, maintenance and re-innovation [20]. 2.2 Potential Application of Digital Twin Technology to Improve Safety Management For this research which is about improving safety, the identification of unsafe behavior is the backbone, supplemented by the detection of unsafe conditions of construction

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equipment and machine and unsafe scenarios of the working environment that can affect the dynamics of construction operations to facilitate the monitoring and early warning of unsafe behavior and conditions. In safety, human is the main subject, and the focus on human dynamics can provide timely tracking to help improve safe behaviors and monitor the occurrence of safety accidents in real time. For example, in the field of public safety, Liu (2020) applied DT to fire safety evacuation drills, mapping the Winter Olympic venues and venue personnel into digital models for algorithmic path planning, so as to choose the best fire safety evacuation plan [21]. Greco et al. (2020) tracked and analyzed the motion data based on the shop floor production personnel to improve production behavior and safety [22]. Zhao et al. (2021) proposed a DT framework for indoor safety tracking: composed of the physical world, IoT devices and Services, the online world, and stakeholder composition. Then, the indoor safety tracking mechanism is proposed: abnormal static monitoring, and self-learning genetic positioning [23]. As for the monitoring of equipment and environmental safety state, Zhao et al. (2019) applied DT in the development of a tool pre-processor to reduce the rework rate of prototype development [24]. Xu and Ye (2022) based on DT using 3D GIS visualization technology, showing the topography in digital 3D space, which promoted the expansion of DT in the field of water conservancy industry safety monitoring business [25]; Wu et al. (2022) integrated the DT, deep learning and mixed reality technologies into the newly developed real-time visual early warning system, presenting danger information in time [26]. For the human-machine-environment safety scenarios, Bao et al. (2022) explored the realization path of DT collaborative technology of human-machine-environment integration from the two cores of environment and task. In the digital environment, to promote the integration and co-creation of the human-machine-environment safety, compared with centralized and one-level research, it can promote the real-time, completeness and adaptability of the entire construction site state detection [27]. It is necessary to realize the unification and integration of various data from different channels, and have enough data processing ability to deal with complicated tasks from different angles. 2.3 Challenges of Applying Digital Twin Technologies in Construction Safety Management In construction safety, the application of DT is still facing many challenges. Hou et al. (2020) stated that the main challenges come from the dynamic and complex nature of construction activities and the immature information synchronization and processing [28]. Yang et al. (2021) also summarized the main challenges faced by DT and mentioned that constructing a high-precision virtual model and ensuring the accuracy of interactive data are two major technical problems in DT research [2]. Similarly, Wei et al. (2021) pointed out that the software architecture design of the DT construction site system is still at a preliminary stage, and the lack of modularity and time-responseoriented system thinking has bound the modularity and expandability of the system [29]. Rasheed, San, and Kvamsdal (2020) summarized that the common challenges faced by DT applications are: virtual and real interaction between physical entities and twins, backward compatibility of models, and higher transparency and interpretability of DT for security [30]. These challenges are summarized in Table 2.

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Table 2. Challenges of applying digital twin technology in construction safety management Category

Challenges

Model Construction

The construction site contains human-machine-environment, so how to build a high precision and high simulation twin model for these three elements?

Virtual reality interaction

How to update dynamic construction information in real time? How twin data can better guide physical entities?

Modular Integration

How to improve time responsiveness? How to meet the needs of multiple subjects? How to promote the scalability of the system?

Interpretability

How to meet the need for a high level of safety and security for DT?

In response to the above challenges and problems, the architectural design and key technical routes of the DT-based behavioral safety identification and early warning system need to achieve high simulation mapping of real physical behavior and environment, with sufficient interactive data processing capability to meet the requirements of realtime visual monitoring, analysis and prediction, fault warning, reverse-time recurrence, and situation prediction for the entire construction site.

3 Conceptual Digital Twin Model for Human-Machine-Environment Safety Monitoring 3.1 Five-Dimensional Digital Twin Model A five-dimensional DT model is needed in order to detect human-machine-environment safety of a construction site, which is divided into a physical entity, a virtual entity, twin data, service and connection as the modeling basis [31]. The overall DT model is shown as Fig. 3. Each of these five modules is described in the following sections. The physical entities [31] are specifically the people and things that actually exist, such as workers/operators, machines and equipment, and the construction environment. By setting up sensors on the construction site, we can extract the data of construction personnel, machines and equipment as well as the environment, and realize the all-round recognition of the construction situation. The DT model is built on the basis of comprehensive and accurate data collection of the construction site and timely acquisition of its dynamic data through intelligent sensing technology. The virtual entity [31] is a digital expression of the physical entity characteristics, which needs to reflect the human-machine-environment interaction, to achieve the effect of simulation and virtual-real interaction. The virtual entity is not just a simple reflection of the site, but a mirror that integrates the different components of the construction site into a variety of data structures and systems. These data structures can deeply reflect the operation mechanism and development situation of the whole construction site.

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Fig. 3. Five-dimensional model of digital twin

The twin data center [31] includes property data and procedure data of the physical entity, and information sharing and value-added are realized through data processing and various algorithms. In the DT five-dimensional model, in addition to the property data and procedure data, there are also historical data and simulation data stored in the cloud to help the model carry out parameter fitting and further optimization of the model, so the data storage volume is large and complex. Therefore, the heterogeneous and unified processing of data as well as high precision computing and large capacity storage are the key aspects to maintaining the normal operation of the twin data center. The service system [31] refers to the intelligent service functions that can be realized by relying on DT, such as simulation, abnormality diagnosis, self-adjustment and optimization, and other services that provide decision support for safety management. In order to facilitate the unified control of site conditions by managers, after intelligent analysis of data and simulation, there should be a visual interface to provide data support for management decisions. The connection [31] is used to reflect the relationship between physical entities, virtual entities, twin data, and services to facilitate collaborative interaction and consistent response in each dimension. The collaborative interaction of all parts should be bi-directional and unified to ensure the dynamic operation and closed-loop optimization of the whole DT five-dimensional model. In the DT five-dimensional model, virtual entities are virtually mapped to physical entities to produce high-intensity real-time simulation data exchange, after which physical entities and virtual entities input various heterogeneous structural data into the twin data, and the twin data center unifies multi-source heterogeneous data and combines technologies such as fusion modeling, data mining and simulation with virtual entities to simulate the situation [15]. Timely discovery of various possible unsafe accidents of man-machine-environment in the construction site, and then the virtual entity feeds back with the predicted results into the visualization platform service, then the tasks of the back office are to perform hazard warnings and communicate statistical information. In

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addition, for perspective simulation and selection of a suitable solution, the twin data will unify all kinds of data and then hand over to the twin data for dynamic simulation, and the twin entity will transmit the simulation results to the service system, which will compare and select the best preview program for different paths. 3.2 Model Operation Mechanism The model is based on the modeling and simulation theory of DT, which realizes the highfidelity real-time mapping of the three elements of the human-machine-environment in the physical entity, and then uploads and stores the operation data as historical data through the cloud server through the fusion and utilization of multi-source data, thus realizing the storage and management of the current operation data and historical data. These data decompose the whole construction site into different data structures for data modeling, thus realizing the multi-dimensional description of the physical entity. Numerical simulation is used to supervise and control the whole process of the entire physical construction site, and situation prediction and panoramic simulation are carried out in different scenarios to realize self-adjustment and learning of the whole DT model (Fig. 4).

Fig. 4. Operation mechanism

4 Technical Elements 4.1 Cyber-Physical Fusion Modeling To build a safety identification and warning system, it is necessary to meet the modeling requirements of different levels and scenarios, mainly in the following aspects: 1) Under the background of DT it effectively represents the linkage between the three elements of the human-machine-environment unsafe condition identification and warning system. The system runs on the basis of a digital construction site generated by DT. How information is called and how information interacts with each other will affect the running state of the whole system, so it is necessary to model the entire physical environment and information integration by coupling.

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2) The integrated DT system, which is mixed with physical and cyber information, is a multi-dimensional and multi-scale hybrid integrality. However, for the data processing and visual user interface, it is necessary to improve the effective fusion ability of multi-source heterogeneous data to ensure the flexibility and applicability of the DT modeling [32]. 4.2 Data Storage and Management The safety identification and warning system are guided by the processing center. By storing and managing the process data, it can realize historical condition playback, multidimensional and all-round data analysis and display. At the same time, it can also provide rich samples for data mining and deep learning, help to discover the potential correlation between data, and realize the portrayal of the whole system operation mechanism, thus realizing a virtuous circle and re-optimizing the system. The process of realization of data storage and management requires a cloud server to distribute the management of massive running data to ensure quick invocation and backup of data. In order to fully ensure the real-time and accuracy of the data call, it is necessary to build a safe and effective data management system and optimize the data distribution architecture, storage mode and call path. 4.3 Model Evolution and Improvement An important function of the safety identification and warning system is to select the solution that can minimize personal and property losses according to simulating multiple paths in uncertain scenes. Based on this, the DT needs to constantly realize self-evolution and self-improvement of the application scenarios in the full process, to ensure that different development paths in uncertain scenarios can be fitted to the real accident situation occurrence to the greatest extent, reduce the uncertainty of information, and improve the accuracy of scheme selection. 4.4 Deep Learning The inherent principle of deep learning is to learn the intrinsic laws of sample data and use neural networks to grasp the characteristics of data in deeper levels. Deep learning requires a large amount of high-quality sample data, and by analyzing the sample data, a more accurate algorithmic model is selected on the basis of merit. The initial algorithmic model is constructed and then fitted based on a large amount of data, and each parameter of the model is precisely adjusted through layers of iterative training. Ma et al. (2022) proposed a fault feature generation and diagnosis method based on the DT model, using the DT simulation method to generate fault feature data, which provides a reliable data source for deep learning and facilitates deep learning techniques for model training, and provides a new optimization idea for deep learning for large-scale complex data [18]. The application of deep learning is to improve the data processing and analysis ability of the whole DT system, constantly realize self-optimization through deep learning, promote the whole system to adapt to changing application scenarios, and achieve the ideal state of self-optimization and self-improvement. This is also a further expansion of the process of data storage and management technology.

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4.5 Unusual State Diagnosis and Trend Prediction Unusual state or condition diagnosis and trend prediction are a kind of ability based on dynamic insight into safety risks, evaluation and prediction. This function can be used to monitor the safety situation of the human-machine-environment all the time. For workers, the changes in their emotions and psychological states can be identified through biometrics, dynamic perception and other technologies, so as to capture the safety risks in time and ensure the safety of personnel. For machinery and equipment, continued monitoring of machinery and equipment as well as trajectory prediction, data processing, machine learning, probability statistics and other technical methods are comprehensively adopted for timely warning of equipment failures to avoid expansion of accidents; For the environment, use image recognition and processing to grasp the dynamics of the environment at all times. The prediction of the trend is based on the diagnosis of unusual states and conditions, using big data analysis and numerical simulation to predict the trend of various elements in the construction site, so as to accurately control the safety risks. 4.6 Data Mining Data mining refers to mining potentially useful information from massive data, with the aim of establishing a decision-making model that can predict the future according to historical data. Simulation is to use one system to simulate another real physical system realistically. In an all-round and multi-faceted way on the basis of mining potential useful information, and further supports the deep mining of data by using simulation to realize the purpose of trend prediction. 4.7 Digital Twin for the Collaboration of Human-Machine-Environment Human-machine-environment itself is not a new concept. Yuan et al. (2020) proposed the theory and method of man-machine-environment condition recognition and specifically demonstrated the key technology of man-machine-environment information sensing in mines [33]. The construction of human-machine-environment virtual-real fusion perception theory aims to realize the digitization, modeling and unified description of mining safe construction perception information by building a mine safety production state knowledge system, which can be helpful to improve the current situation of rich data-poor knowledge in mining big data platforms in the human-machine-environment co-integrated intelligent manufacturing platform, people should be at the center of the physical space, and the task should be the main line of development in the information space [27] (Bao et al., 2022). The physical and twin mapping is shown as Fig. 5 . The twin collaborative technologies oriented to human-machine-environment cointegration start from improving the prediction and early warning capability and selfadaptive capability of the whole system, fully considering the dynamic nature of the environment in construction operations, the autonomous mobility of humans, the controllability of machines and equipment, and the collaborative co-integration needs of human-machine interaction in a dynamic environment [28], and the need to pay attention to the complex heterogeneity of human-machine collaborative operations in the

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Fig. 5. Physical-Twin Mapping and Interaction

dynamic environment in the scenario of safety in real time and real-time interaction. Based on this, the whole twin technology of human-machine-environment integration should firstly plan and monitor the real-time changes of machines and equipment and the dynamic environment in the DT space to guide the workers and operators to carry out coordinated and adaptive operations; secondly, use the controllability of machines and equipment and deep learning techniques to grasp the movement path of intelligent devices, adapt to the environment through self-adjustment of intelligent devices, maximize the machine utilization based on cognitive system analysis and save duplicated labor and improve the comfort of operators’ work. It should be noted that the co-integration of the human-machine-environment is firstly required based on the previously mentioned fusion modeling technology. Based on the integration and promotion of various key technologies such as data mining, whole process data storage and management, the sensing of the real-time state of the human-machineenvironment and the interaction and collaboration of human-machine-environment data ultimately serve the data center and service platform, to solve the current problem of synergy and management between different levels and factors in safety management.

5 Design and Exploration of Application Scenarios 5.1 Abnormal State and Condition Warning and Predictive Maintenance At the present stage, the identification, assessment and control of hazards are fragmented, without forming a linked and smoothly connected synergistic mechanism, and the supervision of construction sites is still mainly manual, with a slow response to hazard identification and fault warning. Through the DT model, based on real-time on-site

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detection of sensors, detectors, intelligent monitoring, and other equipment to master the conditions of people, machines and the environment, set the operation and maintenance interval between the normal state of detection data and abnormal state, when the detection data exceeds the normally defined interval, it is recognized as an abnormal state, issue an early warning, and the twin data center will transmit the control instructions to the physical site and warn the staff through the on-site alarm device [34]. At the same time, the abnormal information and the site’s human-machineenvironment-related information are fed back to the system’s backend, and the situation is reported to the management staff through the visualization interface as well as providing specific attribute data support so that the management staff can combine their own management experience and specific construction site conditions for further construction safety management. The historical data and simulation data stored in the data center over time will be automatically counted and intelligently analyzed for potential hazards, which will assist the manual personnel to conduct regular inspections and predictive maintenance. 5.2 Unsafe Behavior Detection An important part of the conceptual system of the DT is to realize the whole life cycle data management of the object. The whole life cycle data management focuses on the process of construction site abnormal detection: safety monitoring – risk assessment – safety control – problem analysis -measure improvement, the purpose is to break the information barrier and realize closed-loop integrated management. Yu, Zhang and Guo (2019) proposed a refined classification of unsafe behaviors at construction sites, structured storage and construction of behavioral analysis methods to provide a feasible path for safety management at construction sites, while the fusion application of DT is to realize data value-added on a data-driven basis to achieve efficient utilization and closed-loop management of information [35]. When an accident occurs, twin space can monitor, for example, the fatigue state of people, the life span of equipment, and the brightness of the environment based on site data and operation and maintenance data, and when the specific value of the site data exceeds the established range, it can be judged as an abnormal state for prior warning; when an accident occurs, the dangerous situation can be isolated in time through alarms to reduce the deterioration of the accident; the site data of the twin space can also be retrieved in time for accident tracing to help restore the causes and consequences of the accident and provide samples and data support to curb the recurrence of accidents in the future. Targeted improvement measures taken for accident prevention can also be used to calculate the applicability and practicality of the improvement measures through spot monitoring of the site. For how to provide workers with autonomy in learning and avoid formalism, the application of DT can break the status quo of knowledge-based indoctrination, show workers the specific process of safe construction intuitively through on-site VR real-world simulation, let workers participate and correct their operational errors by simulating scenes, and also deepen the understanding of the severe consequences of accidents by showing workers the consequences of unsafe operation through sensing and scene simulation [36].

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5.3 Case Study This case is about the application of DT system for safety prevention and control of the Qinling Tunnel cluster as part of a highway system. The information presented here is mainly cited from the information tweets, the WeChat social media, released by Shaanxi high-speed highway monitoring company [37]. At present, the first phase of project construction has been completed, which is the construction of the DT system platform, consisting of six modules: traffic safety, structure safety, mechanical and electrical facilities safety, surrounding environment safety, traffic emergency management and control, and risk management and control. It integrated the application of BIM, GIS, DT, big data analysis and other technologies to achieve target monitoring and early warning, emergency response, and enhance the comprehensive management and control ability of the tunnel. With the comprehensive application of the DT system of the Qinling tunnel safety prevention and control system, it is expected to reduce the traffic accident rate by more than 40% [38] (Zhang and Bai 2022). Situated in the northwest of China, the Qinling tunnel has complex terrain and changing environment. The tunnel section traffic is accident-prone, and easy to cause traffic congestion or even paralysis, which requires timely monitoring and risk assessment of vehicles. The Qinling tunnel DT system, with the help of DT and the highway tunnel digital mapping, is used for traffic management, the DT use perception equipment to capture its coordinates and operating status, plan vehicle dispatch routes, and coordinate with backend managers for decision-making. For the tunnel maintenance management, traditional manual inspection and mechanical equipment is less efficient, workload is large, and the scope of inspection is limited, maintenance information mainly relies on manual recording and paper text transmission. The use of DT, according to prior three-dimensional modeling of the tunnel, staff can use the DT platform for road section information retrieval and simulation inspection, predicting the load level of the tunnel structure, improve the inspection precision reading and efficiency. The application of DT can provide a deep thinking and forward-looking chip for the construction and operation of the Qinling tunnel, realizing the visualization and monitoring from physical space to DT space, and then from DT space to real space for intelligent control and smart services. This is also a constructive application of DT in the highway construction industry—the typical construction safety high-risk industry. From environmental safety monitoring at the initial conceptual design stage to traffic safety control at the opening stage, and then to system monitoring and early warning on the DT platform at the operation and maintenance stage, all of these prove the technical advantages of DT in construction safety management and whole life cycle safety management in a real-time controllable manner.

6 Conclusion Construction safety is a traditional and vital research field with new elements continuously added, and the mainstream themes include factors such as accident prevention, human factors, risk assessment, safe behavior, and hazard identification. The comparative advantages of the DT’ virtual-reality interaction and real-time prediction are

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closely related to the optimization of these mainstream research themes and are important directions for future intelligent safety management. In this paper, we proposed a conceptual system framework of the five-dimensional model of the DT, for humanmachine-environment safety detection and control based on DT and described the key technologies and specific application scenarios for conducting safety identification and early warning. In the development of the system, the technical framework is specified from three levels of human-machine-environment, showing the operating principles of supervision and control, situational prediction and panoramic simulation, and self-adjustment and learning for the whole process of the early warning and prediction mechanism, and shows the detail requirements of data from five dimensions: physical entity, virtual entity, data, service, and connection. In terms of key technologies and specific application scenarios, the research has specified key technologies such as cyber-physical fusion modeling, data storage and management, model evolution and improvement, deep learning, unusual state diagnosis and trend prediction and DT for the collaboration of human-machine-environment. The design and initial exploration of application scenarios have abnormal state and condition warning and predictive maintenance and unsafe behavior detection. A case is studied to demonstrate the actual and the potentials application of DT systems for safety management in construction projects. The contribution of this research is the development of the conceptual DT system and in-depth discussions of the technologies and techniques needed for human-machineenvironment safety management in construction projects. Future research could continue in the same direction and build on these conceptual systems by investigating the details of these broad discussions and develop DT systems for real construction sites which could provide pilot implementation and post-test evaluation.

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Interview Methods in Construction and Demolition Research: Based on Case Study and Recommended Best Practices Zhikun Ding1,2,3,4 , Xinrui Wang5(B) , Jian Zuo6 , Patrick X. W. Zou7,8 , and Lili Yuan1 1 Key Laboratory of Coastal Urban Resilient Infrastructures (MOE), Shenzhen University,

Shenzhen, China 2 Sino-Australia Joint Research Center in BIM and Smart Construction, Shenzhen University,

Shenzhen, China 3 Department of Construction Management and Real Estate, College of Civil and Transportation

Engineering, Shenzhen University, Shenzhen, China 4 Shenzhen Key Laboratory of Green, Efficient and Intelligent Construction of Underground

Metro Station, University of Shenzhen, Shenzhen, China 5 Department of Construction Management and Real Estate, Shenzhen University,

Shenzhen 518060, China [email protected] 6 School of Architecture and Built Environment, The University of Adelaide, Adelaide, Australia 7 Changan University, Xian, People’s Republic of China 8 Department of Civil and Construction Engineering and Centre for Sustainable Infrastructure, Swinburne University of Technology, Melbourne, Australia

Abstract. Interview holds a prominent place among qualitative research methods, and it has been widely used in construction and demolition waste management research. Based on the literature review of interview-based related research, the status quo of interview applications is analyzed. Consequently, an interview guideline of interview-related methods is proposed according to the ratio between the number of interviewers and interviewees for effective implementation of interview methods. This paper classifies interview methods into four scenarios: Type I (one to one, 1:1), Type II (one to many, 1: n), Type III (many to one, m:1), and Type IV (many to many, m:n). Through a series of case studies, the application of the guide is demonstrated, and the corresponding best practices are summarized. Overall, this paper provides references for researchers to select the most appropriate interview method in construction and demolition waste research, and manage the interview process effectively. Keywords: interview · research method · construction and demolition waste management

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 57–73, 2023. https://doi.org/10.1007/978-981-99-3626-7_5

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1 Introduction Construction and demolition (C&D) waste is defined as the waste generated during construction, renovation, and demolition activities. The inappropriate treatment of C&D waste would impose an adverse impact on the environment [1], and it has been considered an overwhelming waste flow hindering urban sustainable development. Therefore, the C&D waste problem has been a global hot spot, which receives an increasing number of scholars attempting to solve it in a manageable way. Scholars need to obtain empirical data from different stakeholders in the C&D waste industry, and various data collection methods have been adopted by previous C&D waste management studies, including direct observation, questionnaire survey, interviews, direct measurement, tape measurement, and truckload records. Interview methods are one of the top choices and important building blocks for academic research [2]. Firstly, interviews fit in the way that humans express meaning through language. Furthermore, it assists scholars to explore complex issues that are unquantifiable or go beyond the scope of quantitative means [3]. Therefore, it holds a prominent place in social and behavioral research [4], including C&D waste management. The interview has been applied for collecting both quantitative and qualitative data in different sub-pillars of C&D waste management, including waste generation, waste reduction, waste disposal, waste transportation, policy research, carbon emission, and so on. Therefore, the characteristics of C&D waste research require scholars to collect reliable and valid data in various interview scenarios, which makes it more difficult to conduct related research. In literature, a significant amount of research has been conducted using interview techniques. However, research that systematically reviews the implementation of the interview in C&D waste management and discusses their application scopes have yet to be conducted. Scholars need to have a clear understanding of the characteristics and implementation constraints of the interview techniques before making an informative research plan. To fulfill this gap, this paper proposed a guideline of interview methods for qualitative research by addressing the above issues. This framework could help guide researchers to choose the interview methods appropriately and conduct interviews effectively according to the number of interview participants and the ratio between the numbers of interviewers and interviewees. This guideline enables qualitative C&D waste management researchers to assess different types of interview methods, thus assists the decision-making of appropriate techniques for their research.

2 Literature Review The interview is usually a purposeful conversation between more than one person to detect required information [5], and it forms an important building block of research methodology. A literature review has been conducted considering the C&D waste management research using the interview method. therefore, a rigorous literature search process has been carried out based on Web of Science (http://www.webofscience.com/) and Scopus (http://www.scopus.com/), which have been considered to be two influential literature databases [6, 7] containing most of C&D waste management research [8].

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Searches were limited to journal papers written in English, between 1-st January 2010 and 1-st September 2022. The search keywords are selected considering previous C&D waste research reviews, and shown as follows: TITLE-ABS-KEY: (“Interview*” OR “Brainstorming” OR “Nominal groups” OR “Delphi groups” OR “Focus group” OR “Ethnography” OR “Charrette” AND Āconstruction and demolition wasteā OR ĀC&DWā OR ĀCDWā OR ĀCDWā OR Āconstruction wasteā OR Ādemolition wasteā AND Āmanage*ā)

The papers found in the initial search result have been collected and filtered by strict manual inspection. This review focuses on the studies that have (1) claimed to use at least one interview methodology in research. (2) taken the C&D waste management or related diversion activities as the research object. Finally, 89 papers have been collected and analyzed. Overall, this section has been divided into three parts. First, it proposes an overall introduction of interview methods that have been applied in C&D waste management research. Second, it describes the current implementation of interview methods in C&D waste management. Third, it discusses the characteristics of the C&D waste management interview would be discussed. 2.1 The Introduction of Interview Methods The interview structure would influence how the researcher plan or organize the interview process. Therefore, based on the structure, interview methods adopted in C&D waste management research can be classified as follows: structure interview, semi-structured interview, unstructured interview, focus group, Delphi groups, and Charrette. In a structured interview, the researcher follows a specific set of questions in a predetermined order with limited response categories [9, 10]. In a semi-structured interview, the researcher sets the outline of the covered topics, but the interview is developed by the interviewer according to the interviewee’s responses. Semi-structured interview is the most widely used form of interview in qualitative research and can be used for individual or group interview. Unstructured interviews, refers to the form in which researchers conduct interviews without a predefined theoretical framework [11]. Delphi groups refer to a process in which experts are convened to respond to the researcher’s questions via multiple rounds of structured questionnaires [12, 13]. Focus group is one form of group interview and is generally assumed to have derived from the focused interview developed by Merton and his colleagues during the 1940s [14]. Focus group is normally composed of 5 to 10 participants who, with the guidance of a moderator, discuss a particular topic [15]. Group members should be homogeneous [16]. Ethnography refers to the research process in which a researcher closely engages in the daily life of some social setting and collects data by fieldwork such as observation, participation, and then writes accounts of this process. Ethnographic inquiry often

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involves interviews, which may vary from casual conversations to prolonged in-depth interviews [17]. The primary objective of an ethnographic interview is to appreciate or observe the world from the eyes of the person being studied [5]. Charrette is an intensive workshop in which various stakeholders and experts are brought together to address a particular issue. There are two critical elements of charrettes i.e. an educational component (workshop) and an interactive planning component (charrette). Charrette is an intensive face-to-face process designed to bring people from various sub-groups of society into consensus within a short period [18]. Through reviewing previous research, an overall picture of the interview in C&D waste management can be drawn. The semi-structured interview was the most common method (about 40%), followed by focus group (about 14%). Meanwhile, nearly 10% of the research adopted the unstructured interview. The rest research adopted the interview method including Dephi interview, structure interview, and charrette. Especially, Dephi interview and ethnography were only used in very a few studies [19]. 2.2 Current Implementation of Interview Considering the knowledge framework of Ding [20] and Jin [21], research on the application of interview method can be clustered into four sub-domain of C&D waste management, including: (1) Introduction of the current waste management. (2) Influential factors in waste diversion. (3) Estimation and quantification of the waste generation. (4) Planning and summary of empirical waste diversion practices. The basic contents or research structure have been shown in Fig. 1.

Fig. 1. Research domains that adopted interview in C&D waste management research

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The first domain: Introduction of the current waste management mainly introduces the current situation (e.g. obstacles, advantages) of C&D waste management in a specific area, countries or cities, including Australia [22, 23], England [24, 25], Mainland China [9, 10, 26], UAE [9, 27] and Sweden [28]. Table 1(1) depicts the typical research in this domain. In literature, researchers have interviewed stakeholders to collect empirical information for further analysis. Furthermore, interview have also been used for confirming the collected information. Wang [29] have confirmed the information from consultants by interviewing contractors and clients. The second domain: Influential factors in waste diversion mainly explores the factors influencing C&D waste diversion activities, which can be divided into two clusters. First, researchers have mainly adopted the interview for investigating stakeholders’ attitude towards waste management behavior and influential factors, including recycling behavior [30], and waste reduction management [31]. Second, researchers have mainly interviewed stakeholders to explore the crucial factors in the implementation of C&D waste diversion. Depicts the typical research in the second domain. Furthermore, some researchers, especially those who study the critical success factors (CSF) in C&D waste management, have sent the questionnaire to related stakeholders for rating the emphasis of collected factors [32]. The third domain: Estimation and quantification of the waste generation focus on evaluating the generation of C&D waste in a specific region through the mathematics model or technical tools. Table 1(3) depicts the typical research in this domain. Interview and survey are commonly mentioned in these researches. Besides, Zhang and Tan [33] interviewed stakeholders to evaluate the verify the recycling rate in a highway project. Overall, researchers in this domain mainly collect the waste generation information through on-site observation, related documents, or interviews, and may subsequently verify and revise the initial findings through interviewing related stakeholders or experts. The fourth domain: Planning and summary of empirical waste diversion practices shares the practice of C&D waste management, discusses the performance of policy and case studies, and interviews the stakeholders for evaluating the emerging concepts or techniques in C&D management. Figure 4(4) depicts the typical research in this domain. Interview and survey are commonly mentioned in these researches. In regard to the waste management project, research has shared the project experience from Nigerian [34, 35], Shenzhen, China [36], Latvia [37]. Furthermore, innovative concepts and techniques that are used for improving the performance of C&D waste management have also been discussed and evaluated, including building information model [38], big data analysis [39], smart grid concept for C&D waste material trade, and green building [40]. Overall, the frequencies of interview methods used in different research fields are basically.

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Z. Ding et al. Table 1. The typical research in waste management

No.

Authors (year)

Interview methods

Aims

1

Akinade et al., 2017 [41]

Semi-structured interview

This article aims to study the tensions and barriers between participants in the Swedish decoration project

Hoang et al., 2020 [42]

Interview semi-structured

Study the current situation of construction waste management in Peru

Tam et al., 2012 [26]

Interview; group discussion

This study investigates the challenges and countermeasures for C&D waste management in Shenzhen, which is a typical

Lu and Yuan, 2011 [14]

Semi-structured interview

Explore the key factors for the success of construction waste management in China

Bilal et al., 2015 [43]

Focus group

Determine the critical success factors (CSF) for the design for deconstruction to effectively recycle materials

Ogunmakinde et al., 2019 [30]

Interview

This study aims to analyze the behavior of construction stakeholders and the decision of recycled mineral construction materials in the Swiss construction materials market

Jin et al., 2019 [21]

Semi-structured interview

This study aims to provide an accurate model to forecast the rapid growth of municipal waste in Hong Kong and establish corresponding solutions

Ding et al., 2021 [44]

Semi-structured interview

The study surveys 15 construction and demolition sites in Vietnam, to determine the C&D waste generation rate, composition, and current disposal methods (CDW)

Yuan et al., 2011 [9]

Interview

This study aims to examine construction waste generation and management in Taiwan

Ma et al., 2020 [10]

Ethnographic;semi-structured interview

This research aims to understand social-technical conditions that lead to the reuse of recycled building elements

Sundin et al., 2011 [28]

Focus group

This study aims to propose solutions to improve the C&D management in Hong Kong

Zhikang Bao et al., 2020 [45]

Structure interview

This study aims to reveal the views of Hong Kong construction industry participants on the construction waste charging scheme three years after its implementation

2

3

4

2.3 The Factors Influencing the Interview Implementation The C&D waste management is a complex system referring to various elements, which challenge the implementation of interview. Considering the previous research and personal experience, factors influencing the implementation of interview in C&D waste management research can be summarized in Fig. 2, including: (i) The insufficient

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of C&D waste information. (ii) The sensitivity of C&D waste information. (iii) The distinctiveness of interviewees’ occupation.

Fig. 2. Factors influencing the implementation of interview in C&D waste management research.

Insufficient C&D waste information is a serious problem in C&D waste management research [46]. It can be caused by lacking appropriate data collection methods, unawareness of the importance of C&D waste. Several researchers have suffered from the limited sample size. Because researchers should ensure interviewees having sufficient related working experience or knowledge to answer the interview questions, they may not find a large number of interviewees that meet with requirements. Moreover, the C&D waste-related information (including: waste generation, waste diversion method) may be sensitive for government and company. Due to the C&D waste regulations, the company that conducts inappropriate waste diversion method and have overwhelming waste generation would receive fine, and subsequently influence the interest of the company. Because of that, interviewees may be influenced, especially in the situation of having other interviewees in a group interview having dominated voice, and they may keep silent or provide invalid and insufficient information for protecting their interest. In addition, due to the work-intensive and complexity of building construction related-job [47], especially for the on-site worker, it is common to find that interviewees to be absent or request a postponement caused by the accidental on-site events. Overall, the insufficient C&D waste information requires researchers to make the best use of every interview chance. Moreover, the sensitivity of C&D waste information also require the elaborate design of interview manual. Nevertheless, the incidental events present higher requirements for the researcher’s interview skills. Therefore, this paper introduces a decision framework that assists researchers to deal with the changes in

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the number of participants caused by emerging events during C&D waste management research.

3 The Decision Framework 3.1 The Introduction of Decision Framework Considering the existing introduction of interview method and empirical research, a decision framework for developing interview manual is proposed in this study. The number of participants, including both interviewer and interviewee, impose great impact on the conduct of interview. With regard to interviewee, managing multiple interviewees is exponentially more difficult than handling just one interviewee. In terms of the interviewer, it is rather difficult for one interviewer to maintain an effective interview process while keeping manual recording when tape recording was not allowed. It is necessary to involve more than one interviewer to divide the workload, and therefore interviewers can emphasize different points and contribute to each other’s points in the meantime. 3.2 An Illustrative Example in C&D Waste Management Research During the research, the research team has been commissioned by the Government to develop a Guideline for Construction Waste Reduction. This project offered a good opportunity to verify and elaborate on the proposed decision framework. The project team consists of 3 professors and 4 research assistants/students. It should be noticed that the following case studies would focus on summarizing the notice or experience related to the interview instead of discussing the content deduced by the interview. 3.2.1 Scenario One to One (1:1) The main content of this scenario can be summarized in Fig. 3. The main content of this scenario can be summarized in Fig. 3. The first interviewee for this project is a government official, who has at least three years of related work experience and is familiar with the professor in the team. The semi-structured interview was conducted to investigate the current situation of C&D waste management and waste reduction methods. This is a typical case of interviews, i.e. one interviewer vs. one interviewee and semi-structured. In this scenario, the interviewer has to be aware of the potential data bias which may arise from the familiarity between him and the interviewee. Therefore, the interviewer took extra efforts to maintain the objective while interviewing. The interview atmosphere was important to the process as a relaxed atmosphere would enable the interviewee to freely express his opinions. Otherwise, the tense atmosphere can cause the interviewee to be too nervous to fully express his true thoughts.

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Fig. 3. The main content in the scenario (1:1)

3.2.2 Scenario One to Many (1: n) The main content of this scenario can be summarized in Fig. 4. A manager in a construction waste recycling company was invited to fully understand the current status of C&D waste recycling industry. He was very enthusiastic about the issue and promised to invite other stakeholders in the company including the technical director, on-site workers, top and middle managers. However, the company is located quite far from the city where the research team was located. As a result, only the principal investigator, as the interviewer, traveled to the waste recycling company and conducted the focus group interview for investigating the current situation of C&D waste recycling management. The ratio of the interviewer to the interviewee is 1:6.

Fig. 4. The main content in the scenario (1:n)

A briefing session is important. The main purpose of the briefing session is to set the grounding rules of Q&A, clarify the interview objectives, and so on. Therefore, at the start of the interview, the principal investigator should explicitly announce the identities of all participants including name, title, etc. Moreover, the constructive debate should be encouraged and due attention is required for tension between participants during the interview process. Otherwise, different opinions may cause arguments or even conflicts, because the interviewer doesn’t know what interviewees’ interests under the context of the company. Considering the hierarchy among the company stakeholders, the interviewer should ask questions that correspond to the job duties of the interviewee. For example, field

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workers are more familiar with the construction site than the project manager. On the contrary, the project manager is more familiar with the policies, regulations, and the company’s planning than the field workers. Besides, the interviewer should balance the power relations between interviewees. Otherwise, the hierarchical power relation may bring undesirable consensus among interviewees. The diversion from the opinions of the top hierarchy should be avoided. Once it occurs, immediate measures must be taken to break the influence of power hierarchy. 3.2.3 Scenario Many to One (m:1) The main content of this scenario can be summarized in Fig. 5. A manager in a demolition company was invited to be interviewed. A semi-structured interview was adopted for investigating the situation of on-site demolition waste management. Because the C&D waste management information may influence the interests of the company, tape record is not allowed. The ratio between interviewer and interviewee was 7:1.

Fig. 5. The main content in the scenario (m:1)

In this scenario, the manager was the only interviewee for the project team which consists of 3 professors and 4 research students. When multiple interviewers attended, it is necessary to specify the primary interviewer, secondary interviewer, etc. beforehand so that interviews could be conducted in an orderly fashion. The main objective is to allocate the power and obligations among interviewers, and ensure interviews run smoothly. The primary interviewer must consider others’ interests and perspectives, and leave time for others to ask questions. Interviewers must always be conscious of their potential status and gender differences and make sure that each interviewer has an opportunity to ask questions. 3.2.4 Scenario Many to Many (m:n) The main content of this scenario can be summarized in Fig. 6. In this scenario, a group interview is arranged to interview a government official and four technicians at the same time. The technicians are employed by the government to develop a system to facilitate the C&D waste management. Besides, two co-investigators and three MPhil

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the research team members joined the focus group interview. This interview aims to understand the construction and demolition waste management system proposed by Shenzhen government, and request the collected C&D waste data (e.g. annual generation of waste in Shenzhen). The ratio between interviewer and interviewee is 5:5. This is a typical m:n interview.

Fig. 6. The main content in the scenario (m:n)

Though the power issue among the interviewees in group discussion may cause the inactivity of interviewees in a lower managerial level. However, interviewer can also make use of the power relationship to enrich interview contents. In this scenario, the interviewer invites the official who is employer of technicians to facilitate the implement of interview. Meanwhile, the attendance of officials and technicians at the same time can avoid problems caused by the sensitivity of C&D waste information.

4 Discussion 4.1 Clarification of the Relationship Among Participants When the total number of interviewers or interviewees is more than one, power issues (between interviewers, interviewees and in-between) should not be overlooked and the relationship among/between interviewers and interviewees must be properly handled. 4.1.1 Interviewer Perspective When multiple interviewers having different disciplinary backgrounds but cooperate in an interview, it is necessary to clarify rights and responsibilities of each person. The collaborative relationship is intertwined with power issues e.g. who controls the directions of interviewing, who manage the schedule of interview etc. Moreover, the social identities of interviewers in real lives interact with the sense of power during an interview [48]. To maintain the order during interview, the power relationship between interviewers should be clarified under various scenarios.

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4.1.2 Interviewee Perspective Interviewees are likely to adapt to others based on their social status or relationships. Interviewees who know one another before the interview will talk in ways that reflect their roles and relationships beyond the interview. It was called recipient design, which refers to word selection, topic selection, ordering of sequences, opening and terminating conversations, and so on. In the scenario of multiple interviewees, attention needs to be paid on the potential problems caused by hierarchy. Proper measures (e.g. separate interview) need to be conducted to ensure the recipient design effect could be minimized in the context of an explicit hierarchy. 4.1.3 Relations Between Interviewer and Interviewee Issues of power in an interviewing relationship are affected by the social identities that interviewees and interviewers bring to the interview. Our social identities are affected by our experience with issues of class, race, ethnicity, and gender, and these social forces interact with the sense of power in our lives. When the ratio between interviewer and interviewee is 1:n, the difficulty of interviewing management increases with n growing. The interviewer needs to take appropriate measures to ensure all interviewees have the chance to express their opinions. On the contrary, when the ratio is m:1, there should be a primary interviewer to divide the labor and control the interview progress. When it comes to m:n, there should be a balance between what is sought and what is given. 4.2 Time Management Time management is more challenging in the case of m: 1, 1:n, and m:n when more participants are involved in an interview. The principal investigator should bear the time management responsibility of the interview process. The principal investigator must balance the time allocation for each interviewer and interviewee. The new guideline highlights the importance of the time management in various types of interviews from the participants’ perspective. In particular, a detailed schedule should be prepared at the pre-interview stage for Type II, III, and IV interviews. 4.3 Selection of the Most Appropriate Interview Method With the new guideline, researchers can select the most appropriate interview method according to their research context. The steps are shown in Fig. 7. Step 1. Identifying Research Purposes Before selecting an interview method, researchers should understand the purpose of the study and the targets to achieve through the interview. Step 2. Selecting Interview Methods With clear research purposes, researchers can use the guideline to select the most appropriate interview method. For example, if the number of interviewers is 1, Type I and

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II interview could be considered and further choices could be made according to the interviewee number. If collaborations are needed for a research project, Type III and

Fig. 7. Selection and implementation of interview methods

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IV could be considered according to the number of interviewees. with consideration of advantages and disadvantages of various methods, a particular method can be selected. Step 3. Implementing Interviews Interview implementation consists of 3 stages: pre-interview, during interview, and postinterview. At each stage, close attention should be paid to four issues. Before an interview, planning is critical. Interviewees should be contacted to confirm the planned interview schedule. Interview equipment need to be prepared and tested. Interview tasks should be assigned if multiple interviewers or interviewees are involved. During an interview, the interviewer should ensure power balance among all participants. Time management is very important to go through the interview process as expected when there are multiple participants. Similarly, primary focus should be placed on exploring potential and useful information by probes. After interview, data need to be cross-verified and analyzed e.g. by software such as NVivo. Multiple rounds of data analysis and discussions are necessary to test reliability and validity of results. Step 4. Drawing Conclusions An interview-based research report should be prepared after previous steps. The report should be written according to the general qualitative research principles. Limitations of interview data should be described.

5 Conclusions Interview methods are widely used in C&D waste management research for collecting data and verifying results. Researchers could collect a large amount of data from various stakeholders via interview related methods. However, there are issues associated with interview methods applications such as confusing terminology of “interview”, lack of clarity on the number of interviewers/interviewee participants, inflexibility to alter interview methods and inflexibility to combine multiple interview methods in one study. Interview takes a lot of time, thoughtfulness, energy and money. It is more labor intensive than other research methods because researchers have to conceptualize research projects, establish access and make contact with interviewees, interview them, transcribe the data, and work with the data and share what they have learned. The new interview guideline proposed in this research classifies interview techniques into four groups according to the ratio between the number of interviewers and interviewees, i.e. 1:1 (Type I), 1:n (Type II), m:1 (Type III), and m:n (Type IV). Moreover, it helps to enhance the awareness and understanding of various interview related methods. This is beneficial for the future researchers to compare and choose suitable methods according to their research context. Furthermore, it highlights the effect of power distribution between interviewers and interviewees. Author Contributions. Conceptualization, Z.D.; Methodology, X.W.; Formal analysis, J.Z.; P.Z.; Investigation, X.W.; Data curation, X.W.; Writing—original draft preparation, X.W.; Writing—review and editing, J.Z, L.Y.

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Funding. This research was conducted with the support of the National Nature Science Foundation of China (Grant No.71974132), Shenzhen Government Nature Science Foundation (Grant No. JCYJ20190808115809385), Shenzhen Natural Science Fund (the Stable Support Plan Program No.20220810160221001).

Conflicts of Interest. The authors declare no conflict of interest.

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Application of High-Rise Building Fire Rescue Based on BIM and GIS Dongmei Huangfu1,2(B) , Lihui Rong1 , and Guanglan Wei1 1 Department of Architectural Engineering Institute, Dianchi College of Yunnan University,

Kunming 65000, China [email protected] 2 School of Accounting and Finance, Faculty of Business and Law, Taylor’s University, 47500 Subang Jaya, Selangor, Malaysia

Abstract. With the progress of the society and technology, there are more and more high-rise buildings, while followed by high-rise building fire problem has become a worldwide problem. Through stating the number of high-rise building fires, the elaboration of high-rise fire risk and its characteristics, analyse the urgency and difficulties faced by the high-rise building fire rescue. Moreover, make the comparative analysis of data information differences between GIS and BIM. Finally, based on the application of the fire command system of GIS, the fire command flow chart of BIM-GIS applied in building is put forward, which can help improve the high-rise building fire and rescue command management. Keywords: High-rise building · Fire rescue · BIM · GIS

1 Introduction High-rise buildings have the advantages of saving land, facilitating life and improving urban landscape, but also bring many problems to people, one of which is fire. Today’s high-rise buildings are in a variety of shapes, from the building height, building shape, building materials, building structure and building function have undergone great changes, however, this also increases the complexity of building fire-fighting. Compared with ordinary building fire, high-rise building fire has obvious characteristics, such as rapid spread of fire, difficult evacuation, fire-fighting and so on (Qiao, Zhang, Wan, Li 2016), which make it more difficulty to rescue. According to the statistics of China’s Fire and Rescue Bureau of the Ministry of Emergency Management, in the past 10 years, from 2012 to 2021, a total of 1.324 million residential fires occurred in China, causing 11,634 deaths, 6,738 injuries and a direct property loss of 7.77 billion RMB. Moreover, in 2021, there were 32,000 fires, killing 179 people and injuring 422. Among them, high-rise building fires keep rising, a total of 4057 high-rise building fires were reported, 168 people died, the death toll increased 22.6% than last year, and the probability of death in crowded places is relatively high, which mainly concentrated in residential places. Frequent building fires cause a large number of people and property losses and directly affect the economic and social development. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 74–84, 2023. https://doi.org/10.1007/978-981-99-3626-7_6

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The number of deaths, injuries and property losses caused by fires in China from 2015 to 2021 is shown in Fig. 1. Figure (a) shows the number of fire deaths (black bar) and the number of fire injuries (grey bar) and Figure (b) shows the fire property losses (Billion RMB).

2500 2000 1500 1000 500 0 2015 2016 2017 2018 2019 2020 2021 Number of death

Number of injury

(a) Fire casualties

8 7 6 5 4 3 2 1 0 2015 2016 2017 2018 2019 2020 2021 (b) Fire property losses

Fig. 1. Fire Deaths, injuries and property losses in China from 2015 to 2021. Notes: The data are collected from the Fire and Rescue Bureau of the Ministry of Emergency Management of China (https://www.119.gov.cn/article/46rcva01Vzg).

With the progress of society and technology, there are more and more high-rise buildings, followed by high-rise building fire problem has become a worldwide problem. For such high-rise buildings where a large number of people gather, when a fire occurs, safe, effective evacuation and transfer in a short period of time for the high-density people, which becomes the key problem of high-rise building fire rescue. In fire actual combat, on-the-spot command, fire model analysis and emergency measures are important components for the fire-fighting actual combat (Cui 2010). Therefore, the ability of geographic information system (GIS) to collect, manage, analyse and output various geographical real-life information makes it the main technical means of fire command in China at present (Zhang 2007; Su and Wu 2021). However, GIS has some limitations in the application of fire protection. Fire GIS is mainly based on GIS map to provide spatial location information about cities or local areas, and then conduct telephone positioning through the caller number and address, and further analyse fire facilities, water sources and other related information around the building with fire based on fire-fighting data (Su and Wu 2021). But at present, fire GIS cannot realize the command and rescue inside the building. Therefore, the combined application of BIM technology can help to solve this problem. While, BIM has the characteristics of simulation, visibility, coordination, optimization and drawing, which brings new methods and technologies to traditional building fire control design and building fire rescue. The coordinated feature can integrate the actual building into a three-dimensional visual model, so that the fire rescue personnel can master the inside information of the fire building intuitively and quickly.

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2 Difficulties During Fire Rescue of High-Rise Building Compared with ordinary building fire, high-rise building fire has obvious characteristics, which make it more difficulty to rescue. The characteristic of high-rise buildings, such as “high shape, multiple floors, large scale, clustering and full functions” (Zhang 2007; Xu, Wang, Zhou, Lu 2020; Ham and Opdenakker 2021), is an important reason why it is difficult to break through fire-fighting in high-rise buildings (Liu, Liu, et al. 2011). The “chimney effect” of high-rise buildings is significantly enhanced compared with general buildings. Once a fire occurs, the fire is fierce and spreads rapidly. In addition, thick toxic smoke obstructed the rescue process to a large extent, posing a major problem for firefighters to rescue and evacuate people (Su and Wu 2021). Such as the High-rise residential building fire occurred on 24th September 2022, in Urumqi, Xinjiang Province. All the injured were rushed to hospital for treatment due to inhalation of toxic smoke, and 10 died after rescue, 9 patients had moderate inhalation lung injury. Through the collation of relevant literature, the main measures of high-rise building fire prevention are summarized in three aspects: ➀ using the ascending fire truck. ➁ Use helicopter air rescue or fire spraying. ➂ Firefighters rushed directly into the building for rescue (Liu, Liu, et al. 2011). But these three rescue methods for high-rise buildings, also have their own corresponding problems. For high-rise building fire rescue, the height of the fire truck is limited, the efficiency of helicopter air rescue is not high, firefighters directly rush into the building, which is the most common and very necessary rescue way. But for fire rescue, not only to save the lives of the victims is important, but also to protect the lives of firefighters. For such high-rise buildings, when a fire occurs, safe, effective evacuation and transfer in a short time period, which becomes the key problem of high-rise building fire rescue. In fire actual combat, on-the-spot command, fire model analysis and emergency measures are important components for the fire-fighting actual combat. Therefore, higher requirements for the high-rise building fire command are put forward, which are more timely, more efficiently, and more accurately.

3 Application Analysis of BIM and GIS 3.1 Application of GIS in Fire Protection Field With the rapid development of geographic information system (GIS) in the early 20th century, fire GIS system not only has the collection, management, analysis and output functions of common information management system, but also has powerful spatial analysis and decision-making support functions. For example, zoning of disaster risk degree, analysis of disaster type and dynamic evolution of disaster can be effectively and intuitively displayed on electronic maps, and outdoor rescue routes and some auxiliary decision-making can be provided for fire rescue personnel (Su and Wu 2021). Under normal circumstances, GIS technology platform can realize two-dimensional drawing analysis of buildings, but the building fire protection system is complex, with large amount of data information and very tedious. The two-dimensional drawing of buildings can only obtain the spatial topological relationship between the interior of the building and the fire protection equipment, and cannot meet the special functions such

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as perspective thinking of building fire protection (Song 2022). At present, it is impossible to obtain the internal information of buildings only by using the fire GIS system. However, various large public buildings usually have complex internal structures, which makes the fire occurrence points changeable. Therefore, when carrying out the fire rescue work of buildings, it is necessary to respond in the first time when the disaster occurs. It is also necessary to be able to fully grasp the internal spatial structure and detailed information of all buildings related to evacuation in the event of a disaster, and guide the appropriate evacuation channels for evacuation and rescue workers accurately, quickly and dynamically (Liu 2016). Therefore, if the location of the exit and entrance of the building evacuation passage, the configuration information of its internal fire facilities and the information of the internal space structure of the building, which can be provided, the rescue work will be better implemented (Wang, Huangfu, Jia 2015; Dao and Shi 2017). Simplely speaking, GIS has some limitations in the application of fire protection. Fire GIS is mainly based on GIS map to provide spatial location information about cities or local areas, fire facilities, water sources and other related information around the building. But at present, fire GIS cannot realize the command and rescue inside the building. 3.2 Research on BIM in the Field of Fire Protection With the development of computer technology and building information technology (BIM), BIM technology arises at the historic moment. The building model built based on BIM technology is no longer composed of simple elements such as planes, lines and points, but also includes beams, columns, windows, doors, walls and some infrastructure inside the building. BIM technology has the characteristics of simulation, visibility, coordination, optimization and drawing, which brings new methods and technologies to traditional building fire control design and building fire rescue (Jiang and Chang 2017). The simulation characteristics of BIM technology can provide simulation scenarios for evacuation and rescue. So that people can carry out fire evacuation exercises on the computer, which not only cannot participate in the actual exercise to bring the real feeling of the fire scene, but also can appropriately reduce the economic cost of fire exercise. Its visual features provide spatial visual experience for rescue and evacuation. The coordinated feature can integrate the actual building into a three-dimensional visual model, which is convenient for roaming experience, so that the fire rescue personnel can intuitively and quickly master the surrounding situation and building internal information of the fire building (Huang 2022). Optimization characteristics can be used to optimize and compare various building evacuation schemes, and then select efficient rescue evacuation schemes. Its graphability can provide two-dimensional drawings and tables for evacuation and rescue schemes for fire rescue (Wang, Huangfu, Jia 2015; Dao and Shi 2017). According to the analysis of relevant literature, BIM technology in the field of fire protection is mainly divided into five aspects, fire management throughout the whole life cycle of the building, fire facility model database creation, expansion of interface types in fire simulation software, through improving building information data to realize indoor evacuation path planning function, construction of urban fire management platform

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(Qin and Hu 2022; Cui 2010; Saad, Ajayi, Alaka 2022). In addition, there are unknown directions and fields to be further developed (Fig. 2).

Fire management throughout the whole life cycle of the building

Through improving building information data to realize indoor evacuation path planning function

Fire facility model database creation

Research direction of BIM technology in the field of fire protection Construction of urban fire management platform

Expansion of interface types in fire simulation software

Fig. 2. Research directions of BIM used in the field of fire-fighting.

As Fig. 2 shows, there are six circles totally, each of these five circles with words represents the research direction of BIM technology in the field of fire protection mainly including five aspects, and the ellipsis in the circle at the bottom indicates that there are many unknown lines of research. 3.3 Comparison of BIM and GIS Data Information Zhang (2007) pointed out that there are two main aspects in the application of geographic information system in urban fire communication command system. One is the basic means of information expression. The second is the auxiliary decision-making service of case handling. In other words, based on its powerful system database, fire GIS makes use of spatial visualization and spatial guidance functions to comprehensively and multi-level integration of information to achieve intuitive and convenient fire command (Zhang 2007). GIS technology makes it easy to store data in a central location and to establish geographical links between information. In addition, GIS technology creates the possibility to evaluate projects of any scale, further highlighting the importance of GIS technology and its integration with BIM (Siahboomy, Sarvari, et al. 2021). As can be seen from the position and meaning of I (Information) in BIM, data information is a very core element of BIM technology, and also represents architectural geometric information and non-geometric information in the whole life cycle of a building (Cui 2010; Siahboomy, Sarvari, et al. 2021). BIM technology is based on IFC standard to establish a database throughout the whole life cycle of the building. It is mainly divided

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into four layers, including resource layer, core layer, sharing layer and domain layer (He 2011; Lu and El-Gohary 2018). The database not only contains building structure information, but also fire resistance coefficient of building materials, spatial topological relationship, facilities and equipment, surrounding environment, seismic level, lines, pipelines, fire sprinkler, fire hydrant and other data information (He 2011; Zhang, Wang, Al-Hussein, Yin 2022). With building information models becoming more and mor mature, each building will have a corresponding electronic information file, like an ID card (Wang, Huangfu, Jia 2015). Table 1 shows the main fire data information of fire GIS and BIM technology based on IFC standard. Table 1. Comparison between GIS and BIM data in fire-fighting. Type

Data information

Fire GIS data information (Zhang 2007; Su, Wu 2021; Song 2022; Tomar and Bansal 2022)

City map The distribution of city blocks Distribution of major units Distribution of key fire-fighting units Water distribution Distribution of fire hydrant Fire squadron, fire vehicle dynamic distribution and other information of the wide area fire map Emergency fire map Fire zone map Fire plan

BIM fire protection data based on IFC standards (Wang, Ren, et al. 2013; Huang 2022; Qin and Hu 2022; Lu and El-Gohary 2018, 2022; Zhang, Wang, Al-Hussein, Yin 2022)

Architectural location Building and street profiles Building structure, area, height and fire resistance rating Location and number of building evacuation passages and safety exits Fire water sources and fire lanes inside and outside the building Earthquake resistant level Line, pipeline Fire sprinkler, fire hydrant, etc

As can be seen from Table 1, GIS is responsible for data information outside the building, and a specific technique that integrates location-related information with contextrelated details (Siahboomy, Sarvari, et al. 2021), while BIM is responsible for data information inside the building (Liu 2016; Siahboomy, Sarvari, et al. 2021). However, the data information of high-rise building itself is large and trivial, and the spatial topological relationship and other related information of building interior and fire-fighting equipment can be obtained from two-dimensional drawings, which requires higher professional ability of perspective thinking and spatial analysis (Qin and Hu 2022). Such a set of complete building information database and its rapid retrieval and positioning

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functions can make up for some deficiencies in fire GIS command and rescue. Moreover, BIM technology can also realize the 3d visualization and roaming simulation function of buildings, which can not only assist the fire management of high-rise buildings, but also improve the efficiency of fire scene command and rescue. To sum up, the effective combination of BIM technology and traditional building fire management can provide complete and detailed building information for fire rescue and evacuation, and in turn, the relevant information of building fire protection can be fed back to the BIM model, so as to update and improve the BIM model in time. Thus, improving the level of building fire safety (Chen and Ren 2015). BIM technology should give full play to its advantages in data information sharing and exchange, and provide guarantee for the management of administrative law enforcement departments and property management units. The main function of GIS and BIM is to create spatial information model. GIS is mainly used for outdoor modeling, while BIM is mainly used for indoor modeling (indoor equipment information and components, etc. (Bicy, Wang and Zhang 2013). GIS system and BIM model are responsible for the data information outside and inside the building respectively. Based on the advantages of the two, and the combination application of the two, which can help solve many problems existing in the current fire rescue process (Li, Becerik-Gerber, Krishnamachari, Soibelman, 2014). BIM and GIS technologies are two complementing tools, which one cannot replace the other (Siahboomy, Sarvari, et al. 2021). 3.4 Relative Research on the Combination of BIM and GIS in China Relative research about the combination of BIM and GIS in China are not really much more. Taking the literature on BIM and GIS in recent ten years in China as the research object, by using Cite Space software. The following chart shows the CNKI general trend of related research literature (Fig. 3).

Fig. 3. CNKI general trend chart of related research literature.

As Fig. 3 shows, the research on BIM and GIS has attracted more and more attention recently. Figure 4 is the keyword clustering map, through clustering, research hotspots are mainly about CIM, visualization, information management, integrated applications, Bridges, and so on. Therefore, the integration research of BIM and GIS is only about 10 years, and the application is still in the initial stage. In addition, problem about how to predict

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Fig. 4. Cite Space keyword clustering map.

disasters or how to deal with disasters is a very important research field. According to the literature, the integration application of BIM-GIS in the field of emergency and disaster prevention emerged around 2014, so it is a new development direction. In addition, in the urban field and disaster emergency treatment, there are also some relatively deep application precedent. At the same time, According to the theoretical research results, some commercial software have developed.

4 Fire Command Flow Chart of BIM-GIS Applied in Building From the above analysis, in case of building fire, the combination of BIM technology and GIS technology can improve the efficiency of directing rescue inside the building (Li et al. 2014). The output of a geographic information system (GIS) model consists of one or more actual locations. It provides the points associated with the BIM tool and creates a 3D model to visualize the best locations. The combination of GIS and BIM can bring several advantages, including optimal positioning (Siahboomy, Sarvari, et al. 2021). Based on the flow chart of GIS proposed by Liu Jianzhong et al. (Liu et al. 1997) and the research of BIM-GIS integrated model by Mehdi Asgari Siahboomy, Hadi Sarvari, et al. (Siahboomy, Sarvari, et al. 2021), the flow chart of BIM-GIS technology applied to building fire command is proposed. That is, by introducing BIM model into the geographic information system of the urban fire command system, the detailed information about the interior of the building is increased and the 3d visualization and roaming function of the building is more convenient to be realized, so as to strengthen the timeliness, accuracy and efficiency of the fire command and rescue of high-rise buildings, which is shown in Fig. 5. The dotted boxes in bold represent the BIM-GIS fire command process steps. From the alarm system to the dispatch processing system mainly includes three steps. Step 1 aims to identify the fire location. After that, Step 2 combined with fire GIS system to analyse the surrounding conditions around the fire building. Moreover, Step

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3, with the BIM model to view the interior conditions of the of the fire building. After the three steps, the fire command decision for the rescue of high-rise buildings will be better made.

Wired or wireless alarm system

Step 1

Wired or wireless alarm signal

wired alarm signal Precise or inexact positioning

Precise positioning Company name, telephone number, house number, etc

wireless alarm signal

inexact positioning

Road intersections, reference buildings, etc

Wireless alarm point coding location

After the fire location is located, confirm and generate a fire record GIS

Step 2

Combined with the fire GIS system, the optimal path analysis was carried out to clarify the surrounding units and water sources, and fill in the information of fire location, driving route and nature of disaster

BIM Identify the building where the fire occurred, call out its BIM model, and view the indoor information of the building, such as the indoor space structure, number and location of fire facilities

Step 3

Dispatch processing system

Fig. 5. Fire command flow chart of BIM-GIS applied in building.

5 Conclusion From the above analysis that, compared with GIS technology, BIM technology has obvious advantages in the internal information of high-rise buildings to improve the efficiency of high-rise building fire command and rescue. Firstly, it is helpful for the firefighters to recognize the information of the building and to improve the management level of fire emergency plan. The building model based on BIM technology can clearly show the location of fire facilities, fire partition, and the state of evacuation channels (Volk et al. 2014; Siahboomy, Sarvari, et al. 2021). The large amount of information and accuracy can provide certain guidance for the fire emergency plan, and also further improve the command and rescue efficiency of high-rise building fire (Bi 2014). Moreover, it is helpful for ordinary people to learn self-help. For most ordinary people, there are not many opportunities to participate in fire drills. Using the 3d visualization and roaming simulation functions of BIM technology, people can conduct fire simulation exercises on the network (Tomar and Bansal 2022; Wang, Huangfu, Jia, 2015). This can not only reduce the cost of field exercises for ordinary people, but also bring the ordinary people a certain sense of the fire scene.

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In conclusion, the application of BIM technology in high-rise buildings will bring a big impact on the fire command and rescue of high-rise buildings, no matter from the perspective of rescuers and firefighters, or from the perspective of the rescued party and ordinary personnel. The combination of BIM technology with fire GIS platform makes the fire daily work more information, which enhances the fire unified command of the high-rise building, and improves its cooperative engagement, fast reaction ability, and work efficiency. In view of the problems encountered in the current BIM-GIS integration, it is not only necessary for the computer science field to develop and improve the underlying data, but also for researchers in architecture and other fields to build the integration framework. Otherwise, BIM technology should give full play to its advantages in data information sharing and exchange, and provide guarantee for the management of administrative law enforcement departments and property management units. From the point of implementation method, both in technical or management, there are also many difficulties and challenges which need to be discussed and solved. Funding. The research in this paper has been conducted and financially supported under Yunnan Provincial Department of Scientific Education Research Fund Project China of 2022 (No. 2022J1083). We are grateful for all provided financial and technical support.

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Analysis of Flow and Stock of Sand and Gravel in Shenzhen Buildings and Associated Environmental Impact Yao Zhou1 , Feng He2 , Jian Liu3(B) , Jing Bai4 , and Huabo Duan5 1 College of Civil and Transportation Engineering, Shenzhen University, Shenzhen, China 2 Shenzhen Construction Science and Technology Promotion Center, Shenzhen, China 3 Ecological Technology Institute of Construction Engineering, Shenzhen University, Shenzhen,

China [email protected] 4 The Institute for Sustainable Development, Macau University of Science and Technology, Macao, China 5 School of Environmental Science and Engineering, Huazhong University of Science and Technology, Wuhan, China

Abstract. There is a rapidly spread sand and gravel supply shortage to meet increasing construction material needs. A considerable quantity of sand and gravel are stored in the built environment and have the value of being a secondary material resources. Using the bottom-up material flow analysis (MFA), this study estimates the flow and stock of sand and gravel in Shenzhen buildings and evaluates associated environmental impact from the material production stage. The results show that the consumption of sand and gravel in Shenzhen buildings exceeded 439 million tons (Mt) from 1979 to 2019, with an average annual consumption of 10.9 Mt. Equivalently, 598 kilotons (Kt) of the embodied carbon emissions were generated in the production stage of the sand and gravel. The cumulative outflow (measured by the generation of demolition waste) of sand and gravel exceeded 150Mt, accounting for 34% of the total consumption. Moreover, the sand and gravel stocks in Shenzhen buildings have boomed to 302 Mt in 2019 (23 t/cap), and show characteristics of fast inflow and slow outflow. This study helps understand the dynamic metabolism of sand and gravel in buildings and forms the data basis for further research in urban sand and gravel resources, waste management, and environmental strategy. Keywords: Urban buildings · Sand and gravel · MFA · Flow and stock · Carbon emission

1 Introduction Buildings are the foundation of society, providing basic human needs for housing and infrastructure. With increasing construction activity, building density and intensity are also growing. The construction industry has accelerated the consumption of resources. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 85–95, 2023. https://doi.org/10.1007/978-981-99-3626-7_7

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Vast materials have accumulated in buildings and infrastructurein China. Building materials have become the largest input into urban systems after water resourcesand the largest waste category [1]. With the intensified resources and environmental stresses, building stocks have attracted people’s attention because of their vast holding quantity and influence on resources and the environment [2]. Research on building stocks mainly focuses on forecasting resources and energy consumption, waste management and environmental impact. The research objects include metallic materials (e.g., copper, aluminum, iron) and non-metallic materials (e.g., cement, wood, sand, and gravel). And the research aims are different: predicting future inflow and outflow, predicting current or future stocks and their evolution, analyzing the dynamic metabolic of building materials in cities, studying the influence of several parameters on future flow, and analyzing the relationship between flow and stock, etc. The spatial scales of these studies range from global, national, and regional levels, with time scales ranging from a year to a century [3]. At the global scale, Cao et al. used the top-down dynamic MFA to estimate the cement flow, energy consumption, carbon emission, and carbon sink relationship from 1930 to 2100. They described the stocks and flows of cement, future demand, and demolition waste generation [4]. At the national level, Han et al. estimated the material stock accumulation in the infrastructure of 31 provinces in China and analyzed the driving factors of regional differences [5]. At the city level, Mao et al. used a bottom-up MFA to analyze the material stocks and spatial distribution of the building and infrastructure in Beijing in 2018. The results showed that the material composition of the building system in Beijing has a large proportion (96.8%) of non-metallic mineral materials (sand, gravel, cement, bricks, etc.) [6]. Regarding to analyzing environmental impact associated with building materials, Shi et al. predicted the construction materials consumption and carbon emission of residential building and transport infrastructure in China [7]. Sand and gravel are the enormous portions of all classes of solid materials used by humankind, about 68–85% by mass [8]. There is a rapidly spread sand and gravel supply shortage to meet increasing material needs for urban construction. Some studies focus on sand and gravel. Zhong et al. forecast sand demand for residential and commercial buildings in 26 regions around the world in 2060. The results show that annual global building sand demand continuously increases from 3.2 gigaton (Gt) in 2020 to 4.5 Gt in 2060, and more than half of the demand comes from developing countries, led by China [9]. China is the world’s largest producer and demander of sand and gravel. Sand and gravel resources are indispensable raw materials for urban construction. Buildings have the characteristics of large volume and long lifetime, and the stocks of construction sand and gravel are potential urban minerals in the future. Paying attention to the dynamic evolution of sand and gravel stock lays a foundation for subsequent resource and environmental management. Considering that the transport radius of sand and gravel is limited by cost, it generally flow in the city or region, and the study on a city scale is necessary. This study selected Shenzhen, a highly urbanized city in China, as a case to estimate the flow and stock of building sand and gravel from 1979 to 2019 and measured the associated environmental impact (with carbon emission equivalent as the index). The result provides data reference for the sustainable development of urban construction.

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2 Calculation Method and Data Inventory 2.1 Flow and Stock Accounting Model Shenzhen was established as a city in 1979. In the past 40 years, the construction industry in Shenzhen has seen unprecedented development. The total area of buildings completed exceeded 330 million m2 from 1979 to 2019, with an average of 8.08 million m2 completed yearly. Since the statistical data of the building area can be traced back to 1979 at the earliest and considering the urban development of Shenzhen, the influence of buildings before 1979 on this study can be neglected. We selected Shenzhen as the research area from 1979 to 2019 and took the buildings as the research object, including residential buildings and non-residential buildings such as public, commercial, industrial, and other types structures. A flow and stock accounting model is constructed based on the MFA. The flow and stock of sand and gravel are calculated targets. The model is shown in Fig. 1.

Residential

Newly constructed floor area 1979-2019

Lifetime model Material inflows Sand, Gravel 1979-2019

Non-residential

Material intensity Sand, Gravel

Material outflows Sand, Gravel 1979-2019

Mass balance Mass balance

Material stocks Sand, Gravel 1979-2019

Fig. 1. Flow and stock accounting model.

2.2 Flow and Stock Calculation Method MFA assesses the dynamic metabolism of materials within a system boundary defined in space and time, is based on mass balance, and uses material mass as the basic unit to calculate the inflow, accumulation, and outflow of resources entering the urban building system [10, 11]. There are two main approaches: the top-down approach and the bottomup approach. The top-down approach is often used for research at the national or global level, refers to the statistical verification of the economic system’s material input and output data to calculate the system’s material stocks. The bottom-up approach divides the material stocks according to the structure type, defines the standard units material intensity (MI), and multiplies the MI by the corresponding standard unit scale to calculate the material stocks. In consideration of this study focuses on the flow and stock of building sand and gravel in buildings at the city level, the bottom-up approach is adopted in this study. Sand and Gravel Inflow. The sand and gravel inflow refer to the materials that constitutes the new building entity entering the urban building system, and the calculation

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process is mainly related to two key parameters as shown in Eq. 1: 1) annual floor space completed (m2 ) and 2) the MI specifically to each construction sector (kg/m2 ). Min (t) =

n m  

NAi (t) × MIij (t)

(1)

i=1 j=1

where Min(t) is the sand and gravel inflow in year t, the mass of sand and gravel that flowing into the buildings; NAi (t) is annual floor space completed of construction sector i (residential, non-residential buildings) in year t; MIij (t) is the intensity of material j (sand, gravel) per physical size in construction sector i. Sand and Gravel Outflow. The sand and gravel outflow at the time t is defined as the scrap sand and gravel generated from building stocks from the end-use sector i (measured by the generation of demolition waste). Since the building demolition area over the years cannot be directly obtained, the lifetime model is often used to simulate the building demolition. The demolished area in year t can be calculated based on the floor space completed each year before year t and its probability of demolition in year t. According to previous studies, Weibull distribution, Gamma distribution, Normal distribution, and Logarithmic normal distribution are often used to model building lifetime. MIatto’s research shows that logarithmic normal distribution can better represent the accumulation and demolition dynamics of building stocks in Asian cities. Therefore, the logarithmic normal distribution is chosen as the lifetime model of the building in this study [12]. The sand and gravel outflow Mout(t) can be computed by Eqs. 2 and 3. Mout (t) =

t  τ =t0

ϕ(t) =

{Min (τ ) × [ϕ(t − τ ) − ϕ(t − τ − 1)]} 

t t0

√

1 2π · σ · x

·e

− (ln x−μ) 2 2σ

(2)

2

dx

(3)

where Mout(t) is the sand and gravel outflow in year t, the mass of scrap sand and gravel generated from building stocks; ϕ(t) is the cumulative probability and shows the depreciation of the building in year t or the likelihood that it will be demolished; μ is the average lifetime of buildings; σ is the standard deviation, 30% of μ; t0 is the base period (1979). Sand and Gravel Stock. Building sand and gravel stock estimates are determined from mass balance with inflow and outflow. The sand and gravel stock in year t is the previous year’s stock plus the net increase in that year, which equals the sum of the sand and gravel inflow minus the outflow from 1979 to year t (Eq. 4). MS (t) =

t 

[Min (τ ) − Mout (τ )]

(4)

τ =t0

where Ms(t) is the sand and gravel stock in year t, the mass of sand and gravel that stored in the buildings.

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2.3 Embodied Carbon Emission Calculation The total carbon emission of China’s construction sector is about half of the whole society, especially the embodied carbon emission in the production stage of building materials is the primary source [13]. A large amount of sand and gravel used and the resources crisis will affect the transformation of the construction sector to low carbon. Based on the inflow, this study evaluates the environmental impact of the sand and gravel production stage to provide data support for finding carbon reduction paths. Equation 5 shows the calculation of embodied carbon emissions. CEj(t) = EFj × Min, j(t)

(5)

where CEj (t) is the embodied carbon emissions of material j (sand, gravel) in year t; EFj is the embodied carbon emission factor of building material j; and Min,j (t) is the material j inflow in year t. 2.4 Data Inventory The key parameters and data sources used in the research are listed in Table 1. The annual floor space completed of different building types from 1979 to 2019 is quoted from The Shenzhen Statistical Yearbook (1979–2019). The selection of literature data follow timeliness and regional principles. Specifically, the average lifetime of the building (μ) is determined based on the survey data in Shenzhen, the residential building is 22.4a, and the non-residential building is 24.4a [14], and the sand and gravel MI given by Tao, is the average value of the China’s material strength calculated based on the material strength of each province [15]. In addition, The carbon emission factor of sand and gravel is taken from eBalance in kg CO2-eq per t material, a local Chinese database with more China-peculiar life cycle inventory data. Table 1. Summary of the key parameters and data sources. Symbol

Term

Data source

NA

Annual floor space completed for residential Shenzhen Statistical Yearbook and non-residential buildings (1979–2019)

μ

Average lifetime of buildings

Literature research

MI

Material intensity

Literature research

EF

Carbon emission factor for sand and gravel

eBalance database

3 Results and Discussion 3.1 Sand and Gravel Flow Analysis Sand and Gravel Inflow. The inflow of building sand and gravel in Shenzhen from 1979 to 2019 is shown in Fig. 2. The results show that approximately 439 Mt of sand

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and gravel were consumed for building construction from 1979 to 2019 in Shenzhen, with an average annual demand of 10.7 Mt (Fig. 2a). With the rapid development of the construction industry in Shenzhen from 1979 to 2003, the sand and gravel inflow increased continuously, from 0.16 Mt in 1979 to 32.21 Mt in 2003, reaching the peak value. It is closely related to the urbanization construction and the increase of the permanent population. However, the sand and gravel inflow decreased fluctuant after 2003 and reached 9.4 Mt in 2019. The main reason is that the Shenzhen government has issued a series of regulations to strengthen the control of land supply, reducing the heat of housing construction, which is likely to continue in the future. For different materials, the inflow of sand and gravel varies in different periods (Fig. 2b). Due to the similar MI of sand and gravel in construction, the annual inflows of the two materials were similar from 1979 to 2000. From 2001 to 2019, the inflow of gravel was higher than that of sand, mainly due to the improvement of the MI of gravel in construction.

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a

Sand Material inflows in Mt

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40

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0 1979 1984 1989 1994 1999 2004 2009 2014 2019

5

0 1979 1984 1989 1994 1999 2004 2009 2014 2019

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Material inflows in Mt

Residential 15

c

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10

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0 1979 1984 1989 1994 1999 2004 2009 2014 2019

Fig. 2. Sand and gravel inflow during 1979–2019. (a) Total inflow of building sand and gravel. (b) Inflow by materials type. (c) Inflow by buildings type.

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Considering residential buildings versus non-residential buildings, the cumulative sand and gravel consumption of each type accounted for 50% of the total use from 1979 to 2019. However, it can be seen that the material inflow of residential and nonresidential buildings varies in different periods (Fig. 2c). From 1991 to 2010, the annual sand and gravel inflow of residential buildings was significantly higher than that of nonresidential buildings because more residential buildings were constructed. In 1999, the floor space completed of residential buildings was about 1.7 times that of non-residential buildings. However, the sand and gravel inflows of non-residential buildings in 1979– 1990 and 2011–2016 were more enormous. The reason is that Shenzhen strengthened its industrial construction activities, focusing on industrial development to drive economic growth from 1979 to 1990, and intensified the construction of significant livelihood projects to improve the level of urban public service and social governance from 2011 to 2019. Sand and Gravel Outflow. The outflow of building sand and gravel has a rapid growth trend. The scrap sand and gravel (measured by the generation of demolition waste) exceeded 150 Mt, accounting for 34% of the total consumption, with average annual outflows of about 3.7 Mt and reached 13.8 Mt in 2019(Fig. 3a). From the perspective of recycling, if the construction waste can achieve 100% utilization, it means that 13.8 Mt of sand and gravel can be consumed less in 2019, which is undoubtedly beneficial to protecting resources and reducing carbon emissions. Due to the larger MI of gravel used in the building from 2001 to 2019, gravel outflow was more remarkable than sand (Fig. 3b), consistent with the inflow trend. In terms of building type, the annual outflow of residential buildings is larger than that of nonresidential buildings due to the shorter life cycle of residential buildings and the lag in depreciation of non-residential buildings (Fig. 3c).

3.2 Sand and Gravel Stock Analysis The sand and gravel stock increased continuously, from 0.16 Mt in 1979 to 325.21 Mt in 2015, reaching the stock’s peak. Especially from 2000 to 2005, with an annual growth of 20–30 Mt (Fig. 4a). The main reason is that Shenzhen ushered in a construction peak during the period, and the construction of infrastructure-supporting buildings was strengthened. After 2015, the stock slightly decreased to about 302 Mt in 2019. Due to Shenzhen’s urbanization and existing building material stocks having reached a high level, the speed of urban expansion has slowed as compared to the previous period. Based on the stability of the sand and gravel stock development pattern, it can predict that the stock will continue to maintain a downward trend in the next few years.

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10

Gravel(non-residential) Gravel(residential) Sand(non-residential) Sand(residential)

a

Sand Material outflows in Mt

Material outflows in Mt

15

5

0 1979 1984 1989 1994 1999 2004 2009 2014 2019

Material outflows in Mt

10

8

Residential

8

b

Gravel

6

4

2

0 1979 1984 1989 1994 1999 2004 2009 2014 2019

c

Nonresidential

6

4

2

0 1979 1984 1989 1994 1999 2004 2009 2014 2019

Fig. 3. Sand and gravel outflow during 1979–2019. (a) Total outflow of building sand and gravel. (b) Outflow by materials type. (c) Outflow by buildings type.

The total population of Shenzhen had boomed from 0.31 million in 1979 to 13.43 million in 2019. The average per-capita sand and gravel stock increased rapidly from 0.5 t in 1979 to more than 25 t in 1989. After 1989, the average per-capita stock fluctuated and amounted to 22.5 t in 2019 (Fig. 5). This is different from the long-term growth trend of Shenzhen’s total population and the development trend of building sand and gravel stock, which reflects the gradually developed Shenzhen’s strong attractiveness to the people. Moreover, Fig. 5c considers the differences in sand and gravel stocks. It is easy to find more gravel stocks over the same period, trend same as the inflow (Fig. 2b) and outflow (Fig. 3b). The difference in residential and non-residential materials stocks are shown in Fig. 5d, which are highly consistent with the inflow trend (Fig. 2c).

Analysis of Flow and Stock of Sand and Gravel 40

a

200

100

200

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0 1979 1984 1989 1994 1999 2004 2009 2014 2019

20

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c Material stocks in Mt

Material stocks in Mt

150

30

0 0 1979 1984 1989 1994 1999 2004 2009 2014 2019

0 1979 1984 1989 1994 1999 2004 2009 2014 2019

Sand

20

b Total population in million

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Per-capita stocks in ton

Material stocks in Mt

400

93

150

d

Nonresidential

100

50

0 1979 1984 1989 1994 1999 2004 2009 2014 2019

Fig. 4. Sand and gravel stocks during 1979–2019. (a) Total stock of building sand and gravel. (b) Per-capita stock (left-hand axis) and total population of Shenzhen from 1979 to 2019 (right-hand axis). (c) Stock by materials type. (d) Stock by building type.

3.3 Embodied Carbon Emission The consumption of sand and gravel in buildings exceeded 439 (Mt) from 1979 to 2019, including 201 Mt of sand and 238 Mt of gravel. According to the estimation based on the carbon accounting equation, the production of these sand and gravel requires direct discharge of 598 Kt CO2-eq to the environment (42% from sand, 58% from gravel), primarily from the power and energy consumption in the material production stage. This figure is small compared with the cement of the same quality because the carbon emission of cement is mainly from carbonate calcination. However, the consumption of sand and gravel is the largest among all construction materials. Carbon emission reduction is still worth focus.

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30 Carbon emissions in Kt CO2-eq

Sand 25

Gravel

20 15 10 5 0 1979 1984 1989 1994 1999 2004 2009 2014 2019

Fig. 5. Embodied carbon emissions of sand and gravel.

4 Conclusions This study uses the bottom-up approach to calculate the flows and stocks of building sand and gravel in Shenzhen from 1979 to 2019, and draws the following conclusions: From 1979 to 2019, the accumulated sand and gravel consumption of buildings in Shenzhen exceeded 439 Mt, with an average annual consumption of 10.9 Mt. The embodied carbon emission of all the consumed sand and gravel can reach 598 Kt CO2 -eq (42% from sand, 58% from gravel). The cumulative outflow (measured by the generation of demolition waste) of sand and gravel exceeded 150Mt, accounting for 34% of the total consumption. The more significant proportion of sand and gravel outflows in the residential sector is due to the shorter lifetime. In addition, the inflow and outflow of gravel are more extensive than the sand because the MI of gravel is larger in the same period. The building sand and gravel stock in Shenzhen increased from 0.16 Mt in 1979 to 302 Mt in 2019. It has undergone four stages: drastic growth from 1979 to 1999, rapid growth from 2000 to 2005, steady growth from 2006 to 2015, and the stock slowly declining after 2015, which has distinct characteristics of the urban development stage. The per capita sand and gravel stock increased from 0.51 t in 1979 to 22.53 t in 2019. A large amount of sand and gravel resources are still in use. Combined with the inflow and outflow of building sand and gravel, it is found that Shenzhen’s building sand and gravel present the characteristics of fast inflow and slow outflow. While consuming sand and gravel resources, vast demolition waste is produced simultaneously. Future urban construction is facing the dual pressure of resources and the environment. In future research, it is necessary to predict the future consumption trend of sand and gravel, the discharge of construction and demolition waste and the space for recycling, and analyze the resources utilization efficiency of sand and gravel. The carbon reduction path should be studied by examining the environmental impact. More meaningfully,

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how to implement strategy should be explored to reduce the sand and gravel used in construction. Acknowledgments. The research was supported by the Science and Technology Program of Ministry of Housing and Urban-Rural Development (2021-K-113) and the Shenzhen Science and Technology Plan (JCYJ20190808123013260).

References 1. Augiseau, V., Barles, S.: Studying construction materials flows and stock: A review. Resour. Conserv. Recycl. 123, 153–164 (2017) 2. Porhincak, M., Estokova, A.: Comparative analysis of environmental performance of building materials towards sustainable construction. 16th International Conference on Process Integration, Modelling and Optimisation for Energy Saving and Pollution Reduction 35, 1291–1296 (2013) 3. Göswein, V., José, S., Guillaume, H., Fausto, F.: Dynamic assessment of construction materials in urban building stocks: A critical review. Environ. Sci. Technol. 53(17), 9992–10006 (2019) 4. Cao, Z., et al.: The sponge effect and carbon emission mitigation potentials of the global cement cycle. Nat. Commun. 11(1), 3777 (2020) 5. Han, J., Xiang, W.N.: Analysis of material stock accumulation in China’s infrastructure and its regional disparity. Sustain. Sci. 8(4), 553–564 (2013) 6. Mao, R.C., Bao, Y., Huang, Z., Liu, Q., Liu, G.: High-resolution mapping of the urban built environment stocks in beijing. Environ. Sci. Technol. 54(9), 5345–5355 (2020) 7. Shi, F., Huang, T., Tanikawa, H., Han, J., Hashimoto, S., Moriguchi, Y.: Toward a low carbondematerialization society measuring the materials demand and CO2 emissions of building and transport infrastructure construction in China. J. Ind. Ecol. 16(4), 493–505 (2012) 8. Krausmann, F., Gingrich, S., Eisenmenger, N., Erb, K.H., Haberl, H., Fischer-Kowalski, M.: Growth in global materials use, GDP and population during the 20th century. Ecol. Econ. 68(10), 2696–2705 (2009) 9. Zhong, X.Y., Deetman, S., Tukker, A., Behrens, P.: Increasing material efficiencies of buildings to address the global sand crisis. Nature Sustainability 5(5), 389–392 (2022) 10. Myers, R.J., Reck, B.K., Graedel, T.E.: YSTAFDB, a unified database of material stocks and flows for sustainability science. Scientific Data 6(1), 84 (2019) 11. Song, L., et al.: China material stocks and flows account for 1978–2018. Scientific Data 8(1), 303 (2021) 12. Miatto, A., Schandl, H., Tanikawa, H.: How important are realistic building lifespan assumptions for material stock and demolition waste accounts? Resour. Conserv. Recycl. 122, 143–154 (2017) 13. Jang, H., Ahn, Y., Tae, S.: Proposal of major environmental impact categories of construction materials based on life cycle impact assessments. Materials 15(14), 5047 (2022) 14. Zhao, S.C.: Estimation of the demolition waste generation and its management countermeasure by using building lifespan methods. Shenzhen University (2019) 15. Huang, T., Shi, F., Tanikawa, H., Fei, J., Han, J.: Materials demand and environmental impact of buildings construction and demolition in China based on dynamic material flow analysis. Resour. Conserv. Recycl. 72, 91–101 (2013)

Developing Virtual Labs for Engineering Education: Lessons from Leveling Experiment Baoquan Cheng1,2 , Hao Su3 , Dahao Cheng4 , and Xiaowei Luo2(B) 1 School of Civil Engineering, Central South University, Changsha 410075, Hunan, China 2 Department of Architecture and Civil Engineering, City University of Hong Kong, Hong

Kong 999077, China [email protected] 3 School of Advanced Technology, Xi’an Jiaotong-Liverpool University, Suzhou 215123, Jiangsu, China 4 School of Civil Engineering, Anhui Jianzhu University, Hefei 230601, Anhui, China

Abstract. Virtual labs based on serious games have shown their potential to support experiment teaching, particularly for online engineering education during and after the global COVID-19 pandemic. However, how to develop serious games to support experiment teaching is still unclear. This article applied the Unity game engine to develop a virtual lab for the leveling experiment to show the process of virtual lab development. Ten students majoring in construction were recruited to test the developed virtual lab. After the experiment, participants completed a questionnaire including six questions about their feedback on the developed virtual lab. It can be concluded that the develop a virtual lab that allows students to actively join in and experience the whole experiment process better help students master related knowledge and skills. The developed virtual labs can therefore well aid in future experiment teaching of engineering surveying for construction majors. More efforts are needed to enrich the range of experiments and improve the user experience to promote virtual labs in engineering education. Keywords: unity · virtual labs · leveling · experiment teaching · online education

1 Introduction Online education is becoming an efficient complement to traditional face-to-face education modes in higher education with the advancements in internet technologies. Particularly during and after the global COVID-19 pandemic, online education ensures the smooth development of teaching activities on the premise of safety [1]. Integrating online and offline education modes seems the inevitable tendency of higher education [2]. Different from some majors mainly taught in theories, construction major is one of the engineering subjects requiring on-hand practices, and lab sessions play important roles in talent training. However, it is challenging to perfectly replicate these lab sessions in online education [3] . How to conduct efficient experiment teaching online has become an urgent issue in engineering education. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 96–103, 2023. https://doi.org/10.1007/978-981-99-3626-7_8

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In fact, when these experiments are replicated online, they are just like “video games”, in which students (players) are required to finish various tasks according to experiment guidance (game guidance) and thus achieve the experiment goal (win the game). Therefore, serious games, which are designed for a primary purpose other than pure entertainment [4], are expected to contribute to experiment teaching in online engineering education. There have been many virtual lab tools based on serious games developed for various experiments teaching in engineering education [5]. In construction engineering education and training, virtual labs have been applied for architecture and design visualization, construction health and safety training, equipment, and operational task training, as well as structural analysis [6]. Lifelike scenes are the foundation of successful serious game design [7]. How to establish virtual lab environments is always the key to applying various games for experiment teaching. However, few studies have explained the process of virtual lab development in detail. This study, therefore, selected an important experiment for construction majors, i.e., leveling, to develop a simple virtual lab and show how to apply it in construction education. Its advantages and limitations are also discussed. The developed virtual lab is expected to be used for leveling experiment teaching for construction majors, which is particularly suitable for online education. In addition, lessons from this virtual lab development process also provide valuable references for other experiment teachings in engineering education.

2 Virtual Lab Development As mentioned before, the process of virtual lab development can be considered serious game development. Unity, a cross-platform game engine developed by Unity Technologies, can create three-dimensional virtual lab environments as well as interactive simulations and other experiences conveniently, which has been adopted by industries outside video gaming, such as film, automotive, architecture and construction engineering, etc [8]. It is adopted in this study for virtual lab development of the leveling experiment. Developing the virtual lab of leveling experiment needs many materials such as threedimensional models of the automatic level, the level staff, the survey environment, user interface (UI) components such as screws to control the virtual labs, etc. Some materials can be directly found in rich resources provided by Unity like a 3-screw system for leveling the instrument while many materials must be prepared with other tools. For example, the three-dimensional models of automatic level and level staff are developed by 3DMax while the survey environment is created by Autodesk Revit. After all required materials are ready, they should be reorganized in Unity according to the experiment guidance of leveling. Leveling is the most common surveying method for obtaining the level of ground points to a relative datum. As shown in Fig. 1, the leveling experiment includes five main steps, which are setting the instrument (the automatic level and the level staff) in suitable positions, leveling the automatic level with the circular bubbles and the 3-screws systems, bringing the crosshairs in focus by adjusting the eyepiece focus, bringing the staff in focus by adjusting the objective focus, and reading, recording, and processing data. To achieve the function of experiment teaching, the virtual lab should replicate all these steps. First of all, to let the user install the equipment

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in various positions, a mini-map is developed using Unity built-in camera system. This camera is placed in the sky and facing down vertically. It will capture an overhead view and pass into a render texture. The render texture is then drawn on the UI overlay canvas to form a mini-map. Multiple positions have been marked on the mini-map. Users can click this mark to select which device to install (automatic level or level staff). Once the automatic level is installed, an installation event will be triggered to notify the UI overlay canvas to display the circular bubble indicator, the 3-screws system, the eyepiece focus, the crosshairs, the objective focus, and the image. Event triggers are attached to the 3-screws system. When the user operates the 3-screws system, the bubble inside the circular bubble indicator will move accordingly. Similarly, event triggers are also used to adjust the focus of crosshairs and the image. The developed virtual lab is shown in Fig. 2.

Equipment installation

Leveling

Bringing the crosshairs in focus

Bringing the staff in focus

Mini-map

UI overlay canvas ; Event triggers .

Event triggers; Cameras

Event triggers; Cameras

Data recording and process

Leveling experiment steps

Main functions in Unity

Fig. 1. Main steps in the leveling experiment and used functions in Unity.

Fig. 2. The virtual lab for the leveling experiment.

3 Implication and Validation This virtual lab is developed for leveling experiment teaching. To validate the developed virtual lab, this study recruited 10 student participants to test the virtual lab. These 10 student participants are all sophomores majoring in construction majors. They have completed theoretical studies of the leveling experiment. Before the test, researchers

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explain and demonstrate how to use the virtual lab to participants. After that, participants need complete the leveling experiment in the virtual lab and finish the experiment report. Figure 3 shows the participant that is doing the virtual experiment. Figure 4 shows a case of experiment report. At last, they are invited to finish a questionnaire about their feedback on the developed virtual lab. This questionnaire includes six questions as shown in Table 1. For question 1 to question 4, the participants should answer a number of 1 to 7 meaning totally disagree to totally agree. Question 5 and question 6 are open questions. These questions assess usability, teaching effects, realism, prospects, limitations and improvement directions of the developed virtual lab. This question survey comprehensively examines the effects of the lab from various perspective, which is expected to provide valuable references for further improvements to the virtual lab.

Fig. 3. Participant in the experiment.

Fig. 4. A case of experiment report.

4 Results All 10 participants successfully completed the leveling experiment in the virtual lab and obtained acceptable experiment reports. Table 2 shows the mean and standard deviation of question 1 to 4 in from 10 participants.

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No.

Questions

1

I can easily complete the leveling experiment in the virtual lab according to the experiment guidance.

2

I can master the use of an automatic level through the virtual lab.

3

The virtual lab is similar to the leveling experiment in real life.

4

I believe the virtual lab can completely replace experiments in engineering education.

5

What are the shortcomings of the virtual lab?

6

What are your suggestions for improvements to the Virtual Lab?

Table 2. Summary of questionnaire results Questions

Mean

Standard deviation

Q1

6.70

0.64

Q2

5.70

1.47

Q3

3.50

0.92

Q4

4.70

2.00

Ease of use is important to the implication of the virtual lab. If the virtual lab is complicated and difficult to control, it is impossible to be welcome in engineering education. Question 1 examines the usability of the developed virtual lab and its average mark of 10 participants is 6.7. It can be learned that the virtual lab can be easily controlled through the simple UI interface. This means learning to use the virtual lab will not increase many workloads when it is used for experiment teaching, and thus software operation will not become the key barrier in the application and promotion of the virtual lab. The virtual lab is developed for the leveling experiment teaching. Therefore, it should enable participants to master the usage of the automatic level and the method of leveling survey. Question 2 examines the teaching effectiveness of the developed virtual lab and its average mark of 10 participants is 5.7. The results indicated that most students could master the usage of the automatic level and the method of leveling survey by doing the experiment in the virtual lab and the virtual lab can achieve good teaching effectiveness. Virtual labs used for experiment teaching in engineering education should be similar to experiments in real life. Otherwise, knowledge and skills learned in the virtual labs are difficult to be used in students’ future daily work. Question 3 examines the degree of realism of the developed virtual lab and its average mark of 10 participants is 3.5. This result indicates that the present version of the virtual lab still has obvious differences from the real leveling survey. For example, the surveyor cannot control and operate the automatic level by clicking the mouse or keyboard in real life.

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Virtual labs are an emerging teaching method for experiment teaching in engineering education. Question 4 examines the participants’ confidence in the virtual lab replacing the field experiment in the future and its average mark of 10 participants is only 4.7. This result indicates that with the development of computer and internet technologies, virtual labs may well supplement experimental teaching in engineering education. However, it is next to impossible to fully replace filed experiments.

5 Discussion With the rapid advancements in computer and internet technologies, online teaching has obtained significant achievements. However, experiment teaching has become an important bottleneck to the further promotion of online teaching, particularly for engineering majors with great practice like construction subjects. Virtual labs based on serious games have shown their potential to support experiment teaching and various virtual labs have been developed for different experiments and subjects. Compared with other traditional teaching methods used for online teaching such as videos and animations, virtual labs have significantly stronger interactivity [9]. This means virtual labs allowed participants to more actively join in the whole experiment process instead of only passive acceptance of knowledge [10], thus helping them to better master related knowledge and skills [11]. Previous studies also have indicated that using virtual labs based on serious games in experiment teaching can obviously reduce the cognitive workloads of students and boost their study efficiency [12]. In addition, virtual labs can efficiently reduce the cost of experimental teaching [13]. some equipment for experiment teaching is too expensive. Many universities without high funding support cannot afford it. Sometimes experiment teaching also requires the consumption of a large number of drugs, materials and other experimental consumables, resulting in a serious waste of resources. By developing various virtual environments according to the experiment guidance, a set of computers or virtual equipment can be used for teaching different experiments, thus efficiently reducing the cost of experiment teaching. However, virtual labs still cannot fully replace site experiments in experiment teaching at the current stage. This is mainly because it is almost impossible for virtual labs to recreate the experiment in a completely consistent manner. For instance, the control and operation of equipment in real life is always different from that in virtual labs due to the limitation of interaction methods of virtual labs. In addition, it is usually impossible to consider all potential results when designing virtual labs. Unexpected situations in field trials can overwhelm students who have only participated in virtual experiments. At present, the global new crown epidemic has put serious impacts on teaching activities and many universities therefore have to choose to integrate online and offline education modes, which create great opportunities to further the development of virtual labs. More efforts are needed in the future to further develop virtual labs. On the one hand, more virtual labs should be developed to meet the requirements of experiment teaching for various engineering majors. A good hardware and software ecology is the basis for the further promotion of virtual labs. On the other hand, developers should further improve the interaction and experience of using virtual labs to bring them closer to reality.

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6 Conclusions Virtual labs are expected to address the problem of online experiment teaching of engineering majors. This article developed a virtual lab of leveling surveys with the Unity game engine. 10 students majoring in construction subjects were recruited to test the developed virtual lab and all participants were asked to finish a questionnaire after the experiment. At last, the implications and shortcomings of virtual labs are discussed based on previous literature and participants’ feedback. The main conclusions were drawn as follows: (1) The Unity game engine can provide a powerful platform for developing virtual labs. Rich resources and functions provided by the Unity engine and C# scrips can help to achieve various operations in virtual experiments. (2) Compared with other teaching methods, virtual labs based on serious games can improve students’ participation in experiment teaching and thus boost the teaching efficiency for online engineering education. (3) At the present stage, virtual labs still cannot fully replace site experiments in experiment teaching because it is almost impossible for virtual labs to recreate the experiment in a completely consistent manner. (4) To further promote the application of virtual labs, more efforts are needed to enrich the range of experiments and improve the user experience.

7 Funding Statement This research was funded by UGC Special VTL Grant Project, grant number 6430150.

Declaration of Competing Interest. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

References 1. Kabilan, M.K., Annamalai, N.: Online teaching during COVID-19 pandemic: A phenomenological study of university educators’ experiences and challenges. Stud. Edu. Evalu. 74, 101182 (2022). https://doi.org/10.1016/j.stueduc.2022.101182 2. Zhu, W., Liu, Q., Hong, X.: Implementation and Challenges of Online Education during the COVID-19 Outbreak: A National Survey of Children and Parents in China. Ear. Childh. Res. Q. 61, 209–219 (2022). https://doi.org/10.1016/j.ecresq.2022.07.004 3. Kumar, V., Gulati, S., Deka, B., Sarma, H.: Teaching and Learning Crystal structures through Virtual Reality based systems. Adv. Eng. Info. 50, 101362 (2021). https://doi.org/10.1016/j. aei.2021.101362 4. Cowan, B., Kapralos, B.: An Overview of Serious Game Engines and Frameworks. Recent Advances in Technologies for Inclusive Well-Being: From Worn to Off-body Sensing. In: Brooks, A.L., Brahnam, S., Kapralos, B., Jain, L.C. (eds.) Virtual Worlds, and Games for Serious Applications. Cham, Springer International Publishing, pp. 15-38 (2017). https://doi. org/10.1007/978-3-319-49879-9_2

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5. di Lanzo, J.A., Valentine, A., Sohel, F., Yapp, A.Y.T., Muparadzi, K.C., Abdelmalek, M.: A review of the uses of virtual reality in engineering education. Comp. Appli. Eng. Edu. 28(3), 748–763 (2020). https://doi.org/10.1002/cae.22243 6. Wang, P., Wu, P., Wang, J., Chi, H.-L., Wang, X.: A critical review of the use of virtual reality in construction engineering education and training. Int. J. Enviro. Res. Pub. Heal. 15(6), 1204 (2018) 7. Laamarti, F., Eid, M., Saddik, A.E.: An overview of serious games. Int. J. Comput. Games Technol. (2014). Article 11. https://doi.org/10.1155/2014/358152 8. Matallaoui, A., Herzig, P., Zarnekow, R.: Model-Driven Serious Game Development Integration of the Gamification Modeling Language GaML with Unity. 2015 48th Hawaii International Conference on System Sciences (2015). https://doi.org/10.1109/HICSS.2015.84 9. Bartolomé, N.A., Zorrilla, A.M., Zapirain, B.G.: Can game-based therapies be trusted? Is game-based education effective? A systematic review of the Serious Games for health and education. 2011 16th International Conference on Computer Games (CGAMES) (2011). https://doi.org/10.1109/CGAMES.2011.6000353 10. Hauge, J.M.B., Pourabdollahian, B., Riedel, J.C.K.H.: The Use of Serious Games in the Education of Engineers. Advances in Production Management Systems. Competitive Manufacturing for Innovative Products and Services. Berlin, Heidelberg, Springer Berlin Heidelberg (2013) 11. Bhute, V.J., Inguva, P., Shah, U., Brechtelsbauer, C.: Transforming traditional teaching laboratories for effective remote delivery—A review. Edu. Chem. Eng. 35, 96–104 (2021). https:// doi.org/10.1016/j.ece.2021.01.008 12. Chittaro, L., Buttussi, F.: Assessing Knowledge Retention of an Immersive Serious Game vs. a Traditional Education Method in Aviation Safety. IEEE Trans. Visuali. Comp. Graph. 21(4), 529-538 (2015). https://doi.org/10.1109/TVCG.2015.2391853 13. Tsekleves, E., Cosmas, J., Aggoun, A.: Benefits, barriers and guideline recommendations for the implementation of serious games in education for stakeholders and policymakers. British J. Edu. Technol. 47(1), 164–183 (2016). https://doi.org/10.1111/bjet.12223

Insights into the Resource Utilization Behavior of Reclaimed Asphalt Pavement Based on Theory of Planned Behavior from Different Stakeholders’ Perspective Dan Chong1 , Yihao Huang1(B) , and Hongyang Li2 1 School of Management, Shanghai University, Shanghai 200444, China

[email protected]

2 Business School, Hohai University, Nanjing 211000, China

[email protected]

Abstract. The road repair and maintenance process will produce a large amount of reclaimed asphalt pavement (RAP). The abundant generation of construction waste poses a major challenge to the sustainable development of resources worldwide. Based on the increasingly prominent contradiction between environmental resources and construction development, the implementation of resource utilization of recycled asphalt pavement materials is of great significance for resource sustainability. This paper aims to study the factors influencing the behavior of resource utilization of RAP materials from different stakeholders’ perspectives. Based on the theory of planned behavior (TPB), three variables of “government supervision”, “potential benefits” and “project constraints” are introduced to meet the specific situation of road projects, and the model of RAP resource unitization behavior is constructed. Data were collected through questionnaires, and structural equation modeling (SEM) was used to test the theoretical model and research hypotheses. The influencing factors that cause the difference of RAP resource utilization behavior among stakeholders were analyzed through multigroup structural equation. The research findings show that attitude towards behavior, subjective norms and perceived behavioral control have a significant positive impact on RAP resource utilization behavioral intentions. Perceived behavioral control, project constraints and behavioral intention have a significant positive impact on RAP resource utilization behavior. The hypothetical path of potential benefits has a non-significant effect on actual RAP resource utilization behavior. The multi-group structural equation analysis with stakeholders as moderating variables showed that the effects of government supervision, project constraints, and behavioral intentions on actual behavior were more significant for the designer; the effects of attitude towards behavior on behavioral intentions of RAP resource utilization were more significant for the contractor; and the effects of perceived behavioral control variables on behavioral intentions and actual behavior of RAP resource utilization were more significant for the owner. The research is expected to provide a theoretical basis to guide the stakeholders better implementing RAP resource utilization measures.

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 104–124, 2023. https://doi.org/10.1007/978-981-99-3626-7_9

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Keywords: theory of planned behavior · road engineering · reclaimed asphalt pavement · resource utilization behavior

1 Introduction In recent years, the concept of circular economy and sustainable development has attracted great attention in the field of construction industry. China currently has the second-biggest asphalt pavement road transportation network in the world [1]. China’s highway traffic development has gradually transitioned from the era of road construction to the era of maintenance and road repair. Maintenance has become the most heavy and urgent task of the current road development. This process will produce a large number of Reclaimed Asphalt Pavement (RAP) material. RAP refers to the old pavement material obtained from asphalt pavement by means of milling and excavation [1, 2]. According to the data released by MOT China, the amount of RAP generated from rehabilitation of artery highway, including expressway, first and second grade highway, is estimated to be more than 0.16 billion tons annually [3]. If these road wastes are directly landfilled or abandoned without resource treatment, it will not only cause great damage to the ecological environment, but also pose a major threat to the sustainable development of China’s resources. Based on the increasingly prominent contradiction between environmental resources and construction development, it is important to promote the resource utilization of recycled asphalt pavement materials for resource sustainability. Relevant studies have shown that RAP can be remixed with regenerants, new asphalt materials, and new aggregates in certain proportions to obtain a recycled mix that meets the technical specifications [4, 5]. Therefore, the resource utilization of RAP can reduce the demand for the use of original aggregates and also save the amount of asphalt binder required in the production of asphalt mixes [6]. In highway maintenance projects, vigorously promoting the recycling technology of pavement materials can not only save a large amount of material resources and funds, but also avoid environmental pollution, which is a key element in establishing a resource-saving and environment-friendly industry as well as a low-carbon transportation system. This is an important way to realize the circular economy development model and sustainable development strategy. In road construction, the resource utilization management of RAP involves multiple stakeholders. The designer usually focuses on the design of many functions in the case of sufficient funds to ensure that the product complies with the design content. The contractor usually focuses on the cost, schedule, and quality objectives. The owner looks at the implementation of policies and regulations on the project, and the economic and ecological benefits generated by the project. Related studies have shown that the willingness and behavior of stakeholders towards waste management can have a large impact on the management effectiveness. For example, Li et al. found that the design party’s attitude, subjective norm and perceived behavioral control on waste minimization behavior had a significant positive impact on waste minimization behavior [7]. Al-Khatib et al. found that the contractor’s attitude, subjective norm and behavioral willingness on waste management played a key role in waste generation [8]. Kim et al. developed a performance evaluation framework for construction and demolition waste management (CDWM) and

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revealed that the attitude of construction stakeholders towards CDWM emerged as the foremost critical factor [9]. Therefore, the willingness of different stakeholders to use recycling technology for RAP resource utilization during road maintenance construction can be influenced by companies’ attitudes, subjective norm, perceived behavioral control, and behavioral willingness toward RAP resource utilization and recycled asphalt mixes, in addition to government supervision, potential benefits, and project constraints. From the existing literature, the current research on RAP mainly focuses on the analysis of RAP gradation, the design of recycled mix ratio, the design of recycled equipment combination, the analysis of recycled construction process test and the analysis of road index and performance evaluation, etc. [10–14] Few studies have been conducted to explore the behavior of road engineering stakeholders on the resource utilization of RAP. The Theory of Planned Behavior (TPB) is a theoretical tool proposed by Ajzen to explain and predict human behavior and has been applied to several research areas with good explanatory effects [15]. Based on the TPB, this study aims to investigate the potential influencing variables of RAP resource utilization behavior in mainland China. Three contextual elements (i.e., governmental supervision, potential benefits, and project constraints) were introduced to establish the theoretical model of RAP resource unitization behavior. Structural Equation Model (SEM) was applied to validate the theoretical model. The influencing factors that cause the difference of RAP resource utilization behavior among stakeholders were analyzed through multi-group structural equation. This study then proposed the path and promotion strategy to improve the RAP resource utilization behavior, so as to better promote the road resource utilization.

2 Literature Review 2.1 Relation Behavior Research on RAP Management Asphalt pavement regeneration technology is a set of technology to rehabilitate the old asphalt pavement after retreating, recycling, crushing, and re-mixing with new regenerant and aggregate in a certain ratio to obtain a recycled asphalt mixture [16]. Recycling of asphalt pavements dates back to 1915, but it did not become a common practice until the early 1970s when asphalt binder prices skyrocketed as a result of the Arab oil embargo. Asphalt paving technologists reacted to this situation by developing recycling methods to reduce the demand on asphalt binder and reduce the costs of asphalt paving mixtures. In addition, China has used recycling asphalt mixtures for road construction in the 1980s and has gradually extended the asphalt pavement recycling technology for road maintenance. Reclaimed asphalt pavement can save substantial material resources and funds, avoid environmental pollution from waste materials and promote ecological protection on the basis of satisfying pavement performance requirements. Although RAP recycling has been promoted worldwide, the current recycling rate is still at a low level due to the inadequate technical policies, legal framework, and the lack of systematic education and training for the relevant enterprises [17]. Besides, the research on RAP can be broadly divided into five categories: RAP gradation analysis [18], recycled mix ratio design [19], recycled equipment combination design, recycled construction process test analysis [20] and RAP-related index analysis [21, 22]. It can be seen that the current

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research on the resource utilization of RAP has focused on technical aspects such as the testing of RAP performance, but there is a lack of quantitative research on the influencing factors of resource utilization behavior. Therefore, it is necessary to investigate the significant factors influencing the RAP resource utilization behavior of road engineering stakeholders from a management perspective, which can further regulate the resource utilization management of road waste. 2.2 Theory of Planned Behavior The theory of planned behavior was developed based on the Theory of Reasoned Action (TRA) proposed by Ajzen and Fishbein in 1973 [15, 23].It is a theoretical tool to explain and predict human behavior and has been applied in various research fields [24]. This theory believes that attitude towards behavior, subjective norm, and perceived behavioral control are three important predictors of behavioral intention. Attitude towards behavior and subjective norm indirectly affect actual behavior through behavioral intention, while perceived behavioral control predicts behavior directly or indirectly through behavioral intention. The theory of planned behavior (TPB) model is shown in Fig. 1.

Fig. 1. The structural model of the theory of planned behavior

The theory of planned behavior has been applied to the field of management science and engineering, and the scope of studies covers online shopping behavior [25], green consumption behavior [26], low-carbon transportation mode [27] and so on. Besides, TPB is also widely used in waste management behavior research, which is mainly related to electronics industry, the agriculture and construction industry [28]. In the study on the influencing factors of consumers’ intentions to participate in e-waste recycling based on TPB, Xu, et al. [29] has proved that behavioral attitude, subjective norm and perceived behavioral control directly influence consumer intentions of wasted electronics recycling. And it also affected by various moderating factors such as legal advocacy, environmental knowledge and recycling experience. Besides, Yuan, et al. [30] has explored the predictors of project managers’ waste reduction intentions through TPB, and attitude was found to be the strongest predictor of project managers’ waste reduction intentions.

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The theory of planned behavior focuses on the motivations hidden beneath the decisions of managers and can provide an appropriate theoretical basis for examining the incentives for various behaviors. 2.3 Research Gap and Innovation in this Study From the existing literature, the current research on RAP is mainly focused on the analysis of RAP gradation, the mixing design of recycled aggregate, the combination design of recycled equipment, the analysis of recycled construction process and the evaluation of pavement index and performance. Few studies have been conducted to explore the behavior of road engineering stakeholders on the resource utilization of RAP. Based on the TPB, this study identifies potential influencing variables and constructs a research model on the influencing factors of road engineering stakeholders’ behavior towards RAP resource utilization. The paper adopts a questionnaire survey to obtain data, and the theoretical model is validated through Structural Equation Model (SEM). In addition, the Multi-group Structural Equation Model is used to analyze the factors influencing the differences in stakeholders’ behavior towards RAP resource utilization. Finally, this study proposes the pathway and promotion strategy to enhance the resource utilization behavior of reclaimed asphalt pavement, making it possible to improve the utilization of road resources.

3 Hypothesis and Theoretical Model Development 3.1 Hypothesis Development In this study, the hypothesis was developed based on the eight items identified before modelling. It has been proposed that the attitude towards behavior (AB) and subjective norm (SN) make impacts on behavior by influencing the behavioral intention [31]. While the governmental supervision, potential benefits and project constraints have direct impacts on the behavior. In particular, the perceived behavioral control can either influence the behavior through behavioral intention or directly. RAP resource utilization behavioral attitude towards behavior (AB) refers to the subjective evaluation held by road engineering stakeholders on their own side to implement RAP resource utilization behavior. Generally, a good attitude or positive evaluation of expected benefits will enhance the willingness of enterprises to implement specific behaviors and promote the implementation of resource utilization behavior. Numerous studies have shown that positive behavioral attitudes will have a positive promotion effect on behavioral attitudes. For example, Yuan et al. founded that attitude was the strongest predictor of project managers’ waste reduction intentions, followed by subjective norm and perceived behavioral control [30]. Based on the TPB, Zhu et al. studied construction personnel’s construction waste reduction management behavior and found that construction personnel’s attitude toward reduction had the greatest impact on their behavioral willingness [32]. Based on this, the following research hypothesis 1 is proposed in this paper. Hypothesis 1: Attitude towards behavior has a significant positive impact on the behavioral intention to utilize RAP resources.

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The subjective norm of RAP resource utilization (SN) refers to the pressure perceived by the road project stakeholders when deciding to implement the RAP resource utilization behavior. In general, when this pressure is felt to be higher, the willingness to implement the behavior is also stronger. For example, Sia et al. combined the theory of planned behavior variables with norm activation model to predict the behavioral intention to build eco-friendly houses and found that people’s subjective norm could significantly influence their willingness to participate in construction [33]. Based on this, the following research hypothesis 2 was proposed. Hypothesis 2: Subjective norm has a significant positive impact on the behavioral intention to use RAP resources. Perceived behavioral control of RAP resource utilization (PBC) refers to the perceived ease of implementation of resource utilization behavior for RAP by road engineering stakeholders and the assessment of the ability and conditions required to implement the behavior on their side. The easier the perceived controllability is, the stronger the willingness to implement the behavior. For example, Li et al. found a significant positive effect of perceived behavioral control on their willingness to perform waste minimization behaviors, using design parties as subjects [7]. Wang et al. addressed the issue of low motivation of residents to participate in e-waste recycling and found that perceived behavioral control had a significant effect on behavioral willingness [34]. Based on this, this paper proposes the following research hypothesis 3. Hypothesis H3: Perceived behavioral control has a significant positive effect on behavioral intention to use RAP resources. The theory of planned behavior argues that accurate perceived behavioral control reflects the condition of actual behavioral control conditions, and therefore perceived behavioral control can be used as a measure of actual behavior to directly predict the likelihood of behavior occurrence [35]. For example, Yuan et al. studied construction workers’ waste minimization management behavior based on the theory of planned behavior and found that perceived behavioral control had a significant positive effect on construction workers’ minimization behavior [36]. Zhu’s study on construction workers’ behavior toward construction waste minimization found that perceived behavioral control could produce better predictions of actual behavior [37]. Based on this, the following research hypothesis is proposed in this paper. Hypothesis H4: Perceived behavioral control has a significant positive effect on the implementation of RAP resource utilization behavior. Governmental supervision (GS) refers to the influence of legal systems, regulatory systems, and policy measures (punitive, incentive, and mandatory) on stakeholders’ behavior in implementing RAP resource utilization. Wu et al. found that Chinese government departments played an important role in guiding and promoting contractors’ behavior in construction waste management [38].Through empirical analysis, Tam et al. found that certain incentive policies, laws and regulations, and industry norms were important in influencing contractors’ construction waste minimization management behavior [39]. Ajayi et al. found that by transferring construction waste in landfills policy study, legislative and fiscal policies were key drivers of construction waste minimization behavior [40]. Based on this, the following research hypothesis 5 is proposed in this paper.

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Hypothesis H5: Governmental supervision has a significant positive impact on the implementation of RAP resource utilization behavior. Potential benefits (PB) refer to the extent to which the predicted benefits generated by the implementation of RAP resource utilization behavior in road projects by road project stakeholders affect the actual behavior, including economic, social, and ecological benefits. A quantitative practice of the use of RAP in asphalt pavement found that asphalt mixture recycling applications had significant effects in reducing energy consumption and carbon emissions, and could effectively reduce material costs and overall economic costs, which had good social, environmental and economic benefits [4, 41]. Alhamadani et al. investigated the construction companies in Chongqing city with the relative importance of waste minimization strategies and material waste benefit identification, and they found that implementing waste minimization strategies could achieve benefits such as cost reduction and environmental protection [42]. Based on this, the following research hypothesis 6 is proposed in this paper. Hypothesis H6: Potential benefits have a significant positive impact on the implementation of RAP resource utilization behavior. Project constraints (PC) refer to the influence of production factors such as “human, machine, material, method, and environment” on the implementation of RAP resource utilization by stakeholders. Cao et al. analyzed the quality control of major projects in terms of “human, machine, material, method, and environment”, and found that these elements were complementary to each other in the quality control process of major projects. Meanwhile each influencing element and link has an impact on the project duration, cost, and quality [43]. Based on this, the following research hypothesis 7 is proposed. Hypothesis H7: Project constraints have a significant positive impact on the implementation of RAP resource utilization behavior. Behavioral intention to reuse RAP refers to the intensity of the subjective willingness of road projects’ stakeholders to implement reuse of RAP. By conducting a study on solving the problems of landfill shortage and resource conservation, Bortoleto et al. found that stakeholders relied on technical means to reduce waste generation, but the total amount of waste is still rising, and the important reason was that the behavioral willingness of individuals to participate in waste management was relatively weak [44]. Based on this, the following research hypothesis 8 is proposed. Hypothesis H8: Behavioral intention has a significant positive impact on RAP resource utilization behavior. In summary, the research hypotheses are summarized in Table 1 to clarify the hypothesis path of this study more intuitively, where H1, H2, H3, H4, and H8 are the basic hypotheses in the theory of planned behavior model, and H5, H6, and H7 are the new hypotheses added to this paper. 3.2 Theoretical Model Development Based on the indicators identified and hypotheses proposed, government supervision, potential benefits and project restrictions are introduced as extended variables of the influencing factors of resource utilization behavior. And the preliminary theoretical model

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Table 1. Hypotheses in the Preliminary Theoretical Model Hypothesis H1

Attitude towards behavior has a significant positive impact on the behavioral intention to utilize RAP resources

H2

Subjective norm has a significant positive impact on the behavioral intention to use RAP resources

H3

Perceived behavioral control has a significant positive effect on behavioral intention to use RAP resources

H4

Perceived behavioral control has a significant positive effect on the implementation of RAP resource utilization behavior

H5

Governmental supervision has a significant positive impact on the implementation of RAP resource utilization behavior

H6

Potential benefits have a significant positive impact on the implementation of RAP resource utilization behavior

H7

Project constraints have a significant positive impact on the implementation of RAP resource utilization behavior

H8

Behavioral intention has a significant positive impact on RAP resource utilization behavior

of the influencing factors of RAP resource utilization behavior was developed as shown in Fig. 2.

Fig. 2. The Preliminary Theoretical Model

4 Research Design 4.1 Questionnaire Design This study conducted questionnaire design based on the theoretical model and model indicators. The research design process combined with the basic operational steps of the questionnaire survey method, as shown in Fig. 3.

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Fig. 3. The Research Design Process

In this study, the initial research questionnaire was designed by drawing on relevant mature scales and combining the characteristics of road construction. The questionnaire consisted of three parts: (1) Survey background, including a brief introduction of this study and the concept of RAP. (2) Demographic characteristics of the respondents, including information on gender, age, length of service, workplace, and education level. (3) Variables of the measurement model. The study used a 5-level Likert scale measure and initially designed 33 question items to measure eight variables. 4.2 Data Collection The questionnaire mainly targets three stakeholder groups, i.e. the road engineering designers, the owners, and the contractors. They are then differentiated in terms of behavioral intention and actual behavior. A focus group meeting and a pilot study were conducted to improve the reliability and validity of the measurement scales. A total of 55 copies were collected from the pre-survey. According to the respondents’ feedback, the questionnaire’s question set basically covers the factors influencing the behavior of RAP resourcefulness, but some questions need to be changed in detail. (1) Changes to the questions that caused confusion among respondents. For example, in question SN2, the options are set to include various stakeholders, including the respondent’s organization, which may cause ambiguity to

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the respondent, and the party where the respondent’s organization is located should be removed. (2) Make the semantic items in the questionnaire more explicit. For example, “Our senior management highly supports the adoption of RAP as a resource utilization practice” should be replaced with “Our senior management supports the adoption of RAP as a resource utilization practice”. (3) Clarify the research target. The questionnaire set material testing units and material suppliers as stakeholders, but the actual pilot study found that contractors had self-owned asphalt mixture plant and had affiliated material testing technical departments within the asphalt mixture plant. Therefore, the material testing consulting units and material suppliers in the questionnaire were removed from the setting. Based on the feedback of the respondents and data analysis results of their responses, the contents and wording of the questionnaire were adjusted and improved to form the final version of the questionnaire, which is shown in the supplementary material. The formal questionnaire consisted of three parts: (1) Survey background, including a brief introduction of this study and the concept of RAP; (2) Demographic characteristics of the respondents, including information on gender, age, length of service, workplace, and education level; (3) Variables of the measurement model. The third part involves 32 questions to measure the eight constructs by incorporating a 5-point Likert scales, where “1 = strongly disagree”, “2 = disagree”, “3 = neutral”, “4 = agree”, “5 = strongly agree”. A reverse question was designed to test whether the respondents replied to the questionnaire carefully. The questionnaire was distributed through two ways. The first way was to distribute the questionnaires to professionals in road construction companies when holding regular site meetings. The second one implemented the “snowball sampling” strategy to invite the respondents to distribute the questionnaire to their colleagues. The survey period was from May 15, 2019 to July 15, 2019. A total of 300 questionnaires were distributed, 268 questionnaires were returned, 15 invalid questionnaires were excluded. 253 questionnaires were finally valid, with an effective response rate of 84.3%. 4.3 Data Analysis Process The collected data were analyzed by the following steps. Firstly, descriptive statistics of the data and reliability analysis of the questionnaire were performed by using SPSS 24.0. Then the prespecified models and hypothesized paths were tested using structural equation modeling (SEM) with the software of AMOS 24.0, including normality test, validation factor analysis, and hypothesis testing. Finally, the influence effects of different groups implementing RAP resourceful utilization behaviors were analyzed by Multi-group Structural Equation Model Analysis.

5 Results and Discussion 5.1 Descriptive Statistics The background information of the respondents is shown in Table 2. It can be found that 90 respondents (35.6%) were from the design side, while 82 (32.4) and 81 (32%) were from the contractor and owner side respectively.

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D. Chong et al. Table 2. Personal Background Information of the Respondents (N = 253)

Variable Gender Age

n

percentage n

percentage n

Percentage

Designer

Contractor

Owner

Total

Male

57

63.3

71

86.6

58

71.6

186 73.5

Female

33

36.7

11

13.4

23

28.4

67

26.5

16–25

13

14.4

17

20.7

8

9.9

38

15.0

26–35

59

65.6

41

50.0

41

50.6

141 55.7

36–45

18

20.0

18

22.0

17

21.0

53

≥ 46

0

0

6

7.3

15

18.5

21

8.3

42

46.7

27

32.9

30

37.0

99

39.1

35

38.9

30

36.6

29

35.8

94

37.2

11–15

9

10.0

13

15.9

13

16.0

35

13.8

16–20

3

3.3

8

9.8

4

4.9

15

5.9

> 20

1

1.1

4

4.9

5

6.2

10

4.0

Junior

0

0

4

4.9

0

0

4

1.6

Senior

2

2.2

19

23.2

2

2.5

23

9.1

Bachelor 26

28.9

45

54.9

37

45.7

108 42.7

Master

61

67.8

13

15.9

34

42.0

108 42.7

Doctor

1

1.1

1

1.2

8

9.9

10

Working ≤5 experience 6–10

Education level

Percentage n

21.0

4.0

5.2 Reliability and Validity Tests The data were analyzed by SPSS24.0 software, The KMO values were used to determine whether the factors corresponding to each variable were suitable for factor analysis, and then the reliability and validity of the questionnaire were judged by Bartlett’s test and Cronbach’s alpha coefficient, etc. Due to the limited space in this article, the specific meaning of abbreviations can be found in the early paper published at the ASCE Construction Research Conference (CRC) 2020, Tempe, Arizona, USA [45]. The KMO values of the eight latent variables were all greater than 0.6, and the Bartlett’s sphericity test for each variable was 0.000, indicating that the observed variables corresponding to each latent variable were suitable for factor analysis among themselves. When doing factor analysis, only one factor was extracted for each observed variable belonging to each latent variable. The explanatory variance of the variables ranged from 51.05% to 73.84%, except for the percentages of squared and variance of the extracted loadings for the item restriction variables, which were less than 50%, and all of them reached a high level of explanatory validity. In addition, except for individual factors (SN3, GS4, PB1, and PC5) whose loading coefficients ranged from 0.5 to 0.6, most of the indicators had loadings greater than 0.6 on their respective attributed factors, and most of them were above 0.7, indicating that the survey scale in this study had good structural validity. From the Cronbach’s alpha coefficient index, the alpha coefficient

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values of all variables were greater than 0.7, indicating that the questionnaire had good internal consistency, i.e., the reliability met the research needs. 5.3 Confirmatory Factor Analysis Confirmatory factor analysis (CFA) indicated that the observed variables with factor loadings less than 0.5 were deleted for the subsequent multiple regression analysis. As a result, the observation variables SN3, GS4, PB1, and PC5 were deleted since their corresponding factor loadings were 0.401, 0.495, 0.455, and 0.468 respectively. The model was corrected by the correction index provided by AMOS. After the correction, the normalized factor load of the observed variables GS3, GS5, and PC1 were less than 0.5 resulting in their deletion. The CFA for the remaining constructs was employed as shown in Fig. 4. All of the factor loadings are higher than 0.5. The goodness-of-fit indices of the full measurement model are shown in Table 3, which indicates that most of the goodness-of-fit indices satisfy the corresponding acceptable requirement. Before conducting SEM analysis, confirmatory factor analysis (CFA) of the model is required to test the validity of the measurement model. CFA is used to test whether the model between the observed variables in the measurement model and their latent variables fits the observed data, and the process may remove observed variables that do not meet the requirements [46]. VCFA of the model through AMOS 24.0 showed that the standardized factor loadings of the observed variables SN3, GS4, PB1, and PC5 were below 0.5, indicating that the variables did not fit the model and therefore needed to be removed. Then the model was corrected by the correction index provided by AMOS, and the correction resulted in the standardized factor loadings of the observed variables GS3, GS5, and PC1 being lower than 0.5, so these three variables needed to be removed. After removing the non-conforming observed variables, the predefined model plot is shown in Fig. 4, at which the standardized loadings of all observed variables are between 0.5 and 0.95. In addition, the model fitness test is shown in Table 3, the RMR value is less than the critical value of 0.05, and the RMSEA is less than the critical value of 0.08. The GFI and AGFI are greater than 0.8, which are at the acceptable level. The values of IFI, TLI and CFI are greater than 0.9, which are in the good range. The values of PGFI, PNFI and PCFI are greater than the critical value of 0.5, and the model fitness test indicators are the values of PGFI. PNFI and PCFI are all greater than the critical value of 0.5. 5.4 Hypothesis Testing Analysis Table 4 shows the hypothesis testing results for the modified SEM model. The unstandardized estimates of attitude toward behavior, subjective norm, and perceived behavioral control on behavioral intention were respectively 0.249, 0.166, and 0.347, with t-values reaching significant levels. It indicates that the paths from AB, SN, and PBC to BI were positively significant, and thus hypotheses H1, H2, and H3 were supported. The p-value of the path of potential benefits to actual behavior is greater than 0.01, which indicates that the effect of potential benefits on actual behavior is not significant, therefore hypothesis H6 is rejected. The standardized path coefficient of government supervision on actual behavior is negative, so hypothesis H5 is rejected. However, the p-value of the path from GS to B is statistically significant at the 5% level, indicating that

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Fig. 4. The predefined model for RAP resource utilization behavior

Table 3. Goodness-of-fit of the Model Goodness-of-fit measure Absolute fit

Incremental fit

Parsimonious fit

χ2/df

Level of acceptance fit

Fit statistics

SC -> SSC

−0.15

4.59

0.00

Support

H4

PEU × MCS -> SC

−0.10

2.28

0.01

Support

Note: MCS = Management commitment to safety; SC = Safety communication; PEU = Perceived ease of use; DSC = Deep safety compliance; SSC = Surface safety compliance

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Perceived Ease of Use (-) Management Commitment to Safety

Deep Safety Compliance

0.011* Safety Communication

0.000** (+)

Surface Safety Compliance Note: **p 0.001; *p

0.05

Fig. 1. Hypothesis testing

5 Discussion After statistically testing the hypotheses, the results are interpreted to highlight the theoretical and practical implications. First, it was revealed that construction workers’ deep safety compliance was considerably and positively impacted by their safety communication, supporting Hypothesis 1a. The findings of this study provided empirical evidence that strengthening the capacity of construction workers to discuss and report safety concerns could help achieve the objective of deeply complying with safety tasks. This was in line with the findings of Hassan [37], who established that workers’ adherence to safety regulations might be predicted by safety communication in the manufacturing industry. On the other hand, the results were in support of Hypothesis 1b, which stated that construction workers would take adequate time with safety regulations and procedures if they felt comfortable discussing safety concerns with site managers or safety supervisors. In support of this finding, O’Neill [38] concluded that regular counselling and social engagement were essential for maintaining safety in the construction industry. In addition, the results confirmed a strong and consistent association between management commitment to safety and safety communication, supporting Hypothesis 2. This finding manifested that when safety was given a high priority by the management staff, such as site managers or safety supervisors, it could encourage construction workers to actively discuss safety issues and report safety hazards. In support of this finding, Singh and Misra [39] stated that construction workers should be regularly reminded of the value of safety by management staff in order to improve workplace safety protocols. Also, the findings demonstrated that, through safety communication, management commitment to safety could be positively linked to deep safety compliance and negatively associated to surface safety compliance, supporting Hypothesis 3a and 3b. This echoed the finding of Man [23] who stated that mangers should encourage workplace safety since it affected workers’ perceptions of whether they should pay attention to safety on site, and thus achieve different types of safety compliance. Last but not least, this study demonstrated that perceived ease of use could positively moderate the relationship between management commitment and safety communication, supporting Hypothesis 4. In other words, if site managers or safety supervisors

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want to increase construction workers’ safety communication by prioritizing safety on a worksite, they should also ensure that construction workers believe they can simply adhere to safety guidelines and practices. Results confirmed the viewpoint of Zhang [40], suggested that one of the important and direct determinants of construction workers’ willingness to adhere to safety measures could be evaluated by their perceived ease of use.

6 Conclusion This study has drawn upon social exchange theory and technology acceptance model and proposed a model to improve construction workers’ deep safety compliance while reduce their surface safety compliance in Australia. This study provided empirical evidence that safety communication among construction workers could significantly and positively influence their deep safety compliance while negatively affecting surface safety compliance. Moreover, the link between management commitment to safety and deep or surface safety compliance could be mediated by safety communication. Management commitment to safety was also found to contribute to safety communication, while perceived ease of use could moderate this linkage. This study does possess limitations, some of which could be helpful in highlighting future research directions. For example, the antecedents identified in this study are not exhaustive and more constructs from other theories or models could be included. For future studies, researchers would apply other theories to investigate the antecedents of deep and surface safety compliance of workers in the construction industry. As an industry with complex systems, the construction industry involves collaboration among different stakeholders, such as trade contractor, site manager, component suppliers, and the principal contractor, to achieve the goal of eliminating accidents and injuries [41, 43]. It would be interesting to study the impact of deep and surface safety compliance on the safety outcomes of different stakeholders. Also, future research would conduct comparison studies on other industries or countries to highlight any differences. Nevertheless, this study contributes to significant theoretical contributions and practical implications. Theoretical contributions of the study lead to improving construction safety literature by extending social exchange theory and technology acceptance model to safety compliance of construction workers and refining the understanding of two types of safety compliance (deep and surface) in the construction industry. This study also has practical implications for eliminating accidents and injuries on construction sites in Australia. By understanding the difference between deep and surface safety compliance, this research helps to propose strategies and practical guidelines for relevant stakeholders, such as construction companies, governments, and workers’ unions. Also, the findings of this study can guide professionals and construction companies in Australia to reform their attitude towards safety compliance and its antecedents. Last but not least, this study can lead construction safety managers to provide guidance of how to enhance deep safety compliance and reduce surface safety compliance of construction workers.

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GRA-Fuzzy-Based Green Urban Planning Scheme Decision-Making Zhenjun Nie(B) , Chenghao Zhou, and Jihuan Zhuo School of Management Engineering, Qingdao University of Technology, Qingdao, China {niezhenjun,zhuojihuan}@qut.edu.cn

Abstract. In order to improve the sustainability of urban construction and development, governments at all levels in China have launched a large number of policies related to urban green planning one after another. However, the program design of urban green planning itself is a complex system engineering, and the scientificity of program decision is important for the smooth implementation and sustainability effect of urban planning programs. To this end, this paper proposes a GRA-Fuzzy-based urban green planning scheme evaluation model. The method starts from the program itself, analyzes the operation and guarantee measures of the program, and combines with the analysis of relevant literature to establish the evaluation index system of urban green planning program; on the basis of fully considering the uncertainty, coherence, fuzziness, and grayness of the evaluation index of urban green planning program, it combines the non-addressable Shapley value assignment method, fuzzy and gray system theory to form a comprehensive evaluation model of urban green planning. Finally, the empirical study shows that the model can effectively evaluate the urban green planning scheme. Keywords: urban green planning · program evaluation · grey correlation analysis · fuzzy integrated evaluation

1 Introduction In the past thousands of years of urban construction and development, human beings have been taking resources from nature, but rarely considering ecological problems as potential problems of urban development. In recent decades, China’s rapid socio-economic development and urbanization levels have been rising, but the development planning of urbanization is mainly of a rough and loose type, which has not only consumed a lot of human resources, financial resources, and material resources, but also caused great waste and serious damage to the natural environment. The haze phenomenon, which currently is at an extremely severe stage, can clearly show the seriousness of this problem. Since the beginning of the new century, China has gradually become increasingly aware of the importance of ecological and environmental effects in the process of urban construction, which requires urgent attention to environmental protection in China’s urban planning. Fundamental project: National Natural Science Foundation of China (71471094); National Humanities and Social Sciences Foundation of Qingdao University of Technology(2019RW006) © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 225–234, 2023. https://doi.org/10.1007/978-981-99-3626-7_18

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Urban green planning provides a new approach to ensuring the quality of urbanization and environmental protection, and governments at all levels have been exploring reasonable green urban planning solutions from this perspective. During the 12th Five-Year Plan period, China issued the “Implementation Opinions on Accelerating the Development of Green Buildings in China” and the “12th Five-Year Plan for the Development of Green Buildings and Ecological Cities”, pointing out the direction for green urban planning in China’s cities. During the 13th Five-Year Plan period, “The Outline of 13th Five-Year Plan of the National Economic and Social Development of the People’s Republic of China” was released, and the concept of green development was reflected in all aspects. The Outline proposes coordination and linkage of ecological protection and environmental governance to achieve the goal of integrated and efficient development of urban agglomerations. This reinforces the important position of “green sustainability” in urban planning and development. At present, there are nearly 300 urban green planning schemes launched by various cities in China, accounting for nearly 97% [1] of the total number of cities in China. However, there are serious subjective intentions in the current planning schemes, and the schemes are more arbitrary and uncertain, and face more unnoticeable risks in the implementation process, so scientific decision-making methods are necessary for the implementation of urban green planning schemes. However, experts and scholars in the field have focused more on the policy-making, planning and implementation process of urban planning evaluation, and few studies on scheme decision-making have put green planning in the first place. Lu, Ning et al. [2] studied the ecological urban planning scheme earlier and paid attention to the fuzzy uncertainty of urban planning in the evaluation, and combined hierarchical analysis with fuzzy theory to produce a reasonable evaluation of the ecological urban planning scheme. In the quantitative evaluation of the Xuzhou Urban Master Plan (2007–2020), Lin, Liwei et al. [3] considered the sustainability of the plan and included environmental sustainability in the evaluation index system. In order to solve the subjective arbitrariness of green urban planning schemes, they [4] proposed a three-dimensional evaluation model based on the distance measurement perspective and verified the effectiveness of the model through empirical studies in several places. Zhai Huimin et al. [5] evaluated the ecological sponges in Xinyang City by using the ecological suitability evaluation method, carried out planning zoning based on the obtained grayscale map of sponge analysis in Xinyang City, and proposed sponge urban planning measures according to local conditions. In summary, urban planning research is the focus of attention from all walks of life, both the government’s policy research and the academic research process have achieved more research results, and these research results are mostly focused on theoretical research of urban planning. Although sustainable development has been incorporated into the urban planning framework this year, the relevant research has not made great progress, and urban green planning research has been stagnant. The lack of efficient program decision models makes it difficult to provide perfect theoretical guidance for the implementation of green planning programs in China’s cities. In this paper, we combine the Shapley value, fuzzy comprehensive evaluation and grey system theory to establish a decision-making method for urban green planning schemes, taking into account the influence of various uncertainties in the implementation of urban green planning schemes.

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2 Construction of Evaluation Index System Urban planning evaluation is a complex systemic issue, and urban green planning schemes focus on their close connection with the surroundings, which makes them more complex than the preceding urban planning evaluation system. Based on the program itself, this paper also delves into the operation and safeguards of the scheme. Based on the green ecological perspective, it analyzes the policies related to the implementation procedures of the planning program with relevant references [6–8] and engineering examples, and finally constructs the evaluation index system of urban green planning program from four levels, including ecological planning program preparation, economic factors, planning organization and management, and implementation safeguards (Fig. 1).

Fig. 1. Evaluation index system of urban green planning scheme. The index system of urban green planning

3 Construction of Urban Green Planning Scheme Evaluation Model Based on GRA-Fuzzy 3.1 Indicator Assignment The Shapley value mainly reflects the contribution of individuals to the subject and takes into account the cumulative effect of different individual combinations, which is not a simple linear addition but a non-linear, i.e. non-additive relationship. The Shapley value objectively takes into account the importance of non-additive measures among indicators to the evaluation target in the form of combination weights, and the specific calculation steps are as follows.

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Let N = {1, 2, 3, ..., n} be a finite set, S (a random combination of different indicators) be any subset of it, [N , V ] be a pairwise combination of any of the different indicators in the set, V(S) be the corresponding real-valued function, i.e., the influence weight of the combination of lower-level indicators on the higher-level indicators, and V (ϕ) = 0. On the basis of obtaining the influence weights of the combined indicators, the objective weights of each indicator can be calculated as follows. shi (N , V ) =

n 

w(s)[v(s) − v(S/i)]

S⊂N ,i⊂S

where w(s) = (s−1)!(n−s)! , is the probability of indicator pairwise combination, s is the n! number of indicators in the subset S, and S/i denotes the combination of indicators in the combination set S S after subtraction. i 3.2 Quantification of Indicators Based on Fuzzy Comprehensive Evaluation 3.2.1 Definition of Urban Green Planning Program Evaluation Measures The evaluation measures of urban green planning scale the rationality of the planning scheme and the effect of the completed implementation proposed for the characteristics of urban green planning engineering. In order to achieve the quantitative evaluation of qualitative indicators, this study draws on the literature [9] and uses fuzzy measures to divide the evaluation measures into intervals. The evaluation measures are set as [0 ~ 1], and the evaluation measures of each index of urban green planning scheme are divided into four grades: excellent, good, medium and poor, as shown in Table 1. Table 1. Evaluation measures of urban green planning programs. Evaluation level of urban green planning scheme Grade

Excellent

Good

Medium

Difference

Measurements

[1,0.9)

[0.9,0.7)

[0.7,0.5)

[0.5,0]

3.2.2 Fuzzy Comprehensive Evaluation of Evaluation Indexes (1) Set the evaluation level. Set the set U = {u1 , u2 , ..., um }, where u1 , u2 , ..., um is the index for program evaluation. Also determine the corresponding measurement thresholds for each evaluation level according to the program evaluation measures in Table 1: M = (0.95, 0.8, 0.6, 0.25). (2) Establish the evaluation matrix. Experts in the field of urban planning were invited to score the program evaluation indexes ui to obtain a matrix of the affiliation of

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the indexes with respect to V. ⎤ r11 r12 · · · r1m ⎢ r21 r22 · · · r2m ⎥ ⎥ R=⎢ ⎣ · · · · · · · · · · · · ⎦0≤rij ≤1 rn1 rn2 · · · rnm ⎡

where rij is the affiliation of the indicator Aij with respect to the rank vj . (3) Synthesize the fuzzy evaluation matrix. The weights of the second-level indicators are fuzzy synthesized (∧, ∨) ω with the corresponding affiliation matrix R, and the fuzzy evaluation of the first-level indicators is carried out, and the evaluation vectors B˜ i are as follows. ⎤ ⎡ ri11 ri12 · · · ri1m ⎢ ri21 ri22 · · · ri2m ⎥ ⎥ (1) B˜ i = ωi ◦ Ri = (ωi1 , ωi2 , · · · ωin ) ◦ ⎢ ⎣ ··· ··· ··· ··· ⎦ rin1 rin2 · · · rinm

(4) Calculation of comprehensive evaluation value. The traditional fuzzy comprehensive evaluation often uses the principle of maximum affiliation to determine the evaluation level, which to a certain extent leads to the neglect of smaller affiliation information and affects the accuracy. In this paper, the evaluation vector is synthesized with the measurement threshold, and the B˜ i integrated evaluation value X is obtained by single-valued processing. X = B˜ i • M T

(2)

3.3 Gray Correlation Analysis Grey relational analysis (GRA) is a comprehensive evaluation method based on a mathematical theory proposed by Professor Deng Julong [10, 11], which is an extension of the views and methods of system theory, information theory and cybernetics. As one of the classical methods, the gray correlation analysis model whitens the gray system by combing the model, which has the characteristics of simple model, clear concept, strong application and reliable conclusion. It has been widely used in engineering decision-making based on multidimensional objectives [12–14], the. Since urban green planning is a highly complex and uncertain system, the complexity of the system and the limited external knowledge when conducting program evaluation make the program evaluation also a system with unclear information, i.e., a gray system - between a white system with completely certain information and a black system with completely unknown information. Therefore, the evaluation of urban green planning schemes should fully consider the grayness of the evaluation system. Therefore, in order to cope with the above problems, this paper selects the gray correlation method to make comprehensive decisions on urban green planning schemes and find the optimal scheme.

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3.3.1 Determining the Ideal Set of Evaluation Indicators The ideal index set of urban green planning scheme is set X0 = (x01 , x02 , · · · , x0n ) as a comparison sample series. The optimal value in the series can be selected as the maximum value or the minimum value of the program, but the optimal value should not be too high or too low to ensure the scientific nature of the ideal index set. The evaluation set is Xi = (Xik )m×n (i = 1, 2, · · · , m k = 1, 2, · · · , n), where n is the number of evaluation indicators and m is the number of evaluation programs. 3.3.2 Data Processing Since the evaluation indexes themselves have different attributes and different magnitudes and orders of magnitude, it is not practical to compare them directly, so they need to be normalized and dimensionlessized to make them comparable. In view of the above fuzzy comprehensive evaluation method has already achieved the normalization and dimensionless processing of the data of indicators, so there is no need to repeat the processing here. 3.3.3 Calculating the Grayness Coefficient (1) The normalized dimensionless values of the ideal index evaluation set constitute the reference series X0k = (x01 , x02 , · · · , x0n ), and the comparison series are Xik = (xi1 , xi2 , · · · , xin ) , i = 1, 2, · · · , m. (2) Calculate the number of gray correlation coefficients εik between the reference series X0k and the comparison series Xik . εik =

minmin|X0k − Xik | + ρmaxmax|X0k − Xik | i

i

k

k

|X0k − Xik | + ρmaxmax|X0k − Xik | i

k

i = 1, 2, · · · , m k = 1, 2, · · · , n.

(3)

where, εik denotes the correlation coefficient between the reference series and the comparison series at time k. ρ is the discrimination factor introduced to reduce the effect of extreme values, generally ρ = 0.5. (3) Let ri denote the gray correlation between the comparison series and the optimal series, then the gray correlation between the reference series X0k and the comparison series Xik is calculated as follows. 1 εik (i = 1, 2, · · · , m k = 1, 2, · · · , n) n n

ri (k) =

(4)

k=1

ri The larger it is, the closer the comparison series is to the ideal series, i.e., the solution is the optimal solution among the alternatives.

4 Analysis of Calculation Cases A city intends to implement an urban green planning policy in the city. Due to the traditional planning concept and existing planning conditions as well as the complex conditions of urban planning itself, the city may face various complex uncertainties in the

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implementation of urban green planning. According to the actual needs of planning, the urban planning unit has prepared three urban green planning schemes and invited experts in the field of implementation planning to participate in the evaluation to determine the optimal green planning scheme. In this paper, the application process of the program decision model is elaborated as an example. 4.1 Determining Weights Based on Shapley-Valued Non-additive Measures The weighting process of indicators is demonstrated by taking the subordinate indicators of indicator A4 in the evaluation index system of green planning scheme of the city as an example. Based on the statistical data and on-site questionnaires, and confirmed by relevant experts, the combined influence weights of A41 , A42 , A43 and A44 for “implementation guarantee” A are obtained4 , as shown in Table 2. Table 2. Impact weights for the combination of indicators under Indicator A4 “Implementation Assurance”. The impact weight of sub indicator combination of indicator A4 “implementation guarantee” (N,V )

V (s)

1

0.326

2

0.233

3

0.298

4

0.143

(1,2)

0.612

(1,3)

0.631

(1,4)

0.522

(2,3)

0.519

(2,4)

0.349

(3,4)

0.453

(1,2,3)

0.886

(1,2,4)

0.671

(1,3,4)

0.709

(2,3,4)

0.647

(1,2,3,4)

1.000

The sum of the data in the seventh column of Table 3 is the combined weight value of 0.340 for A41 . The same reasoning leads to. w4 = (0.340,0.240,0.295,0.125). Correspondingly, the weights of the other indicators were determined as above, respectively.

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Table 3. Calculation of the weights of indicator A in the evaluation index system of urban green planning scheme41. Weight calculation of index A41 in the evaluation index system of urban green planning scheme S

v(s)

v(S/{1})

v(s) − v(S/{1}

|S|

w(s)

w(s)[v(s) − v(S/{1})]

{1}

0.326

0

0.326

1

1/4

0.082

{1,2}

0.612

0.233

0.379

2

1/12

0.032

{1,3}

0.631

0.298

0.333

2

1/12

0.028

{1,4}

0.522

0.143

0.379

3

1/12

0.032

{1,2,3}

0.886

0.519

0.367

3

1/12

0.031

{1,2,4}

0.671

0.349

0.322

3

1/12

0.027

{1,3,4}

0.709

0.453

0.256

3

1/12

0.021

{1,2,3,4}

1.000

0.647

0.353

4

1/4

0.088

w0 w1 w2 w3

= (0.267, 0.208, 0.301, 0.224); = (0.233, 0.223, 0.247, 0.297); = (0.309, 0.213, 0.285, 0.193); = (0.331, 0.267, 0.183, 0.219).

4.2 Quantification of Indicators Based on Fuzzy Comprehensive Evaluation (1) 20 industry experts were selected to analyze and judge the degree of implementability of the evaluation indicators of the city’s green planning program. ⎡ ⎤ 0.55 0.30 0.10 0.05 ⎢ 0.65 0.25 0.05 0.05 ⎥ ⎥ R4 = ⎢ ⎣ 0.40 0.40 0.10 0.10 ⎦ 0.75 0.15 0 0.10 (2) Calculate the evaluation vector of A 4 based on the weights w4 = (0.340,0.240,0.295,0.125) obtained from Eq. (1) and the above shapley values. B˜ 4 = ω4 ◦ R4

⎡

⎤ 0.55 0.30 0.10 0.05 ⎢ 0.65 0.25 0.05 0.05 ⎥ ⎥ = (0.340, 0.240, 0.295, 0.125) ◦ ⎢ ⎣ 0.40 0.40 0.10 0.10 ⎦ 0.75 0.15 0 0.10 = (0.555, 0.299, 0.075, 0.071) Similarly, the evaluation vectors for the other level 1 indicators in Scenario 1 are B˜ 1 = (0.444, 0.467, 0.071, 0.018); B˜ 2 = (0.377, 0.405, 0.169, 0.049); B˜ 3 = (0.469, 0.399, 0.076, 0.056);

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(3) Calculate the combined evaluation value of Option 1 according to Eq. (2). ⎤ ⎤ ⎡ ⎤ ⎡ 0.444, 0.467, 0.071, 0.018 0.95 0.843 ⎢ 0.377, 0.405, 0.169, 0.049 ⎥ ⎢ 0.8 ⎥ ⎢ 0.769 ⎥ ⎥ ⎥ ⎢ ⎥ ⎢ X1 = ⎢ ⎣ 0.469, 0.399, 0.076, 0.056 ⎦ • ⎣ 0.6 ⎦ = ⎣ 0.824 ⎦ 0.829 0.555, 0.299, 0.075, 0.071 0.25 ⎡

Similarly, it follows that. X2T = (0.766, 0.835, 0.854, 0.793); X3T = (0.803, 0.846, 0.659, 0.851) 4.3 Gray Correlation Analysis From the nature of the scheme here, the optimal reference series can be set as X0T = (1, 1, 1, 1), while the comparison series are the combined evaluation series of scheme 1, scheme 2, and scheme 3, respectively. Again, taking option 1 as an example, from Eq. (3), it is obtained that ε11 =

minmin|X0k − Xik | + ρmaxmax|X0k − Xik | i

i

k

k

|X0k − X1k | + ρmaxmax|X0k − Xik | i

k

0.146 + 0.5 × 0.341 = 0.966 = |X01 − X11 | + 0.5 × 0.341 ε1 = (0.966, 0.854, 0.913, 0.927) And the gray correlation between Option 1 and the optimal option can also be found according to Eq. (4). 1 1 εik = (0.966 + 0.854+0.913+0.927) = 0.913 4 4 4

r1 =

k=1

Similarly, the gray correlation of the two remaining scenarios r2 = 0.891 and r3 = 0.861. In summary, it can be seen that r1 > r2 > r3 , so option 1 should be chosen as the urban green planning option.

5 Conclusion (1) The paper treats the urban green planning process as a gray system, and constructs a Gray-Fuzzy evaluation model that integrates the systematic correlation, fuzzy and gray characteristics of the evaluation indexes of urban green planning schemes by combining the Shapley non-additive measure assignment method with fuzzy comprehensive evaluation and gray correlation analysis. (2) Shapley’s non-additive measure assignment method takes into account the nonlinear interactions among the internal factors of the evaluation system, and its combination

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with fuzzy theory and gray system theory achieves a more objective quantification of indicators that are cognitively unclear and difficult to quantify. (3) The Gray-Fuzzy decision system established in this paper verifies the applicability and feasibility of the decision method in urban green planning scheme decisionmaking through arithmetic examples. Since the uncertainty, fuzziness and grayness of the evaluation system are addressed in this study, it can be applied in similar engineering decision-making problems with minor modifications according to the engineering characteristics.

References 1. Yu, C., et al.: From an eco-industrial park towards an eco-city: a case study in Suzhou, China. J. Clean. Prod. 102, 264–274 (2015) 2. Lu, N., Lu, L., Huo, S., Xu, H.J.: Comprehensive evaluation of ecological urban planning schemes. Urban Issues 04, 22–26 (2006) 3. Lin, L.W., Shen, S.H., Fang, X.U.E., Zhou, Z.X.: Research on the evaluation of urban master planning scheme based on hierarchical analysis method: Xuzhou urban master plan (2007– 2020) as an example. Journal of Suzhou Institute of Science and Technology (Engineering Technology Edition) (02), 61–65 (2010) 4. Ren, H., Du, Y.J., Chen, Y.Q., Ren, P.Y., Dong, H.F.: Research on three-dimensional evaluation model of ecological urban planning scheme from the perspective of distance measurement. Sci. Technol. Prog. Countermeas. 16, 81–85 (2016) 5. Huimin, Z., Wenquan, X., Xianwu, Y., Bing, L., Jingmin, H., Pei, X.: Research on sponge urban planning based on ecological sponge evaluation–Xinyang city as an example. Journal of Xinyang Normal College (Natural Science Edition) 31(03), 443–448 (2018) 6. Miao, S.G., Wang, X.Y., Jiang, W.F., Wang, W.-W., Chen, X.: The influence of green space layout on meteorological environment in urban planning--Chengdu urban green space planning scheme as an example. Urban Planning (06), 41–46 (2013) 7. Yang, Z..: Study on Planning Countermeasures for Urban Landscape Evolution and Convergence in Rapid Urbanization. Central Academy of Fine Arts (2012) 8. Chen, X.: Selection and evaluation of urban construction planning schemes using cost-benefit analysis. Xi’an University of Architecture and Technology (2009) 9. Chen, W., Wang, H., Yan, H., Li, M.: Competency evaluation of construction project managers based on vector entropy cosine. Journal of Civil Engineering and Management 35(02), 32– 38+84 (2018) 10. Fan, Q.: Ecological environment evaluation of Yangtze River economic belt under grey correlation model. Statistics and Decision-making 34(24), 117–119 (2018) 11. Li, M., Chen, L., Wei, L.: Marketing forecast of Shenhua coal based on dynamic market response model and computer simulation. Anhui Agricultural Science 37(30), 14973–14975 (2009) 12. Cong, Q.: Water quality evaluation of Fen River based on improved grey correlation analysis. Water Resources and Hydropower Express 40(05), 35–38 (2019) 13. Jinrong, L.: Evaluation of regional innovation performance based on grey correlation DEA cross-efficiency. Shanxi Science and Technology 34(01), 19–24 (2019) 14. Hao, L., Yanjun, L., Guiyuan, C., Orangezi, Y.: Evaluation of airport operation efficiency based on gray correlation degree. Aviation Computing Technology 48(06), 73–76 (2018)

Path Analysis of Regional Carbon Lock-in and Unlocking from a Qualitative Comparative Perspective Yang Chen1,2 , Tianxin Lai2(B) , Jingke Hong2 , and Yue Teng1 1 Department of Building and Real Estate, The Hong Kong Polytechnic University, Kowloon,

Hong Kong 2 School of Management Science and Real Estate, Chongqing University, Chongqing 400044,

China [email protected]

Abstract. Carbon lock-in is becoming increasingly prominent in China’s industrialization. Understanding regional carbon lock-in and finding the path of carbon unlocking are of great significance for China to achieve carbon peak and carbon neutralization. Based on the qualitative comparative perspective, this paper systematically defined the five stages of regional carbon lock-in (carbon attraction, carbon clustering, carbon lock-in, unlocking transition, and relative unlocking) at the provincial level between 2000 and 2019 and analyzed their historical evolution characteristics. Further, the forming paths of carbon lock-in and unlocking were explored by using fuzzy-set qualitative comparative analysis (fsQCA). The results showed that during the past 20 years, the general trend of carbon lock-in in China was from the gradual formation of carbon lock-in to carbon unlocking. In addition, there were nine paths of carbon lock-in and five paths of unlocking. The carbon lock-in paths can be summarized as Government Driven, Technological Lag, Development Backward, and Extensive Development, and the unlocking paths can be classified as Social Unlocking and Economy Driven. Using a quantitative method to describe the qualitative definition of carbon lock-in and unlocking, this study provided a systematic perspective on the transition of carbon lock-in in China and offered unlocking recommendations for carbon-locked regions. Keywords: carbon lock-in · carbon unlocking · configuration perspective · fuzzy-set qualitative comparative analysis

1 Introduction Climate change is a major global challenge facing humanity today [1]. With the increase of industrialization and urbanization in China, the enormous energy demand for socioeconomic development has led to a continuous increase in carbon emissions. According to the China Energy Statistics Yearbook, China’s total energy consumption grew from 571 million tons of standard coal to 4.87 billion tons from 1978 to 2019, with an average annual growth rate higher than the global average. To address the worsening climate © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 235–252, 2023. https://doi.org/10.1007/978-981-99-3626-7_19

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change and to promote harmony between the economy and environment, the Chinese government has made a series of commitments to take responsibility for reducing emissions [2]. In 2020, China put forward the “30–60 target” of achieving peak carbon and carbon neutrality, i.e., to achieve peak carbon by 2030 and carbon neutrality by 2060. However, the inertia of socio-economic development can bring about a carbon lockin effect, which is characterized by evolutionary inertia and hyper-stable structure [3]. This effect can limit the application and diffusion of low-carbon economic technologies and lock socio-economic development in a high-carbon emission state, thus hindering the region from achieving the dual-carbon goal [4]. Therefore, exploring the formation mechanism and stage characteristics of “regional carbon lock-in” and finding a “regional carbon unlocking” path consistent with regional scenarios will help promote regional sustainable development and low-carbon green transformation. The carbon lock-in effect was first proposed by Unruh [4]. Increasing returns to scale reinforces technology and institutions each other and leads to the modern industrial economy being locked into a high-carbon state. This state may result in persistent market and policy failures and impede the adoption and diffusion of low-carbon technologies. Many scholars have defined the carbon lock-in type in exploring the mechanism of carbon lock-in formation. For instance, Seto, et al. [3] combed through the literature and identified the three main types of carbon lock-in: infrastructure and technology lock-in, institutional lock-in, and behavioral lock-in. Based on the technological perspective, Li and Guo [5] pointed out that the carbon-based technology system will undergo three stages: marketization, institutionalization, and social embedding. Then, the increasing degree of technology lock-in, institutional lock-in, and social lock-in will give rise to carbon lock-in. Therefore, low-carbon technology diffusion and low-carbon economic transformation are the keys to unlock the carbon lock-in. Regarding the carbon unlocking path, previous studies mainly focused on carbon unlocking strategy analysis and policy design based on specific geographical areas or industries, such as carbon tax and carbon trading policy studies [6] and decoupling of carbon emissions [7]. For example, Chen, et al. [8] assessed long-term CO2 emissions in the United States, China, and Japan to address the decarbonization of the power sector. Carley [9] evaluated whether the U.S. power industry has moved away from carbon-intensive technology lock-in. Similarly, Erickson, et al. [10] developed a straightforward method to measure the rate, intensity, and magnitude of carbon lock-in for major energy-consuming departments, such as the power, buildings, industry and transportation sectors. Regarding the judgment of the carbon locking degree, Cai [11] calculated the carbon locking coefficients of 28 industrial sectors in China using the input-output method and found that the locking status of China’s carbon emissions is mainly concentrated in the two states of relative unlocking and growth locking. Scholars have researched the theoretical connotation, formation mechanism, and unlocking path of carbon lock-in. However, there are still deficiencies in the following aspects: (1) The qualitative studies on the formation mechanism of carbon lock-in have been summarized clearly, but there is a lack of corresponding systematic quantitative methods to match them. Although the existing quantitative studies generally use a single indicator (e.g., carbon overload rate, carbon lock index) to measure the degree of regional carbon lock-in, it lacks multidimensional judgments on different stages of

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regional carbon lock-in. It may lead to biased judgments so that the conclusion is not convincing. (2) Studies on the influencing factors and formation paths of carbon lock-in are generally based on reductionism, which focuses on the analysis of the net effect of a single factor on the formation of carbon lock-in. The analysis of marginal effects has disadvantages, such as multicollinearity. Also, it may ignore the impact of multiple core factors on the interactive and combinatorial effects of carbon locking. Based on the system theory, this paper proposed a fuzzy qualitative comparative analysis method (fsQCA) to quantitatively analyze the carbon lock-in status of each province in China and also explored the causes of carbon lock-in formation and the path of carbon unlocking. The main contributions of this paper are: (1) This paper accurately grasps the complexity, systemic and evolutionary characteristics of regional low-carbon development and provides a new perspective for achieving integrated planning of regional sustainable development; (2) This paper integrally considers the judgment conditions of different carbon lock-in stages for 30 provinces from 2000 to 2019, depicts the historical evolution characteristics of carbon lock-in status, and reveal the spatio-temporal evolution trend of the regional carbon lock-in; (3) Based on the principle of complex causality, this paper adopts the fsQCA method to analyze the multivariate paths affecting carbon lock-in, which can better reflect the combined effects of the influencing factors than traditional econometric analyses, thus providing development path recommendations for provinces to achieve carbon unlocking. In the following section, the theoretical analysis and study design are illustrated, and the results are detailedly reported. The final part of the paper presents the major conclusions and the implications for policymakers.

2 Theoretical Framework 2.1 Influence Mechanism of Carbon Lock-in Essentially, the regional carbon unlocking results from the evolutionary game of multiple conflicts. In the temporal dimension, it is the contradiction between the regional high carbonization path dependence and the native interest demands. In the process of carbon lock-in, the regional economy may have a double locking dilemma of “high carbon” and “low level.” Therefore, carbon unlocking needs not only to solve the regional locking problems but also to avoid the competitive squeeze on local economic interests in industrial upgrading and unlocking. From the spatial dimension, it is the contradiction between the space-time heterogeneity of high-carbon development and adequate regional development. Carbon emissions have significant spatial interaction effects and differential distribution characteristics, so it is necessary to consider the consensus of interests and synergy laws to realize the coordinated development of carbon unlocking strategies among regions and industries. From the policy dimension, it is the contradiction between mandatory policies and regional sustainable development goals. Most of the current policy programs pursued by carbon unlocking are radical and coercive, and too much emphasis is placed on achieving short-term goals without a policy design of regional integration. These contradictions are caused by the spatial and temporal heterogeneity of carbon lock formation pathways and carbon metabolism patterns. Based on previous studies,

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this paper investigates the mechanism of carbon lock-in from four aspects: economy, industry, institution, and technology, as shown in Fig. 1. The economic development level strongly influences the degree of carbon lockin [12]. For the crude economic development pattern, the high economic growth will increase the demand and dependence on fossil energy and intensify the carbon lockin. For the intensive economic development pattern, economic growth will promote low-carbon green technology, optimize the industrial structure, and improve the energy utilization rate, thus alleviating the carbon lock-in dilemma. China’s industrial transfer first appeared in the eastern coastal regions, attracting international manufacturing transfers. Subsequently, manufacturing industries gradually shifted to inland areas where factor costs are lower and marginal returns are higher because the marginal effect of industrial inputs and outputs is a unique comparative advantage for the inland regions. In other words, when the marginal returns of regional high-carbon industries are higher, the more likely it is to attract high-carbon industries to enter, gradually making local economic development more and more dependent on high-carbon industries, which in turn leads to higher carbon emissions and deeper carbon lock-in levels [13]. As an essential instrument to integrate resource allocation and deepen the internal division of labor, industrial agglomeration has a double effect on the regional carbon lockin [14]. On the one hand, industrial agglomeration negatively impacts carbon emissions, mainly because of the labor force agglomeration and output scale expansion. Moreover, industrial agglomeration will further enhance the industry’s marginal efficiency and market competitiveness and capture the market share while playing the scale economy effect. Therefore, the higher the concentration of high-carbon industries, the more severe the carbon lock-in. On the other hand, industrial agglomeration can improve resource allocation efficiency, save costs, promote inter-firm exchanges, and accelerate spillover of green knowledge, thereby alleviating carbon lock-in. Industrial structure optimization can influence the regional carbon lock-in in two ways: improving energy efficiency and enhancing modern urbanization [15]. In detail, the advanced industrial structure can improve energy efficiency and reduce the reliance on high-carbon fossil energy. Apart from that, the industrial structure optimization will bring the structural dividend, promoting the quality effect of urbanization and weakening the negative externality originated from the expansion effect of urbanization. The impact of technological progress on carbon lock-in also plays a dual role [16]. On the one hand, technological progress can improve the efficiency of fossil energy use, promote the application of clean energy, and reduce the reliance on carbon-based technologies. On the other hand, technological progress improves energy efficiency and saves energy, leading to an energy rebound effect. In other words, technological progress promotes economic growth and generates new demand for energy, which partially offsets energy savings. As the “visible hand” of government, environmental regulation can be used to regulate the failure of the “invisible hand” [17]. Porter and Vanderlinde [18] have proposed the “Porter Hypothesis,” which argues that appropriate environmental regulation can encourage enterprise innovation and stimulate technological innovation. The government’s implementation of environmental policies, such as setting standards and imposing

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taxes and fees, will increase enterprises’ environmental management costs and emission costs. On the one hand, it may crowd out productive expenditures and reduce output. On the other hand, it may force enterprises to use more energy-saving technologies. However, when environmental regulations are lax, it is likely to trigger a “pollution sanctuary” effect, leading to the relocation of pollution-intensive industries from other regions to the local area, thereby increasing carbon emissions. Informal institutions include ideologies, values, morals, ethics, and customs [19]. When the moral motivation of residents to participate in a low-carbon environment is stronger, the informal institutions are stricter. Firstly, low-carbon culture helps form a social culture of energy conservation and emission reduction; secondly, public participation in low-carbon activities can effectively stimulate enterprises to be more proactive in developing and applying low-carbon technologies in the market competition; finally, the higher the moral motivation of public participation in low-carbon activities, the lower the public tolerance for low-quality ecological environment, and the stricter environmental regulations implemented by the government. In summary, the informal system can influence the development of a low carbon economy from three aspects: consumption, production, and government regulation. Economy

Technology

Economic development

Technological advances

Industry

Institution Environmental regulation Public participation

Marginal benefits Configuration

Industry structure Industrial clustering

Carbon lock-in stage

High/Low carbon intensity

Fig. 1. The research framework of this paper

2.2 Identification of the Carbon Lock-in Stage Based on the definition and features of carbon lock-in in the literature [20, 21], we summarized five stages in Table 1: carbon attraction, carbon clustering, carbon lock-in, unlocking transition, and relative unlocking. Stage 1: Carbon attraction. The main characteristics of this stage are low factor costs, high investment returns, weak environmental constraints, and backward technological

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Y. Chen et al. Table 1. Identification of the carbon lock-in stage.

Stage

Main characteristics

Indicator features

Stage 1: Carbon attraction

High comparative advantage and attractiveness to high-carbon industries

“Low technological progress and loose environmental regulation”; “low technological progress and weak public participation”; or “low technological progress and large scale payoff.”

Stage 2: Carbon clustering

Regional high-carbon industries continue to coalesce, and the industrial agglomeration effect begins to emerge

“High industrial concentration and low technological progress.”

Stage 3: Carbon lock-in

The high-carbon industries “Backward industrial structure and continue to cluster, and their scale high technological progress.” and development will generate substantial technological demand

Stage 4: Economic development creates Unlocking transition the foundation for unlocking and industrial structure optimization brings the motivation for unlocking

“Advanced industrial structure and high technological progress”; or “high economic development, advanced industrial structure, and high technological progress.”

Stage 5: Relative unlocking

“High economic development, advanced structure, high public participation, and high technological innovation.”

Technological innovation, public awareness of environmental protection and the agglomeration effect of the tertiary industry have given the region the power to disrupt the previous carbon-based technology regime

innovation capabilities. This gives the region a high comparative advantage and attractiveness to high-carbon industries. In this stage, technological progress is the leading reference indicator, with environmental regulation, public participation, and pay for scale as complementary reference indicators. Specifically, there are three scenarios: “low technological progress and loose environmental regulation,” “low technological progress and weak public participation,” or “low technological progress and large scale payoff.” Stage 2: Carbon clustering. Regional high-carbon industries continue to coalesce, and the industrial agglomeration effect begins to emerge. Under the influence of the market scale effect and geographic spatial advantage, regional development becomes increasingly dependent on high-carbon industries. Meanwhile, the technological innovation activities of low-carbon industries are continuously suppressed, and the region initially forms a high-carbon path dependence state. This stage is characterized by “high industrial concentration and low technological progress.” Stage 3: Carbon lock-in. When the high-carbon industries continue to cluster, their scale and development will generate substantial technological demand. It will bring

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the development of carbon-based technologies and strengthen the integration between industry and technology. At this point, the region begins to show the intersection of “high carbon path dependence” and “technological progress” The main characteristic of the region is the improvement of technological progress, but the industrial structure is solidified in the secondary industry. The final performance is characterized by “backward industrial structure and high technological progress.” Stage 4: Unlocking transition. With the continuous improvement of economic development and industrial structure optimization, the regions begin to have the conditions for relative unlocking. At this stage, economic development creates the foundation for unlocking, and industrial structure optimization brings the motivation for unlocking. Finally, there are two transition features: “advanced industrial structure and high technological progress” or “high economic development, advanced industrial structure, and high technological progress.” Stage 5: Relative unlocking. In this stage, increasing capacity for technological innovation has given the region the power to disrupt the previous carbon-based technology regime. Increasing public awareness of environmental protection has prompted the government to establish green values and strengthen green policies so that society evolves towards a low-carbon and green development path. Beyond that, the agglomeration effect of the tertiary industry compresses the market space of high carbon emission enterprises. The above conditions provide opportunities for regional low-carbon development from three perspectives: technology, institution, and economy. Therefore, the region has reached the stage of relative unlocking, showing the characteristics of “high economic development, advanced industrial structure, high public participation, and high technological innovation.”

3 Method and Data 3.1 Variables and Data This paper used panel data from 30 provinces in China (excluding Tibet, Taiwan, Hong Kong, and Macao) between 2000 and 2019. The price took the deflator coefficient into account, and 2000 was considered as the base period (year 2000 = 100). Some of the missing data were filled in using linear interpolation and regression prediction methods. The specific variable settings are listed in Table 2 below. 3.2 Data Processing As for the carbon lock-in stage, we learn from Fiss [22] and use the average of the upper and lower quartiles as the threshold value for judging the high or low of each condition. In the causal analysis section of carbon lock-in, this paper employs the fsQCA method. This method, first proposed by Ragin [23], is one that combines the advantages of qualitative and quantitative methods. So far, the QCA method has been used extensively in various fields such as business [24], information [25], environment [26], and society [27]. The fsQCA method is adept at revealing the combination effects between the outcome and its drivers and making explicit the interaction effects between factors [28]. Referring to Fiss

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Y. Chen et al. Table 2. Variable description.

Factor

Variable

Explanation

Data source

Explained variable

Carbon intensity

Carbon/GDP

CEADs

Economy

Economic development

GDP per capita

China Statistical Yearbook

Industry

Industrial marginal benefits

Industrial cost margin

China Industrial Statistics Yearbook

Industry structure

Value added of tertiary industry/value added of secondary industry

China Statistical Yearbook

Industrial clustering

Location entropy value calculated by the employment share of each industry

Local Statistical Yearbook

Environmental regulation

Industrial pollution control investment /local GDP

China Statistical Yearbook on Environment

Public participation

Number of environmental China Environment letters per citizen Yearbook

Technological advances

Number of low carbon patents

Institution

Technology

Incopat, Derwent, and Patsnap database

[22], we transformed the raw data into fuzzy set scores using a direct calibration method where the anchor points were set to the upper and lower quartiles and their means. To ensure a sample size of more than 80% and robustness of results [29], we set the case threshold to 3, the original consistency threshold to 0.782, and the proportional reduction in inconsistency (PRI) to 0.671 . This paper uses fsQCA 3.0 software for analysis. 3.3 Robustness Tests We employed two types of robustness tests: Improving the raw consistency and PRI to enhance the determination threshold for configuration paths; Enlarging the case threshold to ensure the representative of configuration paths. Specifically, we first increased the raw consistency and PRI from 0.782 and 0.67 to 0.828 and 0.70, respectively and reran the operation; On the basis of this, we increased the case threshold from 3 to 4 (reaching 86% sample coverage). The recalculated results from the two settings are almost indistinguishable from the original conclusions, proving the robustness of our results. 1 The raw consistency benchmark of fsQCA analysis must be more than 0.8 accompanied by a

benchmark for PRI score of over 0.65; the higher the value, the more robust the solution.

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4 Results 4.1 Carbon Lock-In Trends We visualize the carbon lock-in stages in each of the 30 provinces from 2000 to 2019, as shown in Fig. 2. From the general historical trend, the state of carbon lock-in in each province is gradually changing from Carbon Attraction (stage 1), Carbon Solidification (stage 2), Carbon Lock-in (stage 3), Unlocking Transition (stage 4), to Relative Unlocking (stage 5) during 2000–2019. The first carbon lock-in was formed in Guangdong province in 2005, and some other provinces then gradually entered this stage. From 2010 to 2014, the country was at the peak of carbon lock-in formation. Carbon lock-in is occurring in an increasing number of provinces, probably due to China’s rapid urbanization and GDP growth-led development model over the past decades; From the perspective of carbon unlocking, the first provinces to reach relative unlocking status were Beijing and Shanghai in 2007. Then, many provinces began to enter unlocking status one after another from 2015 onwards, and 11 provinces achieved relative unlocking by 2019. The trend of carbon unlocking may be a consequence of the increasing national emphasis on carbon emission reduction since the 13th Five-Year Plan. Since the double reduction target of total carbon and carbon intensity was proposed, the strength of the policy is increasing in each province, and the carbon lock-in model is gradually being unlocked. Among the 600 samples, the first stage had the majority of the sample with 212 (35%), and the second stage had 177 (30%). In the last five years, the unlocking transition and relative unlocking stages have increased significantly, with a combined total of more than 50% during the five years. The results show that the development path of the provinces has become more decarbonized and sustainable compared to many years ago. China’s Policies and Actions to Address Climate Change published by the State Council states that China’s carbon emissions intensity in 2020 is 18.8% lower than in 2015, exceeding the binding target of the 13th Five-Year Plan; it is 48.4% lower than in 2005, exceeding China’s commitment to reduce CO2 emissions by 40%-45% by 2020. To show the spatial distribution characteristics and historical evolution of carbon lock-in stages, this paper maps the spatial distribution of carbon lock-in stages in 2005, 2010, 2015, and 2019 respectively, as shown in Fig. 3. In addition, we show the time points of unlocking transition and relative unlocking for provinces in Table 3. From the map, we can see that in 2005, most provinces were in stages 1 and 2 and only Guangdong province formed carbon lock-in (stage 3). In 2010, Guangdong Province, Shanghai and Beijing entered a relatively unlocked state; Most of the central and eastern regions and a few western regions were in a carbon lock-in state; The northeastern regions and most of the western areas were still in stage 1 or stage 3. In 2015, an increasing number of provinces were in the preliminary and relative unlocking stages, mainly located in the eastern coast and central and western areas. By 2019, the number of provinces in the preliminary unlocking and relative unlocking stages peaked at 11, mainly on the eastern coast. The seven provinces in the preliminary unlocking stage were mainly located in the central and western regions. Twelve provinces were still in the pre-carbon lock formation stage. It is noteworthy that these provinces in different stages have spatial contiguity characteristics.

2000 2 1 1 1 2 1 2 1 2 1 2 2 1 1 2 2 1 2 1 2 2 2 2 2 1 2 2 2 2 1

1

Beijing Tianjin Hebei Shanxi Inner Mongolia Liaoning Jilin Heilongjiang Shanghai Jiangsu Zhejiang Anhui Fujian Jiangxi Shandong Henan Hubei Hunan Guangdong Guangxi Hainan Chongqing Sichuan Guizhou Yunnan Shaanxi Gansu Qinghai Ningxia Xinjiang

Carbon lock-in

Fig. 2. The stage of carbon lock-in.

Stage 1

2001 2 2 1 1 2 1 2 2 2 1 2 2 2 1 2 2 1 2 2 2 2 2 2 2 1 2 2 2 2 1 2

2002 2003 2004 2 2 2 2 2 2 1 1 1 1 1 2 2 2 2 1 2 1 2 2 1 2 1 1 1 1 2 2 2 1 2 1 1 2 1 2 1 1 2 1 1 1 1 1 1 2 2 2 1 1 1 2 2 1 2 2 2 2 2 2 2 2 1 2 2 1 2 2 2 2 2 2 2 1 1 2 2 2 2 2 2 2 1 1 2 2 2 1 1 2 Stage 2

2005 2 2 1 2 1 1 1 1 2 2 1 1 2 1 1 1 1 1 3 2 1 1 2 2 2 1 2 1 2 1 3

2006 2007 2008 3 5 5 2 2 2 1 1 1 1 1 1 2 2 2 1 2 3 1 1 1 1 1 1 2 5 5 1 3 3 3 3 3 1 1 1 1 1 1 1 1 1 3 3 3 2 1 3 1 1 1 1 1 1 3 3 3 2 2 2 1 1 1 1 1 1 1 1 1 2 1 2 2 2 1 1 1 1 2 2 2 1 1 1 2 2 2 1 1 2 Stage 3

2009 5 2 1 1 2 3 1 1 5 3 3 1 1 1 3 3 1 1 5 2 1 1 1 2 2 1 2 1 2 2 4

2010 2011 2012 5 5 5 1 3 5 1 1 3 1 1 1 1 1 1 3 3 3 1 1 1 1 1 1 5 5 5 3 3 3 3 3 3 3 3 3 3 3 3 1 1 1 3 3 3 3 3 3 3 3 3 3 3 3 5 5 5 1 1 3 1 1 1 3 1 3 3 3 3 1 2 2 2 1 1 1 3 3 2 2 2 1 1 1 1 2 2 1 2 1 Stage 4

2013 5 5 3 1 1 3 1 4 5 5 5 3 3 1 3 3 3 3 5 3 2 3 3 2 2 3 2 1 1 2 5

2014 2015 2016 5 5 5 5 5 5 3 3 3 1 1 1 1 1 2 3 5 5 2 1 1 4 4 4 5 5 5 5 5 5 5 5 5 3 3 3 3 3 3 1 3 3 3 4 5 3 3 3 3 5 5 4 4 4 5 5 5 3 3 3 2 1 1 5 5 5 3 4 4 2 2 2 2 2 1 3 3 3 2 1 1 1 1 2 2 2 2 2 2 2 Stage 5

2017 5 5 4 1 2 5 1 1 5 5 5 3 4 3 5 4 5 4 5 4 2 5 4 1 4 3 1 1 1 2

2018 2019 5 5 5 5 4 4 2 1 1 1 5 5 1 1 1 2 5 5 5 5 5 5 4 4 4 5 4 4 4 5 4 5 5 5 4 4 5 5 4 1 2 2 4 5 4 4 1 1 4 1 3 4 1 1 1 1 2 1 2 2

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Fig. 3. The evolution maps of the carbon lock-in stages (2005, 2010, 2015, and 2019).

245

246

Y. Chen et al. Table 3. The time points of carbon unlocking provinces.

Province-time

Carbon attraction (Stage 1)

Carbon clustering (Stage 2)

Carbon lock-in (Stage 3)

Unlocking transition (Stage 4)

Relative unlocking (Stage 5)

2000

2006

/

2007

Relative unlocking Beijing

/

Tianjin

2000

2001

2011

/

2012

Liaoning

2000

2003

2008

/

2015

Shanghai

/

2000

/

2007

Jiangsu

2000

2002

2007

/

2013

Zhejiang

/

2000

2006

/

2013

Fujian

2000

2001

2010

2017

2019

Shandong

/

2000

2006

2015

2016

Hubei

2000

/

2010

/

2015

Guangdong

2000

2001

2005

/

2009

Chongqing

/

2000

2010

/

2014

Unlocking transition Hebei

2000

/

2012

2017

/

Anhui

2000

/

2010

2018

/

Jiangxi

2000

/

2015

2018

/

Henan

2000

/

2008

2017

/

Hunan

2000

/

2010

2014

/

Guangxi

2000

/

2012

2017

/

Sichuan

2000

/

2010

2015

/

Yunnan

2000

2002

/

2017

/

Shaanxi

2000

/

2011

2019

/

The spatio-temporal evolution of the carbon lock-in reveals that the eastern region is generally ahead of the central and western regions. After entering the 21st century, the eastern region was given the historical mission of high-quality development. For example, the State Council promotes the eastern region to take the lead in developing and upgrading and supports the construction of Shenzhen as a pioneering demonstration zone of socialism with Chinese characteristics. Along with rapid economic development, the eastern region is the first to enter the carbon lock-in due to its vast energy consumption. Since the eastern regions continue to pursue high-quality development, such as shifting away from high-carbon industries and developing science and technology, these regions lead the way to the relative unlocking stage. The central and western regions have been urbanizing rapidly in recent years and have become the fast-growing urbanization regions in China. These regions have been taking over the high carbon emission industries moved

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out from the eastern regions and using them as the pillar of local economic development, so most of the central and western regions were still in the carbon lock-in stage. 4.2 The Impact Pathway of High and Low Carbon This paper employs the fsQCA method for the high and low carbon intensity from 2000 to 2019. A total of nine high-carbon pathways (C1a-C7) and five low-carbon pathways (C8a-C10) were identified, indicating that the pathways leading to high or low carbon intensity are causally complex and equifinal. 4.2.1 High Carbon Intensity Pathways As shown in Table 4, the coverage of the nine high carbon pathways (C1a-C7) was 0.42, indicating that these pathways covered 42% of the overall sample, which shows that these pathways are representative. In addition, the consistency level of both individual solutions and the overall solution is higher than the minimum acceptable criterion of 0.75, indicating a high explanatory power of the results. For the first three paths C1a-C1c, the three conditions of high industrial marginal efficiency, strong formal institutions, and weak informal institutions are their common core conditions. There are 60 representative cases, such as Inner Mongolia in 2008 and Qinghai in 2009. For these regions, the government’s institutional constraints on carbon emissions are more influential than public awareness, making these regions attract more high-carbon industries, thus leading to more carbon emissions. Thanks to the strong dominance of the government over the environment, we categorize the three pathways as the Government driven types. Paths C2 and C3 are characterized by low marginal industrial efficiency, advanced industrial structure, low industrial agglomeration, and low technological innovation. It shows that the backwardness of technology may be the main reason for the high carbon intensity in the regions. These paths contain 18 cases, with representative cases including Chongqing in 2006 and Qinghai in 2019. Since lagging technological innovation is the core condition, we categorize these paths as Technological lag types. The common characteristics of paths C4 and C5 are low economic development, public participation, and technological innovation. These two pathways show that despite the high level of public participation in the environment, economic and technological development lag still leads to high carbon intensity. Therefore, we categorize them as the Development backward type. Paths C6 and C7 share high economic development and marginal industrial benefits, but their industrial agglomeration and public participation are not high. It can be seen that the high marginal efficiency of industries and the low public awareness of the environment may attract high-carbon industries, which leads to high economic development and carbon intensity. Therefore, we categorize it as an Extensive development type. 4.2.2 Low Carbon Intensity Pathways In addition to analyzing high carbon pathways, as shown in Table 5, we analyzed the pathways leading to low carbon intensity and obtained a total of 5 low carbon pathways

0.170 0.170 0.010 0.007 0.828 0.851 InnerMongolia08, Shanxi07, Qinghai09 Hebei14, Government driven

0.126 0.027 0.837 Gansu14 Shaanxi02

๪

๪

0.080 0.067 0.025 0.017 0.806 0.873 Chongqing06, Jilin17, Qinghai19 Jilin18 Technological lag 0.420

๪

๪

Ⴠ

Ⴠ

๪

0.059 0.088 0.014 0.042 0.883 0.814 Gansu18, Qinghai16, Guangxi19 Ningxia05 Development backward

๪

Ⴠ

๪

Ⴠ

Ⴠ

๪

๪

C5

๪

๪

๪

๪

Ⴠ

Ⴠ

C7

0.104 0.104 0.005 0.026 0.869 0.813 Shandong14, Fujian07, Shandong08 InnerMongolia13 Extensive development

Ⴠ

๪

Ⴠ

๪

Ⴠ

Ⴠ

C6

Type Solution coverage Solution 0.811 consistency Notes: Black circles indicate the high level (or presence) of a condition; circles with crosses indicate the low level (or absence) of a condition; large circles indicate core conditions; small ones, peripheral conditions; and blank spaces indicate “don’t care”.

Typical regions

Raw coverage Unique coverage Consistency

๪

Technological innovation

๪

Ⴠ ๪

๪

Public participation

๪

Ⴠ

Ⴠ

Environmental regulation

๪

๪

๪

๪

Industry clustering

Ⴠ

Ⴠ

Ⴠ

๪

Ⴠ Ⴠ

C4

C3

๪

๪

Industry structure

๪

๪

Ⴠ

C2

C1c

๪

Ⴠ

C1b

๪

Ⴠ

C1a

Marginal benefits

Economic growth

Configurations

Table 4. Configurations for high carbon intensity.

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Table 5. Configurations for low carbon intensity. Configurations

C8a

C8b

C8c

C8d

C9

Economic growth

●

●

●

●

●

Marginal benefits

●

●

⊗

●

⊗

●

⊗

●

Industry structure

⊗

Industry clustering Environmental regulation

⊗

⊗

⊗

Public participation

●

●

●

●

●

Technological innovation

●

●

●

●

⊗

Raw coverage Unique coverage Consistency Typical regions

0.227 0.005 0.841 Jiangsu19, Tianjing19

0.233 0.198 0.039 0.019 0.870 0.787 Beijing19, Guangdong08, Beijing18 Guangdong09 Social unlocking 0.380

0.119 0.006 0.901 Tianjing16, Tianjing17

0.094 0.054 0.788 Jiangsu03, Zhejiang04 Economy driven

Type Solution coverage Solution 0.827 consistency Notes: Black circles indicate the high level (or presence) of a condition; circles with crosses indicate the low level (or absence) of a condition; large circles indicate core conditions; small ones, peripheral conditions; and blank spaces indicate “don’t care”.

(C8a-C10). The high coverage (0.38) and consistency (0.827) of these pathways indicate that the findings are representative and have high explanatory power. The four pathways C8a-C8d share high economic growth, technological innovation, and public participation. The provinces involved in these pathways are at a high stage of economic and technological development, and the public has a strong awareness of environmental protection, so the government does not need to intervene too much. Therefore, we categorize them as the Social unlocking types. Path C9 has high economic development and public participation, but the levels of marginal industrial benefits, industrial structure, industrial agglomeration, and technological innovation are all at a low stage. The results show that these provinces did not focus on technological innovation and industrial structure optimization when developing the economy but on developing the secondary industry and adopted a rough development model. Therefore, we categorize it as the Economy driven type. It is found that all five low carbon pathways are characterized by high economic development and public participation, which indicates that economic development and public awareness in these regions have reached a high stage. It also demonstrates that economic development and public participation positively affect carbon reduction.

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In addition, comparing high and low carbon intensity pathways, the same conditions, such as technological innovation, are insufficient for achieving high or low carbon intensity. Various influencing conditions need to interact to achieve high or low carbon, indicating a causal asymmetry between carbon emission intensity and its influencing factors.

5 Conclusion and Implications Based on provincial panel data from 2000 to 2019, this paper employs the fsQCA method to determine the different carbon lock-in stages of each region during the 20-year period and the pathway analysis of high and low carbon intensity. The following conclusions were mainly obtained: (1) During the 20 years, the trend of carbon lock-in stages in China’s provinces is from carbon attraction, through carbon clustering, carbon lockin, unlocking transition, and finally to the relative unlocking stage; (2) The regions in each stage have spatial adjacency characteristics. The regions that have reached the relative unlocking stage are mainly concentrated in the eastern coastal regions, while the regions in the carbon pre-unlocking stage are basically concentrated in the central and western areas; (3) A total of nine high-carbon pathways and five low-carbon pathways were identified, which shows that the pathways leading to high or low carbon intensity are causally complex and equifinal. (4) We summarize two low carbon intensity paths (e.g., Social unlocking and Economy driven), which can provide lessons on low-carbon development strategies for the high-carbon regions. Recommendations are also draw according to the conclusions of this paper as follows: (1) China has achieved a successful decarbonization transition in the past 20 years, but individual differences in regional endowments have led to different carbon lock-in states. Therefore, each province should develop low-carbon energy technologies, such as wind power, water energy, and other renewable energy, and reduce the use of fossil energy following local conditions. For example, provinces with low-level industrial structures and backward technological innovation should decarbonize and upgrade traditional industries and increase the proportion of high-tech and lowcarbon industries. (2) In the process of carbon unlocking, a few provinces such as Shanxi and Inner Mongolia have been in the carbon attraction (stage 1) and carbon clustering (stage 2) and even appear to shift from stage 2 to 1, which represents that these provinces continue to undertake the transfer of high carbon industries after the carbon clustering stage, thus hovering between the two stages. Therefore, to achieve the dualcarbon goal, local government should adjust the preferential policies for high-carbon industries, such as raising the entry threshold for high-carbon industries, encouraging the transfer of foreign low-carbon industries, and developing local low-carbon industries. (3) The central government should implement the evaluation and assessment of the carbon lock status of each province and develop a reward and punishment system. Provinces that are the first to enter the unlocking phase can organize demonstration activities and share effective patterns and user experiences. Apart from that,

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provinces still in a carbon lock-in dilemma should emulate the successful experiences of other provinces. (4) The crucial positive role of public participation and technological progress can be found in the low-carbon pathways, so provinces need to build a social consensus on environmental protection. Acknowledgment. The authors wish to express their sincere gratitude to the PolyU Start-up Fund (Project No.: P0042633).

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The Influence of Real Estate Investment on Economic Development: From New Production Element Perspective Ben Pang, Rui Liu(B) , and Jingfeng Yuan Department of Construction and Real Estate, School of Civil Engineering, Southeast University, Nanjing, China {liu_rui,jingfeng-yuan}@seu.edu.cn

Abstract. The investment in real estate is an essential part of the regional economic development. In the digital economy, the data element has promoted economic development by accelerating information dissemination. Despite prior studies that have found that real estate investment can positively affect economic development, limited empirical studies explore this relationship based on new production elements perspective. In this study, the data samples are obtained from 31 provinces of China from 2006 to 2019. The OLS regression model is established to evaluate the influence of the investment in real estate on economic development and further examine the change of this kind of influence from regional heterogeneity and temporal heterogeneity aspects. This study demonstrates that the investment in real estate can influence the economy positively when the data element is involved in production. The influence of investment in real estate on economic growth in developed regions is lower than that of undeveloped regions. The influence was positive and significant before 2010, while the influence of the investment in real estate on economic development was negative after 2010. Besides, the data element has gradually become essential in driving economic growth. The government should control the investment in real estate and emphasize the value of data element to facilitate the real estate industry. Keywords: economic development · real estate · new production element

1 Introduction Fixed asset investment can provide impetus to promote economic growth [1]. As a pillar industry of the national economy, the investment in real estate industry has become a significant part of fixed asset [2]. From 2019 to 2021, the proportion of real estate investment is increasing in China from 25.7% to 26.7%. Reasonable investment in real estate can enhance the sustainability of the real estate industry and promote economic transformation [3]. Specifically, real estate investment is crucial in expanding domestic demand [4], enhancing urban infrastructure development [5], promoting enterprise innovation [6], and improving the level of financing and credit [7]. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 253–264, 2023. https://doi.org/10.1007/978-981-99-3626-7_20

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Many studies indicate that real estate investment is important for promoting the development of economy. For example, Y. Kong et al. found that real estate investment could directly promote China’s economic growth [8]. Many conducted studies from different dimensions to further explore how real estate investment can promote economic growth. P. Y. Lai argued that reasonable real estate investment could stabilize the national economy by regulating housing prices [9]. N. T. Nguyen et al. indicated that the real estate industry was capital-intensive and could improve the economy mainly through capital injection [10]. G. Zhang et al. held that the development of related industry chains could be promoted by the investment in real estate. Real estate investment promotes national economic growth through the investment multiplier effect [11]. T. Y. Liu et al. suggested that real estate investment can promote urbanization, thus promoting economic growth [12]. However, some studies pointed out that investment in real estate can weaken or even dampen economic growth. For example, Xu et al. argued that investment in real estate has caused a “blood-sucking effect” on some industries and led to an unbalanced industrial structure, which inhibited the development of other related industries [13]. S. Hwang et al. argued that the investment in real estate would inhibit the development of medium-sized and small enterprises [14]. F. J. Fabozzi et al. suggested that a speculative bubble could be created by blind investment in real estate, which may cause much capital to flood the real estate market [15]. There are continuing debates for the influence of investment in real estate on economic growth in different studies. Usually, prior studies explored the impact of investment in real estate on economic development from traditional production elements perspective [16]. With the advent of the digital economy, the way production elements exert their efficiency is constantly innovated [17]. In this way, data element has been regarded as a new production element [18]. Therefore, the economic benefits of investment in real estate need to be further examined scientifically. This study aims to reassess the economic benefits of investment in real estate when the data element is involved in production. Specifically, a theoretical framework including new production factors, investment in real estate, and economic growth is constructed. Then the OLS regression model is established to evaluate the influence of investment in real estate on the development of economy, and further explore the influence from regional heterogeneity and temporal heterogeneity aspects from new production element perspective.

2 Theoretical Framework In the neoclassical economic growth theory, capital, labor and technology are the three major production elements that affect economic growth [19]. As a form of capital investment, real estate industry investment mainly affects economic growth by injecting capital elements [10]. This study decomposes the capital element into real estate investment and investment in other industries to explore economic development. Economic growth can be expressed in the following forms. Y = F(A, Ktα , Klα , Lβ ) Y represents the total economic output. L represents the labor input. A represents the technical level. α is the elastic coefficient of labor output. β is the elastic coefficient

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of capital output. Kt represents investment in the real estate industry, and Kl represents capital investment other than in real estate. In the era of the digital economy, the data element, which contains huge potential and hidden value, has become an essential factor in facilitating the development of economy [20]. Unlike labor and capital, the data element interacts with other elements to generate value [21]. Specifically, the data element can facilitate the restructuring and upgrading of elements and enhance efficiency through reconfiguration [22]. Data-based management decisions can crack the problems of heterogeneous demand and decision-making errors faced by the traditional enterprise [23]. The productive efficiency of the data element is realized mainly through the superimposed enhancement effect on labor, technology and capita [21]. Therefore, the forms can be extended as follows, and the theoretical framework is shown in Fig. 1.   Y = F D, A, Ktα , Klα , Lβ The data element (D), the real estate industry investment (Kt ), other industrial investment (Kl ), labor input (L), and technical level (A) jointly exert production benefits. Real estate investment Investment in other industries composition Capital element Neoclassical economic growth theory

Labor element Technical element

Economic development

Data element

Fig. 1. The theoretical framework

3 Methodology 3.1 Model Establishment Linear least squares (OLS) regression is mainly adopted to solve for the values of parameters in linear relationships [24]. This method first assumes a linear relationship between the variables studied. Then, the series of empirical data was used to carry out statistical regression and obtain the estimator of the coefficient of the linear equation. The OLS regression is the most widespread tool in empirical analysis to explore the mechanisms of economic development, such as environmental regulation [25], government expenditures [26], trade balance effects [27], and employment [28]. This study chooses the OLS method to explore impact of real estate investment on the development of economy from new production element perspective.

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In the economic activities of real estate investment, the input of capital element is the main form of promoting economic development [29]. To study the influence of real estate investment on the development of economy from new production element perspective, the model constructed in this study is as follows: GDP = α + β0 Estait + βj Xijt + μ GDP represents economic development status. Esta represents real estate industry investment. In this study, Xj represents control variables, including other industrial capital element inputs, labor element inputs, technology level, data element, and government spending level. Meanwhile, α is a constant, β is the coefficient of each variable, i represents the province, t represents the year, and μ is the random disturbance term. 3.2 Variables and Data This study includes explained variable, explanatory variable, and control variables. Panel data of 31 provinces in China (2006–2019) is used, which are from the National Bureau of Statistics, China Investment Statistical Yearbook, and China Fixed Asset Investment Statistical Yearbook. The explained variable is economic growth. Gross National Product (GDP) is an important indicator for measuring the scale of economic development in countries worldwide [30]. This study selects GDP for measurement. Meanwhile, the explanatory variable is investment in real estate (Esta), which is the total investment amount of development activities of real estate development companies and other units, including unified construction, demolition and return of housing buildings, supporting service facilities, and land development works [31]. This study selects the completed amount of real estate investment for measurement. Besides, according to the theoretical framework in this study, economic development is affected by the industrial capital element, labor force, technology level, and data element. The following indicators are selected as control variables. Industrial capital element (asset). According to Chun, this study adopts the amount of fixed asset investment excluding real estate industry to measure this indicator [32]. Labor element (lab). The labor element can directly participate in production and manufacturing to create economic output. Based on S. Hong, the annual employment number was used to measure it [33]. The level of technology (tech). The technology level would affect productivity, thus improving economic development. Referring to H. Liu, the number of patents granted each year is used to measure the technology level [34]. Data element (digtal). The data element enhances the productivity of the original production elements in the form of “empowerment”. There is no scientifically valid and credible measurement method for the data element. According to A.V.Vavilina. The data element was measured by the development level digital infrastructure. In this way, the Internet access port is used to measure data element in this study [35]. Government expenditure level (govpay). The government expenditure level represents the degree of government regulation of the macroeconomy. Referring to J. Li, the ratio of government public budget expenditure to GDP was adopted to indicate the government expenditure level [36].

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4 Results and Discussion 4.1 Correlation Test Before the regression analysis of panel data, a correlation test is carried out first to avoid the problem of false regression in the empirical process. The correlation test avoids the pseudo-regression phenomenon caused by collinearity between the index data [37]. Variance inflation factor is selected in this study to test the correlation of each variable. After data calculation, the variance inflation factor of each variable is less than 10, indicating no obvious collinearity. 4.2 The Influence of the Investment in Real Estate on Economic Development at National Level The results based on OLS regression analysis are shown in Table 1. The coefficient of Eata is 0.111, indicating that real estate industry investment promotes economic development. Real estate investment involves multiple productions, such as land development, architectural design, construction, material procurement, and quality inspection, thus driving economic growth. The asset variable is 0.015 and significant at the 1% level, which is significantly lower than that of the investment in real estate (0.111). The real estate industry is related to many industries, which prompts the development of related industries, and drives economic growth. Meanwhile, the coefficient of the data element is 0.141, indicating that the data element cannot be ignored when analyzing economic growth. The data element plays an important in driving economic development. When the data element is involved in production, the results show that real estate investment positively affects economic development, which is consistent with Y.Kong and Huang [38, 39]. By contrast, Wu pointed out that investment in real estate inhibited economic development because the it could prevent the growth of the traditional economy [40]. It should be noted that this study explores the overall impact of investment in real estate on economic development from the perspective of new production elements. Specifically, the elements that are more in line with the digital economy (e.g., data element) are considered in the research model, thus improving the reliability of results. Table 1. The influence of real estate investment on economic development at national level variables

explanatory control variables variable

Constant term

Esta

asset

lab

tech

digtal

govpay

Cons

0.111***

0.015***

0.013***

0.487***

0.141***

-1.766***

(0.005)

(0.001)

(0.091)

(0.042)

(0.133)

18.308*** (0.729)

Influence coefficient (0.032)

Note: Robust standard errors are in brackets; *** means significant at 1% level, ** means significant at 5% level, * means significant at 10% level

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4.3 Regional Heterogeneity Analysis The country in this study is divided into three regions (the eastern China, the central China, and the western China) according to the regional division standards of the National Bureau of Statistics of China to explore the differences in different regions (see Table 2). The coefficients of Esta in the eastern China, the central China, and the western China are 0.057, 0.011, and 0.260. This indicates that the influence of investment in real estate is not the same across regions when considering the impact of data element on economic development. Among them, the highest coefficient is for the western China, followed by the eastern China, and the central China is the lowest. The development of western China is relatively backward, and its economic foundation is weak. The real estate industry is capital-intensive. It can attract an influx of capital. And capital can provide the impetus for economic growth in the western region. Therefore, the economic benefit of real estate investment in the western China is the most significant. The eastern China is more developed. The benefits of its capital investment are relatively saturated. Therefore, the real estate industry has the lowest economic efficiency in the eastern China. In the western China, the coefficient of Esta (0.260) is higher than that of asset (0.051). On the contrary, the Esta variable is 0.011 in the central China, which is smaller than that of asset (0.027). The coefficients of Esta and asset in developed eastern region are not significant. This indicates that as the economy develops, the marginal benefits of real estate investment diminish and the economic benefits of investment in other industries increase. The real estate industry has an agglomeration effect on capital [12]. It develops together with the economy. Economically developed regions are saturated with capital in the real estate industry. The capital of other industries is “bled out”. Therefore, the economic efficiency of other industries is higher than that of the real estate industry. Besides, the data elements become the driving point of the economy in all regions. The coefficients of digital in eastern, central, and western China are 0.122, -0.037, and 0.163. This means that the influence of the data element on economic development in each region are remarkable except the central. Meanwhile, the west region is more innovative and more capable of mining data value. In digital economy, the results of reassessing the economic benefits of investment in real estate are different from those of the past. During the period of 2006–2019, the influence of the investment in real estate on economic development in eastern China and central China are not significant, while the real estate investment in western China promotes economic development. It is inconsistent with Jing, who argued that the central and eastern China had the highest influence of the investment in real estate on economic growth, followed by the western China [12, 41]. This difference is mainly because the influence of each production element on economic growth are different over time. The research data collected by Jing is from 2000–2016. During this period, real estate was the primary means of driving economic development, especially in the developed regions. However, after 2016, with the advent of the digital economy, the data element is gradually replacing real estate investment as the motive force for economic growth in developed regions. This result indicates a significant temporal heterogeneity of the influence of investment in real estate.

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Table 2. The influence of real estate investment on economic development in each region Variables

East

Central

West

explanatory variable

Esta

0.057 (0.050)

0.011 (0.044)

0.260*** (0.064)

control variables

asset

0.006 (0.006)

0.027*** (0.008)

0.051*** (0.012)

lab

0.010*** (0.002)

0.010*** (0.002)

0.036*** (0.004)

tech

0.185 (0.118)

-0.956*** (0.362)

3.793 (0.544)

digtal

0.122*** (0.055)

-0.037 (0.069)

0.163* (0.089)

govpay

-2.813** (0.734)

0.192 (0.583)

-0.737*** (0.148)

Cons

23.446*** (1.848)

15.916*** (1.719)

6.442*** (1.189)

Constant term

4.4 Temporal Heterogeneity Analysis To evaluate the influence of the real estate investment on the economic growth in different period, this paper divides the 2006–2019 time period into three stages based on the fiveyear plan for real estate industry. The first stage is from 2006 to 2010. During the period of 2006–2010, the real estate industry was suffering extensive operation and growth, and the government intervened moderately in the real estate. The second stage is from 2011 to 2015. During the period of 2011–2015, the government transformed into refined and intensive management, and the overall policy was moderately strict. The third stage is from 2016 to 2019. During the period of 2016–2019, the government has further tightened the regulation of the real estate. Based on this, OLS regression was used to test the results under different period (see Table 3). At national level, the coefficients of Esta are 0.242, 0.057, and 0.034 during 2006– 2015. It shows that the influence of the investment in real estate on economic growth decrease significantly with the development of digital economy. The real estate can attract much capital. However, due to its large capital occupation and long turnaround time, the real estate industry will be overcapitalized. This will lead to less capital flowing into other industries, backward technological innovation and imbalance in industrial development. Ultimately, it inhibits economic development. Therefore, the economic benefits are no longer significant after 2016. The coefficient of Esta in the eastern China is 0.299 before 2010. The coefficient of Esta is not significant after 2010, demonstrating that the positive impacts of real estate investment on economic development only before 2010 in the eastern. Meanwhile, the coefficient of digital is 0.127 and 0.130, which are higher than the coefficients of Esta (0.064, -0.112). It demonstrates that the influence of real estate investment on driving

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economic development have declined. Data element becomes a new driving force to promote economic development. The coefficients of Esta in central China were 0.188 and -0.129 during the period of 2006–2010 and 2011–2015. After 2015, the influence of the investment in real estate is not significant. This indicates that the influence of investment in real estate on economic development decreases greatly in digital economy. Due to overdevelopment of real estate in the central China, the balance of the industry structure would be greatly affected by the much more capital pouring into the real estate industry. Before 2010, the economic benefits of the investment in other industries are not significant. During 2011–2015 and 2016–2019, the coefficients of asset are 0.034 and 0.014, which are higher than those of the Esta (-0.129 and 0). This indicates that investment in other industries can better promote economic growth after 2011. Besides, data element does not contribute to economic growth during 2006–2015, while the coefficient of digtal in central China was 0.179 after 2015. This indicates that the data element has not play a value until 2015 and it began to replace real estate investment to drive economic development since 2015. The realization of the value of data element requires mature technical conditions and market environment, which cannot be satisfied by the backward western China. Therefore, data element is less effective at promoting economic growth than capital, labor and technology in the central China. The coefficients of Esta in the western China are 0.523, 0.164, and 0.123 from 2006 to 2019, respectively, indicating that the investment in real estate industry promotes economic development in the western China since 2006, but this positive influence gradually decreases. The coefficients of aeest, lab, and tech are all significant during 2006–2019, indicating that capital, labor, and technology are all significant in driving economic growth. In the western China, real estate investment will attract much capital, thus promoting economic development. The variations of the influence of the production element in promoting economic growth demonstrate that the data element is gradually replacing capital as the key element driving economic growth in digital economy, which is consistent with Guo and Li [42, 43]. The results also show that the data element has become a driver of economic development. In the early stage of economic development, capital is one of the main elements that promote economic growth. Before 2010, real estate investment promoted economic development significantly in all regions, while the data element is less effective at promoting economic growth. It indicates that the capital element is more efficient at driving economic growth than the data element before 2010. With the development of the digital economy after 2010, the influence of investment in real estate on economic growth has declined and the data element gradually plays a dominant role in driving economic development. During 2010–2019, the real estate investment in the east and central will even inhibit economic development, while the influence of the data element in promoting economic growth changes from insignificant to significant. It indicates that data gradually replace capital as main driving force for economic development.

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Table 3. The influence of the investment in real estate on economic development in different time frames Variables

Time period

Explanatory variable Esta

National

East

Central

West

Control variables

Constant term

asset

lab

tech 0.924***

(0.067)

0.019 (0.014)

0.020***

digtal

govpay

Cons

(0.292)

0.375 (0.279)

-2.348***

(0.003)

(0.223)

12.498*** (1.052)

2011–2015

0.057* (0.032)

0.014*** (0.005)

0.008*** (0.002)

0.123 (0.084)

0.115* (0.066)

-2.287*** (0.136)

23.958*** (0.834)

2016–2019

0.034 (0.034)

0.009*** (0.004)

0.009*** (0.002)

0.167*** (0.097)

0.080* (0.047)

-2.116 (0.158)

26.855*** (0.977)

2006–2010

0.299*** (0.098)

0.002 (0.020)

0.012** (0.004)

0.415 (0.342)

0.285 (0.331)

-5.595*** (1.495)

20.321*** (3.243)

2011–2015

0.064 (0.051)

0.006 (0.006)

0.004* (0.002)

0.071 (0.098)

0.127 (0.078)

-6.115*** (0.872)

34.048*** (2.489)

2016–2019

-0.112* (0.062)

-0.001 (0.005)

0.009*** (0.002)

-0.155 (0.149)

0.130** (0.058)

-6.805*** (1.022)

42.009*** (3.095)

2006–2010

0.188* (0.103)

0.019 (0.022)

0.008** (0.003)

0.320 (1.502)

0.917* (0.429)

-3.365*** (0.763)

18.305*** (1.991)

2011–2015

-0.129** (0.038)

0.034** (0.008)

0.006** (0.002)

-1.392*** (0.277)

-0.050 (0.073)

-4.399*** (0.500)

31.282 (1.548)

2016–2019

-0.046 (0.034)

0.014* (0.005)

-0.001 (0.004)

-0.356 (0.252)

0.179 (0.111)

-4.633*** (0.786)

36.474*** (2.408)

2006–2010

0.523*** (0.185)

-0.001 (0.036)

0.045*** (0.005)

7.351*** (1.310)

1.391 (0.680)

-1.215*** (0.222)

2.238* (1.444)

2011–2015

0.164** (0.063)

0.023** (0.010)

0.029*** (0.004)

2.815*** (0.570)

0.251* (0.133)

-1.295*** (0.146)

12.918*** (1.357)

2016–2019

0.123** (0.057)

0.022* (0.013)

0.028*** (0.006)

1.475*** (0.451)

-0.012 (0.092)

-1.289*** (0.170)

17.769*** (1.513)

2006–2010

0.242***

5 Conclusion Investment in real estate is an essential part of the regional economy. The data element has become a new driving factor for economic growth in the digital economy. Reconsidering the influence of data elements on the real estate industry is necessary for sustainable development. This study is intended to study the influence of real estate investment on driving the growth of economy from the perspective of new production elements. A theoretical framework including new production factors, investment in real estate, and economic growth is constructed. Then, the OLS regression model is established to evaluate the influence of real estate investment, which is been further examined from regional and temporal heterogeneity aspects by considering the new production elements. The specific conclusions are as follows. First, from 2006 to 2019, the investment in real estate promoted economic growth when the data element is involved in production elements. Second, before 2010, the influence of real estate investment on economic development in the eastern China and central China are significant, while after 2010, with the development of the digital economy, the results were reversed. In the western China, the investment in real estate has significantly drove economic development since 2006.

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Third, after 2010, the influence of investment in real estate on economic development has decreased significantly, and data element gradually replace capital element as main driving force for economic development. In practice, the government should reasonably control the scale of the investment in real estate. Meanwhile, utilizing data elements in the real estate industry is imperative. Acknowledgment. Funding agencies: The article is funded by the National Natural Science Foundation of China (contract no.: 72134002) and the Research and Practice Innovation Program of Postgraduate in Jiangsu Province (contract no.: KYCX22_0319).

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A Study of the Relationship Between Psychological Capital and Unsafe Behavior of Construction Workers Wenmin Gao1 , Xiaoli Yan1(B) , and Hongyu Chen2 1 School of Management Studies, Shanghai University of Engineering Science,

Shanghai 201620, China [email protected] 2 No.1 Junior High School in Zhouji Township, Shangqiu Demonstration Area, Shangqiu, Henan Province, China

Abstract. Many studies show that individual unsafe behavior is the leading cause of construction safety accidents, of which 94% of accident-induced causes are related to unsafe human psychology. Psychological capital, as a unique psychological resource of individuals, plays an important role in motivating individuals to implement safe behaviors. Therefore, it is essential to explore the influence of construction workers’ psychological capital on unsafe behaviors, which helps to control their unsafe behaviors and provides strong guidance for construction enterprises. In addition, the psychological capital of construction workers is one of the important endogenous factors affecting unsafe behavior. This research explores the relationship between construction workers’ psychological capital and unsafe behaviors. It carried out surveys of construction workers in Shanghai by questionnaires. The psychological capital, safety attitudes, and unsafe behaviors of construction workers were used respectively as exogenous, meditating, and endogenous variables to construct the structural equation model. The following conclusions were obtained: first, the three dimensions of self-efficacy, hope, and resilience significantly affect unsafe behavior, and the relationship between optimism and unsafe behavior is not significant; second, self-efficacy, optimism, hope, and resilience have significant effects on safety attitudes; third, self-efficacy, optimism, hope, and resilience indirectly affect construction workers’ safety attitudes by influencing construction workers’ unsafe behaviors; finally, we propose management countermeasures to improve the workers’ psychological capital and safety attitude based on the research results. Keywords: Psychological capital · unsafe behavior · safety attitude · SEM

Funded by Shanghai 2020 “Science and Technology Innovation Action Plan” Soft Science Key Project “Key Issues and Countermeasures for Digital Technology (20692101300). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 265–279, 2023. https://doi.org/10.1007/978-981-99-3626-7_21

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1 Introduction Construction offers employment opportunities for millions of workers worldwide (Rostami et al., 2015), but it is one of the most dangerous industries and causes numerous casualties in every country (Skibniewski, 2014). There is a compelling and urgent need to improve the construction industry’s safety worldwide (Kamardeen, 2013). The construction industry is China’s national economic industry but also is China’s second most high-risk industry after the mining industry. The factors such as the complexity of the construction site, the mobility of construction workers, and the hardship of the construction environment make the safety accident rate in the construction industry high, and the construction safety situation is difficult. Although science and technology have been advancing, in recent years, safety supervision and accident warning technology on construction sites have been greatly improved. However, the trend of frequent construction accidents has not been curbed. There are intensive accident causation factors on construction sites, including workers, materials, and equipment (Haslam et al., 2005; Wu et al., 2010). Due to the high accident rate, unsafe worker behavior contributes to more than 80% of accidents (Choi et al., 2017; Willamson and Feyer, 1990). It shows that unsafe human behavior is the main cause of safety accidents. Therefore, for the frequent occurrence of safety accidents, the active control and management of unsafe behaviors of construction workers must be the work focus. About 80% of China’s construction workers are migrant workers with a low education level and weak safety awareness. They do not pay enough attention to observing safety procedures for construction, often ignoring the risks of unsafe behavior. Construction workers work outdoors and in a complex environment, with more areas prone to falls and collisions. Workers always need to pay attention to avoid accidents, and working in such an environment for a long time can easily cause fear, anxiety, and other emotions. The existing training system for construction workers is not perfect. Shortage in effective safety education and practical experiences for construction workers make them unable to operate in a standardized manner, not know the severe consequences of non-standardized operation, ignore the safety hazards during operation, or have a common preconception of safety risks, which leads to safety accidents. It can be seen that the outside world easily influences the psychological state of construction workers to produce bad psychology, which leads to the generation of unsafe behavior. In the previous studies on psychological capital, mainly in the fields of education, family, medicine, and mining, there are very few studies on psychological capital for construction workers. The research on the influence of construction workers’ unsafe behavior mainly focuses on factors such as safety atmosphere and safety culture. The research on individual psychological factors affecting unsafe behavior is less and not deep enough. Therefore, it is crucial to explore how to reduce the unsafe behaviors of construction workers from the perspective of psychological capital. Miao (2006) found that among the factors that cause accidents, 94% of them are related to poor psychological state. Psychological states control human behavior, and safety problems arising from unsafe human behaviors still need to rely on human initiative to solve them. Improving workers’ psychological capital has now become a means to improve safety performance in many enterprises. Based on this, this paper subdivides

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psychological capital into self-efficacy, hope, optimism, and resilience, using questionnaires to obtain data and SEM to explore the influence relationship between psychological capital, safety attitude, and unsafe behavior. Besides, this paper also explores the mediating role of safety attitude between psychological capital and safety attitude and proposes corresponding management countermeasures. This study can expand the theory of construction workers’ unsafe behaviors and contribute to the research and deepening of the management system of construction workers’ unsafe behaviors.

2 Theoretical Basis and Research Hypothesis 2.1 Psychological Capital The famous economist Goldsmith introduced the concept of psychological capital (1997). In his study of the impact of employees’ psychological capital on their wages, he attributed psychological capital to the characteristics that affect individual productivity. Psychological capital is a positive state or competency that can be assessed, developed, and changed during personal growth and development (Luthans et al., 2007). It has been widely used to study its relationship with employee behavior, work engagement, and job performance (Avey et al., 2010; Madrid et al., 2018). Psychological capital is a multidimensional structure. There is no consensus about the structure of its measurement, and its dimensional structure varies from two to five dimensions (He et al., 2019). Among them, the most widely adopted structure is the four-dimensional structure that includes dimensions of self-efficacy/confidence, hope, resilience, and optimism (Luthans et al., 2004). Several scholars have discussed the conceptual and empirical commonalities among the four elements of psychological capital (hope, self-efficacy, resilience, and optimism). The psychological construct of self-efficacy is defined as confidence in successfully performing strenuous activities. Hope is persevering with goals and, if necessary, reselecting strategies and ways to reach them. Optimism is the positive attribution of success and the expectation that good things will come. Finally, resilience is the ability to continue and try to get out of challenges and recover quickly when difficulties and failures are encountered. Luthans et al. (2007) argue that instead of assessing each factor’s contribution to motivating employees and promoting organizational goals, the sum effect of the four elements better explains behavioral inequalities. Therefore, this study divides the psychological capital of construction workers into self-efficacy, hope, resilience, and optimism to develop a research model. 2.2 Safety Attitude As an essential psychological reflection of the individual, the safety attitude of workers directly impacts their behavior. The cognitive-evaluative theory proposed by Lazarus and Folkman (1984) argues that when an individual is subjected to the effects produced by a stressor, whether the individual will be stressed or not is determined mainly by the individual’s cognitive evaluation and coping processes. Cognitive appraisal is the cognitive process by which an individual perceives whether an external environment or

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situation will affect the individual. When construction workers with high psychological capital face stress, they can adopt a safe attitude to resolve the stress and thus reduce unsafe behaviors when facing stress because of their optimistic attitude and high level of self-efficacy. Wang et al. (2016) studied the relationship between organizational safety management and miners’ safety performance in coal mines. They found that miners’ safety attitudes were significantly related to unsafe behaviors. All this affirms that safety attitudes are an essential factor influencing the safety behavior of construction workers, so this paper intends to explore the relationship between psychological capital and unsafe behavior from the perspective of safety attitudes. 2.3 Unsafe Behavior Unsafe behavior is an essential indicator of employees’ work status because people are complex animals. Their behavior, industry, and worker groups, are related, so it has not yet been a unified concept. Reason (1990) believes that human error is unsafe human behavior. Domestic and international scholars have presented their understanding of unsafe behavior based on different perspectives, as shown in Table 1. Neal and Griffin (2000) divided unsafe behavior into safety non-participation and safety non-compliance. Safety non-participation refers to employees who do not actively participate in the work process regarding safety management, such as not actively providing safety support and assistance to workers, not actively learning new safety knowledge, etc. Safety noncompliance refers to employees who cannot strictly comply with safety regulations and safe operating procedures, etc. Subsequent scholars are more inclined toward the concept of unsafe behavior proposed by Neal, so this paper adopts the understanding of Neal and Griffin (2000) and defines construction workers’ unsafe behavior as safety nonparticipation behavior and safety non-compliance behavior of construction workers on construction sites. 2.4 Theoretical Mechanisms and Research Hypothesis 2.4.1 Psychological Capital and Safety Attitude The cognitive-evaluative theory proposed by Lazarus suggests that when an individual is subjected to the effects generated by a stressor, whether the individual becomes stressed or not is determined primarily by the individual’s cognitive appraisal and coping processes. Cognitive appraisal is the cognitive process by which an individual perceives whether an external environment or situation will affect the individual (Lazarus, 1991). When construction workers with high psychological capital face stress, they can adopt safe attitudes to diffuse the stress, thus reducing unsafe behaviors. In summary, construction workers’ psychological capital impacts their safety attitudes. 2.4.2 Psychological Capital and Unsafe Behavior Bandura’s (1991) social cognitive theory states that behavior is not only altered or influenced by the environment but can also be influenced by an individual’s psychological cognition, which is partially dependent on the individual’s characteristics. Psychological capital is a unique resource based on an individual’s positive psychological state

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Table 1. The meaning of unsafe behavior Authors

Meaning

Reason (1990)

Unsafe human behavior consists of two categories: error and negligence. Mistakes are the difference between intention and result; negligence is the deviation between the actual behavior and the plan

Misumi (1993)

An act that has caused or is likely to cause a safety accident in the future

Zhou and Cheng (2008) Human error is a broader concept than unsafe human acts. Human error can occur in all processes of work and all types of workers. An unsafe human act is a particular case of human error Wu (2009)

An unsafe act is considered to be an act that directly causes or extends the loss of a safety accident during construction safety

Neal and Griffin (2000) The unsafe behavior is divided into safety non-participation and safety non-compliance. Safety non-participation refers to employees who do not actively participate in the work process regarding safety management, such as not actively providing safety support and assistance to workers, not actively learning new safety knowledge, etc. Safety non-compliance refers to employees who cannot strictly comply with safety regulations and safe operating procedures, etc

(Bagozzi, 1980). Therefore, individuals’ unique ability helps them produce positive behaviors and achieve acceptable performance. As a salient marker of an individual’s psychological condition, it can be argued that unsafety behavior - one of the components of work-related behavior - can also be influenced by a workers’ psychological capital. Based on this, researchers have conducted studies. Jason et al. (2019) explored the relationship between psychological capital and unsafe behaviors in the workplace using cynicism as a mediating variable. Saleem et al. (2022) also explored the role of psychological capital and job engagement in construction workers’ safety behavior. Wang et al. (2018) investigated the effect of psychological capital on construction workers’ safety behavior using survey data from 352 respondents in the Chinese construction industry, using safety motivation as a mediating variable. The empirical results showed that workers’ psychological capital directly and positively impacted safety compliance and participation. In summary, construction workers’ psychological capital impacts their behavioral choices. 2.4.3 Safety Attitude and Unsafe Behavior Cognitive evaluation theory believes that an individual’s attitude significantly influences their behavior choice. An individual’s attitude belongs to the individual’s psychological state, and behavior choice is the externalized form of this psychological state (Lazarus, 1991). Safety attitude is the psychological state that construction workers judge how they can avoid safety accidents after being stimulated by the outside world in an adverse situation. If construction workers have a strong safety attitude, a clearer understanding of the consequences of unsafe behavior, and the necessary safety precautions, they can

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avoid safety accidents. The attitude-behavior process model, which states that attitudes can describe and predict behavior (Šeibokait˙e et al., 2022) has been widely used in the study of safety behavior. Li et al. (2019) used the Coal Miner Safety Attitude Scale and the Safety Behavior Scale to analyze the influence of safety attitudes on safety behavior and to study the correlation between the four dimensions of safety attitudes and the two safety behaviors. Niu and Zhao (2022) introduced two variables, situational awareness, and task complexity, established a mediated model with moderation to explore the path of safety attitudes on human errors based on the Attitude-Behavior Model. In summary, construction workers’ safety attitudes impact their behavioral choices. 2.4.4 Research Hypothesis This paper subdivides psychological capital into four dimensions of self-efficacy, hope, optimism, and resilience based on previous research. It then uses construction workers’ safety attitudes as a mediating variable to investigate the effect of individual construction workers’ psychological capital on individuals’ safety attitudes and unsafe behaviors. The specific hypothesized relationships are shown in Fig. 1.

S e lf- e ffi c a c y H1a

H2a

Hope

a

S a fe ty tu d e

H1b H3a

H5

H2b H4a

Op

m is im

H3b

U n sa fe b e h a v io r

H4b

R e s ilie n c e

Fig. 1. The hypothesis of the relationship between psychological capital, safety attitudes, and insecure behavior

(1) Self-efficacy is an individual’s subjective judgment that he or she can complete a specific behavior. When an individual is in an adversity situation, the self-efficacy level greatly influences the individual’s behavioral choices. Individuals with high selfefficacy can actively mobilize their positive inner factors, maintain the best state to meet challenges, and have a higher possibility of successfully solving problems when facing difficulties. Accordingly, the following hypotheses are proposed. H1a: Self-efficacy is significantly and positively related to safety attitudes. H1b: Self-efficacy is significantly and negatively related to unsafe behavior.

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(2) For construction workers, the open-air working environment is harsh, and the daily work is repetitive, irritating, and tasteless. Moreover, working in a hot or cold environment challenges the construction workers’ bodies and minds. Maintaining an optimistic attitude during such long hours of repetitive and tedious work can alleviate workers’ negative emotions and, to some extent, prevent construction workers from negatively adopting unsafe behaviors to perform their work. Hence the following hypothesis is proposed. H2a: Optimism is significantly and positively related to safety attitudes. H2b: Optimism is significantly and negatively related to unsafe behavior. (3) Construction workers full of hope in their hearts tend to be more motivated to participate in their work and have a stronger will to achieve success. They will be more willing to participate in various safety meetings, comply with safety rules and regulations, and set clear safety goals for themselves when working. When they encounter difficulties at work, they can persevere to accomplish their goals and meet the difficulties with the most enthusiasm. Accordingly, the hypotheses are formulated. H3a: Hope is significantly and positively related to safety attitudes. H3b: Hope is significantly and negatively related to unsafe behavior. (4) Resilience is the ability to cope and adapt effectively in the face of difficulty or adversity. Resilience helps construction workers emerge from construction accidents stronger and helps them adapt to their work environment and work safely in a positive state. Furthermore, resilient construction workers can withstand blame and advice from their managers after making mistakes and actively correct their mistakes to avoid unsafe behaviors. Thus the hypotheses are as the following. H4a: Resilience is significantly and positively related to safety attitudes. H4b: Resilience is significantly and negatively related to unsafe behavior. (5) Safety attitude is one of the personal attitudes, and it acts as an internal psychological motivation for the employee, which triggers the corresponding behavior. In the process of work, construction workers will react to external stimuli. If they have good psychological capital, the attitude will develop in its desired direction, affecting the individual’s behavioral choices. Thus the hypothesis is proposed: H5: Safety attitudes are significantly and negatively related to unsafe behaviors.

3 Research Design and Methodology 3.1 Scale Design and Data Collection Based on the relevant literature and this paper’s research objectives, this study uses the indicators recognized by scholars, drawing on mature domestic scales. After being selected by experts and scholars in the construction industry, the following indicators were selected. For psychological capital, four dimensions of self-efficacy X1 , optimism X2 , hope X3 , and resilience X4 were measured, where each dimension was four question items, respectively; for safety attitudes, three indicators were measured by cognitive Y1 , affective Y2 , and behavioral intention Y3 and for unsafe behaviors two indicators were measured by safety non-compliance W1 and safety non-participation W2.

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Four academic domain research experts discussed this study’s questionnaire. The researchers met with four staff members to assess the validity of the content by discussing the questionnaire with them and observing whether they were able to understand the written content. In addition to basic information such as age, gender, marital status, education level, and length of service, this questionnaire included 16 items related to psychological capital, 12 items related to safety attitudes, and 9 items related to unsafe behaviors. The questionnaire was scored using a Likert-5 scale (1 = strongly disagree; 2 = disagree; 3 = not sure; 4 = agree; 5 = strongly agree). With the assistance of the Shanghai Songjiang District Construction Project Quality and Safety Supervision Station, several construction projects under construction, covering all types of construction workers as far as possible, were selected for formal research. The questionnaires were given to the workers, and were usually filled in between breaks and during meal times. Appropriate assistance was given to them in filling out the questionnaire so they could respond. Anonymity was the most important aspect, and respondents were assured of the privacy and anonymity of the responses they gave, as well as the academic purpose of the study. As a result, 500 questionnaires were distributed, and 476 were collected, with a recovery rate of 95.20%. After eliminating 124 invalid questionnaires, we finally obtained 352 valid questionnaires with a valid recovery rate of 73.95%. Respondents’ ages ranged from 20 to 59 years old. Regarding work experience, most respondents (72%) have been in their current job for more than five years. In addition, most respondents hold a high school education or less, while only 11% have a college degree or higher. 3.2 Scale Reliability Test The data were first analyzed for reliability through spss24.0 to ensure the validity and reasonableness of the questionnaire. The overall reliability coefficient Cronbach’s α value of this measurement questionnaire was 0.852, as shown in Table 2. According to the differentiation criteria of reliability, the reliability of each variable is greater than 0.8, and the overall scale reliability coefficient is more significant than 0.8, indicating that the reliability of this questionnaire is good. The scale’s validity was tested using KMO and Bartlett’s spherical test chi-square value, where Bartlett’s spherical test chi-square value was 1009.324, and the KMO value was 0.771, which was significant at the level of 0.000, indicating that it is suitable for factor analysis.

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Table 2. Reliability analysis of the questionnaire Variables

Cronbach’s alpha

KMO

self-efficacy

0.911

0.792

optimism

0.934

0.753

wish

0.929

0.821

resilience

0.892

0.788

Security attitude

0.882

0.904

Unsafe behavior

0.901

0.845

Total

0.853

0.771

4 Results and Discussion 4.1 Fitting of the Model After an initial reliability analysis of the model, structural equation modeling (SEM) of psychological capital, safety attitudes, and unsafe behaviors was developed using AMOS 20.0 based on the research hypotheses above. Structural equation models are divided into measurement models and structural equations. The measurement model is the model that describes the relationship between latent variables and indicators. Latent variables are not directly observable, but indicator variables are, so the level of latent variables can be indirectly measured in SEM using indicator variables. The measurement equation can usually be written as follows For the relationship between indicator variables and latent variables. x = x ξ + δ

(1)

y = y η + ε

(2)

where x denotes a vector consisting of exogenous indicators, y denotes a vector consisting of endogenous indicators. x denotes the relationship between exogenous indicators and exogenous latent variables. y denotes the relationship between endogenous indicators and endogenous latent variables, and δ and ε denote measurement error. The following structural equation is usually written for the relationship between latent variables. η = Bη + ζ + ζ

(3)

where η denotes the endogenous latent variable, ξ denotes the exogenous latent variable, and B denotes the relationship between the endogenous latent variables.  denotes the effect of the exogenous latent variable on the endogenous latent variable, and ζ denotes the residual term of the structural equation. The psychological capital of construction workers, including self-efficacy, optimism, hope, and resilience, is the exogenous latent variable of the model with 16 measures.

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Safety attitude is the mediating latent variable with 12 measures. The endogenous latent variable is unsafe behavior with nine measures. The main fit indicators of the model are shown in Table 3, and it can be seen from the table that the model has a good overall fit. Table 3. Goodness-of-fit analysis of structural equation models for psychological capital, safety attitudes, and unsafe behavior Targets

X2 /df

GFI

AGFI

NFI

CFI

RMSEA

Standard Model

1–3

>0.9

>0.9

>0.9

>0.9

0.9

>0.9

>0.9

30

>30

>30

>30

/

/

/

Aiming at the problems existing in the original lighting design, the optimization design is carried out from three aspects: illuminance, uniformity and glare. After simulation calculation, it is found that increasing the power of lights can make the illuminance meet the requirements of healthy lighting, but cannot improve the glare problem. After continuously improving the height and background brightness of lights, the effect of improving glare is not obvious. Therefore, in order to make the lighting design meet the health requirements, the optimization scheme of replacing lights is adopted. Therefore, in order to make the lighting design reach the health standard, the optimization scheme in the form of replacing lights is adopted. The specific contents are shown below. New scheme: in consideration of health and low carbon, further optimize the lighting design of the room, including the form, installation position and power of lights. Based on the optimization of bedrooms, living rooms and dining rooms in original scheme, the kitchens, toilets, corridors and other rooms that exceed the specification requirements are replaced with low-power lights for energy conservation. The specific light layout of new scheme is shown in Fig. 3. 1. In the dining room and living room, the original 5 W bulb is replaced by 16 W ceiling light, and the installation height is changed from 2.4 m to 2.9 m. The change of light form and the increase of installation height can improve the glare problem; 2. In the bedroom, the original 5 W bulbs are replaced by 16 W ceiling lights, and the installation height is changed from 2.4 m to 2.9 m to improve the illumination, glare and uniformity of children’s room and elderly room. However, due to the large space of the master bedroom, although the illuminance and glare meet the requirements, the uniformity cannot be improved. Therefore, add a 10 W ceiling light at the bay window, and the uniformity becomes 0.47; 3. The 16 W flat light is reserved in the kitchen, and the light strip has little effect on lighting, so it is recommended to cancel it; 4. The 16 W flat light is replaced by 7 W ceiling light in the secondary toilet, and the 10 W ceiling light in the main toilet. The price and power of the flat light in the original design are high, so the selection of ceiling light is not only energy-saving but also low cost; 5. Reduce the number of 7 W spotlights from 5 to 2 in the corridors; 6. Replace the 18 W ceiling light on the balcony with 7 W ceiling light. The detailed illuminance distribution of the room can be seen from the Isoilluminance curve in new scheme in Fig. 4. It can be seen from Table 2 that the illuminance

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and uniformity ratio of illuminance in new scheme are significantly improved, and the unified glare rating is also reduced.

Fig. 3. Light layout optimization in new scheme

Fig. 4. Iso-illuminance curve diagram of new scheme

Table 2. Simulation results of new scheme

E2 (lx) (U0 ) UGR

Master bedroom

Children’s room

Elderly room

Guest bedroom

Living room

Kitchen

Dining room

Main toilet

81.1

104

91.3

94.9

107

190

166

129

0.47 21.6

0.57 20.5

0.47 20.4

0.57

0.55

0.52

0.65

/

20

20.2

/

/

/

The optimized scheme not only meets people’s health lighting needs, but also saves energy. The design focus of these places will be described below. The lighting design of the kitchen is more functional than decorative, so the light strip that has little impact on the lighting is cancelled, and auxiliary lighting can be added at the console. The kitchen is a place for family cooking and emotional communication, so the lighting system is required to be practical, safe, beautiful and bright. The optimized kitchen is more suitable for daily needs and avoids energy waste. It is necessary to select the appropriate lighting mode according to the lighting characteristics of the toilet and the changes of human behavior. There are three typical lighting modes in living rooms in China: Central lighting, ambient lighting and key lighting [7]. In most cases, the householder only turns on the central lighting, and the central light plays a very important role in the lighting of the living room, which must meet the requirements of illuminance, color temperature, color rendering, etc. Therefore, in the optimal design, the living room is replaced by a high-power ceiling light to make the light more uniform and meet the requirements of function and illuminance. In new scheme, the installation position of lights and the form of lamps are reconsidered to reduce the impact of glare in the bedroom and living room. After optimization, the illumination, uniformity ratio of illuminance and uniform glare rating of the new scheme meet the functional requirements. In the design of health lighting, we should not only consider some physical parameters, but also consider other actual situations, such

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as the consideration of bedroom users, the use of ambient light in the living room, etc., which are more in line with the requirements of health lighting. 3.3.2 Low Carbon and Energy Saving Optimization According to Standard for building carbon emission Calculation GB/T 51366-2019 [6], it can be found that the carbon emission factor of electric power is 0.5271 in Guangdong province, and the lighting time theory of LED life cycle is 50000 h, the loss coefficient of lights α can be taken according to the actual situation of lights, and the design value of  lights shall be used in this calculation α = 1, and then using the forα ni=1 EF×Pi×Ti . After calculation, the details are shown in Table 3, through mula Cc = 1000A optimization, the energy-saving rate of new scheme is 16%. Considering health and low-carbon energy conservation, new scheme is better than original scheme. Table 3. Comparison of carbon emission and energy saving reduction rates of different schemes Original lighting scheme

New scheme

Total power

220.6 W

185.4 W

Carbon emission per unit area

56.5 kgCO2 /m2

47.4 kgCO2 /m2

Project carbon reduction

/

1583.96t

Energy saving rate

/

16%

3.3.3 Cost Optimization On the basis of meeting the requirements of health and low-carbon energy conservation, cost factors should also be taken into account. The number of lighting fixtures used in the original design is 20, and the total cost of 103 m2 house type is 1370 ¥. After optimization, the number of lights is reduced to 16, and the total cost is 1075 ¥, reducing the cost by 21.5%; The total cost of the original lighting design of the project is about 2.94 million ¥. Which can reduce the cost by 0.63 million ¥. New scheme takes into account the requirements of health and energy conservation, and also achieves the purpose of saving investment and lighting energy consumption. New scheme reselects lights suitable for their functional requirements for light source and power, reduces the number of lights, and also reduces the total lighting power and lighting power density, so as to save investment and lighting energy consumption. New scheme is the best scheme to be recommended, which reduces the cost and energy consumption, and takes into account the requirements of healthy lighting.

4 Discussion This lighting design project has created a good lighting effect through optimization design, that is, a reasonable lighting design can meet the needs of healthy lighting, save costs and resources, and also verify that a reasonable lighting design can achieve the

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purpose of health, energy saving and money saving. However, in the actual promotion, energy conservation in the field of residential lighting may encounter some difficulties. The main reasons are as follows: 1. Lack of professional lighting designers Few colleges and universities in China offer majors related to lighting design. In order to avoid this confusion, the state should pay attention to publicizing the relevant requirements of lighting design and pay more attention to the training of relevant practitioners. 2. The public lacks awareness of lighting energy conservation Due to the influence of culture, education, industrial development and other factors, most residents have no scientific understanding of lighting, and pay more attention to the choice of lamp appearance. Therefore, it should be noted that residents should popularize the knowledge of lighting energy conservation. When there is an inappropriate lighting environment, it can be transformed into a more energy-saving and qualified lighting environment.

5 Conclusions Through the practice of the lighting design project in Foshan, Guangdong Province, we have learned that the existing problems in residential lighting, such as insufficient implementation of the standards, improper selection of light sources and so on, which will have an adverse impact on human health, such as visual system problems such as myopia, biological rhythms and other problems, leading to potential diseases. Therefore, the lighting project practice adopted schemes to optimize the lighting design in terms of health, low-carbon and cost, explored the carbon emission calculation of the whole life cycle of healthy lighting design and light lighting, and summarized the flow chart of residential lighting carbon emission calculation, so as to provide healthy lighting design methods for lighting designers and owners and create a healthy low-carbon lighting environment for people, It also provides ideas for future health lighting design. Due to the complexity of lighting design and the diversity of space, lighting simulation cannot fully consider all the problems of lighting design, but this study has found the existing problems in the field of residential lighting, and the calculation of carbon emissions is only a rough calculation method, which will continue to deepen in the future research. Acknowledgements. This research is financially supported by Practical Research on Low Carbon and Healthy Building System in Fujian and Guangdong for China Construction Fangcheng Investment& Development Group Co, Ltd. (CSCECFC-2022-KJZX--06), Epidemic Prevention System Standard and Consultation for Shenzhen Ping An’s Yinian city project (F202205130008), Research On Intelligent System of Healthy Residential Buildings for Smart Building Research Center of China Real Estate Association, and Research and Verification of Epidemic Prevention System of Residential Buildings (Shenzhen science and technology plan project in engineering construction field in 2021).

Declaration of Competing Interest. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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References 1. Pompei, L., Blaso, L., Fumagalli, S., et al.: The impact of key parameters on the energy requirements for artificial lighting in Italian buildings based on standard EN 15193-1: 2017. Energy Build. 263, 112025 (2022) 2. Ghisi, E., Tinker, J.A.: An ideal window area concept for energy efficient integration of daylight and artificial light in buildings. Build. Environ. 40(1), 51–61 (2005) 3. State Council of China, Proposal of the Central Committee of the Communist Party of China on formulating the fourteenth five year plan for national economic and social development and the long-term goals for the year 2035 (2020). http://www.gov.cn/zhengce/2020-11/03/con tent_5556991.htm 4. China Academy of Building Sciences. Standard for lighting design of buildings GB 500342013. China Construction Industry Press (2014) 5. Xin, Y.: Empirical analysis on ecological improvement in one construction engineering based on building life cycle of carbon emission assessment. South China University of Technology, 2017 6. China Academy of Building Sciences. Standard for building carbon emission Calculation GB/T 51366-2019. China Construction Industry Press, Beijing (2019) 7. Chen, Y., Hao, L., Cui, Z.: Study on the human factors of lighting in neutral color living room. J. Light. Eng. 25(4), 29–34 (2014)

Correlation for Project Decision Making Process Between Green Building Proposal Evaluation and Life Cycle Costing Applications Cuong N. N. Tran1(B) , Nhung T. T. Nguyen2 , and Vivian W. Y. Tam3 1 University of Economics and Business - Vietnam National University, 144 Xuan Thuy Street,

Cau Giay District, Hanoi, Vietnam [email protected] 2 Ho Chi Minh City University of Technology (HCMUT) - Vietnam National University, Ho Chi Minh City, 268 Lý Thu,`o,ng Kiê.t, District 10, Ho Chi Minh City, Vietnam 3 School of Engineering, Design and Built Environment, Western Sydney University, Locked Bag 1797, Penrith, NSW 2751, Australia

Abstract. Choosing an effective and efficient building design choice for energy management and for optimal cash flow utilisation connected with a building’s lifecycle is always a significant challenge for designers in the effort against global climate change. In addition to reducing greenhouse gas emissions, researchers all over the globe are prospering in the development of building optimization models that would reduce a building’s total energy usage. To foster environmentally friendly development, the Vietnamese government deploys tough law enforcement, well-defined green construction regulations, and a variety of other techniques. To encourage construction enterprises to adopt ecofriendly materials in their current and future projects. According to this study, green building assessments will be influenced by a number of significant factors, such as groupings of variables related to “Experience, credentials, and talents.” Group of variables associated with “Motivation to maximise economic performance of the project life cycle,” “Energy conservation,” “Water conservation,” “Materials & Resources,” “Management,” and “Motivation to promote the benefits of market strategy.” The management of energy efficiency ranks first among the categories of factors that impact the approach and assessment of a green building. In addition, this will affect future evaluations of green buildings, such as life cycle cost analyses and environmental impact assessments undertaken during the development of the project. It is vital to identify and prioritise the components that impact the development of a green building. It will provide more precise project development approaches to academics, designers, and practitioners. Keywords: Green building rating systems (GBRSs) · life-cycle assessment · life-cycle energy consumption · life-cycle greenhouse-gas emissions · life-cycle cost · LOTUS

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 475–483, 2023. https://doi.org/10.1007/978-981-99-3626-7_38

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1 Introduction Pollution of the environment, particularly air pollution and the depletion of the ozone layer, is a worldwide concern that threatens the health of people all over the world [1]. In addition to the discharge of automobiles, the manufacture of industrial goods, and the collecting of wastewater that has not been treated, the operations of the construction sector are another significant source of air pollution [2, 3]. Research conducted by the American Green Building Council found that buildings in the United States account for 38% of CO2 emissions, 71% of electricity consumption, 39% of energy use, 12% of water consumption, and 40% of non-industrial waste. These findings demonstrate that the construction industry has a significant impact not only on the environment but also on the economy and society [4]. The building and construction industry in the UK is responsible for a significant percentage of the country’s total energy consumption and CO2 emissions [5]. Construction may also contribute to the depletion of natural resources. There are several examples, such as 40% of the worldwide raw materials used for building each year: raw stone, gravel, sand, and old-growth wood [6]. The construction industry has adopted green building as a standard practice in response to the worldwide environmental problem and climate change [7]. In order to both conserve the environment and increase human well-being in a manner that is in harmony with nature, governments throughout the globe are urging their citizens to come up with creative methods to deal with and limit environmental effects on metropolitan areas. Three sustainability pillars of economic, environmental, and social well-being act as a whole. The interdependence of society, economy, and ecology means that if any one of these components is disrupted, the whole system will be thrown out of balance. Environmental issues and economic development are intertwined, and so are social well-being and the preservation of the natural world. For environmental and social sustainability, however, solutions should be financially feasible. In order to achieve global sustainability, green building construction is becoming more popular. Green building is an evaluation technique that shows human efforts towards overall sustainable development. With the goal of sustainable development, green building is currently a revolution in the field of construction on a global scale as construction works increasingly show greater responsibility for natural resources, with environment, ecology and with the quality of human life through comprehensive efforts in the following aspects: Design, construction, equipment manufacturing, technology, materials, policy and finance. Green building has been shown to be beneficial in several aspects: Environmental efficiency, social benefits via and apparent economic advantages. Determining the elements impacting investment choices in office projects is an essential responsibility of construction management to estimate and analyse construction investment costs for the project life cycle according to standards. Green building is an essential path of construction management; through which it is possible to estimate the profit provided by a project according to green building standards to assist investors stimulate investment in projects according to green building standards. Green construction standards [8], which contribute to increasing productivity, enhancing health, conserving energy, making effective use of resources, and lowering operational costs. New approaches to marketing and a new look for company [9]. With the foregoing challenges, the paper offers the identification of variables impacting investment choices of office

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projects based on cost assessment in the life cycle of the project according to green building standards in the instance in Vietnam is based on LOTUS standard.

2 Enhance Office Building Functionality by Using Green Building Standards In many nations, green building policies have become an integral component of the reform of development policy. Currently, several green building evaluation methods have been developed on a global scale [10]. Numerous studies suggest that LEED, BREEAM, DGNB (Deutsche Gesellschaft fur Nachchaltiges Bauen), and Green Star. Certified green commercial buildings are more valuable commercially, socially, and ecologically than other grading systems. Previous research on the economics of green buildings in the United States, United Kingdom, and Australia determined that green buildings may attain greater sales prices, acquire higher rentals, and have a higher occupancy rate than buildings without green building certifications. In addition, studies indicate that industry validation of ‘green value’ is beginning to have an effect on property prices through decreased building running costs, ease of sale and renting, and tenant retention. Occupants and increasing total occupancy [11–13]. Vietnam is a nation with a strong urbanisation rate in big cities, and the flood of migration to urban regions is increasing, placing strain on infrastructure, housing, and workplaces… Construction and growth region 60% of natural materials, 30 to 35% of total national energy consumption, 30% of clean water supplies, and 30% of CO and CO2 emissions are being used by metropolitan areas in Vietnam, producing the greenhouse effect, climate change, and sea-level rise. For the green building approach of Vietnamese construction industry, LOTUS is the first voluntary green building criteria system built exclusively, continuing the benefits of the LEED and Green Mark systems. It was created by the Public Council. Vietnam Green Program (VGBC - LOTUS). This is an initiative of the Green Cities Fund that is non-profit (California, USA). LOTUS is a standard and goal-setting tool for developing ecologically and health-friendly buildings with reduced running costs (VGBC - LOTUS). Green building development is hampered by high investment costs, according to a number of studies [14–16]. Determining the elements impacting investment choices in office projects is an essential responsibility of construction management to estimate and analyze construction investment costs for the project life cycle according to standards. Green building is an essential work of construction management; through which it is possible to estimate the profit provided by a project according to green building standards to assist investors stimulate investment in projects according to green building standards. Green building standards [8], contributing to enhancing employee productivity, improving health, conserving energy, managing resources wisely and minimizing operational expenses. Branding and transforming the face of company [9]. In light of the aforementioned concerns, the paper outlines the elements influencing office project investment choices based on cost assessments throughout the project’s life cycle in accordance with green building standards in Vietnam, where the LOTUS standard is used. Table 1 introduces some major factors that recent research have shown to influence the development of green buildings. These factors include aspects relating to social, economic and environmental impacts.

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Table 1. Aspects influencing the development of green buildings mentioned in recent studies. No

Author

Research’s topic

Factors affecting the development of green buildings

1

Schleich, et al. [17]

Environmental sustainability - drivers for the real estate investor

- Translation results - Green certification - High market value - High rental income - Cost savings - Image benefits - Government encourages

2

Isa, et al. [18]

Factors Affecting Green Office Building Investment in Malaysia

- Occupancy rate, market and rental value - Profit - Cost savings - Social and environmental benefits

3

Joachim [19]

Modeling demand-supply dimensions of green commercial properties

- Tax incentives - Available green building skills - Incentive for cost savings in the project lifecycle - Motivation for environmental protection, - Benefits in market strategy, - Economic and financial dynamics - Motivation, awareness and responsibility of the business - Government interest - Green building certification policies and motivations - Expected rate of return of developers

4

Onuoha and Finbarr [20]

Analysis of the factors affecting green building investment in imo state, NIGERIA

- Market readiness (size, trends and investment costs market, access to market information) - Economic and financial factors (profitability/expected profit margin, construction cost/green building construction cost, - Policy to encourage the purchase of green buildings) - Government policies (mandatory/voluntary policy for green building development) - Relevant laws and regulations

5

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Factors affecting green building development at the municipal level: A cross-sectional study in China

- Geographical location - Local financial revenue and real estate investment are strong predictors of green building numbers - Mandatory regulations and incentive-based policies - Local design standards

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3 Comparison of LOTUS with Other Green Building Rating tools in the Industry LOTUS is a green building certification system designed by the Vietnam Green Building Council (VGBC - LOTUS) [22] - an international non-profit organisation, a member of the World Green Building Council (WorldGBC) (WorldGBC). After more than 8 years of development, LOTUS Certification currently contains 7 assessment systems, applicable to practically all kinds of construction projects such as non-residential structures, condos, commercial buildings, houses, individual dwellings, small-scale buildings and interior spaces. LOTUS acts as a guiding guideline and goal setting tool to create buildings that are favourable to the environment and inhabitants’ health with reduced running expenses. The score system, prerequisites (GAT), and criteria in the categories are all identical to those of LEED, Green Mark, Green Star, and other well-known green building certification schemes as can be seen in Table 2. In order to be the most appropriate green building certification standard for Vietnam’s built environment, LOTUS differs significantly from LEED in several ways. LOTUS makes frequent mention of Vietnamese legislation, standards, and norms. Table 2. Compare LOTUS certificate with green building certifications in the globe. System

LEED

GREEN STAR

GREEN MARK

LOTUS

Rating

- LEED certified - Silver LEED - Golden LEED - LEED platinum

- 1 star: Not eligible - 2 stars: Not eligible - 3 stars: Not eligible - 4 stars: “Best Method” certification - 5 stars: Green star “Australian Excellence” certification - 6 stars: Green star certification “World leader”

- Green Mark Certification - Green Mark Gold - Green Mark Gold Plus - Green Mark Platinum

LOTUS Certification LOTUS Silver LOTUS Gold LOTUS Platinum

(continued)

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System

LEED

GREEN STAR

GREEN MARK

LOTUS

Rating system

LEED version 4 - Design and Build (BD + C), - Interior design and construction (ID + C), - Building Operation and Maintenance (O + M), - Regional Development (ND)

- Design and build - Interior - Public construction - Works of art

- Residential area - new construction - Residential - existing buildings - No housing - new construction - Existing non-residential buildings

- LOTUS NC v3, applicable to new construction or large-scale renovation projects with a total floor area (GFA) of 2500 m2 or more - LOTUS BIO, applicable to existing buildings - LOTUS Homes, applied to individual housing projects - LOTUS SB, applicable to non-residential projects with GFA less than 2500 m2 - LOTUS Interiors, applying interior finishing project - LOTUS Small Interiors, applying interior finishing project with GFA less than 1000 m2

Criteria

- Location and means of transportation - Sustainable websites - Efficient use of water - Energy and atmosphere - Materials and resources - Indoor environmental quality - Regional priority - Innovation

- Management - Indoor environmental quality - Energy - Transport - Country - Material - Land use and ecology - Emissions - Innovation

- Energy efficiency - Efficient use of water - environment - Indoor environmental quality - Other green features

- Energy - Country - Materials & Resources - Health & Comfort - Location & Environment - Manage - Outstanding performance

Similar criteria for project performance are classified under Credits. The LOTUS project will pick and perform specific quantities and gain points for certification. In general, the more points a project receives, the larger the advantages LOTUS will provide. A higher grade denotes a building that is more energy efficient, uses less water, requires less maintenance, and is more pleasant for its occupants to use. Depending on the final result, the project will get LOTUS Certification at one of many levels. The lowest level of certification is established at 40% of the total score (LOTUS Certification) (LOTUS Certification). LOTUS Silver, Gold, and Platinum certifications are worth 55%, 65%, and 75%, respectively, of the overall score.

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4 The Development of Green Building Evaluation System in Vietnam The periodical study “Vietnam Green Building Market Overview” in the third quarter of 2021 released by the UK Government, IFC (World Bank Group) and SGS demonstrates that the green building market in Vietnam is continually expanding. Development, however it is still modest compared to the amount of building activities and the trend of greening in the area. Accordingly, in the first 3 quarters of the year, there were 201 certified green projects, with a total certified floor area of 5,327,242 m2 (Table 3). Specifically, 53 projects were given EDGE Green Building certifications, accounting for 26%; with a total floor area of 2,655,673 m2 , accounting for 58%. It is recently reported that 113 projects have been awarded the LEED Green Building Certificate, which accounts for 56% of the total floor area, which amounts to 2,322,673 m2 . There are 35 projects received LOTUS Green Building accreditation, accounting for 18%; total floor area reached 348,585 m2 , accounting for 8%. Table 3. Number of certified green buildings as of the third quarter of 2021 in Vietnam. Green building rating tool

Number of projects

Percentage

Floor area

Floor area percentage

EDGE

53

26%

2.655.673 m2

58%

LEED

113

56%

2.322.673 m2

34%

18%

348.585 m2

8%

LOTUS

35

Evaluation of economic efficiency is a problem that Vietnam’s LOTUS green building evaluation system is still inadequate compared to other systems in the world and mainly utilised to evaluate the efficacy of investment projects. Labor. First and foremost, LCCA (Life Cycle Cost Assessment), or any other economic evaluation technique, must identify the economic performance of various building designs and building systems, as well as the resulting consequences and monetary values. LCCA is a method for evaluating an asset’s total cost of ownership during its entire useful life, including the expenses of procurement, use, maintenance, and eventual disposal. The notion of life-cycle costs was first introduced in the mid-1960s to aid the US Department of Defense in procuring military hardware [23]. Furthermore, comparing the life-cycle costs of various options is now widespread practise among US government organizations [24]. A life cycle cost is the present value of an asset’s overall life-cycle cost [25]. This cost covers the original capital cost, financing cost, operating cost, maintenance cost and ultimate disposal cost of the asset at the end of its life. All upcoming expenditures and benefits are lowered to current value utilizing discounting procedures. Using a green building approach, this is also an appropriate solution. Generally, it is the present value of all green building-related expenses during the life cycle in present value terms. A green building’s life cycle costs can be calculated by adding up all the costs associated with the project’s various phases (pre-construction through post-occupancy)

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and dividing them by the project’s specified lifespan (in years) or a predetermined point in time (in percentage terms) to arrive at a life cycle cost estimate [26].

5 Discussion and Conclusion To encourage environmentally friendly growth, the Vietnamese government employs strict enforcement of the law, well-defined green building rules, and a number of other strategies. Construction companies to encourage them to use environmentally friendly building materials in their present and future projects. Green building evaluations will be impacted by a variety of important aspects, according to this research, including groups of variables linked to “Experience, qualifications, skills”, Group of factors related to “Motivation to maximise economic performance of the project life cycle”, “Energy saving”, “Water saving”, “Materials & Resources”, “Management”, “Motivation to promote the advantages of market strategy”. The findings from the study of groups of components by variable reveal that the data explains the variance 68.954%. Group 1: The group of elements connected to “Energy saving” is estimated to have the largest effect on project investment choices according to Lotus Green Building standards based on cost evaluation throughout the project life cycle. Having a variability of the data of 40.157%. Group 2: The group of variables connected to “Experience, qualifications, skills” holds the second place in affecting investment choices with the variability of the data of 6,876. Group 3: The group of elements connected to “The driving force that supports the advantages of the market strategy” holds the 3rd position with the variability of the data of 5,049. Group 4: Group of parameters connected to “Health & Comfort” with data variability of 4,316. Group 5: Group of variables connected to “Policy” with data variability of 3,624. Category 6: There are 3,365 variables connected to “Water saving” in this group. Group 7: Group of elements connected to “Project Lifecycle Economic Performance Optimization Dynamic” with data variability of 3,039. Group 8: There are 2,527 variables connected to “Materials & Resources” use in this group. Energy-saving management ranks first among the categories of criteria that influence the approach and evaluation of a green building. In addition, this will have an influence on further assessments of green buildings, such as life cycle cost analysis and environmental impact assessments conducted throughout the project’s construction. It is essential to identify and order the components that influence the development of a green building. It will provide academics, designers, and practitioners with more accurate project development methodologies.

References 1. Bodansky, D.: The History of the Global Climate Change Regime, vol. 23, no. 23, p. 505 (2001)

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2. Holman, C.: Sources of air pollution. In: Air Pollution and Health, pp. 115–148. Elsevier (1999) 3. Dar, I.Y., Rouf, Z., Javaid, M., Dar, M.Y.: Atmospheric emissions from construction sector. In: Malik, J.A., Marathe, S. (eds.) Ecological and Health Effects of Building Materials, pp. 13–31. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-76073-1_2 4. Leadership in energy and environmental design (2008) 5. Prism Environmental. Construction Sector Overview in the UK, report for the European Commission (2012). http://www.prismenvironment.eu/reports_prism/UK_PRISM_Env ironment_Report_EN.pdf. Accessed 2015 6. Worldwatch Institute. Vision for a sustainable world (2015) 7. Pham, T.L., Nguyen, T.T.: Green building certification as a policy to promote green-buildinga study of Singapore, Taiwan, Australia, UK, US and lessons for Vietnam. Int. J. Sustain. Constr. Eng. Technol. 12(3), 135–141 (2021) 8. Chegut, A., Eichholtz, P., Kok, N.: Supply, demand and the value of green buildings. Urban Stud. 51(1), 22–43 (2014) 9. Diyana, N., Abidin, Z.: Motivation and expectation of developers on green construction: a conceptual view. Int. J. Humanit. Soc. Sci. 7(4), 914–918 (2013) 10. Illankoon, C.S., Tam, V.W., Le, K.N., Tran, C.N., Ma, M.: Review on green building rating tools worldwide: recommendations for Australia. J. Civil Eng. Manag. 25, 831–847 (2019) 11. GBCA. Green building council, Australia (2012) 12. Pitts, J.: Green buildings: valuation issues and perspectives. Apprais. J. 76(2), 115 (2008) 13. Ellis, C.R.: Who pays for green? The economics of sustainable buildings. EMEA Research, vol. 19 (2009) 14. Nguyen, H.-T., Skitmore, M., Gray, M., Zhang, X., Olanipekun, A.O.: Will green building development take off? An exploratory study of barriers to green building in Vietnam. Resour. Conserv. Recycl. 127, 8–20 (2017) 15. Landman, M.: Breaking through the barriers to sustainable building: Insights from building professionals on government initiatives (1999) 16. Pitt, M., Tucker, M., Riley, M., Longden, J.: Towards sustainable construction: promotion and best practices. Constr. Innov. 9, 201–224 (2009) 17. Schleich, H., Lindholm, A.-L., Falkenbach, H.: Environmental sustainability-drivers for the real estate investor. In: ERES 2009, Tukholma, Ruotsi, 24–27 June 2009. European Real Estate Society (2009) 18. Isa, M., Rahman, M.M.G.M.A., Sipan, I., Hwa, T.K.: Factors affecting green office building investment in Malaysia. Procedia Soc. Behav. Sci. 105, 138–148 (2013) 19. Joachim, O.I.: Model of demand and supply factors affecting green commercial properties. Faculty of Geoinformation and Real Estate, Universiti Teknologi Malaysia, Malaysia (2017) 20. Onuoha, I.J., Finbarr, C.C.: Analysis of the factors affecting green building investment in Imo State, Nigeria. J. Environ. Des. (JED), 118 (2020) 21. Song, Y., Li, C., Zhou, L., Huang, X., Chen, Y., Zhang, H.: Factors affecting green building development at the municipal level: a cross-sectional study in China. Energy Build. 231, 110560 (2021) ´ tiêu chí Công trình xanh LOTUS. https://vgbc.vn/ 22. VGBC - LOTUS. Hê. thông 23. Irwin, M.: Measuring Corporate Environmental Performance: Best Practices for Costing and Managing an Effective Environmental Strategy. Irwin/Institute of Management Accountants Chicago (1996) 24. Goh, B.H., Sun, Y.: The development of life-cycle costing for buildings. Build. Res. Inf. 44(3), 319-333 (2016) 25. Addis, B., Talbot, R.: Sustainable construction procurement: a guide to delivering environmentally responsible projects (2001) 26. WGBC. https://www.worldgbc.org/

Exploring the Spatial-Temporal Evolution Characteristics of Urban Eco-efficiency: A Case Study of 276 Chinese Cities Xi Cai, Yu Zhang(B) , Mengxue Li, Liudan Jiao, and Xiaosen Huo School of Economics and Management, Chongqing Jiaotong University, Chongqing, China [email protected]

Abstract. With the unbalanced development of the ecological environment and economic growth, eco-efficiency (EE) has turned into a popular research topic. This paper measures the eco-efficiency values of Chinese cities using the Superslack-based measure (Super-SBM) model with non-desired outputs. Data for the study were obtained from a sample of 276 cities in China from 2015–2019. The outcomes show that: (1) The top three performers in EE are Beijing, Ziyang, and Zhoukou, and the bottom three performers are Wuzhong, Yichun, and Tieling; (2) the amount of cities in the lowest efficiency group decreases, and the average eco-efficiency value increases slightly overall; (3) by region, the average ecoefficiency value is highest in the east, comparable in the central and western areas, and lowest in the northeast; (4) from the period, the eco-efficiency values in the eastern and western regions have not changed much since 2015–2019, but the central region has increased from 0.28 to 0.42 and the northeastern region has decreased from 0.31 to 0.24. The results of the study provide a significant reference for policymakers to consider environmental protection and economic growth in an integrated manner. Keywords: Chinese cities · Eco-efficiency analysis · Super-SBM model

1 Introduction In recent decades, China’s economy has achieved fast growth. In statistical terms, the rate of urbanization has increased from 17.92% in 1978 to 64.72% in 2021. GDP increased from 3,645 billion RMB in 1978 to 1,143,670 billion RMB in 2021, firmly ranking it as the world’s second-largest economy [1]. China’s economy growing, however, comes at the cost of extreme resource ingestion and ecological contamination. Therefore, extreme consumption of natural resources, coal-based energy constructions, and development modes of long-standing high-energy ingesting results in excessive discharge of wastewater, waste gases, and solid wastes, aggravating air, water, and soil pollution [2]. A study by the Rhodium Group states that in 2019 global carbon emissions totaled about 10,285 million tons, with China accounting for 27%, the U.S. for 11%, and India for 6.6%. According to data from the World Energy Statistics Yearbook 2021, China has © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 484–500, 2023. https://doi.org/10.1007/978-981-99-3626-7_39

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maintained its position as the world’s largest consumer of primary energy for 12 consecutive years, with a 26.1% share of the world’s [3]. What is worrisome is that China’s emissions are greater than those of all developed countries combined. This model of growth at the cost of the natural environment not only harshly harms the ecosystem, but is also not conducive to quality development. In the context of natural resource depletion and ecological environmental worsening, the Chinese government realizes that the widespread development model that depends on resource input to attain economic progress is unsustainable. In 2012, the CPC Constitution (Amendment) adopted at the 18th Party Assembly includes “the CPC leads the people in building a socialist ecological civilization” in the Party Constitution, which is the first time in the world that ecological civilization is involved in the action program of a political party. In 2013, the Third Plenary Session of the 18th CPC Central Committee was held, and the Party Central Committee, with Jinping Xi as the essential, positioned the building of ecological civilization in a protuberant place in national governance. In 2014, China revised the Environmental Protection Law, 25 years after the 1989 version, which was called the “strictest” environmental protection law in history. In 2018, the Alteration to the Constitution of the People’s Republic of China was adopted at the first meeting of the 13th National People’s Assembly, incorporating ecological civilization and beautiful China into the Constitution, providing a fundamental national law for the building of ecological civilization. In particular, at the 8th National Session on Ecological Environmental Security held in May 2018, Jinping Xi Thought on Ecological Civilization was formally recognized, which is a significant theoretical achievement in the history of ecological environmental defense in China and provides ideological guidance and practical guidelines for environmental strategic policy reform and innovation. Since then, Jinping Xi’s thought on ecological civilization has become an executive ideology for ecological civilization, green development, and the construction of a “beautiful China” nationwide. It has also elevated the world’s strategic thinking on sustainable development at the international level. For easier reading, we have sorted out the timeline of ecological civilization construction (Fig. 1). There is no doubt that all of the above policies are aimed at achieving sustainable development of the environment and economic growth. From a historical perspective, the idea of sustainable development arose in the 1987 report of the United Nations World Command on the Environment and expansion in a paper entitled “Our Communal Future”. It demands that developers be realized “that meet the requests of the current group without cooperating with the ability of future generations to encounter their requirements” [4]. In this context, eco-efficiency plays the role of a trend indicator toward sustainable development transition [5] and could provide a pathway to it [6]. Accordingly, this paper objects to assessing the eco-efficiency of distinct cities and offering policy recommendations for city managers to endorse sustainable urban growth. An index system for eco-efficiency assessment was constructed in this study. A Superslack-based measure (Super-SBM) model was employed to assess eco-efficiency. A total of 276 cities at the prefecture level in China during 2015–2019 were used for empirical analysis.

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Fig. 1. The development history of ecological civilization.

2 Literature Review Eco-efficiency (EE) was first presented by Schaltegger and Sturm, who saw it as a tool for sustainable development analysis [7]. Subsequently, the idea of eco-efficiency was endorsed by the World Business Assembly for Sustainable Development [8]. As a tool for analyzing sustainable development, the concept has gained widespread care in studies on sustainable development. Previous studies on eco-efficiency can be divided into three main levels: macro, meso, and micro [9]. Micro studies usually focus on firms or industries, and Alves and Medeiros demonstrated that eco-efficiency can lead to lower cost benefits for micro or small firms by applying eco-efficiency to a Brazilian micro car company [10]. Some Meso-level studies have focused on specific cities or regions; Chen et al. evaluated the environmental efficiency of the Yangtze River Economic Zone in China and created that cities in the Yangtze River Estuary were more environmentally efficient than other cities [11]; Li et al. assessed the eco-efficiency of housing growth in three Chinese cities using the ecological footprint method and discovered that Shanghai and Beijing were more eco-efficient than Nanjing [12]. With the overconsumption of natural resources and the increasing worsening of the ecological environment, the idea of eco-efficiency has been presented in an increasing number of fields. This has led to many studies that have been extended to the national and even worldwide levels. Lupan and Cozorici analyzed the eco-efficiency in Romania to provide policy recommendations for a future sustainable development path in Romania [13]. It is extremely critical to choose the appropriate method to measure eco-efficiency values. Typically, two methods are often used to assess eco-efficiency, namely the ratio way and the frontier way. Eco-efficiency can be assessed as the ratio of the worth of what has been shaped to the influences on the environment of the product or service [6]. Yet, it is insufficient to conduct research on eco-efficiency using a single measure [14, 15]. Evaluation of area eco-efficiency should be done by uniting indicators from two or more scopes [9]. It seems that data envelopment analysis (DEA) is the most broadly

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accepted approach joining various inputs and outputs along dissimilar dimensions without any subjective weights for aggregating indicators [14, 16]. However, traditional DEA models suffer from at least three unavoidable problems. First, traditional DEA models measure efficiency only in terms of individual inputs and individual outputs. This is both incomplete and biased [17]. Second, the traditional DEA model treats undesirable outputs inappropriately. But the role of undesirable outputs in eco-efficiency is critical [16]. The last problem is that the efficiency of decision units can only be up to 100%, which makes it impossible to sort and distinguish between decision units [6]. Huang et al. (2014) offered a Super-efficient SBM model of non-consensual output to study the subtleties of area eco-efficiency in China from 2000–2010, which can address the above three questions [18]. Recently, some scholars have measured eco-efficiency or related efficiency, such as environmental efficiency, in Chinese regions based on the improved model described above. Chu et al. assessed the eco-efficiency of 30 district regions in China using the DEA two-stage network framework [19]. Yang and Zhang propose an extended data envelopment analysis model that combines worldwide benchmarking techniques, directional distance functions, and bootstrap methods to study the dynamic tendencies of regional eco-efficiency in China from 2003–2014 [20]. The Super-SBM model, which considers undesirable outputs, was applied by Zhou et al. to assess the eco-efficiency of 21 cities in Guangdong Province, China [21]. Similarly, the model was used by Zhang et al. to assess the pollution regulation efficiency of PM2.5 in 112 cities in China from 2003–2017 [22]. Some other scholars have also conducted empirical studies on the eco-welfare efficiency of selected Chinese cities using the Super-SBM model [23, 29, 37]. At present, research on DEA models and eco-efficiency has made great progress in both theory and practice. The valuable results obtained so far have enriched our understanding of eco-efficiency. However, most of the current research is focused on the macro level at the national and provincial levels. Although a few scholars have conducted studies on ecological efficiency at the provincial level in China, few of them have addressed the regional city level, ignoring the differences in economic and ecological development among cities and lacking theoretical support for improving urban eco-efficiency. Therefore, this paper builds an assessment index system and adopts the SBM model to assess eco-efficiency. Based on 276 prefecture-level cities in China during 2015–2019, this paper demeanor an empirical analysis to seal the holes in previous studies. The remainder of the paper is prearranged as follows. Section 3 describes the model used in this study. Section 4 focuses on the empirical results. Finally, conclusions and related recommendations are deliberated in Sect. 5.

3 Materials and Methods 3.1 The Super-SBM Model In recent years, DEA methods have been broadly used in many study fields such as eco-efficiency evaluation and energy efficiency evaluation [24]. However, Tone pointed out that in the radial DEA model, the calculation of the inefficient decision-making unit (DMU) only covers the proportion of equal scaling down (increasing) of all inputs (outputs) [25]. Furthermore, the DEA approach does not consider non-desired outputs

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(e.g., pollutants, etc.) [26]. Based on this, Tone came up with the SBM model, which removes the DMUs to be evaluated from all DMU reference sets and solves the problem that the radial model’s measurement of inefficiency does not cover the slack variables. However, the SBM model also has some problems. The efficiency values derived from the SBM model range from 0 to 1, and the effective DMU efficiency value is 1. When the amount of DMUs is minor, it is still possible to make a valid distinction between good and bad efficiency values, but when the amount of DMUs is huge, many effective DMUs have an efficiency value of 1, and it is impossible to further sort them. To solve this problem, Tone then came up with the Super-SBM model [25]. In the case of the Super-SBM model, the efficiency evaluation results are no longer limited to the interval of [0–1], and the value of the efficiency can exceed 1 [27]. The Super-SBM model with undesired output is more applicable than the traditional DEA method. Therefore, in this paper, the Super-SBM model is used to measure urban ecoefficiency. The specific steps are described in the following: ➀ Assume there are n DMUs, each DMU with m input indicators (x) can produce s1 desired outputs (yd ) and s2 undesired outputs (yud ), each vector is represented as: x ∈ Rm , yd ∈ Rs1 , yud ∈ Rs2 . If n cities are considered, the matrices X , Y d and Y ud can be formed as follows: X = [x1 , x2 , · · · , xn ] ∈ Rm×n > 0 Y d = [y1d , y2d , · · · , ynd ] ∈ Rs1 ×n > 0 Y ud = [y1ud , y2ud , · · · ynud ] ∈ Rs2 ×n > 0 Then, the set of production possibilities (P) for eco-efficiency can be established as the following equation. P = {(x, yd , yud ) | x > X λ, yd ≤ Y d λ, yud ≥ Y ud λ, λ ≥ 0} where λ is a non-negative intensity vector, representing that the above definition is under the situation of constant proportional return (CRS). Based on this production possibility set (P), the SBM-DEA model with inputs, desired outputs, and undesired outputs, according to Tone, measures as follows:  1 − m1 m i=1 si /xi0 ρ = min s1 d d  2 ud ud 1 1 + s1 +s2 ( r=1 sr /yro + st=1 st /yt0 ) ⎧ xo = X λ + S − ⎪ ⎪ ⎨ y0d = Y0d λ − S d Subject to (1) ud ⎪ y0 = Youd λ + S ud ⎪ ⎩ − S ≥ 0, S d ≥ 0, S ud ≥ 0, λ ≥ 0 where the vector S d represents the slack in the desired output, the vector S ud is the slack in the non-desired output, and the vector S − represents the slack in the input.

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The eco-efficiency value ρ is in the range [0, 1]. For a given DMU to be evaluated, the efficiency is valid if ρ = 1 and S − = S d = S ud = 0. If ρ < 1, the efficiency is invalid. However, when the amount of DMUs becomes huge, the DEA model yields more than one efficiency value of 1. To further distinguish the DMUs with efficiency worth ρ = 1, the Super-SBM model is used to evaluate the efficiency values. ➁ Assuming that DMU(xk , ykd , ykud ) is valid with a worth of ρ = 1, the corresponding Super-SBM models dealing with inputs, desired outputs and undesired outputs are developed as follows: ∗

ρ = min ⎧ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎨ Subject to ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎩

1+

1 s1 +s2 (

m

− i=1 Si /xik s2 ud ud d d r=1 sr /yrk + t=1 st /ytk )

1+ 1 s1 m

 xik ≥ nj=1,=k xij λj − Si− n d ≤ d d yrk j=1, =k yrj λj + Sr  n ud ud ytk ≥ j=1,=k ytj λj + Stud s1 d d s2 ud ud  1 1 − s1 +s r=1 sr /yrk + k=1 sk /ytk > 0 2 S − ≥ 0, S d ≥ 0, S ud ≥ 0, λ ≥ 0 i = 1, 2, · · · , m; j = 1, 2, · · · , n(j  = k); r = 1, 2, · · · , s1 ; t = 1, 2, · · · , s2 (2)

where the vectors Si− , Srd and Stud correspond to the slack in inputs, desired outputs, and non-desired outputs, respectively, and λ is a non-negative intensity vector. The value of the objective function ρ ∗ can be greater than 1. It can rank the DMUs that are valid for the SBM model measurement. Higher values of ρ and ρ ∗ indicate higher urban eco-efficiency. 3.2 Indicator System and Research Data 3.2.1 Indicator System The application of model (1) and model (2) to assess EE requires the selection of a set of input-output indicators. Input: X = [x1 , x2 , · · · , xn ] ∈ Rm×n > 0 Desirable outputs: Y d = [y1d , y2d , · · · , ynd ] ∈ Rs1 ×n > 0 Undesirable outputs: Y ud = [y1ud , y2ud , · · · ynud ] ∈ Rs2 ×n > 0 Choosing suitable indicators of ecological ingesting and ecological outputs is an important stage for EE evaluation. Figure 2 offers a framework for evaluating ecoefficiency input and output indicators. This measure should include ecological resource inputs (e.g. energy and water consumption), ideal output (e.g. GDP), and sub-optimal output indicators (e.g. environmental contaminants). Based on the available studies in the relevant literature and combined with the availability of data, we chose water (x1 ), energy (x2 ), and land (x3 ) as resource inputs. The waste of water resources in cities is considered a serious ecological conservation problem [21]. Energy, on the other hand, is a valuable asset for every country, and for China,

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Fig. 2. Input and output framework for EE.

it is an important economic pillar [28]. Land is also an important parameter as it is the place where people depend for their activities, especially the area of land available for use [20]. Therefore, we use urban electricity to react to energy consumption [29], residential water to represent water consumption [30], and built-up area to represent land input [31]. Concerning former studies, GDP was employed as a desirable output from an economic point of view, represented as y1d . The most representative undesirable outputs for calculating eco-efficiency are environmental pollutants, containing wastewater and exhaust gases. Industrial wastewater volumes are used as undesirable outputs to measure water pollution levels, denoted as y1ud . SO2 and soot/dust emissions are designated as other undesirable outputs to measure air pollution levels, denoted as y2ud and y3ud [26, 32–34]. Table 1 shows the chief features of the input-output indicators. Table 1. Chief characteristics of the input-output indicators. Dimension

Indicator and definition

Unit

Input indicators

Water consumption (x1 ): Per capita water

Ton

Energy consumption (x2 ): Per capita urban electricity

kWh

Land consumption (x3 ): Per capita built-up area

m2

GDP (y1d ): Per capita GDP

yuan

Wastewater (y1ud ): Per capita wastewater SO2 (y2ud ): Per capita SO2 Soot /dust (y3ud ): Per capita industrial fume and dust

Ton

Output indicators

Ton Ton

3.2.2 Research Data The corresponding data for these indicators come from prefecture-level and above cities in China. There are 338 cities at the prefectural level and above throughout China, but some of them have a large amount of missing data that we could not obtain. Therefore,

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we only selected 276 sample cities for eco-efficiency analysis. Since the most complete data in the Statistical Yearbook only go up to 2020, which is actually the value for 2019, we chose the five-year period 2015–2019 as the study period. These five years both experience the end of the 12th Five-Year Plan and occupy most of the 13th Five-Year Plan, and are of significant research value. Data for these 276 cities for 2015–2019 are obtainable from China’s urban statistical yearbook [35], Statistical yearbook of urban construction [36].

4 Results and Discussion 4.1 The Overall Analysis of the EE Application of the methods and data detailed in Sect. 3, with the help of the Max DEA computer program, allows us to obtain EE results for 276 Chinese cities for the years 2015–2019. Descriptive statistics of the results are shown in Table 2. Table 2. Descriptive statistics of eco-efficiency values. N

Minimum

Maximum

Average

Standard deviation

2015

276

.057054

1.235719

.35700709

.242815250

2016

276

.058513

1.296947

.48465919

.277346650

2017

276

.064978

1.171268

.44381316

.273861155

2018

276

.087347

1.178095

.38101689

.249529071

2019

276

.078932

1.200040

.38574625

.225037671

Number of valid cases

276

As a whole, the mean value of EE for each sample city during the study period was 0.41, showing the overall poor performance of Chinese cities in terms of eco-efficiency. This is in line with the findings of Long and Zhang et al. and indicates that China’s overall EE performance is poor [29, 39]. To get further useful information, the three best and worst performing cities are analyzed below to provide reference or warning for other cities. The top three cities are Beijing, Ziyang, and Zhukou. The details of these three cities over the years are shown in Fig. 3. Beijing, the capital of China, unsurprisingly ranked first in this study. Beijing has gone through a phase of rapid economic development. Therefore, it has shifted to a governance model that coordinates economic development with environmental protection. High-tech industries as well as financial and cultural industries have made outstanding aid to Beijing’s economic growth and livelihood enhancement. What surprised us were the second and third ranked cities, namely Ziyang City and Zhoukou City. Neither of these cities is located in a very economically developed area. But without exception, both have good ecological efficiency. Ziyang City has a very special geographical position, located between Chengdu and Chongqing, and is the center of the Chengdu-Chongqing Economic Circle. Therefore, the economic development

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of Ziyang City has been greatly driven. In addition, Ziyang City has been sparing no efforts in ecological protection. It has been awarded the titles “The Model City of Beautiful Environment in Sichuan Province”, “Energy-saving Capital of China” and “Green Ziyang”. Zhoukou is a typical plain agricultural area, located in the southeastern part of Henan Province. In the past five years, Zhoukou City has taken the initiative to connect with developed coastal areas for industrial transfer and has developed well. Particularly, Zhoukou’s outstanding ecological civilization construction work. In 2017, the “double substitution” policy for coal burning was implemented. And the pollution problem of industrial enterprises was rectified.

Fig. 3. Top three EE rankings from 2015 to 2019.

On the contrary, there are numerous cities with deprived EE performance. Among them, the three worst cities are Wuzhong, Yichun, and Tieling. The situation of these three cities is shown in Fig. 4. Wuzhong City is located in the remote western region. Its complex geographical environment and harsh climatic conditions are the main constraints to eco-efficiency. Yichun City and Tieling City are located in the northeastern region of China. With the depletion of urban forestry resources and the execution of the national overall economic transformation policy, Yichun, as a typical forestry resourcebased city, is facing severe challenges. In general, the problems in Yichun City include an unstable state of economic growth, inconspicuous industrial structure adjustment, and a low level of disposable income per capita. Tieling City is one of the old industrial bases in

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Northeast China. In the early stage of development, the industrial system was composed of heavy pollution due to factors such as funding, policies, and development needs. This also led to the difficulty in repairing the quality of the ecological environment. After summarizing the analysis, it can be obtained that cities with poor EE performance are either due to poor economic development or serious environmental pollution. The cities with good EE performance are not necessarily good at both, such as Zhoukou, where the ecological environment outweighs economic growth. From the perspective of sustainable urban development, a more worthy city for us to learn from would be a city like Beijing which has a balance between the two.

Fig. 4. Bottom three EE rankings from 2015 to 2019.

4.2 Dynamic Analysis of the EE The obtained EE values for each year for the 276 sample cities were plotted as a scatter plot as shown in Fig. 5. The number of cities with EE values less than 0.25 is significantly lower in 2019 compared to 2015. Also, the average value in 2019 is slightly higher than that in 2015. This indicates that, as a whole, the national eco-efficiency has improved. This is alike to the findings of Yang et al. and Zhang et al. [20, 29]. Also, we can observe in Fig. 5 that there are only a few cities with efficiency values between 0.7 and 1. This implies a large gap between cities with good and better

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eco-efficiency performance. The problem of unbalanced development across regions in China has been widespread. The Chinese government has long focused more on major eastern cities, especially municipalities like Beijing, Shanghai, and Tianjin [38]. Even after the Chinese government’s promotion policies, the northwest region remains underdeveloped. In these areas, environmental protection has become less urgent than economic growth. Southern region is better than the other regions in terms of environmental protection, as it has less industry and is closer to the coast. Specific regional differences will be analyzed in the following sub-section.

Fig. 5. Scatter plot on the EE values between 276 Chinese cities from 2015 to 2019.

4.3 Regional Comparative Analysis on the EE China is an enormous country, and the dissimilarities among different regions in the east, middle, and west cannot be ignored. To comprehensively analyze the EE performance values among different cities, the distribution of EE values of 276 cities is plotted in this paper and displayed on a map of China, as shown in Fig. 6. For easy observation all cities are divided into four groups and represented by different colors: group 1 (0 ≤ EE < 0.3), group 2 (0.3 ≤ EE < 0.6), group 3 (0.6 ≤ EE < 0.9), and group 4 (0.9 ≤ EE < 1.2). As can be seen in Fig. 6, in 2015, the eastern region had the best EE performance [40, 41], followed by the western & northeastern regions, and the worst performance

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was in the central region. This may be because the developed eastern coastal regions have the highest level of economic development and can use advanced technologies to

Fig. 6. A regional view of EE performance.

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reduce the undesirable environmental impacts of economic development. The western and northeastern regions have the highest environmental quality in the country due to their vast lands and sparse populations, and the low influence of human activities on the environment. In contrast, the central region has a large share of heavy industries, and the waste gas and wastewater emissions are not effectively treated. Therefore, the ecological environment is damaged and serious environmental pollution is caused. However, in 2019, the EE performance ranking is in the following order: east, central, west, and northeast. This is more clearly reflected on the radar chart in Fig. 7. As shown in Fig. 7, the central region has made the greatest progress over the five-year period, with the eco-efficiency value rising from 0.28 to 0.42. The western and eastern regions have not changed significantly. In the Northeast, the eco-efficiency value regressed from 0.31 to 0.24. From the average value of each region, it can also be concluded that the East is the most efficient, the West and Central are comparable, and the Northeast is the worst. This may be due to the generally low eco-efficiency of the western and northeastern regions as a result of the traditional crude production methods they have adopted for a long time. And it is impossible to change this crude growth mode of high consumption, high emission, and low output in the short term, so the enhancement of eco-efficiency is not obvious. The central region, on the other hand, has a better foundation for its eco-efficient

Fig. 7. Radar plot of EE for different regions.

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industries, environmental protection, and other facilities than the west. And 2015–2019 is in the “13th Five-Year Plan” period, which has greatly endorsed the development of various aspects. Not only has the economic development been greatly improved, but also the environmental pollution emission has been effectively controlled.

5 Conclusions and Policy Implications 5.1 Conclusions China’s rapid economic development has also brought about environmental pollution. The Super-SBM model was applied in this study to evaluate urban ecological efficiency. For the empirical study of their levels of urban sustainable development, 276 prefecturelevel cities in China from 2015 to 2019 were used as study objects. The results show that: (1) The top three performers in EE are Beijing, Ziyang, and Zhoukou, and the bottom three performers are Wuzhong, Yichun, and Tieling; (2) the amount of cities in the lowest efficiency group decreases, and the average eco-efficiency value increases slightly overall; (3) by region, the average eco-efficiency value is highest in the east, comparable in the central and western areas, and lowest in the northeast; (4) from the period, the eco-efficiency values in the eastern and western regions have not changed much since 2015–2019, but the central region has increased from 0.28 to 0.42 and the northeastern region has decreased from 0.31 to 0.24. This paper contributes a complete analysis of the eco-efficiency status in China. Sustainability is emphasized as the final goal of urban development rather than the single-minded pursuit of economic development. This paper also builds an index system for eco-efficiency assessment, which can be used directly in studies in other regions. These research findings can provide important references for other regions to improve eco-efficiency. Furthermore, it highlights the direction for policymakers to formulate strategies for urban sustainable development. However, there are some areas of improvement in this article. The first is to improve the indicators whenever new data become available. For example, an increase in solid waste emissions indicators could provide a more accurate picture of environmental pollution. Secondly, the influential factors of eco-efficiency are also worthy of further investigation. 5.2 Policy Implications The results of the study suggest the following policy recommendations to improve the eco-efficiency of Chinese cities and achieve sustainable development. First, play the leading role in high-efficiency cities. Cities with good eco-efficiency performance should be promoted as examples in other cities. At the same time, cities with poor eco-efficiency performance should actively exchange with excellent cities with similar geographical situations and industrial structures. Second, for the more polluted heavy industrial cities, it is necessary to change the mode of economic growth and shift from high-speed development to high-quality development. Further develop clean production, circular economy, and low-carbon economy,

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improve the level of industrial ecology, and deepen the energy-saving and consumption reduction measures further. At the same time to strengthen emission reduction measures, control the number of pollutant emissions and solid waste generation, to lighten the paradox between economic development and the environmental environment. Third, for cities with a backward economy, the future direction of industrial development should be planned in an integrated manner with the environmental carrying capacity as the constraint. The government should actively coordinate the development of secondary and tertiary industries, and gradually eliminate backward production capacity. In addition, it should actively introduce strategic new industries, attract investment and promote the development of import and export trade. Fourth, for traditional agricultural cities, we should vigorously develop green agricultural and livestock products processing industry, encourage the optimization and adjustment of industrial structure, and abolish backward technology and technology. Through the implementation of these measures, the negative influence of economic activities on the natural environment is reduced to a minimum. Resources and the environment are rationally allocated and sustainably developed, thus achieving harmonious development of the economy, society, and the environment. Acknowledgments. This work is supported by National Natural Science Foundation of China (Grant No.72204033), Humanities and Social Science project of Ministry of Education of China (Grant No. 21YJC630169), China Postdoctoral Science Foundation (Grant No. 2022M711457), Natural Science Foundation of Chongqing (Grant No. cstc2021jcyj-msxmX1010), Social Science Planning Project of Chongqing, (Grant No. 2020QNGL25), Science and Technology Research Project of Chongqing Education Commission (Grant No. KJQN202000724) and Humanities and Social Science Research Project of Chongqing Education Commission (Grant No. 21SKJD072).

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Spatiotemporal Evolution the GTFP of the Construction Industry--Empirical Analysis Based on the Yangtze River Economic Belt Hongsheng Kan1,2 , Yuanyuan Dong1 , Bin Hu1 , Jingxiao Yu1 , Yajun Chen1 , and Jinxian Zhao1,2(B) 1 School of Management Engineering, Qingdao University of Technology, Qingdao, China

[email protected] 2 Shandong Smart City Construction Management Research Center, Qingdao, China

Abstract. The green development of the construction industry plays an important role in promoting the ecological civilization of China. By applying the SBM model, the global Malmquist–Luenberger index, and the GeoDetector model, this paper examines the green total factor productivity (GTFP) of the construction industry of the Yangtze River Economic Belt. Results show that ➀ the GTFP of the construction industry of the Yangtze River Economic Belt has demonstrated a growth trend in most years with an average annual growth that exceeds the national level. In the upstream, middle, and lower reaches of the river, the changes in GTFP show great convergence, whereas the technical efficiency and technical progress indices show volatile trends; ➁ the increase in GTFP is seriously polarized and reflects an unbalanced development in the Yangtze River Economic Belt. The highest and lowest GTFP levels are reported in the downstream and upstream areas of the river, respectively. However, opposite trends are observed for the annual GTFP growth rate. This research is the basic work to further find the driving factors of GTFP growth and formulate the policy to improve the GTFP of the construction industry of the Yangtze River Economic Belt. Keywords: Green total factor productivity · spatiotemporal Evolution · SBM model · global Malmquist–Luenberger index · Yangtze River Economic Belt

1 Introduction In his report to the 19th National Congress of the Communist Party of China, General Secretary Xi Jinping put forward the construction of ecological civilization as a millennium plan to ensure the sustainable development of China. This plan highlights the importance of adhering to the basic national policies of resource conservation and environmental protection and achieving a green development mode. The Yangtze River Economic Belt is one of the most economically developed regions in China whose GDP accounted for 46.2% of the country total in 2019. Accordingly, the Chinese government © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 501–515, 2023. https://doi.org/10.1007/978-981-99-3626-7_40

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attaches great importance to the green, coordinated development and ecological civilization construction of the Yangtze River Economic Belt. Researchers have examined the green development of cities, industry, and agriculture in the Yangtze River Economic Belt, but the green development of its construction industry, which is characterized by its high energy consumption and pollution levels, has attracted limited attention. As a pillar industry, construction significantly contributes to the economy and social development of a country while simultaneously consuming a large amount of resources and causing environmental pollution. Statistical data show that the construction industry accounts for 1/4 of the total energy consumption of China and emits 1/3 of its CO2 [1]. The green development and ecological civilization construction of the Yangtze River Economic Belt urgently require a green transformation of its construction industry. Therefore, the temporal and spatial evolution characteristics of green total factor productivity (GTFP) of this industry should be firstly measured in order to analyze its driving factors and further to formulate the policy to improve the GTFP.

2 Literature Review The production efficiency of the construction industry has received limited attention from researchers [2]. While research on the total factor productivity (TFP) of the Chinese construction industry started out late, some researchers, including Zhang [3], Gao [3], Xue [5], and Li et al. [6], have already performed related work in this area. Zhang and Wang [3] examined the production efficiency of the construction industry of Shandong Province via data envelopment analysis (DEA), whereas Wang et al. [7] studied the growth of the total output value of the construction industry of China from 1995 to 2010 along with its contributing factors by using the improved production function model (C–D model) and the Solow residual method. Li [6], Duan [8], Liu [2],Tan Dan [9], Chen [9] and Shi [11] studied the variations in this TFP by using the DEA–Malmquist index method and panel data for different years. Li [12] and Hu [12] also used the Malmquist index to study the changes in the TFP of the Australian construction industry. Duan [14] studied the TFP of the East China construction industry, whereas Wang et al. [15] studied the productivity of the construction industry across six provinces of South China by using a super-efficiency DEA model. Li [16] used the SBM-DEA model to study the changes and decomposition of the TFP of the Chinese construction industry. Feng et al. [17] used the SBM model to measure the energy economic efficiency of this industry. The methods for measuring TFP can be classified into parametric and non-parametric methods. The former is represented by the Solow residual method and the stochastic frontier approach, whereas the latter is represented by DEA. The parametric method presupposes that the form of the production function and the distribution characteristics of the error term will lead to deviations in the analysis. The DEA method effectively avoids this problem by not setting a specific production function. To address the radial and single-angle shortcomings of the common DEA model, Tone [18] proposed a nonradial, non-angle DEA–SBM model. As a static analysis method, combining DEA with the Malmquist index can help researchers dynamically examine the changes in TFP [19] and decompose TFP into the technical progress change index and the technical

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efficiency change index. To address the limitations of the Malmquist index in handling undesired output, Chung et al. [20] integrated the directional distance function containing undesired output into the Malmquist model to build the Malmquist–Luenberger (ML) productivity index model. However, this index is not cyclical and transitive and may lead to unsolvable problems in linear programming. Therefore, Oh [21] combined the concept of global productivity with the directional distance function to construct the global Malmquist–Luenberger (GML) index. As environmental issues have become increasingly prominent in recent years, the economic development of China has demonstrated an urgent need for an accelerated green transformation. Given the significant contribution of industrial development to GDP, resource consumption and environmental costs warrant consideration. GTFP incorporates energy consumption, pollutants, and other undesired outputs into the framework of TFP theory and has become an important indicator for measuring the coordinated development of the economy, its resources, and the environment [22]. Many studies have investigated GTFP accordingly. For instance, Yi et al. [23] studied the GTFP of the Yangtze River Economic Belt, Li et al. [24] studied the GTFP of its major cities, and Wu Chuanqing et al. studied its industrial [25] and agricultural GTFP [26]. However, only few studies have examined the GTFP of the construction industry of this economic belt. Xiang et al. [27] used the GML model to calculate the GTFP of the construction industry of 30 provinces in China from 2008 to 2017. Zhang et al. [28] improved the GML productivity index and calculated the dynamic GTFP of the Chinese construction industry for the years 2011 to 2017. Hu and Liu [13] studied the TFP of the Australian construction industry while taking carbon emissions into account. Gao et al. [30] studied the energy efficiency of the construction industry and its temporal and spatial evolutions. Zhou et al. [31] studied the carbon emission efficiency of the construction industry by using super-efficiency SBM. In sum, while research on the TFP of the construction industry has achieved some progress, several areas for improvement are still present. In terms of research objects, these studies are mainly conducted in China and its central, eastern, and western regions, and only few studies have focused on the Yangtze River Economic Belt despite its outstanding contributions to the economic development of China and its construction industry. In terms of measuring TFP, previous studies have mainly used the DEA–Malmquist index in their dynamic analysis and decomposition of TFP change. While the SBM model overcomes the shortcomings of the common DEA model with the same radial and single angle, the GML index takes into account environmental pollution and other undesirable factors unlike the traditional Malmquist index. Therefore, the GML index based on the SBM direction distance function provides more accurate measurements of TFP change. In examining the driving factors of TFP, previous studies have mainly applied regression analysis, but only few have analyzed the interaction among these factors. To address these gaps, this research takes 11 provincial-level administrative regions in the Yangtze River Economic Belt as its research objects, comprehensively uses the SBM direction distance function and the GML index to measure the GTFP of the construction industry and its changes while taking undesired output into account, analyzes its space– time evolution.

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3 Research Methods and Data Sources 3.1 Research Methods 3.1.1 Global Production Possibility Sets To measure the GTFP of the construction industry and its changes, this paper constructs a production possibility set that includes both “good” and “bad” outputs. Each provincial administrative region is considered a decision-making unit (DMU). Following Färe et al. [33], each provincial administrative region is assumed to use N element inputs x = + (x1 , x2 , · · · , xn ) ∈ R+ N to obtain M expected outputs y = (y1 , y2 , · · · , yn ) ∈ RM and I + undesired outputs (b1 , b2 , · · · , bn ) ∈ RI . Suppose that each period is t = (1, 2, · · · , T ) and each administrative district is k= (1, 2, · · · , K), then the input of the k region and t period The output is expressed as xkt , ykt , bkt , and the current production possibility set P t (x) is expressed as  K K       t t t t P x = yt , bt : zkt ykm ≥ ykm , ∀m; zkt btki = btki , ∀i; k=1 K 

t zkt xkn

≤

t xkn , ∀n;

k=1

k=1 K 

zkt

=

1, zkt



≥ 0, ∀k

(1)

k=1

where zkt represents the weight of each cross section, zkt ≥ 0 represents constant return  t t to scale, and K k=1 zk = 1, zk ≥ 0 represents variable returns to scale. However, in the case of P t (x), technological regression may occur. For this reason, Oh [21] constructed a global production possibility set P G (x) that emphasizes the consistency of the production frontier. This set is expressed as  K K T  T        G t t t P x = yt , bt : zkt ykm ≥ ykm , ∀m; zkt btki = btki , ∀i; t=1 k=1 K T   t=1 k=1

t zkt xkn

≤

t xkn , ∀n;

t=1 k=1 K T  

zkt

=

1, zkt

≥ 0, ∀k

 (2)

t=1 k=1

3.1.2 SBM Direction Distance Function In the condition where slack variables exist, the traditional directional distance function is prone to errors in estimation. Following Fukuyama and Weber [34], this paper defines the global SBM directional distance function considering undesired output as

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− →G  t,k t,k t,k x y b S v x , y , b , g , g , g = max

1 N

N

sxn n=1 gxn

+

1 M+1

M

T  K 

t t zkt xkn + sxn = xkn , ∀n;

t=1 k=1 T  K 

l

sbi i=1 gb i



T  K 

y

t t zkt ykm − sm = ykm , ∀m;

t=1 k=1

zkt btki + sbi = btk i , ∀i;

t=1 k=1

+

2

sx ,sy ,sb

s.t.

y

sm m=1 gym

505

K 

y

zkt = 1, zkt ≥ 0, ∀k; sm ≥ 0, ∀m; sbi ≥ 0, ∀i

(3)

k=1

where xt,k , yt,k , bt,k represent the vector of input, expected output, and undesired output of the k province during t, respectively, g x , g y , g b represent the direction vectors of y input decrease, expected output increase, and undesired output decrease, and snx , sm , sib represent input, expected output, and the slack variable of undesired output. Positive snx and sib indicate that the actual input and undesired output are greater than the boundary y input and output. If sm is positive, then the actual output is smaller than the boundary y b x output. sn , sm , si Represent the amount of excessive input, insufficient expected output, and excessive undesired output. 3.1.3 GML Productivity Index The GML productivity index is a non-parametric measure of changes in production efficiency proposed by Oh [21] that combines the concept of global productivity with the ML index proposed by Chung et al. [20] considering undesired output. This index addresses the limitations of the traditional Malmquist index in dealing with undesired output. Moreover, the ML index is not cyclical and transitive, which may lead to unsolvable linear programming problems. This paper builds a GML index model based on the SBM direction distance function to measure the change rate of the GTFP of the construction industry of the Yangtze River Economic Belt. The K production inputs used by each DMU are denoted as X = + , the M expected outputs produced are Y = (y1 , y2 , · · · , yM ) ∈ (x1 , x2 , · · · , xk ) ∈ RK + RM , and the I type of undesired output produced is recorded as b = (b1 , b2 , · · · , bI ) ∈ R+ I . . The GML index for the GTFP of the construction industry from periods t to t + 1 is constructed as GMLt+1 t

− →G 1 + S V (xt , yt , bt ; gx , gy , gb ) = − →G 1 + S V (xt+1 , yt+1 , bt+1 ; gx , gy , gb )

(4)

− →G − →G where S V (xt , yt , bt ; g x , g y , g b ) and S V (xt+1 , yt+1 , bt+1 ; g x , g y , g b ) represent the global directional distance function in periods t and t + 1, respectively. A GML value greater, equal, and less than 1 indicates an increased, unchanged, and decreased GTFP, respectively. The GML index can be further decomposed into the technical efficiency

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change index (GMLEC) and the technological progress index (GMLTC) as shown in formula (3). GMLtt+1

 tV (xt , yt , bt ; gx , gy , gb ) 1+S =  tV (xt+1 , yt+1 , bt+1 ; gx , gy , gb ) 1+S =

 tV (xt , yt , bt ; gx , gy , gb ) 1+S ×  tV (xt+1 , yt+1 , bt+1 ; gx , gy , gb ) 1+S G

t

 V (xt , yt , bt ; gx , gy , gb ))  V (xt , yt , bt ; gx , gy , gb )/(1 + S 1+S  t+1 , yt+1 , bt+1 ; gx , gy , gb )/ 1 + S G  tV (xt+1 , yt+1 , bt+1 ; gx , gy , gb ) 1+S V (x = GMLEC × GMLTC

(5)

− →t − →t where S V (xt , yt , bt ; g x , g y , g b ) and S V (xt+1 , yt+1 , bt+1 ; g x , g y , g b ) represent the current directional distance function in periods t and t + 1, respectively, and GMLEC represents the change in technical efficiency and measures the catch-up effect on the production frontier from periods t to t + 1. GMLEC > 1 indicates that the gap from the optimal DMU is narrowing, whereas GMLEC < 1 indicates otherwise. GMLTC represents technological progress, and its changes reflect the contribution of the movement of the production frontier to the changes in TFP. GMLTC > 1 indicates that technological progress is present, whereas GMLTC < 1 indicates otherwise. 3.1.4 Geographic Detector Model GeoDetector is a new statistical method proposed by Wang et al. [32] that aims to detect spatial differentiation and its driving factors. GeoDetector includes four detectors for differentiation and factor detection, interaction detection, risk area detection, and ecological detection. The differentiation, factor detection, and interaction detection functions of GeoDetector can be used to explore the spatial differentiation, driving factors, and interactions of the GTFP of the construction industry of the Yangtze River Economic Zone. Limited by the length of the article, this research mainly demonstrates spatial differentiation of GTFP of the construction industry. 3.2 Data Sources This paper studies the GTFP of the construction industry in the 11 provincial administrative regions of the Yangtze River Economic Belt by using 2007–2017 as the sample period. The relevant data are collected from the China Statistical Yearbook, China Energy Yearbook, China Statistical Abstract, and China Science and Technology Statistical Yearbook. To exclude the influence of factors such as inflation, the original data are converted into constant price data with 2007 as the reference year.

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4 Analysis of the Temporal and Spatial Evolution of the GTFP of the Construction Industry 4.1 Variable Selection and Data Description Using the DEA method to measure production efficiency requires a scientific and reasonable selection of input and output indicators, and several principles should be followed in the indicator selection [9, 17]. First, the value orientation should be analyzed. Second, the indicators should meet the evaluation requirements and objectively reflect the productivity level of the evaluation object. Third, avoid strong linear relationships between indicators. Fourth, the data related to the selected indicators are easy to obtain and are reliable. Based on these principles, refer to Li [12], Liu [2], Feng [17], Tan [9], Hu [12], Gao [30], Xiang [27] and other scholars on the construction industry Selection of total factor productivity evaluation, green total factor productivity evaluation, and energy efficiency evaluation index. The input indexes used in this study include labor input, capital input, mechanical equipment input, and energy input. The expected output index is used as the total output value, and carbon emissions is used as the undesired output indicator (Fig. 1). (1) Construction industry input: Labor and capital are the main inputs of the construction industry. Labor input is calculated as the number of employees in the construction industry, whereas capital input is calculated as the total assets of construction enterprises. Mechanical equipment is an important factor in construction production activities, and the input of mechanical equipment is calculated as the total power of self-owned construction machinery and equipment at the end of the year. The energy consumption of the construction industry is calculated based on its main consumption of 12 types of energy, namely, raw coal, briquette, coke, gasoline, kerosene, diesel, fuel oil, lubricating oil, liquefied petroleum gas, natural gas, heat, and electricity. After converting these energy types into “10,000 tons of standard coal,” their sum is computed. (2) Construction industry output: The expected total output value is calculated as the total output value of the construction industry, whereas the undesired output is represented by CO2 emissions. CO2 is the most important undesired output of the construction industry and is also the most critical environmental pollutant. Carbon emissions are calculated by using the carbon emissions coefficient recommended by the United Nations Intergovernmental Panel on Climate Change (IPCC). The index variables and their calculation methods are shown in Table 1.

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Table 1. Selection of construction industry input–output indicators and their calculation methods. Indicator type

Variable name

Calculation method

Investment

Labor input

Number of employees in the construction industry

Capital input

Total assets of construction enterprises

Mechanical equipment input

Year-end total power of self-owned construction machinery and equipment  12 main energy consumption in the construction industry * Energy conversion standard coal coefficient

Energy consumption

Expected output

Total output value

Undesired output

CO2 emissions

Total output value of construction industry  Ci = Eij · ηj j

Note: Carbon emission is calculated following the method proposed by the IPCC: Ci =



Eij ·

j

ηj , where Ci represents the CO2 emissions of the i administrative district, Eij represents the i administrative district, j represents energy consumption, and ηj represents the carbon emission coefficient of j.

4.2 Analysis of the Spatio-Temporal Differences in GTFP By using MAXDEA8.0, the SBM direction distance function, and the GML index, the 2007–2017 GTFP of the construction industry of the Yangtze River Economic Belt, its upstream regions, middle reaches, downstream regions, and various provinces are computed along with the GML (Table 2), GMLEC (Table 3), and GMLTC indices (Table 4). 1.20 1.15 1.10 1.05 1.00 0.95 0.90

2008

2009

2010

2011 GML

2012

2013

GMLEC

2014

2015

2016

2017

GMLTC

Fig. 1. GTFP and its decomposition of the construction industry the Yangtze River Economic Belt in 2007–2017.

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Table 2. 2007–2017 GML index of the construction industry of the Yangtze River Economic Belt, its upstream, midstream, and downstream regions, and various provinces 2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

Shanghai

1.005

0.993

0.985

0.985

0.992

0.997

1.049

1.000

1.000

1.000

Average 1.001

Jiangsu

1.086

0.929

1.159

1.000

0.987

1.008

1.005

1.000

0.951

1.052

1.018

Zhejiang

1.000

0.984

0.971

1.046

0.993

1.006

1.002

0.994

0.986

1.020

1.000

Anhui

0.960

0.972

1.026

1.065

1.038

1.060

0.991

0.971

0.972

1.047

1.010

Jiangxi

0.994

1.007

1.249

1.002

1.002

0.957

0.978

0.980

0.986

1.023

1.018

Hubei

1.067

0.985

1.017

1.095

1.015

1.042

1.021

0.840

1.036

1.022

1.014

Hunan

1.015

1.016

1.056

1.074

0.954

1.016

1.007

0.899

0.968

0.994

1.000

Chongqing

1.017

0.938

1.216

0.944

1.035

1.034

1.056

1.109

0.981

1.011

1.034

Sichuan

1.001

1.016

1.027

1.032

1.117

1.140

0.980

0.876

0.918

0.949

1.006

Guizhou

1.053

1.059

0.980

1.105

1.007

0.989

0.989

0.881

0.931

1.035

1.003

Yunnan

1.034

1.081

1.008

1.125

1.023

1.006

0.958

0.877

0.946

0.957

1.002

Upstream

1.027

1.024

1.058

1.052

1.045

1.042

0.996

0.936

0.944

0.988

1.011

Midstream

1.025

1.002

1.107

1.057

0.990

1.005

1.002

0.906

0.997

1.013

1.011

Downstream

1.013

0.969

1.035

1.024

1.003

1.018

1.012

0.991

0.977

1.030

1.007

Total

1.021

0.998

1.063

1.043

1.015

1.023

1.003

0.948

0.970

1.010

1.010

Table 3. 2007–2017 GMLEC index of the construction industry of the Yangtze River Economic Belt, its upstream, midstream, and downstream regions, and various provinces 2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

Average

Shanghai

1.000

1.000

1.000

1.000

1.000

1.000

1.000

1.000

1.000

1.000

1.000

Jiangsu

1.108

1.016

1.000

1.000

1.000

1.000

1.000

1.000

1.000

1.000

1.012

Zhejiang

1.000

1.000

1.000

1.000

1.000

1.000

1.000

1.000

1.000

1.000

1.000

Anhui

0.867

1.085

1.069

0.938

1.116

1.029

0.990

1.087

0.950

1.042

1.017

Jiangxi

1.018

1.047

1.148

1.000

1.000

1.000

1.000

1.000

1.000

1.000

1.021

Hubei

1.043

1.037

1.074

0.995

1.037

1.000

1.000

1.000

1.000

1.000

1.019

Hunan

0.986

1.029

1.082

1.018

1.000

1.000

1.000

0.973

0.979

0.982

1.005

Chongqing

1.012

1.044

1.210

0.934

1.061

1.084

1.035

1.000

1.000

1.000

1.038

Sichuan

0.949

1.111

1.045

1.054

1.081

1.125

0.948

0.945

0.975

0.936

1.017

Guizhou

1.049

1.104

1.031

1.015

1.078

0.996

0.993

0.988

0.933

1.000

1.019

Yunnan

1.032

1.128

1.034

1.038

1.058

0.990

0.928

1.050

0.930

0.961

1.015

Upstream

1.010

1.097

1.080

1.010

1.069

1.049

0.976

0.996

0.959

0.974

1.022

Midstream

1.016

1.038

1.101

1.004

1.012

1.000

1.000

0.991

0.993

0.994

1.015

Downstream

0.994

1.025

1.017

0.985

1.029

1.007

0.997

1.022

0.988

1.011

1.007

Total

1.006

1.055

1.063

0.999

1.039

1.020

0.990

1.004

0.979

0.993

1.015

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Table 4. 2007–2017 GMLTC index of the construction industry of the Yangtze River Economic Belt, its upstream, midstream, and downstream regions, and various provinces 2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

Shanghai

1.005

0.993

0.985

0.985

0.992

0.997

1.049

1.000

1.000

1.000

Average 1.001

Jiangsu

0.980

0.914

1.159

1.000

0.987

1.008

1.005

1.000

0.951

1.052

1.006

Zhejiang

1.000

0.984

0.971

1.046

0.993

1.006

1.002

0.994

0.986

1.020

1.000

Anhui

1.107

0.896

0.959

1.136

0.931

1.030

1.001

0.893

1.022

1.004

0.998

0.957

Jiangxi

0.976

0.962

1.088

1.002

1.002

Hubei

1.023

0.949

0.946

1.101

0.979

0.978

0.980

0.986

1.023

0.996

1.021

0.840

1.036

1.022

0.996

Hunan

1.029

0.987

0.976

1.055

0.954

Chongqing

1.006

0.898

1.005

1.011

0.975

1.016

1.007

0.924

0.989

1.012

0.995

0.954

1.021

1.109

0.981

1.011

Sichuan

1.055

0.914

0.982

0.979

1.034

0.997

1.013

1.033

0.927

0.942

1.014

0.989

Guizhou

1.004

0.960

0.951

1.089

0.934

0.993

0.996

0.892

0.998

1.036

0.985

Yunnan

1.002

0.958

0.975

1.084

0.967

1.016

1.032

0.835

1.018

0.996

0.988

Upstream

1.017

0.933

0.978

1.041

0.978

0.994

1.020

0.941

0.985

1.014

0.990

Midstream

1.010

0.966

1.004

1.053

0.978

1.005

1.002

0.915

1.004

1.019

0.995

Downstream

1.023

0.947

1.019

1.042

0.976

1.010

1.014

0.972

0.990

1.019

1.001

Total

1.017

0.947

1.000

1.044

0.977

1.003

1.013

0.945

0.992

1.017

0.996

1.042

4.2.1 Overall Analysis The overall GTFP of the construction industry of the Yangtze River Economic Belt showed an increasing trend with an average annual growth rate of 1%, which was much higher than the national growth rate of 0.33% [28]. The highest GTFP growth of 6.3% was recorded in 2010 and was attributed to improvements in technical efficiency (GMLEC1.063). However, this GTFP reported a negative growth in 2009, 2015, and 2016 due to the negative impact of the 2008 global financial crisis and the 2015 domestic stock market crisis. Meanwhile, the negative GTFP growth of 5.2% in 2015 was mainly due to the decline in technological progress (GMLTC0.945). Zhang et al. [28] studied the GTFP of the national construction industry and obtained similar conclusions. The GML index of the national construction industry demonstrated a continuous decline from 2015 to 2017. However, the Yangtze River Economic Belt has shown technological improvements (GMLEC0.993, GMLTC1.017) in 2017. After overcoming the negative impact in 2011, GTFP stopped falling and rebounded. From the perspective of the GML decomposition index, the main driving force for the growth of the GTFP of the construction industry of the Yangtze River Economic Belt is technical efficiency (GMLEC1.015). This conclusion contradicts those of Xiang [27], who used national data. Meanwhile, the main driver for the GTFP growth of the national construction industry from 2008 to 2017 is technological progress [27], whereas the main driver for the GTFP growth of the Yangtze River Economic Belt construction industry is improved technical efficiency. Both the technical efficiency index and the technical progress index show fluctuating trends, and the changes in GMLTC and GML are highly

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synchronized, thereby indicating that technological progress plays an important role in promoting the growth of GTFP in the Yangtze River Economic Belt. 4.2.2 Regional Analysis Based on the GTFP value recorded in 2007, GML is converted into a cumulative index to obtain the GTFP value for the other years [35, 36]. In addition, to analyze the temporal and spatial differentiation of the construction industry GTFP of the Yangtze River Economic Belt, Fig. 2 which made by GeoDetector intercepts the GTFP for years 2008, 2011, 2014, and 2017. From the perspective of the GTFP value, GML value, and decomposition index of the Yangtze River Economic Belt from 2007 to 2017, the GTFP level of the construction industry in the upper, middle and lower reaches of this region follows a tiered structure, with the lowest and highest GTFP levels recorded in the upstream and downstream regions, respectively. The GTFP in these regions shows a fair annual growth rate. By contrast, GTFP decreases successively to 1.11%, 1.06%, and 0.72% for the upper, middle, and lower reaches of the economic belt, respectively. From the perspective of the GML decomposition index, the upstream regions have the highest technical efficiency index, whereas the downstream regions have the lowest. However, the opposite is observed in terms of the technological progress index.

Fig. 2. Spatial differentiation of the construction industry GTFP of the Yangtze River Economic Belt in 2008, 2011, 2014, and 2017.

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The changes in the construction industry GTFP of the upper, middle, and lower reaches of the Yangtze River Economic Belt show high convergence with a simultaneous decline in 2015 and 2016 and growth in the other years. Compared with economically developed downstream regions, the upper and middle regions of the economic belt were more negatively affected by the 2015 stock market crisis. The negative growth in this region is mainly attributed to the simultaneous decline in technological progress in its upper, middle, and lower reaches (GMLTC 0.941, 0.915, and 0.972, respectively). In the upstream region, GTFP maintained a high growth rate of over 4% from 2010 to 2013, with technical efficiency playing a major role (GMLEC 1.080, 1.010, 1.069, and 1.049, respectively) except in 2011 where technological advancement played a major role (GMLTC 1.041). A decline in technological progress was reported in the other years (GMLTC 0.978, 0.978, and 0.994). In the midstream region, the construction industry GTFP maintained a relatively high growth rate from 2010 to 2011, reaching 10.73% and 5.72%, respectively. Technical efficiency and technological progress played a major role in such growth. In the downstream areas, the GTFP steadily increased in most years with an average growth rate of approximately 2% and the highest growth rate of 3.5% recorded in 2010. The construction industry GTFP and its growth in the upper, middle, and lower reaches of the Yangtze River Economic Belt are polarized and reflect characteristics of an uneven development. The average annual growth rate of the construction industry GTFP in the upstream area of Chongqing reached 3.4%, which shows that Chongqing has grasped the strategy of western development and has achieved an obvious catch-up effect. Meanwhile, Yunnan, Guizhou, and Sichuan reported very slow annual GTFP growths of 0.15%, 0.3%, and 0.57%, respectively, due to the 2015 stock market crisis, indicating that their GTFP has regressed. The construction industry GTFP of Hunan in the middle reaches of the economic belt initially increased and then decreased, whereas those of Jiangxi and Hubei experienced growths of 1.79% and 1.39%, respectively. Although the economic development level of these two provinces is lower than that of downstream regions, their technical efficiency has significantly improved due to their increasing industrial development and technological innovation investment. The construction industry GTFP of Jiangsu and Anhui in the downstream areas of the economic belt was at the middle-high and middle-low levels at the beginning of the study period with average annual growth rates of 1.77% and 1%, respectively. These regions achieved a clear catch-up. Zhejiang and Shanghai have a strong economic foundation and an advanced carbon emission technology, thereby explaining their highest construction industry GTFP. However, the GTFP growth of these provinces is the least obvious in the entire region. Figure 2 shows that compared with the levels reported in 2014, the construction industry GTFP in the middle and upper reaches of the upstream region has significantly regressed in 2017 due to the negative impact of the 2015 stock market crisis. Meanwhile, the central and western regions of the economic belt have a relatively slow economic development, which greatly affects their ability to recover.

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5 Conclusions In addition to the SBM direction distance function and GML index, this paper uses panel data of the Yangtze River Economic Zone from 2007 to 2017 to measure its construction industry GTFP, GML indexes, and decomposition indexes. The time series growth and spatial differentiation characteristics of GTFP (along with its driving mechanism) are explored. First, from the perspective of evolution and its driving factors, except for the negative growths recorded in 2009, 2015 and 2016, the overall construction industry GTFP of the Yangtze River Economic Belt has shown an increasing trend with an average annual growth rate of 1%, which is higher than the national average. Technical efficiency plays a major role in the highest GTFP growth (6.3%) reported in 2010. Meanwhile, GTFP experienced the largest negative growth of 5.2% in 2015 mainly due to a decline in technological progress resulting from the 2015 stock market crisis. A high convergence is observed in the construction industry GTFP of the upper, middle, and lower reaches. Both technical efficiency index and technical progress index showed volatile trends throughout the study period, and the changes in the technical progress index and GML index are highly synchronized. In sum, technological progress is an important driving force for the green development of the construction industry. Second, from the spatial differentiation perspective, the construction industry GTFP of the Yangtze River Economic Belt and its growth are severely polarized and show an uneven development. The construction industries in the downstream areas show the highest level of green development, whereas those in the upstream areas show the lowest level. The opposite trends are observed in green development. Chongqing in the upstream region showed an obvious catch-up effect in 2017 during which the province closely followed the highest echelon of GTFP. The annual average GTFP growths of Yunnan, Guizhou, and Sichuan were very slow, and these provinces had the lowest GTFP levels in the entire economic belt. The construction industry GTFP of Hunan initially increased and then regressed. Meanwhile, the GTFP levels of Jiangxi and Hubei experienced a fast growth, Jiangsu achieved a clear catch-up and entered the highest echelon of GTFP, Anhui reported mid-to-low GTFP levels yet achieved a catch-up, and Zhejiang and Shanghai reported the highest GTFP levels.

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Federated Learning Based Collaboration Framework of Data Sharing for Intelligent Design of Residential Buildings Qiqi Zhang(B) and Wei Pan Department of Civil Engineering, The University of Hong Kong, Hong Kong SAR, China [email protected], [email protected]

Abstract. The fast-developed artificial neural network (ANN) technique has been used in the architecture, engineering, and construction domain to facilitate the designing process, such as layout planning and structural designing. However, it is difficult to collect sufficient drawings from data owners for privacy reasons and due to the lack of proper incentive mechanism. To address the challenges, this paper proposes an incentive collaboration framework based on federated learning and game theory to enhance data sharing for intelligent design of building projects. Federated learning can train ANN models on data owners’ local devices and thus avoid directly publicising their drawings. A collaboration matching mechanism is designed based on game theory to encourage drawing owners to publicise their data. A case study is conducted using drawings of residential buildings to validate the feasibility of the proposed framework. Results show that (1) Data owners who provide data with higher quality and larger quantity can trade for more data in collaboration; and (2) The ANN model trained through federated learning on larger quantity of drawings performs better than the one trained on local data. Practically, the innovative framework should facilitate the development of intelligent design models with higher performance to assist building designers. Theoretically, the combination of federated learning and game theory could enhance the knowledge of addressing the data sharing dynamics and complexity in innovative construction such as modular building. Keywords: Intelligent design · Federated learning · Cooperative game theory · Building design

1 Introduction Intelligent design aims to automatically generate drawings according to certain constraints such as building boundaries and programmatic requirements [1]. It can reduce manual design work, accelerate the iterative design process, and promote better design performance. Recent studies have achieved great strides in this direction [1–4]. A series of ANN models were proposed and achieved state-of-the-art performance in layout planning, urban planning, and reinforced concrete design. Despite ANN models’ universality and good performance, it is hard to collect sufficient data to train an ANN model. Some © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 516–532, 2023. https://doi.org/10.1007/978-981-99-3626-7_41

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research tries to alleviate this problem through transfer learning [5]. While the transfer learning-based method has prone to be an effective way to enhance a model’s performance, developing data collecting methods is another possible solution. Most organisations are unwilling to publicise their drawing data in case of unnecessary troubles. As a result, embedded value cannot be mined from these scattered drawings. To enhance design productivity in the architecture, engineering, and construction community, it is necessary to explore techniques and cooperation mechanisms to facilitate drawing sharing while ensuring data contributors’ privacy, benefits, and security. Federated learning is a fast-developing technique enabling ANN models to be trained on local devices and avoiding publicising training data [6]. However, there are two main challenges to utilising federated learning in intelligent design. Firstly, data owners usually view their drawings as strategic resources to train high-performance intelligent models so as to gain competitive advantages in the market. They are not likely to share drawings without equivalent resources in exchange. In the federated learning framework, all the shared data cannot be directly seen and evaluated. This may lead to opportunistic behaviours – some drawing owners may provide low-quality drawings to the community and exchange them for high-quality drawings. The unpredictable asymmetry of trading may finally lead to the “adverse selection” phenomenon of the drawing trading market. Secondly, it is hard to find proper counterparties with equivalent resources and transaction intentions in a market with many drawing suppliers. To tackle such challenges, this paper aims to propose a drawing sharing framework that can promote near-fair deals in the long run. To achieve this aim, three objectives were developed: (1) to propose a cooperation framework for drawing sharing and persevering; (2) to analyse participants’ behaviours in the proposed framework and check whether the “adverse selection” phenomenon can be prevented; and (3) to validate the cooperation framework with a case study. This study contributes to the body of knowledge in two aspects. First, this study is one of the early investigations focusing on data cooperation mechanisms to improve intelligent design model training. Second, a matching mechanism is proposed to promote collaboration equilibrium and enhance trading efficiency among organisations with competitive relationships. Besides, the matching mechanism, combined with the federated learning technique, shows promise in solving the dynamics and complexity of data sharing in innovative construction such as modular integrated construction (MiC). The rest of this paper is organised as below. Section 2 elaborates backgrounds in intelligent design, federated learning, and cooperative game theory. Section 3 introduces the proposed drawing cooperation framework. Section 4 presents the collaboration matching mechanism to achieve gaming equilibrium among participants in the framework. Section 5 validates the proposed framework and strategy through a case study. Section 6 discusses the experimental results and limitations of the current framework. The final section concludes the main findings and lists out future research recommended.

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2 Literature Review 2.1 Intelligent Design Intelligent design aims to generate design content given certain constraints by utilising smart algorithms. Existing approaches to realising intelligent design tools can be divided into generative and deep learning methods. Generative methods, including genetic algorithms, cellular automata, and generative grammar, are usually used in architectural designs and are less suitable for structural designs. Deep learning methods driven by big data have proven useful in both architectural and structural designs [1]. Despite these remarkable achievements, many other sub-tasks remain to be tackled for achieving totally automated design for different buildings [7–10], such as pipeline layout planning, furniture arrangement, fire protection design, and insulation design. Besides, the fastdeveloping off-site construction also makes new demands of intelligent design for more accurate and detailed design in the early stage [11]. Current deep learning methods usually model intelligent design tasks as image generation problems and train relevant ANN models on drawings encoded in image format [2, 3]. Typical data-processing mechanism in intelligent design involves (1) collecting drawings from different organisations, (2) filtering out drawing layers related to tasks and encoding them into image form, (3) designing, training, and evaluating ANN models, (4) vectorising results generated by trained models. One of this mechanism’s main challenges is collecting sufficient high-quality drawing data. One organisation’s drawings may be insufficient to train a high-performance model and need to collect more drawings from other organisations. However, drawings may be considered important resources for some organisations, such as design institutes. The disclosure of drawings can also cause unpredictable trouble for owners. This makes it challenging to collect training data and hinders the conversion from big data to the productivity of the AEC community. As a result, it is important to explore drawing data collaboration mechanisms with data security and commercial value as two primary considerations. 2.2 Federated Learning Federated learning, a machine learning technique first proposed by Google, can train an ANN model on data owners’ local devices and share local model parameters to the global model. This way, one can train ANN models while avoiding access data directly. A typical federated learning algorithm is listed in Table 1. In the context of data sharing for intelligent design, federated learning can ensure drawings’ security. However, three issues should be considered when utilising this technique. First, FL assumes all clients are willing to provide their data to train a model and doesn’t consider how to assign the benefits among participants. In the context of intelligent design, data providers are usually in business competition. Without enough benefits, a client will not participate in the collaboration. Second, since drawings cannot be directly analysed, the quality of shared drawing data can only be identified after an ANN model trains on these drawings. Some malicious participants may use lowquality data to trade with high-quality data. A sustainable data-sharing market should be designed with a mechanism to avoid such opportunistic behaviours. Third, drawings in

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Table 1. Weights averaging federated learning

different areas can vary widely. For example, residential buildings’ pipelines differ from those designed for hospitals. As a result, the pipeline generating models of residential buildings and hospitals should be trained separately on different drawings. The data sharing market for intelligent design should enable users to identify the types of shared drawings before trading and using them. 2.3 Cooperative Game Theory Cooperative game theory aims to analyse coalition patterns among players in a game. Given a predefined payoff function for all players, players’ payoff changes according to the coalition patterns. This paper studies how clients will collaborate to train their personalised intelligent design models in federated learning. Suppose the payoff of each client is the performance of the personalised model. The payoff function cannot be written explicitly and has to be approximated using the evaluation score of the learned models for each client. This is different from the classical cooperative game theory. Given all the shared data and personalised models, a collaboration equilibrium cannot be predicted or calculated directly in the intelligent design data sharing market. There are some studies focusing on finding the collaboration equilibrium in federated learning [12–15]. Most of the research requires obtaining the results of all possible collaborations before finalising the collaboration equilibrium. However, this process can be time-consuming and needs enormous computing power, which is not practical in the drawing sharing market. To tackle this knowledge gap, this article proposes the collaboration matching mechanism to accelerate collaboration in the intelligent design data market while sacrificing part of the temporary equilibrium.

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3 The Proposed Data Sharing Framework The proposed sharing framework consists of three layers, the client layer, the trading layer, and the recording layer. The client layer involves all the clients who own drawings, intelligent design models and computing devices. Clients trade their drawings on the trading layer. Models will be trained on traded data through federated learning. The performances of models will be recorded and uploaded to the recording layer. The records will be referenced by the clients when trading drawings. 3.1 Client Layer All the clients who participated in the data sharing framework constitute the client layer. Three crucial assumptions are made for clients. (1) All the drawings owned by clients can be standardised and marked with a unique string. (2) Not all drawings can enhance one model’s performance due to the uncertain quality and distribution of drawings. (3) All the clients are rational. 3.1.1 Drawings standardisation Drawings in this paper are designs of buildings satisfying the two properties: (1) Contents are divided into multiple layers. (2) Elements with the same semantic are in one layer. Typical drawings are represented in DWG or DXF files. Denote a drawing as R = {li }i=1,2,...N , where li represents the ith layer of the drawing. An intelligent design task can be described as, given constraints C ⊆ R, generate target layer lt that is closest to the real layer lk ∈ R. For example, in a study aiming at generating shear walls, the constraint is a set containing the architectural wall layer, while the target is to generate a layer that is similar with the shear wall layer in this drawing. Generally, a drawing can be training data for multiple intelligent design tasks. In practice, layers’ dividing and naming regulations differ according to organisations and designers. It is necessary to standardise the representation of different drawings to train an intelligent design model jointly. Though formulating regulations is one essential step toward data sharing in intelligent design, it is out of this article’s scope. Assuming all the clients’ drawings are organised in the same standard, a data package D is a set of standardised drawings R. In the proposed data sharing framework, all the data packages contain a fixed quantity of drawings and are the basic trading unit. A client can trade one data package with one data package. To avoid a standardised drawing appearing in more than one data package, a client should declare the hash codes of the contained drawings as their unique content identifiers. A hash code can be generated according to the drawing easily but cannot be decoded with limited time. As a result, publishing hash codes will not lead to data leakage. 3.1.2 Models, Data Quality, and Data Distribution One of the crucial assumptions in machine learning is the distribution similarity between training data and evaluating data. Though drawings can be standardised into the same format, the distribution of drawings may vary a lot. For example, the pipeline layouts of

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hospitals and residential buildings are different. The performance of an intelligent design model designed to generate pipeline layout of hospitals may reduce if the model is trained on drawings of residential buildings. Besides, the performance is also affected by the quality of data. The relationship between model performance and data requires clients select data packages of high-quality and similar content. However, it is challenging to evaluate the quality of a data package in the federated learning context where the data packages cannot be viewed directly. The recording layer is to help clients estimate the quality of data before training. 3.1.3 Rational Clients A client can publish its data packages to the trading layer and can make an order to other clients for data packages. The trading layer will automatically match these orders based on a called collaboration matching mechanism (CMM) introduced in Sect. 4. The CMM ensures the quantity of data packages a client can get is limited according to its contribution to the community. A rational client will make orders to maximise its benefits according to the CMM and recordings. 3.2 Trading Layer The trading layer is charged by a neutral service provider. It collects orders from clients and utilises a series of regulations to promote data trading among clients. The regulations can be summarised in three parts, data packages declaration, order making and collaboration matching. If a client wants to share a new data package in the trading layer, a series of steps are required to declare the legality and quality of the package. (1) Publish the hash codes of drawings in the data package. The trading layer will compare the hash codes with existing ones to avoid repeating them. (2) Declare basic information about the data package, including building types and semantic content of each layer. (3) The trading layer matches a model to some data packages according to the information declared in step 2. The model will be trained on the matched data packages, and the evaluating result of the model will be uploaded to the recording layer. An order is a list containing all the data packages a client wants to trade for. Each client owns an order in the trading layer. The trading layer will match models and data packages according to all the orders using CMM. CMM is an automated algorithm to pair models and data packages according to orders. CMM ensures the data packages that a client shares with the community are equal to those it gets from the community. For example, in a community with five clients, each client declares one data package to the trading layer and wants to train one model. The orders of clients are shown as edges in Fig. 2. There are two possible trading arrangements to ensure the in edges equal to out edges for a client. The CMM tries to find an arrangement maximizing clients’ utility among all the possible solutions. CMM is the most important module of the framework. A strict definition and related solution of CMM will be introduced in the next section.

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3.3 Recording Layer The recording layer saves all the historical training results. When a model and data packages are paired, the pre-performance of the model on evaluating data will be recorded before training. The model will then be trained jointly on the paired data packages and local data using federated learning. Finally, the trained model will be evaluated, and the post-performance will be recorded. The pre-performance, post-performance, model, and data packages constitute a record. The records can be an essential reference for clients to select data packages.

4 Modelling of Data Sharing Problem for Intelligent Building Design This section defines the intelligent design data sharing problem with necessary notations and definitions in the first part. The second part introduces the CMM based on the notations. The last part gives a discussion on clients’ behaviours under CMM. 4.1 Definitions and Notations N

Suppose there are N clients I = {I i }i=1 , and each client is attached with some intelligent j P

j Q

design tasks Ti = {Ti }j=1 and some data packages Di = {Di }j=1 where the drawing    j quantity of each data package Di  is fixed. Each client wants to train personalised j P

intelligent design models Mi = {Mi }j=1 according to their intelligent design tasks. In j

each collaboration, a participating client trains one of its models Mi on data packages {Dkt } including those shared by other clients and owned by itself. There is no guarantee that one client’s model can always benefit from a data package. A client will not be willing to join the data sharing market if the possibility of benefiting from traded data packages is too low. To help clients estimate the quality of data packages, the proposed framework will record the performances of a model before and after training on these j j data packages. Denote all the historical records of data package Di as Xi . Given historical j records of all data packages {X i }, one client I i will list out all the data packages it wants to trade for its models. Trading intentions of all clients can be presented by a directed multi-connected graph G. Figure 1 shows a simple case. The green nodes represent data packages owned by clients. The orange nodes represent intelligent design models. An arrow line starting from a model and ending at a data package means that a client wants to train the model on the data package. Define a ring as a set of lines that satisfies all the lines and connected clients form a cycle. For example, the orange lines form a ring in Fig. 1. Define a subgraph gi of graph G as a coalition, if gi is a ring. Obviously, all the clients in a coalition will share one data package with one client and get one data package from another client. Denote all the coalitions in graph G as C = {gi } where gi is a coalition. A subset C of C is called max non-conflict collaboration, if it satisfies, 

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Fig. 1. An example of trading intentions and different collaboration modes

  (1) edge(gi )∩edge gj = φ, ∀gi , gj ∈ C , i = j, where edge(gi ) presents the set contains all the edges of ring  gi .     edge(gi ) ∩ edge gj = φ for ∀gj ∈ C. (2) gi ∈C 



For example, all the solid lines and nodes connected form a max non-conflict collaboration in Fig. 1. According to condition (1), for any client I i in a max non-conflict collaboration, the edges pointing to I i equals the edges leaving from I i . In other words, more edges directing to client I i means that I i can get more data packages from the trading layer. 4.2 Collaboration Matching Mechanism The order of a client indicates its preference for data packages. Given the order Oi of j client I i , denote Oi as the j th data package on the order. The graph G can be presented as j

j

{Oi }ni=1 . Denote the priority of Oi as R Oi = 1j . For any max non-conflict collaboration 

j C , define the priority of C as R C = R Oi . The CMM algorithm aims j Oi ∈node(C ) to find the C in C with the highest priority. The steps of CMM are shown in Table 2. 









4.3 Clients Behaviours Analysis  

C (I i ). Given Denote a set of all the directed data packages from client in C as Dout a max non-conflict collaboration, the proposed framework will let client I i train its j C i model on Dout (I ). Define the utility a client’s model Mi benefits from a data package j n = max{0, p − p}, where p is the performance evaluated after training as U Mi , Dm n and p is the performance evaluated after training on Dn . on Dm m Ii



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If one of a client’s data packages is shared, the client should provide local computing resources for the buyer. Assume these resources constitute all the loss of sharing a data package and are constant. Denote the loss as l > 0. For a max non-conflict collaboration, the payoff function of client I i can be presented by,

j n − l] F(I i , C ) = [U Mi , Dm n ∈DC (I i ) Dm out 





j n before evaluating, it needs For that a client doesn’t know the actual value of U Mi , Dm j

estimate the results according to the historical records {X i }. According to the rational client assumption, the client will not trade for a data package whose estimated utility is less that l. Low-quality data packages with bad records can be avoided when clients list their orders. As a result, publishing low-quality data packages cannot make the client get data packages from others. In contrast, a high-quality data package may be listed in many orders, which means the data owner can use a high-quality data package to trade for more packages. This way, the framework can promote clients publishing data packages of high quality. Assume a client publishes a high-quality data package. Many other clients will list this package in their orders. The data owner can only list data packages whose quality is similar to its data package. This property can avoid high-quality data packages being traded with low-quality data.

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5 Case Study on Intelligent Design of Residential Buildings To demonstrate the effectiveness of the proposed framework, experiments are conducted on a simple subtask of intelligent design in which all the clients try to train a model to predict the central point of the living room given a house’s boundary as a constraint. In the study of Wu et al., predicting central points is the first step to generate layouts of residential buildings [9]. The proposed ANN was reused in this work. 5.1 Dataset of Floor Plan of Residential Buildings The experiment was conducted on a popular public dataset named RPLAN [9]. RPLAN comprises 80,788 floorplan drawings for residential buildings. Each drawing is presented in the PNG format and has four channels with a resolution of 256 × 256 pixels. The first channel represents exterior walls and the entrance of a house. The second channel shows different rooms and interior walls of the house. Figure 2 shows an example in the RPLAN data set. The left image shows the content of the first channel. The middle one shows the content of the second channel in which the red area represents the living room. The central points of rooms can be calculated according to channel two and are shown in the right one. Viewing each channel of pictures in RPLAN as a layer in drawings, the data format of RPLAN is consistent with the assumptions in Sect. 3. This study randomly selected 1000 images from the dataset and divides them into four groups in a ratio of 3:2:2:1. Each group was assigned to a client in the imaginary market. For each client, additional 100 images were randomly selected as the evaluating dataset from RPLAN. Denote the four clients as I 1 , I 2 , I 3 , and I 4 . Each data package in this market was assumed containing 125 drawings. Denote the data packages owned by I 1 , I 2 , I 3 , and I 4 as {D11 , D12 , D13 }, {D21 , D22 }, {D31 , D32 }, {D41 }. Client I 4 was assumed as an opportunist, and the data package {D˙41 } shared by I 4 was modified according to {D41 }. In this study, labels of living rooms in {D˙41 } were set as “toilet”, while labels of toilets were set as “living room”.

Fig. 2. Visualisation of a drawing in the RPLAN dataset

5.2 Experiments and Results 5.2.1 Environment Setting The experiment was conducted on a computer that runs Windows 10 system. The specification and configuration were as following: (1) CPU: Intel Core(TM) i9-9820X; (2)

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GPU: NVintelligent designIA GeForce RTX 2080 Ti; (3) RAM: 64GB DDR4 Memory; (4) Hard Disk: 512 GB SSD. The basic ANN model was the same as the article by Wu et al. [9]. The federated learning algorithm was set as shown in Table 1. All models were built using python 3.10 and Pytorch 1.11 as the machine learning framework. 5.2.2 Data Pre-processing j

For each data package Di , all the drawings were mapped to its hash code. The hash codes were then published to the trading layer. The trading layer checked these hash codes to avoid data repetition. The result showed all uploaded hash codes were different, indicating there were no repeated drawings in shared data packages. All the data packages were trained and evaluated independently before listing orders. The evaluating results of data packages are listed in Table 3. The results were then uploaded to the recording layer. Table 3. The evaluating results of shared data packages Data package

Model

Pre-error

Post-error

D11

M11

13.51

1.87

D12

M11

40.68

1.91

D13

M11

27.67

1.91

D21

M21

52.00

1.79

D22

M21

32.62

1.82

D31 D32

M31 M31

29.46

1.81

25.64

1.83

D˙41

M41

42.19

3.70

5.2.3 Collaboration Matching The result showed that D˙1 had the highest post error, indicating the data package might 4

be of low quality or heterogeneous to the evaluating dataset. Clients listed their orders according to the records. For client I 1 , the post errors of its data packages {D11 , D12 , D13 } were around 1.9. Data packages with similar post errors were {D21 , D22 , D31 , D32 }. As 2 3 4 a rational client, client I 1 listed {D21 , D31 , D22 , D32 } on its  order. Client I , I and I

1 2 1 2 3 1 2 1 2 3 1 listed D3 , D3 , D1 , D1 , D1 , D2 , D2 , D1 , D1 , D1 and D21 , D31 , D22 , D32 , D1 , D12 , D13 on their orders separately. The orders are summarised in Table 4. The CMM algorithm was then used to find the max non-conflict collaboration C with the highest priority. The found C is shown in Fig. 3 with different rings shown in different colours. The dotted line means the line doesn’t belong to C . 





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Table 4. Orders of clients Client

Order

I1

D21 , D22 , D31 , D32

I2

D31 , D32 , D11 , D12 , D13

I3

D21 , D22 , D11 , D12 , D13

I4

D21 , D31 , D22 , D32 , D1 , D12 , D13

1

Fig. 3. The max non-conflict collaboration with highest priority

5.2.4 Results of Training and Evaluating According to Fig. 3, data packages for each model are listed in Table 5 below. The model of client I 4 was not assigned with any data packages from other clients. Table 5. Data packages assigned to each model according to CMM Model

Data packages

M11

D11 , D12 , D13 , D21 , D22 , D31 , D32

M21 M31

D11 , D12 , D21 , D22 , D31 , D32

M41

D˙41

D11 , D12 , D21 , D22 , D31 , D32

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Each client’s model was pre-trained on self-owned data and then trained through federated learning on traded data packages. Figure 4 shows the loss on different clients’ data packages during federated learning of models M11 , M21 , and M31 . The blue lines represent loss on the self-owned data during federated learning. The orange and green lines represent loss on traded data from two other clients.

(a)

(b)

(c)

Fig. 4. Loss on different clients’ data packages during federated learning

Figure 5 shows the evaluating error before and after training on traded data packages. Each model is represented in different colours. The pre-error and post-error of model M11 are 1.632 and 1.618, about 0.9% improvement. The pre-error and post-error of model M21 are 1.705 and 1.66, about 2.6% improvement. The pre-error and post-error of model M31 are 1.705 and 1.62, about 4.8% improvement. The figure shows that all the models’ performances are improved through federated training on extra data packages. Besides, the ANN model was trained directly on all the data packages of the clients. The evaluating error was 1.51, lower than all the post errors of models trained through federated learning.

Fig. 5. Evaluating errors of clients’ models before and after federated learning

Figure 6 shows the predicting results of different models on an example image. The light green point denotes the ground truth. The black point is generated by model trained on all the packages owned by clients 1, 2, and 3. The light blue, dark blue, red points are generated by M11 , M21 , and M31 separately. The smaller ones are generated before training on traded data packages. The larger ones are generated by models after federated

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learning. Finally, the evaluating results were added to the records to be referenced by clients in the next trading round.

Fig. 6. The results of different models on an example image

Since all the data packages are fully controlled by its owners in this framework, a data package may be available only in a certain period. Consequently, it is impossible to train a model on all data packages simultaneously. Clients need to cascading train their models in some cases. Further study was conducted to evaluate models’ performance during cascading federating learning. The left image in Fig. 7 shows the loss of model M11 in cascading learning. Model M11 was first trained on data packages {D11 , D12 , D13 } of I 1 . The trained model was then trained on D21 and {D11 , D12 , D13 } through federated learning. Following D21 , data packages D22 , D31 , D32 were cascading selected as training data. The left image in Fig. 7 shows the loss on {D11 , D12 , D13 }, D21 , D22 , D31 and D32 during the federated learning of M11 . The right image in shows three models’ performance during cascading training. The result indicates that conducting federated learning on multiple small qualities of data in the cascading scheme may not improve a model’s performance. More research needs to be conducted to enhance models’ performance.

Fig. 7. Loss and eval error of models during cascading federated learning

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6 Discussion This paper proposes a framework for encouraging clients to share their drawings while avoiding publicising drawings directly. Five aspects have been recognised as the main findings. First, this study is one of the early studies focusing on data sharing mechanisms in the construction domain [16]. Privacy considerations hinder drawings sharing among data owners, which makes it difficult to develop high-performance ANN-based intelligent design models. The experiment showed that federated learning can facilitate intelligent design models’ performance while avoiding drawing leakage. Second, the proposed CMM can reduce opportunistic behaviours. Low-quality data can be identified according to training records this. Rational clients won’t list lowquality data packages in their orders. Consequently, an opportunist cannot benefit from published low-quality data packages. Third, models trained through federated learning may decrease in performance compared with those trained directly, which means more data are required to develop satisfied intelligent design models. Fourth, the CMM encourages clients to publicise more data packages. On the one hand, the up limit of data packages one client can acquire in a market depends on the required number of data packages. This property of the proposed CMM ensures clients who share more high-quality data packages can reach more data packages. On the other hand, the limit of one client’s data packages that are shared with the market depends on the number of data packages it requires. This property can protect clients’ data packages from being freely used. Fifth, the CMM and the use of federated learning should add much value to MiC by facilitating data sharing. The data sharing dynamics and complexity in the adaptation of MiC [17, 18] can be addressed through proper benefit allocation and privacy protection. Consequently, the scattered confidential data in different production processes and stakeholders can be integrated and used to enhance the decision-making in MiC. Despite these innovations, this study still has several limitations. First, the experiment was conducted on a simple task, and the drawings were limited to one type. An investigation involving multiple tasks, models, and types of drawings should be conducted. Second, this study assumed clients can infer data quality according to historical records. However, the relationship between models’ performance and training data is complex. It is not rigorous to judge whether a data package is of low quality if a model’s performance is declined after training. A more thorough method to depict the quality of data packages remains to be explored.

7 Conclusions Intelligent design can facilitate the designing process and promote better design performance. A large quantity of data is required for current machine learning techniques used in intelligent design. However, it is difficult to collect enough data such as drawings from relevant stakeholders to train the machine learning models. Therefore, this paper has proposed an innovative data-sharing framework for intelligent design based on federated learning and incentive mechanism to encourage data sharing among drawing owners.

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The proposed framework involves the client layer, trading layer, and recording layer. Clients declare their data packages to the trading layer. The hash codes are used to identify the uniqueness of drawings in the data packages. The CMM in the trading layer matches clients’ models and declared data packages according to orders assigned to each client. Models will be trained on the assigned data packages through federated learning. The recording layer records the history of different models’ performance before and after training on the declared data packages. The records provide crucial information for clients to select data packages and thus can reduce opportunistic behaviours combined with the CMM. The framework provides a novel way to handle the problem of obtaining drawings for learning-based intelligent design models. The federated learning technique is used to avoid drawing leakage. The CMM is proposed to encourage drawing owners to share their drawings. With more data available, the intelligent design model can obtain better performance and provide more solid support to relevant designers. A case study is conducted to test the feasibility and effects of the proposed framework. Results show that the recording mechanism in this framework can filter out data packages of low quality and thus reduce opportunistic behaviours. With federated learning, data packages from other clients can be involved in training a model. The training result shows that the evaluation performances of models are improved by 0.9% to 4.8% through federated learning. This paper contributes to the body of knowledge in two main aspects. First, this study proposes a data cooperation framework based on federated learning to enhance drawing sharing while avoiding privacy leakage. Second, a collaboration matching mechanism is proposed to promote collaboration equilibrium and enhance trading efficiency among drawing owners. Further, the proposed framework provides a novel way of tackling the dynamics and complexity of data sharing in innovative construction such as MiC by integrating federated learning and collaboration mechanism. In future research, the following works are recommended to enhance the intelligent design data sharing framework. First, a more detailed experiment involving multiple models and different types of drawings should be conducted to validate the feasibility of the proposed framework. Second, relations between model performance and data quality should be explored more deeply to help clients recognise suitable data packages according to training records. Third, new federated learning techniques should be developed to address the cascading training problem discussed in this paper. Acknowledgements. The authors acknowledge support from the Research Impact Fund of the Hong Kong Research Grants Council (Project No.: HKU R7027-18).

References 1. Liao, W., Lu, X., Huang, Y., Zheng, Z., Lin, Y.: Automated structural design of shear wall residential buildings using generative adversarial networks. Autom. Constr. 132, 103931 (2021) 2. Zheng, H., An, K., Wei, J., Ren, Y.: Apartment floor plans generation via generative adversarial networks. In: Proceedings of the 25th CAADRIA Conference, vol. 2, pp. 599–608. Chulalongkorn University, Bangkok (2020)

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3. Huang, W., Zheng, H.: Architectural drawings recognition and generation through machine learning. In: Proceedings of the 38th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA), Mexico City, Mexico 18–20 October, 2018, pp. 156–165 (2018). ISBN 978-0-692-17729-7 4. Weber, R.E., Mueller, C., Reinhart, C.: Automated floorplan generation in architectural design: a review of methods and applications. Autom. Constr. 140, 104385 (2022) 5. Zheng, Z., Zhang, Z., Pan, W.: Virtual prototyping-and transfer learning-enabled module detection for modular integrated construction. Autom. Constr. 120, 103387 (2020) 6. Koneˇcný, J., McMahan, H.B., Yu, F.X., Richtárik, P., Suresh, A.T., Bacon, D.: Federated learning: Strategies for improving communication efficiency (2016). arXiv preprint arXiv: 1610.05492 7. Nauata, N., Chang, K.H., Cheng, C.Y., Mori, G., Furukawa, Y.: House-GAN: relational generative adversarial networks for graph-constrained house layout generation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.M. (eds.) ECCV 2020. LNCS, vol. 12346, pp. 162–177. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58452-8_10 8. Nauata, N., Hosseini, S., Chang, K.H., Chu, H., Cheng, C.Y., Furukawa, Y.: House-gan++: generative adversarial layout refinement networks (2021). arXiv preprint arXiv:2103.02574 9. Wu, W., Fu, X.M., Tang, R., Wang, Y., Qi, Y.H., Liu, L.: Data-driven interior plan generation for residential buildings. ACM Trans. Graph. (TOG) 38(6), 1–12 (2019) 10. Hu, R., Huang, Z., Tang, Y., Van Kaick, O., Zhang, H., Huang, H.: Graph2plan: learning floorplan generation from layout graphs. ACM Trans. Graph. (TOG) 39(4), 118–121 (2020) 11. Pan, M., Yang, Y., Zheng, Z., Pan, W.: Artificial intelligence and robotics for prefabricated and modular construction: a systematic literature review. J. Constr. Eng. Manag. 148(9), 03122004 (2022) 12. Ghorbani, A., Kim, M., Zou, J.: A distributional framework for data valuation. In: International Conference on Machine Learning, pp. 3535–3544. PMLR (2020) 13. Cui, S., Liang, J., Pan, W., Chen, K., Zhang, C., Wang, F.: Collaboration equilibrium in federated learning. In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 241–251 (2022) 14. Ghorbani, A., Zou, J.: Data Shapley: equitable valuation of data for machine learning. In International Conference on Machine Learning, pp. 2242–2251, PMLR (2019) 15. Jia, R., et al.: Towards efficient data valuation based on the Shapley value. In: The 22nd International Conference on Artificial Intelligence and Statistics, pp. 1167–1176, PMLR (2019) 16. Li, X., Chi, H.L., Lu, W., Xue, F., Zeng, J., Li, C.Z.: Federated transfer learning enabled smart work packaging for preserving personal image information of construction worker. Autom. Constr. 128, 103738 (2021) 17. Pan, W., Yang, Y., Zhang, Z., Chan, S.: Modularisation for Modernisation: A Strategy Paper Rethinking Hong Kong Construction. The University of Hong Kong, Hong Kong (2019) 18. Pan, W., Hon, C.K.: Modular integrated construction for high-rise buildings. Proc. Inst. Civil Eng.- Munic. Eng. 173(2), 64–68 (2020)

Design for Manufacture and Assembly (DfMA) Communication Network and the Impact of COVID-19 Vikrom Laovisutthichai1,2(B) , Weisheng Lu1 , K. L. Tam3 , and Stephen Siu Yu Lau4 1 Department of Real Estate and Construction, The University of Hong Kong, Pok Fu Lam,

Hong Kong [email protected] 2 Department of Architecture, Chulalongkorn University, Bangkok, Thailand 3 The Estates Office, The University of Hong Kong, Pok Fu Lam, Hong Kong 4 Faculty of Architecture, The University of Hong Kong, Pok Fu Lam, Hong Kong

Abstract. Design for manufacture and assembly (DfMA) encourages upstream and downstream construction stakeholders to involve early and communicate openly before developing a manufacture and assembly-oriented design. However, this communication network is understudied and currently challenged by many infection control measures against the pandemic, e.g., lockdown, isolation, and social distancing. This research, therefore, investigates multi-stakeholder intensive communication underneath DfMA implementation and the impact of COVID-19. It does so by participatory action research, tracing stakeholders’ activities, and thematic analysis. It discovers an underlying decentralized mesh communication network, involving the iteration cycle of inquiry and response, submission and feedback, and reporting and acknowledgment. Amid the pandemic, these actions cannot be taken through traditional communication mediums, forcing stakeholders in the case study to adapt multiple generic, virtual platforms to convey various message forms, including technical information and three-dimensional models, without systematic guidelines or integrated platforms for visualization, validation, and tracking. To stabilize this network in the post-pandemic era, the DfMA practice, together with integrated project delivery (IPD) and building information model (BIM), is highly recommended. The communicators, connections, messages, and mediums visualized in this research are valuable resources for governing future practice and developing an integrated platform-empowered mesh communication in DfMA. Keywords: Collaborative design · Communication model · COVID-19 pandemic · Design for manufacture and assembly · Information management · Integrated project delivery

1 Introduction Design for manufacture and assembly (DfMA) refers to “a philosophy and a methodology whereby products are designed in a way that is as amenable as possible for downstream manufacturing and assembly” [1]. Originated in the manufacturing industry to © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 533–546, 2023. https://doi.org/10.1007/978-981-99-3626-7_42

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break through traditional ‘over-the-wall’ product design and production process, it is considered a new hope to revitalize the traditional, fragmented architecture, engineering, and construction (AEC) project delivery. Many efforts have been made to promote DfMA in AEC, e.g., establishing construction-oriented guidelines [2], harmonizing them with the Royal Institute of British Architects (RIBA) Plan of Work [3], decoding underlying DfMA grammar [4], developing building information model (BIM)-enabled DfMA framework [5], and expanding to offsite interior design and construction [6]. The arrival of DfMA decreases construction costs and time, escalates material consumption efficiency, improves workforce safety, and alleviates the industry’s lackluster productivity [3]. In addition, it can be regarded as a companion of industrialized construction, preventing potential obstacles and facilitating standardized production, crowded transportation, and massive and heavy prefinished components assembly [1]. In parallel with these fruitful benefits, DfMA creates a paradigm shift in design methodology and professional practice in three ways [7]. First, it revolutionizes AEC organizational structure by involving downstream stakeholders, e.g., main contractors, manufacturers, and suppliers, at the early design stage [8, 9]. Second, in harmony with several principles of integrated project delivery (IPD), DfMA encourages collaborative design, open communication with no-blame culture, knowledge sharing, and mutual respect and trust, as they can facilitate interdisciplinary knowledge flow before design, avoid errors and reworks, and ultimately escalate overall productivity [8]. These recommendations change the roles and responsibilities of upstream designers to become learners, collaborators, and negotiators [7]. Third, it transforms the architectural language and grammar from crafting architecture with ornate elements to the selection and composition of standardized building elements with coordinated dimensions [4]. From a communication theory perspective, the DfMA practice establishes a new communication network by inviting new communicators, stimulating communication among upstreamdownstream stakeholders, and incorporating technical messages from manufacture and assembly. While many scholars and organizations worldwide encourage open communication and knowledge sharing among upstream-downstream stakeholders, the underlying communication network is surprisingly understudied. This multi-stakeholder communication is even more complicated in the COVID-19 pandemic, when lockdowns, curfews, social distancing, isolation, border control, and other infection prevention measures radically change our lives, environments, and working procedures in every business worldwide [10]. It also disrupts traditional AEC project delivery, changes physical site visits to remote management and inspection, and moves face-to-face team building and project meetings to online platforms, which inevitably affect the open communication process and achievement of DfMA [11, 12]. This research, therefore, investigates 1) the DfMA communication network and 2) how the COVID-19 pandemic affects this underlying network. A profound understanding of the underlying communication network underneath DfMA will be beneficial for governing future practice and constructing integrated platforms or assistive tools [13, 14].

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2 Literature Review 2.1 Communication Network DfMA and IPD share some common features. Unlike the traditional over-the-wall project delivery process, they promote open, direct, and honest communication among multistakeholder with diverse backgrounds and different opinions as a basis or point of departure for team building, collaborative decision-making, and innovation [8, 9]. One of the effective approaches to empathize with the underlying communication is visualizing through a communication graph or network [18]. It is a convenient way to explain an act of communication, including who, says what, in which channel, to whom, and what impact, in a simplified illustration [19, 20]. In AEC, Titus & Bröchner illustrate the typical communication web in the construction supply chain by placing a project model at the center, surrounded by key stakeholders, and connecting these components using requirement and fulfillment lines [21]. Despite the model’s contextualization and development over time, 1) communicators, 2) messages, 3) mediums, and 4) effects and feedback remain the basic components of most communication graphs and networks [19, 20]. Based on the structural layout and form, there are several network topology types, e.g., chain, circle, star, and mesh [22]. The traditional over-the-wall design and construction, where designers develop and hand over a design without coordination from downstream construction, can be considered a chain or linear network, which triggers more communication errors than other typologies [22, 23]. A circle, sometimes called a ring, represents a circular information flow among nodes in the network [18]. A star is the most centralized one, where all information goes to a central person, while a mesh is more egalitarian, where all participants can have equal access to one another [18]. Notably, it does not imply that the mesh network is the best. Instead, it depends on the communication parties and objectives. The communication network can also be categorized by messages (e.g., verbal, text, construction drawings, and three-dimensional models) or online communication mediums (e.g., social networking, video conference platform, instant messaging, and media sharing network) [24]. Identifying types of networks, stakeholders, messages, and mediums used in an organization, helps us diagnose the strengths and weaknesses of existing practices before noise reduction and communication pattern revitalization [13]. This underlying mechanism is also necessary for innovating assistive tools or integrated platforms-enabled communication among all participants [8, 14]. However, in terms of DfMA, there is still a lack of investigation into how stakeholders communicate, collaborate, and carry out DfMA. 2.2 DfMA and IPD in the New Normal The COVID-19 pandemic has disclosed weak points of existing design and construction project delivery. For instance, infection control measures harshly disrupt the industry’s orthodox communication and exchange modes, depending heavily on face-to-face interactions on a site or in a meeting room [12, 15]. The fragmented construction supply chain

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is not resilient enough, resulting in materials shortage, material cost escalation, significant delay, and productivity decline [11]. However, every coin has two sides. While the traditional method cannot be operated, this situation sheds light on various innovative strategies, e.g., BIM-enhanced architectural education, a blockchain-based system for construction e-inspection, and AEC organization redesign with systems integration [9, 12, 16]. Some scholars expect an increase in IPD adoption due to its capabilities of improving a project’s organizational and cognitive proximities in the pandemic and postpandemic era [17]. They also see DfMA as a new hope to fasten the tedious construction process and fulfill the surging demand for healthcare facilities [9]. It can be inferred that the pandemic creates a paradigm shift in the entire AEC project delivery process, but the DfMA implementation process and its underlying communication network in the new normal have not yet been investigated extensively [17].

3 Research Methods This research adopted a pragmatist philosophical position to conduct participatory action research. Through the lens of pragmatism, human actions, changes, and dynamic relationships between knowledge diffusion and actions are the essence of humankind [25]. Participatory action research is one of the empirical approaches to investigate these social actions, conditions, effects, and their relationships that construct a society through the cycle of planning, execution, and fact-finding [25, 26]. The full involvement in a realworld phenomenon as changers allows researchers to face challenges, experiment, gain insight, propose feasible solutions, and ultimately constitute theoretical and practicable contributions [26]. In AEC, it encourages researcher-practitioner collaborative research, which contributes to more in-depth understandings and more practical solutions [27]. It has been adopted to investigate, for example, greater integration of BIM in the architectural curriculum [16], design for construction waste minimization principles [28], and post-occupancy evaluation [29]. In this research, it was prospected to disclose communication actions, difficulties, and solutions in the DfMA implementation during the COVID-19 pandemic. This research comprises three stages (see Fig. 1).

Fig. 1. Research methods

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3.1 Data Collection This research began with participatory action research. The façade design and construction of a 3-story building in Bangkok, Thailand, was selected as a case study. It aims to be an environmental filter between a controlled indoor environment and outdoor pollution. Several DfMA guidelines and instructions provided in the existing publication, e.g., early involvement of downstream façade contractor, construction knowledge sharing, and design simplification, were adopted to improve buildability, facilitate façade production and assembly, minimize potential construction waste, and improve overall productivity. The project includes four main parties (see Table 1)—the researcher involved in this case study was one of the design team members. The project information, construction drawings, communication records, and chat history were collected with permission for further analysis. Table 1. Stakeholders’ roles and responsibilities in the project case study No Stakeholders

Roles and responsibilities

1

Project owner (O)

Providing the project brief, appointing the project team members, making strategic decisions, and making payments

2

Designer (D)

Collaborating with other stakeholders, developing the building and façade design, and generating construction drawings

3

Main contractor (C)

Undertaking the construction of the building structure and services, collaborating with the façade contractor, and facilitating the façade installation

4

Façade contractor (F) Providing feedback to the façade design, designing its details and joints, manufacturing the façade components, and installing the façade

3.2 Data Analysis A wealth of unstructured, unrelated qualitative data collected from the case study requires a systematic analytical method to manage and streamline them. A thematic analysis was thus conducted. We adapted some instructions proposed by Mills et al.: 1) reviewing all data, 2) using communication model components as a starting list, 3) identifying key stakeholders, 4) categorizing types of communication actions, messages, and channels, and 5) searching for practice lessons [30]. The communication actions timeline was also compared with the number of confirmed cases obtained from the Ministry of Public Health of Thailand (MOPH) [31] and various infection control measures issued by the government. 3.3 Data Visualization To ease the understanding of the underlying communication process, the researchers followed several instructions and illustrated the DfMA communication network [20,

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32]. Four main stakeholders or sources of information were represented in rectangular shapes and placed on a canvas. Then, they were connected by lines, indicating communication actions, messages, and mediums. These lines and information were repeatedly added to the canvas until all actions in the case study were included. Finally, the DfMA communication network was visualized, the impacts of the pandemic on this network were identified, and multi-stakeholder communication in the DfMA implementation in the post-pandemic era was discussed.

4 Results The DfMA implementation in the case study was carried out during the widespread of COVID-19 in Thailand. Practitioners faced many difficulties due to various infection control measures, such as physical distancing, construction site closure, border control, and curfew. They cause skilled labour shortage, supply chain disruption, and 1-year project delay. The multi-stakeholder communication actions throughout this lengthy process were traced, aggregated, and restructured into three main stages: design, preconstruction, and construction (see Fig. 2). First, after the onsite kick-off meeting, the design team played a leading role in the development of the façade concept. It involved multiple inquiries and responses among the project owner, architects, and façade contractor. Following some DfMA guidelines, the façade material specifications, standard material processing, buildability, and cost were extensively discussed before developing, detailing, submitting, and approving the manufacture and assembly-oriented design. During this stage, the Thai government reported the first COVID-19 case, declared a state of emergency, and issued various infection control measures, e.g., curfew, border control, working-from-home, and physical distancing. Practitioners avoided onsite meetings and adapted various online platforms, including instant messaging applications for inquiry and response cycles, calling applications for multiple discussions, and cloud-based storage and e-mail for sharing architectural drawings and three-dimensional models. In addition, they were applied together to finalize, confirm the concept design information, and transfer it to the façade contractor for detailing and generating construction drawings in the following stage. The main contractor also reported the building structure construction delay, resulting in the delay in façade production and assembly. Second, in the pre-construction stage, the façade contractor transformed the concept design into construction drawings. There were lots of discussions between them and the architects on the material specifications, dimension coordination, joint detail, and manufacture, logistics, and assembly process. Some details, e.g., the perforated panel curve, colour, and connections, were revised or redesigned due to the machinery and vehicle limitations. These actions could reduce the risk of construction errors and reworks. Nonetheless, this stage was also affected by the global pandemic, forcing all stakeholders to keep using online communication platforms to discuss, inquire, comment, and approve. The instant messaging application was mostly used to deliver various message forms, e.g., text, drawings, three-dimensional models, and figures, as all members, including the owner and administrator, were familiar with its interface. After the reiterative cycle of design submission, feedback, and revision, the owner and designers approved the façade construction drawings.

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Fig. 2. Multi-stakeholder communication in DfMA amid the COVID-19 pandemic

Finally, the façade contractor played a leading role in carrying out the façade production, transportation, and assembly, while the owner, architects, and main contractor followed, supported, and helped when some difficulties arose. This final stage was

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harshly affected by the COVID-19 Delta variant (B.1.617.2), which causes higher disease severity in populations than the Omicron variant (B.1.1.529) [33]. The number of confirmed cases escalated from ten to approximately 20,000 cases per day in August 2021, disclosing fragility in the public health system of Thailand and other South East Asian countries [34]. The government imposed a wide range of infection control measures, e.g., physical distancing, travel restrictions, quarantine orders, curfew, and factory and construction site closure. Although lots of discussions on the façade manufacture and assembly had been moved to the first two stages, multi-stakeholder communication was still necessary for this critical stage. The façade construction progress, difficulties, and delay were reported through text and verbal communication using instant messaging and calling applications. There were multiple discussions among key stakeholders to resolve unexpected issues caused by this wave. Some details, e.g., façade components connections and installation procedures, were also revised. The restrictions were relaxed in September 2021 [34]. Then, the façade transportation and installation were resumed and finally completed in November 2021.

5 DfMA Communication Network The communication network components, i.e., stakeholders, communication actions, message forms, and mediums, were analyzed and restructured following several illustration instructions. Eventually, the communication network behind the DfMA implementation in the case study was visualized (see Fig. 3). Stakeholders and communication actions: The four main parties involved in the case study were the owner, architects, main contractor, and façade contractor. The architects and façade contractor inquired and responded to many questions about the material specifications, processing, colour, and cost. These actions were to absorb knowledge outside their areas of expertise before developing the preliminary design and generating construction drawings. They also shared the façade design and reported the progress to other parties. The owner responded to the inquiries, provided feedback, and made a decision, while the main contractor acknowledged, coordinated with other parties, facilitated the façade installation, and reported the progress. By visualizing the topography of these communication actions, multi-stakeholder communication in DfMA can be considered a decentralized mesh network, where all parties can inquire, share their knowledge, discuss, and make a decision directly and repeatedly from the beginning. Throughout the façade design and construction with DfMA implementation, there were no centralized nodes or barriers between upstream and downstream stakeholders. Message forms: Multi-stakeholder communication in DfMA comprised a variety of message forms. First, a text message was the most used, but it was not merely a simple one. According to the case study, the construction process, material specifications, and technical terms were explained through text, and some details could not be clearly explained, e.g., the aluminium surface, texture, and colour. The second was a construction drawing to transfer the design and building information to the following stages. Third, quotation, contract, specification, and construction planning documents were sent. Fourth, three-dimensional models in SketchUp were shared among four main parties, especially during the justification of façade components’ dimensions and revision of their curves to comply with the machinery and vehicle capacities. Fifth, as a text

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Fig. 3. DfMA communication network

was unable to convey concise messages and technical terms, figures were used to enhance receivers’ understanding and report the construction progress. Finally, verbal communication is still necessary to share, report, discuss, and resolve unexpected challenges due to COVID-19. Communication mediums: To convey various message forms in DfMA amid the COVID-19 pandemic, traditional face-to-face communication and paper-based submission were obstructed. Instead, practitioners in the case study adopted free online platforms, including 1) instant messaging applications for inquiries, responses, sharing files,

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comments, notifying, and acknowledgment, 2) calling applications for verbal communication, and 3) cloud-based storage and e-mail for submitting the design proposals. Compared to BIM, they are more convenient for some stakeholders with little construction experience, and easier to be accessed via computer, tablet, and mobile phone. However, these platforms brought some drawbacks. Despite the adoption of multiple platforms, several tasks still could not be achieved virtually through online social media. Practitioners thus met up physically onsite for the kick-off meeting, team building, site preparation, signing a contract, quality inspection, and project delivery. Some of these were postponed due to the surge in confirmed cases, resulting in the delay in construction project delivery. Moreover, adopting many generic virtual platforms without a single source of truth made it tedious and time-consuming for practitioners to store great building information offline, manage multiple files, and trace back the design development, previous conversation, decision-making, and report. In the case study, practitioners searched on the platforms one by one to retrieve previous messages or files, increasing the risk of information loss, misunderstandings, and conflicts among stakeholders. In addition, some platforms allowed file storage for only 15 days. Some figures, construction drawings, documents, and models could not be brought back.

6 Discussion DfMA revitalizes the AEC industry’s lack of productivity while revolutionizing the existing professional practice [3, 7]. In harmony with the prior publications, this empirical investigation first details and positions DfMA as an influencer or stimulator, reshaping the communication network among construction stakeholders. DfMA increases the number of communication actions and creates continuous loops of question and answer, submission and feedback, and report and acknowledgment in the early design and preconstruction stages. Various message forms, including text, documents, figures, verbal communication, construction drawings, and three-dimensional models, are transmitted in these loops. Furthermore, this research discloses the underlying decentralized mesh communication network behind multi-stakeholder communication in DfMA. Compared to the prior understanding of linear design and construction, one can infer that DfMA shifts the traditional over-the-wall practice to be more egalitarian, where all parties have equal opportunities to learn and share their knowledge from the beginning. However, this communication network in DfMA is more bewildering amid the COVID-19 pandemic, when lockdowns, social distancing, and other measures prohibit traditional face-to-face meetings. In many small and medium enterprises (SMEs), including this case study, advanced computational technologies or integrated virtual platforms to help manage multi-stakeholder communication and a wealth of building information have not yet been adopted due to a lack of expertise, resistance to change, and high cost [35]. Nowadays, practitioners harness the power of various generic online platforms, especially instant messaging, media sharing, and calling service applications, to share technical information, including material specifications, construction drawings, and three-dimensional models. Without a single source of truth to store and manage a wealth of building information, the current practice is tedious and time-consuming to review, recheck, and retrieve the design and construction process. Furthermore, some activities

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cannot be achieved through these platforms, e.g., trust building, quality assurance, and project delivery. In the post-pandemic era, the use of these virtual platforms will not suddenly disappear. Rather, business meetings and works are prospected to transform again from online to a hybrid of collocated traditional and virtual mediums [36]. To help practitioners throughout this shift, systematic protocols or frameworks are necessary for every organization to govern further multi-stakeholder communication. The underlying communication process visualized in this paper can be seen as a preliminary framework to be incorporated with various computational technologies. BIM, for example, can be integrated as an assistive tool to manage a variety of communication actions and technical information while implementing DfMA. In addition, practitioners may adopt the proposed communication network together with the prior DfMA guidelines [3], knowledge-to-action framework [7], multi-stakeholder model [37], and IPD philosophy [8] to deliver a better and more systematic DfMA practice. To improve communication skills among stakeholders, it can also be included in the existing AEC curriculum, BIM training, and lifelong learning [16]. This research reveals the underlying communication mechanism behind the DfMA realization and visualizes it in a diagram for future practice and tool development. However, it has several limitations. First, participatory action research is adopted to gain insight from a real-world phenomenon, but it is sometimes accused of biases and conflicts of interest. Although these drawbacks are mitigated by adopting a systematic analytical method, more investigations without disturbance from researchers, e.g., interviews and questionnaires, are demanded for validation. Second, a generalization capability of a single case study is criticized by scholars. Additional cases are thus required for validation and fine-tuning the communication network. Third, it investigates the real-world façade design in Thailand without using BIM or other AEC virtual platforms. Due to the project-based nature of the AEC project, readers are reminded to consider differences in context before reusing the findings.

7 Conclusion The traditional over-the-wall design and construction process is considered a linear communication network, where a design is developed and handed over to the following stage without receiving feedback from downstream stakeholders. Through full involvement in the real-world phenomenon, this research corroborates that DfMA revolutionizes this traditional practice by stimulating early communication among key parties, production and construction stakeholders in particular, and creating a decentralized mesh communication network. This network involves the repeated cycle of inquiry and response, submission and feedback, and reporting and acknowledgment, and contains various message forms, e.g., text, construction drawings, and three-dimensional models. Amid the pandemic, many AEC practitioners, especially those in SMEs, convey these messages through instant messaging applications and other generic virtual platforms without integrated tools or systematic protocol, making it arduous to deliver concise messages and trace back discussions, decision-making, and design development. In the new normal era, this communication network will be challenged again by the transformation to a hybrid format. In responding to this change, the DfMA communication

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network discovered in this research can be further incorporated with BIM, IPD, and other computational technologies to govern future practice. This research constitutes both academic and practical contributions. It visualizes the underlying communication network behind the DfMA implementation, and identifies the impact of COVID-19 for governing future practice and developing integrated platforms. Nevertheless, it should be treated as a preliminary version. Future research is recommended to improve and fine-tune this network by investigating different building typologies or conditions. Moreover, the communication actions, messages, and mediums data can be further elaborated, quantified, and visualized in graphs for a better understanding of the communication process and frequency in the DfMA implementation. The communication network and challenges also support the further development of resilient DfMA communication protocol and BIM-enhanced multi-stakeholder communication in DfMA. Acknowledgment. The authors would like to thank the support from the Hong Kong PhD Fellowship Scheme (HKPFs) by the University Grants Committee (UGC). The authors also would like to thank all project stakeholders for providing the opportunity, project documents, and information for this research.

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The Impact of Internationalization on Corporate Social Responsibility: Evidence from Chinese Listed Construction Companies Meiyue Sang(B) , Lingyu Zhang, and Jiayu Li School of Management Science and Real Estate, Chongqing University, Chongqing, China {MeiyueSang,lingyuzhang}@cqu.edu.cn

Abstract. Actively fulfilling corporate social responsibility (CSR) will significantly impact the sustainable development of construction companies. Economic globalization has prompted Chinese construction companies to target overseas markets and increase their turnover. The process of internationalization may influence the CSR of Chinese construction companies. This study aims to analyze the effects of internationalization on the CSR of listed construction companies in China. This study tests the hypothesis empirically by multiple regression analysis with the sample of A-share listed construction companies from 2010 to 2020. The results of this study demonstrate that the degree of internationalization (DOI) has a negative impact on CSR. The findings provide suggestions to Chinese construction companies for implementing CSR activities, especially in the context of globalization. Chinese construction companies should pay more attention to CSR activities to promote the sustainable development of the construction industry. Keywords: Corporate social responsibility · Internationalization · Construction Companies

1 Introduction The construction industry is a conventional and pillar industry in China, and construction companies have accelerated their pace of internationalization since China entered the WTO. Economic globalization has blurred production and operation borders. The “One Belt, One Road” initiative continues and deepens the “Going Global Strategy” for Chinese construction companies. Recently, the vast construction markets in Asia, Africa, and Latin America have created widespread prosperity and opportunities for international construction companies. More and more Chinese construction companies conduct international operations. Recent years have seen a growing trend of Chinese construction companies making considerable inroads into the global market. Engineering News-Record (ENR) has published annual data about the top list of international contractors. 74 Chinese international contractors are on the list, with a global turnover of $120.005 billion. And Chinese construction companies continue to expand their market share steadily. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 547–560, 2023. https://doi.org/10.1007/978-981-99-3626-7_43

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Although Chinese construction companies perform remarkably, they are generally significant but not substantial, and the degree of internationalization (DOI) is still low. The total operating income is relatively high, but the profit is not optimistic, and the growth rate will slow down. Compared with the domestic market, construction companies often face more risks and challenges when carrying out international projects. For example, the Peruvian government imposed a heavy fine on Zijin Mining for failing to disclose the significant social and environmental risks of the Oroblanco Mine project in 2011; Mexico’s government announced the cancellation of the high-speed rail project won by China Railway Construction Corporation in 2014 under pressure from the public. These incidents show that Chinese construction companies have caused social and environmental conflicts due to improper CSR performance. And Chinese companies are stereotyped as lacking warmth by local people, which seriously restricts the “going out” strategy and hinders sustainable development in overseas markets. The ambiguous global environment has caused various uncertainties and negatively impacted the development of construction companies. Chinese construction companies urgently need to establish a good corporate reputation in overseas markets and improve competitiveness. Corporate social responsibility (CSR) has attracted widespread attention. International organizations have successively implemented a series of CSR standards to regulate the correct understanding and fulfillment of CSR. Such as the ISO26000 “Social Responsibility Guidelines” issued by the International Organization for Standardization (ISO) in 2010 and the “Sustainability Reporting Guidelines (G4)” of the Global Reporting Initiative. Chinese Ministry of Commerce has also promulgated the “Guidelines for Social Responsibility of China’s Foreign Contracting Engineering Industry”, “Evaluation Standards for Corporate Social Responsibility in the Construction Industry”, and other standards that encourage Chinese construction companies to assume CSR batteries, promote the standardization of overseas construction companies’ CSR fulfillment. Although research has shown that CSR is one of the key factors contributing to the growth of construction companies, the attitude of international construction contractors toward fulfilling CSR is still unclear. On the one hand, multinational construction companies face different external environments in overseas markets than in home markets, including diverse cultural backgrounds and stakeholders, which pose more challenges to contractors. A series of negative issues led to continuous criticism and attacks on multinational companies. Therefore, based on legitimacy and strategic motivation, these companies will likely respond to stakeholders’ needs and assume more social responsibilities. On the other hand, construction contractors suffer a severe legal crisis due to different laws and regulations, and CSR activities inevitably cost. Construction companies must coordinate all stakeholders and invest more funds and resources to fulfill CSR. The research related to internationalization mainly focuses on the aspects of business performance, risk, and technological innovation. However, few studies are exploring the impact of internationalization on CSR, and the conclusions are inconsistent [1– 3]. Some studies have shown that the internationalization strategy will attract more attention from stakeholders, thus exerting pressure on companies to carry out CSR activities [4, 5]. The research of Cheung et al. [6] showed that higher internationalization is related to better CSR performance. Some scholars found that there seems to be a

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positive correlation between internationalization and CSR [1, 2]. Other studies have revealed different results. Eun-Ho et al. [7] believed that internationalization could lead to decreased CSR performance. Simly and Li found no significant correlation between internationalization and CSR. Rapidly growing globalization requires exploration of the intersection between CSR and internationalization, which remains an underexplored area in academic literature. Past studies have recognized the vital role of internationalization and CSR strategies, but few studies have simultaneously discussed internationalization and CSR as research elements. This research focuses on international construction companies, reveals the current CSR performance, explores the impact of internationalization on CSR, and provides advice on the sustainable development of Chinese construction companies. The rest of this paper is organized as follows. Section 2 reviews the literature on construction CSR, internationalization of construction companies, and the relationship between internationalization and CSR, then we propose a hypothesis. Section 3 describes the data collection and the model design. Section 4 gives the basic statistics, empirical results, and robustness testing. The last part presents the discussion and conclusions of this paper.

2 Literature Review and Hypothesis 2.1 Construction Corporate Social Responsibility There is a paradox in terms of the construction of CSR [8]. On the one hand, the construction industry has contributed considerably to socio-economic growth and social development. For example, it provides many employment opportunities; buildings also provide a place for human economic activities [9]. On the other hand, the construction process harms the environment, construction activities are accompanied by pollution such as dust and gas emissions, noise pollution, waste generation, water pollution, and land. In addition, the primary energy consumption of the construction industry accounts for 30% to 40%, and about 40% to 45% of the global greenhouse gas comes from the construction industry. Overall, construction CSR is complex. Global construction companies step up focus on CSR. Over the past few decades, many top international construction companies have regularly released CSR reports, and CSR records have become increasingly important in the construction industry. CSR can be an effective tool to increase diversity in the construction industry [10]. Dainty, et al. [11] depicted an early picture of CSR activities in construction, including topics such as corruption, community engagement, sustainable development, occupational health and safety, and the role of construction in disaster and poverty reduction. The research of Jiang [12] on Chinese construction contractors found that the critical fields of construction CSR are environmental protection, construction quality and safety, community, employees, customers, and CSR management. Lu et al. [8] explored trends and prospects of CSR disclosure in the international construction industry and found three most commonly reported CSR indicators: “Emissions, Wastewater and Waste”, “Community” and “Occupational Health and Safety”.

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The international standards provide references for construction contractors, including ISO26000: Guidelines for Social Responsibility; “Building Responsible Competitiveness” initiated by the European Commission (2010); the Sustainability Reporting Guidelines issued by the Global Reporting Initiative (GRI), GRI also provides guidelines for different departmental reviews and metrics. In addition, contractors can use procedures such as Social Responsibility 8000, the Global Compact Initiative, and the ECS 2000 standard. However, although CSR has attracted more attention, construction companies are still criticized for ignoring environmental pollution, confronting customers, and being inconsiderate of society [13]. Construction companies are not proactive enough or even passive in fulfilling social responsibilities. In the context of global sustainable development, actively carrying out CSR activities has become a vital issue worth construction companies’ attention. 2.2 Internationalization of Construction Companies Internationalization means for companies to expand beyond the business limits of domestic markets. Internationalization refers to increasing companies’ engagement and turnover in the international market. Construction companies are pinning their hopes on improving global influence by expanding their international business. However, internationalization is a challenging endeavor [14, 15]. According to internalization theory and a resource-based view, internationalization can create benefits. Internalization theory argues that firms can benefit from internationalization by integrating and utilizing firm-specific knowledge and products in different markets [16]. Likewise, according to a resource-based view, a business can achieve a competitive advantage by acquiring resources and capabilities across multiple operations. On the contrary, according to agency theory and transaction cost theory, internationalization may incur costs. Due to possible disagreements between shareholders and proxies, managers may pursue unreasonable investments. For example, managers may increase the company’s size to earn higher compensation, or adopt strategies that reduce their employment risk. These activities can lead to over-investment and add more monitoring costs [17]. Internationalization increases environmental complexity, construction companies face more unfamiliar environments in overseas markets, such as culture, politics, exchange rate factors, etc. Second, the political, economic, cultural, and legal complexities between countries make geographic diversity tend to increase the number and diversity of stakeholders in a company’s external environment [18]. Geographic diversity leads to higher internal and external transaction costs with government officials, suppliers, and customers [19]. 2.3 The Relationship Between Internationalization and Corporate Social Responsibility Companies essentially pursue interests, they will continue to fulfill only when CSR activities can bring benefits. Construction companies always focus on “one-off” traditional projects, and more stakeholders increase the complexity of the construction supply chain. CSR invests in stakeholder management, such as building positive relationships with customers, governments, investors and activists, resulting in improved reputation

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and profitability. CSR can effectively promote the sustainable development of construction companies [20]. Studies have shown that international companies tend to face more trade barriers [21], resulting in more transaction and agency costs [22]. Internationalization means companies have complex stakeholder relationships. The biggest obstacle for multinational companies in emerging economies to promote international business is the acquisition of legitimacy, and a higher level of internationalization means that companies have greatly increased their relationship with influential host country stakeholders and relevant international multilateral organizations. Exposure, due to institutional vulnerabilities in home countries and limited corporate governance disclosures, in turn increases the likelihood that these stakeholders will engage in adverse institutional attributions when evaluating these firms. Therefore, formulating a CSR strategy that meets all stakeholders’ interests is challenging [23]. Similarly, international expansion offers the prospect of economies of scale. It typically requires the company to incur additional costs than it faces domestically, negatively impacting the profit margins [24]. Although internationalization and CSR have been widely practiced, combining two strategies may create negative synergies and amplify the overall risk. As a result, construction companies are hesitant to fulfill their social responsibilities when conducting international projects. With the growing degree of internationalization, construction companies do not necessarily make more efforts in CSR. Therefore, research generally believes CSR investment benefits companies [25]. However, contrary to the above expectations, we assume that Chinese construction companies may take a negative attitude towards undertaking CSR activities with the development of internalization.

3 Research Design 3.1 Data and Sample Collection Taking 126 China’s A-share listed companies in the construction industry as the object. The research sample is the unbalanced panel data of A-share listed construction companies from 2010 to 2020. The data comes from three sources: Wind database, Hexun database, and corporate annual financial reports (manually collected). CSR scores are collected from the Hexun database, and other data are mainly collected from the Wind database. The initial sample is processed as follows: excluding ST, *ST, and samples with missing data, and finally, we get 72 listed construction companies from 2010 to 2020, including 501 pieces. 3.2 Variables Measurement 1. Independent Variable Degree of Internationalization (DOI): The international ratio is selected to measure the internationalization degree of construction companies, representing the ratio of overseas income to total income [26].

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2. Dependent Variable Corporate Social Responsibility (CSR): Considering the authority and availability of data, Hexun.com’s scoring results are used to measure CSR performance. Hexun.com’s CSR evaluation system examines five aspects: shareholder responsibility, employee responsibility, supplier, customer and consumer rights responsibility, environmental responsibility, and social responsibility. The scoring is more comprehensive and objective, and typically, higher scores indicate better CSR performance. 3. Control Variables (1) Corporate financial scale (Total assets): The larger the business, the more resources the company has and it is generally considered to have the ability to generate higher than average profit margins. We use total assets as an indicator to measure a company’s financial size and take its logarithm. (2) Corporate organizational size (Employees): Generally, businesses with more employees tend to be larger. The number of employees is used to represent the organization size of the company, and the logarithm is processed. (3) Net debt: Select “net debt” as another financial variable that can measure the company’s financial risk. Since the variable has negative numbers, we refer to John and Draper [27] and deal with it as follows. L(x) = sign(x) ∗ log(|x| + 1)

(1)

(4) Age: Company’s age generally refers to the number of years that the company has experienced from its establishment to the present. We select age as one of the control variables. (5) ROE: ROE is an essential reference value in financial indicators to measure a company’s profitability. It is the percentage of net profit to average shareholders’ equity. This indicator reflects the income level of shareholders’ equity and is used to measure the efficiency of the company’s use of its capital. A higher ROE presents a higher return on investment for the business. This indicator reflects the ability of own capital to obtain net income. (6) Corporate growth rate (Growth): The growth rate of the company’s operating income is used as an indicator to measure the company’s growth rate. Corporate growth rate = (primary business income of the current period - main business income of the previous period) / primary business income of the last period × 100%. (7) Capital Intensity (CI): Capital intensity is a measure of the effectiveness of resource use, expressed as the investment required to generate a unit of income. CI = sales/ total assets. 4. Empirical Models To analyze the effect of DOI on CSR and verify the hypothesis, this study develops a multi-regression model as follows: CSR = β0 + β1 DOI + β2 AGE + β3 SIZE1 + β4 SIZE2 + β5 Netbet + β6 CI + β7 Growth + β8 ROE + ε

(2)

CSR is the dependent variable, DOI is the independent variable, and AGE, SIZE1, SIZE2, Netbet, CI, Growth, and ROE are the control variables. SIZE1 indicates the

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corporate financial scale, and SIZE2 indicates the corporate organizational size. β0 is an absolute term, β1 −β8 is the regression coefficients of the respective variables, and represents a random disturbance term.

4 Results 4.1 Descriptive Statistical Analysis Before performing regression analysis, we first conduct descriptive statistical analysis on each variable. It can be seen from Table 1 that the average value of CSR is 25.515, the minimum score is −7.17, and the maximum score is 77.92, indicating the poor performance of construction CSR. And CSR performance of companies is significantly different. The average internationalization degree of listed construction companies is 0.148, the minimum is 0, and the maximum is 0.983, showing the construction companies’ internationalization degrees in general and various. The average financial scale is 1,286 billion (100 million yuan), the minimum is 3.204 billion (100 million yuan), and the maximum is 27,330 billion (100 million yuan). The average number of employees is 43343.323 (person), the minimum is 147 (person), and the maximum is 552810 (person). The average net debt is 14.57 billion (yuan), the minimum is −22.73 billion (yuan), and the maximum is 343.6 billion (yuan). The average capital intensity is 0.634, the minimum is 0.085, and the maximum is 1.714. The average growth rate is −0.0046, the minimum is −0.3875, and the maximum is 0.1996. The average ROE is 0.088, the minimum is −0.678, and the maximum is 0.568. Finally, the average age of Chinese construction companies is 17.303 years old, the minimum is 2 years old, and the maximum is 38 years old. We can find significant differences in the scale and development of Chinese listed construction companies. Table 1. Descriptive statistical analysis of variables Number of cases

Minimum

Maximum

Average

Standard deviation

Variance

CSR

501

−7.170

77.920

25.515

16.501

272.283

Employees

501

147

552810

43343.323

101298.530

10261392180

TotalAssets

501

3.20E + 08

2.733E + 12

1.286E + 11

3.382E + 11

1.14379E+23

DOI

501

0

0.983

0.148

0.202

0.041

Net Debt

501

−2.27E + 10

3.436E + 11

1.46E + 10

4.45E + 10

1.97758E + 21

CI

501

0.085

1.714

0.634

0.251

0.063

Growth

501

−0.388

0.200

−0.005

0.209

0.043

ROE

501

−0.678

0.568

0.088

0.108

0.012

Age

501

2

38

17.303

6.919

47.873

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4.2 Empirical Analysis and Results 1. Correlation analysis The correlation between variables was studied and the hypothesis was initially tested. The results are shown in the following Table 2. It can be seen that the CSR of construction companies has a significant negative correlation with the degree of internalization (DOI), the correlation coefficient is 0.102, and the significance level is 5%; CSR and Total Assets (lnTotal Assets) have a significant positive correlation, the correlation coefficient is 0.264, the significance level is 1%; CSR and capital intensity (CI) have a significant positive correlation, the correlation coefficient is 0.264, the significance level is 1%; CSR and ROE have a significant positive correlation, the correlation coefficient is 0.381, the significance level is 1%; CSR and organizational size (lnEmployees) have a significant positive correlation, the correlation coefficient is 0.284, the significance level is 1%; CSR has a significant negative correlation with the company’s age (Age), the correlation coefficient is −0.311, and the significance level is 1%. But construction companies’ CSR performance has no significant correlation with net debt and corporate growth rate (Growth). Table 2. Correlation statistical analysis of variables CSR DOI

0.102**

lnTotalAssets

0.264***

NetDebt’

−0.047

Age

−0.311***

CI

0.264***

ROE

0.381***

Growth

0.005

lnEmployees

0.284***

Note: *, **. Indicate significance at the5% and1% level (both sides), respectively

2. Regression results analysis (1) Multicollinearity test Before the multiple regression analysis, we used VIF to test the multicollinearity. When 0 < VIF < 10, we can judge that each index has no serious multicollinearity problem. The test results show that the maximum variance inflation factor of the independent variable is 4.98, the minimum is 1.07, and the mean is 2.14. No variable has a variance inflation factor higher than 10. The study sample is not affected by multicollinearity. (2) Model selection We applied the Breusch and Pagan Lagrange multiplier (BP) test to check whether a mixed panel model or a fixed-effects regression model was used, and applied

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the Hausman test to check whether a fixed-effects model or a random-effects regression model was used. Based on correlation analysis, we use SPSS 23.0 for regression analysis to judge whether there is a significant correlation between the DOI and CSR. We can find the results in Table 3, there is an important negative linear relationship between DOI and CSR, with a correlation coefficient of −17.016 and a significance level of 1%. Table 3. Multiple Regression Results Variable

Model CSR

DOI

−17.016***

lnTotalAssets NetDebt’ Age CI

4.229** 0.137 −2.531*** 1.335

ROE

34.872***

Growth

−0.975

LnEmployees

−2.919

Constant

−6.584

R2 F

0.287 21.066

Prob > F

0.000

Note:*, **, *** indicate significance at the 10%, 5% and 1% level (both sides), respectively

(3) Robustness test There are many indicators to measure the degree of internationalization. To ensure the robustness of the multiple regression results, we take overseas turnover (OT) as another indicator to measure the degree of internationalization and conduct robustness tests. The results are shown in Table 4. There is a significant negative correlation between DOI and CSR, with a correlation coefficient of −1.226 and a significance level of 5%.

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Variable lnOT lnTotalAssets NetDebt Age CI

Model CSR −1.226** 5.953** 0.136 −2.593*** 4.559

ROE

34.225***

Growth

−1.071

lnEmployees

−2.226

Constant R2 F Prob > F

−32.096 0.280 20.366 0.000

Note:*, **, *** indicate significance at the 10%, 5% and 1% level (both sides), respectively

5 Discussion This study empirically quantifies the impact that DOI has on CSR, the results overcome contradictory findings in the literature. The previous literature indicated that the international environment make pressure on construction companies due to the unfamiliar culture, policy, language, etc., which are beneficial to promoting CSR activities. on the other hand, some study results showed that the DOI is detrimental to CSR. Because international projects and CSR activities since they all imply costs and uncertainty, resulting in a negative synergy. To test the relationship between DOI and CSR, we ran a fixed-effects panel regression, using empirical data from listed Chinese construction companies from 2010 to 2020. Our empirical study results support the hypothesis that the relationship between DOI and CSR is negatively linear, indicating that construction companies’ international strategy is not beneficial for CSR. on the contrary, a large number of overseas projects may decrease the companies’ enthusiasm for implementing CSR activities. Although previous studies have suggested that companies may have more pressure to fulfill social responsibilities in the overseas market. To gain legitimacy and advantages in the host country, CSR and internationalization seem to go hand in hand [28]. Then they have chances to improve corporate image and status, and strengthen their relationship with their stakeholders, which is beneficial for construction companies to gain engineering projects and profits. For example, companies with higher levels of internationalization are more likely to engage in corporate philanthropy [29] to mobilize stakeholder support [30], including building schools, providing more careers for residents and donating medical equipment, etc. Construction companies with positive CSR activities are more likely

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to gain attention from local governments, which have the right to provide preferential policies to companies. However, our research shows that higher levels of internationalization do not necessarily lead construction companies to be more active in CSR. On the contrary, the results of our study seem to support the research of Jung et al. [26] that there are negative synergies between internationalization and CSR strategies. The increase in the DOI has caused construction companies to reduce their CSR performance, which supports our hypothesis. Chinese construction companies conducting overseas projects need to pay additional operation and management costs, such as higher freight and insurance costs, foreign exchange costs, and trade barriers. Due to the large gap between foreign and domestic economic and legal environments, construction companies’ management in the overseas market will become more complex [31]. The international environment adds risks to the survival of construction companies, and even brings a fatal blow to companies, causing the company to go bankrupt. The resources that each company has been limited, and construction companies have paid more costs and faced more risks in the process of carrying out international projects. However, corporate social responsibility also needs to consume a lot of resources and funds, and managers may not understand whether corporate social responsibility can bring advantages to the development of companies, or this kind of advantage is not obvious. Ultimately, international construction companies are hesitant to perform active social responsibility activities.

6 Conclusion Through this study, we evaluate the impact of the degree of internationalization on corporate social responsibility. The research results show that the internationalization of construction companies has a negative impact on CSR performance, which is consistent with our proposed hypothesis. This research brings three main contributions to the literature. First, it contributes to the theory of internationalization. Although construction companies hope to increase their influence and competitiveness by expanding overseas markets, developing international projects is not as simple as imagined. On the contrary, there are various uncertainties in the overseas environment, and construction companies also face more risks in the process of contracting overseas construction projects. Secondly, it helps to understand the transaction cost theory. Construction companies often face more transaction costs in the process of internationalization, which is mainly due to the company’s unfamiliarity with the host country’s environment and the particularity of the local political, cultural and legal environment. The increase in transaction costs has made construction companies begin to consider socially responsible activities. As previous studies have argued, the degree of internationalization increases the transaction cost of enterprises and brings risks to the operation and production of enterprises. It is undeniable that corporate social responsibility activities will also bring more transaction costs to enterprises. To avoid poor financial performance, construction companies may reduce their enthusiasm for fulfilling social responsibility. Secondly, it helps to understand the cost-benefit theory. Enterprises are rational people in principle. Only when the benefits of implementing corporate social responsibility activities are greater than the costs, enterprises will actively

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perform social responsibilities. However, in the process of corporate internationalization, construction companies often only see the costs and resources required to perform corporate social responsibility and do not pay attention to whether corporate social responsibility activities can bring benefits to the company, which is also the enthusiasm of international construction companies for corporate social responsibility activities. an important reason for not being high. Finally, this is one of the few studies focusing on the relationship between the degree of internationalization of construction companies and corporate social responsibility, which enriches the related research on the internationalization of construction companies. The research enriches the cross-research on internationalization and CSR. In general, the performance of Chinese construction companies is not ideal, and they may have negligent management of CSR. And in the internationalization process, Chinese construction companies hold a conservative attitude toward CSR, which needs further improvement. Although China’s construction companies have occupied an increasingly important position in the international market, their turnover and global influence have continued to increase, and their contracted international projects have become more abundant. However, Chinese construction companies often face legal pressure in host countries. The neglect of CSR has led to Chinese construction companies facing condemnation from the international community, which is not conducive to sustainable development. The unstable international environment and fierce global competition have put forward higher requirements for the development of Chinese construction companies. The research results suggest that Chinese construction companies should be further focused on sustainable development. They need to pay attention to the legitimacy issues caused by cultural environment, laws, regulations, and political differences, actively perform social responsibilities to gain support from local stakeholders, and eliminate the misunderstanding of pursuing short-term interests. We study and discuss the relationship between the internationalization of Chinese construction companies and CSR. The research results show that Chinese construction companies are insufficient in fulfilling social responsibilities at the emerging stage, which can guide them to correctly perform social responsibilities and promote the sustainable development of the construction industry. However, due to the particularity of the Chinese market economy, there may be differences in the performance of CSR between stateowned companies and non-state-owned companies. And construction companies may face various stakeholders, and cultural and political in overseas markets, which will lead to heterogeneity in CSR performance. Therefore, it is possible to further study the CSR performance of construction companies in different host countries in the future.

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A Simulation-Driven Data Collection Method of External Wall by Integrating UAV and AR Dianwei Song, Yi Tan, Penglu Chen, and Shenghan Li(B) Sino-Australia Joint Research Center in BIM and Smart Construction, Shenzhen University, Shenzhen, China [email protected]

Abstract. With the development of urban construction, the number of high-rise buildings is increasing, and the diseases of external wall appeared over time. Conventional high-rise building wall inspection is usually inefficient. Unmanned aerial vehicle (UAV) with cameras improves the efficiency of wall inspection, however existing complete data collection method of the whole external wall without any focus is time-consuming. Augmented reality (AR) with simulation can drive the real equipment to execute expected task properly. Therefore, this paper proposes a simulation-driven data collection method of external wall by integrating UAV and AR. This method firstly imports building model and UAV model into AR operation system, and relevant components are added to construct AR interface. After that, the grid map is generated and the voxelized building point cloud data is imported, and the map is integrated into the UAV control system to realize the path search algorithm. Then, a special network connection protocol between Robot Operating System (ROS) and AR is used to link them and realize the control and information transmission between UAV and AR. Finally, a scenario model is built in simulation environment to verify the feasibility of this method. The results show that this method can successfully control the UAV in the AR equipment and obtain the required information and improve work efficiency. This method aims to verify the use of AR to control equipment to achieve intelligent building external wall data collection. In the future, this technology will continue to be expanded and applied in practice. Keywords: Augment reality · Data Acquisition · Network protocol · Simulation · UAV

1 Introduction With the acceleration of urbanization, a large number of high-rise buildings have been built in the city. With the increase of the service life, there are certain risks and defects on the external walls of high-rise buildings. In the past, data acquisition for building external walls often required tools to send workers to target for photography. Later, researchers used the ability of unmanned aerial vehicles (UAV) to access inaccessible areas and flexibility to collect data, but most of the studies are carried out in a fully automated manner [1], and some areas which obviously © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 561–573, 2023. https://doi.org/10.1007/978-981-99-3626-7_44

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do not require patrol inspection are also included in the scope of the route [2, 3]. The whole process has large data volume and takes a long time, which affects the work efficiency. In fact, before data is collected, workers can eliminate these areas without defects obviously, but this is not real-time in a fully automated process and can only modify the plan in advance. Augmented reality (AR), as a new technology proposed in recent years, is able to superimpose reality and virtual environment to meet the needs of many scenes [4]. At the same time, operator can interact with virtual objects in AR to control the corresponding objects in reality, which realizes the linkage between the virtual and real. In order to improve work efficiency and reduce the workload of subsequent data processing, this paper proposes a method of collecting building external wall data by combining UAV and AR. This method integrates the flexibility of UAV and the visualization ability of AR, and combines the judgment and recognition ability of human beings, realizing the collection and display of data simultaneously. The rest of this paper is organized as follows. Section 2 briefly reviews previous related work. Section 3 introduces the general idea and technical points of this method in detail, and Sect. 4 uses a case study to validate the proposed method. Section 5 concludes this research and proposes the future work.

2 Literature Review 2.1 Data Collection of Structure Surfaces Recently, UAV has been already applied to the collection of inspection data of built structures. For example, Neshat and Amin [3] proposed a method for inspecting with LiDAR, which collected the complete surface data by combining the field of view (FOV) of LiDAR and considering overlapping views based on the criticality of the areas. Jung et al. [5] presented a new UAV 3D coverage path planning method, which can inspect high-rise structures. In the method proposed by Henk and Markus [6], a concept for the integration of UAVs for visual inspection tasks was proposed, which can generate collision-free flight paths and take photos in the most favorable position, achieved with virtual previews in the planning application. Tan et al. [1] proposed a method based on BIM model to optimize the UAV flight path for automatic collection of building external wall data, which mainly uses the connotation identity information and coordinate information of wall components in BIM, and carries out route planning and data collection. In addition to buildings, some researchers have also studied other structures. For example, Song et al. [2] presented a method based on BIM model and lidar to plan the path to ensure the maximum scanning coverage of mechanical electrical plumbing (MEP). Ribeiro et al. [7] proposed an innovative method that can inspect a reinforced concrete structure remotely using UAV and image processing. The above-mentioned methods show that UAVs are widely used in the data collection of structure surfaces. However, most of the existing collection methods are fully automated, and the collection time is long and the amount of collected data is large, which is not conducive to subsequent processing. In addition, these studies have almost never considered that the structural surfaces may not need to be inspected completely.

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For some areas without any obvious cracks or do not need to be repaired, they can be removed by temporary decision-making. 2.2 Application of AR Recently, AR has been applied to many industries. For example, in the field of education, AR makes students understand better by visualizing knowledge [8]; In the industrial field, AR is widely used to guide workers to use equipment and load and unload parts [9]. In addition, in the field of civil engineering, AR has also achieved significant results. Agarwal [10] summarized the application of AR in the construction field. Researchers converted 2D drawings into 3D models through AR technology to reduce human and other technical errors in the construction process. Kurmar and Yemineni [11] proposed a method to realize the visualization of smart cities by using AR. This method uses BIM and AR to realize the real digital sense on the mobile platform, and improves the 3D sense of layout planning. Kodeboyina and Koshy [12] introduced a visual construction interface application environment based on Unity engine and mature SDK, which can be used for specific positions and directions in civil engineering. Lin et al. [13] Using headmounted augmented reality devices, Lin et al. added 3D content to realistic drawings, making it easier for observers to read drawings and deepening their understanding from 2D to 3D. This method has proven to be very helpful in building construction. Firstperson perspective was usually taken as the lens, emphasizing immersive experience. Building patrol inspection focuses more on taking the overall situation as a perspective and controlling the whole process from a more general perspective. In addition, most of the studies only focus on AR itself, and do not combine it with other devices. Combining AR with patrol inspection equipment can maximize the visualization characteristics of AR. 2.3 UAV Intelligent Control The UAV can travel in 26 directions and rotate in 4 directions. It can also carry various high-precision lenses to collect data. This unique flexibility and applicability enable UAV to play a key role in various fields. Liu and Shen [14] presented an interface by integrated AR and UAV, which can further set the spatial target positions by intuitive head gaze and hand gesture. Antonio and Uwe [15] designed a robot operation visualization software based on robot operation system (ROS), which is also integrated with UAV. The robot sensor data can be seen through this visualization software, which is valuable for real-time mapping and automatic driving. Singh et al. [16] proposed a framework for agricultural scenarios. This framework uses the UAV intelligent control system and is designed for the particularity of agricultural scenarios to solve the special problems. Guan et al. [17] proposed another method for the power inspection scene, which uses lidar and applies real-time mapping technology to control the movement of UAV, and completes the inspection task in unknown environment in a real-time manner. In addition, considering the potential danger for UAV to carry out experiments in the real environment, some studies are explored in the simulation environment. Guo et al. [18] simulates the downwash gas flow of agricultural UAVs in the simulation environment, and conducts 3D simulation of the gas flow by constructing different agricultural

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environments to find the best spraying method and process of agricultural UAVs. In Hiba’s [19] paper, a simulation environment was created for unmanned agencies in different traffic conditions to test the response ability of different types of UAVs in complex environments. These studies are mainly considered from the UAV itself, and are limited to optimizing its own performance. However, as a tool, UAV should start from its scene and play different roles in different occasions. In addition, most of the studies are full automation of the UAV flight process, lacking human participation and decisionmaking, while in the actual situation, correct guidance by operators can make the work more smoothly.

3 Methodology This section describes the details of the proposed method. Figure 1 introduces the workflow of this method, which includes three aspects: (1) Construction of AR terminal, (2) Control of UAV terminal, and (3) Terminal communication.

Fig. 1. Framework of the proposed method

The purpose of this method is to connect two different terminals, control the UAV in a visual and convenient way and display the relevant information in the data collection process. The AR terminal is responsible for issuing instructions and displaying the data from UAV, so it plays the role of a “client”. The UAV terminal performs tasks by receiving instructions and transmits all kinds of collected data back to the AR terminal, so it plays the role of a “server”. The terminals communication is like a “bridge” to establish a channel so that the data can be transferred smoothly between terminals. This structure composed of “client”, “server”, and “bridge” supports the content of this method. Their mechanism and theory will be introduced in detail in the following sections.

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3.1 Construction of AR Terminal 3.1.1 Scene Construction As the client of the whole process, AR terminal needs to meet the characteristics of being easy to observation and operation. Therefore, it is necessary to import the model required by the scene, like building and UAV model. When importing building model, the target detection area usually represents the external wall of building, other components are not required in this method. And the complete BIM model contains too much information that the current mobile AR devices cannot support loading too large models, so it is necessary to remove all components except external walls in the BIM model in advance. In addition, since the BIM model is composed of multiple components, when importing the model into the unity editor, this structure will lead to excessive duplication of the grid of the model, which wastes memory space. Therefore, The C-Sharp script is used to merge the model Mesh to improve fluency. Moreover, scenarios should also include functions and interfaces associated with UAV, like the change of scale and rotation of the model and the video stream transmission. In UAV system, pictures and video streams are saved from byte stream code, while they are all displayed in the form of texture in unity environment, so when establishing corresponding functions, it is necessary to establish additional texture and adjust parameters in the script to adapt to the format after transcoding. The compile environment used in this paper includes Unity and Universal Windows Platform (UWP), so the corresponding Mixed Reality Analytics Toolkit (MRTK) [20] is selected to establish functions, it can provide mature UI layout and model operation for operators, and reduce the development costs. 3.1.2 Coordinate Transformation As the client and server are in different spatial environments with different coordinate systems, coordinate system conversion is required to connect client and server. This section mainly involves two coordinate transformation: 1. Virtual-real coordinate transformation, 2. Simulation space and flight control coordinate transformation. The virtual-real coordinate system refers to the AR space coordinate system and the simulation environment coordinate system. AR space uses the left-hand coordinate system, while the simulation environment uses the right-hand coordinate system. This method selects the AR space coordinate system and the local coordinate system of the simulation environment, and sets the UAV takeoff point as the origin point of the local coordinate system, so that its local coordinates can be obtained through the relative positional relationship between each point and the origin point. In this way, the influence caused by coordinate error can be eliminated as much as possible, and the coordinates of each point in the space can be obtained more easily as shown in Fig. 2 (a). The coordinate transformation between space and flight control involves the transformation of Euler angle, as shown in Fig. 2 (b). Assuming the coordinates XYZ of ENU coordinate system and NED coordinate system, the conversion formula is shown in Eqs. (1)–(4). While controls yaw conversion, the conversion of Euler angle is carried out in Cartesian coordinate system, each rotation generates a new coordinate system to replace the previous one. The conversion formula is shown in Eqs. (5) and (6), α, β, γ denotes

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the rotation angles generated by rotation around three axes. YawNED = −YawENU + 90

(1)

XNED = YENU

(2)

YNED = XENU

(3)

ZNED = −ZENU

(4)

⎛

⎞ ⎛ ⎞ X X ⎝Y ⎠ = M⎝Y ⎠ Z Z

(5)

⎛

⎞ cosαcosγ − sinαcosβsinγ −sinαcosγ − cosαcosβsinγ sinβsinγ M = ⎝ cosαsinγ + sinαcosβcosγ −sinαsinγ − cosαcosβcosγ −sinβcosγ ⎠ (6) sinαsinβ cosαsinβ cosβ

Fig. 2. Coordinate transformation

According to the two coordinate transformations, the commands sent by the AR terminal can be transmitted to the flight control system and executed in the simulation environment, and the parameters sent by the flight control can also be sent back to the AR terminal for visualization. 3.1.3 Human-Computer Interaction The process that the operator selects the target point in the AR scene, and then the UAV calculates the optimal path in real time and collects it, that is the process of humancomputer interaction. Human-computer interaction refers to the use of the characteristics of human and machinery, complementary advantages and reasonable division of labor to achieve work that any machine cannot independently complete. In this study, operator can quickly determine the target position by observing the whole building using the thought and behavior ability of human. AR can assist the operator in judgment by providing a clearer vision. The data transmitted by the UAV can be displayed in AR in real time and

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fed back to people for judgment. Operator do not need to reach the location of external wall diseases in person, these dangerous behaviors can be completely replaced by UAV. In fact, the ability to make a decision from human in an unknown and complex environment is stronger than computers [21]. Usually, UAVs need to import a priori environment or calculate environment in real time through sensors when performing routing tasks, which is clearly time-consuming and laborious. Although the participation of human consciousness makes the whole process not completely automated, but it greatly improves the work efficiency. 3.2 Control of UAV Terminal The above section mainly introduces the construction principle and technical details of the “client”. As the task executor and data collector, UAV participates in a large part of the work as the “server”. In order to complete all tasks normally, some technologies are involved. This section will focus on these technologies and elaborate their details. 3.2.1 Path Planning During external wall inspection with AR and UAV, the operator only needs to select the target point, and the path planning from the current position to the target point is completed independently by the UAV. In this study, A-star algorithm [22] is used to obtain the shortest path, and the premise of this algorithm is the grid map. Because the data is collected in the simulation environment, the environmental impact around the building is ignored. It is sufficient to import the building model into the space. BIM model has rich geometric information, and exporting it in model can also meet the needs of building 3D grid map. First, a completely empty 3D grid space is created by using the resolution method, and then the BIM model is transformed into point cloud and inserted into this space for updating. Because of point cloud is composed of several points, the security of the aircraft cannot be guaranteed in the occupied map, so it is necessary to expand the model point cloud, and the expansion radius of the point cloud is the radius of the UAV. The grid diagram is shown in Fig. 3, a simple point cloud model is expanded into a grid model and displayed in blue. Such a grid map is the premise of the A-star algorithm utilized in this study, where the grid map includes the “walkable area” and “non-walkable area”. When the operator drags the UAV to the specified position in the AR interface, the coordinates will be released in the form of 3-dimensional space coordinate system first. Since there is an equal proportion conversion relationship between the simulation environment and the AR space, and the reference point of the AR space is the UAV origin in the simulation space, the relative position relationship between the UAV target point and the reference point in the AR space can be multiplied by the scale coefficient of the AR space. The coordinates in the simulation space are obtained, which are consistent in the simulation environment and the grid map environment. The current location information of the UAV can be obtained in the UAV system and recorded on the grid map in the same coordinate system. In this routing request, the grid map has recorded the current and target location points, which are the starting and ending points. Next, the A-star algorithm automatically plans the shortest path in the grid map and outputs the

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Fig. 3. Illustration of created 3D Grid Map

path points. In the simulation environment, the UAV will move according to the path points and finally reach the end point. The current position of the UAV obtained from the simulation environment will also be transferred to the AR interface and represented by another color of the UAV. The final two colors of the UAV should approximately coincide to indicate that the current routing has been completed. 3.2.2 UAV Control System UAV control system is the core when executing aerial missions. This study focuses on how the external commands control UAV command system. There are two kinds of common control, one is the native control mode of Linux, the other is the node control mode of ROS. In this study, Pixhawk 4 flight control relying on ROS is adopted. In ROS [23], various data are transmitted in the form of “messages”, and the two parties transmitting data exist in the form of “nodes”, which are called “server” and “client”. These messages can be delivered in the form of “topic” or “service”, and the relationship between them is shown in Fig. 4(a). Through this mechanism, each node can send or receive multiple data to realize the control system framework of the UAV. Data transmission through nodes can avoid high computational costs. The real-time solution capability of the mobile AR device is limited, and data transfer through the ROS system can help the AR terminal process information quickly. In this method,

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the UAV control system can not only accept the command information from the AR interface, but also feedback the current state of the UAV back to the AR interface in real time. When the path-finding request starts, the UAV control system will first obtain the current coordinate information of the UAV and transfer it to the grid map, and then transfer the target point position transferred from the AR interface to the grid map. When the path-finding begins, the UAV control system will receive the path information from the grid map in real time and publish it in a way that ROS can understand to control the UAV movement in the simulation environment. In addition, the control system will also transmit the current state information of the UAV in real time, such as coordinates, speed, angle, etc., and transmit the information required by the AR interface in the form of communication. 3.3 Terminal Communication After the client and the server are established, a channel needs to be established between them to transfer data, which is the terminal communication. Traditional distributed methods were usually used for communication, which were costly and difficult to modify. Now, based on the characteristics of ROS, researchers have proposed a method called Rosbridge [24], which uses less resources and has fast communication speed. As shown in Fig. 4(b), the distributed communication between nodes can be changed into C/S communication between the client node and the proxy node, then the proxy node forwards the request to the service node, which can avoid communication with the entire platform and reduce costs.

Fig. 4. Terminal communication

The functional components are implemented through the client library of RosBridge, and then the transmission channel is established through WebSocket. WebSocket is a protocol for full duplex communication over a single TCP connection, which allows the server to actively push data to the client. In the WebSocket API, clients and servers only need one handshake to create a permanent connection and two-way data transfer. In this way, information can be transmitted to the back-end control system of the machine. At the same time, machine can send its feedback information to the client. With this proposed process, two terminals can act as both the client and the server, so the information interaction is very convenient.

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4 Case Study In order to verify the feasibility of the proposed method, the model of an office building in Shenzhen University is selected for experiments in simulation environment. The building covers an area of about 8,608 square meters and height is about 26 m. The simulation environment for experiment is gazebo 9.0 based on Ununtu18.04. The AR scenario is built by the Unity editor and deployed on Hololens 2. A four rotor UAV is introduced into the simulation environment. The final simulation environment is shown in Fig. 7(a).

Fig. 6. Building models in different formats

First, the BIM model is simplified on Revit, only the external walls are preserved, as shown in Fig. 6(b). Then convert this model into point cloud format in CloudCompare for rasterization. After that, the point cloud is expanded into a grid map as shown in Fig. 6(d), then the origin point of this map and the UAV takeoff point are aligned in the simulation environment, this enables the path finding to be implemented correctly. When building AR scene, the parent object is used to establish new space to meet the positional relationship like the simulation environment. Then the MRTK toolkit is used to add other functions and windows, such as model movement and video stream display. Two UAVs are also placed in the scene, the black one is interactive, and the red UAV is not. The operator sends the attitude information by dragging the black UAV, while the red UAV receives the information and displays it in real time. The video transmission can also be displayed in the scene, as shown in Fig. 7(b). In order to further verify the effect of this method, five people who have no UAV flight experience and five people who have flight experience were selected for next

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Fig. 7. Scene demonstration

experiment. At first, the joystick is simulated by QCGround in simulation environment. Then participants were asked to use joystick and AR equipment to control the UAV to reach the same position and shoot the same place. The comparison of the time spent is shown in Table 1. The results show that whether they have flight experience or not, using AR equipment can improves their work efficiency. Participants only need to move their arms to move the drone to the position they want, and can easily deflect the drone and view data. In addition, the time in the table does not include teaching. Table 1. Comparison of time spent by participants Group

Number

Time in joystick/(s)

Time in AR scene/(s)

Degree of time saved/(%)

Never Flight

1

87

44

49.4%

Never Flight

2

95

25

73.7%

Never Flight

3

83

40

51.8%

……

……

……

……

……

Have Flight

1

41

40

2.44%

Have Flight

2

37

33

10.8%

Have Flight

3

30

25

16.7%

……

……

……

……

……

5 Conclusions This paper mainly introduces a data collection method of external wall by integrating UAV and AR, and carries out experiments in simulation environment to verify the possibility and simplicity of operation. This method constructs AR scene with a global

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perspective, greatly improving the visualization ability and helping operators execute action easily. In addition, AR visualization strengthens the role of human-computer interaction, gets rid of the heavy patrol task in the past, and reduces working time and data volume. At last, the control mode of the UAV is changed from the joystick to the finger, it makes non-professionals also can easily perform patrol tasks. However, this study still has its limitations. First, the AR scenario is too simple, it has only part of the data transmission, cannot fully reflect the advantages of visualization and human-computer interaction. Second, a single global planning may not be able to adapt to the need in real scene. As a relatively regular model, BIM model may not be consistent with the actual building situation, and an unknown real environment around the building will increase the risk of flying. Finally, this paper only carries out experiments in the simulation environment, without considering the real scene and many other factors. In the future, there are three perspectives for further research: 1. Improving the richness of AR scenes, such as other functions to help operators make judgments. 2. To optimize the path planning process considering add lidar or visual positioning. In this way, many risks in the actual situation can be avoided, and the path can be further shortened. 3. Using a smoother data transmission channel. The higher the bandwidth, the larger the amount of data that can be transmitted, this can also improve work efficiency.

References 1. Tan, Y., Li, S., Liu, H., Chen, P., Zhou, Z.: Automatic inspection data collection of building surface based on BIM and UAV. Autom. Constr. 131, 103881 (2021) 2. Song, C., Chen, Z., Wang, K., Luo, H., Cheng, J.C.P.: BIM-supported scan and flight planning for fully autonomous LiDAR-carrying UAVs. Autom. Constr. 142, 104533 (2022) 3. Bolourian, N., Hammad, A.: LiDAR-equipped UAV path planning considering potential locations of defects for bridge inspection. Autom. Constr. 117, 103250 (2020) 4. Uhm, J.-P., Kim, S., Do, C., Lee, H.-W.: How augmented reality (AR) experience affects purchase intention in sport E-commerce: roles of perceived diagnosticity, psychological distance, and perceived risks. J. Retail. Consum. Serv. 67, 103027 (2022) 5. Jung, S., Song, S., Youn, P., Myung, H.: Multi-layer coverage path planner for autonomous structural inspection of high-rise structures. In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1–9 (2018) 6. Freimuth, H., König, M.: Planning and executing construction inspections with unmanned aerial vehicles. Autom. Constr. 96, 540–553 (2018) 7. Ribeiro, D., Santos, R., Shibasaki, A., Montenegro, P., Carvalho, H., Calçada, R.: Remote inspection of RC structures using unmanned aerial vehicles and heuristic image processing. Eng. Fail. Anal. 117, 104813 (2020) 8. Lima, C.B.D., Walton, S., Owen, T.: A critical outlook at augmented reality and its adoption in education. Comput. Educ. Open 3, 100103 (2022) 9. Wang, Z., et al.: A comprehensive review of augmented reality-based instruction in manual assembly, training and repair. Robot. Comput.-Integr. Manuf. 78, 102407 (2022) 10. Agarwal, S.: Review on application of augmented reality in civil engineering. In: International Conference on Inter Disciplinary Research in Engineering and Technology, p. 71 (2016) 11. Rohil, M.K., Ashok, Y.: Visualization of urban development 3D layout plans with augmented reality. Results Eng. 14, 100447 (2022)

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12. Kodeboyina, S.M., Varghese, K.: Low cost augmented reality framework for construction applications. In: ISARC. Proceedings of the International Symposium on Automation and Robotics in Construction, p. 1. IAARC Publications (2016) 13. Lin, T.-H., Liu, C.-H., Tsai, M.-H., Kang, S.-C.: Using augmented reality in a multiscreen environment for construction discussion. J. Comput. Civ. Eng. 29, 04014088 (2015) 14. Liu, C., Shen, S.: An augmented reality interaction interface for autonomous drone. In: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 11419– 11424. IEEE (2020) 15. Zea, A., Hanebeck, U.D.: iviz: a ROS visualization app for mobile devices. Softw. Impacts 8, 100057 (2021) 16. Singh, P.K., Sharma, A.: An intelligent WSN-UAV-based IoT framework for precision agriculture application. Comput. Electr. Eng. 100, 107912 (2022) 17. Guan, H., et al.: UAV-lidar aids automatic intelligent powerline inspection. Int. J. Electr. Power Energy Syst. 130, 106987 (2021) 18. Guo, Q., et al.: CFD simulation and experimental verification of the spatial and temporal distributions of the downwash airflow of a quad-rotor agricultural UAV in hover. Comput. Electron. Agric. 172, 105343 (2020) 19. Hiba, A., et al.: Software-in-the-loop simulation of the forerunner UAV system. IFACPapersOnLine 55, 139–144 (2022) 20. Nebeling, M., et al.: MRAT: the mixed reality analytics toolkit (2020) 21. Chen, X., Geyer, P.: Machine assistance in energy-efficient building design: a predictive framework toward dynamic interaction with human decision-making under uncertainty. Appl. Energy 307, 118240 (2022) 22. Tseng, F.H., Liang, T.T., Lee, C.H., Chou, L.D., Chao, H.C.: A star search algorithm for civil UAV path planning with 3G communication. In: 2014 Tenth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 942–945 (2014) 23. Quigley, M., et al.: ROS: an open-source Robot Operating System. In: ICRA Workshop on Open Source Software, Kobe, Japan, p. 5 (2009) 24. Crick, C., Jay, G., Osentoski, S., Pitzer, B., Jenkins, O.C.: Rosbridge: ROS for non-ROS users. In: Christensen, H.I., Khatib, O. (eds.) Robotics Research, pp. 493–504. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-29363-9_28

A Systematic Review of Quantitative Measurement Methods for Accessibility of Urban Infrastructure Gunjun Li1 , Zhongwei Xiong2 , and Yanqiu Song2(B) 1 Academic Affairs Department, Central University of Finance and Economics, Beijing, China 2 School of Management Science and Engineering, Central University of Finance and

Economics, Beijing, China [email protected]

Abstract. The current rapid urbanization process has led to an uneven distribution of infrastructure, which has brought about many environmental and social problems. Therefore, it is necessary to have a comprehensive understanding of the construction of urban infrastructure in order to better plan the direction of urban development and cope with social problems such as educational resources, public health, and aging. As a common spatial indicator in urban geography studies, accessibility is an important tool for monitoring and constructing urban development patterns, as well as an indicator of fairness in resource allocation reflecting sociological studies, and the most common method used in the existing literature to evaluate the fairness of infrastructure facilities is also accessibility analysis. The traditional accessibility review is too simple in its methodological analysis, and with the development of information technology, it does not include many new methods in its examination. Therefore, this thesis adopts a systematic review approach to comprehensively analyze the strengths and weaknesses of existing methods, their scope of application, and perspectives of concern, and to gain a clearer understanding of future accessibility method improvements. It is found that the subjective factors of residents, i.e., mobility and consumption level, are rarely considered when examining factors affecting accessibility; current accessibility measures mainly examine accessibility at a certain time slice, i.e., static accessibility, and less research is conducted on dynamic accessibility, which is particularly important for certain facilities, such as emergency medical facilities; accessibility is mainly studied for common green spaces, transportation, and medical facilities. Therefore, accessibility measures are more oriented to spatial accessibility, and less attention is paid to non-spatial accessibility. Keywords: infrastructure · accessibility · systemic overview

1 Introduction The United Nations estimates that the population of urban areas will rise to 66% by 2050, and urbanization will continue to advance. However, rapid urbanization increases socioeconomic inequality (Sampson, 2017; Lusseau and Mancini, 2019), inequality is © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 574–592, 2023. https://doi.org/10.1007/978-981-99-3626-7_45

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reflected not only in income disparities but also in the types of infrastructure used by residents (McConnachie and Shackleton, 2010; Castells-Quintana and Royuela, 2015; Althoff et al., 2017; Mackenbach et al., 2018). The reason for this may lie in the current rapid urbanization and continued economic growth; this rapid development has put enormous pressure on urban infrastructure and land use, and the failure of most cities to respond to this rapid growth in a coordinated manner has resulted in an uneven distribution of infrastructure. The concept of accessibility, first introduced by Hansen (1959), characterizes the ease of overcoming spatial barriers; if the spatial barrier from one point to another is large, the point is poorly accessible, and vice versa. The quality, quantity and type of services provided by a facility also affect the level of accessibility (Handy and Niemeier, 1997). As a common spatial indicator used in urban geography studies, accessibility is an important tool for monitoring and constructing urban development patterns (Bertolini, le Clercq and Kapoen, 2005; Geurs, Krizek and Reggiani, 2012; Ben-Elia and Benenson, 2019), accessibility also reflects the equity of resource allocation in sociological studies, and therefore accessibility is an important indicator of equity (Del Casino and Jones, 2007; Golub and Martens, 2014; St˛epniak and Rosik, 2015; Xing and Ng, 2022). The most common method currently used in the existing literature to evaluate the equity of infrastructure facilities is also accessibility analysis, especially applied to urban green space, healthcare, and education infrastructure (Fig. 1).

Fig. 1. The path of impact of uneven distribution of infrastructure under urbanization

There are also scholars who have reviewed the methods of accessibility, but they are too simple in their methods, and traditional methods such as ANOVA have become obsolete, and with the development of GIS technology, many newly emerged methods are not examined included. Or the analysis of methods is not comprehensive and only focuses on the applicability of the methods themselves. Therefore, it is necessary to conduct a systematic review of the accessibility methods, comprehensively analyze the advantages and disadvantages of the existing methods, the scope of application, and the angle of concern, and have a clear understanding of the future method improvement. In particular, the traditional accessibility analysis methods are not applicable to new concepts such as financial infrastructure and arithmetic infrastructure, which need to be measured and measured from new dimensions.

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2 Review Methodology 2.1 Search Strategy of Measure Methods This dissertation uses a systematic review approach, which is a method of screening, classifying, and evaluating the literature using set search rules for one or more niche issues to efficiently and comprehensively address the questions posed, and allows for prognosis of future trends based on existing research (Mulrow, 1994). Compared to traditional reviews, systematic reviews are highly transparent and reproducible, (Thorpe, 2005; Tranfield, 2003; Robinson, 2015), and are not subject to the perceived bias of researchers (Zhu, 2020). The concept of reachability has been around for a long time, but scholars’ views on the factors to consider for the reachability measure are not uniform. In this thesis, we distinguish objective and subjective factors that affect the accessibility measure from the existing literature. The objective factors include time, space, and infrastructure factors, while the subjective factors include mobility and consumption level factors. Therefore, accessibility measures are also subdivided into overcoming time accessibility, overcoming spatial accessibility, overcoming infrastructure accessibility, overcoming mobility accessibility, and overcoming consumption accessibility (Fig. 2).

Fig. 2. Five factors affecting the accessibility measure

2.2 Literature Results First, according to the set search strategy, literature search was conducted with Web of Science and Scopus as the primary databases because these databases have significant advantages over other data in terms of interdisciplinarity, collection time, and prospective (Mongeon & Paul-Hus, 2016), and with Google Scholar, Wiley, IEEE Xplore as secondary databases for the initial screening of the literature, plus a total of 6439 articles from other sources. Due to the excessive volume of articles in the initial screening, irrelevant research directions (e.g., genetics, oil refining, etc.) were eliminated from the

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search results and duplicates were removed, leaving approximately only 1598 articles. Again, we eliminated the literature that did not match by reading the titles, abstracts and keywords, such as literature where the research object was not urban in scope and qualitative analysis methods were used. After a cursory reading in the previous step, we were able to grasp some proper nouns in the field (e.g., UGS is called urban green space and PPQI is called suburban park quality index), so we searched again by using proper nouns to prevent literature omission. Finally, the full text was read and 103 eligible papers were retained (Figs. 3 and 4).

Fig. 3. Literature screening process

The left side of the above figure shows the number of papers published across years, and it can be seen that the number of scholars’ researches on accessibility was small between 2010 and 2016, and the overall trend was on the rise. In the five years after 2017, scholars’ attention to accessibility issues showed an obvious upward trend, among which the research on urban green space was always the focus of attention; the research on medical care showed an upward trend in general, especially some scholars studied the accessibility of medical institutions in the context of the new coronary pneumonia; while the attention to traffic accessibility showed a downward trend. In terms of the types of journals published, the top five journals with more publications are Sustainability, International Journal of Geo-Information, Journal of Transport

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Fig. 4. Number of papers published and journal data across years

Geography, International Journal of Environmental Research and Public Health, and Cities, with the top five journals accounting for 42% of the total sample size (Fig. 5).

Fig. 5. List of thesis research sites

From the data of the paper studies, domestic scholars are more concerned about accessibility than foreign ones, and the data of many studies also come from domestic sources, followed by Iran, Canada, the United States, Brazil, and Australia, while European and Pacific Rim countries such as Japan are less studied.

3 Overview of the Selected Accessibility Measures Gravitational Model The gravitational model is actually derived from the law of gravitation. The famous physicist Isaac Newton discovered gravity in 1687, and the magnitude of gravity is

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proportional to the product of their masses and inversely proportional to the square of their distances. The American scholar Reilly surveyed 150 cities in the United States in 1931 and proposed the law of retail gravity based on the theory of gravity, summarizing the interrelationship between urban population, distance and retail gravity, i.e., the attractiveness of a city to its surrounding area is proportional to its size (population) and inversely proportional to the square of distance. It is expressed by the formula: where A is the attractiveness of the city to the surrounding area, P is the number of city population, and D is the distance between the city and the surrounding area. A=

P D2

The concept of accessibility was first proposed by Hansen (1959), and accessibility is a characterization of the ease of overcoming spatial barriers, Hansen used the gravitational model as a measure of accessibility, and the formula is as follows: Ai represents the accessibility of spatial location i, Wj is the service capacity of a certain type of infrastructure number j, and the service capacity is usually chosen as the area served by the infrastructure, the number of people served, etc. Sij is the spatial resistance value between spatial location i and infrastructure number j, which is usually measured by linear distance, and α is the gravitational decay coefficient, which is used to characterize the degree of decay of accessibility as the spatial resistance value increases. If the spatial resistance from one point to another is large, the accessibility of the point is poor, and vice versa, the accessibility of the point is good.  Wj × Sij−α Ai = j

However, scholars found through empirical studies that the results of Hansen’s accessibility formula were significantly different from the reality, so Ingram (1970) and Wilson (1971) improved the gravitational model through exponential transformation, respectively. Weibull et al. in 1976 also characterized the service effectiveness of infrastructure with different spatial Tsou (2005) and others used the gravity model to construct a comprehensive equity index, extending the original gravity model for the same type of infrastructure accessibility to multiple infrastructures and incorporating residents’ preference coefficients for different infrastructures. Chang (2011) further refines the gravity model by dividing the accessibility of different modes of travel into walking and driving, which is essentially a further improvement of the influence of weibull population size. The gravity model essentially analyzes accessibility from the perspective of demand and supply, and then characterizes the level of infrastructure equity in different regions by the magnitude of regional accessibility values. The method is more realistic, but the calculated values can only compare relative differences, and there are differences in the calculated values with the specific construction of the formula. 2SFCA Method Luo (2003) proposed the 2SFCA method and investigated the equity of health care facilities, i.e., the demand and supply are searched separately, which can measure the difference of spatial distribution at the microscopic scale and also reflect the degree of

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supply and demand balance. The formula is shown as follows: In the first step, the demand and supply ratio of infrastructure j is calculated by searching all settlements within the threshold value, with infrastructure j as the center and d0 as the threshold value. Where Rj represents the supply-demand ratio of infrastructure j, Sj represents the service capacity of infrastructure j, which is usually measured by factors such as service area and service population, and Pk represents the number of population at spatial location k. In the second step, all infrastructure points within the range are searched with the same threshold d0 , centered on settlement i, and the accessibility of settlement i is calculated. Rj = Ai =



Sj

{ 

} Pk Rj

k∈ dkj ≤d0

j∈{dij ≤d0 }

HUFF Model The Huff model was created by David Huff in 1963 when Huff wanted to measure the attractiveness of different shopping places to consumers, and later scholars also adopted this model to measure the accessibility of infrastructure. Huff model is a stochastic probability model based on the urban road network, and the most obvious difference of Huff compared with other methods is the inclusion of probability factors to measure the subjective propensity of residents to choose infrastructure, which is closer to reality. However, when Huff is applied to measure the infrastructure attractiveness index, scholars basically use the conventional indicators such as service area and number of people, which are actually relatively arbitrary. In addition to the scale of infrastructure, there are also service characteristics of facilities and service quality that are important influencing factors, and possible influencing factors can be taken into account through certain weighting values. Kernel Density Analysis Method The kernel density analysis method is able to estimate the overall distribution from scattered infrastructure distribution data, which will vary the density of the infrastructure in a continuous raster form within a given range, reflecting the clustering characteristics of the infrastructure under space. In simple terms, it is centered on the location of the infrastructure respectively, which is covered with a smooth surface, and its density value is highest at the location of the infrastructure, decreases gradually with increasing distance, and is zero at a given radius. For any location in space, the final density value of the location is obtained by summing up the density values of all infrastructures at that point. The higher the density value, the more infrastructure there is in that location, the more convenient it is for residents to use. The lower the density value, the less infrastructure in the location, the less convenient for the residents to reach the infrastructure. The nuclear density analysis method can be expressed by the formula. 1  x − xi ) k( nh h n

fx =

i=1

Among them, k(x) is the kernel function, different kernel functions have little effect on the density estimation, which can be selected according to the actual demand; h is the

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radius, the larger the radius, the smoother the surface covered on the infrastructure, and the density value within the range changes slowly, and vice versa, the choice of radius will largely affect the final presented results, so the radius needs to be adjusted several times during the operation to better meet the research requirements. Buffer Zone Measurement Method By calculating the number, type, and area of urban green spaces in a certain spatial location within a certain radius distance, or by calculating certain types of elements (such as the number of residential areas or population) within a certain radius distance of infrastructure, the amount of elements is used to characterize the level of accessibility. The former characterizes the amount of resources available to residents within a certain range, while the latter characterizes the amount of elements served within a certain range. The buffer zone measurement method is convenient and concise, but it also has the following defects: 1. Using infrastructure as the point source to establish the buffer zone, it can only distinguish between reachable and unreachable, and the internal differences of the reachable area cannot be analyzed; 2: the service radius of infrastructure is usually artificially set, which is not objective; 3. The service radius varies with different service facilities, and the uniform dependence does not match with the actual situation; 4. Ignoring the influence of mountains, rivers, railroad Lines and other objective factors will overestimate the accessibility level. Minimum Proximity Method By calculating the straight-line distance from any location in space to the closest infrastructure, the distance is used to characterize the proximity of residents to the infrastructure. This method is relatively simple and direct, but ignores many objective problems, such as different service levels of infrastructure, large gaps between the real road network distance and the straight-line distance, and the inability to measure the richness of infrastructure. Minimum Travel Time Method By calculating the travel time from any location in space to the closest infrastructure, this distance characterizes the proximity of residents to infrastructure. Network Analysis Method Network analysis method is to use the network analysis module of arcgis (Network Analyst), using the real city road network topology to build a model, and set the road passage speed under different travel modes to fit the real travel situation according to the actual situation, through the virtual city road network for vector calculation, so as to measure the accessibility of reaching the infrastructure. Network analysis method is based on the principles of graph theory and operations research, mainly including the following four elements: Center that is, the infrastructure to be reached in the urban road network; Node that is, the intersection of urban roads, the intersection will have an impact on pedestrian and vehicular traffic through traffic lights; Link that is, the abstracted urban roads, according to the actual situation is distinguished into different levels of roads such as trunk roads, secondary roads, feeder roads Impedance is the resistance to passage,

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Fig. 6. Schematic diagram of network measurement method

which can be understood as the speed through the road, and there are differences in the passage speed of different levels of roads for different travel modes (Fig. 6). The network analysis method is based on the actual road traffic network, and the calculation is based on vector data, which can simulate the real situation more realistically and has obvious advantages in measuring the accessibility of infrastructure. The network analysis method can also be used for the rational allocation of limited resources, the calculation of the best access routes and the analysis of the service area of public service facilities. However, the network analysis method not only requires sufficient data sources, but also requires manual entry of many data, which is more troublesome to process. The current application of this method is relatively limited. Spatial Autocorrelation Analysis This method mainly uses the Moran index to examine whether the infrastructure between regions is spatially correlated, and uses LISA agglomeration plots and Moran scatter plots to analyze the interdependence and spatial heterogeneity of the infrastructure between regions. This method is suitable for examining the amount of infrastructure distribution in the regional dimension, but it does not portray the infrastructure distribution in great detail. Spatial Syntactic Model The spatial syntactic model was proposed by Bill-Hillier in 1970, and its core lies in abstracting the topological structure from the real layout, calculating the values of topological variables, and analyzing spatially based on the values. The method is based on graph theory and social network analysis, and mainly includes two core steps: firstly, spatial segmentation is carried out, and the methods of spatial segmentation are axis method, convex polygon method, and view area segmentation method, among which axis method is applicable to the segmentation of urban road network, convex polygon method is suitable for the segmentation of street width that cannot be ignored or includes facilities such as urban square and green space, and view area segmentation method

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is mainly for the segmentation of complex buildings, such as parks, shopping malls, libraries, etc.

4 Discussion 4.1 Discussion on the Spatial Externality of Accessibility Approach Spatial externality refers to the impact of our current economic activities on other people and society in space, and in this article it can be derived that spatial externality refers to the impact of our choice of a certain infrastructure on other facilities and things around us in space. According to the concept of spatial externality, we can sort out the methods of accessibility as shown in the following figure. The darker the color of the method in the diagram, the stronger the spatial externality. The minimum proximity method is the earliest and simplest accessibility measurement method, which was widely used in the early period when computer technology was not developed, but this method assumes the same individual mobility, homogeneous infrastructure and the same consumption preference, ignoring the spatial factor, and the obtained results have some reference value, but they are too far from the actual situation and overly magnify the accessibility. The minimum travel time method takes into account individual time constraint factors, mobility ability factors, and residents may travel in different ways, which is an improvement to some extent, but both methods in general do not consider spatial externalities, and only use the minimum distance or the shortest time for facility search. The buffer zone measurement method considers all the infrastructures within a certain radius and expands the range of choices for residents, but the externality is still not considered enough. After the rapid development of GIS technology, Huff model, gravity model, and 2SFCA model further increase the spatial externality from different perspectives. Huff model is based on random probability model, which assigns probability to all infrastructures of the study type in the area, and higher probability means that residents are more likely to be willing to go to that infrastructure. The gravity model considers the attractiveness and spatial resistance of all infrastructures, with attractiveness generally measured by the service scale of the infrastructure and service quality and service content not examined, and spatial resistance generally measured by linear distance. 2SFCA is slightly different from the gravity model, and is essentially a special case of the gravity model. Range of infrastructure service effectiveness, and then examines the accessibility of all infrastructure within a certain range, centered on a certain spatial location. The network measurement method, on the other hand, is based on the real urban traffic road network, and the speed of travel vehicles and the limited speed of different roads are set, aiming to recover the real situation as much as possible, examining the time factor, space factor, and mobility factor, and fully considering the spatial externalities. In general, with the development of technology, the accessibility measure has evolved from the simplest shortest distance measure to the more realistic network measure, which is more accurate in terms of results. Of course, the accessibility measures are well measured in spatial factors, however, they still do not take into account the time constraint of equipment use in temporal factors, the quality and content of infrastructure services, the mobility factor of individual mobility, the consumption factor of consumption level, and the preference factor which is slightly reflected in the

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gravity model. Some scholars believe that accessibility does not fully reflect equity, because the needs of different regions may be different, especially the disadvantaged groups will have a higher than usual demand for infrastructure, so scholars combine accessibility and demand index to correct the bias in this regard (Fig. 7).

Fig. 7. Spatial externality diagram of the accessibility measure

4.2 Discussion on the Static and Dynamic Aspects of Accessibility Methods From the static and dynamic perspectives, although the accessibility measurement is more accurate with the development of information technology. However, most existing accessibility measurement methods basically assume that urban transportation supply and social activities are static and the interaction between residents and facilities is single, and by default residents are considered to use the infrastructure as long as they touch the physical edge of the facility, ignoring the temporal dynamics of the city and the mobility of residents, which overestimates the level of spatial accessibility to a certain extent. As shown in the figure, the three core components that make up the accessibility analysis (residents, transportation, and activity facilities) actually occur in interdependent spatial and temporal dimensions (Fig. 8). The existing method usually takes the street or neighborhood as the smallest unit when examining residents, thus fixing them at home, and the population data is based on census data or nighttime light data, and the accessibility is examined by allocating

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Fig. 8. Relationship diagram of the three core components of dynamic accessibility

the population through the area of the neighborhood and using the center of mass of the neighborhood as the starting point of the measure, which is reasonable. However, in reality, residents travel through the city for work and other reasons, and may conduct different social activities at specific times and places, and do not stay at home most of the time, so the population mobility factor of the city should be examined when examining accessibility. Existing methods also consider only a single mode of travel (walking, bicycle, public transportation, private car) when examining transportation, or use multiple modes of travel to compare the spatial variation in accessibility. However, it may be more convenient for residents to choose a mixed-mode travel mode when traveling, depending on the city’s real-time traffic conditions (congestion, speed limits, turning restrictions, one-way streets). The choice of travel mode also varies as the seasons change, especially for walking and bicycling modes, where the resistance to travel is significantly greater in winter than in summer. Facility factors should be considered in addition to geographic location, but should also examine their working day hours, business hours, and cost of use. 4.3 Discussion on the Parameters Used in the Accessibility Method The current accessibility method has two common methods in measuring distance attenuation, one is to use the distance attenuation function, commonly used distance attenuation function including power function, exponential function, linear function, kernel density function, Gaussian function, 0–1 function six categories. The second is to use the distance decay coefficient β, β takes the value interval usually between 1–2. The current study does not analyze the specific effects of different attenuation functions on accessibility measures, and the commonly applied practice is to use a uniform distance attenuation function or parameter across the study area, with larger distance attenuation effects indicating that human behavior is more sensitive to distance. However, given the diversity of geographic settings (e.g., urban, suburban, and rural areas), and the complexity of urban planning, distance decay may vary between destinations.

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When examining urban population distributions, population counts at the street or neighborhood scale are often examined, and with the development of big data, big data can provide new ways to quantify spatial population through cell phone signals (Xiao, Wang and Fang, 2019) and mobile social software (Song, Richards and Tan, 2020). Although these data are not publicly available, this is a point at which the data can be improved. When considering the quality of service, size, and content of a facility, the paper essentially uses conventional measures such as the number of people served by the facility and the size of the facility, which is not a very complete and effective measure of the variation between facilities, yet it is one of the important factors in examining accessibility. Xing and Ng (2022) used exploratory factor analysis (EFA) to determine the quality of service scores for healthcare facilities, rather than EFA is a commonly used multivariate data analysis method that aims to generalize and explain a large number of observed facts with a minimum number of factors. The principle of this method is to represent the original data structure with fewer dimensions while preserving as much information as possible from the original data. When examining the time cost, some scholars also convert the monetary cost of travel into time cost into consideration, which can be a reasonable improvement to the accessibility estimation. 4.4 Discussion on the Object of Research on Accessibility Methods The current objects of accessibility measurement are basically common public facilities such as green areas, transportation, and medical care, etc. A few scholars have also measured the accessibility of new infrastructure such as charging piles. Traditionally, we regard infrastructure as an independent unit of analysis, but in the face of complex social systems, infrastructure should be interdependent networks that need to cooperate with each other for better operation, for example, the improvement of transportation infrastructure has a great impact on the accessibility of other facilities. In the face of new concepts such as financial infrastructure and computing infrastructure, the current accessibility measurement methods are not well applicable, and the current focus is on spatial accessibility, so more attention and development should be paid to non-spatial accessibility measurement, for example, whether complex network metrics and community stratification methods can be used for infrastructure non-spatial accessibility, which is a more popular network concept. Metrics, this is something that can be worthy of attention.

5 Conclusion This thesis summarizes the current accessibility measures by means of a systematic review, and comprehensively analyzes the strengths and weaknesses, the scope of application, and the angle of concern of the current methods. It can be seen that with the development of information technology, the accessibility measurement methods have evolved from the simplest linear distance measurement to the more realistic network measurement methods, which have more reference value in terms of measurement values; the current accessibility measurement is still in a static range, i.e., it only considers the accessibility at a certain point in time, which is not a good portrayal of the spatial and temporal trends of accessibility, and this change effect is more obvious in the

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cities with traffic congestion. For some infrastructures, such as emergency medical facilities, dynamic accessibility may have more reference value, so with the development of GIS technology, subsequent accessibility studies should shift from static accessibility to dynamic accessibility; when examining the parameters used for accessibility, the traditional single indicator is outdated, and more novel and comprehensive analysis methods should be used; current accessibility measures mainly focus on spatial Current accessibility measures focus on spatial accessibility, but in the face of emerging infrastructure concepts, accessibility measures should also change to non-spatial accessibility with the times.

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Urban Renewal Planning Strategies Guided by Public Values Xu Yu(B) Hainan Province Design and Research Institute Co, Ltd., Haikou, China [email protected]

Abstract. China’s urbanization has entered a stage of renewal and quality development, and urban construction pays more attention to connotation development. Urban renewal planning is not only based on the improvement of its own spatial functions, but also plays a positive role in improving the overall quality of the city. This paper briefly expounds the research background of this topic, focusing on two aspects: the construction of high-quality urban public space and the implementation of the high-efficiency collaborative model of public value, and analyzing and studying the urban renewal planning. In addition, two planning modes of refined improvement and micro-renewal are proposed in the implementation of urban public space renewal, and the implementation path of urban renewal construction that is conducive to public value orientation is explored. Keywords: Public value orientation · Urban renewal · Refined improvement · Micro-renewal

1 Introduction The development goals of cities are closely related to the level of urban economic development. China’s urbanization has entered a new stage of transformation and development that focuses on improving quality. How to gradually change the way of urban development, how to focus on the development of urban connotation, improve urban quality and enhance urban quality have become an important issue in the new normal [1]. At present, many cities will enter the process of land reuse and urban redevelopment. The incremental construction in the past can no longer meet the requirements of urban development in the current era, and cities will turn to the face of stock development. Urban renewal is a self-regulating mechanism in the process of urban development, an important measure for high-quality urban development, and an effective mechanism that can give cities new vitality. It will be an important means of current urban governance. The goal of urban renewal is to solve urban problems that affect or even hinder urban development. These problems have both environmental, economic and social reasons [2]. From the perspective of urban development, urban renewal planning is a work with a relatively prominent public comprehensiveness. During the implementation process, it is necessary to adjust the private and public interests presented in the urban development stage, and at the same time, it can also adopt public intervention methods, © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 593–601, 2023. https://doi.org/10.1007/978-981-99-3626-7_46

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avoiding adversely affecting the urban space. Based on the actual situation, the object of urban renewal planning is the urban built environment, and it has extremely prominent complexity in terms of ownership, and involves many parts of related interest. So in the process of subsequent implementation and its the preparation of urban renewal planning, it should fully demonstrate its own public policy attributes, and always follow the public value orientation [3]. Urban renewal planning involves complex interests and the related parts. In the process of planning preparation and implementation, it is necessary to reflect its public policy attributes and focus on public value orientation, so as to build a highquality institutional platform, and then from the root. It provides guarantee for efficient negotiation between multiple interests and the improvement of the openness of public space itself, and coordinates the interests of all parties. This paper conducts an in-depth study of urban renewal from the perspective of public value, and studies urban renewal planning methods from two aspects: improving public space in central urban areas and ensuring that public value is contained in the whole process of renewal planning.

2 The Development Process of Urban Renewal The idea of urban renewal originated in western countries to solve a series of urban problems in the process of urban construction in the 19th century. Initially it was mainly in response to the post-war decline of the cities, especially in response to rising inequality, poverty, crime and unemployment in decaying inner cities. The process of deindustrialization in the late 1970s and the subsequent restructuring of the global economy in the 1980s became the catalyst for the development of urban renewal strategies in many cities in the United States and Western Europe [4]. Scholars at home and abroad divide the evolution of urban renewal into four stages: urban reconstruction, urban reconstruction, urban revival and current urban renewal. The urban reconstruction phase began after the end of World War II and into the 1960s. The city was severely damaged by the war. Therefore, the government-led comprehensive urban reconstruction work was launched. The updated content in this phase mainly solved the slum environment and living problems, and paid more attention to the urban material space. The reconstruction was the implementation as the large-scale demolition and construction. The urban reconstruction stage was from the 1960s to the 1970s, focusing on the renovation and improvement of the old urban area, under the joint construction of the government and enterprises, and the construction of welfare housing in the community as the main content. The urban regeneration stage was from the 1980s to the 1990s. In this stage, assets and the market were the main forces, the government played a coordinating role, and the old city development model was dominated by real estate. At the same time, it began to focus on urban culture coastal development, and construction of water areas. After the 1990s, urban renewal was proposed. This stage is a multi-dimensional urban renewal that integrates society, economy, culture, and ecology. It is jointly promoted by the government, enterprises and the public. The public participates in policy formulation and urban construction, and pays attention to urban culture and public interests. The evolution of urban renewal in these four stages is characterized by the gradual transformation from government-led to multi-participation of the government, the public as well as the enterprises. It is from urban demolition and reconstruction to a multi-dimensional organic renewal as the concept.

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Domestic urban renewal began in 1949. According to national policy changes and the process of urban development, Yang Jianqiang [5] divided China’s urban renewal into four stages. The first stage was from 1949 to 1977. Most of the cities in the early days of China were old cities built in the semi-colonial and semi-feudal period, showing signs of decline. The urban environment and living conditions were poor. Therefore, the basic sanitation and living conditions of the cities needed to be improved. It has become the focus of urban construction work. The construction policy of “key construction and steady progress” is implemented. The second stage was from 1978 to 1989. During this period, China entered into reform and opening up, and began to establish a series of laws and regulations on urban and rural planning. In terms of the reconstruction of old urban areas, it was proposed that “the reconstruction of old urban areas should be followed by strengthening maintenance, rational utilization, rational utilization, adjustment of layout, following the principle of gradual improvement, unified planning, phased implementation, and gradual improvement of living and traffic conditions, strengthening of infrastructure and public facilities, improving the comprehensive functions of the city”. Beijing, Shanghai, Guangzhou and Nanjing have successively carried out large-scale projects. The renovation of the old city focused on solving housing shortages and repaying infrastructure debts. At the same time, Mr. Wu Liangyong put forward the theory of organic renewal, which has promoted the transformation of Chinese cities from largescale demolition and construction to orderly urban renewal activities. The third stage is from 1990 to 2011. During this period, China’s urbanization process accelerated. The reform of land management and urban housing system greatly promoted the renewal of old cities such as the renewal, functional transformation and redevelopment on both sides of the Huangpu River in Shanghai, etc. The renewal work at this stage covers the renewal of old residential areas, the improvement of infrastructure, the renovation of old industrial areas, the protection and renovation of historical blocks and so on. However, a large amount of capital pours into urban renewal, which also leads to inappropriate residential relocation, resulting in community network breakage and development. Excessive density leads to the deterioration of the living environment, excessive capacity leads to overloaded infrastructure, and improper factory relocation leads to serious problems such as environmental pollution and inconvenience [6]. The fourth stage is from 2012 to the present. During this period, China’s urbanization process has entered the second half. The urban expansion and large-scale demolition and construction brought about by the rapid urban development in the previous stage will be summarized, applying the “stock” thinking that enhances the connotation as the core, even “reduction” planning has become the new normal of China’s spatial planning [7]. Urban renewal has shifted to focusing on the development of urban connotation, starting from the “people’s needs”, paying more attention to the improvement of the living environment, and opening a new situation of urban renewal based on people-oriented and high-quality development. Sonya’s urban double-day weekends and Shanghai’s community micro-renewal and so on is the practical exploration of this period. The evolution of China’s urban renewal has transformed from urban living and environmental improvement during the founding of New China to today’s high-quality development based on humanistic texts.

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3 The Public Value Attribute Model of Urban Renewal Planning Under the market economy system dominated by efficiency, it often leads to the uneven development of urban space [8]. Many existing urban construction and management systems in China have been established to support large-scale and rapid urbanization since the reform and opening up. The work has formed a certain resistance, causing the update process in various places to get stuck from time to time [9]. In the stage of China’s new urbanization, urban renewal is an important means to realize the redistribution of space resources, an important regulatory tool to promote social equity, and has a strong public value attribute. How to redistribute the city’s public resources through urban renewal to achieve a balanced adjustment of public space resources is a problem to be considered in the development of China’s cities today. Therefore, as a public implementation strategy, urban renewal should focus on areas such as urban functional space balance, urban space quality improvement, and public value embodiment, so as to adjust urban functions through the redistribution of public space resources through urban renewal, coordinate the unbalanced development of urban space and alleviate social conflicts. Therefore, this paper starts from the construction of urban public space and the implementation of the efficient and collaborative mode of public value. Building a platform for public participation, promoting people’s participation in the whole process of urban renewal, safeguarding the interests of various social groups, and resolving social conflicts have become an important measure to promote a city’s “sense of happiness”.

4 Implementation Path of Urban Renewal Based on Public Value 4.1 High-Quality Urban Public Space Construction In the many years of the city development, some original functional structures have been unable to meet the development requirements of the current era. In some areas, the effective planning of public space was not implemented efficiently at the beginning of its construction, so it was necessary to update it. The process of urban renewal is also a process of reshaping urban physical space and updating functions, emphasizing the creation of urban physical space, and giving the city a new life with the overall spatial form of the city and a pleasant space close to the human scale. At present, China’s urban renewal planning work should reorganize and shape the existing public space system in the region, so that the regional public space can give full play to its due value, and further promote the optimization and improvement of the overall image of the region. The potential of the region should be tapped at different levels to promote the continuous and steady improvement of the region’s attractiveness [10]. Urban renewal planning must meet the current city’s demand for high-quality urban space functions. The renewal method cannot be carried out in a single mode. In the face of different projects, the implementation method will also vary according to the object. In this paper, two modes of refinement and micro-renewal are used to carry out an in-depth exploration of urban public space and overall pattern as well as to study the implementation methods of urban high-quality urban public space construction and renewal work, thereby creating favorable conditions for urban development.

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4.1.1 Urban Refinement Improvement Refinement improvement urban renewal refers to the redevelopment and utilization of urban land of a certain scale under the guidance and control of general planning, regulatory planning and control, under the requirement of efficient and rational use of land, to improve the quality of urban space and improve urban function. This article believes that there are two main ways, namely replacement update and retrofit update. Replacement renewal refers to the areas in the old urban area where there are many old dilapidated and concentrated houses with incomplete land functions and backward facilities. The spatial functions of this area can no longer meet the needs of urban development. The city adopts the method of land acquisition, demolition and relocation compensation, and then returns funds through the public transfer of land, and conducts block development and reconstruction in a market-oriented way so as to achieve the purpose of improving the living environment, improving urban public service facilities, and renewing the city’s image [11]. For example, the construction of the Haikou Binya Village Reconstruction Project, which was originally an old urban village with a large number of old houses, complicated internal roads, imperfect facilities, and lack of necessary public activity space. Through the replacement renewal of this area, a commercial complex is built along the main road of the city, and a residential area with a good environment is built behind the commercial area. It can give full implementation to its public value, continuously improve its role in the creation of urban activity atmosphere and image building, and at the same time improve the urban functions of the area (Figs. 1 and 2).

Fig. 1. Photos of Binya Village before the update

Renovation is aimed at areas in the city where supporting facilities are less perfect. Some buildings are dilapidated buildings and the surrounding environment is not up to standard. In addition to demolition and reconstruction, local renovations can also be carried out to make up for the shortcomings of insufficient public service facilities by improving infrastructure, demolishing of some unsuitable old buildings to beautify the environment. This can not only alleviate the problems caused by imperfect functions and high building density in the process of subsequent development to build a more harmonious and high-quality environment, but also can effectively improve the quality image of the city. Regardless of whether it is replacement or renovation, in the actual urban renewal planning work, it is necessary to strengthen the reasonable control of the public attributes of the area itself, so that the area can fully display the public value after

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Fig. 2. The updated photo of Binya Village

the completion of the renewal, achieving an effective promotion. Under the guidance of public value, urban space should gradually break through the privacy and closeness of previous urban space by actively introducing public functions, promoting its effective transformation to open public functions, and the organic combination of diverse functions through urban renewal. Thus, it will inject a new vitality into the development of the city. 4.1.2 Urban Micro-update Urban micro-renewal is to meet the needs of community life under the premise of not changing the nature of land use and basically not changing the main structure of the building space. Through transformation, repair and partial renovation, small-scale public spaces or facilities can be used for functions of perfection and quality improvement [12]. The urban micro-renewal mostly takes community space as the object. Through in-depth research on the current situation, in view of the existing problems, considering the needs of the public at all levels, from space improvement to facility improvement, solutions are proposed to make the most use of construction funds, by adopting reasonable and excellent design solutions. The renovation of the old community of Hainan Provincial Design and Research Institute is listed as a demonstration project of the renovation of the old community in Haikou City, which has certain reference in terms of fund raising, administrative approval, project management, operation and maintenance. The source of funds for the project is divided into three parts: unit investment, self-financing by the owner, and government subsidies. That is, the responsible subject first collects self-raised funds (including unit investment and owner self-financing), and then applies to the government the subsidy funds during the process of starting the project renovation or after renovation is completed. The renovation content includes basic, improvement and upgrading. The basic class refers to the renovation of municipal infrastructure, community roads, garbage houses, roof waterproofing, and community wall renovation; the improvement class refers to the renovation of parking sheds, express delivery Parts points, charging parking space renovation, building facade renovation, etc.; the upgrading class is the renovation of cultural facilities and the addition of leisure and fitness facilities. The renovation of the old community of Hainan Provincial Design and Research Institute starts from the perspective of serving the residents and being close to life, without

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involving land use planning and index adjustment, improving the community public space, improving supporting facilities, beautifying the environment, increasing public space and cultural connotation but through small Scale micro-renewal to improve the community environment and improve the quality of life of residents (Fig. 3).

Fig. 3. Renewal scheme of residential quarters of Hainan Design and Research Institute

4.2 An Efficient Collaborative Model for Implementing Public Value The process of urban renewal is a process of negotiation among multiple parts of related interest, a platform for multiple parts of related interest to interact and play gaming. It is also a carrier of the process of spatial governance. For urban renewal, it belongs to a comprehensive construction project, and the renewal planning work carried out for it needs to ensure the deepening of cooperation between various disciplines and all relevant parties, and then build a more scientific and effective coordinated management on this basis. Mechanisms are in place to facilitate centralized and unified arrangements for urban renewal and to fully implement various construction points. Based on this, in the process of updating the planning, it is necessary to actively mobilize the enthusiasm of the staff in the fields of municipal engineering, transportation, architecture and landscape, so as to enable them to achieve information sharing and mutual cooperation, and to implement their high quality integration of design work, and start from the technical level to provide guarantee for the continuous and stable implementation of the project. The planners should give full play to their organizational and coordinating role in this

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process, and urge each professional team to fully apply a unified design concept in their work to provide necessary support for subsequent construction. Relevant staff need to conduct extensive consultation with the general public, and go deep into the site to carry out research work to strengthen the public’s participation in urban renewal planning. The design team should, on the basis of comprehensive consideration of relevant standards and requirements, work with various units in the region. A good communication platform is required to be established between owners and residents, so as to give full play to the public’s supervision role, listen to the suggestions of relevant parties after completing the preliminary design plan, and then make appropriate modifications and adjustments to it. This will promote the plan to make the high-quality implementation achieved in actual operation, which ultimately improves the effectiveness of urban renewal. For urban renewal, the planning work carried out is not only limited to the level of physical space and environment, but is essentially closely related to the overall economic, social and environmental aspects of the city. It also involves relatively complex parts of related interest. Therefore, it belongs to a kind of social system engineering, which reflects the extremely high strategic, policy and comprehensive characteristics, which can effectively realize the further extension of the material space and explore broader and deeper social and economic goals. Based on the current cases of existing urban renewal planning projects in China, the people have developed a strong sense of participation in urban renewal. In th case, in the future, the existing conditions should be comprehensively considered when carrying out renewal planning, and the interests of parts of related interest should be clearly understood. On the basis of the practice process, the balance of the interests of all parties should be promoted to the greatest extent possible. For example, in community micro-renewal, it is needed to actively explore the construction of a diversified financing mechanism with government policy guidance, residents’ coconstruction contribution, enterprise market participation, and all-round social support. In addition, the preparation of urban renewal planning needs to be presented prominently and guided by its public value, it is designed in combination with local development and future construction direction, so as to give full play to the substantive role of public participation and effectively ensure the public value of urban planning [13]. Urban renewal is a work completed by multi-party cooperation and multidepartmental cooperation. Planning and construction should strengthen public participation, guide the multi-dimensional interaction between the government, designers and various rights subjects, and ensure that the opinions of all parts of related interest are heard and implemented. The policy system should be improved, the functional system should be constructed, and the standard system should be deepened. The government manages and controls bidding and construction approval, and revises and improves the update policy in combination with development needs and market feedback to protect public interests. The construction plan should be designed in a comprehensive and integrated manner, and an efficient design team should be formed by professionals in planning, architecture, landscape, and municipal administration to ensure that the public interest is protected. It is required to establish a sustainable goal-oriented urban renewal refined management system so as to improve the multi-subject collaborative urban renewal driving mechanism, and provide scientific support and decision support for the effective implementation of sustainable urban renewal [14].

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5 Conclusion The urban renewal system reflects the governance capacity of a city and is an important part of the modernization of the national governance system and governance capacity. In the stage of stock planning and development, the focus will be on the construction of high-quality urban public space and the implementation of an efficient synergy model for public value. With the goal of improving urban public services and the quality of the built environment, we will continue to strengthen our response to the increasingly complex, diverse and comprehensive governance capacity to solve identified urban issues. In the actual implementation of urban public space renewal, we should implement refined improvement or minor changes according to different objects, and organically carry out renewal and construction. The new city is a complex and organic whole body, which is constantly updated and evolved. From the perspective of public value, urban renewal requires the joint efforts of the government, the market and the society to promote it together. The gradual improvement of vitality has a positive effect on the efficient development of the urban economy and society.

References 1. Yang, J., Du, Y., Wang, Y., Duan, J., et al.: Urban renewal and functional improvement. Urban Plan. 40(01) 2016 2. Liu, B., Liu, J., Cheng, T., Wang, T., He, Q.: Theory and practice of urban renewal in China. China Famous Cities (4), 1–10 (2021) 3. Liu, M., Zeng, Q., Deng, W., et al.: Research on wind environment assessment and planning control of urban design in the central area: taking Guangzhou as an example. Urban Plan. J. (4), 35-42 (2021) 4. Ding, F., Wu, J.: The evolution of concepts related to urban renewal and its practical significance today. Urban Plan. J. (06), 9–19 (2017) 5. Yang, J., Chen, Y.: The development and review of urban renewal in China from 1949 to 2019. Urban Plan. J. 44(02), 87–95 (2020) 6. Yang, J.: Analysis of the main contradictions in the renewal and reconstruction of old cities in China. Urban Plan. Collect. J. (4), 9–12 (1995) 7. Shi, W.: Planning should realize the transformation from increment to stock and reduction planning. Urban Plan. 38(11), 21–22 (2014) 8. Zhang, L.: Discussion on the compilation method of overall urban design under the guidance of public value. Shanghai Urban Plan. (05), 35–40 (2018) 9. Tang, Y.: Analysis of key dimensions and strategies of urban renewal system construction in China. Int. Urban Plan. 37(01), 1–8 (2022) 10. Lu, Y.: Discussion on the main points and methods of planning and implementation evaluation of urban central areas: taking Shanghai Hongqiao Business District as an example. Shanghai Urban Plan. (z1), 109–114 (2020) 11. Liu, B., Liu, J., Chen, T., Wang, T., He, Q.: Theory and practice of urban renewal in China. China Famous City 35(07), 1–10 (2021) 12. Chen, M.: Shanghai practice of urban space micro-renewal. J. Archit. (03), 1–11 (2022) 13. Burke, P., Masterson, J., Malleha, M., et al.: The application of resilience scorecard in planning integration and coordination—taking three coastal cities in the United States as an example. Int. Urban Plan. 36(5), 69–77 (2021) 14. Cao, K., Yu, D.: Research progress and prospect of space-time evolution path and driving mechanism of sustainable urban renewal. Adv. Geogr. Sci. 40(11), 1942–1955 (2021)

Research on the Similarity of Highway Construction Projects Based on EWM-GRA Bo Yu(B) , Liudan Jiao, Yu Zhang, and Xiaosen Huo School of Economics and Management, Chongqing Jiaotong University, Chongqing, China [email protected]

Abstract. At present, the selection of construction solutions for highway construction projects mainly relies on personnel experience for the preparation of construction solutions for target projects, so to make use of the experience of previous highway projects, this paper establishes a decision model for construction solutions of roadbed projects based on case inference techniques. First, several characteristic attributes are extracted from the previous literature and data. The item attributes are extracted from the selected historical items, and the data is dimensionless. Moreover, the characteristic weights are assigned to the attributes using the entropy weight method. Based on this, the search for similar items is completed using gray correlation analysis and the items with the highest similarity are selected. In the last step, experts refer to the projects with the highest similarity after conducting the screening to prepare the roadbed construction plan for the new project. Finally, the model was applied to the reconstruction project of the National Highway G350 Huangshui-Huashi Ling section road in Shizhu County. Nine historical projects in the southwest area were selected for screening. The final results showed that the selected historical projects were mainly similar to the construction schemes of the target projects, which proved the model’s effectiveness and effectively improved the efficiency of the roadbed construction scheme preparation. Keywords: Roadbed construction program · CBR · Entropy power method · Grey correlation analysis

1 Introduction In recent years, China’s construction industry has been steadily advancing from a large construction country to a strong construction country. Since the 14th Five-Year Plan, the strategy of a vital transportation country has been put forward, which has accelerated the pace of digital innovation in the transportation industry. The intelligent and digitalized construction and transportation industry are one of the main directions for the future highquality development of the transportation and construction industry. In the traditional construction industry, the decision of construction plans often relies on the experience of similar projects in the past. The same is true for the selection of construction methods and construction processes for highway construction projects. However, the experience © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 602–614, 2023. https://doi.org/10.1007/978-981-99-3626-7_47

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accumulated in building many highways in the past is vibrant, so the experience of existing projects can be mined and then applied to the decision of construction plans for our new projects. It is challenging to prepare the construction scheme for some projects in traditional highway engineering construction projects because of the special topographical and geological conditions and the complex environmental factors considered. Due to the complexity of the environment of highway engineering construction projects, it is imperative to select reasonable construction methods and technological processes. In this situation, it is necessary to refer to how previous projects choose construction methods and processes in such a complex environment. We call this process experience mining. In the past, only experienced and experienced designers who have participated in such engineering construction can be consulted when encountering unfamiliar engineering types. These designers have much engineering experience, but the human experience is often limited. Because our country has accumulated abundant experience in building many expressways in the past, the experience of designers only accounts for a small part of the experience of the whole project. It is constructive for us to make decisions on the construction scheme by mining the experience of existing projects. How to use past projects for experience mining is a blank at present. Case-based reasoning (CBR) is a widely used method in experience mining. Integrate the historical cases of highway engineering projects, use case-based reasoning technology to quickly and accurately retrieve and match similar projects, and reuse the most similar projects after reference. Experience mining is usually used in industries with a strong experience orientation, such as the current selection of medical cases and the construction industry currently mentioned in this paper. The characteristic is that the matter currently waiting to be solved can be referenced accordingly based on similar cases in the past. Case-based reasoning is a method of experience mining. Reusing experience is a common and effective method of solving problems, which originates from the dynamic memory theory proposed by Schank (1982). Case-based reasoning is a cycle and integration process of solving problems, learning from experience, and solving new problems. CBR is basically in line with human cognitive processes. When people encounter new problems and situations, they usually use their past experiences with similar problems to solve new problems. The new problem is called a target case; past cases are stored in a case database [8]. The process includes case retrieval, correction, case reuse, and case storage (4R) cycle [1]. Its advantage is that it can collect all the cases in this field for integration and then use these historical cases to find solutions to new cases. Its thinking is the same as the process of humans seeking to solve problems. The difference is the enormous data of case reasoning. Among them, the similarity calculation of case retrieval is the core of casebased reasoning. Some scholars have also studied the experience of highway engineering. For example, Li junda et al. applied case-based reasoning model to highway project cost budget [15]. At present, case-based reasoning is not widely used in the construction industry, mainly in the project cost estimation [2], the project transaction opportunity decision-making of the construction project general contracting enterprise [3], and the decision-making of the project delivery system [4], The calculation of similarity is based chiefly on the definition of distance. For example, the most traditional nearest neighbour method is adopted for the similarity calculation matching the emergency decision [5].

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The improved similarity calculation method proposed in [6] is also based on the idea of distance. Similarly, the calculation of similarity in case of retrieval in the gas explosion emergency response application is done by decomposing the attributes of different data types into different attribute types and then classifying them separately for calculation. Finally, the overall similarity is calculated [7]. So it lacks diversity in the similarity calculation of case-based reasoning and is seldom applied in the decision-making of highway traffic project construction schemes. From experience and industry survey data analysis, highway engineering construction projects require practitioners’ experience. However, at present, with the strategy of strong transportation and strong construction, the transportation industry will also usher in wisdom, which cannot rely on practitioners’ experience, but make various decisions through intelligent ways, for example, in this study, for the selection of construction methods and construction process flow, the Case reasoning technology is applied to this, which will help to promote the wisdom of the transportation industry and promote the intelligent generation of construction solutions. First, we need to build a database of highway roadbed special construction plan and its attribute framework, then determine its attribute weights, calculate the similarity based on the weights and feature attributes, and finally, the overall proposed decision model of highway roadbed construction plan based on the case reasoning model.

2 Research Methods This study combines the use of highway engineering experience and case-based reasoning techniques for the construction plan of road base works for highway projects because the quality of road base, as the foundation of highway projects, is related to the life cycle of the whole project. While selecting a roadbed construction plan is the key factor in determining its quality, Therefore, this study takes the preparation of subgrade construction scheme as an example. The construction plan is determined by a combination of its design documents, region, topography, and the needs of the road being built. It provides a new reference basis for the preparation of the construction plan. It reflects the integration of experience-oriented and intelligent construction in the preparation of highway construction projects. Its main steps are as follows [12]. Firstly, it is necessary to select the set of attributes that can represent the highway project and determine the weights based on the data of the attributes, then establish a database of historical projects, then compare the target project with the attribute set of historical projects, that is, the calculation of similarity, and calculate the global similarity based on the weights and the similarity of each attribute. The most similar project is selected as the basis for the construction planning of the new project (Fig. 1).

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Target Project Construcon Program

Historical project database

Determinaon of aributes and calculaon of weights

Case representaon

case retrieval

Most similar Project

Similarity calculaon

Fig. 1. Flow chart

2.1 Selection of Attributes In this study, the basic attributes of the road project and the characteristic attributes of the roadbed project are mainly considered, and the attribute characteristics of these two parts are the important basis for case retrieval. These attributes can be used to represent the construction characteristics of road base works for road construction projects. Further, any of the highway project’s physical geographic features will affect the construction methods for the highway roadbed project. The characteristic properties of the roadbed project compared with the general characteristics of the road project is to consider the more specific way of the roadbed. However, previous studies selected no construction solutions for roadbed projects for their characteristics. Therefore, several of the more important attributes were selected in this study according to books. The factors influencing the preparation of construction plans for several road construction projects were analyzed, from which the characteristic attributes representing road projects were selected. For example, the strength and stability of the roadbed are related to the climate of the roadbed, the width of the roadbed depends on the technical grade of the road, The height of the roadbed depends on the topography and highway longitudinal section design, the slope of the roadbed slope depends on the geology, hydrological conditions, the height of the roadbed and the economy of the cross-section, the slope of the embankment should be based on the physical properties of the filler, the height of the slope and the geological engineering conditions, the slope of the road graben should be judged from the geomorphology and geological structure of its integrity. In this paper, several factors influencing the construction programming of highway projects are analyzed based on the literature, from which eleven characteristic attributes representing highway projects are selected. The following Table 1 also indicates the corresponding data types.

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Attributes

Data Type

Highway grade

String

Total Miles

Numerical

Design speed

Numerical

Road load rating

String

Area (Road Natural Zoning)

String

Climatic conditions

Fuzzy type

Terrain and Landforms

Fuzzy type

Geological conditions

Fuzzy type

Hydrological conditions

Fuzzy type

Roadbed width

Numerical

The average height of road embankment slope

Numerical

2.2 The Dimensionless Data Since the differentiation of data types affects the subsequent calculation of similarity, it is essential to dimensionless the data. For example, fuzzy-type attributes are not well handled in the data. Here they are quantified using fuzzy linguistic levels. All other data types are transformed into numerical data, with regions assigned according to national road natural zoning levels. Highway grade is 5 grades, and climatic conditions, geological terrain, and other conditions of the criteria are quantified by the degree of impact on the road construction project to facilitate the following calculation of similarity. The climatic conditions are classified according to their occurrence frequency. The hydrological conditions are based on whether there is groundwater and the abundance of groundwater, whether there are mountains that affect the construction, and whether the geological conditions have unfavorable geology. The specific criteria are shown in the Table 2. Table 2. Quantification Standards Attribute Name

Quantification criteria

Highway grade

Expressway - 1, Class I - 2, Class II - 3, Class III - 4, Class IV - 5

Road load rating

Highway - Class I - 1, Highway - Class II 2

Area (road natural zoning)

According to the natural zone map (continued)

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Table 2. (continued) Attribute Name

Quantification criteria

Climate

0–3days - 1, 3–10days, 10–15days - 3, 15–20days - 4, more than 20days - 5

Hydrological conditions (the degree of impact on the project)

No- 1, 0–50 km- 2, 50–150 km - 3, 150–250 km- 4, more than 250 km - 5

Terrain topography (complexity)

No- 1, Smaller - 2, General - 3, More complex - 4, Complex - 5

Geology

No- 1, Smaller - 2, General - 3, More complex - 4, Complex - 5

2.3 Case Representation Case representation describes the whole project in the database into a type of data that is easy to store. For example, in the database, a project is described in multiple attributes and outputs (construction plan) so it is easy to store. The case representation involves defining the attributes and structure that describe the case. Case reasoning is also based on the ability to represent cases with reasonable accuracy [11]. Among the collected cases of road construction projects, the number of each historical project M = {1, 2 ,…, m}, i ∈ M is noted, and Ai means the i-th project. The number of its characteristic attributes N = {1, 2, …, j}, j ∈ N, and Aij means the jth attribute value of the i-th historical item. Historical projects are those stored in the project database that may provide the basis for construction programming for new projects, and target projects are new projects in urgent need of construction programming. Let Ai = {A1, A2, …, Am} denote the set of historical projects, Ai denotes the i-th historical case, let A0 denote the target case, it is evident that some construction plan of A0 project here is unknown, i.e. it is the project that needs to use the most similar project among M historical projects as the basis of preparation. At this point, the problem that needs to be solved in this paper is to select the most similar cases for the current construction project A0 based on the experience accumulated in the preparation of the construction plan for project Ai and to prepare the construction plan for the new project based on the selected cases. 2.4 Case Retrieval Case retrieval is a crucial part of the case reasoning system, and the case search result will directly affect the accuracy of the final retrieved items. The main task of case search is to find items similar to the target case through the problem attributes, also known as the K-nearest neighbour method [3]. The case search is divided into two main parts: determining item attribute weights and calculating item similarity. In this study, based on the attribute data of target cases and historical items using the entropy weighting method for weighting and weighted gray correlation analysis for similarity calculation, the attribute data of target items are used as reference data, This is the case retrieval

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through the EWM-GRA method. In contrast, other historical items are used as a control matrix. Finally, the K items adjacent to them are counted, and the nearest item is selected. 2.4.1 Determination of Attribute Weights In highway engineering projects, the degree of influence of each attribute on the final selection of the most similar project is different, so the average weight cannot simply calculate its weight. Compared with the hierarchical analysis method, which relies on human subjective thinking to determine the weights of attributes, the entropy weighting method is an objective weighting method using attribute data, which does not change with human subjectivity and is more in line with the idea and process of intelligent decision-making. Therefore, the entropy method in the objective assignment method is introduced, which refers to the use of the concept of information entropy. The greater the amount of information conveyed by the indicator, the greater the dispersion, and the higher the weight value. The entropy method is closely related to the size of the indicator itself and has a strong mathematical theoretical basis. The entropy method is calculated as follows [9]: (1) First, the data in the attributes are normalized to the data: xij Xij = n

i=1 xij

(1)

Because the road engineering case base is composed of M cases, where I denotes the i-th project, each case contains N indicator feature attributes, and j denotes the j-th attribute feature. Where xij denotes the value of the jth indicator feature attribute of the i-th case (i = 1, 2,…, n; j = 1, 2,…, m); Xij is the value of xij after normalization; (2) The entropy value ej of the jth indicator is calculated as shown in Eq. (2). ej = −k

n 

xijln(xij)

(2)

i=1 1 Among them .k = ln(n) , ej ≥ 0 (3) Define the coefficient of variation gj, as shown in Eq. (3)

gj = 1 − ej

(3)

(4) Determine the indicator weights, the jth indicator weight, as shown in Eq. (4). gj wj = n

i=1 gj

(4)

After the calculation of the above entropy power method, the importance of each attribute in the roadbed construction plan framework of the highway construction project can be obtained to help improve the accuracy of the calculation in the subsequent similarity calculation.

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2.4.2 The Similarity Calculation The similarity calculation in previous studies was mainly done according to its data classification subtypes. For example, the similarity in construction safety control decisions was divided into word-level similarity and sentence-level similarity [3], the data types were divided into qualitative, quantitative and fuzzy types [10]. In this study, after dimensionlessizing the data, the calculation is unified by the gray correlation analysis method, which makes it simpler and more convenient to unify. Calculating similarity by grey correlation: Gray correlation analysis is a method to calculate the degree of similarity between individuals in a system, which has the characteristics of simplicity, speed, and wide application. This paper uses gray correlation analysis to calculate the similarity between the target and similar cases. According to the gray correlation analysis method, the attribute matrix formed by the attribute data of the target case is used as the reference matrix. The attribute matrix of the historical items is used as the control matrix, and the correlation degree of each row in the control matrix with the reference matrix can be calculated, which is the similarity degree. Since the data were dimensionless before, we do not need to consider the dimensionality of the data, and we can directly calculate the dimensionless data when calculating the similarity. The similarity of any single feature attribute ξj(k) between the target case A0 and the similar case Ai can be expressed as Eq. (5) ε i(k) =

minmin|OoK − Oik | + ρmaxmax|OoK − Oik | |OoK − Oik | + ρmaxmax|OoK − Oik |

(5)

where: ρ is the discriminant coefficient, ρ ∈ [0, 1], and its introduction can avoid the calculation being influenced by extreme values, and it is generally more appropriate to take ρ ≤ 0.5. The correlation coefficient indicates the degree of association between each characteristic attribute, so the similarity between the target case and similar cases sj is Si =

n 

wkξ i(k)

(6)

k=1

Here, wk is the kth attribute value of each project and then multiplied by its ξj(k), which distinguishes it from the traditional similarity calculation method and will highlight the quickness of this calculation method. After completing the case search in this way, the most similar projects can be selected from them. The roadbed construction plan of the historical project with the highest similarity can be used as the basis for preparing the roadbed construction plan of the new project. The whole idea diagram for calculating the similarity is shown below (Fig. 2).

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Entropy power method

Determinaon of aribute weights

+

Sim(A0,A1)

ξ1

Sim(A0,A2)

Sim=

GRA

ξ2

... ξn

w1ξ1 +w2ξ 2+Ċ wnξn

Sim(A0,An)

MAXSim(A0,An)

Case retrieval Fig. 2. Case retrieval

3 Case Study In this paper, the reconstruction project of National Highway G350 from Huangshui to Huosiling in Shizhu County is taken as the target case (number 0), and nine other projects are selected as historical projects. The model established above is used to reason about the roadbed construction plan of the target case. The construction plan of the most similar project is selected to compare and analyze with the construction plan of the expert for project A0 to verify the effectiveness of its model for the preparation of the construction plan. Project overview of National Highway G350 Huangshui-Huashi Ling section road reconstruction project in Shizhu County. Shizhu County National Highway G350 Huangshui to Huashi Ling section of the road reconstruction project project project overview: The project is 27.225 km long, a two-way two-lane secondary road with a design speed of 40 km/h and a standard roadbed width of 12m. The project is located in a mountainous area, tectonic denudation of the mountainous terrain, with large topographic undulations and complex geological conditions. The design load is highway-I level. The height of the rift valley slope varies from section to section, and the average height is 16M. Decision-making process based on case-based reasoning: Target case 0 is located in the southwest region of China, so a wide range of cases of highway projects in the southwest region is collected. 9 projects from Chongqing and other regions are collected here as case studies. The highway grade, design speed, climatic conditions, topography and geology, the average height of the embankment slope, and other conditions of each project are normalized according to the proposed method. Then the weights are determined for the collected project data according to the entropy weighting method. Then the data are matrixed, with the data of the A0 project

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as the reference matrix and the other historical project data as the control matrix. The ξj of each attribute of each project is calculated. Then they are weighted to obtain the final similarity and select the most similar project. Cases 0 to 9 are indicated Shizhu County National Highway G350 Huangshui to Huoshiling section of highway reconstruction project, the G348 Rongchang District Banqiao to Anfu highway diversion project, Nanchuan District Provincial Highway S206 Shuijiang Town to Shanwangping Town highway reconstruction project, Nanchuan District S103 half-he field to Yangliuzui highway reconstruction project, Hechuan District Yang old road (Lingchuan to Baiyin section) highway reconstruction project, Mawu to Longtan first-class highway third bid, G351 Monkey Bridge to Tongnan County section of the new reconstruction project Monkey Bridge to Wangshuiya section, Youyang County National Highway G319 Longtan Transit Section New Reconstruction Project, S534 Deng Ying to Shijiao Section Road Reconstruction Project in Qijiang District, Zhongxian to Shibao Along the River Tourism Highway Phase I Project (G348 County to Moujiashan Section), and are indicated by A0 to A9, and attributes 1 to 11 are indicated by B1 to B11, respectively. The characteristic natural values of each case are shown in the Table 3. Table 3. Project quantitative values No

B1

B2

B3

B4

B5

B6

B7

B8

B9

B10

B11

A0

3

27.225

40

1

2

4

4

2

3

12

16

A1

2

5.959

60

1

2

3

2

2

2

24

14

A2

3

12.896

40

1

1

3

5

4

3

8.5

7

A3

3

7.974

40

2

2

3

4

4

3

8.5

10

A4

4

8.467

30

2

1

3

2

1

2

7.5

6

A5

2

14.905

60

1

2

3

3

3

3

20

12

A6

3

12.076

60

1

1

2

2

1

2

12

20

A7

3

12.072

60

1

2

2

3

1

2

12

15

A8

3

30.827

40

1

1

2

4

4

3

8.5

8

A9

3

7.3

60

1

2

1

2

3

2

12

15

After standardizing the data of the above eleven items, the weights of the eleven attributes of the project roadbed construction program are calculated and determined according to the objective weighting method based on the entropy weighting method above. The results are calculated in the EXCEL Table 4 as follows. Table 4. Weight calculation result attribute B1 weight

B2

B3

B4

B5

B6

B7

B8

B9

B10

B11

0.026 0.215 0.041 0.072 0.073 0.076 0.083 0.182 0.030 0.111 0.091

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The attribute data of the A0 project, which is the section of National Highway G350 from Huangshui to Huoshiling in Shizhu County, is used as the reference matrix in the gray correlation analysis matrix, and the data of other projects after quantification is used as the control matrix, and then ξj is calculated using the gray correlation analysis method according to the data in the table. The calculation results of ξj(k) are shown in Table 5. Table 5. The result of the calculation of ξj No

B1

B2

B3

B4

B5

B6

B7

B8

B9

B10

B11

A1

0.60

0.39

0.50

1.00

1.00

0.67

0.50

1.00

0.60

0.33

0.80

A2

1.00

0.49

1.00

1.00

0.50

0.67

0.67

0.33

1.00

0.63

0.47

A3

1.00

0.41

1.00

0.33

1.00

0.67

1.00

0.33

1.00

0.63

0.57

A4

0.60

0.42

0.67

0.33

0.50

0.67

0.50

0.50

0.60

0.57

0.44

A5

0.60

0.52

0.50

1.00

1.00

0.67

0.67

0.50

1.00

0.43

0.67

A6

1.00

0.47

0.50

1.00

0.50

0.50

0.50

0.50

0.60

1.00

0.67

A7

1.00

0.47

0.50

1.00

1.00

0.50

0.67

0.50

0.60

1.00

0.89

A8

1.00

0.79

1.00

1.00

0.50

0.50

1.00

0.33

1.00

0.63

0.50

A9

1.00

0.41

0.50

1.00

1.00

0.40

0.50

0.50

0.60

1.00

0.89

The specific similarity can be obtained by weighting the weights of each characteristic attribute of the road construction project previously calculated by the entropy weighting method and ξj obtained by the gray correlation analysis method. The similarity between the target case and similar cases according to Eq. si is (Table 6) Simi =

n 

wkξ i(k)

(7)

k=1

Table 6. Similarity calculation results No

A1

A2

A3

A4

A5

A6

A7

A8

A9

Similarity

0.66702821

0.58931018

0.59931496

0.49877668

0.629071177

0.616794145

0.687581164

0.672379878

0.651677874

The highest similarity with the target project can be seen in the table, with a similarity of nearly 0.7. Project A7 is also the Youyang County National Highway G319 Longtan Border Crossing Section New Reconstruction Project. The roadbed construction plan of the A7 project was compared with the construction plan given by the experts of the A0 project for validation purposes. For the most similar projects selected to verify the similarity of the construction scheme, most of the embankment filling of Youyang County National Highway G319

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Longtan Transit Section New Reconstruction Project and A0 Project are filled according to the entire width of the cross-section divided into horizontal levels and filled layer upwards by layer, which is the layered filling method. Most of the excavation of soil road rift is done in horizontally layered excavation, and the blasting of stone road rift is done by drilling and blasting. And extensive blasting is prohibited because they are all near residential settlements, and environmental factors must be considered. There are slight differences in construction methods in retaining wall support projects because of their topographical features and the different heights of the road-graben slopes. Therefore, most construction methods in similar cases are similar, and the process flow is not very different. Therefore, the model has validity for the preparation of the construction plan.

4 Conclusion Although selecting roadbed construction methods and processes is critical during construction, there is a lack of a systematic approach to their preparation in the transportation construction industry so that practitioners can better prepare construction plans when they encounter complex highway projects. This study focuses on the idea of preparing a construction plan for a highway construction project and develops a decision-making framework through case-based reasoning techniques. First, a framework representation of the highway construction project is made. The whole project can be represented in an orderly and reasonable way from the selection of attributes at the input end and the output of the construction plan at the output end. Secondly, the entropy method and gray correlation are used to jointly calculate the similarity between items in the case search, effectively improving its calculation efficiency. Finally, the construction solutions of the target cases given by the experts were compared among the construction solutions of the most similar projects. It was found that the difference was not significant, which is consistent with the calculated similarity of 0.688, indicating that this idea is practical for selecting construction solutions. Further, the following conclusions were obtained from the study of this paper. (1) The application of case reasoning technology to the study of road base construction fills the research gap for this content in China (2) The weighting method based on the entropy method avoids the problem that the extreme values generated in the expert scoring excessively influence the results due to subjective reasons, and the weights obtained are more objective. (3) The construction plan of the selected historical project is generally consistent with the construction plan of the target case, which indicates the practicality of this model. Acknowledgements. The authors would like to acknowledge the financial support for this research received from National Natural Science Foundation of China (Grant No. 71901043), Science and Technology. Research Project of Chongqing Education Commission (Grant No. KJQN201900713).

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References 1. Aamodt, A., Plaza, E.: Case-based reasoning: foundational issues, methodological variations, and system approaches. AI Commun. 7(1), 39–59 (1994) 2. Hyung, W.G., Kim, S., Jo, J.K.: Improved similarity measure in case-based reasoning: a case study of construction cost estimation. Eng. Constr. Archit. Manag. 27(2), 561–578 (2019) 3. Su, Y., Yang, S., Liu, K., Hua, K., Yao, Q.: Developing a case-based reasoning model for safety accident pre-control and decision making in the construction industry. Int. J. Environ. Res. Public Health 16(9), 1511 (2019) 4. Zhu, X., Meng, X., Chen, Y.: A novel decision-making model for selecting a construction project delivery system. J. Civ. Eng. Manag. 26(7), 635–650 (2020) 5. Keke, Z., Nianxue, L., Yingbing, L.: STGA-CBR: a case-based reasoning method based on spatiotemporal trajectory similarity assessment. IEEE Access 8, 22378–22385 (2020) 6. Ji, S.H., Ahn, J., Lee, E.B., Kim, Y.: Learning method for knowledge retention in CBR cost models. Autom. Constr. 96, 65–74 (2018) 7. Fan, Z.P., Li, Y.H., Wang, X., Liu, Y.: Hybrid similarity measure for case retrieval in CBR and its application to emergency response towards gas explosion. Expert Syst. Appl. 41(5), 2526–2534 (2014) 8. Zhao, X., Tan, Y., Shen, L., Zhang, G., Wang, J.: Case-based reasoning approach for supporting building green retrofit decisions. Build. Environ. 160, 106210 (2019) 9. Wu, W.: Research on decision making scheme of railway tunnel engineering based on CBR. Sichuan Construction, 42(01), 121–124 (2022). (in Chinese) 10. Le´sniak, A., Zima, K.: Cost calculation of construction projects including sustainability factors using the Case Based Reasoning (CBR) method. Sustainability 10(5), 1608 (2018) 11. Li, H., et al.: Emergency decision-making system for the large-scale infrastructure: a case study of the south-to-north water diversion project. J. Infrastruct. Syst. 28(1), 04021051 (2022) 12. Shao, H., Zheng, W., Tian, W., Pan, S.: Comparative analysis of subgrade design and construction of highway and high-speed railway. Highway, 65(04), 69–73 (2020)

Understanding the Role of Housing in Family Reunion: Evidence from Rural-Urban Migrant Families in China Jun Qiu(B) and Ping Lv Department of Land and Real Estate Management, Renmin University of China, Beijing, China [email protected]

Abstract. Family migration has become the main migration pattern of rural-urban migration in China, which is characterized by phasing and dynamic. Previous studies have focused on the impact of housing on individual migration, paying less attention to family migration, and few studies have examined the impact of housing in different stages of family migration. This paper constructs a staged and dynamic decision-making mechanism based on the Todaro model, which divides family migration into two stages: family separation stage and family reunion stage, and uses data from the 2017 China Migrant Dynamic Survey for analysis. The following conclusions are drawn: housing cost has a significant negative impact on rural-urban family migration of renting households and a significant positive impact on rural-urban family migration of purchasing households. The effect of housing ownership on family migration is significantly positive and mainly plays a role in the family reunion stage. The effect of relative housing deprivation on family migration is significantly negative. The results of this paper will help formulate housing policies that are more compatible with family migration to serve new urbanization. Keywords: Family migration · Housing cost · Housing ownership · Housing deprivation

1 Introduction Since the reform and opening up, along with rapid economic growth, China’s population began to migrate on a large scale. The migration trend, structure, and migration direction have different characteristics over time. From the 1980s to the early 1990s, with the advancement of marketization and industrialization, the scale of China’s migrant population grew significantly, but at that time, the migrant population mainly moved individually, which was called “Pao Dan Bang”. After the 1990s, early migrants began to bring their family members to their current residence and realize family reunion there, and this pattern of family migration gradually became an important form of population This work was supported jointly by the National Natural Science Foundation of China (Grant No. 71673285 and No. 72274207). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 615–632, 2023. https://doi.org/10.1007/978-981-99-3626-7_48

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migration (Li et al. 2020). In recent years, the total number of migrants has tended to stabilize, while family migration has remained China’s population migration development trend. According to the “2018 Report on China’s Migrant Population Development”, the average family size of the migrant population from 2011 to 2016 was about 2.54 people1 . Further studies have found that complete-family migration has become the most common pattern of family migration (Wang et al. 2017). Unlike entire family migration in western countries (Kofman 2004), rural-urban family migration in China is rarely a one-time event, but rather cyclical and temporary (Fan et al. 2011; Li et al. 2009). It usually necessitates the cooperation and efforts of several generations to gradually move into the city. Various scholars have coined terms like “joint family urbanization” (Yang 2019), “gradual family mobility” (Du and Zhang 2010), and “multi-stage relocation” (Sheng 2014) to describe this migration pattern, all of which convey the same message: rural-urban family migration is a multi-staged and dynamic process. Many migrant families in China are faced with the problem of family separation (Chen et al. 2019). Some studies believe that this is the result of the family’s independent choice (Fan 2011), while others believe that this is caused by external factors, which will have a negative social impact (Wei 2018). Studies have discussed the various effects of housing on individual migration, but few studies have focused on the effects of housing on family migration, especially not on the effects of housing at different stages of family migration. This paper constructs a staged and dynamic family migration decision-making mechanism, which extends the Todaro model from the perspective of new economics of labor migration theory, dividing family migration into two stages: the family separation stage and the family reunion stage. Moreover, we employ data from the 2017 China Migrant Dynamic Survey and other data sources for empirical analysis. The results indicate that, for renting households, housing cost has a significant negative impact on rural-urban family migration, and for purchasing households, the impact is significantly positive. Housing ownership has a significant positive effect on family migration. The effect of relative housing deprivation on family migration is negative.

2 Literature Review 2.1 Studies on the Factors Influencing Migration There is a substantial amount of research on the factors that influence population migration. Micro-level factors mainly include individual characteristics and family characteristics (Mohabir et al. 2017), such as social capital and social network (Huang et al. 2018; Chen and Liu 2016), objective and subjective socioeconomic status (Chen et al. 2020), physical and mental health (Huang et al. 2020), hukou, occupation type and other economic status (Zhu 2007; Cao et al. 2015), age and education level (Yue et al. 2010), land tenure security (Ren et al. 2020), land holding (Meng and Zhao 2018), and children-related factors (Wang et al. 2019). Macro-level factors mainly include urban 1 China’s Mobile Population Development Report 2018, National Health Commission, 2018,

Beijing.

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and rural income (Selod and Shilpi 2021), human capital externalities (Liu 2008), household registration system (or hukou system), land policy and housing purchase policy (Su et al. 2019; Gu et al. 2020; Liu (2008), public infrastructure and public services (Lin et al. 2019), amenity (Rosen 1979; Roback 1982), government interventions (Wang et al. 2020) and other urban geography factors and local contextual factors (Tang and Feng 2015; Liu and Wang 2020; Liu et al. 2022). With the promotion of urbanization, many studies have begun to focus on the important role of housing in population migration, including the effects of housing types (Wu and Huang 2020), housing costs (Stawarz et al. 2021), housing prices (Zhou and Hui 2022; Peng and Tsai 2019; Zang et al. 2015), housing tenures (Lux and Sunega 2012) and housing conditions (Mao et al. 2018) on migration intentions and settlement decisions. As family migration gradually becomes the main pattern of population migration, migrating families put forward higher realistic demands for housing affordability, ownership, quality, and living area, which will inevitably influence family migration decisions. Some studies have analyzed the housing situation of migrating families and analyzed differences in residential independence, tenure, and quality in the cities among different types of migrating families (Feng et al. 2017; Lin et al. 2021). However, only a few studies have focused on the relationship between housing costs and family migration outcomes (Withers and Clark 2006; Withers et al. 2008), and very few studies have paid attention to the effects of housing ownership and relative housing deprivation on family migration, especially at different stages of family migration. 2.2 Introduction of Family Migration Theory Migration is a macro social phenomenon as well as a micro individual choice (De Jong and Fawcett 1981). Migration theories based on neoclassical economics, have looked into the macro and micro motivations for population migration. Such as Lewis’s (1954) theory of “dual economy structure”, Lee’s (1966) “push-pull theory” and “Harris-Todaro Model” (Harris and Todaro 1970). All these theories analyze population migration behavior and its motivations from the perspective of individual rationality. With the continuous advancement of population migration research, the complex environment and complicated mechanisms of migration behavior are continuously revealed, and the theoretical perspective of using the household as the unit of analysis provides a new research direction that can better connect micro and macro influences and reveal the intrinsic mechanisms of population migration in a more comprehensible manner (Da Vanzo 1976; Long 1972, 1974; Mincer 1978). While all of these theories focus on entire family migration, some studies have begun to focus on the migration behavior of some members of the family. Stark and Bloom (1985) studied rural-urban migration in developing countries and proposed the New Economics of Labor Migration (NELM) theory, which claims that the costs and benefits of migration are shared among family members and that the motivation for migration is not only to maximize household income but also to reduce risk (Stark and Levhari 1982). Migration outcomes are partly due to interactions within the family on how to share common income obtained through specialization (migration by some, non-migration by others) and cooperation (exchange of risks) (Stark 1991).

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The NELM theory introduced three core concepts. The first is “risk aversion”, which was developed from asset portfolio theory and means that families diversify their sources of income by relocating the most suitable family members to the urban sector, thereby reducing risk (Stark & Levhari 1982). As the migrant obtains more secure employment and accumulates location-specific capital, the risks associated with urban employment and future urban earnings typically diminish (Stark and Lucas 1988). The second is the “economic constraint”, that is, in the face of the shortage of capital accumulation, institutional supply, and social security, rural families will choose to migrate to the city to obtain income and social security. The third is “relative deprivation”, which means that when families make decisions, they consider not only the absolute expected income level, but also the relative income level, and the lower the relative income, the more likely they are to feel relative deprivation and thus more likely to migrate to other places (Stark and Bloom 1985).

3 Theoretical Analysis 3.1 The Todaro Model Todaro proposed a micro-level individual decision mechanism for labor migration, claiming that labor migration is determined by the expected income gap between rural and urban areas and that labor will migrate to cities if the expected income gap is greater than the cost of labor migration (Todaro 1969). The basic expression of Todaro model is as follows2 .  n   p(t)Yu (t) − Yr (t) e−rt dt − C(0) (1) V (0) = t=0

3.2 Staged and Dynamic Family Migration Decision Mechanism Based on the assumption of staged and dynamic migration of families, this paper divides family migration into two stages - family separation stage and family reunion stage. During the family separation stage, the laborers in the family gradually move to the city, and the rest of the family remains in the rural area. During the family reunion stage, the family’s non-laborers start to move out gradually, until all the family members settle in the city and reach the state of complete-family migration. (1) Family separation stage: labor migration decision mechanism.

2 V (0) Denotes the net discounted value of the expected urban-rural income gap for the migrant.

Yu (t) and Yr (t) denote the real wage rates in urban and rural areas in period t, respectively. n is the number of periods, and r is the discount rate. C(0) denotes the cost of migration (e.g., relocation costs), and p(t) denotes the cumulative employment probability of the migrant in period t.

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Based on the Todaro model, the absolute net benefit of labor migration based on family decision is constructed. First, Vi denotes the absolute net benefits of labor i.3  n   p(t)Yu,i (t) − Cu,i (t) − Yr,i (t) + Cr,i (t) e−rt dt − C(0) + Yne,i − Cne,i Vi = t=0

(2) Ri =

i 

Vi + H (t)

i=1

i = θ N1 (θ ≤ 1)

(3)

Ri denotes the entire family’s absolute net benefits. θ denotes the migration rate of laborers in the family, N1 denotes the number of laborers in the family, and H (t) denotes the family’s total property income. According to the NELM theory, family migration is influenced by both absolute net family benefits and relative deprivation, based on which the labor migration decision model is constructed. Mi = M (Ri , RD)

(4)

Mi denotes the probability of labor migration, and RD denotes the relative deprivation. Equation (5) indicates that the probability of labor migration is a function of the ∂Mi i family’s absolute net benefits and the relative deprivation, and ∂M ∂Ri > 0, ∂RD < 0. During this stage, laborers with higher levels of human capital in the family gradually move out, while non-laborers stay at home and share their income in the family through remittances. Therefore, the migration decision in this stage is oriented to economic efficiency, which means economic benefits and economic costs are factors that families are more concerned about. In addition, depending on the family’s risk tolerance, the laborers may choose to migrate entirely at once or gradually. (2) Family reunion stage: non-labor migration decision mechanism. When all of the laborers have migrated to the city, the non-laborers in the family will also start to migrate because the family is unstable in its long separation stage. Vj  denotes the absolute net benefit of non-labor j, and Rj denotes the total family’s absolute net benefits.  n   −Cu,j (t) + Cr,j (t) e−rt dt − C(0) + Yne,j − Cne,j (5) Vj  = t=0

Rj = RN1 +

j 

Vj  + H (t)

j=1

j = ηN2 (η ≤ 1)

(6)

3Y ne,i And Cne,i denote the non-economic benefits and non-economic costs of labor i moving

to the city, respectively.

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η denotes the migration rate of non-laborers in the family, and N2 denotes the number of non-laborers in the family. The migration decision model for the non-labor is constructed as follows. Mj = M (Rj , RD)

(7)

Mj denotes the probability of non-labor migration. Since the non-labor is unable to generate income for the family, the total economic income of the family remains the same as j increases, but the total costs of living increase. Therefore, the migration decision at this stage is psychologically utility-oriented, and non-labor migration depends more on the non-economic benefits and non-economic costs. The non-economic benefits mainly come from the comforting effect brought by the accumulation of family wealth (e.g. the acquisition of housing ownership) and the institutional benefits brought by the public services provided in the city. While the non-economic costs mainly come from the psychological costs of family separation and rural land attachment. However, if the gap between non-economic benefits and costs is insufficient to compensate for the increase in economic costs, the non-laborers will not migrate, and the laborers that have already migrated will choose to return. According to the analysis of the staged and dynamic decision-making mechanism of family migration, the factors affecting the decision of family migration mainly include five aspects: economic benefits, economic costs, non-economic benefits, non-economic costs, and relative deprivation (Fig. 1).

Fig. 1. Factors influencing family migration decisions

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4 Data and Methodology 4.1 Variables and Empirical Model The dependent variable in this paper is “family migration", which is divided into “nonfamily migration”, “semi-family migration” and “complete-family migration” to distinguish the different effects of each variable at different stages of family migration. When only one member of the nuclear family migrates to the city, it is referred to as “non-family migration”. “Semi-family migration” refers to the migration of more than one member of the nuclear family, but not the completion of the entire family migration. “Complete-family migration” refers to the case where the nuclear family has completed entire family migration. Since the dependent variable is polytomous, a mLogit model was used for regression analysis. The main independent variables include “housing cost”, “housing ownership” and “relative housing deprivation”, among which, the proxy variable for “housing cost” is the average monthly housing expenditure in the city, which refers to the monthly rent or mortgage paid by families. “Housing ownership” refers to whether a family owns housing in the city, and is represented by a dummy variable. “Relative housing deprivation” is expressed as the proportion of the sample with better housing conditions than the family’s housing conditions in the total sample of families living in the same city. The controls include both family characteristics and urban characteristics. The family characteristics include income level, living cost, relative income deprivation, family size, migration range, and land ownership, while the city’s household registration system, medical condition, and education level are among the urban characteristics. Land ownership includes ownership of contracted land and ownership of homestead. The relative income deprivation is measured using the Kakwani index (Kakwani 1984). Assuming that the vector X is a sample of all families living in the same place, with the size of n. The families in the sample are arranged in ascending order of total family income, X = (x1 , x2 , . . . , xn ), and the relative deprivation for each family is calculated as follows4 .   n γx+i μ+ 1  xi − xi rdincomei = (xj − xi ) = (8) nμx μx j=i+1

4.2 Data The data used in this paper are primarily sourced from the 2017 China Migrants Dynamic Survey (CMDS) of the National Health Commission. In the questionnaire, individuals were defined as rural-urban migrants if they were aged over 15, had left their rural hukou location, and lived in the surveyed city for more than one month. Economic statistics 4 rdincome Denotes the relative income deprivation of individual i. γ + denotes the proportion of i xi the sample in X with income over xi , μ+ xi denotes the average income of the sample with income over xi , and μx denotes the average income of the total sample X . The value of rdincomei is

within the interval of [0, 1], and the higher the value, the stronger the sense of relative deprivation.

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of the city where families relocated to are used as well, which are primarily sourced from the web of China Real Estate Information5 , and housing prices and rent data are obtained from the Xi Tai national real estate database6 . Table 1 shows the descriptive statistics of variables. Table 1. Descriptive statistics of variables Variable

mean

SD

min

max

Dependent variable

Family migration

2.671

0.584

1

3

Independent variables

Housing cost

806.9

888.5

0

5000

Housing ownership

0.224

0.417

0

1

Relative housing deprivation

0.228

0.259

0

0.971

Income level

7218

3915

2000

30000

Living cost

2976

1724

20

15000

Relative income deprivation

0.275

0.156

0

0.802

Family characteristics

Urban characteristics

Family size

1.485

0.575

1

3

Migration range

2.287

0.765

1

3

Ownership of contracted land

0.576

0.494

0

1

Ownership of homestead

0.749

0.433

0

1

Household registration system

0.543

0.498

0

1

Medical condition

60.68

44.99

17.75

345.4

Education level

684.8

103.8

450.8

1310

5 Empirical Results 5.1 Basic Regression Results As shown in Table 2, we estimate the effects of housing cost, housing ownership, and relative housing deprivation on family migration. Column (1) of Table 2 shows that the coefficients of “housing cost” and “housing ownership” are significantly positive for both semi-family migration and complete-family migration, while the coefficients of “relative housing deprivation” are significantly negative for both when controlling family characteristics. After controlling urban characteristics in column (3), the significance and direction of the coefficients remain constant. Column (2) and (4) reports the estimated relative-risk ratio (RRR), representing the ratio of the probability of an outcome in an exposed group to the probability of an outcome in an unexposed group. According to the values of the relative risk ratio in 5 http://www.crei.com.cn/. 6 https://www.creprice.cn/.

Understanding the Role of Housing in Family Reunion

623

column (4), for each unit increase in housing cost, the probability that migrant families choose semi-family migration over non-family migration increases by 22.04%, and the probability of choosing complete-family migration over non-family migration increases by 45.11%. It’s probably because the higher the degree of family migration, the higher the housing conditions required in the city, resulting in higher housing costs. This endogeneity issue will be further discussed later. In comparison to families without housing ownership, for families with housing ownership, the probability of choosing semi-family migration over non-family migration increases 1.1 times and the probability of choosing complete-family migration over non-family migration increases 16.63 times. For every 1 unit increase in relative housing deprivation, the probability of choosing semi-family migration over non-family migration decreased by 59.45%, and the probability of choosing complete-family migration over non-family migration decreased by 44.23%. Thus, both “housing cost” and “housing ownership” are more influential in the family reunion stage than in the family separation stage, and “housing ownership” is the most influential factor in the family reunion stage and may play a decisive role in family reunion. 5.2 Robustness Test To ensure the robustness of the regression results, this paper replaces the dependent variable with “degree of family migration” and “children migration”. The “degree of family migration” is represented by the proportion of the number of family members who have completed the rural-urban migration to the total number of family members, and “children migration” is represented by a dummy variable, i.e. whether children in the family move to the city with parents. The direction and significance of the regression coefficients are generally consistent with the basic regression. The coefficients of both “housing cost” and “housing ownership” on children migration are significantly larger than their coefficients on the degree of family migration. This further suggests that housing cost and housing ownership have a greater impact during the family reunion stage than during the family separation stage. The above results support the robustness of the basic regression results (Table 3).

6 Endogenous Discussions Due to the possible endogeneity issue of reverse causality among “housing cost”, “housing ownership”, and family migration, this study employs the instruments to test and analyze. To simplify the analysis, “degree of family migration” is used as the dependent variable. The average rent and average housing price in the destination city are the instruments for “housing cost”, and the higher the average rent or average housing price, the higher the family’s housing cost is likely to be. The PIR in the city is the instrument for “housing ownership”, and the higher the PIR, the more difficult it is for families to obtain housing ownership. PIR of the city is calculated as follows7 . PIR =

housing price ∗ 90 income

7 PIR refers to the housing price to income ratio, where housing price refers to the price per unit

area of housing, setting the area to 90 square meters, and income refers to the family average annual income.

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J. Qiu and P. Lv Table 2. Housing and family migration Dependent variable: family migration (1) coefficient

(2)

(3)

(4)

relative-risk ratios

coefficient

relative-risk ratios

1.2290***

0.199***

1.2204***

Base Group: non-family migration semi-family migration Housing cost

0.206*** (16.165)

(16.16)

(15.452)

(15.45)

Housing ownership

0.795***

2.2135***

0.742***

2.1006***

(5.100)

(5.10)

(4.705)

(4.71)

Relative housing deprivation

−0.864***

0.4214***

−0.903***

0.4055***

(−7.644)

(−7.64)

(−7.898)

(−7.90)

Ownership of contracted land

−0.085*

0.9188*

−0.092*

0.9124*

(−1.710)

(−1.71)

(−1.842)

(−1.84)

Ownership of homestead

−0.201***

0.8175***

−0.198***

0.8205***

(−2.969)

(−2.97)

(−2.908)

(−2.91)

0.194

1.2137

0.188

1.2072

(1.587)

(1.59)

(1.448)

(1.45)

0.241***

1.2728***

0.247***

1.2807***

(5.432)

(5.43)

(5.549)

(5.55)

0.1764***

−1.762***

0.1716***

Income level Living cost

Relative income deprivation −1.735*** (−5.254)

(−5.25)

(−5.073)

(−5.07)

Family size (= two-child family)

0.405***

1.4999***

0.425***

1.5293***

(8.544)

(8.54)

(8.839)

(8.84)

Family size (= multi-child family)

0.608***

1.8360***

0.647***

1.9092***

(5.871)

(5.87)

(6.189)

(6.19)

Migration range (= intra-provincial migration)

0.432***

1.5402***

0.385***

1.4689***

(5.788)

(5.79)

(5.033)

(5.03) (continued)

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625

Table 2. (continued) Dependent variable: family migration (1)

(2)

(3)

(4)

coefficient

relative-risk ratios

coefficient

relative-risk ratios

0.715***

2.0449***

0.685***

1.9844***

(10.372)

(10.37)

(9.794)

(9.79)

Household registration system

−0.088*

0.9160

(−1.729)

(−1.73)

Medical condition

0.149**

1.1604**

(2.147)

(2.15)

0.208

1.2308

(1.279)

(1.28)

Migration range (= inter-provincial migration)

Education level cons

−2.998***

−4.817***

(−2.639)

(−2.951)

Complete-family migration Housing cost

0.372***

1.4512***

0.372***

1.4511***

(29.994)

(29.99)

(29.696)

(29.70)

Housing ownership

2.910***

18.3577***

2.869***

17.6263***

(19.422)

(19.42)

(18.929)

(18.93)

Relative housing deprivation

−0.559***

0.5716***

−0.584***

0.5577***

(−5.209)

(−5.21)

(−5.403)

(−5.40)

Ownership of contracted land

−0.161***

0.8511***

−0.173***

0.8413***

(−3.431)

(−3.43)

(−3.656)

(−3.66)

Ownership of homestead

−0.631***

0.5322***

−0.618***

0.5390***

(−9.898)

(−9.90)

(−9.669)

(−9.67)

Income level

−1.471***

0.2297***

−1.407***

0.2448***

(−12.177)

(−12.18)

(−10.942)

(−10.94) (continued)

626

J. Qiu and P. Lv Table 2. (continued) Dependent variable: family migration (1)

Living cost

(2)

(3)

(4)

coefficient

relative-risk ratios

coefficient

relative-risk ratios

0.921***

2.5124***

0.931***

2.5369***

(20.938)

(20.94)

(21.090)

(21.09)

0.0100***

−4.475***

0.0114***

Relative income deprivation −4.602*** (−14.376)

(−14.38)

(−13.293)

(−13.29)

Family size (= two-child family)

−0.209***

0.8114***

−0.205***

0.8148***

(−4.659)

(−4.66)

(−4.504)

(−4.50)

Family size (= multi-child family)

−0.584***

0.5574***

−0.579***

0.5604***

(−5.679)

(−5.68)

(−5.584)

(−5.58)

Migration range (= intra-provincial migration)

0.239***

1.2704***

0.276***

1.3177***

(3.608)

(3.61)

(4.055)

(4.06)

Migration range (= inter-provincial migration)

−0.060

0.9421

−0.050

0.9510

(−0.963)

(−0.96)

(−0.798)

(−0.80)

Household registration system

0.139***

1.1496***

(2.875)

(2.87)

Medical condition

0.048

1.0493

(0.733)

(0.73)

0.503***

1.6530***

(3.256)

(3.26)

Education level cons obs

8.258***

4.038**

(7.391)

(2.529)

45133

45133

45133

45133

t statistics in parentheses. *p < 0.1, **p < 0.05, ***p < 0.01.

6.1 IVtobit Estimates of “Housing Cost” To ensure the accuracy of the estimation results, the sample is divided into rental and purchase samples, and the estimation and testing results are shown in Table 4 and Table 5. According to the estimation results, an increase in housing cost has a significant negative impact on family migration for the rental sample. For the purchase sample, on the other hand, an increase in housing cost has a significant positive impact on family migration. This suggests that high urban rental costs may discourage family migration, while for families that have acquired housing ownership, the higher the housing price, the stronger

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627

Table 3. Robustness test Dependent variable Housing cost Housing ownership Relative housing deprivation

Degree of family migration

Children migration

(1)

(2)

(3)

(4)

0.017***

0.017***

0.249***

0.254***

(38.937)

(39.311)

(31.878)

(32.015)

0.097***

0.095***

2.454***

2.446***

(31.345)

(30.206)

(31.250)

(30.549)

−0.084***

−0.086***

0.018

0.018

(−18.171)

(−18.507)

(0.282)

(0.267)

Family characteristics Urban characteristics

YES

YES

YES

YES

NO

YES

NO

YES

cons

1.749***

1.414***

9.218***

5.883***

(39.125)

(21.567)

(16.496)

(7.108)

45133

45133

45133

45133

obs

t statistics in parentheses. *p < 0.1, **p < 0.05, ***p < 0.01.

the comforting effect of families on housing ownership, and thus even if the burden of mortgage payments is higher, it may promote family migration. Table 4. IVtobit estimates of “housing cost” (rental sample)

Average rent

Stage 1

Stage 2

Housing cost

Degree of family migration

0.301*** (16.50) −0.288***

Housing cost

(−5.99) Control variables

YES

YES

obs

30413

30413

F-statistic

391.86

Weak IV test: AR

45.74***

Exogeneity test: WALD

80.62***

628

J. Qiu and P. Lv Table 5. IVtobit estimates of “housing cost” (purchase sample)

Average housing price

Stage 1

Stage 2

Housing cost

Degree of family migration

0.415*** (3.77)

Housing cost

0.377** (2.43)

Control variables

YES

YES

obs

8017

8017

F-statistic

209.58

Weak IV test: AR

9.66***

Exogeneity test: WALD

9.07***

6.2 CMP Estimates of “Housing Ownership” The CMP model estimation results (see Table 6) show that housing ownership has a significant positive effect on family migration, which is consistent with the basic regression estimation results, indicating that this finding is reliable. Table 6. CMP estimates of “housing ownership”

PIR

Stage 1

Stage 2

Housing ownership

Degree of family migration

−0.003*** (−42.71)

Housing ownership

0.219*** (30.81)

Control variables

YES

YES

obs

45133

45133

F-statistic

533.11

lnsig_2

−1.548*** (−329.58)

atanhrho_12

−0.370*** (−18.64)

Understanding the Role of Housing in Family Reunion

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7 Conclusions and Policy Implications Unlike previous studies, we not only extend our research perspective to family migration and discuss the effects of housing on family migration, but also further divide family migration into two stages: family separation stage and family reunion stage, and discuss the different effects of housing in these two stages. The results are discussed with robustness tests, and we also have conducted an endogeneity discussion using instruments (see Fig. 2).

Fig. 2. Empirical analysis results

According to the results, housing cost has a significant negative effect on family migration of renting households, which means the higher the housing rent, the less favorable it is for family migration. While housing cost has a significant positive effect on family migration of purchasing households, which means the higher the housing price, the more conducive it is for family migration. Our results comparing renting and purchasing households yield a more microscopic conclusion than the existing literature (Stawarz et al. 2021; Withers and Clark 2006; Withers et al. 2008), that there are opposite effects for renting and purchasing households facing rising housing costs. Housing ownership has a significant positive effect on family migration and comparing the effects of housing ownership on semi-family migration and complete-family migration, it is discovered that housing ownership mainly plays a role in the family reunion stage. Relative housing deprivation has a significant negative effect on family migration, meaning that the more severe the relative housing deprivation, the less favorable it is for family migration.

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The Characteristics of Land Use Around Rail Transit Stations in Tianjin, China Yifei Wu, Junhong Zhou, and Yani Lai(B) Department of Construction Management and Real Estate, Shenzhen University, Shenzhen, China [email protected]

Abstract. TOD (Transit-Oriented Development) has been widely accepted in many countries as an important spatial strategy to guide the coordinated development of rail transit and urban spatial structure. However, there is still a lack of evaluation and understanding on the TOD practices in the Chinese cities. Based on site-level land use data of Tianjin in 2019, this study aims to fill this gap by an empirical investigation on the effects of rail transit stations on land use outcomes in Tianjin, China. Land use types are identified within 500 m of rail transit stations in Tianjin. Shannon entropy is used to measure the land use diversity of these areas. Multivariate linear models are established to explore the quantitative relationship between various factors and land use diversity around stations. Our study shows that land use types around transit stations are mainly residential and commercial land. Land use diversity shows a great variation around transit stations in the central and suburban area. According to our results, The density of public transportation and the distribution of schools are positively related to land use diversity. The aggregation of residential land is negatively related to land use diversity around stations in the central area. As for the stations in non-central districts, the aggregation of business land has a significant and positive effect on the land use diversity of the station areas.

1 Introduction The industrial revolution created a large number of employment opportunities in cities. And with the economies of scale, the population continued to influx into big cities, the number of private cars has a substantial increase, resulting in more serious traffic and environmental problems (He 2013). In view of the population and traffic problems of large cities, Cervero (1998) pointed out that the urban population reaching 5 million and the core area population density reaching 15,000/km2 are the thresholds for the construction of rail transit with large passenger flow. At present, major cities around the world are continuing to develop urban rail transit, and academic circles have accumulated certain research results on urban rail transit and urban land use. After the 1980s, the idea of sustainable development and smart growth around the coordination of resources and development was put forward. Public transportation and urban rail transit became hot topics in this field. In this field, Calthorpe (1993) proposed the “TOD” model (Transit-Oriented Development), which is characterized by designing © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 633–648, 2023. https://doi.org/10.1007/978-981-99-3626-7_49

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a high-density and mixed-use community with a convenient and friendly pedestrian environment. Regarding land use along the rail transit line, Green and Jones (1993) pointed out that the development intensity along the rail transit line is significantly higher than other areas of the city. Cervero et al. (1997) conducted in-depth research on the “TOD” model, and proposed the “3D” construction principles for rail transit station areas, namely density (Density), land use diversity (Diversity) and reasonable regional design (Design), emphasizing high-density development to achieve the purpose of intensive development; land use diversity not only refers to a single station, but also to connect different functions around the line network; and good design is to fully consider people’s travel characteristics. However, research in China on rail transit and urban land use started relatively late. Since the twenty-first century, Chen (2000) compared and analyzed the land use patterns along the world’s typical rail transit lines in the research, and believed that the “TOD” model of land development strategy has great reference value for Chinese cities. In further research, scholars found that there are many differences between the actual situation of developed countries and developing countries. In China, the effective integration of station area and traffic is neglected and it can’t realize the organic combination of commerce and transportation (Zhang 2011). As the second city in China with rail transit lines, Tianjin is also in the stage of rapid development of rail transit. The “14th Five-Year Plan” for comprehensive transportation in Tianjin clearly states by 2025, the city’s rail transit operating mileage will exceed 500 km, basically achieving full coverage of key areas. But the current rapid development of urban rail transit does not mean that the complex relationship between rail transit and urban spatial structure has been fully grasped. Since China’s land use and policies are closely related, the study of a city’s land use needs to be carried out in combination with the actual situation of local development and policy orientation.

2 Literature Review Regarding land use along rail transit lines, the TOD (Transit-Oriented Development) model was originally based on the research of many American scholars represented by Calthorpe to solve the problem of traffic congestion and environmental pollution. The core of the concept is to form a high-density land development model along largecapacity public transportation, and to create a humanized employment and living space with mixed land use and pleasant pedestrian environment design (Cevero 1997; Dittmar 2004; Ren 2010). This model has also been effectively applied in Japan and the main form is the integrated development of the station and the city. Through the intensive development of the land around the station, the surrounding area of the station is improved and the necessary commercial facilities are configured (Zheng 2003; Lu 2017). In view of the main characteristics of the TOD model, scholars have summarized the TOD development principles into three important criteria, namely diversification, density and urban design. Scholars generally believe that a community with a compact layout of elements, mixed land use, and pedestrian-oriented design can significantly reduce the long-distance travel needs of residents, and help people choose non-motorized travel (Ewing 2001; Leck 2006; Litman 2012; Wey 2016). Chinese scholars have also introduced the concept of TOD into the development planning and research of urban rail transit. Different

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from the situation of low-density sprawl in foreign countries, most cities in China have relatively high population density, and most of the land use in the central area has been formed. To realize the TOD concept in China, the key is to realize two connotations of “integration of transportation and land use” and “priority of public transportation” (Li 2015; Song 2016). According to the neoclassical location theory represented by Alonso, the different willingness to pay for land and transportation costs of land users will lead to different urban land use types. Moon (1990) found in his research on the San Francisco Bay Area Rapid Transit System and the Washington Metro, that the formation and development of commercial land in suburban stations have improved significantly due to improved traffic accessibility. The related research of Chinese scholars also pointed out that in the core influence area of the subway stations, and there is more commercial land than residential land, but in the suburbs, but there are more residential land transactions than commercial land, which emphasized the different impact of rail transit on the surrounding area around the stations in different location conditions (Zhao 2018). At the same time, the impact of rail transit on land use is also reflected in the different characteristics within different distances from the same station. Cervero’s (2002) study found that the commercial land near rail transit stations increased by an average of 23%. Ning et al. (2005) believed that the introduction of rail transit plays an important role in promoting the scale expansion and upgrading of retail commercial centers, and it is more attractive to commercial activities. Zhang (2014) found in the research that the financial and office in the core business districts of Shanghai are mainly concentrated within 100 m of the stations, of which the willingness to pay for the traffic accessibility is relatively high. For the combined structure of different functional land, Liu et al. (2009) used the land use information entropy to quantitatively study the land use structure around the station, and proposed that in central area the difference among land use type widened; while in the new district, the difference narrowed. Most of the existing studies also believe that the high accessibility of rail transit promotes the increase in the value of the land around the stations, so that high-yield land functions such as office and business gather in the surrounding areas of the rail transit stations. (Cervero 2002; Ning 2005; Zhao 2018) Areas with more bus lines and closer to transfer stations have attracted more kinds of land use functions (Zhou 2021). On a whole, the existing research on urban rail transit mainly focuses on the impact on the surrounding density, real estate price and traffic accessibility of rail transit with a lack of that on the land use type and diversity. Due to the complex factors involved in the research on land use diversity around stations, most of the existing researches on it are qualitative description analysis. And there is a lack of relevant quantitative empirical evidence research. Therefore, this paper will focus on the types and diversity of land use around Tianjin rail transit stations and provide a reference for the optimization of urban rail transit construction in this city.

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3 Data and Methods Tianjin covers an area of 11,919.7 square kilometers, including the central city and suburban areas. The central city includes the main urban area and Binhai New Area. And the main urban area includes six districts in central area and four districts around the central parts. At the end of 2019, Tianjin has opened a total of 6 subway lines, Lines 1, 2, 3, 5, 6, and 9, with a total of 145 stations. This study relies on the data manually collected by the mapping team of the Tianjin Rail Transit Space Optimization Project up to the end of 2019. The data types include land use function and area, which is divided into 6 categories: residential, commercial services, office, industry, public facilities, and infrastructure. And there are 44 subcategories under 6 categories. According to the real_purpo field code, the land use types are classified into residential, commercial, office, industrial, public service (including land for public facilities and infrastructure) and other land (including unconstructed or non-constructed land). This paper also developed new variables for the land use analysis of 145 stations, such as the distance from the site to the center, the density of bus lines, and the number of schools within walking distance. The characteristic data of all stations within a radius of 500 m of 145 stations are obtained by following ways (Fig. 1).

Fig. 1. Types of construction land in Tianjin

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(1) Information entropy The definition of urban land use information entropy is to assume that the land area of a certain city is A, with the land of the city is divided into N types according to Nfunctions, and the area of each function type is Ai (i = 1,2,3,N). Then there is i=1 Ai =A, according to which the percentage of various land areas can be obtained as: N  Pi = Ai /A = Ai / Ai i=1

Obviously Pi has a normalization property, so according to the Shannon entropy formula, the land use information entropy is defined as H=−

N 

Pi ln Pi

i=1

H ≥ 0 is the information entropy, which reflects the diversity of urban land use. When A1 = A2 = · · · = An , P1 = P2 = · · · = Pn = 1/N , the H value reaches the maximum, = Hmax ln N, which means that all types of land use reach a balanced state. (2) Land dominance Index In addition, the land use dominance index is mainly used to describe the degree of control of a few types of land within the station’s influence area. The land use dominance index is calculated as follows: D = Hmax − H = ln N +

N 

Pi ln Pi

i=1

4 Empirical Results 4.1 Spatial Characteristics of Land Use Around the Stations Through the statistics of land use types within a radius of 500 m around the stations, it is found that the surrounding area of the station is mainly for residential and commercial land. There is high degree of compatibility between residential and commercial service functions in central area. Line 1 is the first subway line in Tianjin and it passes through more mature areas in the central urban area, such as Yingkou Road, Xiaobailou and other urban core business area, where the surrounding area of the stations is highly developed, with less industrial land and a prominent proportion of public functions. In contrast, a large proportion of industrial land is distributed around the four districts outside the central area, and the industry is dominated by traditional industrial types such as machinery manufacturing. The line connecting the new district (Line 9) occupies a relatively high proportion of industrial land, mainly for traditional machinery manufacturing, electronic processing and other industries. Some stations are still less developed, failing to make good use of rail transit to promote intensive development.

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From the station level, to analyze the influence degree of rail transit stations on different types, make 500-m equidistant impact circles around each station, and analyze the relationship between the proportion of land use types in different distances around the stations, taking a total of 0–500 m, 500–1000 m, 1000–1500 m for comparison. The results show that rail transit is more attractive to commercial land and the proportion of commercial land decreases with the increase of distance. It is speculated that commercial use land has higher requirements for transit accessibility, because of a higher degree of dependence on passenger flow, and is more willing to bear the rent brought by convenient transportation. On the contrary, the proportion of industrial land increases with the increase of distance. Industrial land is less sensitive to the accessibility of rail transit, which is related to the characteristics of traditional industries in Tianjin. The ability to pay rent premiums for accessibility is weaker (Figs. 2 and 3).

Fig. 2. Types of land use around the stations

0.4 0.2 0 Residential land Commercial land

0-500 meter

Office land

500-1000 meter

Industrial land

Public land

1000-1500meter

Fig. 3. Land use types at different distances around the stations.

4.1.1 Distribution of Different Land Use Around the Stations As shown in Fig. 4, the proportion of residential land around the station is relatively high. In Tianjin, there are 12 stations of which the proportion of residential areas is more than

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40%, so residential land is dominant among all types of land. The average residential land area around the stations in the six central districts is 23.17 hectare, and the average of non-central districts is 19.63 hectare. The proportion of residential land around the station of four districts outside the central area is relatively low, which is directly related to the low development intensity of some stations. The stations with residential land accounting for more than 40% can be regarded as typical residential stations. As shown in Fig. 5, residential stations are mainly concentrated at the junction of the six central districts and the four districts around the city. But there are few business facilities around these stations, and other land types are relatively lacking. So the function of these stations is single. As for commercial land, the average commercial land area around the stations in the six central districts is 22.93 hectare. And the average of non-central districts is 12.723 hectare, which is significantly lower than the six central districts. Over 80% of commercial and service-oriented stations are located in the six central districts. In addition, most of them are located near the subway transfer station. Because the commercial land is highly affected by transit accessibility. Around these stations, the proportion of industrial land is relatively low, which is related to the regional maturity and development planning. Typical industrial stations are mainly distributed in the four districts outside the central area, especially at the end of the line. The industrial area generally cover a large area, and a few are polluted by noise and waste. At the end of the traffic line, the functional structure is relatively simple compared to other types of stations, and there are typical employment-oriented stations, such as Xuefu Industrial Zone, High-tech Zone and Technology Park North. Xuefu Industrial Zone is located in Xiqing District. It mainly undertakes the transformation of scientific research achievements in the university town, and develops the electronic information industry, high-tech digital product innovation and technology export, and electronic logistics industry. Around the steel pipe company station are mainly steel pipe manufacturing plants and other industries. The stations dominated by office land have more types of land use around them. And the stations with the higher proportion of office land are located in the geographic center of the six central districts and of Binhai New Area, mainly for administrative office function. The stations with the lower proportion of office land are located in the four districts around the city, and the surrounding industrial land accounts for a large proportion, mainly for industrial offices. 4.1.2 Station Area Types Based on Land Use Structure We use the area ratio of different types of land around the stations as an indicator for the identification of station area types, and use the site land dominance index to describe the degree of control of certain type of land in the affected area of the stations (Table 1). The station data of different types is superimposed with the Tianjin administrative division map to analyze the spatial distribution. From the functional analysis of Tianjin rail stations, the commercial service type accounts for a large proportion in the central area, more than 80%. Most of commercial stations are in Hexi District and Hedong District; Nankai District, Heping District and other mature urban areas have diverse functions around the stations. A typical business service center group appears in the core

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Fig. 4. Different land use types area around the stations.

Fig. 5. Spatial distribution of different land use types around the stations.

business district of Yingkou Road-Xiaobailou, with Binjiang Road Business District and Xiaobailou Business District nearby, and is in the center of the city’s rail transit network; Erwei Road-Anshan Road and Xiawafang- Tucheng is located in the urban core area and

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Table 1. Classification criteria of station types. Types of Site

Classification Criteria

Residential Station

Land features

Land Dominance Index

Residential land ratio ≥40%

≥0.25

Public Service Station Public service land ratio >15%, Residential land ratio 35%, Residential land ratio 15%, Residential land ≥0.25 ratio 40%

C3 > C2 > C1 . It means that for the primary indicators of population structure, the importance order of the secondary indicator is: Ratio of highly educated population > ratio of labor force population > mechanical rate of population change > natural rate of population growth. (2) Economic Foundation P2 Under the economic foundation level, the evaluation matrix of the secondary indicators (rate of GDP growth C5 , rate of general public budget expenditure C6 , per capita disposable income of urban residents C7 , and proportion of tertiary industry C8 ) is also obtained as follows:

C5 C5 C6 C7 C8

C6

C7

C8

[1,1] [1/ 3,1/ 2] [1/ 5,1/3 ] [1/ 4,1/ 3] [2,3] [1,1] [1/ 3,1/ 2] [1/ 2,1] [3,5] [2,3] [1,1] [1,3] [3,4] [1,2] [1/ 3,1] [1,1]

The indicator weight results are as follows: C5 : [0.1459, 0.1741] C6 : [0.3274, 0.3701] C7 : [0.7646, 0.7891] C8 : [0.4982, 0.4988] From this, the importance order of C5 , C6 , C7 and C8 cloud be obtained as: C7 > C8 > C6 > C5 . It means that for the primary indicators of economic foundation, the order of importance of the secondary indicator is: Per capita disposable income of urban residents > proportion of tertiary industry > rate of general public budget expenditure > rate of GDP growth. (3) Society Development P3 Under the economic foundation level, the evaluation matrix of the secondary indicators (employment rate C9 , proportion of transportation facilities C10 , new residential area C11 , and tap water production capacity C12 ) is obtained as follows:

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C9 C9 C10 C11 C12

C10

C11

C12

[1,1] [3,5] [1/ 2,1] [1/ 2,1] [1/ 5,1/ 3] [1,1] [1/ 5,1/ 3] [1/ 5,1/ 3] [1,2] [3,5] [1,1] [1,3] [1,2] [3,5] [1/ 3,1] [1,1]

The indicator weight results are as follows: C9 : [0.4394, 0.4857] C10 : [0.1301, 0.1542] C11 : [0.6747, 0.7192] C12 : [0.5223, 0.5340] From this, the importance order of C9 , C10 , C11 and C12 cloud be obtained as: C11 > C12 > C9 > C10 . It means that for the primary indicators of society development, the order of importance of the secondary indicator is: New residential area > tap water production capacity > employment rate > proportion of transportation facilities. 3.3 Mutation Model Construction Mutational models are adopted to determine data processing norms for shrinking cities. In the evaluation index system, there are four secondary indicators under each primary indicator. Therefore, a dovetail catastrophe model with four control variables is selected, and the potential function of the primary indicators can be defined as follows: f (x) =

1 6 1 1 1 x + ax4 + bx3 + cx2 + dx 6 64 3 2

The equilibrium surface M can be expressed as: M =

∂f (x) = x5 + ax3 + bx2 + cx + d = 0 ∂x

The set of singularities N is the second derivative of the potential function V (x, u, v, w): 5x4 + 3ax2 + 2bx + c = 0 Then the divergence equation of the butterfly mutation model: a = −10x2 , b = 20x3 , c = −15x4 , d = 4x5 Therefore, if the four variables of the butterfly mutation model are set to be xa , xb , xc and xd , and the order of importance is: xa > xb > xc > xd , the data processing formulas for the four variables of different importance are respectively for: xa = |a|1/2 , xb = |b|1/3 , xc = |c|1/4 , xd = |d |1/5

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Combining Sect. 3.2 (ranking of the importance of indicators) and Sect. 3.3 (data processing formula), the data processing specification of the secondary indicators in the indicator layer C could be determined. When calculating the value of the primary indicators by the value of the secondary indicators, the principle of non-complementarity or complementarity can be determined according to the actual situation: If the relationship between the indicators is not obvious or independent of each other, the noncomplementary principle of minimax is adopted; If the indicators are related to each other and have a certain mutual promotion or restriction effect, the principle of complementarity of taking the average value shall be adopted. Since there is a certain relationship between the evaluation indicators and they are not completely independent, this manuscript adopts the principle of complementarity.

4 Case-Based Interpretation of Urban Shrinkage Catastrophe Theory This manuscript mainly focuses on the general laws of Chinese shrinking cities in three aspects: manifestations, motivational mechanisms, and evolutionary trends. At present, China is in the process of rapid industrialization. One of the important obstacles to be overcome in realizing full industrialization is the imbalance of regional development. Although some large cities are still showing relatively prosperous growth and expansion, some cities, represented by northeastern industrial and mining cities, have begun to shrink. Comparing the urban development models of China and Occident, China’s urbanization is based on the top-level design of the growth model, which is very similar to the early development model of shrinking cities in Europe and the United States, which also provides evidence for the inevitability of urban shrinkage in China. However, as the proportion of shrinking cities increases significantly, how to scientifically understand the transformation mechanism of urban growth and shrinkage, and then change the top-level design model of cities, is a new proposition that Chinese urban researchers cloud focus on. One of the research purposes of this manuscript is to provide reference for China’s shrinking cities to transform their governance mechanisms and planning models. On this basis, this manuscript chooses Ordos City in Inner Mongolia Autonomous Region and Taizhou City in Zhejiang Province as case objects. The evaluation index system of the comprehensive development index of shrinking cities shown in Table 1 is mainly used to describe the development trajectory of shrinking cities rather than judging whether shrinkage exists. Combined with the conceptual definition of shrinking cities (continuous population loss caused by structural economic crises), the case object must be cities that have experienced structural crises that lead to shrinking. Long et al. (2015) gave a general description of China’s shrinking cities (84 cities in China experienced shrinkage between 2007 and 2016) [21], and UN-Habitat has identified about 50 shrinking cities in China in the World Cities Report (2009) [22]. The evaluation criteria adopted by the above two studies are the decrease of permanent resident population in cities for several consecutive years, which cannot reflect the characteristics of structural economic crisis. In other words, in this list of shrinking cities, some cities may be just spurious shrinkage, caused by other reasons besides structural economic crisis, such as statistical restructuring due to administrative division adjustment, natural disasters, or migration

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caused by climate. Such cities cannot be considered as real shrinking cities, and if they are chosen for case study, the research conclusions may be biased. In contrast, Ordos City in Inner Mongolia Autonomous Region and Taizhou City in Zhejiang Province not only appear in the list of shrinking cities in China, but also have shown obvious symptoms of structural crisis such as population loss, economic recession, employment decline and social problems, which belong to the shrinking city in the true sense. Choosing these two cities as research objects can increase the rigor of the research process. The selection of these two cities as research subjects adds to the rigor of the research process. Resource-based cities are one of the most typical representatives of shrinking cities. There are 269 resource-based cities in China, 69 of which have experienced varying degrees of resource depletion and population decline [23]. Foreign case studies on shrinking cities (The EU-funded project selects the case studies of 7 cities in 7 European countries including Leipzig, Liverpool, Ostrava and so on [24]; Case studies on European, American, Latin American and even Asian cities carried out by the Organization for Economic Cooperation and Development [25]; Related research on cities such as Detroit, Pittsburgh, St. Louis, and Buffalo in the Great Lakes region of the United States [26]) also confirm that the shrinking of resource-based cities is a global phenomenon. Therefore, this manuscript also selects Ordos City in Inner Mongolia Autonomous Region as a representative case of resource-based cities. However, Taizhou City in Zhejiang Province is a port city with strong economic vitality and advantageous automobile manufacturing and equipment manufacturing industries, but there is still a shrinking phenomenon. A comparative study of two completely different types of cities is conducive to extracting common factors and forming a general law of the evolution of China’s shrinking cities. The time series data of various indicators of the evaluation system shown in Table 1 are collected, and the data are from the Ordos Statistical Yearbook (2010–2019) and Taizhou Statistical Yearbook (2010–2019). Considering that urban shrinkage is a gradual process rather than a sudden phenomenon, it is necessary to establish long-series time data to comprehensively reflect the characteristics of urban shrinkage. Therefore, the data from 2009 to 2018 is chosen for the study. In addition, since the financial crisis in 2008, Chinese cities have generally entered the stage of a new economic normal, which is an ideal time node for conducting research on shrinking cities. The collected data is processed based on the normalization formula of the butterfly mutation model, and the evolution trajectory of the shrinking cities will be drawn to find the shrinkage mutation points. 4.1 Case 1: Ordos City After the data processing of the butterfly mutation model, the urban comprehensive development index of Ordos can be measured by the numbers between [0, 1], where 0 means the city has completely shrunk, and 1 means the city has not yet shrunk. The lower the value, the lower the level of urban development, that is, the higher the level of shrinkage. The urban evolution trajectory of Ordos City from 2009 to 2018 is shown in Fig. 3:

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Fig. 3. Comprehensive Development Index of Ordos City from 2009 to 2018

If the small turning points of the urban evolution trajectory are ignored (which may be caused by statistical errors and changes in statistical caliber, etc., and cannot be regarded as the abrupt change point), the urban comprehensive development index of Ordos City during 2009–2018 is maintained in the range of 0.5 to 0.7, with the highest reaching 0.646 and the lowest reaching 0.507. The urban evolution trajectory showed serious downward bends in 2011 and 2015, and these two obvious mutation points divided the development curve of Ordos into three stages: before 2011 (stage 1), 2012–2015 (stage 2), after 2016 (Phase 3). (1) Stage 1: Ordos is a typical resource-based city. Its economic growth mainly comes from coal-related industries in the secondary industry, which is dominated by simple coal mining and processing under the extensive growth model. As the first city in China to produce more than 200 million tons of coal, Ordos’s urban economy gained a powerful growth engine after 2002, when coal market prices rose sharply. As of 2011, the economic growth rate of Ordos City has remained above 10%, and the total GDP once surpassed that of Hong Kong City. However, due to the lack of abundant investment channels and the impact of the macro background of the development of China’s real estate market, the capital accumulated in Ordos can only be deposited locally and turned to the real estate market, resulting in an exponential growth in housing prices. In 2006, the average housing price in the city was only 180 dollars per square meter, but by 2010, the average housing price had risen to 150 dollars per square meter, even close to the level of housing prices in megacities such as Beijing and Shanghai. (2) Stage 2: In 2011, the coal market price fell off a cliff, and a large number of small and medium-sized mining enterprises in Ordos closed down one after another. The real estate market has also fallen into a serious debt crisis. The rupture of capital chain leads to the forced suspension of a large number of real estate projects under

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construction, followed by a sharp drop in housing prices and a surge in unemployment. Urban development stagnated and reversed growth, resulting in the first sudden change. Since then, the government has massively cut general public budget spending to inject funds into the industry (the annual growth rate of general public budget spending was more than 40% before 2011, and dropped to less than 10% in 2012), controlled the real estate transaction price and developed the replacement industries of the coal industry (equipment manufacturing base and cloud computing industrial park), etc. Coupled with a brief rebound in the coal market, Ordos appeared on the surface to be on a steady growth trajectory between 2012 and 2015. (3) Stage 3: Although the local government has started to plan to take equipment manufacturing base and cloud computing industrial park as the leading industries for Ordos’s transformation, this city has completely lost the driving force of economic growth after 2015 with the coal market prices fell again the supply side structural reform by the central government, in which the coal industry has been required to cut overcapacity. In 2015, the newly built residential area in Ordos was only 281,100 square meters (6.2628 million square meters in 2012 and more than 1 million square meters in 2013–2014). The annual growth rate of urban general public budget expenditure decreased to negative value from 2016 to 2017, and the growth rate of urban GDP decreased to negative value in 2017. This shows that although the local government has repeatedly cut general public budget expenditures, controlled the size of the real estate market, and adjusted the industrial structure, Ordos has entered the track of urban shrinkage, showing an irreversible shrinking trend. 4.2 Case 2: Taizhou City The time series data of Taizhou is also processed based on the butterfly mutation model, and the urban evolution trajectory curve from 2009 to 2018 is obtained, as shown in Fig. 4: Similar to the development situation of Ordos, if the small and medium turning points of the urban evolution trajectory are ignored, the comprehensive development index of Taizhou City during 2009–2018 is maintained in the range of 0.5 to 0.8, with the highest reaching 0.789 and the lowest reaching 0.552. In 2012 and 2016, the urban evolution trajectory appeared serious downward bending, with two obvious abrupt points. These abrupt points divided Taizhou’s urban development curve into three stages: before 2012 (stage 1), 2013–2016 (stage 2), and after 2017 (stage 3). (1) Stage 1: Taizhou takes private manufacturing industry as the main body (private enterprises account for more than 90% of the total number of enterprises, creating more than 90% of the city’s economic aggregate, more than 80% of employment and more than 70% of fiscal revenue), and its economic development is highly dependent on export trade. The financial crisis in 2008 had a serious impact on Taizhou. The major economic indicators of industries above designated size were all in a state of negative growth, among which the negative growth of foreign trade reached 19%. Therefore, Taizhou’s urban comprehensive development index in 2009 was only 0.552. Since 2009, the local government began to guide private enterprises to merge and reorganize to deal with the problem of broken capital chains, and

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Fig. 4. Comprehensive Development Index of Ordos City from 2009 to 2018

increased investment in Infrastructure projects such as power supply, water supply and pollution control. The introduction of some large enterprises and projects, such as the Taizhou Petrochemical Integration Project (a joint investment of $12.1 billion by PetroChina, Shell and Qatar Petroleum), has also accelerated the recovery of Taizhou’s economy. Therefore, in stage 1, Taizhou’s urban comprehensive development index maintained rapid growth, reaching a maximum of 0.789 in 2012. (2) Stage 2:2012 is the first abrupt change point of Taizhou’s development trajectory. Since 2013, Taizhou’s comprehensive development index has continued to decrease, which is mainly attributed to the following two reasons: First, Taizhou is a diversified economy with the private economy as the main body. The private economy showed strong vitality in the early stage, but its property rights are highly concentrated in one person or a family, making it difficult to form a perfect corporate governance structure. Besides, the strong demand for expansion and the high debt ratio makes it easy to be affected by market fluctuations. With the development of urban economy, on the one hand, the private enterprises in Taizhou have been subjected to macro-control such as loan interest rate and ownership reform, and their comparative advantages have gradually weakened. On the other hand, private enterprises in Taizhou are mostly engaged in terminal business such as spare parts assembly and raw material processing, while the price increase of metal raw materials such as steel, copper, and aluminum is mainly absorbed by the terminal, resulting in a decline in the profits of private enterprises and hindered development. Second, since 2010, Taizhou has increased investment in the real estate market, and its investment in real estate development in 2011 increased by more than 60% compared with 2010. Driven by market factors, although Taizhou’s de-industrialization layout and transformation work has met development needs to a certain extent, due to low consumption levels and excessive transformation efforts, Taizhou’s economy has only

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experienced a short-term rise, while the long-term development still lacks sustained intrinsic motivation. Combining the above two reasons, the comprehensive development index of Taizhou began to decline in 2012 and reached the bottom in 2014 (0.645). Later, Taizhou municipal government also sought economic growth again by cutting fiscal expenditure, introducing foreign capital and state capital and reforming enterprise organization structure. Therefore, the urban comprehensive development index in 2015 and 2016 was better than that in 2014. (3) Stage 3: At the end of 2015, the supply-side reform put forward environmental protection requirements for enterprises, which would further strengthen pollution prevention and control supervision and transform backward industries. Taizhou’s private economy, which was mainly based on industrial manufacturing, was hit again. Moreover, due to the falling international crude oil prices and the poor profitability of the refining sector, the petrochemical joint venture between PetroChina, Shell and Qatar Petroleum which has been highly anticipated in the past few years, aborted in 2016. Under the combined effect of internal and external factors, the comprehensive development index of Taizhou fell again, and 2016 also became the second sudden change in the urban development trajectory. From the perspective of long-term development, global economic depression, Sino-US trade war, RMB depreciation and the emergence of China’s Lewis turning point and other factors will make Taizhou, which mainly has an export-oriented market structure, fall into the dilemma of low growth or reverse growth.

5 Conclusions Through two case studies, it can be found that the comprehensive development index of Ordos and Taizhou has fluctuated greatly, and some common factors can be extracted from the rapid development of the city to the contraction of the city. In view of the fact that city is a complex system, and urban shrinkage also exhibits system characteristics such as multiple feedback, dynamics and nonlinearity, the following will be detailed from the perspective of system: (1) Form of expression. The city is a dynamic open and orderly system. When it evolves from the current steady state to an equilibrium state, it may enter a self-enhancing positive cycle, or may fall into a bad vicious circle. In the past ten years, Ordos and Taizhou have gradually changed from growth to shrinkage. From the perspective of manifestations, economic shrinkage occurs first, followed by population shrinkage. The population shrinkage further strengthens the economic shrinkage, and finally makes the city slide into a negative cycle of Inefficient Lock-in. From the perspective of economic effects, the reduction of economic level is the root cause of population loss and unemployment rising, and it is also the key to determining the growth or shrinkage of cities. From the perspective of population effect, the loss of population causes the decline of regional economic development, while the inflow of a large amount of human capital promotes the economic development of the region. The population impacts on economic growth not only directly as a factor of production, but also indirectly by increasing productivity. Therefore, the economic level and population are important indicators to measure the state of shrinking cities.

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(2) Motivation mechanism. Ordos is a coal resource-based city, with the highest proportion of secondary industry exceeding 60%, and the coal industry accounting for nearly 70% of the above-scale industries. Taizhou is an export-oriented marketoriented city with a private economy as its mainstay, and its private manufacturing sector accounts for 90% of the total economic output. The two cities have different characteristics and development impetus, but what they have in common is their single industrial structure and aging economic structure. The two cities have different characteristics and development dynamics, but the similarities are single industrial structure and aging economic structure. Ordos once attracted a large amount of capital and industry inflows with the advantages of human resources and natural resources, which led to the rapid development of industry and manufacturing. However, with the pressure of resource contradictions and structural problems during the transition period, the slow process of marketization of production factors has led to inefficient allocation of resources, traditional industries suffer regional trauma, and unemployment rate increases. Shrinking phenomena such as population loss and economic recession followed. Although Taizhou’s private economy is active, its chain is too short, and it stays at the middle and low end of the global industrial chain. The long-term aging of the economic structure in the transition period leads to excess production capacity, weak growth, and other problems, resulting in structural economic imbalance. In addition, both cities vigorously developed the real estate market from 2009 to 2011, and the economic output of the secondary industry flowed into real estate development investment on a large scale, showing the characteristics of blind de-industrialization. In addition, both cities vigorously developed the real estate market from 2009 to 2011, and the economic output of the secondary industry flowed into real estate development investment on a large scale, showing the characteristics of blind de-industrialization. Overall, the economic resilience (economic resilience refers to a city’s ability to withstand and absorb shocks, and recover from external shocks)of the two cities is not strong, among which industrial structure is the most important impact factor. This influence is mainly reflected in two aspects: the diversity of industrial structure and types of leading industries. Cities with rich industrial structures are more resilient to shocks, while cities with a single industrial structure have worse economic resilience. The level of economic resilience directly determines the evolution trajectory of cities after encountering external shocks. Some urban systems can gradually recover and evolve to a more advanced equilibrium state, while others have to fall into shrinkage. Once hit by external shocks (falling coal prices in Ordos, rising prices of raw materials for end-part processing in Taizhou, government supply-side reforms, and other macro-control measures), economic growth stagnates and slips into a vicious cycle of economic shrinkage and population shrinkage. (3) Evolution trend. Urban shrinkage brings not only the decay and reorganization of physical space, but also the rupture and reconstruction of social networks, which is difficult to stop in the short term. Urban shrinkage does not fully represent urban decline, but urban decline is usually accompanied by urban shrinkage. Although many scholars have proposed that the shrinking of cities is a normal phenomenon with regularity like its growth, such as the neighborhood life cycle theory

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proposed by Hoove and Raymond (1959) [28]. And from the perspective of urban life cycle theory, cities do not only grow, but also have the possibility of stagnation and decline [29], and there is a life cycle of development and decline [30]. However, if the intervention is appropriate, the city may have a chance to get out of the fate of ‘rise—development—decline—death’, and emerge from recession and regenerate. However, from the development trajectory of Ordos and Taizhou, although the city seems to have entered the growth track after the first abrupt change point, it is difficult to reach the previous comprehensive development index level. Local governments typically take steps to cut fiscal spending or rely more on the economic output of leading industries to ease the further spread of shrinkage. Missing the best opportunity for transformation, once the external development environment continues to deteriorate (trade war, public health emergencies, etc.), cities will fall into irreversible shrinkage under the dual role of external and internal causes. Moreover, under the catalysis of globalization and informatization, urban development has entered the era of flowing space and network system. A general ageing population and slowing economic growth have led to increased competition among cities. It is inevitable that some cities will be eliminated in the competition. In the past, some deindustrialized cities that faced shrinking problems usually took measures to transform their economic methods to find new development models and directions. However, this path has been difficult to reproduce. China’s shrinking cities are facing long-term and irreversible shrinkage.

Appendix The following are the steps to calculate the indicator weights by interval AHP: ˜ = Step 1: Based on the expert scoring method, the interval number judgment matrix A (˜aij )n×n is formed. The scoring principles are shown in Table 2: Table 2. Scoring principles of interval hierarchy method The relative importance of the indicator aij

The Definition of Importance

1

Element i and element j are equally important

3

Element i is generally more important than element

5

Element i is more important than element

7

Element i is very important compared to element

9

Element i is extremely important compared to element j

2, 4, 6, 8

The median value of adjacent indicators

˜ the interval evaluation numbers a˜ ij = [aL , aU ], the Step 2: In the evaluation matrix A, ij ij ˜ can be decomposed into two matrices: AL and AU , where AL = evaluation matrix A ˜ = [AL , AU ] could be obtained from this. (aijL )n×n , AU = (aijU )n×n . And A

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Step 3: Corresponding to the maximum eigenvalues of AL and AU , the eigenvector method is adopted to obtain the normalized eigenvectors X L and X U , respectively. Step 4: Based on AL = (aijL )n×n and AU = (aijU )n×n , calculate α and β: ⎡ α =⎣

n  j=1

⎤1/2 n

1

U i=1 aij

⎦

⎡ n  1 ⎣ β= n j=1

L i=1 aij

⎤1/2 ⎦

Step 5: The weight vector is: ω˜ = [αX L , βX U ].

References 1. Häußermann, H., Siebel, W.: Die Schrumpfende stadt und die stadtsoziologie. Soziologische Stadtforschung 78–94 (1988) 2. Martinez-Fernandez, C., Weyman, T., Fol, S., et al.: Shrinking cities in Australia, Japan, Europe and the USA. Progr. Plann. (105), 1–48 (2016) 3. Oswalt, P., Rieniets, T.: Atlas of shrinking cities = Atlas der schrumpfenden Städte. Hatje Cantz, Ostfeldern (2006) 4. Rybczynski, W., Linneman, P.D.: How to save our shrinking cities. Public Interest (135), 30–44 (1999) 5. Wiechmann, T.: Errors expected- aligning urban strategy with demographic uncertainty in shrinking cities. Int. Plan. Stud. 13(4), 431–446 (2008) 6. Hollander, J.: Polluted and dangerous, America’s worst abandoned properties and what can be done about them. University of Vermont Press, Burlington (2009) 7. Harvey, D.: The limits to capital. Blackwell, Oxford (1982) 8. Harvey, D.: Space of Global Capitalism: Towards a Theory of Uneven Geographical Development. London (2006) 9. Smith, N.: Uneven Development: Natural, Capital, and the Production of Space. University of Georgia Press, Oxford (1984) 10. Blanco, H., et al.: Congested, Crowded and diverse: emerging research agendas in planning. Progr. Plann. (4), 153–205 (2009) 11. Wiechmann, T.K.M., Pallagst, K.: Urban shrinkage in Germany and the USA: a comparison of transformation patterns and local strategies. Int. J. Urban Regional Res. 36(2), 261–280 (2012) 12. Lesthaeghe, R.: The second demographic transition in western countries: an interpretation. In: Mason, K.O., Jensen, A.-M. (eds.) Gender and Family Change in Industrialized Countries, pp. 17–62. Clarendon Press, Oxford (1995) 13. Rust, E.: No Growth: Impacts on Metropolitan Areas. Lexington Books, Lexington (1975) 14. Delken, E.: Happiness in shrinking cities in Germany. J. Happiness Stud. 9(2), 213–218 (2008) 15. Cunningham-Sabot, E., Audirac, I., Fol, S., et al.: Theoretical approaches of shrinking cities. In: Pallagst (Ed.) Shrinking Cities-International Perspectives and Policy Implications, pp. 14– 30. Routledge, London (2013) 16. Bontje, M.: Facing the challenge of shrinking cities in East Germany: the case of Leipzig. GeoJournal 61(1), 13–21 (2005) 17. Schetke, S., Haase, D.: Multi-criteria assessment of socio-environmental aspects in shrinking cities. Experiences from eastern Germany. Environ. Impact Assess. Rev. 28(7), 483–503 (2008)

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18. Pallagst, K., Wiechmann, T., Martinez-Fernandez, C.: Shrinking Cities: International Perspectives and Policy Implications, pp. 59–93. Routledge, London (2014) 19. Lauf, S., Haase, D., Seppelt, R., Schwarz, N.: Simulating demography and housing demand in an urban region under scenarios of growth and shrinkage. Environ. Plann. B: Plann. Design 39, 229–246 (2012) 20. Haase, D., Haase, A., Kabisch, N., Kabisch, S., Rink, D.: Actors and factors in land-use simulation: the challenge of urban shrink-age. Environ. Modell. Softw. 35, 92–103 (2012) 21. Long, Y., Wu, K., Wang, J.H.: China’s shrinking cities and their research framework. Mod. Urban Res. 09, 14–19 (2015) 22. Habitat, U.N.: State of the World’s Cities 2008/2009: Harmonious Cities. Earthscan, London (2010) 23. Wang, C.J., Qu, Y.Y., Wu, X.J.: Research on the economy and population shrinking governance of resource-exhausted cities—based on the realistic analysis of resource-exhausted cities in Heilongjiang Province. Macroecon. Res. 08, 156–169 (2019) 24. Dieter, R., Annegret, H., Matthias, B., Katrin, G.: Addressing urban shrinkage across Europe - challenges and prospects. Shrink smart: the governance of shrinkage within a European context (2012) 25. Cristina, M., Naoko, K., Antonella, N., Tamara, W.: Demographic change and local development: shrinkage, regeneration and social dynamics. In: OECD Local Economic and Employment Development (LEED) Working Paper Series (2012) 26. Wiechmann, T., Pallagst, K.M.: Urban shrinkage in Germany and the USA: a comparison of transformation patterns and local strategies. Int. J. Urban Reg. Res. 36(2), 261–280 (2012) 27. Gromann, K., Bontje, M., Haase, A., et al.: Shrinking cities: notes for the further research agenda. Cities 4, 221–222 (2013) 28. Hoover, E.M., Raymond, V.: Anatomy of a Metropolis: The Changing Distribution of People and Jobs within the NewYork Metropolitan Region. Harvard University Press, Cambridge (1959) 29. Buˇcek, J., Bleha, B.: Urban shrinkage as a challenge to local development planning in Slovakia. Moravian Geogr. Rep. 21(1), 2–15 (2013) 30. Brezis, E.S., Krugman, P.R.: Technology and the life cycle of cities. J. Econ. Growth 2(4), 369–383 (1997)

Battery Storage Analysis for Residential Solar Photovoltaic Systems Zheng Wang, Mark B. Luther, Peter Horan, Jane Matthews, and Chunlu Liu(B) School of Architecture and Built Environment, Deakin University, Geelong, Australia {wangzheng,mark.luther,peter.horan,jane.matthews, chunlu.liu}@deakin.edu.au

Abstract. This paper analyses the impact of using battery storage in solar PV homes. It uses actual PV generation data and smart meter data from a case study of a house in Geelong, Australia, to study this. As the adoption of intermittent solar photovoltaic (PV) systems grows, storage capacity, such as batteries, is required to match unpredictable generation with uncertain demand. The results show that applying a 10 kWh battery to a 10 kW solar PV system can reduce annual imported energy by 95%. In order to make the house grid independent, a 20 kWh battery is required with a payback period of 25 years. In addition, PV self-consumption and PV self-sufficiency rise as the battery capacity increases, but this trend is limited by constrained PV generation in winter. This study demonstrates the feasibility of applying battery storage in solar PV homes, but the characteristics of PV generation and house power demand need to be considered to determine the best combination of PV and battery system size. Keywords: solar photovoltaic · battery storage · self-consumption · payback period

1 Introduction The global energy supply has been severely strained by continued socioeconomic development. According to reports, the world’s energy consumption will rise by 30% by 2040 (International Energy Agency 2019). In Australia, 93% of energy requirements are satisfied by fossil fuels, with residential energy use making up about 11% of total energy use (Department of Industry Science Energy and Resources 2021). Additionally, residential buildings contribute roughly 24% of Australia’s overall electricity consumption and 12% of its overall carbon emissions (Department of Climate Change Energy the Environment and Water 2022). Energy use worldwide and the effects of global warming can be significantly reduced by reducing residential energy consumption. Renewable energy sources, such as solar photovoltaic (PV) power, are valuable sources in terms of meeting global energy use and reducing greenhouse gas emissions because they are sustainable and their costs are decreasing (Khezri et al. 2022). Solar PV systems have been increasingly installed in Australian households. It is reported that over 30% of Australian houses have rooftop solar PV systems (Australian © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 669–678, 2023. https://doi.org/10.1007/978-981-99-3626-7_51

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Renewable Energy Agency 2022). Li et al. (2021) reviewed the solar PV market between January 2010 and September 2020 in Australia and identified that over 18 GW of rooftop solar PV systems were installed during that period. The expansion of solar PV houses has been driven primarily by feed-in tariffs and significant rebate incentives over the past decade (Khezri et al. 2020); however, feed-in tariffs in Australia have fluctuated over time, ranging from AUD 0.60/kWh to 0.05/kWh. Electricity providers are encouraging more renewable energy generation to be consumed on-site rather than sold to the grid, because the grid, designed to supply power rather than receive it, is becoming overburdened. In addition, due to the mismatch between peak PV generation and household energy use, PV self-consumption, which measures the proportion of total PV generation consumed on-site, is relatively low (Wang et al. 2021). One of the solutions to address the above issues is to use an electric battery, which can store excess PV electricity during the day (Ke et al. 2015), and consume it at night or during periods of peak electricity demand (Saez-de-Ibarra et al. 2016). The use of electric batteries in grid-connected PV houses has gained extensive attention recently, and most of the research focuses on the technical feasibility, optimal sizing, and economic viability of PV battery systems. For example, Donnellan et al. (2018) performed a critical capacity analysis for determining the optimal sizing of PV and battery storage for gridconnected solar PV houses, but the PV generation data used in the analysis was produced through simulation. Barcellona et al. (2018) investigated the economic feasibility of adding battery storage to residential grid-connected PV plants by using an approach that optimizes the size of the battery storage, but again, the daily electricity consumption and solar insolation used in the optimization process were not actual data. Importantly, Horan and Luther (2018) used the real measured data to analyse the relationship between the harvested solar energy, PV size, house electrical load, and battery capacity. Later, Horan et al. (2021) examined the energy and cost performance of applying PV battery systems in three case study houses. The authors discovered that a battery, regardless of size, requires a significantly large PV system to charge during wintertime. Our work is also using the actual PV generation data and smart metered data, which measures the amount of electricity imported from and exported into the grid every half hour. In addition, this work aims to use these real-measured data from a case study house to analyse the effect of using an electrical battery on the consumption of renewable energy generation and grid energy. Section two explains the design of solar PV homes with battery storage and a diagram for calculating the energy flows of PV battery systems. Also, a case study house used for this study is introduced in this section. The effect of applying a battery in solar PV homes is demonstrated in section three. Section four presents the cost analysis of applying a battery, and section five draws conclusions from this research work.

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2 Description of Grid-Connected Solar PV Homes with Battery Storage 2.1 Design of PV-Battery System The design of a solar PV home with battery storage is shown in Fig. 1. An inverter is one of the most important components in this design as it connects to the grid, the battery, and the household appliances and provides control. On the one hand, it can convert the DC output from the battery or solar PV to AC and supply it to home appliances or the grid. On the other hand, it can convert the AC supply to 48 V DC, charging the battery. During the daytime, the harvested solar energy will be delivered to home appliances when there is an electrical load. When the harvested solar energy exceeds the electrical load of the house, the excess energy will be used to charge the battery. If there is still excess energy after the battery is fully charged, it will then be exported to the grid. When the house’s electrical load is greater than the harvested solar energy, the battery will be discharged to meet the load. After the battery is discharged, more power will be imported from the grid to continue meeting the house’s electrical load. The battery in this work is assumed to be fully charged when it is installed.

Fig. 1. Simplified diagram of a grid-connected solar PV house with battery storage

A flowchart describing the energy flows in solar PV homes with battery storage is presented in Fig. 2. The explanation of the various symbols is listed in Table 1.

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Fig. 2. Diagram for calculating the energy flows of solar PV homes with battery storage

Table 1. Description of the various symbols Symbols

Description

Symbols

Description

t

Time inverval



Energy difference between the house electrical load and the harvested solar energy during each time interval

t EH

House electrical load during each time interval

EBt

State of charge of the battery

ESt

Harvested solar energy during each time interval

EIt

Energy imported from the grid during each time interval

CB

Battery capacity

EEt

Energy exported to the grid during each time interval

2.2 PV Generation and Smart Meter Data Acquisition from a Case Study House The house used in the case study is located in the Geelong area, Australia. A 10 kW solar PV system has been installed on the roof, and each panel is fitted with a micro-inverter that converts the output to 240 V AC. Therefore, each panel operates independently, and a panel failure does not cause a system failure. The PV system controller records the harvested solar energy every fifteen minutes. A smart meter was installed in 2013 to measure the amount of electricity imported from and exported to the grid in halfhour intervals. A CSV file that includes the last two years of smart meter data, recorded every 30 min, can be downloaded from the electricity provider (Powercor 2022). When a PV system is installed but no battery storage is available, the smart meter measures imported energy as house electrical load minus harvested solar energy and measures

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exported energy as harvested solar energy minus house electrical load. In addition, since the imported and exported energy flow on the same wire, when one is positive, the other has a value of zero. The PV generation and smart meter data of the house in 2021 will be used for the analysis in this work. The time step used in this work is one hour. The equation to calculate the electrical load of the house during each time interval is: EHt = ESt + EIt − EEt

(1)

3 The Effect of Using a Battery To analyse the effect of using battery storage on the consumption of grid and harvested solar energy, the variation of imported energy, exported energy, harvested solar energy, and the electrical load of the house versus battery capacity was calculated and plotted as shown in Fig. 3. A 10 kW PV system harvested 14.36 MWh of electrical energy in 2021.

Fig. 3. House energy summary as a function of battery capacity

When no battery was used, the imported energy in this year was 2.18 MWh, and the exported energy was 12.15 MWh, so the total house electrical load in 2021, calculated using Eq. 1, was 4.39 MWh. It is also clear from the graph that the amount of imported and exported energy decrease as the battery capacity increases. The difference between the energy imported and exported is the same, because it is equal to the difference between the harvested solar energy and the house electrical load, which is a constant value. The figure also demonstrates that the use of a 10 kWh battery reduces the amount of energy imported by 95%, from 2.18 MWh to 0.10 MWh, but the energy exported is still substantial at this stage, at 10.08 MWh. It is suggested that a larger battery may be

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needed to buffer more PV power for home use, but at this point, consideration needs to be given to how battery charging is affected by limited PV generation in mid-winter as well as the cost-effectiveness of the battery, which will be discussed in more detail later. Also, we have found that for the house to be independent of the grid without importing energy, the size of the battery would need to be 20 kWh, which is not shown in the figure. Self-consumption and self-sufficiency are two parameters used for renewable energy systems that describe the relationship between the amount of consumed renewable energy generation and the total renewable energy generation and house electrical demand. In this work, considering that the renewable energy generation system is solar PV, the PV self-consumption measures the proportion of total PV generation consumed on-site, while PV self-sufficiency measures the proportion of house electrical load met by PV generation (Wang et al. 2022). To illustrate the variation of PV self-consumption and self-sufficiency in relation to PV and battery size, the values of PV self-consumption and self-sufficiency for four PV system sizes are plotted in Fig. 4 as a function of battery size. The solid blue line in these graphs represents PV self-consumption, and the dashed red line represents PV selfsufficiency. Each PV system generates a different amount of annual energy, increasing in proportion to its size. As can be seen from the graphs, for all four PV sizing systems, PV self-consumption and PV self-sufficiency increase as the battery capacity increases, but this trend is limited because the amount of PV power generated is related to seasonal

Fig. 4. PV self-consumption and self-sufficiency as functions of PV system and battery sizes

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conditions. Specifically, during the summer months, PV generation is sufficient, and excess PV energy can be used to charge the battery. However, in winter, when PV generation is constrained, the daily PV generation is not enough to meet the daily house load, so the battery cannot be charged effectively. The graph also demonstrates that at a battery capacity of 10 kWh, the PV selfsufficiency rate increases with the size of the PV system, rising from 74.7% for a 2.5 kW PV system to 93.3% for 5 kW, 96.6% for 7.5 kW, and finally 97.6% for 10 kW. But PV self-consumption declines substantially, from 91.4% for 2.5 kW PV systems to 29.9% for 10 kW PV. This is because when the battery capacity is fixed, increasing the size of the PV system can continuously meet the house’s electrical energy demand, but the increase in meeting the house’s electrical energy demand is small compared to the energy exported to the grid. Therefore, it is noted that when installing PV systems and batteries, it is necessary to consider the characteristics of PV generation and house electricity demand in order to determine the best size combination of PV and battery systems. In order to clearly depict the challenge of charging the battery in winter, the imported energy of the house with three different sizes of batteries, 0 kWh, 5 kWh, and 10 kWh, was calculated, as shown in Fig. 5. With the installation of the three different battery sizes, 2.18 MWh, 0.57 MWh, and 0.1 MWh of imported energy will be used, respectively. As seen in the graph, expanding the battery size greatly reduces the quantity of energy imported into the home. Nevertheless, most of the reduction takes place in from spring to autumn. During winter, the house must purchase electricity from the grid to meet the demand because solar PV generation is limited by shorter days, lower solar altitude, and greater night time energy demand in winter. This results in 0.08 MWh of the 0.1 MWh of imported energy being drawn between May and August when a 10 kWh battery is used.

Fig. 5. Imported energy as a function of battery capacity

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4 Payback Period of Applying a Battery This case study house is equipped with a 10 kW solar PV system, but no battery storage. The amount of imported energy in 2021 was 2.18 MWh, and the amount of exported energy was 12.15 MWh. Noteworthy, feed-in tariffs are now on the decline (by around 20% per year on average), while the cost of purchasing electricity from the grid is rising (by 5% per year on average). In addition, electricity suppliers have started restricting the excess PV-generated electricity from being sent back to the grid, so that PV owners may not be able to sell electricity to the grid soon. As a result, the net cost for PV owners to purchase energy from the grid will increase each year. The annual net cost for purchasing electricity from the grid can be calculated as below: C = EAI × PI × (1 + 5%)i−1 − EAE × PE × (1 − 20%)i−1

(2)

where C is the annual net cost for purchasing electricity from the grid. EAI is the annual imported energy, and EAE is the annual exported energy. PI and PE are the prices of electricity purchased from and sold to the grid in 2021, respectively. i is the ith year. According to the data, Victoria’s feed-in tariff for 2021 was AUD 0.067/kWh (Essential Services Commission 2021a) and AUD 0.41/kWh for electricity purchased from the grid (Essential Services Commission 2021b). Therefore, when no battery is installed, the final net cost of purchased electricity is calculated to be AUD 80 in 2021. Adopting battery storage enables part of the excess harvested solar energy to be stored for use during peak hours, reducing the amount of energy that must be imported from the grid and lowering the cost of energy. The price and payback period of the battery should also be considered when installing one at home because they are expensive (assuming an estimate of AUD 850/kWh at the time of writing). The payback period represents the time period to recover the cost of the investment. The annual house electrical load and annual harvested solar energy for a 10 kW PV system are assumed to be constant and equal to the value in 2021. Also, it is assumed that no maintenance costs are required for the battery, so the payback period for using batteries can be calculated using the following equation: p C1 − C2 < B × (n + 1) (3) B×n≤ t=0 (1 + k)t where B is the cost per kWh of battery, and k is the discount rate, and n is the battery capacity in kWh, and p is the payback period. C1 and C2 are the annual net cost for purchasing electricity before and after the battery is installed, respectively, and they are calculated using Eq. 2. When a 5 kWh battery is assumed to be installed in 2021, the amount of energy imported from the grid is 0.57 MWh per year, and the amount of energy exported is 10.54 MWh per year. The discount rate is estimated to be 7%. Therefore, the payback period for a 5 kWh battery is calculated to be 8 years. In addition, it is necessary to analyse the payback period of a 20 kWh battery because it allows the house to become grid independent. The amount of energy imported from the grid is zero per year after a 20 kWh battery is installed, and the amount of energy exported is 9.97 MWh. So, the payback period for a 20 kWh battery is calculated to be 25 years. The payback periods for batteries of different capacities are shown in Table 2.

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Table 2. Payback period of different sizes of batteries Battery capacity (kWh)

Annual imported energy (MWh)

Annual exported energy (MWh)

Payback period (year)

5

0.57

10.54

8

10

0.10

10.08

12

15

0.01

9.98

18

20

0

9.97

25

5 Conclusion This work uses a case study house in Geelong, Australia, to analyse the impact of applying battery storage to residential solar PV systems. The results revealed that a 10 kW solar PV system harvested total electrical energy of 14.36 MWh in 2021, and the amount of imported and exported energy with no battery was 2.18 MWh and 12.15 MWh, respectively. Applying a 10 kWh battery reduces the annual amount of energy imported by 95%, from 2.18 MWh to 0.10 MWh, but the annual exported energy remains high, at 10.08 MWh, thus suggesting that a large battery may be needed to buffer more PV power for household use. Furthermore, the study illustrates that the PV self-consumption and PV self-sufficiency increase as the battery capacity increases, but the trend is limited by constrained PV generation in winter. The study also found that in 2021, the total cost of purchasing electricity from the grid without a battery installation is AUD 80. The payback period is estimated at 25 years if a 20 kWh battery is used to allow the house to be grid independent. This study demonstrates the feasibility of applying battery storage in a solar PV home, but the characteristics of PV generation and house electricity demand need to be considered to determine the best combination of PV and battery system sizing.

References Australian Renewable Energy Agency: Solar energy (2022). http://bit.ly/3XbWa8z. Accessed 9 Jan 2023 Barcellona, S., Piegari, L., Musolino, V., Ballif, C.: Economic viability for residential battery storage systems in grid-connected PV plants. IET Renew. Power Gener. 12(2), 135–142 (2018) Department of Climate Change Energy the Environment and Water: Residential buildings (2022). shorturl.at/ksPY1. Accessed 17 Oct 2022 Department of Industry Science Energy and Resources: Australian Energy Update, Department of Industry Science Energy and Resources (Canberra) (2021) Donnellan, B.J., Soong, W.L., Vowles, D.J.: Critical capacity analysis for optimal sizing of PV and energy storage for a household. In: 2018 IEEE Energy Conversion Congress and Exposition (ECCE), pp. 5125–5132. IEEE (2018) Essential Services Commission: Minimum electricity feed-in tariff (2021a). Accessed 1 July 2021 Essential Services Commission: Victorian Default Offer price review 2021 (2021b). shorturl.at/BNRU1. Accessed 30 Aug 2022 Horan, P., Luther, M.B.: How big should my battery be?. In: International Conference of the Architectural Science Association, pp. 249–256 (2018)

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Making Decisions for Urban Regeneration: A Bibliometric Analysis and Critical Review Yan Liu(B) , Yi Yang, and Haotian Zhang International Research Center for Sustainable Built Environment, School of Management Science and Real Estate, Chongqing University, Chongqing, China [email protected]

Abstract. Urban regeneration provides opportunities to address urban problems in order to achieve sustainable urban development, which has received significant attention worldwide. As the practice of urban regeneration practice is a complicated system engineering, inappropriate urban regeneration strategies may cause huge economic loss, exacerbate social problems, and even hinder long-term development for urban sectors. Scientific decision-making on urban regeneration before its implementation has gradually been regarded as one of the most effective solutions to such a challenging issue. Although existing studies have begun to pay attention to the issue of urban regeneration decision-making, an integrated and systematic review covering the whole decision process has yet to be produced. To overcome the gap, using 293 articles which were published between 1968 and 2022 collected from the Web of Science Core Collection Database, a bibliometric analysis offers the overall development and hot research topics of the existing research in urban regeneration. To better clarify the mechanism behind the urban regeneration decision-making, based on the bibliometric analysis’s results, this paper proposes a novel conceptual decision-making process framework for urban regeneration and critically reviews the previous theoretical landscape in terms of the five components of the framework, namely decision objectives, decision content, decision-makers, decision methods and decision results. The paper also outlines pathways and provides recommendations for future research directions. Keywords: Urban regeneration · Decision-making · Bibliometric analysis · Critical review

1 Introduction The rapid population growth and urbanization in many countries and regions give rise to the need for the regeneration of the urban fabric, in response to the concern of urban sprawl and large quantities of abandoned urban areas (Couch 1990). Urban regeneration has been regarded as a sound approach to reusing resources and improving the urban environment (Zheng et al. 2016); enhancing the function of a city and helping tackle urban challenges such as congested traffic, inadequate public space, and insufficient urban infrastructure delivery (Lai et al. 2014; Zielenbach 2000). In addition, urban regeneration with revitalisation objectives has been shown to be effective in benefiting © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 679–694, 2023. https://doi.org/10.1007/978-981-99-3626-7_52

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urban socioeconomic development through upgrading industries, creating job opportunities, and promoting the active participation of urban residents in urban governance (Chan and Yung 2004). It can be seen that urban regeneration provides opportunities to solve urban problems and bring continuous improvement to urban areas across the aspects of the economic, social, cultural and physical environment (Roberts et al. 2000). Therefore, urban regeneration has attracted much attention worldwide from academics, practitioners and public authorities. And there has been a continuous search for effective and proper urban regeneration strategies through decision-making for maintaining sustainable urban development (Cruz and de Brito 2015). However, urban regeneration is a complicated systematic process, which requires the trade-off and balance between multiple objectives and the needs imposed by various stakeholders including the state, public and private sector. Besides, in implementing urban regeneration practices, the socioeconomic status and development needs of the local context need to be taken into account as well. It is indicated that a more comprehensive and in-depth understanding of urban regeneration decision-making is necessary. Furthermore, as the first stage of urban regeneration practice, scientific decision-making before its implementation is considered the key prerequisite for shaping the quality and sustainability of urban regeneration paradigm. As bad urban regeneration strategies generated in this stage, such as blind large-scale demolition, may cause economic loss, exacerbate social problems, and even hinder urban long-term development. The study by Zhang and Zeng (2016) shows that there were at least 460 million m2 of buildings demolished in China between 2011 and 2015. In light of this, the decision-making upon urban regeneration strategies would directly impact the sustainability of urban regeneration programs and the quality of regeneration outcomes. Only proper decision-making can produce adequate urban regeneration strategies and programs. Although many studies have been conducted from different perspectives of urban regeneration decision-making, discussions by other researchers on it are scattered in various parts of the whole decision-making process. A systematic and integrated review from the perspective of the urban regeneration decision process that summarizes and analyses the relevant existing studies has yet to be conducted. In order to achieve effective and efficient decision-making, it is first necessary to understand every phase of the process and the mechanism behind it. To bridge this research gap, a critical review combining bibliometrics is conducted, which is helpful to identify the research gap and potential future research directions, as well as provide support for scientific decisionmaking of urban regeneration initiatives. The below research question needs to be answered: 1) How to define decision making science for urban regeneration? What is the previous theoretical landscape and how to compare them? 2) A novel conceptual framework for urban regeneration is constructed in this paper. 3) How to use the novel conceptual framework to apply into the urban regeneration practices and contexts?

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2 Methodology 2.1 Data Search Urban regeneration, urban renewal, urban redevelopment, and urban rehabilitation share similar meanings but are used at different periods in different countries or regions. So the search rule was (“urban regeneration” OR “urban renewal” OR “urban redevelopment” OR “urban rehabilitation”) AND (“decision-making” OR “decision”), which was put in the searching criterion Topic in the SCI database (Web of Science Core Collection). Topics were scanned with the search rule mentioned above with a time span of 1900/01/01–2022/8/31 and 297 papers (including articles, review articles, and proceedings papers) were retrieved. After rejecting conference proceedings and reading the abstract and content of each paper, 293 papers were finally selected for the literature review. 2.2 Literature Review Method In this paper, we adopted an integrated review approach combining bibliometrics and systematic critical literature review from both a quantitative and qualitative perspective, which consisted of two phases: – The first phase is bibliometrics analysis based on CiteSpace software. The bibliometric analysis is a quantitative and visual approach that applies mathematical and statistical methods to reveal the bibliographic features and regular patterns from the literature as a whole, like attribute distributions, network relationships, research hotspots and trends (Chen 2016). The results of the bibliometric analysis also assist in the design of a logical framework for the critical literature review that follows. – In the second phase, the critical review is conducted with a logical framework of the urban regeneration decision-making process. The sample of publications was further streamlined to mine deep information. Papers that have been published for many years but with low citation counts were excluded and some papers with reputable publishers were retained.

3 Bibliometric Analysis 3.1 Yearly Trend To trade the evolution of the research trend, the annual number of relevant papers published between 1968 and 2022 is graphed in Fig. 1. It shows a substantial and continuous upward trend since 1968, the year in which relevant studies began. And the number has grown faster and steadily since 2012 particularly, indicating an increasing research interest of scholars in the past decade.

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Fig. 1. Annual number of published papers about urban regeneration decision-making

3.2 Research Topics A co-occurrence analysis of keywords is employed to uncover the relationship and logical structure of current research. The keyword co-occurrence network map was created using CiteSpace as shown in Fig. 2. According to the criteria identified, 347 keywords met the g-index criterion with a total of 347 nodes and 944 edges. Setting a minimum frequency equal to 5, 53 nodes with name labels of keywords were displayed on the map. Nodes in the network map stand for keywords and the size of nodes is determined by their frequency of occurrences. Edges in the network are lines between nodes representing links of keywords, indicating keywords co-occur in the same paper. The keywords presented in Fig. 1 indicate that there has been a large amount of research covering various aspects of the urban regeneration decision-making system, such as regeneration objectives, decision support methods, stakeholders, regeneration strategies, etc. Then we used the algorithm proposed by Kleinberg (2003) to analyze the burst strength of keywords in order to determine the hotness of key topics (i.e. keywords with a sudden increase in the number of citations). Figure 3 shows the burst detection on the keywords from the dataset. It can be seen that “land”, “performance”, “adaptive reuse”, “gentrification” and “policy” have the highest citation burst values. Among them, the keywords like “land” and “policy” are also the most recurrent keywords as shown in Fig. 1, which suggests that there is a significant hot interest in these two topics. Then, if we check this information temporally, the earliest keywords with the longest time spans (in red) are “policy”, “localism” and “growth”. These keywords are being replaced by others such as “gentrification”, “indicator”, “geospatial tool”, “land”, “energy”, “infrastructure”, “performance”, and “public participation”. Figure 4 represents the temporal change of keywords, showing the comparison between traditional and modern in urban regeneration decision-making. Figures 3 and 4 imply a specialization in the field of urban regeneration decision-making from a more theoretical and macro perspective towards the specific elements in the urban system (like land, infrastructure, buildings), scientific decision-making tools, and so on. The goal of regeneration shifts from solving local urban problems to pursuing comprehensive socio-economic and environmental benefits and further sustainability. From demolition and reconstruction to micro-renovation and

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Fig. 2. Network visualization for the co-occurrence of keywords

micro-renewal. At the same time, more attention in this field is paid to the joint participation of stakeholders. Urban regeneration practices and policies have also shifted from demolition and redevelopment to micro-renovation and micro-regeneration. Research scales go from the city and regional level to the community and neighborhood level.

Fig. 3. Top 12 keywords with the Strongest Citation Bursts

The diverse research keywords and topics presented in the above bibliometric analysis provide insights into the complexity of urban regeneration decision-making. Urban regeneration decision-making indicated a series of behaviors in which the relevant stakeholders use scientific techniques and methods to make decisions on the selection of urban regeneration mode, timing design, spatial layout, funding arrangement and benefit allocation during the stages of regeneration planning, scheme design and mid- and

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Fig. 4. Urban regeneration decision-making: from tradition to modernity

late-stage implementation adjustment. On the one hand, the research on urban regeneration decision-making covers not only urban planning and design, but also sociology, management and engineering. On the other hand, it also covers various aspects of the urban regeneration decision-making process and system, including decision-making objectives, decision-making content, decision-makers, and decision-making results like strategies and policies.

4 Critical Review As various issues and research topics are involved in urban regeneration initiatives as shown in Sect. 3, the urban regeneration decision-making process is complicated (Peng et al. 2015). Only by understanding and scrutinizing each component of the decisionmaking process, can proper solutions and strategies for sustainable urban regeneration be proposed. Based on the bibliometric analysis, a novel decision-making conceptual framework of the urban regeneration decision-making process for the critical review is thus developed as shown in Fig. 5.

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Fig. 5. A novel conceptual framework for urban regeneration decision-making

4.1 Decision Objectives Many urban regeneration initiatives have been proposed to drive decision-making to meet various objectives. At the microscopic scale like land, building and infrastructure, studies usually focus on specific urban problems and physical improvement (Ho et al. 2012) At the city or district scale, maximizing the macro-level benefits and enhancing sustainable development for the whole by balancing social, economic, environmental development is the tendency (Moussiopoulos et al. 2010). Economic objective. To reap the economic rewards from the improvement of urban regeneration initiatives, numerous regeneration projects are carried out to boost urban economic growth and increase land value (Ryberg-Webster and Kinahan 2014). Many academics have investigated how economic factors affect decision-making in urban regeneration projects under economic goal orientation (Wang et al. 2014). For example, Zhou et al. (2017) carried out an extensive study incorporating a variety of economic factors, such as cost, investment (risk and reporting), and market issues, all of which were proven to be crucial in projects’ economic sustainability. Social objective. Lots of studies have begun to examine the relationship between urban regeneration and social development to achieve social sustainability in urban regeneration. According to Saynajoki et al. (2014), a living environment that satisfies social sustainability should be carefully constructed, well-planned, as well as maintained and regenerated promptly. And much existing literature tended to concentrate on the causes and effects of gentrification as well as practical methods for solving the social issues in the process of urban regeneration (Falanga and Nunes 2021). Environmental objective. By addressing environmental issues and improving the quality of the living environment for people, urban regeneration serves as a tool to meet environmental development needs (Dennison 2008). For example, in light of challenges faced by China’s cities in reducing carbon emissions, Cheng et al. (2022) proposed a

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decision-making framework of sustainable neighborhood regeneration from the perspective of carbon reduction to promote the implementation of the low-carbon concept in the practice of urban renewal. There are still some research gaps even though many studies have examined the relationship between urban regeneration and various urban development goals. When different objectives conflict, few studies have explored how to balance them, such as macro and specific, development-led and conservation-led or combined social, economic and environmental objectives based on the needs and context of urban development. 4.2 Decision Content Given that the city is a spatial-constructional system, all material elements of the urban planning and design system, as depicted in Fig. 6, should be considered as the content of urban regeneration decision-making. Each element of this system, including the land, building, infrastructure, heritage and each element must be studied comprehensively and deeply to understand.

Fig. 6. Decision content: urban planning and design system in urban regeneration

Land. Land, as one of the key elements in urban spatial systems, is the basis for urban development. Of the papers reviewed, Williams and Dair (2007) referred to a number of cases of brownfield redevelopment projects and presented a framework for assessing the sustainable redevelopment of brownfield sites, which might serve as a reference for future urban regeneration decision-making. G. Liu et al. (2019) adopted the Cellular Automata mode to simulate the dynamic trends in land use change by referring to the possible future scenarios of urban regeneration in Chongqing. However, due to the particular characteristics of the land, meeting the demand for adequate land supply is an ongoing challenge. In addition to applying brownfield redevelopment and adaptive reuse to address this challenge, future research could investigate more useful pathways to perfect land use through effective urban regeneration practices. Building. In terms of buildings, many scholars focused on developing decisionmaking methods to help identify potential buildings that need to be renovated. Langston et al. (2008) introduced an adaptive reuse potential model to help decision-makers to identify the existing buildings with high adaptive reuse potential in the highly-urbanized

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city of Hong Kong. In addition to seeking physical improvements to buildings, future studies should also explore how to realize sustainable housing in urban regeneration. Heritage. Cultural heritage endows the built environment with high historical and cultural value, which is irreplaceable and unrecoverable. Therefore, heritage preservation is increasingly being incorporated into the regeneration plans of many cities worldwide. For example, Strange and Whitney (2003) thoroughly discussed the practices and evolving roles of heritage preservation in the UK around four themes: reorientation, sustainability, planning, and governance. In summary, with respect to the content of urban regeneration decision-making, future study should pay more attention to other elements like infrastructure, housing, etc. In addition, in order to facilitate the overall sustainable development of the city, it is important to deeply and broadly explore the contributions of every element in the urban planning and design system, how they interact with each, how they relate to the objectives of regeneration. 4.3 Decision Makers Due to the widespread realization that stakeholders have a considerable impact on the decision-making of urban regeneration initiatives, multi-stakeholder analyses have become a focus of urban regeneration research (Zhou and Zhou 2015). With multiple stakeholders involved in urban regeneration projects, the decision-making can be systematic engineering, which makes the decision-making process more complex. Large-scale regeneration projects, for instance, such as neighborhood comprehensive renovation, public infrastructure reconstruction, and historic district revitalization generally involve various stakeholders from both public and private sectors, including governments, residents, developers, architects, constructors, and end-users (Fig. 7).

Fig. 7. Decision makers: stakeholders in urban regeneration decision-making

Coordination of relationships between diverse stakeholders is necessary for successful urban regeneration since the various interests of the different stakeholders affect the final decisions in urban regeneration (Zhou et al. 2017). As a result, an in-depth understanding of multiple stakeholders’ demands before making decisions is vital to

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ensure sustainable urban regeneration (Zhuang et al. 2019). Amin and Adu-Ampong (2016) found that multiple stakeholders tended to have different ideas on how to achieve the conservation goals in urban regeneration as they placed different weights on the environment, housing, social welfare, and commercial concerns. Unfortunately, there is currently a lack of research on how to balance interests and alleviate conflicts of benefit between different stakeholders. Stakeholder involvement has been suggested as a critical channel for ensuring inclusive and sustainable urban regeneration (Guo et al. 2018). It is essential to ensure fair decision-making and effective urban regeneration practice supported by multiple forces, which also can help strengthen the urban governance network to prevent further degradation of urban spaces (Miskowiec and Gorczyca 2018). It is suggested that both public and private interests should be highly valued (Capolongo et al. 2019). However, existing studies have identified the lack of an effective public participation mechanism in the decision-making process of urban regeneration (Yung and Chan 2011). For example, Arthurson et al. (2015) revealed that tenants in public housing had few opportunities to participate in and influence redevelopment project decision-making. Additionally, it is still debatable whether public participation can change social stratification exacerbated by the gentrification accompanying the process of urban regeneration. Furthermore, an inappropriate participation mechanism may lead to unequal interest distribution among various stakeholders (Cohen and Wiek 2017). On the other hand, participatory planning cannot be perfect, and some criticisms of strengthening public participation have been raised. For instance, excessive “participation” and the institutional instrument “partnership” advocated by contemporary urban regeneration will be more time-consuming and may result in authoritarian decision-making in the end. 4.4 Decision Methods Reasonable and effective decision-making in urban regeneration needs to be supported by appropriate methodologies and tools. Table 1 classifies the research regarding methods and tools of urban regeneration decision-making into three categories: (1) studies that applied evaluation methods with indicator-based approaches or model tools, focusing on evaluating past, current, or future conditions to decide regeneration strategies; (2) studies that developed group decision-making methods to support various stakeholders participate in the decision-making process of urban regeneration; (3) studies that focused on leveraging big data and information technology to provide more precise information and to build multi-participation platform, which enhances the efficiency and quality of decision-making. It is commonly acknowledged that evaluation can provide information about past experiences, current issues and predict future trends, which may facilitate proposing appropriate strategies (Hemphill et al. 2004). The methods and tools used in the first category are commonly used, which are mature but basic, such as indicator-based evaluation method, multiple criteria decision assessment, analytic network analysis, future scenario simulation, and so on. The advantages are that these approaches are relatively explicit and easy to apply. However, when the number of stakeholders is large or the decision issues are complicated, they may be not sophisticated enough to accurately support decisions sometimes. Therefore, instead of the single use of existing approaches

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Table 1. Classification of existing decision-making methods of urban regeneration Methods and tools Category 1: Evaluation

Example articles A composite index based on indicator system

Greig et al. (2010); Yang (2017)

A decision framework based on Huang et al. (2020); Zheng indicator system et al. (2016)

Category 2: Group decision-making

Category 3: Information technology and big data

Data envelopment analysis (DEA)

Huang et al. (2016); Tang et al. (2020)

Fuzzy Delphi method (FDM) + Multi-criteria decision-making (MCDM)

Chen et al. (2018)

Cellular Automata model

G. Liu et al. (2019)

Post factum analysis (PFA) + Decision aiding

Ciomek et al. (2018)

Multi-criteria decision-making (MCDM) + Analytic Network Process (DANP)

Manupati et al. (2018)

A decision-making platform

Yi et al. (2017)

A GIS-based decision support system for facilitating participatory

Omidipoor et al. (2019)

Crowdsourced data and a presence-and-background learning (PBL) method

Y. L. Liu et al. (2019)

An experience-based mining system

Zhou et al. (2021)

A multi-scale decision model Zheng et al. (2017) (System dynamics modelling + conversion of land use and its effect at small scale modeling + Markov chain prediction + GIS)

or tools, there is a need for several techniques to be blended in order to properly accommodate diverse needs for decision assistance in urban regeneration (Radulescu et al. 2016). The second category is the group decision-making method that is used to coordinate intricate interactions among stakeholders in the decision-making process. Methodologies in this category involve building a platform for group decision-making, determining the weighting of decision-makers, etc. Unfortunately, the current methods in dealing with benefit distribution and balance between various stakeholders are still weak, which can fully employ mathematical or economic models introduced from management science

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and operations research to better simulate, optimize, and coordinate the relationships of multiple stakeholders. The third category of decision-making methods focuses on using big data or information technology tools to establish a decision information database, simulation monitoring system, and decision-making platform. The efficiency and intelligence of these tools help the decision-making to achieve higher goals and levels that are unattainable by traditional decision-making methods. However, the toolkits developed in this category still have certain shortcomings. Firstly, not all of the toolkits for decision support methods have been well-tested. Some toolkits may have potential technological and design issues that could compromise the precision of decision support (Huang et al. 2016; Wang et al. 2015). Then, part of the toolkits has application thresholds like high technical requirements and cost of use, which restricts the applicability and functions of decision support tools for urban regeneration. And some toolkits lack sufficient access to the information and data required for use (Kim et al. 2009). Multi-source heterogeneous data should be further used effectively for more accurate analysis. Besides, there are some general downsides to these above methods. For example, some decision support toolkits have strong local and regional characteristics and are only suitable for certain regions (Ciomek et al. 2018; Culshaw et al. 2006). Some toolkits were developed specifically for use at a particular scale, such as community, city or regional levels (Perez et al. 2018), and multi-scale methods need to be established to enhance the applicability and adaptability of decision-making support. Then, despite the widespread adoption of tools and techniques from the fields of urban studies, geography, and information technology to develop decision support toolkits, management science theories and techniques have not yet been fully integrated into decision-making for urban regeneration. For instance, complex network theory could comprehensively discover and quantitatively analyze internal relationships between multiple subjects in the decision-making process; game theory can be further applied to investigating the issue of benefit distribution between multiple stakeholders in urban regeneration; and dynamic programming aids in optimizing the multi-stage decision-making process in urban regeneration. 4.5 Decision Results Urban regeneration policies, strategies and practice projects, as the results of urban regeneration decision-making, could test the rationality of the decision-making process and in turn, influence the decision-making objectives and processes of future decision-making. The focus on social capital financing, benefit distribution among multiple stakeholders, social welfare, improvement of public engagement, as well as the harmony between “topdown” and “bottom-up” management approaches are several key areas where current urban regeneration policies and strategies still fall short.

5 Discussion and Research Agenda By comprehensively reviewing the existing literature on decision-making in urban regeneration, research gaps and agendas are identified.

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Future research on decision-making objectives for urban regeneration should focus on the following directions: (1) human-centered and placed-based principles in determining urban regeneration objectives (2) the balance between different objectives like macro and specific, development-led and conservation-led or combined social, economic and environmental regeneration objectives according to the needs, use, and value of urban regeneration practice. Future research on decision-making content for urban regeneration should focus on the following directions: (1) approaches for meeting the demand for sufficient land supply by urban regeneration; (2) contribution of every element, and their relationships and interactions of the urban planning system in urban regeneration. Future research on decision-makers for urban regeneration could emphasize three main aspects: (1) rational definition of participation boundaries of all parties concerned; (2) mechanism for balanced benefit distribution among key parties; (3) incentives to promote public participation and consultation. Future research on decision-making methods for urban regeneration could be underlined in the following areas: (1) advanced methods and techniques in management science to be integrated; (2) more applicable and adaptive decision support toolkits to be developed; (3) participatory approaches and tools to improve multiple stakeholders especially public participation and consultation; (4) information technology like cloud technology, multi-source heterogeneous data to be effectively used for more accurate analysis. Future research on decision-making results like policy and strategies for urban regeneration should focus on the following directions: (1) the balance between top–down and bottom–up approaches in urban regeneration strategies; (2) social inequality alleviation policies.

6 Conclusion The decision-making process of urban regeneration is highly complicated, and as such a systematic overview is provided. This paper reviewed existing studies on urban regeneration decision-making published from 1968 to 2022. A bibliometric analysis was first used to quantitatively detect the hot topics and research evolution from traditional to modern in this field. Based on the results of the bibliometric analysis, a novel conceptual framework for urban regeneration decision-making was proposed to help conduct a systemic critical review. Based on this framework, five main components of the decision process in urban regeneration, namely decision objectives, decision content, decision makers, decision methods and decision results, were discussed to identify existing studies’ contributions and their limitations. Specifically, the decision content is identified by material elements in the urban planning and design system. The decision-makers refer to stakeholders in urban regeneration who should participate in the decision-making process. And the decision results in this framework are urban regeneration policies and practical strategies. The novel conceptual framework could also be applied to urban regeneration practices and contexts to help make scientific decisions for urban regeneration. Finally, the discussion section of this paper concluded the future research trends according to the critical review, which could be read as a road map for researchers exploring the field of urban regeneration decision-making.

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A Scientometric Review on Real Estate Investment Trusts: Towards a New Asset-Information-Capital Framework Huicang Wu, Hui Sun(B) , Qian Tian, and Yingzi Liang College of Management and Economics, Tianjin University, Tianjin, China [email protected]

Abstract. Real Estate Investment Trusts (REITs) has caused great concern for its capacity of serving the real economy with the capital market. The research related to REITs is more and more, especially in China. It is vital for interested researchers to get full picture of global research. However, the current review in the field of REITs mainly focuses on the summary of single area of REITs, and there is a lack of systematic review of other latest research progress in this field. This paper reviews the current REITs research with 817 academic papers from Web of Science database by scientometric analysis techniques. Visual analysis of authors, affiliations, countries, and keywords through CiteSpace software reveals the main areas and future trends in REITs research. Based on this, 4 major themes are identified, that is, risk and uncertainty management, capital structure, investment performance, price volatility. Additionally, a new AIC (Asset-InformationCapital) framework is also proposed to structure existing REITs research. Finally, 3 specific future directions are presented to comprise more attention on social performance of REITs, asset choice with environment-oriented configuration, the match between governance and regulation of REITs. Therefore, this paper provides a critical review of the extant REITs literature, and also contributes to the development of REITs practice in China. Keywords: REITs · Scientometric Review · themes · AIC framework

1 Introduction Real Estate Investment Trusts (REITs) is a kind of financial product for real estate securitization. It firstly collects investor funds through the issuance of income certificates, then operates the real estate assets by specialized institutions, and finally distributes the income to certificates holders in a high proportion [1]. REITs first appeared in the United States in the 1960s, bringing new opportunities to real estate investment development, and have been launched in Australia, Japan, Singapore and many other countries since then. As of December 2020, a total of 43 countries and regions have introduced REITs in the world, and the total market value of the public REITs has exceeded $2 trillion [2]. Some financial products with the embryonic form of REITs also began to appear in China at the end of the last century. Since April 30, 2020, with the successive release of a series © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 695–705, 2023. https://doi.org/10.1007/978-981-99-3626-7_53

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of policies, the pilot of REITs has been officially developed. As of December 2021, a total of 11 infrastructure REITs has been listed. Since listing, they all performance well, with an average premium rate of nearly 25% [2]. Over the past few decades, REITs products have gradually matured and related research is flourishing. Research on REITs has covered a wide range of topics, from qualitative analysis to quantitative research, and has explored in depth the regulations and policies, operational models and risks of REITs. In addition, the strong support of the policy and the sound development of the market have made the theoretical research on REITs active again, but the current review in the field of REITs mainly focuses on the summary of single area of REITs, and there is still a lack of systematic and comprehensive analysis of the REITs research. This paper uses CiteSpace, a tool for scientific literature analysis, and selects 817 academic papers from Web of Science to visualize the existing REITs research status and analyze the potential future directions. Based on scientometric analysis, the knowledge structure of REITs research is pictured, 4 research themes are summarized, and then a new AIC framework and 3 future directions are also illustrated.

2 Methodology This study involves three processes to provide a comprehensive review of the state-ofthe-art research on REITs. Figure 1 shows the review procedure and contributions. In the selection process, we first searched the Web of Science database for English articles that contain words “REITs” or “REIT” or “Real Estate Investment Trusts” in their title, keywords, or abstract. And the categories of chosen paper are article and review. The first-round search results in 861 papers from 1985 to 2022. After reading their titles, we excluded 44 papers that are unrelated to REITs, leaving 817 papers for analysis. Also, several industry reports were referenced as complementary materials. The review process used both scientometric analysis and content analysis. Scientometric Analysis is a statistical evaluation of publications. Here we used CiteSpace, an openly accessible utility application for envisioning and breaking down patterns and examples in the scientific literature [3], to conduct the scientometric analysis. Scientometric analysis as the first step provides general knowledge such as popular document, active authors and countries, top keywords, and major themes. Section 3 described the scientometric analysis results. Content analysis is a descriptive, statistical analysis of the key information of publications. Two authors manually read and noted down the research topic, focus, methods, and outcome of each paper for the content analysis. The recorded values were determined by the authors, and the coding reliability was assured by iterative cross-checking. Lastly, by summarizing the takeaways from the quantitative and qualitative review of the 817 papers and industry reports, we proposed a new AIC framework for REITs research. Then we identified 3 future directions that deserve more attention as the major contributions from this study, which will be illustrated in Sect. 4.

A Scientometric Review on Real Estate Investment Trusts Selection Process

Review Process

Academic Papers

Quantitative Review

Web of Science Search

Scientometric Analysis

861 Publications

Excluded 44 Unrelated Articles

Content Analysis

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Contribution

General Knowledge Documents, Authors, Countries, Keywords, Themes, etc. (Section 3)

Specific Knowledge Research Focus, Hot Topics, and a new AssetInformation-Capital framework (Section 4)

817 Publications

Industry Reports

Qualitative Review

3 Future Directions (Section 4)

Fig. 1. Research procedure and contributions.

3 Scientometric Analysis Results 3.1 Popular Publications The analyzed 817 papers can be dated back to 2002, and the number of publications is increasing year by year. While there was a decline during 2014 to 2016, but REITs started to gain wider attention since 2017. Table 1 lists the most cited articles in our dataset. The most cited paper is the paper written by Ling DC et al. [4] and published in REAL ESTATE ECONOMICS. This paper examines U.S. public and private commercial real estate returns at the aggregate level and by the four major property types over the 1994– 2012 time period. And the results suggest that REIT returns do not embed additional commercial real-estate-specific information useful in predicting private market returns. This paper has a high citation and centrality, and occupies an important position in cocitation network where each node is a paper. And the third cited publication is also from the same journal. Boudry WI et al. [5] proved the ability of traditional capital structure theories to explain the issuance decisions of REITs through the issuances made between 1997 and 2006. The second and the fourth cited publications are attempts for REITs ownership and both published in The Journal of Real Estate Finance and Economics: Han B [6] investigated the relation between insider ownership and firm value through empirically, while Devos E et al. [7] observed the REIT institutional ownership dynamics before, during, and after the financial crisis in 2007. Actually, the citation of these paper is not so high, up to 20 times, and difference between them is small, which indicates that the impact of REITs research is not prominent in the Web of Science database.

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Rank

Citation

Centrality

Author, Year

Title

1

18

0.35

Ling DC, 2015

Returns and Information Transmission Dynamics in Public and Private Real Estate Markets

2

11

0.24

Han B, 2006

Insider Ownership and Firm Value: Evidence from Real Estate Investment Trusts

3

13

0.18

Boudry WI, 2010

An Analysis of REIT Security Issuance Decisions

4

10

0.17

Devos E, 2013

REIT Institutional Ownership Dynamics and the Financial Crisis

3.2 Active Authors, Affiliations and Countries Table 2 shows the active authors who have more than 10 publications included in our dataset. Most of the active authors focus on real estate finance and investment. And these publications provide fundamental contributions to this field and received high citations, which can indicate that REITs is an interdisciplinary research field between real estate and finance. Table 2. Active authors in this dataset. No. of Papers

Author

Research Field

Affiliations

Title of the most cited paper (number of total citations of this paper)

16

C F SIRMANS

Real Estate, Housing

Florida State University

The pricing of seasoned equity offerings: evidence from REITs (207)

11

JOSEPH T L OOI

real estate, housing, real estate finance, REITs

National University of Singapore

The growth of REIT markets in Asia (148) (continued)

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Table 2. (continued) No. of Papers

Author

Research Field

Affiliations

Title of the most cited paper (number of total citations of this paper)

11

SEOW ENG ONG

REITs, housing finance, price discovery

National University of Singapore

An analysis of the financing decisions of REITs: the role of market timing and target leverage (111)

10

DAVID M HARRISON

Real Estate

University of Central Florida

Further evidence on the capital structure of REITs (144)

10

CHINMOY GHOSH

finance

University of Connecticut

The pricing of seasoned equity offerings: evidence from REITs (207)

In terms of affiliations, the top 5 active ones are Natl Univ Singapore (38 publications), Univ Connecticut (36 publications), Florida Int Univ (24 publications), Florida State Univ (20 publications) and Mississippi State Univ (19 publications). This finding is consistent with Fig. 2 that the USA is the most active countries in REITs research. Each node represents each country. The outer circle of the node is purple, indicating the greater centrality of the medium. The thicker the purple circle, the more literatures published in the country. In addition, Fig. 3 illustrates the cooperation among researchers. It can be seen clearly that there are 3 clusters, which are WILLIAM G, DAVID C LING and SEOW ENG ONG. And they published a lot of papers with own cooperators. Throughout the picture, there is no outstanding cross-clustering collaboration. This reflects that the research in REITs mainly carried out by region, such as Singapore or USA, and the multiple countries publications are still lack.

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Fig. 2. Collaboration map-Nation.

Fig. 3. Collaboration map-Researcher.

3.3 Keywords and Themes Keywords can be used to identify research themes. CiteSpace offers analysis of Keywords. Figure 4 displays the co-occurrence network of keywords, which can study the relationship between keywords. Ticker links indicate that two words often appear together. And the larger the node, the more the keyword appears. The result shows that the most popular topic in REITs research is return (217 times), performance (151 times), and price (34 times). The second prevailing topic is associated with market (139 times), risk (105 times) and volatility (33 times). The third one is about corporate governance

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(38 times), capital structure (35 times) and ownership (24 times). Additionally, the word of “bond” has the highest centrality (0.66), which also links 3 clustering.

Fig. 4. Co-occurrence Network of Keywords

Furthermore, Log-likelihood Ratio (LLR) algorithm identifies 8 outstanding clustering (Fig. 5). And Table 3 lists the first three keywords in every clustering which includes more than 10 keywords. The value of Silhouette can represent the homogeneity between keywords. Generally, clustering is considered good when the Silhouette value is greater than 0.7. Consequently, 4 themes are identified as the following: (1) Risk and uncertainty management (#0, #6): this theme focuses on some common risk factor including market, policy and financial crisis and their impact on REITs return. During these studies, cross section and bonds are often used. (2) Capital structure (#1, #3, #5): Under this theme,

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Fig. 5. Clustering of Co-occurrence Network

Table 3. Clustering of Co-occurrence Network Cluster ID

Cluster Name

Size

Silhouette

Representative Keywords

#0

uncertainty

28

0.932

market, cross section, bond

#1

capital structure

24

0.966

capital structure, finance, firm value

#2

investment

19

0.909

Performance, investment, commercial real estate

#3

firm

19

0.944

Firm, liquidity, decision

#4

model

18

0.949

Model, price, volatility

#5

agency issues

13

0.991

#6

risk

11

0.995

Return, risk, financial crisis

#7

perverse inflation hedge

10

0.985

Dynamics, equity, interest rate

decisions on issuing REITs or not will depend on the firm value and liquidity. And the difference in capital and ownership structure will also have an impact on the choice. (3) Investment performance (#2): This theme considers the investment nature of REITs. The initial public offering of REITs and its performance are both what investors care about. (4) Price volatility (#4, #7): Because of the nature of finance product, the price of REITs is often be studied, such as its volatility in price and asset pricing model. And equity market is generally compared with REITs to hedge perverse inflation risk.

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4 AIC Framework and Future Directions Inspired by the observations of REITs research, we propose a new AIC framework (Fig. 6) to structure existing REITs research. The framework can also serve as a roadmap to identify currently weak or lacking research areas. The framework consists of three dimensions: Asset, Information, and Capital. Considering the investment nature of REITs, the admittance and check of real estate or asset is necessary. And the check can be divided into 3 areas: the approval of real estate deal, the attraction of asset profit, and the robustness of asset operation. The Information axis describes how REITs could circulate in finance product. As a finance product, REITs is market-oriented and the information of supervision is quite important. Firstly, performance is the first concern and needs to be controlled at all times. Secondly, market information that can easily affect performance should be paid more attention. Lastly, the information disclosure is an important guarantee for REITs operation. The Capital axis is based on ESG theory for reference. The most basic corporate governance includes ownership structure, firm value and mechanisms, which are the primary focus of existing REITs research. Very few researchers paid attention to social and environment, such as green technology, environmental cost accounting, and targeted poverty alleviation. This framework can be used to brainstorm potential research areas by enumerating any combination of the three dimensions. Based on the previous quantitative and qualitative review, we proposed 3 promising future directions in REITs research (Fig. 7). These three areas were selected because they have different positions in our AIC framework and have little overlap, but this list does not represent any ranking. 4.1 More Attention on Social Performance of REITs The first major future direction for REITs research concerns the increasing attention on society of REITs. As the world becomes more demanding for services, opportunities for asset may be expected to increase in aged care and infrastructure. And this kind of asset can provide including profit and fame for REITs. This also agrees with Reddy and Cho [8]. He noted that health care REITs invest in an unexpectedly wide range of properties. And the infrastructure REITs in China or USA and other regions are all increasing rapidly. 4.2 Asset Choice with Environment-Oriented Configuration The second major future direction for REITs research is the combination of multiattribute decision and asset choice. Under the demand of both environment requirements and market impact, asset check has become a multi-attribute decision [9]. Future research should study how to choose reasonable and attractable asset through multi-attribute decision methods to achieve asset resilience during REITs operation.

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mechanism

disclosure

regulation

asymmetry

transparency

market

interest rate

policy

stock

performance

return

price

volatility

Civilized construction valuation

biodiversity

Environment cost

Safe production

Ownership structure

Poverty alleviation dedal

profit

operation

Asset

Green technology

Capital

Fig. 6. AIC framework for REITs research.

perf orm anc e

Performance & Social

Asset & Market & Envrionment

Disclosure & governance & profit

greater society performance

Asset choice with environmentoriented

Which kind of governance can achieve better disclosure

Fig. 7. Future research directions.

4.3 The Match Between Governance and Regulation of REITs Existing research has paid attention on the relationship between REITs return and governance structure [10]. However, few research observed the necessity of REITs regulation, especially the information disclosure. Although there is considerable structural commonality in REITs across the various jurisdictions of the world, a big difference may still exist, especially the structure of REITs in China now, which has an extra layer on the structure. And then the corresponding regulatory system also needs to change.

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5 Conclusion REITs is an important innovation of China’s real estate financing channels and an innovative support of the financial industry for the development of the real estate industry. The research on REITs review can promote the transformation of Chinese real estate enterprises from asset-heavy to asset-light strategy, and it is also an important guarantee for the sound development of trust products in financial innovation tools. This paper presents an extensive, systematic review of REITs research through a scientometric analysis of 817 academic papers, which can supplement the theoretical research in detail and explore the way of REITs management that is line with the national conditions of China. On the basis of scientometric analysis, the popular publications, active authors and affiliations, keywords and themes are all identified. And then, a new AIC framework is further summarized and 3 specific future directions are also illustrated for REITs research, which are deserved more future research endeavors. The findings and recommendations of this study can enrich the potential application of the REITs and offer insights to relevant researchers and practitioners in REITs domain. Some limitations may worth notice that (1) only English papers were reviewed, meaning the exclusion of some advanced work that were publisher in other languages, (2) the research methodology is relatively homogeneous, making the results largely determined by CiteSpace’s ability to extract and process information. Future review of REITs needs to cover more languages and more resources (not only journal papers) and take a more diverse approach.

References 1. Chen, L., Huang, X., Wu, Z.: The research on risk and management of real estate investment trusts in China. In: ICCREM 2020: Intelligent Construction and Sustainable Buildings, pp. 781–792. American Society of Civil Engineers, Reston (2020) 2. Han, Z.F., Zhang, Z.: The Chinese Way of REITs Development. People’s Publishing House, Shanghai, vol. 3 (2021). (in Chinese) 3. Chen, C.: CiteSpace II: detecting and visualizing emerging trends and transient patterns in scientific literature. J. Am. Soc. Inform. Sci. Technol. 57(3), 359–377 (2006) 4. Ling, D.C., Naranjo, A.: Returns and information transmission dynamics in public and private real estate markets. Real Estate Econ. 43(1), 163–208 (2015) 5. Boudry, W.I., Kallberg, J.G., Liu, C.H.: An analysis of REIT security issuance decisions. Real Estate Econ. 38(1), 91–120 (2010) 6. Han, B.: Insider ownership and firm value: evidence from real estate investment trusts. J. Real Estate Financ. Econ. 32(4), 471–493 (2006) 7. Devos, E., Ong, S.E., Spieler, A.C., et al.: REIT institutional ownership dynamics and the financial crisis. J. Real Estate Financ. Econ. 47(2), 266–288 (2013) 8. Reddy, W., Cho, H.: Emerging sector REITs. In: Parker, D. (ed.) The Routledge REITs Research Handbook, Routledge, Abingdon, p. 56 (2018) 9. Bing, H.: Insider ownership and firm value: evidence from real estate investment trusts. J. Real Estate Financ. Econ. 32(4), 471–493 (2006) 10. Jalil, R.A., Ali, H.M.: Performance determinants of Malaysian real estate investment trusts. Jurnal Teknologi 73(5), 321–324 (2015)

The Spatial Relationship Between Rail Transit Network and Population and Employment Density in Tianjin, China Junhong Zhou and Yani Lai(B) Department of Construction Management and Real Estate, Shenzhen University, Shenzhen, China [email protected]

Abstract. The development of rail transit is expected to gather housing and jobs around the station, so as to improve the urban spatial structure for sustainable development. Based on mobile phone signaling data in 2019, this study analyzes the coupling relationship between metro station areas and urban population intensity in Tianjin. Regression models are established to quantitatively evaluate the effects of metro stations on the residential population density and employment density around the station. The study shows that the residential population and employment are significantly concentrated along the rail transit area. The employment density, the proportion of residential land and commercial land are the most influencing factors in residential population density within the station surrounding areas. Whereas, the residential population density, average and house price and the proportion of industrial land are in employment density within the station surrounding areas. Based on the research results, policy implications are proposed to better guide the coordinated development of rail transit and urban spatial structure. Keywords: TOD · urban spatial structures · influencing factors

1 Introduction China’s urbanization rate has exceeded 60% from less than 20% in the early 1980s. With the continuous expansion of urban scale, the monocentric city structure had a lot of problems, such as traffic congestion, jobs-housing imbalance, air pollution, environmental degradation (Liu et al. 2019). Therefore, many first-tier cities in China began to promote the construction of polycentric spatial structure. For example, in Beijing’s urban master planning (2016–2035), Beijing clearly proposed to build an urban spatial structure of “one core, one primary center and one subcenter”, and strive to change the development mode of single center aggregation. In the master planning of Shanghai (2017–2035), Shanghai proposed to build an urban spatial structure of “one primary center, two axes and four wings; multiple corridors, multiple cores and multiple circles”. However, many empirical studies show that China’s first-tier cities have not yet formed a polycentric spatial structure (Deng et al. 2008). As an old industrial city, although Tianjin clearly © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 706–719, 2023. https://doi.org/10.1007/978-981-99-3626-7_54

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proposed to take the central urban area as a primary center and the core area of Binhai New Area as a subcenter as early as the Tianjin urban master planning (2005–2020), some studies have found that Tianjin still shows a strong monocentric concentric circle structure (Jiang et al. 2018). The coupling development between public transport, especially rail transit and population intensity can effectively disperse the city and alleviate various disadvantages of monocentric city structure (Handy 2005). Bothe et al. (2018) find that after 10 years of subway construction, the population and employment around subway stations have increased significantly, effectively alleviating the separation of jobs and housing. Therefore, many big cities in China have begun to work hard to promote the construction of rail transit, like Tianjin. According to the 14th five-year plan of Tianjin comprehensive transportation, the operating mileage of Tianjin rail transit will exceed 500 km in 2025, which was twice as much as the mileage today. The coupling relationship between rail transit and population intensity may vary greatly in different cities, but the research is mostly concentrated in big cities such as Beijing and Shanghai, there is a lack of research on Tianjin. Based on mobile phone signaling data in 2019, this paper studies the coupling relationship between rail transit and the distribution of population intensity in Tianjin, which will enrich the empirical cases of rail transit and its spatial relationships with population and employment density.

2 Literature Review 2.1 Impacts of Rail Transit on Residential Development Nowadays, Scholars hold different opinions about the impact of rail transit on residual space. Some scholars believe that the improvement of traffic accessibility by rail transit system attracts real estate development and plays an important role in gathering people. Jin and Kim (2018) provided evidence that improvement of subway system affects the distribution of population and employment. Particularly, they found that the improvement of subway network has increased the living population of surrounding communities and shortened the average distance from people to subway stations by 1700 m. Kotavaara et al. (2011) examined the effect of accessibility by road and railway network on population change from 1970 to 2007 in Finland by using generalized additive models (GAMs). They found that the Finnish population has concentrated to areas with high road-based potential accessibility (like the rail station areas), especially since the opening in the Finnish economy in the 1990s. Hurst and West (2014) also provided evidence that multifamily housing increase by 11.3% in neighborhoods surrounding Metro Blue stations in Minneapolis during the construction, and 16.6% after opening the rail system. However, some scholars have questioned that rail transit can improve residential population density in the station surrounding areas. They believe that the high land price around the station and the rapid increase of commercial facilities would damage the growth of the residential population. Cervero and Guerra (2011) investigated the relationship between transit and urban densities in USA. The results showed that rail transit systems generated some negative externalities, such as noise and air pollution, which would make people move out to the station areas and harm population density. House price is an important factor that affects people’s choice of living place. Some studies

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assessed the impact of proximity to light rail transit stations on residential property values in Buffalo, New York, indicating that every foot closer to a light rail station increases average property values by $2.31 (using geographical straight-line distance) and $0.99 (using network distance) (Hess and Almeida 2007). What is worse, most of the people living in the narrow houses around the station are low-income people, who rely on rail transit for commuting. The high-income people live in low-density communities or villas in the suburbs, and they travel by cars. Therefore, the increasingly high house rent around the site makes the low-income people have to move farther and farther away from the station, resulting in the decline of the residential population density around the station. In addition, commercial facilities also have a negative on population density around the stations. Dueker and Bianco (1998) surveyed the Eastside MAX rail line’s effect in Portland, OR, on residential density between 1986 and 1995. They found that the net residential population density declines by 1.60% in the station surrounding areas due to a significant increase in commercial facilities. 2.2 Impacts of Rail Transit on Spatial Distribution of Employment At present, there are also many opinions about the impact of rail transit on spatial distribution of employment. Most scholars believe that rail transit can affect the behavior of employees and stimulate the economy and industrial agglomeration by improving traffic accessibility, which can improve the employment density around the station. They believe that when transportation is well-developed, employees are willing to commute longer distances, more people will enter the labor market, and jobs will be transferred to places that are easier to reach and more productive. In addition, the improvement of rail transit will affect the urban spatial relationship, making some industries gather around the station, resulting in agglomeration effect (Baumont et al. 2004). Bothe, Hansen (2018) analyzed the intra-urban employment growth by workplace in regard to the opening of the Copenhagen Metro in 2002. Their results uncovered that in 10 years before the opening of the metro, the employment growth rate in the metro service areas is lower than that of in the non-metro service areas. However, in 10 years after the opening of metro, the employment growth rate in the metro service areas is much higher than that of in the non-metro service areas. Yu et al. (2018) used logistics models to reveal the relationship between rail transit proximity and industrial agglomeration, and they demonstrated that rail transit is positively associated with the clusters of the retail and knowledge sectors. Similarly, Credit (2018) examined the impact of Phoenix’s light rail system on new firm formation in specific industries by using adjusted-interrupted time series (AITS) regression. The findings showed that transit adjacency is worth an 88% increase in knowledge sector new starts, a 40% increase in service sector new starts and a 28% increase in retail new starts at the time the system opened, when compared with automobile-accessible control areas. However, a minority of scholars point out that rail stations tend to be set in areas with high employment density and areas where high-income people work, so rail transit can not improve the employment density around the station. Schuetz (2015) found that new station openings were not significantly associated with differences in retail employment in three of the four MSAs. Canales et al. (2019) also demonstrated that there is no significant relative increase in the level of employment in neighborhoods near rail stations

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post opening of the first light rail line in Charlotte, NC. Particularly, they argued that the line connects to areas with significantly higher shares of high-wage workers and industries, which may not meet the goal of creating more employment opportunities around the stations. In general, the extent of the impact of rail transit on the population and employment density around the stations can vary from place to place, mainly depending on the spatial contexts. For example, rail transit systems in lower density or auto-oriented cities might not attract more population and employment to redistribute around the stations (Billings 2011; Handy 2005).

3 Data and Methodology 3.1 Study Area As one of the earliest cities in China to build rail transit system, Tianjin Line 1 was opened as early as 1984. After that, Line 2, Line 3, Line 5 and Line 6 connecting the six urban districts of the center and line 9 connecting the two city centers were successively put into operation. At present, Tianjin has formed a three-dimensional urban rail transit network in the central area. Therefore, the study area of this paper is 11 administrative districts covered by rail transit network in Tianjin: Heping, Hexi, Hedong, Hebei, Hongqiao, Nankai, Beichen, Dongli, Xiqing and Jinnan Districts and Binhai New Area. As shown in Fig. 1, the study area is divided into three circles: ➀ six central districts, consisting of Heping, Hexi, Hedong, Hebei, Hongqiao and Nankai Districts; ➁ four districts around the center, consisting of Beichen, Dongli, Xiqing and Jinnan Districts; ➂ Binhai New Area. In 2019, there were 145 rail transit stations in Tianjin, including 82 stations in six central districts, 56 stations in the four districts around the center and 7 stations in Binhai New Area. 3.2 Data The mobile phone signaling data in this paper comes from China Unicom, and the data observation period is from November 1 to November 30, 2019. By tracking the users’ trail for one month, the users’ living and working places are extracted. Residential users refer to mobile phone users who are cumulatively observed to live in the same place for more than half of the number of days in one month. That kind of mobile phone user is defined as a stable residential user. To judge whether a user lives in a certain place for a day, we should analyze whether they have stayed in the same place for more than 4 h during the night (21:00 p.m. to 8:00 a.m. the next day). Working users refer to the mobile phone users who are cumulatively observed to work in the same place for more than half of the normal working days in one month. That kind of mobile phone user is defined as a working user. To judge whether a user works in a certain place for a day, we should analyze whether they have stayed in the same place for more than 4 h during the daytime (9:00 a.m. to 17:00 p.m.). The floating population, the elderly, children, housewives and other unemployed people have no place of employment, and the rest have a corresponding place of employment.

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Fig. 1. Distribution of Tianjin rail transit lines

Then, by dividing the grid, this paper puts the living population and working population data which are identified by the mobile phone signaling data into the 500 m * 500 m grids. In the following research, when the buffer area intersects with grid data, first we need to establish Tyson polygon to divide the overlapping part of the buffer area. Then we should intersect the divided buffer area with grid data and eliminate the part of grid area whose area is less than 50%. Finally, we need to overlay the population grid data to complete the grid with an area greater than 50% of the grid area at the intersection and correct the boundary of the buffer zone. 3.3 Methodology The research methods in this paper are as follow: (1) Coverage rates of residents and jobs within rail stations. It enables us to measure the relationship between rail transit and the distribution of residents and jobs. The residential or job coverage rate in this paper is the percentage of the number of residents or jobs within the buffer zone of the rail stations to the number of the total residents or jobs in the city. The equation can be presented as follows: Residential/job coverage rate =

the number of residents/jobs within the buffer zone of the rail stations × 100 the number of the total residents/jobs in the city

When calculating, we first determine the buffer zone as 500 m and 800 m. Then, we count the residents and jobs within the buffer zone of 145 rail stations in Tianjin. Finally, we calculate the percentage to obtain the residential and job coverage rates. The greater the coverage rates are, the better coupling degree between rail transit and jobs-housing space is.

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(2) Regression analysis. There are about 10 variables in this study. So we establish the residence and employment multiple linear regression models respectively to study what will affect the distribution of residential population and employment around the station through R language. Then we can accurately quantify the influence degree of each independent variable through standardized coefficients. The model equation in our study can be presented as follows: Y = β0 + β1 X1 + β2 X2 + . . . + βi Xi + μi where Y is the residential population density or employment density within 500 m of each station, β0 is the intercept of the model, Xi is the independent variable of the model, βi is the estimated coefficient associated with independent variable Xi and μi is the residual term of the model. In addition to studying these two models respectively, this paper also combines these two models to compare and study how to improve the jobs-housing relationship around the station.

4 Empirical Results 4.1 Coupling Relationship Between Rail Transit and Jobs-Housing Distribution in Tianjin City Pan et al. (2007) determined 500 m (walking distance of 10 min) as the core influence range of rail transit stations in the Chinese context. In addition, Wang (2019) uncovered the influence range of rail transit stations ranges from 710 m to 910 m by using questionnaire in Jiangsu. Therefore, this study adopts 500 m and 800 m as the buffer zones for research. After calculation by coverage rate equation, the total number of residents within 800 m of the station is 3908089 and the total number of jobs within 800 m of the station is 2043700. Therefore, the residential coverage rate within 800 m of the station is 25.036% and the job coverage is 22.795%. The total residents within 500 m of the station is 1983911 and the total jobs is 1069232. Therefore, the residential coverage rate within 500 m of the station is 12.266% and the job coverage rate is 11.926%. So we could see that the attraction of rail transit stations to the residents is slightly better than that of jobs. This is because the employment space has higher requirements for the agglomeration effect around the station than the residual space. Table 1 shows how China’s urban comprehensive transport system planning standard divides the minimum coverage rate of stations within 800 m of the station according to different urban population sizes. According to this standard, the minimum coverage rate in Tianjin is 65%. However, the residential and job coverage rates are 25.036% and 22.795% respectively, far lower than 65%. We can find that there is still a large blank area in the coverage of jobs and residents in Tianjin rail transit network. This is because the rail transit mileage in Tianjin is insufficient, and the rail transit network is concentrated in the central urban area, and other areas are very lack of rail transit. In addition, we calculate the residential and job coverage rates of 11 districts in the study area. Table 2 lists the results of 11 districts. The results show that the job coverage of rail transit in six central districts is significantly better than that of the residential

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J. Zhou and Y. Lai Table 1. The standard for urban comprehensive transport system planning

Urban population sizes (10,000 people)

Population density in center area (10,000 persons/km2 )

The minimum coverage of stations within 800 m (%)

≥1000

1.0–2.0

65

500–1000

0.8–1.2

50

300–500

0.5–1.0

35

150–300

0.3–0.8

20

coverage, while the residential coverage of rail transit in the four districts around the city and Binhai New Area is slightly better than that of the employment coverage. This is because a large number of commercial and office land are planned around the rail stations in the central urban area, and industrial clusters have been formed around the station according to the positions of each district. Table 2. The residential and job coverage rates in each district Sub-regions

Administrative Districts

Residential coverage (%)

Job coverage (%)

Six central districts

Nankai

37.43

45.34

Hongqiao

22.70

26.63

Hexi

38.45

42.65

Hedong

37.35

51.19

Four districts around the center

Binhai New Area

Hebei

35.96

42.37

Heping

76.93

82.55

Sub-total

38.52

48.71

Xiqing

7.74

7.66

Jinnan

4.85

4.19

Dongli

10.47

7.77

Beichen

11.66

10.64

Sub-total

8.73

7.66

Binhai New Area

3.01

2.28

12.27

11.93

Total

As the above scope of study mainly covers the whole city of Tianjin, the impact scope of rail transit is actually relatively limited. Therefore, the minimum boundary geometry is set in Arc-GIS (Version 10.2) to obtain the envelope range of Tianjin rail transit network for analysis.

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The average residential population density in Tianjin is 1327 persons per km2 , and the average employment density is 762. While the average residential population density within the envelope of rail transit network is about 9208 persons per km2 , and the employment density is about 4784. So we can find that the residential population density within the envelope of rail transit network is about 6.94 times larger than that of in the Tianjin city, and the employment density within the envelope of rail transit network is about 6.28 times larger than that of in the Tianjin city. When studying further within 500 m around the rail transit stations, we can find that the average residential population density around 145 stations is 19314 persons per km2 , and the average employment density is 10401. Hence, the residential population density around the rail transit stations is about 2.10 times larger than that of in the envelope range, and the employment density around the rail transit stations is about 2.17 times larger than that of in the envelope range. In general, Tianjin Rail Transit has an obvious effect on the aggregation of resident and jobs around the stations. 4.2 Influencing Factors on the Distribution of Population and Employment Around Rail Transit Stations 4.2.1 Descriptive Statistics The descriptive statistics for 145 stations are shown in Table 3. In this research, we quantify the accessibility as the distance from city center, road density within 500 m and the number of bus routes within 500 m. We also set the average housing price, residential population density and employment density as socioeconomic variables. Additionally, we use the proportions of residential land, commercial land, industrial land and office land as land use variables. The road density within 500 m is equal to the ratio of the total length of all types of roads in the study area to the area of the study area, so the road density is calculated based on the following equation: n ϕi li /π R2 A2 = i=1

where ϕi is the weight of different types of roads, 3 for national highway, 2 for provincial highway, 1 for others, li is the length of the road, R is the station radiation radius. 4.2.2 Regression Results Firstly, we study the influencing factors of residents around the station based on the variables such as distance from city center, road density within 500 m, the number of bus routes within 500 m, average house price within 500 m, employment density within 500 m and the proportions of residential land and commercial land within 500 m. To ensure that there is no multicollinearity among independent variables, we first calculate the variance inflation factor (VIF). The variables associated with VIF values of greater than 5 are removed, and the VIF of all variables is less than 5. So no variable need to be removed. The results are shown in Table 4. The residence model has an adjusted R2 of 0.648, indicating that it explains almost 65% of the residential population density around the stations in Tianjin. The employment

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J. Zhou and Y. Lai Table 3. Variables and descriptive statistics

Variable description

Maximum

Minimum

Mean

Median

Residential population density within 500 m (persons/km2 )

74881.4

10.2

16838.7

12834.7

Employment density within 500 m (persons/km2 )

58395.82

57.38

9403.23

6150.22

Distance from city center (km)

21.064

0.000

7.102

5.938

Road density within 500 m (km/km2 )

25.9887

0.4899

10.7429

10.1795

The number of bus routes within 500 m

11.000

0.000

4.241

4.000

Average house price within 500 m (×103 )

63.172

7.392

26.537

24.907

The proportion of residential land within 500 m

0.8864

0.0000

0.2813

0.2874

The proportion of commercial land within 500 m

0.5646

0.0000

0.2408

0.2556

The proportion of industrial land within 500 m

0.55980

0.0000

0.07130

0.01188

The proportion of office land within 500 m

0.30606

0.0000

0.06121

0.04247

density, the proportion of residential land and the proportion of commercial land within 500 m buffer zone are statistically significant as explanatory variables. However, distance from city center, road density within 500 m, bus numbers within 500 m and average house price within 500 m are not statistically significant. The higher the employment density is within 500 m, the higher the residential population density will be. This is because the high accessibility of the rail station can attract a large number of people and commercial facilities to gather around the station, which brings a lot of jobs. With the rapid development of the station areas, the land around the station is gradually saturated. The station need to further expand the land to the surrounding areas. Finally, the station will develop into a new community with concentrated residents and jobs. The higher the proportions of residential and commercial land, the more the residential population density will be. Residential land is the basis for the construction of residential districts and the aggregation of residents. So there is a significant positive correlation between the proportion of residential land within 500 m and the residential population density around the station. Besides, when people choose living places, perfect commercial service facilities have a great attraction to them. The allocation level of commercial and entertainment facilities in this area has gradually become one of the important factors. Within the walking range, people hope to lead the convenient life including living, shopping, leisure and entertainment.

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Table 4. Results of residence regression Model

Unstandardized Coefficients

Standardized Coefficients

Sig

Constant

−92.839

NA

0.98550

Distance from city center

−3.581

−0.001014

0.98651

Road density within 500 m

−141.881

−0.040893

0.52008

The number of bus routes within 500 m

−94.111

−0.016325

0.81620

Average house price within 500 m

−135.726

−0.082625

0.26669

Employment density within 1.082 500 m

0.744479

5.17 × 10−15 ***

The proportion of residential land within 500 m

18373.734

0.186739

0.00339 ***

The proportion of commercial land within 500 m

28879.095

0.267436

0.00022 ***

Model

Adjusted R2 = 0.648

Note: *p < 0.1; **p < 0.05; ***p < 0.01

Then, we study the influencing factors of employment around the station based on the variables such as distance from city center, road density within 500 m, the number of bus routes within 500 m, average house price within 500 m, residential population density within 500 m and the proportions of commercial land, industrial land and office land within 500 m. Similarly, the variables associated with vif values of greater than 5 are removed, and the vif of all variables is less than 5. Therefore, no variable need to be removed. The results are shown in Table 5. The employment model has an adjusted R2 of 0.7689, indicating that it explains over 75% of the employment density around the stations in Tianjin. Road density, bus numbers, average house price, residential population density, the proportions of industrial and office land within 500 m buffer zone are statistically significant as explanatory variables. But distance from city center and the proportion of commercial land within 500 m are not statistically significant. There is a positive correlation between road density and the number of bus routes within 500 m buffer zone and employment density. The more the road density and bus routes within 500 m are, the higher the accessibility of the rail station will be. This is more likely to attract industrial agglomeration to improve employment density. The higher the average house prices are, the higher the employment density will be. In China, house prices are closely related to location. The better the location is, the higher the house prices are. In areas with better location (such as CBD), enterprises are

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concentrated and there are many jobs. So there is a positive correlation between average house price and employment density. The residential population density plays the most important role in the increase of employment density. This is because residents have some daily needs, like shopping and dinning. So the more residents are around the station, the more commercial facilities gather to meet these needs. This can promote the growth of jobs. The proportions of industrial land and office land within 500 m buffer zone are positively associated with the employment density, suggesting that the industrial land and office land can attract firms. However, there is no significant correlation between the proportion of commercial land and employment density. The explanation is the other two land use variables (the proportion of industrial land and the proportion of office land) play a more significant role in the increase of employment density. When we use the proportion of commercial land within 500 m as the only independent variable to establish the model, this variable is statistically significant. When combining the proportions of commercial land, industrial land and office land within 500 m as a single variable, this variable is also statistically significant. With these three variables included in the model, industrial land and office land are the dominant factors positively influencing the employment density while commercial land shows no association. Table 5. Results of employment regression Model

Unstandardized Coefficients

Standardized Coefficients

Sig

Constant

−16410

NA

3.16 × 10−9 ***

Distance from city center

−53.94

−0.0222

0.65885

Road density within 500 m

229

0.095887

0.07061 *

The number of bus routes within 500 m

543.1

0.136863

0.01893 **

Average house price within 500 m

482.7

0.426889

3.35 × 10−12 ***

Residential population density within 500 m

0.3756

0.545612

1.23 × 10−15 ***

The proportion of commercial land within 500 m

1482

0.019945

0.74349

The proportion of industrial 12980 land within 500 m

0.163603

0.00819 ***

The proportion of office land within 500 m

17920

0.114055

0.02011 **

Model

Adjusted R2 = 0.7689

Note: *p < 0.1; **p < 0.05; ***p < 0.01

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5 Conclusions and Implications This empirical study of the coupling relationship between metro station areas and urban population intensity in Tianjin has several important findings. First, rail transit services have an obvious concentration effect on the residential population and employment. From the perspective of function, the coverage of rail transit to residence is slightly better than that of employment. But there is still a large blank area covered by rail transit in Tianjin. Second, as the regression results show, the population density is positively associated with the employment density, the proportions of residential and commercial land within 500 m buffer zone. The employment density is positively associated with road density, the number of bus routes, average house price, residential population density, the proportions of industrial and office land within 500 m buffer zone. Additionally, distance from city center has no significant effect on the residential population density and employment density. This result is in line with the research of AlQuhtani and Anjomani (2021). This is arguably due to the fact that other accessibility factors (road density and the number of bus routes) play an more significant role in the increase of population density and employment density. The population density and employment density around the metro stations are roughly 2.10 times as large as the envelope range. This aligns with the findings of Chen and Pan (2020), who revealed that the population density and employment density are about 2.2 times as large as the envelope range in Shanghai. This result reflects that rail transit plays an important role in gathering population and employment in the Chinese context. By comparing the two models, it is found that the attraction of employment density to residents is greater than that of residents to jobs. This result is broadly consistent with those reported by Jin and Kim (2018) which showed that the effect of employment on population is much larger than the effect of population on employment. These findings have some important policy implications. First, high accessibility of rail station should be encouraged as a key factor to improve the residential population density and employment density within the station surrounding areas. Therefore, when planning rail transit stations, we should make transportation planning around these stations, such as road planning and bus route planning to the city centers. Second, commercial land is very effective in attracting residents, industrial land and office land are very effective in attracting jobs. So commercial land and office land should be more strongly integrated with the planning for metro stations, and we should make high intensity development around the stations. Third, the attraction of station areas dominated by residential districts to jobs is very limited. The employment center areas with good location and complete supporting facilities can attract a large number of residents to live here. Compared with creating jobs in the station areas dominated by residential districts, it’s more beneficial to develop residential areas in the station areas with concentrated jobs. Acknowledgements. We thank the National Natural Science Foundation of China (72174122), National Natural Science Foundation of Guangdong Province (2022A1515011816), and Shenzhen Science and Technology Plan (20200813170728001) for funding this study.

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Model Development to Link Cultural Intelligence and Individual Work Performance: Mediator and Moderator Considerations Djoen San Santoso1 , Jungang Luo1(B) , Hecai Song2 , and Miao Li3 1 School of Engineering and Technology, Asian Institute of Technology, Pathum Thani,

Thailand [email protected] 2 China Construction Civil Engineering CO., Ltd., Beijing, China 3 CSCEC (China State Construction Engineering Corporation) International Construction CO., Ltd., Beijing, China

Abstract. The success of international construction projects depends on the effective performance of construction expatriates. However, cultural issues often hinder their performance in the new overseas working environment. To address this challenge, organizational supports in the process of adaptation and integration are crucial, but often overlooked. This study aims to develop a research model linking cultural intelligence to the individual work performance of construction expatriates by considering other constructs that contribute to this association based on a systematic literature review. Our findings suggest that cultural adjustment plays a mediating role in the relationship between cultural intelligence and individual performance, while perceived organizational support may have a moderating effect. The proposed research model provides a framework for further analysis, enabling organizations to better support construction expatriates in their adaptation and integration process. Keywords: Culture · performance · construction expatriate · overseas assignment · international project · work integration

1 Introduction Technological development has made international communication and cooperation much easier than our ancestors could ever have imagined, and these advances have opened up a new globalization world to us. In the construction industry, a considerable number of professionals have been assigned abroad due to the booming of international projects [1]. It is believed that cultural issue is one of the most significant factors associated with project performance [2]. Due to its labor-intensive nature [3], the cultural diversity in the construction project could have negative effects such as fragmentation, antagonism, mistrust, poor communication, short-term mentality, blame culture, casual approaches to recruitment, machismo, and sexism [4]. Even so, there is still a fundamental lack of appreciation of the importance of culture for construction management [5]. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 720–726, 2023. https://doi.org/10.1007/978-981-99-3626-7_55

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In construction, usually, the practitioners attach great importance to the bidding strategy and business negotiation; After the bid is awarded, they are accustomed to focusing on the control of quality, cost, and time limit in the process of project implementation. The traditional approaches to managing domestic construction projects might be effective. However, in the case of international construction projects, previous research found that cultural differences could be the resources of contractual problems which in turn may lead to schedule delays, increased tension, and breakdown of business relationships [6]. Aside from the construction sector, cultural theories have been widely applied in many industries. Scholars found out that people’s behavioral intention to adopt AR (augmented reality) is dependent on cultural traits [7]; Distinctive culture and language could be the explanation for school bullying and cyberbullying [8]. Meanwhile, culturerelevant concepts have been developed by researchers, such as cultural intelligence (CQ), organizational culture (OC), cultural adjustment (CA), etc. [9–11]. Subsequent studies have been conducted, for example Groves and Feyerherm [12] indicated leaders’ overall CQ has a positive relationship to leader performance and team performance in cross-cultural organizations; Wambugu [13] suggested OC helps employees to direct their ideas toward the set of principles developed. The existing literature was mostly conducted from only one aspect of an individual or organizational level. However, studies that simultaneously considered these two perspectives are rare. So, this raised a new question, what are the relationships between these concepts, especially considering the interaction of CQ and OC in one research model, and how do they affect the expatriates’ performance? In order to understand this question in the construction industry, a research model aiming to explore the relationships between CQ, OC, CA, perceived organizational support (POS), and individual work performance (IWP) is proposed based on a rigorous literature review.

2 Literature Review Kluckhohn defined culture as patterned ways of thinking, feeling, and reacting. It is acquired and transmitted mainly by symbols, constituting the distinctive achievements of human groups [14]. Thus, the majority of people in the culture will do what is appropriate, and culture actually becomes a way of adapting to the existing environment [15]. As a complex and uncertain concept, Hofstede [14] considered culture as a kind of Mental Software, which is something you learn from the social environment. Cultural differences include language, way of thinking, way of communication, and social relations. In this section, the cultural-related concepts (CQ, OC, CA), as well as their possible relationships with POS and IWP from literature, are revealed as follows. 2.1 Cultural Intelligence (CQ) CQ was developed to analyze the adaptive capability of groups or individuals to new cultural settings [16]. Ang et al. [9] highlighted it as a new construct of intelligence and conceptualized it in an intercultural context which consists of four general facets: metacognitive, cognitive, motivational, and behavioral. As a type of intelligence, it enables people to “look beyond their cultural lens” and is critical for cross-culture situations [17].

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In facilitating quantification, some measurements have been suggested. The cultural intelligence scale (CQS) enjoys popularity, and then some revised versions are adopted for specific situations, such as Business Cultural Intelligence Quotient (BCIQ) by Alon et al. [18] and Short Form Measure of Cultural Intelligence (SFCQ) by Thomas et al. [19]. As CQ was defined as a person’s capability for successful adaptation to new cultural settings, one of the most thoroughly researched outcomes of CQ is performance [17]. Previous studies have shown that CQ has both direct and indirect effects on performance. For direct effects, Groves and Feyerherm [12] found CQ was positively related to both leader and team performance on culturally diverse working teams. For indirect effects, Lee and Sukoco [20] indicated that CQ has an indirect effect on performance through cross-cultural adjustment by examining the relationship among them, which means CA could mediate the effects of overall CQ on expatriates’ performance. 2.2 Organizational Culture (OC) OC is perceived as a system of common symbols and meanings, providing shared rules, and managerial cognitive and affective aspects of association in an organization [21]. Cameron and Quinn [10] defined OC as the dominant leadership styles, success languages, values, standards, and beliefs that make an organization unique. Meanwhile, they developed a framework built upon a theoretical model called the “Competing Values Framework.” This framework, based on four dominant organizational culture types (Clan, Adhocracy, Market, and Hierarchy), refers to whether an organization has a predominant internal or external focus and whether it pursues resilience and individuality or stability and centralization. Previous studies suggested that different dominant OC could vary from one organization/industry to another. For example, Nummelin [22] indicated that in the international construction sector, market and hierarchy cultures are usually relatively stronger than adhocracy and clan cultures. Besides, OC was also found associated with project delay and effectiveness in construction projects [23]. Moreover, there are some interesting findings regarding the correlations between OC and CQ. For example, in a study about university students in Hungary, Balogh et al. [24] found that those students with a high degree of CQ would like to work in an adhocracy OC, and the higher a student’s CQ scores are, the more flexible focused OC they prefer. While Li et al. [25] indicated that differences in organizational culture have a moderating effect on the impact of Chinese employees’ CQ on knowledge sharing and sustainable innovation behaviors. 2.3 Cultural Adjustment (CA) Professionals in a new cultural context need time to establish patterns for their adjustment. Black and Stephens [26] defined CA as the expatriates’ ability to fit into the new cultural setting measured by the amount of difficulty experienced in the management of everyday situations. They proposed a three-dimensional construct for cross-cultural adjustment, namely general adjustment, interreaction adjustment, and work adjustment. As noted earlier, CA could mediate the effects of CQ on performance. Such as, Jyoti &

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Kour’s [27] study found a mediating path from CQ through CA to task performance with managers working in banks in India. 2.4 Perceived Organizational Support (POS) POS refers to people’s global beliefs about the extent to which the organization cares about their well-being and values their contributions [28]. It indicates that the organization cares about the employee and is concerned for their well-being. Previous studies have found that POS has moderating effects. In Lee and Kartika’s [29] study, POS has served as an important factor that could moderate the influences of the antecedents on expatriate adjustment and the influences of adjustment on expatriation performance. 2.5 Individual Work Performance (IWP) As noted earlier, the correlations between IWP and CQ, CA, and OC were investigated by scholars. Regarding IWP, Previous studies have two important opinions on it, 1) its concept should be wider than work productivities, and 2) the definition of IWP should be in terms of individuals’ behaviors or actions rather than the results [30]. Campbell [31] defined IWP as behaviors or actions that are relevant to the goals of the organization. Numerous approaches have been made to measure IWP, from generally applicable scales to industrial or positional specifical ones. For example, Koopmans et al. [30] summarized the four dimensions, including task performance, contextual performance, counterproductive behaviors, and adaptive performance, which are very frequently used to describe IWP. While Leung et al. [32] suggested that task performance, interpersonal performance, and organizational performance are the facets that could be adopted to evaluate construction project managers’ performance.

3 Research Framework The objective of this study is to explore the relationships between CQ, OC, CA, POS, and IWP. From the previous studies, cultural theories and relevant concepts have been widely applied in many industries. Still, comprehensive investigations of CQ, OC, CA, POS, and IWP regarding construction professionals have rarely been conducted. In addition, unlike other industrial fields, the international construction industry provides a unique work context. Multi-culture and labor-intensive are the nature of international construction projects [2]. In this study, a systematic literature review has been conducted, and then the research model was proposed based on the findings of previous researchers. Our research framework as shown in Fig. 1 is demonstrated how the concept was developed. Scholars have studied the effects of CQ on IWP which, as noted earlier, could be through the mediating and moderating effects of CA and POS. In addition, OC could also be associated with CQ and IWP. On the one hand, individuals with different CQ would prefer different dominant OC [24]; on the other hand, organizations with a certain dominant OC might also have a preference for the CQ level of employees. In this study, IWP was considered the dependent variable, and OC was considered the independent variable. While POS was regarded as moderating variable in the relationships between

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Research Framework

Journal articles

Literature review

Previous researches

Text books

Problem statement

Research objectives

Identifying CQ,OC, CA, POS, and IWP

Proposing the research model

Fig. 1. Research Framework

CQ and IWP, CA was regarded as mediating variable between CQ and IWP. Then, the correlations of OC and CQ, and OC and IWP were also considered. The conceptual model is proposed as shown in Fig. 2.

Fig. 2. Conceptual Model

4 Discussions This study offers a novel contribution to the literature on international construction professionals by proposing a conceptual model that incorporates five important constructs: CQ, OC, CA, POS, and IWP. The model considers the interaction of CQ and OC, as well as their effects on IWP, based on a systematic literature review. While prior research has investigated these constructs at either the individual or organizational level, our study recognizes that IWP issues in construction projects often involve both organizational and individual dimensions. By proposing a comprehensive model that captures these interrelated factors, our study provides a more nuanced perspective that is relevant to both academia and industry.

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Moreover, we note that the relationship between CQ and OC is likely to be mutually reinforcing, as individuals with higher CQ are likely to exhibit greater adaptability and learning ability, leading to more positive OC perceptions, and vice versa. Furthermore, our review indicates that cultural adjustment can mediate the relationship between CQ and IWP, while POS may moderate this relationship. These findings further highlight the importance of considering both individual and organizational factors in enhancing the performance of construction expatriates. Overall, the proposed model offers a valuable framework for future research on international construction professionals. However, we acknowledge that our study has limitations, such as the focus on only five constructs and the use of a literature review rather than empirical data. Thus, we encourage future researchers to further refine the model by exploring other relevant constructs and conducting empirical studies to validate the proposed relationships.

References 1. Chan, I.Y.S., Leung, M.Y., Liang, Q.: The roles of motivation and coping behaviours in managing stress: qualitative interview study of Hong Kong expatriate construction professionals in mainland China. Int. J. Environ. Res. Public Health 15(3), 561 (2018) 2. Ankrah, N.A.: An investigation into the impact of culture on construction project performance, vol. 405 (2022) 3. Kim, S., Kim, J.D., Shin, Y., Kim, G.H.: Cultural differences in motivation factors influencing the management of foreign laborers in the Korean construction industry. Int. J. Project Manage. 33(7), 1534–1547 (2015) 4. Konanahalli, A., Oyedele, L.O., Von Meding, J.K., Spillane, J.P.: UK expatriate’s skills and competencies influencing their cross-cultural adjustment on international architectural engineering and construction assignments, pp. 762–770 (2011) 5. Fellows, R.: Understanding organisational culture in the construction industry. Constr. Manag. Econ. 28(8), 898–900 (2010) 6. Chan, E.H.W., Suen, H.C.H.: Dispute resolution management for international construction projects in China. Manag. Decis. 43(4), 589–602 (2005) 7. Jung, T.H., Tom Dieck, M.C.: Augmented reality, virtual reality and 3D printing for the cocreation of value for the visitor experience at cultural heritage places. J. Place Manage. Dev. 10(2), 140–151 (2017) 8. Smith, P.K.: Bullying: definition, types, causes, consequences and intervention, social and personality psychology. Compass 10(9), 519–532 (2016) 9. Ang, S., et al.: Cultural intelligence: its measurement and effects on cultural judgment and decision making, cultural adaptation and task performance. Manag. Organ. Rev. 3(3), 335–371 (2007) 10. Cameron, K.S., Quinn, R.E.: Diagnosing and Changing Organizational Culture, Revised edn. Addison-wesley (2006) 11. Black, J.S., Mendenhall, M., Oddou, G.: Toward a comprehensive model of international adjustment: an integration of multiple theoretical perspectives. Acad. Manag. Rev. 16(2), 291–317 (1991) 12. Groves, K.S., Feyerherm, A.E.: Leader cultural intelligence in context: Testing the moderating effects of team cultural diversity on leader and team performance. Group Org. Manag. 36(5), 535–566 (2001)

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13. Wambugu, L.W.: Effects of organizational culture on employee performance (case study of wartsila-kipevu ii power plant). Eur. J. Bus. Manage. 6(32), 80–93 (2014) 14. Hoftstede, G.: Comparing Values, Behaviors, Institutions and Organizations Across Nations 596 (2001) 15. Kim, Y.: Becoming Intercultural: An Integrative Theory of Communication and CrossCultural Adaptation (2012) 16. Earley, P.C., Ang, S.: Cultural Intelligence: Individual Interactions Across Cultures (2003) 17. Fang, F., Schei, V., Selart, M.: Hype or hope ? A new look at the research on cultural intelligence. Int. J. Intercult. Relat. 66, 148–171 (2018) 18. Alon, I., Boulanger, M., Meyers, J., Taras, V.: The development and validation of the business cultural intelligence quotient. Cross Cult. Strat. Manage. 23(1), 78–100 (2016) 19. Thomas, D.C., et al.: Cultural intelligence: a theory-based, short form measure. J. Int. Bus. Stud. 46(9), 1099–1118 (2015) 20. Lee, L.Y., Sukoco, B.M.: The effects of cultural intelligence on expatriate performance: the moderating effects of international experience. Int. J. Hum. Resour. Manag. 21(7), 963–981 (2010) 21. Cheney, G., Christensen, L.T., Conrad, C., Lair, D.J.: The SAGE Handbook of Organizational Discourse. SAGE Handb. Organ. Discourse (2004) 22. Nummelin, J.: Measuring organizational culture in the construction sector - finnish sample, pp. 1–12 (2006) 23. Arditi, D., Nayak, S., Damci, A.: Effect of organizational culture on delay in construction. Int. J. Project Manage. 35(2), 136–147 (2017) 24. Balogh, A.: Relationship between organizational culture and cultural intelligence. Manage. Mark. 6(1), 95 (2011) 25. Li, J., Wu, N., Xiong, S.: Sustainable innovation in the context of organizational cultural diversity: the role of cultural intelligence and knowledge sharing. PLoS ONE 16(5), e0250878 (2021) 26. Black, J.S., Stephens, G.K.: The influence of the spouse on American expatriate adjustment and intent to stay in pacific rim overseas assignments. J. Manag. 15(4), 529–544 (1989) 27. Jyoti, J., Kour, S.: Cultural intelligence and job performance: an empirical investigation of moderating and mediating variables. Int. J. Cross Cult. Manage. 17(3), 305–326 (2017) 28. Eisenberger, R., Huntington, R., Hutchison, S., Sowa, D.: Perceived organizational support. J. Appl. Psychol. 71(3), 500–507 (1986) 29. Lee, L., Kartika, N.: Expert systems with applications the influence of individual, family, and social capital factors on expatriate adjustment and performance : the moderating effect of psychology contract and organizational support. Expert Syst. Appl. 41(11), 5483–5494 (2014) 30. Koopmans, L., Bernaards, C.M., Hildebrandt, V.H., Schaufeli, W.B., De Vet Henrica, C.W., Van Der Beek, A.J.: Conceptual frameworks of individual work performance: a systematic review. J. Occup. Environ. Med. 53(8), 856–866 (2011) 31. Campbell, J.P.: Modeling the performance prediction problem in industrial and organizational psychology (1990) 32. Leung, M., Chan, Y.-S., Olomolaiye, P.: Impact of stress on the performance of construction project managers. J. Constr. Eng. Manag. 134(8), 644–652 (2008)

Understanding Causes and Resolutions of Construction Disputes: A Case Study Haijun Gu(B) , Shang Zhang, and Qianqian Wang Department of Construction Management, Suzhou University of Science and Technology, Suzhou, China [email protected] Abstract. Although there are a few empirical research into the dispute problem in the Chinese construction industry, the causes and effective resolution methods are unknown in the context of COVID-19 pandemic. To investigate the causes and resolutions of construction disputes in China, this research employs the methods of literature review, questionnaire survey and interview. 93 professionals’ viewpoints were used for empirical analysis. The results indicate that the COVID-19 (4.20) has a significant impact on construction disputes, and contractual causes (4.00) are the most common disputes. Diverse stakeholders’ perceptions of the causes of construction disputes varied significantly. Furthermore, most disputes are resolved by negotiation (35.5%) and mediation (49.5%). This research provides professionals with invaluable insights into construction dispute’s types and dispute resolution systems. Moreover, it allows the various construction stakeholders to understand and take into consideration the perceptions of other participants when applying risk and dispute prevention techniques. Keywords: Construction disputes · Stakeholders · Causes · Resolutions

1 Introduction In recent years, construction disputes have occurred frequently in the construction industry. If the disputes cannot be resolved efficiently, it will have negative impacts on the implementation of the project. Data from China Judgement Online show that overall, the number of construction contract disputes increased by 2266.94% in 2021 compared to 2014, while in Suzhou, they increased by 1945.45%. Construction disputes have always been a serious issue in the industry that urgently needs to be studied. However, research in related fields is relatively rare in China. There are just 16 first-tier publications, according to CNKI database which is the largest research database in China (retrieved using “construction dispute” in Chinese as the subject keywords). In addition, no research has been conducted under the context of COVID-19 pandemic, which has significant influences on the implementation of construction projects worldwide. As a result, this research first identified the causes of the construction disputes based on extensive literature review. Second, it summarizes a list of construction disputes that have been resolved in accordance with industry practices. Finally, this study investigated the main causes and most popular ways to resolve construction disputes using questionnaire survey and interview methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 727–738, 2023. https://doi.org/10.1007/978-981-99-3626-7_56

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2 Literature Review 2.1 Definition of Construction Disputes Diverse academics have different perspectives on construction conflicts because of the variety of factors causing disputes. Construction disputes tend to arise from contracts with ambiguous provisions (Tang and Jing 2020). According to Jia et al. (2019), construction disputes are conflicts of interest brought on by inadequate related policies. Guo and She (2013) believe that the construction dispute is the conflict arising from the contract parties’ failure to perform their contractual obligations. Construction disputes, according to Osei-Kyei et al. (2019), are any disagreements between people, groups, or organizations regarding their interests, goals, or priorities. Accordingly, this paper defines the construction dispute as the conflict of interest between the stakeholders caused by the management, contract, force majeure, and other causes. 2.2 Causes of Construction Disputes Based on the literature review, four categories of indicators were identified, and the results are as follows: (1) The managerial causes The managerial causes include a lack of management abilities, which frequently results in construction disputes (Awwad et al. 2016). These causes mostly consist of poor document management (Awwad et al. 2016), schedule delays (Tang and Jing 2020), delay in payment (Wen 2014), and project changes (Zhang et al. 2016). (2) The contractual causes Stakeholder causes (relating to the stakeholder’s low level of performance of the contract) and internal contract causes are the two main categories of contractual causes (which are related to the topic and requirements of the contract itself). Internal contract causes include incomplete or inaccurate information, unfair risk allocation, and conflicts between the owner’s technical specifications and the designs. Contradictory and erroneous information in the contract’s drafting will result in an unfair distribution of risks between the contractor and the owner (Zhang 2020). Stakeholder causes are mainly related to one party’s failure to understand or abide by the contractual obligations. One party’s failure to comprehend or perform its contractual duties is the main cause of disputes. The major reasons for construction disputes during contract execution are confusing contract terms and obligations (Wen 2014). In addition, during the construction process, the owner’s instructions were changed many times, and the owner’s supplied materials failed to be delivered on site in time (the construction conditions could not be provided as agreed in the contract), which also frequently result in disputes (Li and Jing 2013). (3) The force majeure causes The force majeure causes include bad weather, earthquake, epidemic, etc. (Xu 2020). The outbreak of COVID-19 has had a significant impact on the construction industry.

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Relevant control measures have led to the delay of the resumption of work, and the inflation in the prices of personnel, materials, and machines, which has increased the probability of construction disputes (Xu 2020). (4) Other causes Construction disputes has also been caused by changes in laws and regulations as well as differences in culture and values. On the one hand, the contract may not be able to be implemented due to changes in laws and regulations (Li and Jing 2013). On the other hand, because the construction is unique, the stakeholder relationships’ cooperation is just a temporary arrangement. Conflicts between various stakeholders may result from various organizational cultures (Ding 2012). 2.3 Construction Dispute Resolutions This paper divides construction dispute resolutions into negotiation, mediation, dispute review, arbitration, and litigation based on the dispute resolution mechanisms outlined in the Standard Form of Construction Contract for Construction Projects (GF-2013–0201) in China, and international literature (Osei-Kyei et al. 2019). (1) Negotiation Negotiation is a dispute resolution reached by both parties through mutual consultation, which does not involve the participation of third-party institutions or individuals (Osei-Kyei et al. 2019). Negotiation is typically the initial stage in resolving construction disputes since it is less expensive and promotes a more efficient resolution (Yang and Zhang 2012). Negotiation, however, is not always appropriate. Other options are frequently needed when the dispute is significant, and the cost involved are huge. (2) Mediation Mediation refers to a technique for resolving disputes in which a neutral third party negotiates a settlement between the parties after persuading them to do so in compliance with applicable laws or contractual obligations, fostering the parties’ understanding of one another (Xu 2018). Mediation is a voluntary, non-confrontational, informal, confidential, and non-binding method to resolve disputes. Typically, the third party has no right to compel the announcement of the mediation outcome. (3) Dispute review The dispute review is an international construction dispute resolution between mediation and arbitration. It is an independent review expert selected by each party during the project commencement or implementation stage to provide resolutions to the disputes between both parties (Chen 2019). The dispute review usually includes the Dispute Adjudication Board (DAB) and the Dispute Review Board (DRB). Unlike negotiation and mediation, the results reached by the dispute review are legally binding. (4) Arbitration Arbitration refers to a method in which the parties agree to submit the dispute to an arbitration institution, and the arbitrators judge and adjudicate the disputes (Osei-Kyei

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et al. 2019). Different from other resolutions, the parties in arbitration do not strive to reach a mutually agreed resolution but suggest their own resolutions. (5) Litigation Litigation refers to the way by which the disputed party sues the other party to the court with jurisdiction to solve the dispute (Mi 2012). In case of a dispute arising from the contract or related matters of the contract, the parties may bring a lawsuit with the people’s court with jurisdiction under the provisions of the special contract terms (Mi 2012). The outcome of litigation is legally binding on the parties. In addition, due to the high procedural and institutional nature of litigation, the procedure is often more complex than arbitration (Yang and Zhang 2012).

3 Research Method 3.1 Questionnaire Design The questionnaire was designed based on review of high-quality literatures. The questionnaire mainly includes two parts. Part one is to gather the basic information of the respondents. Part two is to solicit the perceptions of the respondents on dispute causes and dispute resolution methods. These include: the scale of the construction dispute causes (13 issues in total) and the construction disputes resolutions (30 questions in total). This part adopts 5-point Likert scale (i.e. 1 = very unimportant/totally disagree, 3 = neutral, and 5 = very important/totally agree). After the completion of the questionnaire design, a pilot survey was carried out among 6 professionals, which resulted in an improved questionnaire in wording and structure. 3.2 Data Collection To study the construction disputes, questionnaires were distributed to the construction practitioners in Suzhou, China. 109 questionnaires were collected which produced 93 valid samples. Among the respondents, the male professionals are 63.4%, and most of them aged 21–30 (55.9%). The type of organizations of the respondents is mainly the contractor (32.3%). Most of the respondents (51.6%) have worked over 6 years. Majority of the respondents (71%) had experience in construction disputes. 3.3 Interviews Six professionals with work experience in the dispute resolution of construction projects were invited in semi-structured interviews using the findings of the survey analysis (see Table 1 for sample details). Half participants from contractor and client were invited as the interviewees, because they are the main participants in the construction dispute (Wen 2014). In addition, half of participants we interviewed were from engineering consultant and law firm because they were more impartial. Hence, they can offer more comprehensive insights into interpreting the survey results. According to Zhang et al. (2022), during the interview process, first, respondents were given the survey’s primary findings before being asked to explain those results.

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Table 1. Interview participants Interview participant

Organization

Years of experience in dispute settlement

Participant 1

Contractor

5

Participant 2

Law firm

5

Participant 3

Engineering consultant

More than 20

Participant 4

Contractor

10

Participant 5

Engineering consultant

More than 20

Participant 6

Client

5

Selected quotations from these interviews were used to enrich the presentation of the following statistical results, providing possible explanations and important pointers for relevant future research. Second, they were requested to assess and confirm the findings of the quantitative survey and offer further commentary as needed. To examine the raw interview data, a content analysis technique and a deductive thematic analysis approach were both used. First, members of the research team became acquainted with all the perspectives gathered from the interviews. Second, significant viewpoints were filtered and rearranged under the heading of the thirteen factors. Third, to further explain and enrich the quantitative survey data, some critical qualitative results were used.

4 Results and Discussion 4.1 Reliability and Validity Tests Data analysis was conducted using the IBM statistical package for social science (SPSS). The analysis included reliability test using the Cronbach’s alpha (Wu 2019), and validity test using Kaiser Meyer Olkin (Wu 2019). The data analysis results show that the Cronbach’s alpha of the scale of construction dispute causes (0.868) and the construction dispute resolutions (0.952) are greater than 0.7, indicating that the scale has a high internal consistency (Wu 2019). Meanwhile, the Kaiser Meyer Olkin of the scales of construction dispute causes (0.816) and the construction dispute resolution (0.864) are greater than 0.7, indicating that the scales have good validity (Wu 2019). 4.2 Main Causes of Construction Disputes To analyze the main causes of construction disputes, this paper conducts a descriptive statistical analysis on the collected data, and the analysis results are shown in Table 1. The following findings can be drawn from Table 1: (1) The mean values of these causes are relatively high (all above 3.5), and the mean value of COVID-19 influence (4.20) is the highest among causes. First, it shows that the causes identified in this paper conform to the current situation of the Chinese

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Dimensions

Cause

Mean

SD

Rank

Managerial

Delay in payment

4.13

1.055

3

Schedule delay

3.97

1.016

8

Project changes

4.04

0.871

5

Poor document management

3.82

1.042

10

Total mean Contractual

3.99 The contractor fails to perform its obligations

4.19

0.970

2

The owner fails to perform its obligations

4.00

1.063

6

Unreasonable contract risk allocation

4.06

0.857

4

Contradictions in contract documents

3.99

0.866

7

Other stakeholders fail to perform its obligations

3.74

0.931

11

Total mean Force majeure

4.00 The influence of COVID-19

4.20

1.007

1

The influence of bad weather

3.53

0.996

12

Total mean Other Total mean

3.85 Changes in laws and regulations

3.88

0.942

9

Differences in culture and values

3.35

0.905

13

3.62

construction industry, as these causes all have influences on the occurrence of disputes. Second, it indicates that the modern construction projects are more likely to generate disputes due to the impact of the COVID-19 (Xu 2020). (2) The contractual cause is the main dimension leading to the construction dispute. In most cases, causes in the managerial dimension and those in the contractual dimension are interrelated. Further analysis indicates that: ➀ Unreasonable risk allocation and contradictions in contract documents are important causes for the contractor and the owner fail to perform their obligations. Firstly, unreasonable contract risk sharing will lead to the unforeseen risks of the contractor and reduce the contractor’s enthusiasm to perform the contract (Zhang 2020). Secondly, because the bidding documents and the construction contract are the performance of the true intention of both parties, when the contract documents are contradictory, construction disputes will frequently arise, resulting in both parties refuse to perform their respective obligations. According to the interview, 33% of respondents commented that “Contradictions in contract documents” is an also important cause of construction disputes. ➁ The cause of “the contractor fails to perform its obligations” is mainly manifested as schedule delay and the cause of “the owner fails to perform its obligations” is mainly manifested as delay in payment of construction costs. Since the contractor is the direct

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party in charge of the project schedule, the construction progress delay is often regarded as the contractor’s failure to perform its obligations (Tang and Jing 2020). It is the obligation of the owner to make payment to the contractor according to the contract, and the owner’s delay in the payment will lead to suspension of work, which is an important cause for the construction dispute (Wen 2014). ➂ Project change is the main cause of schedule delay and delay in payment. Project changes can lead to the changes of cost and schedule, which increase the risk of schedule delays and delay in payment (Osei-Kyei et al. 2019). 4.3 Differences of Stakeholders’ Perceptions on the Causes of Construction Disputes To explore the differences of the stakeholders’ perceptions on the causes of the construction dispute, the one-way analysis of variance of the data on the causes of construction dispute was conducted using SPSS software, and the analysis results are shown in Table 2. Table 3. Differences of diverse stakeholders’ perceptions on the causes of construction disputes Cause

Mean

Total mean

P

Owner

Contractor

Engineering consultant

Other parites

Delay in payment

3.36

4.40

4.48

4.17

4.13

0.001

The owner fails to perform its obligations

3.09

4.23

4.39

4.22

4.00

0.000

The contractor fails to perform its obligations

4.41

3.73

4.48

4.33

4.19

0.015

According to the data analysis results in the table, it can be found that there are perception differences (P < 0.05) among the stakeholders in three causes: “Delay in payment (p = 0.001)”, “The owner fails to perform its obligations (p = 0.000)”, and “The contractor fails to perform its obligations (p = 0.015)”. (1) As to delay in payment, the contractor (4.40), engineering consultant (4.48) and other parties (4.17) have high perception of this cause, while the owner (3.36) has low perception of this cause. The owner’s delay the construction payment is an important reason for the construction dispute (Wen 2014). In the interview, 50% of the respondents (excluding owner) also believed that delay in payment is an important cause of the dispute. (2) As to the owner fails to perform its obligations, the contractor (4.23), the engineering consultant (4.39) and other parties (4.22) have high perception of this cause, while the owner (3.09) has low perception of this cause. From the results of descriptive statistical analysis, the owner fails to perform its obligations is mainly manifested

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in delay in payment, while the regular payment of the construction cost is the owner’s contractual obligation. Therefore, there are perception differences among the stakeholders in “ the owner fails to perform its obligations “. (3) As to the contractor fails to perform its obligations, the owner (4.41), engineering consultant (4.48) and other parties (4.19) have a high degree of perception for the cause, while the contractor (3.73) has a relative low degree of perception. It can be seen from the results of descriptive statistical analysis that the contractor’s failure to perform the contract obligations is mainly manifested as schedule delay. The contractor is the direct party in charge of the project’s schedule, but the reasons for the schedule delay are various. Therefore, the schedule delay is often regarded as the cause, which directly leads to the dispute between the owner and the contractor (Wang 2021). In the interview, 40% of the respondents believed that the schedule delay was an important cause for the construction disputes. 4.4 The Most Frequently Used Dispute Resolution Method According to the frequency statistics, negotiation (35.5%) and mediation (49.5%) are most frequently used in construction dispute, followed by arbitration (4.3%), litigation (7.5%) and dispute review (3.2%). To analyze the reasons, this paper makes a descriptive statistical analysis on the questionnaire data, and the analysis results are shown in Table 3 (Table 4). Table 4. Characteristics of construction dispute resolutions Type Negotiation

Mediation

Litigation

Mean

SD

Rank

Low cost

4.08

1.106

3

Less time

4.08

0.912

2

Conducive to maintaining cooperative relations between all parties

4.23

0.861

1

Confidentiality

3.90

1.001

4

Difficult to reach an agreement

3.45

1.118

6

Not legally binding

3.89

1.146

5

Less time

3.87

0.958

3

Low cost

3.89

0.787

2

Conducive to maintaining cooperative relations between all parties

4.00

0.860

1

Confidentiality

3.67

0.959

5

Delay the best time for litigation

3.66

1.137

6

Not legally binding

3.70

1.101

4

Legally binding

4.33

0.812

1

(continued)

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Table 4. (continued) Type

Arbitration

Dispute review

Mean

SD

Rank

High cost

4.12

0.976

3

Results can be changed

3.46

1.069

6

More time

3.94

0.998

4

Not conducive to maintaining cooperative relations between all parties

4.14

0.973

2

Not confidential

3.88

1.031

5

More equitable results

4.33

0.785

1

Confidentiality

3.94

0.895

2

Simple procedure

3.73

1.002

6

High cost

3.90

1.001

3

Results cannot be changed

3.87

1.034

4

Rigid agreement form

3.81

1.135

5

Low cost

3.53

0.973

6

Less time

3.69

0.847

4

Conducive to maintaining cooperative relations between all parties

3.72

0.839

1

The quality of the evaluation experts is hard to guarantee

3.71

0.995

3

Lack of enforceability of review results

3.58

1.087

5

Not legally binding

3.72

1.117

2

The information in Table 3 reveals that the respondents (mean value > 3.7) had a high level of agreement on the characteristics of the construction dispute resolution methods. (1) Negotiation Negotiation is an important method to resolve disputes (35.5%). Because negotiation is conducive to maintaining cooperative relations between the parties (4.23), low cost (4.08), less time (4.08) needed to resolve disputes and confidentiality (3.90), it has become the preferred dispute resolution method for most parties. However, due to the lack of legal binding force (3.89) and the difficulty in reaching an agreement (3.45), the situation of applying the settlement method is limited, that is, the settlement is applicable to engineering construction disputes with small scale, small differences and without the intervention of a third party (Marques 2018). (2) Mediation Legal binding force is an important reason why mediation has become the most commonly used in dispute (49.5%). Mediation is also conducive to maintaining the

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cooperative relations between the parties (4.00), low cost (3.89), less time needed to resolve disputes (3.87) and confidentiality (3.67). However, due to the involvement of a third party, mediation requires more cost and time, and the confidentiality is not as good as that of negotiation. However, third-party intervention also makes mediation more legally binding (Xu 2018). From this point of view, it can be found that besides the cost and time of dispute resolution, legal binding force is an important consideration when choosing this dispute resolution method. (3) Litigation Litigation is rarely used in construction disputes (4.3%). Due to the “high cost (4.12) “, “not conducive to maintaining the cooperative relations between the parties (4.14) “, “more time needed (3.94) “ and “not confidential (3.88) “, each participant will finally choose litigation when the negotiation, mediation, dispute review and arbitration cannot resolve the disputes (Awwad et al. 2016). Litigation is usually regarded as the final step to resolve disputes. The results of litigation are legally binding, but litigation is not conducive to maintaining the cooperative relations between all parties and will consume a lot of time and resources (Osei-Kyei et al. 2019). (4) Arbitration Arbitration (7.5%) is the most frequently used formal dispute resolution after the failure of negotiation and mediation. The respondents think that the arbitration result is fairer (4.33) and confidential (3.94), but the cost is high (4.12), the result cannot be changed (3.87) and the agreement form is rigid (3.81). In addition, in arbitration, the parties involved in the conflict do not try to find a agreement by both parties, but present their own requirements, and the arbitrators make the final decision, which is legally binding (Marques 2018). (5) Dispute review Dispute Review (3.2%) is rarely used in resolving construction disputes in the Chinese construction industry. The respondents believe that the quality of the review experts of the dispute review is difficult to guarantee (3.71) and is not legally binding (3.72). In fact, China’s dispute review system is still in a development. At present, a mature dispute review expert training and selection mechanism has not been formed, and the ability and reputation of dispute review experts are not sufficient to form a strong support for their review opinions (Chen 2019).

5 Conclusion Based on extensive literature review, this paper explores the main causes of construction disputes and the most frequently used dispute resolution methods. The statistical analysis result of the questionnaire and interview data shows that: (1) Construction disputes are greatly influenced by COVID-19 (4.20), and contractual causes (4.00) are the critical cause of construction disputes, followed by managerial cause (3.99), force majeure causes (3.85) and other causes (3.62).

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(2) Diverse stakeholders’ perceptions of the causes of construction disputes varied significantly. The perception differences of the stakeholders are mainly reflected in the delay in payment (P = 0.001), the owner fails to perform the obligations (P = 0.000), and the contractor fails to perform the obligations (P = 0.015). This indicates that contract obligations and the construction payment are the critical factors to be considered when resolving the construction dispute. (3) Negotiation (35.5%) and mediation (49.5%) are the most frequently used dispute resolution methods in the Chinese construction industry. The findings of this paper are conducive to better solve and reduce the occurrence of construction disputes in the future. Since the empirical investigation are conducted to construction practitioners in Suzhou, the conclusions cannot represent the overall situation in China. Therefore, future cross-regional studies can be conducted to make the findings more comprehensive to the Chinese construction industry.

References 1. Awwad, R., Barakat, B., Menassa, C.: Understanding dispute resolution in the Middle East region from perspectives of different stakeholders. J. Manag. Eng. 32(06), 05016019 (2016) 2. Chen, Q.W.: Research on dispute resolution of international construction projects. South China University of Technology (in Chinese) (2019) 3. Ding, J.: Research on conflict management mechanism of construction project. Constr. Econ. 1, 57–59 (2012). (in Chinese) 4. Gaum, T., Laubscher, J.: The implementation of alternative dispute resolution methods by architectural practitioners in South Africa. Acta Structilia 26(01), 97–119 (2019) 5. Guo, D.D., She, L.Z.: Analysis on issues related to the construction of domestic DAB dispute resolution mechanism. J. Eng. Manage. 27(06), 11–15 (2013). (in Chinese) 6. Jia, H.J., Sun, L.Z., Guo, X.L.: Research on the risk and dispute of construction project provisional valuation. Constr. Econ. 40(04), 69–72 (2019). (in Chinese) 7. Li, K., Cheung, S.O.: Bias measurement scale for repeated dispute evaluations. J. Manag. Eng. 34(04), 04018016 (2018) 8. Li, X.J., Ji, Z.K.: Study on the causes and countermeasures of construction contract disputes. Constr. Econ. 8, 62–65 (2013). (in Chinese) 9. Marques, R.C.: Is arbitration the right way to settle conflicts in PPP arrangements? J. Manag. Eng. 34(01), 05017007 (2018) 10. Mi, Q.: Research on dispute resolution mode of construction engineering. Zhejiang University (2012). (in Chinese) 11. Osei-Kyei, R., Chan, A.P.C., Yu, Y., Chen, C., Dansoh, A.: Root causes of conflict and conflict resolution mechanisms in public-private partnerships: comparative study between Ghana. Cities 87, 185–195 (2019) 12. Tang, Y., Jing, Y.F.: Analysis and solution of the dispute on the completion settlement and price evaluation of the general contracting project. Constr. Econ. 41(S1), 119–122 (2020). (in Chinese) 13. Wang, X.J. (2021). Study on the control of influencing factors of construction contract price disputes. Yangzhou University (2012). (in Chinese) 14. Wen, J.A.: Research on the dispute of payment terms in construction contract. Constr. Econ. 35(08), 83–86 (2014). (in Chinese) 15. Wu, M.L.: Questionnaire Statistical Analysis Practice: SPSS Operation and Application. Chongqing University Press, Chongqing (2019). (in Chinese)

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16. Xu, J.X.: Research on influencing factors and solutions of construction contract price disputes. Nanjing Forestry University (2018). (in Chinese) 17. Xu, K.B.: Influence of epidemic situation on the trial of construction contract dispute cases and countermeasures. J. Law Appl. 7, 29–38 (2020). (in Chinese) 18. Yang, Y., Zhang, J.: Comparative analysis of DAB and domestic legal construction contract dispute resolution. Constr. Econ. 6, 51–54 (2012). (in Chinese) 19. Ye, G., Jin, Z., Xia, B., Skitmore, M.: Analyzing causes for reworks in construction projects in China. J. Manag. Eng. 31(06), 04014097 (2015) 20. Zhang, J.D.: Research on risk sharing of general contracting project of construction engineering. Harbin Institute of Technology (2020). (in Chinese) 21. Zhang, L.Z., Xu, W., Chen, H.: A model for the causes of disputes in engineering construction contracts. J. Civ. Eng. Manag. 33(04), 76–82 (2016). (in Chinese) 22. Zhang, S., Loosemore, M., Sunindijo, R.Y., Galvin, S., Wu, J., Zhang, S.Y.: Assessing safety risk management performance in Chinese subway construction projects: a multistakeholder perspective. J. Manag. Eng. 38(4), 05022009 (2022)

Research on the Impact of Market Sentiment on the Second-Hand Housing Market Deheng Zeng(B) , Jinyu Wang, and Yan Shan School of Management Science and Real Estate, Chongqing University, Chongqing, China [email protected]

Abstract. Market sentiment is an important factor affecting real estate market transactions. In the face of irrational fluctuations in China’s real estate market, “Expectation Stabilization” is regarded as one of the important policy goals of real estate regulation. This paper takes Chongqing as an example to make an in-depth analysis of the impact of market sentiment on the second-hand housing market. Firstly, obtain the corpus of Chongqing real estate industry information from 2019 to 2021 based on the Sina public opinion platform, analyze the sentiment tendency of the corpus and construct the market sentiment index accordingly. Secondly, establish the VAR model of the second-hand housing market by adding variables such as real estate development investment and M2 money supply. Finally, make an empirical research on the impact of market sentiment on Chongqing’s second-hand housing market. The results show that market sentiment has a significant impact on the second-hand housing market in the short term, and there is a hysteresis. Faced with the impact of market sentiment, the response of second-hand housing transaction area is more significant than second-hand housing sales price. Keywords: Market sentiment · Second-hand housing market · Chongqing · VAR model

1 Introduction Since the implementation of housing reform in 1998, China’s housing construction has made rapid progress and housing demand has increased rapidly. Under the influence of multiple factors, housing price has shown a tendency of excessively rising. In order to curb the impact of further inflation of housing prices on real economy of China, the state put forward the concept of “Housing without Speculating” in 2016, with the goal of “Stabilizing land prices, Stabilizing housing prices, and Stabilizing expectations” to ensure the stable and healthy development of the real estate market. The real estate market is considered to be an “inefficient market” with obvious irrational factors in market transactions due to asymmetric information [1]. Behavioral economics believes that in addition to basic values such as macroeconomics and financial environment affecting real estate market transactions, market sentiment is also an important factor affecting its changes. Case et al. (2003) first used the University of Michigan’s Survey of Consumers (SOC) to assess homebuyer expectations as sentiment © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 739–748, 2023. https://doi.org/10.1007/978-981-99-3626-7_57

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values in the real estate market [2]. Chun & Hae-Jung (2014) used the time series method to empirically analyze the research of consumer sentiment on the housing market, and the results showed that consumer purchase intention had a great impact on housing prices [3]. Hui & Wang (2014) found that the housing market sentiment index can reflect the operation of the real estate market and consumer participation, effectively predict the price level, rate of return and transaction volume of the housing market, and maintain a long-term equilibrium with the housing market [4]. Zheng et al. (2016) found that the investor confidence index in the real estate market was an important determinant of real estate price dynamics. The real estate confidence index showed heterogeneity in different cities and time dimensions, depending on the urban demographic structure and the urban housing Supply elasticity [5]. Soo (2018) conducted text analysis of news reports in 34 cities across the United States, based on which housing market sentiment was contructed, and conducted correlation analysis with the sentiment values obtained by Case and Shiller based on questionnaires, finding that real estate market sentiment had a significant impact on future housing prices [6]. Li Shuxin (2018) used text sentiment analysis technology to construct a market sentiment index, and found that there was a transmission mechanism between market sentiment and the real estate market. Among them, the market sentiment in the new commercial housing market had a hysteresis effect on housing prices, but there was no hysteresis in the second-hand housing market [7]. Zhou (2018) collected transaction-by-transaction data from 2015 to 2016 of the second-hand housing market in Shanghai, and constructed a sentiment index through five indirect indicators, including the floor space of newly started residential buildings, the share of housing investment in total real estate investment, and so on. It was found that high market sentiment can lead to high returns in the housing market, but it was difficult to cause a downturn in housing market returns due to low sentiment [8]. Song Dandan et al. (2019) used machine learning algorithms to conduct sentiment analysis on the real estate information of Baidu news column, constructed sentiment variables and attention variables based on it, and established a time-varying parameter vector autoregressive model of housing prices, finding that media sentiment changes can reflect the characteristics of the regulatory policy stage, the change of attention had a transmission effect on the market [9]. Tai Yuhong et al. (2020) constructed a real estate market sentiment index based on online search data and conducted empirical research based on 70 large and medium-sized cities across in China. They believed that the real estate market sentiment had a significant impact on market fluctuations, and the degree of influence showed significant regional heterogeneity [10]. Jia Ting & Zhang Lianying (2021) constructed investor sentiment in the real estate market based on sentiment analysis and incorporated it into a house price regression model. They found that the investor sentiment index had a greater impact on house prices than other economic variables [11]. In recent years, the rapid development of the Internet has made the society as a whole pay more attention to the housing market. When the emotions of micro market participants become a powerful force affecting the operation of the market, a large number of opinions and attitudes which spread through the Internet may aggravate market volatility and even affect the overall market health and stability. This paper constructs a market sentiment index with the help of public opinion corpus, then adds it into the VAR model of the second-hand housing market and conducts the empirical

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research, which makes up for the insufficiency of existing research and provides a new perspective for revealing the changes in the second-hand housing market and promoting the stable and healthy operation of the real estate market.

2 Research Design There is an interaction mechanism between market sentiment and housing market. The market performance can be traced back to every market participant in economic activities. The imperfect rationality of micro individuals and the information asymmetry of the market trigger the generation of individual emotions and group emotions. The essential mechanism of the imperfect rationality of micro individuals is shown as overconfidence and underreaction, anchoring effect, herding effect, etc. The comprehensive effect of these factors leads to the production of housing market sentiment. Affected by market sentiment, the participants in the market are not completely rational in their investment decisions, thus causing irrational fluctuations in the housing market. The housing market is regarded as a complex system, which brings together all kinds of micro individuals to participate in. Market participants generate certain emotions according to relevant information and self-cognition, and make economic decisions according to their emotions, bringing changes to the supply and demand structure and transaction structure of the market. The changes in supply and demand structure and transaction structure of the housing market in turn serve as the feedback information of market participants, on which basis participants adjust their emotions, leading to a new round of market changes. Therefore, there is an interactive mechanism between market sentiment and housing market, and they affect each other. Due to the particularity of the real estate market in China, the second-hand housing is less regulated, the transaction disorder is obvious, and the credit mechanism is missing. Theoretically, it is likely to be affected by market sentiment and thus change. In this regard, this paper puts forward the following hypothesis: market sentiment have an impact on the second-hand housing market. Vector autoregressive model is a model that uses unstructured methods to establish the relationship between variables. Considering the second-hand housing market as a complex system, in addition to market sentiment factors, it also includes various other macro, micro, quantitative and qualitative factors. The VAR model is more suitable for this kind of research which is composed of multiple economic indicators. Therefore, this study constructs two VAR models to test the impact of market sentiment on the second-hand housing market. 2.1 Select Variable Indicators After comprehensively considering the relevance and availability of real data, the final selection of relevant indicator variables is shown in Table 1. 2.2 Construct the Market Sentiment Index Obtain relevant news information on the real estate industry in Chongqing from January 2019 to December 2021 based on Sina Public Opinion. The text of real estate information

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Table 1. Description table of the selection of variable indicators in the second-hand housing market Variable name

Indicator name

Indicator description

Unit

Explained variables

Second-hand housing sales price index (Y1)

Data from the National Bureau of Statistics

–

Second-hand housing transaction area (Y2)

Data from Chongqing Municipal Commission of Housing and Urban-rural Development

Ten thousand square meters

Explanatory variables

Market Sentiment Index (SI)

Built on text analysis

–

Control variables (Supply level)

Real estate development investment (S1)

Data from Chongqing Municipal Bureau of Statistics

Million

Newly built commercial housing transaction area (S2)

Data from Chongqing Municipal Commission of Housing and Urban-rural Development

Ten thousand square meters

Disposable income of urban residents (D1)

Data from Chongqing Municipal Bureau of Statistics

Yuan

Average transaction price of new commercial housing (D2)

Data from Chongqing Municipal Commission of Housing and Urban-rural Development

Yuan

M2 money supply (E1)

Data from the People’s Bank of China

Million

Control variables (Demand level)

Control variables (Macroeconomic level)

Consumer Price Index Data from Chongqing (E2) Municipal Bureau of Statistics

–

was de-weighted, cleaned and abstract. Then call the API interface of Baidu natural language processing technology for text sentiment analysis. The corpus that is conducive to the increase in the real estate transaction volume or price in Chongqing is judged as a positive label “2”. A negative mark “0” is judged to be a corpus that may lead to a decline in the real estate transaction volume or price in Chongqing. The corpus that is irrelevant or impossible to judge the operation of Chongqing real estate market is regarded as a neutral label “1”. Finally, 67 007 positive corpus, 90 303 negative corpus, and 36 501 neutral corpus were obtained. Based on the confidence returned by each corpus, the average confidence of this sentiment analysis was calculated to be 0.799447, indicating

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that the results of the sentiment analysis were reliable. The Chongqing real estate market sentiment index was constructed on a monthly basis. The monthly market sentiment index was calculated by subtracting the number of positive and negative corpora in a month and dividing it by the total number of corpora released in the month. The specific preparation is shown in formula (1): SI _t = (Npos − Nneg)/Nt ∗ 100 + 100

(1)

where, SI _t is the market sentiment index, the number of positive corpora and the number of negative corpora in the month are respectively expressed as Npos, Nneg, Nt represents the total number of corpora in Chongqing in the t month. The final trend of the market sentiment index is shown in Fig. 1.

Fig. 1. Market sentiment of Chongqing’s real estate market from 2019 to 2021

3 Empirical Research 3.1 Data Collection and Processing Collect monthly data from January 2019 to December 2021 for other variables above. Since the construction of the VAR model requires that the data are all stationary time series, it is necessary to carry out data processing. Use the X-12 statistical method to eliminate seasonal factors, and take the logarithm to avoid the interference of heteroscedasticity on the model. All data processing and validation were done with Eviews.10. 3.2 Data Stationarity Test Before entering the specific regression model, the stationarity test and cointegration relationship test of all proxy index data should be carried out first. The results of the ADF unit root test method show that the above variables are all first-order difference stationary time series, which can be considered to be first-order difference stationary.

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The cointegration test results show that there is a long-term cointegration relationship between second-hand housing sales price index Y1 and disposable income of urban residents D1, newly built commercial housing transaction area S2, M2 measure of money supply E1, consumer price index E2, and there is a long-term cointegration relationship between second-hand housing transaction area Y2 and other variables except newly built commercial housing transaction area S2 and consumer price index E2. 3.3 Empirical Research Between Market Sentiment and Second-Hand Housing Market 3.3.1 VAR Model Construction Second-hand housing sales price index Y1, market sentiment index SI, disposable income of urban residents D1, newly built housing commercial housing transaction area S2, M2 money supply E1, and consumer price index E2 were selected to construct VAR01. Second-hand housing transaction area Y2, market sentiment index SI, disposable income of urban residents D1, average transaction price of new commercial housing D2, real estate development investment S1, and M2 money supply E1 were selected to construct VAR02. Before formally establishing the model, it is necessary to select the optimal lag order. The output results determine that the lag periods of VAR01 and VAR02 are both 2. The model verification results in Fig. 2 indicate that the two models are successful and can be used for subsequent analysis.

Fig. 2. VAR01 (left) and VAR02 (right) reciprocal graphs of polynomial roots

3.3.2 Impulse Response Analysis This paper set 10 as the lag period, that is, to simulate the response of market sentiment index SI and the second-hand housing market to each other’s response to the pulse within 10 months due to a shock. As shown in Fig. 3 & Fig. 4 below. Figure 3 shows the response of second-hand housing sales price index Y1 to the impact of market sentiment index SI. When market sentiment index SI is positively impacted by one standard deviation, second-hand housing sales price index Y1 will make a very weak negative reaction at the moment. This reaction bottoms out in the second period, then maintains a negative reaction, and finally gradually tends to zero.

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Fig. 3. Impulse response of second-hand housing sales price index Y1 to market sentiment index SI

This shows that market sentiment SI has a negative impact on second-hand housing sales price index Y1, but the response is relatively weak.

Fig. 4. Impulse response of second-hand housing transaction area Y2 to market sentiment index SI

Figure 4 shows the response of second-hand housing transaction area Y2 to market sentiment index SI. When market sentiment SI is affected by a positive impact, secondhand housing transaction area Y2 shows a high positive response in the current period. This shows that the positive fluctuation of market sentiment SI can have a significant positive impact on second-hand housing transaction area Y2 in a short period of time. While in the first three periods, the positive reaction gradually weakens and shows a negative reaction, and then in the third to forth periods, the negative reaction gradually declines until it becomes stable. 3.3.3 Variance Decomposition Using the previously constructed the VAR model to complete the variance decomposition of market sentiment SI and second-hand housing sales price index Y1, as shown in Table 2. Table 2 shows the contribution ratio of each variable to the fluctuation of secondhand housing sales price index Y1. The fluctuation of second-hand housing sales price index Y1 is largely caused by its own lag factor. The variance contribution rate of its

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Table 2. Variance decomposition of second-hand housing sales price index Y1 in VAR01 Period

SE

LNY1

LND1

LNS2

LNE1

LNE2

LNSI

1

0.0098

100.0000

0

0

0

0

0

2

0.0169

74.0582

1.78E + 01

0.5670

5.0684

0.7790

1.7492

3

0.0189

64.2405

24.9640

1.8330

5.2060

0.9846

2.7719

4

0.0195

61.0375

27.0726

1.8167

5.0264

1.7681

3.2787

5

0.0202

58.5989

27.8908

2.5770

4.8838

2.8195

3.2300

6

0.0229

62.6997

22.4858

4.5749

4.5307

2.8096

2.8994

7

0.0266

64.1730

21.2406

4.8996

3.3896

2.9409

3.3563

8

0.0283

61.3564

23.8843

4.6322

3.2267

2.9345

3.9659

9

0.0289

58.7167

25.3491

5.0246

3.7784

2.9205

4.2106

10

0.0294

57.0715

26.3323

5.5122

4.0865

2.9158

4.0817

own lag item remains at about 74% in the first two periods, then drops slightly to about 60% and maintains stability. Other factors that have a greater impact on the fluctuation of second-hand housing sales price index Y1 are disposable income of urban residents D1 and M2 money supply E1. Among them, disposable income of urban residents D1 rises rapidly to about 25% in the first three periods, and almost remained around 25% in the later periods. M2 money supply E1 rises to about 5% in the first two periods, and then maintains a contribution rate of about 4%. The contribution rate of consumer price index E2 and market sentiment index SI is actually relatively small. The contribution rate of market sentiment index SI is up to 4.21%, and the contribution rate of consumer price index E2 is up to 2.94%. Comparing the contribution rate of E2 and SI in the same period, it is found that the contribution rate of market sentiment index SI is higher than that of consumer price index E2, which shows that market sentiment SI has a certain influence on second-hand housing sales price index Y1, even higher than the consumption level. The variance decomposition of market sentiment index SI and second-hand housing transaction area Y2 is shown in Table 3. As shown in Table 3, in the first three periods, second-hand housing transaction area Y2 is still more affected by its own factors, and it gradually decreases after the fourth period to maintain a level of about 20%. The impact of M2 money supply E1 reaches a level of 43% in the third period, which has exceeded the influence of the lag factor of second-hand housing transaction area Y2 itself. In the later periods, the contribution rate of E1 has fallen, but it still remains at around 30%, which is still a relatively high contribution. The contribution of real estate development investment S1 is relatively high, which is up to 28%, and stays around 10%. The contribution of disposable income of urban residents D1 is low in the first four periods, but it has been rising slowly, reaching a maximum of 20% in the sixth period, and maintaining around 20% in the subsequent period. The growth trend of the variance contribution rate of average transaction price of new commercial housing D2 is similar to that of disposable income of urban residents D1, but its overall contribution rate is lower than that of D1, at the level of 8%. The

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Table 3. Variance decomposition of second-hand housing transaction area Y2 in VAR02 Period

SE

LNY2

LND1

LND2

LNS1

LNE1

LNSI

1

0.5059

100.0000

0

0

0

0

0

2

0.6346

67.3657

0.0007

2.4891

28.8709

0.9747

0.2990

3

0.9746

33.0664

0.41778

1.1081

12.2503

43.2969

9.8605

4

1.1176

25.2230

2.3476

1.6075

15.4724

45.1264

10.2229

5

1.3687

22.5923

17.2925

3.6175

12.8889

31.8147

11.7941

6

1.4585

20.4685

20.1326

7.6552

11.5210

28.5224

11.7003

7

1.5024

19.9185

19.0244

8.7733

12.4468

26.9170

12.9201

8

1.5723

19.8113

17.5954

8.3889

12.2555

28.3641

13.5848

9

1.6404

20.0246

16.7375

7.8721

11.3444

30.4647

13.5566

10

1.6984

19.8274

19.4493

7.6698

11.5889

28.4385

13.0261

impact of market sentiment index SI on second-hand housing transaction area Y2 is higher than average transaction price of new commercial housing D2, and the highest variance contribution rate is 13.6%, which is not different from the contribution rate of real estate development investment S1, indicating that the impact of market sentiment on second-hand housing transaction volume is similar to the impact of market supply.

4 Conclusion This paper constructs the market sentiment index of Chongqing’s real estate market with the help of natural language processing and other technologies, then empirically explores the impact of market sentiment on Chongqing’s second-hand housing market. The second-hand housing market is relatively less regulated by policies, and no relevant policies such as price guidance have been issued, so its short-term fluctuations are easily affected by public opinion. Moreover, market sentiment has a lag effect in response to the impact of the second-hand housing market transaction volume and price, which indirectly indicates that the second-hand housing market information is asymmetric, and that market anomalies need a certain transmission period for sentiment. Faced with the positive impact of market sentiment, second-hand housing transaction area responds quickly, and this positive feedback will probably last for two to three months, while the second-hand housing market sales price has a relatively weakly negative reaction.

References 1. Case, K.E., Shiller, R.J.: The efficiency of the market for single-family homes. Am. Econ. Rev. 79(1), 125–137 (1989) 2. Case, K.E., Quigley, J.M., Shiller, R.J.: Home-buyers, housing and the macroeconomy. In: Richards, A., Robinson, T., (eds.) RBA Annual Conference Volume (Discontinued), Reserve Bank of Australia, Asset Prices and Monetary Policy (2003)

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3. Chun, H.J.: A empirical analysis on the impact of the consumer sentiment on the housing market. J. Architect. Inst. Korea Plann. Des. 30(8), 83–90 (2014) 4. Hui, C.M., Wang, Z.: Market sentiment in private housing market. Habitat Int. 44, 375–385 (2014) 5. Zheng, S., Sun, W., Kahn, M.E.: Investor confidence as a determinant of china’s urban housing market dynamics. Real Estate Econ. 44(4), 814–845 (2016) 6. Soo, C.K.: Quantifying sentiment with news media across local housing markets. Rev. Financ. Stud. 31(10), 3689–3719 (2018) 7. Li, S.X.: Research on investor sentiment index in China’s real estate market and its impact on housing prices based on text mining technology, Shanghai Jiaotong University (2018) 8. Zhou, Z.: Housing market sentiment and intervention effectiveness: evidence from China. Emerg. Mark. Rev. 35(C), 91–110 (2018) 9. Song, D.D., Zhang, D., Yin, Q.W., He, F.M.: Internet news, demanders concern and housing prices: a study based on time-varying parameter vector autoregression model. Southern Econ. 04, 106–128 (2019) 10. Tai, Y.H., Fang, Y.Q., Wang, C.: Research on the impact of real estate market sentiment on commodity housing prices. Econ. Forum 5, 8 (2020) 11. Jia, T., Zhang, L.Y.: The impact of investor sentiment on housing price volatility based on sentiment analysis. J. Eng. Manage. 35(03), 135–140 (2021)

Research on Emergency Decision Making Considering Decision-Maker Peference Based on Improved Regret Theory—A Case Study of Covid-19 Yang Su1,2(B) and Sun Taibao1 1 School of Economics and Management, Anhui Jianzhu University, Hefei 230000, China

[email protected] 2 Anhui Research Center for Construction Economics and Real Estate Management, Anhui

Jianzhu University, Hefei 230000, China

Abstract. For public health emergencies, adopting different response plans often makes the emergencies develop in different ways. In this paper, we propose a methodology for emergency decision-making based on regret theory, which takes into account the preferences of decision-makers. In this paper, we first calculate the utility value of each solution (including the utility value of the loss caused by the emergency and the utility value of the cost incurred in implementing the solution); then, we calculate the regret value of the emergency solution to obtain the perceived utility value of the loss and cost incurred by the decision maker; then, we calculate the combined perceived utility of the emergency solution; then, based on the combined perceived utility, we add the decision maker’s Finally, an example study is presented to show that taking into account the decision maker’s preferences does have a significant impact on the choice of contingency decision. Keywords: Regret theory · Covid-19 outbreak · Delta virus · Public health emergencies · Emergency decision-making · Decision-maker preferences

1 Quotes In recent years, emergencies have occurred from time to time. An emergency is often defined as an unexpected event that occurs unexpectedly, and is often of an immediate or potentially dangerous nature; the 2007 Emergency Response Law defines an emergency Fundamental Project: Key Project of Humanities and Social Sciences of Anhui Provincial Education Department: Evaluation and Prediction of Green Development Level of Anhui Province’s Assembled Buildings Based on DPSIR Model (SK2020A0258); Project of National Natural Science Foundation of China: Risk Transmission Mechanism of Project Portfolio Fragility and Its Metrics (71802003); Project of Humanities and Social Sciences Youth Fund of the Ministry of Education: Evolutionary Path of Project Portfolio System Fragility and Its Control Strategies (18YJC630040) Yang Su (1981-), woman, Wuhu, Anhui, China, Associate Professor, PhD, mainly engaged in emergency management and decision making; risk analysis and management © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 749–764, 2023. https://doi.org/10.1007/978-981-99-3626-7_58

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as a sudden outbreak of events that may cause or bring about serious social damage and requires a timely emergency response. The definition further classifies emergencies into four categories: natural disasters, accidents and disasters, public health events and social security events. In this paper, we take the novel coronavirus as an example and study emergency decision-making from the perspective of major public health emergencies. Public health emergencies, as a kind of emergencies, belong to a kind of compound crisis, often sudden public health events (such as SARS, H1N1 virus or even the new coronavirus in 19) Zhu Zhe especially major public health emergencies will have a multifaceted impact. In the case of the new coronavirus, for example, it has had a huge impact on the social order; secondly, it has had an impact on the behaviour of the government; especially importantly, it has had a devastating impact on the economy of a country and society; and it has also hindered international communication between countries. It is precisely because of the negative impact of public health events that many scholars have been attracted to the study of emergencies and major public health emergencies in order to prevent their impact on human society. The study of emergencies is often divided into two phases, and the more important of these two phases is the problem of emergency response to emergencies. For emergency response, the most fundamental problem to be solved is actually a type of emergency decision-making problem, how to predict the various trends and possibilities of actual emergencies, the losses (either human or economic losses) that will be caused by taking targeted measures for various possible scenarios and the costs of taking preventive and control measures, and outputting a scientific and effective emergency decision-making plan to deal with the risks of emergencies. To date, research on emergency decisionmaking methods for emergencies (including public health emergencies) has attracted the attention of a considerable number of scholars. For example, Li Xinhong, Zhang Yi and Han Ziyue used a Bayesian network to construct an accident evolution model in order to improve the effectiveness and scientificity of emergency decision making for marine oil spills and proposed a feasible and effective oil spill disposal plan. After Bell and Loomes and Sugden put forward regret theory, it has also attracted extensive attention as a typical theory of psychological behaviour in the research related to emergency decision-making. The study of regret theory has been conducted by many scholars. Liu Yang and Fan Zhiping, on the other hand, have studied emergencies from the perspective of “ex ante - ex post” and divided them into two stages, and based on the idea of regret theory, they have constructed a series of “regret” scenarios by simulating the various directions of emergencies and various scenarios. Wang Juan and Dai Fengwei added the concept of hesitation fuzzy set to the regret theory and improved it on the basis of the original single regret theory. Improved, overcoming the influence of limited rationality of decision makers on decision making and improving the effectiveness of coal mine accident risk decision making; Wei Liang,Yang-Ming Wang took public health events as an example and proposed a method based on regret theory and interval evidence inference for scoring, the interval evidence inference method is an effective way to deal with ambiguity and in The article proposes an interval projection measure that considers both distance and direction, which avoids the problem of information loss in the decision-making process and improves the effectiveness of emergency decision-making (risk decision-making) to a certain extent; Yuning Wang, Yingzi Liang,

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on the other hand, apply regret theory to the decision-making of emergency response to urban rail transit storm disasters, proposing a regret-based theory-based emergency decision-making model, proposed optimal pre-disaster prevention methods and continuous response measures for various types of current disaster scenarios, and, at the end of the article, suggested that the adoption of this approach could improve the reliability of public transport services and emergency and risk response capabilities in railway traffic storm disaster emergency and related disaster prevention management. Of course, many scholars have adopted prospect theory more often in their academic research for risk decision making, for example, Zhu Li and Zhu Chuanxi used prospect theory to innovatively propose a hesitant fuzzy meta-decision making method, through a series of weighted calculations, to make a more scientific ranking of scenarios; Fan Zhiping, Liu Yang et al. found that taking different emergency plans Fan Zhiping and Liu Yang found that the adoption of different emergency plans would have different effects on the development direction of the emergency, so they proposed a method of emergency decision-making based on prospect theory, and quantified the decision-makers’ psychological perception of casualties and property damage in the calculation process, which brought great convenience to the subsequent research. In summary, considering that regret theory sometimes plays a decisive role in decision making, it is necessary to study the characteristics of decision makers after considering regret avoidance for such major public health emergencies, in conjunction with considering the different emergency response decisions that decision makers with different preferences will take after facing an emergency, i.e. The influence of decision makers with different preferences on decision making is also considered. Therefore, this paper takes the rebound of the new crown epidemic as an example study, and addresses the emergency decision problem of emergency response with different probabilities of occurrence of various possible scenarios when different epidemic prevention and control plans are implemented. The impact of considering the decision maker’s preferences on the emergency decision.

2 Asking Questions The following symbols or formulas are used to represent the set of values and quantities involved in making emergency decisions following a public health emergency. (1) Q = {Q1 , Q2 , ..., Qn }: : The set of (n) possible scenarios for a contingency, in which Qj represents a scenario that could actually occur in j and j = 1, 2, ..., n. (2) G = {G1 , G2 , ..., Gm }: This refers to the collection of alternative contingency plans or contingency plans that need to be developed in advance for an emergency. Gi represents the alternative contingency plan for i, i = 1, 2, ..., m. (3) S = {S1 , S2 , ..., Sh }: emergencies often result in losses in many ways. S represents the set of indicators of losses from h, where Sk represents the indicator of losses from k and k = 1, 2, ..., h. In practice, emergencies often result in losses in many ways, which in reality can cause damage to personal safety, property and even have a very negative impact on society.

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  (4) Tij = a˜ ij1 , a˜ ij2 , . . . , a˜ ijh , : A vector of loss indicators for a given scenario Qj and a contingency plan Gi , where a˜ ijh represents the value of a loss indicator for a scenario Sk , Qj and a contingency plan Gi .. It should be noted in advance that for a given emergency situation (scenario), the lower the value of the loss indicator, the more effective the contingency plan is for that particular emergency time scenario. In addition, when considering the actual situation, it is easy to see that a lot of information or indicators are very difficult to obtain when an emergency occurs, and that even if specific data is available, it is not as realistic as it could be, as the actual situation tends to  so this paper has chosen to take the form of interval numbers, i.e.  fluctuate, k ks ku a˜ ij = a˜ ij , a˜ ij and aijku > aijks > 0i = 1, 2, ..., m; j = 1, 2, ..., n; k = 1, 2, ..., h to describe the problem. →

(5) P = (pi1 , pi2 , ..., pin ): A vector representing the probability of various scenarios i

(situations) occurring when a plan Gm is implemented. pij in the vector represents the probability of a scenario Qn occurring when a scenario Gm is implemented, n  pij = 1, 0 ≤ pij ≤ 1, i = 1, 2, ..., m is satisfied. Clearly, the probability so j=1

of each scenario occurring in an emergency is necessarily different when different contingency plans are implemented. (6) A = (α1 , α2 , ..., αh ): A vector of weight values for each type of marker that would result from an emergency event, in which αh represents the degree of importance of h  αk = 1, k = 1, 2, ..., h. the loss indicator Sk , proportional to its importance, and k=1

In this paper, the values for αk are given by experts and decision makers through empirical and subjective assessments. (7) L = {l1 , l2 , ..., lm }: The implementation of a specific contingency plan often requires a certain amount of cost input. L represents the cost vector of the cost of implementing a particular plan. li represents the cost of implementing a specific contingency plan. Gm is the cost of implementing a specific contingency plan. The cost in this document is the total cost input for all categories of costs required to implement the emergency response. (8) Z = (z1 , z2 ): This paper has selected two aspects: the emergency and the cost of implementing the contingency plan. Z refers to the weight vector of the damage caused when an emergency occurs and the weight vector of the cost of implementing the contingency plan. z1 and z2 refer to the weights of the damage caused by the emergency and the cost of implementing the contingency plan respectively, again, the higher the weight the greater the importance. z1 + z2 = 1, 0 ≤ z1 ≤ 1, 0 ≤ z2 ≤ 1. (9) ξj : represents some situation decision Qj makers for the loss caused by emergencies and need emergency response cost associated parameters of perception, represents the decision makers for damage caused by an emergency and to plan the cost of the psychological perception of mutual influence of value,the value ξj is often associated with to consider aspects of emergencies, for different values (Gi ) = q1 giS + q2 giL , i = 1, 2, ..., m, i = 1,2.….m,the value range of ξj is as shown in the list below(1).

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Table 1. Range of values for ξj Constraints (type of decision maker)

ξj The range of values

Description of the range of values

Risk-based

0 ≤ ξj ≤ 1

indicates that there is an inverse, reverse interaction between the psychological perception of the damage caused by the contingency and the cost to be incurred, when ξj takes a positive value 0 ≤ ξj ≤ 1 (Here, the risk-based decision-maker seeks to achieve the desired contingency at the least cost, which is the “reverse” referred to above)

Neutral

ξj = 0

Indicates the psychological perception of the offsetting effect of the damage caused and the cost to be incurred by the contingency, taken ξj = 0

Conservative

−1 ≤ ξj ≤ 0

indicates that there is a positive, homogeneous interaction between the psychological perception of the damage caused by the contingency and the cost to be incurred, where ξj takes a negative value, i.e. −1 ≤ ξj ≤ 0, (here, the conservative decision maker seeks to spend the most to achieve the desired contingency, which is the “homogeneity” referred to above))

Figure 1(Decision Tree) below illustrates the problem of risk decision making (emergency decision making) for emergency response (prevention and control of major public health emergencies) in the form of a decision tree, which can be more clearly depicted.

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Y. Su and S. Taibao Q1 p11 p12

Q2

p1n G1 ˄l1

Qn

˅

Q1

G2 ˄l2 ˅

p11 p11

Q2

p11

Qn

Gm ˄lm˅

Q1

p11 p11

Q2

p11 Qn

Fig. 1. Decision Tree

3 Method, Principle and Calculation Steps 3.1 Method and Principle As the scale can cause a series of inconveniences when performing calculations, the scale is eliminated and the data that will be used for emergency decision response is next normalised through a number of operations. The losses caused by the contingency are to the interval of losses that   normalised would be caused by the contingency a˜ ijk = a˜ ijks , a˜ ijku by normalising it to an indicator   ˜ ku by the following formula: with a benefit type between [0,1] b˜ kij = b˜ ks , b ij ij bks ij = bku ij =

ak max − aijku ak max − ak min ak max − aijks ak max − ak min

, i = 1, 2, ..., m, j = 1, 2, ..., n, k = 1, 2, ..., h

(1)

, i = 1, 2, ..., m, j = 1, 2, ..., n, k = 1, 2, ..., h

(2)

Of which   = min aijks |i = 1, 2, ..., m; j = 1, 2, ..., n , ak max = ak min   max aijku |i = 1, 2, ..., m; j = 1, 2, ..., n , k = 1, 2, ..., h The cost values for the implementation of emergency response programmes to emergencies also need to be normalised to a benefit-based indicator between [0,1] βi ,

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normalised by the formula βi =

l max − li , i = 1, 2, ..., m − l min

l max

(3)

where l min = min{li |i = 1, 2, ..., m}, lmax = max{li |i = 1, 2, ..., m}. For practical life, combined with the actual occurrence of emergencies, for the loss brought about by the emergencies and the cost to be paid when responding, and then consider the impact of decision makers for emergency decisions, often decision makers for risk are risk averse [7 ~ 8], so the utility function v(x) should be a monotonically  increasing concave function that satisfies v(x) > 0, v (x) < 0. In this paper, a power function is finally chosen as the method to calculate the utility value, with the following equation. v(x) = xθ

(4)

In the above equation, θ represents the risk aversion factor, 0 < θ < 1, and the smaller θ represents the greater risk aversion of the decision. In the previous section, the concept of “interval numbers” was mentioned in order to provide a more realistic description of the problems that arise in practice in the form of intervals, and to make the study of the problem more realistic. bkij fijk (x) Gm Sk vijk It is not difficult to find that the normalised bkij can be regarded as a random variable within ku the interval [bks ij , bij ], which is considered to be either uniformly distributed or normally distributed. be expressed by the following equation.  vijk =

bku ij

bks ij

v(x) · fijk (x)dx

(5)

Further, the above equation calculates the utility values for the loss indicators, and the equation to calculate the utility values for the scenario Gm for all loss indicators of the contingency vijk , provided that the scenario Gm is implemented and the scenario Qn occurs, is vij =

h 

αk vijk , i = 1, 2, ..., m, j = 1, 2, ..., n

(6)

k=1

It is worth noting that the value of the loss of contingency is in a reciprocal relationship with vij , i.e. the smaller the overall loss due to the contingency, the larger the value of vij , i.e. the more effective the scenario Gm is for the scenario. Qn . For the implementation cost of implementing the contingency programme, the value of the cost of implementing the contingency programme as normalised above βi is substituted into the utility function of Eq. (4) above and the formula for calculating the cost of implementing the programme Gm is obtained as follows viL = (βi )θ

(7)

From the perspective of finite rationality, decision makers, as a combination of rationality and emotion, will often regret the decision they have made when faced with any

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decision, regardless of the contingency plan they have made. Psychological gap is related to the gap between the current solution and the maximum utility that can be obtained when implementing other solutions, and often the larger the gap, the greater the perceived regret of the decision maker; at the same time, as decision makers tend to be risk averse to risk, so it is not difficult to conclude that the regret function of the decision maker for the decision H (v) is a monotonically increasing concave function that also satisfies H (v) > 0 and H (v) < 0, according to the reference, H (v) can be be expressed by the following equation. H (v) = 1 − exp(−μv)

(8)

For the above equation, where μ represents the decision maker’s risk aversion coefficient, μ > 0 and μ, the greater the decision maker’s regret aversion, and v represents the difference between the maximum utility that can be achieved by implementing the current solution and the alternative solution, v ≤ 0. According to Eq. (8), it can be concluded that H (v) ≤ 0 and the larger is, then |v||R(v)|, i.e. the larger the difference between the maximum utility that can be achieved by implementing the current option and implementing the other option, then the stronger the decision maker’s perception of regret. Based on Eq. (8) above, the formula for calculating the regret value of Hij for the scenario Gi for the loss indicator if the scenario Gi is implemented and the scenario Qj occurs is as follows

Hij = 1 − exp −μ vij − vj∗ , i = 1, 2, ..., m (9) where vj∗ == max{vij |i = 1, 2, ..., m} where vj∗ represents the utility value that works best. Following Eq. (9), the corresponding formula for calculating the regret value of the programme Gi for the cost of implementing the programme is, by the same token

∗ HiL = 1 − exp −μ viL − vL , i = 1, 2, ..., m (10) ∗

∗

where vL = max{viL |i = 1, 2, ..., m} and vL represent the utility values corresponding to the lowest cost option for implementing the contingency. For losses caused by contingencies, the perceived utility of the scenario Gi for losses caused by contingencies can be calculated based on the following Eq. (11) for the scenario Qj occurs as gij = vij + Hij , i = 1, 2, ..., m; j = 1, 2, ..., n

(11)

To carry out the next calculation, since in real life the development of events is often elusive, and considering that the evolution of actual contingencies is varied, the perceived utility of the scenario Gi for calculating the losses caused by contingencies in various types of situations giS , is given by giS =

n  j=1

pij · gij , i = 1, 2, ..., m

(12)

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In the same way as Eq. (12), the perceived utility value of the implementation cost of the contingency scenario Gi is used in the following equation giL , i.e. giL = viL + HiL , i = 1, 2, ..., m

(13)

In summary, having obtained the perceived utility values giS and giL , the combined perceived utility of the contingency scenario Gi can be obtained as (Gi ), which is calculated as (Gi ) = q1 giS + q2 giL , i = 1, 2, ..., m

(14)

Although the above equation calculates the perceived utility, it is often only a small number of individuals who ultimately make the decision, so the need to consider the decision maker’s preferences was also illustrated in the previous section, and the following Eq. (15) represents the parameters of the decision maker’s preferences, the specific values chosen are given in Table 1 above. (Gi ) = q1 giS + q2 giL + ξ giS giL

(15)

The final perceived utility, which takes into account the decision maker’s preferences, is then calculated and used to rank the options. 3.2 Calculation Steps Based on the above description, the following steps are listed below for the calculation of a specific regret theory-based emergency decision to respond to an emergency event taking into account the decision maker’s preferences, as follows. Step1: Normalize the  indicator values for losses due to major public health emergen cies a˜ ijk = aijks , aijku and the cost of implementing an outbreak prevention and control programme (ci ), respectively, according to Eqs. (1) to (3). Step2: Calculate Qj the effectiveness value of the scenario for the implementation of the outbreak prevention and control programme (Gi ) for the loss indicators in this paper based on Eqs. (4) to (7)vij and the effectiveness value of the scenario for the implementation of the specific scenario for Zengben viL . Step3: Calculate the regret value Hij for the loss indicator Gi and the regret value HiL for the prevention and control of implementation costs Gi , based on Eqs. (8) to (10), provided that the scenario Qj occurs. Step4: Calculate Gi the perceived utility of the damage scenario giS and the perceived utility of the cost of implementing the prevention and control scenario giL for major public health emergencies according to Eqs. (11) to (13). Step5: Calculate the combined perceived utility of the outbreak prevention and control programme Gi (Gi ) according to Eq. (14). Step6:Calculate Gi the combined perceived utility of an outbreak control scenario that takes into account the decision maker’s preferences according to Eq. (15)(Gi )ξ . Step7:Based on the value of the result (Gi )ξ calculated in Step 6, the final ranking of the outbreak prevention and control options or the selection of the best prevention and control option on merit is determined.

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4 Case Studies Since the end of 2019, the Covid-19 has been sweeping the world, bringing great disaster to the entire human community. After nearly two years of struggle with the Covid-19, the Covid-19 epidemic gradually stabilised, but in 2021.8 the rebound of the epidemic in Nanjing once again sounded an alarm to the national population, with new numbers of the Covid-19 being seen in many parts of the country. This paper demonstrates the potential application of the methodology proposed in this paper through the selection of a practical emergency decision-making scenario for the prevention and control of this epidemic. It is assumed that there are three suspected Covid-19 infections in the population of a district in a province of the country, with a total of approximately 50,000 people in the area, largely clustered in small communities. It is assumed that the area has a low level of population mobility and that the population is relatively stable. But considering that it is a region, outbreak prevention and control is also something that must be taken seriously. The regional leaders organised an emergency response team, listened to the advice of emergency response experts and firstly isolated, examined and treated three newly crowned suspected infected persons, and then proposed four specific prevention and control options for the prevention and control of the epidemic in the region, from which one option was finally selected for implementation: firstly all three options involved the closure of the region (1)G1 On the premise that all cells in the region are closed, each area of The preliminary estimate of the cost of implementing this option (c1) is $400,000; (2)G2 , based on the closure and disinfection of all cells in the area, real-time temperature monitoring and recording at the entrances and exits of the cells, the cost of implementing this option (c2) is $800,000; (3)G3 , based on the disinfection, temperature testing and recording of The cost of implementing this programme is (c3) $1.2 million, based on disinfection, temperature testing and documentation, and strict closure of specific priority areas in the region (in this case, areas where the three suspected infected persons have been in close contact recently). (4)G4 for a more rigorous area closure based on A3, with strict closure for epidemic prevention and control (not only closure, but also disinfection, and strict control of entry and exit, not leaving the community unless necessary, supplies to be supplied by the community) Obviously the cost of this option would be higher, estimated by experts to be c4 at 1.6 million yuan. Having listened to and consulted with the Covid-19 (epidemiologists), they gave several typical scenarios of what could happen:Q1 :Small number of infected people found in close contact with three suspected infected people living in their neighbourhood;Q2 :Multiple infected people found in neighbourhood streets;Q3 : Larger number of infected people found in adjacent neighbourhoods;Q4 : Large number of infected people found regionally. (Q1 to Q4 are progressive, with Q4 having the deepest and worst impact). In this paper, the impact of the new coronavirus on people and society is considered, and the impact of the new coronavirus is felt in many ways. 1) the change in people’s travel habits (masks are required in most cases); 2) the huge impact on people’s livelihoods and even the devastation of some industries; 3) the tens or even hundreds of millions of people infected so far; 4) the huge economic losses caused by the epidemic, etc. Two indicators have been selected for this paper:S1 : the first

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is the number of people infected by the spread of the new coronavirus. S2 : firstly, the number of people infected due to the spread of the Covid-19, in persons;: the economic losses caused by the epidemic, in million dollars. It is clear from the above scenarios for the prevention and control of Neoplasia and some of the possible scenarios, combined with the actual prevention and control of the epidemic, that different epidemic control protocols have different effects on the prevention and control of Neoplasia and the resulting differences are often enormous. As of today, humans are still in the exploratory stage of research on the Covid-19, combining what is currently known about the Covid-19 with the results of research on COVID-19 disease transmission and outbreak control that has been produced by relevant epidemiologists. Recommendations have been made to experts on the probability of the spread of COVID-19 when different epidemiological scenarios are adopted, and on the indicators of the damage that would be caused if various scenarios were to occur. The specific values are shown in Tables 1 and 2. Table 2. Probability of occurrence of each scenario of the covid-19 outbreak under different outbreak control scenarios Programme Gi

Q1

Q2

Q3

Q4

G1

0.4

0.3

0.2

0.1

G2

0.5

0.2

0.2

0.1

G3

0.6

0.2

0.15

0.15

G4

0.7

0.15

0.1

0.05

Table 3. Indicator values for damage caused by each scenario of the covid-19 outbreak under different outbreak control scenarios Programme Gi

Q1 S1

Q2 S2

S1

Q3 S2

S1

Q4 S2

S1

S2

G1

[0,6] [0,15] [20,25] [70,90] [50,60] [160,180] [60,80] [200,220]

G2

[0,4] [0,10] [15,20] [60,80] [40,50] [130,150] [50,60] [180,200]

G3

[0,3] [0,8]

[10,15] [40,50] [30,40] [110,130] [40,50] [160,180]

G4

[0,2] [0,5]

[5, 10]

[20,30] [20,30]

[90,110] [30,40] [140,160]

At the same time, the weighting of the covid-19 loss indicators L1 and L2 according to epidemiologists, disease control authorities and emergency management leaders and decision makers were ω1 = 0.6,ω2 = 0.4, while the weighting of the loss indicators and the implementation costs of several scenarios according to emergency management authorities and epidemiologists and disease control authorities and leaders and decision makers were q1 = 0.8,q2 = 0.2, respectively.. The next part is the exciting part. The next part is a more detailed calculation process for the contingency (risk) decision problem for this covid-19 scenario selection, based on

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the calculations already mentioned in the above article, and a comparison of the scenario selection (priority of the scenarios) without considering regret avoidance, resulting in a suitable scenario selection. The first step is (first) to normalise the indicator values for the losses caused by the recurrence of this outbreak and the implementation cost values for each contingency plan according to the above formula ci . The normalised loss indicator values are shown in Table 4 and the normalised contingency plan implementation cost values are calculated as:r1 = 1;r2 = 0.67;r3 = 0.33;r4 = 0; Table 4. Loss indicator values (after normalisation) Programme Gi

Q1

Q2

Q3

Q4

S1

S2

S1

S2

S1

S2

S1

S2

G1

[0.93,1]

[0.93,1]

[0.69,0.75]

[0.59,0.68]

[0.25,0.38]

G2

[0.95,1]

[0.95,1]

[0.75,0.81]

[0.64,0.73]

[0.38,0.5]

[0.18,0.27]

[0, 0.25]

[0,0.09]

[0.32,0.41]

[0.25,0.38]

G3

[0.96,1]

[0.96,1]

[0.81,0.88]

[0.77,0.82]

[0.5,0.63]

[0.41,0.5]

[0.09,0.18]

[0.38,0.5]

[0.18,0.27]

G4

[0.98,1]

[0.98,1]

[0.88,0.94]

[0.86,0.91]

[0.63,0.75]

[0.5,0.63]

[0.27,0.36]

[0.5,0.59]

It may be assumed that the normalised value of the loss indicator Sk bkij follows   ku a uniform distribution over the interval bks ij , bij , then the corresponding probability density function fijk (x) is fijk

1 ks ku ks ,bij ≤x≤bij bku ij −bij

= {0,

i1,2,...,m;j = 1, 2,...,n;k = 1, 2,...,h

Based on Eqs. (4)–(6), the implementation plan can be calculated in the face of emergencies Gm . and the scenario Qj occurs, calculate the scenario utility values for the loss due to the contingency vij and obtain the following matrix of utility values. ⎡

 VS = vij m×n

⎤ 0.9719 0.7395 0.3590 0.1423 ⎢ 0.9799 0.7873 0.4895 0.3182 ⎥ ⎥ =⎢ ⎣ 0.9840 0.8573 0.6144 0.4321 ⎦ 0.9920 0.9191 0.6919 0.5385

sed on Eq. (7), the utility value of the cost option for responding to emergencies can be calculated as v1C = 1, v2C = 0.67, v3C = 0.33, v4C = 0. Here, the arguments in the utility function are taken from θ = 0.8. Based on the above calculations, the regret values are calculated according to Eqs. (8)–(9),Hij , and the resulting matrix of regret values is as follows. ⎡

 H S = Hij m×n

⎤ −0.0060 −0.0554 −0.3950 −0.4862 ⎢ −0.0036 −0.1409 −0.2243 −0.2465 ⎥ ⎥ =⎢ ⎣ −0.0024 −0.0637 −0.0806 −0.1123 ⎦ 0 0 0 0

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Based on Eq. (10), calculate the regret value for the scenario Gi for the cost of implementing the contingency programme implementation as follows H1L = 0, H2L = −0.0857, H3L = −0.1930, H4L = −0.3499 It is important to mention that the parameter μ = 0.3 in the regret function here. Once the above results were obtained, further calculations were carried out, and according to Eqs. (11)–(12), the perceived utility of the decision maker for each contingency option was calculated to know the damage caused by the contingency as follows. g1S = 0.5500, g2S = 0.6776, g3S = 0.8437, g4S = 0.9284(a) The following are some of the most important features of the new system. The perceived utility values of the decision makers for each contingency option at the time of implementation cost are calculated from Eq. (13). g1L = 1, g2L = 0.6401, g3L = 0.2189, g4L = −0.3499 Next, the combined perceived utility of considering losses due to contingencies for decision makers without considering decision maker preferences, decision makers considering decision maker preferences, and the cost of programme implementation is calculated from Eq. (14) as (G1 ) = 0.64, (G2 ) = 0.6701, (G3 ) = 0.3439, (G4 ) = 0.6727 The combined perceived utility of taking into account the decision maker’s preferences, taking into account the decision maker’s consideration of the damage caused by the contingency and the cost of implementing the programme is calculated according to Eq. (15). Based on table (1) above, the combined perceived utility of the options is calculated by taking ξ = −0.9, ξ = 0, ξ = 0.9, which corresponds to conservative, neutral and risky decision makers respectively, and substituting the decision maker preference parameters as follows. ξ = −0.9 = 0.145, (G2 )(−0.9) = (G1 )(−0.9) 0.2797, (G3 )(−0.9) = 0.1776, (G4 )(−0.9) = 0.9650 (a) The following are some of the most important features of the new system ξ =0 (G1 )(0) = 0.64, (G2 )(0) = 0.6701, (G3 )(0) = 0.3439, (G4 )(0) = 0.6727 (a) The following are some of the most important features of the new system ξ = 0.9 (G1 )(0.9) = 1.135, (G2 )(0.9) = 1.0604, (G3 )(0.9) = 0.5101, (G4 )(0.9) = 0.3803. It is clear from the above calculations that different types of decision makers have different results on the final ranking of the options: for conservative decision makers:G4 > G2 > G3 > G1 ; for neutral decision makers:G4 > G2 > G1 > G3 ; for risky decision makers:G1 > G2 > G3 > G4 .

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From the above ranking of the options it is clear that decision makers with different preferences largely determine the choice of option. Taking into account the decision maker’s preferences and the effectiveness of considering regret avoidance, i.e. without taking into account either regret avoidance or the decision maker’s preferences when making a decision, the decision maker simply uses a mathematical approach to calculate the risk decision taking into account expectations and thus gives the priority of the solution. The specific calculation steps are as follows. Step1:Normalize the data in Table 2 to obtain normalized loss indicator values that are identical to those in Table 3. ku Step2:Assuming that the values of Sk are uniformly distributed over the interval [bks ij , bij ], k

the loss indicator expectation can be calculated for bij Step3:On the basis of step 3, build the corresponding risk decision matrix as in table (5) below. Step4:the other parameters are taken as above and the expected values for each scenario are calculated. Step5:On the basis of Step 4, a prioritisation of the options is calculated, taking into account the weighting of the different options, the damage caused by the contingency, and the cost of implementing the options (Table 5).

Table 5. Risk decision matrix Programme Gi

Q1

Q2

Q3

Q4

S1

S2

S1

S2

S1

S2

S1

S2

G1

0.965

0.965

0.72

0.635

0.315

0.225

0.125

0.045

G2

0.975

0.975

0.78

0.685

0.44

0.365

0.315

0.135

G3

0.98

0.98

0.845

0.795

0.565

0.455

0.44

0.45

G4

0.99

0.99

0.91

0.885

0.69

0.545

0.565

0.315

Based on the above calculation steps, the expected values for each scenario were calculated as E(G1 ) = 0.6569, E(G2 ) = 0.7422, E(G3 ) = 0.8534, E(G4 ) = 0.9145. The final combined ranking values for each option were obtained as (G1 ) = 0.7255, (G2 ) = 0.7278, (G3 ) = 0.7487, (G4 ) = 0.7316.

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Without considering decision maker preferences and without considering regret avoidance, the final ranking of options is G3 > G4 > G2 > G1 , Table 6 below gives the ranking of the scenarios for the different kinds of scenarios. Table 6. Ranking of scenarios under different kinds of scenarios Serial number

Description of the situation

Programme sequencing

1

Consider decision maker preferences and consider regret avoidance

2

Conservative decision makers.ξ = −0.9

G4 > G2 > G3 > G1

3

neutral decision makers.ξ = 0

4

risk-based decision makers.ξ = 0.9

G4 > G2 > G1 > G3 G1 > G2 > G3 > G4

5

No consideration of decision maker preferences, no consideration of regret avoidance

6

No consideration of decision maker preference parameters

G3 > G4 > G2 > G1

5 Conclusion Risk decision making is a popular topic and research object nowadays. In this paper, emergency decision making is investigated in terms of the damage caused by emergencies and the cost of adopting a plan for emergency emergencies, and this paper proposes a regret-averse decision making method for dealing with emergencies based on regret theory and an improved regret theory-based risk decision making method that takes into account the preferences of decision makers. The paper also proposes an improved regret-based risk decision making method that takes into account the decision maker’s preferences. This approach takes into account not only the regret-avoidance characteristics of the decision maker, but also the decision maker’s preferences as an influencing factor in emergency decision making. The paper concludes with a case study comparing the ranking of scenarios when decision maker preferences are not taken into account and when they are taken into account, and in the column where decision maker preferences are taken into account, three types of decision makers are listed and the ranking of scenarios that match the characteristics of decision makers is calculated. It is worth noting that the solution ranking method mentioned in this paper requires only simple and rigorous calculations to obtain satisfactory solution results and has some practical application.

References 1. Du, L.: Research on collaborative emergency response network for major urban emergencies. Harbin Engineering University (2020)

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2. Law Office of the State Council of the People’s Republic of China PRC Emergency Response Law [M. Beijing: China Legal Publishing House, 2007.The People’s Republic of China State Council Legislative Affairs Office. The People’s Republic of China Emergency Response Law. China Legal Publishing House, Beijing (2007) 3. Zhe, Z., Zongchao, P.: Government role determination in public health emergencies: challenges and countermeasures. Southeast Acad. 02, 11–17 (2020) 4. Li, X.H., Zhang, Y., Han, Z.Y., Jia, R.C.C., Wang, J.W., Zhu, Y.J.: Risk and cost-based emergency control decision for marine oil spill incidents. Chin. J. Safety Sci. 31(04), 184–190 (2021) 5. Bell, D.E.: Regret in decision making under uncertainty. Oper. Res. 30(5), 961–981 (1982) 6. Loomes, G., Sugden, R.: Regret theory: an alternative theory of rational choice under uncertainty. Econ. J. 92(368), 805–824 (1982) 7. Yang, L., Zhiping, F., Tianhui, Y., Xiaorong, W.: Pre-event-event two-stage emergency decision-making method for emergencies. Syst. Eng. Theory Pract. 39(01), 215–225 (2019) 8. Juan, W., Fengwei, D., Bo, F.: Research on risk decision-making for emergency response of coal mine accidents. China Sci. Technol. Saf. Prod. 14(06), 21–26 (2018) 9. Wang, Y., Liang, Y., Sun, H.: A regret theory-based decision-making method for urban rail transit in emergency response of rainstorm disaster. J. Adv. Transp. 2020 (2020) 10. Liang, W., Wang, Y.M.: A probabilistic interval-valued hesitant fuzzy gained and lost dominance score method based on regret theory. Comput. Ind. Eng. 159, 107532 (2021) 11. Zhu, L., Zhu, C., Zhang, X.: A hesitant fuzzy risk-based multi-attribute decision making method based on prospect theory. Stat. Decis. Making (17), 68–71 (2014) 12. Zhiping, F., Yang, L., Rongjian, S.: A risk-based decision-making approach for emergency response to emergencies based on prospect theory. Syst. Eng. Theory Pract. 32(05), 977–984 (2012) 13. Chengkun, L., Chen Yunxiang, G., Tianyi, X.H.: A hybrid multi-attribute decision making method based on prospect theory and evidence-based reasoning. J. Natl. Univ. Defense Technol. 41(05), 49–55 (2019) 14. Yanhui, Z., Changfeng, Z., Qingrong, W., Sinan, L., Yangyang, M.: Bi-objective optimization of emergency supplies game deployment considering finite rationality. Chin. J. Saf. Sci. 30(11), 168–174 (2020)

Integrating BIM and Quality Standards for Highway Construction Inspection Xin Xu1(B) , Kaiwen Chen2 , Jingwen Zhou1 , Jiawei Chen1 , and Xin He1 1 Department of Engineering Economics and Engineering Management, Hohai University,

Nanjing, China [email protected] 2 Department of Civil, Construction, and Environmental Engineering, The University of Alabama, Tuscaloosa, USA

Abstract. Construction inspection is critical to ensuring the quality and longterm performance of highway infrastructure. In the current practice the inspectors need to manually gather and interpret the lengthy quality standards for inspecting and evaluating the highway construction work, which is subjective, error-prone, and time-consuming. Many transportation agencies have developed inspection forms to organize construction requirements that reside in textual documents (e.g., standard specifications, construction inspection handbooks, and quality standards) in the format of checklists in order to reduce the workload for inspectors and enhance productivity. However, due to the missing link between the inspection forms/checklists and the construction work under inspection, the inspectors might need extra work to find the applicable forms/checklists for inspection. This paper thus proposes a Building Information Modeling (BIM)-based approach to establish the missing link. First, the semantic structure underlying the quality standards for highway construction (a Chinese quality standard for highway engineering is selected as the case) is analyzed, followed by the re-structuring of the quality requirements as inspection forms. Then the inspection forms are associated with their relevant BIM objects. Last, a BIM-enabled prototype is presented to illustrate the generation of customized inspection forms as construction progresses. With this newly developed tool, field inspectors can get rid of the overwhelming texts in the quality standards and can be equipped with the necessary knowledge regarding what, when, and how to inspect. Keywords: highway engineering · construction inspection · quality standards · BIM

1 Introduction Construction inspection is crucial for ensuring the quality and longevity of infrastructure. Currently, inspectors rely on a manual process of reviewing plans and specifications to identify quality requirements, which is inefficient and prone to errors [1, 2]. Some U.S. states have developed inspection forms in the form of checklists to organize these requirements, such as INDOT’s forms [3, 4] which feature a checklist of questions for © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 765–775, 2023. https://doi.org/10.1007/978-981-99-3626-7_59

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each section of their standard specification. Although the effectiveness of the newly developed checklists in reducing workload and increasing productivity has been proven, a critical bottleneck has been identified: the lack of connection between the checklists and the actual work being inspected, which results in inefficiencies when using the checklists for inspection purposes. This paper thus proposes a Building Information Modeling (BIM)-based approach to establish the missing link. First, the semantic structure underlying the quality standard for highway construction is analyzed, followed by the re-structuring of the quality requirements according to the semantic structure. Then the quality requirements in the standard are linked with their relevant objects in BIM. Last, the temporal and spatial distributions of the quality requirements (i.e., quality requirements organized by construction process and BIM objects) are achieved within a BIM-enabled platform. A user-friendly interface is designed to prove the concept. With this newly developed tool, field inspectors can get rid of the overwhelming texts in the quality standards and can be equipped with the necessary knowledge regarding what, when, and how to inspect. This newly developed tool is expected to greatly reduce the workload for inspectors and enhance the effectiveness of the construction inspection process.

2 Background and Related Studies Public and local transportation agencies, including the U.S. Federal Highway Administration (FHWA), have been striving to enhance the efficiency and effectiveness of construction inspection procedures. While there is no standard approach to construction inspection, field inspectors generally follow a three-step process involving preparation, performance, and reporting [5]. This process requires inspectors to compile relevant information, such as objectives, reference documents, and quality requirements, from textual documents, which can be a time-consuming and inefficient manual process. To address this, digital inspection systems have been developed to reduce inspection workload. The U.S. FHWA offers a quality assurance software package to guide the use of quality assurance procedures in highway construction projects [6]. Other computerbased systems, such as one developed by Battikha in 2002, have integrated inspection requirements with project activities and physical aspects [7]. Despite these advances, many local transportation agencies’ digital inspection systems only address procedural implementation and do not aid in requirements-gathering from textual documents. Some agencies have created checklists based on standard specifications to eliminate the manual requirements-gathering process [1, 4]. However, these checklists are typically static and have varying levels of detail. Although the effectiveness of the newly developed checklists in reducing workload and increasing productivity has been proven, a critical bottleneck has been identified: the lack of connection between the checklists and the actual work being inspected, which results in inefficiencies when using the checklists for inspection purposes.

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3 Methodology – Integrating BIM and Quality Standards A three-step approach, which consisted of analyzing the semantic structure underlying the quality standards, converting the standards into an inspection form, and linking the quality requirements with their relevant objects in BIM, was utilized to integrating BIM and quality standards for highway construction inspection. 3.1 Analysis of the Semantic Structure in Quality Standards A Chinese quality standard entitled Inspection and Evaluation Quality Standards for Highway Engineering was selected in this study for the analysis of the semantic structure, followed by the organization of the quality requirements following the semantic structure. The Inspection and Evaluation Quality Standards for Highway Engineering is intended to enhance the quality management of highway engineering, stipulate the standards for inspecting and evaluating the highway construction quality, and ensure the quality and long-term performance of highway infrastructure. This document serves as the dominant reference for the Chinese Contractors to guide their inspection practices as highway construction progresses. The analysis of the semantic structure started with the work breakdown structure (WBS) of highway engineering, which have been specified in the Appendix A of the standards of interest. A partial view of the WBS is given in Fig. 1. The detailed quality requirements are organized following the WBS by design, which

Fig. 1. A partial view of the highway engineering WBS

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eases the mining process of the highest level of the semantic structure of the standards. As such, a preliminary inspection form can be formulated by simply categorizing the quality requirements based on the predefined WBS. However, this is still far from being implemented for practical purposes and further refinements need to be done by looking into the semantic structure underlying the quality requirements. Below are the four observations after a careful investigation into the text structure and contents of the standards, as highlighted in Fig. 2. First, most of the section/subsection numbers and titles (e.g., 7 Pavement Engineering, 7.2 Cement Concrete Surface) align well with the predefined WBS, which would make it easier for the inspectors to navigate through the lengthy document. Second, each section has a dedicated subsection (e.g., 7.1 General Requirements) to stipulate the quality requirements applicable to all the work within the section. Third, each subsection (excluding the subsection of General Requirements) has three sub subsections (e.g., 7.2.1, 7.2.2, and 7.2.3), each specifying one type of quality requirements, i.e., Basic Requirements, Measured Items, and Quality

Fig. 2. The semantic structure underlying the quality requirements

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Appearance. Fourth, for the requirements regarding Measured Items, they are organized in tables with the following four headings: Item ID, Measured Item, Specified Value or Allowed Deviation, and Check Method and Frequency. The four observations lead to the design of a tabular format for organizing the quality requirements of the standards with a deeper level of detail. The headings of the designed table are given in Fig. 3. The table can be expanded to accommodate the details in Measured Items using the same headings with the original table in the standards. Using the designed table the quality standards with all the applicable quality requirements can be converted into an efficient and easy-to-use inspection form. For demonstration purpose, Table 1 gives a sample inspection form re-structuring the quality requirements in Fig. 2. With this type of inspection form, construction inspectors can avoid being overwhelmed by the lengthy quality standards and can be equipped with the necessary knowledge regarding what, when, and how to inspect.

Fig. 3. The headings of the designed table

3.2 Integration of Quality Standards with BIM While the newly developed inspection form by re-organizing the requirements in the quality standards has the potential to facilitate the inspection efficiency, the static nature of the form remains a critical limitation when it comes to the complex and dynamic jobsite conditions. Establishing the link between the inspection form and the ongoing work on the jobsite could be a valuable way to suit a practical need. BIM is a process of creating and managing information for a built asset throughout its lifecycle from planning and design to construction and operations. BIM can be information-rich by adding more extra information (BIM object plus time, cost, energy, quality, etc.), growing into ndimensional (nD) BIM. A BIM model containing construction information is the digital replicate reflecting the jobsite dynamics. As such, integrating the quality standards with BIM would make the inspection form more adaptive to the changing conditions on the jobsite. The following presents the integration of the inspection form with the highway BIM objects.

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X. Xu et al. Table 1. A Sample Inspection Form

Highway Engineering WBS

Inspection and Evaluation Quality Requirements

Type of Quality Requirements GR

Unit Project

Partitioned Project

Subdivisional Work

R_ Code

R_ ID

Requirement Content

Pavement Engineering

Surface

–

7.1.1

–

The specified value or allowable deviation of the measured items of pavement engineering shall be determined by two categories: expressway/first-class highway and other highways, and the inspection standards for all the thickness of the pavement structure layers shall be the allowable deviation

…

-

…

7.2.1

1

The quality of the base layer should meet the specifications and design requirements, and the surface should be clean and free of floating soil

…

…

–

–

–

–

1

Appearance-limiting defects for the panels in Appendix P shall not be present

Cement Concrete Surface

7.2.2

7.2.3

…

…

…

…

…

…

…

…

…

…

…

BR

MI

Details of Measured Item QoA

√

√ √

√ √

√ √

√

I_ ID

Measured Item

Specified Value /Allowed Deviation

CM& Freq

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

1

Flexural-tensile strength

Within acceptable ranges

See App. C

…

…

…

…

–

–

–

–

–

–

–

–

Note. R_Code: Requirement Code; R_ID: Requirement ID; GR: General Requirement; BR: Basic Requirement; MI: Measured Item; QoA: Quality of Appearance; I_ID: Item ID; CM&Freq.: Check Method and Frequency

A Chinese professional advisory standard entitled Standard for Application of Building Information Modeling in Highway Engineering Construction is used for reference. This standard has mapped the Highway Engineering WBS to their corresponding objects in BIM, as shown in Fig. 4. For example, the construction work of Soft Soil Ground Treatment is mapped to the BIM object of Sand Mat of Subgrade. The inspection form developed in this study categorizes the quality requirements by the Highway Engineering WBS, and thus the quality requirements can be linked to their related BIM objects through the WBS-BIM mappings. However, three limitations were identified during the linking process. First, for the General Requirements in the inspection form, they do not have a specified subdivisional work in the Highway Engineering WBS (see Table 1 for Requirement 7.1.1), which would make it difficult to find their related BIM objects. Second, the WBS-BIM mapping still remains at a relatively rough level. For example,

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the work of Earth Subgrade and Rock-fill Subgrade have the same BIM mapping of Subgrade Structure Layer, and consequently the BIM object of Subgrade Structure Layer would have a number of applicable quality requirements, which would place a further burden on the inspectors to differentiate the requirements varying by the type of Subgrade Structure Layer. Third, one dominant mapping situation is that one subdivisional work may have more than one BIM objects. For example, besides Sand Mat of Subgrade the Soft Soil Ground Treatment work has other mapped objects such as Reinforced Soil Pile and Rigid Pile. This situation would make the linking process more complicated as it is required to read through every quality requirement for picking the most relevant from a candidate set of BIM objects.

Fig. 4. WBS-BIM Mappings

To address the abovementioned limitations, the following operations were performed. For Limitation One, all general requirements were made applicable to the entire unit project (e.g., Subgrade Engineering) rather than any specific BIM object. For Limitation Two, the mapped general BIM objects were further detailed into their specific types which were then associated with their respective applicable quality requirements. For Limitation There, every quality requirement was examined in order to build the most proper link. Table 2 gives a sample resulting linking using the quality requirements regarding Subgrade Engineering.

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X. Xu et al. Table 2. A sample resulting linking

Unit Project

Partitioned Project

Subdivisional Work

Model Object

R_ Code

…

Relevant Object in BIM

Subgrade Engineering

Earth and Rock Engineering

-

-

4.1.1

…

Subgrade

…

…

4.2.1

…

…

…

4.3.1

…

…

…

4.4.1

…

Sand Mat of Subgrade

4.4.2

…

Reinforced Soil Pile

…

…

…

…

…

…

…

…

Earth Subgrade

Subgrade Structure Layer

Rock-fill Subgrade

Soft Soil Ground Treatment

…

Sand Mat of Subgrade, Reinforced Soil Pile, Rigid Pile, etc

…

…

…

…

Earth Subgrade Structure Layer Rock-fill Subgrade Structure Layer

The original inspection form enables the grouping of quality requirements by WBS, generating WBS-consistent sub forms for inspection, and when the schedule information is assigned to the WBS, any sub form that applies can be presented in a timely manner as construction progresses. When further enriched with the BIM associations, the inspection form enables the presentation of quality requirements that are only applicable to any specific BIM object. The two enabling mechanisms (i.e., temporal and spatial distributions of the quality requirements) are realized via the database links as depicted in Fig. 5.

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Fig. 5. Database links

4 Design of a User Form Gor Highway Construction Inspection To prove the concept, Microsoft Power BI was used to design a user-friendly form to support highway construction inspection. Power BI has a great capability of integrating data in varying formats and visualizing them for data-driven decisions that drive strategic actions. For this proof of concept, three data were used, described as follows. First, a sample BIM model for highway infrastructure was created using Autodesk Revit. Second, the Revit model was imported to Autodesk Navisworks for construction simulation, followed by the generation of an Excel file that archives a set of sample highway construction activities, their start dates, end dates, and some representative model screenshots. Third, the inspection form that re-structures the requirements in the Quality Standards as well as their associated objects in BIM is stored as a master Excel file. Last, the Revit model and two Excel files were loaded into Power BI for demonstration. The underlying mechanism for data integration relies on the database links described previously. Figure 6 is the designed user interface for digital highway construction inspection based on the integration of BIM and quality standards. The following functionalities were designed as embedded into the system. • The inspectors can navigate through the 3D BIM model to have an overview of what is or will be constructed on the jobsite. • The inspectors can insert any date (e.g., current date) to check several representative screenshots of the simulated construction process; after checking the construction process, the inspectors can also select the corresponding work in the WBS to generate all applicable quality requirements. • The inspectors can click any element in the 3D BIM model to generate all the quality requirements that apply.

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• The inspectors can also select any type of quality requirements to only display the quality requirements of that type. Automating the requirements-gathering process and presenting them in a consistent and easy to use format would greatly enhance inspection efficiency and accuracy and, consequently, address the resource shortage challenge.

Fig. 6. User Form for Highway Construction Inspection

5 Summary and Conclusions This paper proposes a BIM-based approach to establish the link between the quality standards and the construction work under inspection. The approach consists of three steps, i.e., analyzing the semantic structure underlying the quality standards, converting the standards into an inspection form, and linking the quality requirements with their relevant objects in BIM. Within a BIM-enabled platform, the temporal and spatial distributions of the quality requirements (i.e., quality requirements organized by construction process and BIM objects) are achieved within a few clicks. For proof of concept, a user interface is designed with the desired capabilities, e.g., automatically generating customized checklists with adequate details for construction inspection per user intentions. This system eliminates the manual, time-consuming, and error-prone process of gathering construction requirements from textual documents in current practice, and it overcomes the limitations of the static inspection forms and checklists. With this newly developed tool, field inspectors can get rid of the overwhelming texts in the quality standards and can be equipped with the necessary knowledge regarding what, when, and how to inspect. This newly developed tool is expected to greatly reduce the workload for inspectors and enhance the effectiveness of the construction inspection process.

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References 1. Xu, X., Zhang, Y., Yuan, C., Cai, H., Abraham, D.M., Bowman, M.D.: Risk-Based Construction Inspection, p. 28 (2019).https://doi.org/10.5703/1288284316916 2. Federal Highway Administration (FHWA): Construction Program Management and Inspection Guide (2004) 3. Yuan, C., Park, J., Xu, X., Cai, H., Abraham, D.M., Bowman, M.D.: Risk-based prioritization of construction inspection. Transp. Res. Board, Washington, D.C. (2018). https://doi.org/10. 1177/0361198118782025 4. ODOT: Inspection and Documentation Forms. http://www.dot.state.oh.us/Divisions/Construct ionMgt/Admin/Pages/InspectionForms.aspx. Accessed 26 July 2020 5. Dias, L.A.: Inspecting Occupational Safety and Health in the Construction Industry (2009) 6. Weed, R.M.: Quality Assurance Software for the Personal Computer: FHWA Demonstration Project 89, Quality Management (No. FHWA-SA-96–026) (1996) 7. Battikha, M.G.: QUALICON: computer-based system for construction quality management. J. Constr. Eng. Manag. 128(2), 164–173 (2002)

Influence of Spatial Ability on Virtual Annotation Response in Construction Equipment Teleoperation Xiaomeng Li, Jiamin Fan, and Xing Su(B) College of Civil Engineering and Architecture, Zhejiang University, Hangzhou, China [email protected]

Abstract. Virtual annotations (VA) for compromising visual limitations during construction equipment teleoperation can provide critical instructional information, but it remains to be verified whether an operator can respond to VA timely and accurately while operating. Studies have demonstrated a positive relationship between spatial perception ability (SPA) and teleoperating performance. Many studies considered that those with SPA have a lower mental workload and more attention on other matters. Therefore, this study sought to examine whether an operator with a higher SPA score can respond better to VAs. Quantitative results from the SPA test questionnaire and the teleoperated excavator experiment showed a significant inverse relationship between SPA and the correct response time. It is expected that the results could improve the effectiveness of selecting construction equipment operators and optimize prior management. Keywords: virtual annotations · teleoperation · spatial perception ability

1 Introduction Teleoperation can isolate operators from a hazardous work environment to guarantee their safety and has been studied for aircraft operation and rescue efforts. However, the research on construction equipment teleoperation is mostly in the stage of theoretical development. The main reason is that viewing screen images cannot achieve a sufficient breadth and depth of direct observation compared to naked, making it difficult to estimate distances and detect obstacles. To this end, the introduction of virtual annotations (VA) in the form of symbols or graphics to present key information in teleoperation is expected to reduce the difficulty of obtaining environmental information and reduce some cognitive load to help remote operators detect unexpected situations [1]. However, at present, in the field of construction, although many studies try to use VA as a visual aid, and based on different visual presentation technologies (mostly VR, AR) to improve the safety of construction machinery operation, most studies tend to pay more attention to detection sensing technology, and do not consider whether VA really plays its role in the teleoperation of construction machinery, that is, operators can only show the effectiveness of VA application if they can respond to different VAs in a timely © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 776–785, 2023. https://doi.org/10.1007/978-981-99-3626-7_60

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and accurate manner. In the case of relatively mature VA technology, the remaining variable, the quality and ability of the operator in teleoperation, has a greater impact on the performance of operation and the response to VA. Research has proven that operator’s spatial perception ability (SPA) is an important factor in teleoperation performance. Spatial perception is essentially a process related to interpreting incoming data of an individual’s visual perception, such as images, objects, maps, shapes, as well as scenes of specific situations in the environment. Numerous studies have shown that operators with greater SPA can complete operational tasks faster and perform better [2–4]. The reason is that operators with greater SPA have a lower cognitive load when operating and will form the overall cognitive map faster than those with weak SPA [5]. As mentioned earlier, the introduction of VA reduces some cognitive load. However, VA, as a newly added element, increases the cognitive needs to distinguish different types of VAs and reflect corresponding operations than the previous pure operational tasks. Moreover, VA in the teleoperation of excavator is mainly used to remind obstacles and emergencies. Existing studies have investigated the relationship between SPA and operation performance, without considering external things. Therefore, on the one hand, this paper aims to explore the operational performance of excavators with different SPA in the teleoperation of excavators, which has not been studied; on the other hand, it aims to explore operators’ reactions with different SPA to VA outside excavators operation, in order to provide guidance for operators’ selection and training.

2 Related Work 2.1 Virtual Annotation (VA) Currently, VA is mainly used for navigation and operation guidance by providing annotation. Kleinermann et al. proposed a method that allow non-specialists to add and update VA to existing virtual environments and create navigation paths that allow Web end users to better navigate virtual environments [6]. Gauglitz et al. implement a visuospatial sharing augmentation system to support remote collaborative tasks for equipment maintenance, where local and remote users can communicate well through AR spatial annotation [7]. Leutert and Schilling propose a projection-based AR system for remote equipment maintenance communication in which users can add VA to highlight problematic mechanical parts to aid technical communication [8]. Andersen et al. propose a training system that can display intuitive graphic markers to accurately display the surgical forceps clamping drop point and scalpel cutting route to give trainees effective guidance on surgical operations [9]. These features of VA mainly enhance the operation convenience. In the field of construction equipment operation, researchers focus on safety warnings for on-site operation. Fang et al. develop a framework for active real-time alerting for the safe operation of mobile tower cranes, where VA is used for target location and active alerting [10]. Chen and Teizer present a real-time sensing data collection and visualization technique for site construction safety and activity monitoring, in which VA is used for hazard area warnings and object distance displays in a visualization platform [11].In the field of remote operation, there are few researches on VA. At present, the

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teleoperation of excavator proposed by Hong et al. is relatively complete in theory and experiment. VA helps operators to understand the field situation by highlighting the edge of obstacles or setting radar map on the screen to obtain the relative position of excavator and surrounding objects [12]. In the context of construction equipment teleoperation, operators can obtain spatial information about the surrounding environment with the help of virtual reality. VA system can help identify and detect objects in dynamic construction sites. Although VA has a strong theoretical basis and a mature application mode in other fields, the characteristics of personnel perception, operation and environment in construction machinery teleoperation are different from those in other fields. In remote operation, the operator must not only complete the operation content of the machine itself, but also set aside energy to pay attention to random emergencies, understand VAs, and make environmental observations. It has been reported that handling VAs during operations may distract operators and affect operational performance. For example, in the remote operation experiment of excavator conducted by Hong et al., several subjects reported that VA distracted attention during operation and reduced operation performance [12]. It has been proved that VA can promote the spatial understanding of operators, and reduce operators’ cognitive load. Operators with greater SPA also have lower cognitive load during operation, thus achieving better operational performance. Then, can operators with greater SPA obtain better operational performance and more effective VA responses in the mechanical remote operating system with VA than operators with weaker SPA? Therefore, this paper will conduct an experimental study on the relationship between SPA and VA response, in order to draw relevant conclusions to support the construction of VA system framework for remote operation of construction machinery, and effectively improve operators’ situational awareness in remote operation environment. 2.2 SPA in Teleoperation There is no clear standard for the definition of spatial perception ability in academia. DU et al. believe that spatial perception is the process of determining an individual’s location in space, creating information about space in his mind, identifying and understanding space, finding places, finding routes and describing places, depending on route knowledge obtained through identifying and understanding places [13]. Contero et al. believed that spatial perception refers to the ability to move objects or components in the void with the mind [14]. Generally speaking, we can think that spatial perception ability refers to the ability to understand, infer and remember the spatial relationship between objects or Spaces. It is an important dimension of human’s intellectual ability and an inherent ability. It has the same status as other forms of intelligence, such as expression ability, logical reasoning ability and memory ability. According to the characteristics of spatial perception ability, people unconsciously use this ability in daily life, such as confirming their own position and direction, evaluating the size and distance of objects, recognizing maps, etc. Existing studies have shown that people with high SPA have advantages in STEM fields such as science, technology, engineering and mathematics, and everyone needs to cultivate these necessary skills through basic education [15]. In addition, the potential advantages of high SPA are also found in fields requiring three-dimensional spatial imagination such as surgical

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medicine, architectural design, and remotely operated machinery. For example, Dror et al. argued that pilot selection should be based on spatial awareness, and try to select “natural” (high SPA) pilots. Menaca -Brandan et al. found that astronauts with higher spatial orientation and mental rotation abilities spent significantly shorter operating time and observation time in simulating the operation of the extra-vehicle-operated robotic arm, indicating that space perception ability is related to operational performance [16]. Liu et al. used SPA to predict the performance of astronauts’ teleoperation training, and proved through logistic regression analysis that the performance of the operation of the extravehicular robotic arm could be predicted by SPa-related test results to a certain extent. They believed that astronauts with lower SPA scores could receive training earlier in advance, and astronauts with better space skills could be arranged in a shorter time window more flexibly. Because they are more likely to perform well [4]. Lathan et al. [2] showed that the spatial perception ability of operators varies among individuals and is positively correlated with the remote operation performance. The stronger the spatial perception ability, the better the remote operation performance. Spatial perception may affect people’s integration of spatial information from different perspectives, especially from different reference frames. These studies have demonstrated that SPA is closely related to the operational performance of remotely operated machinery. However, the current research on remote operation with SPA only stays in the mechanical operation itself, without further considering whether different operators in SPA can effectively respond to emergencies in the remote environment. This paper will study and analyze the relationship between spatial perception ability and virtual annotation response, and discuss the characteristics of “people” that affect the overall performance of remote operation, in order to support the early selection of operators and the ability orientation in process training.

3 Experiment In this study, subjects were given 15 min to complete a paper SPA test questionnaire, and on another day, they were required to complete teleoperation experiment in Unity using excavator to transfer balls and react to randomly appearing VA. 3.1 SPA Test Questionnaire The test question set was selected from the well-established test methods, which is shown in Table 1. The final set consisted of 21 questions, and subjects were required to complete the test within 15 min, and the number of correct answers was used as the test score. 3.2 Virtual Annotation Design Two types of VA, ring and cross (refer to Fig. 1), were used to represent random obstacles or contingencies in the construction environment. Subjects need to respond promptly and accurately to them during teleoperation. The ring-shaped VA requires the operator to push the honk button while excavating, and the cross-shaped VA requires the operator to cease operation until the VA vanishes. The VA in this study randomly exhibits different sizes of small, middle, and large at any location on the screen.

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X. Li et al. Table1. Composition of SPA test question set

SPA’s Sub-competencies

Test Question

Question Type

Number

SV

Paper Folding

Single Choice

6

Form Board

Single Choice

3

Card Rotation

Single Choice

3

Cube Comparison

Single Choice

3

PSVT:R

Single Choice

3

Vandenberg MR

Multiple Choice

3

SR

Fig. 1. Diagram of the appearance of ring and cross in teleoperation

3.3 Teleoperation Experiment Design First, subjects were required to fill out the pre-task questionnaire to provide information about gender, age, and previous 3D gaming experience. Then, they should watch a short video introducing excavator operation, and they were given five minutes to familiarize the operation. Next, they should watch another short video introducing the reaction to VA, and two minutes were given to practice it. The last was the ten-minute formal test, in which two types of VA appeared randomly in three sizes and anywhere on the screen, and the number of balls transferred and the number of correct VA responses were displayed at the top left of the screen. The backend will also record the right or wrong and time of each response to VA. 3.4 Experimental Conditions The teleoperation platform is deployed on a computer with 3.70 GHz Intel(R) Core(TM), 64G RAM, and NVIDIA GeForce RTX 2080 Ti with 11,048 MB VRAM. The excavator simulation software is developed in Unity. The screen in front of the subjects was used to display the teleoperation scenario, with a game joystick in each left and right hand and a keyboard placed in the middle for the necessary key control.

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3.5 Participants Participants were recruited from Zhejiang University student population. A total of 30 students participated, 15 were male and 15 were female. Their mean ages were 24.03 years with corresponding standard deviations of 2.03, respectively. All participants have no construction equipment operation experience. 3.6 VA Response Assessment Indices and Research Hypothesis Correct response time and correct response rate are analyzed as the two major assessment indices. Correct response time refers to the duration between a VA appears and the subject correctly responds to it. Correct response rate is the ratio of correct responses over all responses. The research hypothesis is based on the evaluation indicators: A subject with a higher SPA score has a lower average correct reaction time and a higher correct reaction rate.

4 Results 4.1 Descriptive Statistics of SPA Scores Table 2 shows the SPA scores of 30 people. It was found that SPA could not be directly tested for significance with indicators, so SPA was divided into high and low groups according to the mean value. The results of grouped descriptive statistics showed that scores for the high subgroup ranged from 12 to 18 (mean 13.719, standard deviation 1.915), and scores for the low subgroup ranged from 5 to 11.5 (mean 10.036, standard deviation 1.726). Table 2. Descriptive statistics of SPA scores SPA groups

N

Min

Max

Mean

Std

High score

16

12.0

18.0

13.719

1.915

Low score

14

5.0

11.5

10.036

1.726

Sum

30

5.0

18.0

12.000

2.593

Independent samples t-test by gender grouping (p = 0.106 > 0.05) showed that there was no significant difference between genders in SPA scores, but male scored slightly higher than female on average, which is consistent with established research findings that men are superior to women in terms of mental rotation and spatial orientation (Table 3). The results of one-way ANOVA on subjects’ SPA scores for the three types of 3D game experiences (p = 0.171 > 0.05) indicated that there was no significant difference among different experiences in SPA scores, but when looking only at the mean (11.500, 12.714, 14.750), there is indeed a positive relationship between 3D game experience and SPA score.

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X. Li et al. Table 3. t-test of SPA scores by gender grouping

Item

Category Female

15

Gender

Male

15

Never or rarely

21

11.500

2.480

Not often

7

12.714

2.514

Regularly

2

14.750

3.182

3D Game

N

Mean

Std

t-value

Sig.(2-tailed)

11.233

2.638

− 1.668

0.106

12.767

2.390 –

0.171

4.2 Descriptive Statistics and t-test of Correct Response Time for High and Low SPA Score Group Table 4 shows the average correct response time for ring and cross together, ring only and cross only. The independent samples t-test (0.875, 1.003, p = 0.042 < 0.05; 0.899, 1.074, p = 0.025 < 0.05) indicates that high grouping takes less time than low grouping significantly. The result (0.865, 0.979, p = 0.112 > 0.05) shows that there is no significant difference in the mean correct response time to the cross only between two groups. Table 4. Average time for all correct responses for ring and cross SPA Groups

N

Mean

Std

t-value

Sig.(2-tailed)

High score

16

0.875

0.130

− 2.129

0.042

Low score

14

1.003

0.195

ring only

High score

16

0.899

0.146

− 2.416

0.025

Low score

14

1.074

0.233

cross only

High score

16

0.865

0.133

− 1.659

0.112

Low score

14

0.979

0.226

ring and cross

4.3 Correct Response Rate Table 5 shows the correct response rate for three situations. The independent samples t-test (p = 0.197 > 0.05; p = 0.470 > 0.05; p = 0.810 > 0.05) indicates that there is no significant difference in the correct response rate between two SPA groups in all three cases. But the Means (0.815, 0.770; 0.638, 0.586) show a positive relationship between correct response rate and SPA scores. 4.4 Number of Transferred Balls The number of transferred balls, as an indicator of productivity reflecting the level of teleoperation, was also recorded and analyzed. Table 6 (424.94, 311.64, p = 0.014
Y refers to the cause-and-effect occurrence of two or more safety risk factors. Suppose safety risk factors X and factor Y are causal related, and their causal relationship X < cau > Y is shown in Fig. 1(a). The solid arrow line represents that factor X may cause the occurrence of factor Y. Factor X is the cause factor and Y is the result factor. Coupling relationship X < cou > Y means that the simultaneous occurrence of factors could cause huge disturbance to the safety risk state. Figure 1(b) displays the coupling relationship. The dashed arrow line represents the coupling transmission relationship. The arrow line is bidirectional, indicating the coupling relation is mutual.

(a) Causal transmission relationship

(b) Coupling transmission relationship

Fig. 1. Two kinds of risk transmission relationship

4 Research Methods 4.1 Research Design The DEMATEL (Decision Making Trial and Evaluation Laboratory) technique is frequently used to identify the causal relationships among factors [13]. The fundamental idea of the method is grounded upon graph theory which exploits visualization for examining complex problems [14]. In this study, the impacts of causal and coupling relationships are integrated. In doing so, the problems and corresponding effective solutions are outlined below. (1) Accident report reveals the causes of metro construction accident. These documents are open-accessed. Thus, accident reports were collected to help identify safety risk factors. (2) Due to the centrality degree in DEMETAL refers to the comprehensive impacts among risk factors, the centrality degree is used to represent the risk transmission strength (Mi RT ). The centrality degree of causal relation refers to the causal transmission strength (Mi cau ), while the centrality degree of coupling relation refers to the coupling transmission strength (Mi cou ). (3) The DEMATEL method is based on the calculation of node density in complex networks. The more arrows pointed by the nodes in the network, the greater of the influence degree. Also, the more the arrows pointing to the node, the greater

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of the being-affected degree. Therefore, the causal centrality degree (Mi cau ) and coupling centrality degree (Mi cou ) can be algebraically superimposed, representing the transmission strength (Mi RT ), as shown in Eq. (1): MiRT = Micau + Micou

(1)

4.2 Framework of the Method The specific steps of evaluating the transmission strength for metro construction safety risks are displayed in Fig. 2, and are illustrated as follows.

Fig. 2. Framework of the evaluation of risk transmission

Step 1: Data collection. Risk factors are identified by experienced experts from accident reports of metro construction, and their relationships are provided by questionnaires. Step 2: Developing the direct relation matrices. Provide the causal-and-effect and the coupling direct relation matrix separately, using expert interview and questionnaires. Step 3: Data processing. First, normalize the above two matrices separately, by converting the Likert values in the direct relation matrices to values in the [0,1] interval. Then, calculate the total (direct and indirect) relation matrices separately. Detailed equations and process can be found in the reference [13] and [14]. Step 4: Producing the indicators and diagrams. First, calculate the influence degree, being-affected degree, and causality strength, using the total relation matrix of causality. Then, calculate the coupling strength, using the total relation matrix of coupling. Finally, calculate the transmission strength according to Eq. (1).

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5 Data Analysis 5.1 Data Collection By searching the pattern ((“subway” or “urban rail transit” or “metro”) and “construction” and “accident”) in the website of China. 68 safety accidents were obtained, most of them caused deaths and injuries. These reports were published to the public after investigation, describing the scene and the process of the accidents in detail. We carefully read through the reports and selected 48 risk factors occurred in the reports. Then expert experience was utilized to check the initial risk list. Experts were chosen by two criteria. They need to: 1) have at least five years of safety management experience of metro construction projects; 2) obtain at least an undergraduate degree. Thus, ten experts were selected. They were interviewed separately by the researchers. Finally, 37 risk factors were validated and classified into five categories (Table 1). Table 1. Safety risk factors of metro construction projects Categories

Risk Factors

Serial number

Risk Factors

Serial number

Environment (SH)

Natural disaster

SH1

Insufficient exploration or protection of surrounding buildings

SH4

Complex geological conditions

SH2

Rainwater pipeline inspection or maintenance

SH5

Unknown underground hydrological conditions

SH3

Gas pipeline detection or protection

SH6

Insufficient safety management organization

SG1

On-site management chaos

SG7

Improper management of subcontractors

SG2

Insufficient safety protection

SG8

Incomplete safety management mechanism

SG3

Poor coordination of construction organization

SG9

Insufficient safety inspections

SG4

High schedule pressure

SG10

Insufficient safety training

SG5

Insufficient supervision

SG11

Insufficient emergency plans and drills

SG6

Lack of safety awareness

SR1

Construction not as designed

SR5

Defects of construction technology

SR2

Improper lifting of the crane

SR6

Illegal command from supervisors

SR3

fatigued operations of workers

SR7

Illegal operation of construction work

SR4

Management (SG)

Personnel (SR)

(continued)

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Categories

Risk Factors

Serial number

Risk Factors

Serial number

Techniques (SJ)

Insufficient geological survey

SJ1

Quality defects of the structure

SJ6

Design flaw

SJ2

Formwork support system defects

SJ7

Inadequate monitoring

SJ3

Defects of foundation pit support system

SJ8

Improper construction scheme

SJ4

Insufficient remedies

SJ9

Insufficient safety disclosure

SJ5

Improper choice of materials

SW1

Improper selection of machinery and equipment

SW3

Unreasonable stacking of materials and equipment

SW2

Equipment failure or operation

SW4

Materials and equipment (SW)

5.2 Developing the Direct Relation Matrices The direct relation matrices were set based on experts’ experience. The experts are the same people as in risk identification step. Researchers designed two sets of pairwise comparison questionnaires to investigate the causal and coupling relationship. Likert Scale (one and five respectively denote very low and very high) was used for experts to determine the direct relation values. The average of the expert’s scores after removing the highest and lowest scores was determined as the final value of the direct relation matrices. 5.3 Data Processing and Output Using Matlab to normalize the direct relation matrices and produce the total relation matrices. Then, calculate the influence degree, being-affected degree, causality degree, strength, as well as the causal strength, coupling strength and transmission strength. Table 2 shows the above indicators obtained by METETAL technique. Figure 3 displays the influence and being-affected degree of safety risk factors, and Fig. 4 demonstrates the distribution of transmission strength and causality degree.

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Table 2. Indicators obtained by METETAL technique Risk Influence Being-affected Causality Centrality Degree Factors Degree Degree Degree Causal Coupling Transmission Rank Strength Strength Strength (TS) of TS SH1

0.00

0.00

0.00

0.00

0.65

0.65

8

SH2

0.00

0.00

0.00

0.00

0.54

0.54

10

SH3

0.00

0.00

0.00

0.00

0.67

0.67

7

SH4

0.00

0.04

– 0.04

0.04

0.03

0.07

31

SH5

0.00

0.03

– 0.03

0.03

0.38

0.41

15

SH6

0.00

0.02

– 0.02

0.02

0.06

0.08

29

SG1

0.04

0.00

0.04

0.04

0

0.04

32

SG2

0.04

0.10

– 0.06

0.14

0.01

0.15

27

SG3

0.30

0.06

0.25

0.36

0.02

0.38

16

SG4

0.13

0.56

– 0.43

0.69

0

0.69

6

SG5

0.23

0.18

0.05

0.40

0.03

0.43

14

SG6

0.05

0.30

– 0.25

0.35

0.02

0.37

18

SG7

1.05

0.39

0.67

1.44

0.07

1.51

1

SG8

0.00

0.37

– 0.37

0.37

0.54

0.91

2

SG9

0.35

0.00

0.35

0.36

0.02

0.38

17

SG10

0.09

0.01

0.08

0.10

0.03

0.13

28

SG11

0.40

0.00

0.40

0.40

0.06

0.46

11

SR1

0.89

0.00

0.89

0.89

0

0.89

3

SR2

0.25

0.00

0.25

0.25

0.05

0.30

21

SR3

0.12

0.07

0.04

0.19

0.03

0.22

24

SR4

0.00

0.20

– 0.20

0.20

0.62

0.82

4

SR5

0.08

0.16

– 0.09

0.24

0.02

0.26

23

SR6

0.00

0.20

– 0.20

0.20

0.16

0.36

19

SR7

0.01

0.02

– 0.01

0.03

0.01

0.04

33

SJ1

0.03

0.01

0.02

0.04

0.28

0.32

20

SJ2

0.01

0.01

0.01

0.02

0.02

0.04

34

SJ3

0.06

0.09

– 0.04

0.15

0.31

0.46

12

SJ4

0.09

0.16

– 0.07

0.25

0.02

0.27

22

SJ5

0.07

0.10

– 0.02

0.17

0

0.17

25

SJ6

0.00

0.16

– 0.16

0.16

0

0.16

26 (continued)

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Risk Influence Being-affected Causality Centrality Degree Factors Degree Degree Degree Causal Coupling Transmission Rank Strength Strength Strength (TS) of TS SJ7

0.00

0.01

– 0.01

0.01

0

0.01

37

SJ8

0.00

0.30

– 0.30

0.30

0.41

0.71

5

SJ9

0.00

0.43

– 0.43

0.43

0

0.43

13

SW1

0.00

0.00

– 0.00

0.01

0.03

0.04

35

SW2

0.00

0.04

– 0.04

0.04

0.03

0.07

30

SW3

0.00

0.01

– 0.00

0.01

0.01

0.02

36

SW4

0.02

0.31

– 0.29

0.34

0.25

0.59

9

Being-affected degree

Trend line

Influence degree

Fig. 3. Influence and being-affected degree of safety risk factors

6 Discussion Influence degree shows the extent that one factor influences the other factors, while being-affected degree refers to the extent that one factor is affected by the other factors. Figure 3 shows that most factors are distributed around the axis (below the trend line). Some factors have a large influence value, but the being-affected value is small. These factors are: Lack of safety awareness (SR1 ), Insufficient supervision (SG11 ), Poor coordination of construction organization (SG9 ), and etc. On the other hand, some factors have a small influence impact to other factors, but can be strongly affected, such as Insufficient safety checks (SG4 ), Insufficient remedies (SJ9 ) and Insufficient safety protection (SG8 ). Among all the risk factors, On-site management chaos (SG7 ) shows

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Causality degree

Transmission Strength Evaluation of Metro Safety Risks

Transmission strength

Fig. 4. Transmission strength and causality degree of safety risk factors

both great influence and being-affected degree. This indicates that SG7 is very important for metro construction safety. Causality degree indicates the pure impact of one factor on other factors, which is the difference between the influence degree and being-affected degree. It can be seen from Fig. 4 that the causality degree is closely distributed near the abscissa axis, and is basically between the [–0.5, 0.5] region. This indicates that most factors are equally influenced and being influenced. Factors with high causality degree claim that its ability to affect other factors is far greater than the ability to be affected by other factors, including Lack of safety awareness (SR1 ), On-site management chaos (SG7 ), Insufficient supervision (SG11 ), and etc. Figure 4 also displays the risk transmission strength. Risk transmission strength is concentrated in the region of [0, 0.6]. This means that each factor interacts with an average of 0.6 factors, indicating the interactions among safety risk factors of metro construction is strong. Table 2 also reveals the rank of transmission strength. The most transitive factors are On-site management chaos (SG7 ), Insufficient safety protection (SG8 ), Lack of safety awareness (SR1 ), and etc. These factors has the most strong transmission ability. Insufficient monitoring or lack of risk response may lead to the quick amplification of other factors. Thus, engineers shall attach importance to the development of these factors, and take measurements to diminish high transmission risks.

7 Conclusion Safety risk transmission of metro construction is proposed in this paper. To evaluate the value of risk transmission, causal and coupling relationships were integrated based on the DEMETAL technique. A case study of metro construction was provided. The result

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shows that the transmission is strong among metro construction safety risk factors. Key risk factors with strong transmission ability were highlighted, such as onsite management chaos, lack of safety awareness, insufficient safety protection, and etc. This paper provides an adaptive method to evaluate safety risk transmission for metro construction projects. Despite the contributions of the study, some limitations need to be acknowledged. First, risk identification and relationship determination were based on expert experience. This may lead to expert bias and data inconsistency. New information techniques may facilitate automatic risk identification, such as text mining, natural language processing, and etc. Second, more case studies need to be conducted to validate the risk transmission model. Funding:. This research was supported by the National Natural Science Foundation of China (grant number 71901206) and the Social Science Fund of Jiangsu Province (22GLB023).

References 1. Na, X., Jianping, W., Jie, L., et al.: Analysis on relationships of safety risk factors in metro construction. J. Eng. Sci. Technol. Rev. 9(5), 150–157 (2016) 2. Wu, K.-F., Wang, L.: Exploring the combined effects of driving situations on freeway rear-end crash risk using naturalistic driving study data. Accid. Anal. Prev. 150, 105866 (2021) 3. Rodrigues, A.: The role of system dynamics in project management: a comparative analysis with traditional modles. Int. Syst. Dyn. Conf. 14, 214–225 (1994) 4. Xin’e, Z.: Transmission of project investment risk and flexible management. J. Wuhan Univ. Technol. 32(3), 315–319 (2020) 5. Yiyong, X., Wenbing, C., et al.: Research on quality risk transfer model based on quality supply and demand chain. Proj. Manage. Technol. 7(8), 13–17 (2019) 6. Ren, H.: Risk lifecycle and risk relationships on construction projects. Int. J. Proj. Manage. 12(2), 68–74 (1994) 7. Taroun, A.: Towards a better modelling and assessment of construction risk: insights from a literature review [J]. Int. J. Project Manage. 32(1), 101–115 (2014) 8. Etemaneh, H., Tavakolan, M.: Fuzzy weighted interpretive structural modeling: a new method for risk path identification of infrastructure projects. In: Construction Research Congress, pp. 2841–2850 9. Eybpoosh, M., Dikmen, I.: Identification of risk paths in international construction projects using structural equation modeling. J. Constr. Eng. Manage. 137(12), 1164–1175 (2011) 10. Jianxiu, W., Hanchuan, L., et al.: Research on the linkage of enterprise risk conduction: a perspective of business association. Econ. Prob. 1, 89–93 (2015) 11. Fuzhen, C., Yan, Z.M.W., et al.: Risk analysis of coupling domino effect in petroliferous tank farm. China Saf. Sci. J. 10, 111–116 (2017) 12. Jia Lixiao, H., Shenping.: Diagnosis of coupling effect among risk causes of water transportation. Navig. China 4, 91–96 (2016) 13. Mohandes, S.R., Sadegh, H., Fazeli, A., et al.: Causal analysis of accidents on construction sites: a hybrid fuzzy Delphi and DEMATEL approach. Saf. Sci. 151, 105730 (2022) 14. Chun, D., Ying, W., Minglu, G.: Analysis of safety accident influence factors of complex system based on the integration of DEMATEL and ISM. Math. Practice Theory 42(22), 143–151 (2012)

Conceptualizing Key Performance Indicators for Building Critical Infrastructure Resilience Through Public-Private Partnership Godslove Ampratwum(B) , Robert Osei-Kyei, and Vivian W. Y. Tam School of Engineering, Design and Built Environment, Western Sydney University, Penrith, Australia [email protected]

Abstract. Disasters has implications for the sustainable development goals and affect the functional performance of critical infrastructure. Public-private partnership is recognized as a medium to build the resilience of critical infrastructure. While previous literature provides valuable insights into public-private partnerships in building critical infrastructure resilience, key performance indicators for a public-private partnership in critical infrastructure resilience is yet to be established. In this study, a systematic review was conducted to propose a set of KPRI in PPP in critical infrastructure resilience. Using a three-stage literature search, 95 publications were selected for content analysis. A total of 23 key performance resilience indicators were proposed and subsequently grouped under five categories namely, System performance, Human resource, Social, Record Keeping and Equipment KPRI. Considering this study as a pioneer, this study will provide an in-depth information and knowledge needed for stakeholders in critical infrastructure resilience such as policymakers and infrastructure operators in building the resilience of critical infrastructures. Keywords: PPP · resilience indicators · critical infrastructure

1 Introduction Disasters have implications for the sustainable development goals and affect the functional performance of critical infrastructure. Sustainable development is defined as fulfilling the present needs without compromising the capability of future generations [1]. The United Nations introduced the Sustainable Development Goals in 2015, and one of these goals is to take urgent action to combat climate change and its impact. The United Nations predict that medium to large scale disasters will increase 40% from 2015 to 2030 (United Nations, 2015). The United Nations Sustainable Development Goal 11 focuses on making cities and human settlements resilient and sustainable. As a result, infrastructure resilience has drawn the attention both in research and practice. Recent disaster and crisis management calls for collaborative governance to enhance infrastructure ability to quickly recover to normal after any disruptive event such as floodings, fire outbreak, earthquakes, terrorist attacks etc. These disruptive events interrupt the © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 811–822, 2023. https://doi.org/10.1007/978-981-99-3626-7_63

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functioning of infrastructure that are critical to the survival of the society. Examples of critical infrastructure may include energy transmission and distribution networks, telephone communication networks, transportation systems, water and gas distribution systems etc. [2]. Critical infrastructure are the spine and lifeline of the society and has to recover timely for continual service delivery when interrupted by disasters [3]. It is the responsibility of the government to protect citizens from disasters and to sustain life [4] and plays a key role in critical infrastructure resilience [5]. [6] opines that critical infrastructure resilience (CIR) is directly influenced by the participation and involvement of key stakeholders such as public sector i.e., government and its associated agencies and the private sector i.e., infrastructure providers. Public-private partnership is widely recognised as an effective approach to build critical infrastructure resilience [7]. Previous literature has provided valuable insights into public-private partnerships in building critical infrastructure resilience. [8–17]. These studies defined public-private partnership as a teamwork between governmental institutions and private infrastructure providers. [18] defined public-private partnership as the “collaboration between a public sector (government) entity and a private sector entity to achieve a specific goal or set objectives. The specific goal or set objectives is “building resilience of critical infrastructure”. PPPs provides an added value achieved from greater co-operation between public-private sector entities [19]. Considering the definition of critical infrastructure resilience and PPP, a public-private partnership in critical infrastructure resilience can be defined as a program-oriented approach devoid of time periods, aiming for reliable infrastructure performance [20]. Considering that success is the goal of any partnership, it is imperative to ascertain the critical success criteria otherwise known as key performance indicators of a publicprivate partnership formed to build critical infrastructure resilience. While in such an instance, what can be used to measure the successful performance of a public-private partnership set up to build critical infrastructure resilience. Key performance resilience indicators (KPRI) are widely used as target-based quantitative management indicators in performance management systems for different industries [21]. They are used to gauge the success of a PPP to build the resilience of critical infrastructure (i.e., the goal for forming the PPP). Key performance resilience indicators are independent variables (measures, principles, standards) [22–25] for measuring and evaluating an outcome. This research aims to propose a set of key performance resilience indicators (KPRI) that are peculiar to using PPP to build the resilience of critical infrastructure. The proposed indicators will be inspired by synthesizing literature from critical infrastructure resilience domain and public-private partnership domain to develop a set of KPRI in PPP in critical infrastructure resilience.

2 PPP in CIR Public-private collaboration is traditionally viewed as a corporation between government agencies and private sector companies (Geoffrey et al., 2009). PPP in critical infrastructure resilience constitute multiple levels of government such as federal, state, local and private sector stakeholders with different operating conditions [26]. The success or failure of PPP in CIR is dependent on the performance of both parties in partnership. In

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most countries, where critical infrastructure is majorly owned by the government, they may be financially incapable of funding critical infrastructure resilience [18]. Thus, the pursuance of critical infrastructure resilience may cause the public sector i.e., government and the private infrastructure owners or operators to form a collaborative network. The networks may consist of institutions or agencies from both the public and private sectors with some form of oversight responsibility or operation of critical infrastructure. Government are typically represented by responsible agencies [20]. A significant feature of the PPP in CIR networks will be their effectiveness in achieving their goal. Network effectiveness is defined as the attainment of positive network level outcomes that would not be achieved by silo organizations acting independently [27]. The role of the government will be to monitor the private sectors in the networks in addition to coordinating and stimulating functional networks so that they can fulfil their tasks in the best possible way [20]. Thus, selecting the right conditions for the network of participants created for building CIR becomes a crucial responsibility of the government [28]. Some of the conditions include ensuring effective incentives for investing in ex ante resilience measures, constant communication between government and private infrastructure operators, and establishing accountability framework. The success of a public-private partnership formed to build critical infrastructure resilience may be perceived differently by different stakeholders. In construction project management, a project is deemed successful when it meets budget and schedule constraints even though it may not have met factors such as customer needs or achieved a quality commercialization process of the final product [29]. In project management literature, project success criteria are used to measure success whilst success factors facilitate the achievement of success [30]. In that same regard, the performance of public-private partnership in critical infrastructure resilience needs to be gauged to ascertain its success or failure.

3 Research Methodology A comprehensive method was adopted to examine the research studies conducted in critical infrastructure resilience indicators and public-private partnership. Scopus online database was used to search for relevant literature because it covers wide-ranging coverage of academic journals from different disciplines [31]. The indexing process for publications are fast which increases the likelihood of retrieving ore current publications [32]. Figure 1 below outlines the workflow of the study. 3.1 Identification The search for literature was conducted in three stages. The first stage was used to search for literature related to PPP in CIR indicators by using keywords such as “"criteria” OR “indicators” OR “parameters” AND TITLE-ABS-KEY “PPP” OR “public-private partnership” AND TITLE-ABS-KEY ( “CIR” OR “critical infrastructure resilience”. Scopus and Web of Science were used to search for literature in the first stage. Both databases were used in the first stage to establish a gap in PPP in CIR indicators. A second literature search was grounded on only critical infrastructure resilience indicators research domain. The third literature search was focused on “PPP project success criteria”.

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Stage 1

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(literature related to PPP in CIR indicators )

(literature search was grounded on only critical infrastructure resilience indicators research domain)

(literature search was focused on “PPP project success criteria”. )

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Title/Abstract/ Keywords

Title/Abstract/ Keywords

Scopus (1 arƟcle)

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1,338 arƟcles

8 arƟcles

1,347 arƟcles screening to eliminate publicaƟons that were not aligned to this study’s research theme

Screening and Analysing Target Papers (95 arƟcles) content analysis with the intent of deriving key performance resilience indicators (KPRI) that are peculiar to using PPP to build the resilience of criƟcal infrastructure.

Developing PPP in CIR Indicators

Conclusion

Fig. 1. An overview of research process

Project success criteria is a principle or standard by which something can be judged or decided [33]. The definition of “project success criteria” and “key performance resilience indicators” is grounded in the same understanding. By virtue of “public-private partnership” in this study, it was necessary to conduct this third literature search. This was to identify any “PPP project success criteria” that may align with “key performance resilience indicators”. The keywords were grounded on prior related research publications to critical infrastructure resilience indicators and project success criteria. The title/abstract/keyword search bar was used to search for relevant publications based on the obtained keywords. The document type, language and year for the search query had no limitation. The full search code used on 14th April 2022 has been stated below. 1st Literature Search For Scopus: ( TITLE-ABS-KEY ( “criteria” OR “indicators” OR “parameters”) AND TITLE-ABS-KEY ( “PPP” OR “public-private partnership”) AND TITLE-ABS-KEY ( “CIR” OR “critical infrastructure resilience”)). The search code generated only one (1) document. For Web of Science: “criteria” OR “indicators” OR “parameters” (Topic) AND “PPP” OR “public-private partnership” (Topic) AND “CIR” OR “critical infrastructure resilience” (Topic). The search code did not generate any document. 2nd Literature Search A second literature search was grounded on only critical infrastructure resilience indicators research domain. The essence of this second literature search was to examine any literature from the search output that may be relevant in PPP in CI resilience indicators. The full search code used was;

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( TITLE-ABS-KEY ( “criteria”) OR TITLE-ABS-KEY ( “indicators”) OR TITLEABS-KEY ( “index”) OR TITLE-ABS-KEY ( “metrics”) AND TITLE-ABS-KEY ( “critical infrastructure”) OR TITLE-ABS-KEY ( “critical infrastructure resilience”)). The search code generated 1,338 documents. 3rd Literature Search The search was conducted only in Scopus database because the concept of project success criteria has received vast research attention. Hence an additional search in Web of Science could have resulted in duplication of articles. The search code used is: ( TITLEABS-KEY ( “PPP” OR “public-private partnership”) AND TITLE-ABS-KEY ( “project success criteria”) AND TITLE-ABS-KEY ( “success criteria”)). The search code generated only 8 documents. 3.2 Screening and Analyzing Target Papers In all a total of 1,347 documents publications were retrieved from Scopus and Web of Science databases. The title and abstracts of the publication were screened to eliminate publications that were not aligned to this study’s research theme. In circumstances where an inclusion or exclusion decision on a publication was difficult based on the title and abstract, a full text reading was conducted. A total of 95 publications were finally selected for this systematic review. The selected target papers were subjected to thorough content analysis with the intent of deriving key performance resilience indicators (KPRI) that are peculiar to using PPP to build the resilience of critical infrastructure.

4 Key Performance Resilience Indicators in PPP in CIR Success is perceived differently by different stakeholders due to a large diversity of people with different ideas [30]. The perception of a PPP performance or success in CIR may differ. Key performance resilience indicators (KPRI) are used to measure success of a public-private partnership in critical infrastructure resilience. According to [34] the selection of an adequate set of indicators must be (i) coherent and consistent with the principles of resilience, (ii) they must be clear, simple and unambiguous and (iii) they must be representative of system under study which in this study is a public-private partnership in building critical infrastructure resilience. Key performance resilience indicators proposed in this study can be used as the evaluation standard for monitoring the success of a public-private partnership in critical infrastructure resilience. The KPRI proposed in this study exemplifies “RAM” principle. R stands for ‘relevance’ and it indicates the degree of correlation of the indicator to a public-private partnership in critical infrastructure resilience. A means ‘accessibility’ and indicates the ease of obtaining data related to the indicator. The M means ‘measurability’ which is capable of being measured [35]. Success in critical infrastructure performance may be measured using these set of resilience indicators if PPP is used. Table 1 is a list of key performance resilience indicators for a public-private partnership in critical infrastructure resilience. They were grouped under five categories namely, System performance, Human resource, Social, Record Keeping and Equipment KPRI. The indicators were grouped based on its similar contributions towards a PPP in CIR. Each category in relation to the indicators in Table 1 is explained under the subsections of Sect. 4.

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Table 1. Key performance resilience indicators for a public-private partnership in critical infrastructure resilience Categories

Key Performance Resilience Indicators

System performance Decoupling ability of critical infrastructure components High functional service delivery of critical infrastructure Low failure magnitude on critical infrastructure Well-designed critical infrastructure to include resilient features Minimum vulnerabilities within critical infrastructure High-cost benefits Human resource

Adequately Trained personnel Long-term Public-private partnership Consistent hazard assessment

Social

Low impact of disrupted functional performance of critical infrastructure on end-users Low economic impact of disrupted functional service delivery of critical infrastructure

Record Keeping

Availability of Hazard maps which provides information about hotspots areas susceptible to disruptive events Existence of Business Continuity Plan Properly recorded Historical hazard data on critical infrastructure Properly recorded interruption of functional performance of critical infrastructure Consistent records on operational and structural condition of critical infrastructure

Equipment KPRI

Existence of Technology-oriented infrastructure to monitor system performance of critical infrastructure Well-developed Simulation and modelling technologies Existence of Renewable energy systems Well-developed Intrusion detection system Availability of Backup infrastructure components for emergency use Existence of Disruptive events early-warning systems

4.1 System Performance Critical infrastructure must perform at an efficient level where it meets the needs of its users. Constant evaluation of disaster resilience is very effective in the establishment of urban resilience plans [4]. Disruptive events isolation devices as a resilience indicator are indicative how resilient a critical infrastructure will be when faced with any disruptive events. When the fault in distributed systems are isolated by switches, the affected users can be re-connected to another functional feeder [36]. The decoupling function

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protects infrastructure systems from cascading effects among its components. Ability to separate malfunctioned infrastructure components from the remaining infrastructure system to prevent cascading effects. Enhancements of infrastructure systems prior to disruptions can be achieved by allocating resources for intervention that increase the robustness and rapid recovery of critical infrastructure system [37]. Resilient critical infrastructure means downtime will be reduced and be producing at a capacity that will reduce operational cost and maximise efficiency. 4.2 Equipment KPRI Availability of backup power and redundant transmission lines can improve the reliability of electric supply when the main source of power delivery is affected by disruptive events [38]. Emergency equipment such as redundant components of CI should be reliable to ensure its proper usage during any disruptive events. Redundancy is an effective popular approach in improving the reliability performance of critical infrastructure systems [39]. Emergency equipment should be reliable so that it functions correctly when it is needed, and it should be available for use when a crisis occurs [40]. Scenario based analysis and data can be used to anticipate and predict who, where and when disasters will be likely to affect critical infrastructure [41]. It is an adaptive measure where past disruptive events data can be used to make critical infrastructure systems adaptive to future disruptive events. Adaptation is a long-term learning in order to make critical infrastructure resilient [42]. Predicting and modelling the impacts of disruption or failure of critical infrastructure is important in building its resilience [43]. Predicting the impact of failure of infrastructure components is relevant in exploring the issue of critical infrastructure resilience [44]. The significance of predictions is to analyse all available information about the nature of impacts which may be dependent on many external and internal factors within the system [43]. 4.3 Human Resource Collaboration in Public-Private Partnership is about information-sharing and jointly developing strategic plans for critical infrastructure resilience [45]. Top managers should be committed to the resilience building process by training the personnel involved in the functional performance of critical infrastructure [40]. Personnel must have the capacity to detect warning signals, communicate to the stakeholders and analyse triggering events to propose new preventive measures for future [40]. Personnel must have sensemaking capacity to understand an unexpected disruptive event, adapt to it, and make the correction decisions in a stressful situation and without complete information [46]. The human behaviour may initiate a cascading failure either directly or indirectly [2]. Human behaviour may aggravate or diminish the impact of its consequences. Training of personnel is a preparedness action to equip employees to effectively anticipate, respond to and recover from the impacts of disruptive events on critical infrastructure [47]. Hazard assessment is a vulnerability or risk assessment that drives most activities associated with critical infrastructure resilience. Hazard assessment take into consideration the potential consequences both direct and indirect vulnerabilities to specific threat information [48].

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Hazard assessment draws on historical information and future prediction about natural hazards to assess the likelihood or frequency of various hazards [48]. 4.4 Social The proportion of users affected by disruptive events or dysfunctional critical infrastructures is dependent on whether they are in urban poor communities or wealth communities. The severity of financial loss is dependent on the extreme economic loss experienced by stakeholders of the disrupted or damaged critical infrastructure and the negative economic impact of the disruptive events on communities. Chatterjee and Mozumder [49] investigated how hurricane preparation and utility disruption plays a role in the wellbeing of households after a hurricane disaster. Public and private institutions are responsible for ensuring the required technical performance of critical infrastructures prior to any disruptive event, during the event and after the event [50].The participation of the government and private infrastructure operators in post-disaster relief activities plays an important role in restoring the functional performance of critical infrastructure after the occurrence of disruptive events [51]. Links between various government departments ensure the swift and accurate transmission of information in the emergency recovery plan for the functional performance of critical infrastructure [51]. 4.5 Record Keeping Archived observation data of disruptive events is a resilience indicator where historical data on threats to critical infrastructure (CI) can be accumulated [52]. It is an anticipative capacity of CI to make predictions based on archived data on threats or disruptive events. Properly recorded interruption of functional performance of critical infrastructure is a criteria of critical infrastructure resilience in PPP. The performance metrics of infrastructure assets and systems provide another type of fundamental information for resilience analysis [53]. Information gathering equipment are needed to monitor critical infrastructure performance. Consistent record keeping of CI functional performance may be used to simulate some disruptive events using simulations and modelling equipment. The outcome of the simulation and modelling may be used to improve the resilience performance of critical infrastructure. Identifying and understanding hazards and vulnerabilities of a critical infrastructure helps to identify the proper measures to implement for its protection and resilience [2]. Monitoring events to spot signals and indicators that predict the location, timing, and magnitude of future or immediate disruptions [47]. 4.6 Implementing Key Performance Resilience Indicators According to Parmenter [54], “the ultimate success of a change strategy depends greatly on how the change is introduced and implemented rather than on the merit of the strategy itself”. Key performance resilience indicators must be linked to resilience strategies. These resilience strategies are success factors that increase the likelihood of a successful public-private partnership in critical infrastructure resilience [55]. In essence, key performance resilience indicators must be linked to success factors otherwise known

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as critical resilience strategies. Critical resilience strategies are dependent elements or influences that can increase the likelihood of building the resilience of critical infrastructure using the PPP mechanism. While key performance resilience indicators measure performance of a public-private partnership to build critical infrastructure resilience. The implementation phases of the KPRI are Step 1: Stakeholder Engagement. Difference infrastructure operators from different infrastructure sectors may form a PPP in CIR network. These members must understand the need to monitor each KPRI. Step 2: Monitoring and Evaluation. Routine and constant monitoring are needed to measure the progress of KPRI. Constant monitoring and documentation provide streams of information for evaluation. Without routine monitoring of the progress of KPRI there cannot be feedback and reprioritization. Step 3: setting up databases and reporting systems to trap and report measures. Gathering and recording performance measures on database. This database needs to be up to date, complete and made available to all members of PPP in CIR network. An electronic working tool will ensure a high level of consistency. Step 4: Feedback on strategies outlined to achieve KPRI. Feedback is a valuable information that may be used to make important decisions. It is a critical component in improving performance. Feedback may be used to modify KPRI. This will help in adding useful perspective to the resilience strategies. Step 5: Reprioritization. Feedback feeds to reprioritization of KPRI.

5 Conclusion This study sought to propose a set of key performance resilience indicators for a publicprivate partnership in critical infrastructure resilience. The proposed indicators were inspired by reviewing literature from critical infrastructure resilience stream and publicprivate partnership stream to develop a set of KPRI in PPP in critical infrastructure resilience. A total of 23 KPRIs were proposed and subsequently grouped under five categories namely, System performance, Human resource, Social, Record Keeping and Equipment KPRI. The theoretical implications of this study are the indicators that have been developed which is peculiar to a PPP used to build critical infrastructure resilience. For the practical implications of this study, the list of indicators is a tool to track the success of any PPPs set up to build critical infrastructure resilience. This study will provide an in-depth information and knowledge needed for stakeholders in critical infrastructure resilience such as policy makers and infrastructure operators in building the resilience of critical infrastructures. The limitation of this study is that the KPRI in PPP in critical infrastructure resilience has not been validated empirically. In future, the results of this study can be empirically validated, and more studies can be conducted by exploring further the concept of KPRI in PPP in critical infrastructure resilience. Acknowledgement. This paper constitutes a part of a PhD research project being conducted at the School of Engineering, Design and Built Environment at Western Sydney University, Australia. The authors acknowledge the Western Sydney University for funding the research.

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Decoration and Renovation Waste Recycling Intention of Homeowners: A Perceived Value Perspective Xinping Wen1,5 , Zhikun Ding1,2,3,4(B) , and Chunbao Yuan6 1 Sino-Australia Joint Research Center in BIM and Smart Construction, Shenzhen University,

Shenzhen 518060, China [email protected] 2 Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen, China 3 Key Laboratory for Resilient Infrastructures of Coastal Cities (Shenzhen University), Ministry of Education, Shenzhen, China 4 Shenzhen Key Laboratory of Green, Efficient and Intelligent Construction of Underground Metro Station, Shenzhen University, Shenzhen, China 5 Department of Construction Management and Real Estate, College of Civil and Transportation Engineering, Shenzhen University, Shenzhen, China 6 China Construction Seventh Engineering Division. Corp. Ltd., Shenzhen, China

Abstract. Decoration and renovation waste (DRW) recycling is one of the most important solutions to maintain economic development while minimizing environmental contamination related to DRW. Homeowners play a pivotal role in DRW recycling management. However, there is a serious lack of research on DRW recycling behavioral intention of homeowners. The purpose of the current study is to explore the critical factors affecting homeowners’ DRW recycling intention and their mechanisms from a perceived value perspective. A new model that covered environmental values and perceived value was developed, and subsequently was examined via the partial least squares structural equation modeling (PLS-SEM) based on the questionnaire data of 155 respondents. It is found that the proposed model provided an effective framework for explaining homeowners’ intention to participant in DRW recycling. Results revealed that homeowners’ DRW recycling intention was affected by environmental values as well as perceived value. Moreover, perceived value played a significant and positive mediating role between environmental values and DRW recycling intention. Therefore, to enhance homeowners’ intention to engage in DRW recycling, several measures should be taken by both neighborhood committee and government, e.g. launching an advocacy campaign and providing economic supports. Keywords: decoration and renovation waste · recycling intention · perceived value · environmental values · homeowners

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 823–839, 2023. https://doi.org/10.1007/978-981-99-3626-7_64

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1 Introduction China, as a developing country, is striving for progress in urbanization [1]. Continuing urbanization and urban renewal accompanied with a flourishing of the interior decoration and renovation industry [2] has generated a substantial amount of interior decoration and renovation waste (DRW). It is estimated that the quantity of construction waste is approximately 3039 million tons in 2020 [3], of which 25.4% is DRW, but the proportion of DRW was only 6.19% in 2015 (Fig. 1). Clearly, with the continuing growth of personal income and needs for a better life, the quantity of DRW in China is increasing year by year and will continue to increase in the future. 100

80

45.08 51.36

Proportion (%)

57.95

59.13

59.92

61.26

60

40

29.52 31.68 35.86

33.85

20

33.01

32.2 25.4 16.96

0

6.19

7.02

7.07

6.54

2015

2016

2017

2018

Demolition waste

New construction waste

2019

2020

Decoration and renovation waste

Fig. 1. Proportion of different types of construction waste in China from 2015 to 2020. Source: Qianzhan Industry Institute [3]

Today, DRW has not received adequate attention [4], and even has not been clearly defined and delineated [2], although it is as essential as other types of construction waste, i.e. new construction waste and demolition waste. In this study, following the definition given by Sun et al., DRW was defined as the discarded materials generated from the building decoration and renovation activities [5]. Decoration refers to the first decoration of a new building, while renovation is the refurbishment and improvement of an older building that does not meet current living and aesthetic needs [6]. In China, both decoration and renovation activities require no construction permit [7]. Therefore, DRW generated from a wide range of sources are scattered, increasing the difficulty of DRW recycling management. Unlike other types of construction waste, DRW has a much more complex composition [4]. Generally, DRW contains toxic waste materials, such as paint, adhesives,

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gypsum board, sponge rubber material, thermal insulation plastics and synthetic chemicals [2, 6, 8]. Besides, DRW may also be mixed with other household waste, making it difficult to be disposed. In China, 95% of DRW is directly disposed via simple landfilling without any proper treatment [2], resulting in numerous environmental problems e.g. hazardous gas emissions [9], water pollution[8], land occupation and degradation [10]. DRW recycling is one of the most effective ways to reduce environmental burdens and achieve circular economy, while there is a dilemma that the current recycling rate of DRW is extremely low, which is only 5% [11]. Therefore, the necessity to improve DRW recycling has never been more urgent. Homeowners, one of the most important stakeholders, play a vital role in the DRW recycling management. More specifically, homeowners, as the decision makers, need to decide how to dispose of DRW generated from the two disposal strategies, i.e. recycling and landfilling [12]. In addition, homeowners are potential customers who may use recycled products when purchasing materials for decoration and renovation. Therefore, the decision-making behavior of homeowners directly affects the subsequent DRW recycling management. Based on behavioral theories, behavioral intention is an antecedent factor of behavior [13]. In other words, DRW recycling intention to a considerable extent determines whether to perform the recycling behavior. Hence, it is imperative to investigate how to enhance the DRW recycling intention of homeowners so as to promote DRW recycling and alleviate the accompanying environmental pressure. Despite the fact that a large number of studies have focused on construction waste management e.g. waste quantification [14, 15], generation characteristics [8, 16], environmental impact assessment [17] and source reduction [18], little attention have been paid to DRW, and no research has been conducted to explore the homeowners’ intention towards DRW recycling. In general, DRW recycling behavior could be conceptualized as a pro-environmental behavior, which is morally guided by environmental values [19]. Perceived value theory states that individual perceived value is a critical factor and has a crucial effect on their behavior [20], indicating that homeowners with higher perceived value towards DRW recycling are more likely to perform the behavior. Considering that DRW recycling is a kind of pro-environmental behavior guided by personal environmental values, it might be an effective way to explain homeowners’ DRW recycling intention from the perspective of perceived values. Thus, the relation between homeowners’ DRW recycling intention and perceived values is explored in this paper. The aim of this study is to investigate homeowners’ intention to recycle DRW and its determinants by developing a model that incorporates environmental values and perceived value. The specific objectives are: (1) to design a conceptual framework focusing on the perceived values and DRW recycling intention; (2) to examine the proposed structural model and hypotheses based on questionnaire survey data. The findings can provide valuable insights for government to take some effective measures to motivate DRW recycling intention of homeowners.

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2 Conceptual Framework and Research Hypotheses 2.1 DRW Recycling Intention Behavioral intention is defined as an indicator of how hard people are willing to perform the behavior [13]. According to some mainstream behavioral theories such as theory of planned behavior (TPB), theory of responsible pro-environmental behavior (REB) and norm activation model (NAM), behavioral intention is identified as an immediate antecedent of behavior. In the present study, DRW recycling intention is defined as the extent to which homeowners are willing to recycle DRW. In recent years, there has been a growing body of literature on individual’s behavior/behavioral intention in the context of construction and demolition waste management. Li et al. [18] investigated the determinants that influence the construction waste reduction behavior with an extended TPB model. Mak et al. [21] and Jain et al. [22] explored the critical factors of construction and demolition waste recycling. It could be concluded that much of the existing literature focuses particularly on construction and demolition waste reduction and recycling. However, few studies paid attention to the DRW recycling behavior, although DRW has become one of the most important sources of waste. Thus, this study proposed a theoretical framework for DRW recycling intention in which environmental values and perceived value were considered (Fig. 2).

Perceived economic value

Egoistic values

Altruistic values

H2

Environmental values

Biospheric values

Perceived value

H1

H3

Perceived social value

Perceived environmental value

Recycling intention

Fig. 2. Conceptual framework.

2.2 Environmental Values Values serve as a guiding principle in the life of a person for selecting behaviors and evaluating people [23]. Stern et al. proposed a value-belief-norm theory in which three types of values closely related to the pro-environmental intentions and behaviors were identified i.e. egoistic, altruistic and biospheric values [24]. In general, people with strong egoistic values will pay particular attention to their own benefits of pro-environmental

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behavior, while people with strong altruistic and biospheric values will focus on the welfare of pro-environmental behavior for other people and biosphere, respectively. Overall, all three values may motivate people to act in a pro-environmental way [19] and hence are classified as environmental values as a whole. Over the past few decades, a number of studies have emphasized the role of human environmental values in shaping pro-environmental behavior/behavioral intention [25, 26]. In the context of DRW recycling management, there are many heterogeneous homeowners who hold different environmental values. It is inferred that homeowners with higher environmental values will be more willing to send the DRW to the recycling plants instead of landfilling. Likewise, if homeowners hold stronger environmental values, they will tend to purchase recycled DRW materials rather than raw materials. Therefore, the following hypothesis was proposed: H1. Environmental values have a positive effect on DRW recycling intention. It is highlighted that environmental values were considered as a second-order variable containing three first-order observation dimensions, namely egoistic, altruistic and biospheric values. The above relationship is hypothesized between recycling intention and the second-order variable only. 2.3 Perceived Value Perceived value is the consumer’s overall assessment of the utility of a product based on perceptions of what is received and what is given [20]. The concept of perceived value has been successfully adopted in various fields, such as product marketing, e-commerce and tourism. It could be concluded that much of the current research mainly concentrates on the perceived value of a kind of product or service. Although limited research on the perceived value of pro-environmental behavior has been available [27], little attention has been paid to the perceived value of DRW recycling. Perceived value is commonly considered as a multi-dimensional construc t[28, 29]. As Jin et al. summarized that construction waste recycling could generate economic, social and environmental benefits [30], so could DRW. More specifically, landfilling charge could be saved by selling DRW to recycling plants. Meanwhile, DRW recycling helps homeowners make a good public image, and even promote the sustainable development of society [31]. Moreover, DRW recycling programs can bring significant positive effects on environment, like saving land resources, reducing greenhouse gas and harmful dust emissions [2]. Therefore, this study divided homeowners’ perceived value towards DRW recycling into three aspects i.e. perceived economic value, perceived social value and perceived environmental value. As suggested by De Groot et al., environmental values contribute to the formation of perceived value [32]. Such a statement might be extended to the DRW recycling, that is, homeowners’ perceived value towards DRW recycling may be influenced by their environmental values. To be specific, as a guiding principle for evaluating behavior [19], environmental values can assist homeowners in assessing the value of DRW recycling behavior, indicating that the role of environmental values in shaping perceived value is of vital importance. For this reason, homeowners who hold stronger environmental values are more likely to recognize the economic, social and environmental value of DRW recycling.

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With respect to the relationship between perceived value and recycling intention, Ding et al. have demonstrated that perceived value exerts a significant positive impact on behavioral intention [28]. In other words, the higher an individual’s perceived value towards a certain behavior, the more likely to act. In the case of DRW recycling, homeowners maybe diverse in the perceived value of DRW recycling. Generally, homeowners with greater perceived value may have stronger intention to engage in DRW recycling. Therefore, the following hypotheses were proposed: H2. Environmental values have a positive effect on perceived value. H3. Perceived value has a positive effect on DRW recycling intention. Perceived value was regarded as a second-order construct which included first-order variables i.e. perceived economic value, perceived social value and perceived environmental value. Likewise, there is no hypothetical relationship between perceived value and corresponding first-order variables.

3 Methods 3.1 Data Collection In the present study, the target population of the survey was homeowners, each of whom had at least one property and conducted decoration or renovation activities. To ensure the data reliability and validity, a pilot survey was conducted from January 4 to January 11, 2022 in which the draft questionnaire was randomly distributed to homeowners. Based on the survey results and suggestions provided by participants, some items that were ambiguous or difficult to understand were revised to finalize the questionnaire. An online questionnaire survey was carried out to collect empirical data from January 17 to March 22, 2022. Firstly, the questionnaire was posted on the Internet (https:// www.wjx.cn/), and a unique web link was generated for distribution. Secondly, the questionnaire link was randomly distributed to the target population, i.e. homeowners, through WeChat, QQ and email. A total of 203 responses were collected, of which 48 invalid questionnaires with logical errors and inconsistent answers to all items were removed. Finally, 155 valid questionnaires were retained accounting for 76.35% of the total responses. Such a sample size satisfied the “10 times” rule of thumb for minimum sample sizes proposed by Chin [33]. Therefore, the sample was sufficient for subsequent data analysis. Demographic data were presented in Table 1. Table 1. Demographic profile of respondents. Variable

Category

Frequency

Percentage (%)

Gender

Male

81

52.26%

Female

74

47.74%

18–30

12

7.74%

Age

(continued)

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Table 1. (continued) Variable

Education level

Understanding level of DRW

Category

Frequency

Percentage (%)

31–40

71

45.81%

41–50

51

32.90%

51–60

18

11.61%

More than 60

3

1.94%

Junior college and below

35

22.58%

Bachelor’s degree

72

46.45%

Master’s degree

41

26.45%

PhD or above

7

4.52%

Understand

36

23.23%

Know but not fully understand 108 Never heard of it and did not understand it

11

69.68% 7.10%

As shown in Table 1, the percentage of male was 52.26%, which was slightly higher than that of female (47.74%). Among the 155 respondents, over three quarters were between 31 and 50 years old (78.71%). With regard to education level, respondents who received higher education accounted for 77.42% and most of them had a bachelor’s degree. More than 90% of the respondents knew about DRW more or less (92.90%), although nearly 70% of homeowners had heard of it but did not understand it completely (69.68%). 3.2 Measurement Development The latent variables i.e. environmental value, perceived value and recycling intention are rather abstract and it is difficult to observe and measure these variables directly. Generally, each latent variable should be measured by 3 or more items [34]. In order to operationalize these constructs, measurement scales presented in Table 2 were developed. The measurement items of DRW recycling intention were designed by referring to scales of previous research in the broader field of waste recycling. Using a 5-point Likert scale from 1 (Not at all willing) to 5 (Strongly willing), respondents were asked whether they were willing to engage in DRW recycling behaviors. The higher the score of respondents, the stronger the intention to act. Environmental values including three dimensions i.e. egoistic, altruistic and biospheric values were measured by available scales developed by Stern et al. [24]. Such scales have been validated in various studies about pro-environmental behavior [35, 36]. Respondents were asked to rate the importance of these values as “a guiding principle in their lives” [19] based on a 5-point Likert scale ranging from 1(Not at all important) to 5 (Extremely important).

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Perceived value was assessed from such aspects as perceived economic value, perceived social value and perceived environmental value. Several items derived from previous research on waste recycling were employed, and some words were modified appropriately to reflect the context of DWR recycling. Likewise, a 5-point Likert scale from 1 (Strongly disagree) to 5 (Strongly agree) was adopted to measure these items. Table 2. Measurement scale. Constructs

Measurement items

Recycling intention (RI: Not at all willing: 1 –Strongly willing: 5) RI1 I am willing to use recycling products made by DRW. RI2

I tend to sell DRW to recycling companies.

RI3

In the future, I will recycle DRW.

Source

[22, 28]

Environmental values (EV: Extremely unimportant: 1 – Extremely important: 5) • Egoistic values (EGV) EGV1 Individual rights

EGV2

Individual wealth

EGV3

Individual social status

• Altruistic values (AV) AV1 Social justice

AV2

Interests of others

AV3

Social fairness

[19, 24]

• Biospheric values (BV) BV1 Protecting the environment

BV2

Preventing pollution

BV3

Unity with nature

Perceived value (PV: Strongly disagree: 1 – Strongly agree: 5) • Perceived economic value (PEV) PEV1 Selling DRW to recycling companies can make some money.

PEV2

Throwing away DRW is wasteful.

PEV3

Recycling DRW can save raw materials.

•

[30, 37]

Perceived social value (PSV)

PSV1

Recycling DRW can help me make a good impression on others.

PSV2

Recycling DRW can increase new employment opportunities.

PSV3

Recycling DRW can promote the sustainable development of society.

[30, 38]

Perceived environmental value (PENV) • PENV1 Recycling DRW can reduce landfill and save land resources. PENV2

Recycling DRW can reduce carbon emissions and improve the climate.

PENV3

Recycling DRW can reduce harmful dust emissions and improve air quality.

PENV4

Recycling DRW can reduce waste and improve urban environment.

[9]

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4 Data Analysis and Results Structural Equation Model (SEM), a method used to measure latent concepts based on multiple observed indicators [33], was applied to test research hypotheses. It is well known that there are two mainstream SEM techniques i.e. covariance-based SEM (CBSEM) and partial least squares SEM (PLS-SEM). In this study, PLS-SEM was employed to analyze data with the software of SmartPLS 3.0. The reasons for choosing PLS-SEM were explained as following. Firstly, compared with CB-SEM, PLS-SEM is more robust with small sample sizes [39]. Secondly, PLSSEM is often adopted to examine exploratory models, while CB-SEM is frequently applied to validate confirmatory models [40]. Thirdly, PLS-SEM is more effective when the hypothetical model is complex with second-order constructs [39, 40]. In general, PLS-SEM analysis is carried out in a two-step process including the assessment of measurement models and the assessment of structural models [41]. 4.1 Measurement Model Following the suggestion of Urbach and Ahlemann [41], indicator reliability, internal consistency reliability, convergent validity, as well as discriminant validity should be tested to evaluate the measurement models. Table 3. Assessment of measurement model. First order

Items

Loadings

CA

CR

AVE

EGV

EGV1-EGV3

0.824 ~ 0.846

0.784

0.873

0.697

AV

AV1-AV3

0.769 ~ 0.927

0.824

0.896

0.743

BV

BV1-BV3

0.932 ~ 0.959

0.942

0.963

0.897

PEV

PEV1-PEV3

0.814 ~ 0.916

0.825

0.895

0.741

PSV

PSV1-PSV3

0.799 ~ 0.861

0.788

0.875

0.701

PENV

PENV1-PENV4

0.838 ~ 0.926

0.909

0.937

0.787

RI

RI1-RI3

0.800 ~ 0.919

0.841

0.904

0.760

> 0.7

> 0.7

Criterion

> 0.7

> 0.5

Second order

CA

CR

AVE

EV

0.902

0.887

0.727

PV

0.914

0.901

0.753

> 0.7

> 0.7

> 0.5

Indicator reliability refers to the extent to which a variable or a set of variables is consistent regarding what it intends to measure [41]. The traditional criterion of indicator reliability is factor loadings which should be significant at least at the 0.05 level and greater than 0.7 [33]. It is worth noticing that there is no factor loading for multiple second-order constructs. This is due to the fact that such constructs are measured by corresponding first-order constructs. Accordingly, only the first-order constructs have factor loadings. As presented in Table 3, the factor loadings of all items for first-order constructs (ranging from 0.769 to 0.959) exceeded 0.7. Besides, the significances of factor loadings were verified by bootstrapping, a resampling technique. The test results showed that all loadings reached the significant level with a p-value of 0.000 (< 0.05), indicating that all measurement items had sufficient indicator reliability.

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Internal consistency reliability is the stability of the measurement results when the same scale is measured repeatedly. The common criteria of internal consistency reliability are Cronbach’s alpha (CA) and composite reliability (CR), both of which should exceed the threshold value of 0.7 suggested by Chin [33]. The larger the value, the better the reliability. From Table 3, it was found that the CA values (ranging from 0.784 to 0.942) and CR values (ranging from 0.873 to 0.963) of all constructs were larger than 0.7 suggesting that the internal consistency reliability of each construct was adequate. Convergent validity describes the degree of convergence of indicators that measure the same construct. Average variance extracted (AVE) is viewed as a particular measure to examine the convergent validity. A recommended benchmark value proposed by Fornell and Larcker is 0.5 [42]. Table 3 showed that the AVE of each construct (ranging from 0.697 to 0.897) was larger than 0.5, demonstrating that the measurement scales had enough convergent validity. Discriminant validity refers to the degree of difference between various constructs. In order to verify the discriminant validity, Fornell-Lacker criterion was used. From Table 4, it was found that the square root of AVE for each construct was greater than its correlations with any other construct. In this case, the correlations within constructs were higher than the correlations between different constructs, representing an adequate discriminant validity. Table 4. Discriminant validity of the constructs. EV EV

0.853

PV

0.592

PV

RI

0.868

RI

0.506

0.688

0.872

AVE

0.727

0.753

0.760

Note: Values in bold on diagonals are square roots of AVEs and the others are correlations between constructs

4.2 Structural Model After evaluating the measurement models, the structural model was examined by focusing on path coefficients and coefficient of determination (R2). It should be noted that Goodness-of-fit (GOF), one of the most essential criteria for CB-SEM, is not available for PLS-SEM [41], and hence was excluded in this study. Considering that the complex structural model contains second-order constructs and first-order constructs, it is essential to examine the second-order structural model first. As shown in Table 5, all path coefficients from second-order constructs to first-order constructs reached the significant level with a p-value of 0.000 (< 0.05). Moreover, the R2 values (ranging from 0.476 to 0.879) were much higher than the critical value of 0.35. More specifically, the degree of explained variance of environmental values was

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reflected in egoistic values (47.6%), altruistic values (87.9%), and biospheric values (82.5%), respectively. The extent of explained variance of perceived value was reflected in perceived economic value (59.4%), perceived social value (80.8%), and perceived environmental value (85.8%). This process laid a foundation for further testing research hypotheses. Table 5. Assessment of the Second-order structural model. p

R2

9.792

0.000

0.476

69.929

0.000

0.879

46.581

0.000

0.825

0.771***

11.988

0.000

0.594

0.899***

47.609

0.000

0.808

0.926***

60.890

0.000

0.858

Second order

First order

β

EV

EGV

0.690***

AV

0.938***

BV

0.908***

PCV PSV PENV

PV

t

Note: * p < 0.05, ** p < 0.01 and *** p < 0.001

The hypothesis test results were shown in Table 6. It was found that the effect of environmental values on recycling intention was significant (β = 0.152, p = 0.033). As expected, environmental values exerted a significant effect on homeowners’ perceived value of DRW recycling (β = 0.592, p = 0.000). Besides, perceived value further significantly affected the recycling intention (β = 0.599, p = 0.000). Thus, H1, H2 and H3 were all supported. Furthermore, the R2 of perceived value was 0.351 indicating that environmental values explained 35.1% of the variance in perceived value. Chin suggested that R2 values of approximately 0.67 are considered as substantial, values around 0.33 moderate, and values around 0.19 weak [33]. The R2 value of 0.489 illustrated that the proposed model explained 48.9% of the variance in homeowners’ DRW recycling intention, representing a good explanatory power. Table 6. Hypothesis test results. Hypotheses

Paths

β

t

p

f2

Results

H1

EV → RI

0.152*

2.135

0.033

0.029

Supported

H2

EV → PV

0.592***

7.718

0.000

0.540

Supported

H3

PV → RI

0.599***

8.344

0.000

0.455

Supported

PV

R2 = 0.351

RI

R2 = 0.489

Note: * p < 0.05, ** p < 0.01 and *** p < 0.001

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In addition to the above criteria used for hypothesis testing, effect size (f2 ) is also commonly applied to evaluate the size of each path in PLS-SEM model. Generally, f2 values of 0.02, 0.15 and 0.35 indicate the predictor variable’s low, medium, or large effect in a structural model [41]. As presented in Table 6, the f2 value of H1 was 0.029, meaning that environmental values had less influence on recycling intention. However, the f2 values of H2 and H3 were 0.540 and 0.455, both of which were much larger than 0.35. Such results indicated that environmental values had a considerable effect on perceived value, and perceived value subsequently had a large influence on recycling intention. 4.3 Mediation Analysis Based on the aforementioned results, it was inferred that the construct of perceived value could be identified as a mediator between environmental values and recycling intention. In order to examine the mediation effect, Sobel test was conducted. From Table 7, it was found that the direct effect of environmental values on recycling intention was 0.152, the indirect effect was 0.355, and hence the total effect was 0.507. It is concluded that the mediation effect reached the significant level with a p-value of 0.000 (< 0.05) and a t-statistic of 5.646. Following the guidelines of Hair et al., the variance accounted for (VAF) value of below 20%, between 20% and 80%, and exceeding 80% could be considered as no, partial, or full mediation [43]. The VAF value of 0.700 indicated that there was a partial mediation in the proposed model. Therefore, perceived value had a positive mediating effect on the relationship between environmental values and recycling intention. Table 7. Mediation effect test results. Effects

Path

β

Total effect

VAF

t

p

Direct without mediator

EV → RI

0.152

0.507

Not applicable

2.135

0.033

Indirect with mediator

EV → PV → RI

0.355

70%

5.646

0.000

5 Discussion 5.1 Comparison with Existing Literature Considering that DRW recycling, as a type of pro-environmental behavior, is guided by personal environmental values, a new model to explore the critical factors affecting homeowners’ DRW recycling intention was proposed and corresponding hypotheses were tested. The results revealed that environmental values significantly affected homeowners’ intention to recycle DRW. In accordance with the present results, some previous

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studies in the broader field of pro-environmental behavior have confirmed the role of environmental values in shaping pro-environmental behavioral intention [26, 44]. In the context of DRW recycling, homeowners holding larger environmental values will be more likely to act in a pro-environmental way such as selling DRW to recycling companies and adopting recycled products made by DRW. As for the three dimensions under environmental values, altruistic values and biospheric values were more strongly related to environmental values than egoistic values. This finding is in line with that of De Groot and Steg [19], who suggested that pro-environmental behavior require individuals to restrain egoistic tendencies, so does DRW recycling behavior. In addition, environmental values were found to significantly and positively affect perceived value. This result is in agreement with earlier research [32], but different from that of Hänninen and Karjaluoto [44] who reported that customers’ environmental values failed to significantly affect perceived value in supply channel management. The reason for this may have something to do with the properties of the behavior itself. Specifically, unlike supply channel management in Hänninen and Karjaluoto [44], DRW recycling is more about the pro-environmental behavior. In the latter behavior, environmental values are more of a guiding principle for assessing the benefits of recycling DRW. It is not surprised that environmental values of homeowners exerted a significant positive influence on perceived value towards recycling DRW. As expected, perceived value had a significant positive effect on homeowners’ intention to recycle DRW, which seems to be consistent with the work of Ding et al. [28] who found that stakeholders with higher perceived value towards recycled products had a stronger intention to purchase these products. DRW could be viewed as a wrongly placed resource, and its value can be reflected in economic, social and environmental aspects through recycling [30]. Accordingly, when homeowners perceive higher value of recycling DRW, they are more likely to recycle DRW. Moreover, compared with perceived economic value, perceived environmental value and perceived social value accounted for a larger portion of perceived value. A possible explanation might be that recycling DRW in China is still in the primary stage [45] and economic support for recycling activities from government is insufficient [46]. Therefore, for most homeowners, recycling DRW is more of an activity that is beneficial to society and environment, rather than an economical one. Interestingly, perceived value played a significant and positive mediating role in the relationship between environmental values and DRW recycling intention. In other words, environmental values positively and indirectly affected homeowners’ intention to engage in DRW recycling via the mediation of perceived value, although the direct effect of environmental values on DRW recycling intention was also significant and positive. Among the total effects of environmental values on DRW recycling intention, indirect effect (0.355) was much larger than direct effect (0.152), indicating that perceived value played an outstanding and mediating role in shaping DRW recycling intention. Therefore, if homeowners hold stronger environmental values, they will be more likely to identify the value of recycling DRW, and more intend to recycle DRW.

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5.2 Implications The findings provide both theoretical and practical implications for better understanding the determinants of homeowners’ DRW recycling intention. In theory, firstly, the current study provided an empirical support for applying and expanding the theory of perceived value in the area of DRW recycling behavior research. To identify the factors affecting homeowners’ intention to recycle DRW, a new model was developed based on the theory of perceived value and was extended by introducing environmental values. The results showed that all hypotheses were supported. Hence, this study demonstrated that the perceived value theory is applicable in this field. Secondly, this study laid a foundation for future research in the broader field of DRW recycling. Until now, research about DRW recycling behavior is still in the exploratory stage. This study was the first attempt to explain DRW recycling intention. In the proposed model, the R2 value of homeowners’ DRW recycling intention was 0.489 which meant that the model explained 48.9% of the variance in homeowners’ DRW recycling intention, representing a good explanatory power. Therefore, the present study offered an effective framework for interpreting homeowners’ DRW recycling intention. This research also has some practical implications. Firstly, as environmental values had direct and indirect effects on motivating homeowners’ DRW recycling intention, actions should be taken to strengthen environmental values. Despite the difficulty of changing individuals’ value orientations, it is worth trying to enhance their environmental values, especially altruistic values and biospheric values [19]. For example, the routine public promotion about environment protection should be carried out through multiple channels like Internet, billboard, media, magazine and newspaper. This is due to the fact that the formation of environmental values is influenced in a variety of ways. Secondly, given the significant role of perceived value in promoting DRW recycling intention, some effective measures such as an advocacy campaign dedicated to DRW recycling should be launched to help homeowners recognize the benefits of DRW recycling. As discussed earlier, economic benefits should be paid more attention. Therefore, it is suggested that government should provide economic supports for homeowners to achieve significant economic benefits e.g. rewards for performing DRW recycling, subsides for purchasing recycled materials as well as other forms of economic incentives. As a result, homeowners will form stronger environmental values and perceive larger value, and in turn enhancing their intention to participant in DRW recycling.

6 Conclusions The current study proposed a new model that innovatively incorporates environmental values and perceived value to investigate the key factors influencing homeowners’ intention to recycle DRW. Based on questionnaire data and PLS-SEM analysis, it is found that environmental values and perceived value played a significant and positive role in motivating DRW recycling intention of homeowners. The relationship between environmental values and DRW recycling intention was mediated by perceived value. The present study is the first study to explain the influencing factors of homeowners’ DRW recycling intention and their mechanisms from the perspective of perceived value.

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One the one hand, this study offers an empirical support for developing and expanding the theory of perceived value, and simultaneously lays the groundwork for future research in DRW recycling. On the other hand, the findings highlight the role of environmental values and perceived value in shaping DRW recycling intention of homeowners, which enable governments to take measures to motivate homeowners to participant in DRW recycling, e.g. conducting routine promotion related to environmental protection, launching a campaign for DRW recycling, and providing economic supports. In future, the explanation power of the developed model might be further improved by incorporating additional variables, such as environmental consciousness, attitude and moral norms. Furthermore, which environmental values (i.e. egoistic, altruistic and biospheric values) and perceived value (i.e. perceived economic value, perceived social value and perceived environmental value) are most likely to promote DRW recycling intention should be discussed in the future. Acknowledgements. This research was conducted with the support of the National Nature Science Foundation of China (Grant No.71974132), Shenzhen Government Nature Science Foundation (Grant No. JCYJ20190808115809385), Shenzhen Natural Science Fund ( the Stable Support Plan Program No.20220810160221001).

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The Influence of 2D/3D Urban Spatial Form Indicators on Surface Urban Heat Island Based on Spatial Regression Models: A Case Study of Hangzhou, China Haotian Chen and Sheng Zheng(B) Department of Land Management, Zhejiang University, Hangzhou 310058, China [email protected]

Abstract. Rapid urbanization in China has caused the serious urban heat island (UHI) effect, which is endangering the environment where people live. Many studies have demonstrated the significant contributions of two-dimensional (2D) and three-dimensional (3D) urban spatial form indicators to UHI, but further research is expected to conduct given that UHI has spatial non-stationarity and spatial spillover effects of explanatory variables. Spatial regression models are particularly appropriate to solve the spatial non-stationarity problem. This essay seeks to analyze the impact of urban spatial form on UHI taking the spatial spillover effects into account. In this paper, Landsat 8 OLR/TIRS satellite image was used to invert surface urban heat island intensity (SUHII) in the central city of Hangzhou, China. The Moran’s I index for SUHII was 0.457, indicating a substantial positive spatial autocorrelation. After the establishment of 2D/3D urban spatial form indicators at the block scale level, the influence of 2D/3D urban spatial form indicators on SUHII w‘as examined using the spatial lag model (SLM), spatial error model (SEM), spatial Dubin model (SDM), and spatial Dubin error model (SDEM), respectively. Results suggest that SDM and SDEM fit better by considering spatial interaction of urban spatial form indicators. The coefficients for the ratio of impervious surface area (RISA), ABD (average building density), and sky view factors (SVF) were significantly positive, while the effects of the landscape shape index (LSI), average building height (ABH), average forest height (AFH), and normalized difference vegetation index (NDVI) were significantly negative on the SUHII. Additionally, there were spatial spillover effects of LSI, SVF, AFH, NDVI, and SUHII affecting SUHII. The findings imply that the urban form indicators and SUHII will affect the SUHII of adjacent blocks, so it is important to make suitable urban 2D/3D shapes to enhance the urban thermal environment. These findings would be helpful for urban planners to mitigate future UHI effects. Keywords: Surface heat island effect · Spatial regression models · 2D/3D urban spatial form indicators · Spatial autocorrelation · Spatial spillover effects · Hangzhou

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 840–855, 2023. https://doi.org/10.1007/978-981-99-3626-7_65

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1 Introduction Cities contain various production and living activities of human beings, and it is the artificial ecosystem constructed by us in the process of changing the natural landscape. Urbanization has become a global phenomenon since the Industrial Revolution [1]. Rapid urban area expansion, however, is degrading the urban thermal environment [2]. Urban heat island (UHI) is a phenomenon that occurs when the temperature in urban areas is higher than surrounding rural areas [3]. The urban underlying surface typically has a larger heat capacity than the natural surface, causing repeated reflections and the accumulation of solar heat. As a result, the sensible heat flow in the urban region rises while the latent heat flux falls [4]. In addition, the anthropogenic heat from human activities also raise raises the temperature of the city. Moreover, the UHI worsens air pollution, increases regional energy consumption, alters the local microclimate, impairs living comfort, and even harms human health [5]. As reported by previous research, UHI occurred not only in large cities but also in medium-sized cities [6]. If the urban heat island effect is disregarded, it will have substantial negative effects on sustainable urban development and will eventually become one of the biggest risks to human health in cities. UHI is a result of urbanization and the abnormal climate that was generated by the process of frequent human activities. As a severe urban environmental issue, previous research has examined how different classifications of land uses affect UHI, and the results consistently suggest construction land has the largest contributions to UHI [7]. Additionally, it attracted lots of attention that the effects of landscape compositions and configurations on UHI [8–9], and research suggests that the contributions of landscape configurations to the UHI are more than landscape compositions [10]. For example, the frog-jumping urban expansion pattern makes it easier for energy to flow between heat island and non-heat island areas, thus reducing the total heat island intensity [11]. Therefore, it should attach much significance to exploring the impacts of urban spatial form on urban heat island. Recently, a growing number of studies have focused on the connection between 3D spatial form indicators and UHI, which is more thorough in reflecting the influence of urban form on UHI than 2D landscape [12–15]. It is found that high-density and low-rise (HDLR) as well as high density and medium-rise forms presented higher SUHI [16]. Sky view factor (SVF) is also frequently utilized to reflect the urban street form and building density, but its impacts on UHI vary among research and geographical regions [17–18]. Also, some studies have considered vegetation height as a crucial 3D indicator that lowers the LST of the urban region [19]. As to analysis methods, regression models are mainly used in some recent studies. That is because spatial autocorrelation and spatial heterogeneity are exhibited in LST or SUHII, causing the traditional regression model called Ordinary Least Square (OLS) is no longer appropriate [20]. SEM and SLM perform better than OLS at revealing the relationship between urban form indicators and SUHII [21–22]. Some studies recently have pointed out the spatial spillover effects of urban spatial form on urban heat islands, suggesting it would affect the adjacent blocks [23–24]. Therefore, SDM and SDEM, taking the spatial spillover effects of explanatory variables into account, may be more suitable because SLM and SEM ignore them.

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Overall, little research has discussed how urban spatial form indicators affect UHI using SDM or SDEM, probably ignoring the spatial spillover effects of both UHI and urban spatial form indicators. In this paper, using the data primarily obtained from 2018–2020, the study compared four spatial regression models and thus take the spatial spillover effects into account. Ultimately, the more thorough findings about the influence of urban spatial form on UHI would be helpful for urban planners to reduce the UHI in cities.

2 Data and Study Area 2.1 Study Area Located in the southeast of China, Hangzhou is the provincial capital city of Zhejiang Province, east of Hangzhou Bay. The Qiantang River runs through the center of the city. Hangzhou’s entire size was around 16850 km2 . It is worth mentioning that Hangzhou has been one of the "Four New Furnaces" in China, and has become one of the 10 Chinese cities with the hottest weather in summer since 2017. That is why Hangzhou was chosen as the study case in this paper. As can be seen in Fig. 1, the central city of Hangzhou was selected as the study area, including Xihu District, Yuhang District, Xiaoshan District, Shangcheng District, Xiaocheng District, Gongshu District, Jianggan District, and Binjiang District.

Fig. 1. Location and study area.

Using city boundary data can obtain the built-up area of Hangzhou, and the surrounding suburbs of the urban areas are seen as the background area. The built-up urban

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boundary data was collected from Global Urban Boundary Dataset (GUB, http://data. ess.tsinghua.edu.cn/) [25] for 2018. There are lots of studies that explored the driver factors of UHI based on grids level. However, for fine-scale urban areas and urban planning, parcels especially referred to as blocks are the fundamental spatial units. Open Street Map (OSM, https://www.openstreetmap.org/) road networks can identify parcel geometries [26], so it was used to split the urban areas into 3026 block units (Fig. 2).

Fig. 2. Block units in the urban region

2.2 Data Collection 2.2.1 Landsat-8 OLS/TIRS Data Landsat-8 OLS/TIRS data for 2020 were used to inverse the land surface temperature. The spatial resolution of this data is 30m. The remote sensing image selected in this paper was taken at 10:31 a.m. on Sept. 8, in which cloud cover was only 2.12%. The remote sensing image is available from the United States Geological Survey (USGS, https://earthexplorer.usgs.gov/). 2.2.2 Land Cover Data Annual China Land Cover Dataset (CLCD, https://doi.org/10.5281/zenodo.5816591) is produced by Yang Jie and Huang Xin [27]. Landsat data were used to classify the land cover, which resulted in 8 categories with a spatial resolution of 30 m. The CLCD has an overall accuracy of 80%. In this paper, CLCD was collected for 2020. 2.2.3 Building Footprint Data The building is one of the most prominent landscape elements within the city. The building floor data as well as building outline data for 2020 were obtained by Baidu

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Map Services, China (https://lbsyun.baidu.com/). A single building is represented as a polygon with the account of the floor. In this paper, the account of a building’s floor was multiplied by three to obtain the building’s height. 2.2.4 Forest Height Dataset Similarly, the height of the vegetation is one of the important components of the 3D city landscape. Forest height data for 2019 were obtained from Global Land Analysis and Discovery (https://glad.umd.edu/dataset/gedi/) and its spatial resolution is 30 m [28]. 2.2.5 Digital Elevation Model (DEM) and Population Counts Data The study focuses on the impacts of urban spatial form on the UHI. Therefore, the interference of terrain and population must be controlled in the regression models. DEM was derived from the ASTER GDEM V3 dataset to reflect the terrain factor, which was jointly produced by METI (Japan) and NASA (USA) and it is published in 2019. The data set is provided by the Geospatial Data Cloud site, Computer Network Information Center, Chinese Academy of Sciences (http://www.gscloud.cn). The constrained population counts grid data for 2020 are provided by the World Pop Project (https://hub.wor ldpop.org) product with a spatial resolution of 100 m, which was created utilizing the most recent population data from each country. The data were adjusted to match United Nations national population estimates [29].

3 Methods 3.1 Calculation of SUHII 3.1.1 Land Surface Temperature Inversion The atmospheric correction method is an algorithm based on the radiative transfer equation, by which the evaluation of the land surface temperature (LST) is more accurate than others [30]. The fundamental principle of that method is to remove the impacts of the atmosphere on the surface thermal radiation from the total thermal radiation. Eventually, LST can be converted from thermal radiation intensity. LT =

Lλ − Lup × 1 − ε × Ldown ε×τ

(1)

LT is the blackbody thermal radiation brightness. Lλ (W/(m2 ·sr · μm)) is the top of atmosphere radiance recorded by the sensor. ε is the surface-specific emissivity of the band, and the estimation method according to Qin et al. [31]. τ is the atmospheric transmittance. Lup and Ldown stand for upwelling and downwelling path radiance, respectively. All three atmospheric profile parameters were obtained from the atmospheric parameters calculation website of NASA (http://atmcorr.gsfc.nasa.gov/). Then getting the at-sensor brightness temperature and transformed it into degree Celsius (°C) by Eq. (2): B(Ts) =

K2 ln K1 Lt + 1

− 273.15

(2)

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For Landsat-8 Band 10, the K1 (W/(m2 · sr · μm)) and K2 (K) are the calibration constants, which are 774.89 and 1321.08, respectively. After converting B(Ts) from LT , LST can be calculated as follows: 

LST = 1+

B(Ts) 

λ×B(Ts) ρ

× ln ε

(3)

λ and ρ are the wavelengths of emitted radiance, which are equal to 10.9 μm and 1.438 × 10–2 m K, respectively. 3.1.2 Estimation of SUHII In this paper, the urban heat island intensity (SUHII) was determined by calculating the LST difference between each urban grid and the average rural LST [32]. It was derived that the average LST of the urban area grids within that block. The regions beyond the city boundary were regarded as rural areas, which with a more than 50 m of DEM value difference from average urban areas are removed to eliminate the LST difference caused by elevation. In addition, water areas were removed from the rural areas to reduce the residual error resulting from different land cover compositions. SUHIIi = LSTcity i −

1 n LSTrural 1 n

(4)

where SUHIIi is the SUHII of each grid in the  urban area (°C), LSTcity i is the surface temperature of each 30 m × 30 m grid, and 1n n1 LSTrural referred to as average rural LST. If the SUHII of one area is higher than zero, it means that area has no urban heat island effect. 3.2 Spatial Autocorrelation Spatial autocorrelation will lead to faults in OLS results, which reflects the spatial interaction effects of the variables in the real world. Moran’ I index is a good index to estimate spatial autocorrelation. The formula is as follows [33]:   ¯ n i j Wij (Xi − X)(Xj − X)  (5) Moran I =    ( i j Wij ) i (Xi − X)2 where n is the number of blocks; Xi and Xj are the spatial locations of blocks i and j, respectively; Queen spatial adjacency type was used to calculate the space weight matrix called Wij , which rank was 1 and defines the spatial structure between i and j. The values of Moran’I index are in the range of −1 to1. The higher spatial autocorrelation of variables is suggested by the index’s larger absolute value. There is a positive spatial autocorrelation among regions when Moran’I is greater than 0, while less than zero shows negative spatial autocorrelation.

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3.3 Indicators of 2D/3D Urban Spatial Form The urban spatial form is shaped by the urban land cover, urban geometry, and urban morphology [34], so we search for indicators of urban spatial form from these aspects. Given the previous research and availability of data, we estimated the variance inflation factors (VIF), which assess whether the chosen variables exhibit multicollinearity. After that, nine indicators (as seen in table 1) were chosen to reflect the urban spatial form, with the VIF of all those indicators less than 5. It means that there was no multicollinearity between explanatory variables. Table 1. Explanatory variables description Short title

Name

2D indicators RISA

The ratio of impervious surface area

LSI

Landscape shape index of impervious surface area

ABD

Average building density

NDVI

Normalized Difference Vegetation Index

3D indicators ABH

Average building height

ASVF

Average sky view factor

AFH

Average forest height

Control indicators POP

Average population density

DEM

Average digital elevation model data

Calculation method  

(areai )/A (lengthi )/(2 ×



π × sum(areai ))

 area of building footprinti /A (Band5-Band4)/(Band5+Band4) 



(building heighti )/n n

sin γ

i 1 − i=1n  (forest heighti )/N1

 

(population countsi )/A (value of demi )/N2

areai : area of impervious surface grids; lengthi : length of the impervious surface grid; A: area of each block; n: the number of buildings in each block; γi : viewing angle i, and n: the number of viewing angles [18], and the searching radius R (m) was 50 m in this paper; N1: the number of forest height grids in each block; N2: the number of DEM grids in each block. Band 4: the infrared band of Landsat-8 OLS/TIR; Band 5: The red band of Landsat 8 OLS/TIR. 3.4 Spatial Regression Models This paper focused on the influence of urban spatial form indicators on LST in the central city of Hangzhou. The OLS model was carried out in advance to analyze the simple relationship between them initially.

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Beyond that, the spatial regression models were used to control the interference of spatial autocorrelation on the interpretation of the relationship between urban form indicators and SUHII. The two commonly used spatial regression models are the spatial error model (SEM), which posits that the error term could be of spatial autocorrelation, and the spatial lag model (SLM), which presupposes that the dependent variable has spatial autocorrelation [35]. Taking the spatial lag term of the independent variable into account, spital Dubin model (SDM) and spital Dubin error model (SDEM) are the extended forms of the SLM and SEM. All four regression models are established by equations as follows (6 ~ 9) using Geoda software. SEM: y = βi xi + μ, μ = λWμ + μ

(6)

SLM: y = ρWy+βi xi + ε

(7)

SDM: y = ρWy+βi xi + θ i Wxi + ε

(8)

SDEM: y = βi xi + θ i Wxi + μ, μ = λWu + μ

(9)

where y is the SUHII variable; ρ is a spatial regression coefficient suggesting spatial autocorrelation of SUHII; λ is a spatial autoregressive coefficient that reflects the spatial influence of the residuals; βi represents the corresponding coefficient; xi are the explanatory variables and control variables; θi reflect spatial spillover effects of explanatory variables. Wy and Wu are the spatial lag operators of y and the residual u, and ε is the error term.

4 Results 4.1 LST Inversion in the Central City of Hangzhou LST of the central city of Hangzhou was inverted by the atmospheric correction method based on the Landsat 8 OLS/TIRS data. We finish the inversion process and then mapped it shown in Fig. 3, which illustrated that the LST of the study area was in the range of 15.82 °C to 56.07 °C, with an average LST was 34.06 °C. Furthermore, the average LST of the city region was 37.21 °C compared to 34.18 °C in the rural area. Figure 3 illustrated that the high-temperature zones were mainly concentrated in the center study area and the southeast part of the study area. The two zones were separated by the Qiantang River, and the former which is on the north side was relatively higher than the latter. The substantial temperature variation may be linked to Hangzhou’s urban planning decisions, resulting in higher buildings, impervious ground surface, and population density there. Similarly, the lower LST values were beyond the urban built-up area boundary, such as in the northwest Yuhang District and southern Xiaoshan District. The reason for this distribution was that there are mountainous with dense vegetation, fewer human activities, and higher altitudes. Thus, the surface temperature was relatively lower.

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Fig. 3. LST of the study area in 2020

4.2 Spatial Distribution Characteristic of SUHII By Eq. 4, the SUHII within the urban boundary was estimated in the central city of Hangzhou. Figure 4 illustrates the SUHII of various locations within the city region. Compared with LST (shown in Fig. 3), SUHII could reflect the SUHII directly. Similarly, Fig. 4 overlays the high SUHII zones were mainly concentrated in the center area and southeast part of the study area. The no SUHII zones were mainly scattered around the periphery of the city region where the ratio of vegetation cover was higher than elsewhere.

Fig. 4. SUHII of the study area

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Figure 5 (a) displays the total Moran’s I value, which was 0.457, supporting the SUHII spatial autocorrelation hypothesis. That value means there was a significate positive spatial autocorrelation of SUHII, and thus the OLS model may fail. Moreover, there was spatial heterogeneity in the SUHII in the central city of Hangzhou. The High-High cluster areas (H-H) were mainly located in the Jianggan District, Gongshu District, and Xiacheng District, which are the traditional urban areas of Hangzhou. In addition, LowLow cluster areas (L-L) were mainly in northern Yuhang Districts and southern Xiaoshan District. Furthermore, Low-High cluster areas and High-Low cluster areas account for very low percentages, thus the H-H area and L-L areas to almost separate.

Fig. 5. Moran’s I value of SUHII (a) and LISA significance map of SUHII(b)

4.3 Results of the Spatial Regressions 4.3.1 Applicability of Spatial Regression Models Before performing spatial regression, the tests for the Lagrange Multiplier (LM), and the Robust Lagrange Multiplier (Robust LM) [36] were processed to evaluate whether all spatial regression models are applicable or not. Table 2 shows that both tests of LM and Robust LM of SLM and SEM were significant, indicating all spatial regression models are more applicable than OLS. To thoroughly explore the spatial spillover effects of SUHII and urban spatial form indicators, all four regression models were selected and compared the degree of fit between them. Table 2. Lagrange multiplier (LM) diagnostics for spatial dependence. Test

Value

P-value

LM(SLM)

1042.7556

0.00

LM(SEM)

1511.7209

0.00

Robust LM(SLM)

48.8790

0.00

Robust LM(SEM)

517.8443

0.00

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4.3.2 Results of Spatial Regression Models The regression results for the SUHII and its influencing factors are shown in table 3. Except for the coefficients for RISA in SLM, all indicators show at least 95% significance of coefficients. In general, results indicate that LSI, ABH, AFH, and NDVI had significant negative effects on SUHII, while the coefficients for RISA, ABD, and SVF were significantly positive. Regarding 2D indicators, the impervious surface that typically was referred to as construction land greatly contributed to SUHII. The reason could be that the physical properties of impervious surface materials could absorb more solar heat, which mainly manifests as sensible heat. Additionally, the impervious surface area ratios reflect the intensity of human activities. It can be mutually supported by the coefficient for ABD which is larger than RISA. Buildings are one of the main sorts places for humans to live and produce, emitting large amounts of anthropogenic heat. In contrast, NDVI was a very important factor that mitigates the SUHII. During the day, The majority of the solar heat that vegetation absorbed is transformed into latent heat by transpiration, which raises the humidity of the surroundings. LSI characterizes the compactness of the urban impervious surface. The lower LSI indicates that the urban surface shape tends to be more circular and compact. With the compactness of the city increasing, the high-temperature zones in the urban areas accumulate heat that should have spread to the surroundings and thus increasing the urban heat island intensity of the region [37]. It indicates that urban planning for construction land is expected to not only focus on regularity but consider promoting solar heat loss out through reasonable construction land shapes. As to 3D indicators, SVF had a noticeably favorable impact on SUHII in terms of 3D indicators. Combing the negative coefficient for ABH, the results suggest that building clusters with shadows can reduce the SUHII [38]. The ground always received greater solar radiation in open urban areas with higher SVF [39]. Therefore, to reduce the UHI, buildings in the city should be developed vertically upward rather than low-density and low-rise structures. Additionally, the absolute value of the coefficient for AFH is higher than that for ABD, which can be explained by the transpiration of vegetation. Results suggest that in the 3D landscape, the main effect of 3D indicators reducing SUHII is through shading from solar radiation. Three parameters called Akaike info criterion (AIC), Log-likelihood (LL), and Schwartz criterion (SC) were also calculated to compare the advantages and disadvantages of all four models. The AIC is used to compare the fit of different models with different combinations of explanatory variables, and the fit of regression models increases as the AIC value decreases. Conversely, a higher LL value means the model fits better. The fit of models also improves when the SC value increases. It was apparent that the SDM parameters were better than the other regression models in the aspects of LL and AIC. But comparing the SC, the SDEM seems to be more applicable. However, the R2 of SDM was 0.716, while the R2 of SDEM was only 0.665. In general, spatial regression models were superior to OLS. Although all four spatial regression models were applicable in this study, SDM and SDEM were much better than SLM and SEM. Given the SDM and SDEM contain the spatial spillover of dependent variables, the results imply that not only SUHII has spatial autocorrelation, but the explanatory variables have spatial spillover effects.

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Table 3. Regression coefficient of different regression models and its test p-value Model RISA

OLS

SLM 0.147

SDEM

SDM

0.747***

0.58***

0.627***

LSI

-0.149***

-0.097***

-0.058***

-0.109***

-0.094***

NDVI

-8.914** *

-7.4583***

-7.917***

-8.063***

-7.903***

ABD

2.294***

1.656***

2.936***

2.878***

3.008***

SVF

1.557***

1.176***

1.078***

1.137***

1.022***

AFH

-0.140***

-0.116***

-0.122***

-0.126***

-0.122***

ABH

-0.012***

-0.012***

-0.014***

-0.013***

-0.014***

POP

0.000***

0.000***

0.000**

0.000**

0.000***

DEM

0.010**

0.011***

0.013***

0.013**

0.013*

R2

0.417**

SEM

0.545

AIC

10280.6

LL

-5130.29

SC

10340.8

0.672

0.715

0.665

0.716

9501.89

9318.7

9281.56

9275.96

-4739.95

-4649.35

-4621.78

-4617.98

9568.08

9378.88

9395.85

9396.26

* Represent 95% significance

** Represent 99% significance *** Represent 99.9% significance

In response, the spatial regression coefficient (ρ) was examined which reflect the spatial spillover of SUHII and θi which reflect spatial spillover effects of explanatory variables in SDM and SLM. According to Table 4, there was a strong positive spatial spillover of SUHII in both models, which means the higher SUHII in a block significantly increases the SUHII in adjacent blocks. As seen in Table 4, the coefficient for WNDVI is different between the two models, which in SDM was the positive value but in SDEM was the negative value. Moreover, the coefficients for WRISA, WABD, and WABH were significant in SDM while they show insignificance in SDEM. In contrast, WAFH exhibited negative effects significantly, but it was insignificant in SDM. Considering there could be multicollinearity between Wy and Wxi , and the residual term might have spatial autocorrelation, SDEM could show spatial spillover effects of explanatory variables more accurately. Overall, the results suggest that LSI, NDVI, ASVF, AFH, POP have significant spatial spillover effects and that variables affect SUHII not only in their blocks but also in adjacent blocks. Therefore, when the government decides to take measures to mitigate the UHI and improve the urban thermal environment, the spatial spillover effects of 2D/3D urban spatial form are supposed to be considered. For example, if a block tends to mitigate the UHI by expanding the vegetation area ratio or increasing the height of vegetation, it will also have a positive effect on the adjacent blocks to mitigate the UHI. Furthermore, preparing integrated urban planning will be helpful to achieve the goals of improving the urban thermal environment and making human living conditions more comfortable in the city more efficiently.

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H. Chen and S. Zheng Table 4. Regression coefficient ρ of SDM as well as SLM and its test p-value

SDM

SDEM

Variables

θ

p-value

Variables

θ

p-value

WRISA

-0.671*

0.019

WRISA

-0.536

0.107

WLSI

-0.121***

0.000

WLSI

-0.17***

0.000

WNDVI

1.344***

0.021

WNDVI

-2.339***

0.000

WABD

-2.143***

0.000

WABD

-1.208

0.064

WASVF

1.119*

0.044

WSVF

1.671*

0.011

WAFH

0.016

0.129

WAFH

-0.036**

0.002

WABH

0.010**

0.008

WABH

0.003

0.442

WPOP

0.000***

0.000

POP

0.000***

0.000

WDEM

-0.006

0.356

DEM

-0.002

0.812

* Represents 95% significance

** Represents 99% significance *** Represents 99.9% significance

5 Conclusion To create a healthy and livable urban environment, a suitable urban thermal environment for human habitation is the prerequisite. In this paper, the atmospheric correction method was used to inverse LST in the central city of Hangzhou. After that, the average SUHII at the block scale was calculated. Moran’s I index was 0.457, which indicates there was significant positive spatial autocorrelation of SUHII. Therefore, four spatial regression models were used to analyze the influence of urban spatial form indicators on SUHII, and thus the spatial spillover effects of the explanatory variables were considered in the analysis by SDM and SDEM. The results suggest that SDEM and SDM are better fitted than SLM and SEM, and they would play more role in the study of exploring the driving factors of UHI in the future. The findings of the study show that increasing the percentage of vegetated land and increasing the height of vegetation continues to be one of the most crucial measures to mitigate the UHI. Buildings have the largest contributions to increasing the SUHII because of full of human activities and the physical properties of building materials. In contrast to expectations, ASVF and ABH show that a compact urban building distribution form with higher building height can mitigate the UHI during the daytime. Additionally, the relatively complex shape of the construction land could lead to heat dissipation, which requires more effort in urban planning to consider the conduction land shape and ventilation corridors. The urban spatial form also proved to have spillover effects on surroundings blocks. Thus, the findings advise that integrated urban planning is supposed to make, which makes mitigating the UHI problem more efficient and economical.

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Acknowledgments. This research was funded by the National Natural Science Foundation of China (Grant No. 42007194). The authors are grateful to Worldpop (https://www.worldp op.org, accessed on 7th Sept 2022) for making population data in Hangzhou, China. The authors greatly appreciate the free access to Global Urban Boundaries (GUB) data provided by Tsinghua University and the annual China Land Cover Dataset (CLCD) data provided by Wuhan University.

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Application Analysis of Existing Industrial Robots in Precast Concrete Component Factory Jianqiu Bao, Huanyu Wu(B) , Yongqi Liu, Yuang Huang, and Yongning Niu College of Civil and Transportation Engineering, Shenzhen University, Shenzhen, China [email protected]

Abstract. In recent years, the Chinese government has continuously issued relevant policies to promote prefabricated buildings in the construction industry, but there are some issues, such as the manufacturing link of prefabricated concrete components (PC components). Due to professional prefabricated components manufacturing robots usually need to be imported in complete sets, the price is extremely expensive. The manufacturing process of PC components is still largely manually. Large numbers of workers gathered in closed factories are very dangerous under the epidemic. This study summarizes and sorts out the production stages of various PC components. This paper collected intelligent professional robots commonly used in the production of PC components, and summarized them into four platforms: robotic arm, mobile car, parallel axis, and fixed type. For the industrial robots which are very mature, this paper explores the application opportunities of them in the production of PC components through secondary development. In this way, the use cost is lower, the layout is more flexible, and it is conducive to the purpose of large-scale application. Keywords: PC components · Robot · Prefabricated buildings · Manufacturing process

1 Introduction Since the onset of China’s ongoing reform and opening-up policy, its economy has undergone rapid development, particularly in the real estate and related industries. Consequently, the number, scale, and complexity of construction projects have significantly increased, resulting in a range of issues such as resource wastage, environmental pollution, and quality concerns. To address these challenging problems, the prefabricated building industry has once again witnessed a surge in development. Following continuous technological improvements, prefabricated buildings have not only preserved their original advantages, which include significant savings in labor, materials, electricity, and other resources, but have also greatly reduced the impact of construction on the environment and significantly shortened the construction period through industrial production. Furthermore, they have considerably reduced the generation of construction waste during the construction process, promoting the green and sustainable development of urban areas [1, 2]. In addition, prefabricated buildings have made significant strides in addressing other concerns such as earthquake resistance [3] and water resistance [4]. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 856–866, 2023. https://doi.org/10.1007/978-981-99-3626-7_66

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Prefabricated buildings consist mainly of prefabricated steel, wood, and concrete structures. While steel and wood structures have developed relatively maturely, the poor fire resistance and sound insulation performance of steel structures and the low strength and flammability of wood structures make concrete structures the current mainstream. The manufacture, transportation, assembly, and post-maintenance of PC components are integral to the entire life cycle of a concrete structure prefabricated building, with PC component manufacturing being the most basic link in resource consumption and all subsequent processes [5]. In comparison to the automobile and machinery industries, the degree of intelligence and informatization in PC component manufacturing is still relatively low, especially in China, where the manufacturing process for PC components relies heavily on manual operations [6]. The low manufacturing efficiency of PC components has become an urgent problem that hinders the development of prefabricated buildings. The recently released “14th Five-Year Plan for the Development of the Construction Industry” emphasized the need to create internet platforms for the construction industry, develop iconic products of construction robots, and cultivate intelligent construction and prefabricated construction industry bases [7, 8]. The application of intelligent robots in the production process of prefabricated components is a policyconforming and timely development. It can further save labor resources, free people from dangerous, heavy, repetitive production activities, and messy production environments, and it is an effective way to solve the inefficiency of component manufacturing and the shortage of front-line construction workers. Additionally, it can improve the efficiency and accuracy of PC component production, enhance the quality of components, and shorten the manufacturing cycle. Robot Operating System (ROS) is an open-source robot development system that provides a common communication mechanism and ecological environment for robot development. Similar to computer operating systems, ROS can be implemented on different devices and allows for the use of developed function packs on all devices that support this system. This reduces research and development costs, shortens refinement time, and increases deployment flexibility. Furthermore, it facilitates cooperation among robots, enabling the benefits of intelligent robots to be applied as soon as possible. This study focuses on the production of prefabricated components and provides an overview of the production stages of various PC components. It also identifies and summarizes four commonly used platforms for intelligent robots in the production process of professional PC components: robotic arm, mobile car, parallel axis, and fixed. In addition, we collected information on common ROS-based industrial robots available on the market. Finally, feasible solutions for applying existing industrial robots to prefabricated factories are proposed.

2 Literature Review The origins of prefabricated concrete buildings in China can be traced back to the 1950s, during the implementation of the “First Five-Year Plan” aimed at resolving the housing crisis. At this time, the technical expertise of the Soviet Union and Eastern European countries was leveraged, resulting in widespread adoption of prefabricated structure systems. Despite the system’s initial success, the Tangshan earthquake brought to light

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concerns about the seismic performance of prefabricated components, causing a period of reduced popularity. In the 21st century, a renewed interest in prefabricated concrete buildings has emerged due to China’s commitment to achieving carbon neutrality by 2060, including a carbon peak by 2030. As such, research efforts in this area have been revitalized [9]. At present, the research on prefabricated concrete buildings mainly includes: research on the structural technology system carried out by Vanke, Yuanda Housing Industry, and Nanjing Dadi Construction [10]. Xu [11] etc. and Du [12] etc. carried out research on information management system; and research on industrial production of prefabricated components [13]. 2.1 Overseas Research Status At present, the industrial production of prefabricated PC components has been improved. The main way is to use intelligent robots to replace manual labor to achieve more efficient and precise production [14]. For robots in prefabricated components, there has been a long development time abroad. These include major construction equipment providers and research teams. Represented by Germany among equipment suppliers, a relatively complete set of prefabricated factory equipment has been developed. Avermann company in Germany was the first company in the world to offer precast concrete manufacturing equipment. Vollert company, a company with high domestic attention [15], has provided shuttle car, tilt table, hardening chamber, concrete batching machine, lateral lifting trolley, fixed production line of precast concrete components, concrete transport system, stacker, trowel, large plotter, mold cleaning and oiling equipment, pallet A series of prefabricated components such as rotating equipment produce robotic systems. Ebawe company has excellent results in steel bar handling: automatic steel bar bending machine, rotor straightening machine, steel mesh sheet welding machine, steel bar truss shearing and welding equipment, automatic steel bar laying equipment, from steel wire rod to steel bar The finished mesh is then placed on the mold table, and the whole process is produced in real time according to the process, without the need for cutting, welding and storage of steel bars in advance. Weckenmann company has mold products covering most usage scenarios. And Germany’s Sommer and Finland’s Elematic company are the world’s leading suppliers of precast concrete equipment. In foreign research teams, Cuong et al. [16] developed a robot prefabrication system. When using robot prefabrication for redesigned components, they found different modules by comparing with the original components. By only changing the equipment of different modules, it solves the disadvantage that the automatic prefabrication factory cannot flexibly respond to the design changes of prefabricated components. But they also have some problems: Firstly, they are all based on the production regulations of prefabricated components in the country, which leads to unacceptable conditions in China. Taking molds as an example, prefabricated components in China require reserved steel bars at both ends. The molds provided abroad are made of flat components. It is difficult to achieve this requirement. Secondly, all systems are constructed through their own research systems. Devices from different manufacturers are not easily compatible. It is impossible to use the most advantageous equipment of each company to obtain the best results. Thirdly, the flexibility of use is poor. Taking the mold robot provided by Vollert

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as an example, it can only grab the molds provided by the conveyor belt that have been entered into the mold library, and the rest of the molds can only be placed manually. 2.2 Domestic Research Status At present, the research and development of construction robots in China focuses on the application of construction sites. Construction equipment providers represented by Bozhilin et al. [17] already have independent intellectual property rights for a series of equipment ranging from measuring robots to cloth robots and grinding robots. However, the research and application of robot production in the prefabricated component factory is still relatively backward, and there is an efficiency bottleneck. But in this environment, there are still many researchers who have made great contributions to China’s construction industrialization. Li Guiqi et al. [18] A double-joint placing robot for concrete pouring is proposed, which can realize automatic and unmanned concrete pouring. Yang Guang et al. [19] According to the existing specification of prefabricated components, a magnetic side mold with ribs is designed. And a matching manipulator end effector is designed to realize automatic grasping and placement while touching. Xu et al. [20] Based on the robot motion control system, lidar, and image perception technology, a vision-guided robot automatic mold disassembly system is proposed. And the actual test in the factory has achieved a mold recognition rate of 99.5% and a speed of less than 20s for a single mold installation. At present, domestic research mainly selects some specific production stages and conducts independent in-depth development research. Researcher did not choose a suitable platform to develop with a standard protocol system. The equipment is isolated from each other, making it impossible to form a production line, and it is difficult to form a system advantage.

3 Research Methods This study aims to collect information on the production process and available production robots in existing prefabrication factories. The study summarizes and analyzes the types of robots used in this context, utilizing a comprehensive research approach that combines survey methods, literature reviews, in-depth interviews, and interdisciplinary research methods. The technical route adopted is shown in Fig. 1. The research methodology includes: 1. In-depth interviews: Semi-structured interviews are conducted with workers and managers in the factory to identify potential issues in the production process of prefabricated components, including current personnel allocation for each process in the factory. 2. Survey method: A combination of on-site and online surveys is conducted. On-site investigations are conducted by entering precast component factories to record the specific precast component production process and investigate the brand, price, and function of the machinery and equipment used. Online surveys include a review of articles and videos related to prefabricated component manufacturers and the investigation of equipment supplied by leading prefabrication equipment providers.

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3. Literature review method: A review of research on prefabricated robots in universities and research institutions is conducted to supplement the survey method and understand the current research status in the industry. 4. Interdisciplinary research method: The intelligent production of prefabricated components involves multiple disciplines, including civil engineering, machinery, and computer science. The study employs robots in the mechanical category and intelligent control programs in the computer category to address specific problems encountered in the component production process.

•

• •

•

In-depth interview

Survey method Literature review method

Interdisciplinary research method

raising issue

Why is the level of intelligence in prefabricated factories not high

parsing issue

Smart devices are too expensive The equipment arrangement is not flexible enough The products produced do not meet the manufacturing specifications

solving issue

Introduce industrial robots and reduce hardware costs Use the ROS development platform to reduce software development costs and increase equipment flexibility

Fig. 1. The technical route

4 Robots in Component Production 4.1 Data Acquisition To gain a comprehensive understanding of all the processes and machines used in the production of precast concrete components in prefabricated construction, this article mainly employs a method of collecting official website data and conducting on-site questionnaire surveys. The internet data collection is primarily achieved through browsing the websites of major providers of intelligent equipment for precast components, including Avermann, Vollert, Ebawe, Weckenmann, and Sommer from Germany, and Elematic from Finland. In addition, highly influential companies in various fields such as Fanuc, EPSON, ABB, KUKA, JAMONE, ZOLLERN, SIASUN, CSSC, and NDC were also surveyed to collect information on the intelligent equipment and equipment parameters they currently offer.

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The on-site questionnaire survey was designed with the following contents: (1) The respondent’s job position. What is the production process of prefabricated concrete components that you are familiar with? Please describe in detail. (2) What machines have you used in the production of precast concrete components? Please describe them in detail. (3) Does your company use automation technology in the production process? Please describe it in detail. (4) In the production of precast concrete components, what areas do you think need improvement? Please explain the reasons and suggest improvement methods. (5) What do you think is the future development direction of the precast concrete component industry? Please explain the reasons. Due to the impact of the epidemic, the on-site questionnaire was only used as a supplement. It was sent to three precast component production factories in Shenzhen, China, with a total of 50 questionnaires distributed and 34 received. 4.2 Sorting Out the Production Process Currently, there are two primary methods for prefabricated component production: factory prefabrication and on-site prefabrication. Each method has its own range of applications and complements the other. On-site prefabrication is advantageous due to lower transportation costs, but has limitations in component quality control and site space utilization. It is most suitable for large components that are difficult to transport, such as prefabricated bridges and large culverts. Factory prefabrication, while incurring higher transportation costs, enables standardization, mass production, reliable quality, low production costs, and does not occupy site space. It is best suited for small to medium sized components that are easy to transport, such as in housing construction where whole buildings can be broken down into standard components such as beams, plates, columns, walls, stairs, etc. In prefabricated component production, three common manufacturing processes are used: the pedestal method, unit flow method, and conveyor belt flow method. The pedestal method completes all production processes on a fixed pedestal, with operators, equipment, and materials sequentially moving from one station to the next. The unit flow method moves the product along the process line with lifting and transporting equipment, with workers at each station completing their respective procedures. The conveyor belt flow method moves products in sequence on a conveyor belt, with each station completing its specific processes within a flow beat. The assembly line method is best suited for industrial production of prefabricated components and machinery arrangement and utilization.

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Fig. 2. Manufacturing processes

As depicted in Fig. 2, the production processes of prefabricated sandwich thermal insulation exterior walls (Figure A), prefabricated ordinary walls and prefabricated laminated panels (Figure B), and prefabricated beams, columns, and stairs (Figure C) were sorted out through on-site inspections and online searches for relevant information. 4.3 Robotic Summary of the Production Process Through the manufacturing process of common components in house construction, it can be found that many of the manufacturing links of these components are the same. Therefore, the component manufacturing can be divided into the reinforcement cage production module (Figure A), the mold application module (Figure B), and the concrete pouring module (Figure C). And the same manufacturing robots are used in the manufacturing of the components in the same steps in the same module. Figure 3 illustrates the categorization of component manufacturing into three modules: the reinforcement cage production module (Figure A), the mold application module (Figure B), and the concrete pouring module (Figure C). The same manufacturing robots are employed for the production of components within the same module and in the same steps.

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Fig. 3. Production module

The robots utilized in prefabrication factories can be classified into four categories: those based on robotic arm, mobile car, parallel axis, and fixed type, as presented in Table 1. Some of these devices are adaptable to multiple platforms, and the most common ones are listed herein. The first three robot platforms have mature robot products in industrial robots using the ROS operating system. The application of these products in prefabrication plants requires only the transformation of the corresponding scene, enabling fast and cost-effective applications.

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Based on robotic arm

Based on movement

Based on parallel axes

stationary

Edge mold access machine Mold inspection equipment Reinforcement grabbing and placement equipment Embedded part placement equipment Reinforcement lashing equipment Reinforcement mesh testing equipment CNC scribing machine

Ground walking wheels Mold table drive wheels Lift shuttle bus Accessories transporter

Lifting equipment Mold table system Vibrate the leveler Reinforcement mesh welding equipment Reinforcement straightening machine Concrete conveyor Concrete spreader Grinding and polishing machine Surface brushing machine Side mold conveyor

Injector Touch the production equipment Edge mode identification system Mold washing machine Maintenance silos Rebar cutting machine Reinforcement bending equipment Reinforcement joint length equipment Flip mold release machine Concrete mixer

4.4 Application of Existing Industrial Robots Broadly speaking, industrial robots are programmable machines controlled by computers. Its use is very wide, involving all aspects of industry (Table 2). Table 2. Suitable for industrial robots Type

Parameter(max)

Application industry and brand process

Articulated robot

Load: 2300 kg Radius: 5 m

Automobile, 3C product assembly

Fanuc EPSON ABB

Automated guided vehicle

the effective load range is large

Auto parts handling, logistics, catering industry

SIASUN CSSC NDC

Cartesian robot (Gantry robot)

Load: 15,000 kg Radius: 45 m

Logistics, handling code stack, feeding and blanking

KUKA JAMONE ZOLLERN

Table 1 presents a list of robot types suitable for use in the prefabrication process. Compared to industrial production, precast components are characterized by their heavy and bulky nature, with less stringent precision requirements. Thus, when selecting robots for this application, it is crucial to consider the maximum load capacity and achievable radius according to the process. The Articulated robot is the most suitable for application

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in the “Based on robotic arm” category, given its high degree of freedom and flexibility in planning trajectories. Automated Guided Vehicles are flexible and highly automated, making them ideal for use in “Based on movement” robots, with payload capacity varying greatly based on the size of the body. For instance, SIASUN’s heavy-duty model can achieve a load of more than 5 tons or even 10–120 tons. The Cartesian Robot, with its simple structure, wide achievable radius, and large payload, is most suitable for use in “Based on parallel axes” robots, with ZOLLERN’s Gantry Robot, for example, having a payload capacity of up to 15,000 kg, which can handle most parts of prefabricated components.

5 Conclusions This study presents a comprehensive overview of the production process of various precast (PC) components and identifies the intelligent robots commonly used in their production, categorizing them into four platforms: “Based on robotic arm,” “Based on movement,” “Based on parallel axes,” and “stationary.” The hardware features of Articulated robots, Automated Guided Vehicles, Cartesian robots (Gantry robots), and other robots are introduced, along with the use of the Robot Operating System (ROS) software. By leveraging the mature development of industrial robots based on ROS, it is possible to replace them in the production of PC components through secondary development, thereby reducing the research costs of prefabricated equipment, shortening the improvement time, and improving the flexibility of deployment. Thus, the advantages of intelligent robots can be rapidly applied to the production process of PC components. Acknowledgements. This research was conducted with the support of Shenzhen Newly Introduced High-end Talents Scientific Research Start-up Project (Grant No. 827000656).

References 1. Tavares, V., Gregory, J., Kirchain, R., Freire, F.: What is the potential for prefabricated buildings to decrease costs and contribute to meeting EU environmental targets? Build. Environ. 206, 15 (2021) 2. Liu, S., Li, Z.F., Teng, Y., Dai, L.R.: A dynamic simulation study on the sustainability of prefabricated buildings. Sustain. Cities Soc. 77, 17 (2022) 3. Kurama, Y.C., et al.: Seismic-resistant precast concrete structures: state of the art. J. Struct. Eng. 144(4), 18 (2018) 4. Orlowski, K., Shanaka, K., Mendis, P.: Design and development of weatherproof seals for prefabricated construction: a methodological approach. Buildings 8(9), 22 (2018) 5. Li, D.Z., Li, X., Feng, H.B., Wang, Y., Fan, S.S.: ISM-based relationship among critical factors that affect the choice of prefabricated concrete buildings in China. Int. J. Constr. Manag. 22(6), 977–992 (2022) 6. Weizu, Q., et al.: Learning from advanced experience to understand the latest technology in the industry – a survey on the development of concrete process technology in Europe. Concr. World (01), 42–48 (2018)

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7. Ministry of Housing and Urban-Rural Development. (2022). “14th Five-Year Plan” Construction Industry Development Plan. Beijing: Ministry of Housing and Urban-Rural Development 8. Pan, J.H.: Highlighting Carbon Neutrality in Building Beautiful Cities. Chin. J. Urban Environ. Stud. 09(02), 6 (2021) 9. Chi, Y.Y., Liu, Z.R., Wang, X., Zhang, Y.Y., Wei, F.: Provincial CO2 emission measurement and analysis of the construction industry under china’s carbon neutrality target. Sustainability 13(4), 15 (2021) 10. Zhu Juncheng, Y., Huan, S.S.: Rebar connection technology and application of prefabricated monolithic concrete structures. Build. Technol. Dev. 48(17), 13–14 (2021) 11. Xu, Z., Xie, Z., Wang, X.R., Niu, M.: Automatic classification and coding of prefabricated components using IFC and the random forest algorithm. Buildings 12(5), 22 (2022) 12. Du, J., Jing, H., Choo, K.-K., Sugumaran, V., Castro-Lacouture, D.: An ontology and multiagent based decision support framework for prefabricated component supply chain. Inf. Syst. Front. 22(6), 1467–1485 (2019). https://doi.org/10.1007/s10796-019-09941-x 13. Wang, S.Q., Tang, J., Zou, Y.Q., Zhou, Q.H.: Research on production process optimization of precast concrete component factory based on value stream mapping. Eng. Constr. Archit. Manag. 27(4), 850–871 (2020) 14. Melenbrink, N., Werfel, J., Menges, A.: On-site autonomous construction robots: towards unsupervised building. Autom. Constr. 119, 21 (2020) 15. Vollert (2022). https://www.vollert.com.cn/ 16. Kasperzyk, C., Kim, M.-K., Brilakis, I.: Automated re-prefabrication system for buildings using robotics. Autom. Constr. 83, 184–195 (2017) 17. Xiaokang, L., Jianfu, C.: The application status and key technologies of intelligent robots for prefabricated buildings. Autom. Expo 35(07), 66–70 (2018) 18. Guiqi, L., Qiang, Z.: Design and research of a double-joint placing robot based on concrete pouring. China Equip. Eng. 10, 28–30 (2022) 19. Yang, G.: Design and analysis of side-form manipulator for prefabricated concrete components of prefabricated buildings. (Master), Huazhong University of Science and Technology (2020). Available from Cnki 20. Xu, X., Feng, L., Wu, M.: Design of robot system for dismantling and assembling molds based on 3D vision guidance.Lift. Transp. Mach. (04), 41–47 (2020)

Gauging the Knowledge Development of Innovations in Mega-infrastructure Projects Long Li1,2 , Shuqi Wang1(B) , Haiying Luan1 , and Shengxi Zhang3 1 School of Management Engineering, Qingdao University of Technology, Qingdao 266520, China [email protected], [email protected] 2 Antai, College of Economics and Management, Shanghai Jiao Tong University, 200030 Shanghai, China 3 Department of Construction Management, Dalian University of Technology, Dalian 116024, China

Abstract. As the construction industry’s flagships, mega-infrastructure projects are often involved in advanced practices. In these practices, innovative efforts are critical to the sustainability of mega-infrastructure projects. In the past decade, much research has been conducted on innovations in mega-infrastructure projects. However, an in-depth understanding and knowledge guidance in this field is still lacking. Therefore, it is essential to carry out a thorough review to identify current research objectives and potential future paths for research development. In this study, the Scopus database was used to extract articles published in the past 10 years (i.e., 2012–2021) to reveal the knowledge state and knowledge gaps of innovations in mega-infrastructure projects and to clearly and objectively analyse their knowledge structure and relationships. To achieve these goals, a mixed-review approach was adopted, which includes scientometric and systematic analysis. The scientometric approach identified the most influential papers, the co-occurrence network of authors, and keywords clusters. Moreover, the systematic analysis divided the articles according to the research dimensions (antecedent, content, and impact) of innovations in mega-infrastructure projects. The results show that innovation in mega-infrastructure projects has gained more attention over the past decade. In addition, the majority of studies on innovations in mega-infrastructure projects concentrate primarily on the nature of innovation itself, often ignoring the influencing factors of innovation and the impact of innovation. These findings provide practitioners and researchers with a clearer understanding of innovations in mega-infrastructure project and Inspire future research based on identified gaps. Keywords: Innovation · Mega-infrastructure project · Review · Scientometric analysis · Systematic analysis

1 Introduction Mega-infrastructure projects (later abbreviated as “megaprojects”) are usually perceived to be large-scale and complex ventures that take many years to build and cost more than $1 billion [1]. The typical megaprojects include high-speed railways, seaports, airports, © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 867–884, 2023. https://doi.org/10.1007/978-981-99-3626-7_67

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hospitals, dams, the Olympics, ICT systems [1], etc. Megaprojects are not just enlarged versions of normal projects. In fact, they consist of interdependent subsystems aimed at improving socio-economic development or providing public welfare [2]. Due to the technical complexity, and project uniqueness, a conventional innovation paradigm is not suitable for megaproject innovation. Multiple subjects of megaproject need to form a close network to collaborate with other subjects to carry out innovative activities [3]. Several studies have demonstrated the effective implementation of innovations in megaprojects such as the London Heathrow Terminal 5 [4], London Crossrail [5], the Hong Kong-Zhuhai-Macau Bridge [6, 7], Hoover Dam project [8], Channel Tunnel [9], and the Transportation Expansion Project in the US [10]. Despite the growing number of megaprojects, due to the high risks and uncertainties, megaprojects continue to have poor innovation performance records [2]. In fact, knowledge of innovation in general projects often fails to effectively explain complex innovation issues in megaprojects, and cannot effectively reveal their laws and effectively guide practice. According to Davies and Gann [11], one of the reasons megaprojects fail is because their delivery model is incapable of innovating and adapting to changing conditions. Thus, the completion of megaprojects and improvement of their quality and sustainability depend on technological innovations in engineering systems, structures, materials, engineering techniques, equipment, and other areas [12]. While there has been some study on innovation in regular construction projects, attention has so far been less focused on innovation in megaprojects [11]. Therefore, this study aims to systematically examine and carefully classify innovations in megaprojects between 2012 and 2021. The results are anticipated to help researchers better understand earlier study efforts on this subject and provide guidance for future study areas. The two main research objectives of this study are: 1) to summarize the research of innovations in megaprojects and determine the current level of knowledge research and base in this area. 2) to explore research gaps and trends in innovations in megaprojects, and provide guidance on the scientific knowledge and theory in this field. With this background, this study aims to provide a structural analysis of literature reviews on the topic of innovations in megaprojects. For this analysis, from 2012 to 2021, documents were systematically shortlisted for the study. The remainder of this paper is organized as follows. Section 2 discusses the background of the literature. Section 3 describes the research methods used in this study. Section 4 shows in detail the process of data collection for this study. Section 5 provides an overview of innovations in megaprojects through a mixed-review approach and discusses the existing research gaps. Lastly, the conclusions and contributions of this research are summarized in Sect. 6.

2 Background Because of the significant uniqueness and complexity of megaprojects, there is a great deal of research interest in megaproject management, and there is extensive literature on innovation. Innovation in megaprojects has the characteristics of a multi-subject, nonlinear, dynamic, and integrated complex innovation system [13]. Innovation in megaprojects varies from innovation in the manufacturing industry or other industries in two key ways: first, there are different drivers of innovation [5]; second, the organizational

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dynamics at play are different [15, 16]. Generally, project requirements fuel the majority of innovation in megaprojects [3]. Because advanced or innovative practices can involve uncertainty and higher costs, contractors and client organizations frequently reject innovation when current practices or technologies can achieve the project’s goals [5]. Innovation and social responsibility in megaprojects are two key factors in promoting sustainable delivery of megaprojects. The R&D institution has faced numerous challenges as a result of the high level of technical innovation uncertainty in megaprojects. It has also become the stimulus for moral hazard. To reduce information asymmetry and increase the effectiveness of incentives, Jiawei Liu and Guanghong Ma [17] suggest suitable incentive and supervision mechanisms. Worsnop T. et al. [18] concluded from a study of European cross-track that open and closed innovation could coexist if the right environment for communication and exchange was established. Some studies look at innovation as a mediator or mediator to study its relationship with other variables. For instance, a research by Severo et al. [14] investigated the connection between project process innovations and management practices. Innovation in megaprojects may appear in products, procedures, or systems and include useful applications, knowledge, and technological advancements. Qinghua He et al. [19] discussed the intricate connections between social responsibility, innovation, and project success in megaprojects.

3 Research Methods As shown in Fig. 1, there are 3 phases to the research process for this paper. In order to obtain a comprehensive knowledge of the reviewed topic under consideration while addressing the weaknesses in using either the quantitative or qualitative approach in isolation [20], this study primarily used a method called mixed-review. In the present study, a scientometric review as the quantitative method, and systematic review is selected as the qualitative method. The description and advantages of each method are provided below. 3.1 Scientometric Approach The scientometric approach is a method for mapping extant knowledge and its evolution in a field on the basis of significant academic datasets and measuring the influence and citation processes of research [21]. Science mapping can use a large amount of bibliographic data to study the knowledge structure of a topic and visualize important categories and trends to assist relevant decision-making [21]. Numerous tools are available for such a visualization which include VOSviewer, Gephi, CiteSpace, and BibExcel [22]. After understanding the characteristics of various software, three software, HistCite, Gephi, and VOSviewer, were selected for quantitative research. 3.2 Systematic Analysis Following the scientometric mapping analysis, a qualitative analysis was performed on carefully selected papers. It is important to emphasize that the purpose of the qualitative analysis was to provide deeper discussions and insights on innovations in megaprojects

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and to articulate the need for future research [20]. In management and business literature, the systematic analysis method has been developed over the last two decades and plays a significant role in evidence-based research [23]. In contrast to the traditional literature review, a systematic analysis locates the body of existing knowledge and assesses and synthesizes the studies in a repeatable, transparent, and objective way [23].

Fig. 1. Research procedure of this study

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4 Data Collection The data collection process in this study was divided into two stages: bibliometric search and manual screening. As shown in Fig. 2, there are three main steps, and the numbers below represent the amount of database literature after each step has been conducted. The two major stages of data collection are described in detail below. 4.1 Bibliometric Search Since compared to other databases, Scopus has a wider coverage of scientific papers [24], we searched for titles, abstracts, and keywords using an algorithm in Scopus. In this search, only English articles and reviews covering a period from 2012 to 2021 were selected. Then refine the search further by limiting the subject area to ‘computer science’, ‘materials science’, ‘economics, econometrics and finance’, ‘engineering’, ‘social sciences’, ‘energy’, ‘environmental science’, and ‘decision sciences’.

Fig. 2. The process of data collection in this study

The two key concepts in this study are innovation and megaprojects. Although the term megaproject is frequently used, the alternatives being used are mega-project, major project, mega-infrastructure project, major infrastructure project, major construction, major building, large construction project, complex project, mega construction project, or large project. Therefore, in our literature search, we used the terms “megaproject” and the nine alternatives. We used this broad keyword to include as many potentially relevant studies in the original sample [11] as possible, taking into account the variety of ways that researchers may have used the term innovation [25]. The Boolean operator “AND” was used to combine the two thematic areas because this literature search targets research that falls at the intersection of the themes; while the Boolean operator “OR” was

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used to include all related keywords. After a preliminary literature search, 204 studies were obtained, which served as the basis for data screening. 4.2 Manual Screening Despite the fact that a thorough and efficient literature search was conducted, these 204 papers are not necessarily what we need, and may be mixed with literature that is not relevant to the topic. Hence, to improve and filter the literature from the part of the literature search, a manual screening was carried out. Table 1 shows the two criteria for screening papers [26], if an article fulfilled the following two criteria, it was considered irrelevant. The total number of core articles was reduced to 70 after applying the two criteria [27]. Table 1. Criteria for literature exclusion No. Criterion 1

Articles do not deal with innovation, or innovation is not the main focus

2

Articles do not relate to the AEC industry, such as megaprojects in the field of medicine, education

5 Result and Discussion 5.1 Descriptive Analysis 5.1.1 Trend of Published Articles The distribution of these studies from 2012 to 2021 is shown in Fig. 3. In this field, there have only been a few publications (3–5) between 2012 and 2015. Starting from 2016, there has been a clear increasing trend in research on innovations in megaprojects. The highest number of annual publications (14) is reached in 2021. The overall trend of research results over the previous ten years is shown by the red dashed line in Fig. 3. It can be seen that the number of papers in this field has an overall trend of growth, demonstrating that interest in innovations in megaprojects is increasing. However, the overall low number of publications indicates that innovations in megaprojects are an emerging research field that requires continuous exploration and research. 5.1.2 Contributions of Countries/Regions The amount of academic papers in a country or region reveals how far industrial practices in academic fields have advanced there. Therefore, it is crucial to analyse the contributions of different countries or regions to get a sense of the present industrial practices in particular fields [28]. Figure 4 shows the top ten contributing countries in the world, with the UK ranking first with 20 articles, followed by China, Australia, and the US. Interestingly, Chinese researchers have been actively involved in this field, which echoes the

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Fig. 3. The number of articles on innovations in megaprojects in the Scopus in 2012–2021

unprecedented development of megaprojects in China in recent years. In other words, the rapid growth of megaprojects in the Chinese context provides a supportive environment for innovative research in this field.

Fig. 4. Publications distributed by country

5.1.3 Distirbute of Journals 48 journals distribute the 70 selected articles, the distribution of journals is scattered, and part of the journals only published a small number of documents related to innovations

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in megaprojects, which is of little significance to research in this field. Figure 5 shows the journals with no less than two articles. Among them, the International Journal of Project Management (IJPM) and International Journal of Managing Projects in Business (IJMPB) published the highest number of papers, which indicates the importance of IJPM and IJMPB in this field. Followed by Project Management Journal (PMJ, 4), Journal of Management in Engineering (JME, 4), Construction Management and Economics (CME, 3), Production Planning and Control (PPC, 2), Journal of Construction Engineering and Management (JCEM, 2), International Journal of Innovation Science (IJIS, 2), Engineering, Construction and Architectural Management (ECAM, 2), and Buildings (2).

Fig. 5. Number of papers distributed by journal

5.2 Results of Scientometric Analysis 5.2.1 Historiography Analysis and Influential Article The HistCite software’s “Graph Maker” tool has been used to display the mutual citations of the articles that have been published [22]. We used HistCite to create a citation map to show how innovations in megaprojects research have evolved over time, as shown in Fig. 6. There are 6 links and 10 nodes. The name of the first author and the year of publication are marked next to each node, which represents an article [29]. The radius of each node indicates the frequency of citations from publications in this field, which is indicated by a number in the circle. The citation relationships between the publications are indicated by the red arrows [30]. It shows that Gil N. et al. (2012) and Davies A. et al. (2014) published the most highly cited paper with the LCS value of 8, followed by Sergeeva N. et al. (2018) (LCS = 6) and Worsnop T. et al. (2016) (LCS = 5). According to the distribution of highly cited literature in terms of publishing dates, 2016–2019 is the “golden period” for important research, and 7 out of 10 papers are

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concentrated in this time period. The research structure shows that the field of innovation in megaprojects has the characteristics of a strong combination of theoretical research and practical application. It is important to note that a significant portion of the researchers in the highly cited literature combines theoretical methods with case studies, emphasizing the practical importance of research in innovations in megaprojects.

Fig. 6. Historiography analysis and influential articles

5.2.2 Analysis of Influential Authors’ Network To explore the key participants in current related study and their interactions, this study adopted VOSviewer and Gephi to analyze the co-authorship. First, a network was created using VOSviewer, and the minimum publication collection for each author was two documents. 13 researchers were recognized as having met the standard as a result. Then, the map generated in VOSviewer was imported into Gephi for greater visualization and quality with the suggestion of Cobo et al. [31]. Figure 7 shows the results of an analysis of author collaborative literature networks in existing research areas [30]. Each node represents one author, the size of the node represents the number of publications, and links between the authors represent the cooperative relationship established through co-authors in the article [32]. As shown in Fig. 7, there were three networks of group partnerships between international researchers in megaproject innovations literature publications, with the rest being relatively independent and fragmented. The primary study areas targeted within each network group may be further revealed by examining at these collaboration networks. For example, the cluster research network group consisting of He Q., Chan A.P.C., and Chen X. have been highly focused on the relationship between sustainability, stakeholder, and innovations in megaprojects. Table 2 shows the affiliation and country of these 12 authors, as well as their average normal citation. Although it is clear that authors from Tongji University in China have made significant contributions to the field of innovations in megaprojects, the majority of authors still come from developed countries.

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Fig. 7. Mapping of scientific collaboration network of influential authors

This is due to the fact that developed countries are more advanced technologically and give their innovations more attention. 5.2.3 Map of Study Themes Keywords represent the research focus and direction in the current research domain. Because VOSviewer can visualize bibliometric networks, researchers often use it to conduct literature review analysis in the field of construction engineering and project management [33]. Therefore, VOSviewer was selected as the keyword framework for innovations in megaprojects in this study. At least two occurrences were the criterion for the minimum amount of keyword-related documents, and as a result, only 34 out of 584 keywords met the requirements. As shown in Fig. 8, the size of the node indicates the frequency of occurrence of the keyword, and the thickness of the connecting line indicates the degree of correlation between the two keywords [33]. The keyword cooccurrence map contains three clusters: namely 1) megaprojects, 2) project management, and 3) innovation, each cluster is represented by a different color, and their description is shown in Table 3. • Megaprojects (red cluster): The complexity of megaprojects, social responsibility, and the macro environment of economic development are contributing factors to innovation, and at the same time, the realization of innovation in megaprojects is conducive to the sustainable development of megaprojects, and most researchers use a case study approach to analyze innovation in megaprojects. • Project management (green cluster): Project management is a key part of innovation management, mainly including knowledge management, organization management,

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Table 2. Summary of information from the most contributing authors No.

Author

Institution

1

He Q.

2

Chan A.P.C.

3

Davies A.

University College London

4

Sankaran S.

5

Country

Documents

Norm. Citations

Tongji University China

3

3.03

Hong Kong Hong Kong Polytechnic Univ

2

3.83

UK

2

3.74

University of Technology Sydney

Australia

2

3.17

Artto K.

Aalto Universit

Finland

2

3.16

6

Invernizzi D.C.

University of Leeds

UK

2

2.02

7

Locatelli G.

University of Leeds

UK

2

2.02

8

Chen X.

Tongji University China

2

1.83

9

Cantarelli C.C.

University of Sheffield

UK

2

1.71

10

Seliverstov V.E.

Institute of Economics and Industrial Engineering

Russian Federation

2

1.03

11

Procter C.

University of Salford

UK

2

0.79

12

Kozak-Holland M.

University of Salford

UK

2

0.79

13

Brunet M.

HEC Montréal

Canada

2

0.52

information management, risk management, and so on. In the context of the global knowledge-based and information-based era, among which knowledge management and information management are the hot directions of megaprojects innovation. • Innovation (blue cluster): Because of the significant uncertainty of megaprojects’ technology requirements, innovation in megaprojects mainly refer to technological innovation. In recent years, the development of digital transformation has provided an opportunity for technological innovation. Megaprojects involve multiple stakeholders, so decision-making is particularly important and is the key to influencing the next development trend of megaprojects.

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Fig. 8. Mapping of co-occurrence network of keywords

5.3 Systematic Analysis and Findings While a scientometric review can provide insight and clustering of innovation in megaprojects from a quantitative perspective, it cannot identify specific research gaps in this field [34]. Therefore, a systematic analysis is carried out on the same 70 studies used in the scientometric review to provide an in-depth understanding of innovation in megaprojects. Through the study of 70 research systems, we divided them into three dimensions, antecedent, content, and impact, and these three dimensions are described in detail below. • Antecedent: What fosters the creation of innovation? The complexity of the environment [13], the failure of previous projects [5], and other factors have been identified as crucial drivers of innovation generation in the many literatures on megaprojects. We divided the antecedent dimension into internal factors and external factors. Internal factors are the internal reasons why megaprojects promote the development of innovation based on their own development perspective, including technical challenges, or improving performance. Knowledge and resources [6] are also internal factors, and various parties communicate and collaborate by bringing together different knowledge and resources to play a key role in innovation. External factors refer to the external causes that drive innovation from an institutional theory perspective, including socio-environmental, economic conditions, sustainability requirements, and changes in regulation or political environment [35]. • Content: What does innovation consist of? Innovation types include two categories: technological innovation and management innovation. Technological innovations provide services directly related to the

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Table 3. Clusters description Cluster

Label

Occurrences

Links

Avg. citations

Avg. norm. citations

#1

megaprojects

33

20

21.58

1.15

construction

11

11

24.36

1.07

complexity

7

8

26.29

1.24

megaproject social responsibility

6

10

6.67

0.66

surveys

6

8

15.00

0.87

#2

#3

case study

5

7

15.40

0.81

sustainability

4

8

32.25

2.53

developing countries

3

7

53.33

1.55

economics

3

7

3.67

0.67

project management

20

19

34.75

1.46

information management

4

12

33.50

1.00

risk management

4

8

6.25

1.21

knowledge management

3

6

27.00

0.80

innovation management

3

6

28.33

0.71

organization management

3

5

31.00

1.57

innovation

36

19

22.83

1.26

technology

8

12

33.13

1.75

digital transformation

4

7

7.50

0.81

infrastructural development

3

8

30.67

1.69

stakeholders

3

7

35.67

1.59

decision making

3

6

6.33

0.18

climate change

3

5

81.33

3.01

core activities of an organization, including products, processes, and technologies. Technological innovation has become both a crucial need for construction and a useful way to increase the effectiveness of megaprojects. For example, the application of BIM (Building Information Modeling) technology in megaprojects can be of great

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help in cost-saving, time-saving and green construction [36]. Management innovations refer to the management aspects of an organization’s core activities, including the organizational structure and human resources, etc. An effective incentive and supervision mechanism is of great importance for improving the enthusiasm of innovation in megaprojects and the value of innovation achievements [18]. Megaprojects have identified a number of management innovations, including new governance systems, and new organizational structures, etc. • Impact: What innovation will do? The impact of innovation is diverse, and technology diffusion and knowledge diffusion are important manifestations of the impact of innovation. According to innovation diffusion theory, technology innovation diffusion is a process where technology innovation communicates among members of the social system, through certain channels over time [37]. Knowledge diffusion is the process by which the knowledge contained in the literature related to a megaprojects innovation is accessed, absorbed, adopted, and innovated by other scholars through online or offline ways [38]. Successful innovations can be adopted by other megaprojects through diffusion. For instance, introducing new management methods, new production methods, and new technologies can help enhance the production and management process of megaprojects, thus increasing the chances of successful project delivery and ultimately sustainable development. The left side of Fig. 9 presents the number of publications distributed in different dimensions. As shown in the diagram, under the dimensional perspective, it can be obviously seen that the content dimension accounts for the majority, with a total of 38 papers related to the content dimension (55.7%). This is followed by the antecedent dimension (25.7%) and the impact dimension (18.6%), with 18 and 14 relevant papers, respectively. In order to better observe the appearance order of these three dimensions from a temporal perspective, we have counted the years in which they appeared, as shown on the right side of Fig. 9 The colors of this figure correspond to the three colors of the pie chart on the left, which represent three different dimensions of innovation in megaprojects. The vertical axis is the year, ranging from 2012 to 2021, and the horizontal axis represents the number of papers published in different dimensions under the corresponding years.

Fig. 9. Three dimensions and the historical process of innovation in megaprojects division

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In general, the number of papers examining these three dimensions has increased over time. Notably, the antecedent dimension was in the doldrums until 2016, and since 2017, there has been an unprecedented development of the antecedent dimension. The number of papers on the impact dimension has also increased over the years, but the growth trend is not significant. Unlike the antecedent dimension and the impact dimension, the content dimension has been studied by scholars every year since 2012. The above shows that most studies on innovations in megaprojects have focused on the nature of innovation itself, and tend to ignore the factors affecting innovation and the impact of innovation. But as time goes by, people realize more and more that innovation in megaprojects is complex and cannot be deeply understood and analyzed from one dimension alone. Therefore, scholars gradually focus on the factors that influence innovation and the impact brought by innovation. This will help enrich the theoretical system in the field and prepare well for the innovation practice of megaprojects.

6 Conclusion This study offers a thorough evaluation of the field of innovations in megaprojects and summarizes the research status in this field by examining 70 related papers released from 2012 to 2021. Our finding indicates that: (1) The number of publications on “innovations in megaprojects” has clearly increased over the past decade. With 20 documents, the UK was the country with the largest contribution to publications in innovation in megaprojects. The “International Journal of Project Management” and “International Journal of Managing Projects in Business” with 5 documents were the leading source journal for articles in innovation in megaprojects. (2) The most influential paper in the publication of literature on innovation in megaprojects is Gil N. et.al. (2012), and the most published academic researcher is He Q. with 3 papers. The keyword analysis of the innovation in the megaprojects area identified three key themes, namely: megaprojects, project management, and innovation. (3) According to the results of the systematic analysis, it was found that the antecedent, content, and impact aspects were considered in 25.7%, 54.3%, and 20%, respectively, of the innovation studies on megaprojects in the last decade. The contributions of this study can be divided into two aspects: First, it summarizes the current knowledge of research in the field of innovations in megaprojects, improves the knowledge system in the area, facilitates efficient literature searches by relevant researchers, and provides meaningful references for industry practitioners. Secondly, it is essential to discuss the frontiers of innovations in megaprojects in terms of three different research dimensions in order to improve the sustainability of megaprojects and help raise awareness of the innovation of practical studies to explain broader contexts and issues, as well as research gaps. Several limitations of this study must be recognized despite its contributions to the megaproject management field of study. The Scopus search for this research is limited to English-language journal articles. There might be some articles in other databases or in different languages that can help people comprehend this research more broadly. It is

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recommended to expand the scope of the survey of the literature, which can help future researchers explore more research possibilities. Acknowledgments. This research was funded by the China Postdoctoral Science Foundation (2022M712047), the Natural Science Foundation of Shandong Province (ZR2021QG046), and Outstanding Youth Innovation Team Foundation of Shandong Province in China (No. 2022RW036).

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The Analysis of Urban Expansion Based on Space Syntax: A Case Study of the Main Urban Area of Hangzhou, China Yukuan Huang and Sheng Zheng(B) Department of Land Management, Zhejiang University, Hangzhou 310058, China [email protected]

Abstract. As China’s urbanization enters a rapid development stage, the expansion of cities has generated several problems. The road network influences urban form significantly and can contribute to sustainable urbanization. Currently, studies on urban expansion rarely use road networks. This study aims to analyze the spatial aggregation and intensity of urban expansion using road network data by space syntax. The results of spatial autocorrelation analysis show a moderate degree of positive spatial autocorrelation in the main urban of Hangzhou. The high–high agglomeration regions grew gradually and were primarily concentrated in the center, whereas the low–low agglomeration regions were scattered around the boundary. According to the Space Syntax Expand Intensity Index (SS-EII), the main direction of urban expansion was the northeast and north, while the southeast and east expanded at a slower rate. Moreover, the overall intensity of urban expansion was highest in 2018. Finally, the reasons associated with urban expansion are explored, including demographic, economic, policy, and geographical factors. Keywords: Urban expansion · Space syntax · Main urban area of Hangzhou

1 Introduction At present, China’s urbanization is in a rapid development phase, characterized primarily by an increase in the urban population and the expansion of the built-up area. From 1981 to 2018, the area of China’s urban construction land increased from 6720 to 56,075.9 km2 , and the urban population increased from 201.71 million to 831.37 million [1]. Numerous issues have arisen due to the growth of numerous megacities and large cities, including environmental pollution [2, 3] and biodiversity loss [4]. At the same time, the attraction of large cities to small towns and rural areas has led to idle farmland and hollow villages [5]. The Sustainable Development Goal (SDG) target 11.3 underlines the goal of enhancing sustainable urbanization in all countries by 2030 [6]. Prior to investigating urban-related topics, an analysis of the current state and pace of urban growth is necessary. This analysis can also offer suggestions for the advancement of sustainable urbanization [7]. There are many ways to quantify urban expansion, which has been a crucial research topic. First, the most common method of urban area extraction is remote sensing. For © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 885–897, 2023. https://doi.org/10.1007/978-981-99-3626-7_68

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example, by extracting remote sensing images obtained from Landsat satellites in 1980, 1990, 1995, 2000, 2005, 2008, 2010, 2013, and 2015 and combining them with a series of urban sprawl efficiency indices, it is found that Zhuzhou has developed from a double-center city to a multi-center city [8]. Combined with remote sensing images, mainly Landsat-MSS/TM/ETM and Landsat 8 images covering the whole country, a rapid assessment of Wuhan’s urban expansion was made, and future development trends were modeled [3]. Secondly, Defense Meteorological Satellite Program/ Operational Linescan System (DMSP/OLS) nighttime lighting data is also a common source. By comparing the extracted areas with government statistical areas, the optimal threshold for nighttime light can be accurately determined, and urban built-up areas can be extracted. They help analyze the spatial distribution of the built-up area and explore the spatial expansion patterns and spatial-temporal regularities of the Beijing-Tianjin-Hebei Urban Agglomeration [5]. Finally, impervious surface and geographic country data [9, 10] are also standard methods to obtain land use and built-up area. It has been found that the eastern region of China expanded most significantly from 2000 to 2015, while urban expansion also increased in central and western China [11]. Transport has been proven to be an essential factor in the development of urban form. The road network can show the basic form of the city [12]. Meanwhile, the urban transport network facilitates the physical exchange between the city and the outside world. On the one hand, intra-city transportation shortens commute times between regions. On the other hand, the development of external urban transport creates communication links between cities and indirectly promotes the development of urban land use [13]. The planning of transport and roads can help SDG goals to be achieved more quickly [14]. Space syntax was pioneered in the 1970s and has been used in the last decade to study buildings, cities, and regions [15]. This theory can relate urban form (street layout) and function (land use) to road networks [16]. Besides, space syntax is an effective model for building transport road networks at the urban scale [17]. Thus, space syntax is becoming an important method to analyze the relationship between road networks and urban expansion in recent years. A series of variables from spatial syntax, like connectivity, integration, and choice, not only proves the close connection between the degree of aggregation and accessibility of the transportation network and the boundary of urban development [18, 19] but also can simulate future urban expansion [20]. At present, most studies about urban expansion mainly focus on the built-up area of cities but lack attention to the road network. However, space syntax focuses on the subjective perception of people in space and has significant advantages in analyzing the development of road networks. In our research, space syntax was used to calculate the integration of the road network and analyze the spatial aggregation and intensity of urban expansion. The main urban area of Hangzhou was chosen as a suitable study area for this research. The rest of this paper is organized as follows: the second section includes the study area and data; the third section presents the methods; the fourth section contains the results and discussion; the final section provides conclusions.

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2 Study Area and Data 2.1 Study Area Hangzhou is located on the southern flank of the Yangtze River Delta in China and the northern part of Zhejiang Province. It is located in the subtropical monsoon zone and has a subtropical monsoon climate. The city is dominated by hilly and mountainous terrain, accounting for 65.6% of the total area. Unlike cities directly administered by the central government, such as Beijing, Tianjin, and Chongqing, Hangzhou has been directly administered by the provincial government after the fiscal reform in 1978. To reduce financial pressure, local governments develop infrastructure to attract investment and increase tax revenue. Therefore, studying Hangzhou as a provincial capital city can enrich the understanding of the characteristics of China’s urban expansion [21]. The main urban area of Hangzhou includes Shangcheng, Xiacheng, Xihu, Gongshu, Binjiang, and Jianggan (before the administrative division reform in 2021). However, the official urban area of Hangzhou before the 2021 administrative reform also included Xiaoshan, Yuhang, Lin’an, and Fuyang. Xiaoshan and Yuhang were transformed into districts in 2001. Fuyang and Lin’an were incorporated into Hangzhou in 2015 and 2017, respectively. Therefore, there is a lack of social and planning data on the unification of these districts. For the continuity and accuracy of the subsequent study, the six districts of the main urban area of Hangzhou are finally selected as the study area. In 2018, the main urban area had a land area of 705 km2 , a population of 3,913,000, and a gross regional product of 584,185,860,000 yuan (from the Hangzhou statistical yearbook). The main urban area of Hangzhou occupies only 4.2% of the city’s land, yet it hosts 39.9% of the city’s population and provides 43.2% of its gross regional product. Therefore, the main urban area is an important economic center of Hangzhou.

Fig. 1. Location of the main urban area of Hangzhou

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2.2 Data Collection The data set used in this study includes road network data and socioeconomic data of Hangzhou from 2014 to 2018. Road network data is obtained from OpenStreetMap (OSM) (https://download.geofabrik.de/asia/china.html#). OSM has provided all the road network data since 2014, but the data contains some redundant information. Therefore, by creating buffers on the original road network and re-forming the road, the roads are simplified to make them more compatible with space syntax. Socioeconomic data is from Hangzhou Statistical Yearbook (http://tjj.hangzhou.gov.cn/col/col1229453592/ index.html), including population, gross domestic product, and gross secondary industry product.

3 Methodology 3.1 Space Syntax Spatial syntax is a method for quantifying and analyzing space from multiple scales. Correlations are regularly found between graphically based measures of street network configurations (represented as lines) and observed movement patterns in space syntax studies. This conclusion suggests that topological and geometric complexity plays a crucial role in how people navigate the urban grid [22]. The main space syntax methods are convex space analysis, field of view analysis, and axial analysis. Among them, axial analysis was chosen because of its usefulness in analyzing the movement patterns of pedestrians and the activities associated with such movements [23]. Axis refers to the furthest distance from that any point in road space can be seen on a plane [24]. When mapping axes, the principle of longest and least need to be followed to convert the space into axes for the computer software to process next. The axis analysis method is based on the urban road network data. Five periods of road network data for the main urban area of Hangzhou from 2014 to 2018 were used to analyze urban expansion. Integration is a unique concept in space syntax, which refers to the reciprocal of the sum of the shortest paths of an axis and the rest of the axes, and can reflect the degree of road aggregation [25]. Integration is directly proportional to accessibility. The higher the degree of integration, the closer the area is to the city’s heart. With more pedestrian and vehicular traffic, these areas can bring higher economic benefits. Integration is the reciprocal of real relative asymmetry (RRA), and the formula is as follows [26]: RRAi = Dn =

2(MDi −1) (n−2)×Dn

2{n[log2( (n+2)/3)−1]+1} (n−1)×(n−2)

(1) (2)

where n is the space number, MDi is the space’s mean depth value of space i, Dn is used to provide the standardized value for the integration measure, and RRAi stands for the RRA value of space i. For the purpose of studying the spatial aggregation characteristics and intensity of urban expansion, it is necessary to convert the integration of axes into the integration of

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grids. Due to the large area of the study region, the grid established in the main urban area of Hangzhou was 300 m * 300 m. The grid was used to crop the roads using the axis map as the base map. For different study periods, the local integration of each grid was calculated using the length of the road as the weight. 3.2 Spatial Autocorrelation Spatial autocorrelation tests spatial dependence, including global and local spatial autocorrelation [27]. Moran’s I is the most popular indicator to explore spatial autocorrelation. The calculation formula is as follows: Moran s

I =

n

n

i=1

j = 1 ωij (UEi −UE)(UEj −UE)   S2 ni= 1 nj= 1 ωij

(3)

where n is the total number of grids; UEi and UEj represent the urban expansion of grid i and j, respectively; ωij represents the spatial weight; UE is the mean urban expansion, and S2 is the sample variance. Moran’s I has a value between –1 and 1. If the value exceeds 0, there is a positive spatial autocorrelation of urban expansion; conversely, there is a negative spatial autocorrelation. If the value is close to 0, there is no spatial independence. 3.3 Space Syntax Expand Intensity Index Space Syntax Expand Intensity Index (SS-EII) refers to the degree of change in the local integration of the study area grid per unit of time [28, 29]. The index is derived from the traditional formula for calculating the intensity of urban expansion. The calculation formula is as follows: SS−EII =

|It −I0 | T ×I0

n I0 =

i=1 I0i

n

(4)

n ; It =

i=1 Iti

n

where I0 is the average value of local integration of each grid in the study area before expansion; It is the average value of local integration after expansion; I0i is the value of local integration of the i-th grid before expansion; Iti is the value of local integration of the i-th grid after expansion. n is the total number of grids; T is the time interval before and after expansion. The advantage of the SS-EII over the traditional expansion intensity index is that it further captures the driving force of urban expansion - the people who live in urban space. It analyses the structure and condition of urban expansion from people’s subjective perception, making it more reasonable than the traditional urban expansion index. Furthermore, it can be applied to study changes in land prices, migration distribution, distribution of commercial land, and other factors.

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4 Results and Discussion 4.1 Basic Information on the Expansion of the Road Network To analyze the spatial and temporal patterns of urban expansion, preliminary statistics were conducted on the road network in the main urban area of Hangzhou. Then, the city was divided into eight directions, and the percentage of road network expansion length was counted in each direction. Figures 2 and 3 show the results. The results in Fig. 2 show that the number of roads and the total length of roads in the main urban area of Hangzhou continue to grow. In particular, the road network expanded more dramatically in 2017–2018, with an annual growth rate of 101.76% in the number of roads and 36.11% in total length. However, there are also some differences in the expansion patterns of the two phases. The number and length of roads increased at a similar rate in 2015, indicating that Hangzhou was still in the stage of building a general road network framework. In 2018, the number of roads increased significantly more than the length, indicating that the new sections of the city mainly were shorter roads. The main urban areas were beginning to improve the accessibility of local areas within a well-constructed general framework. The urban expansion pattern also shifted to an internal expansion and began to achieve an intensive use of land.

Fig. 2. Changes in the number and length of roads

The distribution of the annual length of new roads is shown in Fig. 3. Firstly, from 2014 to 2016, the new roads were mainly concentrated in the northwest, northeast, and southwest. The greatest percentages in each year were 27.08% in the northwest from 2014 to 2015 and 23.63% in the northeast from 2015 to 2016. The directions with fewer new roads also remained consistent: the north, southeast, and west. Secondly, the new road directions were mainly concentrated in the north, east, and northwest from 2016 to 2017. The northwest had the highest percentage, at 24.96%. The directions with fewer new roads were northeast, southeast, and west, which remained largely the same as before. Finally, the directions with more new roads were concentrated in the north, northeast, and south from 2017 to 2018. Among them, the north accounted for 34.30% of

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the total, which is the highest value in the past years. In contrast, the length of new roads in all other directions remained relatively stable at 6–8%. Overall, the study period can be divided into two phases, with 2016 as the node. From 2014 to 2016, the expansion of the main urban area of Hangzhou remained relatively stable, with the northeast-southwest as the development axis. From 2016 to 2018, road growth began to take a north-south development axis. At the same time, the growth rate in the southwest and northwest, which had been growing significantly, began to decline, and the road expansion showed a polarization trend.

Fig. 3. The ratio of the amount of urban growth in different directions in the main urban area of Hangzhou. Note: N: North; NE: Northwest; E: East; SE: Southeast; S: South; SW: Southwest; W: West; NW: Northwest.

4.2 Integration Change and Spatial Aggregation Characteristics Axial analysis of Depthmap was used to analyze the road network in the main urban area of Hangzhou to obtain the global and local integration from 2014 to 2018. As shown in Fig. 4, the global integration of the main urban area of Hangzhou was distributed in a circle ring, decreasing from the inside to the outside, mainly including four circles. Xiacheng and Shangcheng have always been in the core circle. The northeastern Xihu, the southern part of Gongshu, and the southwestern part of Jianggan started to enter the core circle during the study period. Global integration continues to increase over time, and the spatial accessibility of the urban center is being optimized, showing a trend toward centrality. In contrast, the global integration of Binjiang showed the opposite trend, developing from the central circle in 2014 to the second circle in 2018, which indicates a shift in the urban core. From Fig. 5, the regions with higher local integration are centered around Hangzhou Bypass, Binxing Road, and Qingling Road. From a chronological perspective, the local integration in the north and south has increased significantly. The area is now more easily

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Fig. 4. Global integration in the main urban area of Hangzhou from 2014 to 2018

accessible from a spatial standpoint, allowing for more people and vehicle movement and a greater intensity of urban expansion. Besides, there are differences between regions with high local integration compared to those with high global integration. Areas such as the eastern part of Jianggan, the central part of Binjiang, and the northern part of Xihu all show high local integration, with the main urban area of Hangzhou reflecting a trend toward polycentric expansion. In summary, changes in the pattern of local transport networks cause changes in the structure of the entire transport network. Local integration is more effective in analyzing the intensity of town expansion and has a more significant impact on the spatial changes of the regional network. Therefore, it is suitable for subsequent spatial autocorrelation analysis of town expansion and the calculation of SS-EII. Table 1 lists Global Moran’s I and z-value for the change values of local integration of the grid from 2014 to 2018. The results show that Global Moran’s I was positive, and the z-values were all above 40, which indicates a moderate degree of positive spatial autocorrelation within the study area. Figure 6 presents the results of the Local Moran’s I. First, the expansion of the main urban area of Hangzhou is mainly concentrated in the north. At the same time, the southwestern direction is not significant. Secondly, there are differences in the spatial aggregation characteristics of urban expansion in different years. In 2015, the high–high clustering urban expansion was mainly concentrated in the northwest, while the eastern part of Binjiang showed low–low clustering characteristics. In 2016, the central part of the main urban area showed low–low clustering characteristics, but the border exhibited high–high clustering characteristics. In 2017, the spatial clustering characteristics were the exact opposite of 2016, with the central part showing high–high clustering, while the boundaries were more low–low clustering, indicating that the overall intensity of urban

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Fig. 5. Local integration in the main urban area of Hangzhou from 2014 to 2018 Table 1. The results of Global Moran’s I related to urban expansion from 2014 to 2018 Year

2015

2016

2017

2018

Global Moran’s I

0.26

0.30

0.20

0.33

z-score P-value

54.48 0.00

61.56 0.00

42.20 0.00

68.93 0.00

expansion was also weaker. In 2018, the Shangcheng, Xiacheng, and Gongshu were connected into significant areas of high–high clustering, while many areas of high–high clustering were also present in the central and eastern parts. Compared to earlier, there is a significant increase in the number of high–high clustering areas. In contrast, the low–low clusters are mainly concentrated in the south. Finally, it is worth noting that the degree of aggregation in Xihu is not always significant. On the one hand, Xihu is larger than the other areas, and therefore, the roads are more dispersed; on the other hand, there are many essential landscapes within Xihu, such as the West Lake, which are of great ecological value. Policies need to be developed considering environmental protection, biodiversity, and other factors, which is an important reason for the slow expansion of the West Lake. 4.3 Urban Expansion Analysis Based on SS-EII From 2014 to 2018, the grids were severed for five stages, and SS-EII was measured for each period in each direction. The results are shown in Table 2. First, there are similarities and differences in the intensity of urban expansion each year. In terms of similarities:

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Fig. 6. The local indicator spatial autocorrelation (LISA) cluster map

on the one hand, the northeastern and northern expansions were higher than the average expansion rate, mainly including Gongshu, Xiacheng, and the western part of Jianggan; on the other hand, the eastern and southeastern expansions were lower than the average expansion rate, mainly including the eastern part of Jianggan and Binjiang. In terms of differences, from 2014 to 2017, the southeast gradually displaced the northwest as the other center of urban expansion. In contrast, Hangzhou’s main metropolitan region had strong growth in 2018, with an average SS-EII of 0.23. Second, the SS-EII for all areas from 2014 to 2018 is 0.21, indicating that the main urban area maintained a high intensity of expansion during the study period. The SS-EII of the north and northeast are 0.38 and 0.34, respectively, higher than the SS-EII of all areas. Urban expansion in the northwest, west, southwest, and south is mainly consistent with the overall region. However, the SS-EII of the east and southeast are only 0.09 and 0.11, respectively, indicating that the intensity of expansion in these two directions is lower during the study period. There are differences in the expansion intensity of Hangzhou’s main urban area due to many factors. First, urban expansion tends to occur near urbanized areas, so the population can significantly impact urban expansion [30]. Jianggan, the area with the largest population increase during the study period, has continued to promote the construction of Qianjiang New Town and transportation to meet the infrastructure needs of the large population, taking advantage of the geography of the Qiantang River. The population of Gongshu and Xiacheng has not increased significantly, but the population density is extremely high. Therefore, many new roads are needed to relieve congestion and meet people’s travel needs. Secondly, in highly densely populated regions or cities with high

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Table 2. The results of the SS-EII in different directions from 2014 to 2018 Direction

N

NE

E

SE

S

SW

W

NW

All Areas

2014–2015

0.22

0.25

0.05

0.12

0.09

0.09

0.12

0.24

0.14

2015–2016

0.16

0.35

0.11

0.00

0.18

0.34

0.25

0.11

0.19

2016–2017

0.12

0.12

0.02

0.14

0.15

0.07

0.11

0.08

0.09

2017–2018

0.61

0.26

0.15

0.14

0.18

0.16

0.09

0.18

0.23

2014–2018

0.38

0.34

0.09

0.11

0.19

0.20

0.18

0.19

0.21

levels of economic development, the economy is a more critical factor than population [31, 32], such as GDP and industrial structure. There is a positive correlation between the intensity of urban expansion and GDP, especially in Shangcheng, which has the highest regional GDP. Although it experienced negative population growth during the study period, the expansionary dynamics remained significant. There is an interdependence between land financing, economic development, and urban expansion, ultimately resulting in a dynamic impulse process [1]. Third, policy plays an important role in driving urban expansion [11]. According to the Hangzhou City Master Plan (2001–2020), it is clear that the main urban area of Hangzhou is the center, with the Qiantang River as the axis. A combination of point and axis expansion is used to transform the cluster-like layout with the old city as the core into a cross-river, riverine, networked cluster layout with the Qiantang River as the axis. Therefore, the north, dominated by the old city, and the northeast, through which the Qiantang River passes, both maintain a relatively rapid urban expansion. However, the eastern part of Binjiang, closer to Xiaoshan, is regulated by the green ecological open space and expands relatively slowly.

5 Conclusion This paper used road network data to discuss urban expansion analysis based on space syntax. Then, the urban expansion agglomeration and intensity to the main urban area of Hangzhou were evaluated explicitly in combination with spatial autocorrelation analysis. First, the number and total length of roads increased significantly during the study period. Nevertheless, with 2016 as the cut-off point, road growth showed different characteristics. Secondly, the complex road network data from OSM was simplified by creating buffers to make it more suitable for space syntax. Based on the local integration of the collated grid, the spatial autocorrelation analysis was used to quantify the spatial agglomeration characteristics of urban expansion. The results showed that there was a moderate degree of positive spatial autocorrelation for urban expansion. High–high clustering areas gradually increased, mainly concentrated in the central part. Fewer areas of low–low clustering, but the southern part of Xihu was not characterized by significant spatial aggregation. Finally, the value of SS-EII was calculated to analyze the intensity of urban expansion. The results show that the northeast and north expanded faster, while the opposite was evident in the southeast and east. The overall intensity of expansion was

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most remarkable in 2018. While population, economy, policy, and geographical location are all important factors influencing urban expansion. Acknowledgements. This research was funded by the National Natural Science Foundation of China (Grant No. 42007194). The authors are thankful to OpenStreetMap (OSM, https://www. openstreetmap.org/) for the road data in Hangzhou.

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An Evolutionary Game Analysis of Organizational Relational Behavior in Megaprojects Considering the Reciprocal Preference Chunxi Luo1(B) , Xian Zheng1 , and Chunlin Wu2 1 School of Finance, Zhongnan University of Economics and Law, Wuhan 430073, China

[email protected] 2 School of Economics and Management, Beihang University, Beijing 100191, China

Abstract. The relational behavior of participating organizations is vital to improving megaproject performance. Although extant research has focused on promoting relational behavior by identifying various factors, it neglects the mutual influence of participating organizations to make relational behavior adoption decisions, especially considering the heterogeneous reciprocal preferences of distinct organizations. To fill this gap, this study constructs an evolutionary game model between two parties with different reciprocal preferences to analyze the evolutionary stability of each organization’s choice. We discuss and simulate the impact of initial relational behavior adoption willingness and reciprocal preferences of different participating organizations, resulting in both factors having a positive effect on the adoption of relational behavior. We further tested their relational behavior choices under different work scenarios (i.e., complex subproject, innovative subproject, interdependent subproject, and simple subproject) in megaprojects. The results are that the first three scenarios all end up with relational behavior adopted by both sides, while the last scenarios render non-adoption of relational behavior by both sides. This research promotes a better understanding for the dynamic mutual influence of participating organizations in megaprojects and unveils the driving paths to enhance relational behavior adoption from a perspective of organizations’ reciprocal preferences. Keywords: Megaprojects · Relational behavior · Evolutionary game · Reciprocal preference

1 Introduction Megaprojects are construction projects that cost over one billion dollars, involve complex technical requirements, various participating organizations, high uncertainties, and dramatically influence economic and social development [1]. In recent years, numerous studies have highlighted the importance of high-quality relationship between participating organizations to facilitate megaproject performance [2–4]. Relational behavior has been proved to have a significant effect on mitigating interorganizational conflicts © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 898–910, 2023. https://doi.org/10.1007/978-981-99-3626-7_69

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and enhancing relationship quality [5, 6], which refers to the desired actions taken by participating organizations in megaprojects to develop cooperative relationship [7]. The most three common relational behaviors are flexibility, information exchange, and solidarity, indicating flexible problem-solving; free information exchange among partners; behaving as a whole by partners to fulfill project goals. Despite the benefits of relational behavior to achieve megaproject efficiency and effectiveness, various participating organizations are still reluctant to adopt it for several reasons. First, providing relational behavior to other parties can be both costly and time consuming [4, 8]. Second, the risk of being susceptible to involve in corruption may arise from the overly close relationship between participating organizations implementing relational behavior [8], especially in such costly megaprojects. Third, the temporary and short-term oriented features of megaprojects lead to discontinuous interorganizational relationships, which will discourage the participating organizations to devote great effort to implementing relational behavior [9]. Last but not the least, for unilateral relational behavior, a participating organization implements relational behavior alone without being repaid by its partner [10], leading to no further relational behavior by this organization in the future. In this regard, the adoption of relational behavior should be encouraged by reciprocity of participating organizations, so as to remedy the effort of the relational behavior provider, and in turn, realizing a “win-win” situation. According to the traditional economic assumptions, the participating organizations are rational to maximize their own interests [11]. Selfishness makes the organization always prefer to be the relational behavior recipient rather than the one who provides it. However, one thing to note is that megaprojects appeal participating organizations for tight cooperation to encounter various tough and intertwined tasks, as well as huge uncertainties that they may never met before. In this occasion, relational behaviors by others are essential for each side to fulfill their work and finally realize the overall megaproject goal. Thus, high reciprocal preference counts for each party to achieve lasting support from other parties. Reciprocal preferences are behavioral responses to perceived good and bad intentions from others [12]. Previous studies have confirmed that reciprocal preferences are noteworthy determinants of organizational behavior, especially in the supply chain [13– 15]. Scholars also agreed that the reciprocal preferences of participating organizations in megaprojects impact the adoption of relational behavior [16]. However, the current studies identified reciprocal preferences as a factor for a single party in the “snapshots” of relational behavior adoption and driving. Therefore, not only does it ignore the dynamic reciprocal preferences of both sides impact relational behavior, but it fails to capture a dynamic evolutionary view on relational behavior decisions in megaprojects, especially considering the heterogeneous reciprocal preferences of distinct participating organizations. Given the limited studies on the implications of diverse organizations’ reciprocal preferences to relational behavior in megaprojects from a dynamic perspective, this study will construct an evolutionary game model to investigate three questions: (1) the influence of the reciprocal preferences of distinct organizations on the decision-making of relational behavior; (2) the evolutionary conditions for relational behavior to become a stable behavior by both parties; (3) the evolution of organizational relational behavior

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in different megaproject scenarios. The result will provide strategic suggestions for megaproject managers to promote the sustainable adoption of relational behavior.

2 Establishment of the Game Model The evolutionary game model, which applies a combination of biological evolutionism and game theory [17], provides a necessary tool to analyze problems involving competition, conflict, and cooperation between different parties [18]. In evolutionary game model, the parties make decisions by comparing various outcomes from different strategies and dynamically adjust their strategies in the game procession, resulting in different evolution path of different parties in the system [19]. In megaprojects, the decision of participating organizations on relational behavior is constantly changing according to the surrounding environment. Therefore, the evolutionary game model is suitable for the research questions mentioned above. 2.1 Applicability of Evolutionary Game Theory For participating organizations in megaprojects, there are various drivers and barriers for them to adopt relational behavior. When organizations as game players face the complex issues on whether to adopt relational behavior or not, it is difficult to find the optimal strategy quickly due to incomplete information and rationality, so they learn and adjust constantly to find better strategies in the game. According to evolutionary game model, the authors argued that the research problems are coherent with its main ideas and assumptions. The probability of one strategy being selected increases if the return of an individual from the strategy exceeds the average return of the whole group from the strategy. Specifically, we can set participating organizations with two different degrees of reciprocal preference to discuss how the different reciprocal preferences of participating organizations in megaprojects influence the adoption of relational behavior strategies. 2.2 Game Assumptions and Description Assumption 1. Because of the tensive schedule and the complex issues in megaprojects, it is difficult for a participating organization to complete the task independently, so the assistance of other participating organizations is required. As a result, organizations not only make their own efforts to complete the work assigned by the project but also make collaborative efforts to help other organizations. This means participating organizations make decisions on whether to adopt relational behavior or not. The probability of organization i with high reciprocal preference (HRP) choosing the adoption of relational behavior (ARB) is assumed to be x, thus the probability of organization i selecting non-adoption of relational behavior (NARB) is 1 − x. Similarly, the probability of organization j having a low reciprocal preference (LRP) which is the game opponent for organization i choosing ARB is y, thus 1 − y represents the probability that organization j will choose NARB in a single-encounter game.

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Assumption 2. The relationship between the output of the organization and the effort made is set by referring to the model of Kretschmer and Puranam [20] as π i = θ ai + (1-θ )bji + γ ai bji . The output of organization i in the game is related to its own effort level ai and the collaborative effort bi given by the game object. Parameter θ represents the effect of one’s own effort level on the output, and 1-θ represents the effect of the assistance effort given by the game object on the output. Parameter γ represents the impact of synergy on output. Assumption 3. Organization i participating in the game has two strategies: ARB and NARB. ARB means that organization i is willing to make collaborative efforts in construction activities (bi = 1), and NARB means that organization i does not make collaborative effort to others (bi = 0). Additionally, the actual benefit of organization i is affected by the reciprocal preference. Under the influence of reciprocal preference, the collaborative effort of organization i is influenced by its own reciprocal preference. The higher the reciprocal preference, the more willing to give a higher level of collaborative effort. Then the collaborative effort obtained by organization i is affected by the reciprocal preference of organization j and the relational behavior strategy of organization j, so bji = ηj bj . Assumption 4. During the construction of major projects, various conflicts of interest often occur, and the behavior of inter-organizational relationships is conducive to alleviating potential conflicts and reducing the occurrence of uncertain events. Supposed that in the relational behavior game between major participating organizations, the loss caused by the conflict is s. The probability of potential conflict occurring when both parties select ARB is p1 , the probability of conflict occurring when only organization i chooses ARB is p2 , and the probability of conflict occurring when only organization j chooses ARB is p3 , when both parties The probability of a conflict occurring when both parties select NARB is p4 . We assume that p1 < p2 < p4 , p1 < p3 < p4 . Assumption 5. The efforts of organization i needs a certain cost. Based on the H-M theoretical model [21], the cost function of the organization is as follows: Ci(a, b) = ci (ai 2 + bi 2 )/2, in which ci is the marginal cost coefficient. To simplify the analysis, it is assumed that the cost coefficient of each organization is the same as c. Table 1 gives an account of the parameters and their meanings. For their own task, the organization will put its utmost effort into it. Thus, to simplify the analysis without affecting the overall results, assuming own effort ai = 1. Based on the above five assumptions, a pay-off matrix can be established for the evolution simulation of relational behavior in megaprojects, as demonstrated in Table 2.

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Parameters

Meanings

x

ARB probability that organization i with HRP

y

ARB probability that organization j with LRP

a

Production efforts made by the organizations

b

Cooperation efforts made by the organizations

θ

Degree of influence of the organizations’ productive efforts

γ

Output efficiency of cooperation between the two organizations

p

Probability of the conflict occurring in megaprojects

s

Loss caused by the conflict

ε

Influence of extraneous variables refers to the uncertainty of megaproject

η

Reciprocal preference coefficients of organization

c

Marginal cost of effort made

Note: ARB = adoption of relational behavior, HRP = high reciprocal preference, LRP = low reciprocal preference

Table 2. The pay-off matrix the evolution simulation of relational behavior in megaprojects Organization j with LRP Organization i with HRP

ARB x

ARB y

NARB (1-y)

Ai = θ + (1 − θ )ηj + γ ηj − p1 s − c

Bi =θ − p2 s − c Bj = θ + (1 − θ)ηi − p2 s − c/2

Aj = θ + (1 − θ)ηi + γ ηi − p1 s − c NARB (1-x)

Di =θ − p4 s − c/2 Ci = θ +(1−θ )ηj −p3 s−c/2 Dj =θ − p4 s − c/2 Cj =θ − p3 s − c

Note: ARB = adoption of relational behavior, NARB = non-adoption of relational behavior; HRP = high reciprocal preference, LRP = low reciprocal preference

3 Model Analysis 3.1 Establish Replication Dynamic Equation First, E i1 and E i0 represent the expectations of organization i for ARB and NARB, respectively, as follows:    Ei1 = yθ + (1 − θ )ηj + γ ηj − p1 s− c + (1 − y)(θ − p2 s − c) (1) Ei0 = y θ + (1 − θ )ηj − p3 s − c/2 + (1 − y)(θ − p4 s − c/2)

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Second, based on pay-off matrix 2, the expected return of organization i gained is shown as follows:   Ei = xy θ + (1 − θ )ηj + γ ηj − p1 s − c + x(1 − y)(θ − p2 s − c)   + (1 − x)y θ + (1 − θ )ηj − p3 s − c/2 + (1 − x)(1 − y)(θ − p4 s − c/2) (2) Thus, based on Eq. (2), the replicator dynamics equation for organization i is as follows:   F(x) = dx/dt = x(1 − x)(Ei1 − Ei0 ) = x(1 − x) y(γ ηj − c/2 − p1 s + p3 s) + (1 − y)(−c/2 − p2 s + p4 s)

(3)

Let =  0, get x = 0, x = 1,  F(x) x∗ = c/2 − (p4 − p2 )s / γ ηj − (p1 − p3 − p2 + p4 )s . Similarly, for organization j, the expectations of ARB E j1 and NARB E j0 , respectively, are as follows:    Ej1 = xθ + (1 − θ )ηi + γ ηi − p1 s− c + (1 − x)(θ − p3 s − c) (4) Ej0 = x θ + (1 − θ )ηi − c/2 − p2 s + (1 − x)(θ − p4 s − c/2)   Ej = yx θ + (1 − θ )ηi + γ ηi − p1 s − c + y(1 − x)(θ − p3 s − c)   + (1 − y)x θ + (1 − θ )ηi − c/2 − p3 s + (1 − y)(1 − x)(θ − p4 s − c/2) (5)   F(y) = dy/dt = y(1 − y)(Ej1 − Ej0 ) = y(1 − y) x(γ ηi − c/2 − p1 s + p2 s) + (1 − y)(−c/2 − p3 s + p4 s)

Let =  0, get y =   F(y) y∗ = 1/2c − (p4 − p3 )s / γ ηi − (p1 − p2 − p3 + p4 )s .

0, y

=

(6) 1,

3.2 Stability Analysis of the Evolutionary Game A Jacobian matrix (J) can be used to estimate whether the equilibrium point has reached evolutionary stability, is as follows: J =       (1 − 2x) y(γ ηj − c/2 − p1 s + p3 s) + (1 − y)(−c/2 − p2 s + p4 s) x(1 − x) γ ηj − (p1 − p3 − p2 + p4 )s   y(1 − y) γ ηi − (p1 − p2 − p3 + p4 )s (1 − 2y)[x(γ ηi − c/2 − p1 s + p2 s) + (1 − x)(−c/2 − p3 s + p4 s)]

When Det J > 0 and Tr J < 0, the equilibrium point has reached the evolutionary stable state [22]; when Tr J = 0, the equilibrium point is a saddle point. On these grounds, the partial stability of the evolving system will be further discussed. Condition 1: when −c/2 − p2 s + p4 s < 0, −c/2 − p3 s + p4 s < 0, the system equilibrium state is at point (0, 0), indicating that ARB is the ultimate equilibrium strategy for both parties. Condition 2: when −c/2 − p2 s + p4 s > 0, γ ηi − c/2 − p1 s + p2 s < 0, the system equilibrium state is at point (1, 0), indicating that ARB is the ultimate equilibrium strategy for organization i and NARB is the ultimate equilibrium strategy for organization j. Condition 3: when γ ηj − c/2 − p1 s + p3 s < 0, −c/2 − p3 s + p4 s > 0, the system equilibrium state is at point (0, 1), indicating that NARB is the ultimate equilibrium strategy for the organization i and ARB is the ultimate equilibrium strategy for organization j.

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Condition 4: when γ ηj − c/2 − p1 s + p3 s > 0, γ ηi − c/2 − p1 s + p2 s > 0, the system equilibrium state is at point (1, 1), indicating that NARB is the ultimate equilibrium strategy for both parties. Condition 5: when 0 ≤ x∗ ≤ 1, 0 ≤ y∗ ≤ 1, the point (x * , y* ) exists and is the saddle point. Considering that collaborative effort is higher, relational behavior plays a significant role in mitigating potential conflict in the megaproject, thus contradicting the condition of Condition 2, the equilibrium point (1, 0) does not exist. Similarly, γ ηj − (p1 − p3 − p2 + p4 )s > 0, , the point (0, 1) does not exist.

4 Numerical Simulation 4.1 Base Simulation To show the evolution process of the stable strategy of organizations i and j, combined with the actual situation, we assign values to parameters in the model. When c = 0.5, γ = 0.5, ηi = 0.5, ηj = 0.4, p1 = 0.2, p2 = p3 = 0.3, p4 = 0.6, s = 0.7, the saddle point is (0.571, 0.363), which provides a basis for setting the initial probability of selecting ARB. Therefore, the result is located very close to the saddle point and towards the ideal equilibrium point (1, 1) when set (x, y) = (0.60, 0.40). Therefore, the following simulation results are obtained. Under the initial parameter setting conditions (x, y) = (0.60, 0.40), the probability of organization i and j choosing ARB will converge to 1. The game system will reach an ideal evolutionary stable state as shown in Fig. 1. In Fig. 1, organization i with HRP has a high probability of ARB while organization j with LRP may choose NARB at the beginning of the game. Therefore, the higher reciprocal preference makes organization i reduce the probability of ARB in the beginning period of megaprojects. However, organization j with LRP receiving kindness from organization i encourage it to choose ARB in the next game. Thus, the probability of organization j choosing ARB will increase. When the ARB probability of organization j rises enough, the probability of organization i receiving goodwill increases, and the high reciprocal preference also makes the ARB probability of organization i rise rapidly. In the end, the probability of both parties choosing ARB rises to 1.

Fig. 1. Base Simulation

4.2 The Effect of Initial Willingness on Evolution Results Figure 2 demonstrates that the initial ARB probability of both sides impacts the results of the relational behavior game. As depicted in Fig. 2(a), when the initial probability

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of organization i with HRP choosing ARB is greater than 0.60, both organization i and organization j in megaproject will choose ARB finally and the game system will reach an ideal evolutionary stable state with a faster speed. When the initial probability of organization i choosing ARB is less than 0.60, such as the parameters in Fig. 2(b), the evolution result will evolve to an unsatisfactory state. Similarly, when the probability of organization j with LRP initially choosing ARB is greater than 0.40, the evolution result will evolve to a satisfactory state as shown in Fig. 2(c). When this probability is less than 0.40, such as the parameters in Fig. 2(d), the decision-making of the two sides ultimately is NARB. It appears that the organizations’ willingness of ARB in the initial period of megaprojects has a great influence on the evolution results of relational behavior decisions. Specifically, the initial higher ARB probability, the stronger initial willingness of ARB. Therefore, the initial willingness of ARB promotes the participating organization to select relational behavior as the stable strategy.

Fig. 2. The effect of initial willingness on evolution results

4.3 The Effect of Reciprocal Preference on Evolutionary Results It appears in Fig. 3(a) and (c) that the evolutionary trend of organization i and organization j in megaprojects is similar in the case of no reciprocal preference discrepancy between organization i and organization j. When the reciprocal preference of two sides is at a high level, both organization i and organization j will choose ARB and the evolution result will evolve to a satisfactory state as depicted in Fig. 3(a) and (b). On the contrary, when both parties with low reciprocal preference play the game, the probability of ARB will decrease as shown in Fig. 3(c). NARB will be the stable strategy in the end and the game system will not reach an ideal evolutionary stable state. As shown in Fig. 3(d), when a large discrepancy between the reciprocal preference of two parties exist, participating organizations tend to choose NABR, and the final result of the game will still be an unsatisfactory state. The results show that the degree and the discrepancy of participating organizations’ reciprocity in megaprojects should be highlighted in the relational behavior decisions.

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Fig. 3. The effect of reciprocal preference on evolutionary results

4.4 The Evolutionary Result in Different Megaproject Scenarios According to the literature of Mai et al. [23], there are four categories of subprojects in megaprojects based on interdependency and cognitive incompleteness: (a) Complex subprojects (b) Innovative subprojects (c) Interdependent subprojects (d) Simple subprojects. Therefore, four work scenarios are set in these four subprojects. First, the complex work scenario with high interdependency and high cognitive incompleteness, such as the island tunnel subproject in the Hong Kong-Zhuhai-Macao Bridge (HZMB) project. In this type of work scenario, the impact of collaboration on output is larger, and the probability of potential conflicts due to high cognitive incompleteness also increases. The parameters are set as γ = 0.6, p4 = 0.7, p2 = p3 = 0.4, p1 = 0.2. Second, the innovative work scenario with low interdependency and high cognitive incompleteness, such as the steel structure subproject in the HZMB project. Megaprojects need to solve technical problems that have not been met and solved by predecessors through innovation. Greater risks are encountered in this type of work scenario, so it is expected that there will be a higher possibility of conflict. The corresponding parameters are set to γ = 0.4, p4 = 0.7, p2 = p3 = 0.4, p1 = 0.2. Third, the interdependent work scenario with high interdependency and low cognitive incompleteness, such as the pavement subproject and the marine sub-engineering in the HZMB project. This type of work scenario requires the collaboration of multiple organizations, so the synergy coefficient γ is higher. Thus, the parameters are set as γ = 0.6, p4 = 0.6, p2 = p3 = 0.3, p1 = 0.2. Fourth, the simple work scenario with low interdependency and low cognitive incompleteness such as the housing construction subprojects in the HZMB project. Compared with other types of work scenarios, this type of work scenario requires relatively less collaboration from others and is less likely to conflict. The corresponding parameters are set to γ = 0.4, p4 = 0.6, p2 = p3 = 0.3, p1 = 0.2. The initial value of (x, y) is fixed at (0.60, 0.40), and the settings of the initial parameters are changed to four scenarios as described above. Different simulation results will be obtained as shown in Fig. 3. It can be seen from Fig. 3 that in the scenario of simple work in the megaproject, the probability of organization i and organization j choosing ARB will decrease. The result of the game in the scenario of simple work is both organization i and organization j choose NARB. However, when facing works with high interdependency or cognitive incompleteness, the equilibrium strategy of both sides of the game is to choose ARB. The simulation results of the four scenarios are consistent with the actual case of the HZMB project (shown in Table. 3). The designers of the island tunnel subproject in the HZMB project propose an unconventional design

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scheme. Confronting with a large number of new technologies and frequent changes of technical merit, the management team’s solution is to coordinate and optimize the overall system and the entire process under the condition of association, at the same time, they establish a “partnering” project team to deal with the uncertainty that has always existed in construction of the HZMB project. For the steel structure subproject of the HZMB project, the high-precision and high-volume requirements of construction materials prompt manufacturers to take the initiative to innovate in production, and the participating organization, China Railway Engineering Corporation, has developed welding robots that help improve the production speed and quality of construction materials. As for the marine sub-engineering, various departments of the local Maritime Safety Administration cooperate with each other to ensure the safe environment of the offshore construction of the project (Fig. 4).

Fig. 4. The result of evolution in different scenarios

Table 3. The organizations’ relational behavior in different work scenarios of megaproject Work scenario

High interdependency

Low interdependency

High cognitive incompleteness

(a) Complex work (e.g. the island tunnel subproject) The designers propose an unconventional design scheme for the island tunnel subproject

(b) Innovative work (e.g. the steel structure subproject) China Railway Engineering Corporation, has developed welding robots

Low cognitive incompleteness

(c) Interdependent work (e.g. the (d) Simple work (e.g. the housing pavement subproject and the construction subprojects) – marine sub-engineering) The management team establish a “partnering” project team to deal with the uncertainty

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5 Conclusions and Suggestions Relational behavior is an effective way to improve the quality of inter-organizational relationships in megaprojects. However, due to the influence of reciprocal preferences, it is uncertain whether participating organizations will adopt relational behavior consistently over the lasting construction period. Therefore, this paper constructs an evolutionary game model of the participating organizations with different reciprocal preferences. Furthermore, a numerical simulation analysis was carried out to study the impact of the initial participating organizations’ willingness, the reciprocal preferences of different participating organizations, and four different scenarios of megaprojects on the evolution of the participating organizations’ relationship management strategies. The conclusions and suggestions of this study are demonstrated as follows: (1) The willingness of organizations to adopt relational behavior in the initial period of megaprojects has a great influence on the evolution results of inter-organizational relational behavior. Specifically, increasing the initial probability of participating organizations choosing relational behavior, they tend to adopt relational behavior continually in the procession of construction. Therefore, in order to increase the probability of the participating organizations adopting relational behavior and make the final game result become the ideal state, the top managers of megaprojects can learn from the HZMB project by establishing a cooperative and sharing culture, thus enhancing the probability of relational behavior adoption in the construction process. (2) The reciprocal preference of participating organizations in megaprojects is an important influencing factor for the choice of relationship behavior. Organizations with a high reciprocal preference that adopt relational behavior will expect others to adopt relational behavior and get corresponding rewards. When the reciprocal preferences of both game parties are at the same high level, the result of the game is that both parties implement relational behaviors. However, when the reciprocal preferences of both sides of the game are quite different, the result of the game is not ideal. Therefore, the owner of megaprojects can incorporate the organization’s reciprocal preferences into the selection criteria for contractors, designers, and other participating organizations by considering past collaborative project experience and corporate culture of them. (3) The results of relational behavior games in different scenarios of megaprojects are various. Specifically, in the scenario of simple subprojects, the additional utility of adopting relational behavior is much smaller than that in the other three scenarios. This result indicates that the work with high interdependency and cognitive incompleteness can improve the adoption of relational behavior strategies in megaprojects. Therefore, the managers of each participating organization in megaprojects should proactively identify the interdependency and cognitive incompleteness of task assigned to it. For work with high interdependency or cognitive incompleteness, such as complex work, innovative work and interdependent work, relational behavior should be actively adopted to save time for both parties in the game and reach a consensus on relational behavior strategies more quickly. While for simple work, the adoption of relational behavior is less helpful to it, thus less investment could be made for organizational informal relationship management.

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Our research makes two important contributions to the theory of megaprojects’ interorganizational relationship. First, this paper breaks the previous research perspective of static relational behavior research, establishes the evolution game model of the interorganizational relational behavior in megaprojects, and explores the change law of the relational behavior strategy in the construction process from the dynamic perspective. It is beneficial for the organization to adopt an effective dynamic relationship behaviordriven strategy. Second, this paper specifically focuses the influence of irrational factors, reciprocal preference, on relational behavioral strategies. In particular, the role of the heterogeneous reciprocal preferences of participating organizations in facilitating the adoption of relational behavioral strategies was explored. Regarding future research avenues, we will consider the games among more participating organizations in view of the large number of stakeholders involved in megaprojects. At the same time, since the games among participating organizations should according to some certain social relationship, it can be combined with the social network analysis and the evolutionary game model for further simulation analysis. Besides, the introduction of additional reciprocal preference in this study only is discussed only between the two organizations, which fails to fully demonstrate the heterogeneity of multiple organizations. In addition, an essential factor of organizational relational behavior decision-making, reciprocity preference, is selected to analysis in this study. Nevertheless, it would be beneficial to investigate other factors that may affect relational behavior. Therefore, the subsequent research can further explore the influence of other financial and psychological factors with dynamic and interaction on the evolution of relational behavior in megaprojects. Last but not least, for aspects related to the evolution game model, including more realistic data from the empirical research can be investigated to make the model more convincing. Acknowledgments. This study is greatly supported by the National Natural Science Foundation of China (No. Grants 71901220) and the Fundamental Research Funds for the Central Universities (No. 2722021BZ015). The authors would like to thank the anonymous reviewers for their helpful advice.

References 1. Flyvbjerg, B.: Introduction: The Iron Law of Megaproject Management. The Oxford Handbook of Megaproject Management, pp. 1–18. Oxford University Press, Oxford, UK (2017) 2. Meng, X.: The effect of relationship management on project performance in construction. Int. J. Proj. Manag. 30(2), 188–198 (2012) 3. Xue, J., Yuan, H., Shi, B.: Impact of contextual variables on effectiveness of partnership governance mechanisms in megaprojects: case of guanxi. J. Manag. Eng. 33(1), 04016034 (2017) 4. Zheng, X., Lu, Y., Le, Y., et al.: Formation of interorganizational relational behavior in megaprojects: perspective of the extended theory of planned behavior. J. Manag. Eng. 34(1), 04017052 (2018) 5. Wu, G., Zhao, X., Zuo, J.: Relationship between project’s added value and the trust-conflict interaction among project teams. J. Manag. Eng. 33(4), 04017011 (2017)

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Research on Evaluation of Construction Workers’ Job Satisfaction Based on Improved AHP-FCE Method Peng Peng1 , Weishu Zhao1(B) , Xinran Deng1 , Bao Guo2 , and Weidong Wu3 1 School of Economics and Management, Anhui Jianzhu University, Hefei, China

[email protected]

2 National Time Service Center, Chinese Academy of Sciences, Beijing, China 3 Anhui Construction Engineering Group, Hefei, China

Abstract. In the post-epidemic era, the construction industry has been greatly impacted, and the wave of labor shortages in the construction industry has reappeared. The construction industry has frequently faced the problems of “labor shortage” and “difficulty in recruiting workers”. At the same time, with the accelerated development of China’s population aging process, research on the job satisfaction of construction workers, has certain practical significance for improving the employment happiness of construction workers and retaining the labor force in the construction industry. First, through literature research and on-the-spot investigation, based on 6 dimensions: work itself, work environment, public services, rights protection, leaders and colleagues, salary and welfare, 20 indicators were selected to establish a job satisfaction of construction worker evaluation index system; Secondly, the improved AHP-FCE (three-scale analytic hierarchy processfuzzy comprehensive evaluation) is used to determine the weights of indicators at all levels, and an evaluation model for construction worker’s job satisfaction is built. Finally, the feasibility of the evaluation system is verified through empirical research, and suggestions on improving the job satisfaction of construction workers are put forward in combination with social reality. Keywords: Construction Workers · Job Satisfaction · Improved Analytic Hierarchy Process

1 Introduction After the COVID-19 epidemic for three consecutive years, construction industry is facing the unfavorable situation of “five increase and one decrease”, that is, the cost of resumption of work increases, the hidden dangers of safety increases, the difficulty of fulfilling the contract during the construction period, the difficulty of organizing production factors increases, and the cost of project construction increases, but profits decrease. A large number of construction units and construction workers are put into a dilemma. According to the data of the Construction Industry Association: in 2021, the number of employees in construction industry is 52.83 million, a decrease of 1.56% from the © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 911–926, 2023. https://doi.org/10.1007/978-981-99-3626-7_70

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end of the previous year; In 2020, a decrease of 1.11% from the end of 2019 [1]. At the same time, with the improvement of people’s living standards and acceleration of aging process of the population, construction industry workers’ job with poor working environment, high work intensity, lack of social security, and often delayed wages, are losing its attractiveness. A large number of construction workers in the secondary industry tend to switch to jobs in emerging service industries, such as couriers, takeaways, etc. The new generation of migrant workers also tend to choose these new jobs. The current construction industry in China needs to consider the living conditions of workers and make timely adjustments to meet the needs of social development in order to reduce the loss of workers in the construction industry. Building a reasonable job satisfaction evaluation system for construction workers, identifying key factors and exploring their improvement paths, will help solve the current problems. The Guiding Opinions on Accelerating the Cultivation of Construction Industry Workers in the New Era, issued by the Ministry of Housing and Urban-Rural Development in 2020, clarifies that by 2035, construction workers should have efficient employment: orderly mobility, and construction workers’ rights and interests will be effectively protected, with a sense of gain, happiness, and security fully enhanced, vocational skills training, assessment and evaluation systems are perfected. There will be an army of skilled, knowledge-based and innovative construction workers adhering to the spirit of labor, model workers, and craftsmanship [2]. It is not difficult to see that the job satisfaction of construction workers will be an important factor affecting the quality of the future development of the construction industry.

2 Research Status In a general sense, job satisfaction usually refers to a person’s satisfaction with the work itself and its related aspects (including work environment, work status, work style, work pressure, challenge, relationship, etc.), about a state of mind that has positive feelings [3]. Job satisfaction originated from the Hawthorne experiment by Mayo et al. (1927– 1932), which stated that “the emotion of work will affect their work behavior, and the social and psychological factors of workers are the factors that determine job satisfaction and productivity. Main factor” [4]. The scholar who formally proposed the concept of job satisfaction was Hoppock. Hoppock (1935) first formally defined job satisfaction in his doctoral dissertation as: employees’ satisfaction with environmental factors in both psychological and physiological aspects, that is, employees’ satisfaction with work Subjective responses to situations. These information are sufficient to show the importance of job satisfaction [5]. Herzberg believes that the factors that affect job satisfaction can be divided into three: physical environment factors, social factors and personal psychological factors [6]. However, due to the comprehensiveness and complexity of job satisfaction itself, there are different influencing factors and degrees of influence for different groups in different occupations. Therefore, there is still little universally applicable theory of job satisfaction. In foreign countries, scholars have done a lot of research on job satisfaction. Since it was first proposed in 1935, it has received extensive attention from scholars. In China, the important role of job satisfaction on employees’ work status and organizational performance has been recognized by researchers, but related

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research is still rare, and the research on job satisfaction of construction workers is even rarer. Therefore, it is necessary to discuss the evaluation and improvement of construction workers’ job satisfaction based on the background of the development of China’s construction industry.

3 Improved Modeling Steps for AHP-FCE 3.1 Construct an Evaluation Index Set First, construct a judgment index system for the target which need be evaluated.Generally, the fuzzy comprehensive discriminant model includes three layers of indicators totally, namely the top target layer, the bottom plan layer and the middle indicator layer. The evaluation object (construction workers’ job satisfaction) X is a hierarchical set of indicators [7]. We can establish a second-level index of construction workers’ job satisfaction, the first-level index is established as X i (i = 1, 2, 3, …, n), thus the first-level index system is X = (X1 , X2 , . . . , Xn ) Secondary indicators can be established as follows. Xij = (Xi1 , Xi2 , . . . , Xij ),

(i = 1, 2, 3, . . . , n)

where j is secondary indicators number. 3.2 Establish a Satisfaction Rating Scale Evaluate the satisfaction level of construction workers with V = {V1 , V2 , …, Vn }, which indicates the satisfaction of construction workers, and classifies the evaluation objects V according to different evaluation levels, Pijk = sijk , k = (1, 2, …, n), among them, Vijk represents the number of evaluation objects Xij belonging to the evaluation level k, and s is the total number of evaluation experts. 3.3 Make a Single Factor Judgment to Construct a Membership Matrix To construct the membership degree matrix, the fuzzy relation matrix of the fuzzy comprehensive evaluation method, we need to construct the evaluation results according to the expert evaluation in different single factor fields to obtain the membership degree vector Rn , and then conclude the membership degree vectors of different single factors into the membership degree matrix. Quantitative analysis is carried out for each factor that may affect the evaluation object Xij , and the membership matrix R is finally obtained. Construct the membership degree matrix R: ⎤ ⎡ ⎡ ⎤ R1 a11 · · · an1 ⎢ R2 ⎥ ⎥ ⎢ ⎥ ⎢ R = ⎢ . ⎥ = ⎣ ... . . . ... ⎦ ⎣ .. ⎦ a1n · · · ann Rn

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3.4 Use the Improved AHP Method to Judge the Weight The traditional AHP fuzzy comprehensive evaluation method uses a nine-scale AHP with a scale of 1–9. Although the weight analysis of various factors of the evaluation object is more refined, the process is much more intricated, which is not conducive to experts’ scoring, and the general public is much unfavorable for the evaluation obtained. On account of it is not easy to conprehend. We adopt the improved AHP evaluation method, changing the conventionally 1–9 scale method to a three-scale method (0,0.5,1) that is easier to understand and conforms to people’s thinking consistency, which is more conducive to experts’ scoring. And the evaluation results are more received to the public. Not only is the fuzzy evaluation more accurate, but also greatly simplifies the subsequent steps for consistency checking.It reduces a great deal of calculation, and also ensures the credibility of the evaluation results [8]. We solve the judgment matrix H for the weight of the satisfaction of construction workers, and the elements hij in the matrix are used to analyze and judge the different factors that affect the evaluation object: ⎧ ⎨ (si −sj )(q−1) + 1 n , si ≥ sj s −smin

−1 rij , hij = (max si = s −s (q−1) ) j i j=0 ⎩ +1 ,s < s smax −smin

i

i

where smax = max(si ), smin = min(si ), q = ssmax min Solve the elements mij in the quasi-optimal uniform matrix M: nij = lg hij , uij =

 1 n  nik − njk , mij = 10uij k=1 n

Calculate the discriminant matrix we have already obtained, multiply each row of the discriminant matrix to obtain the result Yi , and open the nth power to obtain the result to form a vector W. The process is as follows:    n mij , Wi = Yi = n Normalize Wi to get W∗i : Wi Wi∗ = n , 1 Wi Finally, the weight vector of n single elements to the evaluation object W = (W1 , W2 , . . . , Wn ) is obtained.

4 Empirical Analysis Anhui Construction Engineering Group Holdings Co., Ltd. is a large-scale modern construction enterprise group, a world-renowned multinational contractor in Anhui Province. In recent years, the Group has firmly implemented the new development concept, adhered to the main line of reform, transformation and development, and promoted

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high-quality development. Give full play to its financial strength and industrial integration capabilities, it has been vigorously promoted the “integration of investment and financing, construction and operation” model. The corporation has taken the lead in establishing industrial investment funds with financial institutions in the province, who implemented a large number of water conservancy. Taking the EPC project of Bengbu Zhongke High-tech Experimental School as an example, this paper conducts an empirical study on the job satisfaction of construction workers in the engineering projects undertaken by Anhui Construction Engineering Group. In this paper, the construction of the evaluation index system of construction workers’ job satisfaction strives to take into account eight principles: the principle of purpose-based, systematicness, consistency, independence, homogeneity, comprehensiveness, comparability and operational [9]. There are many factors that affect the job satisfaction of construction workers. In this research, initially, the literature analysis method is used, a large number of relevant literatures are found and read, and 24 frequently cited keywords are selected through advanced retrieval of “construction workers” and “job satisfaction” as subject headings. From the literature, we summarized a total of 38 main influencing factors that have been sorted out and recognized by current scholars. There are some factors with basically the same meaning, such as “salary status”, “salary”, “income”, etc. They are merged into “income level” and finally the initial identification obtained 25 factors with higher frequency. Secondly, using the expert interview method, conduct telephone interviews with the main project leaders, construction workers’ representatives, and construction team leaders of Anhui Construction Engineering Group, China Construction Eighth Engineering Division Corp., ltd. and its labor dispatch company in Anhui Project Department, so as to grasp the satisfaction of construction workers from the side degree factor information, and further integrate the obtained factors. Besides, through on-the-spot investigation, first-hand information about the satisfaction of construction workers was obtained, and a preliminary understanding of the living conditions of construction workers was gained. Based on the above elements, we screened out the relevant indicators that can be basically consistent with the opinions of all parties, and finally determined 20 indicator factors. Finally,we divided them from six dimensions: work itself, work environment, salary and benefits, rights protection, leaders and colleagues, and public services. The resulting index system is shown in the Table1. 4.1 Model Building of AHP The improved three-scale AHP-FCE method is used to systematically evaluate the constructed index system for job satisfaction of construction workers. The specific steps are as follows: (1) Determine the target: evaluation of construction workers’ job satisfaction. (2) Establish first-level indicators: there are a total of 6 first-level index factors. (3) Establish secondary indicators: there are 20 secondary indicators in total. 4.2 Description of the Indicator (1) The first-level indicator work itself, including four second-level indicators, work intensity, career prospects, place of work, and overtime. Work intensity is the subjective psychological feeling produced by workers during the labor process. During

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Target layer

First-level indicators

Secondary indicators

construction workers’ job satisfaction evaluation A

Work itself B1

Work intensity C1 Career prospects C2 Work location C3 work overtime C4

Work environment B2

Accommodation C5 Health and safety C6 Life convenience C7

Public service B3

employment service C8 Social respect C9 Family relations C10

Rights protection B4

Five insurances and one fund C11 Labor contract C12 Government regulation C13

Leaders and colleagues B5

working relationship C14 Evaluation of leaders C15 Attention of leaders C16

Salary and welfare B6

Income level C17 Salary payment on time C18 Salary justice C19 Welfare policy C20

work, the more nervous, fatigued, and painful the worker is, the greater the work intensity. Excessive work intensity will increase the risk of unsafe behavior by workers and reduce the risk of unsafe behavior. Construction worker’s job satisfaction; career prospect refers to the worker’s perception of the future of the job or industry and the opportunities for development and progress that a person can achieve in the organization. Construction workers have the right to pursue self-improvement. This variable can be considered as a higher level Need, which belongs to the higher needs of the self-actualization level in Maslow’s theory of needs; work location, that is, the geographical location of the work location, construction workers tend to live in the dormitory of the project department according to the situation of the location of the project, and often have to work far away from home; overtime intensity, that is, the total length or frequency of construction workers engaging in excess work during the working week. Due to the characteristics of construction engineering and organizational needs, the current reality of excess overtime work is unavoidable [10–12].

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(2) The first-level indicator Working environment, including three secondary indicators, accommodation, health and safety, life convenience. Accommodation that is, whether the living and dining conditions of construction workers during work meet satisfactory standards, and the temporary facilities on construction sites are generally poor, which has an impact on the job satisfaction of construction workers. Health and safety means that the workplace of construction workers ensures the health and hygiene conditions of employees. The harsh environment of construction projects is easy to cause serious occupational diseases and damage the health of workers. Life convenience, whether construction workers can enjoy convenient living services, including food, clothing, housing and transportation [13]. (3) The first-level indicators salary and welfare, including four second-level indicators, namely income level, salary payment on time, salary justice and welfare policy. Income level, at the level of construction workers, refers to whether they can obtain satisfactory results from participating in labor to solve the problems of survival and development of themselves and their families; Salary payment on time, refer to whether construction workers can receive labor remuneration according to the time specified in the contract, and are in arrears. Salary is a relatively obvious social issue in the construction industry; Salary justice refer to whether construction workers can receive remuneration with reasonable distribution standards, transparent distribution process, and clear division principles; Welfare policy, that is, whether the enterprise or society can provide employees with benefits in the form of benefits policies to protect their living standards and improve their quality of life [14, 15]. (4) The first-level indicators of rights protection, including three second-level indicators, five insurances and one fund, labor contract, and the government regulation. Five insurances and one housing fund, that is, whether the employer can provide construction workers with various security benefits (including endowment insurance, work-related injury insurance, unemployment insurance, medical insurance, maternity insurance, and housing provident fund). In reality, many construction workers work in the status of temporary workers or labor dispatch. The lack of such guarantees affects the satisfaction of construction workers; Labor contract that fully complies with and contains the provisions of the “Labor Law” and its relevant provisions; Government’s regulation, that is, the government’s purposeful market intervention activities to protect the rights and interests of construction workers [16]. (5) The first-level indicators leaders and colleagues, including three second-level indicators, working relationship, evaluation of leaders and attention of leaders. Working relationship, refers to whether there is a good objective emotional connection between employees and leaders and colleagues, such as informal organizations; Evaluation of leaders refers to leaders’ evaluation of subordinates can reflect the organization’s value standards, work Standard, whether it conforms to objective facts and relevant principles; Attention of leaders, that is, whether leaders often respond to or deal with employees’ feedback [17]. (6) The primary indicator of public service, including three secondary indicators, employment service, social respect and family relations. Employment service refers to the ways for construction workers to obtain employment channels, social services for finding jobs, etc.; social respect refers to whether construction workers are welcomed, treated fairly, and respected in society; family relations refer to The family

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relationship of construction workers, including marital status, family harmony, etc., has a certain influence on work efficiency and satisfaction [18–21]. 4.3 Construction of Judgment Matrix and Single-Layer Weight Calculation According to the evaluation index system of construction workers’ job satisfaction established in Table 1, the hierarchical structure is constructed by combining the relationship between each index. Experts, such as representatives of construction workers, team leaders, and the person in charge of the human resources department of Anhui Construction Engineering Group, were invited to compare and score each factor index. The results are as follows, construct a judgment matrix, and calculate the corresponding weights. ⎛ ⎞ 0.5 1 1 0 1 0 ⎜ 0 0.5 1 0 1 0 ⎟ ⎜ ⎟ ⎜ ⎟ ⎜ 0 0 0.5 0 1 0 ⎟ A=⎜ ⎟ ⎜ 1 1 1 0.5 1 0 ⎟ ⎜ ⎟ ⎝ 0 0 0 0 0.5 0 ⎠ 1 1 1 1 1 1 Calculate according to the steps of the improved fuzzy comprehensive evaluation method, then we get the weight of each criterion layer (primary index), use the same method with principle, and build the judgment matrix of the scheme layer (secondary index) to the criterion layer:   WA = WB1 WB2 WB3 WB4 WB5 WB6   = 0.132 0.070 0.037 0.246 0.021 0.494 Use the same method with principle, construct the judgment matrix of secondary indicators (scheme level) to the criterion level: ⎛ ⎞ ⎛ ⎞ ⎛ ⎞ 0.5 0 1 1 0.5 0 1 0.5 1 1 ⎜ 1 0.5 1 1 ⎟ ⎟ ⎝ 1 0.5 1 ⎠ B3 = ⎝ 0 0.5 0 ⎠ B1 = ⎜ ⎝ 0 0 0.5 0 ⎠ B2 = 0 0 0.5 0 1 0.5 0 0 1 0.5 ⎛ ⎞ ⎛ ⎞ ⎛ ⎞ 0.5 0 0 1 0.5 0 0 0.5 0 0 ⎜ 1 0.5 0 1 ⎟ ⎟ B4 = ⎝ 1 0.5 0 ⎠ B5 = ⎝ 1 0.5 0 ⎠ B6 = ⎜ ⎝ 1 1 0.5 1 ⎠ 1 1 0.5 1 1 0.5 0 0 0 0.5 The weight of each scheme layer (secondary index) is calculated according to the improved method: T  T  W 1 = 0.263 0.564 0.055 0.118 , W 2 = 0.288 0.611 0.100 ,  T  T W 3 = 0.637 0.105 0.258 , W 4 = 0.105 0.259 0.636 ,  T  T W 5 = 0.105 0.259 0.636 , W 6 = 0.131 0.292 0.516 0.061 .

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4.4 Calculation of the Composite Weight of Each Layer Element to the Target Layer The comprehensive weight of construction workers’ job satisfaction evaluation objectives is obtained through the above calculation and evaluation results, as shown in Table 2.The overall ranking of indicator weights is shown in Fig. 1.And the weight distribution of indicators can be seen in Figs. 2 and 3. 4.5 Determination of Evaluation Criteria Set The evaluation standard is based on the job satisfaction index scale compiled by Brayfield & Rothe to set the comment set. The Likert five point scoring method is used, which is divided into very satisfied, relatively satisfied, basically satisfied, relatively dissatisfied, and very dissatisfied(Represented by five-star, four-star, three-star, two-star, one-star respectively). And then the corresponding evaluation score of each standard is determined. Here, V is used to represent the evaluation standard set, and then: V = {V 1, V 2, V 3, V 4, V 5}={five - star, four - star, three - star, two - star, one - star} = {[100, 80), [80, 60), [60, 40), [40, 20), [20, 0]}

4.6 Fuzzy Comprehensive Evaluation of Criterion Level According to the actual situation of the EPC project of the Zhongke High tech Experimental School in Bengbu, Anhui Construction Engineering Group, through the collection of relevant data and the use of questionnaires, the research consulted experts such as construction workers’ representatives, team leaders’ representatives, contractors, and human resources department managers of labor dispatching companies, so as to form a 10 member expert group. Sampling process in detail: Initially, we demanded each of these 10 experts to conduct face-to-face questionnaire interviews with at least 10 construction workers participating in the project. Then, the final representative opinions of construction workers are formed by synthesizing the majority opinions respectively. Finally, a matrix is formed after feedback and sorted out for calculation. The evaluation opinions of these experts on the job satisfaction of construction workers were collected. The fuzzy evaluation matrix obtained after sorting out is as follows: ⎛ ⎞ ⎞ ⎛ 0.1 0.3 0.1 0.1 0.4 0.5 0.2 0.1 0.2 0 ⎜ 0.3 0.2 0.2 0.2 0.1 ⎟ ⎟ ⎠ ⎝ PB1 = ⎜ ⎝ 0.1 0.1 0.4 0.2 0.2 ⎠, PB2 = 0.4 0.2 0.2 0.1 0.1 , 0.1 0.2 0.3 0.2 0.2 0.2 0.4 0.2 0.1 0.1 ⎛ ⎞ ⎛ ⎞ 0.2 0.2 0.1 0.3 0.2 0.2 0.4 0.1 0.3 0 PB3 = ⎝ 0.2 0.3 0.1 0.2 0.2 ⎠, PB4 = ⎝ 0.1 0.3 0.2 0.2 0.2 ⎠, 0.1 0.1 0.5 0.1 0.2 0.2 0.3 0.1 0.2 0 ⎛ ⎞ ⎞ ⎛ 0.3 0.3 0.2 0.1 0.1 0.2 0.2 0.2 0.6 0.1 ⎜ 0.3 0.2 0.1 0.1 0.1 ⎟ ⎟ PB5 = ⎝ 0.3 0.2 0.1 0.1 0.1 ⎠, PB6 = ⎜ ⎝ 0.4 0.2 0.1 0.1 0.1 ⎠. 0.2 0.4 0.2 0.1 0.1 0.2 0.3 0.1 0.2 0.2

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Table 2. Comprehensive weight table of construction workers’ job satisfaction evaluation objectives Target layer A

First-level indicators Bi

First-level Weight Wi

Secondary indicators C

Secondary Weight Wj

Weights Wij = Wi ∗ Wj

construction workers’ job satisfaction evaluation A

Work itself B1

0.132

Work intensity C1

0.263

0.035

Career prospects C2

0.564

0.074

Place of work C3

0.055

0.007

Work environment B2

Public service B3

Rights protection B4

Leaders and colleagues B5

0.07

0.037

0.246

0.021

work overtime C4

0.118

0.016

Accommodation C5

0.288

0.020

Health and safety C6

0.611

0.043

Life convenience C7

0.1

0.007

employment service C8

0.637

0.024

Social respect C9

0.105

0.004

Family relations C10

0.258

0.010

Insurances and fund C11

0.105

0.026

Labor contract C12

0.259

0.064

Government regulation C13

0.636

0.156

working relationship C14

0.105

0.002

(continued)

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Table 2. (continued) Target layer A

First-level indicators Bi

Salary and welfare B6

First-level Weight Wi

0.493

Secondary indicators C

Secondary Weight Wj

Weights Wij = Wi ∗ Wj

Evaluation of leaders C15

0.259

0.005

Attention of leaders C16

0.636

0.013

Income level C17

0.131

0.065

Pay salary on time 0.292 C18

0.144

Fair remuneration C19

0.516

0.254

Welfare policy C20

0.061

0.030

Fig. 1. Comparison of weights of indicators.

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construcon workers' job sasfacon evaluaon A 0.132

0.07 0.037 0.493

0.246 0.021 Work itself B1

Work environment B2

Public service B3

Rights protecon B4

Leaders and colleagues B5

Salary and welfare B6

Fig. 2. The weighting diagram of the criterion layer indicators.

Fig. 3. The weighting diagram of scheme layer indicators. (C1-C20)

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According to the steps of the improved AHP method, calculate the weight vector W of each evaluation index, establish a fuzzy evaluation matrix, and use the formula: Y = W × P. To calculate the comprehensive evaluation vector of the criterion level (first level index). YB1 = WB1×PB1 = [0.225, 0.244, 0.185, 0.162, 0.184], YB2 = WB2×PB2 = [0.398, 0.199, 0.181, 0.139, 0.081], YB3 = WB3×PB3 = [0.174, 0.185, 0.203, 0.238, 0.200], YB4 = WB4×PB4 = [0.174, 0.310, 0.126, 0.210, 0.179], YB5 = WB5×PB5 = [0.226, 0.327, 0.174, 0.173, 0.100], YB6 = WB6×PB6 = [0.346, 0.219, 0.165, 0.165, 0.106].

4.7 Fuzzy Comprehensive Evaluation of Target Layer Based on the relevant calculation rules of fuzzy comprehensive evaluation, according to the calculation results of fuzzy comprehensive evaluation of the first level index (criterion level), the fuzzy evaluation matrix of the target level of the project is constructed. The results are as follows: ⎛ ⎞ 0.225 0.244 0.185 0.162 0.184 ⎜ 0.398 0.199 0.181 0.139 0.081 ⎟ ⎜ ⎟ ⎜ ⎟ ⎜ 0.174 0.185 0.203 0.238 0.200 ⎟ PA = ⎜ ⎟ ⎜ 0.174 0.310 0.126 0.210 0.179 ⎟ ⎜ ⎟ ⎝ 0.226 0.327 0.174 0.173 0.100 ⎠ 0.346 0.219 0.165 0.165 0.106 According to the formula Y = W × P, The comprehensive evaluation vector of the target layer is YA = WA × PA = [0.283, 0.244, 0.161, 0.176, 0.136]. According to the principle of maximum subordination, the comprehensive evaluation results of construction worker’s satisfaction can be determined. Through comprehensive analysis, the comprehensive evaluation values can be obtained by quantifying the indicators, and then the quantized comprehensive evaluation results can be obtained. The quantized evaluation standard G here is the median of the corresponding values in the evaluation standard set V, and the quantized comprehensive evaluation value S is: S = YA × G T    T = 0.283 0.244 0.161 0.176 0.136 × 90 70 50 30 10 = 57.24

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4.8 Analysis of Evaluation Results Through the above calculation, according to the principle of maximum subordination, the maximum comprehensive evaluation value of construction workers’ job satisfaction in the project in this paper is 0.283, which belongs to the five-star (very satisfactory level) in the comment collection. This shows that the construction workers’ job satisfaction of the EPC project of Bengbu Zhongke High tech Experimental School is quite excellent. According to the quantitative comprehensive evaluation results, the comprehensive score of the overall evaluation of the project is 57.24, which corresponds to the three-star(basic satisfaction level). Based on the above two evaluation results, the project is generally between basically satisfied and very satisfied, and can be used as a benchmark project for reference.

5 Conclusions and Suggestions It can be seen from the analysis of the evaluation results that the construction workers’ job satisfaction and objective conditions have become an important factor in the high-quality development of the construction industry. The main indicators that have an impact on the construction workers’ job satisfaction include work itself, work environment, public service, rights protection, leaders and colleagues, and salary and welfare. The indicator of salary and welfare accounts for the main one, which shows that the income level of the construction industry, whether the salary can be paid on time, the welfare policy and the fairness of the salary are the highest priority of the construction workers. Among them, the fairness of salary is the main factor, which shows that construction workers advocate the work system of “distribution according to workload, more work, more pay”; Secondly, the indicators of rights protection, work itself, work environment, public service and leaders and colleagues, complement each other, so that the construction workers’ job satisfaction of the project can reach a satisfactory evaluation level. On the premise of these six indicators, a fuzzy comprehensive evaluation model of construction workers’ job satisfaction is established, and the evaluation system is applied and verified through corresponding cases, providing a reference evaluation system of construction workers’ job satisfaction. In order to speed up the cultivation of a new era of construction industry workers, improve the job satisfaction of construction workers, retain the loss of labor in the construction industry, and promote the high-quality development of the construction industry, priority should be given to the following: (1) The government should focus on the income of construction workers, attach great importance to the social problem of “arrears of migrant workers’ wages”, and increase the proportion of payment of construction workers’ wage deposits. China’s government can explore and implement the system of bank payroll for construction workers, which can put an end to illegal subcontracting, illegal subcontracting and other acts, and ensure the income level of construction workers. Enterprises should set up a reasonable wage growth mechanism. The wages of construction workers in China are often adjusted not according to the level of industry development and

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personal ability, but under external circumstances. For example, when there is a “labor shortage”, the wages will increase. In order to promote the healthy development of the construction industry, retain construction workers, and encourage construction enterprises to set up a reasonable wage growth mechanism. The wage growth rate can be reasonably set according to the length of employment, labor proficiency, vocational skill level, qualification certificate and other aspects. (2) Improve the working environment and conditions of construction workers, and enrich amateur cultural life. The government can consider formulating standards for the working environment and conditions of construction workers to ensure the basic living conditions of construction workers. Compared with other jobs, the messy and noisy environment of the construction site and the poor accommodation conditions are significantly affecting the job satisfaction of construction workers. The establishment of unified norms will help to improve the guarantee of basic living conditions for construction workers. Enterprises can establish social support networks to support construction workers. Construction workers often work in the project department far away from the city and are separated from their families for a long time. The formation of formal or informal organizations such as construction workers’ trade unions and fellow villagers’ associations within the enterprise and the strengthening of social contacts and emotional exchanges among construction workers will help to improve the job satisfaction of construction workers. (3) Build career promotion channels and optimize the content of vocational education. The construction workers lack a complete career development system, which makes it difficult to improve their career level and social status. The government should optimize the vocational education and assessment system for construction workers, so as to provide them with a better channel for social progress. Enterprises can set up diversified career development paths for construction workers, conduct career development assessment, develop open, fair and just assessment methods and processes within the enterprise. Finally, the view of occupational equality should be widely promoted in society to change the inherent perception of construction workers and break occupational discrimination.

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Modularization Considerations for Modular Integrated Construction in Hong Kong: A Case Study Jinfeng Lou(B) , Weisheng Lu, Liupengfei Wu, and Frank Ato Ghansah Department of Real Estate and Construction, The University of Hong Kong, Hong Kong, China [email protected]

Abstract. Modularization has become an important strategy to support current enterprises in gaining an edge in a competitive market. The construction industry is also continuing to explore the same trend. Modular integrated construction (MiC) in Hong Kong is one of these notable examples. MiC has been vigorously promoted by the Hong Kong government as an innovative construction method. However, MiC has some features that make it challenging to adopt, e.g., a wider range of stakeholders and a longer supply chain compared to the traditional construction method. This means that the requirements of all parties and subsequent stages should be considered early in the design phase. Modularization, as a critical aspect of MiC design, should also be carefully carried out to achieve smooth construction. This study, therefore, adopts a case study method to explore the modularization considerations in a real-life MiC project in Hong Kong. Four major considerations are summarized, including design with repetitive module units, compact dimensions of module sizes, lightweight module units, and layout adjustment of the structural core. This study will contribute to the design theories and practices by providing evidence-based modularization considerations in modular building design. Keywords: Modular integrated construction (MiC) · modularization · case study · Hong Kong

1 Introduction Modularization is often recognized as a clever way of balancing two conflicting production requirements, in which products are becoming increasingly complex in order to meet people’s diverse needs, but they also need to be simple and standard enough to be efficient for competitive advantage. It is an essential approach to achieving mass customization, a concept that is frequently discussed in the manufacturing industry [1]. Rather than seeing the heterogeneous needs of customers as a threat, this approach regards them as a profitable opportunity [2]. The thinking behind it is to break down the product designs into numerous standardized modules, which in turn can be configured differently to produce distinct products to meet varying needs. The benefits of modularization have been extensively mentioned in the literature, including structured tasks and knowledge, the © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 927–937, 2023. https://doi.org/10.1007/978-981-99-3626-7_71

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harmonized relationship between standardization and customization, extra revenue, and reduced costs [3, 4]. This strategy has long been accepted by numerous industries, e.g., semiconductors, fashion, furniture, food, car, and even tourism [5]. Similar scenarios for the application of modularization strategies can also be found in the construction industry. Buildings have traditionally been conceived as large, complex, highly uncertain, and customized products. The organization of its production is usually in the form of a project-based organization. These characteristics have contributed to the fact that buildings are generally produced based on a temporary project-based organization, in which a variety of professionals collaborate to meet the unique needs of the client [6, 7]. The project team brings various raw materials, e.g., steel, cement, and aggregates, to the site where the building is located and pours and shapes the concrete as a whole. However, the recent trend toward construction industrialization has suggested another innovative construction method, where modularized building structures or components are manufactured offsite and then transported to the construction site for assembly [8]. This construction method is often termed offsite construction (OC), modular construction (MC), prefabricated prefinished volumetric construction (PPVC), modern methods of construction (MMC), and modular integrated construction (MiC). All the terms are widely used in the industry and academia of construction. Some studies even use them interchangeably [9], while others attempt to distinguish the two concepts. They are not mutually exclusive but just emphasize different aspects. To reduce ambiguities, we align the above terms to “modular integrated construction (MiC)”, a term specific to Hong Kong, where this study was conducted. MiC has demonstrated its numerous advantages over traditional cast-in-situ construction in the previous literature. Examples of these advantages include but are not limited to reducing construction duration [10], assuring quality [11], lowering labor demand [12], and improving workplace conditions [13]. Because of these benefits, the Hong Kong government has introduced various preferential policies, practical guidelines, and knowledge-sharing activities to support the development of MiC. According to a report issued by the Hong Kong Construction Industry Council (HKCIC) [14], there is a huge market demand for MiC in Hong Kong in the future. Specifically, the demand for MiC modules will reach 50,300 (equivalent to approximately 596,000 square meters) by 2024 and 241,100 (equivalent to approximately 2,821,600 square meters) by 2029. The huge demand and promising market are driving the entire industry to adopt innovative approaches, technologies, and strategies to accommodate it. MiC projects mainly consist of four main stages, namely, design, manufacture, transportation, and on-site installation. In the design stage, the building design is divided into the design of individual modules and their interfaces. Then, the design drawings are handed over to the manufacturer to develop the shop drawings further and execute the specific manufacturing process. Following this, the modules were packaged for protection and transported to the buffer area within or near the construction site. In the last stage, these modules are on-site assembled in conjunction with the schedule. However, this innovative arrangement of the building supply chain presents some new challenges in the design of buildings, especially the modularization of building design. Firstly, the MiC project introduces a broader range of stakeholders. With the majority of construction work being undertaken in offsite factories, the manufacturer

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and transporter become new and key stakeholders [15]. These stakeholders need to be deeply involved and collaborated from the early design stages, where the stakeholders’ requirements are coordinated, and a great deal of knowledge is incorporated [16]. For example, in modularizing the building design, transport restrictions need to be carefully taken into account. Secondly, the geographically distributed nature of the supply chain makes it even more prolonged. Manufacturing sites and construction sites are often located in different cities or even in different countries. Once the modules have been transported from the factory to the construction site, they are difficult to transport back to the factory for refinement or repair, which means that the modules must come out of the factory perfectly in line with the design intent and the constraints of the construction site. At the same time, this also places greater demands on the modularization of the design, requiring modularization solutions to be capable of meeting the constraints and uncertainties of subsequent stages. All these challenges necessitate a careful look at modularization considerations when designing modular buildings. This study, therefore, aims to investigate the modularization considerations with a focus on the Hong Kong MiC industry. The case study method is adopted to achieve the research aim. The remainder of the paper is organized as follows. Section 2 introduces the literature review on MiC in Hong Kong, the distinction between concepts related to modularization, and MiC design. Section 3 illustrates the research method. Section 4 shows the main findings of this study. Section 5 concludes this study and provides future research recommendations.

2 Literature Review 2.1 Module, Modularity, and Modularization Modularization, as a way to improve the competitiveness of enterprises, is no longer a new word. This concept is derived from the Latin word modulus, which originally meant a unit of length measurement. As the language evolved, it gradually evolved into three words with the same root, i.e., module, modularity, and modularization. The three terms are often mentioned in the literature, and even in some cases, they are mistakenly used. Before going more deeply, a distinction should be made between the definitions of these three terms. Miller and Elgard [3] have reviewed the history of the evolution of these terms to come up with the definitions, as shown in Table 1. It should be noted that the modules are self-contained but not in isolation. These modules are valuable only if they are specified together with the entire system containing other modules and standard interfaces. An outstanding example of modularization is within the computer industry [17]. In the early 1960s, IBM introduced the 360 families of mainframe computers, which revolutionized the whole computer industry. Before these 360 families, computers were typically closed and monolithic systems customized to the user’s needs with limited flexibility. IBM broke down the computers into individual self-contained functional units, e.g., monitor, keyboard, CPU, and central storage. Meanwhile, the interfaces between the modules were standardized to allow various combinations of modules, which ensured the design flexibility and fulfillment of user customization needs.

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Concept

Definition

Module

An essential and self-contained functional unit relative to the product in which it resides

Modularity

A property of a system related to structure and function

Modularization

An activity in which the structuring in modules takes place

2.2 MiC in Hong Kong The construction industry is a highly valued sector in Hong Kong as it provides a boost to the economy and employment and brings about a sound-built environment. However, Hong Kong’s industry has been facing six significant future challenges, including substantial future construction volumes, high costs, unsatisfactory mega-project performance, unsatisfactory site safety performance, declining productivity, and a lack of creativity & innovation, as delineated by the Construction 2.0 Report issued by Hong Kong Government [18]. This report also points to a number of solutions, one of which involves the adoption of MiC. With the definition from HKCIC, MiC is recognized as an innovative construction method where free-standing integrated modules (with finishes, fixtures, and fittings) are produced in a factory and assembled on the construction site [19]. The small size of the local construction site and its proximity to the Pearl River Delta, the “world’s factory”, have prompted Hong Kong to embrace MiC as a possible solution to the housing crisis [20, 21]. Actually, MiC has been outlined as a novel policy initiative by the Chief Executive’s Policy Address in 2017 and 2018, respectively. Moreover, in 2020’s Policy Address, the Chief Executive highlighted the vital role of MiC in completing the quarantine centers and the North Lantau Hospital Hong Kong Infection Control Center rapidly during the Covid-19 pandemic. Therefore, the government has promulgated various policies that actively favor MiC to promote this innovative method. An example of such policies could be the concession for gross floor area (GFA), stating that 6% of the floor area constructed by MiC can be excluded from the GFA, while not being subject to the 10% GFA cap [22]. With the implementation of initiatives such as Northern Metropolis and Lantau Tomorrow Vision, this construction method will be more widely exploited in Hong Kong. 2.3 MiC Building Design The building design is a highly complex task that needs to consider numerous aspects, including functionality, aesthetics, durability, fire safety, waterproofing, and so on. Moreover, MiC building design has to give extra considerations to manufacture, hoisting and assembly, logistics and supply chain, and so on. Design for Manufacture and Assembly (DfMA) has been widely explored to unleash the potential of MiC [23]. The basic idea behind this concept is to consider the manufacture and assembly downstream stages in the upstream design stages [24]. Subsequently, DfMA gradually evolved into DfX, where

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X represents any excellence criteria, e.g., safety, sustainability, and resilience. Behind each X is a wealth of knowledge that needs to be taken into account in the design, and it is so vast that no one person can fully grasp it. As a later phase of design, modularization is also subjected to these X criteria. Therefore, summarizing the modularization considerations could help designers to evaluate the modularized designs more comprehensively and facilitates the development of automatic modularization algorithms. There are generally two design paradigms, including top-down and bottom-up approaches [25]. The top-down method starts from the prescriptive boundary and then develops a detailed design fitting the contextual constraints. For MiC design, the detailed floorplans are further processed through modularization, outputting the designs of individual modules. In the bottom-up approach, a series of pre-designed modules are aggregated in different ways to achieve the overall design. In this case, it is necessary to consider the necessary requirements to be met by each module during the initial design of individual modules. In practice, the MiC building design may not exactly follow one of these methods, but may be more of a hybrid approach combining these two. Whichever method is employed, modularization considerations always need to be fully taken into account in the design.

3 Method This study adopted the case study research method. Yin [26] defines of case study as “an empirical inquiry that investigates a contemporary phenomenon (the ‘case’) in depth and within its real-world context, especially when the boundaries between phenomenon and context may not be clearly evident”. A case study allows investigations of contextual realities and differences between planned and actually happened activities [27]. It enables researchers to gain a holistic view of a certain phenomenon, understand complex real-life activities, come up with hypotheses and generate theories [28, 29]. The case study method is a combination of research methods applied to a specific research subject. Within the case study, multi-source heterogeneous data can be collected from various data collection methods, e.g., interviews, focus group meetings, questionnaires, archive studies, non-participant observation, and so on. Moreover, the case study method is not necessarily limited to a single case, but sometimes multiple cases are acceptable. It all depends on the subject being studied. Bell sees the ability to focus on one specific subject of interest and to use a variety of methods to understand the interactive processes as the most significant advantage of this research method [30]. A real case of a MiC project for university student residences is selected to contribute to gaining a more in-depth view of how designers make decisions about modularization in a MiC project and how modularization considerations influence decisions. The case study remains exploratory because there are few, if any, other studies that point to the modular considerations required for MiC buildings in Hong Kong. In this case study, we mainly adopted interviews, focus group meetings, and direct observation to collect data and then summarized the primary modularization considerations.

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4 Case Study 4.1 Case Description The selected case is an ongoing student residence project located on Hong Kong Island, as illustrated in Fig. 1(a). This project has been recognized as one of the Pilot Projects for MiC in Hong Kong. It includes two 17-story student residence tower buildings on top of a three-story podium structure. A total of 1224 student places will be offered, and supporting facilities such as canteens, common rooms, laundry rooms, and car parks will be fitted into the podium. The podium structures are established by in-situ reinforced concrete construction, while the tower buildings are made up of steel frame modules and structure cores. It adopts the MiC method for the following reason. Firstly, the MiC modules are all fully finished and furnished modular units (see Fig. 1[b]) in order to take advantage of the factory-controlled environment to achieve better workmanship and quality. Secondly, as shown in Fig. 1(c), the project sits on a complex site, which includes schools and roads in the surrounding area. This places high requirements on site formation work and settlement control. More time is required to deal with removing the original vegetation, leveling the site, and performing the foundation pit support. This leaves plenty of time for the design and fabrication of MiC modules. Thirdly, moving most of the construction work to offsite factories can reduce disturbance and nuisance to surrounding schools, e.g., noise and dust generation. Fourthly, the construction site is not located in a busy traffic district, which offers great opportunities for MiC module transportation.

Fig. 1. Overview of the case project

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4.2 Modularization of MiC Design The strategy adopted in this project was to involve the MiC manufacturer or supplier in the building design. Figure 2 shows the whole procurement procedure. In the initial phase, the detailed design was completed, but done in accordance with traditional construction methods. In early November 2018, the detailed design underwent the first revision to adapt it to the MiC construction methods. The first modularization of designs also took place in this phase. The revised MiC designs were then sent to the government for approval. Meanwhile, tendering for the main contractor for this project was commenced. The main contractors were encouraged to bid with their preferred MiC specialists and suppliers. Finally, the awarded tender team made a second revision or modularization to the design, considering its own production, transportation, and installation constraints, etc. In summary, the MiC design was modularized twice to ensure that the final design was compliant and feasible for all parties. These two modularization processes will be described separately next.

Fig. 2. Procurement procedure

Figure 3 illustrates the first modularization of the building design. The floor plans of the two tower buildings are almost the same, with the only difference being the orientation. Thus, Fig. 3 only presents the floor plan of one of the tower buildings. The centrosymmetric floor plan contains a site-built structure core, which is encompassed by two student residence parts to be modularized, i.e., the south and north part. From this figure, we can observe three major changes. First, all student residence rooms are modularized into limited types. Second, the central structure core is enlarged to achieve a more stable structure for the surrounding modules. Third, the toilet layouts are revised to suit MiC modules. After the second modularization, the resulting plan is shown in Fig. 4(a). It can be noted that the dimensions of each module have been further refined. The structural core of each floor is surrounded by 28 modules, among which only five types of modules with different dimensions are identified in this project (see Fig. 4[b]). This round of adjustments mainly changed the shape and size of the modules, which served to reduce the category of modules and facilitate production and transportation. Synthesizing the above two phases of modularizations, some modularization considerations can be summarized as follows. The first consideration is to design with repetitive module units. When modules have a large degree of similarity or exact repetition, many

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Fig. 3. First modularization of the floorplan

Fig. 4. Second modularization of the floorplan

detailed designs, knowledge, physical components, etc., can be shared or even reused. This greatly reduces duplication of effort and increases efficiency. In this project, the types of modules were eventually reduced to five, achieving great economies of scale. Secondly, it is essential to have compact dimensions of module sizes. The layout of each single student room has been optimized so that its area is approximately 6.5 square meters. As illustrated in Fig. 5, each MiC module is less than 2.5m (Width) X 8.4m (Length) X 3.65m (Height). All the sizes and dimensions of MiC modules comply with current local traffic restrictions. Thirdly, MiC module units should have light weights. In

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order to reduce weight, the project used a steel frame design for all modules. Upon careful estimation, the weights of all modules are in the range of 6.5 tons to 14 tons, which is suitable and easy for transporting and hoisting with readily available equipment and factories. Fourthly, after modularization, the building integrity is weakened. Therefore, it is also necessary to adjust the layout of the structural core to provide strong support for the surrounding modules.

Fig. 5. Compact dimension of modules

5 Conclusion Modularization allows for a certain degree of economy of scale and greater production efficiency while meeting diverse customer preferences. In recent years, this strategy has gradually attracted more attention in the field of Architecture, Engineering, and Construction (AEC) industry with the rise of new concepts, such as offsite construction (OC), modular construction (MC), and modular integrated construction (MiC). This study used a case study method to investigate the consideration of MiC design modularity in the selected student residence projects. The MiC design underwent two modularization revisions in this project. Summarizing the two modularization revisions, four primary considerations can be found: design with repetitive module units, compact dimensions of module sizes, lightweight module units, and layout adjustment of the structural core. This study gives practical experience on the modularization of MiC design. Future studies can be conducted to obtain more generalizable experiences by exploring more real-life MiC projects.

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Complexity Management of Emergency Projects from the Perspective of Complex Adaptive Systems Theory—The Case of the National Exhibition and Convention Center (Shanghai) Zhiwei Chen1(B) , Xian Zheng1 , Ju Bai2 , and Tao Huo3 1 School of Finance, Zhongnan University of Economics and Law, Wuhan 430073, China

[email protected]

2 China State Construction Engineering Corporation, Beijing 100029, China

[email protected] 3 China State Construction Engineering Corporation, Shanghai 200000, China

Abstract. As the construction of emergency projects is of great significance for pandemic control, it has received special attention in the project management realm. Emergency projects possess special complexities that distinguish them from general projects, while extant research focuses on rapid construction techniques but ignores project complexity and its management. In this regard, this study identifies the various aspects of the emergency project complexity and figures out the adaptive strategies. Based on the complex adaptive systems (CAS) theory, a typical emergency project in Shanghai, China, named the National Exhibition and Convention Center (Shanghai), has been selected to demonstrate the process of complexity identification and the adaptive strategies extraction through two-stage content analysis. The results suggest that the complexity of the process of emergency project construction involves six dimensions: goal complexity, organizational complexity, task complexity, streamline complexity, professional systems complexity, and environmental complexity. Based on the CAS theory, adaptive strategies can be divided into two categories: proactive and reactive behavior, which should be combined to deal with the aforementioned complexities. This research promotes a better understanding of the complexity of emergency projects and extracts the adaptive behaviors to handle project complexity from the perspective of contractors, to help speed up the construction schedule. Keywords: Emergency project · Complex adaptive systems theory · Project complexity · Adaptive behavior

1 Introduction Recently, COVID-19 is the largest pandemic that brings a terrible crisis and challenging to the whole world. Countries have adopted a large number of emergency measures in response to the pandemic, among which the most important and effective one is the construction of Emergency Projects (EPs) [1]. EPs refer to engineering response © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 938–950, 2023. https://doi.org/10.1007/978-981-99-3626-7_72

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measures that must be implemented in time when a public health emergency or hidden danger occurs that may cause harm to people’s health and life safety [2]. According to a white paper published by the government in June 2020 titled “China’s Actions to Combat COVID-19”1 , EPs have played crucial and irreplaceable roles in response to the pandemic in China. The majority of EPs are field-specialized hospital construction and reconstruction projects [2], which have a short construction cycle, numerous construction participants, high-quality requirements, and a complex construction environment [3–5]. These characteristics lead to great complexity for project managers to cope with, thus great challenging the success of EPs. They also distinguish EPs from other projects, which makes general project management techniques inappropriate for EPs. In this regard, the construction and management process of EPs deserves special attention [6], and it is essential to summarize the complexity and corresponding strategies to guide the practitioners. At present, EP-related studies tend to examine the techniques for speedy building or behaviors promoting the success of EPs [4, 6], but the complexity of such projects is rarely explored. Project complexity has been proven to hinder project performance enhancement [7] and project success [8–12]. Thus, it is vital to increase the focus on project complexity and its control [13]. The complex adaptive systems (CAS) theory is effective to identify the adaptive strategies of firms in confronting various complexities, such as supply chain disruptions [14]. Thus, this study intends to use the CAS theory to unveil and govern the complexity of EPs by addressing two research questions: (1) What is the complexity framework of EPs over the construction period? (2) What are the main management strategies for participating organizations to deal with these complexities in EPs? The organization of the paper is as follows. Section 2 focuses on the EP complexity and management literature, summarizing the research gaps in the literature. Section 3 describes the research methodology and introduces a typical case from the lens of the CAS theory, interpreting the process of data collection and data analysis. Section 4 presents empirical findings, regarding the complexity management framework in the EP context, focusing on the management strategies that dealt with different dimensions of complexity. The last section concludes this study and suggests avenues for future research.

2 Literature Review 2.1 The Complexity of EPs As complexity is a project characteristic, and a project could be characterized by its complexity footprint [15], the project complexity can be inferred from the project characteristics. Based on the works of EPs [2, 4, 6], the characteristics of the EPs’ construction period can be summarized as follows: (1) short construction period and many uncertain affecting factors, (2) many construction participants, (3) tough tasks and high-quality 1 Fighting COVID-19: China in action”, available at: http://www.scio.gov.cn/zfbps/32832/Doc

ument/1681801/1681801.htm.

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requirements, (4) diversity streamlines within construction, (5) the establishment of complex hospital professional systems, and (6) complex construction environment. All the aforementioned qualities can draw forth special challenges to the construction of EPs, deserving broader concern, and can be the basis of complexity identification. To promote the smooth implementation of EPs, a growing number of scholars have paid attention to addressing the technical complexity through rapid construction [3, 4, 6] and organizational complexity regarding participants’ behavior and relationships over the construction period [2, 5]. These studies have laid the foundation for systematically studying the complexity of EPs. Existing complexity research involves diverse project contexts, suggesting various ways to categorize the complexity of a project. Baccarini [16] divided project complexity into two dimensions (i.e. organizational and technological) based on the sources of complexity. Bosch-Rekveldt et al. [17] proposed a TOE (technological, organizational, and environmental) framework to identify the complexity of large engineering projects. Compared with Bosch-Rekveldt et al. [17], He et al. [12] proposed three different dimensions for mega construction projects: goal, cultural, and information. Given the strong situational nature of complexity recognition, which is greatly influenced by project characteristics, this study aims to extract the complexity attributes based on the characteristics of EPs and contributes to the project complexity management realm. 2.2 The Management of EPs Complexity Researchers on EPs mainly focus on construction skills to achieve rapid construction, which needs systematic refinement to establish matching relationships between project complexities and management strategies. Luo et al. [6] found that to achieve the successful development of Huoshenshan and Leishenshan hospitals, a product, organization, and process (POP) modeling approach should be combined with building information modeling (BIM). A step further, Chen et al. [3] proposed more high-tech technologies such as 5G and unmanned aerial vehicles to realize the fast construction of the fabricated steel structure systems in EPs. More than construction skills, Wang et al. [2] explored the social networks in EPs and insisted the establishment of the Construction Community of Anti-epidemic Emergency Projects is an important guarantee to complete EPs. These studies have provided help for further identifying corresponding management strategies of EPs’ complexity. Project management scholars have explored various approaches to handling complex systems. Hertogh and Westerveld [18] summarized four types of large infrastructure projects’ complexity management approaches: internal and content-focus approach, systems management strategies, interactive management strategies, and dynamic management strategies. Rather than describing the details of project management tools, Li et al. [19] highlighted the adaptation process of project organizations to achieve desired performance and success in the face of different environments and dynamic changes in internal and external environments. As aforementioned, EPs have unique characteristics that set them apart from general projects, and different types of projects require different management approaches [17], it is thus vital to take the feature of EPs into account for identifying the corresponding strategies for EPs’ complexity management.

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3 Methodology 3.1 Case Study of the NECC (Shanghai) Reconstruction Project Among the many categories of EPs, the conversion of large-scale public venues into makeshift hospitals is the most effective way to deal with the spread of the pandemic [1]. The NECC (Shanghai) located in Shanghai, China, is a convention and exhibition complex with various functions and has become one of the landmark buildings of Shanghai. To cope with the increasingly severe situation of the pandemic, the government approved the conversion of the NECC (Shanghai) into a makeshift hospital, which was delivered in batches as scheduled on April 9. The reconstruction project involved the construction and renovation of 14 pavilions covering 420,000 square meters and was able to provide more than 40,000 isolation beds, making it the largest makeshift hospital in Shanghai. The condition of the NECC (Shanghai) before and after the reconstruction is shown in Fig. 1. In such a huge and complex reconstruction process of the NECC (Shanghai), the participants overcame plenty of difficulties to complete the project on schedule, which makes the project itself important, typical, instructive, and deserves further study, conforming to the selection criteria for single case study [20]. Considering the availability of data, the study focuses on the construction period from the perspective of the contractor. Due to the particularity of EPs, the general division of project stages is no longer applicable. Therefore, based on the actual situation, the construction period of EPs is defined as: the time from the government order to start the project to the time until the operator (i.e. the medical team) enters the project.

Fig. 1. Before and after the reconstruction of the NECC (Shanghai)2 )

3.2 Theoretical Basis Systems that exhibit the characteristics of complexity are known as CAS [21], and these systems generally have four characteristics [22]: parallelism, conditional action, modularity, adaptation, and evolution. Refers the work of Aritua et al. [21], this study extends the analysis of four aforementioned CAS attributes for EPs as below: 2 The pictures are derived from the publicity platform of the China Construction Eighth Engi-

neering Division Corp., LTD. (https://mp.weixin.qq.com/s/i8wl_HLTTn6O8GGCP3VD5w

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(1) Parallelism. A CAS consists of large numbers of agents that interact by sending and receiving signals simultaneously, producing large numbers of simultaneous signals [22]. Within EPs, organizations as adaptive agents should interact with diverse others simultaneously for various requirements, to meet the goal of completing an EP over several days. (2) Conditional action. The actions of agents in a CAS usually depend on the signals they receive, that is, the agents have an IF/THEN structure: IF [signal vector x is present] THEN [execute act y] [22]. Within EPs, organizations as adaptive agents can adopt different management strategies when facing different challenges, based on the IF/THEN structure. (3) Modularity. In a CAS, the agents can react to the current situation by executing a sequence of rules [22]. Organizations as adaptive agents within EPs can combine different strategies with others to deal with challenges. (4) Adaptation and evolution. The agents in a CAS change over time, these changes are usually adaptations that improve performance, rather than random variations [22]. Organizations as adaptive agents within EPs continuously adjust their behaviors accordingly for substantial reasons, such as the development of the COVID-19 pandemic and the frequently changing construction needs of the owner and the medical teams. In summary, EPs can be regarded as CAS because they have similar characteristics to general complex adaptive systems. Based on the core content of CAS theory and the actual construction experience of the case, the study defines several concepts for the subsequent empirical analysis as below: (1) Adaptive agent. Holland [23] pointed out that it is the interacting adaptive agents that constitute CAS. The adaptability of agents is reflected in the communication of information and resources with other agents and the environment, and the adjustment and change of behavior patterns to achieve their own goals, to adapt to the changing environment. From the perspective of CAS theory, all construction participants of EPs can be regarded as adaptive agents, including contractors, owners, suppliers, governments, the public, etc. [2]. All agents (i.e. organizations) are interrelated to form a CAS (i.e. an EP). Take the interaction between suppliers, contractors, and designers as an example. During the pandemic, traffic control and other measures lead to slow transportation of materials, which leads to resource shortages. When the contractor cannot purchase the materials required by the designer on time, the designer will often modify the original design scheme according to the materials actually purchased by the construction side, prompting the construction plan to move forward. In the process of constantly interacting and adjusting their own behaviors, participating organizations are performing their functions as adaptive agents. (2) Complexity. For CAS, complexity is the embodiment of the vitality of a CAS and is an intrinsic attribute of the system that cannot be directly observed [23]. For the construction of EPs, the inherent complexities of the system are closely related to the characteristics of the project. For instance, the short construction period can lead to time-related complexity; the severe shortage of various resources might lead to resource-related complexity; and a large number of participants may cause organization-related complexity, etc.

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(3) Complex issue. Complex issues originate from complexities and are an external manifestation of them [23]. Not all complex issues need to be solved or corrected, what managers try to solve is a part of complex issues, which is a local manifestation of complexities. In the process of EPs construction, complex issues are manifested as various challenges the participants faced, and their attributes can correspond with complexities. Take organization-related complexity as an example, the organizational complex issues can be a large number of participants and the complicated structure of the whole organization, which lead to great difficulties for project management. (4) Adaptive behavior. Increasing or adjusting the adaptability of agents is an effective way to solve complex issues, the adaptive behaviors of agents are manifested in two types: proaction and reaction [14, 23]. The former emphasizes the precognitive ability of agents and reflects agents’ tendency to prepare in advance when facing emergencies, while the latter highlights agents’ ability to adapt to different situations. Different adaptation behaviors are combined to jointly cope with complex issues and complexities. In the process of EPs construction, the management measures of the participants to deal with various issues are defined as adaptive behaviors. This study mainly summarizes adaptive behaviors from the perspective of the contractor. 3.3 Data Collection This study relies on several data sources: (1) theoretical data, which mainly involves academic papers; (2) archival data, such as project documents, construction standards, and reports from official media; and (3) semi-structured interviews with top managers and chief engineers in the NECC (Shanghai) reconstruction project. This mixed method supported data validation and triangulation [4]. The specific data collection process is described as follows. Step 1, we searched the WOS database using the keywords “emergency project complexity”, resulting in 268 academic papers. After deleting unrelated papers, we finally used 32 academic papers. These materials provided a basis for distilling project complexity. Step 2, we used China’s largest search engine, Baidu, to collect 62 news reports about the NECC (Shanghai) reconstruction project. Step 3, we obtained 43 internal documents from the NECC (Shanghai) reconstruction project; these documents provided essential knowledge and understanding of the project. And step 4, we held several formal semi-structured interviews with experts to verify the scientificity and validity of the complexity framework refined from academic papers. Four engineers participating in NECCE were invited for deep interviews. All the interviews were transcribed into manuscripts for further analysis. 3.4 Data Analysis 3.4.1 Stage 1: The Identification of Complex Attributes Step 1: A literature review. The prominent complex attributes of the EPs construction period were identified through a literature review, implementing the conventional content analysis method. This method is usually appropriate when existing theory or research literature on a phenomenon is limited [24]. The similarity in points and ideas (i.e. the

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complex properties) were then grouped. Through the conventional content analysis, six attributes reflecting the complexities of the EPs’ construction period were identified and are shown in Table 1. Step 2: Experts verification. Eligible experienced experts were selected to have interviews, and each of the interviews cost more than 2 h. Although experts agreed with the above six complexity dimensions, they put forward some opinions on complexity issues. The improved complexity issues are shown in Table 1. 3.4.2 Stage 2: The Identification of Main Adaptive Behaviors The content analysis technique was used again to locate the main adaptive behaviors based on interview and theoretical content [14, 23]. Both conventional and directed content analysis suggested by Hsieh and Shannon [24] were implemented, the coding categories of adaptive behaviors were mainly from previous studies, while main behaviors were codded directly from the text data. The analysis results are shown in Table 1.

4 Empirical Findings and Discussions 4.1 The Complexity Framework for EPs Construction Period Based on the analysis of academic papers and interviews, the complexity of EPs construction period can be listed below: (1) Goal complexity. Goal complexity is derived from the urgent project duration caused by the pandemic and other uncertain factors, as well as the dynamic, unity, and diversity of construction objectives, which is the core dimension of emergency engineering complexity. Due to the uncontrollable development of the epidemic, the construction plan needs to be changed at any time, which brings great uncertainty and difficulties to the project construction. For instance, additional beds for patients are needed to accommodate patients when the outbreak progresses more severely than expected. A change in the number of beds will trigger many changes, such as increasing the number of supplies purchased, adjusting the layout of beds, increasing the amount of work per unit, and so on. Other complexity dimensions are more prominent under the influence of goal complexity. (2) Organizational complexity. Organizational complexity is an important dimension driving the complexity of EPs, which mainly includes organizational structure complexity and organizational members’ complexity. The former reflects the number and level of participating organizations of the contractor, while the latter highlights coordination difficulties caused by the organizational heterogeneity of each participating party. Take the reconstruction of NECC (Shanghai) as an example, more than a dozen construction units participated in the construction of the project, and nearly 10,000 construction workers were engaged in the construction work at the peak time. Construction units need unified coordination and management, so a threelevel organization management structure of “headquarters - each company - each work area” has been formed. And the project managers also formed a special team to manage the large number of construction workers. Such a large organizational scale increases the difficulty of effective project management.

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(3) Task complexity. Task complexity is closely related to the scale and scope of the project, and it is also affected by the dynamic nature of the task and the degree of closeness between tasks. As far as the reconstruction project of NECC (Shanghai) is concerned, the scale of the project is the largest in Shanghai. The characteristics of EPs make the tasks highly dynamic and closely connected, which makes the construction with significant task complexity. Mechanical and electrical installation works, such as the installation of plug boards for isolated beds and other construction tasks, need to be carried out after the completion of the bed layout. If the number of beds increases due to the increasing severity of the epidemic, the bed layout needs to be rearranged, and the already laid wires may need to be re-laid. There is a very strong correlation between the construction tasks, and a change is likely to cause the overall adjustment. This makes it a big challenge to complete the project on time. (4) Streamline complexity. The complexity of streamlines mainly includes three parts: complicated material flow, traffic flow, and information flow, which is a prominent dimension in the complexity of the EP construction period. Specifically, the complexity of material supply reflects the high level of resource demand and difficult deployment in the context of pandemic control; the complexity of vehicle flow stems from the large number and types of vehicles passing through the project site; the complexity of information flow reflects the complicated information caused by the large number and levels of construction participants. Take container procurement as an example. Under the circumstance that many local EPs are being built at the same time, the resources of local container suppliers in Shanghai were almost exhausted. In order to ensure the supply of resources, the project procurement team chose to purchase materials from other places, such as Zhejiang and Jiangsu provinces. Due to the need for permits to travel between the two places during the epidemic control period, it took two to three times longer than normal for supplies to be transported from other places to Shanghai. On account of the importance of materials to project construction, the management of streamline complexity has become a necessary part of EPs’ construction. (5) Professional systems complexity. The complexity of professional systems mainly comes from the characteristics of hospitals. The construction of EPs includes three zones (i.e. a clean zone, a semi-contaminated zone, and a contaminated zone) and two passages (i.e. a passage for medical personnel, and a passage for the patients). Tasks of the separation of the ventilation system and sewage treatment system in three zones, and the installation of professional medical rescue systems are complicated as well. Take the reconstruction of NECC (Shanghai) as an example. Different medical teams will be stationed after the completion of the project construction. Each medical team has different requirements for the layout of medical equipment in the clean zone, which needs to be personalized and adjusted in the construction process. (6) Environmental complexity. The environmental complexity reflects the environmental impact of the project, and also reflects the complexity caused by the dynamic and random development of the pandemic in the project construction location, which is an integral part of the complexity framework. Take the reconstruction of NECC (Shanghai)’s pavilions 5 and 6 as an example. At the beginning of the project, the plan was to provide 6,000 beds for patients. As the epidemic continued to spread and the number of infected people soared, the number of beds was constantly adjusted

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during the construction of the project, and eventually more than 15,000 beds were provided. In this regard, we can tell that environmental factors bring great uncertainty to EPs’ construction. 4.2 The Critical Adaptive Behaviors for EPs Construction Period Complexity The result of content analysis is as follows, which manifests the main measures used by participants. (1) Proactive behaviors (coded as PB). Proactive behavior emphasizes the precognitive ability of participants, reflects participants’ tendency to prepare in advance when facing emergencies, and mainly contains the following categories: pre identification and decomposition of tasks, establishing strategic resource bases, establishing a hierarchical and redundant organizational structure, information sharing platform integration, and construction site and period separation. Take the pre identification and decomposition of tasks as an example. Before the reconstruction of the NECC (Shanghai), the project manager made a list of tasks for each part of the reconstruction project based on the past construction experience, and then assigned each task to each participating unit to ensure that each unit completed the construction task in an orderly manner as planned. This measure enables construction work to be carried out fast and with high quality. (2) Reactive behaviors (coded as RB). Reactive behavior highlights participants’ ability to adapt to different situations, main parts are as follows: design control and optimization, adjust work schedule flexibly, task parallelism, establishing resource allocation standards, and coordination of requirements among stakeholders. Take the design control and optimization as an example. In the construction process of general projects, the project design must be determined before the construction work begins, and this process is cumbersome and time-consuming. However, in the construction process of EPs, the time is not enough to go through the process according to the conventional standards, so the designer will often reverse adjust the project design according to the construction situation, in order to achieve the goal of timely delivery of the project. As shown in Table 1, the behaviors aforementioned must be combined to deal with various dimensions of complexity.

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Table 1. Data analysis results Complexities

References

Complex issues

Adaptive strategies

Adaptive behaviors

Goal

[1–6]

Urgent schedule

PB

• Establish a redundant organizational structure • Construction site and period separation

RB

• Design control and optimization • Task parallelism

Uncertainty of goals

RB

• Adjust work schedule flexibly

Plenty of participants

PB

• Establish a hierarchical organizational structure • Pre identification and decomposition of tasks

RB

• Establish site resource allocation standards

Complex structure

RB

• Coordination of requirements among stakeholders

Massive tasks

PB

• Pre identification and decomposition of tasks • Construction site and period separation

RB

• Task parallelism

Intertwined tasks

RB

• Adjust work schedule flexibly

Abundant vehicles

PB

• Pre identification and decomposition of tasks

Plenty of information

PB

• Information sharing platform integration

Complicated material supply

PB

• Establish strategic resource bases

RB

• Adjust work schedule flexibly • Establish site resource allocation standards

RB

• Design control and optimization • Coordination of requirements among stakeholders • Adjust work schedule flexibly

Organizational

Task

Streamline

Professional systems

[2–4, 6]

[1–6]

[3, 5, 6]

[4, 6]

Healthcare systems Anti-infection systems

Environmental

[2, 4–6]

Changing pandemic

RB

Impact on environment

Note: PB = Proactive behavior, RB = Reactive behavior

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5 Conclusion The construction of EPs is widely regarded as an essential way to control the pandemic. Due to their unique characteristics, EPs can lead to complicated difficulties for project managers to deal with. However, the complexity and corresponding strategies to implement EPs are still unclear. Therefore, this study implemented the lens of the CAS theory to explore what are the components of EPs’ complexity and how to cope with them. Specifically, based on two-stage content analysis, this research investigated the complexity and corresponding strategies of EPs. The major conclusions are: (1) a comprehensive complexity framework for EPs was developed, which consists of 6 complexity dimensions (i.e. goal complexity, organizational complexity, task complexity, streamline complexity, professional systems complexity, and environmental complexity), and 13 complex issues; (2) two types of adaptive behaviors used by the contractor in the process of complexity management are summarized as follows: proactive behavior and reactive behavior. This research contributes to the existing literature and knowledge base about EPs. Firstly, this study extracts the complexity framework applicable to the construction period of EPs, opened the black box of “EPs’ complexity”, and extended the project complexity research to the EP context. Some studies have explored various aspects of EPs’ complexity, such as construction technical-related complexity [3, 6], and participants’ management-related complexity [2, 5]. This study goes beyond the previous academic discussion around EPs’ complexity to explore the comprehensive components. Secondly, this study verifies the applicability of CAS theory in the context of EPs, and divided the complexity management strategies into two categories (i.e. proactive behavior and reactive behavior), which highlights the importance of considering EPs as complex systems and expanded the current body of knowledge in EPs’ management. Various studies have explored project complexity and how to cope with it. For instance, Hertogh and Westerveld [18] concluded four complexity management strategies (i.e. systems management, internal & content focused approach, dynamic management, and interactive management) for large infrastructure projects. More specifically than existing studies, this study proposed corresponding adaptive behaviors to cope with EPs’ complexity. This study provides insights for government emergency agencies and the project’s main stakeholders. The findings might support project managers in their decision-making about EPs’ implementation, and then gives a hand to the rapid construction of EPs, which is a step further for pandemic control. Besides, as it can be further utilized in the future when confronting other public health emergencies [1, 25], the effective experience of EPs’ construction and management should be summarized and made widely known to help practitioners deal with project management issues [25]. In addition, some works need to do for further research: (1) this study uses a single case study, which may have certain limitations, and the research conclusions need to be tested in more cases. (2) The data is collected in China, and an international context is needed to verify the universality of the research conclusions. Acknowledgments. This study was greatly supported by the National Natural Science Foundation of China (No. Grants 71901220), the Fundamental Research Funds for the Central Universities (No. 2722021BZ015), and the Graduate Research and Innovation Platform project Funds of

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Zhongnan University of Economics and Law (No. 202310522). The authors would like to thank the anonymous reviewers for their helpful advice.

References 1. Chen, S., Zhang, Z., Yang, J., Wang, J., Zhai, X., Bärnighausen, T., et al.: Fangcang shelter hospitals: a novel concept for responding to public health emergencies. Lancet 395(10232), 1305–1314 (2020) 2. Wang, X., He, N., Li, X.: Social network analysis of the Construction Community in the anti-epidemic emergency project: a case study of Wuhan Huoshenshan Hospital, China. Engineering Construction and Architectural Management (2022). ahead-of-print, https://doi.org/ 10.1108/ECAM-1108-2021-0724 3. Chen, L., Yuan, R., Ji, X., Lu, X., Xiao, J., Tao, J., et al.: Modular composite building in urgent emergency engineering projects: a case study of accelerated design and construction of Wuhan Thunder God Mountain/Leishenshan hospital to COVID-19 pandemic. Autom. Constr. 124, 103555 (2021) 4. Tan, T., Mills, G., Hu, J., Papadonikolaki, E.: Integrated approaches to design for manufacture and assembly: a case study of Huoshenshan hospital to combat COVID-19 in Wuhan, China. J. Manag. Eng. 37(6), 05021007 (2021) 5. Wang, W., Fu, Y., Gao, J., Shang, K., Gao, S., Xing, J., et al.: How the COVID-19 outbreak affected organizational citizenship behavior in emergency construction megaprojects: case study from two emergency hospital projects in Wuhan, China. J. Manag. Eng. 37(3), 04021008 (2021) 6. Luo, H., Liu, J., Li, C., Chen, K., Zhang, M.: Ultra-rapid delivery of specialty field hospitals to combat COVID-19: lessons learned from the Leishenshan hospital project in Wuhan. Autom. Constr. 119, 103345 (2020) 7. Chapman, R.J.: A framework for examining the dimensions and characteristics of complexity inherent within rail megaprojects. Int. J. Proj. Manag. 34(6), 937–956 (2016) 8. Qiu, Y., Chen, H., Sheng, Z., Cheng, S.: Governance of institutional complexity in megaproject organizations. Int. J. Proj. Manag. 37(3), 425–443 (2019) 9. Li, Y., Han, Y., Luo, M., Zhang, Y.: Impact of megaproject governance on project performance: dynamic governance of the Nanning transportation hub in China. J. Manag. Eng. 35(3), 05019002 (2019) 10. Williams, T.M.: The need for new paradigms for complex projects. Int. J. Proj. Manag. 17(5), 269–273 (1999) 11. Remington, K., Pollack, J.: Tools for Complex Projects: Routledge, Abingdon (2016) 12. He, Q., Luo, L., Hu, Y., Chan, A.P.C.: Measuring the complexity of mega construction projects in China—a fuzzy analytic network process analysis. Int. J. Proj. Manag. 33(3), 549–563 (2015) 13. Luo, L., He, Q., Jaselskis, E.J., Xie, J.: Construction project complexity: research trends and implications. J. Constr. Eng. Manag. 143(7), 04017019 (2017) 14. Zhao, K., Zuo, Z., Blackhurst, J.V.: Modelling supply chain adaptation for disruptions: an empirically grounded complex adaptive systems approach. J. Oper. Manag. 65(2), 190–212 (2019) 15. Bosch-Rekveldt, M.G.C.: Managing project complexity: a study into adapting early project phases to improve project performance in large engineering projects. Delft Centre for Project Management, Delft, Netherlands (2011) 16. Baccarini, D.: The concept of project complexity—a review. Int. J. Proj. Manag. 14(4), 201– 204 (1996)

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17. Bosch-Rekveldt, M., Jongkind, Y., Mooi, H., Bakker, H., Verbraeck, A.: Grasping project complexity in large engineering projects: the TOE (technical, organizational and environmental) framework. Int. J. Proj. Manag. 29(6), 728–739 (2011) 18. Hertogh, M., Westerveld, E.: Playing with complexity. Management and organisation of large infrastructure projects. Erasmus Universiteit, Erasmus Universiteit, Rotterdam (2010) 19. Li, Y., Lu, Y., Ma, L., Kwak, Y.H.: Evolutionary governance for mega-event projects (MEPs): a case study of the world Expo 2010 in China. Proj. Manag. J. 49(1), 57–78 (2018) 20. Yin, R.K.: Validity and generalization in future case study evaluations. Evaluation 19(3), 321–332 (2013) 21. Aritua, B., Smith, N.J., Bower, D.: Construction client multi-projects—a complex adaptive systems perspective. Int. J. Proj. Manag. 27(1), 72–79 (2009) 22. Holland, J.H.: Studying complex adaptive systems. J. Syst. Sci. Complex. 19(1), 1–8 (2006) 23. Holland, J.H.: Hidden Order: How Adaptation Builds Complexity. Addison Wesley Longman Publishing Co., Inc., The United States (1996) 24. Hsieh, H.F., Shannon, S.E.: Three approaches to qualitative content analysis. Qual. Health Res. 15(9), 1277–1288 (2005) 25. Assaad, R., El-adaway, I.H.: Guidelines for responding to COVID-19 pandemic: best practices, impacts, and future research directions. J. Manag. Eng. 37(3), 06021001 (2021)

The Application Status and Outlook of CGE Model in the Construction Sector Under the Dual-Carbon Target Weina Zhu(B) , Jiannan Jiang, Boyang Liu, and Chengshuang Sun School of Economics and Management Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China [email protected]

Abstract. With the global warming and national dual carbon target in China, various carbon emission reduction models and methods at the macro level have been explored. CGE model is one of the main research models to solve environmental protection problem and also is a comprehensive research method. Two of the important applications of CGE model are in the environmental and carbon trading fields, respectively. The relevant literature on the CGE applications in the environment and carbon trading fields in Web of Science was first retrieved and then was analyzed by using literature visualization tool CiteSpace, and finally the current situation of application was reviewed. Correspondingly, in the field of construction, the CGE model has been adopted in environmental impact assessment at the macro level. But it is relatively less used. The main difficulty lies in the huge data demand, the limited abstract and expressions of the real economic situation, and linking the bottom-up micro-analytical model characterizing the construction sector with the top-down CGE model. Based on the research status of the application of CGE model, it can be indicated that the CGE model that represents the industry-related impacts and ripple effects will provide feasible research methods and models for future carbon trading research in the construction sector. Keywords: CGE model · Dual-carbon · Construction sector · Application status and outlook

1 Introduction Global warming is one of the most important challenges all around the world today, which profoundly affects every aspect of human life. To this end, the Paris Agreement set the goal of limiting global average temperature rise to 2 °C by the end of the century, with further efforts of limiting it to 1.5 °C. As a responsible developing country, China is playing an important role in global climate governance. In 2020, China committed to reach carbon peak in 2030 and carbon neutrality in 2060, specifically that China will adopt more vigorous policies and measures. Accordingly, China is developing a program of action and has already started to take concrete measures to ensure that it achieves its goals and contributes to climate change mitigation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 951–959, 2023. https://doi.org/10.1007/978-981-99-3626-7_73

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To achieve the national double carbon target, it not only relies on the efforts of each individual industry or region, but also needs the coordinated emission reduction efforts among various regions and industries. Therefore, more and more researches begin to pay attention to industry correlation and ripple effect. Accordingly, the main research methods and models include input-output method and computable general equilibrium (CGE) model. Therefore, it is necessary to explore the CGE in the context of global warming and “dual-carbon” in China. The CGE model is based on the general equilibrium theory of the famous economist Walras, and describes the relationships of the mutual interactions and influences between the macro economy and independent decision-making economic individuals. In general, CGE model includes three types of entities (residents, enterprises and governments); and sometimes it can also exist in the form of two subjects (residents and governments) and two markets (factor markets and product markets). Figure 1 shows the intrinsic correlations of economic system structure in the CGE model [1, 2]. Compared with the traditional input-output model or linear programming model, the CGE model has the following advantages: (1) there are multiple related entities and multiple markets in the model; (2) many linear functions in the traditional input-output model are replaced by nonlinear functions; (3) there is a market mechanism through price in the model, which is a tool for policy analysis.

Fig. 1. Economic system structure of CGE model [3]

2 Applications of the CGE Model The literature analysis of the CGE model applications in this study mainly draws and displays the selected literature data by means of charts and scientific knowledge maps. By using the CiteSpace tool, the research situation and research trends in this field are analyzed [4].

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2.1 Construction Business License One of the areas of CGE model applications is in environmental protection [5], namely, the environmental CGE models. In the field of environment, the CGE model aims to solving the problems, such as energy consumptions and carbon emissions; and the research scope focuses on countries, regions and industries. At the industry level, the proportions of CGE studies on greenhouse gas emissions were 7.10% in agriculture, 5.20% in forestry/forestry, 3.90% in industry, 3.30% in transportation, and 1.30% in construction [6]. Keywords are highly condensed to the article and reflect the main research content of the literature, so high-frequency keywords are often used to identify research hotspots. In this paper, from 2012 to 2022 (the data in 2022 only includes January-September), the items, “CGE” AND “environment”, were selected to retrieve in Web of Science, and 366 paper were obtained and were imported into CiteSpace for analysis, and finally the Keyword is taken as a node to obtain a keyword map as shown in Fig. 2. At the same time, the keywords are selected with a frequency of not less than 13 as shown in Table 1.

Fig. 2. Co-occurrence keyword network of “CGE AND environment”

It can be seen from the Fig. 2 and Table 1 that by employing the CGE model, these studies on Policy, Impact, CO2 emissions, Economic impact and Environmental impact, Energy, Consumption, Climate change, China, Industry, Technology and so on are current research hotspot. One of the challenges of CGE model applications in this fields is that linking the bottom-up micro-analysis model with the top-down CGE model. And it is an important development area that can analyze specific fields or industry problems based on a solid micro-data foundation in the future. In the transportation industry and industrial carbon emissions, the construction of CGE and other models is mainly realized through link technology. Yang et al. [7] link the bottom-up MAPLE model with the top-down CGE

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W. Zhu et al. Table 1. High frequency keywords of “CGE AND environment”

No

Keyword

Frequency

Centrality

1

CGE

181

0.11

2

Policy

138

0.11

3

Impact

93

0.11

4

CO2 emission

92

0.09

5

Economic impact

77

0.11

6

Environmental impact

54

0.11

7

Energy

53

0.09

8

Consumption

46

0.23

9

Climate change

38

0.13

10

China

37

0.11

11

Industry

13

0.04

12

Technology

13

0.06

model to assess the combined energy, economic and environmental impacts of China’s deep decarbonization pathway (DDP). Studies indicated that carbon emissions can peak by 2030; DDP will bring environmental and economic benefits. Jung et al. [8] constructed a dynamic CGE model to analyze the impact of demographic changes on CO2 emissions in East Asian countries. Studies have shown that there is a positive correlation between GDP and CO2 emissions. Li et al. [9] have established separate pollution treatment departments for solid waste, wastewater and waste gas management, evaluated China’s environmental tax policies by CGE models for classified pollution treatment industries, described pollution treatment processes and finally determined how policies affect production activities. Mittal et al. [10] combined behavioral parameters and transport techniques to develop bottom-up passenger transport models with discrete selection attributes. The model is linked to the CGE model to assess the impact of various factors (e.g., travel time, energy efficiency and environmental awareness) on transport demands, energy consumptions and carbon emissions. Duscha et al. [11] used dynamic CGE models to explore the role of carbon emission targets of the steel industry in the international agreements, in which the steel industry is broken down into two sub-sectors. Studies have shown that sectoral emission reduction targets can effectively address the output impact of climate policy differences. 2.2 Applications in the Field of Carbon Trading The impact of industrial correlation represented by the CGE model has become a research hotspot in the field of carbon trading system. The CGE model is based on the inputoutput model and developed into a model of global equilibrium between supply and demand through the introduction of price adjustment mechanism; It can describe the

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industrial linkage relationship between complex departments and its change law, analyze the influence mechanism and identify the transmission path. In this paper, from 2012 to 2022 (the data in 2022 only includes January-September), the items, “CGE” AND “carbon trading”, were selected to retrieve in Web of Science; and 219 papers were obtained and then were imported into CiteSpace for analysis; and finally, the Keyword is taken as a node to obtain a keyword map as shown in Fig. 3. At the same time, the keywords are selected with a frequency of not less than 11 as shown in Table 2.

Fig. 3. Co-occurrence keyword network of “CGE AND carbon trading”

It can be seen from the Fig. 3 and Table 2 that by employing the CGE model, these studies on CO2 emissions, Emission trading scheme, Climate change, Carbon tax, Impact, Energy, Market, China, Industry, system and so on are current research hotspot. The application of CGE in the environmental field has many similar studies with the application in the field of carbon trading. However, there are some differences between them. First, the total amount of literature on environmental applications is larger than that on the carbon trading, indicating that the former is more mature applications. Second, the applications in the environment are more focused on the “policy”, “impacts”; while the applications in the carbon trading are more focused on the “carbon”, “market”. In the carbon trading field, the CGE model has the relevant research on these aspects: carbon trading industry coverage, the total amount of initial carbon allowances and carbon allowance allocation, as well as carbon trading mechanism systems, of which carbon quota allocation is the most studied. Mu et al. [12] based on the CGE model, a carbon trading module is constructed, and different high-fuel-consuming industries are included in the emissions trading system (ETS) to assess the socio-economic and environmental impact of ETS. Jia and Li [13] established a number of scenarios with different carbon free allowance ratios, and adopted CGE models to explore the relationships between free

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W. Zhu et al. Table 2. High frequency keywords of “CGE AND carbon trading”

No

Keyword

Frequency

Centrality

1

CGE

181

0.11

2

CO2 emission

138

0.11

3

Emission trading scheme

93

0.11

4

Climate change

92

0.09

5

Carbon tax

77

0.11

6

Impact

54

0.11

7

Energy

53

0.09

8

Market

46

0.23

9

China

38

0.13

10

Industry

37

0.11

11

system

13

0.04

allowance ratios and carbon trading prices, as well as the impacts of carbon trading systems on China’s economy and environment. Zhang et al. [14] established a CGE model to analyze the impacts of different ETS quota allocation schemes on China’s power industry and determine the best choice for quota allocation schemes in the power industry. Pang and Timilsina [15] analyzed the economic impacts of the emissions trading mechanism in 31 provinces in China by constructing a dynamic CGE model.

3 Application and Prospect of CGE in the Construction Field 3.1 Application in the Field of Building Environmental Impact Assessment At present, compared into the other industries, CGE models have fewer applications in the construction field, especially in the built environment. Through literature retrieval, the literature on the application of CGE model in the environment of the building field is summarized into these aspects as follows. First, it is to explore the issues of energy-saving and carbon emission reductions by the CGE model. Wang et al. [16] expanded the CGE model, and constructed the framework of the residential building energy-saving CGE model from the perspective of energy and environmental impact. Zhu et al. [17] adopted the CGE model to explore the impacts of energy-saving technological progress in the building materials sectors on both economy and environment of the construction industry. Second, the CGE model is adopted to study the tax impacts in the construction sector. Shi et al. [18] developed a dynamic CGE model that explores the impact of different carbon taxes on energy consumption in the construction sector. The results showed that when the carbon tax is 60 yuan/t, it can not only meet the emission reduction target but also minimize the negative impact on the macro economy.

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Third, from the perspective of building stock (or other factor on the building sector), the CGE model can be used to analyze the national socioeconomic metabolism and the environmental impacts of building stock changes (or other factor on the building sector). Cao et al. [19] integrated Material Flow Analysis and CGE models in China’s construction industry to assess large-scale, long-term socioeconomic metabolism. The findings revealed the impacts of dynamic building stock on CO2 emissions across sectors and the economy; that is, building stock with low saturation of development services and late saturation times can release investment in buildings and cumulatively save up to 25.4 Gt of building carbon emissions, which is equivalent to 2.7 times the national CO2 emissions in 2012. 3.2 Difficulties in the Field of Environmental Impact Assessment of Buildings Recently, the CGE model has formed a relatively mature research framework and analysis paradigm, which can systematically study the impact mechanism of carbon emissions at the national and regional levels. However, its applications still have some difficulties in in the field of environmental impact assessment of buildings. First, CGE model requires a large amount of data and some data are difficult to obtain, so it is difficult to use statistical methods to effectively test these parameters. In the building sector, there are relatively many related industries (e.g., steel, cement and glass etc.), so the demand for data is huge. However, the input-output table is updated every 5 years, which limits the research of this method in this field. Second, computable general equilibrium theory abstracts and expresses the real economic situation in a limited way. For example, the market is perfectly competitive, and the labor is fully employed and the capital is not idle. Therefore, the validity and rationality of CGE model are based on the reasonable abstraction and expression of the real economy. Last but not least, for more targeted industry-level studies, such as carbon emissions research in the construction sector, a more targeted and specific research target analytical framework can be constructed by linking models that characterize the characteristics of the construction industry to CGE models. Therefore, linking bottom-up micro-analytic models with top-down CGE models is an important area of development in the future to be able to analyze specific domain or industry problems based on a solid microdata foundation, and it is also one of most difficulties for research. 3.3 Outlooks in the Field of Environmental Impact Assessment of Buildings With the development of China’s double carbon target and the establishment of unified national carbon trading market, as a public building operation department with great potential for emission reduction, the participation in national carbon trading is conducive to playing the role of energy conservation and emission reduction marketization mechanism, and can also promote the realization of national emission reduction targets. The CGE model has laid a research foundation for carbon trading industry coverage, the total amount of initial carbon allowances and carbon allowance allocation, as well as carbon trading mechanism systems, especially for the power sector. Therefore, the CGE model applications in the field of carbon trading system will provide scientific and

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feasible research methods and models for the near future research of carbon trading in the building sector.

4 Conclusion This paper first reviews and analyzes the application status of CGE model in the field of environmental protection and carbon trading by adopting the Citespace software. Afterwards, the application status of CGE model in the field of construction, its main application difficulties and near future application outlooks are elaborated. It has certain significance for promoting the applications of CGE models in the construction field. Acknowledgments. The authors would like to thank the Basic scientific research business fee project of municipal colleges and universities - QN Youth Scientific Research Innovation Special Project - Young Teachers’ Scientific Research Ability Improvement Plan, 2021–2022, “Research on the Impact Mechanism of Energy-Saving Technology on Carbon Emissions of Buildings Based on CGE Model under the Constraints of Carbon Peak” (X21010) for providing financial support for this project.

References 1. Wang, C., Chen, J., Zou, J.: Computable general equilibrium model theory and its application in climate change research. Shanghai Environ. Sci. (03), 206–212 (2003) 2. Zhang, X.: Basic Principles and Programming of Computable General Equilibrium Models. Green Publishing House, Shanghai (2010) 3. Wang, K., Wang, C., Chen, J.: Simulation of technological change and its application in climate policy model. Chin. Resour. Environ. 18(003), 31–37 (2008) 4. Chen, C.: Searching for intellectual turning points: progressive knowledge domain visualization. Proc. Natl. Acad. Sci. U.S.A. 101(Suppl), 5303–5310 (2004) 5. Wu, F., Zhu, L.: Evolutionary context and application prospect of computable general equilibrium theory model: a literature review. Auditing Econ. Res. 2, 95–103 (2014) 6. Babatunde, K.A., Begum, R.A., Said, F.F.: Application of computable general equilibrium (CGE) to climate change mitigation policy: a systematic review. Renew. Sustain. Energy Rev. 78, 61–71 (2017) 7. Yang, X., Pang, J., Teng, F., et al.: The environmental co-benefit and economic impact of China’s low-carbon pathways: evidence from linking bottom-up and top-down models. Renew. Sustain. Energy Rev. 136, 110438 (2021) 8. Jung, T.Y., Kim, Y.-G., Moon, J.: The impact of demographic changes on CO2 emission profiles: cases of East Asian countries. Sustainability 13, 677 (2021) 9. Li, G., Zhang, R., Masui, T.: CGE modeling with disaggregated pollution treatment sectors for assessing China’s environmental tax policies. Sci. Total Environ. 143264 (2020) 10. Mittal, S., Dai, H., Fujimori, S., et al.: Key factors influencing the global passenger transport dynamics using the AIM/transport model. Transp. Res. Part D: Transp. Environ. 55(8), 373– 388 (2016) 11. Duscha, V., Peterson, E.B., Schleich, J., et al.: Sectoral targets to address competitiveness - a CGE analysis with focus on the global steel sector. Climate Change Econ. 10(1) (2018) 12. Mu, Y., Evans, S., Wang, C., et al.: How will sectoral coverage affect the efficiency of an emissions trading system? A CGE-based case study of China. Appl. Energy 227 (2018)

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13. Jia, Z., Li, W.: The impact of emission trading scheme and the ratio of free quota: a dynamic recursive CGE model in China. Appl. Energy (2016) 14. Zhang, L., Li, Y., Jia, Z.: Impact of carbon allowance allocation on power industry in China’s carbon trading market: computable general equilibrium based analysis. Appl. Energy 229, 814–827 (2018) 15. Pang, J., Timilsina, G.: How would an emissions trading scheme affect provincial economies in China: insights from a computable general equilibrium model. Renew. Sustain. Energy Rev. 145(37), 111034 (2021) 16. Hong, W., Xiaoma, T., Lei, G.: Construction of CGE model for energy efficiency of residential buildings considering energy and environmental impact. Chin. Resour. Environ. 027, 82–91 (2017) 17. Zhu, W., Sun, C., Li, X.: Impacts of energy-saving technological progress in the building materials sectors on both economy and environment of the construction industry. In: Proceedings of 19th International Conference on Construction and Real Estate Management, Beijing, China, 16–18 October (2021) 18. Shi, Q., Ren, H., Cai, W., et al.: How to set the proper level of carbon tax in the context of Chinese construction sector? A CGE analysis. J. Clean. Prod. 117955 (2019) 19. Cao, Z., Liu, G., Zhong, S., et al.: Integrating dynamic material flow analysis and computable general equilibrium models for both mass and monetary balances in prospective modeling: a case for the Chinese building sector. Environ. Sci. Technol. 53, 224–233 (2019)

ISM-MICMAC Model-Based Construction Risk Evaluation for Green Retrofit Project of Public Buildings Shengnan Li(B) , Xiaosen Huo, and Liudan Jiao School of Economics and Management, Chongqing Jiaotong University, Chongqing, China [email protected]

Abstract. As the risks in construction phase of public building green renovation projects are complex, a reasonable evaluation of the risk hierarchy, identification of key risk relationships, and analysis of risk transmission paths make an important foundation for making efficient risk control measures. In this study, 23 risk factors in the construction phase of public building green renovation projects were firstly sorted out, a four-level hierarchical structure of the risk system was derived by applying the ISM modeling method, combing with the MICMAC method to calculate the driving forces and dependencies of risk factors. And to divide the risk factors into three clusters: independent cluster, autonomous cluster, and dependency cluster. The results of the study can provide reference and decision-making ideas for risk management of green retrofit projects in public buildings. The model results show that risk factors including the policy, supervisory responsibility, technical management experience, personnel safety awareness, and equipment operation risks, as deep-level influencing factors, have a high driving force and strong influence, and should be actively controlled as risk sources. Contract and related management risks, which are at the surface level, are not only susceptible to the influence of other levels in the system but also have complex interactions within the levels. Keywords: green retrofit · public buildings · risk assessment · ISM-MICMAC model

1 Introduction In China the urban construction has shifted from rapid development and construction to a stage of development where stock upgrading and quality transformation and incremental structural adjustment are both important, and renewal projects, mainly the transformation and upgrading of existing buildings, have become one of the important ways of urban development. By the end of 2019, the total number of existing buildings in China reached 60.1 billion square meters, of which the total area of existing public buildings was about 12.8 billion square meters. Existing public buildings have the characteristics of large total volume, wide distribution, and diverse types, therefore, it is important to scientifically develop a comprehensive performance improvement and renovation route for existing © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 960–974, 2023. https://doi.org/10.1007/978-981-99-3626-7_74

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public buildings to promote the high-quality development of urban construction [1]. However, due to the relatively short development history of existing building renovation in China, there are many potential risks in the construction process, and the relationship between various risk factors is intricately linked, which can easily form a series of risk chain reactions. Under the requirement of high-quality development of the construction industry, it is important to explore the risk-driven mechanism of green renovation and to evaluate the key risks and their coupling relationships scientifically and accurately in the construction process of green renovation projects of public buildings, to enhance the construction risk management of public buildings renovation. Domestic and international scholars have talked about green building retrofit risks. Liu et al. constructed a two-tier network model based on stakeholder and risk relationships, which provides a reference for risk decision-making [2]; Wang et al. used failure mode and impact analysis of fuzzy inference systems to identify a list of high-risk FMEAs for green retrofitting [3]; Li et al. constructed a social network model based on a multi-subject dynamics perspective and analyzed the key risk relationships affecting the development of the energy efficiency retrofit market in existing buildings [4]; Chen and Li et al. used a fuzzy explanatory structural model to analyze the role of relationships between project risk factors based on an ESCO perspective [5]. Current research on retrofitting risks in existing buildings is mostly focused on independent analysis of risk factors, but there is a lack of research that takes a systematic view to delineate the risk hierarchy and identify key risks and risk pathways. This study combines Interpretative Structural Modeling (ISM) and Matrix Impacts Cross-Reference Multiplication (MICMAC) based on establishing a system of risk indicators for the construction phase of green retrofit projects in public buildings. Applied to a Classification MICMAC to develop a risk factor analysis model for the construction phase of the green renovation of public buildings. The model first quantitatively investigates the hierarchical correlation of the system factors, and then analyses the dependencies and driving forces of the influencing factors, to identify the key risk factors affecting the green renovation of public buildings during the construction process, and provide a theoretical basis for the formulation of effective risk management measures for green renovation project.

2 ISM-MICMAC Integrated Modeling Approach The construction phase of green renovation projects in public buildings is affected by multiple types of risk factors, and there are complex relationships between the factors, with different degrees of influence between the relevant stakeholders as the risks are transmitted. Therefore, it is necessary to effectively identify the key risk factors that affect all stakeholders during the construction phase, clarify the paths of action between the risks, and seek a modeling method that can clarify the hierarchy and internal linkages of the key factors. The ISM method can transform the logical relationships of factors with many variables and ambiguous and complex relationships into an intuitive and visualized recursive hierarchy model [6], but it only reflects the 0 and 1 influence relationship between factors, but not the degree of interaction, and further analysis of the generated structure is

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needed to derive a more accurate relationship structure. Therefore, based on the progressive hierarchy of the ISM, the MICMAC method can be used to classify risk factors into four categories with different characteristics, such as autonomous clusters, independent clusters, dependency clusters, and linkage clusters, which can more intuitively show the status and role of each element [7]. The main research process of the ISM-MICMAC model used in this study is as follows (Fig. 1).

Fig. 1. Research processes based on ISM-MICMAC model

3 Risk Indicator System Development In this study, according to the sources of renovation construction risk factors, the six sources of construction risks, including policy, safety, quality, schedule, contract, and management, were combined with standard codes such as Technical Specification for Energy-saving Renovation of Public Buildings, Intelligent Technical Regulations for Energy-saving Projects in Public Buildings, Regulations on Energy Conservation in Civil Buildings and actual public building renovation cases, and searched in CNKI with the keywords “public buildings”, “green renovation” and “construction risks”. 23 construction risk factors in public building renovation projects were identified as shown in Table 1.

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Table 1. List of construction risk factors in public building renovation projects First level indicators

Second level indicators

Stakeholders

Policy risks

Risk of inadequate policies and regulations related to green building retrofits(R1)

Government

Risk of change in policy adjustment for conversion projects (R2)

Government

Risk of inadequate safety awareness among construction personnel (R3) [8]

Contracting

Risk of improper operation of machine and equipment (R4) [9]

Contracting

Risk of safety incidents due to aging of existing buildings (R5)

Contracting

Lack of risk of engineering insurance related to green retrofit projects (R6) [10]

Owners

Security risks

Quality risks

Risk of insufficient constructability of the retrofit Design design (R7) [11] Risk of substandard quality of green materials or Suppliers equipment (R8) [12]

Schedule risk

Contract risks

Management risk

Risk of inadequate technical level of green renovation construction (R9)

Contracting

Failure to effectively implement green building standards during construction(R10)

Contracting

Risk of untimely supply of green materials or equipment (R11)

Suppliers

Risk of a poorly designed construction schedule (R12) [13]

Contracting

Risk of rework due to a misunderstanding of green building (R13) [14]

Contracting

Force majeure risks such as war, and disaster (R14)

Owners

Contract variation risk (R15)

Owners

Risk of ambiguity in the relevant modification specifications in the contract (R16)

Owners

Risk of default by parties related to the contract (R17)

Owners

Risk of inadequate construction safety management mechanisms (R18) [15]

Contracting

Lack of technical and managerial expertise in green retrofitting (R19) [16]

Contracting (continued)

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First level indicators

Second level indicators

Stakeholders

Risk of inadequate responsibility of the supervising engineer (R20) [17]

Supervisors

Risk of unclear division of authority and responsibility between parties or unsatisfactory communication and coordination (R21) [18]

Contractor, Owner

Risk of a break in the capital chain of the project Owners (R22) Unanticipated risk of increased human and materials costs (R23) [19]

Owners

4 ISM-MICMAC Model Development 4.1 Risk ISM Model (1) Determine the set of construction risk factors R R = {R1, R2, . . . , Rn}

(1)

where Ri (i = 1, 2, . . . , n) denotes the first i element of the system. The set of construction risk factors, R, is determined by Eq. (1) and is shown in Table 1. (2) Construction of adjacency matrix The adjacency matrix between the influencing factors is determined based on whether Ri influences Rj A = (aij )n×n which is defined by  aij =

1, Ri affect Rj 0, Ri not affect Rj

(2)

Based on the risk factor identification list of green renovation construction of public buildings in Table 1, a questionnaire survey was conducted among a total of 18 experts and scholars engaged in engineering project risk management and researchers in related fields in universities, and the experts scored the relationship between each risk factor of green renovation of public buildings, where “1” means that the occurrence of one factor may lead to the occurrence of another factor, and “0” means that there is no influencing relationship. The results of the 18 experts’ scores

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were aggregated and the final neighborhood matrix A was obtained as follows. ⎧ ⎫ ⎪ 01000001010000100000000⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ 10000000000000101000000⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ 00010000000000000100000⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ 00100000000000000000001⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪00000000000000000000000⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 1 ⎨ ⎬ A= 00000000000000101000000 ⎪ ⎪ ⎪ ⎪00000000000000001000001⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ 00000000000000101000101⎪ ⎪ ⎪ ⎪ ⎪00000000000000100000001⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ 0 0 0 0 0 0 1 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 1 ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 1 0 0 0 0 0 0 ⎪ ⎪ ⎪ ⎩00000000000000101000010⎪ ⎭ (3) Calculate the reachable matrix M The reachability matrix M indicates whether a factor in the system is reachable by some path to another factor, i.e. the extent to which the factors at each node are reachable by a certain length of the path between them. The adjacency matrix A is added to the unit matrix I to obtain the matrix (A + I), and a Boolean operation is performed on (A + I) until (A + I )λ+1 = (A + I )λ = (A + I )λ−1

(3)

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then it is called (A + I )λ is the reachable matrix [20]. This study uses MATLAB software to perform Boolean operations and calculate λ = 5, then the reachable matrix is M = (A + I )5 . The results are shown below. ⎧ ⎫ ⎪ 11000001011110111000111⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ 11000001011110111000111⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ 00110000000100111100111⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ 00110000000100111100111⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ⎪ ⎪ ⎪ ⎪ ⎪ ⎪00000100000000000000000⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪00000010000100111000111⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ 0 0 0 0 0 0 0 1 0 0 1 1 0 0 1 1 1 0 0 0 1 1 1 ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ 0 0 0 0 0 0 0 0 1 0 0 1 0 0 1 1 1 0 0 0 1 1 1 ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ 0 0 0 0 0 0 0 0 0 1 0 1 1 0 1 1 1 0 0 0 1 1 1 ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎨00000000001100111000111⎪ ⎬ M = 00000000000100111000111 ⎪ ⎪ ⎪ ⎪ ⎪ ⎪00000000000110111000111⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪00000000000101111000111⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ 00000000000100111000111⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ 00000000000100111000111⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 1 1 0 0 0 1 1 1 ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ 0 0 0 0 0 0 1 0 1 0 0 1 1 0 1 1 1 0 1 0 1 1 1 ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ 0 0 0 0 0 0 0 0 0 1 0 1 1 0 1 1 1 0 0 1 1 1 1 ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 1 1 0 0 0 1 1 1 ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 1 1 0 0 0 1 1 1 ⎪ ⎪ ⎪ ⎩00000000000100111000111⎪ ⎭ (4) Hierarchical treatment of reachable matrices According to the reachable matrix, the set of all factors influenced by factor Ri forms the reachable set L(Ri), the set of all factors influencing Ri forms the prior set A(Ri), and finally, the intersection C(Ri) of L(Ri) and A(Ri) is found [21] as shown in Table 2. Based on the principle that the reachable set is equal to the intersection, the set formed by the highest-level factors is determined, and the corresponding rows and columns of the factors in this set are removed from the reachable matrix to obtain the new reachable matrix. Repeat the above steps until the set of factors is obtained Cq (q = 1, 2, …, n), and the reachable matrix M . All factors in the reachable matrix are crossed out. The above steps were carried out using MATLAB software and the results of this study were C1 = {R5 , R6 , R12 , R15 , R16 , R17 , R18 , R21 , R22 , R23 }; C2 = {R3 , R4 , R7 , R9 , R11 , R13 , R14 }; C3 = {R8 , R10 , R19 }; C4 = {R1 , R2 , R20 }. The hierarchical structure model can reflect the hierarchy structure and action path among the influencing factors. Based on the results of the hierarchy and the interrelationships between the risks, a multi-layer recursive structure model of the factors of the construction process of green renovation projects in public buildings was made (Fig. 2).

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Table 2. Hierarchy of risk factors in the construction phase of green renovation projects in public buildings i

L (Ri)

R1

1, 2, 8, 10, 11, 12, 13, 15, 16, 1, 2 17, 21, 22, 23

A (Ri)

C (Ri) 1, 2

R2

1, 2, 8, 10, 11, 12, 13, 15, 16, 1, 2 17, 21, 22, 23

1, 2

R3

3, 4, 12, 15, 16, 17, 19, 21, 22, 23

3, 4

3, 4

R4

3, 4, 12, 15, 16, 17, 19, 21, 22, 23

3, 4

3, 4

R5

5

5

5

R6

6

6

6

R7

7, 12, 15, 16, 17, 21, 22, 23

7, 19

7

R8

8, 11, 12, 15, 16, 17, 21, 22, 23

1, 2, 8

8

R9

9, 12, 15, 16, 17, 21, 22, 23

9, 19

9

R10 10, 12, 13, 15, 16, 17, 21, 22, 1, 2, 10 23

10

R11 11, 12, 15, 16, 17, 21, 22, 23

1, 2, 8, 11

11

R12 12, 15, 16, 17, 21, 22, 23

1, 2, 3, 4, 7, 8, 9, 10, 11, 12, 12, 15, 16, 17, 21, 22, 23 13, 14, 15, 16, 17, 19, 20, 21, 22, 23

R13 12, 13, 15, 16, 17, 21, 22, 23

1, 2, 11, 13, 19, 20

13

R14 12, 14, 15, 16, 17, 21, 22, 23

14

14

R15 12, 15, 16, 17, 21, 22, 23

1, 2, 3, 4, 7, 8, 9, 10, 11, 12, 12, 15, 16, 17, 21, 22, 23 13, 14, 15, 16, 17, 19, 20, 21, 22, 23

R16 12, 15, 16, 17, 21, 22, 23

1, 2, 3, 4, 7, 8, 9, 10, 11, 12, 12, 15, 16, 17, 21, 22, 23 13, 14, 15, 16, 17, 19, 20, 21, 22, 23

R17 12, 15, 16, 17, 21, 22, 23

1, 2, 3, 4, 7, 8, 9, 10, 11, 12, 12, 15, 16, 17, 21, 22, 23 13, 14, 15, 16, 17, 19, 20, 21, 22, 23

R18 18

3, 4, 18

18

R19 7, 9, 12, 13, 15, 16, 17, 19, 21, 22, 23

19

19

R20 10, 12, 13, 15, 16, 17, 20, 21, 20 22, 23

20 (continued)

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i

L (Ri)

A (Ri)

C (Ri)

R21 12, 15, 16, 17, 21, 22, 23

1, 2, 3, 4, 7, 8, 9, 10, 11, 12, 12, 15, 16, 17, 21, 22, 23 13, 14, 15, 16, 17, 19, 20, 21, 22, 23

R22 12, 15, 16, 17, 21, 22, 23

1, 2, 3, 4, 7, 8, 9, 10, 11, 12, 12, 15, 16, 17, 21, 22, 23 13, 14, 15, 16, 17, 19, 20, 21, 22, 23

R23 12, 15, 16, 17, 21, 22, 23

1, 2, 3, 4, 7, 8, 9, 10, 11, 12, 12, 15, 16, 17, 21, 22, 23 13, 14, 15, 16, 17, 19, 20, 21, 22, 23

Fig. 2. Risk hierarchy

4.2 MICMAC Analysis Based on the reachable matrix, the different types of risk factors are clustered by driverdependency with the help of the cross-influence matrix multiplication method (MICMAC) [22]. The level of driving force indicates the degree of influence of the risk factor on other risk factors and is obtained by summing the elements of the reachable matrix rows. The dependency level reflects the extent to which the risk factor is influenced by other risk factors, and is obtained by summing the elements of the reachable matrix columns. The results are shown in Table 3. Table 3. Risk factor drivers and degree of dependency Risk factors

R1

R2

R3

R4

R5

R6

R7

R8

R9

R10

R11

R12

R13

R14

R15

R16

R17

R18

R19

R20

R21

R22

R23

Driving force

13

13

10

10

1

1

8

9

8

9

8

7

8

8

7

7

7

1

11

10

7

7

7

Dependency

2

2

2

2

1

1

2

3

2

4

4

20

6

1

20

20

20

3

1

1

20

20

20

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Based on the calculation results, the factor driver-dependency distribution is plotted as shown in Fig. 3, and the risk factors are finally grouped into 3 categories: autonomous clusters, independent clusters, and dependency clusters. Risk factors {R1 , R2 , R3 , R4 , R19 , R20 } are in the independent cluster, which are strongly driven but weakly dependent, having a strong influence and are usually unaffected by other factors; risk factors; {R5 , R6 , R7 , R8 , R9 , R10 , R11 , R13 , R14 , R18 } are in autonomous clusters, where they are weakly driven and dependent; risk factors {R12 , R15 , R16 , R17 , R21 , R22 , R23 } are in the dependent cluster, which is strongly dependent and relatively weakly driven.

Fig. 3. Risk factor driver-dependency distribution

5 Discussion A comprehensive analysis of risk factors using the ISM-MICMAC method reveals that risk factors located deep within the explanatory structure model are mostly located in the independent cluster quadrant, while risk factors located at the surface of the explanatory structure model are overwhelmingly located in the dependency cluster quadrant, with a more complex distribution of intermediate risk factors. Based on these research findings, the risk factors were further analyzed. (1) In the independent cluster, policy risks R1 and R2 are at the bottom of the model and are the dominant driving factors. It may include risks such as imperfect supervision systems of green transformation projects, poor guidance of policies and regulations, adjustment of relevant tax policies, and substantial cost increase caused by exchange rate changes. As they are related to government policy, it is usually not easily controlled by indirectly controlling other risk factors, and once occur, it is likely to form a “risk chain”. When dealing with such risks, managers should try to

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control their proliferation. In the early planning process of the project, the owner should pay attention to the feasibility evaluation of the project by experts, make reasonable evaluation and demonstration, and finally formulate feasible response planning; management risks R20 and R19 have a strong driving force and are in the C4 and C3 layers respectively as source risks, with the possibility of occurring at any stage of construction, and once they occur, they will have a greater impact on the transition layer risk factors. The manager should carry out real-time supervision of the performance of the supervisory engineer’s responsibilities, employ experienced green renovation professional and technical management personnel, and at the same time do a good job of preventing the impact of subsequent risks; R3 and R4 in the safety risks are strong drivers and mainly have an impact on C1 surface layer risks. Safety awareness and risks related to equipment operation have a high probability of occurring in the project, making safety supervision measures particularly important. Managers need to actively change the safety attitudes of construction personnel and correct safety misconduct to establish a good safe construction environment. (2) The risk factors in the autonomous clusters are characterized by relatively weak drivers and dependencies and are usually in the middle layer of the ISM model. Safety risks R5, R6, and management risks R18 have the weakest drivers and dependencies in the system and are at the top of the system, so they are judged to be independent of the system to a certain extent. However, in the construction process, due to the aging of the existing building lines, lack of fire fighting equipment, wall peeling, and other old problems, resulting in a lot of various safety accidents, construction units understanding the situation of the project is very critical. Before construction, professional and technical personnel shall conduct a comprehensive safety appraisal according to the project design drawings, completion data, equipment use, and main structure damage, and formulate detailed preventive measures for safety risks according to the appraisal results. Regular collection of unsafe behaviors on the construction site and classification can not only find the defects of safety management but also prevent similar unsafe behaviors that do not appear to a certain extent; quality risks R8 and R10 are at the C3 level of the model and have relatively high drivers, so their risk transmission is strong. If effective measures can be taken to improve them, the risk transmission can be effectively blocked and have a positive impact on systemic risk control. For example, strict control of green materials and green standards can effectively reduce unnecessary rework and material supply problems. The first consideration for the application of new green materials is whether they can meet the functional requirements of the reconstructed building. Before construction, experts must be organized to comprehensively demonstrate the reconstruction plan and the technology to be adopted, formulate a relatively complete and reasonable construction plan, and invite relevant technical personnel to provide on-site construction guidance. To improve the construction efficiency of the transformation, the supply of new materials should keep the quality and quantity; R7, R9, R11, R13, and R14 in transition risk layer C2 are all quality and schedule-related risks, and failure to control them usually leads directly to a series of risks such as contract-related changes, default and cost increases. Transition layer risk is easy to form a risk chain reaction. For example, problems such as insufficient constructability of the design scheme and delayed supply of materials and equipment will directly

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affect the construction schedule, which will lead to contract changes proposed by relevant stakeholders, a series of risks such as communication and coordination, and default of relevant parties, increasing the final expected cost. Managers should strengthen communication with suppliers and improve relevant contracts to ensure that the quality of materials and equipment meets the requirements of green renovation and that materials and equipment can arrive within the supply time required by the contract. An effective design scheme is to consider the design and construction links as a whole in the design stage, to improve the rationalization and effectiveness of resource and facility allocation. Secondly, multi-party participation in the design form can guarantee capacity construction and technical support in the subsequent construction, and realize the overall realization of planning objectives. The ability of green renovation professionals and technical personnel should include the construction experience of new materials and equipment or new technologies, the learning experience of renovation technology cases, and the comprehensive mastery of core technologies. To improve the technical level of green transformation construction, we should strictly examine the professional level of technical personnel. On this basis, the operation deviation caused by a different understanding of green building in the construction process can be solved to a certain extent, and a series of unnecessary rework can be reduced. (3) Dependency cluster risk factors R12, R15, R16, R17, R21, R22, and R23 are at the surface level of the model structure, showing strong dependency but a weak driving force. All risks are contractual or management risks, except for R12, which is a quality risk due to the poor design of the schedule. This cluster of risks is susceptible to other factors, which then cause the most direct damage to the project. In addition, the structural hierarchy diagram shows that there are also complex interactions between the risks within this hierarchy, leading to the conclusion that these seven risk factors are the most difficult to control in the system. Based on this study, subsequent research should focus on exploring the interactions between the above risks in depth and developing reasonable control measures and methods to promote the efficient management of systemic risks. Contract change may be required during the construction of the owners change, design change or poor construction technology can be sexually caused a variety of reasons, may find transformation rules change process is not clear, part of the event, and so on, an agreement is ambiguous in the responsibility division, the default and delay the construction period, finally, the talent machine costs, not exceeding the expectation “of the project. The occurrence of a risk chain often brings huge losses to the project, and all stakeholders will be involved, which makes the project situation further deteriorate. We should not only strengthen the prevention of the source risks but also formulate sound measures and plans to deal with other risks in the chain. For project fund management, the construction party should fully consider the floating cost of human resources and machinery price, prepare plans, and record funds to prevent the termination of the project due to the break of a capital chain. (4) Risk factors in linked clusters are strongly driven and dependent, and when changes occur they have a significant impact on other factors as well as themselves and are strongly unstable. The fact that there are no risk factors in the linked cluster in this

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study indicates that the 23 influencing factors involved are all relatively stable and the system is not completely uncontrollable due to the action of one factor. (5) Based on the above independent risk factors and their impact analysis, it is known that the risk uncertainty of public building renovation projects is relatively large. In addition to formulating prevention and control measures for each risk, it is necessary to conduct a comprehensive analysis of key risk transmission paths. Determine public building green renovation project risk conduction path is a very complex operation system, based on the overall analysis of the explanation structure model, found that a public building green renovation project risk factors with ladder conduction properties, and there are risk conduction paths, policy risk, technical management personnel to lack of experience, the technical level is less than the most significant. The key to determining the risk transmission path is to determine the risk source, risk flow, and risk transmission carrier. Risk source represents the initial origin of project risk from internal and external factors, namely project risk factors [23]. Policies and regulations, supervision responsibility is not in place, the experience of technical management personnel, safety awareness of construction personnel, and other risks are driving risk sources in the system. In addition to policy risks, other risks are highly likely to occur and should be monitored in risk prevention. Risk flow is attached to the project risk carrier, which is a process of the interaction between project risk factors and the spread of economic energy along with the implementation of the existing public building energy conservation renovation project. In the middle layer, such as the risk of delayed supply of materials and equipment, the driving force and dependence are relatively high, and it is easy to occur in the project, which directly leads to the transmission of risks along the chain, such as schedule design, contract change and coordination between parties. The interaction of surface risk factors is complex and can lead to direct loss of the project. Therefore, subsequent research should continue to explore the occurrence probability of each risk, analyze the transmission process of different risk flows, and quantify the degree of influence, to put forward more precise preventive measures. After analyzing the ISM MICMAC model, the following key risk transmission paths can be summarized: ➀ Supervision engineer’s responsibility is not in place → failure to effectively implement green building standards → rework caused by the misunderstanding of green building → contract related parties’ breach → unexpected personnel and machinery cost increase → risk accidents of green transformation of public buildings; ➁ Lack of safety awareness of construction personnel → failure of machinery and equipment or improper operation → contract change → unclear provisions of reform rules in the contract → unclear division of rights and responsibilities of all parties or unsatisfactory communication and coordination → breach of contract by relevant parties → risk accidents of green transformation of public buildings; ➂ Lack of experience in green transformation technology and management personnel → lack of constructability of transformation design scheme → unreasonable design of construction schedule → contract change → the reform rules in the contract are not clear → the division of rights and responsibilities of the parties or the communication and coordination is not ideal → the contract related parties

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breach of contract → the unexpected personnel and machinery cost increase → the risk accident of the green transformation of public buildings.

6 Conclusion This study applies the ISM model and MICMAC analysis to classify the construction risks of public building green renovation projects into a systematic structure hierarchy and conducts an in-depth analysis of the key risks and their relationships. Firstly, based on identifying the list of construction risk factors for the green renovation of public buildings, an ISM model is established with the help of MATLAB software to complete the analysis of the project recursive hierarchy and the interaction relationship; then, the driving force and dependency calculation of the risk factors are combined with MICMAC analysis to draw a driving force-dependency distribution diagram; finally, combined with the ISM-MICMAC analysis, the position of each risk factor in the system and the degree of importance. Based on the results of the analysis, the following conclusions were drawn: the risk factors of the public building green renovation project form a four-level hierarchy, and the 23 risk factors can be divided into three clusters: independent clusters, autonomous clusters, and dependency clusters based on the driver-dependency analysis. The systemic hierarchy and cluster classification can provide decision-making ideas for the risk management of green retrofitting projects in public buildings. Based on the research of project risk hierarchy and the size of the effect, can be further recognized system of a key risk transmission path, and analyzing the risk conduction function relationship between various stakeholders, the stakeholder is conducive to the interests of all parties in cognitive public building green renovation project risk, determine the key risk factors and control measures, It provides a reference for the risk management of green renovation projects of existing public buildings. Acknowledgements. This research is funded by the Humanities and Social Sciences Project of Ministry of Education of China (Grant No. 21YJCZH048), and the Humanities and Social Sciences Research Project of Chongqing Municipal Education Commission (Grant No. 20SKGH096).

References 1. Wang, R., Wang, G., Li, Q., et al.: Analysis of green energy retrofitting practices in existing public buildings. Green Build. 14(1), 49–51, 57 (2022) 2. Semu-Biruk, T.: Fuzzy Analytical Hierarchy Process (FAHP) Approach to Risk Assessment in Ethiopian Building Construction Projects (2017) 3. Wang, Y., Dou, L., Tong, L.S.: Risk evaluation of green retrofitting of existing buildings based on improved FMEA. Ecol. Econ. 34(1), 89–93 (2018) 4. Li, B., Guo, H., Wu, H.: Study on the factors influencing the development of ESCO dynamics of existing building energy retrofit market based on SNA. Sci. Technol. Progress Countermeasures 35(24), 150–154 (2018) 5. Chen, L.W., Li, X., Wang, Z.: Research on risk hierarchy and transmission path of existing public buildings energy retrofit projects based on FISM. J. Civil Eng. Manag. 38(4), 155–162 (2021)

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6. Hou, Y., Li, M., Li, Y.F.: Analysis of the factors influencing the development of assembled decoration based on the ISM model. Constr. Econ. 42(11), 78–84 (2021) 7. Li, L.H., Bai, F.Y., Mao, B.E.B., et al.: Research on the high-quality development of green buildings based on ISM-MICMAC–Shenyang city as an example. Constr. Econ. 43(3), 98–104 (2022) 8. Wang, W., Zhu, Z., Mi, H., et al.: Study on the influencing factors of fire accidents in urban underground integrated pipe corridors based on DEMATEL-ISM. J. Saf. Environ. 20(3), 793–800 (2020) 9. Liu, G., Wen, Z., He, X., et al.: Factors influencing the quality of assembled buildings based on ISM-MICMAC. J. Civ. Eng. Manag. 36(5), 33–39 (2019) 10. Li, J., Pan, Y.H.: Evaluation of the energy-saving retrofitting effect of existing public buildings based on cloud model - an example from Chongqing city. J. Eng. Manag. 35(1), 89–94 (2021) 11. Liang, X.: Public building renovation strategy based on green building technology. China Build. Metal Struct. 8, 82–83 (2021) 12. Li, Q., Tan, Y., Liu, P.: Study on the comprehensive benefits of SD-based energy efficiency renovation of existing public buildings. J. Eng. Manag. 35(5), 7–12 (2021) 13. Li, Q., Xu, H., Niu, C.: Study on the risk of energy-saving renovation projects of existing public buildings under the PPP model. Build. Energy Efficiency 48(11), 140–146 (2020) 14. Tao, K., Guo, H.D., Wang, Y.L., et al.: Risk hierarchy and association mechanism of building energy retrofit projects. J. Guangxi Univ. (Nat. Sci. Ed.) 41(4), 973–981 (2016) 15. Liu, B.P., Liu, Z.R., Long, H.R.: Risk identification and evaluation of deep foundation pit excavation based on WBS-RBS-G1 method. Highway Traffic Technol. 37(4), 119–125 (2021) 16. Gu, X., Fu, Z., Yan, Y.: A study on residual value risk factors of infrastructure construction PPP projects based on SNA. Constr. Econ. 41(S2), 126–131 (2020) 17. Qiao, W., Guo, H.: SNA-based study on the influence factors of cooperation of existing building energy retrofitting bodies. Ecol. Econ. 38(2), 84–90 (2022) 18. Li, Q., Qi, A.: Study on the risk analysis and countermeasures of the whole process of old neighborhood renovation - based on the improved entropy-gray correlation method. Prod. Res. 6, 48–52 (2021) 19. Liu, X., Wang, B., Bai, C.: Risk evaluation of contract energy management based on ANP-grey existing residential building energy retrofit project. Constr. Technol. 45(4), 56–61 (2016) 20. Li, Y.: Application of green energy-saving technologies in the renovation of old industrial buildings. Build. Energy Efficiency 49(1), 121–127 (2021). (in English) 21. Yu, G., Li, H.: Research on the safety impact mechanism of construction of old industrial buildings renovation. Chin. J. Saf. Sci. 31(7), 120–129 (2021) 22. Wu, Q., Sun, J., et al.: Research on construction safety risks in the renovation of old industrial plants. Constr. Econ. 41(4), 70–75 (2020) 23. Zhang, Y., Wang, X.Y.: A study on the causes of unsafe behavior of construction workers based on DEMATEL-ISM-BN. China Saf. Prod. Sci. Technol. 16(11), 110–116 (2020)

Multiscale Evaluation of the Cooling Effect of Greenspace in Urban Environments Jia Siqi and Wang Yuhong(B) Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR [email protected]

Abstract. Urban greenspace is an effective approach to reducing urban heat island (UHI) effects and providing comfort to the nearby occupants. In this study, a multiscale evaluation of the cooling effect of different types of greenspaces was conducted. Studies were conducted among four typical urban communities in two densely urbanized cities - Hong Kong and Singapore. Large-scale land surface temperature (LST) of pixel-level were retrieved from cloud-free satellite images. Long-term hourly meteorological data collected from weather stations was used for validating the LST retrieval results. Based on LST data at the macro-scale, and microclimatic modeling using simulation tools (ENVI-met) at the micro-scale, this study evaluated the performance of different types of greenspaces in cooling the environment of several typical urban communities. For the street-level greenspace, how the major characteristics of greenspace affect the cooling effect of greenspace was identified. Results demonstrate that the cooling effect of greenspace improves logarithmically with an increase in its size and density. Especially, the leaf area index (LAI ) plays a major role in cooling the surrounding environment with the R2 between LAI and the maximum local cool island intensity (MLCII) reaching 0.30 in Hong Kong and 0.33 in Singapore. For the green infrastructure, the simulation results indicate that street trees, green façades, and extensive/intensive green roofs are helpful in reducing air temperature and improving outdoor thermal comfort in the daytime. Among all scenarios, street trees are most effective to reduce air temperature and improve outdoor thermal comfort at the pedestrian-level. In densely populated urban areas, green infrastructure (especially green façade) may be a suitable choice for UHI mitigation, thermal comfort improvement, and cooling energy savings. Keywords: Greenspace · Green infrastructure · Cooling effect · Urban heat island · Land surface temperature · Microclimate

1 Introduction A remarkable phenomenon in modern cities is the urban heat island (UHI) effect, a term used to describe higher temperatures in cities compared to surrounding rural areas [1, 2]. UHI not only traps for heat and atmospheric pollutants but also deteriorates the conditions of living environments and increases energy consumption. Consequently, © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 975–987, 2023. https://doi.org/10.1007/978-981-99-3626-7_75

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UHI affects the health, leisure activities, and well-being of urban dwellers. Due to the increasing UHI effect in cities and its serious consequences, there is an urgent need to develop effective UHI mitigation strategies. Among typical mitigation strategies, urban greenspace is the most widely used approach to control UHI through various mechanisms: e.g., evapotranspiration, shade provision, and increased albedo [3]. In addition to cooling the actual space, urban greenspace is also able to influence the surrounding area, and this phenomenon is called the urban greenspace cooling effect. Steady progress has been made in understanding and enhancing the cooling effect of greenspace in urban settings [4, 5]. However, in densely populated urban areas, the addition of new urban greenspace is quite limited by the high density of buildings and urban infrastructures. This calls for more innovative approaches to incorporating greenspace in urban areas, such as using street trees, green façades, and green roofs. Hitherto, strategies incorporating different kinds of greenspace to cool the urban environment are not rigorously and comprehensively scrutinized. Given the above-mentioned research gap, a multiscale evaluation of the cooling effect of different types of greenspaces was conducted in this study. The evaluated urban greenspace included street-level greenspace with different characteristics (e.g., coverage and intensity) and green infrastructure. Based on the land surface temperature (LST) derived from satellite imageries at the macro-scale, and meteorological parameters modeled using microclimatic simulations at the micro-scale, much-detailed evidence for the effectiveness of urban greenspace on cooling surrounding environments of different urban contexts can be provided. For the street-level greenspace, how major characteristics of greenspace influence the cooling effect of greenspace was identified. For the green infrastructure, the performance of street trees, green façades, and extensive/intensive green roofs in air temperature reduction and thermal comfort improvement was assessed. The findings of this research are anticipated to help mitigate UHI and its deleterious impacts in high-density cities and contribute to thermally comfortable living conditions and sustainable urban development.

2 Methodology 2.1 Study Area Studies were conducted in two densely urbanized regions - Hong Kong and Singapore. Both cities are facing a rapid pace of urbanization, which can potentially trigger an increase in the UHI effect and endanger the cities with further environmental issues [6]. Hong Kong usually has long and hot summers lasting from late April to September, while Singapore is a typical tropical country, characterized by hot and humid climates throughout the year. In two regions, four typical urban communities were selected as study areas, including (1) high-rise residential built-up areas next to CBD; (2) parks in CBD; (3) high-rise commercial built-up areas in CBD; and (4) high-rise residential builtup areas in sub-urban areas. A total of twenty sites were selected among communities. The descriptions of each community and site are presented in Table 1.

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Table 1. Four typical urban communities in Hong Kong and Singapore Community

Site No

Sites in Hong Kong

Sites in Singapore

High-rise residential built-up areas next to CBD

1

Mong Kok

River Valley

2

Yau Ma Tei

Novena

3

Whampoa

Cantonment

4

Kowloon Park

Pearl’s Hill City Park

5

Danger Flag Hill

Mount Emily Park

High-rise commercial built-up areas in CBD

6

Central

Shenton Way

7

Admiralty

Raffles Place

High-rise residential built-up areas in sub-urban areas

8

Tin Shui Wai

Punggol

9

Yuen Long

Bedok

10

Fanling South

Bukit Gombak

Parks in CBD

2.2 Retrieval of Land Surface Temperature from Satellite Imageries LST has been used as a proxy for urban temperature in UHI studies. In general, there are two approaches to obtaining the LST: (1) ground-based measurement and (2) remotesensing measurement. Although the former measurement provides accurate surface temperatures at a location, it cannot reflect spatially continuous LST information over vast areas. Instead of ground-based observations at locations with limited space, the remote sensing technique has been a good alternative to provide spatially representative LST observations over large areas from regional to global scale [7, 8]. In this research, LST was derived from the cloud-free Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) images. All satellite imageries used in this research were collected from the U.S. Geological Survey (USGS) (https://earthexplorer.usgs.gov/). The TIRS bands 10 with 100 m resolutions and the OLI spectral bands 2, 3, 4, 5 with 30 m resolutions were used for the LST estimation. The selection of acquisition dates was restricted by the availability of cloudless imageries during the study period. The details of the selected Landsat 8 imageries covering study areas are shown in Table 2. Table 2. Details of the selected Landsat 8 imageries Year

Imagery ID

Path Row

Date of acquisition

Resolution (m)

Hong Kong 2019

LC81210452019263LGN00

121/045

20/09/2019

30/100

Singapore2018

LC81250592018144LGN00

125/059

24/05/2018

30/100

The single-channel algorithm was used to retrieve LST from Landsat 8 TIRS band 10 images. This LST retrieval procedure has been widely applied in existing studies [9–11]. Figure 1 (a–b) shows the derivation results of the LST in Hong Kong and Singapore.

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The long-term meteorological measurement by fixed weather stations in Hong Kong and Singapore was used for the validation of LST retrieval results. A total of 42 weather stations managed by the Hong Kong Observatory and 28 weather stations set up by the National University of Singapore were used (see Fig. 1 (c–d) for their distributions). The comparison between the LST and hourly near-surface air temperature during the LST acquisition time (10:00–11:00 AM local time) in Hong Kong and Singapore is shown in Fig. 1 (e–f). Hong Kong

(a) LST

Singapore

(b) LST

(c) Distribution of weather stations

(d) Distribution of weather stations

(e) LST validation

(f) LST validation

Fig. 1. LST retrieval results; distributions of weather stations; and LST validations using the near-surface air temperature in Hong Kong and Singapore

The model error (in Hong Kong, RMSE = 1.14; in Singapore, RMSE = 1.13), which fell within the error band of the measured data, indicates good overall qualitative agreement between the LST and the near-surface air temperature. In general, LST appears to result in warmer values compared to the air temperature. This can be explained by

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that LST increases faster than near-surface air temperature in warm urban environments due to intense radiative heating [12]. 2.3 Microclimatic Modeling with ENVI-Met ENVI-met - a Computational Fluid Dynamics (CFD) based micro-climate and local air quality model, has been widely applied to account for the thermal exchange within street canyons [13]. Key input parameters include weather conditions, initial soil wetness and temperature profiles, structures, the physical properties of urban surfaces, and plants [14]. ENVI-met can simulate the long-wave and short-wave radiative exchange within the plants, the plant canopy effects on convective heat transfer, evapotranspiration from soil and plants, heat conduction in the soil layer, and moisture-dependent thermal properties [15–17]. ENVI-met is used in this study to simulate the air temperature and outdoor thermal comfort within the modeled environment incorporating different kinds of greenspaces. To assess outdoor thermal comfort, several universal thermal indices integrating environmental factors and the energy balance of the human body have been developed [18]. These indices can translate the evaluation of a complex outdoor climatic environment to a single value that can be easily understood and interpreted [19]. In this study, one typical index - Universal Thermal Climate Index (UTCI) [20] was used to represent outdoor thermal comfort of each location. Four commonly used heat mitigation strategies were studied, including three forms of green infrastructure (extensive green roof, intensive green roof, and green façade), and one form of street-level greenspace – street trees. Extensive green roof is covered with grass only and is designed to be virtually self-sustaining. Intensive green roof is usually associated with roof gardens. In general, because of the thicker growing medium and higher canopy density of the intensive green roof, it is expected to deliver higher levels of evapotranspiration and shading than the extensive one. Another popular form of green infrastructure is the green façade/wall, which is a vertical greening technology using climbing plants or vertically applied growth medium with planting boxes. The computational domain in this study is set as X × Y × Z = 250 m × 250 m × 200 m. The grid resolution is 2 m. Both buildings and streets in the case study area are carefully modeled. The base scenario (reference) is developed to represent the current outdoor microclimate, urban layout, and surface conditions of the study area. Additional simulation scenarios are developed for evaluating the influences of adopting different heat mitigation strategies on outdoor microclimate. Figure 2 (b-e) displays the layouts of each mitigation strategy under the same urban morphology, compared to a control scenario (see Fig. 2 (a)). 2.4 Identification of Greenspace Characteristics and Cooling Effect Indicators 2.4.1 The Greenspace Characteristics The cooling effect of greenspace largely depends on greenspace coverage (Pgreen ) and canopy density. Leaf area index (LAI ) and green plot ratio (GnPR) were introduced to quantify the intensity of greenspace. LAI is defined as the total one-sided area of leaf tissue per unit ground surface area [21]. GnPR is expressed as a ratio of total leaf area to

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(a) Reference

(b-c) Extensive/intensive green roof

(d) Green façade

(e) Street trees

Fig. 2. The layouts of commonly used UHI mitigation strategies

total site area [22]. It is calculated as the sum of the LAI of each species multiplied by its canopy area and then divided by the site area. In addition, even though a modelled land parcel may not contain vegetation, if it is close to greenspace (e.g., urban parks) that has cool air, LST of the parcel may be reduced [23]. Therefore, distance to greenspace (Dgreen ) was considered as one of the potential parameters that affect the cooling effect of greenspace. In this study, the greenspace with area larger than 0.09 ha was included, such as relatively small, medium, and large urban parks, street, and roadside greenways, but excluding the single isolated tree or single row street trees [24]. The locations and sizes of greenspace were identified from the land utilization maps of each year. The intensities of greenspace were estimated from the normalized difference vegetation index (NDVI) derived from satellite imageries. 2.4.2 The Cooling Effect Indicators To estimate the cooling effect of greenspace, two typical indicators were adopted – the maximum cooling distance (MCD) and the maximum local cool island intensity (MLCII) [23]. In general, with the distance from the boundary of the greenspace, the cooling effect of greenspace decreases and eventually reaches equilibrium with its surrounding [24]. The distance between the greenspace and the first LST peak in its surrounding is defined as MCD [23]. In this study, MCD was determined by creating a series of 10 m-width buffer rings up to 500 m starting from the boundary of each greenspace, then calculating the mean LST within each buffer ring and comparing that to the mean LST of greenspace. MLCII is defined as the difference between the LST corresponding to the MCD and the mean LST of the greenspace [23]. It is calculated as according to Eq. (1). MLCII = TS − Tgreenspace

(1)

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where Ts is the LST corresponding to where the MCD occurred, and Tgreenspace is the mean LST of the greenspace.

3 Result Analysis and Discussion 3.1 Land Surface Temperature of Greenspace Table 3 displays the correlation between the LST and major characteristics of greenspace (Pgreen , LAI , GnPR, Dgreen ) of each pixel among four typical urban communities in Hong Kong and Singapore. Table 3. Pixel-level correlations between the LST and major characteristics of greenspace (a) Hong Kong Indices

Community 1

Community 2

Community 3

Community 4

Pgreen

−.019**

−.046**

.045**

−.011*

LAI

.023**

−.264**

.181**

−.163**

GnPR

−.077**

−.229**

.155**

−.223**

Dgreen

.059**

.072**

.116**

.265**

.188**

−.085**

−.015

−.012*

(b) Singapore Pgreen LAI

−.180**

−.104**

−.050**

−.013**

GnPR

−.225**

−.112**

−.067**

−.012*

Dgreen

.064**

.039**

.225**

.073**

Note: **. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed).

Table 3 indicates that greenspace is effective in cooling the space of its actual coverage. The impacts of increasing the coverage and intensity of greenspace on LST reduction were observed in most communities of both Hong Kong and Singapore. Especially, LAI and GnPR can decrease the regional LST significantly. However, in community 3 of Hong Kong, the areas covered by high-density greenspace even presented higher surface temperature than the locations covered by no greenspace or low-density greenspace, as shown by the positive correlation between the intensity of greenspace and LST (see the 4th column of Table 3 (a)). This result is explained by the shadow effect of obstacles in the high-density CBD [24]. 3.2 The Cooling Effect of Street-level Greenspace In study area of Hong Kong, there are totally 241 greenspaces among all studied communities. MCD ranges from 0 to 212 m with a mean value of 75 m, while MLCII can reach 3.77 ◦ C with a mean of 0.84 ◦ C. In study area of Singapore, a total of 281 greenspaces

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are included. MCD extends to 202 m with a mean value of 63 m, and MLCII reaches 1.04 ◦ C with a mean of 0.36 ◦ C. . The correlation between major characteristics of each greenspace and its cooling effect (MCD and MLCII) is shown in Table 4. Table 4. Correlations between major characteristics of each greenspace and its cooling effect Hong Kong

Singapore

MCD

MLCII

MCD

MLCII

Size of greenspace

.134*

.243**

.288**

.295**

LAI of greenspace

.441**

.473**

.477**

.583**

GnPR of greenspace

174*

.356**

481**

.535**

Table 4 reveals the correlation between greenspace characteristics and its cooling effect is more significant in Singapore than that in Hong Kong. The result can be explained by the difference of quantities of greenspace in the two regions. The highdensity greenspace in Singapore contributes significantly to its cooling effect. In study area of Singapore, there are more tree-type or shrub-type greenspace with a mean LAI of 0.91. The size of greenspace is relatively large with a mean value of 417 m2 . However, most greenspace in study area of Hong Kong is covered by grass with a mean LAI of 0.35 and is small with a mean size of 200 m2 . Results indicate the large, continuous, and high-density greenspace has a quite strong cooling effect on its surroundings; while the small, dispersed, and low-density greenspace (e.g., grass, and other low plants) is less effective in cooling the surrounding environment. Previous studies demonstrated that small-sized greenspaces with low LAI are often more susceptible to urban and anthropogenic influences, therefore even increasing sensible heat gain [3]. Results also indicate the Pearson correlation between the MCLII and greenspace characteristics is higher than that between MCD and greenspace characteristics. Thus, MCLII is used to explore the impacts of greenspace characteristics on the cooling effect, as plotted in Fig. 3. Figure 3 indicates that MCLII of greenspace improves logarithmically with an increase in its size and density. Among the three studied characteristics of greenspace, LAI has the most significant impact on the cooling effect of greenspace with the coefficient of determination (R2 ) at over 0.30 in both cities. Although size and GnPR were also found to influence the cooling effect of greenspace, relatively high R2 values were only observed in Singapore (R2 = 0.24 for the effect of GnPR on MLICC). Compared to the intensity of greenspace, greenspace coverage plays the least role in affecting the cooling effect. 3.3 The Cooling Effect of Green Infrastructure A comparison among three typical green infrastructure (extensive green roof, intensive green roof, and green façade) and one street-level greenspace was conducted. The diurnal profiles of average air temperature and UTCI values at the pedestrian level (at a height of 1.5 m) of the walkways for the control scenario and four simulation scenarios were

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Fig. 3. Relationships between MCLII and three characteristics of greenspace

analyzed using ENVI-met. Figure 4 presents the diurnal air temperature variations for all scenarios. Note that the temperature shown in the figure is the average temperature of all the points on the walkways for each simulation scenario.

Fig. 4. Diurnal profiles of air temperature for all the scenarios. Note: RE = reference, EGR = extensive green roof, IGR = intensive green roof, GF = green façade, ST = street tree.

Overall, the simulation results indicate that street trees, green façades, and both types of green roofs are effective in reducing the air temperature in the daytime. The results also

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suggest that roof or wall-mounted strategies are less effective than street-level strategies. Among all scenarios, street trees cause a maximum reduction in air temperature by 0.20 ◦ C. Differences between the simulation scenarios change with time. In general, the differences start to rise at around 7:00, reach the maximum between 14:00 and 16:00, and decline drastically at about 17:00. The figure reveals the effectiveness of different types of heat mitigation strategies in cooling the surrounding environment, especially between 13:00 and 17:00 when it is the hottest. Next, Fig. 5 shows the diurnal UTCI variations for all scenarios.

Fig. 5. Diurnal profiles of UTCI for all the scenarios

The UTCI values of different scenarios demonstrate similar diurnal patterns, with the maximum values occurring at around 16:00 and minimum values at around 06:00. In the daytime, the value of UTCI is generally greater than 32 ◦ C, indicating a high possibility of pedestrians feeling strong thermal discomfort while walking in the study area. The value of UTCI reduces dramatically during the sunset period from over 40 ◦ C to lower than 30 ◦ C. At night, the UTCI value is relatively low, suggesting that pedestrians may feel moderate or no thermal stress. Among all scenarios, street trees result in the lowest values of UTCI in the daytime, with a maximum UTCI reduction of 0.8817 ◦ C occurring at 11:00. The UTCI values of all other heat mitigation strategies are only slightly lower than those of the reference. Overall, street-level greenspace is more effective in both reducing the air temperature and improving the thermal comfort of the pedestrian-level environment than the green infrastructure. However, although some existing studies demonstrate that there is no cooling effect of green roofs on pedestrian-level microclimate [17, 25], this study still finds some cooling effects of roof or wall-mounted heat mitigation strategies.

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4 Conclusion In this study, a multiscale evaluation of the cooling effects of different types of urban greenspaces was conducted. Based on the land surface temperature (LST) derived from satellite imageries, how the coverage and intensity of street-level greenspaces influence the LST and the cooling effect was evaluated at the macro-scale. Based on the meteorological parameters modeled using ENVI-met, the performance of different types of green infrastructure in cooling the surrounding environment was assessed at the micro-scale. The research findings are helpful to enhance the cooling effect of urban greenspace and provide insights for using green infrastructure beyond the ground level in cooling the pedestrian-level environment. The major findings are concluded as follows: • Street-level greenspace is effective to cool both the actual coverage space and its surrounding area. The cooling effect of greenspace improves logarithmically with an increase in its size and density. Especially, the leaf area index (LAI ) of greenspace plays a major role in determining its cooling effect. Urban trees are most effective to reduce air temperature and improve outdoor thermal comfort at the pedestrian-level during summertime in studied urban communities of Hong Kong and Singapore. • Green infrastructure can reduce air temperature and improve outdoor thermal comfort in the surroundings. Although ENVI-met simulation results showed that roof or wallmounted strategies (e.g., extensive green roof, intensive green roof, and green façade) are less effective than street-level strategies, green infrastructure, especially green façade, can be adopted for UHI mitigation and thermal comfort improvement in high-density urban areas. Some limitations of this study may be overcome in future research. The primary limitation concerns the satellite imagery. In general, under relatively warm and moist atmospheric conditions, it is highly possible for satellite imageries to contain cloudcontaminated spots. To overcome this limitation, some high-resolution satellites and effective detective schemes for cloudy conditions should be adopted. The second limitation is the technical constraint of ENVI-met simulations, anthropogenic heat releases from transportation, air conditioning, and other factors were not considered. Future research may incorporate anthropogenic heat release in the modeled area with integrated tools. Acknowledgement. This paper is funded by the Research Grant Council (RGC) of Hong Kong Special Administrative Region Government (Grant No. E-PolyU502/16, R5007–18). This work is also supported by the research project - The use and development of remote sensing technologies for biodiversity and habitat assessment of environmentally sensitive areas, funded by the Research Institute for Land and Space, The Hong Kong Polytechnic University.

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3. Wong, N.H., Tan, C.L., Kolokotsa, D.D., Takebayashi, H.: Greenery as a mitigation and adaptation strategy to urban heat. Nat. Rev. Earth Environ. 2, 166–181 (2021) 4. Thom, J.K., Coutts, A.M., Broadbent, A.M., Tapper, N.J.: The influence of increasing tree cover on mean radiant temperature across a mixed development suburb in Adelaide, Australia. Urban For. Urban Greening 20, 233–242 (2016) 5. Morakinyo, T.E., Kong, L., Lau, K.K.-L., Yuan, C., Ng, E.: A study on the impact of shadowcast and tree species on in-canyon and neighborhood’s thermal comfort. Build. Environ. 115, 1–17 (2017) 6. Aflaki, A., et al.: Urban heat island mitigation strategies: A state-of-the-art review on Kuala Lumpur Singapore and Hong Kong. Cities 62, 131–145 (2017) 7. Bechtel, B., Zakšek, K., Hoshyaripour, G.: Downscaling land surface temperature in an urban area: a case study for Hamburg. Germany, Remote Sens. 4, 3184–3200 (2012) 8. Fabrizi, R., Bonafoni, S., Biondi, R.: Satellite and ground-based sensors for the urban heat island analysis in the city of Rome. Remote Sens. 2, 1400–1415 (2010) 9. Chen, X.-L., Zhao, H.-M., Li, P.-X., Yin, Z.-Y.: Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes. Remote Sens. Environ. 104, 133–146 (2006) 10. Yin, C., Yuan, M., Lu, Y., Huang, Y., Liu, Y.: Effects of urban form on the urban heat island effect based on spatial regression model. Sci Total Environ 634, 696–704 (2018) 11. Siqi, J., Yuhong, W.: Effects of land use and land cover pattern on urban temperature variations: a case study in Hong Kong. Urban Climate 34, 100693 (2020) 12. Sun, T., Sun, R., Chen, L.: The trend inconsistency between land surface temperature and near surface air temperature in assessing urban heat island effects. Remote Sens. 12(8), 1271 (2020) 13. Hien, W.N., Ignatius, M., Eliza, A., Jusuf, S.K., Samsudin, R.: Comparison of STEVE and ENVI-met as temperature prediction models for Singapore context. Int. J. Sustain. Build. Technol. Urban Dev. 3, 197–209 (2012) 14. Berardi, U.: The outdoor microclimate benefits and energy saving resulting from green roofs retrofits. Energy Build. 121, 217–229 (2016) 15. Sailor, D.J.: A green roof model for building energy simulation programs. Energy Build. 40, 1466–1478 (2008) 16. Yang, Y., Gatto, E., Gao, Z., Buccolieri, R., Morakinyo, T.E., Lan, H.: The “plant evaluation model” for the assessment of the impact of vegetation on outdoor microclimate in the urban environment. Build. Environ. 159, 106151 (2019) 17. Zölch, T., Maderspacher, J., Wamsler, C., Pauleit, S.: Using green infrastructure for urban climate-proofing: an evaluation of heat mitigation measures at the micro-scale. Urban For. Urban Greening 20, 305–316 (2016) 18. Xi, T., Li, Q., Mochida, A., Meng, Q.: Study on the outdoor thermal environment and thermal comfort around campus clusters in subtropical urban areas. Build. Environ. 52, 162–170 (2012) 19. Chen, L., Ng, E.: Simulation of the effect of downtown greenery on thermal comfort in subtropical climate using PET index: a case study in Hong Kong. Archit. Sci. Rev. 56, 297–305 (2013) 20. Bła˙zejczyk, K., et al.: An introduction to the universal thermal climate index (UTCI). Geogr. Pol. 86, 5–10 (2013) 21. Chen, J., Black, T.: Foliage area and architecture of plant canopies from sunfleck size distributions. Agric. For. Meteorol. 60, 249–266 (1992) 22. Ong, B.L.: Green plot ratio: an ecological measure for architecture and urban planning. Landsc. Urban Plan. 63, 197–211 (2003) 23. Cheng, X., Wei, B., Chen, G., Li, J., Song, C.: Influence of park size and its surrounding urban landscape patterns on the park cooling effect. J. Urban Plan. Dev. 141, A4014002 (2015)

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Blockchain-Based Decentralized Reputation Framework: Understanding the Residents’ Satisfaction About Living House with Trustworthiness Consideration Xing Pan, Botao Zhong(B) , Luoxin Shen, Jun Tian, Xueyan Zhong, and Xiaowei Hu School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan, China {d202081131,dadizhong,shenluoxin,tianjun2001,m202271573, m202271540}@hust.edu.cn

Abstract. Learning the residents’ perspectives on living houses and their satisfaction feedback is meaningful for enterprise reputation service. Some channels (systems of surveys) have already been established in our daily life to help residents publish the residents’ satisfaction feedback. However, related satisfaction feedback data’s authenticity and integrity cannot be guaranteed in centralized architecture channels currently, which limits the authenticity of reputation judgment of related enterprises (e.g., the realtor company, the tenement company, and others) building organizations. To track the issues, a blockchain-based decentralized reputation framework is designed to ensure the tamper-proof and traceability of residence satisfaction feedback. The framework involves two parts: (1) building a residence satisfaction evaluation model; and (2) defining one type of smart contract to create an interactive and efficient feedback-sharing method in a decentralized blockchain architecture, which could effectively achieve the goals of transparency and trustworthiness. This study provides a novel solution for understanding the residence satisfaction with living houses with trustworthiness consideration, inspiring more discussions about blockchain technology. Keywords: Building residence · Satisfaction feedback · Residence satisfaction evaluation model · Blockchain · Smart contracts · Tamper-proof and traceability

1 Introduction In the civil engineering field, learning residents’ feedback about the living houses is pivotal for multiple stakeholders to build reputation service[1, 2]. Drawing on the multiple-stakeholder perspectives, residence satisfaction feedbacks play an important part in influencing multiple stakeholders’ decision-making. More precisely, (1) drawing on the buyers, higher residence satisfaction about living house elicits more purchases from buyers, which reflects the realtor’s reputation on a certain level; (2) drawing on the enterprises/residence building organizations, enterprises through the residence satisfaction evaluation can understand the demand and expectation of residents for products. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 988–997, 2023. https://doi.org/10.1007/978-981-99-3626-7_76

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And enterprises can learn about customers’ complaints and dissatisfaction with housing quality to promote the continuous improvement of housing quality and continuously enhance the competitiveness of enterprises. For example, higher reputation scores of house quality improve enterprises’ behavior on entrepreneurs’ willingness to partner decisions. In short, residence satisfaction is the source of profits for enterprises of the development organizations. To carry out the customer satisfaction strategy and implement customer satisfaction management, a fundamental problem is whether residence satisfaction can be measured scientifically and accurately. In the residence satisfaction evaluation process, there are existing a variety of channels (e.g., building quality complaint systems and living satisfaction surveys) in our daily life. However, Various Internet + quality supervision platforms and other technical systems are mostly centralized systems dominated by the government or trusted third parties, which reduces the mutual trust and enthusiasm of participants to share their satisfaction feedback. A centralized system also has the risk of data tampering, which brings challenges to the credibility of quality data, poor traceability, and difficulty in accountability [3]. In many instances, the stakeholder has the incentive to tamper with the residents’ satisfaction feedback to require a great reputation when an award involves [4]. For example, on 8 Sep. 2017, the tenement tampered with residents’ feedback about building satisfaction for getting more bonuses from realtors. Finally, the realtor and tenement involve in a lawsuit arose, which is a very expensive process when paying legal and expert fees. To ensure multiple stakeholders (i.e. enterprises/building development organizations and buyers) acquire genuine reputation judgment, the residence satisfaction feedback should be ensured anonymity and authenticity. Blockchain, with its distributed storage of information, makes it difficult to tamper with data and enables easy tracking of information [5]. Additionally, its ability to automatically execute smart contracts converts trust in contracts from a human-based system to a machine-based one, built on code trust. These features provide a strong foundation of trust for quality regulation on the Internet and are expected to significantly transform the model and process of quality governance [3, 6]. In this study, a blockchain-based decentralized reputation framework is designed to ensure the tamper-proof and traceability of residents’ satisfaction feedback. The blockchain framework consists of three modules, namely, the data collection layer, the blockchain service layer, and the function layer. The framework involves two parts: (1) building a residence satisfaction evaluation model; and (2) defining one type of smart contract to create an interactive and efficient feedback-sharing method in a decentralized blockchain architecture, which could successfully accomplish the objectives of openness and reliability.

2 Blockchain-Based Decentralized Reputation Framework for Residence Satisfaction Evaluation A blockchain-based decentralized reputation framework is designed to ensure the tamper-proof and traceability of residents’ satisfaction feedback. This framework comprises four layers, as follows: (1) data collection layer, (2) blockchain storage layer, and

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(3) function layer. Figure 1 shows the overall flow of the framework; it consists of the following: (1) As Information Technology (IT) continues to grow in popularity, various channels such as building quality complaint systems and resident satisfaction surveys have been made available for residents to provide their feedback. Typically, residents are asked to evaluate several factors, including building quality, district environment, infrastructure facilities, location conditions, and property management, to comprehensively assess their satisfaction with their residence. The feedback data, such as building quality satisfaction surveys or building quality complaint comments, along with the corresponding quantized scores given by residents, can then be recorded in the systems; (2) To ensure the authenticity and reliability of recorded feedback information, a decentralized blockchain architecture based on blockchain technology is proposed, which includes a smart contract for creating an interactive and efficient feedback-sharing method. It is noteworthy that the procedure for evaluating resident satisfaction in the smart contract is automated through blockchain technology. This enhances the management of automatic resident satisfaction evaluations; (3) Taking into account the requirements for information sharing, this blockchain primarily involves multiple parties, including residents, enterprises/development organizations, and potential buyers. The decentralized reputation framework, based on blockchain technology, is designed to understand residents’ satisfaction regarding their living conditions with a focus on ensuring trustworthiness. The framework includes several functions, such as: a) management of resident certification and authorization; b) management of building feedback data storage; c) management of building feedback data retrieval and query.

Building quality Housing design type

Residence ID Data collection layer

The evaluation index of living satisfaction evaluation

Intital residence feedback data

Residence ID

Supporting infrastructure Public service facility Residential environment

Residence Comments; Living satisfaction score data

Property management Locational conditions

A

Blockchain storage layer

Residence ID

B C Data on-chain

Smart contract

Comment ID Comment data Living satisfaction score ID Living satisfaction score data

Blockchain

Function layer

Residence certification and authorization management

Building feedback storage management

data

Building feedback data retrieval and query management

Fig. 1. Blockchain-based decentralized reputation framework

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In this study, this proposed blockchain-based decentralized reputation framework is designed to ensure the tamper-proof and traceability of residents’ satisfaction feedback for executing the 3 distinct layers as shown in Fig. 1 leverages two parts: (1) building a resident residence satisfaction evaluation model; and (2) defining one type of smart contract to create an interactive and efficient feedback sharing method in a decentralized blockchain architecture. 2.1 A Residence Satisfaction Evaluation Model To explore the value of learning the resident’ perspectives on a living house and their satisfaction feedback, specialized residence satisfaction evaluation is an important tool to carry out the optimal allocation of residents’ resources. In the current conditions of the Internet era to gradually transform into online trading, the role of product reputation management has become more and more prominent. Building housing living perception refers to the level of residents’ perception of their housing quality. It comes from the comparison between the performance or output of housing products envisaged and residents’ expectations. It refers to a different function after the comparison between the results that residents can perceive afterward and their expectations beforehand. It depends on the resident’s perception of the housing product or its services compared with the residents’ expectations before the acceptance. Different residents have different satisfaction with different housing quality, showing individual differences. The design of the evaluation index system of residents’ satisfaction with living housing should adhere to the following principles: (1) Principle of comprehensiveness, (2) principle of independence, (3) principle of importance, (4) principle of difference, and (5) principle of comparability. According to previous research [7], seven first-level indicators are selected to be the living satisfaction evaluation index system as shown in Fig. 2, such as building quality, house type design, infrastructure facilities, public service facilities, community environment, property management, and location conditions.

Fig. 2. The evaluation index system of residence satisfaction evaluation

One panel is established to the weight of the evaluation index system. A total of 48 surveyors from Huazhong University of Science and Technology (23 masters, 20 doctors,

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and 5 teachers) participated in the experiment. Weight satisfying all the aforementioned requirements was selected through a pre-study interview process, as detailed in Table 1. Table 1. The weight of residence satisfaction factors The residence satisfaction factors

Selection operations

Weight

Building quality

26

0.13

House type design

28

0.14

Infrastructure facilities

37

0.19

Public service facilities

19

0.10

Community environment

35

0.18

Property management

28

0.14

Location conditions

23

0.12

2.2 Blockchain-Based Residence Satisfaction Evaluation Management 2.2.1 Blockchain Participants’ Permissions for Residence Satisfaction Evaluation Considering the requirements in information sharing, this blockchain mainly involves multiple residents, enterprises/development organizations, and potential buyers. In the residents living, they are the participants and have their responsibility. Table 2 shows the permission of different participants on the chain. I Table 2. Participants’ permissions and lifecycle on the consortium blockchain Participants

Permissions

Lifecycle on the chain

The residents

Upload

Always on the chain

The enterprises/development organization

Query

Always on the chain

The potential buyers

Query

Always on the chain

Query

Upload

2.2.2 Blockchain-Based Architecture for Information Tamper-Proof Blockchain is regarded as a subversive technology that opens a “new era of trust”. It can well realize the decentralized information interaction and provides a secure and stable trust environment for its participants [8]. Briefly, a blockchain is a distributed ledger to store data [9]. The smart contract in a blockchain network provides the rules to share data among organizations [10]. The hashing algorithm and other technologies enable multiple information to collectively maintain a tamper-proof ledger and promote the

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collaboration of participants [11]. Briefly, blockchain technology has the advantage of ensuring construction equipment security information is immutable, traceable, and transparent, therefore improving participants to share information in a secure and trustworthy system. A Blockchain-based architecture involves a combination of technologies. The key concepts of blockchain are fourfold as shown in Fig. 3: (1) P2P network refers to an information network system where all network nodes are independent and equal. Each node provides and shares data with other nodes. Unlike the central information integration system, each node in the P2P network architecture can both receive and send information. Moreover, it has the capability of disseminating block data, participating in consensus, and competing for bookkeeping rights; (2) Hash functions are used to convert raw data into a fixed-length hash. Hash functions can be used for many different applications such as encryption, data integrity checking, and data indexing; (3) The consensus mechanism is a critical component of blockchain technology. It addresses the challenge of determining who writes data and how to maintain consistency of data across all nodes in a distributed network environment; (4) A smart contract is a computer agreement designed to facilitate the performance of a contract. Its working principle is to convert the agreement of the contract into if-then statements, similar to a programming language. The contract is then installed into hardware or software that can automatically execute it. Based on the characteristics of computer logic operation, the contract will trigger and execute the corresponding operation when the preset conditions are met.

Fig. 3. Potentials in living satisfaction feedback management

2.2.3 Smart Contract Design for Information Sharing Each time resident feedback is offered, it is submitted through a smart contract that validates and implements all transactions. To ensure the security and traceability of the resident feedback data and the corresponding quantized score, a smart contract is developed. These text-recorded feedback data are completed on time by each resident separately.

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In the actual information feedback and sharing, there are many potential falsification and modification behaviors. Nowadays, a resident often gives a feedback to a seller after purchasing a good from it. This feedback is accumulated to form the reputation of the seller. Generally, a seller would modify the previous engineering data to get a good reputation. To ensure the security characteristics for building feedbacks, a smart contract is designed to distribute store resident satisfaction feedback data. Pseudocode 1 displays the storage function for resident satisfaction feedback data and the implementation function of residence satisfaction evaluation.

Pseudocode 1 Input: Resident satisfaction feedback data= {Resident satisfaction feedback, The corresponding quantized LSE score for each LSE factor, The weight of each LSE factor} Output: TxID, BlockID, TxHash Function: 1:

If Building_quality_ feedbacks = Null then

2:

Return “Key cannot be Null !”

3:

End

4:

Else then

5:

If (the quantized score of Building_quality is not vaild or int)

6:

or (the quantized score of House_type_design is not vaild or int)

7:

or (the quantized score of Infrastructure_facilities is not vaild or int)

8:

or (the quantized score of Public_service _facilities is not vaild or int)

9:

or (the quantized score of Community_environment is not vaild or int)

10:

or (the quantized score of Property_management is not vaild or int)

11:

or (the quantized score of Location_conditions is not vaild or int) then

12:

Return “Incorrect Building_quality_feedback data !”

13:

End

14:

Else then

15:

Comprehensive

evaluation

of

quantized

LSE

score

∑(Weight_of_each_LSE_factor×Each_LSE_factor_score) 16:

Resident satisfaction feedback data←Comprehensive evaluation of quantized LSE score

17:

Get BlockID from block

18:

Tx ← satisfaction feedback data

19:

Write Tx into blockchain

20:

If Tx successfully write then

21:

Return TxID, BlockID, TxHash

22:

End

23:

Else then

24:

Return “Written failed !”

25:

End

26: 27:

End End

←

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3 Evaluation of Blockchain-Based Decentralized Reputation Framework One panel is established for evaluating the blockchain system framework with effectiveness and feasibility considerations. Five experts made up the panel. Five specialists are requested to manually assess the system. Considering different levels of understanding of blockchain technology in the public, we made a detailed description of all the evaluation objects to discuss the necessity level and logical rationality level. Experts answer the questions using a five-point Likert scale[12], which is divided into five categories: “strongly agree”, “agree”, “neutral”, “disagree”, and “strongly disagree”, marked as 9, 7, 5, 3, and 1 respectively. The following topics were the subject of the questions: 1. Functions of the blockchain-based decentralized reputation framework for residence satisfaction evaluation; 2. Performance of the proposed residence satisfaction evaluation model; 3. Performance of the blockchain-based decentralized reputation framework. Each expert is asked to evaluate the blockchain-based decentralized reputation framework. In Table 3, the average score of necessity level and logical rationality level are 8.6 and 7.8. These respectable scores supported the logic and application potentiality of the proposed blockchain-based decentralized reputation framework. The 7.8 means that the framework can be used but need further improvements in blockchain practical application in the future. For example, more factors (e.g., cost and practical application policies) need to be considered further when blockchain application. As shown in Table 3, it seems the proposed framework meets the requirements for authentic and reliable resident reviews and ratings and is innovative. The experts affirmed the merits of blockchain in data trustworthiness and found the framework promising and open to further development. For future understanding of the residents’ satisfaction with living houses with trustworthiness consideration, the proposed blockchain-based decentralized reputation framework is a concrete reference. Table 3. Shows the results for the panel. Aim

Functions/ Performances /Operations/Designs

Necessity level

Logical rationality

A blockchain-based decentralized reputation framework is designed to ensure the tamper-proof and traceability of resident’ satisfaction feedback

Functions of the blockchain-based decentralized reputation framework for residence satisfaction evaluation

9

9

Building feedback data 9 storage management

9

Building feedback data 9 retrieval and query management

7

Resident certification and authorization management

(continued)

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Aim

Functions/ Performances /Operations/Designs

Necessity level

Logical rationality

Performance of the proposed residence satisfaction evaluation model

The seven first-level indicators

9

9

The weight for each first-level indicator

7

7

The index and weight update

9

9

Design of consortium of participating members

7

7

Blockchain-based residents’ satisfaction feedback information tamper-proof

9

7

Smart contract design for residence satisfaction evaluation service

9

7

To ensure the security and traceability of information

9

7

8.6

7.8

Performance of the blockchain-based decentralized reputation framework

Average

4 Discussion and Conclusion A novel blockchain-based decentralized reputation framework to ensure the tamperproof and traceability of resident satisfaction feedback is presented, which strengthens the monitoring of resident residence satisfaction evaluation and then reflects the service provider’s reputation on a certain level, contributing to the realization of multi-party trust and efficient collaboration. This framework comprises four layers, as follows: (1) data collection layer, (2) blockchain storage layer, and (3) function layer. The blockchain system framework is evaluated with effectiveness and feasibility considerations. As a result, it is shown that the frameworks can support the logic and fundamentals for understanding the residents’ satisfaction about living house with trustworthiness consideration. The traceability of resident satisfaction feedback data can benefit supporting decision-making in real-time. To this end, the paper’s contribution is twofold: (1) This study provides a solution for understanding the resident’s satisfaction with living houses with trustworthiness consideration, inspiring more discussions about blockchain. This is an example of applying blockchain to ensure the tamper-proof and traceability of residents’ satisfaction feedback; (2) This study develops a residence satisfaction evaluation model. The smart contracts are designed to execute storage functions for resident satisfaction feedback data and

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the implementation function of residence satisfaction evaluation. These functions enhance the residence satisfaction evaluation management. Declaration of Competing Interest The authors declared that they have no conflicts of interest to this work. Acknowledgments. The authors would like to acknowledge the financial support provided by the National Natural Science Foundation of China (Grant No.72271106) and the National Key R&D Program of China (2022YFC3801700).

References 1. Shao, Z., Bi, J., Yang, J., Ma, Z.: Indoor PM2. 5, home environmental factors and lifestyles are related to sick building syndrome among residents in Nanjing. China. Build. Environ. 235, 110204 (2023). https://doi.org/10.1016/j.buildenv.2023.110204 2. Zhou, Y., et al.: Universal health coverage in China: a serial national cross-sectional study of surveys from 2003 to 2018. Lancet Public Health 7(12), e1051–e1063 (2022). https://doi. org/10.1016/S2468-2667(22)00251-1 3. Pan, X., Zhong, B., Sheng, D., Yuan, X., Wang, Y.: Blockchain and deep learning technologies for construction equipment security information management. Autom. Constr. 136, 104186 (2022). https://doi.org/10.1016/j.autcon.2022.104186 4. Qi, S., Li, Y., Wei, W., Li, Q., Qiao, K., Qi, Y.: Truth: a blockchain-aided secure reputation system withgenuine feedbacks. IEEE Trans. Eng. Manage. (2022). https://doi.org/10.1109/ TEM.2021.3128930 5. Mahmudnia, D., Arashpour, M., Yang, R.: Blockchain in construction management: applications, advantages and limitations. Autom. Constr. 140, 104379 (2022). https://doi.org/10. 1016/j.autcon.2022.104379 6. Li, J., Greenwood, D., Kassem, M.: Blockchain in the built environment and construction industry: a systematic review, conceptual models and practical use cases. Autom. Constr. 102, 288–307 (2019). https://doi.org/10.1016/j.autcon.2019.02.005 7. Ma, J.: Evaluation of urban residents’ satisfaction with housing quality, HZAU (Huazhong Agricultural University) (2008) 8. Sheng, D., Ding, L., Zhong, B., Love, P.E.D., Chen, J.: Construction quality information management with blockchains. Autom.Constr. 120, 103373 (2020). https://doi.org/10.1016/ j.autcon.2020.103373 9. Xue, F., Lu, W.: A semantic differential transaction approach to minimizing information redundancy for BIM and blockchain integration. Autom. Constr. 118, 103270 (2020). https:// doi.org/10.1016/j.autcon.2020.103270 10. Zhang, Z., Yuan, Z., Ni, G., Lin, H., Lu, Y.: The quality traceability system for prefabricated buildings using blockchain: an integrated framework. Front. Eng. Manage. 7(4), 528–546 (2020). https://doi.org/10.1007/s42524-020-0127-z 11. Mhaisen, N., Fetais, N., Erbad, A., Mohamed, A., Guizani, M.: To chain or not to chain: a reinforcement learning approach for blockchain-enabled IoT monitoring applications. Futur. Gener. Comput. Syst. 111, 39–51 (2020). https://doi.org/10.1016/j.future.2020.04.035 12. Zhong, B., He, W., Huang, Z., Love, P.E., Tang, J., Luo, H.: A building regulation question answering system: a deep learning methodology. Adv. Eng. Inform. 46, 101195 (2020). https:// doi.org/10.1016/j.aei.2020.101195

Research on Job Stressors and Mental Health of Construction Practitioners in China Qianqian Xu1(B) , Shang Zhang1 , Lilin Zhao2 , Mingsen Dai1 , Haoxiang Li1 , and Haijun Gu1 1 Department of Construction Management, Suzhou University of Science and Technology,

Suzhou, China [email protected] 2 School of Architecture, Civil and Building Engineering, Loughborough University, Loughborough, UK

Abstract. Affected by the characteristics of the construction industry, construction practitioners suffer from many job stressors, leading to high risk of poor mental health. By adopting a mixed research method, this paper aims to identify construction practitioners’ job stressors and unravel the relationship between job stressors and mental health status. A literature review, a questionnaire survey, and interviews were conducted to achieve the research objectives. Factor analysis results indicate seven major job stressors among Chinese construction practitioners, including: job demand, welfare and social economy, workplace injustice, personal and interpersonal relationship, work-family conflict, job role and workplace condition. Descriptive analysis results show that construction practitioners had serious depression (56.5%) and anxiety (51.9%) problems, but the stress condition (22.1%) was in a moderate level. Furthermore, the results of the correlation analysis reveal that the key job stressors affecting construction practitioners’ depression were work-family conflict stressors (0.591), personal and interpersonal relationship stressors (0.577) and welfare and social economic stressors (0.531), whereas the main job stressors affecting construction practitioners’ anxiety were personal and interpersonal relationship stressors (0.601) and work-family conflict stressors (0.578). The findings of this paper will serve as a theoretical foundation for construction companies to implement efficient mental health management strategies for their employees. Keywords: Construction practitioners · Job stressors · Mental health

1 Introduction According to the official data released by the World Health Organization (WHO) in March 2022, the global cumulative number of COVID-19 infections has reached 400 million, and the cumulative death toll has reached 6.08 million. In the situation of such a raging COVID-19 epidemic, professionals in various industries are under greater pressure (Nikolaidis et al. 2022). Due to the labor-intensive nature, complex personnel composition, and poor working conditions of construction industry (Li et al. 2020), construction practitioners historically work in high-stress environments and are exposed to © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 998–1010, 2023. https://doi.org/10.1007/978-981-99-3626-7_77

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a variety of job stressors, which may lead to a series of mental health problems. For example, Boschman et al. (2013) suggested that workload stressors were the direct causes of depression among construction practitioners. Kamardeen and Sunindjjo (2017) also found that work-family conflict stressors were significantly related to the mental health status of construction practitioners. Pamidimukkala and Kermanshachi (2021) further pointed out that stressors such as workplace atmosphere, safety of work environment, job stability and family environment are the main causes of mental health risk among construction practitioners under the COVID-19 pandemic. Due to the characteristics of the construction industry, it is noted that the mental health risk of construction practitioners is greater than that of other industries (Nwaogu and Chan 2021). According to the CNKI database, it was found that relevant studies on the relationships between job stressors and job performance, work behavior or turnover have been widely addressed in the literature (Li and Li 2013). However, the relationship between job stressors and mental health have received less attention, especially among Chinese construction practitioners. Considering the significant impact of job stressors on construction practitioners’ mental health during the COVID-19 period, this study aims to unravel their relationship through empirical investigation in the Chinese construction industry. The findings of this research can not only provide a theoretical basis for researchers exploring construction practitioners’ mental health status in the COVID19 period, but also offer implications for construction companies seeking for efficient mental health management strategies.

2 Literature Review 2.1 Job Stressors of Construction Practitioners Nwaogu et al. (2022) defined stressors as risk factors that can cause individuals to feel stressful or generate other potential mental diseases. Construction practitioners have a wide range of job stressors (e.g., work environment, organizational culture) and personal stressors (e.g., relationship, work-family conflicate) which could result in serious psychological illnesses (Kamardeen and Sunindijo 2017). Researchers have categorized job stressors into many categories based on their nature and source. For example, Cavanaugh et al. (2000) divided job stressors into obstructive stressors and challenging stressors. Sunindijo and Kamardeen (2017) divided the job stressors of construction practitioners into four aspects: physical factors, organizational factors, job demands, and job roles. Yu et al. (2021) divided job stressors of project managers into interpersonal relationship stressors, role ambiguity stressors, work task stressors, organizational structure stressors, work-family conflict stressors, and career development stressors. This paper adopts Sunindijo and Kamardeen (2017)’s category of construction practitioners’ job stressor, which will be described in detail below. Physical stressors mainly refer to the working environments and conditions of construction practitioners, including poor weather conditions and harsh environments. (Campbell 2006). Since construction practitioners usually work on sites, the poor weather conditions may exacerbate the difficulty of work, resulting in the generation of stress (Yu et al. 2021). In addition, poor working conditions, such as high potential hazards, dust,

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and noise, are considered contributing factors leading to the generation of bad mental states of construction practitioners (He and Peng 2014). Organizational stressors (e.g., organizational culture) are those derived from organizations that can cause individuals to feel stress, which can directly affect the individual’s behavior and performance. Lack of training, insufficient labor force, job discrimination and harassment are also organizational factors that can affect construction practitioners’ psychological status (Sunindijo and Kamardeen 2017). Job discrimination and harassment can be summarized as workplace injustice stressors (Chan et al. 2020). In contrast to men, female construction practitioners suffer more severe workplace injustice stressors, which are the primary contributors to their poor mental health issues such as stress (Sunindijo and Kamardeen 2017). Job demand stressors are pressure factors generated from people pushing themselves to perform tasks at work. These factors typically involve long working hours, heavy workload, high requirements of work quality, and tight project schedules (Campbell 2006). According to Boschman et al. (2013), long working hours or excessive workload would lead to the generation of stress and depression. Job role stressors refer to the pressure factors brought by the role, such as role ambiguity, insufficient job supports, poor career development, and interpersonal conflicts (Campbell 2006). Unclear cognition of job responsibilities, conflict with colleagues, and less work supports will hinder individual’s work performance and lead to stress (Gao and He 2019). 2.2 Mental Health Status of Construction Practitioners The World Health Organization (2020) defined mental health as a state of well-being in which a person may have a normal life, reaches his or her full potential, and makes a positive contribution to society. Poor mental health issues can be harmful to individuals and even to the society (He et al. 2016). Sunindijo and Kamardeen (2017) noted that over 70% of construction practitioners experience adverse mental health conditions. Burki and Talha (2018) also demonstrated that the suicide rate among British construction practitioners was 3.7 times of the national average. It can be seen that construction practitioners are more likely to experience mental health problems than the general populations (Nwaogu et al. 2022). Chan et al. (2020) found that construction practitioners mainly suffer from depression, anxiety, stress, post-traumatic stress disorder and other adverse mental health conditions. Pamidimukkala and Kermanshachi (2021) further noted that depression, anxiety, and stress are the most common ailments among construction practitioners.

3 Research Methods 3.1 Questionnaire Design The questionnaire for this study consists of three parts: demographic information, job stressors, and mental health condition. (1) Demographic information section. It was used to gather the respondents’ background information, including their gender, age, position, type of work unit, salary and marital status.

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(2) Job stressors setion: The scale of job stressors compiled by Sunindijo and Kamardeen (2017) was used in this paper. It consists of 38 items and uses a four-point scale from 0 to 3 (never-occasionally-often-almost always), with the higher score indicating that the person experiences more severe job stressors. It included the dimensions of physical conditions, organizational factors, job demands, and job roles. (3) Depression Anxiety and Stress Scale (DASS-21): The original DASS was created by Lovibond and Lovibond (1955), which comprises 42 elements to measure the emotional states of depression, anxiety, and stress. The short form is called DASS21, which has the same excellent reliability and validity as the DASS-42. Through a review of studies on the mental health of construction practitioners, Chan et al. (2020) discovered that the DASS-21 was the most popular measurement tool utilized by researchers. Therefore, this study adopted the simplified Chinese version of DASS-21 revised by Gong et al. (2010), which includes a total of 21 items in three dimensions: Depression, Anxiety and Stress (with seven items in each dimension). Also, it uses a four-point scale from 0–3 (never-occasionally-often-almost always). The scores of each dimension can be divided into “normal”, “mild”, “moderate”, “severe” and “very severe” according to the critical values (Lovibond and Lovibond 1955). 3.2 Data Analysis Methods First, the validity and reliablity of the data gathered from the questionnaire were tested using SPSS software. Second, to reduce the dimensionality of job stressors, factor analysis was utilized. Descriptive statistical analysis was then used to assess the mental health conditions of construction practitioners affected by the COVID-19 epidemic. Finally, the relationship between job stressors and mental health conditions of construction parishioners was investigated by using correlation analysis.

4 Data Analysis Results 4.1 Demographic Information Analysis The objectives of this study were to investigate the construction practitioners in China, including the owner, the supervision personnel, the design personnel, and others working in the construction industry. Questionnaires were distributed online in Jiangsu, Shanghai, Zhejiang and other regions. A total of 155 questionnaires were distributed and 142 returned. In order to eliminate response bias, responses that are incomplete, or have extreme or neutral rates were removed, resulting in a total of 131 valid questionnaires for further analysis. Table 1 presents the demographic information of the respondents. It can be seen from the table that the respondents are mainly male (66.4%), which is consistent with the current situation of personnel structure in the construction industry in China. Also, majority of the respondents (69.4%) were aged 30 or more, suggesting they are experienced construction personnel. The results also show that most of the respondents are married (75.6%), and the salary range of the respondents is widely distributed. In addition, most of them (67.9%) are from design companies and more than half of the respondents are designers (54.2%).

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Q. Xu et al. Table 1. Demographic Information of Respondents (N = 131)

Demographic information

Sample Percentage Demographic information

Sample Percentage

Gender

Male

87

66.4%

18–24

6

4.6%

Female

44

33.6%

25–29

34

26.0%

Marriage Married Unit

Position

Age

99

75.6%

30–34

36

27.5%

Unmarried

32

24.4%

35–39

21

16.0%

Owner unit

26

19.9%

40–44

18

13.7%

Design unit

89

67.9%

45–49

6

4.6%

Supervision unit

5

3.8%

50–54

6

4.6%

Consulting unit

11

8.4%

55–59

3

2.3%

Owner

7

5.3%

≥60

1

0.7%

General manager

2

1.5%

≤5000

11

8.4%

Project manager

11

8.4%

5001–7000

19

14.5%

Management 19 personnel

14.5%

7001–9000

24

18.3%

Designer

71

54.2%

9001–11000

20

15.3%

Supervision engineer

4

3.1%

11001–13000 14

10.7%

Cost engineer

8

6.1%

13001–15000 7

5.3%

Professional

5

3.8%

>15000

27.5%

Others

4

3.1%

Salary

36

4.2 Questionnaire Reliability Test Cronbach’s Alpha coefficient was used to test the reliability of the questionnaire data. Cronbach’s Alpha coefficient greater than 0.8 indicates high reliability of the questionnaire data, and the higher the alpha suggesting higher reliability (Wu 2010). The job stressors and mental health scale were analyzed by SPSS software, and the Cronbach’s Alpha coefficients were 0.952 and 0.965, respectively. Therefore, the data collected in this study were reliable. In addition, if the Cronbach’s Alpha coefficient obtained by deleting an item is not less than the overall value, it indicates poor internal consistency between the items, so the item should be deleted (Wu 2010). After testing the internal consistency of each item, it was found that two stressors of “A2 The tasks you perform do not match your skills” and “A37 Extra care needs for family members” remained 0.952 when they were

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deleted. Hence, these two items were removed, and the remaining 36 job stressors were used for factor analysis. 4.3 Factor Analysis of Job Stressors Kaiser-Meyer-Olkin (KMO) and Bartlett Sphericity Test were conducted to test the suitability of factor analysis of job stressors. The results showed that its KMO value was 0.878, higher than the threshold of 0.80, indicating strong correlations between the variables. In addition, the p value of the Bartlett’s test of sphericity is 0.000, which is less than the significance level of 0.05, indicating that there are common factors among the indicators and it is suitable for factor analysis (Wu 2010). In this paper, the principal component analysis method and the maximum variance rotation method were used to conduct factor analysis on the collected data. A total of seven factors with eigenvalues greater than 1 were extracted, and the explanatory quantities of each factor were 13.16%, 12.12%, 11.01%, 10.81%, 7.95%, 7.76% and 4.97%, respectively. The cumulative explanation of the total variance was 67.783%, which exceeded the threshold of 60%, indicating that the seven extracted components are the most important ones that can represent the 36 variables (Wu 2010). The factor analysis results also revealed that the two stressors of “A19 Excessive formalization or centralization and rigidity in the organization” (0.375) and “A1 Unpleasant nature of work” (0.477) had load coefficients that were less than 0.50, indicating that the consistency between the two items and other items were low (Leung et al. 2017). As a result, these two items were eliminated from further analysis. The final stage was to evaluate the internal consistency of common factors, and the findings revealed that each component had a Cronbach’s Alpha coefficient higher than 0.7, indicating a high level of internal consistency that could be examined in the following phase (Wu 2010). The results of the factor analysis are shown in Table 2. The seven components were further labelled based on the factor loadings of the variables, which will be discussed in the following sections. (1) Factor 1 contains six indicators: “Excessive workload”, “Time pressure”, “Long working hours”, “Working night shifts”, “Inflexible work schedule” and “Unable to predict working hours”, which mainly reflect the work tasks borne by construction practitioners and the stressors caused by the work tasks. Existing studies have summarized such indicators into the dimension of job demand (Campbell 2006). Therefore, factor 1 is named as job demand in this paper. (2) Factor 2 contains six indicators: “Low wage”, “Unstable work”, “Poor welfare”, “Career stagnation”, “Poor living conditions” and “Financial difficulties”, which mainly reveal construction practitioners’ welfare and income, personal developments, and the conditions of the living. According to the classification of job stressors by Chan et al. (2020), this paper named factor 2 as welfare and social economy. (3) Factor 3 contains seven indicators: “Underestimation of personal skills”, “Gender and racial discrimination”, “Underappreciated efforts”, “Harassment”, “Bullying”, “Violence” and “Traumatic events”, which mainly reflect the unfair treatments of construction practitioners in the workplace. This type of job stressors is the most prominent stressor among female construction practitioners (Kamardeen

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Q. Xu et al. Table 2. Factor Analysis Results of Job Stressors

Job stressors

Factors 1

A4 Excessive workload

0.760

A5 Time pressure or work progress is tight

0.798

A6 Long work hours

0.871

A7 Working night shifts

0.746

A8 Inflexible work schedule

0.635

A9 Unpredictable work hours or shifts

0.592

2

A13 Insufficient salary or wage for the work

0.782

A21 Job insecurity

0.549

A22 The welfare is not good

0.773

A23 Career stagnation or lack of career development opportunities

0.630

A33 Housing, accommodation, or living conditions

0.577

A34 Financial difficulties

0.629

3

A20 Undervaluing of your skills or qualifications

0.561

A24 Differential treatment because of gender,ethnic background, etc

0.553

A25 Lack of appreciation or rewards for efforts

0.511

A26 Sexual harassment at work, unwelcome or inappropriate comments or behaviors by colleagues, superiors, clients, etc

0.572

A27 Violence at work, slander, humiliation, intimidation, abuse, or aggression, etc

0.754

A28 Violence at work, assault, threat, etc

0.662

A38 Previous exposure to traumatic events or depression episodes, death of relatives or friends, assault, depression, etc

0.739

4

5

6

7

(continued)

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Table 2. (continued) Job stressors

Factors 1

2

3

4

A15 Inadequate communications between work colleagues and superiors

0.572

A16 Social or physical isolation from others

0.728

A17 Poor relationships with superiors

0.719

A18 Conflicts with coworkers or colleagues

0.773

A36 Poor personal health conditions

0.638

5

A29 Work-home conflicts, lack of family time because of work

0.595

A30 Low support at home

0.693

A31 Dual career challenges (working couples struggling to balance family affairs)

0.666

A32 Poorly functioning home, tense relationships between couples or family members

0.636

A35 Excessive responsibilities in personal life

0.516

6

A3 Role ambiguity (unclear job roles and responsibilities)

0.769

A12 Lack of job autonomy (lack of control over workload or content or participation in decision making)

0.579

A14 Low level of support for problem solving (support from colleagues or leaders)

0.574

7

A10 Poor work environment (space constraint, extreme weather, excessive noise, poor air or water quality, odors or chemicals, unsafe conditions)

0.685

A11 Unfavorable equipment conditions (unsuitable, faulty or inadequate)

0.660

Cronbach’s Alpha

0.890 0.859 0.859 0.864 0.844 0.738 0.793

Note: Extraction method: principal component analysis; Rotation method: Kaiser normalized maximum variance method; The rotation has converged after 10 iterations

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(4)

(5)

(6)

(7)

Q. Xu et al.

and Sunindijo 2017). Chan et al. (2020) named this type of stressors as workplace injustice, which is also used as the name for this component. Factor 4 contains five indicators: “Communication problems”, “Social disorder”, “Bad relationships with one’s leaders”, “Have contradictions with his colleagues” and “Health conditions”, which mainly reflect the situations when construction practitioners have problems getting along with their colleagues. These job stressors are collectively referred to as interpersonal relationships (Yu et al. 2021). Since this factor in this research also includes the index of individual physical conditions, component four is named as individual and interpersonal relationship. Factor 5 contains five indicators: “Work and family conflicts”, “Low family supports”, “Pressure of balancing work and family”, “Family members are strained” and “Too much responsibility in personal life”. They mainly reflect the contradictions of construction practitioners between work and family. According to the categorization of such indicators by other researchers (e.g., Chan et al. 2020; Yu et al. 2021), this component is named as work-family conflict in this research. Factor 6 contains three indicators: “Role ambiguity”, “Lack of decisions”, and “Lack of work supports”, which mainly reflect unclear job roles of construction practitioners and lack of resources such as work support and rights. This component is named as job role (Campbell 2006). Factor 7 contains two indicators: “Poor working environments” and “Inadequate working equipment”, which reflect the working conditions required by construction practitioners. Based on the description of such indicators by Kamardeen and Sunindijo (2017), factor 7 is named as workplace condition in this paper.

4.4 Descriptive Analysis of Mental Health Status According to Lovibond and Lovibond (1955) and Li (2019), the DASS-D scores of 0–4, 5–6, 7–10, 11–13 and ≥ 14 indicate normal, mild, moderate, severe, and very severe depression, respectively. The scores of DASS-A include 0–3, 4–5, 6–7, 8–9, and ≥ 10, indicating the anxiety level of normal, mild, moderate, severe, and very severe, respectively. The total scores of DASS-S include 0–7, 8–9, 10–12, 13–16, and ≥ 17, suggesting normal, mild, moderate, severe, and very severe stress levels, respectively. Based on the above divisions, the data of construction practitioners’ mental health status are analyzed and presented in Table 3. The results show that 74 respondents (56.5%) had depressive symptoms (with mild, moderate, severe, and very severe scores), 68 respondents (51.9%) had anxiety symptoms, and 29 respondents (22.1%) had stress symptoms. This indicates that depression and anxiety are the main mental health problems that construction practitioners in China tend to experience. It can be seen that 52 (39.7%) and 55 (42.0%) respondents had moderate or higher levels of depression and anxiety, respectively, indicating that many Chinese construction practitioners are suffering from serious depression and anxiety-related mental health problems. Interview results explained that the COVID-19 pandemic has caused tremendous uncertainty to their work, leading to excessive psychological burdens and mental problems. Results also show that only 16 respondents (12.2%) had moderate or higher levels of stress and no one had extremely high level of stress, suggesting that the stress levels among construction practitioners are within an acceptable range. Interview results

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Table 3. Mental Health Status of Construction Practitioners (N = 131) Levels

Mental health status Depression

Normal

Anxiety

Stress

Sample

Percentage

Sample

Percentage

Sample

Percentage

57

43.5%

63

48.1%

102

77.9%

Mild

22

16.8%

13

9.9%

13

9.9%

Moderate

38

29.0%

34

26.0%

9

6.9%

Severe

9

6.9%

7

5.3%

7

5.3%

Very severe

5

3.8%

14

10.7%

0

0.0%

explained that most of the respondents have a high educational background, which means they can take effective measures (e.g., exercises) to relieve pressure from their job. 4.5 Correlation Analysis Between Job Stressors and Mental Health Status In order to explore the correlation between job stressors and mental health status of construction practitioners, Pearson correlation analysis was conducted with the extracted seven components of job stressors (e.g., job demand, welfare and social economy) and the mental health status (i.e., depression, anxiety, stress). Table 4 shows the correlation analysis results between the job stressors and mental status. Table 4. Correlation Analysis Results between Job Stressors and Mental Health Status Job stressors

Mental health status Depression

Anxiety

Stress

Job demand

0.404**

0.403**

0.449**

Welfare and social economy

0.531**

0.495**

0.537**

Workplace injustice

0.499**

0.468**

0.505**

Personal and interpersonal relationship

0.577**

0.601**

0.615**

Work-family conflict

0.591**

0.578**

0.614**

Job role

0.344**

0.290**

0.345**

Workplace condition

0.396**

0.400**

0.399**

Note: ** indicates significant association at 0.01 confidence level (two-tailed)

As shown in Table 4, all the seven factors of job stressors have significant positive relationships with the poor mental health status of construction practitioners, which indicates that the more serious job stressors are, the worse mental health status of construction practitioners would experience. The correlation coefficients of job demand, welfare and social economy, workplace injustice, personal and interpersonal relationship, and

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Q. Xu et al.

work-family conflict stressors with the mental status of construction practitioners are all above 0.4, suggesting that these five job stressors have the most significant impacts on the mental status of construction practitioners. The findings are consistent with existing studies. For instance, Pamidimukkala and Kermanshachi (2021) suggested that job demand and work-family conflict stressors are the main factors causing depression and anxiety among construction practitioners. In addition, Nwaogu et al. (2022) also found that rewarding workers for their accomplishments, organizing job tasks properly, and prohibiting harassment are the most effective measures to improve the mental health status of construction practitioners. Results show that the main job stressors affecting depression are work-family conflict (0.591), personal and interpersonal relationship (0.577), and welfare and social economy (0.531), while the main job stressors producing anxiety are personal and interpersonal relationship (0.601) and work-family conflict (0.578). In addition, the main factors affecting work stress are personal and interpersonal relationship (0.615), workfamily conflict (0.614), welfare and social economy (0.537), and workplace injustice (0.505). Based on these findings, it can be concluded that personal and interpersonal relationship, work-family conflict, welfare and social economy are the key factors causing the mental health risks of construction practitioners during the COVID-19 pandemic. As a tradition, Chinese employees attach great importance to interpersonal communication and family. Therefore, when interpersonal communications are blocked or the conflicts between work and family are serious, construction practitioners are prone to have negative mental conditions (Kamardeen and Sunindijo 2017). Through the investigation of psychological harm experienced by construction practitioners, Sun et al. (2022) also found that factors such as interpersonal conflict and salary significantly negatively affect individuals’ mental health. In addition, the results of interview also confirmed that the three job stressors above are key factors affecting the mental status of construction practitioners.

5 Conclusion Under the COVID-19 pandemic, construction practitioners are more likely to experience mental health problems and are subject to a variety of job stressors. However, few studies have examined the relationships between job stressors and the mental health of construction practitioners in China. In order to identify the types of job stressors that construction practitioners experience, a questionnaire survey was conducted to construction practitioners in Yangtze delta river area. Factor analysis results showed that construction practitioners mainly suffer from stressors related to job demand, welfare and social economy, workplace injustice, personal and interpersonal relationship, workfamily conflict, job role and workplace condition. The descriptive analysis of mental health status revealed that the stress of construction practitioners is moderate, but they have more serious depression and anxiety problems. In addition, the results of the correlation analysis and the interviews revealed that job stressors significantly and positively affect the poor mental health status of construction practitioners. Among them, personal and interpersonal relationship stressors have the most significant impacts, followed by work-family conflict, welfare and social economy stressors.

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The findings of this study provided a thorough explanation of the job stressors that construction practitioners experience and how they affect their mental health. Workers in the construction industry can concentrate on managing mental health from three perspectives: interpersonal relationships, work-family conflict and welfare economy, as these are the main factors leading to poor mental health of Chinese construction practitioners. The limitation of this paper is that the investigation was conducted in one of the most developed region in China. Hence the results can not represent the general profile of the Chinese construction industry. A comparative research with those of other regions in China on this topic is therefore a valuable direction in the future.

References Nikolaidis, A., et al.: Heterogeneity in COVID-19 pandemic-induced lifestyle stressors predicts future mental health in adults and children in the US and UK. J. Psychiatr. Res. 147, 291–300 (2022) Boschman, J.S., Van Der Molen, H.F., Sluiter, J.K., Frings-Dresen, M.H.: Psychosocial work environment and mental health among construction practitioners. Appl. Ergon. 44(5), 748–755 (2013) Burki, T.: Mental health in the construction industry. Lancet Psychiatry 5(4), 303 (2018) Cavanaugh, M.A., Boswell, W.R., Roehling, M.V., Boudreau, J.W.: An empirical examination of self-reported work stress among US managers. J. Appl. Psychol. 85(1), 65–74 (2000) Campbell, F.: Occupational Stress in the Construction Industry. Chartered Institute of Building, Berkshire (2006) Chan, A., Nwaogu, J.M., Naslund, J.A.: Mental ill-health risk factors in the construction industry: systematic review. J. Constr. Eng. Manag. 146(3), 04020004 (2020) Sun, C.J.Y., Hon, C.K.H., Way, K.A., Jimmieson, N.L., Xia, B.: The relationship between psychosocial hazards and mental health in the construction industry: a meta-analysis. Saf. Sci. 145, 105485 (2022) Nwaogu, J.M., Chan, A.P.C., Naslund, J.A.: Measures to improve the mental health of construction personnel based on experts’ opinion. Am. Soc. Civil Eng. 38(4), 04022019 (2022) Kamardeen, I., Sunindijo, R.Y.: Personal characteristics moderate work stress in construction professionals. J. Constr. Eng. Manag. 143(10), 04017072 (2017) Lovibond, P.F., Lovibond, S.H.: The structure of negative emotional states: comparison of the Depression Anxiety Stress Scales (DASS) with the beck depression and anxiety inventories. Behav. Res. Ther. 33(3), 335–343 (1995) Leung, M.Y., Liang, Q., Chan, I.: Development of a stressors-stress-performance-outcome model for expatriate construction professionals. J. Constr. Eng. Manag. 143(5), 04016121 (2017) Nwaogu, J.M., Chan, A.: Work-related stress, psychophysiological strain, and recovery among on-site construction personnel. Autom. Constr. 125, 103629 (2021) Pamidimukkala, A., Kermanshachi, S.: Impact of COVID-19 on field and office workforce in construction industry. Project Leadersh. Soc. 2, 100018 (2021) Sunindijo, R.Y., Kamardeen, I.: Work stress is a threat to gender diversity in the construction industry. J. Constr. Eng. Manag. 143(10), 04017073 (2017) Gao, J.M., He, W.P.: Stress management mechanism based on individual resilience: a moderated mediation model. Manag. Sci. 32(04), 117–129 (2019). (in Chinese) Gong, X., Xie, X.Y., Xu, R., Luo, Y.J.: Test report of Depression-Anxiety-Stress Inventory (DASS21) in Chinese college students. Chin. J. Clin. Psychol. 18(04), 443–446 (2010). (in Chinese) He, Q., Peng, R.: Investigation and analysis of mental health status of construction practitioners. J. Eng. Manag. 28(05), 154–158 (2014). (in Chinese)

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He, G.J., Li, W.J., Chen, H., Zhao, X.H.: Influence of imported stressors on mental health of employees. Mod. Prev. Med. 43(01), 32–35+139 (2016). (in Chinese) Li, X.D., Fei, Y.F., Yang, F.: Research review on occupational mental health of construction practitioners. China Saf. Sci. J. 30(9), 202–210 (2020). (in Chinese) Li, Z.B., Li, R.: Review of research on challenge-hindering stressors. Foreign Econ. Manag. 35(05), 40–49+59 (2013). (in Chinese) Li, M.: Potential categories of “3+2” vocational nursing students’ emotional behavior problems and cumulative effect of risk factors. Shandong Univ. (2019). (in Chinese) Yu, S., Li, X.D., Yang, F.: Research on stress, burnout and performance of project safety managers. J. Eng. Manag. 35(03), 100–104 (2021). (in Chinese) Wu, M.L.: Statistical Analysis of Questionnaires: SPSS Operation and Application. Chongqing University Press (2010). (in Chinese)

Heterogeneous Local Policy Responses to Housing Market Regulation: An Interpretive Framework and Evidence from 177 Chinese Cities Yuesong Zhang1 , Shuhai Zhang1(B) , Wei Jing1 , and Dapeng Xiu2 1 School of Public Administration and Policy, Renmin University of China, Beijing, China

[email protected] 2 Research Institute of China Real Estate Data, Beijing, China

Abstract. Housing market regulation is a regular policy tool globally while it manifests uniqueness in China at the meantime. Chinese state government has initiated a number of constrained policies on housing market since 2011 and the policy intensity changes from time to time. Most notably, a wide range of differences can be witnessed within the housing regulation policy content across cities. Noticing that past research efforts have focused on the multifold consequences of regulation policy, it’s necessary to explore the heterogeneous local responses to housing market regulation, and the underlying reasons. This paper establishes a framework to interpret the variation of housing regulation policy across cities, including enabling factors, constraining factors and the balance of the two. An index ‘regulation policy intensity’ is proposed to measure to what extent local policy intervenes local housing market. Text analysis and regression examination are explored to achieve the above research goals. The results show that: under the risk of economic downturn, a city doesn’t tend to propose high-intensity restriction policy when its finance heavily depends on land revenue. Cities with higher house price growth rate, higher housing-price-to-income ratio, higher real estate investment accounts and named by the central government to regulate housing prices are more likely to propose stricter regulation policy content. These factors relate to the external pressure a city faces in its decision making. The differentiated local regulation policy intensity reveals the compromised outcome of local government between political pressure and development risk. Keywords: Housing policy · Regulation · Purchase limitation · Policy intensity

1 Introduction Economic prospects often accompany with dramatic price increase of real estate, which is known as real estate bubbles or booms. For a time, it was considered not a serious public problem and consequently the best way is to leave it to the market (Xu & Chen, 2012). The premises behind such a conclusion are that any interventions to prevent a boom might lead to market distortions. In addition, the costs and effectiveness of intervention © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 1011–1026, 2023. https://doi.org/10.1007/978-981-99-3626-7_78

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are too complex to manage (Glaeser, Huang, Ma, & Shleifer, 2017). However, increasing reflections have been raised as the serious recession originated from the real estate bubble in the U.S. Gradually, consensus have been achieved with relate to policy intervention on real estate market in both academic and practical domain (Cai, Henderson, & Zhang, 2013; Hartmann, 2015). China has experienced real estate market prosperity along with its rapid and largescale urbanization process, in particular for those mega cities which migrant having been flowing into. Real prices grew by a double-digit annual growth rate from 2003 to 2013 (Glaeser Edward, 2013; Wu, Gyourko, & Deng, 2016). Real land prices in 35 large Chinese cities increased almost five-fold between 2004 and 2015. Consequently, the price-income ratio in these mega cities has experienced a dramatic rising. As a response, the Chinse government has adopted a series of policies such as regulation on mortgage, adjustment of tax rate for real estate transactions and valuing, in order to curb the growth of housing prices. The state policy house also requests various city government to propose local purchase limit policy that sets conditions on house purchase and restricts the number of houses that one family could buy. In comparison to the general policy response to real estate boom, the regulation policy agenda of China appears in a stricter and more regulative term due to a series of contextual reasons. First of all, China has experienced a swift and massive process of urbanization in the past three decades with about 14 million migrants moving into cities annually. These new urban inhabitants produce skyrocketing demand for real estate products. Second, the commoditization of residential real estate started late since 1998. In comparison to the booming demand, the real estate supplies large lag behind. In addition, government monopolized land market and industrial land is the main concern in land supply. Therefore, land is expensive and taxes are high, which kept the development of real estate market in term of institutional restrictions (Andrew & Meen, 2003; Tu, Ong, & Han, 2009). Third, real estate becomes main choice of individual investment of citizens, as well as the so-called hot money (speculative funds) (Love & Zicchino, 2006). A large proportion of real estate sales manifests short-term characteristic of investing. Fourth, property tax of residential houses is exempted from conscription. Due to the above reasons, dramatic increase of real estate price has been witnessed in many cities of China. For instance, it is reported that real prices grew by 13.1 percent annually from 2003 to 2013 (Richard & Nancy, 2003). Gradually the real estate price becomes a social burden and increase risks on long term economy development has been noticed, which calls for a government intervention. Due to the broad intervention of housing market in the world, the impact of policy intervention on real estate booms has been a popular research topic. Appropriate policy response to real estate booms is heavily discussed, most of which are based on the evaluation of policy effect. Macro prudential measures are considered to have some advantages due to the fact that they can be more precisely targeted at specific risks (for instance, excessive leverage); These measures can be particularly helpful in countries with fixed exchange rate regimes or in currency unions. However, evidence so far suggests that these measures may not always be effective; especially under a lax monetary policy stance and pressure from external demand. When this happens, monetary policy has to play a complimentary role, and may have to be used to lean against the wind

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(François & Sven, 2006; Song, Martin, & Bob, 2010). Financial regulation (Jeremy, 1995) and the effect of monetary policy are also research hotspots. Research in China also shows an attention on the effect of lately emerged limitation policy on real estate purchase (Tu, Ong, & Han, 2009). Nevertheless, past research has shown an emphasis on the evaluation of policy effect and ascribe it to the implementation of regulation policy. Such a favor reveals that regulation policy is mostly regarded as an exogenous variable. However, such an assumption is not in line with reality under many circumstances. Many factors have influenced the decision making of local government in the formulation of local regulation policy. As a response to such a research insufficiency, this paper aims to answer the question that are there any significant differences lying behind local regulation policy on real estate market. And more importantly, how to explain such a potential difference. In order to achieve the above objectives, we will also formulate an index to evaluate the level of policy intervention. By doing so, the main contribution is to explore the reasons behind the policy differences and propose an interpretation mechanism. This supplements current research by highlighting that the effect of regulation policy on real estate market is not only influenced by the post-policy factors, but also the pre-policy and in-policy factors. Decision making period of regulation policy is already creating differences in policy effect. This is expected to better explain the heterogeneity of intervention policy across cities. Moreover, it is also beneficial to the central government to strengthen the policy effect by proposing more substantial articles that should be visible in local policy. To address the above issues, the rest content of this paper is organized as follows. In the next section, a comprehensive research context would be introduced, including a reflection on the evolutionary path of housing market regulation in China and the general method and data used. In the third section, we will propose an analytical framework which elaborates why differences exist in regulation policy on real estate market across cities. The main findings based on the survey data will be discussed in Section Four. The final section will summarize the main conclusion of the paper and indicate policy implications.

2 Methodology 2.1 Theoretical Framework In the formation process of real estate restriction policy, central government and local government are two important roles. Central government will guild the local governments to carry out their own policies. General articles of regulation policy are made and detailed content is left empty for local government to fill. This relation can be explained by principal-agent theory. The principal-agent theory developed gradually in the late 1960s and early 1970s. Spencer and Zeckhauser put forward the mathematical model to explain the relationship between shareholders and agents in the organization (Michael & Richard, 1971). Then the theory was further promoted by Mirrless and others (James, 1976). In 1990, Holmstrom et al. regard government officials as agents to learn the Principal agent relationship in government departments, which made the theory applied to the field of government administration (Fudenberg, Holmstrom, & Milgrom, 1990). The principalagent theory focuses on the incentive problem in the principal-agent relationship earlier,

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and then the research content is deepening, focusing on the research on the complex principal-agent relationship in many scenarios. The basic logit of principal-agent theory is that the client grants some decisionmaking power to the agent in order to maximize the utility of their resources, and the agent is required to provide services or acts conducive to the interests of the client. Agent is also an economic man who pursues the maximization of his own utility. In the case of inconsistent interests and information asymmetry, the agent may damage the interests of the client, that is, the agency problem. The principal needs to create incentives for the agent to take the desired action (Rose-Ackerman, 1997) (Fig. 1).

Fig. 1. Analytical framework of regulation policy decision process

The local government urge the local government to implement the real estate regulation polity in order to keep social stability and curb housing market disorder. As the responsible department for real estate market, ministry of housing and urban and rural construction has been urging the city government taking actions to curb the rising of housing price. As the client, local governments judge the benefits rationally of policy implementation. A series of factors from two main aspects largely determine whether local government will put forward such a regulation and what kind of policy it will adopt. First is triggering factors that drive local government to formulate regulation policy. This mainly lies on the administrative order from higher level government. Moreover, pressure for local government also comes from increasing costs for both residence and business, which harm local economy and leads to social complaints as well.

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On the other hand, constraining factors also exist which keep local government from laying down a substantive regulation policy. Such factors relate to the local economic performance, revenue income and prosperity of real estate industry, all of which might be weakened by a regulation policy. A limitation on purchase will lower demand for real estate products and build a supply-demand balance supported by government intervention. The stabilization of housing price will further reduce the willingness of real estate developers to buy and develop new land parcels. The direct consequence of such a change is the decrease of land revenue for local government and real estate industry related GDP. In addition, indirect consequences such as a drop of job opportunities and real estate related industries are also too substantive to be ignored. Concerning the above risks, local government would rather be maintaining the rising tendency than changing it. In face of both triggering factors and constraining factors, various local government will make diverse responses. The balance point of triggering and constraining factors will be different from cities to cities because the pressures they confronted and the risks they would suffer from a regulation policy are different. Therefore, the formal policy is a comprehensive expression of local government’s choice after considering various options. The policy texts could tell us how these local governments made their decisions and where they choose to stand in balancing pressures and risks. 2.2 Policy Intensity We acquire 177 regulation policy from 70 cities which formally published one or more than one version of intervention policy on real estate market. Counting Word Frequency (CWF) method is used to study the 177 regulation policies. First, we briefly read all texts of these policy. Then we listed the frequency of all Chinese words in texts using Rost CM 6.0 (a software for Chinese content analysis), and excluded all conjunctions, adverbs, and adjectives (Chinese does not have articles), as well as all words in charts. The most common word is compared with the central theme of real estate regulation. Then we chose the words related to such a theme and we are able to obtain six main items and thirty sub categories to describe the intensity of regulation policy (See Table 1). The promulgation of policies is one aspect. The first key item is government department publishing the regulation policy, which manifests the administrative position and potential importance of the policy. Three sub categories including municipal government, multiple department and Housing department exist in policy texts, indicating a descending policy intensity. Second item is spatial territory. Not all regulation policy covers the whole administrative area. Actually, many regulation policies even only cover part of the urban area. The specific measures of the policy are on the other hand. With regard to purchase restriction, regulation policy designed different content for residents with and without household registration. Except free purchase, restriction on purchase mainly put regulation on the qualification and number of houses. Qualification often requests the potential buyers having local household registration or a certain time of social insurance. Number of houses are mostly limited to 1 or 2. The intensity of purchase restriction is classified into 4 levels: strict qualification restriction means that the residents of the city are required to limit the purchase of one set of housing; non-residents of the city provide

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Y. Zhang et al. Table 1. Items and categories describing the intensity of regulation policy

Items

Categories

Number

Government department

Municipal government

134

Multiple department

14

Housing department

29

Administrative area

65

Partial administrative area

19

Urban area

35

Spatial territory

Partial urban area

58

Strict qualification restriction

6

Qualification restriction

102

Number restriction

52

Free purchase

17

Mortgage restriction

More Strict restriction

55

Normal restriction

122

Sale restriction

Restriction on holding time

13

Normal restriction

164

Purchase restriction

valid temporary residence permits and certificates of paying social insurance or individual income tax in the city for more than five consecutive years; qualification restriction mainly means that non-residents are required to provide proof of paying personal income tax or social insurance (urban social insurance) for more than one year or two years in the city. Number restriction means the residents and non-residents can only have houses below two or three sets of. Free purchase means the city do not restrict purchasing houses. Restrictions on mortgage are mainly set on the proportion of down payment and the interest rate. The detailed mortgage policy is affected by central government. For example, on April 15, 2010, the State Council issued measures to require that the down payment of loans should not be less than 50% and the loan interest rate should not be less than 1.1 times of the benchmark interest rate for families who purchase a second house with loans. With the change of central guidance and central bank policy, the mortgage will have similar regulation direction. Only a few cities will have stricter mortgage policies compared to the policies of other cities in the same year. The mortgage restriction will be divided into two categories. Stricter cities will be pointed out. The intensity of sales restriction will also have two levels. The first is that the new houses purchased by households can be transferred only after they have obtained the real estate certificate for a specified period of time. This is a strict control measure of housing supply side. Other measures of sales restriction consist of differentiated tax rate when selling the house.

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2.3 Models and Variables According to the above framework, we propose two hypotheses with regard to the relation between the intensity of local regulation policy and pressures, risks local government are under. 1) Administrative pressure from higher level government will urge local city government to formulate regulation policy with substantive restrictive instrument. Therefore, the higher pressure that local government is receiving, the higher level of intensity local regulation policy would be. 2) The risks of GDP growth rate decrease, local revenue loss, and a reduction of job opportunities resulted from regulation on real estate market will hinder local government from putting strict regulatory policy. Therefore, the higher risks local government is suffering, the lower level of intensity local regulation policy would be. The econometric model for the examination of the relation between the intensity of local regulation policy and pressures, risks local government are under can be specified as: PI = F(P, R) + e where PI stands for the intensity of regulation policy, which is a continuous variable calculated by the items mentioned in Sect. 2. P stands for variables of pressures. It consists of variables of HP, PIR and CI, and R stands for variables of risks including variables of GR, LRR and TAR. Item e stands for random error. This paper uses multiple linear regression model for data analysis and theoretical test. The model is as follows. Y = α + β1 HP + β2 PIR + β3 CI + β4 GR + β5 LRR + β6 TAR + e HP refers to housing price growth rate of a city. The higher the growth rate is, the more pressure the city is under. PIR is the housing price income ratio, which actually reflects to what extent local residents are able to afford houses. Moreover, higher level government will call a roll to warn those cities which is considered having real estate bubbles. It is expressed by the dummy variable CI. GR refers to gross domestic product rate. We use this variable to examine which type of local government is more willing to propose a regulation policy, those with higher growth rate or the opposite. LRR is the ratio of land sale income and local revenue. It indicates the level of reliance of local government on real estate industry. IRF is the proportion of investment in the construction of real estate investment in fixed assets. This variable reflects the market capacity of real estate. The data for the above explanatory variables are collected in Chinese city statistical year book and the land resources year book of China. With relate to the explained variable, policy intensity is indicated by different restriction levels in Table 1. Purchase restriction is the most direct restriction methods, which set with a score of 1, 4, 7 and 10 based on the relative importance and substantive effect on the regulation in the table. But if the purchase restriction is only implemented in partial administrative areas. The original restriction level will be ranked lower level. Mortgage restriction and sale restriction also have different restriction levels and their

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affect will be added to purchase restriction. If the city has more strict mortgage levels or sales restriction, it will be scored 1; if not, the score is 0. This difference reflects the basic and fundamental effect of purchase restriction. Delphi Method is employed to synthesize of all the items to get the index of policy intensity. Variables from different aspects will be gradually added up to give a comprehensive indication of real estate intervention policy intensity. Firstly, we examine the policy intensity on purchase restriction, which is expressed by PI1 . Purchase qualifications and quantity are directly controlled from the demand side to affect the real estate market. Secondly, the variable of real estate mortgage restriction is added to the purchase restriction to indicate the difficulty to acquire the property. Finally, the sale restriction is also considered and the three variables are added together to indicate the comprehensive policy intensity. The sale restriction can decrease the housing supply and speculation behaviors so as to prevent real estate overheat (Yan & Hongbing, 2018). That step-bystep analysis can contribute to understanding how local government has proposed the regulation policy. By referring to past research, there has not been any typical variables needed to be controlled (Fig. 2).

Fig. 2. The indication of dependent variable

2.4 Data The dependent variable is calculated by systematic evaluation index which are summarized from the detailed regulation policy. These policies are collected from government official websites and websites of Housing and Urban Rural Development Bureau in various cities. The independent variables are various statistical data. The data mainly come from «China Urban Statistical Yearbook», «Statistical Yearbook of Urban and Rural Construction in China» and Statistical yearbook of special provinces. According to the model, the original data is being calculated to get the specific variables in the paper. Table 2 is the basic statistical analysis of independent variables (Fig. 3).

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Fig. 3. The specific intensity of each city’s regulatory policy

The figure above shows the value of PI1 , PI2 and PI3 , which indicate the intensity of one city’s regulatory policy. Due to the city may have different real estate regulation policies in different years, a city will appear more than one times. Beijing is an irreplaceable political center, and the demand of real estate market is very strong. Beijing’s development strategy in recent years lead to the real estate market as a whole has become tight. As can be seen from the figure, P1, P2 and P3 of Beijing are very high, which are located in the upper right corner of the three-dimensional map. As we can see, the comprehensive regulation intensity can be divided into four groups. The policy intensity of Changchun, Wuhu and Kaifeng is relatively low, while some big city such as Shenzhen and Guangzhou are relatively high. This conclusion is consistent with general cognition.

3 Results and Discussion We have run three models for each of the explained variable, PI1 , PI2 , and PI3 respectively. Therefore, nine models have been estimated. The estimation results are shown in Table 2, Table 3 and Table 4. The first three models were employed to verify the influencing factors of PI1 . With the support of the combined stepwise method, two hypotheses with regard to purchase restrictions can be validated separately. Model 1 indicates the effects of pressure related variables onto the policy intensity. According to the result, a significantly higher policy intensity was found in cities with higher housing price growth rate, in cities with higher

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ratio of housing price to income, and in cities which have been named and shamed by higher level governments. Model 2 indicates the effects of risk related variables onto the policy intensity. According to the regression output, the coefficients of GDP growth rate are not significant, which reveals that effect of GDP on real estate intervention policy is not proved in Chinese cities. On the contrary, the variable LRR shows a result is in line with the hypothesis. Cities that are relying on land revenue income to a larger extend relate to lower level of regulation policy intensity. This actually reflects a strong motivation of local governments on a stable land leasing to earn land revenue (Yuan Gao et al., 2019a, b). In comparison to the GDP growth, land revenue improves the local government’s capacity to pay in a direct way. Therefore, the worries on the negative causes of regulation policy on local revenue lead to a lower level of policy intensity. However, the conclusion of IRF is contrary to the hypothesis. The cities with high IRF are more likely to carry out high-intensity intervention policy. The influence of this variable on real estate regulation policy has been tested. Cao et al. found that the IRF has a positive impact on the implementation of urban purchase restriction policy. He believed that if IRF is high, the probability of local governments be named and shamed by upper-level government will be bigger (Cao &Wang et al. 2015a, b). Compared with the results of LRR, local government is the direct beneficiary and supervisor of land revenue. But real estate investment is more vulnerable to the financial regulation of the upper government, which leads to different results. IRF will have a more negative impact on the real estate market, so it needs local government as managers to increase the policy intensity to restrain the unreasonable local economic activities. Local governments will be influenced by their own development needs and external pressure at the same time. These two forces are unbalanced under different circumstances. Therefore, the specific direction of variable influence should be clarified to understand the mechanism of the local real estate restriction policy. Regression results maintain when the two categories of variables representing for pressures and risks were combined in Model Three. The level of significance and the direction of variables’ influence remain the same. This demonstrates the stable relations between these variables and policy intensity. The overall results show a significant effect of most explained variables that except the GDP growth rate. As the only negative factor, the coefficient of LRR is −0.717, which shows that LRR have negative influence on regulation policy intensity. Facing the urgent need of urban development and construction, it is difficult for cities with high dependence on land finance to have high enthusiasm to implement strict purchase restriction policy. With relate to PI2 , Model 4, 5 and 6 indicates the effects of pressure related variables, risk related variables and the comprehensive variables onto the policy intensity of purchase and credit. Housing belongs to high price durable goods (Jiang, Sun, & Webb, 2013). Most people choose to buy houses through loans (Xiaoning & Qing, 2014). Therefore, loan restrictions set a greater threshold for home buyers, to a certain extent, curbed speculation. PI2 represent the overall difficulty of house purchase by adding the factor of loan restriction, which is similar to PI1 and can more comprehensively measure the intensity of purchase restriction policy. The results show that cities with higher housing price growth rate, higher ratio of housing price to income and being called the roll by higher level government will implement higher intensity policy in statistics. At the

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Table 2. Estimation results of regression Explained Variable

Explanatory Variables

Model 1

Model 2

Model 3

PI1

HP

19.352* (1.92)

20.962** (2.11)

IIR

2.598*** (3.42)

1.945** (2.47)

CI

0.950*** (2.81)

0.731** (2.09)

GR

2.090(0.80)

2.424(0.95)

LRR

−0.909*** (−3.21)

−0.717*** (−2.60)

IRF

4.570*** (4.11)

2.624** (2.21)

3.921*** (10.83)

4.685*** (7.83)

3.692*** (5.87)

R-sq

0.165

0.120

0.213

Observations

177

177

177

_cons

Notes: The table reports the estimates of a OLS model over 199 samples. In the table ∗∗∗ indicates significance at 1%, ∗∗ at 5%, and ∗ at 10%, respectively.

Table 3. Estimation results of regression Explained Variable

Explanatory Variables

Model 4

Model 5

Model 6

PI2

HP

19.523* (1.94)

20.980** (2.11)

IIR

2.860*** (3.78)

2.26*** (2.87)

CI

0.921*** (2.74)

0.713** (2.03)

GR

1.970(0.75)

2.147(0.84)

LRR

−0.906*** (−3.18)

−0.704** (−2.56)

IRF

4.509*** (4.03)

2.409** (2.03)

3.911*** (10.83)

4.802*** (7.98)

3.757*** (5.97)

R-sq

0.176

0.117

0.220

Observations

177

177

177

_cons

Notes: The table reports the estimates of a OLS model over 199 samples. In the table ∗∗∗ indicates significance at 1%, ∗∗ at 5%, and ∗ at 10%, respectively.

meantime, risk related variables are also significant except GR. The coefficient of LRR is negative, which means the LRR has inverse effect on PI2 . The IRF has positive effect and the coefficient is 4.509, which means IRF has bigger influence than LRR. The R2 of pressure related variables are 0.176, while the R2 of risk related variables are 0.117. These demonstrates that regulation policy on purchase and credit has closer relation to upper-level government regulation rather than local preference. This conclusion is consistent with the research of Liu Qing et at., which believe that vertical compulsive pressure from the central government and the horizontal competition pressure among the

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same level cities will significantly promote the adoption of the housing purchase restriction policy, but the compulsive mechanism is the leading mechanism of the diffusion of China’s housing purchase restriction policy (Liu & Zhi et al. 2019a, b). The direction and significance of variable in comprehensive model 6 are in consist of Model 4 and Model 5. Compare to model 1-model 3, these three models have don not change much. This result shows that PI2 and PI1 have the same effect on the indicator representation of real estate regulation policy. Individual cities such as Beijing and Shenzhen, due to their higher administrative level and more vigorous housing demand market, have more stringent loan restriction policies. Table 4. Estimation results of regression Explained Variable

Explanatory Variables

Model 7

Model 8

Model 9

PI3

HP

25.227** (2.48)

26.195*** (2.65)

IIR

2.400*** (3.13)

1.854** (2.36)

CI

0.866** (2.55)

0.584* (1.67)

GR

0.112(0.04)

0.355(0.14)

LRR

−1.136*** (−4.02)

−0.965*** (−3.51)

IRF

4.173*** (3.75)

2.412** (2.04)

4.301*** (11.79)

5.688*** (9.51)

4.648*** (7.40)

R-sq

0.159

0.130

0.223

Observations

177

177

177

_cons

Notes: The table reports the estimates of a OLS model over 199 samples. In the table ∗∗∗ indicates significance at 1%, ∗∗ at 5%, and ∗ at 10%, respectively.

With relate to PI3 , Model 7, 8 and 9 indicates the effects of different variables on comprehensive real estate intervention policy which include restrictions on purchase, credit and sale. Consistent with the above models, the pressure related variables and risk related variables are significant. IRF have positive influence on policy intensity, which indicate high IRF will make the city noticed by upper-level government and local government do not want to continue the great real estate investment proportion. In comparison to model 7 and model 8, variables in model 9 are with less changes. PI3 contains the most real estate regulation contents, which better represents the comprehensive intensity level of China’s real estate regulation policy. The robustness of the empirical results fully verifies the theoretical framework of this paper. It is worth noting that sales restriction affects the real estate market from the supply side, so it has different mechanisms. Therefore, the variable coefficients of PI3 changes more than that of PI1 and PI2 . Based on the observation and analysis of the cities that carry out sales restriction cities, they have strong time concentration effect. S. Cho et al. have studied the neighborhood spillover effects between housing prices (Cho, Kim, Roberts, & Kim, 2012). It can be referred that sales restriction may have more obvious spillover effects on time and space dimensions. Although variables above can explain the regional heterogeneity of the restriction

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policies, such policy differences may be also influenced by the spillover effect. This conclusion needs further confirmation. The above regression results fully show that the real estate regulatory policies of different cities are quite different. This shows that the real estate regulatory policy is not simply exogenous, but closely related to the actual situation of the government. Therefore, to clarify the influencing factors of the real estate regulation policy can help central government formulate more appropriate policies to promote the governance levels of the real estate market.

4 Conclusions and Policy Implications The rapid rise of the new round of residential housing prices has not only seriously affected the security of the financial sector and real economy, but also caused dissatisfaction among the majority of low- and middle-income people (Dong-mei, Jian-jiang, & Qing, 2011; Kerwin et al., 2013). In this context, the central government decided to introduce a policy of regulation on real estate market through restricting on purchase, sales, mortgage etc. By considering the costs and benefits of different strategic choices, the central government of China has proposed administrative order to implement regulation policy on housing. Such a policy is considered to safeguard national economic security and obtaining public support. Policy effect has been a research hotspot since then and the main research efforts have been invested into how these policies have been implemented and how these policies have contributed in controlling the booming real estate market (Yang, Wang, & Wang, 2016). The inefficiency and ineffectiveness therefore have been ascribed to the policy implementation. Factors during the policy making period has been ignored. This paper focus on the local regulation policy content by building a theoretical framework to elaborate the underlying mechanism of regulation policy decision making. The heterogeneity of local regulation policy is explained and tested. The study of real estate regulation policies in 177 Chinese cities shows that: the heterogeneous local policy responses to housing market regulation is affected by administrative pressure and local development risks. There are several factors affect policy intensity which are indicated by different regulation methods. The empirical analysis results indicate that local government is motivated by self-development, so the city will not carry out highintensity restriction policy if its finance is heavily dependent on land revenue. At the meantime, external pressure is also the affection factors on policy decision process. Cities with high house price growth rate, high housing-price-to-income ratio and named by the central government to regulate housing prices are more likely to carry out strict regulation policy content. If a city’s real estate investment accounts for a large proportion of fixed asset investment, this city will also carry out stricter regulation policies. Restricted purchase is a helpless action of local governments under pressure from higher levels of government. Although the purchase restriction policy will affect the local government’s fiscal revenue, the local government can only choose to support because the central government decides on the promotion and appointment of local officials (Yan & Hongbing, 2018). In order to reduce the impact of purchase restrictions on the local economy and finance, local governments will make a fuss about the content

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of the purchase restriction policy as much as possible, that is, reduce the degree of purchase restriction through content setting. The pressures of higher-level governments in different cities are different. Therefore, the urban purchase restriction policy shows local differences. Mortgage restriction is designed to reduce housing market transactions, which is mainly decided by the central government and the Central People’s Bank (Zheng & Wang, 2021). Only the cities of higher administrative level are likely to carry out its own mortgage policy which are different from other cities. At the meantime, these cities such as Beijing and Shenzhen are also facing more administrative pressure because of distinctive housing market which are originated from the economic and political advantages. Sales restriction have different regulation logic: it is a regulation tool from the supply side and aimed at curb housing speculation. At the meantime, the sales restriction appears later than sales restriction and mortgage restriction, the evidence results of sales restriction have a little difference from other two restriction methods. The above analysis fully reflects the perspective of principal-agent theory; the local government will judge whether it is in line with their own interests to a certain extent in the face of the overall goal of the central government. When agency dilemma occurs, how to make their interests consistent is the way to solve the problem of policy implementations. Theoretically, it is necessary to reduce local governments’ development risk of implementing the real estate regulation policy; at the same time, solving information asymmetry through a perfect regulatory system can also be effective. Guided by comprehensive development notion and establishing a diversified evaluation system for local government development can better implement the social policies of the central government. It is also essential to reduce the information asymmetry between central and local governments through big data and other technical means to strengthen the access to local information. Recent global financial crisis has placed the housing market at the center stage of economic policy discussions on financial stability. While the advantages of a deep mortgage market cannot be ignored, it is now also widely recognized that housing credit excesses can happen and that their far-reaching negative consequences warrant a reassessment of how macroeconomic policy should look at real-estate market developments (Hwang & Suh, 2021). At present, the city is experiencing the adjustment and upgrading of industrial structure. How to adjust urban investment and reduce the dependence on the real estate industry have gradually become the development direction of local governments. Real estate market is changing rapidly. Timely adjustment to the development trend of the real estate market can play a good effect. Calling the role of high price cities is an effective way to stimulate the local government formulate substantive regulation policy content. It needs the central government have a correct judgement of local housing market. The ratio of house price to income and the growth rate of house price in a city can directly reflect the local market situation, and taking appropriate control measures when the market is overheated can get the positive response of local governments. Besides, lower the local reliance on land revenue are also necessary to trigger the local government increase the regulation intensity. Gao Ya et al. suggests that it is important to improve the sustainability of housing policies, which has significant policy implications for obtaining a well-functioning housing market (Gao, Li, & Dong, 2019a). The sustainability of housing policies needs voluntary cooperation from local governments.

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If the local government can have more pressure and less risks about housing regulation, the policy sustainability can be achieved. The effect of housing regulation lies not only in implementation, but also, and even more importantly depending on the policy making. This should be realized and made use of by the state government in order to improve the efficiency of state regulation policy in various cities. Because of the heterogeneity of real estate regulation policies, the central government is supposed to have differentiated requirements on real estate markets for various cities. The local government should also find its position at national real estate markets and carry out proper policies. The final target is to have stable house price, healthy market and good social environment.

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A Double Deep Q-Network-Enabled Two-Layer Adaptive Work Package Scheduling Approach Yaning Zhang1,2 , Xiao Li2(B) , Chengke Wu2,3 , and Zhi Chen1 1 School of Management, Northwestern Polytechnical University, Xi’an, China 2 Department of Building and Real Estate, The Hong Kong Polytechnic University, Hong Kong,

China [email protected] 3 Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

Abstract. Adaptive project scheduling is paramount for project success. However, it is challenging for industrialized construction (IC) projects to their fragmentation with spatial-temporal distributed work packages (e.g., tasks in production, transportation, and on-site assembly). To achieve adaptive project scheduling in IC, this study proposes a double deep Q-network (DDQN)-enabled two-layer adaptive work package (D2 -TAWP) approach. First, the project scheduling process is transformed into a Markov decision process to model the sequential decisionmaking process of scheduling; Second, a two-layer adaptive scheduling approach is developed to schedule tasks of work packages dynamically. Finally, the effectiveness of the D2 -TAWP approach is validated by experimental simulation. The results indicate that the D2 -TAWP approach can effectively perform work package scheduling compared to traditional heuristics, which paves the way for the next-generation distributed scheduling of IC projects. Keywords: industrialized construction · project scheduling · work package schedules · deep reinforcement learning

1 Introduction Industrialized construction (IC) projects have benefited and will continue to benefit the construction industry by reducing construction waste, improving quality control, and reducing on-site accidents [1]. IC is the paradigm of producing prefabricated products in a controlled factory environment and transporting them to the construction site for assembly [2]. Delays in IC projects still frequently occur since the complexity of coordinating work package schedules across manufacturing, transportation, and on-site assembly [3]. Furthermore, IC projects involve multi-specialty and cross-domain knowledge in producing facilities, and it requires assigning tasks to various subcontractors, specialized work teams, or even robotics and automatic machinery [4]. Therefore, it is necessary to propose an effective scheduling method for coordinating work packages with different resource and task features [5].

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 1027–1041, 2023. https://doi.org/10.1007/978-981-99-3626-7_79

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Work Breakdown Structure (WBS) is an effective tool for project planning and scheduling by hierarchically decomposing the total work scope of a project. With the WBS, different types of work in an IC project can be broken down into work packages [6]. A work package is the smallest element in WBS for planning and scheduling one or more executable tasks [7]. To reduce delays in IC projects, project managers must perform the scheduling in such multi-work package scenarios. Moreover, these work packages, located at different stages of the project and with dynamics, bring new challenges to IC project scheduling. Work packages can be considered as special subprojects with finer granularity [8]. Thus, the multi-work package scheduling problem (MWPSP) is more complex than the multi-project scheduling problem (MPSP). But the solution for MPSP can share similar principles with MWPSP. For example, MWPSP can be regarded as an NP-hard problem. Because MPSP has been proven to be an NP-hard problem and it is reducible to MWPSP [9]. The exact algorithm has difficulties solving large instances of such problems because of its computational complexity. Moreover, the heuristics can quickly obtain an approximate optimal solution. Thus, many heuristics have been developed to solve such problems [10]. Among them, priority rule-based heuristics is gaining more applications in scheduling practice due to its simplicity and effectiveness [11]. Few studies have developed priority rules-based heuristics for MPSP. For example, Félix Villafáñez et al. proposed a heuristic that used the parallel schedule generation scheme (P-SGS) and minimum task total slack (MIN-SLK) as a priority rule to solve the basic MPSP [12]. Liu et al. proposed new priority rules to solve global resource conflicts in a multi-project environment [13]. All of the above studies examine priority rule-based heuristics at a multi-project level. However, there is still lacking a discussion at a multi-work package level. Thus, challenges remain when implementing the IC project scheduling at the work package level. For example, 1) the work packages for IC projects are spread over different phases of the project and different geographical locations. Therefore, each work package has different resource and task features. There is no single scheduling rule that works best across all work packages. 2) A priority rule is usually set ahead of the work package schedule. As the project progresses, the predetermined priority rule may no longer fit to current work package schedule. To address the above limitations, this study aims to develop a double deep Q-network (DDQN)-enabled two-layer adaptive work package (D2 -TAWP) scheduling approach. Three objectives are also designed: 1) to model sequential decisions for MWPSP; 2) to design DDQN and heuristic for solving MWPSP; 3) to validate the D2 -TAWP approach in simulation-based experiments. The following contents are organized as follows. Section 2 introduces existing research in MWPSP and deep reinforcement learning(DRL) in scheduling. Section 3 delineates the research method, including the MWPSP modeling, DDQN-based dynamic priority rule generation, and dynamic priority rule-based heuristics algorithm for adaptive scheduling. Section 4 presents the experimental results of applying the proposed D2 -TAWP approach to demonstrate its usefulness in MWPSP. Section 5 discusses the contributions and limitations of the D2 -TAWP approach. Section 6 concludes the study.

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2 Literature Review This study involves two relevant topics: MWPSP and DRL in scheduling. Since most of the studies focus on MPSP and MWPSP is an extension of it. The review of MPSP helps identify the limitations of current methods. Moreover, studies of DRL in scheduling reveal and justify the applicability of DRL methods in addressing these limitations. e.g., using DRL to achieve a real-time selection of priority rules for adaptive scheduling of work packages. 2.1 The Multi-project Scheduling Problem Managing multiple projects is a complex decision-making process where a number of work packages must share a set of limited resources concurrently. Tian et al. [14] proposed a critical chain resource-constrained multi-project scheduling model with a hierarchical strategy to solve multi-project scheduling plans. In response to the NP-Hard problem’s features, Longqing Cui et al. [15] developed a Variable Neighborhood Search Algorithm (VNS) to solve the problem in a reasonable time. Liu et al. [16] proposed a multi-PR heuristic (MPR-H) to minimize the expected total tardiness cost of MPSP. Chen et al. [17] has compared the hybrid genetic algorithm (HGA) and heuristic algorithm in different cases. The study proved that the superiority of HGA became increasingly significant when problem complexity increased. To prove that the performance of priority rules is highly dependent on project context, ElFiky et al. [18] presented an application of 17 priority rules to a portfolio of deep-water construction projects. Although the above studies demonstrated that work package features and states influenced priority rule-based heuristics, they failed to give a solution to achieve real-time dynamic scheduling. 2.2 DRL in Scheduling Reinforcement learning is a learning and decision algorithm based on state or action values oriented to long-term goals. It has also emerged as an effective scheduling solution method in recent years. Ren et al. [19] introduced the pointer network to the artifacts with the highest priority in the current state, thus creating an artifact ordering based on the input set of artifacts. Park et al. [20] used graph neural networks to select processes directly from the graph that can be performed. Numerous studies also used reinforcement learning to optimize the model parameters for adaptive scheduling. For example, Shahrabi et al. [21] used reinforcement learning to update the parameters of the variable neighborhood search at the re-scheduling instead of optimizing the initial scheduling. Li et al. [22] considered the genetic space as the action strategy space and the fitness function as the reward function for Q-learning to speed up the convergence of the genetic algorithm. DRL is very effective in making quick scheduling decisions based on the scheduling information, meaning it can be applied to implement adaptive scheduling of work packages with different task and resource features, which, however, are not covered in existing studies.

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3 Research Method This study proposes the D2 -TAWP approach for MWPSP in IC projects. As illustrated in Fig. 1, the approach includes two parts: a DDQN at the upper layer to achieve the real-time selection of priority rules and the heuristic at the lower layer to set the start time for the work packages. The upper layer DQN algorithm Loss function gradient descent

Replay memory

Send the quadruple

Replace target parameters

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Fig. 1. A double deep Q-network-enabled two-layer adaptive work package scheduling approach

3.1 MWPSP Transformation The Markov decision process is a mathematical model of sequential decision and is one of the foundations for reinforcement learning. The work package scheduling process is a typical sequential decision process. The tasks in a work package are sequentially scheduled for start times and allocated resources. The key to applying RL in solving MWPSP is to transform scheduling problems into Markov or semi-Markov decision problems, including defining states, actions, and reward function.

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3.1.1 Scheduling State The defined scheduling states need to reflect the real-time scheduling progress of the work package. Scheduling states can be reflected by various aspects of the work package, such as the progress of scheduling, task duration, and resource conditions. Existing studies have used a few measures of project networks to indicate the scheduling states, such as network complexity (NC), resource factor (RF), and resource strength (RS). This method has two limitations: (1) the measures cannot reflect the scheduling process in real-time. (2) a group of different projects can have the same measures, but projects with the same measures may have different scheduling states. Therefore, using the measures of project networks does not provide an accurate reflection of the scheduling states of work packages. This study proposes a method to transform work packages into multichannel images for feature extraction to solve the above problems. Convolutional neural networks (CNN) can be used to perform feature extraction directly from the raw work packages information. The details of the transformation are described below. First, the image size should be determined to facilitate work package information being transferred. The work packages have different tasks, and the transformed images will be different sizes. Consider setting the image size equal in height and width to facilitate subsequent convolution operations. For a work package √ with m tasks, its channel width and height can be roundly calculated using h = w =  m. For example, a work package with 30 tasks will be organized into 6 × 6 images. The first 30 pixels are real tasks, and the other pixel grids are filled with dummy tasks with a duration of 0 and a required resource of 0. The second that needs to be determined is the image channels to which the work package will be transformed. This study organizes the tasks of a work package into three image channels, such as the channel of task duration, the channel of scheduling results, and the channel of resource utilization. The task duration channel is a matrix consisting of each task’s duration. The duration of the scheduled task is 0, and the duration of the dummy task is 0. The scheduling result channel is a matrix composed of the finish time of each task. If the task is not scheduled, its completion time is recorded as 0. The resource utilization channel represents the utilization rate of resources during the work package execution. It is calculated as Eq. (1):  xmn · rmnk (1) resource utilization = k  k Rk where rmnk is the resource k of task m of work package n, Rk is the availability of resource k and xmn is 0–1 decision variables in Eq. (2):  1, task m of work package n is executing xmn = (2) 0, otherwise

3.1.2 Action Definition Priority rules are defined as actions in the Markov decision process. The agent chooses to execute different priority rules and transmits the action instructions to the work package.

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Work packages’ tasks are scheduled according to priority rules combined with a schedule generation scheme. The work packages update states and receive scheduling rewards. This study selects eight priority rules commonly used in project scheduling problems to form the action space (see Table 1). These priority rules are related to the project network, critical paths, and resources. These priority rules comprehensively consider the priority of tasks in terms of network-based rule(NBR), critical path-based rule(CPBR), and resource-based rule(RBR). Table 1. Priority rules for defining actions No.

Priority

Extremum

Description

Type

rule 1

SPT

min

Select the task with the shortest processing time (SPT)

2

LPT

max

Select the task with the longest processing time (LPT)

3

MIS

max

Select the task with the most immediate successor (MIS)

4

MISD

max

Select the task with the most immediate successor duration

5

LST

min

Select the task with the latest start time (LST)

6

LFT

min

Select the task with the latest finish time (LFT)

NBR

(MISD)

7

SLK

min

Select the task with the minimal slack (SLK)

8

TRD

max

Select the task with the total resource demand (TRD)

CPBR

RBR

3.1.3 Reward Function The reward is a value the environment feeds back to the agent after the agent acts. The reward function is the mapping between the reward and the triplet of state, action, and next state. The agent’s learning goal is to maximize each step’s cumulative reward. The setting of the reward function should be closely related to the goal of project scheduling. The purpose of project scheduling in this paper is to minimize the makespan of the project. So, using the negative project makespan as the reward single, that is: Ut = −max{fij }

(3)

where t is the time step during the scheduling and fij is the finish time of the completed task i of work package at the current t. It can be viewed as a discrete t step in the RL method. Let rt = Ut − Ut−1 denotes immediate rewards, then the cumulative reward R can be calculated as follows: R= =

T  t=1 T  t

rt U (t) − U (t − 1)

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= U (1) − U (0) + U (2) − U (1) + . . . + U (t) − U (t − 1) = U (t) − U (0) = U (t)

(4)

3.2 The Upper Layer DDQN The DDQN at the upper layer is built to select the best priority rule for task scheduling. It can extract the scheduling features of each work package in real time and make intelligent decisions through neural networks. The specific settings are as follows: A deep convolutional neural network (CNN) is used in a deep Q-network model for calculating the Q-values. Specifically, the input of the CNN is the three-channel images which describe the task duration, scheduling result, and resource utilization by each channel. The output of the CNN is the predicted Q-values of the individual actions and the optimal Q-network. The following section represents the process of scheduling a work package. The completion of one work package scheduling is called an episode. The specific training steps are described below: First, the scheduling environment should be initialized, including the action-value function Q with random θ and the target action-value function Q with weights θ = θ and the algorithm hyperparameters such as replay memory D with capacity M , exponents α and β, εmax and εmin . Second, at any time t during an episode, the agent selects an action at according to the obtained state st from the lower layer heuristic algorithm. The choice of action at is based on behavior policy involving the Exploration-Exploitation trade-off in the RL method. Exploitation refers to using currently known information to optimize the agent’s performance, and the expected reward is usually used to evaluate the agent’s performance. Exploration refers to gaining more knowledge by interacting with the environment. Common methods, including the greedy method, epsilon-greedy, epsilondecreasing strategy and SoftMax can be used to balance “exploration” and “exploitation”. The proposed algorithm uses the ε-decreasing strategy, in which the probability of εdecreases over time and gradually transfers exploration to exploitation. When ε becomes a minimal value, it becomes an approximate greedy method and selects the optimal action with significant probability. The action selection rule based on this strategy is as follows:  argmax Q(a) with probability 1 − ε a (5) a= random with probability ε 



where ε is the probability of randomly selecting an action and is updated by the following Eq. (6). n_iter

ε = εmin + (εmax − εmin ) × e ε_decay

(6)

where εmax is the initial maximum value of ε, εmin is the minimum of ε in the end, n_iter is the current step counter, and ε_decay is the epsilon decay. At last, the agent selects the action at according to formula (3), then updates ε and transfers at to the lower layer heuristic algorithm. After the calculation by the lower layer

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heuristic algorithm, the reward obtained by taking action and the following scheduling states are obtained and transmitted back to the agent by the work package. Store the quadruple obtained at this time step into the replay memory D. Every period K, k transitions are sampled based on different probability, and the importancesampling weight for each transition is calculated. Given the sampled transitions, a loss is calculated from a Q-value and target value. The learning algorithm performs a gradient descent step with respect to θ on the loss. The accumulated weight change updates the network parameter on all the sampled transitions. The weights of the target Q-network are periodically replaced with those for the Q-network until the optimal Q-network is obtained. The pseudo-code of the upper layer DDQN training process is shown below.

3.3 The Lower Layer Heuristic Algorithm This section presents a dedicated priority rules-based heuristic at the lower layer to generate schedules. The algorithm’s inputs are the current work package scheduling state and the action from the DDQN algorithm. The outputs are the next state and the reward.

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First, we need to define a work package for the scheduled set SC d , the unscheduled set SC u and the decision set DE. SC d is a set of tasks that have been scheduled for a start time. SC u is a set of tasks that have not been scheduled for a start time. DE is a set of tasks that meet logical relationships and resource needs but have not been scheduled for a start time. In the beginning, all tasks belong to SC u . Tasks that satisfy the logical relationships and resource constraints in SC u that can be scheduled for start time and move to DE. Based on the actions made by the upper-layer DDQN, the tasks in DE are selected to schedule the earliest start time. So SC d , SC u and DE are updated. The quadruple is calculated and sent to the upper-layer DDQN. This cycle continues until the entire work package is scheduled. Repeat the above steps until the entire work package is scheduled. The pseudo-code of the lower layer heuristic is shown below.

4 Experiment Experiments have been designed to evaluate the performance of the D2 -TAWP algorithm for MWPSP. In the experiments, this study compares the performance of D2 -TAWP algorithm, random priority rule-based(RPR) heuristic, and eight single-priority rulebased(SPR) heuristics. RPR is to randomly select the priority rules (e.g. in Table 1) to assign tasks in each scheduling iteration. This algorithm’s performance can represent the performance of the eight priority rules selected in this study, which can be used as a benchmark for the D2 -TAWP’s improvement. We compared three approaches for scheduling multi-work package IC project instances with the optimization objective of minimizing project makespan. There is a strict precedence constraint between work packages in each project, which means that a work package can only start when all the tasks are completed in its predecessor work packages [23]. 4.1 Data Briefing To verify the effectiveness of the D2 -TAWP algorithm, simulated IC project instances are generated using RanGen2, which provides diversified project networks with massive data compared with using single case study-based real-world IC projects [24]. RanGen2 can

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generate a random network by presetting the project network parameters. The parameter symbols and meanings are shown in Table 2. Table 2. Meaning of the project parameters Parameters

Meaning

I2 value ∈ [0, 1] Representing the complexity of the project network; when I2 = 0, all tasks are in parallel; when I2 = 1, all tasks are serial RF ∈ [0, 1]

Representing the demand for various types of resources for the tasks; when RF = 0, tasks do not need any resources; when RF = 1 tasks need all of the resources

RS ∈ [0, 1]

Representing the resources constrained situation; If RS is smaller, the greater the degree of resource constraint

Three typical work packages - production, transportation, and on-site assembly are considered in this experiment. As tasks, resources demand, and task dependencies vary in real-world IC project, different project parameters are used for different instances. One instance of its feature parameters is shown in Table 3 [24]. Ten instances of IC projects are generated with this parameter setting. Table 3. The feature parameters of work package scheduling production

transportation

assembly

Number of tasks

20

10

30

Number of resources type

4

2

6

I2 value

0.6

0.7

0.5

RF

1

1

1

RS

0.8

0.2

0.5

The current method of determining hyperparameters is still based on respective experimental tests. For instance 1, the validation results are analyzed under different replay buffer size, gamma, learning rate and update frequency of the target network. We performed a convergence analysis on experimental results. As shown in Fig. 2, 3, 4 and 5, because of an Exploration-Exploitation trade-off exits, the agent did not converge fast after exploring the optimal value but continued with more exploration attempts. For the replay buffer size, setting a larger replay buffer size achieved better convergence, while a larger value slowed down the convergence speed. Gamma is the discount rate, indicating that the higher the value, the fewer rewards are discounted. For the learning rate, a larger learning rate prevented the agent from converging, while a lower learning rate slowed down the convergence rate. For the target network update frequency, a lower update frequency of the target network achieved better convergence

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due to the smaller size of constructed project instances. The speed-up of update frequency led to an increase in computational cost and slowed down the convergence. According to the above analysis, the finalized hyperparameters are shown in Table 4.

Fig. 2. Convergence analysis on different values for replay buffer size

Fig. 3. Convergence analysis on different values for gamma

Fig. 4. Convergence analysis on different values for learning rate

Fig. 5. Convergence analysis on different values for target network update frequency

4.2 Result Analysis The experimental results are shown in Table 5. The results from the RPR heuristics are the average of ten repetitive experiments. As seen in Table 5, although SPR heuristics

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Hyperparameters

Values

References

Number of training

10000

HanBaoan, et al. [25]

Epsilon

Max = 0.9, Min = 0.05

Ian Osband et al. [26]

Buffer size

50000

William Fedus et al. [27]

Gamma

0.99

HanBaoan, et al. [25]

Learning rate

10–5

Long Wen et al. [28]

Target network update frequency

100

Z.Wang, et al. [29]

Batch size

512

Chun-Cheng Lin et al. [30]

perform well in a few instances (e.g., LST, LFT), no single priority rule can be adapted to all instances. For example, instances 3 and 10 are based on the TRD and LPT heuristics, respectively, to find the shortest makespan. In contrast, the D2 -TAWP approach obtaines the shortest makespan in all project instances. So, it is necessary to choose adaptive priority rules for IC projects in different scheduling states. The D2 -TAWP approach proposed in this paper can achieve more robust results than the SPR heuristic and the RPR heuristic. The primary evidence is that the D2 -TAWP does not leverage the simple mixture of the priority rules but performs an intelligent selection of priority rules based on the real-time progress of the work package by using the trained DDQN. The experiment results show that the D2 -TAWP algorithm achieves a 1.97% improvement over the best priority rule (LST) in the SPR heuristics and a 4.81% improvement over the RPR heuristic. In summary, the D2 -TAWP has a better performance in terms of solution quality and a greater generalization across different instances.

5 Discussion The proposed D2 -TAWP is a new approach to the project scheduling problem, particularly for IC projects with multiple work packages. Compared with the previous studies, the algorithm novelty is summarized as follows. First, a CNN-based feature extractor is developed for retrieving features of the work package schedule. Work package scheduling features are extracted through three channels: task duration, scheduling result, and resource utilization. Previous studies obtained work packages’ features by configuring project parameters, e.g., NC and RS. However, these methods are difficult to reflect changes in schedule states during multiple work package scheduling [31]. Therefore, the proposed feature extraction method comprehensively reflects the work package scheduling state in real-time. Second, the proposed D2 -TAWP with deep reinforcement learning can update priority rules dynamically as the project progresses. The priority rules in existing studies are predetermined before scheduling a work package [16]. However, the predefined priority rules may not apply to various and dynamic scheduling states of multiple work packages. The experiments have indicated that the D2 -TAWP approach dynamically selects

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Table 5. Experimental results for algorithm comparison (Makespan) Project

D2 -TAWP

RPR

SPT

LPT

LFT

LST

TRD

SLK

MIS

MISD

1

209

220.3

233

222

211

209

230

214

216

224

2

224

239.6

241

234

232

232

253

232

237

248

3

263

264.8

270

264

265

263

263

274

267

267

4

209

215.5

229

213

213

210

232

217

225

221

5

220

226.1

237

227

224

227

236

226

228

235

6

209

221.3

229

236

213

210

228

218

220

224

7

187

207

212

210

189

194

215

193

200

200

8

208

212.4

219

214

211

208

215

213

214

212

9

201

210.2

212

220

204

204

216

204

216

221

10

207

228.1

234

219

226

223

237

230

227

232

Average

213.7

224.5

231.6

225.9

218.8

218

232.5

222.1

225

228.4

the appropriate priority rule for every scheduling state and reduces the makespan by 3%–6%. Despite these contributions, the study still has several limitations. First, this study only uses simulation instances to verify the effectiveness of D2 TAWP. However, real-world IC projects should involve diversified tasks and a wider variety of resources. The algorithm’s performance should be further evaluated in the MWPSP of real-world IC projects. Second, this study only focuses on the strict precedence relations among work packages. Namely, only three sequentially scheduled work packages: production, transportation, and on-site assembly, are considered in this study. The generalized precedence (e.g., complex coupled relations) is not investigated with more fine granular work packages inside production, transportation, and on-site assembly.

6 Conclusion IC project is the leading enabler of construction 4.0 as it follows industrialized principles. Adaptive scheduling is critical to IC project success. This study proposes a D2 -TAWP approach to realize adaptive project scheduling. First, the IC project scheduling problem is reduced as an MWPSP, which can be modeled as a Markov decision process. Second, a two-layer adaptive scheduling approach is developed, and the DDQN designed in the upper layer guides the heuristic in the lower layer to solve the MWPSP. Finally, the approach’s effectiveness is verified by simulation-based IC project experiments. The experimental results show that the D2 -TAWP can get the optimum solution in all instances compared with the RPR heuristic and SPR heuristic. This indicates D2 -TAWP’s ability to intelligently select priority rules based on the real-time scheduling states of work packages rather than through empirical decisions. Therefore, less experienced project managers can directly use D2 -TAWP for project scheduling, reducing the learning cost of

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understanding projects with different features. And the D2 -TAWP provided a reference for the intelligent scheduling of IC projects. Several topics remain open for future studies. First, the D2 -TAWP still relies on priority rules to generate project schedules. The future study will investigate the end-toend adaptive scheduling from task to schedule by using reinforcement learning methods. Using reinforcement learning to construct mapping relationships between work packages and schedules instead of relying on priority rules to generate schedules. Second, the stochastic model with varied task durations will be developed to enhance the robustness of the adaptive scheduler used in real-world situations. In conclusion, the present study can improve the adaptive scheduling of IC projects while encouraging further research in this field. Acknowledgement. This research was supported by grants from the Research Grants Council of the Hong Kong SAR of China, and the National Natural Science Foundation of China, The University of Hong Kong (RGC Project No.15219422 & G-HKU502/22, NSFC Project No. 72201228, HKU Project No. 2201100548, 2023A1515011162).

References 1. Razkenari, M., Bing, Q., Fenner, A., Hakim, H., Costin, A., Kibert, C.J.: Industrialized construction: emerging methods and technologies. 352–359 (2019) 2. Razkenari, M.A., Fenner, A.E., Hakim, H., Kibert, C.J.: Training for Manufactured Construction (TRAMCON) – Benefits and Challenges for Workforce Development at Manufactured Housing Industry, Modular and Offsite Construction (MOC) Summit Proceedings (2018) 3. Li, C.Z., Xu, X., Shen, G.Q., Fan, C., Li, X., Hong, J.: A model for simulating schedule risks in prefabrication housing production: a case study of six-day cycle assembly activities in Hong Kong. J. Clean. Prod. 185, 366–381 (2018) 4. Li, X., Wu, C., Xue, F., Yang, Z., Lou, J., Lu, W.: Ontology-based mapping approach for automatic work packaging in modular construction. Autom. Constr. 134, 104083 (2022) 5. Chen, Z., Demeulemeester, E., Bai, S., Guo, Y.: Efficient priority rules for the stochastic resource-constrained project scheduling problem. Eur. J. Oper. Res. 270(3), 957–967 (2018) 6. Sutrisna, M., Ramanayaka, C.D.D., Goulding, J.S.: Developing work breakdown structure matrix for managing offsite construction projects. Archit. Eng. Des. Manag. 14(5), 381–397 (2018) 7. Li, X., Wu, C., Yang, Z., Guo, Y., Jiang, R.: Knowledge graph-enabled adaptive work packaging approach in modular construction. Knowl.-Based Syst. 260, 110115 (2023) 8. Servranckx, T., Vanhoucke, M.: A tabu search procedure for the resource-constrained project scheduling problem with alternative subgraphs. Eur. J. Oper. Res. 273(3), 841–860 (2019) 9. Gonçalves, J.F., Mendes, J.J.M., Resende, M.G.C.: A genetic algorithm for the resource constrained multi-project scheduling problem. Eur. J. Oper. Res. 189(3), 1171–1190 (2008) 10. Chen, H., Ding, G., Zhang, J., Qin, S.: Research on priority rules for the stochastic resource constrained multi-project scheduling problem with new project arrival. Comput. Ind. Eng. 137, 106060 (2019) 11. Browning, T.R., Yassine, A.A.: Resource-constrained multi-project scheduling: priority rule performance revisited. Int. J. Prod. Econ. 126(2), 212–228 (2010) 12. Villafáñez, F., Poza, D., López-Paredes, A., Pajares, J., Olmo, R.D.: A generic heuristic for multi-project scheduling problems with global and local resource constraints (RCMPSP). Soft. Comput. 23(10), 3465–3479 (2018). https://doi.org/10.1007/s00500-017-3003-y

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13. Liu, D., Xu, Z., Li, F.: A three-stage decomposition algorithm for decentralized multi-project scheduling under uncertainty. Comput. Ind. Eng. 160, 107553 (2021) 14. Tian, M., Liu, R.J., Zhang, G.J.: Solving the resource-constrained multi-project scheduling problem with an improved critical chain method. J. Oper. Res. Soc. 71(8), 1243–1258 (2020) 15. Cui, L., Liu, X., Lu, S., Jia, Z.: A variable neighborhood search approach for the resourceconstrained multi-project collaborative scheduling problem. Appl. Soft Comput. 107, 107480 (2021) 16. Liu, D., Xu, Z.: A multi-PR heuristic for distributed multi-project scheduling with uncertain duration. IEEE Access 8, 227780–227792 (2020) 17. Chen, J.C., Lee, H.-Y., Hsieh, W.-H., Chen, T.-L.: Applying hybrid genetic algorithm to multimode resource constrained multi-project scheduling problems. J. Chin. Inst. Eng. 45(1), 42–53 (2022) 18. Owida, A.: Resource constrained multi-project scheduling using priority rules: application in the deep-water construction industry. In: International Conference on Industrial Engineering and Operations Management (2020) 19. Ren, J.F., Ye, C.M., Yang, F.: A novel solution to JSPs based on long short-term memory and policy gradient algorithm. Int. J. Simul. Model. 19(1), 157–168 (2020) 20. Park, J., Chun, J., Kim, S.H., Kim, Y., Park, J.: Learning to schedule job-shop problems: Representation and policy learning using graph neural network and reinforcement learning. Int. J. Prod. Res. 59(11), 3360–3377 (2021) 21. Shahrabi, J., Adibi, M.A., Mahootchi, M.: A reinforcement learning approach to parameter estimation in dynamic job shop scheduling. Comput. Ind. Eng. 110, 75–82 (2017) 22. Li, Z., Wei, X., Jiang, X., Pang, Y.: A kind of reinforcement learning to improve genetic algorithm for multiagent task scheduling. Math. Probl. Eng. 2021, e1796296 (2021) 23. Li, C.-L., Hall, N.G.: Work package sizing and project performance. Oper. Res. 67(1), 123– 142 (2019) 24. Vanhoucke, M., Coelho, J., Debels, D., Maenhout, B., Tavares, L.V.: An evaluation of the adequacy of project network generators with systematically sampled networks. Eur. J. Oper. Res. 187(2), 511–524 (2008) 25. Han, B.-A., Yang, J.-J.: Research on adaptive job shop scheduling problems based on dueling double DQN. IEEE Access 8, 186474–186495 (2020) 26. Osband, I., Blundell, C., Pritzel, A.,Van Roy, B.: Deep exploration via bootstrapped DQN. In: Advances in Neural Information Processing Systems, vol. 29 (2016) 27. Fedus, W., et al.: Revisiting fundamentals of experience replay. In: International Conference on Machine Learning, pp. 3061–3071 (2020) 28. Wen, L., Li, X., Gao, L.: A new reinforcement learning based learning rate scheduler for convolutional neural network in fault classification. IEEE Trans. Ind. Electron. 68(12), 12890– 12900 (2021) 29. Wang, Z., Schaul, T., Hessel, M., Hasselt, H., Lanctot, M., Freitas, N.: Dueling network architectures for deep reinforcement learning. In: Proceedings of the 33rd International Conference on Machine Learning, pp. 1995–2003 (2016) 30. Lin, C.-C., Deng, D.-J., Chih, Y.-L., Chiu, H.-T.: Smart manufacturing scheduling with edge computing using multiclass deep Q network. IEEE Trans. Ind. Inf. 15(7), 4276–4284 (2019) 31. Li, H., Cao, Y., Lin, Q., Zhu, H.: Data-driven project buffer sizing in critical chains. Autom. Constr. 135, 104134 (2022)

Research on the Differences of Job Stressors Among Construction Project Managers in China Haoran Xu(B) , Shang Zhang, and Qiqing Zhong Department of Construction Management, Suzhou University of Science and Technology, Suzhou, China [email protected]

Abstract. Construction project managers are under high level of job stress today because of the characteristics of contemporary construction projects, such as large investment, tight project schedule, high requirements and significant influence on the society and public. Different types of construction project managers have been under different job stressors. Based on literature review, this paper adopts questionnaire survey and interview methods to identify the job stressors experienced by Chinese construction project managers and analyze the differences of various construction project managers’ job stressors. The results indicate that there is no significant difference in job stressors for construction project managers with different genders. Significant differences exist in the career-development job stressors of construction project managers with different ages. In addition, there are significant differences in the job stressors of the organizational structure of construction project managers in different organizations. The research results can provide reference for the job stress management of construction project managers in the Chinese construction industry. Keywords: Construction project managers · Job stressor · China

1 Introduction The construction industry, which has been a cornerstone of the growth of the Chinese economy, is expanding in scope and employing more people as a result of the real estate market’s growth and the nation’s infrastructure’s rapid development. By the end of 2021, the total output value of China’s construction industry reached 2,9307.931 billion Yuan, accounting for 25.86 percent of GDP, and the number of employees in the construction industry reached 52,829,400 (National Bureau of Statistics). Construction project manager, as a significant part of Chinese construction industry workers (Bi et al. 2016), play a crucial role in the success of construction projects. According to Wu and Nie (2020), high job stress is a characteristic of workers in high-risk industries like construction. Job stress is prevalent among construction project managers (Jebelli et al. 2019). Contemporary construction projects are characterized by large investment, tight schedule, high requirements, complex technology and significant influence (Zhang et al. 2020). Moreover, the outbreak of COVID-19 pandemic © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 1042–1054, 2023. https://doi.org/10.1007/978-981-99-3626-7_80

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has increased the difficulty of project management. As a result, the job requirements for construction project managers become more and more stringent, and their job stress rises accordingly. High job stress not only has a serious negative impact on the physical and mental health of project managers in the construction industry (Chan et al. 2020), but also leads to an increase in public expenditure on occupational health services. Moreover, it is easy to lead to the decline of job satisfaction and performance, job burnout, leave or resignation of project managers (Leung et al. 2017; Liang et al. 2021), thus affecting the realization of the construction objectives. Different types of project managers have significant differences in personality traits and job responsibilities. For instance, male project managers bear more financial burden on their families and have more demanding requirements in their work, whereas female project managers devote more time and effort to taking care of their families (Song 2017). Project managers with high level of education also have more demanding requirements in terms of jobs, development opportunities and salary (Huang 2020). Project managers’ job stability in state enterprises is higher than project managers in private enterprises (Lu 2014). Therefore, this paper adopts an empirical investigation method to identify the factors of Chinese project managers’ job stressors through factor analysis, and analyze the difference of job stressors of different kinds of project managers to provide a reference for the job stress management of project managers in the Chinese construction industry.

2 Literature Review Job stressor is the various factors that lead to job stress, and job stressors are the foundation of research on job stress (Sun et al. 2021). Because different research fields and subjects have their own characteristics, no uniform standard for classifying job stressors has been formed in previous research. Currently, job stressor research focuses on industry characteristics, and job stressor structure is analyzed based on job characteristics of research objectives (Huang 2020). Leung et al. (2010) divided job stressor into job task stressor, organizational stressor and individual stressor when studying job stress in the field of construction project management. Based on this classification criteria, this paper will further investigate the job stress of Chinese construction project managers. 2.1 Job Task Stressor Job stress is the job itself that contributes to stress, including: work overload and role ambiguity (Leung et al. 2017; Zhang and Sun 2018; Sun et al. 2021). Work overload is when the amount of work is more than the individual can handle or the difficulty of the work is greater than the person can handle. Work overload can be caused by frequent overtime work, multiple tasks in hand, high level of responsibilities, and high job demands (Leung et al. 2008). Role ambiguity refers to situations where individuals are uncertain about how to perform their work because their specific tasks, responsibilities, or job requirements at work are unclear. Because of the large investment and enormous scale of modern construction projects, project managers tend to have unclear roles if they are unclear about the specific tasks, responsibilities, and powers of each individual project manager (Liang et al. 2021).

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2.2 Organizational Stressor The factors leading to job stress by the organization or within the organization are called organizational stressors, which mainly include organizational structure, work environment and organizational support (Haynes and Love 2004; Leung et al. 2008; Bowen et al. 2014). Organizational stress happens due to a complex organizational structure, long processes, many rules and regulations, and centralized organization (Leung et al. 2008). In addition, complex organizational structure and long organizational processes tend to cause the project information flow is not timely, not only affects communication and feedback among project managers, but also easily affects the project management progress, which causes problems for project managers. Workplace stressor refers to various environmental impact factors that lead to workplace stress. Research by Liang et al (2021) has found that poor working conditions (e.g. crowded working conditions, noise, lack of sanitary facilities, extreme temperatures, etc.) are prone to stress among construction workers. Organizational support mainly includes support from superiors, colleagues, and subordinates (Lu 2016). Studies have found that cooperation between colleagues and cooperation between subordinates is conducive to reducing job stress. 2.3 Individual Stressor Personal factors contributing to occupational stress are called individual stressors. Individual stressors include personality traits, interpersonal relations and work family conflict (Wang 2014; Leung et al. 2017; Zhang and Sun 2018). Individual characteristics are divided into Type A personality and Type B personality (Zhang and Sun 2018). Leung and Chan (2012) found that employees with Type B personality traits were more satisfied with the status quo, had no desire for advancement and were less stressed about their jobs than those with Type A personality traits. Relationships mainly include relationships with superiors, colleagues or subordinates. Research studies have shown that close and harmonious relationships between construction workers and superiors, colleagues or subordinates can enhance job performance and job satisfaction, thus mitigating the impact of job stress (Wang 2014; Huang 2020). A work-family conflict arises when employees are too busy with work to spend a lot of time with family and friends. Project managers tend to have less time spending with their families in recent years because of the project’s tight schedule. Even project managers working not far from their home are subject to work-family conflict because of the need for long hours (Joy et al. 2021). 2.4 Career Development Stressor Career development stressors are related to job promotion, skill enhancement, payment and career development prospects (Leung et al. 2007; Zhou 2011; Xu 2014). Xu (2014) found that the main causes of intellectual worker stress are single career paths, lack of learning opportunities, and lack of social status. Wang (2014) also found in his research on construction workers that vague career prospects, less scope for advancement, and declining industry growth all contribute to stress. Furthermore, research by

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Huang (2020) on architects in China has found that unreasonable compensation packages (e.g., no corresponding wage subsidy for extended overtime) may also lead to job stress.

3 Research Methodology 3.1 Questionnaire Design The questionnaire is based on extensive literature review, referring to Leung et al. (2007), Chan et al. (2018), and Bowen et al. (2014). The questionnaire consists of three parts. The first part is to collect basic information of the respondents. The second part is job stressor survey and the third part is open question. Five-point Likert scales were used to measure the level of agreements of the respondents on the questions, with “1” indicating total disagreement and “5” expressing complete agreement. 3.2 Data Collection A questionnaire was distributed to project managers to investigate the differences in the job stressor of project managers in the Chinese construction industry. Influenced by COVID-19 epidemic outbreak, the distribution of the questionnaire takes two forms: face-to-face and online. The questionnaire survey was conducted to the following types of organizations: clients, general contractors, subcontractors and suppliers. Overall, 41 face-to-face and 171 online survey samples were collected, and 191 valid questionnaires obtained for analysis. Basic information on respondents is provided in Table 1. Table 1 shows that the majority of the respondents in this survey were male project managers (87.4%); More than 80% of the respondents were more than 26 years old and 53.4% were older than 30 years; The sample size was evenly distributed among the three types of organizations. Highest number of respondents (47.10%) were general employees followed by heads of departments (42.90%) and senior managers (10.00%).

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H. Xu et al. Table 1. Demographic profile of survey respondents

Variables

Categories

Frequency

Proportion (%)

Gender

male

167

87.40

female

24

12.60

21–25 years old

38

19.90

26–30 years old

51

26.70

31–35 years old

30

15.70

36–40 years old

40

17.80

41–45 years old

22

11.5

46–50 years old

11

5.80

Age 51 and older

5

2.60

Client

65

34.00

Contractor

68

35.60

Subcontractor/supplier

58

30.40

Age

Organization type

Position

General staff

90

47.10

Head of department or discipline

82

42.90

Senior manager

19

10.00

4 Statistical Analysis 4.1 Reliability Tests As the KMO sample measure is closer to 1, the correlation scale is better suited for exploratory factor analysis. Values of KMO above 0.9 indicate that it is suitable for exploratory factor analysis, KMO between 0.8 and 0. 9 indicates that it fits exploratory factors analysis, and KMO values below 0.6 indicate that they are not suitable for explorative factor analysis (Ma 2002). KMO of the job stressor scale in the present study was above 0.9 and Bartlett’s ball P physical examination was below 0.05, suggesting that the job stressor scale in this study is well suited for exploratory factor analysis. In addition, each indicator in turn needs to be removed for subsequent reliability tests, and if Cronbach’s Alpha coefficient is greater than the overall value (0.944 in this paper), because it does not contribute to overall reliability. In the case of the KMO of the whole scale of the job stressor, the value of 0.920 > 0.9, the Bartlett Sphere test P is 0.000 < 0.05, and the number of valid scales of sample size and work stressor is greater than 5:1. 4.2 Exploratory Factor Analysis of Job Stressors The common factor in this paper was extracted by principal component analysis, and the extracted factor was rotated orthogonal by maximum-variance method (Ma 2002). In this paper, exploratory factor analysis resulted in 9 common factors of job stressor with

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variance interpretation percentage of 11.83%, 11.66%, 9.87%, 9.55%, 8.40%, 8.02%, 6.83% 5.11% and 2.11%, respectively. The overall variance interpretation percentage of 76.38% > 70% (Wu 2018) with each common factor corresponding to eigenvalue higher than 1. The results of rotating the composition matrix are shown in Table 2. Table 2. Rotated composition matrix

Factors

Item

Item description

Facto

Alph

r load

a

A1

Often working overtime adds to my job stress.

0.614

A2

The amount of work was overwhelming.

0.779

A3

Multitasking has put lots of stress on my work.

0.735

A7

The long working hours put me under a lot of stress.

0.548

Work overload

0.839

The repetitive and tedious nature of my work added to my A4

0.506 stress.

Role

0.538 ambiguity

Lack of clarity about my job responsibilities or tasks became A6

0.674 a major source of stress.

A5

The responsibility of my job adds to the stress.

0.508

The quality of the project is very demanding and has A8

0.714 become the main stress of my work.

High job

0.849 requirements

The progress of the project is very demanding and has A9

0.638 become the main stress of my work.

A10

The high cost of the project is the main stress of my job.

0.725

(continued)

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H. Xu et al. Table 2. (continued) Construction projects have high safety requirements, which 0.751

A11 has become the major stress of my job. Multiple rules and regulations, complex organizational Organizational

B1

0.627 structure and lengthy processes lead to job stress.

0.706

structure B2

The lack of decision making at work was stressful for me.

0.634

The company work environment is not good (noisy, B3

0.710

Poor working

crowded), let me feel very stressed.

environment

The remoteness and mobility of my workplace added to the

0.715 B4

0.704 stress. The company's leadership did not support my work, which

B5

0.771 added to my stress. My colleagues in the company do not cooperate with my

Organizational B6

0.802

0.878

work, which adds to my job stress.

support

The subordinates of the company did not cooperate with my B7

0.795 work, which increased my job stress.

C1

I'm under a lot of stress to deal with leadership.

0.467

C2

I'm under a lot of pressure to deal with colleagues.

0.780

C3

I'm under a lot of pressure to deal with subordinates.

0.771

Interpersonal 0.867

relationship

I had so much invested in my job that I often had family C4

0.740 issues.

Work-family

0.815 conflict

Often my family or friends complain that I always give C5

0.889 priority to work and spend too little time together.

D1

Difficult career advancement adds to my job stress.

0.837

The difficulty of getting a professional raise adds to the D2

0.632 stress.

Career

0.863 development

My stress is compounded by the fact that the job outlook in D3

0.829 the construction industry is less ideal.

D4

Low pay, poor benefits and low wages added to my stress.

0.792

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For item C1, the corresponding factor load value was 0.467 < 0.50, indicating that this item had low consistency with the other statements. Consequently, item C1 was removed from the job stressors in interpersonal relationships. Cronbach Alpha coefficient corresponding to role ambiguity was 0.538 < 0.55, which indicates that the overall reliability of role ambiguity had low consistency, and this stressor was removed. Finally, after removing the factors that did not meet the requirements, exploratory factor analysis on job stressors obtained eight factors, namely: work overload, high job requirements, organizational structure, poor working environment, organizational support, interpersonal relationship, work-family conflict, and career development. (1) A1, A2, A3 and A7 are work overload factors. Work overload is defined as work that is too large or difficult to perform beyond an employee’s normal work ability, typically in the form of overtime, multitasking, or too much responsibility (Leung et al. 2008). (2) A5, A8, A9, A10, and A11 are related to the high job requirement factors. High job requirements refer to the high requirements of the project in terms of cost, schedule, quality and safety management. That is the project must be completed within the limited time and budget cost, and ensure that the project safety and quality meet the relevant requirements (Zhu 2021). These factors will increase the project manager’s stress if they can not meet or have challenges to meet. (3) Organizational structure factors include B1 and B2. Organizational structure stress is related to a complex organizational structure, tedious management processes, complicated rules and regulations, and limited decision-making power, which leads to increased task allocation and difficulty (Leung et al 2017; Zhang and Sun 2018). (4) B3 and B4 are related to poor working environment factors. Poor working environment mainly includes noisy or crowded working space, remote workplaces and poor transport options (Leung et al. 2010; Liang et al. 2021). (5) B5, B6 and B7 are organizational support factors. Organizational Support stressors include organizational factors associated with job stress such as lack of superior support, lack of cooperation from colleagues, and lack of cooperation from subordinates (Sun et al. 2021). (6) C2 and C3 are interpersonal relationship factors. Interpersonal relationship job stressors are defined as the amount of time and effort individuals have to invest in managing relationships with their superiors, colleagues and subordinates (Lu 2016). (7) C4 and C5 are work-family conflict factors. Work-family conflict occurs when individuals have limited energy and time to balance work and family life (Leung et al. 2016; Zhu 2021). (8) D1, D2, D3 and D4 are career development factors. Career development stressors include, low payment, less opportunities for promotion and raising salaries, poor job prospects and difficulties in upgrading professional skills (Zhou 2011; Xu 2014). 4.3 Difference Analysis of Job Stressors for Different Types of Project Managers In order to reveal the differences of job stressors among project managers of different genders, ages and organizations, SPSS software was used to analyze the differences of job stressors. The Kolmogorov-Smirnov assay (K-S assay) was first used to analyze

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the normality of the job stressor data, which revealed that the sample data is not normally distributed (P = 0.000 < 0.05). Therefore, non-parametric tests were used for comparison. Mann-Whitney U test was used for two independent samples (gender) and Kruskal-Wallis H test was used for multiple independent samples (age and organization). 4.3.1 Differences in Stressors Among Project Managers with Different Genders In this paper, the Mann-Whitney U-shaped test method was used to explore the differences in job stressors of project managers of different genders in the construction industry in China. The statistical analysis results are shown in Table 3. Table 3. Differences in stressors among project managers with different genders Variables

Average Male

P-value Female

Work overload

3.67

3.57

0.661

High job requirements

3.64

3.51

0.379

Organizational structure

3.66

3.58

0.563

Poor working environment

3.28

3.08

0.315

Organizational support

3.24

3.04

0.412

Interpersonal relationships

3.28

3.19

0.493

Work-family conflict

3.46

3.25

0.406

Career development

3.76

3.92

0.366

Note: * denotes p-value < 0.05, indicating significant difference

As shown in Table 3, there is no significant difference in job stressors among project managers of different genders in the Chinese construction industry. It can also be seen from Table 3 that women have higher career development stressor in professional development than men, while men have higher job stressor in professional fulfillment than women in all seven categories except career development stress. Major reasons include: (1) Under the influence of traditional attitudes (i.e. men are considered more suitable for jobs related to construction), women not only have fewer opportunities than men to advance in careers, but also have difficulty in reaching the top and middle levels of project management. As a result, women project managers have a greater source of career development stress than men (Peng 2011). (2) Men’ s social role differs from that of women’s (i.e., men are considered to be the “backbone” of the household’ s economic income in China), and the middle and upper positions of construction projects are mostly occupied by men, resulting in more intense and difficult tasks for male project managers as a whole. Therefore, the overall job stressor of men is larger than that of women (Cui 2018).

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4.3.2 Differences in Stressors Among Project Managers of Different Ages In this paper, the Kruskal Waillis test was used to examine the differences in job stressors among project managers of different ages in the construction industry. The results are shown in Table 4: Table 4. Differences in stressors among project managers of different ages Variables

Mean value

P-value

21–25

26–30

31–35

36–40

41–45

46–50

Above 50

Work overload

3.66

3.68

3.75

3.72

3.38

3.57

3.75

0.600

High job requirements

3.51

3.64

3.54

3.81

3.49

3.76

3.76

0.410

Organizational structure

3.65

3.66

3.88

3.75

3.32

3.55

3.10

0.092

Poor working environment

3.30

3.17

3.42

3.27

3.25

3.23

3.00

0.904

Organizational support

3.20

3.20

3.18

3.26

3.14

3.58

2.87

0.774

Interpersonal relationships

3.21

3.34

3.32

3.29

3.05

3.46

3.10

0.799

Work-family conflict

3.34

3.33

3.72

3.37

3.41

3.50

3.70

0.695

Career development

3.92

3.82

4.01

3.74

3.48

3.52

3.05

0.011*

Note: * denotes p-value < 0.05, indicating significant difference

Table 4 shows that there were significant differences in job stressors of career development among project managers of different age in the Chinese construction industry (P = 0.011 < 0.05). Project managers under 40 years of age have significantly higher level of perception on job stressors in career development than project managers over 50 years. Project managers under the age of 40 are in the ascent phase of their careers and are under intense stress to get promoted. In contrast, project managers older than 50 years tend to have become middle and senior managers of enterprises, in turn they tend to have higher position and high salaries (Zhang 2009). Therefore, career development of project managers under 40 years old is more stressful. The career development job stressors of project managers aged 41–45 (3.52) were significantly lower than those of project managers aged 21–25 (3.92) and 31–35 (4.01). Major reasons include: (1) Because project managers between 21–25 years old are new to the workforce, and their early career development involves various types of training, familiarity with the company, and the need to respond to various competency assessments within the company or project, their professional development work may be a source of considerable stress (Sai 2020).

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(2) Most project managers between 31–35 years old have got married and have higher salary expectations. In addition, project managers aged 31–35 years have some work experience and are in a process of promotion to a department head. Therefore, project directors between 31–36 years old have more stressors for their career development (Wang 2014). (3) Usually, 41–45 year old project managers have become middle positioned managers of companies or projects, with relatively high salaries and social status. Moreover, due to very limited senior management positions, their expectation to move to senior management positions is relatively low. Therefore, 41–45 year old project managers have relatively few stressors for their career development (Peng 2011). 4.3.3 Differences in Job Stressor for Project Managers of Different Organizations Since the organization types are larger than two categories, the Kruskal Waillis test is used to analyze the differences in the job stressors of project managers in different organization types in the Chinese construction industry. The results of this analysis are shown in Table 5: Table 5. Differences in job stressors of project managers of different types of organizations Variables

Average Client

P-value Contractor

Subcontractor/supplier

Work overload

3.62

3.70

3.65

0.798

High job requirements

3.67

3.59

3.69

0.637

Organizational structure

3.75

3.75

3.41

0.016*

Poor working environment

3.21

3.27

3.30

0.718

Organizational support

3.13

3.23

3.28

0.680

Interpersonal relationships

3.28

3.33

3.19

0.716

Work-family conflict

3.47

3.36

3.47

0.631

Career development

3.87

3.72

3.75

0.299

Note: * denotes p-value < 0.05, indicating significant difference

As shown in Table 5, there are significant differences in job stressors of the organizational structure for project managers of different types of organizations in construction industry (P = 0.03 < 0.05). The organizational structure job stressors for project managers/suppliers in the Chinese construction industry (3.41) are significantly smaller than for project managers of client organizations (3.75), and for project managers of general contractors (3.75). As the lowest level of project participants are contractors/subcontractors, their organizational structure is simpler, less structured than for project managers and general construction contractors (3.75).

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5 Conclusion As the Chinese construction industry develops rapidly, project managers face increasing job stress. Job stressors vary among different types of project managers. Drawing on literature review, questionnaire survey and interview results, this paper identifies eight key factors of job stressors for construction project managers, and analyses the differences in job stressors among different types of construction projects managers (gender, age and organization). There were significant differences in career development job stressor among project managers of different age groups, with those aged under 40 significantly greater than those aged over 50, those aged between 41–45 (3.52) significantly lower than those aged between 21–25 (3.92) and those aged between 31–35 (4.01). There were significant differences in organizational structure job stressor for project managers of different organization types, with subcontractor/supplier project managers having significantly lower organizational structure job stressor (3.41) than general contractor project managers (3.75) and client project managers (3.75). The research results can provide reference for the project manager’s job stress management in the Chinese construction industry.

References Bi, T.P., Yang, X.M., Gao, D.Y.: Analysis on the development situation of construction enterprises in China under the new normal economy. Constr. Econ. 37(04), 5–7 (2016). (in Chinese) Wu, N., Nie, L.: Effects of job stress on the safety behavior of site builders: the role of mental toughness regulation. Saf. Environ. Eng. 27(06), 119–125 (2020). (in Chinese) Jebelli, H., Choi, B., Lee, S.H.: Application of wearable biosensors to construction sites. I: Assessing workers’ stress. J. Constr. Eng. Manag. 145(12), 04019079 (2019) Zhang, S., Sunindijo, R.Y., Loosemore, M., et al.: Identifying critical factors influencing the safety of Chinese subway construction projects. Eng. Constr. Archit. Manag. (2020) Chan, A.P.C., Nwaogu, J.M., Naslund, J.A.: Mental ill-health risk factors in the construction industry: systematic review. J. Constr. Eng. Manag. 146(03), 04020004 (2020) Leung, M.Y., Liang, Q., Chan, I.: Development of a stressors-stress-performance-outcome model for expatriate construction professionals. J. Constr. Eng. Manag. 143(5), 04016121.1– 04016121.11 (2017) Liang, Q., Leung, M.Y., Ahmed, K.: How adoption of coping behaviors determines construction workers’ safety: a quantitative and qualitative investigation. Saf. Sci. 133, 105035 (2021) Song, J.: Effects of job stress on overall well-being: Intermediating work values and teamwork. Zhejiang University (2017). (in Chinese) Huang, J.J.: Study on the relationship between job stress and job satisfaction of architectural architects in China. Suzhou University of Technology (2020). (in Chinese) Lu, L.: Study on the relationship between job stress, job satisfaction and separation tendency of technical workers in construction industry. Central China Normal University (2014). (in Chinese) Sun, C.L., Zhang, S., Zhong, Q.Q., Zhu, Y., Zhou, H.: Study on job stressor of construction project cost professionals in China. Eng. Econ. 31(01), 18–22 (2021). (in Chinese) Leung, M.Y., Chan, I.Y.S., Yuen, K.W.: Impacts of stressors and stress on the injury incidents of construction workers in Hong Kong. J. Constr. Eng. Manag. 136(10), 1093–1103 (2010) Zhang, H., Sun, Y.F.: Stress Management of Project Managers in Construction Enterprises. China Construction Industry Press, Beijing (2018). (in Chinese)

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Leung, M.Y., Zhang, H., Skitmore, M.: Effects of organizational supports on the stress of construction estimation participants. J. Constr. Eng. Manag. 134(02), 84–93 (2008) Haynes, N.S., Love, P.E.E.: Psychological adjustment and coping among construction project managers. Constr. Manag. Econ. 22(02), 129–140 (2004) Bowen, P., Edwards, P., Lingard, H., Cattel, K.: Workplace stress, stress effects and coping mechanisms in the construction industry. J. Constr. Eng. Manag. 140, 04013059 (2014) Lu, W.H.: Study on the impact of job stress on project performance. Suzhou University of Technology (2016). (in Chinese) Cui, X.Y.: Relationship between job stress, job satisfaction, and career success among managers of construction enterprises: the role of psychological capital as an intermediary. Sichuan Normal University (2018). (in Chinese) Wang, X.N.: A study on the relationship between job stress, organizational support and work commitment in state-owned construction enterprises. Beijing Jiaotong University (2014). (in Chinese) Leung, M.Y., Chan, I.Y.S.: Exploring stressors of Hong Kong expatriate construction professionals in Mainland China: focus group study. J. Constr. Eng. Manag. 138(01), 78–88 (2012) Zhu, Y.: Research on the job stress of project managers in China: based on the whole process of project implementation. Suzhou University of Science and Technology (2021). (in Chinese) Leung, M.Y., Skitmore, M., Chan, I.Y.S.: Subjective and objective stress in construction cost estimation. Constr. Manag. Econ. 25, 1063–1075 (2007) Zhou, Y.Z.: A study on the relationship between job stress, job satisfaction and job commitment of subway construction managers. Zhejiang University (2011). (in Chinese) Xu, Y.: Study on the relationship between job stress and job performance of knowledge-based employees in architectural design industry. Southwest University of Finance (2014). (in Chinese) Chan, I.Y.S., Leung, M.Y., Liang, Q.: The roles of motivation and coping behaviours in managing stress: qualitative interview study of Hong Kong expatriate construction professionals in Mainland China. Int. J. Environ. Res. Public Health 15, 561 (2018) Ma, Q.G.: Management Statistics: Data Acquisition, Statistical Principles, SPSS Tools and Applied Research. Science Press, Beijing (2002). (in Chinese) Nunnally, J.C.: Psychometric Theory. McGraw-Hill, New York (1978) Wu, M.L.: Questionnaire Statistics and Analysis Practice-Operation and Application of SPSS. Chongqing University Press, Chongqing (2018). (in Chinese) A study on the relationship between job stress, self-efficacy and work commitment in construction technology management. Hunan Normal University (2011). (in Chinese) Zhang, Z.N.: Relationship model analysis of organizational support, work-family conflict, and job satisfaction. Zhejiang University (2009). (in Chinese) Sai, Y.X.: A study on the relationship between job stress and the propensity of Laotian construction workers to leave their jobs: the mediating role of burnout. Guangxi University (2020). (in Chinese)

Research on the Influencing Factors of Concrete Waste Production in the Whole Process Zhi-yu Huang1(B) , Qi-li Li2 , Ye Liu2 , Yan Li2 , and Rui Liu2 1 Department of Chongqing University of Science and Technology, Chongqing, China

[email protected] 2 School of Chongqing University of Science and Technology, Chongqing, China

Abstract. In recent years, the amount of commercial concrete increases year by year, many concrete wastes cause the waste of resources, but also increases the production cost of commercial concrete companies. Aiming at concrete order, production, transportation and installation of the whole production process, through literature study and expert interview, identify 31 waste, the impact of factors, questionnaire, and then use SPSS20.0 software for recycling of 211 valid questionnaire summary and statistics today, using the factor analysis method to draw eight main influence factors, According to the weight, the importance of each factor is sorted, and corresponding suggestions and improvement measures are given, so as to provide reference for the source reduction of construction waste and realizing industrial optimization and transformation.. Keywords: Concrete waste · Whole process optimization management · Factor analysis · Common factor

1 Introduction Concrete has the characteristics of good durability, high compressive strength, strong plasticity, simple production process and low price, so that it has become the largest consumption of the world’s most resources consumption of building materials [1, 2]. But concrete in the production and construction process, will produce a lot of waste slag. Each year, more than 125 million tons of freshly mixed and unhardened concrete are returned to mixing plants as waste for various reasons [3]. According to incomplete statistics, China’s total ready-mixed concrete output in 2017 was 2.298 billion m3, while every 1 m produced 3 On average, concrete will produce 0.04 tons of waste slag in mixing stations [4], the preliminary estimate is that domestic mixing plants need to process more than 90 million tons of concrete waste every year. Mixing plants usually dispose of waste slag by transporting it to places designated by sanitation authorities for stacking or burying, which not only occupies a large amount of land and damages the environment, but also costs a lot of additional expenses such as management, transportation and resettlement [5]. With the implementation of the Environmental Protection Law and the Law of the People’s Republic of China on the Prevention and Control of Environmental Pollution by Solid Waste, the emission reduction and disposal of solid waste in construction has © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 1055–1068, 2023. https://doi.org/10.1007/978-981-99-3626-7_81

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attracted widespread attention. Follow the principle and purpose of “zero discharge of waste” [6]. Research on the optimization management of the whole process of concrete waste production chain can help reduce solid waste in construction, meet the development needs of the construction industry, and further keep up with the national environmental protection development strategy. The whole process production chain concrete waste refers to the waste produced in the whole process of production such as concrete ordering, production, transportation, and installation, which belongs to the construction waste and building materials production waste in the construction solid waste [7]. At present, the research on concrete waste mainly focuses on the recycling of concrete waste. Concrete waste is specially treated as the admixture for making concrete, brick, and block. This kind of research makes concrete waste have more recycling value, and a series of research results have been achieved [8–11].The second type of research is related to the construction solid waste management of concrete waste. This kind of research literature is few, and mainly focuses on the quantification of construction solid waste and the source reduction. For example, based on the construction waste treatment model and experience of developed countries, Rong Yuefang [12] according to Rong, it is necessary to reduce the production of construction waste through scientific management and guidance before it is generated. Li [13, 14] suggested that the solid waste quantification work can improve the level of solid waste management, and that requiring the contractor to estimate the total amount of solid waste in the solid waste management plan is helpful to realize the reduction of solid waste in the construction process. Qian-Kon wang [15] based on R language algorithm and technology mining technology, the waste production rate of concrete and other materials in the construction process is analyzed. It is pointed out that the decline of solid waste production rate is not significant, the waste concrete production rate is still at a high level, and the solid waste management level in China has significant regional differences. Lu Hao and Cui Jun Feng [16, 17] from the perspective of mixing station and construction site, the generation source and existing problems of concrete solid waste were discussed. Although the relevant literature has been explored at different levels to a certain extent, most of them still need to be improved. In terms of recycling and utilization of concrete waste resources, first of all, there is a gap in legislation and limited legal effect [18]. Secondly, there is a lack of large-scale practical production and engineering application, and the treatment cost of concrete waste is high and the product value is low [19]; However, the source reduction management is lack of pertinence, most of which is to analyze the overall construction solid waste, lack of discussion on the source reduction of a certain type of waste, especially lack of research on the whole process of concrete waste generation and quantitative research on the influencing factors of waste. The source reduction management is lack of pertinence, most of the overall construction solid waste analysis, lack of a type of waste source reduction research management. Due to the instability of concrete as a semi-finished material in transportation and construction, as well as the large number of personnel involved and the complex types of machinery and equipment involved, the reduction research based on the whole process is relatively few. Therefore, this paper studies the influencing factors of the whole process of concrete waste production chain, and pays attention to the reduction of concrete waste from the

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perspective of the whole process, which is conducive to reducing the production of concrete waste, improving the economic benefits of concrete enterprises, and promoting the sustainable development of the construction industry.

2 Research Methods 2.1 Literature Research Method Through the literature research method, the whole process of concrete waste is studied. Firstly, this paper sorted out and determined the influencing factors of concrete waste by searching the literature. Secondly, it is divided into four stages according to the concrete production process, so as to facilitate the follow-up research work. 2.2 Expert Interview Method Discuss and analyze a specific matter with six experts in the commercial concrete industry, collect their opinions through one-on-one interviews, and collate the results. After repeated discussion, the influencing factors were added and deleted, and the factors with similar consciousness were combined to finally improve the influencing factors of the whole process of concrete waste, which provided a basis for the production of the questionnaire. 2.3 Factor Analysis Method This paper uses SPSS20.0 software and factor analysis model to carry out factor analysis on the index data of influencing factors of concrete waste production chain in the whole process. According to the correlation difference among the 31 influencing factors of concrete waste, the original variables observed in concrete waste are divided into several groups with fewer number. By analyzing the rotated factor loading matrix, the common factors of the influencing factors of concrete waste in the whole process can be analyzed.

3 Identification of Influencing Factors 3.1 Literature Research to Identify Influencing Factors Relevant literature was retrieved by searching keywords such as “concrete waste” and “concrete production management” in databases such as CNKI and Wan fang Data. First, the possible causes of concrete waste were identified from the literature. Then, combined with the actual situation of the whole process of concrete production, these reasons are preliminarily summarized for these indicators, and the initial influencing factors are obtained. Finally, according to the whole process of production, the influencing factors are divided into four stages: ordering, production, transportation and construction, which is convenient to correctly sort out and summarize the influencing factors, and more conducive to carrying out the follow-up expert interview.

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3.2 Determining the Influencing Factors Through Expert Interviews According to the identified influencing factors, 6 experts were invited to interview and modify the influencing factors. Six experts were involved in concrete ordering, production, transportation and construction stages respectively, and their working years were all more than 10 years. They were more familiar with the direct factors of concrete waste, which was conducive to obtaining accurate, objective, and practical influencing factors. A few indicators inconsistent with the facts were deleted, and the indicators not identified by literature research were added, and the indicators with similar meanings were merged. Such as the “lack of raw material inspection process” and “concrete mix proportion and raw materials are incompatible,” “improper mixing time control” into “the lack of strict quality control of concrete process”, the “performance” “aggregate quality of raw material and gradation” “slump after the loss during” such as combined into “not according to the construction site, the weather and other circumstances considering concrete slump”; Delete “extreme seasonal climate”, “construction site and surrounding environment” and other factors that do not conform to the actual situation; Increase the influencing factors that may appear in the actual production, such as “the buyer provides wrong ordering information”, “the seller fills in the wrong basic information”, “the production scheduler frequently, misspends “, “the driver negligently takes the wrong project or the wrong label”, “the vehicle in transit is damaged or has a traffic accident” and so on. After numbering, the list of influencing factors is finally obtained (Table 1). Table 1. Identification of influencing factors of concrete waste in the whole process classification

Factors affecting the

coding

Order phase

The buyer filled in the wrong concrete label or part

X1

Seller fills in wrong basic information

X2

Communication barriers exist among departments of the commercial Concrete company, resulting in order errors

X3

Concrete operators lack professional concrete basic knowledge

X4

Production phase

Production personnel of concrete mixing station have weak X5 awareness of waste production Lack of maintenance and maintenance of concrete production equipment and facilities

X6

The amount of concrete is inconsistent, and the actual amount of concrete is less than the expected amount

X7

The mixer discharge device is faulty, or the diameter is too large

X8 (continued)

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Table 1. (continued) classification

Factors affecting the

coding

Lack of rigorous concrete quality control process

X9

Failure to consider concrete slump according to actual conditions such as construction site and weather

X10

Site technical service personnel fail to feedback the site situation in time

X11

Lack of reward and punishment measures for concrete waste X12

Transport phase

Concrete operators lack professional concrete basic knowledge

X13

Control system sensor stuck by a foreign object

X14

The residual concrete after unloading by mixing truck is not X15 cleaned in time Transportation distance is too long

X16

Unreasonable choice of transportation route increases the transportation time

X17

Vehicle damage or traffic accident in transit

X18

Driver negligently goes to the wrong item or wrong number X19 The site construction organization design is not reasonable, X20 and the waiting time for pouring is too long

Casting stage

Improper use and maintenance of tank truck, and the phenomenon of sticking tank appears

X21

The template has problems such as lax seam and mold explosion

X22

When the pouring is about to end, the concrete of the replenishment is not accurately calculated

X23

Pumping line connection problem

X24

Improper operation of concrete unloading personnel

X25

Pump tube swing control is not good

X26

Aging and damaged pumping equipment

X27

The casting process is not tight enough and takes too long

X28

Pipe blocking and pipe bursting occur during pouring

X29

Improper placement of pumping lines

X30

Casting allowance in untreated pumping line

X31

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4 Questionnaire Distribution and Data Processing 4.1 Questionnaire Distribution This survey mainly adopts two forms of electronic questionnaire and paper questionnaire, and the respondents are mainly practitioners of commercial concrete and construction engineering enterprises, mainly in Chengdu. The survey content mainly included the nature of the work unit, working years, education level and the degree of influence. The basic information of respondents is shown in Fig. 1 and Fig. 2. Degree of influence is scored by 5 points, which are very small, small, general, large, and very large in order, with 1–5 points respectively. The higher the score, the greater the impact. A total of 350 questionnaires were distributed, 1 of which 211 were valid, with an effective recovery rate of 60.3%. The scores of various influencing factors are shown in Fig. 3.

Fig. 1. Distribution of respondents’ work units

Fig. 2. Distribution of respondents’ working years

4.2 Data Processing 4.2.1 Reliability and Validity Analysis of the Questionnaire Reliability and validity are a basic measure to evaluate the reliability and validity of data. Therefore, the reliability and validity of the questionnaire should be tested [20]. In terms

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Fig. 3. Distribution of scores of each influencing factor

of the reliability of the questionnaire, Cronbach’s α coefficient was used for internal consistency test, and SPSS20.0 reliability analysis showed that Cronbach’s α coefficient was 0.723. In terms of validity, KMO test and Bartlett spherical test showed that the KMO value was 0.881, which was greater than 0.6, indicating that the validity was very good and suitable for factor analysis. The P value was less than 0.001, which was passed Bartlett’s sphericity test. The overall results showed that the collected questionnaire data met the requirements of factor analysis for validity analysis, and the questionnaire data were suitable for factor analysis. 4.2.2 Factor Analysis Factor analysis can not only divide complex variables into several categories, but also reduce the dimension of original data and reproduce the relationship between original variables and common factors [20]. Therefore, this study used factor analysis method to synthesize a group of complex variables into a few common factors, so as to reduce the dimension of complex samples and concentrate information. 4.2.3 Extraction of Feature Vectors and Eigenvalues The eigenvalue refers to the contribution degree of each factor before the index rotation. The larger the eigenvalue, the greater the factor contribution. According to the information in the total variance interpretation table, the factor with an eigenvalue greater than 1 is extracted as the common factor. Each factor has a variance interpretation rate, which represents the information extraction degree of the factor for the analysis items of 31 influencing factors. In factor analysis, the main concern is the rotated data, and the larger the variance explanation rate is, the more information the common factor contains in the original data. If the extracted eigenvalue is greater than 1 and the variance explanation rate is higher than 70%, the analysis is proved to be effective [21]. The eigenvalues and variance contribution rates of the factors were obtained by combining the correlation coefficient matrix. A total of 8 common factors had eigenvalues

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greater than 1, and the cumulative contribution rate was 86.648%, as shown in Table 3. The result show that 8 common factors can be extracted, and these 8 common factors can extract 86.648% of the information of 31 analysis items, and the information integrity of the extracted common factors is guaranteed. To determine the variables contained in each common factor more accurately, the Kaiser normalized maximum variance method was used for orthogonal rotation, and the explained variance and variance contribution rate of the original variables were redivided to obtain the rotated factor load and factor load matrix. To sum up, 31 influencing factors generated by concrete waste in the whole process were successfully transformed into 8 common factors through dimension reduction by factor analysis, as shown in Table 2. 4.2.4 Extract Common Factors and Name Them In this study, the Kaiser normal maximum variance method was used to carry out orthogonal rotation of the original data to obtain the rotated component matrix. Then, the common factors were named and explained according to the high load index of each common factor. X7, X15, X23 and X31 are related to the residual or residual of concrete in the machinery during the three stages of concrete transportation, installation, and construction. Therefore, the common factor 1 is named as the residual of concrete. X9, X10 mainly represents the concrete factory and field quality, so the public factor 2 named concrete quality and quality management defects; X8, X12, X24, X26, X29 and in the concrete in the production of transportation construction three stages of throwing and waste related, so the public factor 3 named as the throwing and waste of concrete; X4, X5, X13, X19, X25 are related to human negligence or improper treatment in the whole process of concrete production, so the public factor 4 is named as the quality of personnel and professional level; X1, X2, X3, X11 are all error information or effective information is timely feedback, so the public factor 5 is named as communication and coordination problem; X20, X22, X28, X30 are all related to the problems existing in the way or method during construction casting, so the common factor 6 is named as unreasonable casting technology and working procedure. X6, X14, X21 and X27 are all related to the presence of foreign bodies and caking in facilities and equipment, so the public factor 7 is named as the maintenance of facilities and equipment; X16, X17 and X18 are all related to transportation lines and transportation equipment, so the common factor 8 is named as transportation state and line planning problem. The results and load values of each influencing factor are shown in Table 4. Eventually received eight public factors, namely won eight major influencing factors, they are “concrete residual surplus”, “concrete defect quality and quality management”, “concrete away and waste”, “personnel quality and professional level”, “communication problems”, “not reasonable casting process and process”, “equipment maintenance”, “Transportation status and route planning problems”.

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Table 2. Total variance interpretation and factor weights Total Variance Explanation Initial eigenvalues

Extract the sum of squared payloads

Rotate the load sum of squares

The factor weights

A total of

Percentage of variance

Cumulative %

A total of

Percentage of variance

Cumulative %

A total of

Percentage of variance

Cumulative %

1

9.893

31.912

31.912

9.893

31.912

31.912

4.674

15.078

15.078

2

3.996

12.892

44.803

3.996

12.892

44.803

4.267

13.765

28.843

14.88%

3

2.974

9.593

54.396

2.974

9.593

54.396

3.539

11.415

40.257

11.07%

4

2.832

9.136

63.532

2.832

9.136

63.532

3.528

11.379

51.637

10.54%

5

2.308

7.445

70.976

2.308

7.445

70.976

3.396

10.956

62.592

8.59%

6

2.184

7.045

78.022

2.184

7.045

78.022

2.805

9.05

71.642

8.13%

7

1.642

5.296

83.318

1.642

5.296

83.318

2.768

8.928

80.57

6.11%

8

1.032

3.33

86.648

1.032

3.33

86.648

1.884

6.078

86.648

3.84%

36.83%

5 Discussion The weight of each common factor can be determined by the ratio of its variance contribution rate to the total cumulative variance after normalization. That is, factor weight = factor variance contribution rate/total cumulative variance × 100%. The results are shown in Table 3. According to the above formula, it can be concluded that the common factor with the highest weight is “residual of concrete”, accounting for 36.83%. The weights of other common factors are as follows: “Concrete quality and quality management defects” accounted for 14.88%, “concrete throwing and waste” accounted for 11.07%, “personnel quality and professional level” accounted for 10.54%, “communication and coordination problems” accounted for 8.59%, “unreasonable casting technology and working procedure” accounted for 8.13%. “Facilities and equipment maintenance” accounted for 6.11%, and the public factor with the lowest weight was “transportation state and route planning”, accounting for 3.84%.

6 Recommendations According to the above studies, they can be sorted according to their weight, from high to low, they are respectively “concrete residual”, “concrete quality and quality management defects”, “concrete throwing and waste”, “personnel quality and professional level”, “communication and coordination problems”, “unreasonable casting technology and working procedure”, “facilities and equipment maintenance”, “transportation state and route planning problems”. In order to further do a good job in the whole process of concrete waste reduction work, we need to focus on the following work. 1) reduce the residual of concrete

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Table 3. Factor analysis results of influencing factors of concrete waste chain generation in the whole process. Serial number

Factors affecting the

load

Factor 1: Residual residue of the concrete

X7. The amount of concrete is inconsistent, and the actual amount of concrete is less than the expected amount

0.888

X31. Casting allowance in untreated pumping line

0.88

X23. When the pouring is about to end, the 0.878 concrete of the replenishment is not accurately calculated X15. The residual concrete after unloading 0.855 by mixing truck is not cleaned in time Factor 2: Concrete quality management defects

Factor 3: Concrete throwing and waste

Factor 4: Personnel quality and professional level

Factor 5: Communication coordination problem

X10. Failure to consider concrete slump according to actual conditions such as construction site and weather

0.936

X9. Lack of rigorous concrete quality control process

0.93

X29. Pipe blocking and pipe bursting occur during pouring

0.926

X8. The mixer discharge device is faulty, or the diameter is too large

0.915

X12. Lack of reward and punishment measures for concrete waste

0.908

X24. Pumping line connection problem

0.905

Pump pipe swing control is not good

0.903

X4. Concrete operators lack professional concrete basic knowledge

0.914

X13. Production scheduler multiple, missend,

0.898

X19. Driver negligently goes to the wrong item or wrong number

0.889

X5. Production personnel of concrete mixing station have weak awareness of waste production

0.881

X25. Improper operation of concrete unloading personnel

0.876

X11. Site technical service personnel fail to feedback the site situation in time

0.894 (continued)

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Table 3. (continued) Serial number

Factors affecting the

load

X2. Seller fills in wrong basic information

0.888

X3. Communication barriers exist among departments of the commercial Concrete company, resulting in order errors

0.882

X1. The buyer filled in the wrong concrete 0.872 label or part Factor 6: Unreasonable casting process and procedure

Factor 7: Facility equipment maintenance

Factor 8: Transportation state and route planning issues

X20. The site construction organization design is not reasonable, and the waiting time for pouring is too long

0.901

X22. The template has problems such as lax seam and mold explosion

0.894

X30. Improper arrangement of pumping lines

0.889

X28. The casting process is not tight enough and the casting time is too long

0.866

X6. Lack of maintenance and maintenance 0.795 of concrete production equipment and facilities X27. Aging and damaged pumping equipment

0.762

X21. Improper use and maintenance of tank truck, and the phenomenon of sticking tank appears

0.752

X14. Control system sensor stuck by a foreign object

0.726

X17. Unreasonable choice of transportation route increases the transportation time

0.93

X18. Vehicle damage or traffic accident in transit

0.924

X16. Transportation distance is too long

0.918

First, the deviation of component size and concrete production measurement value should be avoided, and after concrete unloading, the generation of concrete mixing truck residual material should be reduced. Unavoidable surplus should be combined with the site situation in time to use. At present, the quantity of construction waste is increasing day by day, and there are many unstable factors [22], we should make the best use of everything. 2) Strengthen concrete quality management

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Strengthen the quality control of materials. Comprehensive and strict quality inspection should be carried out before materials are admitted. The same time, strengthen the quality control of the production process, and ensure that the concrete slump meets the construction requirements. Before the concrete configuration, in the actual production, observe the change of the data situation at any time, and stop production immediately in case of fluctuations or other abnormal phenomena. Strengthen quality control in the production process. In addition to the uniformity and constructability of the concrete mixture, we always pay attention to whether there is any abnormality in the production weighing system. Avoid throwing and waste of concrete Regularly maintain the concrete unloading device to avoid the waste of a large amount of concrete caused by abnormal unloading and leakage. A scientific and reasonable pumping speed should be set to prevent pipe bursting and pipe blocking. The corresponding concrete waste reward and punishment measures should be formulated, and the construction site should be managed according to the system, which can arouse the enthusiasm of the site staff. Improve the quality and professional level of personnel Cultivate staff’s awareness of concrete waste reduction and publicize and advocate concrete waste reduction education knowledge and technical knowledge. Managers should establish a timely follow-up mechanism, carry out assessment for employees, and urge relevant personnel to constantly reflect. Strengthen communication and coordination Avoid the generation of concrete waste due to basic information error. Help construction companies reduce communication barriers and ensure information integrity with the help of information tools. Through the Internet and information technology means, to do a good job of coordination in all aspects, to ensure the efficient operation of information interaction between the mixing station and the construction party. Optimize casting technology and working procedure Select the team with standard technology, high quality of formwork assembly and sound formwork production and installation, which is conducive to reducing concrete waste. 4D project management information system of BIM technology is applied to the construction of construction engineering, realizing the visualization of construction engineering, optimizing the construction process, and reducing the possibility of concrete waste. Maintenance of facilities and equipment Responsible personnel should ensure that there is no material leakage and other problems in the equipment. In the process of maintenance, the equipment should be washed or cleaned to avoid dry caking and corrosion of the equipment caused by dust accumulation. At the same time, each mixer truck manufacturer should optimize the design of concrete tank truck barrel and blade. At present, most manufacturers use the same diagonal number of spiral blades, so the residual rate of mixing truck unloading needs to be improved [23]. Transportation state and route planning According to the construction demand and transportation distance, according to the real-time traffic situation of the line, reasonable planning of transportation routes. Concrete production and transportation guidelines should be created according to

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the situation of the mixing station and trained to the driver of the mixing truck, to eliminate hidden risks in the process of transportation and reduce the generation rate of concrete waste.

7 Conclusion Through literature review, field investigation and expert opinions, 31 factors affecting the whole process production chain of concrete waste are sorted out. Then, a Likert scale questionnaire was issued to collect the required data. Factor analysis was used to reduce the dimension of the influencing factors, and eight main influencing factors were obtained. Finally, the factors were sorted according to their weights. Among them, the largest weight is “residual of concrete”, which emphasizes the importance of reducing the residual and residual rate of concrete, and finally puts forward countermeasures and suggestions to reduce the whole process of concrete waste production. Through the study of this paper, it is expected to provide reference for the source reduction of construction waste and the realization of industrial green industrialization optimization transformation. Of course, this study still has the following limitations: there may be other interaction between the influencing factors of the whole process of concrete waste production, which needs further study in the future.

References 1. Alam, Md.A., Habib, Md.Z., Sheikh, Md.R., Hasan, A.: A study on the quality control of concrete production in Dhaka city. IOSR J. Mech. Civil Eng. (IOSR-JMCE) 89–98 (2016) 2. Patek Gursel, A., Massenet, E., Horvath, A., et al.: Life-cycle inventory analysis of concrete production: a critical review. Cem. Concr. Compos. 51, 38–48 (2014) 3. Kazaz, A., Ulubeyli, S., Er, B., et al.: Fresh ready-mixed concrete waste in construction projects: a planning approach. Procedia Eng. 123, 268–275 (2015) 4. Hei, J., Song, X., Liang, L.: Effect of concrete slurry water on properties of C30 concrete. Commer. Concr. (05), 69–72 (2018) 5. Man, L., Wang, Y., Li, Y., Xie, Y., Zhang, R., Tian, Y.: Four wastes treatment of green concrete batching plant. Concr. Cem. Products (10), 84–86 (2019) 6. Wang, W., Mao, B., Qi, H.: Research on greenness evaluation of construction waste source reduction construction mode. Ind. Saf. Environ. Protect. 41(03), 88–91 (2015) 7. Li, J.: Research on recycling and resource reuse of construction solid waste. Green Build. 13(01), 68–70 (2021) 8. Liu, Q., Xiao, J., Pan, Z., Li, L.: Modeling study of waste concrete aggregate and waste brick aggregate recycled concrete. J. Build. Struct. 41(12), 133–140 (2020) 9. Xu, L., Liu, Y.: Study on compressive strength and thermal properties of recycled concrete blocks. New Build. Mater. 44(01), 60–63 (2017) 10. Chen, W., Liu, M., Zhang, M., Wu, F., Zhou, X.: Experimental study on local compression performance of reclaimed concrete cross-hole hollow block wall. J. Central South Univ. (Nat. Sci. Ed.) 52(11), 4063–4073 (2021) 11. Wang, J., Hou, Z., Zhang, K., Wang, B., Li, H.: Experimental study on mechanical properties of recycled concrete in multi-layer cementitious material system. Mater. Rep. 36(12), 97–104 (2022) 12. Rong, Y., Yao, T., Sun, X.: Research on coping strategies of construction waste reduction planning in Beijing. Mod. Urban Res. (03), 62–68 (2021)

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13. Li, J., Ding, Z., Mi, X., et al.: A model for estimating construction waste generation index for building project in China. Resour. Conserv. Recycl. 74(5), 20–26 (2013) 14. Lu, W., Yuan, H., Li, J., et al.: An empirical investigation of construction and demolition waste generation rates in Shenzhen city, South China. Waste Manag. 31(4), 680–687 (2011) 15. Wang, Q., Hu, R., Ren, Z., Chen, L., Tu, J., He, Y.: Estimation of solid waste generation at construction site. China Environ. Sci. 39(04), 1633–1638 (2019) 16. Cui, J., Li, S., Xu, P., Li, Z.: Saving and reusing technology of concrete at construction site. Build. Constr. 40(11), 1959–1962 (2018) 17. Lu, H., Zhang, E.: Management and recycling of solid waste in ready-mixed concrete enterprises. Guangdong Build. Mater. 33(03), 13–15 (2017) 18. Huang, Z., Lang, H., Ma, M.: J. Southwest Normal Univ. (Nat. Sci. Ed.) 46(10), 91–98 (2021) 19. Li, Y., Li, J., Tan, Q., Liu, L.: Analysis of development and driving force of urban solid waste treatment industry in China. China Environ. Sci. 38(11), 4173–4179 (2018) 20. Lin, X., Tan, F.: J. Wuhan Univ. Technol. (Inf. Manag. Eng. Ed.) 39(05), 625–629 (2017) 21. Pang, J., Zhu, H.-B., Liu, K.: Study on influencing factors of green office building cost based on factor analysis method. Value Eng. 37(05), 205–207 (2018) 22. Li, H., Li, Y., Lang, H.: Estimation and prediction of construction waste production: a case study of Chongqing. Guangxi Urban Constr. (11), 125–127 (2021) 23. Chen, J., Tian, S., Gong, Q., Sun, Y., Cao, F.: Design and manufacture of mixing tank for concrete mixer truck based on extension theory. Mach. Tool Hydraulics 48(16), 45–51 (2020)

Measurement of Carbon Emission Rebound Effect of Construction Industry Based on Technological Progress Li Wen and Xiaoli Yan(B) School of Management Studies, Shanghai University of Engineering Science, Shanghai, China [email protected]

Abstract. In the context of the “carbon peak and carbon neutral” goal, promoting energy carbon emission reduction in the construction industry has become an urgent problem. However, there are few studies on energy carbon emissions in the construction industry, especially the impact of technological progress as an essential productivity factor on energy carbon emissions is unclear. This paper measures the carbon emission rebound effect of the construction industry based on technological progress from 2002 to 2019 with the DEA-Malmquist index method to measure the total factor productivity. The results show that the rebound effect from 2002 to 2019 is a partial rebound phenomenon, and the effect from 2014 to 2019 is more evident than before. Therefore, it indicates that the rebound phenomenon of carbon emissions in the construction industry should be paid attention to, and there is still much room for improvement in the emission reduction work. Because of this, reasonable carbon emission reduction policies should be formulated to reduce the rebound effect of technological progress on energy carbon emissions in the construction industry. Keywords: Technological progress · Construction industry · Carbon emissions · Energy rebound effect

1 Introduction The proposal of the “carbon peak and carbon neutral” goal not only points out the way forward for China’s green and low-carbon transition but also injects new momentum into promoting global climate governance. As the focus of the carbon emission field, the carbon emission of the construction industry has always attracted much attention. Currently, China’s construction industry faces problems such as sizeable total carbon emissions, low energy use technology, and efficiency. Still, it faces significant pressure to achieve sustainable development under “carbon peaking and carbon neutrality” requirements. Considering the upstream and downstream industries, such as building materials production and transportation, the annual carbon emission in China’s construction sector accounts for more than 40% of the total carbon emission. Therefore, the construction industry’s carbon emission is an essential part of China’s carbon emission, and the importance of its emission reduction cannot be ignored. So it is urgent to solve the problem of carbon emissions in the construction industry [1]. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 1069–1083, 2023. https://doi.org/10.1007/978-981-99-3626-7_82

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Energy consumption is the primary source of carbon emissions [2]. Most of the existing energy carbon emissions research is made from the country’s overall level. For example, Wan XW et al. considered four scenarios (energy conversion, energy capital savings and loans, energy exports, and cement production carbon emissions). They adopted the energy consumption and input-output method to analyze China’s energy carbon emissions structure on the supply and demand sides, then provided policy recommendations for China’s structural emission reduction [3]. Xuan D et al. adopted a difference-in-difference model to explore the effect of carbon emission trading policies on carbon emission reduction based on a quasi-natural experiment of China’s carbon emission trading pilots. They examined the mediating effects of total energy consumption, technical research level, and the energy consumption structure [4]. Yu Y et al. analyzed the driving factors of carbon emissions from energy consumption by introducing the indicators of energy trade in China from 2000 to 2014 [5]. However, there are few studies on energy carbon emissions in the construction industry, and the energy reduction work of the construction industry should have its particularity. As an essential part of China’s energy carbon emissions, its emission reduction issues cannot be ignored. In addition, in recent years, there have been more and more studies on the carbon emission rebound effect. For example, based on the energy consumption data from 2000 to 2019, Liu K et al. calculated the carbon emission of six provinces of the central region, and then by calculating the contribution rate of technological progress to both economic growth and carbon emission intensity, calculated the carbon saving amount and carbon rebound amount, then obtained the rebound effect value of carbon emission [6]. Chen Q et al. used the IPCC carbon emissions accounting method, then proposed an elasticity approach, constructed an individual fixed effect variable coefficient panel data model, and explored the direct CO2 rebound effect (CRE) in urban households at the provincial level [7]. However, technology, as an essential factor of productivity, whether its progress will lead to an increase in energy demand while improving the energy efficiency of the construction industry and thus have an energy rebound effect is yet to be known. This paper analyzes the impact of technological progress on energy carbon emissions by incorporating the energy rebound effect and obtains relevant inspirations to provide theoretical reference and practical guidance for reducing the energy carbon emissions of the construction industry.

2 Action Paths and Hypotheses Technological progress has both sides of broad and narrow sense. The narrow sense of technological progress only refers to technological progress (such as the adoption of new technologies or the invention of new products). In contrast, the broad sense of technological progress includes not only scientific and technological progress but also management progress (such as the improvement of management efficiency and production experience accumulation), resource allocation efficiency improvement, knowledge progress (such as the ability and knowledge to form and manage large enterprises) and other “soft” technological progress [8]. This paper mainly examines the generalized technological progress. For the construction industry, the generalized technological progress includes technological innovations such as clean energy technology, design and measurement

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technology (BIM, GIS, etc.), construction operation and maintenance technology (big data, artificial intelligence, intelligent robots, etc.), and other technologies, as well as the ability to rationally allocate resources (integration and distribution of input elements, etc.), management progress (management method innovation, management tool innovation, production experience accumulation, etc.) and knowledge progress (management theory learning and innovation, etc.) and other soft technologies. The path analysis of the carbon emission rebound effect of the construction industry based on technological progress is as follows (Fig. 1).

Fig. 1. The path of carbon emission rebound effect of the construction industry based on technological progress

Technological progress will improve energy efficiency in the construction industry, affecting energy carbon emissions. However, at the same time, technological progress will increase the energy demand, and the increase in demand will lead to an increase in the carbon emissions of energy. Therefore, this paper introduces the energy rebound effect to analyze the impact of technological progress on the energy carbon emissions of the construction industry. In this process, relevant energy-saving policies will also impact energy efficiency. Relevant studies have proved that, for example, American scholar Jacobsen (2000) believed that when analyzing long-term energy demand, it is necessary to analyze the technological progress and the impact of policy measures on energy consumption in the same period [9]. Therefore, this paper considers relevant policies when analyzing the energy rebound effect. In general, technological progress

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will have a particular impact on the energy carbon emissions of the construction industry through the energy rebound effect. According to the above analysis of the action path, the following research hypotheses are put forward: Technological progress will promote the increase of energy demand in the construction industry while promoting energy carbon emission reduction, resulting in a rebound effect of carbon emissions.

3 Rebound Effect Measure 3.1 Data Sources Because of the availability of data, this paper uses the relevant data of 31 provinces and cities in China (excluding Hong Kong, Macao &Taiwan) from 2002 to 2019 as a research sample to measure the rebound effect of carbon emissions in the construction industry based on technological progress. The data sources are the “China Statistical Yearbook,” “China Construction Industry Statistical Yearbook,” and “China Energy Statistical Yearbook.” (1) Output (Y ): the total output value of the construction industry from 2002 to 2019 is taken as an indicator. The unit is 100 million yuan. In order to eliminate the impact of price changes, it was calculated at the constant price in 2000. (2) Capital investment (K): The total assets of construction enterprises from 2002 to 2019 are used as the indicators, including fixed assets, current assets, and construction in progress. The data for 2005 and 2013 are from the “China Construction Industry Statistical Yearbook,” and the rest comes from the “China Statistical Yearbook,” which is also calculated at constant prices in 2000. (3) Labor input (L): This paper uses the number of employees in the construction industry in each region from 2002 to 2019 as labor input data. (4) Carbon emission data: Since the official government has not released specific carbon emission data, this paper selects five types of construction industries, including electricity, diesel, coal, gasoline, and fuel oil, which are usually used in the construction industry, and its carbon emissions were measured based on its carbon emission factor and its consumption in the construction industry from 2002 to 2019. (Note: The carbon emission coefficient adopts the default value of IPCC Carbon Emission Calculation Guide [10], carbon emission = converted standard coal coefficient * IPCC carbon emission coefficient * the annual consumption of this energy in the construction industry, the converted standard coal coefficient is from “China Energy Statistical Yearbook”. Among them, the carbon emission coefficient of standard coal: the recommended value of the National Development and Reform Commission Energy Research Institute is 0.67, the reference value of the Japan Institute of Energy Economics is 0.68, and the reference value of EIA is 0.69. Therefore, the paper calculated the “carbon emission coefficient” of 1 kg standard coal as 0.68. Moreover, the carbon emission factor of electricity is calculated as 0.272 kgC/kWh). The descriptive statistics of the data are shown in Table 1.

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Table 1. Descriptive statistical results of the rebound effect of carbon emissions in the construction industry variable

Number of samples

maximum value

minimum

median

average value

standard deviation

output (100 million yuan)

558

21441.18

22.02

1578.43

2755.72

3378.04

Capital investment (100 million yuan)

558

72361.94

31.03

1424.39

2608.31

4111.94

labor input (person)

558

8110275

23000

748025

1213529

1422895

Carbon emissions (10,000 tons of carbon)

18

402.37

115.68

263.45

254.20

94.32

3.2 Model Construction The measurement of technological progress has always been a complex economic problem. In the absence of a better alternative method, economics generally uses “Total Factor Productivity” to represent technological progress (Solow, 1956; Denison, 1967) [11]. At present, there is more and more research on technological progress. In terms of measurement methods, Caves et al. (1982) first applied the Malmquist Index (1953) to the measurement of productivity changes and then combined it with the DEA theory established by Charnes et al. (1978), its application in productivity measurement is increasingly widespread. The current research generally adopts the non-parametric DEA-Malmquist index constructed by Fare et al. (1994) [12]. 3.2.1 DEA-Malmquist Productivity Index Method This paper adopts the non-parametric DEA-Malmquist index constructed by Fare et al. (1994). It takes each province as a decision-making unit to measure the total factor productivity of China’s construction industry. Here, t represents the year. CTFP represents the total factor productivity of the construction industry. GCTFP represents the total factor productivity growth rate of the construction industry. GY represents the economic (output) growth rate. Y represents the output, and the input vector is x. The output vector  is u, and the distance functions of (xt , ut ) in period t and period t + 1 are Dt+1 xt , ut , Dt (xt , ut ). (x ,u ) For period t, the Malmquist productivity index is given by: Mt = Dt D t (x t ,ut ) . Similarly, the Malmquist productivity index of year t + 1 can be obtained. Malmquist CTFP index is the geometric mean of the index in period t and period t √ + 1: CTFP t = Mt × Mt+1 . t+1

t+1

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The total factor productivity growth rate of the construction industry in year t + 1 t+1 −CTFP t is GCTFP t+1 = CTFPCTFP × 100%. t The economic growth rate of the construction industry in year t + 1 is GY t+1 = Yt+1 −Yt × 100%. Yt Then the contribution rate of technological progress to the economic growth of the t+1 construction industry in year t + 1 is σt+1 = GCTFP GY t+1 × 100%. 3.2.2 Construction of the Carbon Emission Rebound Effect Model Based on Technological Progress First, the theoretical formula: Rebound effect: RE = Rebound consumption/Expected savings * 100% = E0 −E2 E0 −E1

E2 −E1 E0 −E1

×

× 100%. 100% = 1 − Second, the rebound effect of carbon emissions is based on technological progress: t represents the year, CI represents the carbon emission intensity of the construction industry, CE represents the carbon emission amount of the construction industry, Y represents the gross production value of the construction industry, and CER represents the carbon emission reduction of the construction industry, then CE t = CI t · Yt . Expected Savings = CER = Yt+1 × (CI t − CI t+1 ). Since technological progress will bring about the economic growth of the construction industry, let b represent the contribution rate of the economic growth of the construction industry brought about by technological progress, then rebound consumption = Therefore, the carbon emission rebound effect of construction industry: RE = RC/CER * 100% = According to the calculation results, the results of the rebound effect can be analyzed as shown in Table 2: Table 2. Analysis method of the rebound effect RE Name

Performance

Explanation

>1 tempering effect

The improvement of energy efficiency has increased carbon consumption, resulting in increased carbon emissions

The expected increase in energy efficiency has had the opposite effect of reducing carbon emissions

=1

Carbon savings offset new increases in carbon emissions due to improvements in energy efficiency

The carbon emissions of the building industry have not changed due to the improvement of energy efficiency, which also represents the ineffectiveness of related policies

full rebound effect

(continued)

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Table 2. (continued) RE Name

Performance

Explanation

middle of station hall > side of platform > side of station hall. Platform fire is less stable and more dangerous than station hall fire, and the impact of fire location on the smoke spread and personnel evacuation is closely related to building structure. The research results can provide a reference for fire fighting and escape strategies in subway station fires and can also provide a basis for the architectural design of subway stations. Keywords: Subway fire · Fire location · Simulation · Temperature · CO Concentration

1 Introduction Rail transit has developed into one of the main urban travel modes due to its fast, punctual, safe and comfortable characteristics. In the post-epidemic era, it has surpassed public transport several times to become the public transport with the largest share of passenger traffic [1]. However, because its structure is usually located underground, its station has the characteristics of a small space, relatively closed and large passenger flow, so once an earthquake, flood, fire and other events occur, it will seriously threaten the safety of © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 1315–1327, 2023. https://doi.org/10.1007/978-981-99-3626-7_102

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passengers’ lives and property. The survey shows that fire accident is the first significant subway station disaster, accounting for nearly 30% of subway accidents worldwide [2]. Therefore, it is of great significance to study the characteristics and laws of subway station fire. The subway space structure is different with different fire source locations. The fire plume impacts with walls, ceilings, stairs and other obstacles to form different plume shapes and development paths, thus affecting the temperature and smoke propagation in the fire. In disaster events caused by fire, smoke is the main factor leading to casualties [3]. Therefore, a large number of scholars are committed to the study of smoke propagation laws. At present, experiments and simulations are mainly used. For example, Xu et al. [4, 5] conducted fire experiments by establishing a scale model and summarized the distribution law of temperature and smoke layer height. Zhong et al. [6] used full-scale experimental methods to simulate the evolution process of subway fire accidents and obtained smoke diffusion characteristic parameters of several typical station fires. B. Giachetti et al. [7] proposed a sub-scale model based on fr Froude number similarity to analyze smoke diffusion according to subway geometry (number of openings) and ventilation volume flow. Yuan et al. [8] studied the influence of airflow velocity on smoke diffusion in subway tunnels by controlling the running speed and direction of vehicles. Peiyunqiu et al. [9] studied the propagation law of smoke in subway station train tunnels under different ventilation conditions. The above scholars use the method of doing experiments to carry out research, which can accurately simulate the firing process and obtain the propagation characteristics of smoke when a fire occurs in a subway station. However, this method inevitably causes damage to buildings and costs high. It is not suitable for a large number of repeated experiments. Therefore, other scholars are committed to simulation methods and simulating the process of fire in building space with the help of pyrosim, FDS and other software to study the diffusion law of smoke in a fire. For example, Xi et al. [10–12] simulated the diffusion law of smoke when a fire occurred in the train, tunnel, platform and other positions in the subway station through FDS software. Zhu et al. [13] studied the distribution of smoke when a fire occurred in the middle of the middle layer of a multi-storey subway station. Yuan et al. [14, 15] studied the movement and prevention of smoke in the event of a fire on the hall floor of a particular subway station through numerical simulation. Mao[16] and others used numerical simulation methods to analyze the movement process and prevention and control mode of smoke in the station and escalator passage when the fire occurred in different areas of the deeply buried station. Cheng et al. [17] studied the smoke diffusion at two different fire locations and different smoke exhaust modes on the hall floor of a subway station. The above research mainly aims at the diffusion of smoke in local space when a fire occurs at a particular position in the subway station. However, passengers are often distributed at different positions in the public areas of various floors, such as the station hall and platform, at the same time and are always in a flow state. Therefore, this paper takes Shixi station of Guangzhou Metro Line 10 as an example to study the propagation law of smoke at different fire locations in the whole public space, including the platform floor and the station hall floor.The above research mainly focuses on the diffusion of smoke in the space when a fire occurs in a certain local space of the subway station, such as the entrance and exit passageway

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of the platform floor and the station hall floor. However, in the entire subway station space with diverse and complex building structures, the impact of the fire location on the smoke and temperature transmission law still needs to be studied. The study of smoke propagation law under different fire source locations can provide reference for passengers’ safe escape in case of fire in subway stations and the design of subway station building fire protection. This paper takes Shixi Station of Guangzhou Metro Line 10 as the research object, studies the development path of fire plume and smoke plume with different fire source locations, summarizes the propagation law of smoke and temperature when the typical location of the subway station catches fire, in order to provide theoretical reference for architectural design and personnel evacuation.

2 Principle and Simulation Parameter Design 2.1 Principle and scene design When a fire occurs in a conventional building space, the flame above the fire source and the smoke flow generated by combustion are called fire plumes. The smoke flow area above the flame area is called smoke plumes. When the vertical upward fire plume is blocked by the ceiling, the hot smoke will flow horizontally along the ceiling to form a ceiling jet. In the early stage of fire, the general development path of fire plume and smoke plume is shown in Fig. 1 below. When the building space structure changes, the development path of plume changes with the change of structure. The subway station is an underground multi-storey building space. Its internal space structure is complex and varies with different locations. Therefore, once a fire occurs in the subway station, the fire location will have an important impact on the development and propagation path of smoke. When the fire source is located in the center of the room, the vertical upward movement of the plume is axisymmetric. If the fire source is close to the wall or corner, the restriction of the solid outer wall on air entrainment will affect the shape of the plume, and strengthen the expansion and spread of flame and smoke on the vertical wall. In addition, building structures such as stairs will hinder the general development path of plume and thus affect the propagation law of smoke.

Fig. 1. General development path of fire plume and smoke plume

Therefore, according to the space structure of conventional subway stations, the following four typical fire positions are set up, as shown in Table 1:

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Location of the fire

scenario

Middle of station hall

1

2

Building space structure

Typical open structure

floor

One side of station hall

3

4

One side wall obstacle

floor

Middle of platform

Obstacle of stairs on both

floor

sides

One side of platform floor

Illustration

Wall and stair obstacle

2.2 Simulation principle of PyroSim PyroSim is a software developed by the National Institute of standards and technology of the United States, which is specially used for dynamic fire simulation. Based on computational fluid dynamics, the software simulates and predicts the flow of toxic gases such as smoke and CO in fire, the distribution of fire temperature and smoke concentration. The reaction process conforms to the mixture fraction combustion model, as shown in formula 1. Cx Hy Oz Nv Otherw + VO2 O2 → vCO2 CO2 + vH2 O H2 O + vCO CO + vSoot Soot + vN2 N2 + vother other

(1)

The reaction rate is calculated by using Arrhenius equation, as shown in formula 2. K = Aexp( - E/RT)

(2)

where A is the pre exponential factor or frequency factor,E is the activation energy, R is the gas constant,T is the thermodynamic temperature. 2.3 Meshing The test shows that D * is generally 4–16 times of the grid size, which is usually expressed as [18]: 2  5 Q ∗ D = (3) √ ρ∞ Cp T∞ g

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where Q is the total heat release rate, in kW; ρ∞ is the ambient air density, in kg/M3 ;Cp is the specific heat of ambient air, in kJ/kg·K;T∞ is the ambient air temperature, in K;G is gravitational acceleration, in M/S2. The fire heat release rate Q is taken as 2500 kw, the specific heat Cp of ambient air is 1.005 kJ/kg·K, the ambient air density is taken as 1.29kg/m3 , the ambient air temperature is 299.15 k, and the gravitational acceleration is 9.8 m/s2 . When the parameter values are taken into the formula, D* is calculated to be 1.34 m, and the theoretical optimal value of the grid size D* is 0.096 m–0.335 m. The smaller the grid size and the closer the size in the three directions of X, Y and Z, the higher the simulation accuracy, but the larger the running memory required. By comparing the simulation results of d = 0.1 M, d = 0.3 m and d = 0.5 m, it is finally determined to use multi grid simulation. The grid size at the near fire source is 0.5 m × 0.5 m × 0.5 m, grid size of non critical position is 1.0 m × 1.0 m × 1.0 m. 2.4 Analog Parameter Setting The investigation shows that baggage fire and common decoration material fire are the most common causes of fire in subway stations. Therefore, the paper will simulate the fire of typical building material foam and set the following parameters [16]: The combustion type conforms to t2 fire, the fire growth coefficient is 0.0469, and the heat release rate Q of the fire source is 2.5 MW. Since the subway station’s air conditioner is turned on, the ambient temperature is set to 26 °C, and the air humidity in Guangzhou is between 45% and 77% a year. The simulated environment is taken as the middle value, the initial relative humidity is set to 50%, the ambient atmospheric pressure is one standard atmospheric pressure, and the reaction type is a polyurethane combustion reaction. According to the principle of 6 min escape, The simulation time is set to 360 s.

3 Case study 3.1 Modeling Taking the Shixi station of Guangzhou Metro Line 10 as an example, the subway station model built by Revit software in a 1:1 ratio is shown in Fig. 2. Both sides of the subway station are equipment areas, and the middle is a public area. The public area is about 72 m long and 20 m wide. It is divided into two floors, of which the first underground floor is the station hall floor. Four exits include exit A, exit B, exit C and exit D. The second underground floor is the platform floor. Both sides of the platform are directly connected to the train tunnel. There are stairs from the platform to the station hall floor, including two escalators, one elevator and one stair. First, build a subway station model as shown in Fig. 2 through Revit and output it as an FBX file. Then, import the model into pyrosim software to carry out fire simulation.

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Fig. 2. Model of Metro Station

3.2 Setting of Fire Location According to the structural characteristics of the subway station, four fire locations are set, as shown in Fig. 3. Locations 1 to 4 are located in the middle of the station hall, the side end of the station hall, the middle of the platform layer, and the platform layer’s side end. All four locations are common building materials foam fire, and the simulation time is 360 s.

Fig. 3. Four Fire Locations

Fig. 4. CO Concentration Monitoring Equipment

3.3 Setting of Measuring Device As shown in Fig. 4, the smoke detection equipment GASA01 to GASD05 are set on the station hall floor, platform floor and the place 1.5 m above the stairs where the passenger evacuation route passes, which is used to measure the CO concentration in the fire process, and a 2D temperature slice is placed on the longitudinal section of the escalator and step centerline in the y-axis direction as shown in Fig. 5, which is used to display the distribution and change of high-temperature airflow in the fire process.

Fig. 5. Temperature and Flue Gas Slice Position

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4 Analysis Results 4.1 Temperature Distribution Law

a

90s

a

90s

b

180s

b

180s

c

270s

c

270s

d

360s

d

360s

Fig. 6. Temperature slice of fire location 1

Fig. 7. Temperature slice of fire location 2

a

90s

a

90s

b

180s

b

180s

c

270s

c

270s

d

360s

d

360s

Fig. 8. Temperature slice of fire location 3

Fig. 9. Temperature slice of fire location 4

Figure 6, 7, 8 and 9 show the temperature slices of the fire locations 1 to 4 at the stairs in the Y-axis direction when the simulation reaches 90 s, 180 s, 270 s and 360 s, respectively. Generally, the human body tolerates 60 °C [12] under the principle of 6min escape for adults. The black area in the temperature slice is the distribution position of 60°C airflow. It can be seen from the figure that the high-temperature airflow starts to spread from the fire source, first spreading upward to the ceiling. Gradually spreads around and settles from high to the ground. The temperature decreases with the increased distance from the fire source.

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As shown in Fig. 7, when the fire is located in the middle of the station hall, the plume spread conforms to the plume diffusion model of fire in general building space. First, the fire source spreads vertically to the ceiling, and then diffuses around the ceiling through the ceiling jet. With the continuous accumulation of smoke, the ceiling smoke gathers and sinks slowly. As shown in Fig. 8, when the fire source is at a wall blocking position on one side, the solid wall restricts the air entrainment during the plume process, thus changing the shape and development path of the plume, so that the fire plume on one side of the wall spreads along the wall, the smoke plume collides with the wall, accelerates its spread along the wall and outside the wall, and accelerates the gathering of smoke in the ceiling through the ceiling jet. As shown in Fig. 9, when the fire source is located in the middle of the platform floor, and there are stair obstacles distributed on both sides, the smoke will collide and sink at the stair after flowing through the ceiling, accelerating the gathering of smoke at the ceiling. At this time, the fire source is equivalent to a narrow space, so the smoke will quickly sink from the ceiling to the ground within 360 s. As shown in Fig. 10, when the fire source is located on one side of the platform floor and close to the stairs, the plume development path on the wall side is the same as Scenario 2, and the staircase side is the same as Scenario 3, and the smoke jet flows through the ceiling and gathers in a large amount at the ceiling. When the fire breaks out on the station hall floor, the fire spreading speed is relatively fast due to the relatively sufficient oxygen. At this time, the farther the escape path and exit are from the fire source, the more favourable the escape conditions are. Therefore, as shown in the section, the fire in the middle of the station hall leaves less evacuation time and less buff space for passengers than at the side end of the station hall. Figure 4 and Fig. 5 show that within the range from the ground to 1.5 m above the ground if the air temperature is less than 60 °C, passengers can evacuate to the exit through the stairs and the hall floor. When a fire breaks out on the platform floor, the fire intensity change varies according to the fire location. In the middle of the platform, the fire intensity first expands and then rapidly decreases. At the same time, at the side end of the platform, the fire intensity first slowly increases and then rapidly expands. It can be seen from the analysis that there are many barriers, such as the stair floor in the middle of the platform, and the airflow is slow. In addition, when the fire breaks out, the hot air flows evenly from the fire source to the surrounding, and the air supplement effect is poor. Therefore, the fire first increases and then decreases with oxygen concentration change. In addition, there are glass barriers on both sides of the escalator, so the smoke will first spread to the station hall through the escalator, and the passengers on the platform floor can evacuate to the station hall through the escalator. In contrast, when a fire breaks out at the side of the platform, the high-temperature airflow will quickly spread to the station hall through the escalator. At this time, the tunnel entrance and the stairway entrance act as air inlets, while the elevator entrance acts as air outlets, meeting the conditions for the chimney effect. Therefore, the fire expands rapidly. The high-temperature air flow quickly spreads to the station hall floor through the elevator near the fire. Passengers can evacuate to the station hall floor through the step ladder.

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4.2 Distribution Law of CO People will soon faint due to poisoning when the CO concentration in the environment exceeds 6.25 × 10−4 kg/m3 [12]. In this paper, the CO concentration slices are set in the subway station model’s three spatial directions of X, Y and Z. On the XY two-dimensional plane, the slices are located 1.5 m from the platform floor. On the XZ two-dimensional plane, the slices are located at the center line of the stairs. On the YZ two-dimensional plane, according to the most dangerous principle, when the fire is located on the station hall floor, the slice is arranged at the exit. The slice is located at the stairway entrance when the fire is located on the platform floor. Figure 10, 11, 12 and 13 below shows the CO concentration slices of the fire simulation to 90 s, 180 s, 270 s and 360 s when the fire point is located in four positions. The black area in the slice is the CO concentration of 6.25 × 10−4 kg/m3 .

a 90s

a

90s

b

180s

b

180s

c

270s

c

270s

d

360s

d

360s

Fig. 10. CO concentration slice of fire location 1

Fig. 11. CO concentration slice of fire location 2

As shown in Fig. 10, when a fire breaks out in the middle of the station hall, CO first spreads to the top of the fire source, then quickly spreads to both sides along the ceiling, accumulates in the corner and sinks along the side wall. When the smoke does not fill the station hall, the co concentration is the highest at the corner of the side wall and the ceiling, decreasing from the corner to the fire source. When the passenger flow is large, passengers quickly gather at the exit during the evacuation. The CO concentration here is high and sinks rapidly. It sinks below 1.5 m from the ground within 180 s, so passengers cannot evacuate safely. As shown in Fig. 11, when a fire breaks out at the side end of the station hall, co will diffuse to the top of the fire source, then diffuse to the other side of the station hall along the ceiling, and the gas diffusion speed is slow. It can be seen from the analysis

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a

90s

a 90s

b

180s

b

180s

c

270s

c

270s

d

360s

d

360s

Fig. 12. CO concentration slice of fire location 3

Fig. 13. CO concentration slice of fire location 4

that the side end is close to outlet a and outlet D, and part of the flue gas will diffuse into the outlet channel, thus slowing down the spread speed of the flue gas in the station hall. In this case, passengers can evacuate through exit b and exit C on the other side within 360 s. As shown in Fig. 12, when a fire breaks out in the middle of the platform, the CO concentration increases slowly in the first 90 s of the fire, accumulating in the middle of the platform and the stairs leading to the station hall. In this case, the station hall meets the safety evacuation conditions. However, the passengers on the platform cannot evacuate safely before a large amount of CO accumulates. There is no safety buffer area on the platform floor. As shown in Fig. 13, when a fire breaks out at the side of the platform, the CO gas concentration is low, and the diffusion is slow in the first 270 s of the fire. In the period from 270 s to 360 s, the CO concentration suddenly increases, and the propagation speed is accelerated. It can be seen from the analysis that the airflow temperature at the fire location and the nearby escalator rises around 270 s, acting as an air outlet, while the airflow temperature at the tunnel entrance and the stairway entrance is relatively low, acting as an air inlet, forming a chimney effect, Therefore, the reaction speed of combustibles is accelerated. The concentration of CO gas is rapidly increased. 4.3 Growth Law of Temperature and CO Concentration at The Most Unfavorable Position Since the monitoring points of temperature and CO concentration are arranged symmetrically concerning the fire source, take the X direction as the coordinate axis and take the average value of the points with the same distance from the fire source to obtain

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the temperature values at 5 m, 15 m and 25 m from the fire source in the middle of the station hall and the CO concentration values at 5 m, 10 m and 15 m from the fire source in the middle of the platform, and draw the broken line diagram as follows:

Fig. 14. Temperature Variation with Distance Fig. 15. Variation of CO Concentration with Distance

According to Fig. 14, it can be observed that the temperature decreases with the increase of the distance from the fire source. The temperature rises relatively fast in the 120s to 240s, then tends to be stable. The temperature growth rate at the 35 m position increases first and then decreases. The reason is that the position is far from the fire source, and the high-temperature airflow settles from top to bottom after arriving, so the temperature growth is slow. After the airflow sinks, the temperature rises rapidly. However, evacuation exits a and D are on both sides of the position, and the hightemperature airflow spreads to the channel. Therefore, the temperature rise rate decreases after the 300 s. In this case, the temperature on the escape path is lower than 60 °C within 360 s, meet the passenger evacuation conditions. It can be seen from Fig. 15 that after the initial stage of fire, the CO concentration increases first and then decreases with the increase of the distance from the fire source. The concentration near the fire source is the smallest, consistent with the law shown in the slice. The CO concentration increases rapidly between 120 s and 240 s and then tends to stabilize. Therefore, the first 120 s of fire is the key time for fire extinguishing. To sum up, in the fire simulation of different locations, the evacuation conditions for passenger evacuation are shown in Table 2, where L represents the left escalator from the platform to the station hall, R represents the right escalator from the platform to the station hall, a, B, C and d represent the shortest path to the four exits in the station hall, 1 represents meeting the evacuation conditions, and 0 represents not meeting the evacuation conditions: It can be seen from the above table that when a fire breaks out in the middle of the station hall or platform, the temperature within 6min can meet the passenger evacuation conditions. However, the CO concentration in the escape route is too high to meet the safety evacuation conditions. In case of fire at the side end of the station hall, the temperature in the space below 1.5 m from the ground is less than 60 °C, and the local CO concentration is less than 6.25 × 10−4 kg/m3 . Passengers can evacuate through exit B and exit C. When a fire breaks out at the side of the platform, the temperature and

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location

Temperature (°C) ≤60 °C

safe

CO concentration ≤6.25 × 10−4 kg/m3

safe

path

LA

LB

LC

LD

RA

RB

RC

RD

LA

LB

LC

LD

RA

RB

RC

RD

1

1

1

1

1

1

1

1

1

yes

0

0

0

0

0

0

0

0

no

2

1

1

1

1

1

1

1

1

yes

0

1

1

0

0

1

1

0

yes

3

1

1

1

1

1

1

1

1

yes

0

0

0

0

0

0

0

0

no

4

0

0

0

0

1

1

1

1

yes

0

0

0

0

1

1

1

1

yes

CO concentration at the side of the fire source does not meet the evacuation conditions. However, passengers can safely evacuate through the remote fire side steps.

5 Conclusion When the two risk factors of high-temperature airflow and CO concentration are considered, the most unfavourable fire location in the multi-exit subway station is different. When considering temperature factors, the order of risk from high to low is the middle of Station Hall > side of platform > middle of platform > side of station hall; When considering CO concentration, the order of risk from high to low is as follows: middle of platform > middle of Station Hall > side of platform > side of station hall, and when considering the two factors, the hazard ranking is: middle of platform > middle of Station Hall > side of platform > side of station hall. Platform fire is more dangerous than station hall fire. The simulation results show that the smoke spreads faster, broader, and less stable when the fire occurs on the platform. Due to the influence of the building structure, the closeness of the platform is higher than that of the station hall. The chimney effect is easy to form, so the platform layer is moIn addition, considering the conditions beyond human tolerance, the temperature is more than 60 °C. The CO concentration is more than 6.25 in the space below 1.5 m from the ground × 10−4 kg/m3 . The diffusion speed of CO is faster and broader than that of high-temperature airflow, which is the most important factor threatening the safety of passengers. The influence of fire location on the smoke spread and evacuation is closely related to building structure. The simulation results show that setting barriers on both sides of the stairs in a platform fire can effectively suppress the air replenishment speed, thereby indirectly controlling the oxygen concentration and slowing down the fire spread speed. In addition, the separation of escalators and escalators is more conducive to the evacuation of passengers in fire, and the closer the stairway is to the exit of the subway station, the shorter the evacuation path. Acknowledgements. The authors would like to acknowledge the financial support for this research received from National Natural Science Foundation of China (Grant No. 71901043), Science and Technology Research Project of Chongqing Education Commission (Grant No. KJQN201900713).

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Inclusion of Durability of Recycled Aggregate Concrete in Life Cycle Assessment (LCA) Weiqi Xing1(B) , Vivian W. Y. Tam1 , Khoa N. Le1 , Jian Li Hao2 , and Jun Wang1 1 School of Built Environment, Western Sydney University, Sydney, Australia

[email protected] 2 Department of Civil Engineering, Xi’an Jiaotong-Liverpool University, Suzhou, China

Abstract. From the life cycle perspective, service life of concrete is a crucial part to be evaluated but is normally overlooked in the environmental impact assessment. While recycled aggregate concrete is regarded as an alternative material to virgin aggregate concrete, CO2 Concrete has the enhanced mechanical and durability properties matching to those of virgin aggregate concrete beneficial from carbon conditioning treatment. To validate its potential on the environment reservation, this study attempts to conduct a comparative analysis of life cycle assessment (LCA) for virgin aggregate concrete and CO2 Concrete, with two functional units (FU) applied. When considering volume and compressive strength of concrete, CO2 Concrete is much preferred for the utilization to construction. However, involving the durability parameters in FU greatly affects the performance of CO2 Concrete. Results illustrate that the selection of FU is crucial to LCA study even though the same system boundary is applied, and the inclusion of durability parameters of concrete is essential to generate an unbiased LCA results. It is also suggested that various scenarios regarding different FU criteria are presented to make a more informed decision, since there is no standard for durability indicators of concrete to be included in the evaluation. Keywords: Construction and demolition waste · Environmental impacts · Functional unit (FU) · Life cycle assessment (LCA) · Recycled aggregate concrete

1 Introduction Concrete is a durable composite material by its nature. Most concrete structures are designed to last for 50 or more years to facilitate the economic development and urbanization requirement [1]. Although several prediction models for estimating the service life of concrete structure have been developed for different deterioration mechanisms, durability is still a critical issue of concrete as the expectation to extended service life is great but the durability cannot be instantly measured and visualized [2]. To get out of dilemmas of natural resource depletion, carbon emissions, land use, etc., concrete made by recycled aggregate from construction and demolition waste has been popular in both academic and industry [3, 4]. Lots of studies have proved that recycled © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 1328–1336, 2023. https://doi.org/10.1007/978-981-99-3626-7_103

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aggregate concrete is a promising alternative to virgin one in structural usage in terms of mechanical properties, environmental impacts, and cost [3, 5, 6]. However, compared with the conventional concrete, the durability of recycled aggregate concrete is unclear and the opinions of professionals towards it are inconsistent. Generally speaking, the durability of recycled aggregate concrete is largely influenced by the adhered mortar of recycled aggregate [7], and its characteristics on drying shrinkage, deformation, and chloride penetration resistance are less desirable than virgin aggregate concrete [8]. There’re some standards and codes of practice for the engineers to conduct a solution for a concrete structure fulfilling all the requirements of engineering and durability with the design lifespan, such as Eurocode 2, and AS3600. Despite that, it seems that only the Indian Standard IS: 383 regulates the guideline to allow up to 20% of recycled aggregate to be used in reinforced concrete [9]. Lacking relevant standards for implementing the recycled aggregate in concrete mixes, together with less reliable of durability, result in a low availability of recycled aggregate concrete in the structural construction. Service life defined in the design phase is claimed suitable to be extended to life cycle assessment (LCA) and the functional unit (FU) nomination [1, 2], whilst limited studies involve long-term durability properties and service life of recycled aggregate concrete in LCA [10]. In fact, scholars have realized that employing the unit volume of concrete as a FU reflects the variations of environmental performance from mix design only, and mechanical strength-based FU for different concrete products is still not sufficient to achieve the comparative purpose in LCA. As a result, it is necessary to normalize the durability related parameters into FU to generate unbiased LCA results for recycled aggregate concretes. This study therefore aims to evaluate the environmental performance of recycled aggregate concrete concerning both compressive strength and durability parameters and compare the results with those of virgin aggregate concrete. Besides, the results under different FU scenarios are analyzed to show the significance of inclusion of service life of concrete in LCA.

2 LCA of Concrete Concerning Durability Parameters Durability of concrete is defined as the ability to resist weathering action, chemical attack, and abrasion while maintaining the desired engineering characteristics. Durability of concrete can be tested subjected to shrinkage, reversibility, impermeability, chloride penetration resistance, carbonation resistance, frost resistance, and alkali-silica reactions [7]. In LCA studies, durability parameters are seldom detected since it is particularly difficult to make all concrete’s parameters identical for FU selection. In that case, Panesar et al. [11] investigated the impacts of six types of FU to the environmental performance, when rapid chloride permeability was regarded as a representative indicator for durability. Zhang et al. [12] tried to quantify the carbon emission of concrete by different FUs including volume, compressive strength, and chloride penetration resistance. Whilst some other scholars attempted to employ other parameters to involve the durability in a FU, these include carbonation depth [1], the amount of concrete to deliver a certain strength and service life [13], and additional volume of concrete for meeting strength, serviceability and durability requirements [14]. In addition, many studies evaluate the environmental behavior of concrete with the assistance of service life prediction model [15].

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Suggestion to include both strength and durability in FU is urgently called for [10, 15]. There seems an agreement on the environmental performance of concrete that higher impacts are investigated if durability of the product is considered in LCA, compared with the value based on volume and strength-based FU. Specifically, durability-related parameter controlled the overall impacts of concrete examined and decision-making would be significantly affected when LCA result was of concern [11]. Zhang et al. [16] also gave an example explaining the effect of varied durability of concrete products throughout the whole life cycle, which more durable products provide less subsequent environmental impacts after the construction phase. Facts shown in various studies illustrate the importance of taking the durability of concrete into account before assessing its sustainable characteristics, otherwise opposite conclusion might be drawn due to the misunderstanding.

3 Research Methodology 3.1 Goal and Scope The goal of this study is to evaluate and compare the environmental impacts of virgin aggregate concrete, and concrete made by carbonation treated recycled aggregate (the patent name is CO2 Concrete) produced in Australia, with various FUs applied to LCA model in terms of concrete’s volume, strength, and durability parameters. Variables selected to fabricate different scenarios of FU are (1) 1 m3 of ready-mix concrete; (2) 28-day compressive strength of concrete; and (3) drying shrinkage, reversibility and permeability of concrete following Australian standards. Consequently, three FUs are: • FU1 – Considering volume and strength of concrete; • FU2 – Considering volume, strength and durability of concrete. According to extensive literature review, strength of concrete is convenient to be normalized in FU as mix design can be altered to compensate for the loss of strength due to the replacement of recycled aggregate. Nowadays with the knowledge advances, recycled aggregate concrete can be as strong as virgin ones without more cement added, contributing to more favored environmental behaviors [3]. On the contrary, involving the durability into FU is a challenge since no one knows exactly which parameters should be considered, and inconstant exposed environment makes the service life prediction complex [16]. To address the issue, Panesar et al. [11] and Zhang et al. [12] make use of the difference of variables examined between two comparative products to define a FU: Value of variable 2(alter Value of variable 1(alter material) × Value of variable 1(base material) Value of variable 2(base Value of variable n(alter material) × ... × Value of variable n(base material)

FU =

material) material)

where n corresponds to each variable identified in a targeted FU. In this study, 28-day compressive strength will not be adjusted in FU since CO2 Concrete has been verified to have a similar engineering performance of virgin aggregate concrete [17], and more importantly, it is less reasonable to simply adjust FU by the ratio

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of compressive strengths, since the concrete strength is determined by mix design but not adding more concrete into a FU. On the contrary, durability is suitable to be normalized by method proposed above, as equivalent amount of concrete indicated by such a ratio is required for repair and maintenance purposes during the service life. To fulfil the requirement of FU, 28-day compressive strength of CO2 Concrete is comparable to that of virgin aggregate concrete (within 2% of variation) to ensure the consistency of strength. As a consequence, three pairs of virgin aggregate concrete and CO2 Concrete with water-to-cement ratios of 0.4, 0.45, and 0.5 are selected, which mix designs are identical but aggregate type are different. The calculated FUs for each pair of concrete products are listed in Table 1, which mix design and mix code can be found in Tam et al. [17] and Xing et al. [10]. Table 1. Comparisons of characteristics among different LCA approaches Mix code

Pair 1 0.4–0%, VAC, GP

0.4–30%, CO2 Concrete, GP

Pair 2

FU1

1

1

1

1

1

1

FU2

1

2.023

1

1.068

1

1.434

0.45–0%, VAC, GP

Pair 3 0.45–100%, CO2 Concrete, GP

0.5–0%, VAC, GP

0.5–50%, CO2 Concrete, GP

3.2 Life Cycle Inventory and Impact Assessment Life cycle inventory comprises all the inputs and outputs within the system boundary of cradle-to-gate, through collecting the data of raw materials production, transportation, and concrete mixture. Regarding recycled aggregate, the upstream end-of-life impacts are attributed to it while the impact of landfilling is avoided. In this study, LCA model is developed in line with the Australian context as displayed in Fig. 1, and the establishment process of model can be found in Xing et al. [10] in details. The life cycle impact assessment methodology applied to this study is CML 2001 (baseline), consisting of 11 indicators that abiotic depletion potential (ADP), abiotic depletion potential-fossil fuels (ADP_ff), acidification potential (AP), eutrophication potential (EP), freshwater aquatic ecotoxicity (FAETP), GWP, human toxicity (HTP), marine aquatic ecotoxicity (MAETP), ozone layer depletion potential (ODP), photochemical oxidant creation potential (POCP), and terrestrial ecotoxicity (TETP).

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Coal mine

Coal

T:By road; R:By rail; S:By ship

R:43.3km

Alternative fuel facility

Alternative fuel

Oversea cement facility Limestone quarry

Imported clinker

Gypsum quarry

T:41.8km

Integrated cement facility

Limestone T:64.9km, R:49.4km

Gypsum T:30km, R:25km, S:2,325km

Recycled steel Avoided impacts of C&D waste disposal T:15km

Avoided impacts of steel

Steel plant

C&D waste

Coal-fired power plant Basalt quarry Recycling plant

T:25km

Residues

Landfill Residues

Cement

Sand T:40.7km, R:39.7km

Sand quarry

Demolition site

1 cubic meter concrete product

T:63.4km, R:25km, S:10,800km

Gas supplier Chemical supplier

T:25km

Sand T:32km

GGBS T:78km, R:50km, S:8,021km

FA T:210km, S:264km

VA T:47km

Concrete batching plant

RA T:20km

CO2 T:25km

SP T:30km

Emissions, Wastes, Co-products and Other releases

Fig. 1. LCA model and its system boundary of concrete manufacturing process

4 Results and Discussions 4.1 Environmental Impacts of Concrete under Different FUs Three pairs of virgin aggregate concrete and CO2 Concrete are evaluated by using the established LCA model. In the assessment, the variations of each indicator range from -6.33% to + 2.05% when compared to virgin aggregate concrete. It can be obviously seen that under the same mix design and the compressive strength level, using carbonated recycled aggregate is generally beneficial to the environment to some extent (Table 2). The environmental behaviors of CO2 Concrete in ADP and AP are poorer than virgin aggregate concrete when Pair 2 and Pair 3 are evaluated. It is mainly caused by CO2 gas used for carbonation treatment of recycled aggregate, when the production of ammonia generates a significant amount of SO2 and NO2 which impacts are partially attributed to its by-product CO2 gas. However, according to the results of Pair 1, the additional impacts brought from the consumption of CO2 gas are offset by 100% of recycled aggregate incorporation, leading to net savings on all impact categories. This implies that a trade-off is necessary to be made when engineering properties of concrete and its environmental performance conflict.

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Table 2. Environmental impacts of virgin aggregate concrete and CO2 Concrete under FU1 Indicator

Unit

Pair 1

Pair 2

Pair 3

VAC

CO2 Concrete

VAC

CO2 Concrete

VAC

CO2 Concrete 1.65E−04

ADP

kg Sb eq

1.83E−04

1.80E−04

1.73E−04

1.76E−04

1.63E−04

ADP_ff

MJ

1.76E+03

1.73E+03

1.62E+03

1.60E+03

1.47E+03

1.46E+03

AP

kg SO2 eq

1.49E+00

1.49E+00

1.35E+00

1.37E+00

1.21E+00

1.22E+00

EP

kg PO4 3− eq

2.57E-01

2.53E-01

2.34E-01

2.25E-01

2.10E-01

2.05E-01

FAETP

kg 1,4-DB eq

2.24E+01

2.17E+01

2.09E+01

1.98E+01

1.94E+01

1.89E+01

GWP

kg CO2 eq

5.24E+02

5.13E+02

4.75E+02

4.45E+02

4.25E+02

4.10E+02

HTP

kg 1,4-DB eq

9.41E+01

9.14E+01

8.65E+01

8.38E+01

7.89E+01

7.76E+01

MAETP

kg 1,4-DB eq

7.67E+04

7.50E+04

7.10E+04

7.00E+04

6.54E+04

6.49E+04

ODP

kg CFC-11 eq

6.60E−06

6.25E−06

6.18E−06

5.79E−06

5.77E−06

5.57E−06

POCP

kg C2 H4 eq

4.05E−02

3.95E−02

3.68E−02

3.41E−02

3.31E−02

3.17E−02

TETP

kg 1,4-DB eq

1.06E+00

1.05E+00

9.74E−01

9.58E−01

8.85E−01

8.77E−01

Nevertheless, if durability parameters of concrete are taken into account, CO2 Concrete outweighs the impacts of virgin aggregate concrete (Fig. 2), highlighting the inferior quality of recycled concrete on long-term behavior and longevity. In this study, LCA results are very sensitive to durability parameters of concrete, which are consistent with the statement by Panesar et al. [11]. The results shown in Fig. 2 also reflect that FU selection is a crucial step for an LCA study that antithetical opinions can be held from different perspectives. Specimen ‘0.45–100%, CO2 Concrete, GP’ has the overall best performance among three CO2 Concrete products evaluated. Since its compressive strength reaches to 35.3 MPa and the durability is similar to virgin aggregate concrete, it seems to be a suitable product altering low to medium strength conventional concrete for structural purpose. On the other hand, other two specimens are subjected to the durability issues, so that additional amount of concrete is required for repair and maintenance purposes. Extra environmental impacts are generated during its service life to compensate for the loss of durability and acquire the same functionality as virgin aggregate concrete. 4.2 Importance of Inclusion of Durability of Concrete in LCA Choosing unit volume as a FU of concrete is the most convenient way but not adequate to address the properties of concrete. Therefore, one of the most important indicators for concrete that compressive strength is usually supplemented to volume in FU to carry out a comparative analysis of different concrete products precisely, as FU1 used in this study. Despite that, FU combining volume and compressive strength seems still insufficient to conduct a LCA with no controversy. As stated before, the durability of concrete affects its serviceability and lifespan directly, which is the rationale of covering durability parameters into LCA. For instance, CO2 Concrete is undoubtedly an environmental-friendly recycled aggregate concrete and its net savings on emissions encourage potential stakeholders

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Fig. 2. Relative environmental impacts of CO2 Concrete under two FUs using virgin aggregate concrete as a reference when water-to-cement ratio is (a) 0.4 (b) 0.45, and (c) 0.5

who are environmental consciousness to apply the product to their projects. Nevertheless, they would be less informed for their decision-making to the subsequent impacts in the service life if different scenarios of LCA results are not disclosed to them. Evidence in the comparison of impacts under FU1 and FU2 explicitly emphasizes the importance of inclusion of concrete’s durability properties in LCA, as the results vary significantly under different FUs. When FU2 is calculated to match the equivalent FU to virgin aggregate concrete, it is found that CO2 Concrete performs better than virgin aggregate concrete on some aspects while the rest parameters for durability determines the environmental impacts of CO2 Concrete directly. In this regard, an issue in respect to which durability properties should be included in FU is essential to be solved before LCA study is conducted. Since it is impossible to take all the parameters into consideration, it is suggested to understand the durability of both plain concrete and structural concrete, and to identify the key factors that can be applied to FU of LCA. In addition, developing various cases of LCA is helpful to find the range of impacts and the corresponding optimized solution.

5 Conclusion Durability of concrete is an important feature in use, and it influences concrete’s performance and determines the service life that should also be embedded in an LCA study. In this study, two different FUs concerning volume, compressive strength, and durability of concrete are established to assess the environmental impacts of virgin aggregate concrete

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and recycled aggregate concrete – CO2 Concrete, by employing CML 2001 (baseline) methodology. By conducting the comparative analysis, CO2 Concrete is more beneficial to the environment under FU1 when its mix design and compressive strength are similar to virgin aggregate concrete. Nevertheless, durability largely affects the performance of CO2 Concrete under FU2 that additional amount of concrete is required to compensate for the loss of durability and obtain the same functionality to virgin aggregate concrete, contributing to increased environmental burdens. The results also address the importance to integrate the durability parameters of concrete into LCA, while which parameters should be included are uncertain. Therefore, various scenarios regarding different FU criteria can be presented to make a more informed decision.

References 1. Marinkovi´c, S., Carevi´c, V., Dragaš, J.: The role of service life in life cycle assessment of concrete structures. J. Clean. Prod. 290, 125610 (2021). https://doi.org/10.1016/j.jclepro. 2020.125610 2. Alexander, M., Beushausen, H.: Durability, service life prediction, and modelling for reinforced concrete structures – review and critique. Cem. Concr. Res. 122, 17–29 (2019). https:// doi.org/10.1016/j.cemconres.2019.04.018 3. Xing, W., Tam, V.W.Y., Le, K.N., Hao, J.L., Wang, J.: Life cycle assessment of recycled aggregate concrete on its environmental impacts: a critical review. Constr. Build. Mater. 317, 125950 (2022). https://doi.org/10.1016/j.conbuildmat.2021.125950 4. Hao, J., Yuan, H., Liu, J., Chin, C.S., Lu, W.: A model for assessing the economic performance of construction waste reduction. J. Clean. Prod. 232, 427–440 (2019). https://doi.org/10.1016/ j.jclepro.2019.05.348 5. Tam, V.W., Soomro, M., Evangelista, A.C.J.: A review of recycled aggregate in concrete applications (2000–2017). Constr. Build. Mater. 172, 272–292 (2018). https://doi.org/10.1016/j. conbuildmat.2018.03.240 6. Behera, M., Bhattacharyya, S., Minocha, A., Deoliya, R., Maiti, S.: Recycled aggregate from C&D waste & its use in concrete–A breakthrough towards sustainability in construction sector: a review. Constr. Build. Mater. 68, 501–516 (2014). https://doi.org/10.1016/j.conbui ldmat.2014.07.003 7. Guo, H., et al.: Durability of recycled aggregate concrete – a review. Cement Concr. Compos. 89, 251–259 (2018). https://doi.org/10.1016/j.cemconcomp.2018.03.008 8. Xiao, J.: Guidelines for recycled aggregate concrete materials and structures. In: Recycled Aggregate Concrete Structures. Springer Tracts in Civil Engineering, pp. 611–632. Springer, Berlin. https://doi.org/10.1007/978-3-662-53987-3_15 9. Thomas, J., Thaickavil, N.N., Wilson, P.M.: Strength and durability of concrete containing recycled concrete aggregates. J. Build. Eng. 19, 349–365 (2018). https://doi.org/10.1016/j. jobe.2018.05.007 10. Xing, W., Tam, V.W.Y., Le, K.N., Butera, A., Hao, J.L., Wang, J.: Effects of mix design and functional unit on life cycle assessment of recycled aggregate concrete: evidence from CO2 concrete. Constr. Build. Mater. 348, 128712 (2022). https://doi.org/10.1016/j.conbuildmat. 2022.128712 11. Panesar, D.K., Seto, K.E., Churchill, C.J.: Impact of the selection of functional unit on the life cycle assessment of green concrete. Int. J. Life Cycle Assess. 22(12), 1969–1986 (2017). https://doi.org/10.1007/s11367-017-1284-0

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12. Zhang, Y., et al.: Effect of compressive strength and chloride diffusion on life cycle CO2 assessment of concrete containing supplementary cementitious materials. J. Clean. Prod. 218, 450–458 (2019). https://doi.org/10.1016/j.jclepro.2019.01.335 13. De Schepper, M., Van den Heede, P., Van Driessche, I., De Belie, N.: Life cycle assessment of completely recyclable concrete. Materials 7(8), 6010–6027 (2014). https://doi.org/10.3390/ ma7086010 14. Marinkovi´c, S., Dragaš, J., Ignjatovi´c, I., Toši´c, N.: Environmental assessment of green concretes for structural use. J. Clean. Prod. 154, 633–649 (2017). https://doi.org/10.1016/j.jcl epro.2017.04.015 15. Van den Heede, P., De Belie, N.: Environmental impact and life cycle assessment (LCA) of traditional and ‘green’ concretes: literature review and theoretical calculations. Cement Concr. Compos. 34(4), 431–442 (2012). https://doi.org/10.1016/j.cemconcomp.2012.01.004 16. Zhang, Y., Luo, W., Wang, J., Wang, Y., Xu, Y., Xiao, J.: A review of life cycle assessment of recycled aggregate concrete. Constr. Build. Mater. 209, 115–125 (2019). https://doi.org/10. 1016/j.conbuildmat.2019.03.078 17. Tam, V.W., Butera, A., Le, K.N.: Mechanical properties of CO2 concrete utilising practical carbonation variables. J. Clean. Prod. 294, 126307 (2021). https://doi.org/10.1016/j.jclepro. 2021.126307

Industrialized Construction Firms and Digitally-Enabled Product Platforms: An International Case Study Shanjing Zhou(B) Department of Civil and Environmental Engineering, Centre for Systems Engineering and Innovation, Imperial College London, London SW7 2AZ, UK [email protected]

Abstract. Construction firms are taking up both digital delivery and product platforms for industrialized construction to benefit from economies of scale and scope. The digitally-enabled product platforms becomes important, which such platform is defined as a collection of common and stable modules and interfaces that can derive products effectively using digital delivery. Existing construction management literatures have focused on the usage of product platforms; however, there is relatively less on platforming, which encompasses both the development and implementation of digitally-enabled product platform. This paper takes a comparative case study approach from nine international case firms to examine how construction firms strategize for platforming. Findings show that three typologies of platforms that firms developed a kit of parts only, and also developed structured interface, and also developed design rules. This paper articulates the influencing role of customer requirement certainties across multiple market segments in shaping these strategies. The contribution is to extend work on construction product platform strategies, by providing a novel classification of platforming strategies with a focus on digitally-enabled product platforms, under varied certainties of customer requirements across market segments. This has implications for practitioners and opens new areas for research, taking the characteristics of customer requirements within or across markets into account in strategic decision-making on digitally-enabled product platforms. Keywords: industrialized construction · product platform · digital delivery · firm strategy

1 Introduction Industrialized construction is a way to construct buildings by leveraging technologies and principles from advanced manufacturing and digital delivery. Proponents of industrialized construction have been learning from the manufacturing sector and applying its principles since the last century (e.g., Barlow et al. 2003; Gann 1996). When industrialized construction firms try to construct using common modules and reuse these modules to generate different types of buildings (Barlow et al. 2003, Gann 2000), developing and © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 1337–1356, 2023. https://doi.org/10.1007/978-981-99-3626-7_104

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implementing a digitally-enabled product platform becomes important; because firms need to reassess their strategies to derive products, while such a platform can configure the final products using a set of common modules (i.e., kit of parts), interfaces, and design rules through digital delivery effectively (Lobo and Whyte 2017, Meyer and Lehnerd 1997; Simpson 2004; Ulrich and Eppinger 2012). The idea behind digitally-enabled product platforms in construction has recently received attention in various countries (WEF 2016, 2018). In tackling some global challenges such as health crises, e.g., the COVID-19 pandemic, and housing crises, many governments have utilized digitally-enabled product platforms to provide affordable housing, quarantine camps, vaccine centers (e.g., Pancevski 2022; The Standard (HK) 2022a, 2022b; Wilson 2020). There have been substantial initiatives in Hong Kong, mainland China, Singapore, and the UK (BCA 2018, 2020; DEVB 2018, 2020, 2021; HM Government 2018, 2020; MOHURD 2020a, 2020b), along with an increased interest in industrialized construction in the US (Pullen et al. 2019). As these governments are encouraging the take up of digitally-enabled product platforms for industrialized construction, construction firms operating in these countries face challenges. One set of challenges they face is technical, e.g., new structural connections (Abu-Salma et al. 2021; Vella et al. 2018). Another set of challenges they face is managerial, e.g., involving the strategic decisions within the firm about both the organizational and technical aspects. There has been some work on firm industrialized construction strategies (e.g., Lessing and Brege 2018; Pan et al. 2012) and the use of digitally-enabled product platforms (e.g., Jones et al. 2021; Veenstra et al. 2006). However, relatively less attention has been paid to platforming, covers both the development and implementation of digitally-enabled product platforms. Therefore, the motivation of this paper is to study platforming towards digitally-enabled product platforms. Thus, the research question of this paper is how industrialized construction firms strategize for the development and implementation of digitally-enabled product platforms. It builds on emerging research on construction product platforms (e.g., Jansson et al. 2014; Jones et al. 2021; Veenstra et al. 2006); thus, it examines how industrialized construction firms mobilize the platform elements while developing the platforms. This paper considers the broad literature on the digitally-enabled product platform, and how current literatures situate and extend it in construction. Then this paper outlines the research methods adopted. It explains the reasons why the multiple-case study was selected as research methods. It then illustrates the case selection and sampling strategies for the overall study. After introducing the research settings, it then illustrates data collection and analysis, the validation strategies. The findings section and analysis of the data set collected. It explores how firms mobilized platform elements, i.e., digitally-enabled kit of parts, digitally-enabled interface, and digitally-enabled design rules, and examines how all case firms mobilized the platform elements. Next, it compares and contrasts their strategies before synthesizing them into three types of firms. Finally, it suggests three types of platform strategies that construction firms can develop and implement. The discussion section synthesizes the finding. By theorizing firms’ strategies transition into the development and use of digitally-enabled product platforms, the main contribution is to extend the construction platform literature, by synthesizing three strategies that construction firms can mobilize platforms into construction. The final section presents

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the conclusions of this paper. To address each research question to examine the firms’ strategies for platforming, the paper finds – as firms move from towards digitally-enabled product platforms, it argues that firms can choose among three platforming strategies based on the varied certainties of market requirements. Acknowledging the limitations of this paper, it suggests future research directions and implications to practice.

2 PERSPECTIVE: Three Platform Elements and Customer Requirements Platforming encompasses the development and implementing of digitally-enabled product platforms (Meyer et al. 2018; Zhang 2015). There has been a trajectory of research on ‘product platform’ from the 1990s with the development of electronic devices, automobiles, etc., from mass-production product industries (Gawer 2014). Product platforms enable firms to develop a stream of derivative products based on a set of predefined subsystems and interfaces effectively (e.g., Baldwin and Clark 2000, p. 77; Chai et al. 2012; Jiao et al. 2007, p. 7; Meyer and Lehnerd 1997, p. 39; Ulrich and Eppinger 2012, pp. 55–56). In order to achieve economies of scale and scope, there is a need to use common elements that are shared and reused across products to derive future products effectively (e.g., Jiao et al. 2007, p. 7; Meyer and Lehnerd 1997, p. 39; Simpson 2004, p. 4). Thus, this section synthesizes these common elements into kit of parts, interface, and design rule. The digitally-enabled product platform is a collection of modules and interfaces that can derive products effectively using digital delivery (Lobo and Whyte 2017; Meyer and Lehnerd 1997; Simpson 2004; Ulrich and Eppinger 2012). Based on the literature, this section conceptualizes the three key elements of digitally-enabled product platform, as digitally-enabled kit of parts, digitally-enabled interfaces (including related specifications), and digitally-enabled design rule. The following section defines these platform elements, and explains how these platform elements situate in industrialized construction (Fig. 1).

Digitally-enabled product platform

Digitally-enabled kit of parts

Digitally-enabled interface

Digitally-enabled design rule

Fig. 1. Three elements of digitally-enabled product platform

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2.1 Digitally-Enabled Kit of Parts To group modules in a library in deriving future products, a kit of parts or product library is defined as a collection of discrete building modules that are pre-engineered and designed for manufacturing and assembly in a variety of ways as a finished building (Gibb and Alistair 2001, pp. 3–4; Gibb 1999; Howe et al. 1999, p. 165; Zhao et al. 2018). With a digitally-enabled kit of parts, modules (or components) can be engineered and defined before design and manufacturing pre-engineered in a digital environment and format (e.g., Cao et al. 2021). A module is designed and manufactured, ready for onsite assembly with predefined interfaces, at varying product architectures or prefabrication levels (Gibb and Pendlebury 2006; Gosling et al. 2016; Peltokorpi et al. 2018). Different combinations of modules cover structural, mechanical, electrical and plumbing, and other services. For example, structural modules can be composed of structural components, made of steel, reinforced concrete, timber, or other composite materials (Gosling et al. 2016). This paper chose the process-based classification (Gibb 1999; Jonsson and Rudberg 2014, 2015) to illustrate the modular kit of parts of each case firm and later examine strategies of digitally-enabled product platforms. Therefore, this paper defines digitally-enabled kit of parts is a set of kit of parts using digital delivery such as BIM (e.g., Cao et al. 2021; Gan 2022b), which is composed of a combination of modules depending on the specific product architectures or prefabrication levels (Gibb and Alistair 2001, pp. 3–4; Gibb 1999; Howe et al. 1999, p. 165). 2.2 Digitally-Enabled Interface Managing the interface between modules is important, as it can determine coordination across stakeholders responsible for adjoined modules associated with the interface (Gann 2000, pp. 41,127; Gibb 1999, p. 45; Jensen et al. 2012, p. 6). This is also critical to the production of modules in achieving economies of scale as the interface determines the interchangeability of modules by different stakeholders across the value chain (Gann 1996, p. 439). In this paper, digitally-enabled interface is defined as a set of digital and physical specifications or protocols to define interactions and relationships between modules (Chen and Liu 2005, p. 773; Ulrich 1995, pp. 421–422), which is useful for product configuration. In industrialized construction, interfaces can be digitally defined and codified, such as BIM objects containing interface requirements (e.g., Jensen et al. 2012, p. 6; Tetik et al. 2019, p. 10). Interfaces can be open or closed, depending on whether they are compatible or interoperable with external at both subsystem (modules) and systems levels (Chai et al. 2012, p. 456; Chen and Liu 2005, p. 773). Platform owner firms can share interfaces externally using an open interface (Meyer and Lopez 1995, pp. 296–297). Digitally-enabled interfaces can enable modules from either the same or different stakeholders in the value chain to interact, such as manufacturers, designers, and final users (Jensen et al. 2012, p. 7). For example, as shown in Fig. 2, if there are two adjoined modules owned by separate different module developers, interfaces owned by a structural module developer, need to define interface specifications for electrical and mechanical module developers.

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Fig. 2. Schematic illustration of a digitally-enabled interface

Digitally-enabled interfaces can store dependence requirements between modules, including engineering, architectural, production, assembly, and others (e.g., Jensen et al. 2012, pp. 6–7; Wikberg et al. 2014, p. 205). Without digital delivery, interfaces between modules can also be defined and then interacted manually through drawings or other paper-based media. Platform owner firms, through digitally-enabled interfaces, can interact with external stakeholders. For example, external module developer firms can use the digitally-enabled interfaces provided by platform owner firms to make their modules compatible with the product platforms. 2.3 Digitally-Enabled Design Rule for Future Products Digitally-enabled design rule refers to a set of digitally-codified protocols, standards, and specifications for product reconfiguration and deriving future products (e.g., Baldwin and Clark 2000, p. 77; Meyer and Lehnerd 1997, p. 39). These digitally-codified protocols, standards, or specifications are in a machine-readable format and mostly process information containing the design, manufacturing, and assembly (including transportation) constraints (Soman and Whyte 2020), which can be used for data-driven design such as generative design integrating with algorithms (Gan 2022a, 2022b). This is different from a digitally-enabled kit of parts, which does not contain processes-related constraint information (Soman and Whyte 2020). Platform owner firms can use such digitally-enabled design rules to derive and reconfigure products based on defined kits of parts and interfaces across value chains (e.g., Jensen et al. 2012, pp. 6–7; Singh et al. 2017; Wikberg et al. 2014, p. 205). Product platforms can embed design rules into product development for industrialized construction, by effectively reutilizing past knowledge and processes (Malmgren et al. 2011, p. 710). As shown in Fig. 3, digitally-enabled product platforms can transfer the learning from downstream value chains and feedback the rules to upstream owners and users of product platforms (Jensen et al. 2012, p. 7; Malmgren et al. 2011, p. 710).

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Fig. 3. Design rules transfer across value chains

Modules at the sub-system level can be efficiently recombined into a complete product system using digitally-enabled design rules (Jensen et al. 2012, p. 7). Design rules can come from the value chain, e.g., end users, manufacturers, site assembly, and designers (Malmgren et al. 2011, p. 710). From end-users, there can be parameters in relation to floor size and exterior. From module developers, there can be requirements including, 1) what module manufacturers need, such as bill of materials, product lifecycle management; 2) what module designers need, such as design standards, and regulations that designers need to fulfill end-user requirements (Malmgren et al. 2011, p. 702). For example, some platform owner firms in Lessing and Brege (2018)’s study used design rules for structural and assembly design. 2.4 Customer Requirement in Products Platforms The purpose of product platforms is to derive future products with commonly-used platform elements (Chai et al. 2012; Meyer and Lehnerd 1997). Existing literature (e.g., Simpson 2004) recognizes the role of customer requirements in mobilizing platform elements in product platform development. Customer requirements for industrialized construction also play an important role in defining the product requirements so as to influence the setup of production systems (Barlow et al. 2003; Barlow and Ozaki 2005). Considering customer requirements from specific market segments matters for product platform development, because such requirements influence whether product platforms can meet future customer demands across multiple market segments (Simpson et al. 2001, p. 3). Yet, how should firms take the customer requirements into developing product platforms? This can be before or after freezing the module design through a topdown or bottom-up approach (Simpson et al. 2001). Firms using a top-down approach can take customer requirements into product platform development in the beginning before decomposing building systems into modules, while firms with a bottom-up approach can take customer requirements based on a set of decomposed existing modules to develop product platforms (Simpson et al. 2001). A notable example in the aircraft manufacturing industry is A320, which shares systems, flight decks, and other subsystems across A330/340 and A350 and A380 families (Airbus 2022). Another example is Volkswagen’s MQB platform which can be reused across A, B, and C segments of passenger car markets (Volkswagen 2022).

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Existing literature on product platforms focuses on design or production stages in mass-production or process-intensive industries (Zhang 2015). Unlike mass-production industries, products in industrialized construction are often related to ‘the degree of customization’ and ‘complexity’ across modules (Gann 1996, p. 449). Thus, generating future building systems from product platforms of industrialized construction requires customization due to relatively high costs (Hobday 2000). Concepts generated in other industries may not hold and require ‘adaptation’ in the construction project-based context (Jansson et al. 2014, p. 72). Because platform elements and related competences need to fit the diverse market needs in the construction context (Jansson et al. 2014). Construction firms can develop product platform strategies while taking customer requirements (Jones et al. 2021; Kudsk et al. 2013; Shafiee et al. 2020; Veenstra et al. 2006). Jones et al. (2021, p. 13) indicate that construction firms, rather than developing the product platform in a relatively standalone approach (i.e., top-down or bottomup), can develop product platforms ‘iteratively’ based on a series of modular kit-ofparts based projects (Jones et al. 2021, p. 13). Although Kudsk et al. (2013) advocate top-down as a preferred approach for product platform development to take customer requirements in the beginning, firms may have established modular kits of parts for those which transitioning into digitally-enabled product platforms. As firms try to use product platforms for various products, we need to have a more granular understanding of how firms mobilize platform elements across market segments, including not only those modular kits of parts, but also interfaces, and design rules (Jiao et al. 2007; Simpson 2004; Simpson et al. 2006).

3 Research Methods 3.1 Research Setting The study examines the leading industrialized construction firms globally that developed and implemented digitally-enabled product platforms. The firms studied operated in Singapore, Hong Kong and mainland China, the UK, and US. Internationally, there is a trajectory to adopt industrialized construction to improve productivity in delivering buildings and infrastructure (WEF 2016, pp. 19–20). In these countries or regions, there were incentives either from governments or the capital investment to promote the adoption of industrialized construction or digitally-enabled product platforms. These policies include: a Hong Kong mandate for modular integrated construction (MiC) in public-funded building projects since 2020 (DEVB 2020), a Singapore policy to meet 70% Design for Manufacture and Assembly (DfMA) adoption rate in 2025 (BCA 2020, p. 1) and support for industrialized and prefabricated building in the 13th five year plan (MOHURD 2017, pp. 4–5). In addition, there was a growing interest in the adoption of industrialized construction in the US, with potential funding from capital investment (Pullen et al. 2019). Thus, construction firms operating in these countries (or regions) with interest in industrialized construction.

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3.2 Methods To address the research question, this paper used an multiple-case study approach (Eisenhardt 1989; Fellows and Liu 2015; Schweber 2016; Taylor et al. 2011) – a wellestablished approach relying on interpretation from both researchers and informants (Gioia 2020, p. 24). This study used secondary and primary data from nine internationally – leading construction firms with strategies for platforming. It used a protocol to carry out semi-structured interviews and collect other data (Miles et al. 2014). These data include 7845-page archival documents, consisting of annual reports, slide decks, technical specifications, etc.; 1062-min videos and audios; 1627-min recordings of 30 in-depth interviews with executives and product development leads; and seven factory and office visits.

4 Findings In this section, to address the research question, the focus is on how firms start the development and use of a digitally-enabled product platform, which is a collection of predefined modules and interfaces that can derive a variety of products effectively using digital delivery. This section first gives an overview of the data set of nine firms. It then describes how these firms developed digitally-enabled product platforms based on three common elements, i.e., digitally-enabled kits of parts, interface, and design rules. Next, the paper compares and contrasts their platforming strategies, articulating three types of firms that mobilized these platform elements in developing digitally-enabled product platforms with a different focus on customer requirements. Findings show firms developed different levels along the journey to digitally-enabled product platforms, thus, the paper typologizes them into 3 types. The paper extends the discussion and interpretation of the findings later. 4.1 Three Types of Platforming Strategies The section compared and contrasted their digitally-enabled kit of parts, interfaces and design rules that were developed and implemented by each firm case. As shown in Table 1, Firms A, B and G were found to adopt an ad-hoc approach to develop and then use interface specifications. They appeared to be uninterested in developing design rules. Another cluster of firms, Firms C and E, developed interface specifications based upon digitally-enabled kit of parts. Firm C, instead, drafted well-structured interface specifications and design rules, such as standard typical drawings, design manuals to use its product platforms for new buildings. Firm E had a similar approach in developing and using these platform elements. More importantly, four leading case firms, Firms D, F, H and I had were identified to develop their platforms into digitally-enabled platform elements, with a high certainty of customer requirements across multiple markets. The following sub-sections summarize the three types of strategies in which firms operate platform elements.

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Table 1. Overview of case firms on digitally-enabled product platforms Digitally-enabled kit of parts

Digitally-enabled interface

Digitally-enabled design rule

Characteristics of customer requirements

Type 1

Yes

No

No

Highly focus on single market segment, with low certainty about other market segments

Type 2

Yes

Structured

No

Increased certainty across multiple market segments

Type 3

Yes

Yes

Yes

High certainty on future building systems across multiple market segments

4.1.1 Digitally-Enabled Kit of Parts with a Focus on a Single Market The first type identified (Type-1) of firms was identified as firms that have only developed a digitally-enabled kit of parts. With less focus on future customer requirements across other markets, they did not develop digitally-enabled interfaces or design rules. The evidence is shown in Table 2. Table 2. Overview of Type-1 case firms Case Code

Digitally-enabled kit of parts

Digitally-enabled interface

Digitally-enabled design rule

Characteristics of customer requirements

A

BIM-based; non-volumetric preassemblies: precast slab, façade, balcony; volumetric preassemblies: bathroom units, plant room module with interior fitting-out and MEP); modular buildings: residential flat modules

Typical details in drawings, yet not in a systemic way; not yet in digital format

Manually and ad-hoc to derive future products

Mainly on housing without any specific focus on other markets

(continued)

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Case Code

Digitally-enabled kit of parts

Digitally-enabled interface

Digitally-enabled design rule

Characteristics of customer requirements

B

BIM-based; non-volumetric preassemblies: panel slab, hollow core slab, façade wall; modular buildings: Pre-fabricated bathroom unit, concrete PPVC module

Not predefined, loosely stored interface specifications

Manuals, on manufacturing requirements

Mainly on housing without any specific focus on other markets

G

BIM-based; Typical detail No non-volumetric drawings but not in preassemblies: precast digital concrete units incorporating façade, precast trenches for MEP; volumetric preassemblies: DfMA module, MEP module; modular buildings: MiC units equipped with plumbing and drainage

Mainly on housing without any specific focus on other markets

Firms A, B and G defined kits of parts into BIM models. However, they did not define interfaces between modules or subsystems in the digital format or the design rules for creating future products. These three firms focused on customer requirements from one single market, i.e., housing. To summarize, these firms did not seek to define interfaces or design rules formally, not even to codify these rules in the digital format. 4.1.2 Clearly-Defined Interface Based on a Digitally-Enabled Kit of Parts This section summarizes Type-2 firms. Transitioning from digitally-enabled and modular kit of parts, Type-2 firms were found to develop structured interface specifications based on their digitally-enabled kits of parts. Emerging findings indicate these firms started developing these specifications into digitally-enabled interfaces. Using digitallyenabled interfaces, Firm E started to transform design manuals and typical drawings into digital tools to configure inter-module interfaces. Firm C drafted the internal standards specifying how to design the interfaces, and published them as industry standards, and shared them with its stakeholders. By doing so, these firms were found to use digitally-enabled interfaces to configure plant rooms and interconnections between modules at the sub-system level. As for design rules, these firms were found to have drafted specifications such as codes of practice or design manuals and then started transforming these into digitally-enabled design rules.

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Because Firms C and E have started to witness the potential benefits from broader market segments, they started to take the customer requirements into interfaces and design rules while driving them to continue bring digital delivery towards digitallyenabled product platforms. Table 3 offers an overview of the evidence on how Firms C and E used digitallyenabled kits of parts, interface, and design rules. Table 3. Overview of Type-2 case firms Case Digitally-enabled kit of Code parts

Digitally-enabled interface

Digitally-enabled design rule

Characteristics of customer requirements

C

BIM-based; non-volumetric preassemblies: 8 types: integrated floors, internal walls, external walls, integrated kitchens and baths, elevators, integrated interior decoration systems, and intelligent building management systems

Standard drawings, technical standards, interfaces between modules can be digitally configured

Follow standards, but no digital rules to generate future products

Across 5 markets (office, housing, hotels, hospitals, schools)

E

BIM-based, Roadmap; Design non-volumetric guide for modules preassemblies: concrete floor, beam, MEP module, wall; volumetric preassemblies: Internal room pod, plant room

In development, no formalized approach yet; some digital configuration rules, “achieve quality and programme requirements”, “manufacturing constraints” (Firm website)

Across 3 markets (housing, school then office)

4.1.3 Towards Cross-Market Platforms: A Complete Digitally-Enabled Product Platform This section summarizes Type-3 firms. These firms were found to develop into a complete set required platform elements, i.e., digitally-enabled kit of parts, digitally-enabled interfaces, and digitally-enabled design rules. These firms were found not only to develop digitally-enabled kits of parts based on BIM tools, but also codified interface specifications into BIM as digitally-enabled interfaces, which can generate inter-module connections with a high level of detail.

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This evidence indicates a high certainty of customer requirements in developing and operating digitally-enabled product platforms, while considering multiple markets and potential changes of future products. These firms also developed codes of practice and digitalized those manufacturing and assembly requirements in their platforms. Firms D, F, H, and I can use their platforms to derive a variety of building systems in compliance with their code of practice and other building regulations. As shown in Table 4, Firms D, F, H, and I codified the interfaces into digital specifications; and developed digitally-enabled design rules in addition to the digitally-enabled kit of parts and interfaces. Platforms developed and operated by these firms can derive building projects digitally, in which platform users can use digitally-enabled design rules to reconfigure existing modules. It explains how each case firm operated with its platform elements to derive future-ready building projects – linking with the characteristics of customer requirements. 4.2 Summary of Findings This section synthesizes firm strategies of digitally-enabled product platforms into three types. Through this data set, findings show three types of firms that mobilize platform elements into their platforming strategies. By unpacking and evaluating the platform elements and their customer requirements of case firms, analyses suggest firms can mobilize platform elements in three types of approaches – driven by various certainties of customer requirements. Findings show that firms with the Type-1 approach chose to develop the digitallyenabled kit of parts only. As customer requirements were largely from one single market segment and firms were less certain about requirements across other market segments, Type-1 firms did not seek to define or standardize interfaces between modules, or apply design rules. The platforms of Type-1 firms stored and reused existing modules, but the interface would be defined manually when there were a new design. As design rules were not developed, deriving of products would be carried out manually. Considering expanding the customer requirements towards multiple markets, it identified that firms with the Type-2 approach chose to develop the digitally-enabled kit of parts and interfaces. With an increased certainty of customer requirements across different market segments, Type2 firms developed digitally-enabled kits of parts and established structured interface specifications. These firms, i.e., Firms C and E, developed interface protocols into standard details, while formalizing design rules into internal standards or design manuals. In contrast, this paper identified that firms with the Type-3 approach chose a complete digitally-enabled kit of parts, interfaces, and design rules. This type of firm had higher certainties on customer requirements from multiple market segments. As a result, these firms developed and updated their platform regularly to derive future building systems while accommodating new requirements from ongoing customer requirements. In addition, it found that an increasing number of interface specifications and design rules had been transformed into digitally-enabled interfaces and digitally-enabled design rules. With relatively more expectations on future requirements, firms used these Type-3 platforms to derive building systems by linking requirements from the value chain and

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Table 4. Overview of Type-3 case firms Case Code

Digitally-enabled kit of parts

Digitally-enabled interface

Digitally-enabled design rule

Characteristics of customer requirements

D

BIM-based; non-volumetric preassemblies: timber, steel, CLT panel, line boards, M&E module; modular buildings: school, office, prison, housing

Connection details between modules or components were defined and configured digitally

Different building systems can be generated using BIM and design rules, for schools and housing

Expanded across 5 market segments (pharmacy factory, then school, housing, office)

F

BIM-based; non-volumetric preassemblies: structural component and system; volumetric preassemblies: modular MEP system, modular partition

Connection detailing tools, composed of proprietary codes of practice for design, manufacturing an assembly; compliant interface specifications can be generated;

Different building systems can be generated; incorporating structural analysis, manufacturing, and assembly constraints

Initially across 7 markets (healthcare, housing, research, commercial, academic, housing, underground)

H

BIM-based; non-volumetric preassemblies: door, windows, wall panel, metal deck; volumetric preassemblies: bathpod; modular buildings: school, healthcare, retail

Codified architectural design, schedule, pricing, and engineering data

Future building systems can be generated using predefined algorithms and design rules, for different types of schools, restaurants

Gradually expanded to four segments (school, housing, education, healthcare)

I

BIM-based; volumetric preassemblies: bathroom pods (modular electrical system, steel frame, plumbing system

Interfaces can be generated digitally

Different bath pods can be generated considering manufacturing, logistics, supply chains, engineering rules

Not predefined, but used for different markets

users. Across three types of strategies, findings indicate all firms positioned digitallyenabled kits of parts at the core, while the digitally-enabled interfaces and design rules were of various completeness.

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5 Discussion By theorizing firm strategies for platforming, i.e., development and implementation of digitally-enabled product platform, main findings relate to the industrialized construction and construction platform strategy literatures (e.g., Hall et al. 2019; Jones et al. 2021; Lessing and Brege 2018). The section discusses the findings in relation to two inter-related literatures associated with the industrialized construction firm and construction platform strategies: Focusing on the three key platform elements and customer requirements, this section classified three strategies that use a kit of parts only, and also use structured interface, and also use design rules. This relates to the construction product platform literature. This section firstly discusses the findings and draws implications to the literature on construction platform literature by classifying three strategies or three types of firms that use a kit of parts only, standardized interfaces, and design rules. It then discusses the role of customer requirement certainty across multiple market segments in shaping these strategies. 5.1 Towards Digitally-Enabled Product Platforms: Three Strategies First, the three typologies of platforming strategies this paper identified relate to existing construction product platform literature (e.g., Jones et al. 2021; Veenstra et al. 2006) by typologizing three platforming approaches that construction firms may adopt. It found that all three types of firms developed the digitally-enabled kit of parts as their first priority, while adopting relatively different approaches to mobilize interfaces and design rules. Findings show Type-1 firms, facing uncertainties across other market segments, chose to develop the digitally-enabled kit of parts only, while developing and using interfaces or design rules in an ad-hoc and unstructured way. All three Type-1 firms, i.e., Firms A, B, and G, operated in specific countries or regions (e.g., Hong Kong, Singapore, or both) in which the market demand for housing s is steadily high and regulated by the government. Therefore, governments may play a dominant role in promoting and regulating the use of kits of parts and modularization in industrialized construction. These construction firms followed the government policies of industrialized construction closely. For example, all three firms were required to get the pre-approval from governments in order to use volumetric preassemblies or modular buildings in their housing building systems. Yet, there was no certainty of customer requirements and demand for other markets outside the housing. The Type-2 firms, instead, with a growing certainty of customer requirements across multiple market segments, chose to develop further into structured interfaces. It found that Firm C and Firm E targeted multiple markets along with the product platform development and deployed projects. The Type-3 firms with high certainties of customer requirements across multiple market segments, developed a compete digitally-enabled product platforms. This concurs with previous studies on product platforms (Jones et al. 2021; Kudsk et al. 2013; Shafiee et al. 2020; Veenstra et al. 2006), while this research extends this by highlighting the role of the customer requirement certainty in the product platform development. To develop into a complete set of digitally-enabled product platforms,

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these three typologies suggest that firms need a substantial level of certainty on customer requirements in their targeted market segments to develop a well-defined and complete digitally-enabled product platform. When such requirements are not well defined and uncertain, construction firms are not able to develop their interface specifications or design rules into well-defined digitally-enabled interfaces or design rules. Through analyses of platform elements used by case firms, this paper extends the literature on construction product platforms (Jones et al. 2021; Kudsk et al. 2013; Shafiee et al. 2020; Veenstra et al. 2006), by identifying three strategies that firms iteratively developed digitally-enabled product platforms, not even of various completeness. It found these three firm typologies chose to develop the product platforms while utilizing their unfinished product platforms to deliver projects by developing an ‘iterative’ approach.. This iterative approach concurs with the study by Jones et al. (2021, p. 13) on platforms development in construction, showing that construction firms can develop product platforms ‘iteratively’ based on customer requirements, instead of developing the product platform in a relatively standalone approach (i.e., top-down or bottom-up) (e.g., Meyer and Lehnerd 1997; Simpson et al. 2001). In the next section, conclusions and implications to research and practice are discussed.

6 Conclusions The overall contribution of this paper is to articulate how industrialized construction firms strategize for development and implementation of digitally-enabled product platform. Construction firms need to strategize before they developed and implemented platforms. Such decisions often involve changes in platform elements. Therefore, we need a better articulation of strategies for how firms developed and implemented platforms. Such articulation can also help firm executives and policy makers to navigate their relevant decisions. The paper addresses the research question, to characterize firm strategies for development and implementation of digitally-enabled product platforms in industrialized construction. It provides three novel typologies to classify and articulate such platforms and their relationships with the customer requirement certainties. This section firstly summarizes the conclusions drawn upon findings and discussion. Then, it elaborates on the implications to the research and practice. Finally, this chapter draws limitations of the research and suggests future research can build upon. 6.1 Digitally-Enabled Product Platform: Three Strategies Depending on Customer Requirement Certainty Across Multiple Market Segments The key contribution from this paper is to classify three strategies in firms’ development and implementation of digitally-enabled product platforms, which extends the construction product platform literature (Jones et al. 2021; Kudsk et al. 2013; Shafiee et al. 2020; Veenstra et al. 2006). Findings suggest that the customer requirement certainty is important in influencing the firm’s platforming strategies, By examining nine internationally-leading construction firms and their platforming practices, this study synthesizes three types of platforming strategies, including: Type-1

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firms mobilizing kit of parts only with a certainty of a single market segment, yet lower uncertainties across other market segments; Type-2 firms clearly defining the interfaces based on a kit of parts with increased certainties across multiple market segments, and Type-3 firms: toward a complete set of platform elements with high certainties across multiple market segments. The analyses suggest that the customer requirement certainty across multiple markets matters. With various levels of such certainties, the study shows how firms can mobilize elements inside digitally-enabled product platforms into three strategies. This paper also contribute to the product platform literature (e.g., Meyer and Lehnerd 1997; Simpson et al. 2001) from a construction perspective. This study concurs with the ‘iterative’ approach (Jones et al. 2021, p. 13), which firms can take customer requirements in the platform development along with the ongoing deployed projects, instead of developing the platforms in a standalone approach. 6.2 Implications to Policy and Practice This paper has implications for executives. For those firms developing and implementing digitally-enabled product platforms, understanding the targeted market segment matters, as firms may need certainties of product requirements, preferably from multiple segments, before making strategic decisions to invest in developing product platforms. 6.2.1 Implications and New Directions for Scholarship This study explores how firms mobilize platform elements as their digitally-enabled product platforms by synthesizing three types of platforming strategies. More in-depth future research can address why firms choose these different strategies from other perspectives. For example, future research can consider the economics of ‘demand’ side rather than ‘supply’ side (Gawer 2014, pp. 1240–1241). Other studies can consider a longitudinal analysis of the Type-3 platforms with a complete set of platform elements to explore the market consequences of future generations of products. Cao et al. (2021); Jensen et al. (2012); Malmgren et al. (2011) have explained the use of the modular kit of parts across value chains or construction stages. Jansson et al. (2014) explore how to mobilize standardized product platforms into a diverse project portfolio. Relatively less research has been conducted to understand how platforms can derive future products. This study suggests that construction firms can develop platforms that effectively derive future products by leveraging the abilities enabled by digital delivery, such as BIM and generative algorithms. With a complete set of platform elements, future research can explore how firms can derive future products based on updated versions of digitally-enabled product platforms using a generational approach.

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Estimating Embodied Carbon Reduction in Modular High-Rise Residential Buildings Through Low Carbon Concrete Siwei Chen1(B) , Yang Zhang1 , Yue Teng2 , Chi Sun Poon3 , and Wei Pan1 1 Department of Civil Engineering, The University of Hong Kong, Hong Kong SAR, China

[email protected]

2 Department of Build and Real Estate, The Hong Kong Polytechnic University, Hong Kong,

China 3 Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University,

Hong Kong, China

Abstract. Modular construction has attracted wide attention from both academia and industry and has been viewed as a novel approach to improve construction sustainability. Nevertheless, few studies have focused on examining the embodied carbon (EC) emissions of modular buildings. This paper aims to assess the EC of a modular concrete high-rise residential building case by comparing it with a conventional prefabricated building case in Hong Kong and examining the EC reduction efficiencies of several low carbon concrete solutions. The EC during the cradle-to-site stage of both building cases was assessed using the process-based life cycle analysis method. A total of 10 scenarios of different low carbon concrete solutions were set, and their EC reductions were compared. The EC per construction floor area for an averaged floor of the modular building case was quantified as 532.1 kgCO2 -eq/m2 , higher than that of the conventional prefabricated building case (558.1 kgCO2 -eq/m2 ). The largest EC reduction reached 23.8% in the modular building case, which was achieved by the combined use of rice husk ashes and the recycled concrete aggregates. Findings in this paper lay a good foundation for exploring novel low carbon concrete solutions for EC reduction of modular buildings in future research. Keywords: Modular building · Embodied carbon · Life cycle assessment · Low carbon concrete

1 Introduction The construction industry plays a critical role in response to the climate emergency as it accounts for 39% of energy-related emissions globally [1]. Amongst all carbon emissions, the embodied carbon (EC) is released within a much shorter period of time compared with buildings’ long service period, contributing to more annual carbon intensive impacts [2, 3]. Therefore, the precise assessment and reduction of the EC are of great importance during the whole life cycle carbon assessment of buildings [4]. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 1357–1369, 2023. https://doi.org/10.1007/978-981-99-3626-7_105

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Modular construction, as an innovative building method, has been attracting intensive attention from both the academia and industry [5]. Modules, as the essential functioning units, are generically premanufactured interiorly, and sometime exteriorly, which largely shorten the required construction time compared with traditional construction methods or prefabricated methods [6]. Compared with traditional cast-in-situ and 2D prefabricated panelized building systems, modular construction also possesses an army of advantages which may reduce EC emissions, e.g., less waste generation, greater recycling and reusing potential, and fewer design changes [7]. However, despite the fact that modular buildings have been widely constructed, the investigations into the EC emissions of modular buildings have been rarely identified amongst literature and reports. Moreover, little research has been conducted on the EC reduction potentials by applying low carbon concrete materials in modular buildings. Concretes, as the staple of the construction industry, 30 billion tons of concretes are utilized annually worldwide, and the concrete consumption is estimated to be rising even more rapidly than that of steel or wood [8]. Common solutions in reducing the EC of concretes comprises using supplementary cementitious materials (SCMs) and recycled concrete aggregates (RCAs) [9, 10]. The use of SCMs significantly decreases the usage of ordinary Portland cement (OPC) in concretes [11]. However, the limited supply of prevailingly used SCMs from industrial by-products has significantly restricted its application in a large industrial scale. Using RCAs is efficient in reusing demolition wastes and economizing the land resources for waste disposals [12]. Nevertheless, the incomplete processing of RCAs struggles to maintain a balance between adequate working performances and EC savings [13]. Therefore, reducing the EC and examining the EC reduction efficiency of novel low carbon concrete solutions are rather important in achieving overall EC reductions for concrete buildings [14, 15]. This paper aims to assess the cradle-to-site EC emissions of concrete modular highrise residential buildings and investigate the EC reduction efficiency of different low carbon concrete solutions in the concrete modular and prefabricated buildings. Processbased life cycle assessment (LCA) method was adopted for quantifying the EC emissions. A modular concrete high-rise residential building project in Hong Kong is selected for the case study, and its EC results are compared with those of a concrete prefabricated high-rise residential building case. Novel low carbon concrete solutions such as utilizing rice husk ashes (RHAs) and well-processed RCAs are also examined regarding their EC reduction performances. The structure of this paper is as follows: following this introduction, Sect. 2 explains the methodology. Section 3 presents the EC results of the two building cases and EC reduction performance of various low carbon concrete materials, as well as corresponding discussions. Section 4 draws the conclusions of this paper.

2 Methodology This paper mainly adopts the process based LCA method for the EC assessment of the modular building case and PC building case [16]. The EC emissions during the cradle-to-site stage is focused on the analysis. Carbon inventory analysis is conducted to identify the major activities and processes during the considered life cycle stages of the

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two different construction approaches. Then, case study method is adopted to provide a detailed analysis of the EC emissions and the EC reduction performances of different low carbon concrete solutions. 2.1 Building’s Carbon Inventory Analysis for the EC Assessment 2.1.1 Identification of EC Emission Sources During the Cradle-to-Site Stage Although the modular concretes residential buildings and prefabricated concrete residential buildings have some different prefabrication and construction processes, they generally share the same life cycle stages for the EC assessment. The cradle-to-site stage of the two types of buildings generally covers four stages (Fig. 1), i.e., S1) raw material extraction and production, S2) raw materials transport to factory, S3) manufacturing and production of modules or prefabricated components, and S4) prefabrication products transport from the factory to the construction site. Since the structural materials take the major part for EC emissions in concrete buildings that can be more than 80% [3], this paper only considers the concrete and reinforcement in the superstructure for the EC assessment. Architectural elements such as the finishes, insulations, building services, decorations and furniture are excluded for calculations.

Fig. 1. The cradle-to-site EC emissions sources for the case buildings

2.1.2 Data Collection and EC Calculation Two types of data, i.e., the emissions factors (EFs) and the data of project activities, are obtained via different data collection channels. The EFs are mainly collected from Ecoinvent Database 3.4 embedded in Simapro 9.2. Relevant data has been slightly adjusted by

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replacing certain process or energy supplies with firsthand data collected from local construction industry. The data of project activities, e.g., materials consumption, transportation mode and power of machinery, is collected by site investigation, bill of quantities, and personal interview. The EC calculation model is devised based on PAS 2050 [17]. The basic principle is to quantify the emissions by multiplying the activity data and corresponding EFs cumulated in different stages from S1 to S4, as shown below:  Qm × em (1) EC1 = EC2 =



EC3 = EC4 =

Qm × Dmt × et 



(2)

Qr × er

(3)

Qp × Dpt × et

(4)

Total EC = EC1 + EC2 + EC3 + EC4

(5)

where EC1 , EC2 , EC3 and EC4 are the EC emissions of corresponding life cycle stage, Qm denotes the engineering quantities of the material m (kg or t or m3 ), em represents the EF for the material production (kgCO2 -eq/kg or kgCO2 -eq/m3 ), Dmt indicates the distances of transportation of material m (km), et is the corresponding EF for the transportation (kgCO2 -eq/tkm), Qr denotes the consumption quantity of resource r (MJ or kWh), er is the EF of r consumption (kgCO2 -eq/MJ or kgCO2 -eq/kWh), Qp stands for the engineering quantities of the precast or modular products (kg or t), and Dpt is the transportation distance of the precast or modular products. The total EC emissions during the cradle-to-site stage is the sum from EC1 to EC4 . 2.2 Case Study A concrete modular high-rise residential building and a conventional prefabricated highrise residential building in Hong Kong are selected for the case study. The information such as major construction materials and transporting distances of the case buildings is summarized in Table 1, 2 and 3. 2.3 Reducing EC Through Low Carbon Concrete Solutions Utilizations of SCMs and recycled aggregates (RAs) in concrete have been recognized as two effective ways of reducing the EC [18]. According to previous studies, three types of SCMs and one type of RAs are studied in this paper, i.e., fly ash (FA), ground granulated blast-furnace slag (GGBS), RHA and RCA. In total, 10 scenarios of different SCMs and RCA are devised to investigate their EC reduction performances in the case buildings, as shown in Table 4. The FA and GGBS, probably as the most reputational SCMs, have been intensively investigated amongst literature [3, 10]. The FA or GGBS incorporated concretes

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Table 1. Basic information of the two buildings Specifications

Modular building

Prefabricated building

Number of floors

27

32

Number of Units

300

448

CFA (m2 )

639

1112

Floor height (m)

2.75

2.75

Structure type

Concrete, with modules

Concrete, with precast components

Service life

50 years

50 years

Table 2. Consumption of major structural materials of the two buildings Categories Concretes

Steel

Specifications

Modular building

Precast building

Precast or module concretes

C45 (8,208 m3 ), C60 (281 m3 )

C35 (11,515 m3 )

Cast-in-situ concretes

C45 (2,992 m3 ), C60 (50.8 m3 )

C35 (1,832 m3 ), C45 (9,020 m3 )

Reinforcing steel bars (Precast or module)

1,236 ton

2,052 ton

Reinforcing steel bars (Cast-in-situ)

209 ton

1,337 ton

have comparable performances as ordinary concretes whilst possessing much less EC. However, its limited production has extensively restricted its large-scale applications in construction [19]. Unlike the prevailingly used SCMs, which are generated as industrial by-products, a few other types of SCMs are the derivatives of biomasses [20, 21]. Amongst them, the RHAs have been drawing attention across the world in the recent years. RHA is the agricultural by-products of paddy rice husk combustion, which is mostly generated in incinerators or power plants. Previous research has reported the promising prospects of incorporating RHA in concrete without impairing its working performances [22]. In Asian countries, the biggest advantage of RHA over conventional industrial by-products SCMs can be characterized as its profuse amount of production, which could economize up to 42 MT of OPC usage in China if largely utilized [23, 24]. Nevertheless, it is extremely hard to estimate the precise carbon footprint during the whole life cycle of RHA. This is fundamentally ascribed to its hard-to-quantify CO2 absorption in photosynthesis as biomasses, different thermal conversion reactions and power generation efficiency in incinerators, counted life cycles, etc. [25]. Therefore, the EF of RHA in this study considers only its grinding process after generation from the incinerator [26]. After adjusting with the local EC database, the EF for RHA is 0.0021 kgCO2 -eq/kg. Meanwhile, the EFs for FA and GGBS are 0.008 and 0.083 kgCO2 -eq/kg, respectively.

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Categories

Description

Cement and SCMs

From mill to concrete 267 batching plant in Hong Kong

307

From mill to factory in Guangdong

100

100

70

110

Natural aggregates (NAs) From sizing plant to construction site in Hong Kong

140

180

From sizing plant to manufacturing factory in Guangdong, China

350

350

From demolishing site to processing factory in Guangdong (average)

40

40

From processing factory to manufacturing factory in Guangdong

30

30

From processing factory to construction site in Hong Kong

100

140

From steel factory to manufacturing factory in Guangdong

130

130

From steel factory to construction site in Hong Kong

28

28

Modules or components

RCAs

Steel bars

From manufacturing factory in Guangdong to the construction site in Hong Kong

Modular building Precast building

Using alternatives of natural aggregates has been identified as another efficacious means of reducing EC in concretes [27, 28]. Amongst them, the most utilized alternative is still from the concretes, i.e., the recycled concrete aggregates, due to its profuse supply in building demolition. However, primarily due to the incomplete detachment of cement residues, additional treatment is needed to produce high-quality recycled aggregates. Otherwise, a higher cement content is required to enhance interior bonding to reach certain concrete strength [29], which inevitably increases the total EC. To use the completed processed RCAs, this paper adopts a complete set of procedures are adopted to produce higher quality of RCAs, as devised amongst literatures [30, 31]. By incorporating with local EC database, the EF for completed processed RCA is determined as 0.0064 kgCO2 -eq/kg, while the EF for NA is 0.002 kgCO2 -eq/kg.

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Table 4. Concrete designs for each scenario No.

Composition

Aggregates type

No.

Composition

Aggregates type

1

100% OPC

100% NAs

6

10% RHA +90% OPC

100% NAs

2

10% FA +90% OPC

100% NAs

7

35% RHA +65% OPC

100% NAs

3

35%FA +65% OPC 100% NAs

8

100% OPC

50% NAs +50% RCAs

4

10% GGBS +90% OPC

100% NAs

9

100% OPC

100% RCAs

5

35% GGBS +65% OPC

100% NAs

10

35% RHA +65% OPC

100% RCAs

3 Results and Discussions By applying the corresponding data and methods, the EC emissions from the case buildings are calculated and reported. According to the calculated results, the low carbon concrete solutions addressing the carbon intensive materials are further explored. 3.1 Results of the EC Emissions of the Case Buildings To simplify the results and provide a more direct view of EC proportions of structural materials in the buildings, the EC of the buildings are categorized into four main parts in Fig. 2, i.e., precast concrete, in-situ concrete, precast reinforcements, and in-situ reinforcements. For example, the EC emissions of transporting cement or aggregates for producing modules and prefabricated components, the EC emissions for manufacturing concrete modules, and the EC emissions in transporting modules are classified as the EC in precast concrete part. EC emissions pertaining to transporting cement or aggregates for producing in-situ casting concretes are classified as the in-situ concrete part. EC emissions related to the steel reinforcements used on sites are categorized as the in-situ steel reinforcements part. Concerning the EC emissions in the modular building, the total EC emission per CFA for an average floor (total EC divided by CFA and the number of total floors) is calculated as 532.1 kgCO2 -eq/m2 . Over half of the total EC emissions, around 57.8%, are induced by precast concrete, followed by the in-situ reinforcements part (20.9%) and the in-situ concretes part (17.8%). Meanwhile, the least EC is contributed by the cast-in-situ steel reinforcements, covering 3.5%. In terms of the prefabricated building, the overall EC per CFA for an average floor is 558.1 kgCO2 -eq/m2 , 4.8% more than the modular case. The largest contributor of EC is the cast-in-situ concretes followed by the precast concrete, sharing the similar proportions (over 31%). Altogether the reinforcements parts contributed nearly 36% of the total EC emissions.

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Fig. 2. EC emissions per CFA of an average floor

The composition of EC sources is significantly different in the two buildings. Comparing with the prefabricated case, the precast concretes cover a significant larger proportion, and the cast-in-situ reinforcements contribute to an apparent smaller percentage in the modular case. Reasons behind such deviations could be well established. The modular building has a much higher percentage (over 70%) in the usage of precast concrete as most of the concrete are integrated into modules. Precast concretes are subjected to more transporting distances comparing with cast-in-situ concretes, which improves the EC emissions. On the other hand, most of steel reinforcements used in modular buildings are integrated into or onto modules to avoid the in-situ placements so as to accelerate the construction pace. Therefore, despite the fact that the concretes are cast-in-situ for certain components, e.g., the semi precast slab above modules and the inter module connecting holes, their interior reinforcements are pre-placed and thus categorized into the precast part. Generally, the discrepancies between the EC compositions can be primarily attributed to the high prefabrication essences of modular buildings. 3.2 EC Mitigations Using Low Carbon Concrete Solutions Low carbon concrete solutions (i.e., the 10 scenarios) are considered for both the castin-situ concretes and precast concretes in the two buildings (Fig. 3). For both the case buildings, scenario 1 is set as the baseline scenario as no low carbon concrete solutions was adopted. The EC for scenario 1 in the modular building and the prefabricated building are assessed as 532.1 and 558.1 kgCO2 -eq/m2 , respectively.

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By adopting SCMs in concrete solely, 10% to 35% replacement of OPC leads to 3.9% to 17.8% EC reductions. Using RHA to replace 35% OPC contributes to the most EC reduction amongst other SCMs, reaching 17.8% and 15.1% in the two cases (scenario 7). By replacing 35% of the OPC with FA, EC emission reductions in modular and prefabricated cases are cut down by 17.7% and 15.1%, respectively. GGBS is the least EC reduction efficient SCM, achieving 4.6% and 3.9% EC reductions by replacing 10% of OPC in the two cases (scenario 4). In terms of the sole utilization of RCAs in scenario 8 and scenario 9, the EC reductions are much less than the SCMs. Replacing all the NAs with RCAs in concretes results in up to 4.2% and 2.4% EC reductions in the modular and prefabricated cases. In general, the highest EC reduction is noticed in scenario 10 in the modular building case (35% RHA +100% RCA), where 23.8% of EC reduction is achieved. Meanwhile, the least reduction happens in scenario 8 in the prefabricated case (50% RCA), reaching 1.2% in total. Generically speaking, in the same scenario, more EC reduction and a larger reduction proportion is achieved in the modular case. This could be essentially ascribed to the higher proportion of EC emissions brought by concretes in the modular building. An interesting fact is noticed by dividing the EC reduction potentials with the corresponding replacing ratio. The results are basically a constant for one type of SCMs adopted in one specific building, regardless of their replacement ratios to OPC. Therefore, in order to better characterized the EC reduction potential of each SCM excluding their OPC replacement ratio, a reducing -replacing factor (RRF) is introduced as follows: RRF = REC /RSCM

(6)

where the RSCM is the replacement ratio by SCM (%) and REC is the EC reduction potential (%). By applying such a factor, the RRFs for FA, GGBS, RHA and RCA are calculated as 0.505, 0.459, 0.510 and 0.042 for the modular case, respectively. Meanwhile, for the prefabricated building, the RRFs for FA, GGBS, RHA and RCA are calculated as 0.431, 0.391, 0.431 and 0.024, respectively. Apparently, the RRF for RHA in the modular case is the largest while the RRF for RCA in the prefabricated case is the least. Concrete incorporating RHA is the most EC reduction efficient solution since more EC can be mitigated using the same replacing ratio. Conversely, concrete incorporating RCA is the least EC reduction efficient solution. Such discrepancies could be primarily attributed to that cement is the main contributor of EC in concretes, reaching up to 80% in some cases [13]. Meanwhile, the EC of aggregates constitutes a much smaller fraction of the total concrete EC. Moreover, not only can the RRF be used as an indicator for assessing the EC reduction efficiencies of the different low carbon materials, but also can it describe the buildings’ sensitivity to EC reductions. For example, in this study, by replacing OPC with the same proportion of RHAs, more EC can be reduced in the modular case, which indicates that the modular case is more EC reduction sensitive. Since the primary use of construction materials can be determined at the design stage, practitioners are encouraged to improve the EC reducing sensitivity of the building through structural design optimization. While calculating the EC reductions induced by replacing aggregates, it is noticed that the processing of RCA is cumbersome and carbon-intensive, and thus the RCAs’ EF

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Fig. 3. EC per CFA of an average floor in different scenarios: a) modular building, b) prefabricated building.

is always higher than that of the NAs. However, the EC for transporting the aggregates constitutes a significant fraction in the total aggregates-related EC. It is estimated that if the total transporting distance of RCAs were less than 92.9% of the transporting distance of NAs in this study, utilizing RCAs in concretes could result in net EC reductions. As shown in the modular case, the total distance of RCAs is 20% of that of the NAs in the precast concrete parts while the transporting distances are equal for RCAs and NAs in the cast-in-situ concrete part. As a result, the EC of the modular case is reduced by up to 4.2%. In practical engineering projects, the aggregates are normally transported from far distances, either by water or land transportations, leading to significant EC emission increases. A well-planned recycled aggregates processing factory location, which cuts down the total transporting distance of recycled aggregates before utilization, can render great advantages of RCAs in reducing the buildings’ total EC and economizing land resources for construction waste disposals.

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4 Conclusions This paper estimates the EC of two case buildings and examined the potential of EC reductions through adopting different low carbon solutions. The major conclusions are drawn as follows: 1) The EC per CFA for an average floor of the modular case is generally higher than that of the prefabricated case, reaching 532.1 and 558.1 kgCO2 -eq/m2 , respectively, amongst which the concrete is the primary EC contributor, constituting around 70% of the total EC in both cases. 2) The maximum EC reductions are achieved in scenario 10, 18.2% and 23.8%, for both cases where both the RCAs and RHAs are used. For the same scenario, more EC can be mitigated, and a larger reduction proportion of EC reductions is achieved in the modular case. 3) Concretes incorporating RHAs is the most EC reducing efficient solution amongst all SCMs, the RRF of which are 0.510 and 0.431 in the two cases, respectively. Not only can the RRF be used as an indicator to describe the EC reduction potentials of different low carbon solutions, but also can it describe the buildings’ sensitivity to EC reductions. 4) Utilizing the RHA in concretes is highly effective in reducing the total EC emissions. Up to 17.8% EC emissions are saved by replacing 35% of OPC in the modular case building. Comparing to industry by-products SCMs, the use of agricultural byproducts SCMs excels in the profuse amounts of production and generating much less EC emissions during the production process. 5) Despite the fact that the EC reductions for utilizing the RCAs (up to 4.2%) are much less than that of the SCMs, it is significant in reducing construction demolishment and waste. Minimizing transportation distance is crucial in reducing the total RCAsrelated EC emissions. The findings in this paper should help to conduct scenario and sensitivity analysis in future research for exploring novel low carbon solutions. Future research is recommended to explore other potential agricultural by-products and recycled wastes to reduce the EC emissions for construction materials. Acknowledgements. The work presented in this paper was supported by the General Research Fund (Project No. 17201120) and the Collaborative Research Fund (Project No. C7047-20GF) of the Hong Kong Research Grants Council.

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Machine Learning Approach to Examine the Influence of the Community Environment on the Quality of Life of the Elderly Qi Liang1(B) , Yang Zhou2 , and Qin Li2 1 Department of Civil Engineering and Geomatics, Southwest Petroleum University, Chengdu,

China [email protected] 2 School of Civil Engineering and Geomatics, Southwest Petroleum University, Chengdu, China

Abstract. The quality of life (QoL) of the elderly has gradually become the focus of contemporary research. Elderly spent certain time staying at the community in their daily life, while studies have claimed the close relationships between built environment and the QoL of the elderly. With the advancement in the analytical tools, this paper aims to apply the machine learning approach to empirically examine the influence of the community environment on the QoL of the elderly. After extensive literature of relevant knowledge, a questionnaire survey was administered among the elderly. The collected quantitative data were subjected to a series of mathematical and statistical analysis analyses, and regression models for the relationship between community environment and the QoL of the elderly were established through support vector machine method. The results show that: 1) both the factors related the space and environment of the community can influence the QoL of the elderly; and 2) it was interesting to note that none of the facilities factor in the community imposes impact on their QoL. Practical recommendations are put forward according the research results in order to improve the community environment for the elderly, including building enough space, optimizing layout of monitoring equipment, maintaining ventilation to ensure air quality, and so on. This paper mainly contributes to apply the machine learning approach for examining the influence of community environment on the QoL of the elderly, which should enhance current body knowledge about the research related to the built environment for the elderly. The research findings should be helpful for the policy makers, facilities managers and academics to effectively improve existing practices regarding the management of community environment for better QoL of the elderly. Keywords: Elderly · Environmental factors · Facility management · Machine learning · Quality of life

1 Introduction According to the aging definition by United Nations, a country or region has entered an aging society if people aged 65 or above accounting for more than 7% of the total population [1]. Aging has become a global issues. The elderly population aged 65 and © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 1370–1381, 2023. https://doi.org/10.1007/978-981-99-3626-7_106

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above accounts for 17% of the total population in the United States, 16% in Russia, and 18.8% in Britain. According to latest statistics, China also has more than 190 million elderly, accounting for 13.5% of the total population by the end of 2020 [2]. Nowadays, aging in place becomes the major way to accommodate the elderly in their later life [3]. Elderly is vulnerable to the influence of living environment that consists of both indoor individual unit flat and outdoor community area. Elderly spent certain time in the communities for various activities, including shopping, social interactions and outdoor exercise. The space, services and facilities of the community may have a significant influence on the daily life of the elderly [4]. Given the amount of the elderly in the community in daily life, it is necessary to study the influence of the community environment in order to ensure health and welling of the elderly. This paper sets to examine the influence of three aspects of the community environment on the quality of life (QoL) of the elderly through hybrid use of both traditional statistical analysis and machine learning approach. After an extensive literature, a questionnaire survey was administered among the elderly to collect quantitative data for empirical test of research hypothesis and development of the environment–QoL model for the elderly in the community. Statistical methods were used in the study for data analysis, including reliability analysis and correlation analysis. Machine learning method was then used for data analysis on top of traditional statistical methods. The final conclusion was drawn based on the results by both analyses. Current study presented an attempt to use machine learning to study the influence of environment on QoL for the elderly. It was expected that through the application of both traditional and innovative methods, the final conclusion would be reliable.

2 Literature Review 2.1 Quality of Life QoL is a broad concept to comprehensively measure and evaluate the quality of human life [5]. It roughly includes five aspects, including psychological, physical, independence, environmental and social QoL [6]. Elderly are often suffering various health symptoms, such as sensory, disability [7]. In fact, physical QoL concerns the health status of the elderly, and has been regarded as the most important factor in evaluating the QoL. The elderly are generally sensitive, prone to emotional instability and poor psychological state, which often lead to mental illness and impaired QoL [8]. Thus, psychological QoL is another important aspect to evaluate the QoL. Independence reflects the elderly’s capability to perform various tasks in their daily life without help from others. If the elderly can walk by themselves and have no obstacles to movement, they can complete many things, such as going out shopping, walking, participating in sports [9]. Environmental QoL concerns the perceptions of the elderly on their living environment, including information acquisition, provision of entertainment and so on. If the environment is not appropriate and can’t meet the demands of the elderly, their health and well-being will be harmed. The social QoL refers to the elderly’s relationships with others. It is important for the elderly to have adequate company and keep relationships with others to certain level, so as to ensure a satisfactory late life [10].

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2.2 Community Environment According to the facilities management, there are three key aspects of the living environment including space management, building services and supporting facilities [11]. Space management refers to management of available space for certain function requirements of space [12], which mainly included space and privacy [13]. Adequate space in common area is associated with willingness of movement, which can improve physical and psychology health and environmental satisfaction of elderly [6]. Properly managed monitoring equipment could reduce elderly’s worry about privacy violation and negative emotions (e.g., anxious, depression) [14], which is benefit for elderly’s psychological health. Building services are the necessary functions or equipment to meet the needs of the elderly. Due to the physiological degeneration of their visual system, the elderly will need higher brightness level than the younger one, and more artificial lighting to compensate for their declined visual capability [15]. Good ventilation can improve the air freshness and maintain the thermal comfort of the human body [16], which is important for elderly to stay comfortable and away from infections. The introduction of outdoor air can significantly improve the perceived air quality of the human body, create a good sleep environment and ensure the sleep quality. Supporting facilities are set up to meet specific needs of the elderly so as to improve the QoL of the elderly, such as allowing entertainment by provision of recreational facilities, facilitating location by establishment of various signage. Recreational facilities within the community, such as parks, squares, shopping malls, chess and card rooms, can encourage the travel willingness and promote social interactions of the elderly [17]. Signage is important for the elderly who often has declined eyesight and mental capability, as it can guide the direction of the elderly and help them avoid danger [14]. 2.3 Support Vector Regression Support vector machine (SVM) is a supervised machine learning method suitable for model recognition, data classification and regression analysis. It has strong mathematical foundation and system theoretical support [18]. Support vector regression (SVR) is a method derived from SVM to solve regression prediction. Its basic principle is to solve the problem with the training error as the constraint condition and the minimum confidence time as the final optimization goal [19]. Compared with traditional mathematical statistics, SVR has wide application in research filed, because of its strong learning, anti-interference capacity and simple operation [20]. SVR has been used to predict and establish model in architectural, construction and engineering researches [21]. For example, SVR has been applied to predict the settlement of buildings, the bearing capacity of stone columns, building energy consumption, the distribution of bending moments of reinforced concrete structures, and estimation of the shear strength of reinforced concrete shear walls [22, 23]. It has also been regarded as a reliable method to develop building energy consumption model and building life cycle carbon emission model [24]. It can also be used for quality cost forecasting as well as play an important role in bridge design management projects [25, 26]. In current study, SVR regression model was applied to explore the relationship between community environment and QoL for the elderly.

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3 Conceptual Model According to relevant theories, models and literatures on the QoL of the elderly and environmental attributes, this paper proposed a theoretical model that display hypothesized relationships between QoL of the elderly and environmental attributes (see Fig. 1). It hypothesized that three groups of environmental attributes in community, including space management, building services and supporting facilities, can impose significant influence on the QoL of the elderly. Community Environment

Space management: privacy, space Building services: lighting, ventilation Supporting facilities: signage, recreational facilities

Quality of Life

Physical health Psychological health Independence Environmental life Social relationship

Fig. 1. Conceptual Community Environment–QoL Model for the Elderly

4 Research Methodology 4.1 Questionnaire Survey and Sample After literature review, a questionnaire is designed with inclusion of validated scales. It consists of three parts: 1) demographic information; 2) the QoL of the elderly, including physical health, psychological health, social relationship, independence, and environmental QoL; and 3) the satisfaction with community environment, including space management, building services, and supporting facilities [9, 13]. The questionnaire items were measured by five-point Likert scale, from 1 (very dissatisfied or very disagree) to 5 (very satisfied or very agree) to show the elderly’s opinions towards community environment and to report their QoL. The questionnaire was administered to elderly people aged 65 and above who have adequate cognitive and expressive abilities through internet and offline distribution. A total of 120 questionnaires were collected, of which 81 were valid. The valid data were all from the elderly over 65 years old. 77.78% of the respondents were 65–79 years old, and 22.22% were 80 years old and above. The proportion of men and women is relatively balanced, with male participants accounting for 46.91% of the total sample. The detail demographic information of the participants are shown in Table 1.

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Q. Liang et al. Table 1. Demographic Information

Factors

Options

Frequency

Percentage (%)

Age

65–79 years

63

77.78

80 years and over

18

22.22

Gender

Men

38

46.91

Women

43

53.09

Education level

Primary School

39

48.15

Junior High School

12

14.81

High School/Secondary School Health status

Living conditions

3

3.71

Others

27

33.33

Healthy

12

14.81

General

54

66.67

Unhealthy

15

18.52

6

7.41

Living with a partner

24

29.63

Living with children

25

30.86

Living with partner and children

24

29.63

2

2.47

Solitary

Others

4.2 Statistical Analyses The empirical data were analyzed by statistical techniques, including reliability test and correlation analysis. In this study, SPSS software was used to conduct these analysis methods. Reliability test was used to test the internal consistence of community environmental factors and QoL factors. The correlation analysis is to explore the strength and direction of the relationship between community environment and the QoL of the elderly. After verification the data will be more reliable. Based on the results of the statistical analyses, SVR analysis was then performed to validate the model. 4.3 Support Vector Regression SVR method was applied to the data for the examination of the relationships between community environment and QoL of elderly. The SVR is one kind of machine learning techniques, which is used to establish regression model and operated on MATLAB. The specific steps of using SVR to examine the relationships between community environment and QoL are as follows: 1) data pre-processing to allow for the use of SVR model; 2) divide data into training set and test set in a ratio of 7:3; 3) normalize the data and simplify the calculation method; 4) use the training set to complete the creation of SVR model, and use the test set to test the model to get the predicted value; 5) simulate and predict the model, and compare the predicted value with the training value; and 6) present the results graphically.

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For a set of data {(x1, y1), (x2, y2), (x3, y3)…(xi, yi)}, where xi ∈ Rn is the input value, yi ∈ R is the associated output value of xi, and n is the number of data points, after constant changes and parameters adjustment, the Eq. (2) is finally obtained from the original linear equation (1). The Eq. (2) is the formula for calculating SVR. f(xi ) = ωT ϕ(xi ) + b

(1)

ϕ(xi ) is a kernel function, ω and b are the parameters to be identified in the model  n  f(xi ) = βi − βi* k(x, xi ) + b (2) i=1

βi∗

βi and are support vector parameters, k(x, xi ) is the inner product [27]. In this study, R2 and MSE were used to evaluate the model accuracy. The larger the R2 value of each independent variable is (the closer to 1), the more accurate the model is. MSE can evaluate the degree of change in the data. The smaller the value of MSE (close to 0), the better the accuracy of the prediction model in describing the experimental data [28]. Equations for these criterion are as follows: 2   i yˆ i − y¯ 2 (3) R =1−  y − yi )2 i (¯  2 1 n  yi − yi (4) MSE = i=1 n



where yi is the mean of the yi values, yi is the y regression estimate

5 Results 5.1 Reliability Test The reliability test was conducted using Cronbach’s alpha value. Alpha value at 0.7 set as the threshold for the inclusion of related factors [29]. According to Table 1, all factors had a higher Cronbach’s value than 0.7, ranging from the lowest at 0.720 for psychological health (Q2) and social relationship (Q5) to the highest at 0.785 for signage (F5). Thus, all factors had an alpha value higher than 0.60 as shown in Table 2. 5.2 Correlation Analysis The results of correlation show that: 1) privacy is negatively correlated with environmental life (Q4: −0.261; P < 0.01); 2) space has a positive relationship with physical health (Q1: 0.383; P < 0.01), psychological health (Q2: 0.477; P < 0.01), independence (Q3: 0.211; P < 0.05), environmental life (Q4: 0.449; P < 0.01), and social relationships (Q5 0.342; P < 0.01); and 3) ventilation is correlated with physical health (Q1: 0.382; P < 0.01), psychological health (Q2: 0.483; P < 0.01), environmental life (Q4: 0.435; P < 0.01), and social relationship (Q5: 0.344; P < 0.01) (see Table 3). The statistical significance of the identified relationship is also pointed out by using the asterisk mark ‘*’ (p < 0.05) or ‘**’ (p < 0.01).

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Factors

Number of items

Alpha (α)

Mean

Q1-Physical health

6

0.728

3.105

Q2-Psychological health

6

0.720

3.743

QoL

Q3-Independence

3

0.751

3.717

Q4-Environmental life

6

0.740

3.797

Q5-Social relationship

3

0.720

3.597

Community Environment Components Space Management F1-Privacy

2

0.784

3.040

F2-Space

7

0.731

3.833

F3-Lighting

2

0.776

3.070

F4-Ventilation

4

0.728

3.830

Building Services

Supporting Facilities F5-Signage

3

0.785

3.207

F6-Recreational facilities

5

0.762

3.242

Table 3. Correlations between the community environment factors and the QoL factors Factors

Q1

Q1-Physical health

1

Q2

Q3

Q2-Psychological health

.633**

1

Q3-Independence

.451**

.416**

1

Q4-Environmental life

.368**

.634**

.343**

1

Q5-Social relationship

.532**

.576**

.660**

.403**

1

F1-Privacy

−.078

−.171

−.160

−.261**

−.067

F2-Space

.383**

.477**

.211*

.449**

.342**

F3-Lighting

.130

.060

.062

.147

.045

F4-Ventilation

.382**

.483**

.189

.435**

.344**

F5-Signage

−.184

.007

−.137

.009

−.102

F6-Recreational facilities

.028

.140

−.024

.127

.161

Note: ** - Correlation significant at the 0.01 level (2-tailed). * - Correlation significant at the 0.05 level (2-tailed).

Q4

Q5

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5.3 Support Vector Regression In each SVR model, a QoL factor (e.g., physical QoL) was entered as the dependent variable of the model and all the community environment factors were used as the independent variable of the model. In total, five SVR models were established (see Table 4). Model 1 shows that space and ventilation have an impact on the physical QoL of the elderly, explaining 35.6% of the variance and achieving MSE value at 0.212. Model 2 show that space and ventilation have an impact on the psychological QoL of the elderly, and 39.7% of the variance and MSE value at 0.081 are obtained by this model. As for Model 3, only 10.1% of the variance was explained and is 0.293 of MSE is explained. This model revealed that space had impact on the independence of the elderly. Model 4 indicates privacy, space, and ventilation have significant effects on environmental QoL of elderly, explaining 52.1% of the variance and 0.047 of MSE. Model 5 suggest that space and ventilation have an effect on the social relationships of the elderly, with 29.4% of the variance explained and 0.156 of MSE obtained. Table 4. SVR regression model for the community environment factors and the QoL factors Models

R

R2

MSE

1. Physical health ← Community environment factors F2 Space

0.597

0.356

0.212

F4 Ventilation 2. Psychological health ← Community environment factors F2 Space

0.630

0.397

0.081

F4 Ventilation 3. Independence ← Community environment factors F2 Space

0.318

0.101

0.293

4. Environmental life ← Community environment factors F1 Privacy

0.722

0.521

0.047

F2 Space F4 Ventilation 5. Social relationships ← Community environment factors F2 Space

0.512

0.294

0.156

F4 Ventilation

6 Discussion Based on literature review, this study puts forward a preliminary model of community environment and QoL of the elderly. A total of 81 valid data is recovered using the verified questionnaire, and the SPSS software was applied to test the data’s reliability and correlation. The SVR prediction model on MATLAB is used to establish the

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influence relationships between community environment and QoL. Based on these analytical methods, the community environment–QoL of the elderly model is established (see Fig. 2). Quality of life Space management Privacy

Space

Building services

Ventilation

Physical health

Psychological health

Independence

Environmental life

Social relationship

Fig. 2. Community Environment–QoL Model for the Elderly. Note: → - significant positive - significant negative relationship confirmed by correlation and SVR regression analyses. relationship confirmed by correlation and SVR regression analyses.

6.1 Space Management and the QoL of the Elderly The finding shows the space has significant impact on QoL of the elderly, which coincides with previous studies [13]. Space is an important factor that can influence all five aspects of QoL. This finding is understandable from the elderly perspectives. Space in community area was concerned with spatial layout. It is difficult to meet the daily needs of the elderly if the community area is limited. Lack of function space (e.g., supermarkets, shopping malls, parks and gymnasiums) will prevent the elderly from physical exercise and causes difficulties for them to travel. Inadequate space in the community (e.g., parks) cause inconveniences for elderly to rest and talk, impairing their social relationship with neighbors eventually. This study found that privacy affects the QoL of the elderly. Actually, privacy has long been considered as an important contributor to the health and welling of elderly [14], while excessive CCTV camera in community may impair privacy of elderly. When the elderly feel their privacy has been violated, they may reduce their activities in the community areas, even if they believe this would have a negative impact on their lives. In this regarding, their environmental QoL would be harmed.

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6.2 Building Services and the QoL of the Elderly Ventilation in community area is closely related to the air quality and environment pollution. Current study revealed ventilation can positively affect elderly’s physical health, psychological health, social relationship and environment life. This finding extended previous study which showed ventilation in common area could positively affect elderly’s QoL [13]. The polluted environment will certainly endanger the health of the elderly, leading to illnesses such as respiratory diseases. If elderly worried about air quality of community area, negative emotions (e.g., anxiety, depression) will certainly appear. It was interesting to find in this study that ventilation does not affect independence, which is different from previous study finding [13]. Perhaps, most elderly in current study are aged 65–79, whose action ability is stronger than other aging stages. Different from previous studies [9], lighting, in terms of brightness and number of street light, were found not to affect the QoL of the elderly in current study This finding should be understandable with consideration of the sample. On one hand, the surveyed elderly were generally in good health condition, and it is likely for them to have good eyesight as well; On the other hand, elderly have lived here for a long time and are familiar to the community. In this regard, it was understandable that light was not a key factor influencing their QoL. 6.3 Supporting Facilities and the QoL of the Elderly In this study, none of supporting facilities could affect the QoL of the elderly, which is different from the previous research findings. Previous studies found signage could affect QoL of the elderly [9]; it could not only help elderly identify their direction and facilities, but also avoid safety risk [14]. However, in this study, it revealed no relation between QoL and signage. Perhaps, the elderly participants of current study have been living their residence for a long time, and thus they are very familiar with the community and could move freely without signage. It was found in current study that recreational facilities didn’t affect QoL of the elderly neither. In daily life of Chinese family, certain amount of elderly live with their children and often take the responsibility of household and take care of grandchildren. It is very likely that few elderly have time to use recreational facilities. Thus, there is no relationship between recreational facilities and the QoL of the elderly.

7 Recommendations This study shows that space, privacy and ventilation can influence the QoL of the elderly. Based on these results, the paper puts forward some suggestions. In view of the space, the designer should fully consider the degradation of the physiological and psychological capacity of the fragile elderly. Enough space (e.g., spacious squares) should be allowed to serve the elderly better who used walking aids. In order to maintain the security of the community without invading the private activities of the elderly, well-distributed and reasonable number of monitoring equipment should be well planned in the community. In terms of ventilation, various measures should be taken to improve QoL of elderly, such

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as improving the number of cleaners and sweeper car to keep the clean of the road, the installation of enough trash cans to classify garbage, and so on. In addition, government should strengthen management over hygiene and pollution issues, such as avoidance of waste incineration plants in the community, prohibiting the fireworks and firecrackers, etc.

8 Conclusions The current study set out to examine the influence of community environment on the QoL of the elderly. Reliability test and correlation analysis were used to perform basic data analysis, and SVR in machine learning methods was conducted to examine the results. Based on the three methods of analysis, the final findings show: the privacy of community environment negatively influences environmental QoL of the elderly; the space positively affects all aspects of the QoL of the elderly, including physical health, psychological health, independence, social relationships and environmental life; and ventilation has a positive effect on physical health, psychological health, social relationships and environmental life of the elderly. Practical recommendations were made to improve the existing community environment, including building enough space (e.g., spacious squares), optimizing layout of monitoring equipment, employing multiple cleaning personnel, classifying garbage in the community, and strengthening management over hygiene and pollution issues by the government. Current study contributed to mix the use of traditional statistical techniques and machine learning SVR methods to study the influence of environment on the QoL of the elderly.

References 1. Ma, C., Zheng, K.: Study on reemployment of retirees in second tier cities from the perspective of active aging - a case study of Hefei. Mod. Mark. (Bus. Ed.) 09, 18–19 (2020) 2. National Bureau of Statistics: Main data of the seventh census (2021). http://www.stats.gov.cn/ ztjc/zdtjgz/zgrkpc/dqcrkpc/ggl/202105/t20210519_1817693.html. Accessed 12 May 2021 3. Wen, T.L., Zhang, M.Y.: Current situation and development strategies of home care services in Tianjin. Tianjin Econ. 09, 39–42 (2014) 4. Zhang, J.Q., Liu, H., Qi, Y.Q., et al.: Analysis of living environment and life satisfaction of the elderly in Beijing. Prog. Geogr. 34(12), 1628–1636 (2015) 5. Zhang, F., Li, D.: How the urban neighborhood environment influences the quality of life of Chinese community-dwelling older adults: an influence model of “NE-QoL.” Sustainability 11(20), 5739 (2019) 6. Leung, M., Liang, Q.: Developing structural facilities management–quality of life models for the elderly in the common areas of public and subsidized housings. Habitat Int. 94, 102067 (2019) 7. Iancu, I., Iancu, B.: Designing mobile technology for elderly: a theoretical overview. Technol. Forecast. Soc. Change 155, 119977 (2020) 8. Yu, M.S.: Analysis of the psychological characteristics of the elderly and recommendations for mental health care. Psychol. Monthly 16(03), 202–203 (2021) 9. Leung, M.Y., Wang, C., Chan, I.Y.: A qualitative and quantitative investigation of effects of indoor built environment for people with dementia in care and attention homes. Build. Environ. 157, 89–100 (2019)

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10. Kong, Z.Y.: The impact of social participation on the quality of life of the elderly. Word Surv. Res. 04, 72–80 (2021) 11. Leung, M.Y., Famakin, I.O., Wang, C.: Developing an integrated indoor built environment– quality of life model for the elderly in public and subsidized housing. Eng. Constr. Archit. Manag. 26(7), 1498–1517 (2019) 12. Yuan, J., Li, L., Wang, E., Skibniewski, M.J.: Examining sustainability indicators of space management in elderly facilities—a case study in China. J. Clean. Prod. 208, 144–159 (2019) 13. Leung, M.Y., Yu, J., Chong, M.L.: Impact of facilities management on the quality of life for the elderly in care and attention homes–cross-validation by quantitative and qualitative studies. Indoor Built Environ. 26(8), 1070–1090 (2017) 14. Fleming, R., Goodenough, B., Low, L.F., Chenoweth, L., Brodaty, H.: The relationship between the quality of the built environment and the quality of life of people with dementia in residential care. Dementia 15(4), 663 (2014) 15. Chen, Y.D., Fan, J.Y., Zhou, T.: A theoretical approach for therapeutic artificial supplementary lighting in elderly living spaces. Build. Environ. 197, 107876 (2021) 16. Yang, B., Olofsson, T.: A questionnaire survey on sleep environment conditioned by different cooling modes in multistorey residential buildings of Singapore. Indoor Built Environ. 26(1), 21–31 (2016) 17. Onunkwor, O.F., Al-Dubai, S.A.R., George, P.P., et al.: A cross-sectional study on quality of life among the elderly in non-governmental organizations’ elderly homes in Kuala Lumpur. Health Qual. Life Outcomes 14(1), 1–10 (2016) 18. Jiang, J.T., Wen, Z.Y., Wang, Z.K., et al.: Parallel and distributed structured SVM training. IEEE Trans. Parallel Distrib. Syst. 33(5), 1084–1096 (2021) 19. Liu, F.Y., Wang, S.H., Zhang, Y.D.: Overview of support vector machine models and applications. Comput. Syst. Appl. 27(04), 1–9 (2018) 20. Mu, H.S., Zhai, X.D., Tu, X., et al.: Research on fault prediction method of electronic equipment based on improved SVR algorithm. In: Chinese Automation Congress (CAC), pp. 3092–3096. IEEE (2020) 21. Chen, J.G.: Prediction of building settlement based on support vector machine model. Math. Pract. Theory 43(12), 137–140 (2013) 22. Li, L., Zheng, W., Wang, Y.: Prediction of moment redistribution in statically indeterminate reinforced concrete structures using artificial neural network and support vector regression. Appl. Sci. 9(1), 28 (2018) 23. Payam, P., Hosein, N.: Shear strength estimation of reinforced concrete walls using support vector regression improved by Teaching–learning-based optimization, Particle Swarm optimization, and Harris Hawks Optimization algorithms. J. Build. Eng. 44, 102593 (2021) 24. Li, X., Yao, R.: A machine-learning-based approach to predict residential annual space heating and cooling loads considering occupant behaviour. Energy 212, 118676 (2020) 25. Chou, J.S., Pham, A.D.: Hybrid computational model for predicting bridge scour depth near piers and abutments. Autom. Constr. 48, 88–96 (2014) 26. Niu, J.G., Gao, C.Y., Xing, X.Q.: Quality cost forecast of the construction enterprise based on SVR model. In: Advanced Materials Research, vol. 594, pp. 3011–3014. Trans Tech Publications Ltd. (2012) 27. Meng, Z., Sun, H., Wang, X.: Forecasting energy consumption based on SVR and Markov model: a case study of China. Front. Environ. Sci. 10, 883711 (2022) 28. Abdolahzadeh, M., Schmalz, B.: Assessment of wavelet-SVR and wavelet-GP models in predicting the groundwater level using areal precipitation and consumption data. Hydrol. Sci. J. 67(7), 1026–1039 (2022) 29. Hair, J.F.: Multivariate Data Analysis. Prentice Hall, Upper Saddle River (2009)

Assess the Reusability Potential of Building Products in an Early Design Stage Qi Han(B) and Nick Kentie Department of Built Environment, Eindhoven University of Technology, Eindhoven, The Netherlands [email protected]

Abstract. Reusability is a key aspect to achieving the transition from a linear economy with the “take-make-dispose” principle to a circular economy that follows the “reduce-reuse-recycle” principle. A literature review and expert interviews are conducted to identify the influencing factors of reusability and its assessment criteria. A distinction is made between three pre-conditional factors (Disassembly, Toxicity and Logistics) and six non-pre-conditional influencing factors (Data management, Standardization, Quality, Financial value, Over-dimensioning and Contracting). Expert panels are conducted to verify the conceptual model built on these factors and to determine the relative importance (weights). Subsequently, an assessment tool is developed to determine building products’ reusability potential in an early stage of the building process. The tool consists of three parts: a list of product properties, the input of assessment criteria, and a weighted average for calculating the potential. It takes three steps to determine the reusability potential. The assessment tool is tested and validated using a case study, where traditional products are compared to circular products. Keywords: Reusability · Influence factor · assessment criteria · product property

1 Introduction Since the Industrial Revolution, the growth of the world economy has resulted in the emergence of a linear economy that follows the “take-make-dispose” principle. This principle leads to global warming, depletion of the earth, and a large amount of waste generation. The construction sector was responsible for 50% of raw materials consumption and about 35% of all CO2 emissions. In addition, the construction sector consumed 40% of total energy consumption and accounted for 40% of the total waste generation (Khodeir & Othman, 2018; Meuffels & Hoppe, 2021). The extraction and processing of primary raw materials are responsible for 90% of water consumption, 90% of biodiversity loss and 50% of CO2 emissions, which are problematic for the environment. In addition, the raw materials are finite and are not reused enough (Meuffels & Hoppe, 2021; Wautelet, 2018). To change the current system, a transition from a linear economy to a circular economy is needed to reduce the depletion of sources and prevent global warming. The © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 1382–1395, 2023. https://doi.org/10.1007/978-981-99-3626-7_107

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Dutch government’s goal is to have a fully circular economy by 2050 (Coenen, 2019). Reusability is one of the key aspects to achieve a circular economy. Reusability is a pre-condition for enabling a circular economy. Even though reuse is one of the most important aspects of enabling the circular economy, there is no unambiguous method yet to assess whether a building product can be reused or what the possibility is that a building product will be reused at the end of the lifecycle. Limited research has been conducted into the factors that influence reusability. No research has been carried out that provides an overview of all the factors that influence the reusability potential of building products. Furthermore, there is no supporting framework to assess the reusability potential of new circular building products, especially the reusability potential of individual building products in an early design stage when making changes is still easy. The limitations in current research can be solved by developing a reusability assessment tool including the most important influencing factors. The problem definition leads to the following main research question: How can the reusability potential of building products be assessed in an early stage of a building process utilizing an assessment tool? The reusability potential is the potential that a building product can be reused at the end of its lifespan, which is assessed in the design phase and can be expressed in a score between 0 and 1. The following section provides a brief background of reusability and the influence factors. In Sect. 3, the process of developing the assessment tool is introduced, and Sect. 4 presents the results of testing the tool with a case study. In the last section, a short conclusion and discussion are provided.

2 Literature Review 2.1 Reusability in the Built Environment Building and demolition waste is mostly recycled in the Netherlands. Almost 95% of the waste is recycled and 85% of that is used in civil engineering as a filler, road base or foundation material. An example is the roof tiles from an old building that will be crushed and used as a road base in the civil engineering sector. Furthermore, the recycling process of this type of low-value reuse consumes a lot of new energy, water, and machinery and also destroys value (Meuffels & Hoppe, 2021). As such, a large stake takes place within low-value reuse, in other words downcycling. Only 3% of the waste in building construction is returned to this sector (Circle Economy et al., 2015; Schut et al., 2015). Reuse in the construction sector is increasing. However, currently, it is mostly done in low-value applications. The circular economy aims to ensure that no new raw materials are extracted and that the raw materials are kept in the production chain as long as possible. And in the circular economy short loops, such as maintenance and reuse are preferred over long loops, such as recycling and recovery (Coenen, 2019). Reuse should be seen as a priority in comparison to recycling. Reusing requires much less processing before it can be used in another application. Unlike recycling, which requires more processing such as labour and energy (Hobbs & Adams, 2017). The 10R model is also used in this study as a reference for reusability (Cramer, 2014). It starts with the Ladder of Lansink (Recycling, 2019; Van Dijk, 2018). Reusability is

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determined with the following 5R principles: Reuse, Repair, Refurbished, Remanufacture and Repurpose to aim for a lifespan extension of the products and components. Table 1 gives a detailed definition of the R’s covered by reusability in this study. Table 1. Reusability potential with their definitions of the R principles Reuse

Using a product for the same application in its original form. Either with or without little modification and enhancement

Repair

Repairing or maintenance of a broken product for use in the intended application

Refurbish

Renovating and improving a discarded product to a good condition by replacing faulty parts or using parts of another discarded product

Remanufacture

Creating a new product in the same application by using parts of a discarded product

Repurpose

Using a discarded product in another application than originated without modification

The circular economy aims to reuse materials, products, and components in the most high-valuable way possible, which preserves their value. To determine the reusability potential, it is a good addition to make a distinction between high-value and low-value reuse. High-value reuse is at least reused on the same level. For example, a roof tile is used as a roof tile and a support beam is used as a support beam. When a door is repurposed for a table (stuff layers), it is reused but the initial value is lost. In this case, the R principle “Re-purpose” is not a part of high-value reuse. 2.2 Influence Factors It is not clear in an early stage of the building process which criteria need to be met to determine whether a product can be reused when it is disassembled from the building at the end of the lifecycle. Various factors influence the reusability potential of a building product through the technical side, the process side and the finance side. Twenty different sources (Table 2) have been used to investigate the influencing factors, which results in a total of 19 general influencing factors, covering 1) technical-based factors: adaptivity, disassembly, material quality, standardization, toxicity; 2) process-based factors: aesthetic, certification, data management, dematerialization, development, geographical, guarantees, law and regulations, logistics, supply and demand, time, willingness; 3) financial-based factors: financial, storage and third market.

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Table 2. A literature overview of the number of factors Nr.

Sources

Nr.

Sources

1

13

Hobbs, G., & Adams, K. (2017)

11

2

Webster, M., & Costello, D. (2005)

2

5

Akanbi, et al. (2018)

12

4

Circo (2020)

3

1

Van Vliet, M., Grinsven, J. van, & Teunizen, J. (2021)

13

4

Kuppevelt, M. Van, & Stoutjesdijk, P. (2020)

4

6

Coenen, T. (2019)

14

4

Meuffels, J., & Hoppe, F. (2021)

5

2

Durmisevic, E. (2006)

15

1

Nederland Circulair. (2015)

6

3

Gladek, E. (2019)

16

2

Heijer, R., & Kadijk, J. (2020)

7

7

Beurskens, P., & Durmisevic, E. (2017)

17

3

Park, J. Y., & Chertow, M. R. (2014)

8

3

Platform CB’23. (2020)

18

8

Platform CB’23. (2019)

9

6

Van Dijk, E. (2018)

19

4

Kozminska, U. (2019)

10

5

European Environment Agency. (2020)

20

2

Durmisevic, E., Beurskens, P., Adrosevic, R., & Westerdijk, R. (2017)

3 Reusability Assessment Tool 3.1 Design Science Approach The Design Research Methodology from Blessing and Chakrabarti (2009) is the methodology and supporting framework used in this research. It contains the literature review, the expert interviews, the expert panels, and the case study. Subsequently, ethics and data management are discussed, and a conclusion is drawn. 3.2 Expert Interview Resulted in 19 Factors A semi-structured interview is conducted with 19 experts from the following seven working fields: Consultant, Platform, Demolisher, Architect, Contractor, Housing corporation and Government. The main goal of this interview is to gain insight into what is essential to enable high-value reuse and what influences the re-usability potential of a product in practice. The interviews are conducted to validate and elaborate on the findings from the literature. The program ATLAS.ti is used for the coding and analysis of the interviews. A total of 188 codes were coded in the first step of open coding, and 23 code groups are the results of axial coding in the second step. The comparison between the literature and the expert interviews shows that there are many corresponding influencing factors. However, not all factors can be implemented in the assessment tool. It is important to investigate which factors have the most influence on the reusability potential and which factors are measurable.

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The factors are converged into a shortlist of nine influencing factors and their assessment criteria by performing three consecutive steps. First, the most important factors according to the literature are identified based on the frequencies mentioned. Next, the most important factors according to expert interviews are identified based on the coding analysis. Third, the measurability of the factors is assessed with three categories (measurable, discrete measurable, and not measurable) based on definition, assessment and data requirements. There is a distinction between three pre-conditional influencing factors (Disassembly, Toxicity and Logistics) and six non-pre-conditional influencing factors (Data management, Standardization, Quality, Financial value, Over-dimensioning and Contracting). It is clear that if a building product cannot be disassembled, if it contains toxic substances or if it cannot be transported to another location, there is no possibility for the building product to be reused. Therefore, the pre-conditional influencing factors have a major consequence on the reusability potential of a building product and have to be met. A threshold assessment criterion is proposed for each pre-conditional factor to be met to have the potential that a building product will be re-used at the end of its life span. 3.3 Expert Panel Resulted in 7 Factors and Their Weights The conceptual model is developed using the nine influencing factors and then tested by the expert panel. Six experts participated. It became apparent that two factors would not directly influence the reusability potential or would simply be too difficult to quantify. This resulted in the deletion of the two influencing factors: standardization and over-dimensioning. Standardization covers an extensive definition. Some examples are modularity, prefabrication, uniformity, and standardized dimensions, which are difficult to quantify. Over-dimensioning interferes with the core idea of the circular economy. In addition, a fine adjustment to the assessment criteria is performed. Contracting should in this case only be assessed on the availability of a return guarantee and the Data Management factor should only be assessed on the availability of a material passport since this would have the most impact on the reusability potential. An important step in the process is assigning weights to the influencing factors and their assessment criteria. Multi-Criteria Decision Making (MCDM) tools can help to achieve the goal. In this research, the common Analytical Hierarchy Process (AHP) is used to determine the weights of the influencing factors and the assessment criteria with input from the experts (Saaty, 2008). Pairwise comparisons were performed with input from all expert panels. Then, using the geometric mean and the normalization, a weighting list was obtained, and the ranking of the factors was determined (Fig. 1). In addition, the assessment criteria of the factors are also verified by the experts. Using these results, the conceptual model is improved to develop the assessment tool. 3.4 The Assessment Tool The assessment tool consists of three parts and takes three steps to determine the reusability potential (Fig. 2). For now, Microsoft Excel is used as the software platform. In the

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Fig. 1. Importance weights of the seven factors

future, this tool may also be implemented as a platform in the form of a web application or app and should be incorporated into the circularity assessment in a BIM-based environment.

Fig. 2. The framework of the assessment tool

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Step 1 – Setting up the Bill of Materials (BoM) First of all, a BoM is set up for all the individual building products in a building project. The user of the tool must ensure that all information is available for the calculation of the reusability potential. It is possible to make assumptions about some influencing factors to check the impact of certain choices, especially if some data is not yet available. Figure 3 shows an example of a building product with the collected data. Step 2 – Fill in the input parameters After collecting the required data, the data is entered into the assessment tool of the corresponding influencing factors. By clicking on the factors in the tool, the different assessment criteria can be filled in according to the collected data of a building product from step 1. Filling in the assessment criteria will automatically result in the corresponding weights and grading. Figure 4 shows an example of the steps to assess the influencing factor in the reusability potential assessment tool. All the influencing factors with their corresponding assessment criteria and scores are assessed after this step. Step 3 – Calculation of the Reusability Potential The final step of the assessment tool is the calculation of the reusability potential. The assessment tool will calculate the reusability potential of a building product by using three different weighted average formulas: 1) Weighted Arithmetic Mean, (WAM), 2) Weighted Harmonic Mean (WHM), 3) Weighted Geometric Mean (WGM). The weighted averages are calculated by all the influencing factors, the assessment criteria, and the corresponding relative importance (weights). Figure 6 shows an example of the three calculated weighted averages (of the reusability potentials). Users can decide which formula is the most appropriate to apply in practice. In addition, it is indicated in the red text “Pre not met”, when a pre-conditional factor is not met, which could result in a very low reusability potential. Figure 5 shows an example.

Fig. 3. Step 1 – Bill of Materials (BOM)

Fig. 4. Step 2 – Input Parameters

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Fig. 5. The influencing factor assessment

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Fig. 6. Step 3 – Weighted averages of the reusability potential

4 Case Study A case study was developed in cooperation with a company. To perform the case study, the layers of the Brand model (Brand, 1994) are used, covering the layer of the space plan, the layer of the skin, and the layer of the structure. The wall is an inner wall, the sandwich panel is used for wall finishing and the hollow-core slab is concrete flooring. This case study is performed by a combination of sensitivity and scenario analysis. For each layer of the Brand, a traditional product and a circular product are chosen. These two products are compared with each other, and the results of the reusability potential tool are discussed. After that, for each product different scenarios are set up, which are also assessed and discussed (see Table 3 for an overview), focusing on assessing individual building products. Adjusting some inputs and making as-assumptions on the input for the factors are used to build multiple scenarios, which provides an insight into what happens when changes are made to the influencing factors and the impact of the factors on the reusability potential of the products. In order to specify the case study, a few boundaries are set up. First of all, the layers of Brand are used as the main category for the products. This is because the layers are used to determine the quality factor. Furthermore, this model does not consider the attachment between products and how a product is assembled within the building, since this is already incorporated in the Disassembly Index. Finally, the assessment tool is focused on assessing individual building products. Impressions of the products that are used in this case study are displayed in Fig. 7. 4.1 Traditional Product vs. Circular Product The first step in performing the case study is to create the Bill of Materials (BoM). This is a list of products that will be used during the case study. A BoM can serve as a material passport for construction projects and additional information can be added to the products. The BoM is set up in BCI Gebouw as shown in Fig. 3. In BCI Gebouw

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Layer Space plan: Wall

Layer Skin: Sandwich panel

Structure: Hollow-core slab

Traditional product - Base scenario - Various scenarios

Traditional product - Base scenario - Various scenarios

Traditional product - Base scenario - Various scenarios

Circular product - Base scenario - Various scenarios

Circular product - Base scenario - Various scenarios

Circular product - Base scenario - Various scenarios

(2021), the company database is used. Therefore, all Disassembly Indices are already determined by experts within the company. In practice, these indices can be adjusted, but for the input of this case study, it is a perfect starting point. The results of the inner wall (Fig. 8a) show that the traditional product scores very low on the reusability potential because the product cannot be disassembled. Since Disassembly is a pre-condition for the reusability potential, this results in a 25% reusability potential for the WAM formula and because a pre-condition is not met it results in a 0% reusability potential for the WHM and WGM formula. Currently, the circular product has no residual value data, it has a lower score on the reusability potential in general. The results displayed in Fig. 8b are the results from the Skin layer where the traditional sandwich panel is compared to a circular panel. Both panels score equally on the Disassembly index. The traditional PIR sandwich panel gets a lower score on the Toxicity factor. However, it is not toxic according to the DGBC report and it is not on the C2C Banned List of Chemicals, therefore it scores a category 3. The other, not pre-conditional factors have a lower score and therefore result in an overall low score. The WHM formula results in the lowest reusability potential because the factor with the highest weighting has the most impact on the final result. The Falk CradleCore panel has the maximum score on every factor, the reusability potential will be 100%. The results for the Structure layer are displayed in Fig. 8c. The traditional product has almost the same result as the traditional inner wall of the Space plan layer. The reason is that this product is not demountable. As a result, it scores 22%, 0% and 0% on the reusability potential using the three formulas respectively. On the other hand, the circular one has almost a reusability potential of 100%. Because the technical lifespan of the products is lower than the corresponding years from the Layer of Brand where the product belongs, it will not get a reusability potential of 100%. 4.2 Product Scenarios and Sensitivity Next, four or five scenarios are assessed per product. In some scenarios, only one parameter of a factor is adjusted and in others, several parameters are adjusted. This makes the analysis a combination of sensitivity and scenario analysis. The products and scenarios of the space plan layer are discussed below as an example. First, for the Space plan layer, the results of the four different scenarios of the Metalstud wand are shown in Table 4. When the product can be disassembled the reusability

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Fig. 7. Examples of impressions of the products

potential of the WAM will be higher. However, the reusability potential for the WHM and WGM will still be zero because a pre-condition is not met. When the parameters for the Disassembly and Toxicity factor are adjusted, there is reusability potential. When the technical lifespan will be lower in practice, the reusability potential will be lower. However, this is not as significant as when the product has a positive residual value. In Table 5, the results of the Juunoo wall are shown. This circular alternative contrasts with the Metalstud wand. However, according to references from the company, it does not have a residual value at this moment. Disassembly is a pre-condition therefore scenario 2 will have a reusability potential of 0% if using the WHM and WGM. For the WAM formula, it scores significantly lower because the factor Disassembly is the

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Fig. 8. The results of the reusability potential (traditional vs. circular alternative)

most important factor with the highest weighting. When the Juunoo wall will have a residual value, then the reusability potential will be 100%. When a return guarantee is not included, it will result in a lower score than the base scenarios. However, when no material passport is available, this will have a more significant impact on the reusability potential. A similar scenario analysis was also conducted for the skin layer addressing the PIR Sandwich panel and the Falk CradleCore, as well as for the structure layer addressing the traditional Hollow-core slab and the demountable Hollow-core slab. The case study also provides some insight into which formula would best suit the practice. The Disassembly, Toxicity and Logistics factors serve as pre-conditional factors that will result in a no reusability potential for a product when it does not meet. Therefore, it is not recommended to use the Weighted Arithmetic Mean (WAM) to assess the reusability potential. The Weighted Harmonica Mean (WHM) and Weighted Geometric

Assess the Reusability Potential of Building Products Table 4. Results of the Metalstud wand scenarios Scenarios

WAM WHM WGM

1 Base (traditional)

0.25

0.00

0.00

2 can be disassembled

0.47

0.00

0.00

3 demountable non-toxic

0.63

0.32

0.61

4 demountable, nontoxic, less lifespan

0.57

0.31

0.58

5 demountable, 0.76 nontoxic, with residual value

0.43

0.73

1393

Table 5. Results of the Juunoo wall scenarios Scenarios

WAM WHM WGM

1 Base (circular)

0.87

0.55

0.83

2 cannot be disassembled

0.65

0.00

0.00

3 with positive financial value

1.00

1.00

1.00

4 return 0.83 guarantee not included

0.48

0.79

5 no material passport

0.36

0.66

0.71

Mean (WGM) can both be used to assess a zero partial outcome on the reusability potential. It can be concluded that the factors with the highest importance should have the most impact on the assessment of the reusability potential. This is also suggested by the expert panels. Therefore, the Weighted Harmonic Mean is expected to best suit in practice.

5 Conclusion and Discussion Reusability is a key aspect of enabling and promoting a circular economy in the future. This research aims to provide insight into the factors that influence the assessment of the reusability potential of building products and how these factors can be applied through the development of a practical assessment tool. To achieve the aim of this study, a literature study, nineteen expert interviews, a six-expert panel and case studies were conducted. Reusability is part of the technical cycle, and it can be concluded that the shorter the feedback loops are, the better the circularity. Therefore, products need to be reused at the highest level possible. In total seven influencing factors are selected that are determined for the assessment of the reusability potential: 1) Disassembly: Disassembly potential of a product; 2) Data management: Availability of a materials passport; 3) Toxicity: If a product is toxic; 4) Quality: Technical lifespan relative to the building system layer; 5) Financial value: Residual value of a building product; 6) Logistics: Transportability of building products; 7) Contracting: Availability of a return guarantee. The first three are pre-conditional factors. Disassembly and Toxicity have the most impact on the reusability potential. Data management also has a significant impact on the reusability potential due to the availability of a materials passport. The influencing factors were combined into a practical assessment tool for assessing the reusability of building products. Per factor, the assessment criteria are defined

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based on the information that is collected about the product (data input). The factors are individually assessed based on the assessment criteria and will result in an overall weighted average. This assessment tool can be used in practice to support and manage decision-making for more circular and sustainable building products. Therefore, this research and the developed assessment tool contribute to the big challenge we are facing today of making circularity in the built environment measurable and the move towards a circular economy by 2050 for the Netherlands. There are some limitations of this study. This research has not been able to implement all factors in an assessment tool and only seven factors were implemented. Therefore, it is recommended to conduct further research into the factors that have not been implemented. It is also recommended to investigate the correlations and causation among the factors as they may strengthen or weaken each other. The reusability potential can be determined at different levels. For example, at the product level, the element level, and the building level. This research only focused on the assessment of the reusability potential on a product level. It would be recommended to investigate if this assessment tool can be extended and used on other levels or other stages of the building process by changing some of the influencing factors or criteria. For example, Adaptivity should be added when the reusability potential of a whole building is assessed and the NEN2767 (2011) condition measurement might be useful for determining the Quality factor of a building product during the use or demolition stage. The developed assessment tool for assessing the reusability potential is a stand-alone model. The next step should be to integrate this tool into existing measuring instruments for the circularity of buildings and further develop it as a BIM-based tool to simplify the input step where the bill of material is required.

References Akanbi, L., et al.: Salvaging building materials in a circular economy: a BIM-based whole-life performance estimator. Resour. Conserv. Recycl. 129, 175–186 (2018). https://doi.org/10.1016/ j.resconrec.2017.10.026 Beurskens, P., Durmisevic, E.: Increasing reuse potential by taking a whole life-cycle perspective on the dimensional coordination of building products. In: Vital Cities and Reversible Buildings: Conference Proceedings, October 2017 BCI Gebouw: Uitgebreide toelichting BCI Gebouw (2021). https://bcigebouw.nl/uitgebreide-toe lichting/ Blessing, L.T.M., Chakrabarti, A.: DRM: a design research methodology. In: Blessing, L.T.M., Chakrabarti, A. (eds.) DRM, a Design Research Methodology, pp. 13–42. Springer, London (2009). https://doi.org/10.1007/978-1-84882-587-1_2 Brand, S.: How Buildings Learn: What Happens After They’re Built. Viking (1994) Circle Economy, TNO, & Fabric: Amsterdam Circular - A Vision Roadmap for the City and Region, October 2015. https://assets.websitefiles.com/5d26d80e8836af2d12ed1269/5ede5a 03e4cd056426b86d8b_20152115-Amsterdamscan-reportENwebsinglepage-297x210mm.pdf Circo: Ontwerp strategieën (2020) Coenen, T.: Circular bridges and viaducts - development of a circularity assessment framework (2019) Cramer, J.: Elementaire deeltjes 16 - Milieu. Athenaeum (2014)

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Durmisevic, E.: Transformable building structures - design for disassembly as a way to introduce sustainable engineering to building design & construction (2006) Durmisevic, E., Beurskens, P., Adrosevic, R., Westerdijk, R.: Systemic view on reusepotential of building elements, components and systems-comprehensive framework for assessing reuse potential of the building element, pp. 275–280, June 2017 European Environment Agency: Construction and Demolition Waste: challenges and opportunities in a circular economy. 8 January 2020. https://www.eea.europa.eu/publications/constructionand-demolition-waste-challenges/at_download/file Gladek, E.: The Seven Pillars of the Circular Economy (2019). https://www.metabolic.nl/news/ the-seven-pillars-of-the-circular-economy/ Heijer, R., Kadijk, J.: Verkenning schone en smet(te)loze materiaalstromen (2020) Hobbs, G., Adams, K.: Reuse of building products and materials – barriers and opportunities, June 2017 Khodeir, L., Othman, R.: Examining the interaction between lean and sustainability principles in the management process of AEC industry. Ain Shams Eng. J. 9(4), 1627–1634 (2018). https:// doi.org/10.1016/j.asej.2016.12.005 Kozminska, U.: Circular design: reused materials and the future reuse of building elements in architecture. Process, challenges and case studies (2019). https://doi.org/10.1088/1755-1315/ 225/1/012033 Van Kuppevelt, M., Stoutjesdijk, P.: Indicatoren voor circulariteit in de Bouw (2020) Meuffels, J., Hoppe, F.: Circulaire materialen in de bouw (2021) Nederland Circulair: High-Value Reuse in a Circular Economy (2015). https://www.circulairond ernemen.nl/uploads/27102a5465b3589c6b52f8e43ba9fd72.pdf NEN 2767-1 (foreword), October 2011. www.nen.nl Park, J.Y., Chertow, M.R.: Establishing and testing the “reuse potential” indicator for managing wastes as resources. J. Environ. Manag. 137, 45–53 (2014). https://doi.org/10.1016/j.jenvman. 2013.11.053 Platform CB’23: Kernmethode voor het meten van circulariteit in de bouw (2019) Platform CB’23: Meten van circulariteit - Werkaf-spraken voor een circulaire bouw (2020) Recycling: Recycling niveaus (2019). http://www.recycling.nl/. Assessed 18 Apr 2022 Saaty, T.L.: Decision-making with the analytic hierarchy process. Int. J. Serv. Sci. 1, 83–98 (2008). https://doi.org/10.1504/IJSSCI.2008.017590 Schut, E., Crielaard, M., Mesman, M.: Circular economy in the Dutch construction sector (2015) Van Dijk, E.: Circulair sturen op hoogwaardig her-gebruik van toegepaste en toe te passen materialen (2018) Van Vliet, M., van Grinsven, J., Teunizen, J.: Circular Buildings – een meetmethodiek voor losmaak-baarheid 2.0 (2021) Wautelet, T.: Exploring the role of independent retailers in the circular economy: a case study approach (2018). https://doi.org/10.13140/RG.2.2.17085.15847 Webster, M., Costello, D.: Designing structural systems for deconstruction: how to extend a new building’s useful life and prevent it from going to waste when the end finally comes. In: Greenbuild Conference (2005)

Risk Assessments with Probabilistic Linguistic Information for Green Building Projects - The Case of Vietnam Lina Wang(B) and Daniel W. M. Chan(B) Department of Building and Real Estate, The Hong Kong Polytechnic University, Hung Hom Kowloon, Hong Kong, China {linawang,daniel.w.m.chan}@polyu.edu.hk

Abstract. Risk assessment is a key component of green buildings. In green building projects, the risk evaluation process is facing great uncertainties, like uncertain conditions, unreliable evaluation models, etc. Effective risk management depends on using the right risk evaluation models. Hence, this study aims to develop a novel risk structure matrix with probabilistic linguistic information for green building projects. To evaluate risks using probabilistic linguistic information, a risk structure matrix is firstly constructed. After that, the consensus reached by the group on the risk structure matrix has to be validated and confirmed. To demonstrate the effectiveness of the unique risk structure matrix, a case study was conducted. The research results have provided an alternative viewpoint for assessing risks, leading to improved risk management in green buildings. Keywords: Risk assessment · Green building projects · Probabilistic linguistic information · Vietnam

1 Introduction Green building (GB) is a significant topic for sustainability projects. The US Environmental Protection Agency states that the goal of GB is to increase resource efficiency and satisfy environmental responsibility across design, construction, operation, maintenance, renovation and deconstruction [1]. The benefits of GB can be classified into three categories: environmental, economic and social [2]. Compared with traditional projects, GB projects may recycle 96% of their waste and emit 33% of electricity and water [3]. Due to the advantages of GB decreasing or eliminating damages to the environment, and improving the quality of life of people, GB projects have received attention from researchers [4]. However, the development of GB projects still faces some problems, like economic, resource risks, policy changes, and stakeholders’ preferences [5]. The GB projects demand higher requirements than general projects in all aspects, including risk management [6]. No construction project is risk-free, which is the same as GB projects [8], as stated by Latham [7]. Risk management of GB projects aims to ensure the success of GB projects, which can be classified into three parts: risk identification, risk evaluation, and risk response stages. Risk evaluation, which is in the middle stage, © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 1396–1404, 2023. https://doi.org/10.1007/978-981-99-3626-7_108

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is related to risk identification and risk response. Moreover, risk analyses and evaluations integrate qualitative analyses with quantitative calculations, which include three processes: (1) The process of figuring out if a risk is likely to exist; (2) The process of analysing the likely outcomes of risks; and (3) The process of evaluating how risks will affect projects [9]. The techniques for evaluating risk probability focus on the likelihood of loss, which may ignore risk features [10]. Besides, the evaluation results of risks may disregard the relationship of risks. It is challenging for specialists to accurately assess a project’s complexity, periodicity, and hesitancy. With these limitations, the following list of the paper’s contributions is provided: 1. Create a risk evaluation model with complex linguistic expressions. Due to the uncertainty, dynamic, and complex life cycle, it is significant to select an efficient tool to express evaluation information. The probabilistic linguistic term set (PLTS) is created in this study to indicate risks from probability and impacts perspectives. 2. Built a novel risk structural matrix. After selecting a proper complex linguistic expression, it is essential to build a risk structure matrix to explore the relationship between risks. Besides, the group consensus of the risk structure matrix needs to be checked and revised for algorithms. 3. Apply the risk evaluation model to a GB project [11], and make sensitive analysis to display the efficiency of the proposed method. The structure of this study is as follows: Sect. 2 reviews risk management of GB projects and complex linguistic information; Sect. 3 represents the methodology applied in this research; Sect. 4 applies the proposed method to a case study. Finally, conclusions and future research are remarked in Sect. 5.

2 Literature Review Fuzzy sets may not be a better tool for expressing experts’ evaluations in terms of the dynamic, complexity, and uncertainty of risk management. Regarding the limitations of fuzzy sets, some complex linguistic information instead of fuzzy sets improve expression limits during the evaluation process. There are so many types of complex linguistic expressions, the hesitant fuzzy linguistic term set (HFLTS) [12], the PLTS [13], the weakened hedged of linguistic terms (LTWHs) [14], etc., which focus on the consistency and consensus of preference relations (PRs). Zhang suggested a consensus index to calculate the agreement degree for decision makers’ evaluations with hesitant multiplicative PRs, which provides a perspective to determine the consensus level with the Monte Carlo simulation [15]. Regarding the characteristics of a social community, Chu et al. developed a consensus-reaching model with two stages during transforming the large-scale group into a small group [16]. Liu et al. built a multi-phase algorithm to reach the consensus degree, which considers both the majority and the minority rules in terms of the incomplete PLPRs [17]. A consensus threshold is calculated based on subjective views in the consensus reaching process, which may alter the level of consensus reached. Building a random consensus index as a reference object, which can automatically measure admissible consensus thresholds, addresses this limitation [18]. In the existing studies, some studies attempted to create risk evaluation models for profoundly evaluating risks of projects. Risk evaluation models have been created using

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many methods, including the analytical hierarchy process (AHP) [19], fuzzy set theory [20], the Monte Carlo Simulation method [3], social network analysis [21], etc. For instance, Geographical information system, with the AHP method, presents a multihazard map to assess uncertain information, which indicates that the uncertainty of landslides and floods locate at a low or very low degree [22]. Li et al. presented a hybrid approach of fuzzy set and Monte Carlo Simulation to evaluate the power system of risk factors, which regards probable degrees of risk factors [23]. Moreover, the fuzzy sets assess risk factors in terms of the fuzzy transformation principle and the rules of maximum membership. Such as Zhao et al. proposed a risk assessment model to evaluate risk factors of GB projects with fuzzy set theory from the occurrence probability and impact level of risk factors [24]. Social network theory takes GB projects as a system environment, which is related to various relationships. For instance, Yang and Zou suggested an SNA model evaluate and analyze risk factors in a GB project from stakeholders’ views, which can simulate and test the effectiveness of risk mitigating measures [2]. Yang et al. proposed an interactive network model related to stakeholders of GB projects, which provides a novel direction for the interaction between stakeholders and the risk evaluation process in GB projects [25]. Regrading to the aforementioned, there are some discussions about these evaluation methods. For the AHP method, the three levels (i.e. the objective level, the criterion level, and the index level) and the judge matrix provide a combination of subjective perception and objective reasoning. This work handles the uncertain risk preferences of professionals considering the judgment matrix, while it may be ineffective in dealing with a project with various risk factors. In the Monte Carlo Simulation, a probability distribution indicates risk results and probability degrees. Instead of only providing value when analyzing CRFs, this work presents a potential extension for experts. The risk factors are considered independent, however, it is complex to determine the degrees of risk allocation. Besides, it is terrible to indicate the probable degrees of CRFs with values in the actual operation of a project. The SNA indicates risk with complex linguistic expressions through the adjacent matrix. The AHP, the Monte-Carlo simulation, the fuzzy sets, and the SNA evaluate CRFs with the possibilities of risk occurrence and the degree of risk loss, while these approaches may not further judge professional’ assessments no matter in the form of indicating information or figuring out the possibility of an undetermined degree. The complex linguistic expressions set, like the PLTS, suffices for dealing with different CRFs with various weights and evaluating criteria under an ambiguous environment.

3 Methodology for the Study The risk decision matrix is crucial to the risk evaluation model. In general, the group consensus of the decision matrix needs to be checked and revised during the decisionmaking process so as to ensure the evaluation results [18]. Regarding the significance of consensus threshold to consensus reaching process, Wang et al. believed that the consensus threshold should be calculated by GCI = RCI × , where the random consensus index (RCI) is calculated by Algorithm 1 [18],  is risk preference, which is determined by decision-makers. The weight of decision-makers is w = (w1 , w2 , ..., wm ), and m is

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the preference reference group.      RCI = E GCI = m f =1 wf GCI P e,g

(1)

where 







GCI P e,g = d P e , P g = 

d le , l g



2 n(n−1)

n−1 

n 

i=1 j=i+1

      (k) (k) d l ij , l ij e



 1 #le

(k) (k) (k) (k)

= × pαij,e − sαij,g × pαij,g

s k=1 αij,e 2#le

g

(2)

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4 Case Study: The Case of Vietnam According to [11], there are 250 survey questionnaires were sent to professionals. And the respondents were selected from the Vietnam Green Building Council, GB consultants and GB contractors with rich GB risk contractors. Finally, 58 valid responses were used as the inputs for the risk evaluation model, and the reliability test value is 0.9, which indicates that the data are reliable [11]. For [11], there are six risk groups, which are human resource and technical risk in the construction phase (HC), performance risk in the operation phase (PO), human resource risk in the design phase (HD), financial risk (FR), regulations risk (PR), and green material risk (MR). In terms of Algorithms 1–2, then part of GCI for [11] are listed in Table 1. Table 1. Value of RCI with respect to PLTSs m

3

4

5

6

7

8

9

10

t=3

0.4982

0.4969

0.56

0.6162

0.6128

0.6978

0.665

0.6583

t=5

0.5072

0.5183

0.5943

0.6286

0.6411

0.7218

0.7012

0.7257

t=7

0.5361

0.5546

0.6113

0.6499

0.6464

0.7325

0.7267

0.7417

t=9

0.583

0.5719

0.646

0.673

0.6772

0.7537

0.7303

0.7502

t = 11

0.6052

0.6014

0.6743

0.6846

0.696

0.7636

0.7654

0.7912

In this paper, we assume that weights of probability of occurrence (P), impact level (I), and risk manageability (M) are wp = 0.33, wI = 0.33, and wM = 0.34, respectively. And take GCI = 0.05891 as an example to illustrate the problem, then ⎛ ⎞ s1 (0.03) s2 (0.1) s1 (0.03) s2 (0.19) s3 (0.31) s4 (0.36) s5 (0.1) ⎜ s1 (0) s2 (0.05) ⎜ s1 (0.03) s2 (0.19) s3 (0.36) s4 (0.31) s5 (0.1) ⎟ ⎜ ⎜ ⎟ ⎜ s (0) s (0.07) ⎜ s (0) s (0.09) s (0.43) s (0.36) s (0.12) ⎟ ⎜ 1 ⎜ 1 ⎟ 2 3 4 2 5 ⎜ ⎜ ⎟ P = ⎜ s1 (0) s2 (0.1) s3 (0.43) s4 (0.36) s5 (0.1) ⎟I = ⎜ s1 (0) s2 (0.09) ⎜ ⎜ ⎟ ⎜ s1 (0.02) s2 (0.17) ⎜ s1 (0.02) s2 (0.26) s3 (0.45) s4 (0.24) s5 (0.03) ⎟ ⎜ ⎜ ⎟ ⎝ s (0.02) s (0.21) ⎝ s (0.03) s (0.24) s (0.38) s (0.26) s (0.09) ⎠ 1 2 3 4 1 2 5 s1 (0.03) s2 (0.19) s3 (0.38) s4 (0.33) s5 (0.07) s1 (0.02) s2 (0.02) ⎛ ⎛ ⎞ s0.0266 s0.307 s1 (0.02) s2 (0.17) s3 (0.53) s4 (0.17) s5 (0.1) ⎜ s0.0099 s0.274 ⎜ s1 (0) s2 (0.17) s3 (0.47) s4 (0.26) s5 (0.1) ⎟ ⎜ ⎜ ⎟ ⎜ s ⎜ s (0) s (0.12) s (0.55) s (0.22) s (0.1) ⎟ s0.1872 ⎜ ⎜ 1 ⎟ 2 3 4 0 5 ⎜ ⎜ ⎟ M = ⎜ s1 (0) s2 (0.21) s3 (0.53) s4 (0.22) s5 (0.1) ⎟G = ⎜ s0 s0.2682 ⎜ ⎜ ⎟ ⎜ s0.02 s0.4606 ⎜ s1 (0.02) s2 (0.26) s3 (0.43) s4 (0.28) s5 (0.02) ⎟ ⎜ ⎜ ⎟ ⎝s ⎝ s (0) s (0.24) s (0.5) s (0.21) s (0.05) ⎠ s ⎛

1

2

3

4

5

s1 (0) s2 (0.17) s3 (0.5) s4 (0.26) s5 (0.07)

⎞ s3 (0.33) s4 (0.4) s5 (0.14) s3 (0.24) s4 (0.47) s5 (0.24) ⎟ ⎟ s3 (0.28) s4 (0.45) s5 (0.21) ⎟ ⎟ ⎟ s3 (0.45) s4 (0.4) s5 (0.07) ⎟ ⎟ s3 (0.5) s4 (0.24) s5 (0.07) ⎟ ⎟ s3 (0.29) s4 (0.38) s5 (0.1) ⎠ s3 (0.34) s4 (0.43) s5 (0.19) ⎞ s1.1742 s1.2344 s0.566 s1.0734 s1.3832 s0.731 ⎟ ⎟ s1.2639 s1.3684 s0.7145 ⎟ ⎟ ⎟ s1.4118 s1.3024 s0.4505 ⎟ ⎟ s1.3791 s1.0144 s0.199 ⎟ ⎟ ⎠ s s s

0.0165 0.4602 1.1733 1.1304 0.3985

s0.0165 s0.2542 s1.2228 s1.3568 s0.548

GCI (P) = 0.2127, GCI (I ) = 0.5462, GCI (M ) = 0.5046

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Then the consensus degrees of P, I, and M are lower than GCI . And assume β = 0.1, the adjusted P 1 , I 1 , and M 1 are listed as follows: ⎛ ⎛ ⎞ ⎞ s0.0269 s0.3143 s1.1498 s1.255 s0.5594 s0.0269 s0.2963 s1.1558 s1.271 s0.5794 ⎜s ⎜s ⎟ ⎟ ⎜ 0.0119 s0.2846 s1.0741 s1.3689 s0.7079 ⎟ ⎜ 0.0089 s0.2566 s1.0381 s1.4329 s0.7779 ⎟ ⎜ s ⎜ ⎟ ⎟ ⎜ 0 s0.1865 s1.2665 s1.3756 s0.7031 ⎟ ⎜ s0 s0.1825 s1.2215 s1.4116 s0.7481 ⎟ ⎜ ⎟ 1 ⎜ ⎟ 1 P = ⎜ s0 s0.2614 s1.3996 s1.3162 s0.4555 ⎟I = ⎜ s0 s0.2594 s1.4056 s1.3322 s0.4405 ⎟ ⎜ ⎜ ⎟ ⎟ ⎜ s0.02 s0.4665 s1.3762 s1.009 s0.1941 ⎟ ⎜ s0.02 s0.4485 s1.3912 s1.009 s0.2141 ⎟ ⎜ ⎜ ⎟ ⎟ ⎝ s0.0179 s0.4622 s1.17 s1.1214 s0.4037 ⎠ ⎝ s0.0169 s0.4562 s1.143 s1.1694 s0.4087 ⎠ s0.0179 s0.2668 s1.2145 s1.3531 s0.5282 s0.0169 s0.2328 s1.2025 s1.3931 s0.5882 ⎛ ⎛ ⎞ ⎞ s0.0259 s0.3103 s1.1258 s1.179 s0.5594 s0.0266 s0.307 s1.1742 s1.2344 s0.566 ⎜s ⎜s ⎟ ⎟ ⎜ 0.0089 s0.2806 s1.1071 s1.3489 s0.7079 ⎟ ⎜ 0.0099 s0.274 s1.0734 s1.3832 s0.73 ⎟ ⎜ s ⎜ ⎟ ⎟ ⎜ 0 s0.1925 s1.3025 s1.3196 s0.6931 ⎟ ⎜ s0 s0.1872 s1.2639 s1.3684 s0.7145 ⎟ ⎜ ⎜ ⎟ ⎟ M 1 = ⎜ s0 s0.2834 s1.4296 s1.2602 s0.4555 ⎟G 1 = ⎜ s0 s0.2682 s1.4118 s1.3024 s0.4505 ⎟ ⎜ ⎜ ⎟ ⎟ ⎜ s0.02 s0.4665 s1.3702 s1.025 s0.1891 ⎟ ⎜ s0.02 s0.4606 s1.3791 s1.0144 s0.199 ⎟ ⎜ ⎜ ⎟ ⎟ ⎝ s0.0149 s0.4622 s1.206 s1.1014 s0.3837 ⎠ ⎝ s0.0165 s0.4602 s1.1733 s1.1304 s0.3985 ⎠ s0.0149 s0.2628 s1.2505 s1.3251 s0.5282 s0.0165 s0.2542 s1.2228 s1.3658 s0.548       GCI P HC = 0.0213, GCI I HC = 0.0546, GCI M HC = 0.0505 Therefore, the ranking of different groups and risk codes are illustrated in Fig. 1.

Fig. 1. The ranking of different groups with GCI

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According to Fig. 1, the risk group ranking are related to GCI . The most significant risk group is FR with GCI = 0.05891, which is different from the crucial of HD in terms of GCI = 0.17673, GCI = 0.29455, and GCI = 0.41237. According to [11], HD is the most significant risk group. The result displays that if GCI = 0.17673, GCI = 0.29455, or GCI = 0.41237, the risk assessment matrix can reach group consensus, and the result meets most experts’ viewpoints. The risk groups of HD, FR, and PR are critical with GCI = 0.17673, GCI = 0.29455, or GCI = 0.41237, „ which can be listed as following reasons: (1) The result is understandable in terms of GB projects with various techniques and innovations in the design stage in Vietnam. (2) Financial risk is a critical risk group for large and complex projects. Especially, GB projects adopt environmental materials, which displays that financial risk is a fundamental problem. (3) Regulation risk is significant in the implementation stage. The approve-permit process requires the participation of various stakeholders, which may cause construction delays in GB projects. Figure 1 also displays the significant risk factors of each risk group. For HC, HC3 (project management consultant and project team lack experience in construction management of GB projects) is the critical one, which is consistent with previous studies [26]. The lack of experience of project managers in GB projects is an important problem in Vietnam, as mentioned in [11]. PO3 (lack of experienced management agency in the operation stage) is the most significant one for PO of GB projects, which may be difficult to deal with uncertain conditions of GB projects. For HD, HD4 (lack of experience of designers about GB) is the weighty risk factor, which is related to designers’ experience. For GB projects, the sustainable designs are remarkable, so the experience of designers is crucial for risk evaluation. FR5 (price inflation of construction materials and labour) has been researched in previous studies [27]. Due to Covid-19, the price inflation of construction materials and labour may fluctuate. Besides, the cost of green materials and labour for GB projects is higher than traditional projects. Complex planning approval and permit procedures (PR1 ) are tough to control, which could lead to delays in the construction of GB projects, and even cause low-quality projects. For MR, MR2 . (limited availability and reliability of green materials and products suppliers) is linked to the limited availability and reliability of materials. In Vietnam, conventional materials still have major products in the construction industry. The development of GB materials depends on technologies, which is a momentous barrier to the development of GB projects.

5 Conclusions GB projects, as sustainable construction, aim to mitigate the negative impacts of materials on the environment. Similar to conventional projects, GB projects still encounter various types of risks during the life cycle. In this paper, a risk matrix was built with PLTSs to display experts’ viewpoints. In addition, random algorithms were used to check and improve the group consensus. Furthermore, this study applied the risk matrix with reaching the group consensus to evaluate risk factors in GB projects in Vietnam. And the results are consistent [11], demonstrating the effectiveness of the suggested approach. Moreover, unlike [11], this technique displays significant risk factors for each risk group.

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This study has also some inherent limitations, which may be future research topics. First, the data displays that each of the linguistic scales have probabilities, while the linguistic scales or probabilistic degrees of PLTSs may be lacking. Besides, the data were collected from questionnaire surveys, which may be limited to subjective evaluations. Moreover, the data were gleaned from Vietnam, which does not indicate various categories of GB projects. As far as limitations are concerned, future research can explore PLTSs with information incomplete conditions, construct different risk assessment models for GB projects, and collect data with different qualitative and quantitative methods.

References 1. US Environmental Protection Agency: Definition of green building (2016). https://archive. epa.gov/grenbuilding/web/html/about.html#1. Accessed 10 May 2021 2. WorldGBC: About green building–the benefits of green buildings (2021). https://www.wor ldgbc.org/benefits-green-buildings. Accessed 10 May 2021 3. Bird, G.A.: Monte Carlo simulation of gas flows. Annu. Rev. Fluid Mech. 10(1), 11–31 (1978) 4. Debrah, C., Chan, A.P.C., Darko, A.: Artificial intelligence in green building. Autom. Constr. 137, 104192 (2022). https://doi.org/10.1016/j.autcon.2022.104192 5. Gan, X.L., Zuo, J., Ye, K.H., et al.: Why sustainable construction? Why not? An owner’s perspective. Habitat Int. 47, 61–68 (2015) 6. Li, Z.D.: Research on risk management of green building development. In: IOP Conference Series: Earth and Environmental Science, vol. 647, no. 1 (2021). https://doi.org/10.1088/ 1755-1315/647/1/012146 7. Latham, S.M.: Constructing the team: final report of the government/industry review of procurement and contractual arrangements in the UK construction industry (1994) 8. Nguyen, H.D., Macchion, L.: Risk management in green building: a review of the current state of research and future directions. Environ. Dev. Sustain. 25, 2136–2172 (2022). https:// doi.org/10.1007/s10668-022-02168-y 9. Wang, J.Y., Patrick Zou, X.W.: Infrastructure Project Risk Management. China Building Industry Press, Beijing (2010) 10. Dikmen, I., Budayan, C., Talat Birgonul, M., et al.: Effects of risk attitude and controllability assumption on risk ratings: observational study on international construction project risk assessment. J. Manag. Eng. 34(6) (2018). https://doi.org/10.1061/(ASCE)ME.1943-5479.000 0643 11. Nguyen, H.D., Macchion, L.: A comprehensive risk assessment model based on a fuzzy synthetic evaluation approach for green building projects: the case of Vietnam. Eng. Constr. Archit. Manag. (2022). https://doi.org/10.1108/ECAM-09-2021-0824 12. Rodriguez, R.M., Martinez, L., Herrera, F.: Hesitant fuzzy linguistic term sets for decision making. IEEE Trans. Fuzzy Syst. 20(1), 109–119 (2011) 13. Pang, Q., Wang, H., Xu, Z.S.: Probabilistic linguistic term sets in multi-attribute group decision making. Inf. Sci. 369, 128–143 (2016) 14. Wang, H., Xu, Z.S., Zeng, X.J.: Linguistic terms with weakened hedges: a model for qualitative decision making under uncertainty. Inf. Sci. 433, 37–54 (2018) 15. Zhang, Z., Pedrycz, W.: Iterative algorithms to manage the consistency and consensus for group decision-making with hesitant multiplicative preference relations. IEEE Trans. Fuzzy Syst. 28(11), 2944–2957 (2019)

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16. Chu, J., Wang, Y., Liu, X., et al.: Social network community analysis based large-scale group decision making approach with incomplete fuzzy preference relations. Inf. Fusion 60, 98–120 (2020) 17. Liu, P.D., Wang, P., Pedrycz, W.: Consistency-and consensus-based group decision-making method with incomplete probabilistic linguistic preference relations. IEEE Trans. Fuzzy Syst. 29(9), 2565–2579 (2020) 18. Wang, H., Yu, D.J., Xu, Z.S.: A novel process to determine consensus thresholds and its application in probabilistic linguistic group decision-making. Expert Syst. Appl. 168, 114315 (2021). https://doi.org/10.1016/j.eswa.2020.114315 19. Saaty, T.L.: What is the analytic hierarchy process? In: Mitra, G., Greenberg, H.J., Lootsma, F.A., Rijkaert, M.J., Zimmermann, H.J. (eds.) Mathematical Models for Decision Support, vol. 48, pp. 109–121. Springer, Heidelberg (1988). https://doi.org/10.1007/978-3-642-835 55-1_5 20. Zadeh, L.A.: Fuzzy sets. Inf. Control 8(3), 338–353 (1965) 21. Wolfe, A.W.: Social network analysis: methods and applications. Contemp. Sociol. 91(435), 219–220 (1995) 22. Bathrellos, G.D., Skilodimou, H.D., Chousianitis, K., et al.: Suitability estimation for urban development using multi-hazard assessment map. Sci. Total Environ. 575, 119–134 (2017) 23. Li, W.Y., Zhou, J.Q., Xie, K.G., et al.: Power system risk assessment using a hybrid method of fuzzy set and Monte Carlo simulation. IEEE Trans. Power Syst. 23(2), 336–343 (2008) 24. Zhao, X., Hwang, B.G., Gao, Y.: A fuzzy synthetic evaluation approach for risk assessment: a case of Singapore’s green projects. J. Clean. Prod. 115, 203–213 (2016) 25. Yang, R.J., Zou, P.X.W., Wang, J.Y.: Modelling stakeholder-associated risk networks in green building projects. Int. J. Proj. Manag. 1, 66–81 (2016) 26. El-Sayegh, S.M., Manjikian, S., Ibrahim, A., et al.: Risk identification and assessment in sustainable construction projects in the UAE. Int. J. Constr. Manag. 21(4), 327–336 (2021) 27. Hwang, B.G., Shan, M., Supa’at, N.N.B.: Green commercial building projects in Singapore: critical risk factors and mitigation measures. Sustain. Cities Soc. 30, 237–247 (2017)

Mechanism and Collaborative Governance of Public Participation in Urban Renewal Project Hao Liu1(B) and Beibei Zhang2 1 School of Economics and Management, Anhui Jianzhu University, Hefei, China

[email protected] 2 Anhui Province Real Estate and Housing Provident Fund Research Institute, School of

Economics and Management, Anhui Jianzhu University, Hefei, China

Abstract. Public is considered to be one of the most important participants in urban renewal projects. The mode of public decision-making has gradually changed from government-led to multi-subject consultative. A good mechanism of public participation can balance interests of all parties and improve decisionmaking effectiveness. Hence, it is of great practical significance to study public participation mechanism of urban renewal projects from the perspective of collaborative governance. Through previous analysis, it can be proved that the subject, process and environment of participation can affect the performance of urban renewal projects. Then theoretical model and hypothesis were put forward. Empirical analysis was conducted by Structural Equation Model. The results showed that 22 observation variables have passed confirmatory factor analysis, whose factor loadings are all greater than 0.6. The participation subject, participation process and participation environment had significant positive effects on performance of urban renewal projects, and the path coefficients are respectively 0.399, 0.288 and 0.275. On basis of model results and collaborative governance theory, the collaborative governance mechanism of urban renewal projects was designed from government level, public level and social organizations level. Finally, suggestions were put forward for each subject in order to realize effective public participation. Keywords: Urban renewal · Public participation · Collaborative governance · Mechanism

1 Introduction In recent years, some cities have gradually stepped into the era of stock, controlling the increment and activating the stock has gradually become the new normal of urban space transformation. As an important way of urban development transformation, urban renewal is of great significance in activating urban stock land, meeting housing demand and improving the future development space of the city. To a greater extent, urban renewal projects are closely related to the vital interests of the public. In order to enable multiple subjects to fully express their own interest demands, balance the interest relationship © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 1405–1418, 2023. https://doi.org/10.1007/978-981-99-3626-7_109

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between the parties, and reduce the conflicts between the participants, public participation is considered to be an effective means to solve the current dilemma [1]. The purpose of public participation is to maximize the respect, recognition and active participation of the public in urban development decisions, objectives and measures through the collaborative cooperation of the government, enterprises, residents and other stakeholders, so as to realize sustainable development of the city and community in the construction [2]. The Central City Working Conference has clearly pointed out the need to transform the mode of urban development, improve the urban governance system, coordinate the three main bodies of the government, society and citizens, and enhance the enthusiasm of all parties to promote urban development. In addition, the government needs to innovate the governance model, respect the public’s right to know, participate in and supervise urban development decisions, and encourage social organizations and the public to participate in the construction and management of cities. However, at present, there are some problems in the process of public participation in urban renewal projects, such as low overall participation rate, low participation status, narrow scope of participants and passive participation process, which significantly affect the effectiveness of public participation and hinder the promotion of urban renewal work [3]. Therefore, in order to promote the success of the project and let the public actively participate in social governance, it is of great significance to analyze the impact of public participation on the performance of urban renewal projects from the perspective of multi-subject collaborative governance, and explore the collaboration mechanism among subjects.

2 Previous Research 2.1 Urban Renewal The concept of urban renewal first originated in the West, which proposed a series of solutions to cope with the problems in urban development [4]. In the early stage of urban renewal, large-scale demolition and reconstruction has been applied to urban construction as the main means. However, this extensive development mode has destroyed the healthy operation of urban economy and brought irreversible influence to social development [5]. Therefore, scholars at home and abroad have conducted a large number of studies on urban renewal and sustainable development in recent years. CiteSpace 6.1.R3 software was used to visualize the literature related to urban renewal at home and abroad. The Chinese literature is sourced from CSSCI, and the English literature is sourced from the SCI core database. The publication time is limited to 2017–2022. The keyword co occurrence analysis is carried out, and the results obtained by hiding the search term “urban renewal/urban regeneration” are shown in Fig. 1. It can be seen that the research hotspots of Chinese literature on urban renewal in recent six years are mainly old residential areas, stock planning, community governance, public participation, etc. In terms of stock planning, urban sprawl has led to the shortage of land resources, which has promoted the transformation of urban development from “incremental expansion” to “stock optimization”. Through the exploration of community space governance mode, the key issues of stock urban design are analyzed [6, 7]. In terms of public participation, the transformation of old residential areas and historical blocks is closely linked to the public interest to a greater extent. The government also

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pays more attention to the importance of government enterprise cooperation and public participation, and guides the public to participate effectively in public decision-making through institutional innovation and top-level design, so as to promote the modernization of urban governance capacity and governance system [8, 9]. The research focus of English literature mainly focuses on land use, sustainability, community, governance, etc. In the process of rapid urban expansion in China, many sustainability problems have emerged, including land shortage, low land use rate, environmental pollution, etc. [10]. Therefore, by optimizing urban and rural space and studying the coupling and coordination between regional subsystems, we can provide theoretical support for rural revitalization and social sustainable development [11]. In the planning of urban renewal, community is considered as the best scale for analysis, and spatial decision support system is used to evaluate according to the six sustainable development goals of the community, so as to promote the sustainable development of urban renewal [12].

Fig. 1. Research Hotspots of urban renewal in 2017–2022

2.2 Public Participation The concept of public participation can be traced back to the rule period of Greek citystates, and it was mainly applied in sociology, public management, urban planning and other fields [13]. Public participation is a process of two-way communication between the government, enterprises and the public, and good communication can alleviate the conflicts of interest between the parties [14]. Therefore, as a way of multi-subject cooperation, public participation can directly or indirectly participate in public decision-making to ensure the openness, fairness and scientificity of the final decision-making results. CiteSpace 6.1.R3 software was used to visualize literatures related to public participation at home and abroad. The Chinese literatures were sourced from CSSCI, and the English literatures were sourced from SCI core database. The publication time was limited to 2017–2022. Hiding the search term “public participation” yields the results shown in Fig. 2. It can be seen that the research hotspots of public participation in Chinese literature in the past six years mainly include environmental governance, social governance,

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collaborative governance and urban renewal. In terms of environmental governance, the practical effectiveness of public participation is considered an important component. In terms of social governance, public participation is considered to be the main way can effectively relieve the contradictions of society, is by the residents, community groups and other organizations at the grass-roots level to ensure that the interests of the whole community and reallocating the bottom-up process [15, 16]. In a major public decisions, government related department must organize the public and social organizations, such as collaborative governance, Thereby improving the quality of decision making [17]. Research hotspots in English literature focus on citizen Science, management, governance, etc. Globally, public science projects have been used as a highly effective tool to increase public awareness of the natural world, engage the public in conservation efforts, and increase public scientific literacy [18]. In terms of governance, a large number of scholars have studied public participation in the governance of the environment, climate change, urban space and other aspects in order to effectively deal with environmental pollution and urban expansion [19].

Fig. 2. Research Hotspots of public participation in 2017–2022

In the field of urban renewal, the research on public participation in China started late and developed immature, and has not formed a complete system and mechanism. In the process of urban renewal in the West, public participation has always played an important role, and after long-term practice and exploration, it has formed a systematic system design and practical experience. Through the analysis of the research hotspots of urban renewal and public participation, it can be found that in urban renewal projects, public participation is increasingly regarded as a “bottom-up” renewal method for communities to achieve social equity, cultural inheritance and sustainable urban renewal [20]. Through the optimization of urban renewal governance model and mechanism, it guides the effective participation and full consultation of multiple subjects, and seeks the optimal solution of urban renewal governance through co-construction, co-governance and sharing.

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3 Mechanism of Public Participation in Urban Renewal Project 3.1 Theoretical Model and Research Hypothesis The success of urban renewal projects is closely related to the degree of public participation. Active and effective participation behaviors can directly affect the final decision, not only help to stimulate public participation confidence, improve public ability, but also improve the quality of decision-making and scientific decision-making through brainstorming. In addition, since all stakeholders can participate in the decision-making process, it is beneficial to solve the possible risks in the later stage in advance, reduce social conflicts, and thus achieve the role of reducing costs and improving project performance. An open and transparent participation process can enhance the public’s trust in the government, increase the public’s concern about urban governance affairs, and thus participate in public decisions spontaneously [21, 22]. In addition, social organizations also need to use their knowledge to provide professional advice to the public, guide the public to make effective decisions, and supervise the decision-making process of the government, so that the government can take more public opinions into account in the final decision [23]. The government’s practical support and public participation in the important driving force, on the one hand guarantee information publicity channels, make project information timely and effectively communicate to the public, thus information needed to facilitate the public fully understand the absorption, and make reasonable decision, on the other hand through policy guarantee and system construction, let the public and social organizations in the exercise of rights laws, To further promote the process of urban renewal projects. To sum up, from the perspective of collaborative governance, combined with the characteristics of urban renewal projects, the influencing factors of public participation can be summarized into three dimensions: Subject of participation, process of participation and environment of participation, and the following three structural equation model assumptions are proposed, as shown in Fig. 3: H1: The subject of participation has a positive effect on the performance of urban renewal projects;

Fig. 3. Path hypothesis

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H2: The process of participation has a positive effect on the performance of urban renewal projects; H3: The environment of participation has a positive effect on the performance of urban renewal projects.

3.2 Observed Variables Selection Effective public participation can significantly affect the performance of urban renewal projects, which requires the public to fully express their opinions, and the government should communicate with the public in the process, and adopt reasonable opinions of the public in the final decision. Literature research method and expert interview method are comprehensively used to select observation variables, and 32 different variables are identified from the subject of participation, process of participation and environment of participation. According to the frequency of the summary, 22 variables with a frequency ratio of more than 40% are determined. The specific classification is shown in Fig. 4.

Fig. 4. Observed variables of public participation

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4 Empirical Analysis 4.1 Distribution and Collection of Questionnaires Based on the characteristics that public participation cannot be directly quantified, this paper designed a five-level Likert scale questionnaire, with the influence degree of 1–5 increasing step by step. In order to ensure the validity of the questionnaire, the subjects of the questionnaire were the surrounding residents who knew about the urban renewal project, the staff of relevant government departments, the management personnel of construction units, experts and professors of universities, and other stakeholders. Electronic questionnaires were distributed to each subject through the network, and a total of 302 questionnaires were collected in two months. After removing 20 questionnaires with obvious data regularity and inconsistent answers, 282 valid questionnaires were left, with an effective rate of 93.38%. The respondents were from relevant government departments, construction units and residents around the project, accounting for 9.27%, 21.91% and 36.8% respectively, which met the requirements of structural equation model analysis. 4.2 Data Quality Analysis SPSS 24.0 was used to test the reliability and validity of the data. The data analysis results showed that the Cronbach’s α value of the sample population was 0.923, which met the requirement of greater than 0.7, and the questionnaire had high reliability. At the same time, KMO and Bartlett sphericity test were used to test the validity of the questionnaire results. The data analysis result was KMO = 0.929, and the significance of Bartlett sphericity test value was 0.000. The results of two tests indicate that the data can be analyzed by factor analysis. 4.3 Confirmatory Factor Analysis The Structure Equation Model mainly deals with the relationships between latent variables and observed variables and within latent variables, so as to obtain the direct, indirect and total effects of independent variables on dependent variables [24]. AMOS 24.0 software was used to conduct confirmatory factor analysis on three potential variables and 22 observed variables of the model, and the rationality of index selection was determined by constructing a first-order confirmatory model. The model establishment and operation results are shown in Fig. 5. It can be seen from the figure that the standardized factor loads of each path are all greater than 0.6, reaching the ideal standard, and the significance level of each index is all significant at 0.001, indicating that the quality of the measurement model is good. The consistency of the selected indicators was observed by calculating the combination reliability CR of each potential variable and the average variance extracted value AVE. It can be seen from Table 1 that the CR and AVE values of the subject of participation, the process of participation and the environment of participation are all greater than 0.8 and 0.5, respectively, indicating that each potential variable can be described by the component observation variable and has good convergent validity.

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Fig. 5. First-order confirmatory factor analysis

Table 1. Convergent validity of the model Latent variables Subject of participation

Convergent validity AVE

CR

0.5185

0.8659

Process of participation

0.515

0.894

Environment of participation

0.5465

0.9056

4.4 Hypothetical Path Analysis In order to verify the rationality of the hypothesized, build public participation in urban renewal project performance influence the structure of the model, the model results as shown in Fig. 6, you can see, in between the main body, process and environment of participation has a significant impact on each other, and the H1, H2, H3 three influence path hypothesis are P < 0.001 level significantly, If the path coefficient is greater than 0, it means that there is a positive impact, and the hypothesis can be verified.

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Fig. 6. Validation of path hypothesis. Note: * p < 0.05, ** p < 0.01, *** p < 0.001.

The fitting results of the structural model are shown in Table 2. According to the fitting value and fitting criteria, all the fitting indexes meet the requirements, and the fitting degree of the model is good, indicating that the actual data has a high degree of fit with the established model, which can support the results of path analysis. Table 2. Model fitness evaluation table Fitting index

CMIN/DF

RMSER

RMR

IFI

CFI

TLI

Fitted value

1.768

0.052

0.041

0.943

0.943

0.936

Adapter standard

(1, 3)

0.9

>0.9

Judge

satisfy

satisfy

satisfy

satisfy

satisfy

satisfy

4.5 Results The results of model operation are used to judge whether the proposed hypothesis is supported. The path coefficients of the three hypotheses are all greater than 0 and pass the significance test of P < 0.001, indicating that the three hypotheses are all valid. Based on the output results of the structural equation model, the following conclusions can be drawn: (1) According to the significance level and standardized path coefficient of each hypothesis, it can be obtained that the influence degree on the performance of urban renewal project from large to small is the participating subject, the participating process, and the participating environment. (2) In terms of participation subject, the factor loads of the awareness, ability and channels of public participation are 0.738, 0.723 and 0.723 respectively, indicating that in terms of participants, it is necessary to cultivate the awareness of public participation

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by expanding publicity and education, improve the ability of public participation, and enrich the channels of public participation. (3) In terms of participation process, the degree of public opinion influence decisionmaking, the effectiveness of public participation in the result feedback and conflict management factor load of 0.823, 0.755 and 0.744 respectively, suggests that the government department in the final decision whether or not to accept the opinions of the public, and whether to inform the public participation in decision-making in the process of participation in the important position, the result of the In addition, conflicts arising in the process of public participation need to be dealt with promptly and effectively. (4) In terms of participation environment, the factor loads of the ease of obtaining project information, the allocation of activity funds and the degree of decentralization of government power were 0.810, 0.780 and 0.778, respectively, indicating that public participation requires more convenient access to project information, and government departments are required to provide sufficient activity funds in the process of participation. And it should be backed by government support to give the public greater power.

5 Collaborative Governance Mechanism for Urban Renewal Project 5.1 Mechanism Design Based on Collaborative Governance The concept of collaborative governance was initially considered as the allocation or sharing of discretionary power to other subjects except the government, so as to achieve win-win cooperation and achieve the public goals designated by the government. In this process, the government is still the leading role in the joint governance of multi-subjects [19]. In China, collaborative governance usually refers to the active participation of the government, enterprises, social organizations, citizens and other subjects in public affairs. In the process of practice, all subjects are willing and motivated to play their own values and roles, so as to achieve an efficient and orderly governance situation [25]. Its connotation can be summarized as the following three aspects: first, multi-subject participation, in which the government plays a leading role, there are certain initiative and interaction between the parties; Second, there is a common and clear goal, and all parties are willing to coordinate and cooperate to achieve the goal. Third, multi-subject to create value together, share risks, share value results. In urban renewal projects, the government, the public and social organizations need to participate in decision-making, balance the interests of all parties, and achieve multiparty governance [26]. Collaborative governance emphasizes the transformation of government functions and governance methods, and focuses on the participation of social organizations and the public in decision-making. Public participation aims to improve the democratic and scientific nature of public decision-making, so as to optimize the way of government governance [27]. The subject diversity, synergy and dynamics of collaborative governance theory are consistent with the purpose and idea of public participation. Collaborative governance theory provides the foundation for public participation, and public participation is the condition and path for realizing collaborative governance.

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Urban renewal projects are closely related to the interests of the public, and it is more necessary for the public to participate in the decision-making process through collaborative governance, so as to give play to the advantages of multiple subjects and improve the scientificity of decision-making. Based on the theory of collaborative governance, the governance mechanism as shown in Fig. 7 is proposed.

Fig. 7. Collaborative governance mechanism

5.2 Government As the main maker of public decisions, the government plays a decisive role in the final outcome of the consultation. In the process of public participation, the degree to which the government adopts public opinions will directly affect the enthusiasm of the public. Therefore, it is necessary to formulate a perfect comprehensive evaluation system for public opinions, and carry out expert argumentation for the problems that reflect strongly. Adopt reasonable opinions from the public and reflect them in the final decision. The government should give corresponding reasons and give timely feedback to those opinions that have not been adopted. The degree of decentralization of government also affects the enthusiasm of public participation to a large extent. Greater freedom and autonomy can help the public exercise their rights in the process of participation and contribute to the final decision. In this process, as the number of equal exchanges increases, the public will also have more trust in the government and feel that they have influenced the city management, so they will be more willing to contribute their share in the process of participation. In the environment of public participation, government departments should increase the disclosure degree of project information. Urban renewal projects are closely related to the interests of the public, and more importantly, the government needs to ensure the authenticity and accessibility of information. In order to enhance the effectiveness of public participation, the public’s right to know about the updated project information must be guaranteed. Through the establishment of the whole process

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of information disclosure system, increase the channels of information disclosure, make full use of the network channels for dissemination, the information related to the project should be placed in the position of easy access, so that more people can participate in the process and make it convenient to put forward targeted opinions. Avoid the problems of low effectiveness of public participation and low willingness to participate caused by information asymmetry. In addition, the establishment and improvement of the legal guarantee system for public participation can also motivate public participation, guarantee the exercise of public rights through the system, improve the public participation mechanism, and give play to the guiding role of the legal mechanism for ordinary people, so as to enhance the enthusiasm and effectiveness of public participation. 5.3 Public As the main stakeholders of the urban renewal project, the public should pay attention to the awareness and ability of the participating public. The public should pay attention to the information released by the competent authorities and can voluntarily form a working group to generate influence within the public. Pay attention to the communication within the organization, expand the scope of influence, and absorb more participants to join. In addition, by setting up a separate budget for the organization of regular training to focus on hot issues, this can significantly increase the public’s interest in and understanding of public events and enhance their ability to participate. Qualified public representatives can clearly express the opinions of most people. When selecting public representatives, legal person public representatives of various interest classes can be determined according to the degree of interests involved in urban renewal projects, so as to ensure that the interests of all parties can be taken care of and reduce social conflicts. Representatives can also be selected according to their understanding of the project. Government public decisionmaking requires the participation of those who have certain professional knowledge and are willing to participate, which can not only ensure the rationality and scientificity of the final decision, but also improve the effectiveness of public participation. In terms of the participation process, to improve the extent to which public opinions influence decisionmaking, the government needs to ensure the effectiveness of public participation through practical actions, so that the public can realize that they can have an influence on decisionmaking, so that they can feel satisfied, and pass this awareness to more people to expand the scope of participation. The public should also actively seek technical support from external experts, participate in relevant professional knowledge training, form public opinion groups, raise questions and discuss them in meetings, so as to improve the rationality of expressing opinions. 5.4 Social Organization As indirect stakeholders of urban renewal projects, social organizations should play their advantages and actively participate in the process of public participation. On the one hand, experts and scholars can make use of their professional knowledge to visit and investigate urban renewal projects, form investigation reports, and feed back the results to the government and the public to provide reference for the final decision. On the other hand, special forums are organized to provide professional knowledge and

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technical support to the public, timely answer questions, improve the ability and awareness of public participation, and guide the public to put forward reasonable and effective opinions. News media departments need to give play to their own value by tracking and reporting the project process to objectively convey information to the public, so that the public can judge the rationality of the decision. The supervision mechanism is a necessary means to ensure the transparency and integrity of the decision-making process. The government should open the supervision channels so that the public can participate and express their opinions. In addition, a third-party supervision organization, composed of representatives from all sides, can be established to provide suggestions from a relatively independent perspective and urge the management department to seriously implement the decisions.

6 Conclusion In urban renewal projects, public participation in decision-making has gradually become a topic of widespread concern in the society. Paying attention to the important role of public participation in the process of urban renewal is conducive to the benign interaction of multi-subject collaborative governance and the harmony and stability of the whole society. The results show that subject, process and environment of participation all have significant positive effects on the performance of urban renewal projects, and the most influential factor is the participants. According to the empirical results, the collaborative governance mechanism of urban renewal project is designed, which has reference significance for the government to conduct social governance. The government, social organizations, external experts, news media and the public are all actively involved in public decision-making. Through coordination and cooperation, all parties can form a situation of mutual contact, decision-making through consultation and sharing risks to promote the realization of public interests. Acknowledgments. This work was supported by the Major Projects of Humanities and Social Sciences of Anhui Provincial Department of Education under Grant No. SK2019ZD51.

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The Influence of Institutional Regulation on Megaproject Social Responsibility: The Moderating Effect of Political Connection Delei Yang1 , Jiawen Li1 , Qinghua He2 , Jun Zhu2(B) , and Kexin Dong1 1 School of Construction Management and Real Estate, Henan University of Economics and

Law, Zhengzhou 450016, China 2 School of Economics and Management, Research Institute of Complex Engineer and

Management, Tongji University, Shanghai 200092, China [email protected]

Abstract. Institutional regulation is a crucial factor to improve megaproject social responsibility (MSR) and promote the sustainability of these megaprojects. However, the current stream of literature has rarely explored the impact of institutional regulation on MSR. This study examines how various factors of institutional regulation in megaprojects affect MSR behavior based on institutional theory. Using 217 survey data from China, the relationship between institutional regulation and MSR behavior was analyzed. The empirical results reveal that four types of government regulatory factors, namely institutional completeness, regulatory normality, institutional constraints, and policy incentives, significantly positive influence MSR behaviors, with institutional constraint having the most significant influence. It also finds that political connection positively moderates the influence of institutional regulation factors on MSR. Following new perspectives of MSR governance, these findings provide practical suggestions for both policymakers and managers on the implementation and governance of megaprojects. Keywords: Megaproject social responsibility · Institutional regulation · Political connection · Structural equation modeling

1 Introduction Nowadays, the world has entered the “trillion dollar era” of megaprojects [1]. Megaprojects plays an essential role in promoting regional economic development, upgrading public services and enhancing national influence [2]. Many megaprojects such as the Hong Kong-Zhuhai-Macao Bridge and the Beijing Daxing International Airport have been completed and have started operation. Megaproject social responsibility (MSR) refers to “the policies and practices of the stakeholders through the whole project life cycle that reflect responsibilities for the well-being of the wider society” [3]. Given that the construction of megaprojects requires increasing consideration of economic, social, and environmental issues, MSR has become a key factor for megaproject sustainability. However, the ensuing lack of social responsibility has brought unprecedented challenges © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 1419–1436, 2023. https://doi.org/10.1007/978-981-99-3626-7_110

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to the sustainable development of megaprojects. The absence, impropriety, or alienation of social responsibility, such as greenwashing behaviors, quality defects, environmental damage, and corruption directly leads to a series of social problems that go beyond the scope of the project [4]. Solving the problem of social responsibility governance of megaprojects has garnered much regard in both academic and industrial fields. Government always plays an important role on the megaproject implementation. In China’s government-market co-effect institutional context, MSR performance is not only influenced by the market, but must also be subject to institutional regulation. Institutional regulation made by government promotes it through top-down constraints on the specific behaviors of various megaproject stakeholders using government policies and regulations. Specifically, it includes legal systems and government supervision, industry standard regulations, engineering-related legal provisions, and rules and other systems. Currently, MSR research has focused either on macro-level factors or on projects through the lens of economics, but to a large degree it is the regulation factors of the micro-level that have received less attention. Meanwhile, unique properties of megaprojects, the influence of institutional regulation on MSR is consequently more complicated. However, there has been rare empirical studies to unveil the effect of different institutional regulation factors on MSR. Therefore, this study intended to draw upon institutional theory to examine the contribution of institutional regulation (i.e., institutional completeness, regulatory normality, institutional constraints, and policy incentives) in promoting MSR behavior. Motivated by this, the paper tries to address such questions from both theoretical and empirical perspectives. From the perspective of institutional theory, we explore the impact of institutional regulation on MSR. Based on a sample of 217 questionnaires in China, this paper proposes and tests an integrated model based on the relationship between institutional regulation and political connection and MSR. The current study is comprised of three parts. First, institutional regulation factors in MSR fulfillment are identified and screened, where an institutional regulation scale is constructed based on it. Second, the effects of each factor of institutional regulation on MSR is explored, and the relationship between them is then tested. Third, the moderating effect of political connection on MSR and factors of institutional regulation are further examined. This allows for the better understanding of the possible interactions between institutional regulation, political connection, and MSR. Practical guidance is provided for MSR governance by effectively playing the role of institutional regulation.

2 Theoretical Foundation and Hypotheses Development 2.1 MSR The long lifecycles, huge investment, complex stakeholders, and its special strategic significance of megaprojects attributes also determine the need of MSR to be given its due regard. Consequently, other than ensuring the basic requirements of project quality and schedule, megaprojects must also fulfill social responsibility in terms of environmental sustainability and social stability. Simultaneously, the potential negative effects of the project are reduced to a minimal level. Given the sustainability of megaprojects, MSR has increasingly become a key area of focus.

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MSR is divided into four dimensions: legal responsibility, economic responsibility, political responsibility, ethical responsibility [4]. These four dimensions respond positively to institutional pressures, enabling the successful construction and operation of megaprojects by ensuring project sustainability [5]. From the perspective of megaprojects, social responsibility practices have been investigated from different perspectives. From a theoretical perspective, scholars have focused on theoretical foundations and covered a wide range of topics, including organizational models, governance strategies and socially responsible behavior, and evaluation systems [4]. From a societal perspective, there is evidence that MSR fulfillment can contribute to national economic growth, job creation and improved regional social stability. However, a plethora of economics literature tends to focus on MSR theoretical studies and regional macro level, mainly exploring the externalities of megaprojects, but research efforts at the micro level on the factors influencing MSR are insufficient, especially the research related to institutional regulation. 2.2 Institutional Regulation The main factors affecting MSR fulfillment can be divided into internal drivers and external regulatory factors. The internal drivers are in MSR fulfillment facilitates the megaproject participants to gain potential competitive advantage. It also contributes to a good organizational culture and promotes organizational performance. External regulatory factors, such as industry norms, government regulations, and market competition, can also significantly influence MSR behavior. In general, MSR behavior in a region is significantly improved when national governments or international organizations promulgate relevant institutional regulations, and the establishment of MSR governmental regulatory agencies and related policies and institutions can help to create institutional pressure on giant project participants to consciously fulfill MSR during project construction and operation. Typically, MSR behavior in a region is significantly improved after national governments or international organizations enact relevant institutional regulations. From the view of institutional regulations, these may lead to legitimacy regarding the extent to which MSR behaviors are accepted or accommodated [6]. Thus, this study examines the impact of these four factors of institutional regulation on MSR. Accordingly, through a systematic analysis of the current situation of institutional regulation of MSR in China and combined with relevant megaproject cases, institutional regulation is divided into four dimensions: institutional completeness, regulatory normality, institutional restraint, and policy incentive. 2.2.1 Institutional Completeness and MSR Based on institutional theory, the regulatory influence on organizational behavior originates from the formal pressure exerted by the government [7]. Following the practice of MSR governance in China, an independent MSR management agency is yet to be established, but each institution and department performs its functions separately, with obvious compartmentalized characteristics. Hence, the overall effect of institutional regulation is limited leading to the generation of behaviors that exhibit deficiencies in social

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responsibilities. Moreover, the government-led model is prone to failures in the regulation of MSR behavior, such as the intensification of social conflicts and the unbalanced distribution of powers and responsibilities. Therefore, by improving the MSR institutional regulation mechanism, the functions of different institutions and departments can be clarified, and government behavior can be effectively regulated, fundamentally promoting the full play of institutional regulation in the implementation of MSR. Based on the abovementioned analysis, the following assumption is made: H1: The institutional completeness of institutional regulation is positively related to MSR. 2.2.2 Regulatory Normality and MSR Based on institutional theory, organizational behavior can be effectively regulated by the legitimacy pressures of formal regulation [8]. Formal regulation includes laws, norms, and mandatory requirements. Furthermore, regulatory normality positively influences MSR behavior—when the regulatory system is more standardized, institutional regulation becomes more stringent. Therefore, punitive measures for illegal behavior of megaproject participants become more effective, making megaproject stakeholders more inclined to actively undertake MSR. Meanwhile, although the existing local regulations play a certain role in regulating MSR, the restraining force remains limited. Therefore, it is difficult to play an effective role in the punishment of behaviors that exhibit deficiencies in social responsibilities. Based on the abovementioned analysis, the following hypothesis is proposed: H2: The regulatory normality of institutional regulation is positively related to MSR. 2.2.3 Institutional Constraint and MSR In a sound institutional environment, organizations must gain regulatory legitimacy to achieve their long-term development, which mainly comes from the institutional constraints imposed by institutional regulation [9]. Additionally, the optimal goal of the megaproject participants is to achieve the unity of economic and social benefits. However, the characteristics of large-scale projects to provide basic public services for social production, development, and affect people’s lives determine its inevitably difficulty to achieve substantial economic benefits in the short term [10]. Consequently, the megaproject participants tend to adopt the attitude of “no action” or “less action” to fulfill their responsibilities based on the goal of profit maximization. When strengthening institutional constraints, megaproject participants meet the legality conditions by actively fulfilling the MSR, thereby reducing the cost of non-compliance. Examples include fulfilling economic responsibilities by completing project schedules on time and fulfilling ethical responsibilities by investing more funds to reduce environmental impact. Clearly, institutional constraints of institutional regulation directly affect specific MSR behaviors of megaproject participants. Given this analysis, the following hypothesis is proposed: H3: The institutional constraint of institutional regulation is positively related to MSR.

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2.2.4 Policy Incentive and MSR Institutional theory emphasizes the interaction between the government and the organization, where different regulatory policies are employed by the government to influence the specific behavior of organizational members [11]. The government promotes the fulfillment of MSRs for megaproject participants through incentives. From the perspective of economic interests, the motivation of MSR fulfillment depends on the expected benefits and costs. During insufficient internal motivation to fulfill MSR, the government effectively increases the expected benefits of the megaproject participants, motivating them to fulfill the MSR more actively by adopting incentive regulation. Additionally, the megaproject participants respond positively to the government regulatory requirements. For example, the participants strictly comply with laws, regulations, and industry standards, thereby making reasonable use of natural resources and actively maintain social stability. In return, the government provides policy preferences and resource support to the participants such as tax relief policies, bank loan support and project support. In this way, the MSR cost is indirectly reduced, driving more megaproject participants to actively fulfill MSR. Following the abovementioned views, the following hypothesis is proposed: H4: The policy incentive of institutional regulation is positively related to MSR. 2.3 Moderation Effect of Political Connection Based on institutional factors, the MSR behaviors of participants are driven by political and other external institutional forces. Meanwhile, many megaprojects built include a large part of political projects pursuing non-financial target benefits, such as the South-toNorth Water Transfer program and the Hongkong-Zhuhai-Macao bridge [12]. Fulfilling the MSR is not only the due obligation and moral requirement of the participants but is also their duty and legal requirement. If the participant fails to fulfill its due social responsibility, it will not only face the problem of violation, but may even lead to serious political problems. Additionally, based on the special public facilities attributes of megaprojects, they are usually entrusted by government departments to market participants for project construction. Simultaneously, many project managers take additional posts in official institutes, allowing formal or informal connection to exist between participants and government agencies or officials, giving rise to the said political connection [13]. On the one hand, the megaproject participants with political connections can be more timely and fully aware of the government’s expectations of their MSR performance, proactively fulfilling MSR to meet the government’s motivation and obtain more project opportunities and resource support [14]. On the other hand, MSR deficiencies are avoided because of political incentives such as job promotion. Executives of companies with a high degree of political connection also give up short-term interests to maintain their political advantage, actively fulfilling MSR and showing due ethical awareness and responsibility. Summarily, the fulfillment of MSR by megaproject participants with political connection is more influenced by government regulatory pressure. Therefore, the following hypotheses are proposed: H5a: Political connection positively moderates the relationship between institutional completeness and MSR.

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H5b: Political connection positively regulates the relationship between regulatory normality and MSR. H5c: Political connection positively moderates the relationship between institutional constraint and MSR. H5d: Political connection positively moderates the relationship between policy incentive and MSR. Figure 1 shows the conceptual model that outlines relationships between institutional regulation, government connection, and MSR.

Fig. 1. Conceptual model

3 Method 3.1 Questionnaire Design and Validation A questionnaire was employed as the primary data collection method. The questionnaire design followed standard design procedures to improve accuracy and reliability. First, this initial questionnaire was developed using information derived from the literature review and megaproject observation. Then, several interviews were conducted with experts to verify the relevance and clarity of the questions in the questionnaire. Finally, a pilot study was conducted with 15 senior managers in the megaproject field to further ensure the validity of the measurement items before starting formal data collection. Based on feedback from the pilot study, further improvements were made to the

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structure and wording of the questionnaire. The items were quantified by the adoption of a five-point Likert scale with the range from strongly disagree to fully agree. 3.2 Measurement To measure MSR, nineteen items modified from the scales developed by Lin [3] were adopted. Four components of the MSR, including legal responsibility (LEG), ethical responsibility (ETH), economic responsibility (ECO), and political responsibility (POL). Political connection (CON) was measured with the 4 items scale proposed by Yang [14]. Institutional regulation is a complex construct, with no uniform standard having been established for its dimensional classification. Therefore, to ensure the validity of the content, we developed an institutional regulation scale mainly based on existing indicators. Institutional regulation is a multidimensional structure consisting of 18 items on four subscales: institutional completeness (MEC), regulatory normality (NOR), institutional constraint (SYS), and policy incentive (INC). 3.3 Sample and Data Collection A questionnaire survey was conducted to collect sample data. The research samples were derived from China’s megaprojects in the construction sector. To a large extent, the questionnaire for professionals could reduce the sampling error. The research questionnaires were distributed to the respondents using online method. In particular, we ensured that the interviewed experts were involved in the construction of at least one megaproject. To maximize the consideration of variability, the questionnaire was sent to participants located in different geographical locations, involved in different scales, and at different stages of the project life cycle. A total of 250 questionnaires were received and 217 of them were confirmed to be valid, with an effective response rate of 86.8%. The characteristics of respondents are shown in Table 1. Table 1. Descriptive statistical analysis of samples Type

Classification

Age

24 Age and below

Frequency

Percentage (%)

Type

Classification

57

26.3

Years of work

6–10 Year

25–35 years old

136

62.7

35–45 years old

16

7.4

Ownership

Frequency

Percentage (%)

6

2.8

10 More than years

10

4.6

Central enterprises

53

24.4

(continued)

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D. Yang et al. Table 1. (continued)

Type

Profession

Classification

Percentage (%)

Type

Classification

Frequency

Percentage (%)

45 Age and above

8

3.7

Local state-owned enterprises

66

30.4

Civil Construction

72

33.2

Private enterprises

78

35.9

Engineering Management

86

39.6

Other

20

9.2

Cost Finance

9

4.1

6

2.8

Accounting & Finance

10

4.6

103

47.5

Other Years of work

Frequency

3 Within one year 3–5 year

Education level

Specialized and below Undergraduate

40

18.4

Master

96

44.2

150

69.1

PhD and above

12

5.5

51

23.5

4 Analysis and Results 4.1 Validity and Reliability Test Factor analysis was used to investigate the validity of the measurement scales, which were assessed by KMO values and Bartlett’s sphere test. Empirically, the KMO value greater than the baseline value of 0.6 and a significant p-value of less than 0.05 for the Bartlett’s sphere test were shown to demonstrate the applicability of the factor analysis and the adequacy of the sample [15]. The KMO value of this questionnaire is 0.900, which is more suitable for factor analysis, and the Bartlett sphere test is 0.000, with significant statistics and a strong correlation between various indicators. Table 2 presents Cronbach’s alpha value, deleted Cronbach’s alpha value, average variance extracted (AVE) value, and combined reliability (CR). Cronbach’s alpha coefficient was adopted to measure the overall as well as the internal consistency reliability of each dimension. The appropriate value of Cronbach’s alpha was 0.70. The alpha values for the entire scale were above 0.7, indicating high overall reliability for the entire scale. In addition, each variable was treated with one deletion, and if the reliability index did not significantly improve after deletion, the variable was considered to have good reliability for the measurement question. The results showed that all measures had good reliability. CR and AVE were employed to test for convergent validity. CR values were all greater than 0.815. These results indicate that the questionnaire is reliable. Also, the AVE values were all above the standard value of 0.5, indicating that all constructs have acceptable convergent validity. In addition, the CFA fit statistics were χ2 /df = 1.805, RMSEA = 0.061, GFI = 0.876, CFI = 0.933, IFI = 0.934, TLI = 0.922, which values are all greater than the

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standard value. It can be concluded that the measurement model fits well with the sample data and the theoretical model setting is acceptable. Table 2. Measurement, Reliability and Validity Result Code

Item description summary

Item deleted Cronbach’s Alpha Value

Cronbach’s Alpha

CR

AVE

MEC1

Set up an independent megaproject social responsibility management agency

0.805

0.842

0.844

0.522

MEC2

Establish an internal supervisory system for government regulatory agencies

0.820

MEC3

Establish a social supervisory system for government regulatory agencies

0.824

MEC4

Enhance the professional quality of institutional regulation personnel

0.813

MEC5

Enhance the legal quality of institutional regulation personnel

0.787

NOR1

Promulgate megaproject 0.751 social responsibility special laws and regulations

0.812

0.815

0.525

NOR2

Construct megaproject social responsibility evaluation index system

0.771

NOR3

Popularize policies and regulations related to megaproject social responsibility

0.793

NOR4

Punish the lack of megaproject social responsibility

0.741

SYS1

Require mandatory publication of social responsibility reports

0.861

0.888

0.890

0.669

(continued)

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D. Yang et al. Table 2. (continued)

Code

Item description summary

Item deleted Cronbach’s Alpha Value

SYS2

Require periodic disclosure of environmental testing reports

0.864

SYS3

Establish an information 0.869 disclosure mechanism

SYS4

Build a file of records for fulfilling responsibilities

0.830

INC1

Offer tax relief for enterprises with good performance

0.851

INC2

Offer government 0.848 subsidies for enterprises with good performance

INC3

Offer loan support for enterprises with good performance

0.841

INC4

Offer priority access for resources to enterprises with good performance

0.839

INC5

Offer more project opportunities for enterprises with good performance

0.825

LEG1

Construct strict 0.814 accordance with relevant regulations and industry standards

LEG2

Compliance with the principles of public competition in the industry

0.814

LEG3

Fulfillment of international peer construction standards

0.830

Cronbach’s Alpha

CR

AVE

0.868

0.871

0.575

0.845

0.846

0.532

(continued)

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Table 2. (continued) Code

Item description summary

Item deleted Cronbach’s Alpha Value

LEG4

Timeliness and effectiveness of disclosure of project-related information

0.825

LEG5

Seriously obey the requirements of institutional regulations

0.784

ECO1

New technology adoption and degree of engineering innovation

0.878

ECO2

Construction quality and 0.885 safety conditions

ECO3

Project cost control and financial status

0.894

ECO4

Control of project duration

0.856

POL1

Maintaining stable community relations

0.806

POL2

Increase community employment

0.831

POL3

Carry out community benefit activities

0.832

POL4

Prevention of engineering corruption

0.829

POLl5

Public event emergency response

0.805

ETH1

Occupational health protection of construction employees

0.888

ETH2

Human care situation of 0.878 construction employees

ETH3

Rational use of resources during the construction phase

Cronbach’s Alpha

CR

AVE

0.906

0.910

0.760

0.851

0.853

0.532

0.900

0.903

0.762

0.874

(continued)

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D. Yang et al. Table 2. (continued)

Code

Item description summary

Item deleted Cronbach’s Alpha Value

ETH4

Ecological protection of 0.888 the construction area

ETH5

Environmental protection of communities in construction areas

CON1

Strength of relationship 0.787 with relevant central government departments

CON2

Strength of relationship with relevant departments of provincial governments (including municipalities directly under the central government)

CON3

Strength of relationship 0.798 with related state-owned enterprises

CON4

Strength of relationship with relevant industry regulatory institution

Cronbach’s Alpha

CR

AVE

0.835

0.840

0.523

0.861

0.794

0.788

4.2 Hypothesis Testing 4.2.1 Main Effects Structural equation modeling was employed to test the direct effects in the constructed models. SEM is particularly effective for testing complex models that fit small samples and relationships between observed or unobserved variables due to the limitations of megaproject samples. To better study the overall influence of government regulatory factors on the MSR, the four variables of legal responsibility, economic responsibility, political responsibility, and ethical responsibility of megaprojects were taken as the mean value and there in recorded as variable MSR. Table 6 presents that the standardized path coefficients among the latent variables were significant, and all hypotheses passed the T-test, and hypotheses H1, H2, H3, and H4 were supported, as shown in the Table 3, and the model path coefficients were plotted as shown in Fig. 2.

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Table 3. Hypothesis test results Hypothesis

Paths

Path coefficient

T-value

Result

H1

MEC→MSR

0.247***

3.346

Support

H2

NOR→MSR

0.254***

3.396

Support

H3

SYS→MSR

0.480***

6.854

Support

H4

INC→MSR

0.241***

3.888

Support

Note * (P < 0.05), ** (P < 0.01), *** (P < 0.001)

Fig. 2. Structural equation path diagram of the influence of institutional regulation on MSR

4.2.2 Moderating Effects Hierarchical regressions were used in this study to examine the presence of moderating effects. Table 4 presents the moderation effect of political connection. Results reveal that the value of R2 increases gradually with the constant addition of variables, which indicates that the interpretation ability of the model is constantly improved. The regression coefficient of the interaction effect were all significant. Therefore, H5a, H5b, H5c, H5d were supported.

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D. Yang et al. Table 4. Tests for Moderating Effects of Political Connection

Moderating variables

Variable

β

R2

CON

MEC

0.197***

0.036

NOR

0.233***

0.047

SYS

0.129*

0.015

INC

0.279***

0.071

5 Discussion Thus, one of the study’s purpose is to explore the direct effects of institutional regulation on MSR terms. Another is to explore the moderating role of political connection. Using a questionnaire survey data of megaprojects, institutional regulation factors were identified and empirically tested. Results revealed that the stronger the institutional completeness, regulatory normality, institutional constraint, and policy incentive of institutional regulation, the more proactive the major project participants are in fulfilling MSR. Meanwhile, political connection does play a moderating role between institutional regulation and MSR realization. 5.1 Effects of Institutional Completeness on MSR The institutional completeness significantly affected MSR, thus supporting H1. The results also supported the previous view that lack of government regulations is a factor that stifles social responsibility [16]. The establishment of a systematic institutional regulatory body will enhance their sense of urgency in fulfilling their social responsibilities and prompt them to take more active and positive social responsibilities in maintaining community stability. Take the South-North Water Transfer Project as an example, the State Council’s South-to-North Water Diversion Project Construction Committee was established to regulate the MSR behavior, assuming the overall responsibility of supervising MSR. In addition, the South-North Water Transfer Project Office and several implementation teams was established to assume the responsibility for regulation implementation. This model forms a top-down systematic regulatory system, which provides a guarantee for the improvement of regulatory institutions and the efficiency of regulation based on the supervision system. The institutional perfection effectively promotes the fulfillment of MSR for the South-North Water Transfer Project in terms of resettlement, maintenance of regional social stability, and water environmental protection. 5.2 Effects of Regulatory Normality on MSR The findings show that regulatory normality were positively related to MSR, thus supporting H2. The introduction of a special law for MSR, including comprehensive and detailed regulations on the various contents involved in it, establishing a strong external constraint mechanism for MSR performance and reducing the problem of MSR deficiency caused by the irregular legal environment, which was consistent with previous

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scholars, who found that socially responsible practices are subject to regulatory pressure, but limited regulatory pressure is attributed to regulatory ambiguity [17], and regulatory effectiveness are important factors affecting social responsibility [18]. 5.3 Effects of Institutional Constraint on MSR This paper postulates that institutional constraint has positive, significant effects on MSR (H3). The results of the structural model support this premise. The results also supported the previous view that the absence of regulatory restrictions by the government reduces environmental responsibility [19]. In the case of the Three Gorges Dam, for example, the Three Gorges Dam Construction Committee and relevant provincial government departments have established strict rules and regulations for megaproject participants in ecological protection, such as the requirement to issue regular environmental monitoring bulletins [20], which has an important impact on the development of social responsibility. They also monitor and publicize the protection of rare animals, which has played a significant role in promoting MSR fulfillment. 5.4 Effects of Policy Incentive on MSR The results confirmed that policy incentive had significant direct effects on MSR, which support H4. The cost investment of MSR is huge, such as pollution control and safety production facilities acquisition. Moreover, it is difficult to bring economic benefits in the short term, which violates the business goal of profit maximization. Because of the strong contrast between high cost of fulfilling responsibilities and weak external regulation, the emergence of MSR deficient behaviors is inevitable. Due to insufficient intrinsic motivation for MSR performance, the incentive reinforcement is used to enhance the motivation of MSR performance by the participants [14]. For example, the incentives of awarding recognition, tax relief, and loan support indirectly reduce the cost of MSR fulfillment of the participating companies and balance their interests. 5.5 Moderating Effects of Political Connection In this study, political connection has been used as a moderator to find its moderating effects on institutional regulation and MSR. The findings reveal that political connection positively moderates the relationship between institutional regulation and MSR. The higher the degree of political connection of the megaproject participants, the more significant the effect of institutional regulation on MSR fulfillment. Therefore, the results of this study provide the evidence to accept H5a, H5b, H5c, H5d. In fact, the higher the degree of political connection means the more sensitive it is to government regulatory requirements and also that it costs more to fulfill more social responsibilities. However, the higher degree of political connection implies the greater their advantages are in terms of legal protection and access to resources. Despite the high cost of fulfilling MSR, megaproject participants proactively fulfill MSR to maintain good government relations. Megaprojects are usually financed by the government and entrusted to project legal entities, which play an important decision-making role

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in resource allocation and other aspects of megaprojects [21]. Based on the interests of enhancing social identity and establishing a good corporate image, the megaproject participants fulfill MSR in a proactive manner, such as maintaining community stability, promoting community employment, and improving the ecological environment, to close the relationship with government organizations and obtain more project resources. They also provide many public functions and receive extensive media attention, which increase their social value and enable them to gain greater social identification. These help companies exert greater influence and provide them the opportunity to gain additional political publicity and resource support [22]. In megaprojects, political connection plays a vital role in MSR fulfillment. This study has found that the empirical evidence of the phenomenon regarding political connection can positively influence the relationship between institutional regulation and MSR.

6 Conclusion and Implications This paper is an exploratory study in the field of MSR and institutional regulation. Based on institutional theory, a literature review identifies and screens four categories of governmental regulatory factors: institutional completeness, regulatory normality, institutional constraint, and policy incentive. Coinciding with our research hypothesis, the empirical results show that four types of institutional regulatory factors have positive effects on MSR performance with institutional constraint having the most significant one. It also finds that political connection plays a significant moderating role in the relationship between institutional regulation and MSR. These moderating effects are most significant in the institutional regulation dimension of institutional constraint. For megaprojects with high complexity and uncertainty, institutional regulation can facilitate MSR fulfillment. This study, therefore, provides actionable insights for decision makers and policy makers in megaproject management to improve the behavior of MSR. First, managers should encourage major project participants to strive for MSR in practice from a sustainability perspective, given its positive impacts. These include regular social responsibility reports, public environmental monitoring reports, information disclosure mechanisms, and public participation. Second, the deficiencies of institutional regulation and imperfect regulatory system in China’s current institutional environment have led to the failure of institutional regulation, which provides opportunities for negative performance or non-performance of MSR by megaproject stakeholders such as corruption, collusion, and deception. Therefore, there is a need to further develop targeted laws and rules that emphasize the establishment of effective MSR specific institutional arrangements to improve the monitoring mechanism and regulate MSR performance in megaproject management. Third, incentive mechanisms play an important role in the case of insufficient endogenous motivation for social responsibility performance in megaprojects, such as the incentive of giving good performers priority access to resources and more project opportunities. Efforts in economic incentives, such as tax breaks, environmental subsidies, and loan support are recommended to increase the incentive and improve MSR fulfillment behaviors. Several limitations of this paper deserve to be addressed in future research. First, in the questionnaire survey, the research data were assessed based on the subjective

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perceptions of the respondents. In order to enhance the objectivity and accuracy of the results, it is necessary to enrich the research method and expand the sample size in future studies. Second, it is also necessary to acknowledge that the findings reflect the current situation in China, thus it may affect the generalizability to other countries. Therefore, researchers in different countries around the world could conduct similar surveys and further studies to explore the situation in other countries. Finally, although there are many stakeholders in megaprojects, this paper has conducted a study with the main megaproject participants as the main body of research and has not yet included other stakeholders. Therefore, it is worthwhile for future research to explore institutional regulation and MSR from the perspective of different stakeholders. Acknowledgments. This research was funded by the National Natural Science Foundation of China (Grant Nos. 71801083; 71901113).

References 1. Flyvbjerg, B.: What you should know about megaprojects and why: an overview. Proj. Manag. J. 45(2), 6–19 (2014) 2. Ma, H., et al.: The societal governance of megaproject social responsibility. Int. J. Proj. Manag. 35(7), 1365–1377 (2017) 3. Zeng, S.X., et al.: Social responsibility of major infrastructure projects in China. Int. J. Proj. Manag. 33(3), 537–548 (2015) 4. Lin, H., et al.: An indicator system for evaluating megaproject social responsibility. Int. J. Proj. Manag. 35(7), 1415–1426 (2017) 5. Qiu, Y., et al.: Governance of institutional complexity in megaproject organizations. Int. J. Proj. Manag. 37(3), 425–443 (2019) 6. Campbell, J.L.: Why would corporations behave in socially responsible ways? An institutional theory of corporate social responsibility. Acad. Manag. Rev. 32(3), 946–967 (2007) 7. Li, X., et al.: Institutional pressures on corporate social responsibility strategy in construction corporations: The role of internal motivations. Corp. Soc. Responsib. Environ. Manag. 26(4), 721–740 (2019) 8. Campbell, J.L.: Institutional analysis and the paradox of corporate social responsibility. Am. Behav. Sci. 49(7), 925–938 (2006) 9. Farzin, Y.H., Kort, P.M.: Pollution abatement investment when environmental regulation is uncertain. J. Public Econ. Theory 2(2), 183–212 (2000) 10. Hu, J., Liden, R.C.: Making a difference in the teamwork: linking team prosocial motivation to team processes and effectiveness. Acad. Manag. J. 58(4), 1102–1127 (2015) 11. Scott, W.R.: The institutional environment of global project organizations. Eng. Proj. Organ. J. 2(1–2), 27–35 (2012) 12. Hu, Y., et al.: Grasping institutional complexity in infrastructure megaprojects through the multi-level governance system: a case study of the Hong Kong-Zhuhai-Macao Bridge construction. Front. Eng. Manag. 5(01), 57–68 (2018) 13. Li, S.X., et al.: Where do social ties come from: institutional framework and governmental tie distribution among Chinese managers. Manag. Organ. Rev. 7(1), 97–124 (2011) 14. Yang, D., et al.: Non-economic motivations for organizational citizenship behavior in construction megaprojects. Int. J. Proj. Manag. 38(1), 64–74 (2020) 15. Hair, J.F., et al.: Multivariate Data Analysis, 5th edn. All Publications (1998)

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16. Muthuri, J.N., Gilbert, V.: An institutional analysis of corporate social responsibility in Kenya. J. Bus. Ethics 98(3), 467–483 (2011). https://doi.org/10.1007/s10551-010-0588-9 17. Khan, M., Lockhart, J., Bathurst, R.: The institutional analysis of CSR: learnings from an emerging country. Emerg. Markets Rev. 46, 100752 (2021) 18. Halkos, G., Skouloudis, A.: National CSR and institutional conditions: an exploratory study. J. Clean. Prod. 139, 1150–1156 (2016) 19. Graafland, J.: Economic freedom and corporate environmental responsibility: the role of small government and freedom from government regulation. J. Clean. Prod. 218, 250–258 (2019) 20. Alshbili, I., Elamer, A.A.: The influence of institutional context on corporate social responsibility disclosure: a case of a developing country. J. Sustain. Finance Investment 10(3), 269–293 (2020) 21. Li, X.H., Liang, X.: A confucian social model of political appointments among Chinese private-firm entrepreneurs. Acad. Manag. J. 58(2), 592–617 (2015) 22. Sun, J., Zhang, P.: Owner organization design for mega industrial construction projects. Int. J. Proj. Manag. 29(7), 828–833 (2011)

Quality Control in Modular Construction Manufacturing During COVID-19: Process and Management Standardization Zhongze Yang(B) and Weisheng Lu Department of Real Estate and Construction, The University of Hong Kong, Hong Kong, China [email protected]

Abstract. With the renaissance of modular construction, quality control (QC) in the manufacturing stage warrants more attention as an important management process. However, many project managers are not familiar with the QC in the manufacturing stage, and the transportation limitation caused by the COVID19 further increases the problem complexity. This paper aims at evaluating the performance of QC using popular QC tools in the manufacturing and discussing its effeteness on the process and management standardization. The evaluation and analysis were based on a real life modular construction project and an e-inspection application used in the manufacturing factory. The results showed that there were several out-of-control QC problems in the factory when the modules in progress and project complexity increased. More quality problems emerged after the 3D assembly of the module, especially during production works related to ceiling and wall installation. The results further proved the poor performance of the current QC in the factory and the importance of the process and management standardization. This paper pinpoints the importance of QC in modular construction manufacturing and its reference to improve the process and management standardization. Keywords: COVID-19 · Modular construction manufacturing · Quality control · Process and management standardization

1 Introduction Modular construction has been seen as one of the opportunities to improve productivity in the construction industry by shifting the traditional on-site construction system to a more efficient manufacturing-style production system [1]. This in-factory production system allows construction modules having the potential to achieve high level of standardization and apply philosophies and technologies in the manufacturing industry such as production lines, lean manufacturing, automation, and robotics [2]. The successful worldwide application and wide discussion in the literature show the benefits of modular construction involving reducing the construction schedule and cost, improving the labor safety, and cutting the carbon emissions [3]. As an innovative approach, modular construction changes the construction process and the environment, and challenges the construction project management especially in the manufacturing stage [4]. In the post © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 1437–1447, 2023. https://doi.org/10.1007/978-981-99-3626-7_111

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COVID-19 era, the cross-border transportation obstacle in some countries and regions still remains, which continually changes the project management especially the quality control in the modular construction factory, since it is difficult to dispatch as many inspectors and managers as usual. Quality control (QC) is a crucial process for the cost management and project completion [5]. Inspection is a valuable tool for QC to check the components at various stages with reference to certain predetermined criterions. In modular construction manufacturing, QC has its uniqueness combining the characteristics from both construction and manufacturing industry, with multiple stakeholders having different management duties. QC plays an important role and failed quality inspection can lead to rework or remanufacturing, which is expensive and time-consuming [6]. Many techniques are applied to improve the QC in modular construction manufacturing such as the building information modeling (BIM), three-dimensional (3D) laser scanning technology, and internet of thing (IoT) [7]. With the renaissance of modular construction, QC in the manufacturing stage warrants more attention as an important management process. However, many project managers are not familiar with the QC in the manufacturing stage, and the transportation limitation caused by the COVID-19 further increases the problem complexity. More studies are required for the QC in the modular construction manufacturing. Some researchers tried to establish a quality information system for modular construction factory to improve the efficiency of the quality management [8]. Some studies focus on the application of lean production theory in the modular construction manufacturing to improve the QC process and achieve the construction industrialization [9]. The quality of modular products can be affected by many factors such as materials, tools, machines, and working conditions. Therefore, it is indispensable to figure out what is happening in the factory and what are the main quality issues. Meanwhile, the standardization of the production process and management is important for QC, however, the related studies are limited. This paper tries to evaluation the performance of QC in the factory and explore the process and management standardization based on the analysis results. A mobile application was developed for quality inspectors in the factory to record the inspection results, which provides the basis of the data collection and statistical analysis. The rest of the paper is organized as follows: the next section is a brief literature review of QC and modular construction manufacturing. Afterwards, the data collection will be reported in Sect. 3. Data analyses, results, and findings of QC performance and its effects on the process and management standardization will be reported in Sect. 4. Discussion and conclusion will be drawn in the last section.

2 Quality Control in Modular Construction Manufacturing Modular construction manufacturing is important for the project delivery and gains gradual focus in recent years. For example, Yu et al. [10] developed and implemented a production system using the effective application of lean tools in a modular building company for six months and demonstrated its efficiency. Moghadam [11] proposed an integrated production schedule line model applying BIM and lean tools on a modular

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construction manufacturing project. Zhong et al. [12] introduced a multi-dimensional IoT-enabled BIM platform (MITBIMP) to improve the information traceability in the manufacturing and supply chain process in prefabricated construction. Zhang Y. [13] presented a framework to help improve the performance of the modular construction manufacturing production line using value stream mapping. Xie et al. [14] established a simulation and optimization system for determining energy consumption in the manufacturing process of modular construction. Innella et al. discussed the implementation of lean techniques in all the production stages in the modular building industry through a systematic review. However, limited literatures focus on the QC in the modular construction manufacturing. QC is a systematic control of various factors to maintain a desired level of quality in a product, which relies on effective feedback and correction [15]. QC involves a set of specific procedures including planning, coordination, inspection, testing, and conformance reporting, with various techniques applied to monitor its process and avoid the unsatisfactory quality performance [16]. QC is a precise and complex process, and effective QC can reduce the possibility of mistakes and reworks, which in turn helps improve the productivity, reduce the manufacturing cost, and avoid disputes [17]. QC in the construction industry is different from that in the manufacturing industry since almost all construction projects are unique and traditional on-site construction cannot provide a production environment with consistent conditions that affected by kinds of uncertainties [18]. As an innovative approach, modular construction moves most of the on-site construction works into a factory to produce the modular unit as a product in the manufacturing environment [19]. Therefore, the QC in the modular construction manufacturing is a process having the characteristics from both construction industry and manufacturing industry. Relevant improvements can be achieved in QC management through the proper application of manufacturing theories [20]. There are seven popular tools for QC in the manufacturing industry to help figure out the characteristics of the process: Pareto chart, check sheet, cause and effect diagram, scatter diagram, histogram, graphs or flow charts, and control charts [15]. A Pareto chart contains both bars and a line graph to identify the quality problems by arranging them in decreasing order of importance [21]. Check sheet is a kind of systematic data record and cause and effect diagram is a fish-bone diagram to figure out the reason of the quality problem. Scatter diagram is used to find the relationship of a variable and the quality following the cause and effect analysis. Histogram is applied to large amounts of data to show the frequency distribution of some quality characteristics. Graphs or flow charts are used to show the operational procedures and simplified systems. Control charts are used to monitor the process and show the performance with two limits, which are upper control limit (UCL) and lower control limit (LCL) calculated based on the average values [22]. These seven tools can be divided into two groups: qualitative tools including check sheets, cause and effect diagram, and flow charts; quantitative tools including Pareto chart, scatter chart, histogram, and control chart.

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3 Data Collection Data in this paper is collected from a real modular construction project. The focal project is a high-rise building procured by a client in Hong Kong. It comprises two 17-floor towers on top of a 3-level podium. The podium and the major structural parts (e.g., core wall) of the two towers still adopt traditional cast in-situ construction while the rooms and toilets adopt modular integrated construction (MiC) technique. This MiC part is composed by around 1200 modular units. Owing to its labor shortage, high construction cost, and low productivity, Hong Kong has over the years relocated its prefabrication and MiC production to mainland China’s PRD, to exploit its wider space and cheaper labor and materials. The PRD has another name of “the World’s Factory”, which manufactures the world’s most computers, mobile phones, and construction products. A typical prefabrication or MiC project in Hong Kong is like “designing in the head office, manufacturing in the PRD, and assembling on-site in Hong Kong”; a clever strategy. Overall, such projects are dominated by construction project clients and managers who are familiar with project design and on-site management. They are increasingly familiar with the cross-border LSC. However, what is happening in the MiC factories is largely like a “black box”. This is the point of departure of this research. A mobile application is developed by our team for quality inspectors in the factory to confront the transportation barriers caused by COVID-19. The designer and the main contractor decompose the manufacturing processes into 50 holding points for quality inspection before next trade commences. All the workflow and inspection requirement information is preset in the application, so that inspectors can follow the guideline oneby-one to record the inspection results and submit them into the system for approval by relevant stakeholders. The inspection records involve the module ID, inspection content, timestamp, inspection results, and the related inspection figures to show the details. Therefore, the application is a real time monitoring of the factory production and QC. The application and database are shown in Fig. 1. The range of the data collection is from January to April, and the amount of the inspection data is 1,889 rows. Microsoft Excel 2016 was used to process and analyze the data.

Fig. 1. E-inspection application

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4 Data Analyses and Findings Statistical analysis and quantitative QC tools in manufacturing industry were applied in this paper to figure out the characteristics of QC and evaluate its performance in modular construction manufacturing. The general statistics of production progress were first summarized, and then QC tools were applied to evaluate the performance of QC. 4.1 In-Factory Production of Modular Construction As the general statistics shown in Fig. 2, there are totally around 200 modules in production. At the beginning of the mass production in January, the speed of the production was slow, while from March, the speed of production started to accelerate. Therefore, the cumulative line in grey of total modules showed a slow climbing trend. The blue bar is the total amount of modules in a daily progress, and the red bar is the amount of the new modules emerging in that day. In addition to the general increase trend, there are no apparent patterns among the daily progress, which indicates the underperformance of the production plan. According to the feedback of inspectors, the factory reported a production delay on March 29 and April 14 due to the implementation of some restrictive regulation in order to prevent the further widespread of COVID-19 pandemic and the material shortage caused by the delay of the importation and delivery in the affected area. Therefore, the progresses of most of modules lagged behind the production plan, resulting in irregular production schedules progress. The application has not only the e-inspection function but also the monitoring function for the stakeholders. Although modular construction manufacturing reduces many uncertainties in the on-site construction process, there are still some factors such as the material shortage, machine breakdown, and labor operation that affect the production progress. The delay in the manufacturing stage has a profound impact on the delivery of the whole project. Thus, the process and management standardization is important in the modular construction

Fig. 2. Production progress in the factory

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manufacturing to figure out the problems and ensure the smooth production schedule. It is a big challenge for construction practitioners who are not familiar with the manufacturing management and factory environment. 4.2 QC Performance Evaluation in the Factory Three quantitative QC tools including Pareto chart, histogram, and control charts were selected to figure out the characteristics and evaluate the performance of QC in modular construction manufacturing. Pareto chart can find the main causes of the quality problems, the histogram can show the frequency of some quality characteristics, and the control chart can distinguish special causes of variations from common causes of variation. 4.2.1 Pareto Chart By counting the failed inspection results, Pareto chart can sort the reasons of poor quality and arranging them in decreasing order of importance to help decide which manufacturing unit should be addressed first. There are thirteen tasks reported failure inspection in the statistical period. According to the statistical results shown in Fig. 3, the most frequent reason of failed inspections came from the “Ceiling stud & Rockwool

Fig. 3. Pareto chart of failed inspection

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in-fill” task, which accounted for 31.47% of the total number of failed inspections. The next frequent reason was the “MEP 1st Fixing (Surface Conduit, Sprinkler Pipe, Cable Wiring, Plumbing Pipes, Air Duct & Ventilation Fan)”, which accounted for 20.28% of the total number of failed inspections. These two tasks accounted for more than half of the total number of reported inspection failures, which indicated the importance of the QC management in the production of theses two tasks. Using Pareto chart, the priorities of different reasons for quality problems were identified. According to the analysis results, more quality problems emerged after the 3D assembly of the module, which indicated the production complexity increased when the module had the 3D shape. Therefore, factory should focus more on the production standardization and process management after assembled stage to ensure the quality of the module. According to the category of production tasks, most of the quality problems were related to “wall and ceiling”, such as “Ceiling stud & Rockwool in-fill”, “Ceiling fire board installation”, and “Top coat paint (wall and ceiling)”, which was another control point for factory manager. 4.2.2 Histogram The histogram chart was used to show the general situation and the relative frequency of the data on a continuous scale. As shown in Fig. 4, the blue bar is the number of modules that complete the related inspection task, and red line is the number of failure inspections. The relationship between the number of modules and the failure inspections is the inspection failure rate, which is an important ratio to guide the QC management. According to the results, the inspection failure rate of the task “Touch-up galvanized paint (Zinc Rich Primer)” is relatively low, which is only 1%. For task “Ceiling stud & Rockwool in-fill” and “MEP 1st Fixing (Surface Conduit, Sprinkler Pipe, Cable Wiring,

Fig. 4. Histogram of modules in production

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Plumbing Pipes, Air Duct & Ventilation Fan)”, the number of failure inspections is even larger than the number of modules. This is because there are several reworks to finally reach the quality requirements of the responsible stakeholders. Therefore, the results of the histogram chart further proved the poor performance of the current QC in these two tasks and the importance of the process and management standardization. The histogram chart can also monitor the production process of the modules in the factory. Thirty-nine out of fifty tasks were inspected in total. According to the results, we could find most of the modules finished the 3D assembly and touch-up galvanized paint; around half of the modules finished the rebar fixing, concrete pouring, and concrete curing; and around 19 modules have been already finished since the cabinet installation was almost the end of the manufacturing. Therefore, the project was mainly in the early stage of the whole production period. 4.2.3 Control Chart Control chart was calculated according to the sample average value (p) and sample standard deviation. There are two limits: upper control limit (UCL) and lower control limit (LCL), which are calculated based on three-sigma rule of thumb. These two limits are constructed to identify the out-of-control status and detect variations outside the normal operational limits. As shown in Fig. 5, the blue line is the daily inspection failure rate; the red line is the average value of the total inspection failure and total inspection; the grey line is the LCL and the yellow line is the UCL. When the daily inspection rate exceeds the range between the LCL and UCL, it indicates that the QC status is out of the regular control. According to the results, the production in April met serious quality management problem with sharply increasing inspection failure rate. The daily inspection failure rates exceeded the control range for six times in April, which indicated the factory did not

Fig. 5. Control chart of modules in production

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have efficient QC management when the modules in progress and project complexity increased. Therefore, the control chart is like an alarm to monitor the QC in the factory. Project manager needs to response quickly to the out-of-control variables to figure out the potential risks and problems to assure the smooth and standard operation in the factory.

5 Discussion and Conclusion The renaissance of modular construction around the world is a chosen strategy enabled by logistic and supply chain integration to respond to construction’s chronical problems. It epitomizes a non-trivial construction innovation by moving many on-site works to factories for massive production. However, the predicament is that manufacturing of modular construction in factories is largely like a black box unknown to many construction project and production managers. QC plays an important role in the modular construction manufacturing management, since the rework or re-manufacturing caused by the quality issues for modular construction is quite expensive. This research aimed to contribute to this problem by applying popular quantitative QC tools in manufacturing industry to modular construction manufacturing to evaluate the QC performance in the factory. It benefited from a dilemma faced by a modular construction project manufactured in the Pearl River Delta of China. Real-life manufacturing process and time data was collected from the factory for the complete statistical analysis. The results showed that there were several out-of-control QC problems in the factory especially in April, when the modules in progress and project complexity increased. There was an increasing trend of productivity; however, there were no apparent patterns among the daily progress, which indicated the underperformance of the production plan. More quality problems emerged after the 3D assembly of the module, especially during production works related to ceiling and wall installation, which indicated the production complexity increased when the module had the 3D shape. The most frequent reason of failed inspections came from the “Ceiling stud & Rockwool in-fill” task and the “MEP 1st Fixing (Surface Conduit, Sprinkler Pipe, Cable Wiring, Plumbing Pipes, Air Duct & Ventilation Fan)”. The results of the histogram chart further proved the poor performance of the current QC in these two tasks and the importance of the process and management standardization. The contribution of this paper is two-fold. Firstly, it pinpoints that QC plays an important role in the modular construction manufacturing, and deserves more attention from practitioners in the construction industry. By the application of quantitative QC tools, the project manager can monitor the performance of quality management in the factory and adjust the QC measures to ensure the quality of modules. Secondly, the QC evaluation can provide feedbacks and references to the process and management standardization. The modular product has high level of customization, but common characteristics of production process and process standardization can still be summarized through the experience accumulation and analysis. Therefore, the evaluation and analysis of the QC continuously is essential for the improvement of process and management standardization. Future work to extend this study lies in several aspects. First, the extensive analysis will be conducted after the completion of the mass production to get the complete

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evaluation of the QC performance. With the help of the application, more related research can be implemented. Secondly, more modular construction factories and projects should be collected and explored to enrich the practical QC data. The QC management may be different when the features of modular product change significantly. Thirdly, other techniques can be explored the possibility of application as manufacturing industry to help further improve the QC efficiency. More specific data and related researches are required for further explorations.

References 1. Afifi, M., et al.: Discrete and continuous simulation approach to optimize the productivity of modular construction element. In: ISARC: Proceedings of the International Symposium on Automation and Robotics in Construction. IAARC Publications (2016) 2. Neelamkavil, J.: Automation in the prefab and modular construction industry. In: 26th Symposium on Construction Robotics ISARC (2009) 3. Subramanya, K., Kermanshachi, S., Rouhanizadeh, B.: Modular construction vs. traditional construction: advantages and limitations: a comparative study. In: Creative Construction eConference 2020. Budapest University of Technology and Economics (2020) 4. Wu, L., et al.: Linking permissioned blockchain to Internet of Things (IoT)-BIM platform for off-site production management in modular construction. Comput. Ind. 135, 103573 (2022) 5. Bae, J., Han, S.: Vision-based inspection approach using a projector-camera system for off-site quality control in modular construction: experimental investigation on operational conditions. J. Comput. Civ. Eng. 35(5), 04021012 (2021) 6. Al-Najjar, B.: Total quality maintenance: an approach for continuous reduction in costs of quality products. J. Qual. Maintenance Eng. 2(3), 4–20 (1996) 7. Li, H., et al.: Improving tolerance control on modular construction project with 3D laser scanning and BIM: a case study of removable floodwall project. Appl. Sci. 10(23), 8680 (2020) 8. Shin, J., Choi, B.: Design and implementation of quality information management system for modular construction factory. Buildings 12(5), 654 (2022) 9. Goh, M., Goh, Y.M.: Lean production theory-based simulation of modular construction processes. Autom. Constr. 101, 227–244 (2019) 10. Yu, H., et al.: Lean transformation in a modular building company: A case for implementation. J. Manag. Eng. 29(1), 103–111 (2013) 11. Moghadam, M., Alwisy, A., Al-Hussein, M.: Integrated BIM/Lean base production line schedule model for modular construction manufacturing. In: Construction Research Congress 2012: Construction Challenges in a Flat World (2012) 12. Zhong, R.Y., et al.: Prefabricated construction enabled by the Internet-of-Things. Autom. Constr. 76, 59–70 (2017) 13. Zhang, Y.: A framework to improve modular construction manufacturing production line performance (2017) 14. Xie, H., et al.: Simulation of dynamic energy consumption in modular construction manufacturing processes. J. Archit. Eng. 24(1), 04017034 (2018) 15. Kumar, S.A., Suresh, N.: Production and Operations Management. New Age International (2006) 16. Arditi, D., Gunaydin, H.M.: Total quality management in the construction process. Int. J. Project Manag. 15(4), 235–243 (1997) 17. Chen, L., Luo, H.: A BIM-based construction quality management model and its applications. Autom. Constr. 46, 64–73 (2014)

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Factors Influencing the Promotion of Green Building Materials: Perspective of Multiple Stakeholders Guanying Huang1,2 , Dezhi Li1,3(B) , and S. Thomas Ng2 1 Department of Construction and Real Estate, School of Civil Engineering, Southeast

University, Nanjing, China [email protected] 2 Department of Architecture and Civil Engineering, School of Engineering, City University of Hong Kong, Hong Kong, China 3 MOE Engineering Research Center of Building Energy Equipment and Environment, Southeast University, Nanjing, China

Abstract. The promotion of green building materials has become a critical solution for most countries to realize low-carbon development, and this would involve multiple stakeholders. In order to formulate a more comprehensive promotion mechanism, this paper analyzed the factors affecting green material promotion from the perspective of multiple stakeholders. Firstly, a comprehensive literature review and the Delphi method of multiple stakeholders were adopted to determine the influencing factors. Then, the DEMATEL-ISM was employed to analyze the centrality, causality and hierarchical structure of the influencing factors. The results indicate that policy system is the cause factor with the highest causality degree, cost of adoption is the effect factor with the lowest causality degree, and initiative of enterprise is the influencing factor with the highest centrality degree. In the hierarchy structure, policy system is at the root level, initiative of enterprise and professional standard are at the deep level, maturity of industrial chain and technical feasibility are at the middle level, and social opinion, cost of adoption and performance of material are at the shallow level. Finally, three practical implications were proposed, including the government’s role to improve the policy system, the association’s need to formulate series of standards for industrial chain, the enterprise’s aspiration to increase investment in research and development. This study provides guidelines for China and other similar economies to formulate promotion mechanisms of green building material promotion and adoption. Keywords: Green building materials · Influencing factors · Multiple stakeholders · China

1 Introduction Global warming caused by excessive carbon emissions has become a problem to be solved by the whole world. Intergovernmental Panel on Climate Change (IPCC) stated that the anthropogenic carbon emissions must fall to net zero by 2050 to achieve a stable © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 1448–1461, 2023. https://doi.org/10.1007/978-981-99-3626-7_112

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global warming of 1.5 °C [1]. Construction sector is the largest emitter of carbon dioxide (CO2 ), consuming 40% of energy and producing 36% of carbon emissions in the world [2]. It is expected that the construction sector will generate more than 40 billion tons of CO2 per year in the 2030s [3]. The embodied carbon emissions in building materials is the predominant part in construction industry, with the high emission intensity during the production stage [4]. For example, as the most common building material, the cement sector accounts for 8% of global carbon emissions, which is the second largest industrial carbon emitter after the power sector [1]. Green building materials can achieve low carbon emissions in their production or maintenance stages through recyclability or high performance properties, and their promotion has been regarded as the critical means to achieve the carbon neutrality goals by many countries [5]. For example, South Korea’s certification system for green building materials as managed by the Ministry of Environment includes the environmental products declaration (EPD) certification, low carbon certification, carbon footprint certification, and eco-label certification [6]. Sweden and Denmark have introduced a certification program of green building materials to improve the sustainability of building materials [7]. Canadian government departments give priority to green building materials with EPDs when they purchasing building materials [8]. Japan has established a Comprehensive Assessment System for Building Environmental Efficiency (CASBEE), which can assess the carbon emissions of major building materials [9]. Green building materials have been widely studied by many scholars in the world, and the relevant researches have promoted the low-carbon development of the construction sector. Most scholars focus on improving the properties of green building materials. For example, Tawasil et al. assessed the physical and mechanical properties of agricultural waste (coconut fiber and sawdust) as green building materials [10]. Sadok et al. carried out an assessment of potential using calcined dredged sediments as cementitious substitutes [11]. Abu-Hamdeh et al. applied paraffin-wax and graphene in flat plate solar collectors and compared the energy efficiency [12]. Coffee husk was used as reinforcement for polypropylene in false ceiling and insulation panels, and its mechanical properties, flame retardancy, water stability, sound, and thermal insulation were assessed by Ilangovan et al. [13]. Some studies focused on the life cycle assessment (LCA) of building materials. For example, Xu et al. performed LCA analysis on bamboo construction materials to assess the potential carbon storage and reduction of CO2 [14]. Chen et al. assessed the annual embodied energy consumption and carbon emissions of the 10 most extensively used building materials in China [4]. Limphitakphong et al. selected four educational buildings to represent the reinforced concrete structures in Thailand and assessed the embodied carbon emissions of building materials used [15]. Reasonable promotion mechanism is one of the key measures to ensure green building materials can achieve the carbon reduction goals. The supply chain of green building materials involves multiple stakeholders, and they are influential in promoting the use of green building materials, but only few scholars have conducted comprehensive studies to unveil their views, and those studies only focused on the single perspective of government departments [16] or constructors [17].

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As the largest developing country in the world, China has become the largest carbon emitter since 2009 [2]. According to the China Association of Building Energy Efficiency, the carbon emissions of building materials production accounted for 28% of that of the national in 2019, making it the top contributor in China. In 2020, the Chinese government has made a commitment to achieve carbon peak in 2030 and achieve neutrality in 2060. The low-carbon transformation of building materials is an important measure for China to achieve this commitment. The Chinese government has issued a series of policies to support the production, certification and application of green building materials. In 2020, China’s Ministry of Finance and Ministry of Housing and Urban-Rural Development has jointly identified 6 pilot cities for the promotion and application of green building materials. In 2021, the national output value of green building materials has exceeded 65 billion CNY. This research will comprehensively uncover the factors affecting the promotion of green building materials, and determine the properties and hierarchical relationships of those factors. It is imperative for China and other similar economies to formulate more sustainable promotion mechanism. The remainder of this article is structured as follows. Section 2 introduces the methodology of analyzing the influencing factors of promoting green building materials. Section 3 shows the causality, centrality and hierarchical structure of the influencing factors. Section 4 discusses the main findings and proposes three implications. Section 5 presents the conclusions, limitations and future work of this research.

2 Methodology An integrated qualitative and quantitative approach was adopted to analyze the influencing factors of green building materials’ promotion. First, a comprehensive literature review and the Delphi method were used to determine the list of influencing factors. Secondly, the decision-making trial and evaluation laboratory (DEMATEL) method was adopted to analyze the centrality and causality of each influencing factor. Finally, the interpretive structural modeling (ISM) method was employed to establish the hierarchical structure model among the influencing factors to determine their interaction mechanism. The specific research framework is shown in Fig. 1. 2.1 Identification of Influencing Factors A comprehensive literature review was carried out to identify the primary influencing factors of promoting the use of green building materials. By searching “green building materials” or “green construction materials” in the title column of the search engine in “Web of Science Core Collection”, 123 articles were retrieved. By screening the contents of the articles, 11 primary influencing factors were identified. Then, the Delphi method was used to determine the final list of influencing factors. The green building materials industry chain involves many stakeholders, including government departments, associations, producers, purchasers (investors), users (constructors). Multiple types of stakeholders (Table A1 in Appendix A) of the green building materials pilot cities in China were invited to review and supplement the list of influencing factors, and all experts were with more than 5 years of practical or research experience in related fields.

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Fig. 1. The flow chart of the methodology

After three rounds of anonymous questionnaire survey, 8 final influencing factors were unanimously agreed by all experts, as shown in Table 1. Table 1. Final influencing factors of promotion of green building materials Code

Influencing factor

Justification

F1

Policy system

[18, 19]

F2

Initiative of enterprise

[20, 21]

F3

Professional standard

[6, 22]

F4

Maturity of industrial chain

[23, 24]

F5

Technical feasibility

[25, 26]

F6

Social opinion

[27, 28]

F7

Cost of adoption

[25, 29]

F8

Performance of material

[30, 31]

2.2 Analysis of the Influencing Factors by DEMATEL DEMATEL is a multi-criteria decision-making method proposed by the “Battelle Memorial Institute Geneva Research Center” in 1973 [32]. It is mainly used to determine the key influencing factors, mainly including the following four steps.

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(1) Construct the direct influence matrix The set of influencing factors for the promotion of green building materials is defined as A = (a1 , a2 , a3 ,…,an ). The effect of ai on aj is defined as aij , which is divided into five grades: very high, high, moderate, low and none, and represented by 4, 3, 2, 1 and 0 respectively. P experts are invited to provide the aij between the n influencing factors. The aij provided by each expert constitute the direct influence matrix X, as shown in Eq. (1). ⎡ ⎤ 0 a12 · · · a1n ⎢ a21 0 · · · a2n ⎥ ⎢ ⎥ (1) X=⎢ . . . . ⎥ ⎣ .. .. . . .. ⎦ an1 an2 · · · 0 P

p p=1 aij

p

where, aij = , aij represents the degree of influence of ai on aj scored by the pth P expert. (2) Normalize the direct influence matrix The direct influence matrix X is normalized to the matrix D, as shown in Eq. (2). ⎤ ⎡ 0 d12 · · · d1n ⎢ d21 0 · · · d2n ⎥ 1 ⎥ ⎢ n (2) D= ×X =⎢ . . . . ⎥ ⎣ .. .. . . .. ⎦ max j=1 αij 1≤i≤n

dn1 dn2 · · · 0

(3) Construct the comprehensive influence matrix According to the normalized direct influence matrix D, the comprehensive influence matrix T can be obtained by summing the direct and indirect influence of each factor, as shown in Eq. (3). ⎤ ⎡ t11 · · · t1n

⎥ ⎢ T = lim D + D2 + D3 + . . . + Dm = D × (1 − D)−1 = ⎣ ... . . . ... ⎦ (3) m→∞

tn1 · · · tnn

(4) Centrality and causality analysis The influencing degree fi and influenced degree ei can be calculated according to Eqs. (4) and (5), which respectively represent the comprehensive influence of ai on all other factors and the comprehensive influence of all other factors on ai . Then, the centrality Mi and causality Ni can be calculated according to Eqs. (6) and (7). The centrality Mi reflects the influence intensity of the factor and the degree of its correlation with other factors. The causality Ni reflects the type of influencing factors. If the value of causality is positive, it means that the influence of this factor on all other factors is greater than the influence of this factor by other factors, which belongs to the cause factor. On the contrary, if the cause degree is negative, it belongs to the effect factor. If the value of causality is 0, it indicates that the influence exerted by the factor is equal to that borne by the factor. fi =

n  j=1

tij

i = 1, 2, . . . , n

(4)

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ei =

n 

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tji

i = 1, 2, . . . , n

(5)

Mi = fi + ei

i = 1, 2, . . . , n

(6)

Ni = fi − ei

i = 1, 2, . . . , n

(7)

j=1

2.3 Analysis of the Interaction Mechanism by ISM ISM method was proposed by Warfield in 1974 to build the hierarchical model of influencing factors [33], which mainly includes the following three steps. (1) Construct the overall influence matrix According to Eq. (8), the overall influence matrix H can be calculated based on the comprehensive influence matrix T in Eq. (3). ⎡ ⎤ h11 · · · h1n ⎢ ⎥ H = T + E = ⎣ ... . . . ... ⎦ (8) hn1 · · · hnn

where, T represents the comprehensive influence matrix and E represents the identity matrix. (2) Construct the reachable matrix After setting an appropriate threshold λ, the reachable matrix K = kij n×n can be calculated based on the overall influence matrix H according to Eqs. (9) and (10). kij = 1 if hij ≥ λ (i, j = 1, 2, . . . , n)

(9)

kij = 0 if hij < λ (i, j = 1, 2, . . . , n)

(10)

(3) Build the hierarchical model The reachable set R and the antecedent set S can be obtained according to Eqs. (11) and (12). Then, the influencing factors can be divided into different levels based on Eq. (13).   Ri = aj |aj ∈ A, kij = 1 , (i = 1, 2, . . . , n) (11)   Si = aj |aj ∈ A, kji = 1 , (i = 1, 2, . . . , n)

(12)

Ri = Ri ∩ Si , (i = 1, . . . , n)

(13)

3 Results The experts as shown in Table A1 of Appendix A were invited to score the influencing factors in Table 1. Based on the DMEATAL-ISM method, the causality, centrality and hierarchical model of the influencing factors can be obtained as follows.

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3.1 Causality and Centrality Analysis In order to consider the opinions of multiple stakeholders comprehensively, the scores of the 13 experts in Table 1 were averaged to obtain the direct matrix X as shown in Table 2. According to Eqs. (2) and (3), the comprehensive influence matrix T was calculated. According to Eqs. (4), (5), (6) and (7), the causality and centrality of each influencing factor were calculated, as shown in Fig. 2. Table 2. The direct influence matrix X F1

F2

A3

F4

F5

F6

F7

F8

F1

0

3.5

3.1

0.4

0.3

0.5

0.7

0.5

F2

0

0

0.8

3.4

2.9

2.7

3.4

1.8

F3

0

0.2

0

3.3

3.2

0.8

2.8

3.2

F4

0

1.1

0.7

0

0.9

3.1

3.8

0.7

F5

0

0.4

0.8

1.7

0

0.4

3.4

3.7

F6

0

1.7

0.4

0

0

0

0.4

0

F7

0.5

2.1

0.5

1.4

0.2

0

0

0

F8

0

0

1.8

0

0

1.2

0

0

Initiative of enterprise

⏐

Cost of adoption

⎡

⎣Professional standard

Centrality degree

Maturity of industrial chain

⎣Technical feasibility

Performance of material

⏐

⎣Social opinion

Policy system

Causality degree Fig. 2. Causality degree and centrality degree of influencing factors

As shown in Fig. 2, there are four influencing factors whose causality degrees are positive. They are defined as cause factors, which are considered to have a tendency to

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affect other factors. Among the four factors, the causality degree of policy system (1.27) is the highest, which is followed by initiative of enterprise (0.60), professional standard (0.59) and technical feasibility (0.19). Four influencing factors with negative causality degree are defined as the effect factors, which are considered to be easily affected by the other factors. Among the four effect factors, the causality degree of cost of adoption (−0.99) was the lowest, which is followed by performance of material (−0.72), social opinion (−0.66) and maturity of industrial chain (−0.13). As for the centrality degree, initiative of enterprise (2.51) has the highest centrality degree, indicating that it is the most important factor. The cost of adoption (2.27), maturity of industrial chain (2.21) and professional standard (2.20) have relative high centrality degree, indicating that they are also relatively important in promoting the use of green building materials. The relatively low centrality degree of technical feasibility (1.87), performance of material (1.42), social opinion (1.36) and policy system (1.27) indicates their relatively low importance, despite they cannot be ignored. 3.2 Hierarchical Model Based on the ISM method, the H matrix can be calculated from the T matrix according to Eq. (8). According to the principle of the moderate number of nodes [34], the threshold of this study was set as 0.2. Then, the reachable set R and the antecedent set S can be obtained, and the influencing factors were divided into four levels. Curves and directed arrows were used to represent the influence relationship between the factors, resulting in a hierarchy diagram as shown in Fig. 3.

Fig. 3. Hierarchical structure diagram

The policy system (F1) is at the root level, which is the root factor influencing the promotion of green building materials. It has a direct impact on the initiative of enterprise (F2), professional standard (F3) and cost of adoption (F7). Therefore, government departments should formulate reasonable incentive mechanisms and punishment measures to promote the use of green building materials, which will hopefully stimulate the enthusiasm of enterprises and promote the formulation of industrial standards. In addition, reasonable tax subsidies and other financial policies will directly affect the cost of adopting building materials.

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Initiative of enterprise (F2) and professional standard (F3) belong to the deep level, and are involved in the enterprise and industry respectively, which are the two most important unofficial stakeholders. Both of them affect the maturity of the industrial chain (F4), technical feasibility (F5), and cost of adoption (F7). In addition, the initiative of enterprise (F2) also has a direct impact on social opinion (F6), because enterprises are the most active subject in the market and play a guiding role in public opinion. Complete standard (F3) will also standardize and improve the performance (F8) of green building materials. Maturity of industrial chain (F4) and technical feasibility (F5) are located in the middle level, which are influenced by the deep factors as well as influencing the shallow ones. Maturity of industrial chain (F4) will not only create good social opinion (F6), but can also reduce the cost of promoting green building materials (F7). Technical feasibility (F5) affect the adoption cost (F7) and performance (F8) of green building materials. Social opinion (F6), cost of adoption (F7) and performance of material (F8) are shallow factors. They are the most direct factors affecting the promotion of green building materials. Social opinion (F6) could affect the attitude and responsibility of all stakeholders towards green building materials. The cost of adopting green building materials (F7) is the most concerned matter of enterprises. The performance of green building materials (F8), such as durability, thermal performance, and recyclability, could all directly affect their likelihood of adoption.

4 Discussions and Implications The influencing factors and their influencing mechanism of promotion of green building materials were analyzed by DEMATEL-ISM. Then, the more comprehensive discussions were carried out and three implications for different stakeholders are proposed. 4.1 Discussions (1) Discussions on influencing factors Initiative of enterprise (F2), cost of adoption (F7) and maturity of industrial chain (F4) are the three factors with the highest centrality degree, whose related stakeholders are all enterprises. Therefore, enterprises play the most critical role in the promotion of green building materials. They are the main subject of the use of green building materials, which should be given enough concerns from the government and industry associations. Besides, the policy system (F1) is the cause factor with the highest causality degree. A reasonable policy system will affect many stakeholders in the promotion of green building materials. The effect factor with the lowest causality degree is cost of adoption (F7), cost is the factor affecting the promotion of green building materials most directly, and it is the most easily affected factor. Therefore, when stakeholders make efforts to promote green building materials, the cost of adoption should be considered as an important indicator.

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(2) Discussions on interaction mechanism The policy system (F1) is the only influencing factor at the root level, which indicates the guiding role of government departments in the promotion of green building materials. Initiative of enterprise (F2) and professional standard (F3) are influencing factors at the deep level, indicating that enterprises and industry associations are also the key subjects to promote green building materials. As the basic levels of the influencing hierarchical structure, the three subjects will significantly affect the middle and shallow factors. The cost of adoption (F7) is affected by most of the factors, and it is affected by the factors at the root level, deep level, and middle level factors at the same time. It shows that although the promotion of green building materials will bring a certain increase in cost to enterprises at the present stage, the cost increase will be effectively controlled through the cooperation of multiple stakeholders and the implementation of promotion measures. 4.2 Implications (1) The government should improve the policy system As the most powerful manager and promoter of the industry, the government needs to promote the application of green building materials by formulating more comprehensive policies such as tax relief and financial subsidies and mandatory building materials market access standards. At present, the production of building materials in China is monitored by the Ministry of Industry and Information Technology, the transportation of building materials is controlled by the Ministry of Transport, and the use of building materials is looked after by the Ministry of Housing and Urban-Rural Development. At present, policies for green building materials are mainly scattered in the policy system of various departments, and there are few policies specifically formulated for green building materials. Therefore, government should jointly release more detailed green building material policies in the future. In addition, the application of green building materials has significant regional heterogeneity, so the government needs to formulate diverse policies for different regions to achieve a more targeted promoting effect. (2) The association should formulate series of standards for industrial chain As the liaison in the industry, the association plays a very important role in connecting different stakeholders. The industry chain of green building materials is long and it involves many stakeholders. The industry association should formulate more standards for the different aspects of green building materials, so as to improve the standardization level and operation efficiency of the industry chain. The industry association should organize different stakeholders to communicate and cooperate to enhance the robustness of the industry chain. At present, China has established the China Building Materials Federation as well as many associations in different provinces and cities to promote the development of building materials. These associations should actively respond to the requirements of the government, and demand enterprises to follow the government initiatively, in order to maintain a good atmosphere for the development of the industry.

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(3) The enterprise should increase investment in research and development Enterprises are the actual implementers in the promotion of green building materials, and they are also the most closely related to the subject. In fact, enterprises are most familiar with the performance and market demand of green building materials. At present, China’s building materials industry has been connected to the carbon trading market, and a more low-carbon building materials production process will directly reduce the production cost of enterprises. While the living standard of citizens is getting higher and higher, the performance of green building materials shall be improved continuously. However, as China’s building materials production enterprises are still very rugged, scientific research investment is not high. Therefore, enterprises should increase investment in research and development so as to reduce the production cost of enterprises and enhance the competitiveness of enterprises, and this can further promote the sustainable development of building material industry.

5 Conclusions Promoting green building materials to achieve low-carbon development goals has become a global consensus. This paper analyzes the factors affecting the promotion of green building materials from the perspective of multiple stakeholders, so as to provide guidance for formulating a comprehensive promotion mechanism. Comprehensive literature review and the Delphi method were adopted to determine the eight key factors affecting the promotion of green building materials in China, and the DEMATEL-ISM was adopted to analyze the influencing factors and their interaction mechanism. The results show that policy system is the cause factor with the highest causality degree, cost of adoption is the effect factor with the lowest causality degree, and initiative of enterprise is the influencing factor with the highest centrality degree. In the hierarchy structure, policy system is at the root level, the initiative of enterprise and professional standard are at the deep level, maturity of industrial chain and technical feasibility are at the middle level, and social opinion, cost of adoption and performance of material are at the shallow level. After a thorough discussion of the results, three practical implications are proposed for different stakeholders, including the government to improve the policy system, the association to formulate series of standards for industrial chain, and the enterprise to increase investment in research and development. There are some limitations of this research which deserve further improvement in the future work. First, this research only reviewed the relevant academic literature, and the text mining approach can be used to identify the influencing factors from various documents in the future. Second, this research only identified and analyzed the influencing factors of the promotion of green building materials, but did not examine the effect of the factors. The simulation of the influence effect based on the multi-agent approach shall be considered in due course.

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Appendix A

Table A1. Profile of the experts Code Gender Organization Industry and information technology bureau in government

Position

A

Male

B

Female Industry and information technology bureau in government

Director

C

Male

Housing and urban-rural development bureau in government Director

D

Male

Housing and urban-rural development bureau in government Deputy director

E

Male

A large building materials production company

Manager

F

Male

A large building materials production company

Senior engineer

G

Female A large building materials production company

Senior engineer

H

Male

A large real estate company

Manager

I

Female A large real estate company

Manager

G

Male

A large construction company

Manager

K

Female A large construction company

Manager

L

Male

An association for civil engineering

Director

M

Female An association for civil engineering

Director

Deputy director

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27. Agyekum, K., Adinyira, E., Oppon, J.A.: Factors limiting the adoption of hemp as an alternative sustainable material for green building delivery in Ghana. Int. J. Build. Pathol. Adapt. 40(2), 202–218 (2022) 28. Agyekum, K., Kissi, E., Danku, J.C.: Professionals’ views of vernacular building materials and techniques for green building delivery in Ghana. Sci. Afr. 8, e00424 (2020) 29. Wang, Y., Liu, J.: Optimal mix ratios of green building materials and construction cost control. Annales De Chimie-Science Des Materiaux 45(4), 297–306 (2021) 30. Streimikiene, D., Skulskis, V., Balezentis, T., et al.: Uncertain multi-criteria sustainability assessment of green building insulation materials. Energy Build. 219, 110021 (2020) 31. Huang, J., Zhou, M., Yuan, H., et al.: Towards sustainable construction materials: a comparative study of prediction models for green concrete with metakaolin. Buildings 12(6), 772 (2022) 32. Gabus, A., Fontela, E.: Perceptions of the world problematique: communication procedure, communicating with those bearing collective responsibility. Battelle Geneva Research Centre, Geneva, Switzerland (1973) 33. Warfield, J.N.: Developing subsystem matrices in structural modeling. IEEE Trans. Syst. Man Cybern. SMC-4, 74–80 (1974) 34. Shen, J., Li, F., Shi, D., et al.: Factors affecting the economics of distributed natural gascombined cooling, heating and power systems in China: a systematic analysis based on the integrated decision making trial and evaluation laboratory-interpretative structural modeling (DEMATEL-ISM). Energies 11(9), 1–28 (2018)

Examining the Use of BIM-Based Digital Twins in Construction: Analysis of Key Themes to Achieve a Sustainable Built Environment Karoline Figueiredo1 , Vivian W. Y. Tam2(B) , and Assed Haddad1 1 Programa de Engenharia Ambiental, Universidade Federal do Rio de Janeiro, Rio de Janeiro,

Brazil [email protected] 2 School of Engineering Design and Built Environment, Western Sydney University, Sydney Olympic Park, Australia [email protected]

Abstract. Pursuing more sustainable construction projects has become a global priority. The construction industry is responsible for the massive use of freshwater resources and fossil fuels and several other environmental impacts, in addition to considerably affecting the gross domestic product (GDP) worldwide. In this vein, it is crucial to find strategies to develop a sustainable built environment based on a triple-bottom-line (TBL) strategy, concurrently considering environmental, social, and economic factors. The application of BIM-based Digital Twins seems to offer a tenable solution for overcoming the challenges related to achieving sustainability in the construction and real estate sectors. This concept is associated with developing a digital counterpart of the facility to assist the decision-making process throughout its life cycle, using real-time data and an actual connection between the 3D digital model and the physical asset. A BIM-based Digital Twin can be advantageous for a single building or an entire city and is, therefore, often related to the development of smart cities. This study’s novelty is presenting a structured literature review that defines the most recent developments in BIM-based Digital Twin applications for the real estate and construction sectors regarding sustainability goals. Based on this literature review, the authors present a discussion of how the knowledge acquired so far can be diffused into the built environment. Keywords: Sustainable Construction · Real Estate · Building Information Modelling (BIM) · Digital Twin

1 Introduction The Digital Twin concept has been discussed in many industries and sectors for years. In the construction and real estate sectors, this concept still presents divergences in its definition and application. A Digital Twin is generally understood as a series of accurate digital models representing a physical asset’s real-time characteristics, state, and behaviour during its entire lifespan [1]. Regarding the application of this concept © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 1462–1474, 2023. https://doi.org/10.1007/978-981-99-3626-7_113

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into the built environment, the benefits of employing a Digital Twin of a building include real-time data visualisation, ongoing asset monitoring, and the growth of self-learning skills [2]. Evidence suggests that the Building Information Modelling (BIM) methodology is a crucial step in developing Digital Twins in the built environment. The BIM methodology represents an innovative work philosophy with which a physical asset may be planned, designed, built and managed within a single 3-D model, allowing a highly collaborative process that involves architects, engineers, real estate developers, builders, manufacturers, and other construction experts. When using BIM-based tools, practitioners can generate a 3-D parametric and data-rich representation of the facility [3]. Therefore, all information related to the physical asset can be centralised within the 3-D digital model, which facilitates performing different types of computer simulations and improves the decision-making process throughout the whole building life cycle. Nonetheless, the current state of BIM only offers the asset’s static data and is typically incompatible with the Internet of Things (IoT) integration [4]. When evaluating the application of BIM-based Digital Twins in the built environment, it is expected to use 3-D digital BIM models as the first step towards creating a digital counterpart of the facility that is updated with real-time data, in addition to assessing the performance of what-if scenarios. In this context, the application of BIM-based Digital Twins seems to offer a tenable solution for overcoming the challenges related to developing a smart and sustainable built environment. Several difficulties arise when attempting to develop sustainable building projects, including the need to manage a sizable amount of data [5], communication failures due to the presence of numerous professionals involved in the process [6] and information loss throughout the whole building life cycle [7]. Using a BIM-based Digital Twin has excellent power to solve these problems, and some practices are already discussed in the literature. However, research on this topic continues mainly at a theoretical level, and therefore, much still needs to be studied for the BIM-based Digital Twin application to be efficient in developing sustainable buildings. The novelty of this paper is related to the presentation of a structured and comprehensive literature review, defining the state-of-the-art of BIM-based Digital Twin applications to achieve sustainability in the construction and real estate sectors. A discussion of how the knowledge acquired so far can be diffused into the built environment is presented.

2 Materials and Methods A thorough literature review is suggested to provide a state-of-the-art of BIM-based Digital Twin applications to achieve sustainability in the construction and real estate sectors. This literature review is expected to allow a profound discussion about this subject, with the definition of potential improvements and applications. The following steps were performed in conducting this method: Stage 1 consists of searching for relevant articles and filtering them based on the topics that need to be addressed. Stage 2 represents the descriptive analysis of the selected papers using text data mining and clustering. Stage 3 involves the evaluation of the filtered

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documents. Finally, stage 4 defines potential BIM-based Digital Twin applications to improve construction and real estate sustainability. 2.1 Stage 1 In order to determine the most recent research status on the BIM-based Digital Twin concept in the built environment, a bibliometric survey was carried out in November 2022, considering SciVerse Scopus as the search engine due to its comprehensive and user-friendly interface. The first search formula was determined as follows: ((“BIM” or “Building Information Model” or “Building Information Modeling” or “Building Information Modelling”) AND (“Digital Twin” or “data-driven simulation” or “cyberphysical system” or “cyber-physical building”)). These keywords were chosen to incorporate more papers related to this research’s theme since the use of the expression “Digital Twin” is recent in the construction industry. Then, (“Sustainability” or “Sustainable”) keywords were also added to the search formula. Only English-language materials were taken into account during this process. As shown in Fig. 1, 427 papers were found involving the use of the BIM methodology and the Digital Twin concept, with 174 journal articles, 179 conference papers, 29 review articles, 23 conference reviews and 22 book chapters. Unfortunately, BIM-based Digital Twin applications for achieving sustainability still need to be discussed more in the literature, which is proven from only 51 papers on this topic. After title and abstract screening, 22 articles were filtered to be evaluated. Based on this screening, it was clear that many articles cite keywords such as sustainability, only referring to possibilities for future research and not addressing this issue in depth.

Fig. 1. The process adopted in this study for the literature review

2.2 Stage 2 A descriptive analysis of the filtered documents was conducted in order to comprehend the nature of the research themes that have developed around BIM-based Digital Twin

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and sustainability. The country that has published more papers in this research field is the United Kingdom, with twelve publications, followed by China and Italy, with nine publications each, as seen in Fig. 2.

Fig. 2. Territories that published the most in this field of research

Besides, a co-occurrence analysis was carried out in order to determine the relatedness of keywords based on the number of documents in which they occur together. VOSViewer software was used for this, with a minimum number of occurrences of a keyword determined as five. As shown in Fig. 3, the Digital Twin concept in the construction industry is closely linked to the BIM methodology and typically involves using the Internet of Things (IoT) concept, Blockchain technology and Geographic Information Systems (GIS). In turn, when analysing the keyword cluster involving Digital Twin and

Fig. 3. Co-occurrence analysis regarding the analysed papers

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sustainable development, highlighted in red in Fig. 3, it is possible to observe that most papers are related to Smart City and life cycle assessments. Finally, the term “literature review” appeared several times in the title and abstract screening, which makes sense since most publications on the topic are limited to the theoretical level so far. 2.3 Stage 3 The evaluation of the papers is summarised in Table 1, categorised according to the pillar of sustainability that each paper is most related to (i.e., environment, society, and economy). Table 1. Most significant publications found in the literature review search Environmental Pillar Ref.

Source

Evaluation of the study

[8]

Journal: Waste Management

From a point cloud collection using scanners, the authors developed a BIM model of buildings in Hong Kong. They generated a Digital Twin-based demolition plan and the waste transportation plan

[9]

Journal: Sustainability (Switzerland)

This paper discusses the usage of a BIM-based digital twin for sustainability assessment via the presentation of a case study that encompasses the design and use phases with a primary focus on energy efficiency

[10]

Journal: Frontiers in Sustainable Cities

This paper reviews the application of Digital Twin to improve sustainability in Positive Energy Districts (PED), divided into three categories: improved BIM model, semantic platforms, and AI-enabled tools for data analysis

[11]

Journal: Frontiers in Built Environment

Despite the Digital Twin concept being mentioned in the title and abstract, the article presents a case study only focused on BIM. The authors focused on the carbon emission calculation in the railway station building

[12]

Journal: Energies

The authors present a case study on optimising maintenance processes and energy efficiency to transform port areas into Zero Energy Districts (continued)

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Table 1. (continued) Environmental Pillar Ref.

Source

Evaluation of the study

[13]

Journal: Buildings

This paper presents a theoretical framework for adopting environmentally sustainable blockchain-based Digital Twins using several BIM dimensions

[14]

Journal: Energies

This paper suggests a workflow to use BIM to perform what-if tests to determine the energy consumption of a building. The authors briefly addressed Digital Twin, and the case study did not use real-time data

[15]

Journal: Sustainability (Switzerland)

The case study based on sustainability and vulnerability audit for subway stations does not use real-time data or a real connection with the physical asset, representing only a digital shadow and not a Digital Twin

[16]

Journal: Sustainability (Switzerland)

The paper presents a systematic mixed-review methodology on the use of BIM to improve building end-of-life decision-making. The authors briefly cite the use of Digital Twin throughout the paper

Economic Pillar Ref.

Source

Evaluation of the study

[17]

Journal of Cleaner Production

This paper discusses the real-time monitoring of cost and security in prefabricated construction with the purpose of influencing sustainability

Environmental and Social Pillars Ref

Source

Evaluation of the study

[18]

Journal: Sustainability (Switzerland)

This paper explores the concept of a smart university campus and discusses the ability of universities to contribute to local sustainability projects. A University in Barcelona, Spain, was used as a case study, in which environmental aspects and occupants’ emotions were monitored (continued)

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Environmental Pillar Ref.

Source

Evaluation of the study

[19]

Journal: Urban Planning

This paper discusses the application of Digital Twin in large panel system (LPS) retrofit projects. It presents an analytical tool for community consultation that enables virtual testing of technical and urban solutions

[20]

Journal: WIT Transactions on the Built Environment

The paper presents a case study related to the renovation of Italy’s national entity for electricity. For this, a Digital Twin was used based on cloud computing, artificial intelligence, machine learning, big data and BIM. The main goal was achieving an active collaboration of all the parties involved

Environmental and Economic Pillars Ref.

Source

Evaluation of the study

[21]

Journal: Journal of Cleaner Production

The authors focus on visualising detailed materials information, schedule, predicted budgets and sustainable carbon footprint over the whole life cycle of railway infrastructures. Still, they do not discuss the connection with the physical asset, thus only partially addressing the Digital Twin concept

[22]

Journal: Frontiers in Built Environment

The authors propose a Digital Twin framework for light rail track slab systems that can perform real-time lifecycle assessments with a focus on cost, carbon emission, and energy consumption. The case study presented did not show a real connection with the physical asset

Papers in which no sustainability pillar was profoundly addressed Ref.

Source

Evaluation of the study (continued)

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Table 1. (continued) Environmental Pillar Ref.

Source

Evaluation of the study

[23]

Journal: Computers and Electrical Engineering

The authors propose a BIM-IoT-based framework to provide a Digital Twin platform limited to real-time monitoring and construction schedule management of road construction. The framework validity was proved on a real pavement construction site. Sensor devices were installed on the rollers before compaction. Sustainability aspects should have been profoundly addressed

[24]

Journal: Sustainable Cities and Society

This study discusses the application of Digital Twin to develop smart cities. For this, the authors propose the integration of BIM and geographic information system (GIS) data. However, the possibility of achieving sustainable standards in smart cities should have been discussed more

[25]

Journal: Sustainability (Switzerland)

This paper analyses the utilisation of BIM for lean purposes through a literature review and identifies dominant clusters of research topics. The Digital Twin concept is briefly discussed

[26]

Journal of Physics: Conference Series

The paper elaborates on transforming the current static digital city into a digital twin city with dynamic online interactivity. The authors propose capturing panoramic images and videos daily to manage and monitor work progress more precisely

[27]

Journal: Buildings

The paper presents a theoretical framework for integrating IoT, BIM, Digital Twin and blockchain throughout projects’ lifecycles

[28]

Annals of the Photogrammetry, Remote The authors discuss some challenges to the Sensing and Spatial Information Sciences Digital Twin application in urban planning and management and compare this concept to City Information Model (CIM). The authors conducted a scientific literature review, analysing 68 scientific documents. This investigation’s conclusions show various definitions of CIM and Digital Twin in the literature, and these concepts remain fuzzy. Sustainable aspects were briefly mentioned (continued)

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Environmental Pillar Ref.

Source

Evaluation of the study

[29]

International Conference on Smart Infrastructure and Construction 2019

This paper proposes developing a Digital Twin model, using the Cambridge campus as a case study and presenting a system architecture for this implementation at a building level. The authors used IoT sensors to acquire data from the assets, which were then integrated into the digital model. BIM tools were utilised to generate the three-dimensional model

2.4 Stage 4 Based on the literature review, it is possible to list potential BIM-based Digital Twin applications to achieve sustainable outcomes in the construction and real estate sectors. The idea of developing a digital counterpart of the facility to assist the decision-making process can be advantageous for a single building, a quarter or an entire city. On the one hand, a BIM-based Digital Twin may be related to the smart networking and control of domestic appliances, locking mechanisms, heating systems, and other electronic apparatus and IoT sensors for domestic use. On the other hand, this idea can be extrapolated, as developing a Smart City would be the next obvious step in this approach. Regarding achieving a sustainable built environment, a BIM-based Digital Twin can contribute to the three main pillars of sustainability: environment, society and economy. The idea of physical and digital assets coexisting and feeding each other with data and information has an enormous impact on different areas, including a better provision of energy and water, people’s health and education, and the overall operational cost of buildings, thus affecting environmental, social and economic aspects. Some papers have already presented specific goals for using BIM-based Digital Twins to achieve sustainable outcomes, such as maximising the recycling and reuse of demolition waste [8] and developing Zero Energy Districts [12]. However, the literature review search found no paper simultaneously addressing the three pillars of sustainability through the application of BIM-based Digital Twin. Another illustration of how BIM-based Digital Twins can improve sustainability, which has not been deeply discussed in the literature so far, is the application intended to improve the Life Cycle Sustainability Assessment (LCSA). This methodology consists of an interdisciplinary framework that integrates the triple dimension of sustainability by investigating the economic, social, and environmental impacts throughout a product’s whole life cycle. This framework can be applied to buildings and infrastructures. Real estate developers, architects, engineers and decision-makers can utilise building LCSA to offer a documentary foundation of the sustainable decisions employed. The data monitoring directly impacts the validity of the findings reported in an LCSA. With a BIM-based Digital Twin, all information and data will be stored in a centralised way, with

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the possibility of collaborative work in real-time, and this can facilitate more sustainable results.

3 Discussion The BIM-based Digital Twin concept emerges as a facilitator for professionals associated with the built environment to achieve specific results, including sustainability outcomes. However, there is still much to be debated and encouraged among experts to utilise this concept in real projects, given that the construction and real estate sectors have historically been hesitant to accept technological innovations. Along with investments made by the Government and businesses, a fundamental paradigm shift is also necessary among professionals and researchers associated with construction. Several papers found in the literature review search discuss the use of Digital Twin but do not present an in-depth explanation of its application in building projects. Besides, several articles use the expression “Digital Twin”, but in practice, they do not apply this concept since they do not use real-time data or a real connection with the physical asset, representing only a digital shadow of the facility and not a Digital Twin. The terms BIM and Digital Twin should not be used interchangeably, as a pure BIM model usually involves only static data related to the building. However, it is undeniable that creating a Digital Twin of a construction asset becomes much easier when starting from a 3-D BIM model, which already has several geometric and semantic information in a centralised way. In turn, a challenging issue that arises when a BIM model is updated to a Digital Twin is related to the interoperability requirements in the BIM domain. The Industry Foundation Classes (IFC) data model is a standardised and digital way to describe the building data by codifying the identity, attributes, semantics, and relationships of objects used in a BIM project. However, when utilising real-time data to create a Digital Twin, a massive amount of information relies on semantic web technologies. In this context, ontology representations of the IFC schema are necessary to structure better the interoperability of BIM-based tools, such as the Web Ontology Language (OWL) for IFC called ifcOWL. This language intends to exploit data distribution, extensibility of data, querying, and reasoning, but its application has been briefly addressed in the literature so far. It is essential to point out that, in order to utilise Digital Twins to improve sustainable outcomes of physical facilities, it is imperative that information and control systems be applied, in addition to the insertion of a new organisation structure. Some articles have already started to address this need when discussing the integration of BIM-based Digital Twins with Blockchain. Blockchain is a Distributed Ledger Technology that forms a database with interconnected data blocks cryptographically protected against tampering [30]. Both the construction industry and real estate can benefit enormously from Blockchain since this information technology can offer a tamper-proof solution throughout the information supervision of building processes. Nevertheless, an in-depth discussion about new organisational structures needs to be raised in the literature, and real case studies need to be evaluated in this domain. Ultimately, communication between Academia and the public and private sectors must be intensified. The possibilities for applying BIM-based Digital Twins to achieve a

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sustainable built environment are numerous and directly depend on advances in research in this regard. From the literature review presented in this research, many applications are still discussed preliminarily and still at the theoretical level. Therefore, innovative research that works collaboratively with researchers, the Government, industry leaders, and other organisations seems crucial and urgent.

4 Conclusion Through a literature review, this study proposed a discussion on applying the BIM-based Digital Twin concept to achieve sustainability. In this context, it is essential to highlight that sustainability is based on a triple-bottom-line approach comprising environmental, social, and economic aspects. The impacts of these three categories must be considered balanced. From the method proposed in this article, 427 documents were found related to using BIM-based Digital Twin in the built environment. Nonetheless, only 51 documents were related to sustainability in some way, among which only 22 papers proved to be helpful for the discussion proposed in this work. Unfortunately, the discussion of this topic in the literature is still immature, concentrated at the conceptual and theoretical levels. Among the few articles that present applications in case studies, some misuse the Digital Twin expression, not using realtime data or a real connection with the physical asset. However, with the growing rate of studies published in this field, the research will advance towards a direction that will encourage BIM-based Digital Twin applications to achieve sustainability in the construction and real estate sectors, simultaneously considering the three sustainability pillars. Acknowledgement. This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) – Finance Code 001.

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Investigating the Competency of Project Managers in the Chinese Construction Industry: A Case Study Haoyu Wang(B) , Shang Zhang, and Chen Wang Department of Construction Management, Suzhou University of Science and Technology, Suzhou, China [email protected]

Abstract. The competency of project managers has critical influences on the success of construction projects. However, the research on the importance of competency factors and the actual competence level of project managers in the Chinese construction industry remain rare. On the basis of literature review, this paper develops the professional competency index system of Chinese construction project managers, which includes 3 dimensions and 7 indicators. Based on a questionnaire survey, descriptive statistical analysis and importance-performance analysis (IPA) were conducted to explore the difference between the importance of professional competency factors and the actual level of their competencies, to understand the weak areas of project manager’s competency and to further improve their professional competencies. The results show that the actual level of all professional competency indicators is lower than their corresponding importance level, indicating that in general, the professional competency of project managers in the Chinese construction industry needs to be comprehensively improved. Among them, the largest gap measured in mean value differences is the “knowledge” dimension, and in terms of factors the gaps for “professional knowledge”, “practical ability” and “career planning ability” are the most significant between the perceived importance and actual levels. These are the aspects that project managers need to focus on improving. The research results in this paper provide theoretical reference for further improvement of the competences of project managers in the Chinese construction industry. Keywords: Project manager · Professional competence · IPA method · China

1 Introduction In recent years, China’s construction industry has developed rapidly. According to the 2021 economic annual report released by the National Bureau of Statistics, the total output value of the construction industry exceeded 29 trillion Yuan, with a year-onyear growth of 11%. However, the development of project management in China has presented three new trends: increasing proportion of large-scale construction projects, increasing technological challenge of construction, and increasing requirements on fast © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 1475–1484, 2023. https://doi.org/10.1007/978-981-99-3626-7_114

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delivery of construction projects (Li et al. 2020). The rapid development of the Chinese construction industry and the new trend of project management have put forward new requirements for the professional competence of project management teams. The improvement of professional competency of construction project managers has a significant role in achieving the success of construction projects. In the implementation process of construction projects, if they lack professional competence, it is difficult to ensure the high quality delivery of construction projects. However, the research results of professional competency of construction management personnel in China are scare. Based on literature review, this paper employs the questionnaire survey method to identify key competency indicators of project managers, analyze the gap between their perceived importance and the actual level of these factors. The results will facilitate a more comprehensive understanding of the current competency level of construction project managers and the weak areas for further improvement.

2 Literature Review 2.1 Definition of Professional Competence Professional competency can be traced back to the 1980s. Hackett et al. (1985) identified the behaviors and abilities that are critical to women’s career and defined them as professional competency. In the 1990s, Arthur et al. (1995) established the “Intelligence Career Framework”, which pointed out the three basic professional skills and knowledge corresponding to occupational skills necessary for individuals in occupations: “Knowing why”, “knowing how”, “Knowing whom” (see Fig. 1). 2.2 Development of Professional Competence Index System for Project Managers A small number of research focusing on the professional competency of project managers globally. Dang et al. (2020) constructed a SWOT analysis matrix for project managers and evaluating the professional competency of project managers by focusing on their personality, responsibility carrying ability and relevant management experience. Wang et al. (2021) conducted semi-structured interviews and questionnaire survey with different stakeholders (teachers, students and enterprises) in the construction industry, and concluded that lifelong learning is the primary ability that professionals should possess, while self-management, initiative spirit and construction management are also important abilities. Wei et al. (2019), based on the actual work of project managers and related theories of project management, constructed an evaluation index of project manager competency from four aspects: interpersonal relationship, project management, professional knowledge and professional quality, and used analytic hierarchy process (AHP) to calculate the relative importance weight of indicators at all levels. Based on the background of Industry 4.0, Ribeiro et al. (2021) determined the gap between the capabilities of traditional project managers and the new capabilities stipulated by industry 4.0, and analyzed the new capabilities and qualities that project managers should have from the aspects of soft skills and hard skills. Tabassi et al. (2016) identified ten types of

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Fig. 1. Three forms of “knowing” competence and accumulation of professional capital

competencies and qualities that project managers should possess from the perspective of sustainable construction projects. The results show that strategic view is the most important factor, followed by critical analysis ability and communication skills. On the basis of literature review, this paper preliminarily identifies the indicators of the professional competency of construction project managers, and then revises them in combination with the construction practice and the opinions of interviewed experts to establish a professional competency index system of construction project managers. This results in a total of 3 dimensions and 7 indicators, as shown in Table 1.

3 Questionnaire Survey and Statistical Analysis 3.1 Questionnaire Design This questionnaire is divided into two parts. The first part intends to collect personal basic information of the respondents. The section part includes professional competency index scale. Among them, personal basic information includes four contents, mainly including the respondent’s age, gender, years of working experience and job position. Professional competence index scale (3 dimensions with 7 factors) includes two parts, the first part is to evaluate the importance of each factor measured through the perception of the respondents, using 5-point Likert scale measurements (“1” represents “strongly disagree”, “3” represents “neutral”, “5” represents “strongly agree”). The second part

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Dimension

Factor

Reference

Knowledge(C1)

Professional knowledge(C11)

Ribeiro et al. (2021); Zhang et al. (2019); Liu (2018); Peng (2017); Song (2017); Yang et al. (2017); Lv et al. (2016); Tan (2015); Bredillet et al. (2015); Jia (2013); Men et al. (2013)

Ability(C2)

Practical ability (C21)

Liu (2018); Peng(2017); Song (2017); Yang et al. (2017); Ekrot et al. (2016); Tan (2015); Jia et al. (2013); Men et al. (2013); Zhang et al. (2013); Lin (2012);

Learning ability (C22)

Liu et al. (2018); Peng (2017); Song (2017); Yan (2016); Tan (2015); Jia et al. (2013); Zhang et al. (2013);

Innovation ability (C23)

Gong et al. (2020) Ni et al. (2018); Liu (2018); Peng (2017); Elaine Chao (2017); Zhang et al. (2013); Lin (2012);

Career planning ability (C24)

Lu et al. (2021); Cui (2019); Zhao (2017); Song (2017); Peng (2017); Tabassi et al. (2016); Liu (2012);

Mental adjustment ability (C31)

Peng et al. (2021); Guo et al. (2019); Peng (2017); Song (2017); Yan (2016); Jia et al. (2013);

Communication ability (C32)

Dang et al. (2020); Peng (2017); Zhao (2017); Hodgson et al. (2015) Jia et al. (2013); Zhou et al. (2013); Wang et al. (2010)

Quality(C3)

Note: the Code of C represents the abbreviation of Competency

measures the actual level of the competency of project managers,using the same factors in the first part. Similarly, 5-point Likert scale was also used for evaluation (“1” means “strongly disagree”, “3” means “neutral” and “5” means “strongly agree”) . 3.2 Data Collection The questionnaire survey was mainly administrated through online technique using the software of Questionnaire Star. A total of 102 valid questionnaires were collected, which produced a total of 96 valid questionnaires. Among them, the number of respondents aged between 21 and 30 accounted for 56.25%. In terms of gender, males account for 67.70% of the respondents, which is in line with the majority of male practitioners in the construction industry in China. From the perspective of roles, it included project managers, technical staff, and design managers.

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3.3 Data Analysis In this paper, SPSS26.0 software was used for statistical analysis of the collected data. Cronbach’s α coefficient was used to test the reliability of the scale. It is generally believed that the reliability is acceptable when the α coefficient is above 0.700. The Cronbachα coefficient values of the professional competency importance index scale and the actual level scale were 0.926 and 0.883 respectively, indicating that the reliability of the questionnaire data was high. KMO value was used to test the validity of the scale. It is generally believed that when the KMO value is greater than 0.7, the validity of the scale is good. The KMO values of the professional competency index scale and the actual level scale are 0.907 and 0.870 respectively, indicating that the measurement items of the questionnaire are accurate and effective and can be used for data analysis. 3.4 Analysis on the Difference of Professional Competence Index of Project Managers In order to understand the significant differences of each factor, 96 valid questionnaires were imported into SPSS26.0, and the difference analysis was carried out. The results indicated that the data are normally distributed. Therefore t-test was conducted to know whether the importance level of the factors and the actual level of the factors have significant difference, with 95% confidence interval. The results are shown in Table 2. Table 2. Difference analysis of professional competency indicators of project managers Dimension

Index

Importance

Performance

Difference t

P

Mean Mean Mean Mean Value sorting Value sorting Knowledge(C1) Professional 4.146 1 knowledge(C11)

3.740

5

0.423

5.036 0.000

Ability(C2)

Quality(C3)

Pratical ability(C21)

4.135 2

3.740

4

0.394

4.759 0.000

Learning ability(C22)

4.083 5

3.823

3

0.276

2.496 0.014

Innovation ability(C23)

3.781 7

3.583

7

0.186

2.012 0.046

Career planning 4.094 3 ability(C24)

3.708

6

0.387

3.952 0.000

Mental adjustment ability(C31)

4.063 6

3.854

2

0.225

2.654 0.010

Communication 4.094 4 ability(C32)

3.917

1

0.181

2.396 0.016

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As can be seen from Table 2, the p values are less than 0.05 for all the factors measured, indicating that the difference is significant between the importance and actual level of project manager’s competency. In addition, the mean values of all the factors in terms of importance is greater than those of the actual level, which indicates that the actual level of competency for project managers in the Chinese construction industry is lower than the importance of these factors. This necessitate a prompt action to improve the competency of project managers in China. 3.5 IPA Analysis In order to compare differences more clearly, the Importance-Performance Analysis (IPA) method is used. The perceived importance of professional competency indicators is shown in the horizontal axis (X), and the perceived actual competency level is shown in vertical axis (Y). The values for the importance of professional competency indicators and actual competency level of project managers were presented in Fig. 2.

Fig. 2. IPA analysis results of professional competence of project managers

The results in Fig. 2 show that: The first quadrant is the area of high importance - high actual performance level. In this quadrant, there are three influencing factors: learning ability (C22), psychological adjustment ability (C31) and communication ability (C32), and this quadrant is the dominant region. The third quadrant is the area of low importance - low actual performance level. There is one influencing factor of innovation capability (C23) in this quadrant, which is the opportunity region. It indicates that the importance of professional competency index in this region is not high, and the actual level is also very low, which is a competency index for secondary improvement. This is different from our previous cognition. Li et al. (2019) claimed that the cultivation of project managers’ innovation ability should be

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given priority. More and more enterprises also also developed new requirements on the innovation ability of construction management graduates in the human resources policy (Lu et al. 2016). The fourth quadrant is the area of high importance-low actual performance level. In this quadrant, there are three factors: professional knowledge ability (C11), practical ability (C21) and work planning ability (C24), indicating that although the above indicators are very important for the professional competence of project management personnel, their actual level is relatively low. Therefore, These areas needs prompt focus on improving the competencies of the project managers in the Chinese construction industry: (1) Professional knowledge (C11) “Professional knowledge ability” is the most important professional competency factor of construction project managers (ranked the first in importance ranking), while the actual level achieved in construction practice is relatively low (ranked the fifth in the actual level ranking), so it is the factor that needs to be improved significantly. At present, more and more enterprises realize that the importance of project management, and the professional knowledge of construction project management is becoming more systematic and standardized in China (Yin et al. 2015). Peng et al. (2021) also found that professional and technical knowledge ranked first among all the areas. Chen et al. (2007) divided China’s project management into three areas in the life-cycle of construction project: planning and control, coordination, and development. It can be clearly seen that the requirement of professional knowledge and ability for an outstanding project manager is constantly increasing with the modernization of construction project. As the growing scale and complexity of construction projects, the construction organizations needs more competent project managers, otherwise various problems will occur such as safety accidents (Wen 2014). (2) Practical Ability (C21) “Practical ability” is ranked second in importance and fourth in actual level, so it is important to pay more attention to this factor. Practical ability refers to the ability to apply the knowledge and skills mastered to solve practical problems. Practical ability is important as project managers need to solve many problems need on construction sites during the implementation of construction projects (Yang et al., 2017). Guo et al. (2019) noted that in the training of construction management professionals, theory and practice should be considered at the same time to improve the training performance. (3) Career Planning Ability (C24) “Career planning ability” ranked third in importance, but sixth for the actual competence of project managers. Career planning ability refers to the ability of individuals to establish action plans and programs to achieve career development goals based on their individual circumstances. The types and difficulties of projects are different, so are the related tasks undertaken by project managers. However, each project manager has different characteristics, and it is impossible for a project manager to know all aspects, and it is impossible for a project manager to be familiar in each field (Chen et al. 2017). After analyzing the necessary skills and responsibilities of traditional project managers and Industry 4.0 project managers, Ribeiro et al. (2021) concluded that there will be professional project managers related to each element of Industry 4.0 in the future. Therefore, it

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is essential for project managers to have good planning skills and to position themselves according to their skills and the requirements of important professional organizations. Ekrot et al. (2016) found that the development perspective of project managers is positively correlated with the maintenance of project management ability, while managers without career planning ability tend to be weak in ability. It further demonstrates that career planning ability is an indispensable part of career competency both from the perspective of the needs of the development of industry and the needs of the development of job seekers themselves.

4 Conclusion On the basis of literature review, this paper constructs an index system of professional competency of construction project managers with 3 dimensions and 7 indicators. Using the questionnaire survey method for data collection and IPA data analysis method, the importance of the factors and the actual level of professional competency of project managers in the Chinese construction industry were evaluated and compared. The results show that the perceived actual levels of professional competency are under their corresponding importance levels. This means that, in general, there is still room for improvement of the professional competency of project managers in the Chinese construction industry. Among the three dimensions, “knowledge” has the largest gap (0.406) measured in mean values, which should be paid the most attention. Among the seven corresponding factors, “professional knowledge and ability”, “practice ability” and “career planning ability” have the most significant differences between the importance and actual level, which need to be improved by Chinese project managers. The increasing complexity of modern construction projects provides more opportunities for project managers to demonstrate their capabilities, but they also need to timely identify their individual capabilities and improve them continuously to better cope with the changes in their career. The results of this paper will help to understand the professional competency level of project managers in the Chinese construction industry and take effective measures to improve the weak areas. However, because the survey is mainly conducted in the Yangtze River Delta, it cannot represent the overall situation of professional competency of construction project managers in the Chinese construction industry. In addition, the comparison between importance and actual level of competency factors is in a exploratory form. A low degree of importance does not necessarily mean that the particular factor can be ignored in the practice.

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Study on Collaborative Development Planning of Airport and City Guangtao Zhang(B) School of Southwest Petroleum University, Chengdu, China [email protected]

Abstract. As an important transportation hub in the development of the city, the regional development pattern of the airport is an important indicator of the city’s economy. The formation of corridor development zone between the airport and the city will gradually become the new development area of the city’s economy. The development of the airport has resulted in traffic congestion in the surrounding area, residential noise, and land use restrictions. This paper will explain how to effectively deploy the contradiction between airport development and urban development from two aspects of noise and airport operation efficiency and demonstrate it through practical cases. Keywords: Airport · City planning · Urban planning · Location analysis · Air Traffic flow · Airway

1 Introduction to Urban and Airport Development At the present stage, China has entered the stage of rapid urbanization. The multi-polar development trend of urban agglomeration such as Beijing-Tianjin-Hebei, Yangtze River Delta and Greater Bay Area has emerged in Chinese cities, gradually driving the western Chengdu-Chongqing Economic circle, Xi ‘an, Urumqi and other cities to build regional transportation hubs, so as to promote the national economic construction. Airport construction is certainly one of the most effective ways to build urban agglomerations or large urban transportation hubs. The airport plays an irreplaceable role in the transportation hub with its high speed and long-distance transportation, so the construction of the airport plays a pivotal role in the development and construction of the city. Transportation hub layout is also an important work in urban planning. How to make transportation hub efficiently provide convenient platform for passenger transport and freight transportation, combine with urban planning, comprehensively and intensively develop urban industries, rationally and effectively use land resources, and make urban transportation match the rapid development of urban economy [9]. Due to the development of the city, the airport will gradually increase the flow of flights, and then the flow of people will also increase, which will lead to the airport surrounding industries including logistics, bus, subway, and other service industries will also increase their business volume. Because of its own human traffic turnover and its irreplaceability and other advantages of related industries, the airport has greatly © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 1485–1498, 2023. https://doi.org/10.1007/978-981-99-3626-7_115

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driven the regional economy around the airport. With the development of the city, the airport area will be formed around the airport, in which logistics, aviation food, jet fuel, aircraft manufacturing, aircraft repair and other advantageous industries are concentrated here, and the airport regional economy is bound to develop rapidly [10]. However, airport development has a great impact on the traffic, land use and ecological damage in the surrounding areas. How to adjust the contradiction between airport development and urban development has become an important problem in many cities at this stage. FAA mentioned in AIP [1] that the biggest contradiction between airport and urban development is the problem of noise. Many old airports in China must be relocated or built because of the huge impact of noise on surrounding residents. U.S. reconstruction engineering for Boston airport [2] because of its huge airport flight delays, caused using the airport have a huge urban resident, in it’s at the same time improve the flight delays, involving airport reconstruction, a series of problems such as land requisition again, therefore, the airport site selection and the airport around the importance of sustainable development planning for the airport late. In this paper, strategic suggestions are put forward in the site selection of the airport and the development of the surrounding area of the airport in the later stage, which can minimize a series of adverse effects such as flight delay and aircraft noise after the operation of the airport and can predict the contradiction between the airport and urban development.

2 Description of Urban and Airport Development Conflicts 2.1 Noise Impact Analysis The development of the city depends on the transportation facilities, in the vicinity of the main road, depending on favorable transportation resources, economic development, forming a new type of economic mixed area. The development of the whole city also depends on convenient and reasonable transportation facilities, and the rationality of the layout of transportation facilities has a profound impact on the development of the city. As an important transportation hub in urban development, the economic development of the airport is an important indicator of urban economy. The corridor development zone formed by the airport and the city will gradually become a new development area of urban economy. However, the frequent takeoff and landing of aircraft over the airport lead to huge noise caused by aircraft. The following Fig. 1 [3] shows the impact range of various types of aircraft noise. The Fig. 1 shows the noise range of each type of aircraft obtained by the FAA through flight tests. As shown in Fig. 1, the different aircraft types are listed: Boeing 737-300; Boeing 727-EM1; Boeing 757PW; Douglas DC8-73 model; Boeing 747-200 aircraft during flight noise range. In addition, Fig. 1 shows that the aircraft is 4 nautical miles away from the runway entrance in the landing stage and 6 nautical miles away from the runway head in the takeoff and climbing stage, which have a great impact on ground noise. As shown in the figure, the plane is closest to the ground during take-off and landing, which has the greatest impact on the ground noise. The following Fig. 2 [3] shows the comfort evaluation of noise in various types of living places. As shown in the figure, residential areas are highly sensitive to noise. When the noise decibel is above 65, people will generally feel uncomfortable. Industrial parks also feel uncomfortable when the

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Fig. 1. Influence range of different models

decibel level reaches more than 65. The tolerance of entertainment places to noise has been strengthened, most of them can accept the noise decibel reached 75 states; Commercial, industrial and agricultural land can generally accept noise above 75 decibels. Aircraft during take-off climb close to the ground, the sensitive area with the ground noise decibels than residence, industrial park, the range of entertainment can accept, so the rational planning of land surrounding the airport, the airport perimeter building facilities from aircraft noise, relieve the contradictions of city and airport development, the direction, and the goal of overall urban planning. 2.2 Analysis of Flight Delay Factors During Airport Operation Due to the development of cities, more and more people will take planes, which will lead to the growth of flight volume. With the growth of flight volume, the service volume of airports will also increase, and most airports will be saturated. Once the airport enters the state of saturation operation, the operation efficiency of the airport will decrease, including a series of operational efficiency reduction of ground service, logistics transformation, flight operation and so on. A little special situation will lead to flight delay, and eventually it will enter a vicious circle, resulting in high flight delay. However, if an airport wants to expand its operating capacity once it is in a high saturated operating state, it needs to expand its operating space. However, with the development of airports, cities are also developing, and land around airports is in short supply. At present, most of the land around old airports in China cannot be used for airport development. The operating cost of airports increases year by year, leading to the relocation and abandonment of many inner-city airports. With the growing demand for civil aviation in China, the contradiction between inner city airports will become more

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Fig. 2. The comfort evaluation of noise in various types of living places

and more obvious soon. How to manage the development contradiction between the two sides can be summarized as the following two points: Reasonable analysis of air traffic situation in the early stage of airport site selection, reasonable planning, and utilization of land around the airport during airport operation are the key to solve this problem. 2.3 Study on the Strategy of Conflict Allocation Between City and Airport Development In terms of time, the contradictory deployment of airport and city development can be divided into pre-deployment and post-deployment, and the overall planning of airport and city development can be carried out in the early stage of airport site selection, and the coordination of airport and city to improve the development planning during airport operation. The pre-planning stage of the airport is the best period for collaborative planning between the city and the airport. Choosing the appropriate site as far as possible will be conducive to the comprehensive development of the city and the airport and bring considerable economic benefits to the city. During the operation of the airport, the planning communication between the airport and the city should be enhanced, and the coordinated development should be carried out under the framework of territorial space and multi-plan integration. This paper analyzes the conflict between deployment cities and airport development in the stage of airport site selection and puts forward strategic suggestions.

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3 A Study on Conflict Management Strategies for Urban and Airport Development In terms of time, the contradictory deployment of airport and city development can be divided into pre-deployment and post-deployment, and the overall planning of airport and city development can be carried out in the early stage of airport site selection, and the coordination of airport and city to improve the development planning during airport operation. The stage of airport pre-planning is the best period for collaborative planning between the city and the airport. Choosing the appropriate site as far as possible will be conducive to the comprehensive development of the city and the airport and bring considerable economic benefits to the city. During the operation of the airport, the planning communication between the airport and the city should be enhanced, and the coordinated development should be carried out under the framework of territorial space and multi-plan integration. 3.1 Noise Contradiction Allocation Strategy Between City and Airport Development in Airport Site Selection Stage 3.1.1 Analysis of Noise Affected Areas in Urban and Airport Development With the gradual development of the city, the flight volume of the airport gradually increases, and the impact of airport noise on Japanese dramas is obvious. The noise affected area outside the airport is the main direction for aircraft to take off and land along the airport runway. The corridor effect plays a role in the development of city and airport. Relying on the advantages of convenient transportation within the connection range between city and airport, most cities will take the connection between city center and airport as the urban development area. How to make such areas avoid the influence of airport noise? In the stage of airport site selection, the preferred site will minimize the contradiction brought by the development of the city and the airport. 3.1.2 The Strategy of City and Airport Development Contradiction in Airport Site Selection Stage Aircraft need to try to keep the wind to take off and land in order to gain as large as possible lift guarantee the safety of the aircraft in low altitude, the airport site selection stage is on the basis of the various plans to choose site meteorological information for a long time, push the show site is roughly constant wind and other meteorological information related to the flight, so each site can determine the main direction of the runway. Based on obtaining the main direction of the runway at each site to be selected, the approximate takeoff and landing area of the aircraft in the site can be measured, which is naturally the area closest to the ground, and the area with the greatest impact on urban noise. The connecting area between city and airport is the golden area for the economic development of city and airport, which should be avoided as much as possible to coincide with the takeoff and landing area of aircraft. In the stage of airport site selection, all factors can be considered comprehensively while paying more attention to the contradictions caused by this factor to the long-term development of city and airport.

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3.1.3 Airport and City Location Analysis of Copenhagen Airport in Denmark Copenhagen Airport, Denmark, is located about 8 km south of Copenhagen city center. It has a long history, but its service quality is second to none in Europe. After many renovations, it has become one of the four major airports in Europe. As shown in Fig. 3, although Copenhagen Airport is very close to the city, it has become an important transportation hub in the region due to its unique position in Scandinavia, which carries a huge number of flights. At close range the airport to the city, the city center and airport nest area has a larger building density, but the airport agency perfect landing direction away from the urban development areas, obviously the airport agency along the stretching direction is the most serious areas, aircraft noise around airport almost no residential buildings along the direction of the runway, buildings are rarely, but cultivated land and vegetation. As shown in Fig. 4, the noise area of Copenhagen is also near the airport where few people live. Up to now, there are a large range of cultivated land and vegetation areas around the airport, which has reserved enough space for airport reconstruction and expansion. However, in the south and north of the airport, there are more buildings and more obvious lights at night. This area not only avoids the influence of airport noise, but also is close to the airport. Such areas can provide better living places for airport staff and provide better services for the airport.

: Copenhagen Airport main takeoff and landing directions : The center aero of Copenhagen city center

Fig. 3. Location map of Copenhagen Airport and city

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Runway main take-off and

Fig. 4. Map of noise sensitive areas in Copenhagen

Copenhagen has always been recognized as the “Capital of Design”, “fairy tale kingdom” and “Livable city” in Europe and even the world. The city plans and lays out urban transportation, architecture, industry, agriculture, and other livelihood facilities on a humanized scale [6]. Copenhagen based on overall urban regional planning goal, coordinated development in different regions to establish a complete system of urban and rural areas, to prevent urban over-expansion cultivated land, make the whole city under the condition of intensive for reasonable development, the government actively coordinated stakeholders’ conflict of interest is in the development of the city most people at the same time guarantee the collective interests [7]. In the heart of the city of Copenhagen has high building density, urban advocates public travel, complete the completion of the entire city of bike lanes, convenient travel needs of people at the same time, greatly saving street area of the car, make the city closer together, each business body commodity denser show the people of the city, make people choose a more diverse selection, it takes less time to buy things [8]. Downtown Copenhagen downtown intensive and compact planning and development, making the surrounding area of farmland, forests, lakes and swamps to protect ecological environment, is each related interest group contradictions in the urban development to further ease, and the boundary of the airport in the urban area, by the city economic development along with the increase of the flights are to minimize noise pollution to the surrounding areas, The contradiction between the coordinated development of the city and the airport can be greatly alleviated. At the same time, a large area of land can be reserved for the airport to expand the capacity, increase the flight volume, and provide a solid foundation for the new runway construction.

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3.2 Analysis of Allocation Strategies for Urban and Airport Development Affected by Air Traffic Operation Efficiency Urban development will lead to an increase in flight volume. Many urban airports in China are already overloaded, but most of the old airports are facing a shortage of surrounding land and cannot be expanded any more. The site selection of many new airports, such as Shanghai Pudong Airport [4], considers the expansion of the airport, reserving a large amount of land for expansion of the airport, and retaining a lot of permanent farmland and ecological habitats around the airport, without excessive commercial development. The intensive planning of the surrounding land during airport operation can effectively solve the contradiction of saturation of the operating capacity caused by the development of the city and the airport and win a large amount of land for expansion around the airport [13]. But land resources are always limited, the airport is not likely to an unlimited expansion, but because of city population, economy, energy, trade expansion of many factors, still growing, in addition to consider the rational development of land from the air traffic efficiency should also find breakthrough, effectively ease the city and the airport development in terms of operating efficiency to both sides of the conflict, The efficient air traffic environment is undoubtedly one of the most effective means to adjust the contradiction between urban and airport development. 3.2.1 Take Chongqing Jiangbei Airport a day Flight for Example After taking off from the airport, the aircraft joins the air route along the established departure route to the destination airport and joins the incoming route over the destination airport to land at last. The process is basically to fly along the fixed route. First, flights are divided into scheduled flights and non-scheduled flights. Scheduled flights refer to flights that will be flown over a long period of time, while non-scheduled flights refer to new flights added within a short period of time. If there is no major event in the daily flight, it is relatively stable in a quarter, that is, the daily flight in one place will not change much basically. Therefore, it is of practical significance to extract the situation of daily flight for analysis. The density of aircraft flow in the airspace over which an airport is located can be obtained according to the number of aircraft flights and the flight distance in the daily route. Different routes will have different aircraft densities, and the flight densities in each small area of the airspace will also be different. On August 22, 2022, according to Chongqing jiangbei airport flight dynamic as shown, the plane into the end zone of Chongqing from different directions, all directions of the plane along the route in the airspace, enter the terminal to enter the port routes, Chongqing jiangbei airport flights also fly out of the departure route into the airspace in the terminal area en-route to a destination. The number of aircraft of each inbound and outbound route in the terminal area on that day can be summarized as follows: On August 22, 2002, the number of departing flights from Chongqing Jiangbei Airport was 268. Departing east, take off to waypoint GUTVI to join the airspace middle route, number of aircraft on that day is 96

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Departing southbound, take off to waypoint UNRIX to join the mid-air route. Number of aircraft for the day is 93 Departing west, take off to the waypoint PINAB to join the air route. The number of aircraft on that day is 8 Departing northbound, take off to the waypoint SOSLI to join the air route, the number of aircraft that day is 71 On August 22, 2002, the number of inbound flights at Chongqing Jiangbei Airport was 276. From the waypoint OPAMU to the terminal control area of Chongqing, the number of aircraft on that day was 98 Southbound port, from waypoint QJG into Chongqing terminal control area, the number of aircraft on that day: 94 Westbound and northbound inbound from the waypoint AKREB into the Restart Terminal Control Area, number of aircraft that day: 93 At present, China’s civil aviation operation is in the state of epidemic prevention and control, which cannot reflect the normal air traffic flow. This quantitative method can be used as a theoretical analysis method. Chart 5 and Chrat 6 respectively show the inbound and outbound flights of Chongqing on August 22, 2022 (Figs. 5, 6).

waypoint

Departure direction

Flight volume

GUTVI

east

96

UNRIX

south

93

PINAB

west

8

SOSLI

north

71

Fig. 5. Distribution of departures from Chongqing in August 22, 2022

3.2.2 Analysis of the Proposed Site with Air Traffic Density Chongqing newly built Airport to choose many sites, Bishan, Tongliang, Tongnan, Fuling, Nanchuan County and so on. The inbound and outbound air routes in the flight terminal area over Chongqing are shown in the Fig. 7. According to the flight data of a

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waypoint

Arrival direction

Flight volume

OPAMU

east

98

QJG

south

94

AKREB

West,north

93

Fig. 6. Distribution of arrival from Chongqing in August 22, 2022

certain day, the width of each inbound and outbound air route is determined according to the proportion of the number of aircraft on each air route on a certain day to show the size of aircraft flow on the air route. Main use of Chongqing airport departure runway north direction, sometimes it will because of the wind changes into a take-off to the south, based on empirical data, in the process of long-term experience, the south airport runway landing time of about a third of the north, so you can see in figure track width of the runway of the airport landing south accounted for a quarter of total north landing for three-quarters of total amount.

: runway : newly built airport site : arrival airway : departure airway

Fig. 7. Chongqing newly built airport site with airway traffic density analysis

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As shown in the Fig. 7, the proposed site is far away from the dense area of air traffic, but close to the city center. As a new airport site, sufficient space can be reserved for the air traffic control department to plan its airspace, so that the two large airports can live in harmony in the airspace and greatly increase the operation efficiency. There is also enough room for the deployment of air conflicts for the long-term growth of air traffic flow. The site is closer to the center of Chongqing than other areas located in the southwest of Chongqing, which provides a reliable site for the layout of the new airport urban transportation hub. 3.2.3 Suggestions on Improving Operation Efficiency in Airport Site Selection Obvious figure insulating medium density larger areas concentrated in the eastern area of Chongqing, in the process of site selection to choose suitable distance such highdensity airspace, air traffic controllers will have wider deployment of airspace, air traffic department for the new airport planning new departure routes can also have more ample space, natural air traffic complexity will be lower. In the process of airport site selection, the proposed site and surrounding airports are also very important factors to measure. The farther the site is from the high-traffic airport, the larger the safety interval between planes, the larger the space of conflict between air traffic control deployment, and the lower the complexity of air traffic deployment conflict. The proposed site is far away from high-density airspace and high-traffic airports, which can significantly reduce the difficulty for air traffic control authorities to deploy air-aircraft conflicts and plan airspace. With the development of the city, flight flow increases, and air traffic conflicts become obvious. Reserving sufficient space in the site selection of the airport can better optimize the airspace structure, improve the operation efficiency of air traffic, and then reduce the impact of the contradiction between the airport and urban development due to the decrease of operation efficiency.

4 Strategy for Urban and Airport Development During Airport Operation Airport planning is incorporated into the integrated urban planning, and land is rationally utilized by means of integrated urban planning such as territorial space and regional planning. Fully measure the urban traffic density, for the crowded city to build efficient rail transit, road traffic to relieve the pressure of large urban traffic. Beijing Daxing [5] airport site selection is a typical example for urban traffic stress relief, most traffic is concentrated in the north of the Forbidden City in Beijing area, so the selection of the large airports in southern cities, such as groups, Langfang city and building group south to build rail and highway traffic, evacuation city dense traffic. The development of airports should give play to their advantages as urban transportation hubs to facilitate people’s travel and improve the operation efficiency of urban transportation. Airport development should be from the surrounding area development, and surrounding towns group development, surrounding towns provide plenty of service for the airport staff and service facilities of the airport at the same time also can provide convenient transportation service for surrounding towns, advantage, both sides can get into

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full play, disadvantage also can be complementary, and promote the overall development of a larger scope. Large airports at home and abroad are closely connected with the surrounding towns, and the quality of the interaction between the two sides directly affects the comprehensive development of the airport and the city. Therefore, the development of the city and the airport needs to deal with the overall coordination problem of the connected areas of the two sides, complement each other in comprehensive development, and achieve a win-win situation. Around the airport should pay more attention to the sustainable development of land use planning, try to consider to the airport for urban transport hub status, a reasonable forecast city flight increment for a long time, try to set aside enough land for expansion of the airport development needs, effective reasonable development land surrounding the airport, as shown in Fig. 2, the surrounding land development and utilization of the airport as far as possible thinking of building noise sensitivity low building, For example, factories, industrial parks and schools should avoid buildings with high noise sensitivity such as new residential and living areas. When planning airspace, the airport can consider the growth trend of airport operation flow, make a reasonable forecast, fully reserve space for the growth of airport flights, coordinate the surrounding airspace users, and jointly plan and use airspace reasonably. According to difference planning, at present, most large and medium-sized cities face two new airports, many Chinese cities to become “one city two airport” model, in this kind of mode, can reasonably arrange flight schedules, almost night city airport, will be moved to the new airport city night flight, reduce the old airport noise impact on urban residents. Shanghai Hongqiao Airport takes advantage of the operation of Shanghai Pudong International Airport, takes full account of its relatively close to the urban area, and stops night flight operation without special circumstances, which sets an example for the harmonious development of the city and the airport. Many international airports also integrate the flight operation advantages of “two airports in one city” or “multiple airports in one city”, fully and reasonably arrange the flight operation time, and alleviate the contradiction brought by the development of cities and airports.

5 Conclusions Most of the traffic jams near the airport, flight delays, the airport nearby residents reflect the noise etc. Examples show that the best airport site selection should be a large space, where land price is low, if there are any flight delays, the problem such as noise, traffic congestion, can easily build new modern facilities of solution, and residents complain that it will reduce. But a large airport requires labor from at least one small or mediumsized town, and it is impossible to find that many people in “a large empty lot.” Even if the airport is on a large open space, how to make the city and the airport develop together and make the city prosperous is by no means a simple issue. Many contradictions caused by the development of cities and airports need to be sorted out carefully. It is necessary to meet the integration of all elements on the ground and consider all elements in the air, and then connect all elements together for unified evaluation and planning, to make decision-making suggestions for the development of cities and airports and make them sustainable.

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With the development of the city, the number of flights in the airport will gradually increase as the number of people taking planes increases. When the capacity of the airport reaches its limit, the airport needs to increase the runway or build more convenient transportation hubs in the terminal to evacuate more and more people. However, most of the airport sites in China have a long history and are close to the city center. The economic demand after the reform and opening has caused the airport periphery to be overdeveloped. The infill planning around most airports and the development of a lot of commercial land have resulted in the convenient traffic advantages around airports, but the noise also bothers the residents there, causing a lot of contradiction between the city and the airport. There is no reserved land around the airport, so it cannot add runway, subway, high-speed rail, and other facilities to expand the airport capacity. The airport cannot timely expand the flight capacity, which will greatly reduce the operation efficiency of the airport. Most domestic and foreign research on the collaborative development of cities and airports focus on urban planning. This paper makes strategic analysis and suggestions on the collaborative development planning of cities and airports from the perspective of air elements. Faced with the first problem of ambient noise caused by the airport, this paper combined with the Danish airport case, proposed that the airport noise affected area should be considered in the airport site selection planning process, and concluded that both sides of the runway end are the areas with the greatest noise impact, and this area should avoid the development area where the city and the airport form a corridor. This factor should be combined with air meteorological observation and urban terrain in airport site selection planning. Facing a second airport efficiency, this article put forward at the beginning of the airport site selection plan should locate the area of air traffic flow is low, can be evacuated air traffic density, and rational use of airspace, air traffic controllers also facilitate direct air traffic, pilots are also more cooperate and security, in the process of analysis to the GIS tools such as system analysis. Due to the length of the paper, there is no in-depth analysis of quantitative indicators such as air traffic density, airspace integration degree and selection degree in airport site selection. It is hoped that the algorithm will be further deduced in future studies.

References 1. Airport Improvement Program Handbook, U.S. Department of transportation, Federal aviation administration, effective date: February 26, 2019 2. Airside improvements planning project logan international airport Boston, Massachusetts, Department of transportation, Federal aviation administration, New England Region (2002) 3. Land use compatibility and airport, Federal aviation administration (2019) 4. Provisions on the Administration of Civil Airports, Civil aviation administration of China (2012) 5. Liu, W.J.: Beijing’s new airport and urban regional development. J. Beijing Plan. Constr. 04, 39–43 (2012) 6. Zhao, S.Q., Liu, S.Y., Dang, L.Q.: A theoretical study of humanized city construction - a case study of Copenhagen. J. Urban Architecture 18, 81–83 (2022) 7. Wang, Z.C., Ma, L.S., Emir, S.: Copenhagen livable concept and city building model. J. Residential Real Estate 11, 75–80 (2022)

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8. Jing, H.Y, Song, Y.J. A quantitative study on spatial compactness of urban center based on GIS – a case study of Copenhagen. J. Dynamic (Eco-city Green Build.). 2016(03), 52–58 9. Sao, C.F. Layout planning and functional design of urban agglomeration comprehensive transportation hub. In: Bejing: Publishing House of Electronics Industry (2021). ISBN: 978– 7–1214–2448–9 10. Liu, W.J.: Portal-type transportation hub and urban (cluster) spatial planning. J. Civ. Aviat. Manage. 10, 33–37 (2017) 11. Alvaro, R.S., Luis, R.A.: A preliminary framework for managing airport capacity and demand from an economic perspective. J. Aircr. Eng. Aerosp. Technol. 94(9), 1463–1480 (2022) 12. Song, J.W.: Analysis on the method of airport land function Expansion under the influence of airport noise: a case study of the planning of the core area of Chongqing airport economic demonstration zone. J. Eng. Constr. Des. S1, 65–69 (2021) 13. Lazaro, F. B.: Malaga costa del sol airport and its new conceptualization of hinterland. Department of Economics and Business Administration, University of Malaga, Malaga, Spain (2021)

Research on the Causes of Safety Accidents in Super High-Rise Buildings—Empirical Analysis Based on Bivariate Probit Model Bing Zhang1 and Qian Lu2(B) 1 Department of Engineering Management, Yangzhou University, Yangzhou, China 2 School of Architectural Science and Engineering, Yangzhou University, Yangzhou, China

[email protected]

Abstract. In this paper, the causes of super high-rise safety accidents are analyzed based on the Bivariate Probit model with 185 typical accident cases in recent years. The accident information is preprocessed and classified from three dimensions, i.e. accident characteristics, project characteristics and organizational characteristics. The empirical results indicate that there is a positive correlation between the fatal accidents and the economic loss accidents, and the 12 selected variables such as accident type, accident hazard source, accident occurrence stage, building purpose, project area, and whether the company has construction experience in super high-rise buildings, have significant effects on the occurrence of the two types of accidents. Keywords: Super High-rise Building · Safety Accidents · Bivariate Probit Model · Case Library

1 Introduction Due to the particularity of the structure and working environment, the development of super high-rise buildings is accompanied by huge safety risks. Although it plays an important role in the global and national economy, it has a “mixed reputation” for its high accident rate and danger. Statistics from the National Construction Safety Supervision Information System show that 5 of the 23 large and above construction safety accidents that occurred in 2019, China, were super high-rise building safety accidents, resulting in 32.71% of the total death toll. In the construction process of super high-rise buildings, the accident rate and damage degree caused by high-altitude construction and deep foundation pit excavation are much higher than in other types of buildings, and the falling is also a continuing threat (Li et al. 2018). It leads to very serious consequences of super high-rise building safety accidents, not only threatening public safety but also having a great negative impact on society. Therefore, it is of great significance to study the factors influencing the safety accidents of super high-rise buildings and improve their safety management level of it. Although it has been recognized that effective safety management should be carried out on super high-rise buildings, there are still few achievements in the whole research © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 1499–1521, 2023. https://doi.org/10.1007/978-981-99-3626-7_116

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field, most construction companies still rely on intuition and oral analysis when designing safety management schemes and preventing safety accidents (Zhan 2021). It is still a great challenge in the whole industry for managers to improve safety management levels and reduce the accident rate. At present, studies on the cause of safety accidents in high-rise buildings mainly focus on risk source identification based on accident causation theory and risk assessment based on fuzzy evaluation network, fault tree analysis, Bayesian network and analytic hierarchy process (Fang and Ma 2021). Questionnaires and expert judgment are the common ways to obtain subjective data for qualitative research. For example, Lu and Yuan (2020) applied the analytic hierarchy process (AHP) to analyze the risk factors of safety accidents in the construction of super high-rise buildings and build an evaluation model. Fang and Ma (2021) constructed the risk load measurement metrics of super high-rise buildings’ construction safety accidents by literature research and expert investigation and proposed a safety risk assessment method based on reliability theory. Despite these studies focusing on the influencing factors of accidents, there are few quantitative analyses of the influence of accident severity based on real accident case data, and the interaction between factors is ignored, making it difficult to clarify the influence differences of various risk factors on accident severity. However, numerous studies have demonstrated that the econometric models can effectively analyze the causes of accidents based on the microscopic data of real cases, screen and extract the influencing factors of accident severity. It can also explore the influence differences between accident inducing factors and accident severity from the perspective of quantitative analysis. For instance, Cai et al. (2021) identified the factors influencing the severity of ship accidents and analyzed the specific influence degree of each factor based on the data on Marine ship accidents. And researches on determinants of traffic accident casualty severity based on traffic accident data, considering with different injury degrees (Lu, et al. 2019) and collision types (Hu, et al. 2018). In addition, the study on influencing factors of engineering corruption severity based on typical corruption case data by Li et al. (2013) is carried out, and so on. Given this, considering the extensive application of econometric models in the laws of accident cause, this study applied an econometric model to conduct empirical and quantitative analysis on the severity and influencing factors of safety accidents in super high-rise buildings and explored the influence laws of safety accident severity of super high-rise buildings. In addition, the severity of safety accidents in super high-rise buildings is usually characterized and divided by the number of casualties or direct economic losses caused by the accident. However, most existing studies only consider the single index of accident severity as a dependent variable for cause analysis, while ignoring the differences and connection of accident severity between primary and secondary responsible persons (Peng and Wang 2022). The bivariate probit model can not only establish the quantitative relationship between multiple independent variables and two dependent variables but also considers the correlation between two dependent variables (Fu et al. 2019). So, in this study, we constructed an accident case and accident characteristic index based on previous super high-rise buildings safety accident data, selected deaths accidents and economic loss of accident as dependent variables, and explored the risk factors of two types of accidents of significance and the influence of impact by bivariate

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probit model, so as to better put forward safety management measures from key risk factors, and improve the safety management level of super high-rise buildings.

2 Literature Review and Hypothesis As we know, there are many factors affecting the safety accidents of super high-rise buildings. From the modern system safety theory, hazard sources of safety accidents mainly include unsafe behaviors of people and the unsafe states of objects, as well as the influence of organizations, the supervision of unsafe behaviors, the preconditions of unsafe behaviors, and so on. The causation mechanism is very complex. At present, although there are differences on the perspectives of researchers regarding the influencing factors of safety accidents in super high-rise buildings are differently, they are not “discord”. The objectives of the above researches are highly consistent, which means that they all aim at finding the incentives and hidden dangers that affect the occurrence of safety accidents in super high-rise buildings from various perspectives, deeply exploring the causation mechanism of safety accidents in super high-rise buildings and discussing how to effectively improve the safety management level of super high-rise buildings and prevent the frequent recurrence of accidents. In previous studies, safety accidents in super high-rise buildings were often characterized by a series of discrete characteristic variables. Many researchers focused on the micro factors that induce accidents. However, due to the difficulty in obtaining the micro index data of safety accidents, some scholars proposed that the analysis of influencing factors of safety accidents should not only contain the micro factors that affect the occurrence of accidents (Trucco et al. 2008), but also pay attention to the engineering characteristics accident characteristics and other macro-level factors. Therefore, in this research, we combined macro indicators and microdata to analyze the influencing factors of fatal accidents and serious economic loss accidents in super high-rise building safety accidents from three aspects: safety accident characteristics, engineering project characteristics and organizational behavior characteristics. Meanwhile, we also put forward some corresponding assumptions. (1) Accident characteristics In previous studies, accident characteristics include accident type, accident hazard source and accident occurrence stage and so on, which mainly reflect the basic characteristics of the accident itself. In studies that focus on the main causes of safety accidents of super high-rise buildings, the researchers divided the main accident types into fire, falling from a height, foundation pit collapse, and object strike (Larsson and Field 2002), and concluded that different influencing factors of safety accidents of super high-rise buildings are different (Yi and Langford 2006), different influencing factors have different degrees of impact on the severity of accidents. In terms of accident hazard identification, most studies focused on micro factors such as the illegal operation of construction personnel and construction machinery failure (De Oa and LóPEZ, 2013). Cao and Goh (2019) took human factors as the research subject, and their qualitative analysis showed that the main causes of high-rise building accidents are lack of training of workers, unskilled workers, unsafe behaviors of workers and insufficient understanding of hazardous activities at the work site. At the same time, it is determined that human factors

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have the greatest impact on the construction of the safety management system (Ismail, et al. 2012), and other common accident hazard sources also include material factors, management factors and environmental factors (Hu et al. 2017). Many studies also tend to analyze and identify the influencing factors of building safety from different construction stages, extract risk factors from design and construction, decoration, operation and maintenance, building demolition and other stages, and then build a risk evaluation index system by using structural equation model (Li et al. 2019), fuzzy fault tree, HSE (Chang and Wu 2018) and other methods, and finally identified key risk factors. Based on this, we put forward the following assumptions: H1: different accident types have different impacts on fatal accidents and serious economic loss accidents; the human factor has the greatest impact on the two types of accidents among the several types of accident hazard sources; the influence of the accident stage on the two kinds of accidents is regular. (2) Project characteristics To a certain extent, the project characteristics reflect the disaster-forming environment of safety accidents. These characteristics include building height, building application, city location and region location, engineering geological conditions, etc. The Code for Fire Prevention Design of High-rise Civil Buildings points out that the higher the building is, the more difficult it is to evacuate people when an accident occurs, and the greater the possibility of serious accidents. To some degree, building purpose reflects the complexity of building construction and the ability to deal with emergencies after accidents. Therefore, it is one of the important characteristics of a project. In the existing academic research, building application is mainly divided into three categories, namely residential buildings, comprehensive buildings, and commercial buildings. In addition, some other factors, such as the engineering geological conditions of the project location (Guo, 2013) and the region (Lei and Ji 2010), are listed as important indicators of project characteristics and project risk assessment. Meanwhile, Zhao et al. (2020) found that there is an imbalance in the space of construction safety accidents, that is, the occurrence rate and severity of safety accidents are different among different cities and regions. Therefore, we set up the following assumptions: H2: building height has a positive impact on the occurrence of fatal accidents and serious economic loss accidents; there are differences in the possibility of fatal accidents and serious economic losses in buildings of different applications; the soil quality of the city, region, and place where the super high-rise building project is located has a negative impact on fatal accidents and serious economic loss accidents. (3) Organizational characteristics Organizational characteristics mainly represent the basic situation of the accident organization unit and reflect the management ability of the organization to deal with the accident, including the qualification of the construction unit, the number of administrative penalties, the nature of the enterprise, the experience of handling super high-rise building safety accidents. Organizational management is a very important factor in the safety management and control of construction sites (Kwon et al. 2004). Large construction units with technical and financial support are often able to support the construction and safety management of super high-rise buildings (Sunindijo and Zou 1988). In this

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study, the organizational characteristics are characterized by the above four indicators. The qualification of the construction unit, the nature of the enterprise, and the number of administrative penalties mainly represent the organizational status of the construction unit, and the experience of super high-rise building safety accidents represents the ability to deal with accidents. Based on this, we set up the following assumptions: H3: the qualification of the construction unit is negatively correlated with the occurrence of the two types of accidents, that is, the higher the qualification, the lower the accident incidence; the number of administrative penalties is positively correlated with the occurrence of the two types of accidents; It significantly affects the occurrence of the two types of accidents that whether the enterprise is a central enterprise or not and whether there is safety accident experience in super high-rise buildings. As safety accidents of super high-rise buildings often result in serious economic losses and casualties, this paper also assumes that: H4: in safety accidents of super high-rise buildings, there is a positive correlation between serious economic loss accidents and fatal accidents.

3 Model Construction and Resolution 3.1 Variable Selection According to the content of literature review, we selected the number of accident deaths and direct economic losses as dependent variables, and selected three dimensions of accident characteristics, project characteristics and organizational characteristics as independent variables for regression analysis (Zhang, et al. 2019). In combination with the safety accident cases and accident investigation reports of super high-rise buildings, we also extracted the data containing these 15 variables and obtained the optional values of characterization attributes after classification and assignment, as shown in Table 1. Table 1. Variables in Econometric Models

accident severity

accident characteristics

Variables

Definitions

S_people

Classification of accident fatalities: 0–3 = 1; 4–10 = 2; 11–30 = 3; 31 and above = 4

S_money

Direct economic loss level: 0–50 thousand = 1; 51 thousand – 2 million = 2; 2.01–5 million = 3; 5.01–10 million = 4; more than 10 million = 5

A_type

Type of accident: fire = 1; fall from height = 2; collapse = 3; other types = 4

A_level

Accident level: general accident = 1; major accident = 2; major accident = 3; major accident = 4

A_source

Accident risk sources: human factor = 1; management factor = 2; environmental factor = 3; other factors = 4

A_stage

Accident stage: main construction stage = 1; decoration stage = 2; operation and maintenance stage = 3; other stages = 4

(continued)

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Project Features

Variables

Definitions

P_high

Building height: 100m to 200m = 1; 200 m to 400 m = 2; above 400 m =3

P_use

Building use: Residential = 1; Commercial = 2; Mixed-use = 3; Others =4

P_city

City level: Tier 1 & new Tier 1 = 1; Tier 2 = 2; Tier 3 = 3; Tier 4 = 4; Tier 5 = 5

P_geology

Soil condition: Class 1 soil = 1; Class 2 soil = 2; Class 3 soil = 3; Other soil quality = 4

P_area

Location: CBD core area = 1; urban area = 2; new area and suburbs = 3

organizational characteristics O_qualification Qualification of construction unit: special grade = 1; first grade = 2; second grade and below = 3 O_punishment

Number of administrative penalties: 0–10 times = 1; 11–30 times = 2; 31–100 times = 3; more than 100 times = 4

O_center

Whether it is a central enterprise: yes = 1; no = 2

O_experienced

Experience in super high-rise building construction: yes = 1; no = 2

Note: P_city refers to the State Council’s “Notice on Adjusting the Criteria for Urban Scale Division”; P_area refers to the regional division standards and development orientation of each city; P_geology refers to the research results of “Urban Underground Space Development and Utilization” of the Chinese Academy of Sciences; O_qualification refers to “Qualification Grade Standards for Construction Enterprises”; O_punishment data comes from the website of the Ministry of Housing and Urban-Rural Development

3.2 Research Design In order to effectively analyze the influence factors and influence degree of super highrise building safety accidents, we construct the model based on four steps. Firstly, in the first step, we collect the super high-rise building safety accident cases in recent years and preprocessed the data, and then, built the database of past accident cases to analyze the accident characteristics based on the data distribution. The second step is to extract the analysis indicators and influence factors based on the literature and test the selected indicators to determine the correlation of indicators and the applicability of the model. The third step is to select the discrete model and estimate the model according to the test results and identify the influential factors, then calculate the marginal effect and analyze the influence degree of each factor on the construction safety accidents. Finally, to ensure the accuracy of the model estimation results, we also change the model to test the robustness of the results. The process is shown in Fig. 1.

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Fig. 1. Research technology roadmap

3.3 Bivariate Probit Regression According to the National Regulations on Reporting, Investigation and Handling of Production Safety Accidents, the severity of production safety accidents is divided into four classes, which are special major accidents, major accidents, larger accidents, and general accidents, based on the casualties or direct economic losses caused by the accidents. At present, most studies only considered the severity of the accident, or a single index in the number of casualties and economic losses of the accident, as a dependent variable for cause analysis, ignoring the relationship and difference between the severity of primary and secondary liability accidents, and also ignoring the relationship and impact between the two types of accidents. At the same time, due to the contingency and particularity of safety accidents in super high-rise buildings and the information blockade of accident

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information by local governments, it is very easy to lead to occurrence of difficulties in accident data collection and small data sample size. Therefore, it is impossible to accurately obtain all the microdata of accident cases and to pay attention to the full sample of accidents, but only part of the published safety accident information and accident investigation reports can be accessed. Existing studies tend to equate part of the observable samples with all the actual accident samples by using a single variable model for estimation, so it is easy to cause estimation errors (Ma and Yang 2020). The bivariate probit regression model can not only analyze the correlation and significance between accident influence factors and the occurrence of safety accidents but also considered the correlation between the frequency of fatal accidents and economic loss accidents. It can effectively support the study of the cause analysis and impact degree of safety accidents in super high-rise buildings. To avoid the above situations, the bivariate probit model considering partial observability is selected in this study. The accident severity is divided into two parts: the number of casualties and the economic loss of the accident. The relationship between the fatal accidents and the serious economic loss accidents in the safety accidents of super high-rise buildings and the accident severity is empirically tested. 3.4 Model Solving The selected bivariate probit model is a joint model obtained by simultaneous equations of two binary dependent variables. The two equations have the same independent variables, which mean that if the results of the two dependent variables are not related, they can be regarded as two independent binary probit models. When the covariance of the error term is not equal to zero, it indicates that there is an interactive relationship between the two dependent variables. For the bivariate probit model, both equations are based on the basic form of the probit model:  Prob(Y = 1) =

βX

−∞



eβ X ϕ(t)dt =  1 + eβ X

(1)

where Y is the intention of accident occurrence, ϕ(t) is the cumulative distribution function of standard normal distribution, β is the parameter, and the explanatory variable X is each factor that affects the occurrence of accidents. The potential variables that cannot be observed are expressed as:   ∗ Y1 = β1 X1 + ε1 (2) Y2∗ = β2 X2 + ε2 where Y1∗ and Y2∗ are the unobservable latent variable, X1 and X2 are the influence factor vectors of safety fatal accidents and serious economic loss accidents of super high-rise buildings respectively, β1 and β2 are the coefficient vector to be estimated, ε1 and ε2 are the random disturbance term and follow a two-dimensional joint normal distribution, and the correlation coefficient between the two is ρ, which is defined by:       0 ε1 1ρ ∼N , (3) ε2 0 ρ1

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where ρ is the correlation coefficient of ε1 and ε2 , if ρ is Significant and equal to 0, indicating that the disturbance term is independent and uncorrelated. When Y1∗ > 0, it indicates that it is a fatal accident, the same as Y2∗ > 0 indicates that a serious economic loss accident has occurred. Therefore, Y1∗ and Y1 and Y2∗ and Y2 can be established by the following equations:  1 (fatal accident), Y∗1 > 0 (4) Y1 = 0 (no fatalities), others  1(Serious economic loss accident), Y∗2 > 0 Y2 = (5) 0(General economic loss accident), others For the dependent variables (Y1 , Y2 ) of the bivariate probit model, four results can be obtained, namely (1, 0), (0, 1), (0, 0), (1, 1), that is, fatal accidents and general economic loss accidents occur, only serious economic loss accidents occur, only general economic loss accidents occur, and both fatal accidents and serious economic loss accidents occur. In this study, the maximum likelihood estimation is used for regression. The probabilities of Y1 and Y2 observations can be expressed as: P(Observe = 1) = P(Y1 ∗ Y2 = 1) = P((Y2 = 0|Y1 = 1) ∗ P(Y1 = 1) = ϕ(β1 X1 , β2 X2 , ρ) P(Observe = 0) = P(Y1 ∗ Y2 = 0) = P((Y2 = 0|Y1 = 1) ∗P(Y1 = 0) + P(Y1 = 1) = 1 − ϕ(β1 X1 , β2 X2 , ρ)

3.5 Marginal Effect Since the selected discrete model is nonlinear, the β coefficient generated by the model estimation is not the true marginal effect of the variable. It can only represent the positive and negative cases of influence, but cannot effectively represent the degree of influence. So, the average marginal effect is used to estimate the impact degree of each independent variable on the accident. For the frequency m of a certain type of accident, when other independent variables remain unchanged, the average marginal effect of the independent variable X is: AME =

∂P(Y = m) ∂X

(6)

4 Results Analyses 4.1 Statistical Analyses 4.1.1 Data collection According to the Regulations on Reporting, Investigation and Handling of Production Safety Accidents and other relevant national laws of China, the solvation results of accidents, except those which should be kept secret according to law, shall be open access to the public by the government responsible for the investigation of accidents or the relevant

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departments and agencies authorized by the government. Meanwhile, safety accidents of super high-rise building construction tend to cause significant negative social impact, and their case information is also widely scattered on major news websites and media. Therefore, to ensure the authenticity and effectiveness of the past cases data of super high-rise building safety accidents, and to obtain and understand more comprehensive real accident information and real accident data, we mainly collected the information of super high-rise building safety accident cases and accident investigation reports published by the websites of the Ministry of Housing and Urban-Rural Development and the emergency management departments of all provinces and cities and other government information portals of China in recent years, and extensively consulted the information published by the news media as the case information supplement. The information from different channels focuses on different points, and there are significant differences in the detail in the information description. Therefore, this study processed the accident cases obtained from unofficial platforms and websites and then extracted valuable accident information. 4.1.2 Data Description Based on the principles of data collection, we collected the investigation reports and accident information of typical super high-rise building safety accidents in China since 2010, and extracted the values of the 2 dependent variables of accident casualties and direct economic losses from the accident investigation reports. In order to study the influencing factors of accidents from different levels, three kinds of data were constructed. One is the data of accident characteristics, including four indicators of accident type, accident grade, accident hazard source and accident occurrence stage, the data of the four indicators are extracted and classified according to the accident investigation report. The second is the project feature data, including building height, building use, the city and region where the project is located, and engineering geological conditions. Among them, the building height and use data are obtained from the accident investigation report. The city and region where the project located, and geological conditions data are classified and assigned with reference to the research results such as the Notice on Adjusting the City Size Classification Standards and the Development and Utilization of Urban Underground Space. The third is the data of organization characteristics, including the qualification of the construction unit, the number of administrative penalties, the nature of the enterprise, and whether there is experience in high-rise building safety accidents. The data is from the website of the Ministry of Housing and Urban Rural Development. The data we collected mainly include four types of super high-rise building fire accidents, foundation pit collapse, and high falling accidents. During the collection process, case data lacking key information were removed, and the data were effectively classified, assigned, and processed. Based on this, we collected 206 typical accident cases since 2010, screened 21 cases lacking key information, and selected the remaining 185 effective cases for research and analysis.

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4.1.3 Descriptive Statistics To analyze the influence degree between each factor and the dependent variable death accident and economic loss accident, the discrete probit model is selected. Before regression analysis, the case index data screened above needs to be discretized. That is, the selected qualitative indicators and classification indicators are transformed into discrete indicators with values of 0 and 1 by introducing dummy variables, where A_type*, A_level*, O_experienced*, and other indicators are the reference benchmarks of corresponding indicators. The descriptive statistical results are shown in Table 2. Table 2. Descriptive statistical results of explanatory variables and dependent variables Variables

Describe

M

SD

Accident characteristics A_*

—

—

—

Accident type

A_type1

Fire accident: A_type1 = 1,others = 0

0.281

0.451

A_type2

Falling from height: 0.330 A_type2 = 1, others = 0

0.471

A_type3

Collapse accident: 0.124 A_type3 = 1, others = 0

0.331

A_type*

Others

—

—

A_level1

General accident: A_level1 = 1, others = 0

0.719

0.451

A_level2

Major accident: A_level2 = 1, others = 0

0.178

0.384

A_level3

Major accident: A_level3 = 1, others = 0

0.0811

0.274

A_level*

Catastrophic accident

—

—

A_source1

Human factor: A_source1 = 1, others =0

0.519

0.501

A_source2

Management factor: A_source2 = 1, others =0

0.330

0.471

A_source3

Environmental factors: A_source3 = 1, others =0

0.0108

0.104

A_source*

Others

—

Accident level

Accident hazard source

— (continued)

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Variables Accident stage

Describe

M

SD

A_stage1

Main construction stage: 0.562 A_stage1 = 1, others = 0

0.497

A_stage2

Decoration stage: A_ stage2 = 1, others = 0

0.157

0.365

A_stage3

Operation and maintenance phase: A_stage3 = 1, others = 0

0.232

0.424

A_stage*

Others

—

—

Project features P_*

—

—

—

Building height P_high1

The building height is 0.676 100 m to 200 m: P_high1 = 1, others = 0

0.469

Building use

City Level

Soil quality

Area

P_high*

Above 200m

—

—

P_use1

Building for residential use: P_use1 = 1, others =0

0.595

0.492

P_use2

Building for commercial 0.0865 use: P_use2 = 1, others =0

0.282

P_use3

Comprehensive building: P_use3 = 1, others = 0

0.409

0.211

P_use*

Industrial and other uses —

—

P_city1

First-tier cities: P_city1 = 1, others = 0

0.205

0.405

P_city*

Second-tier cities and others

—

—

P_geology1

Special soil: P_geology1 0.562 = 1, others = 0

0.497

P_geology*

Normal soil

—

P_area1

CBD core area: P_area1 0.427 = 1, others = 0

0.496

P_area2

Urban: P_area2 = 1, others = 0

0.514

0.501

P_area*

Suburbs

—

—

— (continued)

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Table 2. (continued) Variables

Describe

M

SD

Organizational characteristics O_*

—

—

—

Construction unit qualification

O_qualification1

Special grade and first grade: O_qualification1 = 1, others = 0

0.589

0.493

O_qualification*

Others

—

—

Administrative penalties

O_punishment1

The number of administrative penalties is 0–30 times: O_punishment1 = 1, others = 0

0.730

0.445

O_punishment*

Above 30

—

—

Center or not

O_center1

The construction unit is 0.232 a central enterprise: O_center1 = 1, others = 0

0.424

O_center*

No

0.670

0.471

O_experienced1

Yes: O_experience1 = 1, others = 0

0.589

0.493

O_experienced*

No

—

—

S_people1

Fatal accident: S_people1 = 1, others =0

0.616

0.488

Experienced

Accident severity S_* Fatal accident

S_people* Property damage incident S_money1

S_money*

No fatalities

—

—

Major property damage accident: S_money1 = 1, others = 0

0.259

0.440

General property damage incident

—

—

In terms of accident characteristics, the occurrence frequency of fire and falling from height is relatively high, accounting for 28.1% and 33% respectively. The main accident hazard sources are human factors and management factors, accounting for 84.9%. The main accident stage is the construction stage, accounting for more than half. According to the Regulations on Reporting, Investigation, and Handling of Production Safety Accidents, the collected accidents are mainly ordinary, accounting for 72.5%. In terms of project characteristics, 20.5% of super high-rise building projects are located in first-tier cities and new first-tier cities, and the regional level is mainly CBD and urban areas, accounting for 94.1%. That the construction and production of super

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high-rise buildings are inseparable from the economic level and development status of the city where they are located, the CBD area of cities with relatively rich economies is often a “blessed place” for super high-rise buildings. Under the two evaluation indicators of building height and building use, the proportion of super high-rise buildings with a height of 100 to 200 m is relatively large, and the proportion of buildings with a building use of residential and comprehensive buildings is more than 80%, which also reflects that with the continuous development of the national economy, super high-rise buildings are no longer limited to the development of landmark buildings, nor are they committed to challenging the “highest” height competition game, turn to the development of residential projects that conform to the local economic development level and population distribution, which is consistent with the “Notice of the Ministry of Emergency Management on Strengthening the Planning and Construction Management of Super High-rise Buildings” issued by the Ministry of Housing and Urban-Rural Development. In terms of organizational characteristics, the proportion of non-central enterprises is 67%, which is much higher than the 33% of central enterprises, and 76.8% of the units have had safety accident experience in super high-rise buildings before the accident project. Among the construction qualification evaluation indicators, the special grade and first-grade qualifications account for 58.9%, and the second-grade and below qualifications account for 41.1%. The number of administrative punishments received by the unit is 0 to 30, accounting for 73%. 4.1.4 Pearson Correlation Test The Pearson test was adopted for correlation analysis to measure the degree of correlation between the independent variables and the two types of accidents. The correlation heat map is shown in Fig. 2. In the figure, red represents positive correlation, blue represents negative correlation, and the depth of color represent the strength of correlation. The results show that the characteristic variables of accidents are strongly correlated with the occurrence of deadly accidents and serious economic losses accidents, and are positively correlated. There is still a certain degree of negative correlation between project characteristic variables and the occurrence of the two types of accidents, model estimation can be used to further analyze the impact degree. In addition, organizational characteristic variables have a weak correlation with the occurrence of the two types of accidents, which can be screened, eliminated, or replaced (Bie 2019). At the same time, correlation test results show that there are different degrees of correlation between the selected 13 independent variables, so it is necessary to conduct a multicollinearity test for variables before model regression.

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Fig. 2. Pearson correlation test results

4.2 Model Estimation Analyses 4.2.1 Multicollinearity Test To ensure the effective estimation of the model and the accuracy of the analysis results, the variance expansion coefficient (VIF) is used to test the multicollinearity of the explanatory variables. The results are shown in Table 3. The maximum VIF value is 1.75 and the minimum VIF value is 1.15, both of which are far less than 10. There is no multicollinearity of all variables, so the bivariate probit model can be used for regression analysis. Table 3. Multicollinearity test results of explanatory variables variables

VIF

variables

VIF

variables

VIF

variables

VIF

O_qualification

1.75

O_experienced

1.67

P_high

1.50

A_type

1.47

P_area

1.47

O_center

1.38

P_city

1.37

P_geology

1.33

A_source

1.31

O_punishment

1.27

P_use

1.16

A_stage

1.15

A_level

1.11

MEAN 1.38

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4.2.2 OLS Regression Analysis Before probit model regression, the Ordinary Least Squares (OLS) regression model is proposed to test the error term. The data used in the OLS regression model is shown in Table 3. The classification variables S_people (the number of deaths in accidents) and S_money (the direct economic losses of accidents) were selected as dependent variables, and 13 indicators of accident characteristics, project characteristics, and organizational characteristics were selected as explanatory variables. The model was estimated using STATA, the results show that the overall model is significant and has a good fitting degree. Meanwhile, the distribution of error term is close to the normal distribution, so the probit model can be used. Kernel density estimate of normality test of the error term is shown in Fig. 3.

Fig. 3. Kernel density estimate for error term normality test

The correlation analysis of the error term shows that the correlation coefficient is 0.245, and there is a positive correlation between S_people and S_money. It is recommended to use a bivariate probit model for analysis. 4.2.3 Probit Regression Analysis The bivariate probit model was analyzed with the fatal accident as the dependent variable Y 1 and the economic loss accident as Y 2 . In the bivariate probit model, when all the dummy variables mentioned above are used as independent variables to regress the two dependent variables of Y 1 and Y 2 , the regression results of the model do not converge, and some variables are deleted by STATA. In combination with the official documents of STATA, we considering that the reason is there are multiple collinearity between some dummy variables. Meanwhile, the bivariate probit model is an extension of the probit model, they have the same basic model structure. When the disturbance term between dependent variables is not considered, two independent probit models can be used separately for analysis. So we choose the probit model regression and the multicollinearity analysis to screen significant dummy variables as independent variables of the bivariate probit model.

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(1) Ordered probit regression According to the variable assignment and code representation in Table 3, the dependent variable and explanatory variable after discrete processing are respectively substituted into the ordered probit regression model, and the model is estimated by STATA, the results in Table 4 show that the ordered probit models of fatal accidents and economic loss accidents are statistically significant in the F-test, and the model has a good fitting degree. Table 4. Estimated results of ordered probit regression model variables

Type of accident

accident level

accident hazard

accident stage

S_people

S_money

β

S.E

P

β

S.E

A_type1

−5.783***

1.236236

F = 0.0000

Prob > F = 0.0000

Note: ***, **, * indicate that the explanatory variables are significant at the level of 1%, 5% and 10% respectively

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For fatal accidents, 8 experienced independent variables passed the significance test, respectively, i.e. A_type1 and A_type3 in accident characteristics, A_source2, P_use1, P_use3 and P_area2 in project characteristics, O_center, and O_experienced in organizational characteristics. For economic loss accidents, 8 variables passed the significance test, i.e. A_type1, A_type3, A_source2, A_stage2, A_stage3, P_use2, P_area1 and O_experienced. Based on the analysis, 12 significant variables from three dimensions of accident characteristics, project characteristics, and organization characteristics were screened out. (2) Bivariate probit regression Based on the probit model regression and multicollinearity analysis results, the above 12 significant explanatory variables and the building height P_ High1 is the independent variable in the bivariate probit model, and the model estimation results are shown in Table 5. It can be seen that the bivariate probit model has overall significance and good fitting. The value of the coefficient β represents the influence of the corresponding explanatory variable on the dependent variable. If the parameter is positive, the incidence of death accidents and economic loss accidents will increase with the increase of the explanatory variable. Table 5. Estimation results of bivariate probit regression model variables

S_people(Y1 ) β

Type of accident

S_money(Y2 ) S.E

P

β

S.E

P

A_type1

−3.175***

.5393064

chi2 = 0.0000

Prob > chi2 = 0.0002

Prob > chi2 = 0.0000

Note: ***, **, * indicate that the explanatory variables are significant at the level of 1%, 5% and 10% respectively

5 Conclusion Combined with the actual cases of super high-rise buildings’ safety accidents in recent years, the ordered probit model and the bivariate probit model are used to analyze the influencing factors and degree of safety accidents, and to explore the impact and relationship between human fatalities and economic losses. The study concludes that there is a positive correlation between fatalities and economic losses in the safety accidents

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of super high-rise buildings. The study identified 12 variables in 7 aspects that passed the significance test and have a certain impact on the occurrence of the two types of accidents, such as accident type, accident hazard source, and accident occurrence stage. As some variables failed to pass the significance test, it is impossible to analyze their impact on the two types of accidents. It is necessary to collect more sample data in the later stage for more perfect analysis. The study shows that the probability of fatal accidents and serious economic losses caused by fire and collapse is significantly higher than other types of accidents, and the probability of two types of accidents caused by management factors is also significantly higher than other factors. The probability of the two types of accidents in the operation and maintenance stage is significantly lower than that in other stages. The probability of two types of accidents is also significantly lower than that in non-central enterprises and inexperienced units if the construction unit is a central enterprise and has construction experience in super high-rise buildings. This study provides ideas for the management of safety accidents in super high-rise buildings, due to changes in the management department’s policy on the publication of accident investigation reports in recent years, the case data collected in this study are few, which may lead to a shallow level of analysis. In the future, a large number of typical cases will be further collected and supplemented, to conduct a more in-depth and accurate study.

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Research on the Carbon Emission Prediction of Chongqing Transportation Industry Based on Scenario Analysis Ying Liu1(B) , Liudan Jiao1 , Ya Wu2 , and Liu Wu1 1 School of Economics and Management, Chongqing Jiaotong University, Chongqing, China

[email protected], [email protected]

2 College of Resources and Environment, Southwest University, Chongqing, China

[email protected]

Abstract. The “double carbon” target reflects the new goals and requirements of China entering a new stage of development. In order to achieve the goal of Chongqing’s carbon peak, it is a timely and urgent problem to analyze the carbon emission level of Chongqing’s transportation industry. Based on the data on per capita GDP, passenger transport turnover, freight transport turnover, energy intensity, urbanization rate, private car ownership, and energy structure of Chongqing from 2000 to 2020, this paper applies the extended SRTIRPAT model to predict the carbon emissions of Chongqing’s transportation industry from 2021 to 2035 with different scenarios. Finally, this paper puts forward some appropriate suggestions based on the prediction results. Keywords: transportation industry · carbon emission forecast · scenario analysis

1 Introduction With the rapid development of the economy, the use of a large amount of fossil energy has led to a continuous increase in carbon dioxide and other greenhouse gases. Global warming, haze pollution and other problems have become increasingly prominent. The time limit and route for achieving the “double carbon” goals put forward in the outline of the National “14th Five-Year Plan” clearly put forward the goal of “reaching the carbon peak by 2030 and achieving carbon neutrality by 2060” [1]. As a pillar industry of our national economy, transportation industry is also a main source of carbon dioxide emission, and will become one of the important objects of our energy saving emission reduction [2]. In recent years, Chongqing, as the only pilot carbon market in the western region, accounts for 11% of the total carbon emissions from its transportation industry. As an international comprehensive transportation hub in the western region and an international gateway hub, Chongqing’s transportation industry is extremely developed. Chongqing’s comprehensive transportation “14th Five-Year plan” (2021–2025) proposes that we should focus on the completion of five basic networks and six transportation systems and strive to promote the formation of “high-speed rail thousand kilometers, port © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 1522–1537, 2023. https://doi.org/10.1007/978-981-99-3626-7_117

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and navigation ten thousand tons, double airport hubs, county and county high-speed, group hardening road” traffic development pattern. It is expected that the transportation industry of Chongqing will continue to develop and expand in the future, and the carbon emissions will continue to increase, and the difficulty of emission reduction will continue to increase. Therefore, it is imperative to analyze the influencing factors of the transportation industry in Chongqing and forecast the carbon emissions based on this. Because the transportation industry is difficult to reduce emissions, and to achieve the “3060” dual carbon target, the transportation industry is an important part. To achieve the dual carbon target as soon as possible, it is necessary to understand the influencing factors of carbon emissions and the future emissions. Therefore, domestic and foreign scholars have conducted in-depth research from different aspects such as carbon emission influencing factors and carbon emission prediction. In terms of influencing factors analysis of carbon emissions, Zhang et al. [3] summarized the measurement methods of road traffic carbon emissions through a literature review and divided the factors affecting carbon emissions into three categories: demandside factors, supply-side factors and environmental measurement factors. Chou-tsang Chang et al. [4] took Taichung metropolitan area as the research object, conducted statistical analysis on numerical data and urban spatial information, and drew the overall carbon budget of the metropolitan area, which provided a theoretical basis for the municipal government to implement proposed policies in the future. Ren Feng and Long Dinghong [5] used the extended STIRPAT model to analyze the influencing factors of carbon emissions in Guangdong Province. The results showed a positive correlation between population size, affluence, industrial structure and carbon emissions. At the same time, there was a negative correlation between opening to the outside world, technological level, energy structure and carbon emissions. Guo et al. [6] mainly discussed the impact of GDP on carbon emissions of the transportation industry in 30 provinces of China and studied the role of other economic factors in the nonlinear relationship between GDP and carbon emissions of the transportation sector. The analysis of the whole panel data showed an inverted U-shaped curve relationship between GDP and carbon emissions in the transportation sector. Liu et al. [7] adopted the input-output method to test the carbon dioxide emissions caused by energy consumption in the transportation industry and adopted the structural decomposition method to conduct an in-depth analysis of carbon dioxide emissions in the transportation industry. The results show that road transport activities, consumption expenditure in the transport industry, the influence of other heavy industries, and the adjustment of energy structure and energy intensity are the main reasons for controlling carbon emissions in the transport industry. Liu Yanhui and Li Yang [8] analyzed the influencing factors of carbon emissions in the transportation industry of Hubei Province based on the LMDI decomposition method. They concluded that the continuous development of the economy was the main reason for the continuous increase in carbon emissions. Transportation energy intensity has a significant negative impact on total carbon emissions. For the prediction of carbon emissions, Hong Jingke et al. [9] innovatively constructed a new comprehensive assessment model, including China’s terminal sector— Rice-LEAP model, and dynamically simulated China’s carbon peaking path and global climate change trend from 2020 to 2050 by setting reference scenarios, carbon emission

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constraint scenarios and supply-side structural reform scenarios. Chang-zheng zhu, etc. [10] based on the period 2000–2019 China’s carbon emissions, use of ridge regression to build carbon forecast model, and through the scenario analysis of China’s transportation industry projections on carbon emissions, argues that China should take effective measures, efforts to achieve general low-carbon or enhanced low-carbon scenarios, so that the transport carbon emissions to peak as early as possible. Ye et al. [11] proposed a new data-driven carbon emission prediction decision model based on the Extended Confidence rule Base (EBRB) inference model. They tested the carbon emission management data of 30 provinces in China. The experimental results showed that the model would provide a powerful reference value in policy decision-making. To sum up, scholars focus on transport carbon emissions gradually increased, more of the research on carbon emissions around the factors influencing carbon and carbon emissions to predict two aspects, but for the selection of influence factors, and the choice of prediction method has not yet formed a unified standard, remains to be further in-depth study. Therefore, based on the extended STIRPAT model, this paper takes Chongqing’s transport industry as an example to specifically analyze seven factors affecting carbon emissions in the transport industry, including per capita GDP, passenger transport turnover, freight transport turnover, energy intensity, urbanization rate, private car ownership and energy structure. According to the carbon emission data of the Chongqing transport industry from 2000 to 2020, the extended STIRPAT model is used to set different carbon emission scenarios to predict and analyze carbon emission, explore the situation of carbon peak in the Chongqing transport industry, and put forward suggestions for improvement, in order to promote Chongqing’s economic development and reduce carbon emission as much as possible. I was accelerating the formation of a low-carbon society.

2 Methods and Data 2.1 Carbon Emission Calculation Method According to the 2006 IPCC National Greenhouse Gas Inventory Guide, there are generally two methods to calculate carbon emissions from energy consumption in the transportation industry: top-down and bottom-up. For the former, Carbon emissions are calculated based on the product synthesis of vehicle energy consumption and energy carbon emission conversion factor. For the latter, carbon emission is calculated by the mileage of various transportation modes and their unit energy consumption to obtain the energy consumption of each type of transportation. On this basis, it can be multiplied by the corresponding energy carbon emission coefficient. The data involved in the bottom-up calculation method are not included in China’s existing statistical system, and it is relatively difficult to obtain the data. Therefore, this paper adopts the top-down method to calculate Chongqing’s transportation carbon emissions based on the energy consumption of Chongqing’s transportation industry: C=



Ci =

 i

Ei × LCVi × CCi × CORi ×

44 12

(1)

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where: C – the total carbon emissions generated by energy consumption; Ci -- the amount of carbon dioxide produced by the consumption of type i for a particular energy; i – energy types, which are raw coal, gasoline, kerosene, diesel oil, fuel oil, natural gas and electricity in this paper; (Table 1 Correlation coefficients of carbon emissions from various energy sources). Ei –the physical energy consumption of type i; LCVi – the average low heating value of energy source of the type i; CCi -represents the carbon content per unit calorific value of energy source of the type i; CORi -- represents the carbon oxidation rate of the combustion process of energy source of the type i; 44/12 – represents the conversion coefficient between carbon and carbon dioxide. Table 1. CO2 emission coefficient of various energy sources Types

Average low calorific value KJ/kg(m3 )

Fold standard coal coefficient kgce/kg(m3 )

Carbon per unit calorific value tC/TJ

Carbon oxidation rate %

Discount the carbon dioxide emission factor kg co2/kg(m3 )

raw coal

20908

0.7143

26.37

0.94

1.9003

gasoline

43070

1.4714

18.9

0.98

2.9251

kerosene

43070

1.4714

19.6

0.98

3.0334

diesel

42652

1.4571

20.2

0.98

3.0959

Fuel oil

41816

1.4286

21.1

0.98

3.1705

Natural gas

38931

1.33

15.32

0.99

2.1650

electricity

0.5810

Among them, the average low calorific value of energy and the broken coal coefficient are from China Energy Statistical Yearbook 2021. The carbon content per unit calorific value and carbon oxidation rate are from Guidelines for Compilation of Provincial Greenhouse Gas Inventory (Climate [2011] No. 1041, National Development and Reform Office). Before March 31, 2022, according to Corporate Greenhouse Gas Emissions Accounting Methods and Reporting Guidelines for power generation facilities (Huanban Climate [2021] No. 9), the paper requires that the grid emission factor for 2021 annual emissions be adjusted to 0.5810 tCO2/MWh. 2.2 Construction of Carbon Emission Prediction Model IPAT model combines environmental problems with human factors to reflect the impact of population scale, wealth level and technological level on the ecological environment [12], whose expression is as follows: I = P × A × T

(2)

The impact of regional ecological environment I is attributed to population size P, wealth level A and specific technological level T. Carbon dioxide emission is often regarded as one of the important indicators to evaluate environmental impact I [13].

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Due to the limitations of IPAT model in practical application, scholar York et al. improved IPAT model and proposed STIRPAT model (Eq. 3). I=aPb Ac Td e

(3)

where: I–represents environmental impact, such as carbon emission; P -- population size; A – wealth; T–technical level; a – model coefficients. b, c and d represent variable indices, respectively, and e represents random error terms. Therefore, this paper applies STIRPAT model and uses per capita GDP to represent the economic development level of Chongqing, corresponding to the wealth degree in the original model. Passenger and cargo turnover represents the influence of Chongqing traffic itself, corresponding to the population size in the original model; Energy intensity and energy structure correspond to the technical level in the original model; Urbanization rate and private car ownership represent social influencing factors, in order to study the carbon emission level from the three aspects of population, economy and technology. The model expression is as follows: C = αAβ1 Bβ2 Dβ3 Eβ4 Fβ5 Gβ6 Hβ7 e

(4)

where: C – the total traffic carbon emission of Chongqing (ten thousand tons of carbon). A – per capita GDP (yuan/person); B and D -- Passenger turnover (10,000 km) and freight turnover (10,000 t km), respectively; E and F – energy intensity and energy structure; G and H -- urbanization rate and private car ownership; e – the random disturbance term; β1 , β2 , β3 , β4 , β5 , β6 , β7 –elastic coefficients; a – constant term. In order to determine the coefficient conveniently, take the logarithm of both sides of (Eq. 4) and obtain: lnC = lna + β1 lnA + β2 lnB + β3 lnD + β4 lnE + β5 lnF + β6 lnG + β7 lnH + lne

(5)

2.3 Data Sources Since the existing statistical data only contains the energy consumption data of the transportation industry, storage industry and postal industry, the energy consumption of the transportation industry is not counted separately, considering the relatively small proportion of warehousing and postal services [14]. Therefore, the main energy consumption of the Chongqing transportation industry from 2000 to 2020 (as shown in Table 2) is obtained from China Energy Statistical Yearbook, which are raw coal, gasoline, kerosene, diesel oil, fuel oil, natural gas and electricity. The interpolation method was used to complete the missing data. This study selected per capita GDP, passenger transport turnover, freight transport turnover, energy intensity, urbanization rate, private car ownership and energy structure as the main influencing factors to analyze their impact on carbon emissions in Chongqing’s transportation industry. Each influencing factor index is shown in Table 3.

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Table 2. Main Energy Consumption of Chongqing Transportation Industry (2000–2020) time

raw coal (104 t)

gasoline (104 t)

kerosene (104 t)

diesel (104 t)

Fuel oil (104 t)

Natural gas (108 m3 )

electricity (108 kw.h)

2000

21.2400

24.6700

5.9000

35.0000

1.1000

0.0600

6.4400

2001

21.2500

25.5100

5.9100

36.4200

1.1000

0.0600

6.7900

2002

21.2500

25.5800

5.9300

38.9700

1.1000

0.0600

6.7900

2003

21.2800

26.0700

5.9300

41.2100

1.1000

0.0600

6.0000

2004

21.3000

41.6500

9.2100

138.7400

2.8613

0.1000

6.0000

2005

21.2600

45.6100

9.3000

146.2400

3.5704

0.1000

8.0100

2006

21.2100

51.5900

20.0900

146.2400

3.0361

0.1000

6.3500

2007

21.9300

51.6400

24.0100

190.0100

5.2300

0.1000

8.0000

2008

24.9500

58.7500

27.3500

216.1400

5.9500

0.1100

9.3400

2009

24.9400

22.8500

30.9300

219.0200

5.0500

1.7500

9.1700

2010

28.4300

25.1500

38.4200

270.4000

6.2200

2.1400

9.5200

2011

32.2700

28.5400

40.1400

282.5700

6.5000

3.4500

11.6300

2012

36.5900

31.9900

44.9900

316.7100

7.2800

7.0300

13.3500

2013

42.5200

36.7200

49.3500

351.7400

8.9100

7.1500

15.0600

2014

14.5200

46.7200

51.5000

321.5900

10.9900

7.1900

16.4700

2015

14.8300

55.5600

56.8200

396.1300

12.4500

7.5600

17.8600

2016

16.6400

56.6800

73.8200

420.1000

12.4900

6.8800

18.8800

2017

16.1200

62.3400

76.1100

442.5800

12.7300

7.0700

23.7900

2018

4.2100

147.5300

93.3000

278.9000

14.8600

7.1000

29.1900

2019

4.5200

165.2800

100.6900

264.3000

17.1800

8.1200

32.3000

2020

3.1600

170.8400

85.1200

246.1000

11.5500

6.9900

30.9200

Table 3. Influencing factors of carbon emissions from Chongqing Transportation Industry (2000– 2020) time

GDP per capita Yuan/person

Passenger turnover/104 km

Freight turnover/AFTK

Energy intensity t /104 yuan

Urbanization rate /%

Private car ownership/car

energy structure

2000

6383

2577859

3063900

1.1356

35.6000

409059

0.6636

2001

7096

2662900

3253200

1.0965

37.4000

495798

0.6607

2002

8079

2776900

3376300

1.0628

39.9000

633218

0.6833

2003

9311

2526100

3680300

1.0297

41.9000

781250

0.7032

(continued)

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Y. Liu et al. Table 3. (continued)

time

GDP per capita Yuan/person

Passenger turnover/104 km

Freight turnover/AFTK

Energy intensity t /104 yuan

Urbanization rate /%

Private car ownership/car

energy structure

2004

10934

2994200

5180300

0.9449

43.5000

839352

0.6546

2005

12335

3018038

6248968

0.8779

45.2000

1107266

0.6423

2006

13915

3015761

8213853

0.8563

46.7000

1320442

0.6565

2007

16966

3938936

10497955

0.8111

48.3000

1444881

0.6002

2008

20865

4430156

14864332

0.6847

50.0000

1628164

0.6018

2009

23346

4814394

16442995

0.6613

51.6000

2037034

0.6066

2010

28084

5497718

20103977

0.6184

53.0000

2759728

0.5944

2011

35017

6808274

25302835

0.5429

55.0000

3379098

0.6406

2012

39548

7553916

26480626

0.5032

57.0000

3898647

0.6108

2013

44049

6520061

22932580

0.4779

58.3000

4076180

0.6320

2014

49062

7257895

25888734

0.4516

59.6000

4410723

0.6033

2015

53398

7895976

27063382

0.4317

60.9000

4623231

0.5768

2016

59433

8048052

29647694

0.3939

62.6000

5102500

0.5395

2017

65538

8697933

33707601

0.3614

64.1000

5674952

0.5377

2018

69901

9053806

35936344

0.3452

65.5000

6317233

0.5436

2019

75828

9676925

36105414

0.3257

66.8000

6539509

0.5284

2020

78173

6338680

35246991

0.3048

69.5000

7648814

0.5158

3 Case Analysis 3.1 Calculation of Carbon Emissions Combined with the primary energy consumption of the Chongqing transportation industry (2000–2020), as shown in Table 2, the carbon emissions of the Chongqing transportation industry from 2000 to 2020 are obtained through Eq. 1. The results are shown in Fig. 1. The results showed that the carbon emissions of Chongqing transportation industry presented an upward trend from 2000 to 2020, and the carbon emissions of Chongqing transportation industry reached the peak in 2017, which was also consistent with the data trend of China Carbon Emission Database (CEADs). At the same time, the historical data shows that the carbon emissions of Chongqing transportation industry gradually increased from 2,809,814 tons in 2000 to 18,934,329 tons in 2020, with an annual average growth rate of 12.14%. 3.2 Analysis of Influencing Factors of Carbon Emissions In order to clarify the internal relationship between the influencing factors of carbon emissions in the Chongqing transportation industry, it is necessary to analyze the influencing factors further. This paper uses the ordinary least square method to determine the coefficients of the extended STIRPAT model (Eq. 5) by SPSS software. The regression results are shown in Table 4. The results show that the VIF value of energy structure H

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104t

2500 2000 1500 1000 500 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

0

Fig. 1. Carbon emissions of Chongqing transportation industry from 2000 to 2020

is slightly higher than 10, and the VIF values of other variables are much higher than 10, indicating a multicollinearity problem among all variables. Table 4. Linear regression results of ordinary least squares method model

Unstandardized coefficient

Standardized Coefficients

t

significant

Collinearity statistics

B

Standard error

Beta

(constant)

−1.351

10.817

−.125

.903

lnA

1.510

1.339

1.804

1.127

lnB

−.124

.503

−.083

−.246

lnD

.469

.291

.595

1.609

.132

.016

62.356

lnE

1.956

1.150

1.209

1.701

.113

.004

230.050

lnF

2.596

3.110

.726

.835

.419

.003

344.578

lnG

− .718

.733

−.935

−.979

.345

.002

414.881

lnH

−.588

1.231

−.074

−.478

.641

.091

11.010

tolerance

VIF

.280

.001

1166.972

.809

.019

52.388

In order to eliminate the problem of multicollinearity among variables, this paper uses ridge regression to re-analyze. The basic idea of ridge regression is to give up the unbiasedness of the least square method and obtain a more realistic and reliable regression method with regression coefficients at the cost of partial information loss and precision reduction. Ridge regression results were obtained by running the set syntax program (syntax shown in Fig. 2). As can be seen from Table 5 , R2 = 0.91, F statistic is 30.22, and Sig. (F statistic) is 0, indicating the reliability of the regression results. Therefore, the carbon emission prediction model of Chongqing is as follows:

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INCLUDE'H:\SPSS\Samples\English\Ridge regression.sps'. RIDGEREG DEP=lnC/enter lnA lnB lnD lnE lnF lnG lnH /START=0 /STOP=1 /INC=0.02 /K=0.22 Fig. 2. Ridge regression syntax program

Table 5. Ridge regression results variable

coefficient

standard error

Standardized Coefficients

T statistic

lnA

0.1134

0.1515

0.1355

7.4840

lnB

0.1870

0.7930

0.1260

2.3583

lnD

0.1875

0.0345

0.2380

5.4413

lnE

− 0.0923

0.0560

− 0.0570

− 1.6472

lnF

0.6104

0.1164

0.1707

5.2458

lnG

0.1225

0.2112

0.1596

5.7795

lnH

− 0.5184

0.5222

0.0655

− 0.9928

constant

− 1.9045

1.3027

0.0000

− 1.4619

Note: R2 = 0.91, F statistic = 30.22, sig. (F statistic) = 0

lnC = −1.9045 + 0.1134lnA + 0.1870lnB + 0.1875lnD − 0.0923lnE + 0.6104lnF + 0.1225lnG − 0.5184lnH

(6)

According to Eq. 5, per capita GDP, passenger transport turnover, freight transport turnover, urbanization rate and private car ownership play a positive role in promoting carbon emissions in the transportation industry, while energy intensity and energy structure play a negative role in inhibiting carbon emissions in the transportation industry. According to the results of Ridge regression, (1) Urbanization rate is the primary factor leading to increased carbon emissions in Chongqing’s transportation industry. By 2021, Chongqing’s urbanization rate will exceed 70%, ranking eighth in China. Urbanization has improved people’s living standards, facilitated travel, and led to a significant increase in transportation energy consumption. (2) Passenger and freight transport turnover are also important factors leading to increased carbon emissions in Chongqing’s transport industry. Since the “13th Five-Year Plan”, the rapid promotion of a large number of major national strategies, such as the integrated development of transportation in the Chengdu-Chongqing economic circle and the high-quality development of the inland international logistics hub, has dramatically increased the turnover of passenger and cargo and promoted carbon emissions. Therefore, how to effectively and reasonably

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control transportation energy consumption is a critical issue in realizing energy conservation and emission reduction while realizing high-level urbanization construction and promoting the rapid development of transportation.

4 Scenario Analysis According to Eq. 6, based on the extended STIRPAT model, the carbon emission prediction model of the Chongqing transportation industry can be obtained as follows: C = exp (−1.9045 + 0.1134lnA + 0.1870lnB + 0.1875lnD - 0.0923lnE + 06104lnF + 0.1225lnG - 0.5184lnH)

(7)

In order to verify the accuracy of the prediction model, this paper simulated the historical data of Chongqing from 2000 to 2020 and compared the simulation results with the historical data. It can be seen from Fig. 3 that the historical data are generally consistent with the simulated data. According to Eq. 8, the average relative error is 1.37%. Therefore, it is feasible to use Formula 7 to predict traffic carbon emissions in Chongqing. e=

1 n

    y −y i=1 y

n



∗ 100%

(8)



where: e—the average relative error; y—Simulated value; y—real value.

2500 2000 1500 1000 500 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

0

Carbon emissions (tons)

Simulated data

Fig. 3. Comparison of historical data and simulated data

4.1 Scenario Setting In the scenario analysis of carbon emission in the Chongqing transportation industry, the way to achieve a carbon peak in the Chongqing transportation industry should be analyzed by adjusting the annual average growth rate of the influencing factors of carbon emission according to the actual situation of Chongqing’s sustainable development and

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energy technology level. Therefore, five scenarios are set. In this paper, scenario 0 is taken as the benchmark scenario. Scenario 1 is the situation where the growth rate of the carbon-enhancing factor increases at a low speed, and the growth rate of the carbon-inhibiting factor increases at a high speed. Scenario 2 is the situation where the growth rate of the carburizing factor and the growth rate of the carbon inhibiting factor increases at a low speed. Scenario 3 is the situation where the growth rate of the carbonenhancing factor increases at a high speed, and the growth rate of the carbon-inhibiting factor increases at a low speed. Scenario 4 is the situation where the growth rate of the carburizing factor and the carbon inhibiting factor increases at high speed. The scenario Settings are as follows (Table 6): Table 6. Setting of the growth rate of each influencing factor Model

Low

Middle

High

Time

Carbon increasing factor

Carbon suppression factor

A

B

D

F

G

E

H

2022–2025

6.0%

5.5%

4.72%

−0.0001%

15.96%

−21.5%

−16.12%

2026–2030

5.0%

3.5%

2.72%

−0.0006%

13.96%

−23.5%

−14.12%

2031–2035

4.0%

1.5%

0.72%

−0.0010%

11.96%

−25.5%

−12.12%

2022–2025

7.0%

7.5%

6.72%

0.99%

16.96%

−11.5%

−6.12%

2026–2030

6.0%

5.5%

4.72%

0.94%

14.96%

−13.5%

−4.12%

2031–2035

5.0%

3.5%

2.72%

0.90%

12.96%

−15.5%

−2.12%

2022–2025

8.0%

9.5%

8.72%

1.99%

17.96%

−1.5%

3.88%

2026–2030

7.0%

7.5%

6.72%

1.94%

15.96%

−3.5%

5.88%

2031–2035

6.0%

5.5%

4.72%

1.90%

13.96%

−5.5%

7.88%

GDP Per Capita (A): In 2020, Chongqing’s per capita GDP was 78,173 yuan per person. During the 13th Five-Year Plan period, the gross regional product of Chongqing reached 2.5 trillion yuan, with an average annual growth rate of 7.2%. Outline of the 14th Five-Year Plan for Chongqing’s National Economic and Social Development and the long-term goals for the 2035 period pointed out that the leading indicators of the city’s economic and social development during the 14th Five-Year Plan period required that the per capita GDP of the region reach 102,000 yuan by 2025. The projected target for economic and social development in 2022 states that all households’ per capita disposable income will increase by around 7% for the whole year. Therefore, the per capita GDP growth rate for the base scenario 0 is set: 7% for 2021–2025, 6% for 2026–2030, and 5% for 2031–2035. Other control scenarios are adjusted accordingly on baseline scenario 0. Other control scenarios are adjusted accordingly on baseline scenario 0. Passenger Transport Volume (B): The passenger transport volume in 2020 was 63,386.8 million km. Due to the epidemic’s impact, passenger transport volume decreased significantly. With the easing of the epidemic, the recovery momentum initially appeared. According to the 2021 Annual Report on Transportation Development

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of Chongqing Central Urban Area, the total mileage of urban roads in the central urban area reached 6,025.9 km in 2021. The average daily passenger volume of the rail network was 3.06 million, and the average daily passenger volume of the ground bus network was 3.929 million, up 31% and 17.3%, respectively. The vitality of public transport has returned to the pre-pandemic level. Therefore, the growth rate of passenger turnover in the baseline scenario 0 is set: 7.5% during 2021–2025, 5.5% during 2026–2030, and 3.5% during 2031–2035. Other control scenarios are adjusted accordingly on baseline scenario 0. Freight Turnover (D): The freight turnover in 2020 was 35,246,991 million ton-km. Since the outbreak of the epidemic during the Spring Festival of 2019. As an important transportation hub in western China, Chongqing has also been affected to a certain extent. In terms of volume, the freight turnover in 2020 is roughly the same as that in 2018, or even slightly less than that in 2018. Chongqing modern logistics industry development” 14th Five-Year Plan (2021–2025) points out that by 2025, the total amount of freight transportation will reach 1.6 billion tons, about two percentage points increase in the proportion of railway freight volume, the annual growth of multi-modal freight volume is more than 20%, 1–2 percentage points reduce the total cost of social logistics and GDP ratio. Therefore, the growth rate of freight turnover in the baseline scenario 0 is set as 6.72% during 2021–2025, 4.72% during 2026–2030, and 2.% during 2031–2035. Other control scenarios are adjusted accordingly on baseline scenario 0. Energy Intensity (E): The energy intensity of Chongqing was 0.305 in 2020, which measures the energy consumption per unit of GDP of the transportation industry. According to the 14th Five-Year Plan, Chongqing will vigorously develop a circular economy and reduce energy consumption per GDP by 14% and water consumption per unit of GDP by more than 15% by 2025 compared with 2020. Chongqing will follow the principle of “reduction, reuse and resource recovery” to provide resource guarantee for sustainable economic and social development and significant improvement of ecological civilization construction. Therefore, the growth rate of energy intensity for the baseline scenario 0 is set: −11.5% for the period 2021–2025, −13.5% for the period 2026–2030, and −15.5% for the period 2031–2035. Other control scenarios are adjusted accordingly on baseline scenario 0. Urbanization Rate (F): The urbanization rate of Chongqing was 69.5% in 2020. According to the development goal of the 14th Five-Year Plan, the urbanization rate of Chongqing’s permanent resident population will reach 73% by 2025. Chongqing New Urbanization Plan (2021–2035) pointed out that the new urbanization target of permanent population urbanization rate reached 80%, the urban population reached 28 million, and the new urbanization has been realized. Therefore, the urbanization growth rate of the baseline scenario 0 is set as 0.99% during 2021–2025, 0.94% during 2026–2030, and 0.90% during 2031–2035. Other control scenarios are adjusted accordingly on baseline scenario 0. Private Car Ownership (G): In 2020, private car ownership in Chongqing reached 7,648,814, and the average annual growth rate of private car ownership in Chongqing during the 13th Five-Year Plan period was 10.65%. In recent years, with the continuous development of the economy and society, the total number of vehicles in all provinces in our country is still in the rapid growth trend. Therefore, the urbanization growth rate

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of the baseline scenario 0 is set: 16.96% during 2021–2025, 14.96% during 2026–2030, and 12.96% during 2031–2035. Other control scenarios are adjusted accordingly on baseline scenario 0. Energy Structure (H): The energy consumption of Chongqing’s transportation industry is mainly fossil energy such as coal, gasoline and diesel. In 2020, the total energy consumption of the Chongqing transportation industry was 8,849,371 tons of standard coal. The energy structure is to analyze the proportion of raw coal consumption in energy consumption. During the 13th Five-Year Plan period, Chongqing vigorously promoted the supply-side reform of the coal industry, constantly eliminated backwards production capacity, and ensured safe production. The coal output gradually decreased. In 2020, the raw coal consumed by the transportation industry was 31,600 tons, only accounting for 1/19 of that in 2016. Therefore, the urbanization growth rate of the baseline scenario 0 is set as −6.12% during 2021–2025, −4.12% during 2026–2030, and −2.12% during 2031–2035. Other control scenarios are adjusted accordingly on baseline scenario 0. 4.2 Scenario Prediction and Analysis According to the above scenarios, Eq. 6 is used to calculate the carbon emissions of Chongqing’s transportation industry under different scenarios. The calculation results are shown in Table 7. Table 7. Carbon Emissions of Chongqing Transportation Industry under different scenarios (Uint: 104 t) Year

scenario0

scenario1

scenario2

scenario3

scenario4

2022

2764.390522

2398.93445

3122.5192

3317.533221

2548.75766

2023

3042.639144

2459.681798

3652.658175

4000.131569

2693.668648

2024

3348.89477

2521.967428

4272.803748

4823.177797

2846.818628

2025

3685.976434

2585.830295

4998.237172

5815.569728

3008.676033

2026

4056.986918

2651.310338

5846.834141

7012.151052

3179.735927

2027

4680.893713

2668.088606

6700.454912

8285.764622

3299.351234

2028

5400.748485

2684.973052

7678.701831

9790.704002

3423.466229

2029

6231.306666

2701.964347

8799.769953

2030

7189.592864

2719.063169

2031

8295.249828

2032

9279.835844

11568.98479

3552.250183

10084.51076

13670.25384

3685.878731

2736.270196

11556.81998

16153.17537

3824.534118

2702.837573

12977.6372

18709.42456

3896.590333

2033

10381.28508

2669.81344

14573.13237

21670.20164

3970.004124

2034

11613.46836

2637.192807

16364.78071

25099.52339

4044.801069

2035

12991.90286

2604.970743

18376.69768

29071.53725

4121.007228

As seen from Fig. 4, if the development of carbon emissions continues following the benchmark growth rate according to various influencing factors, the carbon emissions

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12500 10500 8500 6500 4500 2500

Fig. 4. Carbon emission prediction in scenario 0

5000 4000 3000 2000 1000 0

scenario1

scenario4

Fig. 5. Carbon emission prediction in scenarios 1 and 4

35000 30000 25000 20000 15000 10000 5000 0

scenario2

scenario3

Fig. 6. Carbon emission prediction in scenarios 2 and 3

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will continue to increase rapidly, and there will be no turning point. In Fig. 5, the paper compares scenario 1 and scenario 4, and found that under the premise that the growth rate of carbon inhibiting factor is higher than the benchmark growth rate, and the growth rate of carbon increasing factor is lower than the benchmark situation, the carbon peak occurs in 2031. The carbon emission at this time is 27.3627 million tons. Figure 6 is a comparison between scenario 2 and scenario 3. It is not difficult to find that reducing the growth rate of the carbon-enhancing factor can effectively reduce carbon emissions under the premise of a low growth rate of the carbon-inhibiting factor. However, the two growth modes of scenario 2 and Scenario 3 cannot appear as an inflexion point. By analyzing the reasons for the inflexion point in scenario 1, we can conclude that a lower urbanization level and a lower energy structure can effectively reduce carbon emissions. Therefore, reducing the urbanization rate is an effective means to reduce carbon emissions. However, with the development of the economy and society, increasing the urbanization rate is an inevitable trend, so it is not feasible to reduce carbon dioxide emissions by reducing the urbanization rate. Similarly, per capita GDP can promote an increase in carbon emissions. However, with the economy’s and society’s development, per capita GDP is bound to rise. Private car ownership is also another important factor to promote carbon emissions. The public is encouraged to buy new energy vehicles, increase the proportion of new energy vehicles in private cars, and strive to improve the utilization rate of clean energy to a certain extent, so as to reduce carbon emissions in Chongqing’s transportation industry. At the same time, the government should increase corresponding subsidies and preferential policies to guide consumers to practice low-carbon. Energy intensity and energy structure are closely related to the level of technology. Therefore, it is necessary to increase the investment and research on clean energy and use it to replace traditional energy with high pollution and emission.

5 Conclusion This paper uses the extended STIRPAT model to study the impact degree of seven influencing factors on carbon emissions of the Chongqing trading transport industry, including per capita GDP, passenger transport turnover, freight transport turnover, energy intensity, urbanization rate, private car ownership and energy structure, and sets up a benchmark scenario and four control scenarios. The carbon emission of Chongqing’s transportation industry in the future is predicted. The results show that: (1) per capita GDP, passenger transport turnover, freight transport turnover, urbanization rate and private car ownership positively affect transport carbon emissions, while energy intensity and energy structure have a restraining effect on carbon emissions from transportation. (2) In addition to the urbanization rate and per capita GDP, passenger transport turnover, freight transport turnover and private car are the main factors that directly lead to increased carbon dioxide in the transport industry. So it is an effective measure to reduce the pressure of carbon emission reduction in transport by controlling passenger and freight transport, improving transport structure and encouraging people to choose reasonable travel modes. (3) Due to limited energy and time, this paper only takes Chongqing as an example for research. Next, a comparative study may be conducted on transportation carbon emissions in several cities, in order to provide reference for relevant research on transportation carbon emissions.

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Acknowledgements. The authors would like to acknowledge the financial support for this research received from. National Natural Science Foundation of China (Grant No. 71901043), Science and Technology. Research Project of Chongqing Education Commission (Grant No. KJQN201900713).

References 1. Wang, X.L., Huang, Y.S., Liu, S.J.: Potential assessment of optimizing energy structure to carbon intensity target in Hebei province. J. Oper. Res. Manage. 29(12), 140–146 (2020) 2. Huang, Z.H., Ji, L., Yin, J., et al.: Study on the peak path of carbon dioxide emission from road traffic in China (in Chinese). Environ. Sci. Res. 35(02), 385–393 (2021) 3. Zhang, L.L., Long, R.Y., Chen, H., Geng, J.C.: A review of China’s road traffic carbon emissions. J. Clean. Prod. 207, 569–581 (2019) 4. Chang, C.T., Yang, C.H., Lin, T.P.: Carbon dioxide emissions evaluations and mitigations in the building and traffic sectors in Taichung metropolitan area. J. Clean. Prod. 230, 1241–1255 (2019) 5. Ren, F., Long, D.H.: Calculation of carbon emissions in Guangdong Province, analysis of influencing factors and selection of prediction model. Ecol. Econ. (05), 21–27+32 (2022) 6. Guo, M.Y., Chen, S.L., Zhang, J., Meng, J.: Environment Kuznets curve in transport sector’s carbon emission: evidence from China. J. Clean. Prod. 371, 133504 (2022) 7. Liu, M.Z., Wang, J.F., Wen, J.X., et al.: Carbon Emission and structure analysis of transport industry based on input-output method: china as an example. Sustain. Prod. Consumption 33, 168–188 (2022) 8. Liu, Y.H., Li, Y.: Transport carbon emissions measurement and influencing factors of decomposition, Hubei province (in Chinese). J. Statistics Decis. 38(15), 88–92 (2022) 9. Hong, J.K., Li, Y.C., Cai, W.G.: Carbon peak-to-peak path simulation in China based on rice-LEAP model (in Chinese). Resour. Sci. 43(04), 639–651 (2021) 10. Zhu, C.Z., Yang, S., Liu, P. B., et al.: Study on carbon dioxide peak of transportation industry in China. Transp. Syst. Eng. Inf. 1–11 (2022) 11. Ye, F.F., Yang, L.H., Lu, H.T., et al.: A novel data-driven decision model based on extended belief rule base to predict China’s carbon emissions. J. Environ. Manage. 318, 115547 (2022) 12. Zhang, G.X., Su, Z.X.: The yellow river basin and the influence factors of carbon decomposition in transport situation prediction (in Chinese). J. Manage. Rev. 32(12), 283–294 (2020) 13. Wu, Y., et al.: A new panel for analyzing the impact factors on carbon emission: A regional perspective in China. Ecological Indicators 97, 260–268 (2019) 14. Hu, M.F., Zheng, Y.B., Li, Y.H.: Forecasting of transport carbon emission peak in Hubei province under multiple scenarios. Acta Sci. Circum. 42(4), 464–472 (2022)

Life Cycle Application and Optimization of BIM+VR in Hospital Buildings Yuyang Liu, Rong Leng, Lan Luo(B) , and Qiushi Bo School of Public Policy and Administration, Nanchang University, Nanchang 330031, Jiangxi, China [email protected], [email protected]

Abstract. With the advent of the post-epidemic era of COVID-19, the whole life-cycle of hospital building construction and management has become one of the engineering community’s top priorities. This is a time when there is a great need for more advanced technology to oversee and update the construction of hospital buildings. The BIM+VR model can significantly improve hospital management efficiency and uncover new management opportunities. In this paper, the possibility of using BIM+VR in each phase of the project was scored and elaborated on by experts, and in a particular hospital construction project, the application and optimization of the model are proven. Finally, the whole life-cycle application potential of BIM+VR is predicted. The findings reveal that the hospital building model generated using the parametric modeling technique may be swiftly updated, restructured, and optimized utilizing a virtual reality environment. When project issues arise, the BIM+VR model can also provide rapid feedback. In certain instances, repetitive modeling is avoided by combining BIM and VR models, which also improves project efficiency and controllability. The combination of the BIM+VR model offers some relief from repeated modeling, enhances project efficiency and controllability, and enriches project management technology approaches by presenting opportunities for improvement in the creation of the whole life-cycle management of hospital buildings. Keywords: hospital buildings · BIM · VR · project management

1 Introduction The pandemic caused by COVID-19 is the most serious worldwide health issue during the previous decades due to the quick growth in confirmed cases and a massive spike in hospitalisations [1], and increasing the demand for specific treatment facilities. China has reported a total of 82,830 confirmed cases as of April 26, 2020, with 77,474 recoveries and 4,634 deaths [2]. As a result, hospital building construction is beginning to receive widespread attention. To prevent the further spread of the virus, China built two new hospitals (Leishenshan and Huoshenshan) in Wuhan and also converted existing facilities to 16 module hospitals [3], following these examples, the UK also turned convention centres into seven Nightingale hospitals [4]. The evolution of medicine in the © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 1538–1552, 2023. https://doi.org/10.1007/978-981-99-3626-7_118

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epidemic period has brought new disclosures to medical architecture, which has steadily expanded from a single functional building type to a complex system. However, the physical building layouts of normal hospitals present limitations, such as the inability to provide or convert to negative-pressure isolation rooms and current medical information systems to fulfill the needs of unique pandemic patients [5]. Meanwhile the rapid shift from a single-function building type to a complex system has experienced numerous problems, such as operational safety issues, the increase in the number of wards each with specific use demand. In addition, several mechanical and electrical systems, including cold/heat sources, water supply and drainage, power transformation and distribution, lighting, weak current, ventilation/air conditioning, and elevator systems, are incorporated. The most unexpected component of all is managing people in the home, such as guest behavior or staff collaboration on different jobs [6]. Hospital buildings have not just the same challenges as public buildings, but also those exclusive to the medical industry [7]. In hospitals, there are specialized systems such as the medical gas system, the pneumatic logistics system, and the medical sewage system, among others. Throughout everyday activities, all patients, family members, clinicians, and administrative personnel may generate thick and complicated intersections. The solution of these difficulties is particularly crucial in the development of medical buildings. BIM (Building Information Modeling) is one of the most promising and established solutions to the management difficulties of large medical facilities [8]. The BIM technology can expedite the construction sector even more. BIM can establish a virtual three-dimensional model of building construction projects and provide comprehensive and consistent building information. BIM provides a graphic platform with three-dimensional model building entities for facility managers to retrieve, analyze, and process diverse data through a unified software interface [9]. Langni Deng, et al. [10] believe that from the emergency rescue simulation based on BIM, the response information is more intuitive and can be utilized for safety education and training, directing construction workers towards safe construction, along with the drafting of emergency plans to decrease accidents, casualties, and economic losses. Despite accomplishments of BIM, there remain four major issues in building operation management, particularly in big hospital buildings: (1) Existing conventional BIM systems lack a dynamic representation of real-world buildings [11]. For example, unexpected congestion at hospital entrances is hazardous and should be reported immediately. The majority of building data is imported and then kept unaltered on current platforms. Managers are unable to get the most recent status whenever they choose. (2) There are still complications associated with getting digital data from different sources. In a hospital, around 20 systems are possible sources of dynamic data. Each system has its own hardware, interfaces, and data formats. Moreover, sensor data might amass quite rapidly. Data transformation techniques and storage operations for such large volumes of data must also be enhanced [12]. (3) Standard management software in use, such as BIM platforms, focuses mostly on browsing or examining business data using a three-dimensional model, but not on mass analysis. Feedback approaches would be insufficient if a separate offline

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database analysis were utilized to fill this gap. Therefore, improved data analysis in the form of timely recommendations for decision-making is essential. (4) Traditional BIM models are not customized for unique contexts and special patients, such as those with disabilities, dwarfs, etc. This can sometimes create associated safety hazards. Virtual Reality (VR) and Augmented reality (AR) are types of Mixed Reality (MR) technologies that allow interactions of digital artifacts in conjunction with the real world through three-dimensional model digital projections. AR superimposes threedimensional model artifacts into the real world without setting up a separate virtual environment, allowing physical and digital objects to coexist and interact in real Time [13]. In VR technology, on the other hand, users acquire three-dimensional model digital projection and associated data in a fully immersive virtual environment. VR technology allows users to experience prearranged scenarios within which they can interact separately from the real world. The difference and connection between AR and VR are shown in Fig. 1. VR technology, also known as spiritual realm technology, can use computer simulation to create a three-dimensional virtual space, providing users with visual, auditory, and even tactile sensory experience, is a multi-source information fusion, interactive three-dimensional dynamic scenes, and physical behavior of the interactive system [14]. In construction projects, the combination of BIM and VR technology has become increasingly prevalent in recent years. Based on the use of BIM technology for project simulation and management, the introduction of VR technology can significantly improve the visualization performance of BIM technology in order to address the aforementioned issues. BIM modeling of hospital buildings is carried out using Revit, sketch up,rhino, fuzor these software, and the completed BIM model is loaded into Unity3D, a VR engine, and the interface between BIM and VR is completed within Unity to achieve an external VR device interface, which provides technical support for rapid VR of BIM models and saves modeling workload [15]. The operation of the BIM+VR model is demonstrated in Fig. 2.

Fig. 1. The difference and connection between AR and VR

The integration of virtual reality and BIM projects with modeling enables architects and engineers to discover space difficulties, collisions, and design limits. In addition, they provide a shared platform for project participants to interact and make choices based

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Fig. 2. The operation of BIM+VR model

on correct facts [16]. The advantages of combining BIM and VR in construction projects are as follows: (1) Identify errors early in the construction project Some design discrepancies are not obvious on two-dimensional engineering drawings and may be too intricate to be spotted simply by the three-dimensional model model alone. In addition, it is crucial to detect faults and conflicts early on in the project to prevent discovering them late in the design or building phases. Undiscovered faults can result in budget overruns and project delays. Using BIM data and VR models to forecast building project concerns is the most accurate way. (2) Optimize cost and schedule The construction business is very competitive, not only in terms of completing projects, but also in terms of saving time and controlling costs; thus, digital transformation may help the sector improve how it monitors costs and plans operations. VR is a useful tool for reducing construction costs and avoiding delays, as it enables access to BIM models and the identification of opportunities for optimizing costs and schedules at each stage of the construction process. (3) Improve cooperation with stakeholders To avoid creating information silos for the project, the greatest challenge for construction projects is communication between stakeholders, particularly for large or complex projects. With BIM and VR, you can share all the data required by each team, providing an environment that allows them to work together on a simulated building even if they are off-site. In addition to revealing faults on the model to those concerned, it also makes it feasible for multiple teams to better coordinate their work. (4) Improve delivery quality

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BIM data in a virtual environment enables quicker, more reliable choices and improved stakeholder communication. The quality of the project as a whole will be enhanced through enhanced collaboration among all project participants and budgetary consideration. There will be fewer errors and misunderstandings as a consequence of improved team communication. (5) Avoid expensive rework VR improves communication between different project participants and reduces rework, delays due to missing details or incorrect plans, quick access to the latest plans including bookmarks and detected problems, and tracking of daily activities, which will keep all participants informed about the project and allow for more efficient problem-solving. (6) Make the project more customer-friendly The combination of BIM and VR provides clients with access to previously unavailable information. VR technology will make it easier for clients to comprehend estimates, timelines, and building procedures than certain paperwork and twodimensional designs. This will also help clients make more informed decisions based on their understanding of the building project. A BIM+VR hospital structure designed application and optimization using the idea of “whole life-cycle integration” is offered as the illustrative case in this study. In Sect. 2, through expert scoring, the innovative application of BIM+VR in the whole life-cycle was explored is shown. Section 3 for a typical case, combining expert scoring and project specifics, study the optimization potential of BIM+VR in each phase of the project whole life-cycle and explain the optimization results. Conclusions and discussions are provided at the end.

2 Possible Application Innovation of BIM+VR Technology in the Whole Life-Cycle of Projects According to the whole life-cycle theory proposed by PMBOK 7.0, the project is divided into seven phases: initialization, investment decision, survey and design, procurement and bidding, construction, acceptance and handover, and operation. The BIM+VR technology has a variety of applications and creations based on these seven phases. The questionnaire was designed using a 5-point scale, and each observed variable was scored on a 5-point scale. (1 = strongly disagree, 2 = disagree neutral, 3 = neutral, 4 = agree, 5 = strongly agree). 4 = Agree, 5 = Strongly Agree). The experts responded to the questionnaire, rated the extent to which the item descriptions matched. The specific information of the experts are shown in Table 1. The actual the higher the score, the more the expert agrees with the content of the item. The survey of the experts participating in the work of significant projects, accurately reflect the situation of the application capability of hospital projects. Combining the whole life-cycle with particular situations, the utility scores of BIM+VR were computed using the Delphi technique on 13 experts, and the findings are displayed in Table 2.

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Table 1. The specific information of the experts Participants

Gender

Occupation

Years of Work Experience

Education

No.1

Male

University professor

>20 years

Doctor

No.2

Female

University professor

>15 years

Doctor

No.3

Male

BIM construction projects engineer

>5 years

Doctor

No.4

Male

BIM construction projects engineer

>7 years

Master

No.5

Female

BIM construction projects engineer

>10 years

Master

No.6

Male

BIM construction projects engineer

>8 years

Doctor

No.7

Male

Researcher of BIM field

>7 years

Doctor

No.8

Female

Researcher of BIM field

>5 years

Master

No.9

Male

Researcher of BIM field

>5 years

Master

No.10

Female

Researcher of BIM field

>9 years

Doctor

Table 2. The scoring results of BIM+VR utility by experts in the whole life-cycle phase of the project in this phase Phases of projects

Score and the number of experts scoring in that band 1 (strongly disagree)

2 (disagree neutral)

3 (neutral)

4 (agree)

5 (strongly agree)

initialization

2

5▲

5

1

0

investment decision

1

2

3

5▲

2

survey and design

0

0

0

6

7▲

procurement and bidding

2

2

4▲

3

2

construction

0

0

0

5

8▲

acceptance and handover

0

1

6▲

4

2

operation

1

1

1

5▲

5▲

Based on the analysis of the Delphi scoring results, it is concluded that the utility and significance of BIM+VR are in the “investment decision”, “survey and design”, “construction”, and “operation” phase. The highest scores in these four phases indicate that experts believe that the use of BIM+VR models in these four phases is the most

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helpful in improving the construction and use of hospital buildings. The specific utility and significance of BIM+VR in these four phases are as follows. (1) Investment Decision If BIM is a vehicle for project management and data integration, then the beginning of BIM use in the construction industry is the first major milestone in the construction industry’s move toward digitalization. Likewise, the use of VR in BIM estimation of costs and planning time allows evaluators to assess whether what they plan matches the information and ideas provided by other participants, using VR visualization of data in BIM. With VR simulations to visualize and contextualize BIM data, each participating team can actually explain the issues of the task and make it well understood by the other participants, resulting in smoother communication and eventually a much shorter face time and cheaper expenses. (2) Survey and Design When constructing a hospital, it is necessary to plan lines of sight for doctors and nurses to examine patients. The traditional process is to start with the architect’s design concept and design the exterior and interior structural drawings, followed by the structural engineer’s mechanical calculations and the HVAC engineer’s review of the structural soundness and safety. VR intervention allows these three parties to intervene early in the design phase and collaborate to improve the design, thus efficiently advancing the project process. During the interior design and construction phase, the structural engineer walks the user through the design in 2D mode, whilst the user may browse and explore the design from their perspective in VR mode, which raises more possible difficulties. Virtual reality becomes the key to avoiding design problems in this manner. In other words, VR enables clients to participate in the design phase, so preventing time-consuming and perhaps costly redesigns. In addition, VR provides a set of metadata that is not available from traditional BIM models alone. VR also provides information about how users interact with the model, such as what they are looking at and the path they are roaming. This is particularly significant for enhancing the ergonomics of structures. (3) Construction Digital prototypes (DMUs) can help everyone better understand the different finish functions and better comprehend large, complex projects. Complex projects may have several end-user types - if you consider building an airport, there will be customers, service desk employees, security personnel, and maintenance personnel. They all use the same facilities for different purposes. In this situation, it is crucial to comprehend the various traffic flow spaces and directions. You can view the BIM model in a virtual environment using VR software. Immersive worlds are a useful method for visualizing logistical obstacles and building effects. If your project requires the use of cranes or if there are nearby facilities that cannot be shut down during development, you may better plan for them with virtual reality software. (4) Operation Using VR to experience and evaluate the safety of a building model can provide building and maintenance personnel with a clear understanding of where equipment is located and aid in the creation of safety plans for areas that are difficult to access due to security or safety clearances, such as operating rooms. The BIM model contains information containing the names and characteristics of the chosen equipment.

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This data can serve as a solid foundation for 3D documentation. Facility managers may utilize them to educate and train their workers more effectively. Participants in a virtual reality (VR) construction safety event might be construction workers, construction managers, and drivers transporting supplies. By utilizing VR to load building models, users may learn about potential dangers in a virtual environment, such as falling items, fence safety risks, fire, construction environment collapse, and several other virtual situations. In accordance with the specified escape routes and emergency exits during emergency drills. Through such training, participants in the construction industry can reduce the likelihood of safety incidents and increase their productivity. BIM modeling is carried out using Revit software tools, and the completed BIM model is loaded into Unity3D, a VR engine, and the interface between BIM and VR is completed in Unity to realize the interface with external VR devices, which provides technical support for rapid VR of BIM models and saves modeling workload. Through the comparative analysis of relevant domestic and foreign specifications, virtual fire scenarios are realized within VR scenes, different fire conditions and modes are set; categories of pedestrians are divided and parameters are given, and C# programming and finite state machine principles are combined to realize the Agent to switch states and perform corresponding behaviors according to environmental conditions autonomously. Combining the numerous benefits of BIM and VR technologies, and constructing the Agent model to provide a more realistic simulation of pedestrian evacuation, the developed V system is also applicable to scientific research and engineering. In summary, BIM+VR technology has been tested to have an impact and utility on all phases of the project life-cycle, and in “architectural and engineering phases”, “cost estimation and planning stage”, “coordination and building process”, “Facility management and use of VR building safety experience and assessment during operation” and “fire, earthquake and other situations” these five phases can get the maximum performance, which can theoretically improve the efficiency and effectiveness of the project.

3 Case Background and BIM+VR Application Optimization The new emergency medical complex of Shanghai Tenth People’s Hospital is attached to the existing outpatient building, with a total construction area of 11,550 m2 , including 6 floors above ground with an area of 6,700 m2 and 2 floors below ground with an area of 4,850 m2 , integrating the functions of outpatient, emergency, medical technology, and surgery. The construction of the project will help the hospital improve the emergency treatment environment and enhance the level of emergency services, and also lay solid hardware support for the development of emergency disciplines and better match the functional positioning of the medical emergency center in the northern part of Shanghai. The project was awarded the Excellent Award in the housing construction category of the Technical Solution Award of the Second Shanghai BIM Technology Application Innovation Competition, using BIM+VR technology to provide interactive 3D dynamic visualization and system simulation of entity behavior, bringing an immersive experience to medical staff, and the BIM unit is Shanghai Corey Sincere Construction Project Management Co. An aerial view of the project is shown in Fig. 3.

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Fig. 3. Aerial view of Shanghai Tenth People’s Hospital

At the early stage of the construction of this project, a whole life-cycle BIM implementation plan was formulated, based on the national and local standards and basic application points of BIM application guidelines, combined with the characteristics of the project, the innovative application mode of “BIM +” was brought into play, taking BIM as the building information carrier, improving the breadth of application through complex engineering project management, combining with SPEC technical specification requirements to develop the depth of application, relying on the cloud platform to achieve the integration of the whole process information, through the “BIM+PM+SPEC+cloud platform” management model, to help the hospital building intelligent construction, to ensure that the project schedule, quality, investment, safety, civilized construction and other objectives of the realization. The result of adopting BIM+VR technology for operating room roaming in this project is demonstrated in Fig. 4.

Anesthesia Cabinets UPS power (builtin) Quadruple viewing lights Medicine cabinet

Mechanical cabinet Operating room interior effect

Operating room connecting corridor

Operating room hand washing sink

Operating table

Power bank

BIM + VR immersive experience

Operating room indoor roaming

Fig. 4. The effect of using BIM+VR technology for operating room roaming in this project

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The project has made full use of the advantages of BIM and cloud platform technology such as virtualization, visualization, collaboration, and accessibility in program development, schedule and quality control as well as safe and civilized construction, etc. The specific application results are as follows. (1) Real-time communication with clinical departments to improve communication efficiency and implement departmental needs, more than 120 departmental suggestions have been obtained, involving nearly a dozen departments such as testing, radiology, pharmacy, operating room, emergency medicine, etc. The implementation rate is close to 100%, effectively reducing the risk of “late rework due to inadequate communication of departmental needs”. (2) Using BIM 3D display and collision detection function to eliminate conflicts between different professions and different pipelines, more than 200 collision points were found through BIM collision detection to ensure the quality of drawings, reduce the incidence of visas later, and contribute to the realization of the project’s overall investment control objectives. (3) BIM quantity calculation is compared to the traditional list quantity calculation, making it easier for the hospital to analyze the quantity difference and ensure the accuracy of the quantity list in the bidding process; a total of 9 items with a larger quantity difference are identified, including 3 items in the civil part and 6 items in the installation part; this effectively reduces the risk of quantity and price claims by the construction unit in the future. (4) BIM+proposed method, adjusting the construction method (from large excavation to reverse method), combining BIM simulation and analysis functions, simulating and optimizing the traffic organization scheme of the hospital in each stage of construction, forming thematic analysis reports, and proposing a total of 10 traffic management measures in each stage. Previewing in advance, sorting out the risk prevention and control points in each stage of traffic organization and adopting countermeasures to ensure the regular operation of the hospital. (5) Through the entire process of utilizing cloud platform technology, we are able to realize the sharing of platform resources, create a digital, dynamic, and full participation management mode, identify and resolve problems in a timely manner, and significantly improve construction productivity. 733 project site graphics, 115 site work conditions, 290 fixed-point inspections, and 252 foundation pit monitoring, for a total of nearly 7623 documents, have been created to date. Based on the BIM model of this case and other applications, according to the scoring of the BIM+VR model in the whole life-cycle of the project by experts in Sect. 2, combined with the specific situation in the construction and operation and maintenance process of this case, it is considered that the application of BIM+VR in this project can be optimized to the following results under certain conditions, as shown in Table 2 (Table 3).

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Table 3. Project whole life-cycle BIM+VR application optimization before and after comparison Phases of the project

Before optimization

After optimization

initialization

Developed a whole life-cycle BIM implementation plan

-

With or without optimization

investment decision Using BIM collision detection function to eliminate different professions and pipeline conflicts; using parametric simulation to present different schemes

Use VR equipment to ▲ enable investors to enter the operational hospital scene in advance, helping investors to make better investment decisions

survey and design

Combine SPEC technical parameters to generate BIM section layout models ahead of interior design bids

According to the project ▲ drawings using VR+BIM model to build a virtual site and the A-party communication, platform sharing, co-working, able to customize the needs according to different roles

procurement and bidding

With the BIM+cloud platform, real-time communication with clinical departments to improve communication efficiency and implement procurement and bidding requirements

construction

The whole process 4D BIM, first simulation after construction, to achieve progress control, advance preview

Construction of ▲ holographic virtual site through BIM+VR model, indoor safety simulation for each step of construction, to achieve the effect of safe construction (continued)

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Table 3. (continued) Phases of the project

Before optimization

After optimization

With or without optimization

acceptance and handover

Cloud platform site management: information sharing on site work conditions, fixed-point inspection, pit monitoring, project acceptance, document management, etc. to create a whole process, digital and dynamic site management

-

operation

BIM+inverse method, simulate in advance the risk control points of traffic organization in each stage and take countermeasures to ensure normal operation of the hospital

Fire control and crowd ▲ density prediction through BIM+VR model, able to simulate disaster escape

According to the results of the study in Table1 and Table 2, combined with expert opinion and the specific situation of the case itself to consider, the potential prognosis of BIM+VR optimization for this project is shown in Fig. 5. According to the visualization results of the optimization potential of BIM+VR technology for the whole life-cycle of this project in Fig. 5, it can be concluded that the project can use BIM+VR technology to optimize the staging process in the “investment decision”, “survey and design”, “construction”, “acceptance and handover”, and “operation” stages, and the optimization potential in the operation stage is the highest, reaching 1.5 times. Experts believe that BIM+VR technology can play a good role in daily maintenance, improving the interaction between patients and medical staff, private customization of services for special patients, and the process of fire simulation and escape.

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operation

acceptance and handover

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survey and design

investment decision

1.6 1.4 1.2 1 0.8 0.6 0.4 0.2 0

initialization

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Fig. 5. The potential prognosis of BIM+VR optimization of whole life-cycle of the project

4 Discussion and Conclusion Within the scope of this research project, both the practical implementation and theoretical improvement of BIM+VR technology throughout the entirety of the life-cycle of hospital construction projects were investigated. The findings of this study were obtained through the use of an expert scoring system for BIM+VR technology at each stage of the full life-cycle of a healthcare project. These four stages—”investment decision,” “survey and design,” “construction,” and “operation”—can obtain better theoretical application results. And in the case of the selected medical building, we combined the results of the expert scoring with the circumstances of the project to come up with the BIM+VR model for the project. This model can be applied to five out of the seven stages of the project to optimize its efficacy and efficiency, and the specific optimization effect is described. However, the cost of the project is not included in this study. Additionally, figuring out how to use the BIM+VR model for agile management might be a potential area for future research. In the current situation, the conventional method for BIM has been significantly enhanced. The birth of VR has brought people a different perceptual interaction experience, and the combination of BIM and VR The combination of BIM and VR technology can break through this limitation, and it can more profoundly The combination of BIM and VR virtual reality technology can break through this limitation, it can show the construction plan, model and sample in front of people virtually. The complexity of the project itself, the characteristics of more programs need to compare, select and determine, precisely The combination of BIM and VR technology is very necessary, using BIM for 3D modeling, exporting high-definition rendered images, and virtual reality display with VR technology as a medium, so that all people can to visit, learn and compare

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the construction effect visually and immersively. It can be envisioned, In the future construction and construction management, the combination of BIM and VR virtual reality technology will show The combination of BIM and VR virtual reality technology in future construction and construction management will show great advantages and show strong vitality. Acknowledgments. This study is supported by the National Natural Science Foundation of China (72061025, 71901113), the Natural Science Foundation of Jiangxi Province in China (20212ACB214014) , the Social Science Foundation of Jiangxi Province in China (21GL05), and Jiangxi Postgraduate Innovation Special Fund Project (YC2022-s179). Conflicts of Interest. The authors declare no conflicts of interest.

References 1. Anter, A.M., Oliva, D., Thakare, A., Zhang, Z.: AFCM-LSMA: new intelligent model based on L´evy slime mould algorithm and adaptive fuzzy C-means for identification of COVID-19 infection from chest X-ray images. Adv. Eng. Inform. 49, 101317 (2021) 2. National Health Commission of the PRC, Updates on the epidemic (in Chinese,2020.4.26). http://www.nhc.gov.cn/xcs/yqtb/list_gzbd.shtml 3. Luo, H., Liu, J., Li, C., Chen, K., Zhang, M.: Ultra-rapid delivery of specialty field hospitals to combat COVID-19: lessons learned from the Leishenshan hospital project in Wuhan. Autom. Constr. 119, 103345 (2020) 4. Bushell, V., Thomas, L., Combes, J.: Inside the O2: the NHS nightingale hospital London education center. Interprofess Care. 34, 698–701 (2020) 5. Jin, T., et al.: SARS-CoV-2 presented in the air of an intensive care unit (ICU). Sustain. Cities Soc. 65, 102446 (2021) 6. Peng, Y., Zhang, M., Yu, F., Xu, J., Gao, S.: Digital twin hospital buildings: an exemplary case study through continuous lifecycle integration. Adv. Civ. Eng. 2020, 1–13 (2020) 7. Yu, P., Wen, W., Ji, D., Zhai, C., Xie, L.: A framework to assess the seismic resilience of urban hospitals. Adv. Civ. Eng. 2019, 7654683 (2019) 8. Hu, Z.-Z., Tian, P.-L., Li, S.-W., Zhang, J.-P.: BIM-based integrated delivery technologies for intelligent MEP management in the operation and maintenance phase. Adv. Eng. Softw. 115, 1–16 (2018) 9. Luo, L., Chen, H., Yang, Y., et al.: A three-stage network DEA approach for performance evaluation of BIM application in construction projects. Technol. Soc. 71, 102105 (2022) 10. Deng, L., Zhong, M., Liao, L., et al.: Research on safety management application of dangerous sources in engineering construction based on BIM technology. Adv. Civ. Eng. 2019, 7450426 (2019) 11. Gao, X., Pishdad-Bozorgi, P.: BIM-enabled facilities operation and maintenance: a review. Adv. Eng. Inform. 39, 227–247 (2019) 12. Stojanovic, V., Trapp, M., Richter, R., Hagedorn, B., D¨ollner, J., Alamaniotis, M.: Towards the generation of digital twins for facility management based on 3D point clouds. In: Proceedings of the 34th Annual ARCOM Conference, Belfast, UK (2018) 13. Ke, S., Xiang, F., Zhang, Z., Zuo, Y.: A enhanced interaction framework based on VR, AR and MR in digital twin. Procedia Cirp 83, 753–758 (2019) 14. Lu, Q., Xie, X., Parlikad, A.K., Schooling, J.M., Konstantinou, E.: Moving from building information models to digital twins for operation and maintenance. In: Proceedings of the Institution of Civil Engineers—Smart Infrastructure and Construction, pp. 1–9 (2020)

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15. Lin, C.-L., Chen, J.K.C., Ho, H.-H.: BIM for smart hospital management during COVID-19 using MCDM. Sustainability 13(11), 61–81 (2021) 16. Gao, Y., Meng, J., Shu, J., Liu, Y.: BIM-based task and motion planning prototype for robotic assembly of COVID-19 hospitalisation light weight structures. Autom. Const. 140, 104370 (2022)

In-Depth Understanding of Construction Robot Research a Bibliometric Analysis Yongqi Liu, Huanyu Wu(B) , Yuang Huang, and Jianqiu Bao College of Civil and Transportation Engineering, Shenzhen University, Shenzhen, China [email protected]

Abstract. Labor shortage and low labor productivity are critical issues in the construction industry. Robots, as powerful technologies to improve the productivity in digitization era, are being used to assist in addressing those challenges. Particularly in recent years, interest in construction robot and related research has risen remarkably. To gain a deeper understanding of this burgeoning research field, the study provides a bibliometric analysis of 473 related papers retrieved from WoS (Web of Science). Performance analysis and science mapping were used to identify the research trend, analyze the relationship among authors, identify the top publication sources, analyze the region activity, as well as knowledge base and dominant research sub-field. The results indicated that the number of papers focusing on construction robot has been continuously growing after 2013. ‘Automation’, ‘system’ and ‘design’ are the most addressed topics in construction robotics. The study therefore would be valuable for providing a better perspective for practitioners and researchers to understand the development of construction robots and facilitating the building of intellectual wealth of robots in the construction industry. Keywords: Construction robot · Automation · A bibliometric analysis

1 Introduction With the development of robotic technologies, automation has grown in almost all production domains, which has brought a significant increase in productivity. The exception is the construction industry, which has been slow in adopting new technologies. The Mckinsey Global Institute survey has revealed labor productivity in the construction sector has been on a slow rise [1]. Therefore, the construction industry must accelerate the adoption of advanced technologies to significantly improve its productivity to meet demand challenges for now and future [2]. As one of the cutting-edge technologies, robotic technologies are powerful tools to boost productivity by performing simple and repetitive tasks or accomplishing tasks that are beyond human capabilities. The advantages of adopting robotic technologies have been widely documented in the literature. For example, bolting robots enables efficient and high-quality junctions of steel structures. But it was only with the introduction of Industry 4.0 that the application of robotic technologies in construction industry received particular attention. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 1553–1565, 2023. https://doi.org/10.1007/978-981-99-3626-7_119

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Because of these benefits and research trends, construction robots has resulted in a rapid increase in the number of research works and publications on construction robots. It makes it difficult to identify the research interests and gaps, which also means that there is a risk to ignoring important areas and questions for research and practical application. To solve this scientific problem, several literature reviews have made significant contributions. Gharbia et al. (2020) [2] added new insights to the body of knowledge by analyzing existing construction robots for on-site construction through a systematic review approach. The same research methodology was used in Qi et al. (2021) [3] and Wang et al. (2020) [4], which provided a scientific map of the current status of digital technology utilization in construction from different perspectives. Li et al. (2021) [5], on the other hand, researched the knowledge domain of advanced technologies in the management of prefabrication construction based on a bibliometric approach. Despite the great contributions of those reviews, there are t still some limitations. Firstly, results based on the literature review method may be impacted by the subjectivity to some extent. Furthermore, the existing bibliometric-based reviews focus on a wide range of technologies and were not target at robotic technologies. Therefore, as an attempt to fill this gap, the study presents a review specifically focusing on construction robotic technologies based on the bibliometric approach. It contributes to the area in several ways, e.g., identifying research trends, as well as dominant research sub-fields, citation patterns, key research institutions, leading research scholars; providing a better perspective for practitioners and researchers to understand the development of construction robots, and facilitating the building of intellectual wealth of robots in the construction industry.

2 Research Method The study used the bibliometric research method to analyze the existing literature on the application of robots in construction. To figure out the intellectual structure of this scientific domain, the research procedure is developed as shown in Fig. 1, including bibliometric tools selection, data collection, bibliometric analysis, and in-depth discussion of findings. 2.1 Data Collection The bibliographic records were retrieved from the Web of Science (WoS), rather than other databases, such as Scopus and Google Scholar. The rationale behind this is that compared to other databases, WoS has a wider coverage in science and technology [6], and is widely adopted in similar bibliometric research [7–9]. The string ‘robot’ or ‘robotic’ can form lots of phrases with ‘construction’ that only belong to the field of robotics and have no relevance to the construction industry (i.e., robot construction kit, construction of a robot). So the search term (robot* AND construction) was used for literature searching only in AK (author keywords) and TI (title) to ensure accuracy and relevance by narrowing the search scope in records. In addition, to track back publications related to construction robots, no ‘date range’ limit was set and the ‘language’ was limited to ‘English’. It is important to note that the collected data includes both article

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AK=(robot* and construction) OR TI=(robot* and construction) AND LA=(English)

Data collection

Web of science

Step1:Keywords searching

Step2:Eliminating irrelevant papers

A total of 473 bibliographic records were filter as the dataset Web of science Excel

Performance Analysis: evaluate scientific actors • Author • Region

Bibliometric analysis CiteSpace VOSviewer Pajek Scimago Graphica

• • •

Science mapping: display structure and intellectual connections Co-authorship network/ Collaboration network Co-citation network Keywords co-occurrence analysis network

Research trends Dominant research sub-fields Citation patterns

In-depth discussion Research gaps and future trends

Fig. 1. Review workflow of construction robots bibliometric

and conference papers. Indeed, conference papers add more noise to the collected data and make it more complex to analyze [10]. However, the number of journal articles related to construction robots is small (total 280 journal articles after screening) that it is not enough to provide a comprehensive view of this new scientific domain. Moreover, some representative papers are published in the type of conference paper at important conferences (i.e., Wawerla et al. (2002) [11], focusing on the efficient coordination strategy for mobile robots to autonomous construction, which has been cited 51 times). As of July 3, 2022, 1,071 publications were initially identified. After the preliminary search, further fine screening of the literature is needed to determine which are the most relevant to the construction robot. There are three criteria for screening:1) Papers should focus on construction robots, whether it is the theoretical analysis or the technical research of practical application. Otherwise, it will be eliminated; 2) First-hand research papers were included, and review papers were excluded; 3) If the paper has been published through both journal and conference, then select the latest one as the analysis data. Finally, 473 records were filtered as the dataset for the analysis of the study.

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2.2 Analysis Tools Selection and Techniques 2.2.1 Analysis Tools Selection CiteSpace, VOSviewer, HistCite, SciMAT, and Sci2tools, are widely used science mapping tools [12], with each having its strengths and weaknesses. Consequently, it is necessary to use different tools to conduct different kinds of analyzes. In the study, after evaluating the capabilities of various bibliometric tools, CiteSpace and VOSviewer were selected. CiteSpace can create networks with different layouts and assign names to detected clusters with different metrics, While VOSviewer provides a more powerful user graphic-interface that simplifies the process of science mapping [13]. More information and attributes about CiteSpace and VOSviewer can be found in Refs. [14, 15]. Furthermore, Pajek, a program package for analysis and visualization of large networks [16], was also used as an additional network processing and beautification tool, as well as Scimago Graphica. As for performance analysis, the expected analysis goal can be achieved by using Excel combined with the analysis function of WoS. 2.2.2 Analysis Techniques The two main procedures of bibliometric analysis: performance analysis and science mapping [17], were used in the study to explore the construction robot research field. First of all, performance analysis is conducted to evaluate scientific actors and the impact of their activity [18].In this part, papers are counted and analyzed by year, author, and region, to provide an overall picture of current research state. Then, science mapping is used to display the structure and intellectual connections of the construction robot research domain [19, 20]. Specifically, co-authorship network, co-citation network, and keywords co-occurrence analysis network are generated to discover research trends, dominant research sub-fields and citation patterns.

3 Results 3.1 Trend of Research on Construction Robot The first study on construction robot appears to be Fahlman (1974) [21], published in ARTIFICAL INTELLIGENCE, which developed a computer program for robots to generate plans for building specified structures. This implies that research related to construction robot has been around since the 1970s. Figure 2 shows the annual publication number of selected papers from 2010 to 2019. Before 2013, there was no regularity in the number of related papers changed from year to year due to immature robot technology and unrecognized problems in the construction industry. In 2013, the concept of Industry 4.0 was proposed, which has great potential in improving productivity and quality for the manufacturing environment. And the specific definition of it for the construction industry is “a plethora of interdisciplinary technologies to enable digitization, automation and integration of the construction process at all stages” [22], with reference to technologies including robotic technologies. Therefore, the number of papers has increased steadily since 2013, which also reveals a gradual growth of interest in construction robots research. However, the number of studies focusing on construction robot is still relatively small, which proves the research field still needs more attention.

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Fig. 2. Trend in research publications on construction robots

3.2 Authorship Analysis The top 10 most productive scholars were identified based on the number of published papers, along with their total citations and average citations. As can be seen from Table 1, Bock T is the most influential scholar, with the highest number of publications, total citations and average citations. Regarding the number of published papers, Lee ranked second, followed by Kamat. For average citations, Pan is in second place with 20.88 average citations and Gramazio is in third place with 20.71 average citations. Table.1. Authors published most construction robots related papers Authors

Affiliation

Number of papers

Citation

Average Citations

Bock T

Technical University of Munich

12

335

27.92

Lee S

University of Michigan

11

120

10.91

Kamat VR

University of Michigan

10

144

14.4

Yamada H

Gifu University

10

33

3.3

Hong D

Korea University

9

116

12.89

Chu B

Kumoh National University Technology

8

110

13.75

Jebelli H

Pennsylvania State University

8

26

3.25

Pan W

University of Hong Kong

8

167

20.88

Gramazio F

ETH Zurich

7

145

20.71

Han CS

Chungbuk National University

7

89

12.71

Collaborations among researchers facilitate academic exchange and promote productivity [23]. A co-authorship network was conducted by CiteSpace, as shown in Fig. 3. To deliver the information more clearly, the minimum number of citations was set at 3

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and the selection criteria was top 25 per slice, which means selecting the top 25 levels of most cited or occurred (over 3 citations) items from each slice. As illustrated in Fig. 3, the co-authorship network consists of 28 nodes and 29 links, which mainly includes 12 clusters. There are three different forms of clusters: closed-loop circuits, two-nodes clusters and single-node clusters. Closed-loop circuits generally indicate the researchers in these circuits have strong collaboration, such as the cluster of Bock T, Pan W, Pan M and Linner T. Correspondingly, a two-nodes means that scholars cooperate only with each other and a single-node cluster represents scholars work alone by themselves. It is worth noting that the 12 clusters are isolated, which indicates communication and collaboration in this research field are insufficient.

Fig. 3. Co-authorship network of construction robots

3.3 Region Activity Analysis To identify active countries and their collaboration in the construction robotics research area, a network was created using VOSviewer and Scimago Graphica. After the minimum number of documents was set at 3, a network with 24 nodes and 54 links was generated in VOSviewer, and its layout was beautified in Scimago Graphica. As shown in Fig. 4, the United States has been the leading position in the number of published papers in this field, followed by China and South Korea, which published 80 and 52 papers respectively. Judging by the number of published papers, most countries are not paying much attention to construction robotics. In terms of collaboration among countries, although the United States has the highest number of publications, the strength of collaboration with other countries is not as strong as that of China, which has strong collaboration with countries such as Singapore, Denmark, etc. In addition, it is intuitive that the network is sparse and

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not fully connected, which indicates that collaboration among countries is not sufficient and still needs more further collaboration.

Fig. 4. Region collaboration network of construction robots

3.4 Co-citation Analysis The concept of co-citation was first described by Small [24]. It occurs when two or more documents are cited together by other papers. Since the purpose of co-citation analysis is to find out links based on cited references, it can be used to identify the knowledge base of a specific field [25]. As shown in Fig. 5, a co-citation network was created in CiteSpace. As the goal is to identify the knowledge base through references, the minimum number of citations was set at 2 and the selection criterion was the top 30 per slice. Meanwhile, the links scope was set across slices and Pathfinder method is used to prune sliced networks. After a co-citation network with 232 nodes and 539 links was generated, those nodes are clustered using the field of study as the source of the cluster labels. A total of 36 clusters were gathered and the biggest 7 of them were set to visible. Each node consists of concentric circles of different colors. The purple color of the outermost circle represents the centrality of the node, which is an indicator to measure the importance of the node [26]. The larger centrality of one node, the more data will pass through it [27], such as node [28], node [29], etc. The different colors of the inner circles represents that the references are cited in different years, and the corresponding color bar is in the left corner of the Fig. 5. Each cluster means those references could classified to a specific research field, and number of clusters reflects, to some extent, the multidisciplinary character of construction robotics research fields. However, it is meaningless to analyze every 36 clusters, and 7 clusters with sizes of 15 nodes or more

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are selected in the study, which are “Multidisciplinary Engineering”, “Construction & Building Technology”, “Industrial Engineering”, “Robotics”, “Automation & Control Systems”, “Civil Engineering” and “Computer Science, Interdisciplinary Applications”. From those clusters, it fully illustrates that the knowledge base of construction robotics covers a wide range of research areas, and it varies according to the specific problem, but the all of them is related to Robotics and Engineering. And in terms of numbers of cocitations, the most cited references are distributed in the research field of “Construction & Building Technology” and “Engineering Industrial”.

Fig. 5. Co-citation network of construction robots

3.5 Keywords Co-occurrence Analysis Keywords represent an overview of the content of existing research, and a network of keywords provide an opportunity to identify the interests of research, and the connections among them [30]. Thus, a keywords co-occurrence network was generated by combining VOSviewer and Pajek. There are three key steps in the process of creating the network. Firstly, to reduce the impact of various keywords expression, some regular words and phrases used in literature research were deleted, e.g., “construction”, “robotics”, “construction robotics”, “robotic construction”, “robotics in construction” and “robotics and automation in construction”. Also, different expressions of the same meaning of keywords were merged into the same plural forms or broader, such as “mobile robot” versus “mobile robots”, “field robots” versus “field robotics”, “building information modeling” versus “BIM”, “virtual-reality” versus “virtual reality”, “construction automation” versus “automation”, “robotic manipulators” versus “manipulators”, “performance evaluation” versus “performance”, “technological innovation” versus “innovation” and so

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on. Secondly, the minimum of co-occurrence of keywords was set to 3 after multiple experiments, and among all of the 1,528 keywords, 108 meet the threshold and were selected for analysis. Thirdly, to optimize the network, the network is imported into Pajek and the “In y direction” layout is used. Finally, in order to display all extracted keywords as much as possible, some nodes are labeled manually. The result is shown in Fig. 6, with a total of 6 cluster, each row representing one cluster. The larger size of the nodes in Fig. 6 means that the keyword received more interests. It can be seen that “automation” is the most popular keywords in construction robotics research, followed by “design” and “systems”. “Fabrication”, “additive manufacturing”, “BIM”, “safety”, etc., are also key topics has been most researched.

Fig. 6. Keywords co-occurrence network of construction robots

In terms of co-occurring keywords, research on construction robots falls into two groups. The first being research on theoretically analysis, which focus on the benefits and barriers analysis, adoption evaluation and prospect prediction. The other group is devoted to practical applications of construction robots, with emphasis on technical problems of adoption processes. The development of construction robots is not only a matter of techniques, but also needs to be supported from the point of view of effectiveness and management. Keywords such as “management”, “adoption”, “barriers”, “opportunities”, “future”, “performance”, “productivity”, etc., provide a good overview of such research. Research on the practical application of construction robotics can be sorted according to application, techniques, and type of robots, based on co-occurring keywords. There are two special keywords “automation” and “system”. Automation is the main purpose for the adoption of construction robots, the size of which is the largest in Fig. 6, and it covers all major sub-research topics such as design automation and digital fabrication. Another special keyword is “system”, which also has relatively lager for the reason that

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robots is required to work as systems, so the research on the robotic system is also of great interest. From the point of application, as can be seen from Fig. 6, “design” and “fabrication” are two main applications of construction robots. As for fabrication, the most commonly used robotic techniques is additive manufacturing/3d print. The materials and types of robots for fabrication tend to be the differences among those studies. In terms of particular techniques, Additive manufacturing/3D printing is the most investigated robotic technique in the construction industry. Different types of robots are used in practical application, such as mobile robots, field robots, climbing robots and so on. It is important to note that the discussion from these 3 categories does not imply that studies in each category are independent. On the contrary, they are interconnected, even as a whole. It is simply discussions from different perspectives with different emphasis of attention.

4 Discussion There has been a growing interest in research construction robots, and this activity has been thoroughly reviewed in this study through performance analysis and science mapping. The review has been conducted to generate different types of networks on authors, regions, references and keywords by applying different software (e.g., CiteSpace, VOSviewer) on 473 papers retrieved from Web of Science, as well as relevant data were statistically displayed. Based on the analysis of above networks and data, the following conclusions can be drawn: (1) The change in the number of papers did not show any regularity until 2013. After the conception of Industry 4.0 was introduced, the number of papers focused on construction robotics increased continuously, which indicated construction robotics has attracted substantial attention within the construction industry. But the total number of papers also suggests that more attention is still needed to obtain a comprehensive insight of this new research field. (2) As can be seen from the collaboration networks of authors and regions, nodes are not sufficiently connected to other nodes, rather they form small networks, which are scattered and decentralized, especially in co-authorship network. This distribution of networks suggests that academic collaboration and exchange among regions and scholars is not sufficient. And there is a need to further break through regional and community boundaries to spark more creative ideas and explore more worthwhile areas of research through more collaboration. (3) From the keywords co-occurrence network, it can be concluded that some robotic technologies are being emphasized, such as additive manufacturing and the combination of BIM and robots. While other robotic technologies have not received enough attention, such as slam. On the one hand, the theoretically research on construction robotics focused on analyzing the barriers and opportunities of adopting robots and neglected the studies of human-robot collaboration workflow and management models. On the other hand, in terms of practical application research, much attention is paid to the mechanical results of construction robots, while ignoring the practical construction situation. Therefore, new workflow and management models need to be developed in the further research to fit the application of construction

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robots, and the effects of real construction scenarios in adopting construction robots need to be considered, so as to promote the application of robots in the construction industry.

5 Conclusions The study conducted a comprehensive review on construction robotics research field in terms of research trends, collaboration between scholars and regions, references and keywords based on bibliometric approach. The results show that construction robotics is indeed received interest, but there is still a lack of consideration in human-robot collaboration management and practical application scenarios, and the scope of research should be broadened from these aspects. In general, although this study is relatively complete, it has limitations, as it cannot guarantee coverage of all papers related to construction robotics only based on one database and is subject to errors when performing bibliometric analysis. However, it is not deniable that such a bibliometric review targeted at construction robotics is novel in existing studies and filled the research gap in this area to some extent. Besides, this research is valuable in providing practitioners and researchers with a better perspective on the development of construction robotics and contributing to building the wealth of knowledge on robots in the construction industry. Acknowledgements. This research was conducted with the support of Shenzhen Newly Introduced High-end Talents Scientific Research Start-up Project (Grant No. 827000656).

References 1. Mckinsey&Company, Improving construction productivity, https://www.mckinsey.com/ business-functions/operations/our-insights/improving-construction-productivity (2017) [Accessed Web Page], 2. Gharbia, M., Chang-Richards, A., Lu, Y.Q., Zhong, R.Y., Li, H.: Robotic technologies for on-site building construction: a systematic review. J. Build. Eng. 32, 15 (2020). https://doi. org/10.1016/j.jobe.2020.101584 3. Qi, B., Razkenari, M., Costin, A., Kibert, C., Fu, M.Q.: A systematic review of emerging technologies in industrialized construction. J. Build. Eng. 39, 12 (2021). https://doi.org/10. 1016/j.jobe.2021.102265 4. Wang, M., Wang, C.C., Sepasgozar, S., Zlatanova, S.: A systematic review of digital technology adoption in off-site construction: current status and future direction towards industry 4.0. Buildings 10(11), 204 (2020). https://doi.org/10.3390/buildings10110204 5. Li, C.Z., Hu, M.C., Xiao, B., Chen, Z., Tam, V.W.Y., Zhao, Y.Y.: Mapping the knowledge domains of emerging advanced technologies in the management of prefabricated construction. Sustainability 13(16), 31 (2021). https://doi.org/10.3390/su13168800 6. Falagas, M.E., Pitsouni, E.I., Malietzis, G.A., Pappas, G.: Comparison of PubMed Scopus, Web of Science, and Google Scholar: strengths and weaknesses. Faseb J. 22(2), 338–342 (2008). https://doi.org/10.1096/fj.07-9492LSF 7. Shkundalov, D., Vilutiene, T.: Bibliometric analysis of building information modeling, geographic information systems and web environment integration. Autom. Constr. 128, 15 (2021). https://doi.org/10.1016/j.autcon.2021.103757

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Domain Ontology Development Methodology for Construction Contract Saika Wong1 , Jianxiong Yang2 , Chunmo Zheng1 , and Xing Su1(B) 1 College of Civil Engineering and Architecture, Zhejiang University, Hangzhou, China

[email protected] 2 College of Management and Economics, Tianjin University, Tianjin, China

Abstract. The objective of this research is to present a method for developing the domain ontology of construction contracts. Ontology is a promising tool to organize domain knowledge and improve knowledge sharing. In this article, we propose a mixed contract ontology development approach. We first define the root classes by expert knowledge; then collect various concepts to build the subclasses and their interrelations. Finally, we transfer the contract ontology into a knowledge graph based on the results to test the ability of the ontology in implementation. The method provides a reference for the creation of the construction contract ontologies and serves as a fundamental support for the development of construction contract knowledge graphs. Keywords: Construction contract · Domain ontology · Contractual management

1 Introduction With the advent of Construction 4.0, projects in the Architecture/Engineering /Construction (AEC) sector tend towards multidisciplinary and become larger and more sophisticated. This scenario has led to project contract management becoming more challenging and, consequently, creating more issues [1], like claims, which could have been prevented in all probability if contract management had been conducted properly. Studies have reported that many legal issues can be avoided by administering all parties and coordinating all project documents [2, 3]. This situation has received a lot of attention and has generated corresponding technologies. Ontology, which provides a framework for representing, sharing, and managing domain knowledge via a system of conceptual taxonomies, interrelations, also plays a significant role in solving the problem. For example, the construction claims ontology bridges the knowledge gap between the construction teams and domain experts by representing the claims management knowledge of experts [4]. The idea of using ontology in construction projects is not new, to the best of the author’s knowledge, the research conducted in the field of ontology development for construction can be divided into two aspects: (1) Development of the ontological framework; (2) ontology construction. In these respects, some studies have achieved promising results. However, little work has been done on designing the development methodology © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 1566–1575, 2023. https://doi.org/10.1007/978-981-99-3626-7_120

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of a domain ontology for the construction contract. There is a need to build a systematic approach to develop construction contract ontology. In this paper, we proposed an ontology-developing method in the domain of construction contract management. The overall strategy is to use a combination of a topdown approach, designing top-level concepts and their interrelations, and a bottom-up approach, generalizing the domain concepts after collecting construction contractual concepts from contract clauses. At the same time, the ontology developed using the proposed method is machine-readable and knowledge-graph oriented, which will serve as the foundation of an automated contract processing tool.

2 Related Works 2.1 Domain Ontology in Construction Domain ontology is the conceptual model and logical foundation of Knowledge Graphs (KGs) about a specific field, which aims to describe all the related concepts in a particular domain of interest [5]. Therefore, the creation of domain ontology involves a large quantity of specific terminology, concepts, and relationships between concepts [6]. Before proceeding to the next section, it is essential to be noted that there have been several studies on building domain ontology in the construction sphere. For example, EI-Diraby et al. [7] did the groundwork for developing domain ontology in the construction field, which defined taxonomy for core construction concepts as well as developed a unified ontological model to capture the major entities, properties, and interrelations in construction. A series of domain ontologies relevant to the construction area emerged soon afterward. Like Ontology for Construction Claim Knowledge (OCCK) [8], Domain Ontology for Construction Knowledge (DOCK) [9], and Ontology for Delay Analysis in Construction (ODAC) [10]. These contributing studies can be a solid foundation for developing construction contract ontology. However, the extant ontologies on the concept of construction contracts have not been systematically summarized and only marginally covered. It is worth noticing that Niu and Issa [11] have developed a taxonomy for the domain ontology of construction contractual semantics, which is a concrete application to the construction contract, after [8]. Although the taxonomy has been done, it requires further development on binary relations between concepts as well as complex concept representation to build the ontology. 2.2 Ontology Development Methodology At present, the main methods used in conducting ontology development can be divided into the following two types. First, developing the ontology in an ontology description language with the help of domain experts [12], namely a top-down ontology development approach, such as the skeletal methodology [6], METHONTOLOGY [13], the sevensteps method [14] which is a practical and often-used guide of developing ontologies to the present day. Second, learning or discovering the domain ontology from structured data or unstructured data [15], usually text, in other words, a bottom-up ontology development approach. The ontology developed by the former is entirely manual and

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developed by different domain experts can vary greatly. Therefore, the ontology developed in this way is highly subjective, which not only requires ontologists to have a comprehensive understanding of the relevant domain and knowledge of ontology development simultaneously but also highly demands cooperation between domain experts and ontologists. To address the drawbacks of developing the ontology by hand, the latter approach simplifies the workload of manual ontology development and improves the quality of ontologies. It employs automated or semi-automated methods, usually involving NLP (Natural Language Processing) techniques, to build ontologies. The process mentioned above is a common approach to building ontologies. However, these methods have been developed during the development of domain-specific ontologies, thereby having limited application scope and few relevant technologies. More importantly, the ontology constructed using the above approaches is not oriented towards using KGs. On the other hand, although previous studies have illustrated the development of ontologies in the field of construction, there is a lack of systematic summaries, neglect of ontology sharing and reuse, and no unified theory has yet emerged to guide ontology development methods in the construction contract management domain.

3 Methods The following methodology for ontology development is a set of guidelines on carrying out the activities in every ontology development process, taking the characteristics of the construction contract and the future use of knowledge graphs into consideration. The whole lifecycle of the ontology development methodology for the construction contract is illustrated sequentially, see Fig. 1.

Fig. 1. Architecture of ontology development framework

3.1 Specification The scope of the ontology and the essential elements involved are needed to be defined before the ontology is built to restrict the ontology to the precise application. It should first define the domain of specialty and scope covered by the ontology with domain experts. After that, the definition of the objectives for the establishment of the ontology and the

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application scenarios should be accomplished. Last but not least, it is the identification of the ontology end-users and knowledge sources. Since we are focusing on construction contracts, the sources of data are unstructured data. Some informal competency questions will help limit the scope of the ontology during the whole process. To sum up, the work done at the specification phase is analogous to a feasibility study. Before proceeding to the conceptualization phase, the exploration of potentially existing ontologies is worthwhile. There are two direct ways to reuse the existing ontology: (1) Extraction of concepts and interrelations among concepts. Subsequently, these elements can be directly utilized at the conceptualization phase as domain concepts; (2) Importing the ontology, which is already in machine-readable form, into knowledge-representation systems [6], such as Protégé [16]. Afterward, applying the customized criteria to evaluate whether the ontology is suitable for the required application circumstances. 3.2 Competency Questions Competency questions are helpful in most stages of ontology development, during the conceptualization phase, scope restrictions and terminology extraction will become easier with the help of competency questions. Competency questions can be wide as well as specific, for example, what terms to use when expressing a concept in a construction contract? What properties are vital to describe a specific concept? This series of questions should arise from the application of ontology. With the answers to the corresponding competency questions, the scope and concepts of the domain ontology can be indicated. 3.3 Conceptualization The goal of the conceptualization phase is to transform the informal cognitions of the domain into a more formal specification using intermediate representations (IRs) based on tables and graphical symbols, like the Glossary of terms. In short, the idea of conceptualization is to generate an application-oriented collection of domain terminology in the form of tables or graph notations. Specifically, it requires concept elicitation, also known as term extraction, of knowledge sources. The concept elicitation is to produce a list of terms as comprehensive as possible by enumerating essential terminologies of the construction contract without concern about overlapping among concepts, relations between terms, or whether the concepts are classes or properties. It is worth noticing that the conceptualization process will be more effective with the help of competency questions and the official index. 3.4 Formalization To further develop structured knowledge and enhance the correlation between them, the formalization of concepts and terms is adopted in this stage. This is the manual process of linking disorganized and unrelated concepts according to the established rules. To be more specific, a taxonomy construction, ad-hoc binary relation acquisition, ontological model development, and properties definition of concepts collected about construction contracts should be attained.

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In respect to the taxonomy construction, since construction contracts involves many specific concepts of the construction industry, the definition of root concepts can refer to some existing classifications that are commonly used by the industry. Subsequently, the organization of concepts collected into a hierarchical taxonomy according to the definition of the root classes is followed. Concepts of similar nature will be grouped together as instances to form a subclass, which is the class below root concepts. The process of collecting ad-hoc binary relations falls into two parts: (1) Manually defining the interrelations between the root concepts according to the characteristics of their definition; (2) Extracting the interrelations between the concepts involved. In this way, interrelations among concepts need to be extracted based on the semantic information contained in the knowledge source, and the extracted relations are those between non-root concepts. The product of the first process is a relational diagram whereas the second is a relational table since a table involves interrelations that are more diverse and complicated than a diagram. The ontological model serves as the basic framework for describing the whole ontology, not only to allow the user to understand intuitively the interrelations between the most fundamental concepts involved in the ontology but also to control the overall structure of the organization of concepts. In other words, it focuses primarily on depicting the essence of the construction contract, and the ontological model is developed by interrelating the root concepts and their relations. The property definition of classes is a way to enrich the expression of ontology. When referring to a properties’ definition, there are two types mentioned in existing studies, Object property and Data property. The former is the external connections between two concepts, namely the interrelations among concepts. On the other hand, the latter is the intrinsic relations that describe relationships between concepts and data values. In this study, the properties definition of classes is specific to the Data properties. This can be done through the extraction of conceptual features. In summary, formalization is a decisive step for ontology development, its outcome is the fundamental core of the whole ontology. The critical task it covers is defining classes and class hierarchies, defining class properties, and defining the interrelations of classes. Additionally, constructing the class hierarchy and defining properties of concepts are tightly interconnected, which is no order of priority. 3.5 Implementation To ensure the ontology is machine-readable, it is necessary to transfer everything prepared previously into the target ontology expressed in formal ontology description languages like OWL (Web Ontology Language), RDF (Resource Description Framework) etc. At this stage, there are two major tasks: (1) Encoding the domain ontology; (2) Creating instances of classes. Both tasks are carried out in an ontology-development environment like Protégé. The codification of ontology models can provide a more rigorous format than natural language, enhance machine readability, perform automatic translation from the human-understandable level to the computable level, and facilitate automatic logical reasoning of ontology models. Meanwhile, one of the essential issues to consider when creating instances of classes in the hierarchy is whether a concept is an instance (individual in some studies) or a class. In our case, in consideration of the later

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use of a knowledge graph and to better represent the complex knowledge of concepts involved in construction contracts, our suggestion is to treat concepts other than those of the root classes as instances. Since the concepts involved in the construction contract are already at the most fine-grained level, it is better to establish the interrelations among them when they are treated as instances. 3.6 Evaluation The Evaluation phase is the final stage of one complete ontology development lifecycle. To be specific, the evaluation of the quality of ontology is to ensure the correctness of ontology for later use. Ontology evaluation can be carried out from two perspectives: application-based evaluation and evaluation based on the ontology itself. The former is evaluated primarily from the perspective of application effectiveness. In this approach, a specific application is selected, and the performance of an ontology can be evaluated by comparing the application under “with” or “without” the corresponding ontology. In contrast, the latter mainly focuses on the evaluation of the concepts and relationships in the developed ontology. The former is the primary evaluation method of interest in this study, as the latter’s evaluation criteria target ontologies developed by automated approaches. One of the typical approaches to application-based evaluation is competency questions. Unlike the other phases, evaluation of the ontology requires the translation of competency questions into Description Logic axioms to ensure that the ontology can answer the initial set of competency questions. Considering the complexity of knowledge representation in contractual clauses, it is sound to excerpt some clauses to form the competency questions to verify the correctness of interrelations between concepts involved in clauses. If there are no corrections, it will result in a revised ontology after the final evaluation step. A final evaluation step is carried out prior to the implementation step to incorporate the required amendments after the evaluation of the ontology. To sum up, the overall goal of ontology evaluation is to prove compliance between the ontology built and the actual application, and it is worth pointing out that this phase is not the end of ontology development, as Fig. 1. Indicates that the process starts from formalization to evaluation is an iterative development process. In other words, ontologists need to perform the cycle of iterative development until the target ontology achieves the ideal level, in which the target ontology completes the evaluation tasks.

4 Case Illustration 4.1 Data Preparation In order to identify the ontology for the domain knowledge of construction contracts, the Core Clauses of the Engineering and Construction Contract (NEC4) [17] have been chosen as the knowledge source. Since the Core Clauses cover a wider variety of clauses as well as contain a broad range of concepts, they are more representative than other clauses and provide sufficient data to illustrate the generality as well as validation of the proposed methodology.

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The starting point is to manually identify the important terms involved in the clauses to build a glossary of terms. One of the efficient ways is to refer to the index section that is officially provided by the NEC. The approximate 1100 terms listed in the index section provide a good initial source to generate the glossary of terms. To form a more comprehensive glossary of terms, it is necessary to encompass the synonyms of a term and arrange an initial class for it. 4.2 Data Formalization Once the glossary of terms is completed, the root classes can then be defined. Seven toplevel concepts have been designed based on the characteristics of the concepts involved in the NEC, see Fig. 2. Further, with the progress of a refinement of the root concepts, which developed a more detailed classes (subclasses) for each root concept, a class hierarchy, i.e., taxonomy, was built. After the formation of the taxonomy, the binary relations between the concepts can be extracted from the text of clauses. Note that, relations can be divided into two types depending on the object to which they are oriented, which are relations at the classes level and relations at the instances level. Although the relations at the instances level are also a crucial part of the ontology development, here the focus is only on extracting relations at the concepts level from the text to avoid unnecessary confusion, the relations diagram among the major root concepts is shown in Fig. 2. The final work of completing the formalization is the ontological model development, which is relatively a simple task when having the seven top-level root concepts and the interrelations among them. The consequent ontological model with the top-level root concepts is: “Within the Work Environment, a set of Actors uses a group of indispensable Resources to produce a collection of Products by conducting a series of Behavior following certain Processes, in accordance with their Promises.”

Fig. 2. The ad hoc relations diagram between seven top-level root concepts

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4.3 Results The final result of the domain ontology for NEC core clauses is developed in Protégé which is an extensible open-source environment, that supports collaborative development and W3C (World wide Web Consortium) standards. With the above-mentioned data, the structure of the ontology can be easily formed by importing data sequentially, see Fig. 3. Each yellow node represents a root class or a subclass respectively, and the purple node corresponds to an instance. The arrow with various colors indicates different relationships between the two nodes. The structure of the ontology enables a clearer picture of the relationship between the parties involved in the NEC contract and the various events that are present in the contract.

Fig. 3. Contract ontology with protégé

At the end of the case illustration, the potential for converting a domain ontology developed with the proposed method into a knowledge graph was assessed. The final construction contract is aiming to construct and improve the performance of the contract knowledge graph for various downstream tasks by serving as a priori knowledge. The relevant KG was developed based on the above ontology. Since the ontology is developed in a standard ontology description language, also known as a semantic web language, like OWL, RDF, and so on, it can be accomplished by importing the ontology directly into a database storage environment. As shown in Fig. 4, a more detailed interrelations are demonstrated in the graph, further revealing relationships that are indirectly observable, such as identical object property relationships, i.e., multiple nodes sharing the same relationships simultaneously.

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Fig. 4. The Contract KG based on the domain ontology

5 Conclusions and Discussion In this study, we propose a series of processes for developing construction contract ontology that is oriented to the application of knowledge graphs. The lifecycle of which can be concluded as specification, conceptualization, formalization, implementation, and evaluation, in which the formalization is the core session and the iterative development starting from the formalization to evaluation. The result of the domain ontology is capable of revealing the relationships between entities, i.e., relationships between classes or instances, in which the implicit knowledge can be acquired with the formation of a knowledge graph by further cooperating with knowledge representation and inference engine. In addition, there are some works that need to do for further research: (1) Evaluation criteria and validating competency questions development in the construction contract domain. (2) An adjustment of the taxonomy and interrelations between major classes. As the goal of building ontologies is to serve as a priori knowledge base for KGs and building KGs requires a set of knowledge representation methods, the existing methods are insufficient to support the knowledge representation of contractual texts, thus the knowledge representation methods need to be tailored to the characteristics of contractual texts. As a result, the construction of the taxonomy and interrelationships requires a redefinition in terms of the knowledge representation, where a critical factor is the issue of terms granularity, i.e., how long a term can be an instance.

References 1. Hackett, J.: Construction claims: Current practice and case management. Informa Law from Routledge (2021) 2. Abdel-Khalek, H.A., Aziz, R.F., Abdellatif, I.A.: Prepare and analysis for claims in construction projects using Primavera Contract Management (PCM). Alexandria Eng. J. 58(2), 487–497 (2019) 3. Bakhary, N.A., Adnan, H., Ibrahim, A.: A study of construction claim management problems in Malaysia. Proc. Econ. Fina. 23, 63–70 (2015)

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Carbon Emission Reduction Indicators in Green Building Evaluation System Based on Meta-analysis Xinru Qu and Xiaojing Zhao(B) School of Management and Economics, Beijing Institute of Technology, Haidian District, Beijing 100081, China [email protected]

Abstract. Green building evaluation systems are certification tools developed to enhance building performance and reduce carbon emission. To meet the increasingly aggressive “carbon neutrality” and “carbon peaking” targets set by different countries, it is commonly believed that it is vital to identify the critical carbon emission reduction indicators from the green building evaluation systems and assess their impact on carbon emission. The paper thus aims to identify the common carbon emission reduction indicators of green building evaluation systems and to ascertain the supplementary carbon emission reduction indicators of different countries to achieve the “carbon neutrality” and “carbon peaking” targets using a meta-analysis approach. Firstly, this paper refined 11 articles regarding the impact of carbon emission indicators in the green building evaluation systems on the emission reduction effect. Secondly, the paper compared and analyzed the carbon emission reduction indicators in different green building evaluation systems such as LEED, BREEAM, Green Star, and CASBEE. Thirdly, the paper identified the common carbon emission indicators, including energy, transport, materials and resources, pollution and waste, and regional optimization. Comparing the green building evaluation systems can ascertain the emission reduction indicators that need to be supplemented to achieve the “carbon neutrality” and “carbon peaking” targets in different countries. The research findings provide references for improving the green building evaluation systems and help enhance the realization of carbon neutrality and carbon peak targets worldwide. Keywords: Carbon emission · Green building · Evaluation system · Meta-analysis · Emission reduction

1 Introduction Climate change and global warming have long been major concerns worldwide. From the Kyoto Protocol in 1997 to the Paris Agreement in 2015, international treaties, carbon reduction targets, and strategies have been enacted to restrain climate change [1]. As the world’s largest carbon emitter, In 2020, China put forward the mark to achieve a “carbon peak” by 2030 and strive to be “carbon neutral” by 2060. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 1576–1584, 2023. https://doi.org/10.1007/978-981-99-3626-7_121

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The construction industry is long being accused of low energy efficiency and high carbon emission, thus becoming the critical industry sector for carbon emission reduction. According to the Research Report on China Building Energy Consumption released by China Building Energy Efficiency Association in 2018 and 2020, the total carbon emission generated from the whole life cycle of the construction industry in 2016 and 2018 were 1.960 billion tons and 4.930 billion tons, respectively. It accounts for 19.00% and 51.30% of the total national carbon emissions, respectively [2, 3]. The main reason for the significant change in the proportion is that other industries’ carbon emissions are controlled more quickly. The real estate industry is still in the development stage. The construction cycle and use cycle of the building is relatively long. It isn’t easy to control carbon emissions in a short period. Therefore, studying the carbon field in the construction industry is necessary. Different countries have different carbon emission standards in green building evaluation systems. This paper compares the green building evaluation systems of the United States, the United Kingdom, China, Australia, Japan, and other countries. LEED in the United States was chosen because it is currently considered the world’s most typical and influential green building evaluation system. BREEAM in the United Kingdom was selected because it is the first green building evaluation tool developed. China’s evaluation system of green building (ESGB) was chosen because China has the most significant building market in the world and is also a major carbon emitter in the world. Green Star from Australia and CASBEE from Japan were selected mainly because they represent Australia and other countries in the southern hemisphere and other countries in Asia, respectively. Based on the Meta-analysis method, this paper screens 11 kinds of literature related to carbon emission of the green building evaluation systems of the countries mentioned above. Through data analysis, the paper finds out the different indicators affecting carbon emission and analyzes their weight in each country’s green building evaluation system. It provides data support for improving the international green building evaluation systems.

2 Literature Review With the advance of international policies, the green building evaluation systems of various countries are becoming more and more perfect according to the actual national conditions and international development. Scholars have conducted comparative studies on green building evaluation systems in many countries. Norouzi et al. (2020) [4] compared and analyzed China, the United States, and the United Kingdom’s green building evaluation systems from ecological, social, and economic aspects. Wang et al. (2012) [5] explored the similarities and differences between ESGB and LEED sustainable site indicators based on comparative analysis. Elena et al. (2017) [6] enumerated these differences to understand these evaluation systems better and extract the main influences on building design. Ravi et al. (2017) [7] compared the history of green construction and sustainable building in the two countries from the historical dimension. They explored the reasons behind the green building evaluation systems in Japan and India. In addition to macro comparison, many researchers have conducted a systematic and in-depth study on a green building evaluation system. Lei et al. (2022) [8] conducted a

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quantitative analysis and visualization of the LEED system. Alhamlawi et al. (2021) [9] conducted a comprehensive evaluation of Dubai’s Al Sa’ FAT building evaluation system. They applied it to local residential and commercial building prototypes to achieve energy efficiency analysis for building performance simulations. Kamsu et al. (2019) [10] used a system developed by concept maps and semantic Web to check the sustainability criteria of BREEAM. Sheau et al. (2014) [11] examined four aspects from the perspective of project stakeholders from Japan: concept, motivation, motivation, and barriers. It reveals the differences between the construction sector and regional personnel in Japan from the perspective of the construction community. It provides suggestions for the further development and implementation of CASBEE. In addition, the application value of the green building evaluation system has also been paid attention to by researchers around the world. Sasquia et al. (2019) [12] included 276 Brazilian buildings up to 2016 into the evaluation system according to statistical methods and analyzed the impact of the LEED system on regional sustainable development in Brazil. María et al. (2020) [13] mined BREEAM by investigating seven cases, explored its implications for hotel design, and studied the changes implemented to achieve its goals. Although there are many studies on green building evaluation systems, some studies have explored the indicators of carbon emission in some individual green building evaluation systems. However, little literature has made a horizontal comparison of carbon emission in various green building evaluation system policies and classified their carbon emission indicators. This paper thus addresses this gap by using meta-analysis to summarize carbon emission reduction indicators and provide data support for improving the international green building evaluation system.

3 Research Methods and Data Analysis 3.1 Meta-analysis: Literature Search and Screen In the literature retrieval process, we used the green building evaluation system and building carbon emission as keywords to search in China National Knowledge Infrastructure (CNKI), Wanfang Data, and Google Academic. There were no language or study type restrictions in the retrieval process, and the retrieval time of Chinese and foreign languages was from the establishment of the database to June 2022. After searching the literature, we conducted literature screening and set two inclusion criteria. First, the research object of literature needs to be various countries’ green building evaluation systems. Second, the research index of the literature should select the building carbon emission evaluation systems of different countries as the main content of the paper. In addition, determine four exclusion criteria for the article. First, to ensure the literature’s timeliness, the selected papers should be published after 2015. Second, the literature that did not specify the required research indicators was not included in the screening scope. Third, after reading the abstract and the full text in detail, it is found that it has nothing to do with the research content of this paper. Fourth, there is duplication in the literature.

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A total of 197 pieces of literature were retrieved for this article, including 18 duplicated literature. According to the inclusion and exclusion criteria of literature, 78 pieces of literature were excluded due to early publication year, and 66 pieces of literature were found to be irrelevant after reading the abstract. 35 articles were read, 24 of which did not detail the research indicators needed. Finally, 11 studies [14–24] were obtained for qualitative research and quantitative research of meta-analysis. The literature search and screening process are shown in Fig. 1 (Fig. 2).

Fig. 1. Literature search and screening process

Fig. 2. The proportion of different green building evaluation systems in the paper

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3.2 Results of Data Analysis 3.2.1 Determine Green Building Carbon Emission Indicators Model By sorting out the contents of the above 11 pieces of literature, the leading indicators related to carbon emission in green building evaluation systems are sorted out. Similar indicators are combined with items, which can be divided into six aspects. They are energy, materials and resources, emissions, pollution and waste, transport, and regional optimization. The primary purpose of this paper is to explore the carbon emission reduction indicators in green building evaluation systems. Since indicator emissions are the core of the integrated carbon emission theoretical framework model, the other five hands can be divided into direct and indirect indicators. In the process of building construction, the utilization of energy resources is an important part, including the utilization degree of conventional energy and alternative energy and the utilization degree of renewable energy. Traditional energy sources, such as oil and gas, often directly emit carbon dioxide into the environment in the process of construction. Therefore, energy can be regarded as a direct impact indicator. Regarding the utilization of building materials, the use degree of green building materials and the reuse degree of materials are essential components of the green building evaluation system. The use of relevant green materials can directly affect the carbon emission of buildings. Therefore, the material is also regarded as a direct impact indicator. During the construction process, vehicles will transport construction materials and personnel, directly affecting carbon emissions. For the other two indicators, pollution and waste include the treatment of solid waste, harmful gas, liquid, radioactive material, etc. These substances need additional therapy after construction and use, producing carbon emissions. Therefore, pollution and waste are considered indirect effects. In addition, some of the environmental load indicators are also incorporated into pollution and waste due to them having pollution indicators. Regional optimization mainly relates to the degree of reuse of buildings and the renewal of building supporting facilities. In the process of reuse and renewal, new carbon emissions will be generated. Therefore, regional optimization is regarded as an indirect impact indicator. According to the above information, the primary green carbon emission indicators model can be obtained, as shown in Fig. 3. 3.2.2 Handle Abnormal Data After the data summary, the proportion of carbon emission indicators of different green building evaluation systems in other studies can be obtained. Select system indicators with more than or equal to 4 data to get a box diagram, as shown in Fig. 4. According to the box diagram, it can be seen that there are four outliers in the data, which respectively appear in the materials and resources of BREEAM, energy of BREEAM, and materials and resources of LEED. In the subsequent calculation, these outliers must be removed before further analysis.

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Fig. 3. Green building carbon emission indicators model

Fig. 4. Box chart of indicators with data quantity larger than 4

3.2.3 Integration and Induction of Carbon Emission Indicator Data According to the new data set obtained after processing the above data, the average ratio of all policies in each green building evaluation system occupied by policies related to different indicators affecting building carbon emission can be calculated. According to the relevant data, the stacking bar chart and mulberry chart can be made, as shown in Figs. 5 and 6. From the above two figures, it can be seen intuitively that energy, materials and resources account for the most significant proportion of building carbon emission policies, followed by emissions, pollution and waste. In comparison, the part related to regional optimization accounted for a relatively small proportion. As can be seen from the mulberry chart, the two indicators of energy and materials are involved in more than half of the total scope.

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Fig. 5. (a) Stacked bar chart of the relationship between systems and indicators, (b) Sankey diagram of the relationship between systems and indicators

4 Discussion It can be seen that green building evaluation system bias varies in different countries as the two most comprehensive evaluation systems [25], LEED in the United States, involves every aspect. The most prominent indicator is emission, which directly measures the carbon emission of buildings. BREEAM in the UK focuses on energy and emissions without some provisions for quantifying materials and resources. Like LEED, BREEAM places more emphasis on emissions. The Green Star system belonging to Australia involves all aspects except regional optimization. The Green Star focuses more on materials and resources than the previous two systems. CASBEE in Japan only considers energy, materials and resources, as well as pollution and waste. China’s ESGB has more policies on pollution and waste than other systems. This may be because, in ESGB, the indicator of pollution and waste is quantified by the environmental load. This indicator covers a broader range. Besides, ESGB pays more attention to regional optimization. In other regions of the world, few countries have written this indicator into the evaluation system, which may be related to China’s high population density and building density. Different countries need to determine the direction of carbon emission reduction according to the development needs of countries and cities. For example, developing countries with high population density pay more attention to regional optimization in the evaluation process. Some developed countries consider more about pollution and waste. Each region needs to adjust the index weight according to the actual building development to reflect the carbon emission situation preferably.

5 Conclusions This paper summarizes and integrates the carbon emission-related indicators in the green building evaluation systems of various countries and expounds on the internal links of the system. In this paper, the literature is screened based on the meta-analysis, 11 kinds of literature are brought into the statistical analysis, and the green building carbon emission indicators model is obtained. Then discuss various indicators. The conclusions are as follows: Firstly, each country’s green building evaluation system has its characteristics and commonalities. The most focused hands of each system are energy, materials and

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resources, and emissions. Secondly, the indicators that affect carbon emission can be divided into direct and indirect, and direct hands significantly impact carbon emission. Thirdly, countries have fewer regulations on carbon emissions generated by transportation during construction and by optimization during use. This paper aims to increase the international research on carbon emissions of the global green building evaluation system, provide theoretical support for the improvement of the system worldwide, and achieve the international goal of the “carbon neutrality” and “carbon peaking” targets.

References 1. Special Report: Global Warming of 1.5°C [R]. Inchon, South Korea: The Intergovernmental Panel on Climate Change (IPCC) (2018) 2. China Building Energy Consumption Research Report of 2018 [R]. Shanghai, China: China Association of Building Energy Efficiency (2018). (In Chinese) 3. China Building Energy Consumption Research Report of 2020 [R]. Beijing, China: China Association of Building Energy Efficiency (2020). (In Chinese) 4. Norouzi, N., Soori, M.: Energy, environment water and land-use nexus based evaluation of the global green building standards. Water-Energy Nexus 3, 209–224 (2020) 5. Wang, Z.Q., Hu, Q.: The comparative study on the sustainable sites indicators between ESGB and LEED. Appl. Mech. Mater. 253–255, 249–253 (2012) 6. Bernardi, E., Carlucci, S., Cornaro, C., Bohne, R.A.: An analysis of the most adopted rating systems for assessing the environmental impact of buildings. Sustainability 9(7), 1226 (2017) 7. Ravi, S., Dwayne, X.L.: Green building evaluation systems: comparative review of IGBC green homes and CASBEE for detached homes. Int. J. Earth Sci. Eng. 10(4), 793–805 (2017) 8. Lei, M.Z., Cui, T.A.: Scientometric analysis and visualization of global LEED research. Buildings 12(8), 1099 (2022) 9. Alhamlawi, F., Alaifan, B., Azar, E.: A comprehensive assessment of Dubai’s green building rating system: Al Sa’fat. Energy Policy 157, 112503 (2021) 10. Kamsu-Foguem, B., Abanda, F. H., Doumbouya, M. B., Tchouanguem, J.F.: Graph-based ontology reasoning for formal verification of BREEAM rules. Cogn. Syst. Res.55, 14–33 (2019) 11. Wong, S.-C., Naoya, A.: Stakeholders’ perspectives of a building environmental evaluation method: the case of CASBEE. Build. Environ. 82, 502–516 (2014) 12. Obata, S.H., et al.: LEED certification as booster for sustainable buildings: Insights for Brazilian context. Resour. Conserv. Recycl. 145, 170–178 (2019) 13. María, M.S.-B., Paula, T.-T., Carlos, R.-D., Rafael, E.H.F.: Implications of BREEAM sustainability evaluation on the design of hotels. Sustainability 12(16), 6550 (2020) 14. Sánchez Cordero, A., Gómez Melgar, S., Andújar Márquez, J.M.: Green building rating systems and the new framework level (s): A critical review of sustainability certification within Europe. Energies, 13(1), 66 (2019) 15. Doan, D.T., Ghaffarianhoseini, A., Naismith, N., Zhang, T., Ghaffarianhoseini, A., Tookey, J.: A critical comparison of green building rating systems. Build. Environ. 123, 243–260 (2017) 16. He, Y., Kvan, T., Liu, M., Li, B.: How green building rating systems affect designing green. Build. Environ. 133, 19–31 (2018) 17. Lwin, M., Panuwatwanich, K.: Current Situation and Development of Green Building Evaluation System in Myanmar [J] MATEC Web of Conferences. Vol. 312. EDP Sciences (2020)

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18. Braulio-Gonzalo, M., Jorge-Ortiz, A., Bovea, M.D.: How are indicators in green building rating systems addressing sustainability dimensions and life cycle frameworks in residential buildings? Environ. Impact Assess. Rev. 95, 106793 (2022) 19. Shan, M., Hwang, B.G.: Green building rating systems: Global reviews of practices and research efforts. Sustain. Cities Society 39, 172–180 (2018) 20. Wu, Z., Shen, L., Ann, T.W., Zhang, X.: A comparative analysis of waste management requirements between five green building rating systems for new residential buildings. J. Clean. Prod. 112, 895–902 (2016) 21. Yang, W.K., Deng, Y., Wu, X.L., Xiang, Y.L., Chen, C.: Research on green building evaluation index system under the guidance of government based on ANP method. Appl. Mech. Mate. 744, 2301–2305 (2015) 22. Zhang, X., Yi, S.L.: Green building evaluation methodology under ecological view. J. Discrete Math. Sci. Cryptograp. 20(1), 79–90 (2017) 23. Zhao, X.X., Yuan, Y.B., Zhang M.Y.: Comparative study on carbon reduction index of green building evaluation system. Architectural Sci. 32(10), 136–141 (2016). (In Chinese) 24. Awadh, O.: Sustainability and green building rating systems: LEED, BREEAM, GSAS and Estidama critical analysis. J. Build. Eng. 11, 25–29 (2017) 25. Lee, W.L.: A comprehensive review of metrics of building environmental evaluation schemes. Energy Build. 62, 403–413 (2013)

A Comparative Analysis of Green Construction Material Certification Systems Jindao Chen(B) School of Civil Engineering and Engineering Management, Guangzhou Maritime University, Guangzhou, China [email protected]

Abstract. Due to the resource-intensive nature of infrastructure and buildings, the construction industry consumes a lot of resources, which have significant adverse effects on the environment, economy, and society. The choice of construction materials has a critical impact on a building’s operating energy usage, related CO2 emissions, and indoor air quality. It is commonly acknowledged that green construction materials (GCMs) could reduce the adverse effects while improving buildings’ performance. To encourage GCMs, distinct organizations and regions have established different green construction material certification (GCMC) systems. This study aimed to analyze the similarities and discrepancies in requirement categories and detailed requirements for specific materials among various GCMC systems. The findings show that resources, health, energy, and environment are shared interests of Cradle to Cradle Certified (C2CC), Mainland China, Hong Kong, India, and Singapore GCMC systems. C2CC highly values social justice. Both Mainland China and India GCMC particularly take into account the effectiveness or quality of the products. The grades for goods or materials in various GCMC systems are very variable. The requirements for ready-mixed concrete in Mainland China, Hong Kong, and India GCMC differ significantly. Hong Kong GCMC only considers the carbon footprint for ready-mixed concrete, while the other two systems focus on several attributes. The GCMC in India, as opposed to those in Hong Kong and Mainland China, has given credit points to the benchmark for each criterion. The findings can help regional or organizational decision-makers better comprehend the variations across different GCMC systems and enhance their GCMC system. Keywords: Construction Industry · Green Construction Materials · Certification · Standards · Carbon Emissions

1 Introduction The construction industry consumes many construction materials (e.g., cement, concrete, steel, sand, gravel) due to the resource-intensive characteristic of buildings and infrastructure. According to Horvath (2004) [1], the construction industry consumes the most materials by weight, and natural aggregates (e.g., gravel, sand, crushed rock) are the most used material in construction. Torres et al. (2017) [2] further opined that sand and © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 1585–1601, 2023. https://doi.org/10.1007/978-981-99-3626-7_122

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gravel dominate primary construction materials, accounting for 79% of the total natural resources used for construction. With fast urban development and expansion worldwide, the demand for construction materials increases rapidly, posing a significant challenge to global sustainability. Producing construction materials induces severe environmental, economic, and social impacts. On the one hand, extracting construction materials could have considerable environmental impacts on habitats and ecological biodiversity [2]. Besides environmental impacts, excessively exploiting natural resources may induce a scarcity of construction materials in regions so that they have to buy these materials from other places and suffer a high supply chain risk and economic cost [3]. On the other hand, manufacturing construction materials consumes plenty of energy and induces enormous CO2 emissions. According to the International Energy Agency (IEA), cement production accounted for 2.5% and 6.8% of global energy use and anthropogenic CO2 emissions, respectively [4, 5]. Chen et al. (2022) [6] demonstrated that construction materials and their upstream activities (e.g., electricity production) contributed over 90% of the total CO2 emissions induced by construction in China in 2017. Furthermore, manufacturing construction materials could influence public health and cause severe economic loss due to health damage. The health damages caused by concrete production-related pollutants and greenhouse gas (GHG) emissions could reach 75% of the cement and concrete industry’s added value [7]. In addition to the above production impacts, construction materials can significantly influence buildings’ operational energy use, associated CO2 emissions, and indoor air quality, which is the most significant component of indoor environmental quality. According to Balo (2015) [8], using advanced insulation materials with an optimum insulation thickness could save buildings’ operational energy consumption. Najjar et al. (2019) [9] echoed that applying alternative components in building envelopes could reduce 45% of buildings’ annual energy use and over 30% of GHG emissions. Construction materials used in buildings may emit contaminants such as volatile organic compounds (VOCs), causing the so-called sick building syndrome, a severe issue facing humans nowadays [10]. Adopting green construction materials (GCMs) is widely recognized as an effective way to mitigate the production impacts and increase buildings’ performance [11–13]. GCMs have less natural resource consumption and ecological and environmental impacts than traditional materials from a life cycle perspective [14]. GCMs reduce the use of natural resources by containing more recycled content and being reusable or recyclable, which are essential for addressing the increasingly severe construction and demolition waste issue. According to Orsini and Marrone (2019) [15], GCMs can decrease GHG emissions by as large as 40% compared with traditional materials. Selecting GCMs is usually considered in green building certification systems as an essential strategy to improve building energy performance and indoor air quality [16]. Green construction material certification (GCMC) is critical for promoting the adoption of GCMs. There is much information on GCMs, including the number of recycled materials, production energy consumption, emissions, quality, etc. Acquiring authentic

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information on GCMs is a critical challenge for consumers due to the information asymmetry between producers and consumers [17]. To pursue extra profits and competitiveness, producers tend to engage in greenwashing, providing consumers with misleading or inaccurate green information about materials, which can seriously deteriorate consumer confidence in green materials [18]. Certification is an efficient way to reduce the information asymmetry between GCMs’ stakeholders and thus can enhance the use of GCMs [19]. Different regions and organizations have developed various GCMC systems. For example, China, India, Korea established nationwide GCMC systems, while organizations in the US and UK developed multiple GCMC systems with different focuses. Comparing these GCMC systems is helpful for decision-makers of a region or an organization to better understand the differences among various GCMC systems and improve its GCMC system. There are a lot of studies comparing green building certification systems in different regions. For example, Zhang et al. (2017) [20] compared the green building certification schemes in China, Britain, and the United States from energy-saving, water-saving, material-saving, site selection, and outdoor and indoor environmental quality. Doan et al. (2017) [21] compared four green building certification systems, including LEED, BREEAM, CASBEE, and Green Star. They found that these systems include three common categories: indoor environment quality, energy, and material. In addition to the above four systems, Mattoni et al. (2018) [22] considered one more system, ITACA. They investigated these systems’ differences from perspectives of site, water, energy, comfort and safety, materials, and outdoor quality. Lu et al. (2019) [23] compared Mainland China’s, the US, and Hong Kong’s green building certification systems focusing on construction waste management. However, very few studies have conducted a comparative analysis of GCMC systems of different regions and organizations. Only Staniszewski et al. (2017) [24] compared China’s GCMC system with the US one from three aspects: governance and implementation, product testing and oversight, and strategies to boost the number of certified products. Though their study has provided a general comparison of GCMC systems, the research fails to analyze the differences in GCMC standards. Therefore, this study aims to compare various GCMC systems worldwide comprehensively. The specific objectives are to investigate the similarities and discrepancies in requirement categories and detailed requirements for specific materials among various systems. The remaining study is structured as follows. Section 2 presents related background about environmental labeling, selection of GCMs, and GCMs-related requirements in green building standards. Section 3 presents the research methodology. Section 4 demonstrates the results, followed by Sect. 5, which concludes the study.

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2 Background 2.1 Environmental Labeling and Certification System Structure There are three types of environmental labeling defined by the International Organization for Standardization (ISO), including Type I, Type II, and Type III (Table 1) [25]. Type I, based on ISO 14024, is a multi-attribute and third-party environmental labeling, requiring a third-party agent to assess whether product life cycle environmental impacts meet a predetermined standard. In contrast, Type II, based on ISO 14201, is a single-attribute and self-declared environmental labeling that manufacturers can declare. Third-party verification is not mandatory for this labeling, compromising the labeling’s credibility. Type III, conforming to ISO 14025 and called Environmental Product Declaration, requires a company to publish detailed life cycle environmental impacts of products, such as energy use, emissions, and resource consumption. The information about the actual environmental impacts needs to be verified by a third-party agent. Type III labeling does not contain requirement standards for materials/products compared to Type I and II. Table 1. Types of environmental labeling. Type

ISO number

Description

Type I

ISO 14024

Multi-attribute and third-party environmental labeling

Type II

ISO 14021

Single-attribute and self-declared environmental claims, e.g., energy use, emissions, or water use

Type III

ISO 14025

Comprehensive environmental impact disclosure, known as Environmental Product Declaration (EPD)

According to Jahn et al. (2005) [26], Type I and II labeling systems share a similar structure, which consists of manufacturer, customer, consumer, certification body, standard owner, accreditation body, and control body (Fig. 1). The manufacturer produces goods based on the standards developed by the standard owner and calls the third-party certification body to investigate whether the products conform to the standards. The certification body is accredited by the accreditation body. The control body, either a private organization or a public institution, monitors the certification process and ensures the certification is operated correctly. If the certification is approved, the manufacturer acquires a product label. There are two certification systems depending on the type of the standard owner, namely a government or private organization.

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Control body

Accreditation

Certification body

Monitoring

Certification Products

Standards Standard owner

Manufacturer

Customer

Consumer

Fig. 1. Basic structure of certification.

2.2 Green Construction Material Selection There are many criteria for GCMs, such as cost, energy use, CO2 emissions, recycled content, toxicity, and performance, some of which conflict with each other. How to select GCMs under multiple conflicting criteria has attracted much attention from practitioners and academics. Many scholars developed multi-criteria decision-making (MCDM) methods to solve the selection problem. Sandanayake et al. (2020) [27] identified the indicators of cost and environmental impacts of green concrete and developed an MCDM method to optimize the cost and GHG emissions for green concrete selection. Govindan et al. (2016) [28] took into account the economic, environmental, and societal dimensions of the criteria of GCMs and established an MCDM method to select the materials. They demonstrated that potential for recycling and reuse was the most critical green criterion and wool brick was the most sustainable brick. Khoshnava et al. (2018) [29] considered resource efficiency, indoor air quality, energy efficiency, water efficiency, and affordability as the five categories of criteria of GCMs, while Tian et al. (2018) [30] categorized the criteria into the economic, environment, society, and technical dimensions. The above studies have identified the criteria of GCMs and their weight, indicating what properties GCMs should possess and the importance of the properties. Economy, environment, and society are generally the three dimensions of the criteria, which assure that GCMs are also sustainable. However, these studies usually fail to consider how to set a benchmark for each vital criterion, which is the basis of establishing an evaluation standard for GCMs. 2.3 GCMs-Related Requirements in Green Building Standards Many green building standards have set some related requirements for GCMs. The rapid development of green buildings has spurred the need for GCMs. The GCMs-related requirements in green building standards interact with the GCMC standards, which are an essential component for GCMC systems. China’s Assessment Standard for Green Building (ASGB), the US Leadership in Energy and Environmental Design (LEED) v4.1 for new construction, and the UK Building Research Establishment Assessment Method (BREEAM) for new construction are the three widely used green building standards. The GCMs-related requirements in green building standards are shown in Table 2. In general, indoor air quality and materials are the two GCMs-related criterion categories

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in the above green building standards. Compared with LEED, ASGB and BREEAM pay special attention to the durability of materials. ASGB presents a specific requirement for choosing green building materials. In contrast, LEED and BREEAM do not show specific requirements for GCMs but have more detailed requirements for materials, for example, Building life cycle assessment and Environmental Product Declarations. Table 2. GCMs related requirements in green building standards. Standard

Criterion category

Criterion

Requirement

ASGB

Safety and durability

Durability

Improve the durability of building components Improve the durability of building structure materials

Health and comfort

Indoor air quality

The selected decoration materials meet the requirements of the limit of harmful substances in the current national green product evaluation standards

Resources saving

Prerequisite

The building materials produced within 500km shall be more than 60% of the total Cast-in-place concrete shall be ready-mixed concrete, and construction mortar shall be ready-mixed mortar

Material saving and green materials

Reasonable selection of building structure materials and components (high performance) Choose recyclable materials, reusable materials, and waste building materials Choose green building materials (continued)

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Table 2. (continued) Standard

Criterion category

Criterion

Requirement

LEED v4.1 for new construction

Materials and resources

Building life-cycle impact reduction

Option 1: Building and Material Reuse Option 2: Whole-Building Life-Cycle Assessment

Environmental product declarations

Option 1: Environmental Product Declaration Option 2: Embodied Carbon/LCA Optimization

Sourcing of raw materials

Responsible Sourcing of Raw Materials

Material ingredients

Option 1: Material Ingredient Reporting Option 2: Material Ingredient Optimization

BREEAM for new construction

Construction and demolition waste management

Option 1: Diversion

Indoor environmental quality

Low-emitting materials

Use low-emitting materials on the building interior

Indoor air quality

Prerequisite

Indoor air quality (IAQ) plan

Emissions from construction products

Three out of the five or all of product types meet the emission limits, testing requirements and any additional requirements

Environmental impacts from construction products

Building life cycle assessment

Materials

Option 2: Waste Prevention

Environmental Product Declarations (continued)

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Standard

Criterion category

Criterion

Requirement

Responsible sourcing of construction products

Enabling sustainable procurement

Designing for durability and resilience

Protecting vulnerable parts of the building from damage

Measuring responsible sourcing

Protecting exposed parts of the building from material degradation Material efficiency

At the Preparation and Brief and Concept Design stages, set targets and report on opportunities and methods to optimize the use of materials Develop and record the implementation of material efficiency Report the targets and actual material efficiencies achieved

3 Methodology Due to the absence of standards in Type III labeling, this study only considered Type I and II labeling. There are many Type I and II GBMC systems and many kinds of GCMs in the world. It is necessary to pick some of these certification systems and GCMs for in-depth analysis. In general, Type I and II GBMC systems are led by two agents: governments and organizations. Based on the number of attributes considered, Type I and II GBMC systems can be single-attribute or multi-attribute. For example, Energy Star, WaterSense, and Forest Stewardship are single-attribute. At the same time, Green Seal, Cradle to Cradle Certified (C2CC), GREENGUARD, Green Squared, Mainland China GCMC, Hong Kong GCMC, India GCMC, and Singapore GCMC are multi-attribute. In addition, some of these certifications only cover one kind of construction material/product. For example, Green Squared only covers products utilized in a tile installation. A detailed introduction to the certifications mentioned above, except Mainland China, Hong Kong, India, and Singapore GCMC, can be seen in the work by Vierra (2016) [25].

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Since cement and concrete are widely recognized as emission-intensive construction materials, the certification systems considered in this study should cover the above two materials. Thus, this study picked C2CC, Mainland China GCMC, Hong Kong GCMC, India GCMC, and Singapore GCMC for comparison, all of which include cement and concrete-related products. The user guidance and standards of the above five certification systems were collected to evaluate the similarities and discrepancies in requirement categories. The detailed requirements for ready-mixed concrete, which was selected as a case for comparison in this study, in Mainland China, Hong Kong, and India GCMC were acquired for further comparison.

4 Results 4.1 Comparison of Requirement Categories in GCMC Systems Table 3 shows the basic information and requirement categories in C2CC, Mainland China, Hong Kong, India, and Singapore GCMC systems. C2CC is led by Cradle to Cradle Products Innovation Institute (C2CPII), an independent and nonprofit organization. Building materials are one kind of materials and products covered by C2CC. C2CC includes five standard requirement categories: material health, product circularity, clean air & climate protection, water & soil stewardship, and social fairness. Material health denotes that the product’s chemicals and materials are chosen to preserve human health and the environment. Product circularity means that products are actively cycled in their designated cycling route and purposefully created for future usage. Clean air & climate protection requires that air quality, renewable energy supply, and climate change mitigation benefits from product manufacturing. Water & soil stewardship requires product manufacturing to protect watersheds and soil ecosystems. Social fairness means companies are dedicated to defending human rights and using fair and ethical business procedures. Four levels could be assigned to each requirement category: Bronze, Silver, Gold, and Platinum. In order to attain the target accomplishment level within each category, the product must fulfill all requirements for that level and those at all lower levels. Mainland China GCMC is managed by the Science, Technology, and Industrialization Development Center of the Ministry of Housing and Urban-Rural Development of China. Mainland China GCMC focuses on building materials and has four standard requirement categories: resource, energy, environmental, and quality. Each requirement category contains several indicators and benchmarks with three levels: One-Star, TwoStar, and Three-Star. It should be noted that different construction materials have different indicators in each requirement category, which is quite different from C2CC. For example, the environmental property for precast components requires Environmental Product Declaration and carbon footprint analysis. At the same time, ready-mixed concrete considers water-soluble hexavalent chromium content, ammonia release, industrial wastewater per product unit, and specific activity. Hong Kong GCMC, officially called Hong Kong Green Product Certification, is organized by the Construction Industry Council of Hong Kong. Hong Kong GCMC covers 28 categories of construction products and materials. Among these categories,

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cement, ready-mixed concrete, and reinforcing bar/structural steel only consider carbon footprints by quantifying greenhouse gases. The remaining 25 categories focus on human toxicity, resource consumption, and ecosystem impact. Products and materials can be graded by five levels: Green, Bronze, Silver, Gold, and Platinum. India GCMC, called India Green Product Certification, is led by the Green Products and Services Council of India. India GCMC focuses on building products and related technologies. Product design, product performance, raw materials, manufacturing process, waste management, product stewardship, and innovation are the seven requirement categories in the certification system. India GCMC only has two grades, namely Yes or No. If a construction product achieves 50 or more among 100 credit points, such product is graded as Yes, which means this product is green. Singapore GCMC, officially called Singapore Green Building Product Certification, is managed by Singapore Green Building Council. Building materials are the products of focus in the certification system. There are five requirement categories in Singapore GCMC: energy efficiency, water efficiency, resource efficiency, health & environmental protection, and other green features. Four grades could be assigned to building materials: Good, Very Good, Excellent, and Leader. It can be seen that resources, health, energy, and environment are generally the common concern of the above certification systems. C2CC pays special attention to social fairness. Particularly, Mainland China and India GCMC take product quality or performance into account. The grades for materials or products in various GCMC are pretty different. Table 3. Basic information and requirement categories in GCMC systems. No GCMC systems

Type of Managing standard or institution certification

Products of focus

Standard requirement categories

Levels of certification

1

Type I

Building materials, interior design products, textiles and fabrics, paper and packaging, and personal and homecare products

Material health, Product Circularity, Clean air & climate protection, Water & soil stewardship, and Social fairness

Five-tier system (bronze, silver, gold, and platinum)

Cradle to Cradle

Cradle to Cradle Products Innovation Institute C2CPII

(continued)

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Table 3. (continued) No GCMC systems

Type of Managing standard or institution certification

Products of focus

Standard requirement categories

Levels of certification

2

China green Type I construction material certification

Ministry of Housing and Urban-Rural Development of China

Building materials, including 51 categories

Resources, Energy, Environment, and Quality

Three-tier system (one-star, two-stars, and three-stars)

3

Hong Kong Type I CIC green product certification, China

Construction Industry Council of Hong Kong

Building products and materials, including 28 categories

Carbon footprint, Human toxicity, Resource consumption, and Ecosystem impact

Five-tier system (green, bronze, silver, gold, and platinum)

4

India Green Type I Product Certification (GreenPro)

Green Products and Services Council of India

Building products and related technologies

Product Yes/No design, Product performance, Raw materials, Manufacturing process, Waste management, Product stewardship, and Innovation

5

Singapore Type I Green Building Product Certification Scheme

Singapore Green Building Council

Building materials

Energy efficiency, Water efficiency, Resource efficiency, Health & environmental protection, and other green features

Four-tier system (Good, very good, excellent, and leader)

4.2 Comparison of Requirements for Ready-Mixed Concrete in GCMC Systems Table 4 shows the requirements for ready-mixed concrete in Mainland China GCMC system. For example, all levels in this certification system requires that the waste utilization rate during the production process should reach 100% and the proportion of mixed

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solid waste materials should be 30% or more. It should be noted that there are some standards cited in these requirements. For example, the three-star requires that production energy consumption per unit of product should reach 1 degree, which is specified in GB 36888–2018. The detailed requirement for this grade is that production and transportation energy consumption per unit of product should be 0.30 and 1.85 kgce/m3 or lower, respectively. Table 4. Requirements for ready-mixed concrete in Mainland China GCMC system. Requirement Requirement category

Unit

Resources

Waste utilization rate during production process

%

100

Proportion of mixed solid waste materials

%

≥ 30

Production energy consumption per unit pf product

—

2-degree

Proportion of local materials

%

≥ 95

mg/t

≤ 200

Energy

One-Star

Environment Water-soluble hexavalent chromium content

Two-Star

Three-Star

1-degree

mg/m2 ≤ 0.2

Ammonia release

Quality

Benchmark

Industrial wastewater per unit of kg/m3 product

0

Specific activity

I Ra

—

≤ 0.6

Ir

—

≤ 0.6

The ratio of the measured standard deviation to the upper limit of that of corresponding intensity grade

—

≤ 1.0

≤ 0.8

Ratio of the measured strength and the design strength

—

≥ 1.0 and ≤ 1.3

≥ 1.15 and ≤ 1.25

Water-soluble chloride ion content

%

0.06

Durability Anti-seepage grade

—

P8-degree P10-degree P12-degree

Grade of chloride ion penetration resistance

—

II-degree

Anti-carbonization grade

—

III-degree

IV-degree

Anti-freezing grade

—

F300

F400

Source: China Green Building Material Assessment Standards [31]

III-degree

IV-degree

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Table 5 describes the requirements for ready-mixed concrete in Hong Kong GCMC system. Only greenhouse gases per unit of product are considered for ready-mixed concrete. For example, the Platinum-level for C30 requires its embodied carbon emissions intensity should lower than 252 kg CO2 e/m3 . Table 6 displays the requirements for ready-mixed concrete in India GCMC system. The credit for product design, product performance, raw materials, manufacturing process, waste management, product stewardship, and innovation are 5, 20, 30, 10, 25, 5, and 5 points, respectively. India GCMC puts much emphasis on raw materials. Among this requirement category, the use of supplementary cementitious material (Credit 3.1), use of recycled aggregates (Credit 3.2), and fine aggregate (Credit 3.3) amount to 5, 10, and 15 points, respectively. Taking Credit 3.2 as an example, when the proportion of recycled aggregates reaches 50% or higher, it can get 2 points. It can be seen that these three GCMC have quite different requirements for readymixed concrete. Mainland China and Hong Kong GCMC have clearly set the benchmark for each requirement. Hong Kong GCMC only considers the carbon footprint for readymixed concrete. Compared with the above two GCMC, India GCMC has associated the benchmark for each requirement with credit points. India GCMC could grade readymixed concrete as a green product if the credit points achieved are 50 or more. Table 5. Requirements for ready-mixed concrete in Hong Kong GCMC system. Concrete Grade

C30

C35

C40

C45

C50

C60

C70

C80

Eda

296

323

350

373

396

443

490

490

< 298

< 318

< 337

< 337

< 417

< 417

Certification Level (kg CO2 e/m3 ) Platinum

< 252

Gold

252–280 275–306 298–332 318–354 337–375 337–420 417–465 417–465

< 275

Silver

281–310 307–339 333–367 355–391 376–415 421–464 466–514 466–514

Bronze

311–340 340–372 368–403 392–429 416–455 465–509 515–563 515–563

Green

> 340

> 372

> 403

> 429

> 455

> 509

Source: Hong Kong Assessment Guides and Quantification Tools [32]

> 564

> 564

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Credits

Criteria

Credit Points

1

Product Design

5

2

Concrete Performance

20

Credit 2.1

Low Density Concrete

20

Credit 2.2

Self-Compacting Concrete

20

Credit 2.3

Concrete with high SCM (Supplementary Cementitious Material) 20

Credit 2.4

Pervious Concrete

20

Credit 2.5

High Strength Concrete

20

3

Raw Material

30

Credit 3.1

Use of supplementary cementitious material

5

Credit 3.2

Use of recycled aggregates

10

Percentage Substitution ≥ 50%

2

Percentage Substitution ≥ 60%

4

Percentage Substitution ≥ 70%

6

Percentage Substitution ≥ 80%

8

Percentage Substitution ≥ 90%

10

Credit 3.2

Fine Aggregate

15

4

CO2 Emission per tonne of concrete over base year

10

5

Manufacturing Process

25

Credit 5.1

Energy Efficiency

5

Credit 5.2

Water Efficiency

6

Credit 5.3

Particulate emission reduction

4

Credit 5.4

Waste or Concrete Sludge Management

9

Credit 5.5

Renewable Power

9

6

Product Stewardship

5

Credit 6.1

Education

3

Credit 6.2

Quality Management System

1

Credit 6.3

Extended Producer Responsibility: Institute a system for product take-back for recycling or safe disposal

1

7

Innovation

5

Credit 7.1

Innovation

4

Credit 7.2

Other Credentials, Awards and Accolades

1

Total Points Source: India GreenPro Standard for Ready Mix Concrete [33]

100

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4.3 Conclusions This study has compared the requirement categories in C2CC, Mainland China, Hong Kong, India, and Singapore GCMC systems and the requirements for ready-mixed concrete in Mainland China, Hong Kong, and India GCMC systems. The main findings are as follows. The above five certification systems share a similar interest in resources, health, energy, and environment. C2CC takes into account social justice, which denotes that businesses are committed to upholding ethical business practices and protecting human rights. Particularly, Mainland China and India GCMC systems consider the performance or quality of goods. The grades for materials or products in various GCMC are quite different. There are significant differences in the requirements for ready-mixed concrete between Mainland China, Hong Kong, and India GCMC systems. Hong Kong GCMC only considers the carbon footprint for ready-mixed concrete, while the other two systems focus on several attributes. In contrast to the GCMC in Hong Kong and Mainland China, the GCMC in India has assigned credit points to the benchmark for each criterion. This study could help decision makers of a region or organization to better understand the differences among various GCMC systems and improve its GCMC system. However, the study only presented a preliminary comparative analysis of various GCMC systems. Future study should discuss the reasons why the discrepancies among various GCMC systems exist and how to improve each GCMC system.

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Game Engine-Based Synthetic Dataset Generation of Entities on Construction Site Shenghan Li1(B) , Yaolin Zhang2 , and Yi Tan2(B) 1 Sino-Australia Joint Research Center in BIM and Smart Construction, Shenzhen University,

Shenzhen, China 2 Department of Construction Management and Real Estate, The Shenzhen University,

Shenzhen, China [email protected]

Abstract. Computer vision has been widely used in construction sites for progress monitoring, and safety monitoring. However, collecting data from construction sites and labeling them into datasets is a time-consuming, labor-intensive, and costly task. Therefore, a synthetic dataset generation approach for construction site entities based on the game engine is proposed to solve the problem of the lack of construction site datasets. In this research, construction site scene models are formulated by grouping existing digital on-site assets, and image annotation and camera calibration files are automatically generated by developed scripts in the selected game engine. The movement of the model is also controlled by developed scripts and the scene is rendered using High-Definition Rendering Pipeline (HDRP) to obtain high-resolution images. Components such as transform and Box Collider are used to get the coordinates of the object relative to the camera and the size of the bounding box, and to automatically generate the labels. In addition, the focal length, field of view (FOV), and other parameters of the camera component are utilized to calculate the camera Intrinsic when generating calibration files. By this method, a large amount of synthetic data can be quickly acquired and labeled, significantly reducing the time of dataset generation of on-site entities. Finally, the computer vision model trained on the synthetic dataset achieved 91.6% mAP on the real dataset. Keywords: Computer vision · Construction site · Game engine · Synthetic Dataset

1 Introduction Computer vision-based object detection models have proven to be efficient and powerful in several fields. A well-trained model can help users to perform vision-based tasks. Most current models in computer vision are supervised learning models, which infer a distribution function by learning a set of labeled data and can map that function to new data. Therefore, the data set directly affects the performance of computer vision-based detection models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 1602–1614, 2023. https://doi.org/10.1007/978-981-99-3626-7_123

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With the development of computer vision, image-based computer vision has evolved from image classification to recognition and even segmentation, while the annotation of data has evolved from simple categorical labels to 2D bounding box (bounding box) labels and mask labels for object objects. In addition, as the variety of collected data increases, computer vision begins to be used for the recognition and segmentation of point clouds. By lending data with depth information like point clouds and camera calibration data, the dataset can restore the 3D bounding box of the detected objects on the images, represented as the KITTI dataset. Many annotation tools have been developed to assist users in the annotation of datasets, such as LabelImg, VOTT, and point-cloud-annotation-tool. Even so, this whole process of dataset creation, from pre-collection of data, and data processing to data annotation, is accompanied by significant time, economic and labor costs. Moreover, there is still a lack of datasets on construction sites, especially those with 3D annotation of images, which limits the application of computer vision in entity recognition on construction sites. To address this situation, this research provides a game engine-based synthetic dataset generating method. This research uses Unity3D, a game development platform, for its high-quality 3D game development capabilities, various 3D assets and convenience. In Unity, a virtual scene of construction site is created to generate high-definition (HD) synthetic images. At the same time, through programmed scripts to control the events in Unity, labels of target entities on the virtual scene of construction site and calibration files of cameras are generated automatically. Then the obtained images, labels and calibration files are saved as a synthetic dataset. At last, this research uses a manually labeled dataset based on real images to verify the effectiveness of the synthetic dataset. In the following sections, the related works, methodology, and experiments are discussed in detail.

2 Related Works The use case of the proposed method in this article is to generate synthetic datasets in a virtual environment to solve the problem of difficult access to construction site datasets. To address this issue, explorations have been done by previous authors, and their research is summarized in the following sections. 2.1 Synthetic Dataset with 2D Annotation Generative image techniques provide an efficient and fast way to acquire large amounts of data for computer vision model training. The most widely used computer vision models are based on 2D annotated datasets, which represent datasets usually annotated by 2D bounding boxes, masks or 2D pose. Many explorations have been done by previous researchers in the synthetic dataset with 2D annotation. In the field of autonomous driving, Lu et al. used a game engine to generate images in the background of cities, deserts, and forests with mask labels [1]. The model trained using the synthetic dataset achieved better training results than the real dataset. Also, the method of generating synthetic datasets substantially reduces the time consumption compared to using UAVs to collect real data of the same scenes and annotate the data.

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Richter et al. generated 25,000 realistic images and mask annotations in the game Grand Theft Auto V (GTA5) by reconstructing the association among image patches through the communication between the game and graphics hardware [2]. In the field of construction, Kolar et al. use a virtual guardrail model overlaid with a real background to generate synthetic data as a training set, and the real images are then collected as a test set [3]. The performance of different image classification models on the site is verified using the training set and the test set. While the research by Zheng et al. generates the module installation dataset through a virtual environment, then, the model is trained using synthetic images plus a limited set of real images [4]. The results show that in the case of limited real images, generating synthetic images can significantly improve the performance of the module detection model. Soltani et al. proposed a method to automatically annotate images of the excavator 3D asset with the 2D bounding box, and give the annotated images a realistic background to generate synthetic datasets [5]. The detection results of the HOG detector using this method are better than the real images of construction equipment based on manual annotation. Instead of using virtual asset images or virtual backgrounds, research (Calderon et al.) automatically annotated real images to obtain 2D pose data by controlling the pose of the 3D asset to fit the pose of the excavator in the real image [6]. What’s more, Wang et al. used AR techniques to generate synthetic datasets with the 2D bounding box using camera-captured background images of the construction site and a 3D reconstruction model of the crane bucket [7]. Then, the crane construction efficiency was analyzed using the model trained from the synthetic dataset. The above-mentioned studies are all for the current mainstream computer vision models that require datasets with 2D annotations. These annotation methods or the generation of synthetic data have huge advantages in terms of time compared to manual data collection and annotation. 2.2 Synthetic Dataset with 3D Annotation In the real world, the acquisition of 3D annotation of datasets is considered to be a costly and time-consuming task. After years of development, great efforts have been made by previous researchers in datasets with 3D annotation, which provides us with datasets for training 3D target detection, such as KITTI [8], Apollo Scape [9], CityScapes 3D [10], nuScenes [11], Repo3D [12] and so on. In contrast, previous work on synthetic datasets has been rather limited. Used the CARLA simulator, an open-source simulator for autonomous driving research, to generate a dataset containing various data such as point clouds and RGB images with 2D bounding boxes, masks, 3D bounding boxes, and other annotations that can be adapted to a variety of tasks. Sun et al. introduced SHIFT, the largest multi-task synthesis dataset for autonomous driving, which contains image data in different weather and scenarios as well as annotations for various mainstream perception tasks, including the 3D bounding box [14]. These synthetic datasets for autonomous driving provide a resource for training computer vision models with inexpensive but realistic data that reflects real-world conditions.

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Most of the above studies have used real contexts as well as virtual assets to generate synthetic datasets with some success. However, there is a lack of a method to generate datasets with 2D and 3D annotations for construction sites.

3 Methodology The unity store has great assets for developers to use, including many with HD materials and construction site-related assets. This research uses the existing 3D assets with HD materials in the Unity store, including the scene and entities on construction site, as the foundation for the following research. As shown in Fig. 1, the proposed framework can be divided into 2 parts, including the generation of the dataset with 2D and 3D bounding boxes. The method proposed in this research obtains these two different datasets in four steps, which are formulated as HD Image Generation, 2D Bounding Box Generation, 3D Bounding Box Generation, and Calibration Generation. The synthetic datasets with the 2D bounding box can be

Fig. 1. Research framework

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generated by HD Image Generation and 2D Bounding Box Generation, while the synthetic datasets with the 3D bounding box can be generated by all four steps, represented as KITTI. The brief introduction of each step is as follows: HD Image Generation: Using the High Definition Render Pipeline (HDRP) of Unity and scripts to catch the rendered HD image of the scene. 2D Bounding Box Generation: Using Mesh Filter or Mesh Collider to acquire the mesh models of the parts of target models. Then, through coordinate transformation, the vertices of mesh models are projected to the screen and the outer points of 2D bounding box are calculated. 3D Bounding Box Generation: Using the Box Collider component to edit the orientation bounding box of 3D assets, and using scrips to calculate the size, rotation and position of the 3D bounding box. Calibration Generation: Using the camera components to achieve the necessary parameters of camera intrinsic generation. 3.1 Generation of Dataset with 2D Bounding Box The dataset with 2D bounding box has two components, one is the image data and the other is the annotation information corresponding to the image data. Therefore, this section will proceed to cover HD Image Generation and 2D Bounding Box Generation. HD Image Generation According to the introduction of the package manager in Unity, HDRP utilizes Physically-Based Lighting techniques, linear lighting, HDR lighting, and a configurable hybrid Tile/Cluster deferred/Forward lighting architecture, allowing users to render realistic images. Through HDRP and existing 3D assets with HD materials, a scene of construction site was built to simulate the real construction site. By reducing the variation between the two domains of the synthetic dataset and dataset of the real world, it can be foreseen that synthetic datasets-based, well-trained object detection models can adapt to real-world detection tasks better. Before acquiring HD images, there are some pre-set approaches in the camera. In Unity, the GameObject camera got a camera component, which allows users to set the projection mode to render objects and set anti-aliasing to get high-quality images. Especially, if needed, the camera component offers adjustable parameters to simulate the physical camera, including sensor size, iso, shutter speed, focal length and so on. Also, in the game view, the user can adjust the resolution and aspect ratio of the image. With these settings, the quality of the synthetic images can be guaranteed. Then, to control the camera and entities on the construction site, this research programs C# scripts and adds them to the camera and target entities. The programmed C# scripts on the camera allow users to control the camera’s transformation and catch the screen to generate HD images, while C# scripts on the entities can not only control its transformation but also can control its components’ movement. In this research, the entities’ transformation and their components’ movement are set to change automatically in every frame. And the camera is set to manually control and catch the screen at constant intervals. Therefore, when activating the play mode in Unity, the entities work following

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the scripts, and users can control the camera through the keyboard and mouse. At certain intervals, the camera will catch the screen and generate images automatically. 2D Bounding Box Generation The 2D bounding box is the exact rectangle box that encloses the target object. There are two representations of 2D bounding boxes, one with the coordinates of the upper left and lower right corners, and the other with the coordinates of the center of the rectangle and the width and height. Which 2D bounding box representation is used depends on the needs of the chosen computer vision model. This research iterates the vertices coordinates of the mesh model, projects them to the image plane, and calculates the maximum and minimum values of their image coordinates to generate the 2D bounding box. Usually, a complicated entity is created separately, and the finished model usually composes of different parts. In Unity, there are different LOD models used in the same parts, the low LOD models are used for collision calculation and the high LOD models are used for model rendering. These two different LOD models are used to generate different kinds of precision 2D bounding boxes. GetComponentsInChildren < MeshFilter > ()

(1)

GetComponentsInChildren < MeshCollider > ()

(2)

The approach to generating the 2D bounding box based on two different LOD models is similar. This research, by C# script, uses Function. (1) to access high LOD mesh and uses Function. (2) to access low LOD mesh. The meshes in Unity contain vertices coordinate arrays in the local coordinate system, which can be obtained using the C# script. Then use the function TransformPoint() in unity to convert these vertices’ local coordinates to world coordinates and then use WorldToScreenPoint() to image coordinates. After calculating the maximum and minimum values of their image coordinates (xmin , ymin , xmax , ymax ), the 2D bounding box can be generated. For the other 2D bounding box representation, the center coordinates of the 2D bounding box and the width and height of the bounding box can also be obtained from these coordinates. Figure 2 shows the generation of the 2D bounding box.

Fig. 2. 2D Bounding Box Generation. (a) 2D Bounding Box of each component, (b) 2D Bounding Box of the Entity.

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Fig. 3. The precision of 2D Bounding Box. (a) Mesh of Mesh Collider. (b) Mesh of Mesh Filter. (c) Generated Bounding Box of Mesh Collider. (d) Generated Bounding Box of Mesh Filter.

As shown in Fig. 3, the 2D bounding box generated by the low LOD mesh is not completely close to the entities, while the 2D bounding box generated by the high LOD mesh can be completely close to the entities. However, using a high LOD mesh model consumes more computational resources, resulting in a slower speed of 2D bounding box generation. As the camera moves away from the entities to obtain a larger view, the deviation in the 2D bounding box generation based on the low LOD mesh can be negligible on the image coordinate system. Therefore, to generate the 2D bounding box more quickly and stably, this research recommends using the low LOD mesh and using high LOD mesh instead only when the low LOD mesh does not exist. 3.2 Generation of Dataset with 3D Bounding Box Usually, 3D box annotation of an object does not require 2D bounding box information of the object. However, this study refers to the annotation method of the KITTI dataset. In the annotation file of the KITTI dataset, in addition to the information on the 3D bounding box, the information on the 2D bounding box is also added. Therefore, in this article, the generation of the dataset with 3D bounding box contains four parts, besides the two parts previously mentioned, it will also contain 3D Bounding Box Generation and Calibration Generation, which will be described in the following sections. 3D Bounding Box Generation Different from the 2D bounding box, the label of a 3D bounding box consists of information about dimensions, position, and rotation. The KITTI dataset is one of the most widely used datasets in the field of autonomous driving. The 3D bounding box annotation of vehicles, pedestrians and other objects in KITTI is shown in Function (3).    (3) Dimensions(H , W , L), Position(x, y, z), Rotation Rotationy , Alpha Dimensions(H , W , L) is the dimensions of the 3D bounding box, including height, width, and length (in meters). Position(x, y, z) is the camera coordinate of the 3D bounding box bottom center (in meters).  assumes that the object only has  The KITTI dataset the yaw angle. Thus, in Rotation Rotationy , Alpha , Rotationy is the yaw angle in the camera coordinate, and Alpha is the observation angle of the object. All of the above parameters are shown in Fig. 4. This research uses the Box Collider component to attach an orientational 3D bounding box for the target entity, whose dimensions can be edited manually. In addition,

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Fig. 4. 3D Bounding Box and Rotation. (a) Size of 3D Bounding BOX, (2) Rotations.

the dimensions of the 3D bounding box can be obtained by accessing the Box Collider through the C# script. In Unity, each object has a transform component, which contains position information, rotation information, and scale. To get the position of the target entity, the programmed C# script obtains the position of the entity and camera, the target entity position in the camera coordinate can be calculated in Eq. (4). EntityPositionCamera = EntityPositionWorld − CameraPositionWorld

(4)

As for the Rotation of the 3D bounding box, this research based on the angle assumption of the KITTI dataset calculates the Rotationy and Alpha. Therefore, the camera rotation and target entity rotation are set to yaw angle only, which is the rotation of the y-axis within Unity. Rotationy , the rotation in camera coordinate, can be calculated in Eq. (5). Rotationy = Entityy − Cameray

(5)

Entityy is the yaw angle of the target entity in the world coordinate of Unity, with a plus or minus sign to indicate its direction of rotation. Similarly, Cameray is the yaw angle of the camera in the world coordinate. After calculation of Eq. (5), Rotationy is converted from the angle to the radian and controlled in (−π ∼ π). Then, based on the position of the target entity in the camera coordinate, the Theta shown in Fig. 4(b) can be calculated. Thus, Eq. (6) shows the calculation of Alpha. Alpha = Rotationy − Theta

(6)

Calibration Generation In general, when different types of data are collected by multiple cameras, such as point clouds and RGB images, a coordinate transformation matrix between different cameras is required to convert the coordinates under different camera coordinate systems. Therefore, calibration files in such datasets contain camera-to-camera transformation matrices, as well as intrinsic matrices for converting 3D camera coordinates to 2D image coordinates for RGB cameras.

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In reality, 3D bounding box annotation of objects in images requires the following processes: data collection, data processing, annotation of data with 3D characteristics (e.g., point clouds), and annotation coordinate transformation. Different from realistic methods of collecting data and labeling, this research only uses a single camera to collect synthetic HD images in Unity. Thus, the generation of the camera calibration file does not need to consider the camera-to-camera coordinate transformation matrix, but only the camera intrinsic matrix. The camera intrinsic matrix is shown in Eq. (7). ⎛

⎞ fx s u0 K = ⎝ 0 fy v0 ⎠ 0 0 1

(7)

fx and fy are the focal lengths in the x and y directions. s is the axis skew which is 0 in this research. u0 and v0 are the offsets from the image center to the origin of the image coordinate. Usually, the origin of the image coordinates is set at the lower left corner of the images. Therefore, u0 and v0 are related to the screen width and height set in Game View, which can be calculated in Width/2 and Heigth/2. Therefore, only fx and fy need to be calculated to obtain the camera’s intrinsic matrix. fx =

u0 tan(ax /2)

(8)

fy =

v0 tan(ay /2)

(9)

To calculate the fx and fy , the programmed C# script achieves the vertical field of view (FOV) (denote by ax ) by Camera.fieldOfView. Based on the aspect ratio of the screen, the horizontal FOV (denote by ay ) can be calculated. Thus, Eq. (8) and Eq. (9) show the calculation of fx and fy . ⎞ ⎛ ⎛ n ⎞ n xpixel xcamera ⎜ n ⎟ n ⎠n ∈ [1, 8] (10) z n ⎝ ypixel ⎠ = K · ⎝ ycamera n zcamera 1 After obtaining the camera intrinsic, the 8 camera coordinates of the corner points of the 3D bounding box can be transformed into the image by Eq. (10). The 8 camera coordinates of the corner points of the 3D bounding box can be calculated by using the annotation of Function (3). The 3D annotation of the target object is visualized in Fig. 5.

4 Experiment Due to the lack of a 3D bounding box dataset for construction sites currently, this research will validate the effectiveness of a synthetic dataset with 2D bounding boxes generated in Unity. Excavators are important entities on the construction site, and there exists an important significance of excavation recognition through computer vision. Therefore, in this research, the excavator was chosen to evaluate the synthetic dataset effectiveness.

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Fig. 5. 3D Bounding Box Visualization

4.1 Dataset Preparing The movability of the excavator’s components, including cab, boom, arm and bucket, allows the excavator to have different 2D bounding boxes in different positions. First of all, this research creates the terrain in Unity and arranges the camera, excavators and other assets to get a realistic scene by rendering under HDRP. Then, the basic parameters of the camera such as FOV and pixels are set. After that, the scene images are captured through a programmed C# script, and the corresponding annotations for the excavator are generated. By using the method proposed above, a synthetic dataset of 401 images and corresponding annotations of different poses of the excavator in different scenes was collected. In addition, excavator videos are collected from the Internet, and 172 frames are evenly extracted and annotated to create a real dataset. Figure 6(a) shows the synthetic dataset while Fig. 6(b) shows the real dataset.

Fig. 6. Part of the Train and Valid Dataset. (a) Synthetic Dataset, (b) Real Dataset

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4.2 Model Training Yolov5 is used for training and validation, which is a single-stage detector that maintains a very fast speed while balancing accuracy. Meanwhile, the synthetic dataset is used as the train set and the real dataset is used as the valid set. In the experiments, training and testing were performed on a mobile workstation with windows 10 OS, which has an NVIDIA GeForce RTX 3080 Laptop GPU, 16 GB and an AMD Ryzen9 5900HX CPU. The virtual environment was built based on Yolov5 open-source code, including Python-3.9.0, Pytorch-1.9.0 + cu111 and Yolov5 requirements. In training, the SGD optimizer is utilized to update the parameters of gradient descent. The initial learning rate was set to 0.01, the momentum to 0.937, and the weight decay to 0.0005. Also, with the total epochs of 200 and the batch size of 8, it took 1 h to complete training. 4.3 Result and Discussions The Precision-Recall curve (Fig. 7) shows that the model trained on the synthetic dataset achieves a mean average precision (mAP) of 91.6% in the validation set that uses real data. It means that the synthetic dataset generated by the virtual environment can effectively fit the real situation. The performance of the trained model in the valid set is shown in Fig. 9.

Fig. 7. Precision-Recall Curve

However, reviewing the loss function curves of the training process (Fig. 8), the training loss (obj_loss) is decreasing while the validation loss is increasing, which presents an overfitting situation. This research attributes this situation to the difference in complexity between the virtual model and the real environment, the homogeneous environment in which the synthetic images were acquired, and the small amount of training data set. Even so, with the help of Unity, users have the flexibility to import their models and build their scenes, thus increasing the number of scenes and models. This also means that the synthetic images will tend to be similar to the real ones. In addition, in our finite experiments, the average annotation speed of an image is 31.21 s (2–3 objects to be annotated), while the method used in this research can automatically acquire images and their annotations at a fixed time (usually 1–2 s). Compared with manual data acquisition and manual annotation, the method in this study shows a great speed advantage.

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Fig. 8. Train and Valid Loss

Fig. 9. Performance on the Valid set. (a) Label, (b) Prediction

5 Conclusions Computer vision is used on construction sites to help make construction sites more informative and intelligent. However, the application of computer vision in construction sites is very limited due to the lack of data sets, which also limits the development of information technology in the traditional civil engineering industry. Usually, the datasets are created by collecting the corresponding data on the Internet or in the real environment, processing the data, and then manually annotating them, which is time-consuming for the whole process. Therefore, this research proposes a game engine-based synthetic dataset generation of entities on the construction site. The method enables the capture of realistic HD images within the game engine by controlling entities in the game engine through the programmed scripts, and generating the corresponding annotation and camera file by using scene, model information, and camera parameters. Through training with the synthetic dataset, the model reaches an mAP of 91.6% on the validation set that uses real data, reflecting that the synthetic dataset in a completely virtual environment can well reflect reality. However, the method proposed in this research also has some limitations. On the one hand, the model assets and scene assets are not enough, which leads to a large number of images generated in a single scene, in a single weather condition, not able to cover all situations occurring in reality, thus leading to the phenomena of overfitting in the

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training process. On the other hand, this research controls the size of the 3D bounding box by manually setting the parameters of the Box Collider component, which is not yet able to automatically adapt the size of the 3D bounding box for some movable entities, such as excavators. Furthermore, the effectiveness of the method proposed in this study for generating synthetic datasets with 3D bounding box cannot be verified, due to the non-existence of datasets annotated with 3D bounding box about construction sites. Even so, the application of the presented research method to transfer learning can be expected. Future attempts can be made to verify whether the synthetic dataset can improve the training of the model with a small amount of labeled data. In addition, in the future, researchers can try to introduce oblique photography models to solve the problem of lack of virtual scene assets, and also improve the problem of the difference between virtual scenes and real scenes.

References 1. Lu, T., Huyen, A., Nguyen, L., Osborne, J., Eldin, S., Yun, K.: Optimized training of deep neural network for image analysis using synthetic objects and augmented reality. Pattern Recogn. Track. XXX 10995, 106–116 (2019) 2. Richter, S.R., Vineet, V., Roth, S., Koltun, V.: Playing for data: ground truth from computer games. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 102–118. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46475-6_7 3. Kolar, Z., Chen, H., Luo, X.: Transfer learning and deep convolutional neural networks for safety guardrail detection in 2D images. Autom. Constr. 89, 58–70 (2018) 4. Zheng, Z., Zhang, Z., Pan, W.: Virtual prototyping-and transfer learning-enabled module detection for modular integrated construction. Autom. Constr. 120, 103387 (2020) 5. Soltani, M.M., Zhu, Z., Hammad, A.: Automated annotation for visual recognition of construction resources using synthetic images. Autom. Constr. 62, 14–23 (2016) 6. Calderon, W.T., Roberts, D., Golparvar-Fard, M.: Synthesizing pose sequences from 3D assets for vision-based activity analysis. J. Comput. Civ. Eng. 35(1), 04020052 (2021) 7. Wang, D., et al.: Vision-based productivity analysis of cable crane transportation using augmented reality-based synthetic image. J. Comput. Civ. Eng. 36(1), 04021030 (2022) 8. Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? the kitti vision benchmark suite. IEEE Conf. Comput. Vis. Pattern Recogn. 2012, 3354–3361 (2012) 9. Huang, X., Wang, P., Cheng, X., Zhou, D., Geng, Q., Yang, R.: The apolloscape open dataset for autonomous driving and its application. IEEE Trans. Pattern Anal. Mach. Intell. 42(10), 2702–2719 (2019) 10. Gählert, N., Jourdan, N., Cordts, M., Franke, U., Denzler, J.: Cityscapes 3D: dataset and benchmark for 9 DoF vehicle detection, arXiv preprint arXiv:2006.07864 (2020) 11. Caesar, H., et al.: nuScenes: a multimodal dataset for autonomous driving. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11621–11631 (2020) 12. Ye, X., et al.: Rope3D: the roadside perception dataset for autonomous driving and monocular 3D object detection task. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 21341–21350 (2022) 13. Weng, X., et al.: All-in-one drive: a large-scale comprehensive perception dataset with highdensity long-range point clouds (2020) 14. Sun, T., et al.: SHIFT: a synthetic driving dataset for continuous multi-task domain adaptation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 21371–21382 (2022)

Effects of Inter-organizational Activities on Construction Project Resilience in the Context of COVID-19 Pandemic Kangda Wan1 , Liyue Tan1 , Shiyu Bian1 , and Wenxin Shen2(B) 1 School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China

{21241275,21241273,21241259}@bjtu.edu.cn

2 Department of Construction Management, School of Economics and Management, Beijing

Jiaotong University, Beijing 100044, China [email protected]

Abstract. Construction projects are vulnerable to sudden crises such as the COVID-19 pandemic. Hence, many construction companies are increasingly realizing the importance of project resilience. However, most of the current studies have remained on the organizational resilience level, and there is a lack of research on project resilience. This study develops a theoretical model to explore the relationships among inter-organizational activities, organizational crisis awareness, organizational crisis response, and project resilience in the context of COVID-19 pandemic; and empirically tests the proposed model using data collected from 98 projects during the pandemic. Results show that inter-organizational activity is an important factor affecting project resilience. Meanwhile, organizational crisis awareness and organizational crisis response play important mediating roles. This paper reveals the significance of inter-organizational activities in improving project resilience and provides a new perspective for further exploring project resilience. It also helps construction enterprises formulate additional appropriate strategies to deal with sudden crises in the post-pandemic era. Keywords: Resilience · Crisis management · Inter-organizational activities · Construction management

1 Introduction With extensive complexity and uncertainty, construction projects are easily and unavoidably affected by extreme and continuously changing outside conditions [1]. Owing to the COVID-19 pandemic, many construction projects have experienced a shortage of manpower and material resources and a broken capital chain, resulting in the suspension of projects. However, some projects showed high-efficiency reactions to interruption, rapid resumption of operation, and on-time delivery [2]. Given the constantly increasing uncertainty of the nature of economics and society in the post-pandemic era, the construction industry is becoming increasingly interested in research on construction project resilience [3]. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 1615–1626, 2023. https://doi.org/10.1007/978-981-99-3626-7_124

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Under the background of these projects, project resilience is an important but underdeveloped research topic. Given the features of construction projects’ project resilience, adverse events have varying impacts on project stakeholders (e.g., owners, architects, contractors, and suppliers). Hence, project stakeholders’ ability or willingness to cope with and adapt to adversity also varies. In extreme situations with limited resources and critical time (e.g., crises and natural disasters), project stakeholders may have conflicts of interest in resources and survival, and they may be unable to cooperate to overcome problems. Under such circumstances, partnering and boundary activities can help [4]. However, only a few empirical studies have explored the relationships among interorganizational activities, organizational crisis awareness, organizational crisis response, and project resilience. This study used the COVID-19 pandemic as a basis to investigate the direct relationships among inter-organizational activities and organizational crisis awareness, organizational crisis response, and project resilience, as well as the impact of organizational crisis awareness and organizational crisis response on project resilience.

2 Theoretical Background Organization studies have commonly defined an organization/system’s abilities or processes to respond to and recover from adverse situations with the least influence on stability and functionality [5]. Organizational resilience mainly involves three types of abilities: abilities to be aware of, respond to, and recover from crises. Organizational crisis awareness emphasizes the perception of change, understanding of the new environment, and cultivating the recognition of the core values and vision of organizations. Such awareness helps organizations make a rapid and accurate assessment in the face of unexpected or difficult situations, and assists organizations formulate coping strategies [6]. Organizational crisis response refers to the ability of organizations to immediately respond and resist the negative effects of risks to protect their functioning from being completely destroyed. Recovery refers to the ability of organizations immediately recover from crises. Inter-organizational behaviors refer to actions to interact and build relationships with relevant organizations in the external environment [7, 8]. Frequent inter-organizational activities can help companies gain more effective messages, contribute to learning, recognize potential dangers in advance, and strengthen organizational awareness of crises. When dangerous situations happen, companies are supposed to obtain outside information and coordinate the related resources to immediately adopt the corresponding measures to deal with inter-organizational behaviors: adjust plans and resource arrangements to reduce losses brought by risks to organizations and rapidly adapt to the results they have brought through promptly communicating with the owners and suppliers of materials and machines. Hence, we formulate the following hypotheses: H1: Inter-organizational activities are positively associated with organizational crisis awareness. H2: Inter-organizational activities are positively associated with organizational crisis response. This study defines construction project resilience as the ability of construction project teams to answer effectively, remain, or immediately recover their functioning, adapt,

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and study when risk accidents happen. Given that construction projects are composed of multiple organizations (e.g., owners, designers, contractors, suppliers), and face a highly complicated environment full of uncertainty in most cases. Accordingly, the key to lifting construction project resilience is the project team’s capability to coordinate limited resources and social relationships across organizations. How project stakeholders cooperate and coordinate with each other decide a project’s success [9]. Inter-organizational activities in construction projects incorporate the following aspects [10]. First, resources and information are acquired. For example, contractors must work based on technical blueprints collected from architects [11]. Second, information is disseminated. That is, external groups involved should be updated on organizations’ operations and progress. Given that construction activities are highly interdependent and presumably change, designers and contractors should promptly update technical information to guarantee the precision and coherence of information [12]. Third, cooperation and negotiation aim to address technical and managerial issues. Communicating design issues with outsiders, acquiring feedback, and coordinating and negotiating with others to solve disruptions and crises are included in these activities. To prepare and moderate risks or crises, proactive inter-organizational activities should be conducted [13, 14]. However, as many crises cannot be predicted, the focus is directed at the ability of systems to recover over time [15]. In addition, interdependent creeping disruptions (e.g., undiscovered design mistakes, alterations in projects, and misconceptions between project members) commonly occur. Such crises necessitate inter-organizational activities to promote urgency among project participants. In a resource-scarce situation, project teams are suggested to positively engage in inter-organizational activities compared with those operating in a resource-abundant situation [16]. Teams can be motivated to search for external opportunities by the urgent need for essential resources to address crises [17]. Organizations must participate in a series of inter-organizational activities, including exchanging information and coordinating with stakeholders, to maintain functioning and thrive amidst shocks and stressors [18]. Teams obtain additional resources and messages from organizations to survive in the plight of inter-organizational activities. Through boundary reinforcement, teams underscore the primacy of the project group, stimulate members to contribute to project tasks and strengthen their determination as they adapt to and recover from adversities. Through prompt inter-organizational communication, resilient projects demand the ability to actively adjust to changing and fast-paced changes by expanding informational inputs and reallocating resources [19]. Under challenging conditions, project stakeholders with high task interdependence must coordinate efforts to achieve corporate goals [20]. Projects that carry out efficient inter-organizational activities can gain resources and messages and buffer external demands [7]. Therefore, we hypothesize as follows: H3: Inter-organizational activities are positively associated with project resilience. In the aspects of mechanisms and processes, various information sharing mechanisms [15]enable the effective and prompt circulation of information within organizations. Similar to the “prophet of spring River and warm duck”, mechanisms also enhance the possibility of awareness of adversity to start the prevention plan on time. Mature

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management processes [21] and flexible decision-making mechanisms [15] make organizations considerably flexible and agile in the face of risks and crises. Therefore, we present the following hypothesis: H4: Organizational crisis awareness is positively associated with organizational crisis response. Projects are part of organizations’ businesses and temporary organizations, so there exists a close relationship between project resilience and organizational resilience. Organizational resilience is defined as “an ability to help the organization adapt to changes, answer shocks and gain growth and prosperity.” Similar to organization resilience, project resilience is the norm and continues throughout the project life cycle. Organizational resilience can relatively affect whether or not projects being implemented would be able to immediately respond and recover after suffering the strike. For example, organizations that lack awareness of crisis are content with things as they are. Moreover, they have difficulty detecting signs of danger. When facing problems brought by risks (e.g., interruption of the supply chain, increasing prices), projects may be unable to promptly offer and implement solutions and adapt to changes. Organizational vulnerability is affected by the level of organizational management and control capabilities (e.g., psychological readiness, resource readiness, organizational resilience, etc.). If organizations have strong management and control abilities, including adequate psychological and resource preparations, and strong organizational adaptability, their projects will likely have low vulnerabilities to adversity events, particularly disaster crises, and black swan events). Therefore, the degree of organizational management and control abilities can affect the vulnerability of a project in the process of resource rearrangement and adjustment ability under the conditions of adversity events, thereby affecting resilience. We use the preceding analyses as bases to formulate the following hypotheses: H5: Organizational crisis awareness is positively associated with project resilience. H6: Organizational crisis response is positively associated with project resilience.

Fig. 1. Conceptual model

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3 Research Methodology 3.1 Data Collection A questionnaire was designed to test the assumption of the proposed model. In the questionnaire, respondents were required to provide their perceptions of organizational crisis awareness, organizational crisis response, inter-organizational activities, and project resilience in their selective projects. Multiple paper copies of the questionnaires were dispensed to 152 Chinese construction companies during the Project Management Congress 2020. There were 98 valid questionnaires being totally received. The average project investment is 16.6 million yuan. Of the valid questionnaires returned, nearly 40% were project managers, 11.9% were management members of organizations, and the remainder were key project members. For the average project period, nearly 40% were over 6 months. Approximately 61.2% of the respondents were from private companies, 14.3% from state-owned companies, and 15.3% from international companies. 3.2 Measures A five-point Likert scale, which ranges from 1 (strongly disagree) to 5 (strongly agree), was used to measure the construction projects in the model (shown in Table 1). Table 1. Variable Measurement

Inter-organizational Activities

Variables

Explanations

References

Inter-organizational Activities (IA1)

Our organization frequently interacts with external organizations to obtain critical information, resources, and support

Shen et al. (2021) [10]

Inter-organizational Activities 2 (IA2)

Our organization can coordinate with other stakeholders to achieve common goals

Inter-organizational Activities 3 (IA3)

Our organization can improve members’ recognition of the organization by enhancing their boundary awareness and shaping their identity (continued)

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Organizational Crisis Awareness

Organizational Crisis Response

Project Resilience

Variables

Explanations

References

Organizational Crisis Awareness 1 (OCA1)

The organization Kutsch et al.(2015) encourages members to [22] share events of risk and uncertainty in their work, even if these aspects are beyond their responsibilities

Organizational Crisis Awareness 2 (OCA2)

Our organization can accurately predict and identify risks and plan for them

Organizational Crisis Awareness 3 (OCA3)

Our organization uses smart tools and technologies to anticipate risks

Organizational Crisis Response 1 (OCR1)

When crises occur, our Kutsch et al.(2015) organization is able to [22] adjust to the consequences

Organizational Crisis Response 2 (OCR2)

When crises occur, our organization can provide relevant information in a calm, orderly, and controlled manner

Organizational Crisis Response 3 (OCR3)

Our organization provides employees with an appropriate level of freedom to enable the development of creative crisis solutions

Project Risk Prediction In our organization, Kutsch et al.(2015) (PR1) risk and uncertainty are [22] seen as “good things” to be wary of Project Risk Response When crises occur, our (PR2) project team can respond immediately Project Recovery (PR3)

Our project team members reflect on their actions during the crisis and constantly learn and improve to cope with future events

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4 Result 4.1 Descriptive Statistics We used SPSS 26.0 to calculate the means and Pearson correlation among the four variables. The results are shown in Table 2. Inter-organizational activities, organizational crisis awareness, and organizational crisis response are remarkably correlated with project resilience, with the correlation coefficients being 0.58, 0.64, and 0.62, respectively. Therefore, inter-organizational activities and the other three variables have a high degree of correlation. The linear correlation indicates that inter-organizational activities are a key factor in studying organizational activity and project resilience, which is consistent with our research theme. Moreover, the correlation coefficient between organizational crisis awareness and project resilience was 0.61 (p < 0.01), and the correlation coefficient between organizational crisis response and project resilience was 0.47 (p < 0.01). Therefore, organizational crisis awareness and organizational crisis response have a strong impact on project resilience. Moreover, inter-organizational activities can indirectly affect project resilience by affecting organizational crisis awareness and organizational crisis response. Therefore, the orderly development of inter-organizational activities is an effective way to improve project resilience. Table 2. Descriptive Analysis for Constructs Means SD

Cronbach’s α CR

1

2

3

4

1. Organizational Crisis Awareness

3.36

0.85 0.77

0.80 0.75

2. Organizational Crisis Response

3.63

0.75 0.80

0.84 0.64** 0.81

3. Inter-organizational 3.80 Activities

0.73 0.81

0.82 0.58** 0.64** 0.77

4. Project Resilience

0.68 0.73

0.73 0.61** 0.47** 0.62** 0.71

3.64

Note: SD = standard deviations; CR = composite reliability; values in diagonal are the square root of the average variance extracted (AVE); non-diagonal values are latent variable correlations; ** = correlation is significant at the 0.01 level (two-tailed)

4.2 Convergent Validity Analysis Convergent validity of the scale refers to the fact that the observation indicators measuring the same categorical variable will fall on the same common factor. This aspect is used to test the degree to which the observation indicator measures the categorical variable it measures. Convergent validity is often analyzed using three methods: item validity, combined validity (CR) of latent variables, and mean average variance extraction (AVE). The internal consistency reliability coefficient Cronbach’s α of the scale was calculated using the 98 valid samples of the formal survey. As shown in Table 2,

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all factors have Cronbach’s α between 0.73 and 0.81, which are greater than the threshold of 0.7 [23]. Cronbach’s α for organizational crisis awareness, organizational crisis response, inter-organizational activities, and project resilience is over 0.7 and has good measurement reliability. In addition, factor loading (ranging from 0.56 to 0.92) is larger than 0.5, the recommended value. CR (ranging from 0.73 to 0.84) is greater than 0.7, the recommended value [24], and AVE (ranging from 0.50 to 0.66) is above 0.5 [24]. The results indicate that the scale has good internal consistency and convergent validity. 4.3 Discriminant Validity Analysis Discriminant validity is mean the low degree of correlation or striking difference among latent variables. Table 2 is a matrix of correlation coefficients among the variables in this research, in which the value of 1 on the diagonal is replaced by the average variance extraction of the latent variable. If the value of the cell corresponding to the diagonal position is larger than the value of the corresponding row and column, the degree of discrimination between different variables is considered higher, which is the criterion to judge the discriminant validity among variables [24]. Table 2 indicates that the discriminant validity of the variables in this study is good at the conceptual level. In summary, the variables corresponding to the research model proposed in this study are considered appropriate. 4.4 Model Fit Analysis When the factors in the model and scale have been fixed, confirmatory factor analysis (CFA) can be used to test whether or not the model fits the actual data collected. Table 3 shows the fitness index and ideal fitness index value of this structural equation model [25]. The result of the goodness-of-fit (GOF) statistics of the model met the recommended criteria presented in Table 3. This result implies that the data fit the model well. Table 3. Overall SEM Model Results: Goodness-Of-Fit Measures CMIN/DF

RMSEA

TLI

IFI

Recommended value

0.9

> 0.9

Model results

1.502

0.072

0.942

0.959

PCFI

PNFI

> 0.5

> 0.5

0.696

0.645

Note: CMIN/DF: Chi-square/degree of freedom; RMSEA: Root-mean-square error of approximation index; TLI: Tucker–Lewis index; IFI: Incremental fit index; PCFI: Parsimonious comparative-fit-index; PNFI: Parsimonious normed fit index

4.5 Structural Model Evaluation Figure 2 demonstrates the result of the structural equation model. In Hypothesis 1, it’s suggested that inter-organizational activities have a positive and remarkable effect on organizational crisis awareness. The path coefficient between

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Fig. 2. The structural model

inter-organizational activities and organizational crisis awareness is markedly significant (β = 0.67, p < .001), proving that Hypothesis 1 is tenable. Hypothesis 2 states that inter-organizational activities have a positive influence on organizational crisis response (β = 0.37, p < .05). Thus, Hypothesis 2 is supported. There is a positive and remarkable relationship between inter-organizational activities and project resilience (β = 0.63, p < .001). This result proves that inter-organizational activities have a significant influence on project resilience. Therefore, Hypothesis 3 is supported. Hypothesis 4 demonstrates that organizational crisis awareness has an influence on organizational crisis response. Moreover, the relationship between organizational crisis awareness and organizational crisis response is also significant (β = 0.49, p < .01). Hence, Hypothesis 4 is supported. The results also show that the relationship between inter-organizational activities and organizational crisis is partially mediated by organizational crisis awareness. That is, inter-organizational activities directly facilitate organizational crisis response and also indirectly improve organizational crisis response by strengthening organizational crisis awareness. However, the relationship between organizational crisis response and project resilience is statistically insignificant. Therefore, Hypothesis 5 is not supported. A positive relationship exists between organizational crisis awareness and project resilience, as proposed in Hypothesis 6 (β = 0.55, p < .01). The result shows that inter-organizational activities can indirectly affect project resilience through organizational crisis awareness. The value of R2 of project resilience is 0.78, indicating that the three predictors in the model collectively explained 78% of the variance in project resilience. Note that interorganizational activities account for 45% of the variance in project crisis awareness and 61% of the variance in organizational crisis response is accounted for by interorganizational activities and organizational crisis awareness. This result demonstrates that inter-organizational activities are indispensable in improving project resilience, thereby achieving better project performance.

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5 Discussion and Contribution This study established a theoretical model of the influence mechanism of interorganizational activities on project resilience (see Fig. 1) and carried out tests (see Tables 2 and 3). According to the results, inter-organizational activities can directly affect project resilience, but project resilience can also be indirectly affected by influencing organizational crisis awareness and organizational crisis response. This outcome also indicates that increasing inter-organizational activities is indispensable for improving project resilience, thereby achieving better project performance [18]. Interorganizational activities enable organizations to establish extensive contacts with external organizations to collect additional valuable information and resources [19], which is helpful in immediately reducing information asymmetry and detecting adverse situations. Risks are identified in advance and prevention plans are made. Therefore, interorganizational activities are helpful in enhancing their perception and insight into the surrounding environment and improving their risk awareness [13, 14]. At the start of the outbreak of COVID-19 in China, for example, the China Construction Third Bureau received orders to rush to build a 1,000-bed hospital. With the close cooperation and coordination of the government, the general contractor, multiple subcontractors and suppliers, Huoshenshan Hospital was completed in ten days. Meanwhile, the timely prediction of crisis (organizational crisis awareness) also helps organizations to have a timely response time and collect the resources needed to prepare for risks in advance to better cope with them. Organizations associated with the project, when the organizations considerably predict crises, can also help the project team immediately gain familiarity with the crises, thereby reducing the impact of the crisis on the project. Moreover, good organizations can provide the project with better back support for recovery after the crisis to provide a better environment for the project, thereby increasing and improving project crisis resilience. The test of interorganizational activities on the structural equation model of project resilience in this paper shows that Hypothesis 5 is not valid and negatively correlated. We speculate that the reason is that some construction enterprises choose conservative strategies in the face of risks, temporarily stop supporting resources for projects that are not their main business, withdraw the resources of the projects to the organization, and prioritize ensuring the orderly development of organizational activities. This situation will make the organization minimally impacted and significantly respond to crises. However, it will reduce project resilience and project performance. By studying project resilience, a popular topic with theoretical breakthroughs and practical urgency, the theoretical contributions of this paper are mainly reflected in the following aspects. First, an increasing number of studies have focused on the resilience of construction enterprises [3]. However, most of them have remained at the level of organizational resilience and lacked research on project resilience [1]. The current study extends organizational resilience to the project level, deeply analyzes the conceptual connotation of project resilience, and proposes a fundamental difference between project and organizational resilience. The research results enrich the existing conceptual research on project resilience and also expand the theoretical system of project management. Second, most studies on resilience have remained at the technical level of projects, and only a few studies have discussed project resilience from the management level [22, 26].

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This study aims to provide feasible suggestions for improving project resilience from the management perspective. Third, in the COVID-19 pandemic context, given the impact of the extreme environment faced by engineering construction in the pandemic context [1], this paper reveals the significance of inter-organizational activities in improving project resilience. This result will help compensate for the currently limited projectlevel resilience research. This study provides a new perspective to further explore the important role of project resilience.

6 Conclusion This study created a theoretical model to research how inter-organizational activities influence organizational crisis awareness and organizational crisis response and, therefore, project resilience. With the help of the 98 questionnaires collected from various construction projects, the following conclusions are presented. First, inter-organizational activities have a significantly positive effect on organizational crisis awareness and organizational crisis response. Second, inter-organizational activities can positively influence project resilience, and their direct impact on shaping project resilience is substantially stronger than that of the other two determinants in the model. Third, organizational crisis awareness partially mediates the relationship between inter-organizational activities and project resilience. Nevertheless, our research has some limitations. First, crisis events may have different degrees of impact on projects in different stages (design, construction, and completion), but this aspect was not discussed in this study. This paper can be further improved in future research. Second, the theoretical model can be tested with a larger sample size in the future. Acknowledgement. This research was supported by the National Natural Science Foundation of China (Grant No. 72201027) and the China Postdoctoral Science Foundation (Grant Nos. 2021T140047 and 2020M680333), and Beijing Training Program of Innovation and Entrepreneurship for Undergraduates (Grant Nos. 202310004003).

References 1. Naderpajouh, N., Matinheikki, J., Keeys, L.A., Aldrich, D.P., Linkov, I.: Resilience and projects: an interdisciplinary crossroad. Proj. Leadersh. Soc. 1(6), 100001. Elsevier Ltd (2020) 2. Wang, W., et al.: How the COVID-19 outbreak affected organizational citizenship behavior in emergency construction megaprojects: case study from two emergency hospital projects in Wuhan, China. J. Manag. Eng. 37(3), 04021008 (2021) 3. Lim, H.W., Zhang, F., Fang, D., Peña-Mora, F., Liao, P.-C.: Corporate social responsibility on disaster resilience issues by international contractors. J. Manag. Eng. 37(1), 04020089 (2021) 4. Brown, C., Seville, E., Vargo, J.: Measuring the organizational resilience of critical infrastructure providers: a New Zealand case study. Int. J. Crit. Infrastruct. Protect. 18, 37–49. Elsevier, B.V. (2017) 5. Sutcliffe, K.M., Vogus, T.J.: Organizing for resilience. Posit. Organ. Scholarsh, Found. New Discipline 94, 110 (2003) 6. Lazarus, R.S.: Psychological Stress and the Coping Process. McGraw-Hill, New York (1966)

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7. Ancona, D.G., Caldwell, D.: Beyond boundary spanning: managing external dependence in product development teams. J. High Technol. Manag. Res. 1(2), 119–135 (1990) 8. Marrone, J.A.: Team boundary spanning: a multilevel review of past research and proposals for the future. J. Manag. 36(4), 911–940 (2010) 9. Kahn, W.A., Barton, M.A., Fisher, C.M., Heaphy, E.D., Reid, E.M., Rouse, E.D.: The geography of strain: organizational resilience as a function of intergroup relations. Acad. Manag. Rev. 43(3), 509–529 (2016). Colin M. Fisher, University College London, [email protected] Emily 10. Shen, W., Tang, W., Wang, Y., Duffield, C.F., Hui, F.K.P., Zhang, L.: Managing interfaces in large-scale projects: the roles of formal governance and partnering. J. Constr. Eng. Manag. 147(7), 1–12 (2021) 11. Shen, W., et al.: Enhancing trust-based interface management in international engineeringprocurement-construction projects. J. Constr. Eng. Manag. 143(9), 1–12 (2017) 12. Tang, W., Duffield, C.F., Young, D.M.: Partnering mechanism in construction: an empirical study on the Chinese construction industry. J. Constr. Eng. Manag. 132(3), 217–229 (2006) 13. Trump, B.D., Linkov, I.: Risk and resilience in the time of the COVID-19 crisis. Environ. Syst. Decisions 40(2), 171–173 (2020). https://doi.org/10.1007/s10669-020-09781-0 14. Wang, Z., Liu, Z., Liu, J.: Risk identification and responses of tunnel construction management during the COVID-19 pandemic. Adv. Civ. Eng. 2020, 1–10 (2020) 15. Williams, T.A., Gruber, D.A., Sutcliffe, K.M., Shepherd, D.A.: Organizational response to adversity: fusing crisis management and resilience research streams. Acad. Manag. Ann. 11(2), 733–769 (2017) 16. Choi, J.N.: External activities and team effectiveness: review and theoretical development. Small Group Res. 33, 181–208 (2002) 17. Faraj, S., Yan, A.: Boundary work in knowledge teams. J. Appl. Psychol. 94(3), 604–617 (2009) 18. Du, W.D., Pan, S.L.: Boundary spanning by design: toward aligning boundary-spanning capacity and strategy in it outsourcing. IEEE Trans. Eng. Manag. 60(1), 59–76 (2013) 19. Vogus, T.J., Sutcliffe, K.M.: Organizational resilience: towards a theory and research agenda. In: 2007 IEEE International Conference on Systems, Man and Cybernetics, pp. 3418–3422 (2007) 20. Morgeson, F.P., Humphrey, S.E.: The work design questionnaire (WDQ): developing and validating a comprehensive measure for assessing job design and the nature of work. J. Appl. Psychol. 91, 1321–1339 (2006) 21. Linnenluecke, M.K.: Resilience in business and management research: a review of influential publications and a research agenda. Int. J. Manag. Rev. 19(1), 4–30 (2017) 22. Kutsch, E., Hall, M., Turner, N.: Project Resilience: The Art of Noticing, Interpreting, Preparing, Containing and Recovering. Ashgate Publishing, Ltd. (2015) 23. Sharma, S.: Applied Multivariate Techniques, pp. 116–123. Wiley, New York (1996) 24. Fornell, C.G., Larcker, D.F.: Evaluating structural equation models with unobservable variables and measurement error. J. Mark. Res. 18, 39–50 (1981) 25. Bentler, P.M.: Comparative fit indexes in structural models. Psychol. Bull. 107(2), 238–246 (1990) 26. Rahi, K.: Project resilience: a conceptual framework. Int. J. Inf. Syst. Proj. Manag. 7(1), 69–83 (2019)

Conceptualizing Community Participation in the Context of Megaprojects-Induced Internal Displacement Shuang Zhang(B) , Jamie Mackee, Michael Sing, and Liyaning Maggie Tang School of Architecture and Built Environment, The University of Newcastle, Callaghan, NSW, Australia [email protected]

Abstract. The complicated and uncertain character of megaprojects requires appropriate analysis of affected communities to achieve project objectives and accommodate megaprojects-induced internal displacement participation mechanism. Although previous scholarly works have contributed to the development of public participation theory, these theories have not been fully acknowledged from the affected community’s perspective in practices, especially in megaprojectsinduced internal displacement. In this study, an extensive literature review was conducted to get a general knowledge of the relationship between the willingness to participate and other prior key determinants. While no cohesive national governance strategy for megaprojects-induced internal displacement has been developed, the lack of recognition for the issue at the governmental level contributes to internal displacement risk creation, placing vulnerable communities at little or no influence on the process. Therefore, this study proposes a conceptual model of megaprojects-induced internal displacement that can be used to diagnose and assess willingness in community participatory research and practice. The findings will help academics and decision-makers be concerned about the key participation determinants to avoid ambiguity and disparity in understanding the adoption progress and status of community participation. Keywords: Community participation · Internal displacement · Resilience · Megaprojects

1 Introduction Megaprojects, which are larger than $1 billion in value, are becoming the default [1–3] and are sometimes also called “major program(me)” that can be used to define large public development investment projects [4]. Since 2012, Megaprojects have driven the current surge in Australia’s transport project investments [5]. It has been a traditional policy measure since the 1970s, when there is an economic downturn to turn to megaprojects to revive the economy [6–9]. Although the known challenges such as these projects are often complex, take many years to complete, and need ongoing maintenance [10, 11], these projects are the centrepiece investments for a nation’s long-term economic growth [12, 13]. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 1627–1637, 2023. https://doi.org/10.1007/978-981-99-3626-7_125

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Previous megaprojects management was mainly impelled by practical application and defined from concepts, instruments and normative handbooks [14, 15]. Yet, megaprojects to be contrived as a tool is not accurate. As megaprojects’ physical and economic scales became more extensive, the range of people engagement has increased [16]. The land acquisition process for megaprojects directly affects the internal displacement of communities, whether in rural or urban areas [17, 18]. They transform goods and people, involving the displacement of earth, materials, and machinery for massive construction workloads and the displacement of people and sometimes communities [19, 20]. Megaprojects-induced internal displacement occurs worldwide, and this widespread phenomenon has impacted the local communities [21, 22]. Sometimes it may cause controversy [23] and attract great local communities’ attention and political interest because of substantial direct and indirect impacts from the perspectives of the economy, environment, and society [24]. Infrastructure Australia’s latest Infrastructure Priority List noted the need to focus on regional projects affecting population moves [25]. Changing demographics also involves a greater focus on the holistic needs of communities and places, which requires a shift in the broader community engagement practices [17, 26, 27]. For example, the 2018 Infrastructure Decision-making Principles reform put the community at the centre of project decision-making [28]. In 2019, the National Community Engagement for Infrastructure Forum identified the need to deepen community engagement and encourage relevant stakeholder engagement at each phase of the project development, from problem identification to project delivery – including ideas generation and strategic plan development, and implementation [29]. Affected community participation has been a concern in construction management research over several decades; however, it is somewhat under-explored in megaprojectsinduced internal displacement. Therefore, this research aims to assess the interrelationships among construction management-related community participation factors and their influence on megaprojects-induced internal displacement performance. The structure of the paper is as follows: Sect. 2 provides a historical context to megaprojects-induced internal displacement and an introduction to community participation; Sect. 3 details the design of the exploratory study. Following this, a conceptual model is synthesized from the literature in Sect. 4. Finally, Sect. 5 concludes the findings of the research, together with the limitations identified and opportunities for future research are explored.

2 Background 2.1 Historical Context Historically, megaprojects-induced internal displacement, mainly due to hydropower and mining projects, is a well-known global phenomenon [30, 31]. These megaprojects often lead to internal displacement affecting millions of people [32]. As of 2020, statistics reported by ICOLD [33] were 58,713 dams globally. For instance, China’s Three Gorges Dam (TGD), the largest hydropower project in the world, has received extensive coverage and examination due to its enormous impacts, such as massive internal displacement [34– 36]. The international displacement required for TGD involved a population of over 1.3 million [23], spanning 17 years from 1992 and 2008 [37]. According to the Australian

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Bureau of Resources and Energy Major Projects (REMP) December 2020 report; out of 58 major projects in the resources and energy sector at the committed stage worth over AU $217 billion, 12 are megaprojects which account for half of the value of projects [38]. For example, in 2011, all the residents in the town of Acland in Queensland, except for one man who refused to displace, were inevitably internally displaced to give way to the Acland Coal Mine in Australia [17]. Internal displacement is a vital process related to the movement of people affected by a project, often a broader community [39, 40]. A community is a group of individuals who share interests and may participate in collective action in geographical regions or settings [41, 42]. When affected communities are required to move, or when their access to land is restricted, it is known as a displacement [43]. Although there were slight differences between these documents about the definition of displacement, specialists divide internal displacement into two types [44–46]: 1) physical displacement (move from place of usual residence to another location); 2) economic displacement (loss of assets or access to assets that leads to loss of income or means of livelihood). 2.2 Community Participation The construction of megaprojects depends highly on local conditions and the social acceptance [47, 48]. Furthermore, failure to muster greater social support in Environmental Impact Assessment (EIA) can result in forced communities’ displacement. For example, the internal displacement of 10 indigenous communities, approximately 10,000 people, in Malaysia’s Bakun Hydro-electric Project (BHP) led to strong protests from Non-Government Organizations (NGOs) [49]. The opposition from pressure groups (such as NGOs and mass media) was almost because of substantial direct and indirect impacts of megaprojects-induced internal displacement on communities in the vicinity [50]. Opposition from pressure groups has also unveiled the vital role of affected communities’ input in the EIA [51]. Pre-phase evaluations in megaprojects ignore community participation, mainly [52]. Community participation, including “community empowerment,” can help a project achieve long-term benefits and identify any insight into potential opposition from the public [48]. The definition of community participation in this study holds that affected communities who are required to move or when their access to land is restricted have a right to be involved in the decision-making process, and their participation will influence the decisions.

3 Research Methodology In this exploratory research, Fig. 1 illustrates the three steps of this research framework. Step 1: Developing the research protocol is essential in reviewing quality literature. The data was collected through the keywords formed by “Community Participation” and the other Megaprojects-related words. Step 2: This stage was conducted to purify articles elicited from the literature. First, the title and abstract screen were conducted to narrow down the retrieved data. In the second stage of filtering, excluded the irrelevant studies after a full-text review.

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Step 3: This step was conducted to identify determinants. Factors were extracted from the selected publications and categorized into five groups. After completing these three steps, the selected publications covered various perspectives of public participation in megaprojects and the retrieved data obtained from literature were analyzed to develop themes. Step 1

Step 2

Step 3

Filtering Containing keywords in title and abstract?

NO

Screen out

YES

Data Collection Develop the research protocol

Data Analysis

Data Retrieval Conduct the screen process

Develop the conceptual model

YES

Filtering NO

Relevant in paper content?

Screen out

Fig. 1. The steps of the research

4 Determinants and Model Development 4.1 Determinants of Community Participation 4.1.1 Accountability for Community Participation In the context of megaprojects, accountability refers to the definition of the monitoring of arrangements and incentives during project development through a participatory approach [53], including project accountability and social accountability [54, 55]. Project accountabilities refer to the effects of community participation in the decision-making and implementation of internal displacement to achieve sustainability [56]. Recent studies validate participation in megaprojects development as a potentially pivotal approach to establishing social accountability [57]. Therefore, affected communities’ willingness to participate in the internal displacement scheme is derived from accountabilities for community participation in achieving economic development and facilitating the implementation of megaprojects. Hence, the constructed hypothesis is as follows: H1: Accountability for community participation, e.g., project decision-making accountability, project implementation accountability, and social accountability, have a positive impact on community participation in megaprojects-induced internal displacement.

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4.1.2 Transparency for Community Participation Transparency of community participation refers to disclosing accurate and timely information for essential knowledge [58]. Ensure participative and transparent processes that engage all relevant communities through appropriate forms [59]. Therefore, transparent guidelines on compensation for each displacement type must be disclosed and discussed with affected communities [32]. In particular, an alternative project solution is inevitably a critical consideration for the affected community during the internal displacement mechanism [60]. Construction project management studies literature has also proved that a lack of reliable and consistent information and two-way communication between the government places the community unwilling to be involved in the preconstruction stage [61, 62]. This study constructs the following hypothesis: H2: Transparency of community participation positively impacts community participation in megaprojects-induced internal displacement. 4.1.3 Barriers to Community Participation Several factors compromise community participation. It includes a lack of technical support in the planning system or the cost of participation in terms of time or money [63, 64]. The status of lay knowledge is known to raise concerns over being heard in the participation [65]. According to Wang, et al. [66], there were controversies between the government and communities about the difficulty in accessing these information participation policies. These factors are regarded as the main factors hindering community participation. Considering the above reasoning, this study proposed that: H3: Barriers to community participation compromise communities’ willingness to participate in megaprojects-induced internal displacement. 4.1.4 Motivation for Community Participation Motivation refers to the psychological factors that drive behaviour, persist in the course of action, and satisfy a desire or belief [67]. Motivation for community participation can the interests of individuals from being self-oriented to being community oriented. Previous studies have examined the factors influencing participation in the motivation [61, 68]. Woo, et al. [69] indicated that economic and policy factors include reasonable standards for compensation and incentives under the current system that motivate the community participation in renewable energy projects in South Korea. Through the implementation of megaprojects, the affected communities participating in internal displacement scheme tends to be inspired by personal interests and seeks to promote better governmental governance. Therefore, this study proposes the following hypothesis: H4: Motivation positively impacts community participation in megaprojects-induced internal displacement. 4.1.5 Expectations for Community Participation Expectations are the interests or strong beliefs that something will happen [70]. Although expectations are far from uniform and further influence perceptions in megaprojectsinduced internal displacement, this will likely lead to megaprojects being delayed or even

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cancelled if decision-makers cannot meet the expectations of affected communities [71]. Expectations could primarily focus on individual preparedness options to mitigate risk. Therefore, they inevitably relate to one’s life context and local resources [72]. They also relate to bringing benefits, including the availability of job opportunities [73]. Hence, the constructed hypothesis is as follows: H5: Expectations positively influence community participation in megaprojectsinduced internal displacement. 4.2 Conceptual Model Development The research hypotheses were developed based on the literature review to identify the role of community participation in megaproject-induced internal displacement. Therefore, determinants for conceptualizing community participation were extracted and categorized into five groups. This was followed by the development of relationships between the determinants, and then their relationships were interpreted in light of the literature, as illustrated in Fig. 2.

What are the determinants in light of a literature review ?

H3: Barriers

H4: Motivation Community Participate H5: Expectation

H1: Accountability H2: Transparency

Cross-integration with the economy, ecology, and society

Impacts of displacement Mitigate

Community Participation

Gain Willingness

Megaprojects-induced Internal Displacement

Raise Controversy

Nonfulfillment Participation

Intensify

Physical displacement

Economic displacemnt

How do follow-up quantitative research ?

Land acquisition scheme Collaborative Governance

Fig. 2. The conceptual model of community participation

From Fig. 2, the interrelationships among construction management-related community participation factors could be identified as five main categories as accountability for community participation, transparency of community participation, barriers to community participation, motivation of community participation and expectation of community participation. The determinants are selected, and their relationships are hypothesized based on the quality and availability of primary data. The conceptual model illustrated that the nonfulfillment participation raises controversy during the megaprojects-induced internal displacement. Furthermore, the scale of displacement directly determines the utilization of land, as well as the displacement

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impacts input from economic, ecological and social aspects. Therefore, enhancing the megaprojects-induced internal displacement performance in the early stage, attention was supposed to be continuously focused on community participation. A more robust understanding is that in periods where the relationship between the affected community and land is moderated. When the primary attention is concentrated on the indicators related to affected communities’ participatory inputs, the emphasis should be shifted towards creating a collaborative and inclusiveness mechanism at the local government level.

5 Conclusion Megaprojects have become a focus of attention at the intersection of theory and practice [74] and provide insights for academic research into construction management. Studies argue that communities to be resettled might bear negative impacts even before their physical displacement [75, 76]. Potent issues of controversy linger as a result. The literature review develops several testable hypotheses from several global articles to further frame this study’s research problem and highlight the need to identify and evaluate community participation. The conceptual model of community participation in megaprojects-induced internal displacement, as presented in this study, focuses on two concepts: 1) How does community participation mitigate controversy associated with megaprojects-induced internal displacement and intensifying land acquisition schemes in megaprojects construction management? 2) What determinations are expected to better shape community participation in internal displacement by quantifying the community participation in follow-up research? This study contributes to knowledge on how the community participates in mitigating controversy associated with internal displacement and intensifying land acquisition schemes in the context of megaprojects management. The study was limited to the theoretical findings, and the findings were focused on developing a preliminary mode, opening up new ways for researchers to assess project management from the affected community’s perspective. Subsequently, further studies are recommended to validate the relationships between community participation determinants by collecting more quantitative data. To conclude, it is a better understanding of community participation could be achieved by identifying the determinations of (a) accountability for community participation; (b) transparency of community participation; (c) barriers to community participation; (d) motivation of community participation and (e) expectation of community participation, and why affected communities (either physical displacement or economic displacement) inappropriately respond to megaprojects. Furthermore, the research also contributes to practice by providing a reference for decision-makers to implement megaprojects better.

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Real-Time Detection and Tracking of Defects in Building Based on Augmented Reality and Computer Vision Wenyu xu, Yi Tan(B) , and Shenghan Li Department of Construction Management and Real Estate, The Shenzhen University, Shenzhen, China [email protected]

Abstract. Condition assessment and health monitoring (CAHM) of buildings require effective and continuous detection of any changes in the material and geometric properties of components to detect defects in time. However, traditional manual-based detection methods are inefficient and error-prone. Smartphone/tablet-based detection has achieved real-time detection of the CAHM with improved efficiency, however inspectors still need to hold the smart devices in hands, resulting in inconveniency and uncomfortable working experience. In this study, a head mounted display (HMD)-based collaborative method for realtime detection and tracking of defects (i.e., crack, swell, peel, seepage, and mould) in building was developed by combining an object detection algorithm you only look once version 5 (YOLOv5) with multi-object tracking algorithm Deepsort. According to the analysis of the experimental results, the developed method is promising and efficient to detect and track various types of building defects. Keywords: Augmented reality · Computer vision · Defects · Detection and tracking

1 Introduction The condition of most buildings begins to decline after they have been put into service, either due to external factors (e.g., weather) or internal factors (e.g., inadequate maintenance) [1]. Building deterioration is inevitable and even the latest advances in building technology do not seem to have reduced the incidence of defects [2]. Reports around the world show staggering numbers for the extent of building defects and the corresponding financial loss [3]. Therefore, it’s very important to understand the different types of defects that affect the overall condition of buildings. By regular condition assessment and health monitoring (CAHM), maintenance and repairs can be carried out in time before building defects become more serious [4, 5], which helps to extend the service life of existing buildings. Based on relevant information provided by the client such as photographs, notes and drawings, the traditional method of CAHM is to employ well-trained and experienced professionals (e.g., engineers and architects) to record the condition of buildings. Such traditional method is time-consuming, laborious, and expensive [6, 7]. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 1638–1650, 2023. https://doi.org/10.1007/978-981-99-3626-7_126

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With the development of computer vision-based object detection methods, its excellent accuracy and robustness make related object detection be widely used in CAHM as an alternative to manual inspection methods to improve the automation of inspection. For example, Kumar et al. developed a detection system based on YOLOv3 for high-rise civil structure. The results showed that the proposed method provided reliable performance with an accuracy of 94.24% [8]. Similarly, Jiang et al. presented an improved concrete defect object detection methodology based on YOLOv3. The proposed method saved more labor costs and achieved higher accuracy than traditional concrete defect objection algorithms [9]. The recent breakthroughs in computer vision have also led to the development of smartphone/tablet-based applications. Farmers ran computer vision-based object detection programs on smartphones which can detect early disease in plants [10]. Christodoulou et al. developed a smartphone-based method for detecting roadway defects through image signal flows [11]. Furthermore, Perez and Joseph presented a smartphone app, which can detect four types of defects in buildings, namely cracks, mold, stain and paint deterioration [12]. However, during the inspection process, the inspectors cannot release their hands as they need to hold the device, resulting in inconvenience and uncomfortable working experience. Compared to the existing extensive research, limited research focused on the development of head mounted display (HMD)-based CAHM system for building defects. In order to fill this gap, this paper developed the HMD-based collaborative method for real-time detection and tracking of defects (i.e., crack, swell, peel, seepage, and mould) in building by combining an object detection algorithm you only look once version 5 (YOLOv5) with multi-object tracking algorithm Deepsort. This effort aims to make the CAHM system more efficient, faster, and provide the inspectors with a more comfortable interactive experience.

2 Methodology This section presents the development process of the CAHM system based on YOLOv5Deepsort, which consists of the following main parts (Fig. 1). Firstly, a websocket-based communication is established between the server and the client to obtain a real-time stream from the HoloLens 2 (HMD device) camera. Then, the real-time video stream is processed based on YOLOv5-Deepsort and the detection results are obtained. Next, the detection results are transferred to Unity 3D based on the UDP protocol and the obtained 2D pixel points are converted into 3D spatial points by a matrix transformation. Finally, the CAHM system released to the Universal Windows Platform (UWP) platform to enable development in HoloLens 2 via Microsoft Visual Studio. 2.1 YOLOv5-Deepsort Model 2.1.1 Object Detection Model To determine the classification and positioning of building defects, YOLOv5 object detection algorithm is employed. The YOLOv5 algorithm is one of the more advanced single-stage target detection algorithms, which can guarantee detection accuracy with

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Fig. 1. Overview of the proposed system

less time. The structure of the YOLOv5 model is shown in Fig. 2. The algorithm consists of four parts: input, back, Neck, and Output. The Cross Stage Partial network (CSP) acts as the backbone of YOLOv5, whose architecture is responsible for exacting features from the input images.

Fig. 2. The structure of the YOLOv5 model

The neck part of YOLOv5, like that of YOLOv4, still uses the structure of Feature Pyramid Network (FPN) and Path Aggregation Network (PAN) to improve the target localization accuracy (Fig. 3). It is worth noting that the neck structure of YOLOv4 uses all normal convolutional operations, while the neck of YOLOv5 uses the CSP2 structure designed by CSPNet, thus enhancing the network feature fusion capability.

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Finally, feature maps are generated to locate the object at different scales and output the image with classification and positioning results.

Fig. 3. Semantic information flow based on FPN and PAN

2.1.2 Deepsort Model After detecting building defects with classification and position, the Deepsort algorithm was used for real-time tracking of building defects. Deepsort algorithm is an extension of the Sample Online and Real-time Tracking (SORT) [13]. It relies on creating "tracks" representing tracked objects, taking YOLOv5-based detection results as input (confidences, bounding boxes, and features), where the confidences are mainly used to filter the bounding boxes. In addition, bounding boxes and features are matched to the tracker computed later. Using the Kalman filter algorithm, a tracker is generated based on the object from the previous frame [14]. Kalman filter algorithm is a filtering algorithm based on optimal estimation, which is divided into two processes: prediction and update: • Prediction: The position and speed of the current frame is predicted from parameters such as the position of the target frame and the speed of the previous frame. • Update: The predicted and observed values based on the normal distribution are linearly weighted to obtain the current predicted state of the system. The corresponding targets are obtained based on the target box and features are extracted from these targets. Similarity calculations are then performed on the trajectory and appearance features of the target and tracker. For the trajectory matching calculation, the model uses the Mahalanobis distance Eq. (1) to measure the difference between the tracker and the target [15]. t (1) (i, j) = (tj − yi )T Si−1 (tj − yi )

(1)

Sil

represents the covariance

where tj represents the target j, yi represents tracker i and of t and y.

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The model measures the similarity between the target and the tracker by the cosine distance Eq. (2) (i)

(i)

t (2) (i, j) = min{1 − riT rk |rk ∈ Ri }

(2)

(i)

where 1 − riT rk represents cosine distance. To predict ID more accurately, the cosine distance is used to measure the apparent characteristics of the track and the apparent characteristics corresponding to the detection [16]. Combining Eqs. (1) and (2), the comprehensive matching degree formula of the model is obtained. ci,j = λt (1) (i, j) + (1 − λ)t (2) (i, j)

(3)

After the similarity calculation, the model also constructs a similarity matrix by calculating the IoU of the tracker and the target, which finally gives the cost matrix. Finally, the predicted tracker is matched to the target in the current frame based on the Hungarian algorithm and the parameters of the Kalman filter are updated according to the matching result to track the positions of multiple targets [17]. 2.2 Evaluation of Model Performance To guarantee the accuracy of building defects detection, the following metrics are implemented to evaluate the model’s performance: precision, recall, and mAP (Mean Average Precision). Precision answers the question of how many images were correctly labelled out of all those labelled as positive by the classifier (including TP and FP), while recall answers the question of how many of all positive images in the dataset (including TP and FN) were correctly labelled as positive. Thus, high accuracy means more correct detections in the detection results, while high recall means that fewer detection targets are missed in the detection results. The calculation function is as follows: Precision =

TP × 100% TP + FN 1  1 1 = Pm (Rm )dRm × 100% 2 0

Recall = mAP0.95

TP × 100% TP + FP

(4) (5)

(6)

m=0

The first letter represents the correctness of this prediction, T is true and F is false; the second letter represents the category predicted by the classifier, P represents the positive samples predicted and N represents the negative samples predicted [18, 19]. Our study uses the mean accuracy (mAP) to measure the performance of the model. The meaning and consensus of the relevant parameters are as follows: • • • •

TP (True Positive): the prediction result and ground truth are positive samples. FP (False Positive): the detection result is negative, but the prediction result is true. TN (True Negative): the prediction result and ground truth are both negative samples. FN (False Negative): the detection result is positive instead of negative.

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2.3 Server-Client (S/C) Architecture After obtaining the detection and tracking results based on the YOLOv5-Deepsort model, the HMD-based CAHM is implemented through a server-client architecture, which is mainly composed of two parts: (1) Visualization of detection and tracking results and (2) PUN and Azure-based collaborative application. 2.3.1 Visualization of Detection and Tracking Results Before using the PUN and Azure-based collaborative method, appropriate software and hardware are selected to realize visualization of detection and tracking results, which included: • Unity 3D: Unity 3D engine is used as the base platform for developing the augmented teaching environment. It is a commonly used platform for developing both 2D and 3D games, AR/VR applications, and simulations that can be deployed across multidevices. • An AR-compatible computer that allows developing and running AR applications, and • Microsoft HoloLens 2 unit, which is considered as one of the most popular HMD for AR applications. After processing the real-time video stream based on YOLOv5-Deepsort, the detection results are obtained. Next, the detection results are transferred to Unity 3D based on the UDP protocol to convert 2D pixel points into 3D spatial points via matrix transformation. Finally, the CAHM system is released to the Universal Windows Platform (UWP) to enable development in HoloLens 2 via Microsoft Visual Studio. 2.3.2 PUN and Azure-Based Collaborative Approach The development of the collaborative method is mainly divided into two parts: PUN plugin and Azure spatial anchors cloud service, which are realized in four steps (Fig. 4): (1) PUN PUN is a Unity 3D plug-in for multi-user game. First of all, before creating a PUN application in Unity 3D, we turn on the InternetClient, Microphone, SpatialPerception, and other features in Unity 3D. Secondly, PUN app is created on the official website of Photon with Photon account to obtain the APP ID. Finally, after importing PUN into the Unity 3D project, PUN successfully connected to the Unity 3D. (2) Multi-user connection The communication among many users in PUN uses server-client network architecture. Clients connect to the server through the network and send requests to the server through the network. The server receives the request of clients and responds to the clients with data. In the PUN application, server matches an APP ID to realize data transmission between server and clients. (3) Object sharing

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This step is divided into two parts: creating prefabs and synchronizing network. In order for each client to see the same interactive object, it is necessary to create and place object prefabs in the NetworkRoom. When users connect to the Internet, they can see the same objects. Then, using Microsoft Visual Studio and C # programming language, we created the scripts needed to realize network synchronization. (4) Azure Spatial Anchors cloud service To collaborate with objects in one place, we use the Azure Spatial Anchor cloud service provided by Microsoft. Firstly, user A needs to perform a series of binding operations, including starting an Azure session, creating an Azure binding, and sharing an Azure binding. Then, the physical space location of user A will be serialized and transferred to the Azure Spatial Anchors cloud. Then other users start the Azure session and use the Azure ID to find the deserialized location information in the cloud. Finally, other users will get the shared Azure anchor.

Fig. 4. The development process of PUN and Azure based collaborative method

Finally, Unity 3D is used to integrate real-time video streaming and collaborative approach based on PUN and Azure. The Unity 3D program is packaged to the Universal Windows Platform (UWP) and published through Microsoft Visual Studio to develop CAHM application in HoloLens 2 for real-time detection and tracking.

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3 Experiments and Results In this study, a collaborative head-mounted display (HMD)-based approach was developed for real-time detection and tracking of defects in buildings (i.e., Crack, Blister, Peel, Seepage, and Mould). In order to verify the effectiveness and applicability of this method, HMD-based CAHM was used to detect the building defects of the College of Civil and Transport Engineering building in Shenzhen University. 3.1 Data Processing The dataset of the experiment is constructed from the public dataset and photos taken by camera from the Shenzhen University Academic Building. The total number of images in the dataset is 1,986 of five types for building defects (Fig. 5), including Crack, Blister, Peel, Seepage, and Mould. However, the acquired images were insufficient to detect these five building defects and the lack of data could lead to some problems such as overfitting of the training process, poor detection accuracy, and poor generalization.

Fig. 5. Examples of dataset

Therefore, the data augmentation on the dataset was implemented (Fig. 6). Data augmentation is usually used to generate similar but different data to augment the dataset while reducing the reliance of model on certain features. In addition to using single data augmentation (e.g., geometric transformation, contrast, and brightness transformation), combined data augmentation method was also used, including Gaussian blur, pixel inversion, relief effects, and other enhancement. The combined probabilities of these enhancements are continuously tuned to generate better image effects. The final training, test, and valid sets have 7,220, 2,406, and 2,406 images respectively.

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Fig. 6. Examples of data augmentation

3.2 Results In this study, 7,220 images were trained with 300 Epochs using the YOLOv5l model, and 2406 images were used for validation after each Epoch. The trained model was also evaluated for validation using a test set of 2406 images. The proposed object detection model can detect defects from both static images and live video streams. Figure 7 shows example results for building defects detection in images obtained by running the model on a desktop computer with GPU. The association labels indicate the class of defects predicted by the model. In addition, the accuracy percentage indicates the confidence level of the model in this prediction. In general, there are two types of parameters in model, normal parameters, which can be estimated from the data, and hyperparameters, which can only be specified by design based on human experience. Optimal hyperparameters usually need to be determined manually. To achieve automatic optimization of hyperparameters, this study used genetic algorithm which did not rely on conditions (e.g., derivability conditions) to select the optimal hyperparameters, such as the learning rate. The Precision-Recall curve for the test results is shown in Fig. 8 (a). The closer the curve is to the top right corner, the better performance of the model. As can be seen from the Fig. 8 (a), Peel has the highest mAP value of 97.3%. The crack, blister, seepage, and mould have mAP values of 95.4%, 95.5%, 93.1% and 93.9% respectively. Figure 8 (b) shows the loss values of the validation set during the training process. The loss values are divided into three categories: Bounding box loss value, object detection loss value (val Box), and classification

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Fig. 7. Example results for building defects detection

loss value (val Classification). Overall, the model reached convergence, proving that the training of the model was effective and the model can be used in the next step of the study.

Fig. 8. The results of YOLOv5l model (a) Precision-Recall curve for yolov5 (b) Loss values of the validation set in training

A comparison of the detection and prediction results shows that the model is accurate enough for subsequent studies, although there were cases of missed detection or inaccurate prediction of bounding boxes. Subsequently, object features were extracted based on the Deepsort network to help the object tracking detect and match the bounding boxes and trackers of the network. The example results for building defects tracking is shown in Fig. 9 (a). Finally, the real-time video stream on the HMD was returned to

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the real-time video stream with the detection and tracking results through the built S/C architecture and displayed on the HMD, as shown in Fig. 9 (b).

Fig. 9. HMD-based CAHM (a) Example results for building defects tracking (b) Visualize detection and tracking results based on HoloLens

4 Conclusions CAHM is a growing field of research. However, traditional CAHM has led to uncertainty in inspector judgment due to subjective and inaccurate decisions made in the field. Although computer vision-based CAHM mobile applications reduce subjective judgment errors by inspectors, inspectors need to hold the device in their hands during the inspection process and cannot free their hands which create an uncomfortable experience for inspectors. In this current research, we developed a head mounted display (HMD)-based collaborative method for real-time detection and tracking of defects (i.e., crack, swell, peel, seepage, and mould) in building was developed by combining an object detection algorithm YOLOv5 with multi-object tracking algorithm Deepsort. The obtained results have shown that our HMD-based inspection system can detect all types of defects under investigation with high accuracy and near real-time inference time. The results also have shown that our models generally have high recall and precision. The obtained results can be further improved by using larger datasets and higher resolution input images in future detection and tracking tests. However, this means that powerful computing resources are required to complete. This study is more effective in detecting and tracking some common construction defects which affect the condition of concrete buildings because the dataset involved in the proposed YOLOv5-deepsort model is mainly different types of construction defects in concrete. However, the developed framework is flexible and future work considers expanding the dataset by adding more defects under different building materials to enhance the generalization capability of the model.

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Acknowledgement. This research is supported by the Shenzhen Science and Technology Project (No. JSGG20210802153801004).

References 1. Shruthy, P., Gurmu, A.: Framework for building defects and their identification technologies: case studies of domestic buildings in Melbourne, Australia. In: Proceedings of the 54th International Conference of the Architectural Science Association, pp. 1–10 (2021) 2. Xu, Z., Li, S., Li, H., Li, Q.: Modeling and problem solving of building defects using point clouds and enhanced case-based reasoning. Autom. Constr. 96, 40–54 (2018). https://doi.org/ 10.1016/j.autcon.2018.09.003 3. Hopkin, T., Lu, S.-L., Sexton, M., Rogers, P.: Learning from defects in the UK housing sector using action research. Eng. Constr. Archit. Manag. 26(8), 1608–1624 (2019). https://doi.org/ 10.1108/ecam-04-2018-0146 4. Mohseni, H., Setunge, S., Zhang, G.M., Wakefield, R.: Condition monitoring and condition aggregation for optimised decision making in management of buildings. Appl. Mech. Mater. 438–439 (2013) 5. Shamshirband, S., Mosavi, A., Rabczuk, T.: Particle swarm optimization model to predict scour depth around bridge pier (2019). https://doi.org/10.20944/preprints201905.0121.v1 6. Noel, A.B., Abdaoui, A., Elfouly, T., Ahmed, M.H., Badawy, A., Shehata, M.S.: Structural health monitoring using wireless sensor networks: a comprehensive survey. IEEE Commun. Surv. Tutorials 19(3), 1403–1423 (2017). https://doi.org/10.1109/comst.2017.2691551 7. Kong, Q., Allen, R.M., Kohler, M.D., Heaton, T.H., Bunn, J.: Structural health monitoring of buildings using smartphone sensors. Seismol. Res. Lett. 89(2A), 594–602 (2018) 8. Kumar, P., Batchu, S., Swamy, S., N., and Kota, S. R.: Real-time concrete damage detection using deep learning for high rise structures. IEEE Access 9, 112312–112331 (2021). https:// doi.org/10.1109/access.2021.3102647 9. Jiang, Y., Pang, D., and Li, C.: A deep learning approach for fast detection and classification of concrete damage. Autom. Constr. 128, 103785 (2021). https://doi.org/10.1016/j.autcon. 2021.103785 10. Ramcharan, A., Baranowski, K., McCloskey, P., Ahmed, B., Legg, J., Hughes, D.P.: Deep learning for image-based cassava disease detection, Front. Plant. Sci. 8, 1852 (2017). https:// www.ncbi.nlm.nih.gov/pubmed/29163582 11. Christodoulou, S.E., Kyriakou, C., Hadjidemetriou, G.: Pavement patch defects detection and classification using smartphones vibration signals and video images. In: Mobility Patterns, Big Data and Transport Analytics, pp. 365–3802019https://doi.org/10.1016/B978-0-12-812 970-8.00014-2 12. Perez, H., Tah, J.H.M.: Deep learning smartphone application for real-time detection of defects in buildings. Struct. Contr. Health Monit. 28(7), e2751 (2021). https://doi.org/10.1002/stc. 2751 13. Bewley, A., Ge, Z., Ott, L., Ramos, F., Upcroft, B.: Simple online and realtime tracking. In: 2016 IEEE International Conference on Image Processing (ICIP) (2016) 14. Kalman, R.E.: New approach to linear filtering and prediction problems transaction of the ASME. J. Basic Eng. 83, 95–108 (1960) 15. Maesschalck, R.D., Jouan-Rimbaud, D., Massart, D.L.: The mahalanobis distance. Chemometr. Intellig. Lab. Syst. 50(1), 1–18 (2000) 16. Ge, Y., et al.: Tracking and counting of tomato at different growth period using an improving YOLO-deepsort network for inspection robot. Machines 10(6), 489 (2022). https://doi.org/ 10.3390/machines10060489

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17. Wright, M.B.: Speeding up the Hungarian algorithm. Comput. Oper. Res 17, 95–96 (1990) 18. Modarres, C., Astorga, N, Droguett, E.L., Meruane, V.: Convolutional neural networks for automated damage recognition and damage type identification. Struct. Control Health Monit. 25(10), e2230 (2018) 19. Tang, Z., Chen, Z., Bao, Y., Li, H.: Convolutional neural network-based data anomaly detection method using multiple information for structural health monitoring. Struct. Control Health Monit. 26(1), e2296 (2019). https://doi.org/10.1002/stc.2296

Precise Urban Green Volume-Enabled Building and Environment Simulation: Sub-meter Voxel Modeling of Airborne and Hand-Held 3D Scans of Urban Trees Qianyun Zhou1(B) , Jiajia Wang1 , Bin Chen2 , and Fan Xue1 1 Department of Real Estate & Construction, The University of Hong Kong, Hong Kong, China

[email protected] 2 Division of Landscape Architecture, The University of Hong Kong, Hong Kong, China

Abstract. High-rise high-density cities around the world suffer from severe urban heat island effects. Greenery has the potential to heal urban microclimates, such as shading, lowered air temperatures, and increased humidity, apart from other benefits to urban health. Existing numerical simulation studies employing simplified, proxy greenery models have validated the potential at a macro level; However, the human-centric three-dimensional nature of greenery (e.g., tree crown volume, canopy density, and leaf area index) was ignored, leading to inaccurate results for buildings and blocks, especially in the high-rise high-density settings. This research proposes a precise voxel modeling of urban green volume for building and environment simulations. First, two scans, i.e., the airborne scan of tree canopies and hand-held LiDAR scan of lower parts, are registered and merged into a voxel model at 0.5-m resolution. Then, simulations and analysis of the voxelized green volume are conducted using Rhinoceros and Grasshopper. A preliminary experiment of shading on a university campus in Hong Kong proved the concept of this study. Future work will be directed to analysis of the voxel resolution and more types of simulations such as winds and urban thermal exposure. Keywords: Urban green volume · Urban heat island · Building and environment simulation · LiDAR point cloud · Voxelization · Numerical simulation

1 Introduction The intensive urbanization process and anthropogenic activities have increased the vulnerability of cities to climate change. The climate hazard is expected to trigger more frequent natural disasters, such as severe and protracted heat waves, which further harm economic, social, and environmental perspectives of the built environment [1]. For intensive land use and high-density cities, the increasing and long-term air temperatures are frequently associated with intense urban heat island (UHI) effects. UHI not only leads to heat stress among citizens but also raises urban energy consumption (e.g., air conditioning in buildings) and has a detrimental influence on the sustainability of the urban environment [1]. Thus, United Nations’ Sustainable Development Goal-11 (SDG-11) calls for actions to resolve sustainability issues for cities and communities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 1651–1659, 2023. https://doi.org/10.1007/978-981-99-3626-7_127

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Existing studies in the literature have confirmed urban greenery’s positive and negative effects on mitigating UHI. For example, the crown shape and leaf features have been shown to have cooling potential due to their influence on solar exposure, air humidity, and wind environment [2]. Morakinyo, et al. [2] found that dense foliage tall tree species play a significant role in cooling cities through field surveys and model validation, while Chen, et al. [3] extracted and mapped greening coverages with satellite remote sensing to analyze the multi-scale difference in greenspace exposure. Yet, Oshio, et al. [4] showed that urban greenery can significantly reduce wind speeds in streets, thus hindering the local microclimate. Therefore, holistic and accurate quantitative studies are demanded to assess three-dimensional greenery. In recent years, 3D scanning, including airborne drone photogrammetry and mobile LiDAR, has been widely used for building and city information models [5]. The related remote sensing technologies obtain the coordinates (X, Y, Z), sometimes with the color (R, G, B) or laser intensity (I), from the points on the objects. Airborne 3D scan data was used to assess urban greening and its effects on mitigating UHI [4]. However, the airborne data that reflects the top parts of tree crowns cannot provide sufficient 3D vegetation features holistic and accurate quantitative studies. This study presents a sub-meter voxel modeling method for integrating airborne and hand-held 3D scans of urban trees for building and environment simulation. The main contribution of this study is twofold. Firstly, the findings plot a technological pathway to integrate multi-source 3D scans of urban trees into computable voxels of 3D greenery; Secondly, it showed that the sub-meter green volume model could improve the accuracy of building and environment simulation at an ignorable cost of computational load.

2 Methodology This study employs a point cloud-based scan registration for 3D greenery. First, photogrammetry of airborne scans generates high-resolution 3D color points composed of 18,431,241 points. Then, a total of 558,158 points of urban greenery can be collected from semantic analysis of urban models based on machine learning [5, 6] or by color filtering and segmentation, which separates target greenery from the surrounding environment, as shown in Fig. 1. Then, handheld LiDAR scanning collects more of a human perspective on the three-dimensional greening features, with 18,146,233 colorful points. The collected information (e.g., the bottom of the tree crown, tree trunks, and ground shrubs) aptly complements the airborne scans. Similarly, the points of interest of 3D greenery can be collected using automatic or manual processing (e.g., functions in Cloud Compare). Figure 2 shows an example of pre-processed handheld laser-scanned greenery data. A complete 3D greenery data registers the airborne and handheld scans, as shown in Fig. 3. The distance between the overlapping parts of the two source point clouds is calculated by the distance function in Cloud Compare, resulting in 0.3702m for average distance and 0.3104 for root-mean-square deviation (RMSD). The registered 3D greenery data is saved as a point cloud in the LASzip Compressed Lidar (.las) format, consisting of spatial coordinates (X, Y, and Z), source of points, and related variables.

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Fig. 1. Pre-processing of airborne scanning data

Fig. 2. Pre-processing of handheld scanning data

Fig. 3. Registration results of airborne (in dark green) and handheld (in light green) 3D scans

Then, a voxelization modeling method for greening crown shapes is applied to crown volumes for measurement and visualization. A voxel is a 3D cubic representation of volume. Poux [7] presents an automation workflow for transforming point clouds into 3D voxels with Python codes. The voxelization process in this research is based on three Python (ver. 3.8) libraries: laspy (ver. 2.0.3), open3d (ver. 0.11.2), and NumPy (ver. 1.21.2). The voxels are assembled from the dataset’s spatial bounding box, and the voxel location is generated as a relative value to the initial bounding box. The voxel model can be transformed into other 3D formats, such as.obj format, for further computation and simulation. Such transformations can be done through the open3d library. As a result, 3D greenery volume is calculated by voxel quantities, as shown in Fig. 4.

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Fig. 4. Transforming the point cloud of a tree to voxel model

Numerical simulations can finally be conducted using the voxel model. Numerical simulation is often promising for assessing the influence of urban greening on the environment. Previous research has utilized environmental models to study the association between greening and urban air temperatures, local microclimate, and outdoor thermal comfort [2, 4]. Although parametric simulation tools are becoming increasingly wellestablished, the 3D features of greening in urban microclimate studies were generally replaced by theoretical, empirical values, or oversimplified (e.g., a conical-shaped tree crown). The complex characteristics of real greenery (e.g., the density of tree canopy, tree crown volume, and leaf area index) were seldom addressed in numerical simulations [8]. Therefore, it is essential to represent the urban environment via a high-precision vegetation modeling and simulation. In addition, the voxel-based approach allows for the numerical simulation of the urban thermal environment with changeable resolutions.

3 Preliminary Experiment 3.1 Study Area and Settings The preliminary study was established on a university campus (22°16 58.17 N, 114° 8 18.25 E) in sub-tropical Hong Kong, as shown in Fig. 5. The city suffers from the severe UHI effect, while greenery significantly provides outdoor thermal comfort through shading [2]. Both the airborne and handheld scans were collected around Mong Kwok Ping Garden by the authors using a DJI Marvic 2 drone (with Pix4D Mapper) and a Paracosm PX-80 scanner, respectively. The scanned 3D point clouds were preprocessed in CloudCompare (including de-noising, filtering, and segmentation) and then the composite sources were registered and merged. The two types of point cloud data were then exported for simulations and further processing.

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Fig. 5. Location of study area

The voxelization process was coded on the Spyder IDE environment, which reads the full 3D greenery data (.las format). The selection of voxel size significantly impacts the spatial structure and volume of the vegetation (especially tree canopies). In general, larger voxels produce faster visualization results but only summarize the trees’ structural information. In contrast, smaller voxels can convert more comprehensive vegetation information but require a longer time [8]. For this experiment, the transformed voxel model was set up at 0.5m resolution to balance computational time with the abundance of greenery information, as shown in Fig. 6. Lastly, simulations were based on Rhino/Grasshopper3D, with an open-source environmental plugin named Ladybug V.1.5.0. The daylighting engine in Ladybug V.1.5.0 follows ray-tracing algorithm from Radiance and combines imported weather data files (.epw) to calculate readable plots as well as interactivity data. [9] In the ray-tracing algorithm for computing daylight, the following integral equation enables recursive evaluation for testing points on each surface [10]: 







2π π Li (θi , ∅i )ρbd (θi , ∅i ; θr , ∅r )|cosθi |sinθi d θi d ∅i

Lr θr, ∅r = Le θr, ∅r +

(1)

0 0

where: θ means the surface normal polar angle; ∅ means the surface normal azimuthal angle; Le θr, ∅r  is the emitted radiance; Lr θr, ∅r is the reflected radiance; Li (θi , ∅i ) is the incident radiance; ρbd (θi , ∅i ; θr , ∅r ) is the function for bidirectional reflectance-transmittance distribution.

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The greening model was imported into Rhinoceros in.obj format, and the surrounding built environment was obtained from OpenStreetMap (https://www.openstreetmap.org) by blender. The daylight experiment started by importing both geometries and local meteorological data (https://www.ladybug.tools/epwmap/), and then simulated direct sunlight hours in Hong Kong during the summer (June to August). It also measured the total sunlight hours, single simulation time, and green volume under different greening model accuracy.

Fig. 6. Voxelized 3D greenery model of the study area

3.2 Results This experiment compares and analyses the results of five digital modeling simulations: the model of the built environment without greenery, the oversimplified proxy greenery model, the airborne/handheld scanned voxelized greenery model, and the hybrid voxelized greenery model. The numerical simulations were compared with consistent.epw weather data, the target season (summer), and the surrounding built environment. Table 1 depicts the simulation results for the five greening precision models, as well as the greening volume, total sunshine duration, and simulation completion time. It can be observed that the integrated greenery voxel model demonstrates the lowest total solar exposure hours, which means that this model provides the largest area of shade.

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Table 1. Comparison of radiation simulation results with varying accuracy models Type

Experimental Subjects

Total

Time/

Volume/m3

Green

Simulation Visualization

Radiation/h

s

1

No greenery model

-

596.63

40s

2

Conventional over-

3200

579.71

70s

1841

561.46

75s

3022.25

576.24

90s

4863.25

560.38

100s

simplified proxy greenery model

The airborne scanned 3

greenery model (no handheld, 0.5m resolution)

The handheld scanned 4

greenery model (no airborne, 0.5m resolution)

The precise urban green 5

volume model (This study, 0.5m resolution)

The experimental results indicated that registering two source point cloud data increased the overall volume of greenery by 34.2% over the traditional simplified version of the greening model. Moreover, with the increase of the projected area, the total radiation duration is correspondingly reduced by 3.33%. This sub-meter voxel model collects point cloud data from a multi-directional field of view, which complements airborne photogrammetry with handheld scanning data, and makes the numeric model more closely

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resemble real-world vegetation. This method boosts the precision of the digital simulation, contributing to further urban microclimate analysis. Furthermore, the voxelized greenery model offers an automated solution for quantifying greening volumes.

4 Discussion and Future Work Urban greenery is commonly convinced to relieve urban heat, due to its function of providing shadows. [2] However, the quantity and density of urban forests can also impede the urban wind environment (especially in intensive development areas), causing an impact on the local microclimate [4]. The majority of computational simulations used for validation employ hypothetical greening models. These digital representations of vegetation are frequently replaced by empirical values or overly simple geometries, making the simulation results that do not accurately reflect the real urban environment [8]. This research proposes a technical solution for integrating scanned point cloud data from multiple sources, acting as the semantic enrichment of city information models. [5] Subsequently, a Python-based automated program efficiently converts the point cloud model into a sub-meter quantifiable voxel model. This voxel model can contribute to urban greening planning by raising the accuracy of numerical simulations without increasing the computational load considerably. Nevertheless, this study also has some limitations. Firstly, the voxelized greening model was only presented at a resolution of 0.5m in the experiments, thus lacking comparative analysis of the impact of other resolution values of voxel models on environmental simulations. The optimal voxel resolution for specific urban morphologies (e.g., high density, low density, enclosed areas, semi-open areas, open areas) should be further investigated. Moreover, this study only employed point cloud processing software to align multi-source scanned data. A more intelligent and automated technique may be required for future experiments on complex urban environments to extend the model’s applicability. Finally, the initial simulation validation in this test has focused on the shading effects of urban greenery, and future work will pay more attention to urban wind simulation (CFD) and outdoor thermal comfort studies.

5 Conclusion Urban greenery has a positive impact on improving urban thermal comfort; however, the volume and density of the tree crown may also affect the urban microclimate. In recent years, numerical simulations have been frequently employed to validate and quantify the influence of greenery on urban outdoor comfort and environment, but lacking highly accurate vegetation models. In this study, the fusion of multi-source scanned greening point clouds (detailing the three-dimensional attributes of the top and middle crown layers, as well as the lower shrub vegetation) is proposed and automated to generate a sub-meter resolution voxelized greening model. This semi-automated technique enables the reconstruction of the urban forest from an actual physical environment at a low cost, thereby effectively boosting the accuracy and credibility of the environmental simulation. Future work includes optimal voxel resolution in urban environments with different densities and comprehensive urban microclimate simulations (e.g., CFD wind environment studies).

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Acknowledgment. The work presented in this paper was supported by the Hong Kong Research Grants Council (RGC) (Grant No. T22–504/21-R).

References 1. Rosenzweig, C., Solecki, W.D., Hammer, S.A., Mehrotra, S.: Climate change and cities: first assessment report of the urban climate change research network. Cambridge University Press, Cambridge (2011) 2. Morakinyo, T.E., Lau, K.K.-L., Ren, C., Ng, E.: Performance of Hong Kong’s common trees species for outdoor temperature regulation, thermal comfort and energy saving. Build. Environ. 137, 157–170 (2018). https://doi.org/10.1016/j.buildenv.2018.04.012 3. Chen, B., et al.: Beyond green environments: multi-scale difference in human exposure to greenspace in China. Environ. Int. 166, 107348 (2022). https://doi.org/10.1016/j.envint.2022. 107348 4. Oshio, H., Kiyono, T., Asawa, T.: Numerical simulation of the nocturnal cooling effect of urban trees considering the leaf area density distribution. Urban For. Urban Green. 66, 127391 (2021). https://doi.org/10.1016/j.ufug.2021.127391 5. Xue, F., Wu, L., Lu, W.: Semantic enrichment of building and city information models: a ten-year review. Adv. Eng. Inform. 47, 101245 (2021). https://doi.org/10.1016/j.aei.2020. 101245 6. Li, M., Xue, F., Wu, Y., Yeh, A.G.O.: A room with a view: automatic assessment of window views for high-rise high-density areas using city Information Models and deep transfer learning. Landscape Urban Plan. 226, 104505 (2022). https://doi.org/10.1016/j.landurbplan.2022. 104505 7. Poux, F., Billen, R.: Voxel-based 3D point cloud semantic segmentation: unsupervised geometric and relationship featuring vs deep learning methods. ISPRS Int. J. Geo-Inf. 8(5), 213 (2019). https://www.mdpi.com/2220-9964/8/5/213 8. Xu, H., Wang, C.C., Shen, X., Zlatanova, S.: 3D tree reconstruction in support of urban microclimate simulation: a comprehensive literature review. Buildings 11(9), 417 (2021) 9. Pak, M., Smith, A., Gill, G.: Ladybug: a parametric environmental plugin for grasshopper to help designers create an environmentally-conscious design. In: Building Simulation Conference Proceedings (2013) 10. Ward, G.J.: The RADIANCE lighting simulation and rendering system. In: Proceedings of the 21st Annual Conference on Computer Graphics and Interactive Techniques, pp. 459–472 (1994)

Exploring Anti-rumor Behaviors in Mega Projects on Sina Weibo: A Text Mining Analysis Chen Shen1(B) and Xiangyu Li2 1 Department of Public and International Affairs, City University of Hong Kong, Hong Kong,

China [email protected] 2 Department of Architecture and Civil Engineering, City University of Hong Kong, Hong Kong, China [email protected]

Abstract. Conflicts caused by mega projects (MPs) always generate strong public opposition and pose significant threats to social harmony. Combined with ubiquitous online access, the spread of online rumors is unparalleled. However, rumor rebuttal on social media has been largely overlooked in engineering sociology. This study attempts a cross-disciplinary approach by crawling data related to antirumors of a representative MP from Sina Weibo to identify anti-rumor topics and online stakeholders, and investigates the effectiveness of anti-rumor strategies. The results suggest that the sentiment-based echo chamber effect is not significantly present in both participant and strategy networks. Anti-rumor messages of traditional media and elites are effective, while that of self-media are mainly ineffective. Meanwhile, anti-rumor strategies have different effectiveness in three frameworks. Refutation and guide strategies are effective in the assessment and risk perception framework, sarcastic and disbelief strategies are counterproductive in the risk perception and progress framework, while interrogatory strategy has opposite effects in the assessment and risk perception framework. This research can contribute to developing a systematic understanding of anti-rumor communication and provide recommendations for authorities to intervene social conflicts caused by MPs. Keywords: anti-rumor · strategy · stakeholder · mega projects · social media · LDA

1 Introduction With the acceleration of urban development, interest in mega projects (MPs) is rising. In a complex urban context, there is often an antithetical relationship between the significance of MPs and public attitudes (Wang et al. 2021a). MPs play a crucial role in developing comprehensive urban carrying capacity and improving reginal economic competitiveness (Hawken et al. 2021). While the negative externalities caused by MPs, including potential health hazards and environmental pollution, may generate public opposition, threatening project success and social harmony (Wang et al. 2021b). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 1660–1672, 2023. https://doi.org/10.1007/978-981-99-3626-7_128

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Meanwhile, the undeniable negative impacts of MPs would be greatly amplified by word-of-mouth rumors. Combined with ubiquitous online access, social media platforms have become the trumpets for information diffusion, and the spread of online rumors was previously unparalleled (Pal et al. 2020). The misinformation not only affects public risk perceptions of MPs but also aggravates the unwarranted panic (Paek and Hove, 2019). Various conspiracy theories have even become a stumbling barrier in forming a good government image, making it more difficult for authorities to successfully and smoothly build MPs (Wang et al. 2022). Moreover, as a double-edged sword, social media provide a new opportunity to break down public stereotypes about MPs. Government departments, relevant organizations, news organizations, and even the general public can actively participate in refuting rumors to prevent the escalation of MP conflicts. Unfortunately, as online communication becomes increasingly personalized, anti-rumor actions are not entirely effective and can even trigger a “backfire effect”. Against this backdrop, it is strongly recommended to evaluate the effectiveness of online anti-rumor actions in MP conflicts. Nevertheless, existing studies on MPs mainly focus on public participation, antecedents of risk perceptions, or rumor propagation via questionnaire survey, scenario experiment, and parameter simulation (Dai et al. 2022). In addition, most anti-rumor research focuses on social emergencies, policy dissemination, and political activities (Tsai et al. 2020). However, in engineering sociology, rumor rebuttal on social media has been largely overlooked, especially the specific transmission process of anti-rumors and the selective acceptance behavior of online participants. To bridge the above knowledge gap, this study attempts a cross-disciplinary approach by crawling data related to anti-rumors of a representative MP from Sina Weibo to address three questions: Who is involved in refuting rumors of MPs? What are the topics and strategies in anti-rumors of MPs? How effective are different participants and strategies in refuting rumors of MPs?

2 Literature Review 2.1 Social Impacts of MPs MPs, i.e., the Sponge City Program, chemical industrial park, and energy projects, can make up for the inadequacy of existing carrying capacity and accelerate sustainable urban development (Dogan and Stupar, 2017). These projects can also be controversial given their vast organizational complexity, large scope, and costly schemes. It demonstrates that the planning, implementation, and operation of MPs can yield economic benefits and improve industrial innovation (Zheng, 2020). However, MPs may compromise the natural environment, the daily life of residents, or social trust, thereby intensifying disputes and triggering social conflicts (Bixler et al. 2020). What makes public opposition caused by MPs even more prevalent is the use of social media platforms. In the zettabyte era, the emergence of social media created unprecedented cyberspace where online stakeholders can share their emotions, opinions, and perspectives anytime and anywhere (Tanaka and Hirayama, 2019). Yet, social media has also become a catalyst for breeding rumors and disinformation, which exacerbates online MP conflicts (Kwon et al. 2016). The messages related to MPs spread by cyberspace

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presents a new challenge for conflict management. However, although a stream of studies has begun to focus on social impacts of MPs, most of them pay attention to driving determinants of social acceptance and public participation (Petrova, 2016). More attention should be paid to the application of real-time data from the Social Network Sites (SNS), which can reflect the behavioral intentions of dynamic participants. 2.2 Online Rumors and Anti-rumors Public attitude towards MPs is determined not only by the actual project risks they face but also by subjective judgment (Paek and Hove, 2019). Unlike experts, laypeople are inadequately aware of project hazards and are vulnerable to external information. Ensuring the correctness of information on social media platforms is critical to develop accurate risk perceptions, reduce public uncertainty and enhance public acceptance (Kwon et al. 2016). However, online rumors often interfere with these efforts. Rumors refer to the messages disseminated without official verification and are not entirely based on the facts, which may have severe consequences for social trust and social stability (DiFonzo and Bordia, 2000; Kim and Kim, 2017). Rumors can influence individuals to disregard expert advice and government counsel, and mislead the public into taking unnecessary or counterproductive actions, such as offline demonstrations and violent protests (Wang et al. 2021c). When rumors spread on social media, some participants, such as government agencies, news organizations, and public representatives, may adopt different anti-rumor strategies, including refuting rumors with evidence, firmly denying rumors without any reason, sternly criticizing rumor spreaders, etc. (Coombs and Holladay, 2004). The essence of anti-rumor behaviors is to provide correct information to efficiently counteract the consequences of rumors (Wang et al. 2022). The source of anti-rumor messages, the characteristics of content, the identity of anti-rumor receivers, and communication channels can significantly influence the effectiveness of anti-rumors (Cinelli et al. 2021). Although research has concentrated on online rumor propagation and mitigation, most previous studies employed simulation models based on empirical data to reflect the relationship in realistic situations. Parimi and Rout (2021) proposed a genetic algorithm for selecting minimal anti-rumor users in social networks. Nevertheless, in reality, events are changeable, and the effectiveness of anti-rumors can be determined by participant types, strategy categories, etc., which highlight the limitations of machine-based simulation (Zhao et al. 2013). Therefore, it is essential to utilize actual data to understand the mechanism of anti-rumor strategies for urban administration when MPs-related conflicts occur. 2.3 Echo Chamber Effect on Social Media Social media platforms, such as Sina Weibo, Facebook, Twitter, Reddit, and Gab, accommodate a wide variety of users and views, allowing users with different social backgrounds and knowledge reserves to share information (Bunker and Varnum, 2021). Online participants with similar interests or beliefs show a tendency to prefer congenial information, leading to the formation of attitudinally homogeneous clusters - known as

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“echo chambers”. Echo chambers refer to the homogeneous networks where individuals mutually perpetuate worldviews and immunize themselves from other perspectives, contributing to polarization and even radicalization (Buder et al. 2021). Therefore, in the high-choice media landscape, the homogeneous informational clusters of echo chambers may fuel these dynamics and foster misinformation spreading, making it more challenging to refute rumors (Pariser, 2011). The polarization caused by echo chamber effect has been addressed by using a variety of methodologies ranging from comparative case studies and experimental work computer simulations (Geschke et al. 2019). Compared with offline networks, actual networks of online participants could achieve diversified information disclosure, and users even quite explicitly look for cross-cutting dialogue (Buttliere and Buder, 2017). Recently, the homogeneous hypothesis of echo chambers has been questioned by many scholars. Overall, these adversarial debates about homogeneity and heterogeneity on social media present an even more interesting puzzle for the research of anti-rumors. On the one hand, the echo chamber effect establishes a relationship between polarization and homogeneity (Pariser, 2011; Schmidt et al. 2017). On the other hand, recent literature establishes fairly good evidence of the relationship between polarization and heterogeneity (Bail et al. 2018; Wang and Song, 2020). Therefore, the echo chamber effect in cognition and interaction behaviors can be used to understand the effectiveness of anti-rumor strategies.

3 Methodology The first step of this study was to crawl and filter data from social media platforms. Then, cleaning the original data. Finally, three techniques were adopted to extract meaningful insights from online data. Specifically, the Latent Dirichlet Allocation (LDA) model was utilized to identify the frameworks of anti-rumors. Comment sentiment analysis was utilized to evaluate the effectiveness of anti-rumors, and network analysis was utilized to visualize the interaction between rumors and anti-rumors. 3.1 Data Collection and Preprocessing Different from previous studies that adopted dynamic game model or multi-objective algorithm (Parimi and Rout, (2021)), based on the objective data, this research selected a controversial MP for the case study. Case selection follows three principles: (i) the selected project should be one of the most typical and representative MPs; (ii) the selected project should be certain controversial and has recently generated an extensive online debate, which can provide a large amount of available data. Thus, Maoming PX (paraxylene) project was selected as a case study. Maoming PX project is one of the vital mega projects in Guangdong Province with an investment of 3.5 billion RMB (≈550 million USD). In order to improve the development of the petrochemical industry, Maoming PX project was initiated by authorities. Although this PX project can guarantee a sufficient supply of PX for national petrochemical production, negative externalities caused concerns and strong opposition from citizens. This project has been followed and discussed by netizens for nearly a month on Sina Weibo.

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The data collection process followed a similar process of Son et al. (2019)’s research. Firstly, using keywords “Maoming PX” and “PX project”, GooSeeker, a website crawler, was called to obtain original posts and reposts within the time range of 27/03/2014– 27/04/2014. A total of 66831 messages were initially obtained, corresponding to 5,658 original posts and 61,173 reposts. Then, following Wang et al. (2022)’s method, this research invited two trained professionals to annotate and screen out original posts suspected of being rumors or anti-rumors. Next, repeat the manual coding process by another two professionals to ensure accuracy. Finally, remained 334 original rumors consistent with the checklist of rumor text, and 1013 original anti-rumors in line with the list of anti-rumor strategies. This research also deleted 980 original anti-rumors with less than ten comments to exclude the samples with low impact. Remained 33 original anti-rumor posts contrasted with recognized rumors, corresponding to 5791 comments. 3.2 Content Analysis 3.2.1 Identification of Anti-rumor Participants To mine the community structure of anti-rumor participants and ensure classification accuracy, referring to the participants’ classification on social media by Wang et al. (2022), the codebook was determined as six anti-rumor participants types: 1) “government”, refers to the official electronic accounts registered by government departments; 2) “traditional media”, refers to the official electronic accounts registered by traditional newspapers, i.e., People’s Daily, Xinhua News Agency, and Global Times; 3) “selfmedia”, means independently operated social media accounts, usually run by writers and commentators; 4) “NGO”, refers to the official electronic accounts opened by nongovernmental organizations, i.e., ENGOs; 5) “elites” are representatives of the public with high social status and whose content has sufficient impact, i.e., professors, lawyers, and entrepreneurs; 6) “individuals”, refer to the accounts set up by ordinary citizens. This research marked all anti-rumor participants according to user names, industry categories, profiles, and authentication descriptions. 3.2.2 Framework Modeling of Anti-rumors Anti-rumors under different topic frameworks reflect the targeted clarification and guidance of rumor refuting subjects. Hence, to guarantee the integrity of anti-rumor topic coding, this study utilized LDA algorithm to explore the number of anti-rumor topics and the keyword groups. Functions are as follows: N          p Zd ,n θd p Wd ,n Zd ,n , β (1) P θd , Zd ,n , Wd ,n α, β = P( θd |α) n=1

¨ p(wm |α, β) =

p(θm |α) ∗ p(|β) ∗ p(W| α, β ) =

M m=1

Nm 1=1

  p wm,n |θm ,  d dθm

p(wm | α, β )

(2) (3)

α and β are parameters that are sampled once; Nm refers to the length of the document;  refers to the word distribution of topic; M means the number of documents.

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3.2.3 Definition of Anti-rumor Strategy Diverse strategies under frameworks have different effects to the echo chamber effect of online anti-rumors. This study classified anti-rumor messages into different strategies following Choemprayong et al. (2017)’s research, compiling six response strategies: refutation, disbelief, attack, guide, sarcastic, and interrogatory. Specifically, refutation response refers to the messages that provide evidence to refute rumors; Disbelief response refers to the messages that express disbelief in rumors without any reason; Attack response refers to the messages that sternly criticize rumor spreaders and warn or directly take action against rumor mongers; Guide response refers to the messages that propose rational courses of action to counter rumors; Sarcastic response refers to the messages that ridicule others’ beliefs or comments in support of rumors; Interrogatory response refers to the messages that critical inquiry about rumors. 3.2.4 Effectiveness of Anti-rumors After the identification and topic modeling, this study measured the effectiveness of antirumors. Although some scholars consider reposting as a sign of approval, participants add their own perspectives and ideas when they comment on anti-rumor messages, and to some extent, the emotion of their comments can reflect their standpoint. By developing the rumor refutation effectiveness index (REI), Li et al. (2021) found that their emotions were positive when people agreed with a statement. Therefore, this study measured the sentiment of each comment under anti-rumor posts to capture the effectiveness of antirumors. If the sentiment of a comment is positive, then it means this participant agrees with the anti-rumor, so this anti-rumor is effective for this participant and vice versa. Following An et al. (2021)’s research, the sentiment lexicon combined with syntactic analysis was used to perform a fine-grained comment sentiment classification. Based on the SnowNLP package in Python, this research filtered through every comment under all anti-rumors and derived the sentiment score of each comment. Then, this research standardized the sentiment intensity value. If the standardized sentiment value is larger than 0.55, then the comment is positive; if it is smaller than 0.45, then the comment is considered negative; otherwise, the comment is neutral. Formulas are as follows: m n Valueadv1 ∗wp ∗(−1)t1 − Valueadv2 ∗wn ∗(−1)t2 (4) S= i=1

i=1

Sv = (Si − min {Sj })/( min {Sj } − min {Sj }) 1≤j≤n

1≤j≤n

1≤j≤n

(5)

In the equation, S is the sentiment value of each comment; Valueadv1 and Valueadv2 are the intensity of the degree adverb; n and m are the amount of positive and negative sentiment words, respectively; wp and wn are the weight value of each positive and negative sentiment word; t1 and t2 are the number of negation words before sentiment words; Sν is the standardized sentiment intensity value. 3.3 Anti-rumor Network Analysis Based on the classification of participants and their commenting data, the participant network was constructed to observe rumor refuting effects of different participants by

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using Gephi. The Gephi graphics software is an open-source software that can visualize networks. For the anti-rumor participant network in this research, each node denotes a participant, and a line between nodes represents the commenting relationship between two participants. The color of each node denotes participant types or comment sentiment, and the size of each node denotes the topic out-degree. Then, the chord diagram was utilized to visually display the degree of homogeneity based on participants’ identity and comment sentiment in the anti-rumor networks. Moreover, according to the original antirumor posts and their commenting data, affiliation networks under different frameworks were established to map the relationship between anti-rumor messages and comment sentiment. Gephi and the chord diagram were also used to visualize anti-rumor strategy networks based on participants’ standpoint.

4 Results 4.1 Framework of Online Anti-rumors The LDA technique was employed to extract anti-rumor topics of the corpus. Following Han et al. (2021)’s research, this study made a trade-off between topic differentiation and model perplexity. Thus, 12 topics were extracted and described according to the logical connection of high-frequency feature words (see Table 1). To better generalize the antirumor messages, 12 topics were further divided into three frameworks. Specifically, the progress framework clarifies rumors related to project progress that causes public attention, including project start-up, suspension, and offline protests; the assessment framework clarifies rumors related to the public attitude towards the PX project or the authorities; the risk perception framework clarifies rumors related to the risks of the PX projects. In Table 1, anti-rumors related to the progress, assessment and risk perception framework reached 46.62%, 33.53% and 19.85%, respectively. Table 1. Topic and framework summary of online anti-rumors Framework

Topic No

Progress framework

T1 (24.68%)

Maoming PX, efficiency, rumors, punish, transfer, suspension

T5 (7.42%)

Respect, stop, limit, launch, gather, site location, settle, continue

T6 (6.14%)

Hearings, interviews, policy, law, response, start, handle

T9 (4.19%)

Compensation, statement, policy, citizen, decision, prepare

T10 (4.19%)

Riot, protect, police, witness, bloodless, demonstration, suppress

Assessment framework

Risk perception framework

Salient terms (partial)

T3 (11.96%)

Economic, development, industrialization, conscience, trust

T4 (10.70%)

EIAR, experts, Baidu, Tsinghua, ignorance, comment, statement

T7 (6.07%)

Scientific, truth, public opinion, transparency, defend, government

T8 (4.80%)

Responsibility, sensible, rational, democracy, victim, justice, right

T2 (12.98%)

Low toxicity, safe, healthy, secure, protection, save, doctor

T11 (4.19%)

Controllable, risk, emission, odorless, Hypotoxicity, rumors

T12 (2.68%)

Mature technology, safe, handle, operation, specialized, experts

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In addition, this study generated the anti-rumor propagation map of participants, as shown in Fig. 1. Most of the participants who participated in rumor countering were selfmedia (50.00%), followed by traditional media (17.81%), elites (17.34%), individuals (10.37%), NGOs (4.14%), and government agencies (0.34%). Individuals, elites, and NGOs mainly post anti-rumor messages related to assessment framework, self-media and traditional media mainly post anti-rumor messages related to progress framework, while government agencies post anti-rumor messages related to assessment and risk perception framework. Moreover, most anti-rumor strategies in three frameworks are refutations (76.68%), followed by guide (13.05%), sarcastic (8.40%), disbelief (1.71%), interrogatory (0.12%) and attack (0.03%).

Fig. 1. The flow of anti-rumors

4.2 Anti-rumor Strategy Network To gain insights into the effectiveness of diverse anti-rumor strategies under different frameworks, this study also generated comment networks under different anti-rumor frameworks. As shown in Fig. 2, most anti-rumor strategies in the assessment framework are refutations, followed by sarcastic, interrogatory, and guide. The sentiments of the comments under refutation, interrogatory, and guide strategy were mainly positive, while the sentiments of the comments under sarcastic strategy were mainly neutral. As shown in Fig. 3, most anti-rumor strategies in the risk perception framework are sarcastic, followed by interrogatory, disbelief, guide, and refutation. Moreover, the comments under sarcastic, interrogatory, and disbelief strategies were mainly negative, while the comments under refutation and guide strategies were mainly positive. As shown in Fig. 4, most anti-rumor strategies in the progress framework are guide strategy, followed by attack, disbelief, and sarcastic strategy. The comments under guide, disbelief, and sarcastic strategy were mainly negative, while those under attack strategy were mainly positive.

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Fig. 2. Comment network (a) and co-comment network (b) colored by sentiment in the assessment framework

Fig. 3. Comment network (a) and co-comment network (b) colored by sentiment in the risk perception framework

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Fig. 4. Comment network (a) and co-comment network (b) colored by sentiment in the progress framework

5 Discussion Based on sentiment analysis, this study investigates anti-rumor effect of diverse strategies. The empirical results suggest that anti-rumor strategies have different effects in the assessment, risk perception, and progress frameworks. Details are as follows: Firstly, in the assessment framework, refutation, interrogatory, and guide strategies are effective, while sarcastic strategy is ineffective, which are consistent with previous studies. DiFonzo and Bordia (2007)’s empirical evidence showed that refutation is the most effective anti-rumor strategy as it can reduce netizens’ uncertainty by providing evidence to explain why rumors should not be believed. Paek and Hove (2019) also indicated that the valid anti-rumors were not passive ones (i.e., sarcastic) but rather more aggressive ones (i.e., refutation and interrogatory). Moreover, in the context of online MP conflicts, guide strategy is also critical for countering rumors. Citizens seek information more intensely than ever, as engineering issues require unfamiliar expertise. Increased fear and uncertainty make individuals more vulnerable to rumors, while guide strategy can help people evaluate the value of MPs via evidentiary materials. Within the assessment framework, outcomes suggest that each anti-rumor strategy, regardless of type, seems to reduce more or less rumor-related beliefs, which highlights the importance of MP conflict communication. Secondly, in the risk perception framework, refutation and guide strategies are valid, while sarcastic, interrogatory, and disbelief strategies are counterproductive. The opposite effects of evidence-based refutation/guide strategy and groundless sarcastic/disbelief strategy emphasize the significance of countering rumors based on factual foundations. Given the complexity of MP conflict communication and the rapidity of information diffusion through social media, response processes could be more complicated than other crisis events or social hotspots (Jia et al. 2011). Therefore, in order to enhance

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public ability to make rational judgments, it is necessary for rumor breakers to correct the misunderstanding of MPs by providing empirical evidence. Additionally, it is worth noting that interrogatory strategy in the assessment framework is effective, but in the risk perception framework has the opposite effect. Interrogatory strategy asks critical follow-up questions about rumors to reveal the limitations of arguments in support of rumors (Goh et al. 2017). Nevertheless, it is unavoidable and undeniable that MPs have negative externalities such as health risks, pollution risks, and toxic risks. Thus, this strategy loses effect in the risk perception framework. Finally, in the progress framework, attack strategy is effective, while guide, disbelief, and sarcastic strategies are counterproductive. Paek and Hove (2019) found that attack strategy would have adverse consequences if users attacked rumors by threatening punishments. However, this study showed that attack strategy is valuable in the progress framework. This may be because the progress framework clarifies rumors related to project progress that cause public attention, including project start-up, suspension, and offline protests. The specific progress of MPs is an objective fact so that attack strategy can suppress rumors via legal actions. Interestingly, guide strategy is helpful in the assessment and risk perception framework, but it is counterproductive in the progress framework. The construction of MPs is closely related to the daily life of the surrounding residents, but guidelines and directions are mainly published by authorities, i.e., experts, celebrities and industry associations Goh et al. (2017), most of them are not locals. Therefore, individuals are less likely to believe in guide strategies released by authorities due to public stereotypes.

6 Conclusion Drawing on a cross-disciplinary approach, this study identifies anti-rumor topics and online stakeholders who participate in refuting rumors related to MPs and investigates the effectiveness of anti-rumor strategies. The results suggest that the sentiment-based echo chamber effect is not significantly present in both participant and strategy networks. Moreover, anti-rumor effects of online stakeholders are totally different, traditional media and elites actively participated in refuting rumors, and their anti-rumor messages are effective, while comments under anti-rumor messages of self-media are mainly negative. In addition, anti-rumor strategies have different effectiveness in three frameworks. Refutation and guide strategies are effective in the assessment and risk perception framework, sarcastic and disbelief strategies are counterproductive in the risk perception and progress framework, while interrogatory strategy has opposite effects in the assessment and risk perception framework. Hence, careful deliberation should be paid to response strategies when countering online rumors. Treating different misinformation equally without considering rumor characteristics may adversely influence MP conflict management. Theoretical contributions to the existing knowledge are as follows. Firstly, previous studies paid more attention to online rumor propagation (Parimi and Rout, 2021; Wang et al. 2021c). This research makes up for the knowledge gap by focusing on anti-rumor strategies in online MP conflicts. Practitioners can use empirical findings to improve communication efficiency in rumor management. Secondly, by utilizing text mining

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and topic recognition techniques, this research presented and tested anti-rumor effect of different online stakeholders. The identified participants can help understand the importance of supporting multi-level participation in MP conflict communication. Finally, this research demonstrates the significant role of context-based anti-rumor strategies. Moreover, the findings highlight several practical implications. Firstly, this study highly recommends deploying differentiated anti-rumor strategies under different circumstances: for rumors within assessment and risk perception framework, evidencebased strategies such as refutation and guide are better than passive anti-rumors; while for rumors within progress framework, aggressive strategies such as attacks are more effective. Secondly, with the rapid development of information communication technology (ICT), it is essential for government agencies to cooperate with non-governmental actors to conduct collaborative governance of online MP rumors. Government should establish or improve relevant notices to manage the operation of self-media. When rumors spread, government can interact with traditional media as well as self-media to seize the discourse power. Finally, government should develop a rumor detection system with a thorough warning mechanism to quickly disseminate multi-topic and comprehensive information about MPs as soon as online conflicts break out to prevent information vacuum and cognitive bias.

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Spatial Characteristics Analysis of COVID-19 in Guangdong and Suggestions for Community Prevention Fan Wu, Yuxuan Li, Junjie Ma, Zilin Chen, and Weijia Luo(B) School of Civil Engineering and Transportation, South China University of Technology, Guangzhou, China [email protected]

Abstract. As a major public health event, the impact of the COVID-19 outbreak was significant. Based on data from December 2021 to March 2022 for the medium to high-risk areas reported in Guangdong Province and their corresponding street communities, the paper studied the spatial characteristics of the epidemic and the gathering distribution of points of interest (POI) and traffic points, and the association between them, which is analyzed by kernel density analysis, coldhot spot analysis, and spatial autocorrelation analysis. It was found that the spatial distribution of the epidemic in Guangdong Province had significant positive spatial clustering characteristics, and the spatial clustering pattern of the epidemic was roughly the same as the clustering distribution pattern of POIs and traffic points. Keywords: COVID-19 · Community Governance · Spatial Characteristics Analysis · Point of Interest

1 Introduction The outbreak of COVID-19 is one of the most serious challenges to public health security governance worldwide nowadays. Historically, urban public health emergencies have been one of the important reasons for promoting urban planning and habitat. The existing studies have mostly focused on the characteristics of virus, medical treatment means, etc., and relatively few studies have been conducted on the spread and risk of epidemics in cities using spatial analysis methods [1]. Thus the correlation between urban spatial environmental factors and the risk of epidemics has not been identified [2]. Therefore, the analysis of the factors influencing the spread of epidemics is conducive to predicting the trend of its spread and formulating relevant strategies that can provide favorable support for prevention and response to public health emergencies. The authors would like to express their gratitude for the research funding support projects: Young Scholar Program of High-End Science and Technology Innovation Think Tank 2021 Annual Project given by the China Association for Science and Technology (project number 2021ZZZLFZB1207150), and Guangzhou Philosophy and Social Science Development 14th FiveYear Plan Joint Project given by the Guangzhou Social Science Planning Office (Project Number 2021GZGJ41). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 1673–1681, 2023. https://doi.org/10.1007/978-981-99-3626-7_129

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With an urbanization level close to that of developed countries, Guangdong has the largest and fastest growing population in China and a high density of economic activities. Therefore, it is of great significance to analyze the community spatial distribution characteristics of the epidemic in Guangdong. The paper selected intermediate, high-risk areas of COVID-19 in Guangdong and their corresponding street communities from December 2021 to March 2022 as the research objects, and used kernel density analysis, cold-hot spot analysis, and spatial autocorrelation analysis based on point of interest (POI) data and rail traffic data to reveal the community spatial distribution characteristics, and then provide suggestions for community epidemic prevention.

2 Literature Review 2.1 Community Epidemic Prevention As the basic component unit of urban, communities are the epitome of urban social life and have the basic elements of life and spirit [3]. Urban communities have extremely high population aggregation and become a high incidence of epidemic transmission and infection after the outbreak of COVID-19. Tu explained the embodiment of the role of communities in the prevention and control of epidemic from four aspects: organizational function, propaganda function, control function, and care service function [4]. Yan emphasized the importance of community emergency management mechanism in epidemic prevention and control [5]. Many scholars have suggested that there are shortcomings in Chinese urban community management in terms of community boundaries, community resource dispatch, and community resident management [6]. Hence, the community perspective is very important in the face of epidemic prevention and control, and it can reduce the risk and the negative impacts at the first time of the epidemic spread. 2.2 The Spatial Distribution Characteristics of Infectious Diseases In the study of spatial distribution characteristics of various infectious diseases, scholars analyzed the spatial and temporal distribution characteristics, individual and spatial clustering characteristics of tuberculosis [7], SARS [8], Middle East respiratory syndrome [9], and MERS CoV [10] using cold-hot spot analysis, geographically additive variables model, and complete Bayesian methods. Regarding the spatial distribution characteristics of COVID-19, Lv used Crystal Ball and Geographic Information Systems (GIS) to analyze the spatial and temporal characteristics of the epidemic in Hubei Province over a period of time using spatial autocorrelation and mathematical statistics, and put forward targeted ideas for epidemic prevention and control [11]. Similarly, Jia [12] and He [13] used statistical methods and geographic techniques to analyze the diffusion and spread process, spatial variability characteristics, and influencing factors of the epidemic in Guangxi Province and Fujian Province. Dehghan has spatially analyzed and predicted the COVID-19 situation in various Asian countries, and proposed that epidemic prevention and control requires intervention through coordinated measures and public actions at the level of each national government, he recommended paying extra attention to the epidemic spillover from neighboring countries [14].

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To sum up, most studies have used GIS to analyze the spatial and temporal distribution patterns and influencing factors of the epidemic in countries, provinces, and cities at a large scale. This paper focused on the common characteristics of the spread of the epidemic from a community perspective, so as to provide suggestions for community emergency management during the occurrence of major public health events.

3 Research Design 3.1 Research Strategy and Data Sources After screening the collected epidemic data, this article selected the main medium and high-risk areas reported by Guangdong Province from December 2021 to March 2022 for research. In order to visually represent the severity of COVID-19, based on the official list of medium and high-risk communities, considering the definition of the epidemic risk level and duration of the epidemic, this article respectively assigned values 1 and 3 to the medium and high-risk areas, and accumulated them according to the number of days of duration, then got the community’s epidemic risk index based on the value assignment of community’s epidemic severity. As shown in Fig. 1, 19 medium and highrisk communities and streets with large indexes were screened out after the assignment, and ArcGIS was used to analyze the spatial characteristics of the epidemic distribution, POIs, and traffic points distribution in these areas.

Fig. 1. Selected Communities and Their Assignment

The data used in this paper were obtained from the Guangdong Health and Health Commission portal and Dongguan Health and Health Bureau, BigeMap, OpenStreetMap, and Baidu Map open platform, with a 2 km radius circle around the community as the delineation of POI around the community.

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3.2 Research Methods 3.2.1 Spatial Autocorrelation Analysis 3.2.1.1 Global Spatial Autocorrelation Global spatial autocorrelation describes the overall distribution of a phenomenon, and is used to analyze the distribution and aggregation of the epidemic in various communities across the province. The calculation formula of Moran’s I is as follows:     n ni=1 nj=1 Wij (xi − x) xj − x   (1) Moran s I =   (i = j) n n n 2 i=1 j=1 Wij i=1 (xi − x) In the formula, n represents the sum of the epidemic risk index of each community; Wij is the spatial weight matrix, which represents the degree of connection between the i community (street) and the j community (street); represents the epidemic risk index of a community (street); xi represents the epidemic risk index of a community (street); x represents the mean value of the epidemic risk index of the community (street). For Moran’s I, Z-score and P-value are used to test the significance level of spatial autocorrelation, and the formula for Z-score is as follows: I − E(I ) (2) Z(I ) = √ Var(I ) where E(I ) and Var(I ) are the expected value and variance of Moran’s I, respectively. 3.2.1.2 Local Spatial Autocorrelation This paper is mainly used to quantitatively describe the similarity or correlation degree of the epidemic risk index between each community and neighboring communities. The calculation formula is as follows:    n(xi − x) m j=1 wij xj − x  Local Moran s I = (3) (i = j) n 2 i=1 (xi − x) The meaning of each variable in formula (4) is the same as formula (2). 3.2.2 Cold-Hot Spot Analysis The cold-hot spot analysis is used to identify the aggregation and dispersion trends of the spatiotemporal data of the epidemic situation in prefecture-level cities in Guangdong. The regional cold and hot spots are often measured by the Getis-Ord Gi∗ index, and the calculation formula is as follows:  n n n n 2 a x − x a x ij j ij j j=1 xj j=1 j=1 j=1 ; S = − x2 Gi∗ =  ; x = (4)  2 n n n n s n a2 − a j=1 ij

j=1 ij

n−1

where xj represents the epidemic risk index of j community (street); aij represents the spatial weight between i community (street) and j community (street); n represents the sum of the epidemic risk indices in Guangdong; x represents the mean; S represents the standard deviation. If the result is positive, it belongs to the hot spot area; otherwise, it belongs to the cold spot area.

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4 Results and Analysis 4.1 Development and Distribution of COVID-19 in Guangdong As shown in Fig. 2, the spatial distribution density was obtained by kernel density method. Analysis results show that in this round of epidemic situation, high-risk areas of Guangdong mainly focus on Dongguan and Shenzhen. Dalang town of Dongguan is the epidemic area. In addition, Shenzhen has formed a set of epidemic focus in the lower intensity. In Shenzhen, there are 3 hotspots of highest intensity, which are mainly located in Nanshan district, Luohu district, and Baoan district. Shenzhen has become a hot spot of the epidemic due to its functions as a city. As a special economic zone, Shenzhen has a large population flow due to its frequent economic exchanges, which triggers the import and export of epidemic diseases. Relevant studies also show that population flow provides an effective way for the transmission of infectious diseases [15]. The high frequency of population flow in Shenzhen also makes Dalang Town, which is close to Shenzhen, vulnerable to the interference of Shenzhen epidemic. Therefore, Dalang town has a high risk of outbreak importation and is easy to become the hardest hit area of the epidemic. In the areas mainly discussed in this study, the duration of the epidemic was all short, which means that the epidemic was controlled and alleviated in a short time. 4.2 Spatial Agglomeration Characteristics of COVID-19 in Communities It can be seen from the above that the epidemic risk index presents a relatively stable positive spatial agglomeration pattern with urban agglomeration as the core. The global autocorrelation Moran’s I index was used to analyze the spatial agglomeration characteristics of cumulative confirmed cases in communities in Guangdong. As shown in Fig. 3, the Moran’s I index is 0.080, indicating that the epidemic risk index shows a spatial correlation; The z-score is 13.23 and the p-value is close to 0, indicating that this feature is significant. This reflects that the cumulative confirmed cases in Guangdong are not randomly distributed, but have significant spatial agglomeration characteristics.

Fig. 2. Kernel Density of the Epidemic in Guangdong (2021.12–2022.03)

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Fig. 3. Epidemic Spatial Autocorrelation Report (2021.12–2022.03).

Fig. 4. Clustering and Outliers Analysis (2021.12–2022.03).

After further local autocorrelation analysis shown in Fig. 4, it was found that the spatial agglomeration pattern of the epidemic situation had not changed. The distribution of the COVID-19 in Guangdong showed the characteristics of local high-value agglomeration, which was in line with the objective law of infectious diseases. The high-high cluster is mainly distributed in the downtown area of Shenzhen and Dalang Town, Dongguan. These two places had a serious epidemic situation, and the surrounding economic development level is high, the population mobility is strong, and the turnover in the city is high, so that there are greater risks in epidemic prevention and control. 4.3 Spatial Distribution Characteristics of POIs and Traffic Points As shown in Fig. 5 and 6, analysis of hot-cold spots of eight types of POI data in Guangdong shows that the spatial distribution pattern of POI in Guangdong roughly corresponds to the spatial agglomeration pattern of the epidemic. The pattern of hot spots is stable, concentrated in PRD Metropolitan area. Dongguan and Shenzhen are POI hotspots, the POI hotspots have complete functional facilities and frequent human activities. The frequent activities and exchanges have accelerated the flow of people and increased the risk of the spread of the epidemic. According to the clustering and outlier analysis of POI and traffic points in Guangdong shown in Fig. 7, it is found that the spatial agglomeration pattern of POI and traffic points also roughly corresponds to the spatial agglomeration pattern of the epidemic, the PRD region is mainly characterized by high-high cluster and low-high cluster. Convenient transportation increases the risk of infection with COVID-19 in the PRD region, which is consistent with previous studies suggesting that transportation accessibility will promote the large-scale spread of infectious diseases [16]. The reason is that areas with developed transportation can promote economic development rapidly, the connection between urban and rural areas and between regions is increasingly frequent, cultural and economic exchanges and trade inevitably bring about population flow. For example, Shenzhen’s POIs and traffic points are high-high cluster. The whole city has a superior geographical location and a rich economic foundation. The traffic network is developed and the degree of opening up is high, thus promoting local cultural exchanges and foreign trade exchanges, the possibility of the spread of the epidemic has been increased

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largely. The POIs and traffic points near Dalang Town, Dongguan City are mainly lowhigh cluster, and the nearby metropolitan area are well connected with each other, but the traffic connection with Dalang Town is less, Dalang Town is relatively isolated, and there is less external traffic inflow. Due to the epidemic, Dalang Town has implemented a long-term traffic control policy, and the epidemic is not easy to spread to the outside world.

Fig. 5. POI Cold-Hot Spots in Guangdong

Fig. 6. Clustering and Outlier Analysis of POI

Fig. 7. Clustering and Outlier Analysis of Traffic Points

4.4 Community Prevention and Control Advice After analyzing the POI data of the 20 communities in Dongguan and Shenzhen shown in Fig. 8, the study found that among the POI around the communities in Dongguan, the POI types with a higher proportion are residential community POI and shopping POI, respectively accounting for 35.2% and 33.5%, while residential areas and shopping places usually have a large density of people. Observing the activity track of confirmed cases announced in Dongguan, these people have also stayed in such places many times, so during community epidemic prevention and control, more attention should be paid to the management and control of personnel in the surrounding communities and shopping places to ensure that suspected cases are found in the first place. According to the statistics of POI around medium and high-risk communities in Shenzhen, the distribution of POI around the communities is significantly denser, and the types of POI with higher proportion are shopping, transportation facility, and residential community, accounting for 26.7%, 26.5%, and 18.8%, respectively. During the epidemic prevention and control in the community, in addition to paying attention to the inspection of personnel in public spaces such as communities and shopping places, it is also necessary to pay attention to the risk of transfer of confirmed cases in the operation of public transportation, and focus on the passengers who take the transportation together.

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Fig. 8. Percentage of Various POIs Around Medium and High-risk Communities in Dongguan (left) and Shenzhen (right)

5 Conclusion and Discussion This paper analyzed the spatial characteristics of community outbreaks in Guangdong by kernel density analysis, cold-hot spot, and spatial autocorrelation analysis, for the sake of predicting the epidemic spread trend. The study shows that the spatial distribution pattern of epidemic communities has significant spatial clustering characteristics, and the clustering degree of various POIs and traffic points roughly overlaps with the clustering pattern of epidemic to a certain extent. Thus, it can be concluded that the distribution of POI and traffic points may have a correlation with the spread of the outbreak, and this correlation has an impact on the spread of the epidemic by affecting people’s movement. Specifically, POI hotspot areas are well-equipped with various functional facilities, while traffic points gather in areas with well-developed transportation. Both areas are characterized by frequent human and economic activities, and the frequent interaction of activities accelerates the movement of people and increases the risk of epidemic transmission. The epidemic prevention and control strategies developed for communities with different types of characteristics should also be different. Communities with a large number of POIs and gatherings should pay more attention to the control of people in the community surrounding neighborhoods and shopping places, and emphasis should be placed to the screening of people in public spaces to ensure that abnormal people are found at the first time; communities with a relatively high number of transportation points in the surrounding area, strengthen the spillover of confirmed cases and pay attention to the risk of transferring confirmed cases when public transportation is in operation; communities with a relatively low number of transportation points in the surrounding area, pay attention to the control of imported cases. Based on the results of the analysis of the spatial distribution characteristics of the epidemic, this paper explores different community prevention and control measures, aiming to improve the ability of communities to cope with the impact of public health emergencies. However, there are still some shortcomings in this study. First, it is difficult to obtain the official epidemic data of community due to privacy, so the analysis results are somewhat different from the actual situation. In addition, this study only conducted a qualitative analysis of the relationship between POI data and the spatial characteristics of

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the epidemic. In the future, the relationship will be quantitatively analyzed by combining spatial autocorrelation and other methods.

References 1. Feng, J., Tang, S., Chuai, X.: The impact of neighbourhood environments on quality of life of elderly people: evidence from Nanjing, China. Urban Stud. 55(9), 2020–2039 (2018) 2. Tan, Z.B.: Reflections on territorial space planning caused by public health emergencies. China Land 2020(03), 8–12 (2020) 3. Tönnies, F.: Gemeinschaft und Gesellschaft: Grundbegriffe der reinen Soziologie. Peking University Press (2010) 4. Tu, Q.L., Zhang, T.S.: Function of grassroots community in novel coronavirus pneumonia prevention and control. J. Hubei Norm. Univ. (Philos. Soc. Sci.) 40(03), 60–64 (2020) 5. Yan, Y.: Systems for community emergency response: the building of structural factors and resilience capacity. Stud. Party Govern. 2022(02), 108–117 (2022) 6. Xue, Z.L., Song X.: Community participation in emergency management of megacities. Shanghai Cult. 2022(08), 13–19 (2022) 7. Nie, T.Y., Chen, W., Zhang, J.Y., et al.: Analysis of spatial distribution characteristics of pulmonary tuberculosis cases in Hefei City, 2009–2020. Chin. J. Antituberculosis 44(09), 947–953 (2022) 8. Meng, B., Wang, J., Liu, J., et al.: Understanding the spatial diffusion process of severe acute respiratory syndrome in Beijing. Public Health 119(12), 1080–1087 (2005) 9. Adegboye, O.A., Ezra, G., Fahad, H.: Spatial modelling of contribution of individual level risk factors for mortality from Middle East respiratory syndrome coronavirus in the Arabian Peninsula. PLoS ONE 12(7), e0181215 (2017) 10. Al-Ahmadi, K., Alahmadi, S., Al-Zahrani, A.: Spatiotemporal clustering of Middle East respiratory syndrome coronavirus (MERS-CoV) incidence in Saudi Arabia, 2012–2019. Int. J. Environ. Res. Public Health 16(14), 2520 (2019) 11. Lyu, Z.H., Cheng S.W.: Research on the temporal and spatial characteristics of the COVID-19 in Hubei Province with the use of Crystal Ball and GIS. J. Cent. China Norm. Univ. (Nat. Sci.) 54(06), 1059–1071 (2020) 12. Jia, Y.H., Hao, L.X., Tan, Y.Y., et al.: Study on epidemic characteristics and spatial differences of COVID-19 in Guangxi. Geom. Spat. Inf. Technol. 45(08), 1–4 (2022) 13. He, Z.W., Chen, N.: Spatial distribution characteristics and influence factors of COVID-19 in Fujian Province. Geospat. Inf. 20(07), 15–21 (2022) 14. Dehghan, S.Z., Shahnazi, R.: Spatial distribution dynamics and prediction of COVID-19 in Asian countries: spatial Markov chain approach. Reg. Sci. Policy Pract. 12(6), 1005–1025 (2020) 15. Kraemer, M.U., Yang, C.H., Gutierrez, B., et al.: The effect of human mobility and control measures on the COVID-19 epidemic in China. Sci. 368(6490), 493–497 (2020) 16. Sun, M.L., Guan, L., Zhao, J., et al.: Legislative considerations on traffic travel of MDRTB/XDR-TB patients from the perspective of infectious disease prevention and control. Chinese J. Antituberculosis 42(12), 1268 (2020)

Public Evaluation of the Effects of River Restoration Projects on Social Benefits Yang Chen, Yuhong Wang(B) , and Charissa Chi Yan Leung Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China {yang-cee.chen,charissa.leung}@connect.polyu.hk, [email protected]

Abstract. River systems worldwide are severely degraded due to diverse threats. River restoration aiming to reverse negative impacts has become a priority strategy for enhancing social benefits and river ecosystems in urban development. River restoration projects provide multiple ecosystem services to human beings, and social benefits derived from cultural services have been underestimated and have attracted increasing interest from researchers and policymakers. The purpose of this research is to investigate the public evaluation of the impacts of river restoration projects on social benefits. Two restored river sites in Hong Kong were selected as the case sites. The questionnaire survey was implemented to collect the public evaluation of the social benefits derived from the cultural services of the restored projects, and their visiting patterns and feelings. Regarding the evaluation of the cultural services, the public thought the restored rivers contribute to public health, quality of life, and recreation activities. Additionally, the respondents thought there is a substantial improvement in the aesthetics of the environment. Many people are supportive of similar projects in future implementation. The results summarized from the 158 responses show that local citizens frequently visit the restored river sites for diverse motivations. People generally feel good about air condition and sounds at the restored sites. Most people are in a good mood when staying at the restored sites, indicating that river restoration contributes to the public’s healthy living. A large proportion of people think there are no negative impacts of projects though some people are concerned about insects, guano, and animal disease. Generally, the public holds a positive view of the projects. The research finding enables policymakers to understand the public’s opinions on the river restoration projects and increase their confidence in implementing more similar projects in the urban environment. Keywords: River restoration projects · Public evaluation · Social benefits · Cultural services

1 Introduction Rivers are an essential part of many cities and are home to over 500 million people worldwide [1]. However, despite playing a critical role in human development, many rivers are severely degraded and under threat due to human needs, urbanization, and © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 1682–1691, 2023. https://doi.org/10.1007/978-981-99-3626-7_130

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environmental challenges [2, 3]. The consequences of river degradation result in devastating social impacts and degraded river ecosystems [4]. Aiming to reverse the negative impacts on human beings and improve river ecosystems in the long run, river restoration has become an increasingly popular strategy in urban sustainable development [5, 6]. Such restoration projects have included the reintroduction of natural elements into channelized rivers such as replacing the concrete riverbed with eco-friendly materials, introducing native wildlife, and redesigning and reconstructing the river channels to mimic a natural river course [7]. Applying such ecological enhancements to river restoration helps return nature to cities while creating more aesthetically pleasing surroundings, promoting biodiversity and habitat diversity, and providing opportunities for recreational activities [8]. These are significant in improving social benefits and the river ecosystems. The social benefits of river restoration are defined as the ecosystem services (ES) derived from river ecosystems that directly affect human beings [9]. River systems provide diverse ecosystem services (ES) to human beings [10]. Previous studies on river systems have mostly focused on provisioning services (e.g., water supply, food provision) and regulating services (e.g., water quality, erosion prevention) [11]. Regarding river restoration, the majority of studies that are concerned with ES have focused on biodiversity and regulating services [12–14]. The cultural services (CS), which refer to the nonmarket benefits people obtain from ecosystems (e.g., recreational, aesthetic, and spiritual benefits), are typically overlooked and often underestimated [9]. With the important role of the public in cities and social benefits being emphasized in river restoration [15], as the most recognizable and easily accessible type of benefit to the public, CS have gained increasing interest from environmental researchers and policymakers [16]. In this case, the social benefits of river restoration from the perspective of CS are rarely monitored, quantified, and evaluated while they are deserved to be studied and clearly understood [4, 17, 18]. This study aims to contribute to filling this research gap by conducting a questionnaire survey at two restored river sections located in the New Territories of Hong Kong. Hong Kong is one of the most populated cities in the world but is well-endowed with green spaces and water resources. By reinventing the “Blue and Green Systems” in the built environment, the Hong Kong government aims to increase the livability and sustainability of the city and enhance the health and well-being of citizens. The information and feedback obtained from the citizens directly reflect how much the public can benefit from the restored river sites, which is significant in enabling policymakers to understand the effects of river restoration on CS, how much the citizens can benefit from the restored rivers, and obtaining social insights for future similar projects.

2 Methodology 2.1 Case Sites Two restored river sites serve as case studies: the Ma Wat River (MWR) and Lower Lam Tsuen River (LTR) sites (Fig. 1). The restoration projects for MWR and LTR were conducted during a similar period, and the restored ecological features were similar, including riparian vegetation, sinuosity configuration, current deflector etc. Therefore,

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the two sites were combined as one to be analyzed in this study. MWR and LTR are two originally concrete-based drainage channels for drainage purposes but lost ecological, aesthetic, and recreational values. With the “Blue-Green Infrastructure (BGI)” concept of promoting landscape greening, biodiversity, and environmental beautification while maintaining the drainage capacity, Hong Kong Drainage Services Department (HKDSD) has actively introduced ecological enhancements to a wide range of river channels during the past two decades. The restored MWR and LTR are two of the representative 26 green river sections with the BGI initiative [19]. Located in the northern New Territories, MWR flows northwards through rural areas and industrial areas. The enhancement site is an 80-m section (N22°29 21.331 E114°8 49.633 ) restored in 2015 near Jockey Club Road and Tong Hang Village. LTR lies in the south of MWR, which is a main river in the Eastern New Territories. The restoration reach locates in the lower reach of LTR, passing through the urban area of Tai Po. The enhanced 40-m section (N22°27 0.492 E114°9 30.478 ) is located near Mui Shue Hang Playground and was restored in 2016.

Fig. 1. The landscape of the two river restoration projects

2.2 Data Collection and Analysis The questionnaire survey was conducted from March 2018 to April 2018 at MWR and LTR, and there were in total 158 questionnaire samples collected. The questionnaire comprised three parts: socio-demographic information, evaluation of the CS and the overall projects, visiting patterns and feelings. The socio-demographic variables aim to know the population status of the surveyed group. The basic demographic information include gender, age, education level, occupation, and monthly personal income. The CS items designed in this study are closely related to human beings, including public health, quality of life, recreation opportunities, and visual improvement. Additionally, the respondents were invited to evaluate the overall project and their prospects for similar projects in the future. The public’s visiting pattern refers to their visiting motivations, frequency, and feelings. The feelings include the respondents’ sense of the surrounding air and sounds, their mood in the environment, and their consideration of the negative impacts of the projects. The results of the questionnaire survey were listed in Excel and analyzed by SPSS Statistic 23. The reliability was tested as 0.893 via Cronbach’s Alpha

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index, which is considered reliable since it is over 0.70 [20]. The correlation among the factors is significant at the 0.01 level, indicating the data are valid.

3 Results 3.1 Socio-demographic Information of the Respondents The composition of the respondents is shown in Fig. 2, the majority of respondents are residents (77.9%) living in the surroundings of the MWR and LTR, and the others are visitors (10.1%) and people just passing by (12%) the sites. Table 1 presents the socio-demographic profile of the respondents and their evaluation of the overall projects. Males (54.8%) are a little over-represented than females (45.2%), and respondents are dominated by the old (>55 years old) (50%). There are more than half of the respondents attained tertiary education (54.4%) and the employed proportion is 53.2%. For the monthly personal income, a large proportion of the respondents (41.4%) earn less than HKD 10,000, and only a small proportion of people (17.9%) have a high income (>HKD 40,000). The mean score and standard deviation of the mean score from each demographic group on the projects are also presented. Generally, females gave a higher score to the projects than males. The youth (18–54 years old) think more highly of the projects than the young (12–18 years old) and the old (>55 years old). People who obtained tertiary education marked high scores on the projects with a mean value of 7.51. The homemakers mark the least scores on the projects than the other groups (e.g., employed, out of work, student, and retired). It seems that people who earn more money are more supportive of the restored projects.

Fig. 2. The composition of the surveyed respondents

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Socio-demographic variables

MWR/LTR (% of respondents)

Mean (S.E.)

Male

54.8

7.02 (2.092)

Female

45.2

7.28 (1.692)

Gender

Socio-demographic variables

MWR/LTR (% of respondents)

Mean (S.E.)

Employed

53.2

7.24 (1.574)

Out of work

3.2

7.20 (0.837)

Homemaker

9.0

6.71 (2.431)

Occupation

Age (years old) 12–18

3.8

7.00 (0.894)

Student

5.8

7.78 (1.394)

18–34

17.9

7.75 (1.351)

Retired

28.8

7.07 (2.290)

35–54

28.3

7.41 (1.575)

Monthly personal income (HKD)

>55

50.0

6.76 (2.246)

40,000

17.9

7.38 (1.299)

Tertiary education

54.4

7.51 (1.453)

Education level

3.2 Public Evaluation of the CS of the Projects Figure 3 (a) shows the respondents’ evaluation results of the projects, mainly focusing on the CS, specifically the efficacy of river restoration projects on public health, quality of life, recreation opportunities, visual improvement, and the overall score of the projects. The mean scores of public health, quality of life, and recreation opportunities are acceptable and distinguished little, with the mean values of 6.62, 6.80, and 6.78 respectively. Compared to the above three items, the mean score of visual improvement is much higher with a value of 7.54. Generally, the respondents think the restored river sites are acceptable with a mean value of 7.17. Additionally, respondents were asked whether the government should continue similar restoration projects in Hong Kong. The

Fig. 3. Respondents’ evaluation of the river restoration projects

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majority of people (78.3%) are supportive of these projects, and the others hold a neutral view (12.1%) and a negative view (9.6%) on future project implementation (Fig. 3(b)). 3.3 Visiting Patterns and Feelings 3.3.1 Respondents’ Visiting Motivations and Frequency Respondents were motivated by a variety of motivations to the restored river sites, including strolling, passing by, walking the dog, observing the wildlife, exercising, and other activities (e.g., photography) (Fig. 4(a)). Some respondents were just passing by the sites (36.1%). Many people went to the sites for strolling (46.2%) and exercising (22.8%). A few people came here for observing the wildlife (10.8%) and walking their dogs (3.2%). Only several people (1.3%) came for other purposes (e.g., photography). These results indicate the restored river sites provide recreational opportunities for people’s healthy lifestyles. In terms of visiting frequency, a large proportion of people (69.4%) always visit the sites, a small proportion of people visit the sites with a low frequency (17.2% for sometimes and 10.2% for seldom), and it was the first visiting for the 3.2% respondents (Fig. 4(b)). The diverse motivations and high visiting frequency indicate the restored river sites play an essential role in people’s daily life, which reflects the social benefits derived from the CS for human beings.

Fig. 4. Respondents’ visiting motivations and frequency towards the river restoration projects

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3.3.2 Respondents’ Feelings at the Restored Sites Respondents were asked about their sense of smell and sense of sound at the restored river sites. The majority of people think the air there is good, which is reported as very fresh (20.3%) and fresh (65.8%). A few people (11.4%) do not have much feeling about the air quality, but a small proportion of people think the air there is bad, representing as smelly (1.3%) and very smelly (0.6%). Concerning the sounds (e.g., sounds of birds, water, insects, leaves, and construction) at the restored river sites, the results are similar to the public’s sense of smell. Most people think the sounds are good, with 15.8% of respondents saying very pleasant and 68.4% saying pleasant. About 13.9% of the respondents hold a neutral view of the sounds, and only several people think the sounds are unpleasant. Sense of smell and sense of sound are two sensory assessments that are used to test the perception of naturalness [21]. This result indicates respondents’ positive feedback on the naturalness perception of the restored river sites (Fig. 5).

Fig. 5. Respondents’ sense of smell and sense of sound at the two restored sites

Respondents were asked about their feelings about the surrounding environment, including positive feelings (e.g., feeling happy, forgetting trouble, and reducing anxiety) and negative feelings (e.g., annoying, irritating, and sad). Based on Fig. 6(a), almost all of the respondents show positive feelings when staying at the restored river sites, particularly there were two-thirds of people (67.7%) thought they were feeling happy at the sites. This outcome shows the respondents gain spiritual benefits from the restored river sites. As for the views on the negative impacts of the projects, many people believed (72.3%) that the projects have no negative impacts on the environment (Fig. 6(b)). However, some people have concerns about the increase of insects (12.6%), the increase of the guano (11.3%), and animal disease concerns (3.8%).

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Fig. 6. Respondents’ feelings about the surrounding environment and their views on the negative impacts of the projects

4 Conclusion This research investigated the public views on the effects of river restoration projects on social benefits based on two river restoration projects in Hong Kong. Some of the respondents are typical visitors (10.1%), reflecting that the restored sites work as an attractive landscape to attract people to come. Respondents generally think the restored river sites are acceptable, though different groups mark diverse scores of the sites. Respondents think three social benefits (namely, public health, quality of life, and recreation opportunities) derived from CS of the restored rivers are acceptable with mean values over 6.50. Another CS item of CS (visual improvement) related to aesthetics received a much high score of 7.54, showing the efficacy of the river restoration projects in improving urban greening and the environment. As for the necessity of whether to implement more river restoration projects in the future, some people hold negative or neutral views while most people (78.3%) show a supportive attitude. The respondents frequently came to the river sites for diverse motivations (e.g., strolling, walking the dogy, observing the wildlife, exercising, etc.), indicating the restored river sites provide recreational spaces to the citizens. Additionally, respondents generally feel good about the air condition and sound at the restored sites, showing a perception of the naturalness of the restored river sites. Respondents generally have positive feelings (e.g., feeling happy, forgetting trouble, reducing anxiety) when visiting the sites, showing the restored sites can provide spiritual benefits to the public. From the perspective of project implementation, many people are concerned about some negative issues (e.g., increasing insects, increasing guano, and animal disease) while more than two-thirds of people (72.3%) think the projects do not bring any negative impact to the surrounding environment. The results demonstrate that the river restoration can bring social benefits to the citizens and get their support for future project implementation. Hong Kong is trying to create an enabling built environment for benefiting people of different demographic groups to fulfill their fullest potential. This research outcome directly presents people’s views on river restoration which provides insight for policymakers to understand the feedback and gain confidence in implementing more similar projects for the city and its citizens.

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Acknowledgement. This paper is based on the research project (E-PolyU502/16) funded by the Research Grant Council (RGC) of the Hong Kong Special Administrative Region Government. The research is part of the study entitled Urban Nature Labs (UNaLab), funded by the European Commission (EC)’s Horizon 2020 Research Scheme, Hong Kong RGC, and other research partners. This research is also supported by the research project (1-CD86) funded by the Research Institute for Land and Space, The Hong Kong Polytechnic University. The authors appreciate the support from the Hong Kong Drainage Services Department (HKDSD).

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19. Hong Kong Drainage Services Department (HKDSD) (2022). https://www.dsd.gov.hk/Eco DMS/EN/River_Channels/River_Channels.html. Accessed 23 Feb 2022 20. Bolarinwa, O.A.: Principles and methods of validity and reliability testing of questionnaires used in social and health science researches. Niger. Postgrad. Med. J. 22(4), 195 (2015) 21. Jorgensen, B.S., Stedman, R.C.: A comparative analysis of predictors of sense of place dimensions: attachment to, dependence on, and identification with lakeshore properties. J. Environ. Manage. 79(3), 316–327 (2006)

Secure Version Management of BIM Using Blockchain and Smart Contract Cluster Xingyu Tao, Moumita Das, Yuhan Liu, Peter Kok-Yiu Wong, Xingbo Gong, and Jack C. P. Cheng(B) Department of Civil and Environment Engineering, The Hong Kong University of Science and Technology, Hong Kong, China [email protected], [email protected]

Abstract. Versioning in Building Information Modeling (BIM) is essential for design collaboration. However, current version control systems risk data manipulation because they rely on centralized versioning architecture, which might result in rewriting, losing design traceability, and causing arguments. Blockchain technology offers a decentralized, immutable, and traceable database model, making it a possible solution for secure version management. Therefore, this study presents a blockchain-based framework with two key contributions. First, a distributed versioning environment is established, leveraging blockchain and common data environment (CDE). Second, a smart contract cluster (SCC) is developed to automate versioning operations in the blockchain. The proposed framework is evaluated and validated in design scenarios based on an actual project. Results show that the blockchain is a promising solution for efficient and secure BIM versioning. Keywords: BIM · Blockchain · Version management · Smart contract · Common data environment

1 Introduction BIM versioning means mange versions of BIM data, (including files or elements) to keep all project members working in the up-to-date BIM data. Version management of BIM has long been a challenge in collaborative design. Project members must continually update models, track, and propagate changes throughout the design process to ensure that designers from all domains work on the up-to-date versions of BIM data [1]. One solution to improve BIM versioning is to build a common data environment (CDE), which is recommended by the latest ISO 19650 standards [2] as “an agreed source of information for any given project or asset, for collecting, managing and disseminating each information container through a managed process”. A CDE benefits version management by providing a common protocol for version update approval/review, defining four containers for holding BIM data in various version states, and controlling version naming conventions. However, most current CDEs and versioning systems are based on a centralized repository managed by a single project administrator or a third-party vendor [3]. Consequently, unknown to other members, malicious members can delete, manipulate or © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 1692–1700, 2023. https://doi.org/10.1007/978-981-99-3626-7_131

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tamper with version records, risking a loss of design change traceability, authenticity, and availability. Additionally, the low robustness and stability of a centralized database frequently lead to server failure. Version data would invariably become temporarily or permanently unavailable as this database is the only source of version information. Blockchain technology is an emerging and promising solution that provides a decentralized, immutable, and traceable data storage and management paradigm to achieve secure BIM versioning. In the construction industry, preliminary explorations of blockchain implementation have been carried out in applications such as supply chain management [4], payment automation [5], and CDE management [6]. A smart contract deployed in a blockchain network is machine-readable code that can self-execute when certain predefined conditions are met. However, integrating smart contracts with blockchain is under-explored for supporting BIM versioning events (e.g., version update initialization and approval). Therefore, this paper proposed a blockchain-enabled CDE (BECDE) framework to manage BIM versions in a decentralized fashion. In addition, a smart contract cluster containing four smart contracts is developed in the BECDE to facilitate versioning automation.

2 Research Methodology The research methodology involves three steps. Firstly, a blockchain-enabled CDE (BECDE) framework is proposed to illustrate the overall logic of versioning in a distributed environment. Secondly, four blockchain smart contract are developed to support versioning activities in a CDE. Thirdly, these smart contracts are validated in a design example to validate their workability (Fig. 1). • Step 1. A blockchain-enabled CDE (BECDE) framework is proposed. Firstly, four core versioning activities are identified. Next, a BECDE framework leveraging blockchain and private IPFS is established. Blockchain is inherently unsuitable for storing largesized files (e.g., a BIM model in hundreds of megabytes) due to the block size limit; thus, decentralizing design documents and design records necessitates coupling blockchain with other storage systems like the InterPlanetary File System (IPFS). IPFS is a content-addressable and distributed file system that stores and shares large files with high throughput [7]. This framework illustrates an overall versioning mechanism by (1) showing how versioning activities interact with distributed databases in a CDE workflow and (2) presenting the logic of how the proposed SCC supports secure and efficient BIM versioning. • Step 2. An SCC is developed. Four smart contracts are designed namely, (1) version update initialization (VUI) smart contract, (2) version update approval (VUA) smart contract, (3) automatic version update (AVU) smart contract, and (4) version transaction query (VTQ) smart contract. These four smart contracts constitute a cluster to automate BIM versioning. • In Step 3. Validation and evaluation are performed. The framework is applied in a design example with three scenarios to demonstrate the feasibility of the SCC. Additionally, this study measures the security performance of the SCC.

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Fig. 1. Research Methodology

3 Blockchain-Enabled CDE Framework 3.1 Identification of Versioning Activities Identifying core versioning actions is a fundamental step in developing versioning methods. Besides necessary and generic versioning actions (e.g., commit and pull changes), BIM versioning in a CDE requires additional activities, especially version change approval. The CDE workflow requires an approval transition for any version update or status change to ensure data suitability and liability. Additionally, various approval administrators differ at various phases of version revisions [8]. In summary, four activities are identified: (1) version update initialization (VUI), (2) version update approval (VUA), (3) automatic version update (AVU), and (4) version transaction query (VTQ). 3.2 BECDE Framework Figure 2 is the proposed BECDE framework that integrates blockchain, CDE workflow, and smart contracts. Four main activities identified in Sect. 3.1 are shown. • Activity 1. Initialize version update. Project member 1 builds a BIM model in his or her local WIP database. He or she would commit the BIM model and model issues to a “PRE-SHARED approval container” before sharing this revised model with all members to obtain permission for version changes. Members of this approval layer (i.e., private IPFS network 1) are currently the only ones with access to unapproved model data. By using the VUI smart contract, version metadata will be represented in a transaction and published to a blockchain. All project participants will be aware that this BIM model is awaiting approval.

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• Activity 2. Check version compliance. After receiving a version modification request, the approver will examine his or her blockchain ledger to compare the model to the delivery plan and established standards. To retrieve the current version of the data on the blockchain ledger, the VTQ smart contract will be used. • Activity 3. Approve/Reject version update. Using a VUA smart contract, a transaction is proposed and stored in a blockchain that includes version metadata, approver choices (such as approval or rejection), and evidence (such as a signature). • Activity 4. Update version in a blockchain. To determine the value of the model’s most recent version, the approver will use the AVU smart contract. The calculation results will then be automatically synchronized to all ledgers by this smart contract to maintain a consistent and current version across the blockchain network.

Fig. 2. BECDE framework

3.3 Development of SCC Figure 3 depicts the interaction between an SCC and a blockchain. A VUI transaction can be recorded and disseminated using a VUI smart contract. Approvers deliver their decisions by leveraging a VUA smart contract to commit a VUA transaction to a blockchain. Additionally, based on a specific AVU transaction, an AVU smart contract automatically lets approvers determine and modify the version value. A VTQ smart contract assists in examining any previously recorded transactions in a blockchain. Five steps are involved in a VUI smart contract algorithm. In Step 1, a VUI function in the smart contract pre-executes a new transaction with a group of endorsers to prevent

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Fig. 3. Interaction Between SCC and Blockchain

illegal transactions. If the transaction data model and function name are correct, the VUI smart contract returns a blockchain read set, a write set, and signatures from endorsers. In Steps 2 and 3, the VUI smart contract packages all the information into a new block and pushes it to an ordering service responsible for broadcasting new blocks (Step 4) using a consensus algorithm. Before adding this new block to his/her local ledger, every member will verify its legality (Step 5). A new block will be chained if results are validated, and all members are notified that a model is seeking version update approval. The VUA smart contract algorithm follows the same execution process as the VUI smart contract. But the transaction in VUA will contain the decision and signature. Similarly, an AUV smart contract will perform a pre-execution to validate input legality (Step 1). Step 2 checks the current version “a” of a given file ID; then, an AUV function returns an updated version value by calculating “version delta+a”. This calculation is automatically performed on all blockchain ledgers. As a result, the new version value is written to a blockchain world state, where members can easily access the version status and work on up-to-date BIM data (Step 3). In addition, this calculation action will be recorded in a block and be chained to the existing ledger (Steps 4–7). A VTQ smart contract aims at inquiring about historical block data for traceability purposes. In Step 1, the VTQ function will check if the input ID exists. If yes, it will return the transactions of the given ID. Otherwise, VTQ function will reject the query.

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4 Validation and Evaluation 4.1 Validation in Design Example Blockchain is still an emerging technology that has not received enough awareness and mainstream adoption in the construction industry. Thus, this section leverages a scenariobased method to apply the BECDE framework in a design collaboration example to illustrate its feasibility and performance. Figure 5 shows the process map of version update approval. An MEP designer, MEP002, initialize a version update request of model “TESTMODEL-MEP-001” in version P01. Another MEP designer, MEP001, is the approver in the MEP domain. He/she downloads the model and issues files using. MEP001 checks the model quality and its version compliance. Then, he/she endorses this version update by invoking a VUA smart contract to mark the version status and add approval proofs to the VUA transaction. Successfully sharing the VUA transaction indicates that the “TESTMODELMEP-001” model in version P02 has been approved and can be used by other project disciplines. However, the VUA transaction only provides a version change proof and does not update the version value in the blockchain. Therefore, the approver invokes an AVU smart contract to upgrade the version value to P02 and synchronize it to all ledgers. Figure 6 shows the results of version approval and updates in the blockchain. 4.2 Security Valuation The Hyperledger community has created the open-source and static program “Revivecc” to examine the security flaws in smart contracts [9]. This part uses Revive-cc to find five common dangers in the codes of smart contracts. These five security metrics are (1) blacklisted chaincode imports, (2) global state variables, (3) goroutines, (4) phantom read of ledger, and (5) range over map. Figure 5 indicates that the SCC contains none of the above flaws, issues, or warnings (Fig. 4).

Fig. 4. Results of SCC Security Evaluation

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Fig. 5. Roadmap of BECDE Validation

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Fig. 6. Validation Results in the Blockchain

5 Conclusion This paper explores a blockchain-enabled CDE solution for secure BIM versioning. Two research objectives have been achieved. Firstly, a BECDE framework that leverages private blockchain and IPFS is proposed to provide a technical architecture for managing BIM versions in a decentralized collaboration environment. Secondly, a smart contract cluster (SCC) is developed to enable versioning activities to interact with a blockchain. Beyond recording and querying version transactions, new smart contract algorithms (e.g., AVU) are also designed to perform version value computation, enhancing versioning automation and data consistency among peers. Finally, the proposed BECDE framework is demonstrated and evaluated in a design example, showing that it is a promising solution for secure and efficient versioning. Further research will focus on connecting the BECDE framework to existing BIM collaboration systems.

References 1. Zada, A.J., Tizani, W., Oti, A.: Building information modelling (BIM)—versioning for collaborative design. Comput. Civil Build. Eng. 512–519 (2014)

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2. ISO. ISO 19650–5:2020 Organization and digitization of information about buildings and civil engineering works, including building information modelling (BIM) — Information management using building information modelling — Part 5: Security-minded approach to information management (2020) 3. Nizamuddin, N., Salah, K., Azad, M.A., Arshad, J., Rehman, M.H.: Decentralized document version control using ethereum blockchain and IPFS. Comput. Electr. Eng. 76, 183–197 (2019) 4. Hamledari, H., Fischer, M.: Measuring the impact of blockchain and smart contracts on construction supply chain visibility. Adv. Eng. Inform. 50, 101444 (2021) 5. Das, M., Luo, H., Cheng, J.C.P.: Securing interim payments in construction projects through a blockchain-based framework. Autom. Constr. 118, 103284 (2020) 6. Tao, X., Das, M., Liu, Y., Cheng, J.C.P.: Distributed common data environment using blockchain and interplanetary file system for secure BIM-based collaborative design. Autom. Constr. 130, 103851 (2021) 7. Tao, X., Liu, Y., Wong, P.K.-Y., Chen, K., Das, M., Cheng, J.C.P.: Confidentiality-minded framework for blockchain-based BIM design collaboration. Autom. Constr. 136, 104172 (2022) 8. Preidel, C., Borrmann, A., Oberender, C., Tretheway, M.: Seamless integration of common data environment access into BIM authoring applications: The BIM integration framework, European Conference on Product and Process Modelling, Limassol, Cyprus, pp. 119–128 (2017) 9. Sivachokkapu, Revive-cc (2021). https://github.com/sivachokkapu/revive-cc. Accessed 16 Mar 2022

Facilitating Integration in Complex Projects: A Case Study Yinbo Li1 , Cheryl Shu-Fang Chi2 , and Yilong Han1(B) 1 School of Economics and Management, Tongji University, Shanghai, China

[email protected] 2 The Walt Disney Company, Burbank, USA

Abstract. The increasing complexity of engineering projects is a result of multiple stakeholder interfaces, interdependent processes of different tasks, and large amounts of information generated and flowing within project networks. To cope with complexity and deliver quality projects, the current literature argues that integration is needed in the mindsets and skills of both project managers and project teams. However, how to facilitate integration through project management, especially in complex environments, has yet to be explored with empirical evidence. We studied an ongoing construction project with highly complex characteristics and examined the transition from fragmented management practices to an integrated approach and how it helped improve the project team’s ability to manage project complexity. The results of the case study reveal the elements of integration management and its dynamic nature, where routine development and boundary spanning are critical to its effectiveness. Keywords: Boundary spanning · Case study · Complex project · Integration management · Routine

1 Introduction and Background Interdependencies among various parties and activities are common in complex projects and represent a significant cost if not properly managed [1, 2]. In construction projects, multiple participants and concurrent engineering lead to more interdependencies that need to be coordinated back and forth, as design, engineering and production work is done in parallel rather than sequentially [3]. Grandori [4] argues that it requires collective action or integration for teams to work with interdependencies. Integration can be considered as the merging of different disciplines or organizations with different goals, needs, and cultures into a cohesive and mutually supportive unit [5]. Project integration requires project participants from different organizations to work together to achieve common goals by sharing information, aligning processes, and aligning organizational cultures [5]. There are different approaches to integration, some technology oriented, some relational and some contractual [6]. Integration at the project level can be achieved by combining several coordinating mechanisms. The use of a coordinating mechanism in a given context when a coordination gap arises is as © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 1701–1709, 2023. https://doi.org/10.1007/978-981-99-3626-7_132

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important as the mechanism itself [7, 8]. However, few studies in project management have investigated what coordination is needed for integration and how it is formed. Routines, usually defined as recurring patterns of behavior of multiple organizational members involved in performing organizational tasks [9], exist in coordination activities. Routines play an important role in the development of operational capabilities [10–12]. The emergence of a routine is a result of knowledge accumulation, which improves organizational efficiency [13]. A routine also helps organizational members to share understanding by forming their connections [9]. Although routines are based on designed activities, they do not emerge as ready-made procedures, but are constituted through actors’ enactment and iteration between the abstract concept of the routine and its performance [10]. The interactions of people in routines involve boundary spanning. A boundary is a sphere of activity that marks the limits of a domain, including physical, geographical, social, cognitive, relational, cultural, and disciplinary [14, 15]. Boundary spanning is a set of communicating and coordinating activities performed by individuals within or between organizations to integrate activities across these different contexts [14]. In boundary spanning, boundary objects are defined as objects that are flexible enough for users to develop their own understandings, yet specific enough to maintain a common meaning across different users to facilitate cross-boundary knowledge sharing [16, 17, 19]. Specific individuals also act as boundary spanners when working across organizational boundaries [20–22]. They often act as conduits for information sharing, facilitating knowledge flows and conflict resolution. However, few studies have investigated the role of boundary spanning and routines for integration, despite their relevance to the coordinating mechanisms involved in integration. However, the functions and formation of integration routines have been little studied in the project management literature. To understand how to facilitate integration in a complex environment from an empirical perspective, we conducted a case study of a complex construction project. Through data collection from multiple sources and data analysis, we revealed the elements of the integration mechanism and its dynamic nature, in which developing routines was found to be effective in facilitating integration, and boundary spanning was critical for developing integration routines.

2 Methodology This study adopts an inductive theory building approach from a single case to answer the research question with an embedded within-case analysis [23–25], which allows us to make comparisons within the case. The reason for adopting this approach is that the case is critical to our theoretical propositions because it allows us to examine how integration is facilitated in a real complex project from a longitudinal perspective. We first established some constructs through literature review and initial fieldwork, then collected data using triangulated methods to improve reliability and reduce subjective bias, including semistructured interviews with project team members, project documents, field notes, and meeting minutes. Interviews were conducted by one interviewer and one note taker, and key notes were reviewed and further discussed by project members. After recursive data collection and coding analysis using NVivo 12 software, we completed our constructs and formed propositions about their relationship.

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The research setting is a complex project, and we focus on the complexity that the project with multiple disciplines whose interfaces need to be coordinated by professionals [26]. Our case is a large themed entertainment project in China with a construction area of over 10,000 square meters and a total investment of over 1 billion RMB, using the traditional design-bid-build contracting method. The case study lasted 12 months and began in the construction phase, when the main structure of the building was completed and part of the decoration work had begun. The project involved multiple disciplines, particularly those related to themed entertainment facilities, making it more complex than traditional projects, and the client had sought to facilitate the integration of the multiple disciplines. The client had an all-professional in-house consulting team with key roles including project managers, designers (including entertainment designers), construction managers, planning and control team, and BIM managers.

3 Results and Findings This section presents the findings of the study and reveals how the project team facilitated integration after identifying integration needs and trying integration methods. Quotes from project members are extracted from interview transcripts and meeting notes. Project member interviews were conducted in two sets, one at the beginning and one at the end of our case study. 3.1 Recognition of Integration Needs The project was in trouble at the beginning of the study when there was a delay of at least 8 weeks: The client found a large amount of substandard work during the inspections, where there were many “drawings delivered to the site without coordination with other disciplines” or “construction quality problems”, then ordered the general contractor (GC) to rework. One of the most frequently mentioned reasons for the situation was the lack of competence of the contractors (GC and its subcontractors) to handle the complexity of the project: “The root cause is the low bid at the beginning, and the price was unreasonable. This project is so complex that they cannot handle it” (Client’s Construction Manager). The client’s team saw the need to help GC improve the situation and get the project back on track. After several discussions, meetings about the recovery plan were first held within the client’s team. The theme of “trust, connection, and respect” was emphasized to address the conflicts within the client’s team over the responsibilities of shop drawing (SD) review and site inspection. “I think the whole team is in a pretty frustrated mood right now, not that people aren’t trying to solve problems, but they don’t seem to be working together” (Client’s Designer). In this phase, we derived the integration needs shown in Fig. 1. The coordination gap between the contractors’ capabilities and the project complexity exceeded the client’s expectations, creating new coordination needs in the form of information, process, and organizational integration. Although the client had previously asked the GC to take on more responsibility, as the coordination gap widened, the client realized that it had to change first.

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Fig. 1. Identified integration needs of the project

3.2 Taskforce: Design Formal Integrative Mechanisms The recovery plan was formally implemented, and a Supplemental Agreement was signed between the client and the GC, with changes to the GC’s management team and new incentives and penalties based on the newly adjusted schedule. In addition, an integration mechanism was developed, with weekly “Taskforce” meetings organized to facilitate communication and collaboration both within the client’s team and between the

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client and the GC. The intention was to “bring up and resolve all issues that arise on the project from shop drawings to site inspections at Taskforce meetings” (Client’s Project Manager). There were 4 series of Taskforce meetings, each with a theme of interior, facade, area development (AD), and mechanical, electrical, and plumbing (MEP), led by the client’s four project managers. Attendees included the client’s designers from all disciplines (including entertainment), the client’s construction managers, and the GC’s and subcontractor’s designers and construction managers. Tracking logs were designed and implemented to record meeting decisions and outcomes: For each issue raised and discussed at the meeting, a responsible individual (RI) would be assigned to ensure feedback at the next meeting, and new decisions and outcomes would continue to be recorded until the issue was resolved. “Taskforce is the highest priority, you should show up as long as the issue affects you, and you should raise an issue as long as you think it might affect others” (Client’s Project Manager). For example, the entertainment set installation plan (led by the entertainment designers) and the scaffolding erection and dismantling plan (led by the construction managers) were highly interdependent and needed to be mutually adjusted. Before the start of each meeting, the client’s project manager would send out an email outlining the topics that might require cross-team coordination after discussions within each team. Some topics were brought up to give everyone the big picture, such as the latest overall progress. A lot of information was shared, even if some of it was not directly related to the issues to be addressed. The four Taskforce teams held 83 meetings in 8 months (some were canceled due to the Omicron impact) and resolved 334 issues in two major categories: design coordination and on-site construction coordination, the latter accounting for 70%. As the recovery plan brought the project back to normal, the number of issues discussed and resolved in each meeting gradually decreased, from an average of 6.5 in the first week to 2.6 in the last week. Attendance also dropped, as project managers invited people based on who was involved in the issues. At the same time, there were complaints that Taskforce was taking up extra time: “Colleagues on site don’t always have time to come to the meeting room for every issue” (Client’s Construction Manager). One project manager simply adjusted the meeting time to be as short as possible based on the number of topics. In general, the role of Taskforce diminished. 3.3 Integrated Digital Delivery: Make Integration as Routines Three months into Taskforce, the client realized it was not sustainable: “When the issues tend to be more predictable, they should be addressed in the normal processes without the need for four more meetings” (Client’s Project Manager). Process integration was identified as the key: “The process from model to construction to site inspection and the roles of different teams should be reorganized, especially how designers and construction managers can work better together” (Client’s BIM Manager). Previously, designers were responsible for coordinating the design intent of various disciplines, especially those related to theme entertainment, while construction managers were responsible for coordinating with the GC to ensure that contractors built to the client’s intent and standard. Given the existing contractual relationship and organizational structure, the client decided to run an IPD-like pilot called Integrated Digital Delivery (IDD) which was

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more focused on the use of digital tools, in a single unit of the project, after getting support from the GC. A new team and a series of meetings were organized, with the team configuration similar to Taskforce, but with “more open-minded communicators willing to explore better ways of doing things” selected from the client and contractors. The central theme of the meeting was design coordination, with the process redesigned to connect otherwise separate model review and SD review. The model review was mainly a clash detection meeting led by the BIM manager, while the SD review was not necessarily a meeting: The client’s designers reviewed the contractor’s SD, commented on the drawing, and assessed whether it met the requirements. In IDD, the elements of SD review such as design intent and constructability have been consolidated into model review. In addition, IDD requires that construction managers be involved in model review to both “get an overall picture of the design requirements” and “provide feedback on constructability as well as on-site progress”. “In this way, IDD establishes a complete chain of information” (Client’s BIM Manager). The process redesign challenged the existing division of organizational responsibilities. The client’s designers and construction managers, who were used to the previous SD review process, had to put more effort into reviewing models and communicating directly with contractors in meetings. The BIM manager at IDD took on greater responsibility, not only to manipulate the model in the meeting, but also to lead the designers, construction managers, and contractors into discussing a model review process that was more design-oriented. And the BIM manager also led a three-week pull plan discussion at the start of each review meeting to better connect the SD schedule with the on-site status. “It’s quite challenging, I have to go beyond my previous responsibilities and learn more about design and project schedule to lead this review meeting” (Client’s BIM Manager). The BIM manager led the team in reviewing the model, while others provided comments, such as whether the lighting installation was in line with the entertainment designer’s intent, or whether it could be built without clashing with other equipment on site. And then the team determined the following action items, such as how and when to update the model. “It’s faster to iterate the design this way than to give the contractor comments on the drawing, because we basically come to a conclusion right in the meeting” (Client’s Designer). IDD held 21 meetings in 5 months and resolved 149 issues, 90% of which were related to design coordination. The model review coordination efforts were reflected in the SD as expected. “We were able to do the most important and time-consuming design coordination work more efficiently in IDD than we could before” (Client’s Designer).

4 Concluding Remarks The case study demonstrates the importance of integration management and how it is designed and enacted as a routine after integration needs are identified, as shown in Fig. 2. The elements of integration as a coordinating mechanism and its dynamic nature in complex projects are demonstrated in our case study. In complex projects, interdependencies emerge as the project evolves, especially in the construction phase, with more unpredictable relationships between activities as the effects of concurrent engineering

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Fig. 2. A model of integration mechanism in a complex project

and design changes emerge [2, 3]. Project partners may encounter coordination gaps where the required coordination is greater than the actual coordination [8]. When they face this gap and start to deal with it, they will create new ways of coordination [7, 27]. Therefore, it is necessary to understand how coordinating activities evolve and selforganize around the project goal [28]. Our findings indicate that the coordination gap between the capabilities of the project team and the complexity of the project is mainly in information, process, and organization. After recognizing this gap, the project team first establishes a new consensus using a supplemental agreement that includes formal coordinating mechanisms such as contracts, plans, roles, and incentives, and then organizes the Taskforce team to further integration through new team configurations and issue-oriented discussions. However, the Taskforce is dependent on the newly raised issues and is not proactively integrated into existing processes, so its role diminishes as the project returns to normal. The integration mechanism is dynamic as the coordination gap between the project team’s capability and the project’s complexity changes. The case study also demonstrates the role of routines in achieving integration and how routines are constructed and enacted, and in particular the role of boundary spanning in this process. Although the concept of integration is well described in the project management literature [5, 6, 29], there have been few empirical studies of the mechanism of integration in specific projects. The findings suggest that the new process in IDD creates structural interventions that require the team to develop an initial shared understanding of goals and a willingness to collaborate beyond their previous responsibilities. These interventions are further enacted through concrete actions and interactions between participants to the point of forming formal routines [10, 11]. Through knowledge sharing and information exchange in the routines, shared understanding between teams is further deepened and team capability is enhanced [9]. In particular, we find that the BIM manager in the IDD team takes on the role of the primary boundary spanner, driving the formation of the integration routine by crossing conventional responsibilities and

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leading the conversation as an intermediary between different parties [20, 21], suggesting the importance of the soft (communication) skills of the BIM manager. This finding echoes the literature that treats BIM as a boundary object, where actors perceive their BIM-related roles as more relational than technical, requiring more soft than hard skills [19, 30]. The findings therefore have implications for better integrating BIM into project management. The case study is based on qualitative data to reveal the elements and mechanism of integration, with limited quantitative analysis to reflect the effectiveness of integration. Future research is suggested to construct structural indicators to investigate the value of integration at a more detailed level from empirical data, with more approaches such as multi-case study or long-term longitudinal single-case study.

References 1. Shokri, S., Ahn, S., Lee, S., Haas, C.T., Haas, R.C.G.: Current status of interface management in construction: drivers and effects of systematic interface management. J. Constr. Eng. Manag. 142(2), 04015070 (2016) 2. Morris, P.W.: Managing project interfaces–key points for project success. In: Cleland, D.I., King, W.R. (eds.) Project Management Handbook, 2nd edn., pp. 16–55. John Wiley & Sons (1997) 3. Fischer, M., Ashcraft, H.W., Reed, D., Khanzode, A.: Integrating Project Delivery. John Wiley & Sons, Hoboken (2017) 4. Grandori, A.: An organizational assessment of interfirm coordination modes. Organ. Stud. 18(6), 897–925 (1997) 5. Baiden, B.K., Price, A.D., Dainty, A.R.: The extent of team integration within construction projects. Int. J. Proj. Manage. 24(1), 13–23 (2006) 6. Ospina-Alvarado, A., Castro-Lacouture, D., Roberts, J.S.: Unified framework for construction project integration. J. Constr. Eng. Manag. 142(7), 04016019 (2016) 7. Jarzabkowski, P.A., Lê, J.K., Feldman, M.S.: Toward a theory of coordinating: creating coordinating mechanisms in practice. Organ. Sci. 23(4), 907–927 (2012) 8. Gerwin, D.: Coordinating new product development in strategic alliances. Acad. Manag. Rev. 29(2), 241–257 (2004) 9. Feldman, M.S., Rafaeli, A.: Organizational routines as sources of connections and understandings. J. Manage. Stud. 39(3), 309–331 (2002) 10. Feldman, M.S., Pentland, B.T.: Reconceptualizing organizational routines as a source of flexibility and change. Adm. Sci. Q. 48(1), 94–118 (2003) 11. Felin, T., Foss, N.J., Heimeriks, K.H., Madsen, T.L.: Microfoundations of routines and capabilities: individuals, processes, and structure. J. Manage. Stud. 49(8), 1351–1374 (2012) 12. Roscoe, S., Cousins, P.D., Handfield, R.: The microfoundations of an operational capability in digital manufacturing. J. Oper. Manag. 65(8), 774–793 (2019) 13. Peng, D.X., Schroeder, R.G., Shah, R.: Linking routines to operations capabilities: a new perspective. J. Oper. Manag. 26(6), 730–748 (2008) 14. Schotter, A.P., Mudambi, R., Doz, Y.L., Gaur, A.: Boundary spanning in global organizations. J. Manage. Stud. 54(4), 403–421 (2017) 15. Aldrich, H., Herker, D.: Boundary spanning roles and organization structure. Acad. Manag. Rev. 2(2), 217–230 (1977) 16. Levina, N., Vaast, E.: The emergence of boundary spanning competence in practice: implications for implementation and use of information systems. MIS Q. 29(2), 335–363 (2005)

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17. Carlile, P.R.: Transferring, translating, and transforming: an integrative framework for managing knowledge across boundaries. Organ. Sci. 15(5), 555–568 (2004) 18. Star, S.L., Griesemer, J.R.: Institutional ecology, translations’ and boundary objects: amateurs and professionals in Berkeley’s museum of vertebrate zoology, 1907–1939. Soc. Stud. Sci. 19(3), 387–420 (1989) 19. Papadonikolaki, E., van Oel, C., Kagioglou, M.: Organising and managing boundaries: a structurational view of collaboration with building information modelling (BIM). Int. J. Project Manage. 37(3), 378–394 (2019) 20. Ryan, A., O’Malley, L.: The role of the boundary spanner in bringing about innovation in cross-sector partnerships. Scand. J. Manag. 32(1), 1–9 (2016) 21. Williams, P.: The competent boundary spanner. Public Adm. 80(1), 103–124 (2002) 22. Fellows, R., Liu, A.M.: Managing organizational interfaces in engineering construction projects: addressing fragmentation and boundary issues across multiple interfaces. Constr. Manag. Econ. 30(8), 653–671 (2012) 23. Eisenhardt, K.M.: Building theories from case study research. Acad. Manag. Rev. 14(4), 532–550 (1989) 24. Eisenhardt, K.M., Graebner, M.E.: Theory building from cases: opportunities and challenges. Acad. Manag. J. 50(1), 25–32 (2007) 25. Yin, R.K.: Case study research and applications: Design and Methods, 6th edn. Sage, Thousand Oaks (2017) 26. Anderson, P.: Perspective: complexity theory and organization science. Organ. Sci. 10(3), 216–232 (1999) 27. Pauget, B., Wald, A.: Relational competence in complex temporary organizations: the case of a French hospital construction project network. Int. J. Project Manage. 31(2), 200–211 (2013) 28. Ahern, T., Leavy, B., Byrne, P.J.: Complex project management as complex problem solving: a distributed knowledge management perspective. Int. J. Project Manage. 32(8), 1371–1381 (2014) 29. Demirkesen, S., Ozorhon, B.: Impact of integration management on construction project management performance. Int. J. Proj. Manage. 35(8), 1639–1654 (2017) 30. Bosch-Sijtsema, P.M., Gluch, P., Sezer, A.A.: Professional development of the BIM actor role. Autom. Constr. 97, 44–51 (2019)

Construction Industry Job Image Analysis Among Job-Seekers Based on Social Media Perspective Angela Palaco and Xing Su(B) College of Engineering and Architecture, Zhejiang University, Hangzhou, China {22012320,xsu}@zju.edu.cn

Abstract. Construction is considered one of the world’s oldest industries. On the other hand, the emergence of new technologies has led to a significant change in job popularity among the younger generation. With COVID-19 affecting the world, many new phenomena were triggered, including the job image of the Construction Industry, causing direct consequences on the sector, such as the reduction of available labour. This study analyzes how social media data affected the construction industry’s image during the pandemic. Comments were collected on Twitter for 3 years from 2019 to 2021, followed by NLP (Natural Language Processing) methods to process the data through sentiment analysis. Specifically, this research provides insight into what motivates the younger generation to join the construction industry. The outcome can also assist construction companies in improving their incentives among the suggested dimensions and sectors to enhance the recruiting rates among young job seekers. In addition, it also provides a deep understanding of how the pandemic changed the generation’s perception of such a traditional sector. The study discovered the topics that were most frequently discussed during the peak of the pandemic, as well as how they affected construction companies. Construction jobs and work environment can be designated as one of the peak topics during this time, as well as Leadership and Management, implying that they may be a leading cause of employee turnover. The findings can directly help company behavioral management decisionmakers develop and evaluate initiatives to improve construction companies’ job image. Keywords: Construction Industry · Construction Companies · Social Media · Job Image · NLP · Topic Modelling · COVID-19

1 Introduction The construction industry is one of the oldest sectors in the world. It is associated with and characterized mostly by old and traditional ways compared to new forthcoming, and emerging industries. As a general data collection method, social media platforms give construction businesses and workers avenues to communicate their experiences and feelings. [1]. Since approximately the year 2000, as digital media has reshaped contemporary © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 1710–1722, 2023. https://doi.org/10.1007/978-981-99-3626-7_133

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society and institutional life, many studies have examined how new technologies feature in day-to-day organizational behavior and interaction. [2, 3]. Simultaneously, construction industry practitioners, including companies and workers, began to use social media platforms to promote and market their business, [3, 4] allowing society in general and especially the younger generation to have an insight into the internal environment that this industry provides. On the other hand, previous studies have shown that the construction industry faces a labour shortage that worsens yearly [5]. The survey by [6] emphasized that the shortage of skilled labor continues to be a challenge for the construction industry. In the study by [7], about three-quarters of construction managers have experienced a shortage in filling positions for artisans/skilled labor in their projects. In addition, researchers have identified many more negative factors affecting the industry, such as higher rates of employee turnover [8] use of traditional techniques, rise in entrepreneurship, poor image of the industry, unfriendly/harsh working conditions, inadequate training, low participation of women, and globalization as contributing factors spurring the shortage in skilled labor experienced in the construction industry [9–11]. COVID-19 in Construction With the rise of COVID-19, nations worldwide implement risk mitigations measures in order to avoid the spread and mass contamination [12]. Prior to COVID-19, the construction sectors already had a higher index of exposures and vulnerabilities to public health, safety and risks. However, the pandemic trigger on the economic crisis may increase the risk of suicide among a significant portion of the workforce [12, 13]. Many Institutions started to predict the impacts, in which, the World Bank states that the global recession caused by COVID-19 will be the most impactful since the World War II. Furthermore, the majority of economies will experience per capita output declines not seen since 1870. Numerous industries are currently experiencing significant job losses as a result of COVID-19’s impact. According to the International Labour Organization, the second quarter of 2020 saw a 17.3% decrease in work hours compared to the fourth quarter of 2019 resulting in the loss of 495 million full-time jobs. In addition, most economies will experience output declines per capita not seen since 1870 [14]. Due to COVID-19’s impact, numerous industries are currently experiencing significant job losses worldwide. This trend continued into the third quarter, and future projections suggested that the following times would possess worse statistics than previously anticipated.[12, 15]. The construction industry, has been severely affected by COVID-19 pandemic, being these effects on reflected on it’s economy. However, the construction industry has better prospects for recovery than other sectors due to its ability to create jobs. Recovery measures can help the sector move toward sustainability and digitalization. It is critical to conduct a study that does not rely on surveys, which can be biased. Unlike job surveys, social media allows people to freely express themselves without fear of repercussions. Many sectors have suffered severe consequences since the outbreak of the COVID19 pandemic. or instance, suicide rates in the construction industry have been high, particularly among low-skilled workers, and there are concerns that the effects of the COVID-19 pandemic could exacerbate this issue [13, 16]. Knowledge Gap and Research Aim Many factors, including labor shortages, decreased use of traditional techniques,

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increased entrepreneurship, a poor image of the industry, unfriendly/harsh working conditions, inadequate training, low female participation, and globalization, have contributed to the construction industry’s skilled labor shortage [9–11]. All of these factors have an impact on the construction industry’s image and the desire of job seekers to pursue a career in this field, aggravating, even more, the lack of workers in the industry. And, with the occurrence of COVID-19, it is clear that the situation has been aggravated, [13, 16] making it critical to examine the effects on people’s perceptions of the construction industry job image. With the digital media era, in which new industries are being developed, with new technologies and even more appealing features to the younger generation, it is believed that improving the industry’s job image may also improve the industry’s job image problems that are directly related to the factors above mentioned. Previous studies have researched how different clusters inside the industry express their opinions on social media [17]. Another study has performed a day-to-day organizational conduct and interaction [2, 3]. However, there is still a lack of understanding of how job-seekers perceive the industry, nowadays, and the COVID-19 pandemic had a greater impact on how people can perceive and look at a job in the industry compared to others sectors. This knowledge gap has led this study to examine how COVID-19 affected people’s perception of the construction industry’s job image. This study aims to provide insight into what motivates the younger generation’s perception of the construction industry, more specifically companies, assisting them in improving their attributes among the suggested dimensions to enhance their recruiting rates among young jobseekers. In addition, it also provides a deep understanding of how the pandemic changed the perception of these generations of the construction industry. These findings can provide recommendations for increasing recruitment rates among young job seekers in the construction sector. The study will also enhance the understanding of how the COVID-19 pandemic affected the general discussion on social media related to the construction industry and companies, consequently understanding the critical factors among the industry affected. This study will benefit both employers and job seekers by establishing a link between what job seekers desire and what firms offer and then improving these key areas based on the research results.

2 Literature Review 2.1 Social Media Usage in the Construction Industry Social media platforms, as a general way to collect data, provide construction companies and workers with channels to express their experiences and feelings [1]. At the same time, practitioners in the construction industry, including companies and workers, begin to use social media platforms to promote and market their businesses [3, 4]. As digital media is transforming contemporary society and institutional life, starting around 2000, many studies have been conducted on how new technologies feature in day-to-day organizational conduct and interaction [2, 3]. A study on Twitter was performed to analyze the actual state view of the construction industry based on users’ comments [17]. Different data-analysis methods were used for the specific themes, such as Stanford Natural

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Language Processing (Stanford NLP) for sentiment analysis. It is stated that construction workers tended to have a higher proportion of negative messages, and construction companies tended to publish positive news about their projects and accomplishments. Reduced amount of social media usage to build an image. Previous studies concluded that there is a reduced amount of social media platforms in the construction industry compared to other sectors, realizing that there is more negative sentiment towards the industry than positive. [3, 18–23] It recommended four areas where action was needed to reform the industry’s image, starting with ‘engaging young people and society at large [24]. 2.2 Social Media Influence on Mindset Changing [25] performed a study to understand the main sphere for image formation for tourist destinations, based on the interpretation of how and who is leading the image formation process based on Twitter. According to the latest statistics, 22 percent of the world’s population is active on Facebook as of June 2017, most of which are the youth. According to research, Facebook mostly led to feelings of insecurity and being unaccomplished in young people, but mostly in those in their mid to late twenties, when they saw their former friends update their accounts with images from travels to exotic locations or have bought a new car or house. Social media is considered to be braining social change. In that case, it is a significant tool to mold or educate society in terms of a subject. [26] found that individuals with more depressive symptoms were more likely to report higher levels of Facebook addiction. Similarly, [27], among other scholars, also found an association between social media addiction and depression. 2.3 Job Satisfaction and Job Determinant Index (JDI) Job satisfaction can be defined as the employee’s emotional response to the tasks and the natural and social environment of the workplace. [28–30]. To evaluate job satisfaction, employee surveys are commonly used [31, 35]. The Job Descriptive Index (JDI) is the most widely used measurement tool, and its reliability and validity have been shown to be strong [30, 32, 33]. The JDI assesses five facets of job satisfaction, including satisfaction with coworkers, the work itself, pay, opportunities for promotion, and supervision [34]. Existing studies suggest more than five facets [31, 35–37], however for this study we will consider the initial five in order to establish the relationship between the traditional facets and the ones found in our study.

3 Methodology To achieve the goals, the research conducted the data extraction (comments) from Twitter, followed by the pre-processing data stage, and lastly, the use of NLP (Natural Language Processing) through sentiment analysis to analyze and categorize the opinions based on the comments themselves and JDI (Job Descriptive Index) aiming to understand the critical factors that suffered influence among the pandemic that might have a direct effect

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on the job image among young people on construction (Fig. 1). In total, 427 854 tweets were collected and analyzed across three different years, from 2019 to 2021. Each year, four different trimesters were examined individually to provide a comprehensive and insightful preview of the situation. Purpose

Method

Literature Review

Research on previous studies

Data Collection

Twitter (Crawling Method)

Sample Size Determination

Based on data acquired

Data Analysis

NLP – (Natural Language Processing), Sentiment Analysis

Discussion and Conclusion

Fig. 1. Methodology

4 Data Acquisition and Pre-Processing From Twitter, data were collected according to Table 1. In total, 427 854 tweets were extracted and analyzed in stages based on the year’s four trimesters. Table 1. Data Collection YEAR 2019

TRIMESTER First

39,146

2/4

Second

37,275

3/4

Third

38,856

4/4

Fourth

SUB-TOTAL 2019 2020

(2019/01/01)–(2019-12-31)

38,638 153,915

1/4

First

41,950

2/4

Second

40,608

3/4

Third

37,790

4/4

Fourth

SUB-TOTAL 2020 2021

NO. TWEETS

1/4

(2020/01/01)–(2020-12-31)

31,866 152,214

1/4

First

31,625

2/4

Second

31,001

3/4

Third

30,554

4/4

Fourth

SUB-TOTAL 2021

(2021/01/01)–(2021-12-31) Total Tweets

28,545 121,725 427,854

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Figure 2 depicts the fluctuation of the number of tweets over the period studied, and it is possible to see that from the fourth trimester of 2019 to the second trimester of 2020, there is an increase in the number of tweets, which coincides with the pandemic’s actual peak period.

No. Tweets 45,000 40,000 35,000 30,000 25,000 1/4 2/4 3/4 4/4 1/4 2/4 3/4 4/4 1/4 2/4 3/4 4/4 2019

2020

2021

1+2 Fig. 2. Total Number of tweets collected.

From Table 1, the comments were filtered based on the topics directly related to the five factors we intended to analyze, namely, Management and Leadership, Advancement Opportunity, Pay and Benefits, Work-Life Balance, and Culture. The stage was divided into the following steps: 1) Data Cleaning: Data cleaning was performed to remove the URL, @ mentions, hashtags, punctuation marks, and letter repetitions. 2) Upper to Lowercase: Each of the terms was lowercase. 3) Tokenization: Each of the documents (comments) was tokenized. 4) Stop Word Removal: Stop words were removed from each document. 5) Stemming: Stemming was done on each of the tokens using the Porter-Stemmer algorithm. 6. NGram Creation and Addition: Bigrams and Trigrams were generated using words that appeared together and were added to the document. 7. Stop Word Removal: Stop words were removed after the text had been stemmed and bigrams and trigrams generated. 8. Pruning.

5 Data Analysis Following the pre-processing stage, sentiment analyses were performed for each year, stating the level of positive, negative, and neutral feelings that the comments reflect. Similarly, to point 4, the period between the fourth term/trimester of 2019 and the second term/trimester of 2020 sees an increase in the number of tweets and coincides with the co-vid19 peak period, as well as an increase in negativity (see Fig. 4a)) and a decrease in positivity (see Fig. 4b)). In addition, the word cloud generated a composition of words used in this specific text or subject, with the size of each word indicating its frequency or importance (Fig. 6).

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Fig. 3. Sentiment Analyses Variation for the three years

Fig. 4: Negative sentiment variation among the period in the study. b) Positive sentiment variation among the period in the study.

Figure 7 shows the count of words that consists on illustrating the proportion of each topic in the whole corpus. This graph helps us determine which topic is the most dominant one across all the documents and which topics are the least. In which, words like “work”, and “project” are seen as the top general mentioned.

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Fig. 5. World cloud for the words with the most positive frequency for 2020.

Fig. 6. World cloud for the words with the most negative frequency for 2020.

5.1 Key Themes of Research on Remote Work in Relation to the COVID-19 Pandemic The particular topics identified by the algorithms are presented in the Discussion section. After identifying the core and latent topics in the dataset, we performed a content analysis [38]. First, we pre-analyzed the 5 JDI related factors, namely, coworkers, work itself, pay, opportunities for promotion, and supervision [37] and performed a relationship with the topics found in our study with the LDA topic modeling, with an additional factor discovered with the performance of our study, being the new factors organized into 1. Work Condition/Environment, 2. Management and Leadership; 3. Financial Benefits; 4. Advancement Opportunity; 5. Financial and Benefits; 6. Personal Characteristics; Each factor was analyzed to identify its correspondence with the topics. All 427 854 tweets were annotated according to the pre-identified topics. We selected one topic at a time and retrieved its associated tweets for further analysis.

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a) First Trimester

b) Second Trimester

c) Third Trimester

d) Fourth Trimester

Fig. 7. Top-mentioned words for 2020.

JDI Dimensions • 1. Coworkers • 2. Work itself • 3. Pay • 4. Opportunities for promotion • 5. Supervision

Our Study Dimensions • 1. Work Condition/Environment • 2. Management and Leadership • 3. Financial and Benefits • 4. Advancement Opportunity • 5. Family and Personal Beliefs • 6 .Personal Characteristics

Fig. 8. JDI vs Selected Studied Dimensions

6 Discussion From Table 1, it is possible to analyze that 2020, the year of the pandemic, registered the highest amount of tweets, due to the higher amount of time that people were staying home, resulting in higher social media traffic, as well as higher tweets generation, on the same note, from Fig. 3, it is possible to analyze that the year of 2020, also had both higher values of positive sentiments, as well as higher negative sentiments, meaning that during the pandemic, there was an increase on the negative feelings towards construction companies, with an higher frequency of negative words like money, hire, need, government, taxes, employee, and safety; terms all related to the dimension No. 3. Financial and Benefits from our studied dimensions, as well as the JDI Dimensions (see Fig. 8), meaning that the subject related to money and benefits increased during the pandemic, making people express more about this issue; The need to hire, being employed and get better positions at work is also a hot topic among the negative facet of the data collected, being that topic part of the dimension No. 4 of our study dimensions (see Fig. 8). What

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can be associated with the mass layoffs, as well as, the temporary or permanent closure of several companies and consequently jobs during the pandemic. All the issues addressed are directly related to the 5 JDI factors mentioned above: coworkers, the work itself, pay, opportunities for promotion, and supervision. In addition, all the factors analyzed had a significant influence on how the image of the construction industry is perceived, but, for the first part of the study, the results show a higher significance of negativity towards the factors related to Financial and Benefits, as well as, Advancement Opportunity, showing that these issues might have been triggered during the pandemic. Most positive words are directly associated with general daily words, like better, work, price, development, etc., not giving an accurate and direct correlation with the JDI factors addressed in the paper, showing that there is more accuracy on the negative impacts, as well as, higher negative direct effects on the industry than positive (see Fig. 5).

7 Conclusion An extensive literature review was effectuated, an attempt to validate the problem studied in this paper, in which, a) There was already a decrease in the Construction Industry Image, including companies among the younger generation that are the higher users of social media, due to the lack of new methods and strategies to market the industry and promote their activities to target the youth. b) with the covid-19 pandemic encounter in 2020, there was a trigger on all the negative points that the industry was already facing with the lack of new strategies, making it even harder for the industry to recover from the negative sentiments most people already have, mostly related to the 5 JDI like coworkers, the work itself, pay, opportunities for promotion, and supervision. With the rapid explosion of new jobs that are more convenient (like online jobs), the industry and companies must focus on improving the factors that were mentioned in this paper to avoid the mass emigration of workers to other sectors. Due to the reduced levels of Bias of the methods implemented in the study, it is safe to state that our research provides a perspective from the current jobseekers and workers, about the construction industry from one of the most used and reliable social media platforms in the modern days. Currently, Twitter is the most used social media platform worldwide, in which users express their most truthful and raw opinions, making it a great tool to study people’s perspectives on different subjects. For this specific case, the image that construction companies had before. During and after the Covid-19 pandemic period, what may help in decision-making for different companies when hiring, improving their recruiting as well as retention rates. to reduce the labor shortage that the same industry has been suffering. One additional finding that our study performs is the 6th dimension related to personal characteristics, in which, with the rebranding of the world and the effect of social media on the mindset changing (see point 2.4), the personal aspects, physical or even emotionally started to have a big impact on how the workers perceive themselves at work. Among this topic words like woman, and need, are frequently mentioned in the context of inclusion of the woman in the construction Industry sector.

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8 Limitations and Future Research It is important to note some limitations of this report, the first of which is that data collection was limited to construction companies. Second, the analysis was made based on a simple analysis method, in which future research can and is currently working on the use of new and more accurate methods. In other words, the methods used are still the initial method for analysis, in which, to get a deeper understanding of the perception of the topics being mentioned on social media, methods with higher accuracy like topic modeling are being applied to the data presented to improve. This study opens the door to several possible analyses, such as the comparison of different sector’s job image based on the social media collection data, as well as, the perception of different genders or generations (age).

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Critical Risks Associated with Blockchain Adoption in China’s Construction Supply Chain Xiaoyue Lv, Zhaoqian Liao, and Lin Yang(B) School of Civil Engineering, Wuhan University, Wuhan 430072, China {2021202100058,2021282100088,yang.lin}@whu.edu.cn

Abstract. With the successful landing of blockchain in more and more industries, the construction industry is on a path to finding possible application scenarios of blockchain. Blockchain, described as “open and transparent”, “safe and trustworthy”, and “immutable and traceable”, is expected to break the barriers to trust in the construction supply chain (CSC). However, the application of any new technology will generate more unknown risks due to the increase of uncertainties. As a result, the objective of this paper is to find the critical risks associated with blockchain adoption in China’s CSC. Through a literature review, this research identified 26 risks of blockchain application in the CSC, including seven types of risks: technical risk, industry risk, social risk, cost risk, management risk, legal risk, and force majeure risk. Then, a risk network was set up to analyze the importance of risk nodes by considering risk associations. Results showed that “Technical suitability”, “Public attitude”, and “Information asymmetry” were perceived to be the critical risks. This research had great reference significance to the future development of blockchain applications in China’s CSC. Keywords: Blockchain · Construction Supply Chain (CSC) · Risk · Smart contract

1 Introduction As an important production industry in the national economy, the construction industry is one of the most complex and peculiar industries in the world [1]. Construction projects are one-off, fluid, collaborative, and therefore often accompanied by a highly integrated supply chain. The CSC starts from the effective demand of the owner and is based on the whole process of the project. Through the control of cash flow, material flow, and information flow in CSC, the project participants are connected into an overall network chain structure. Project participants often place high expectations on CSC, which needs to achieve multiple goals such as schedule, quality, safety, cost, and transaction trust. However, due to the lack of trust and transparency in some existing CSCs, problems such as the “broken chain” and “bullwhip effect” are emerging one after another [2]. The addition of blockchain is like a “timely rain”, making the CSC break the barriers to trust to achieve the goals as expected.

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 1723–1736, 2023. https://doi.org/10.1007/978-981-99-3626-7_134

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Blockchain, an important part of the new generation of information technology, integrates distributed network, encryption technology, smart contract, and other technologies. Blockchain is regarded as a shared database technology, and the information stored in this database has the characteristics of “transparency and openness”, “nontampering”, “secure and trustworthy”, and “traceable”. Up to now, the applications of blockchain have begun to emerge in the supply chain of some other industries. For example, blockchain can promote the sharing and transmission of supply chain financial data. Tencent Cloud’s blockchain supply chain finance (warehouse receipt pledge) solution was a good example [3]. In the logistics industry, the application of blockchain to the logistics supply chain can improve logistics efficiency and enhance the ability of item traceability and anti-counterfeiting. Suning Tesco’s blockchain platform has achieved material traceability, tracking the entire process of items from manufacturers’ purchases into the warehouse to sales through serial code association [4]. In the food supply chain [5], the pharmaceutical supply chain [6], and other scenarios, the blockchain has shown its irreplaceable value. With the increasing maturity of blockchain, some scholars have proposed that the future of blockchain in the construction industry will be implemented in suitable application scenarios [7, 8]. In the construction industry, blockchain has explored applications in payment security [9], BIM modeling [10], supply chain management [11], government regulation [12], and other scenarios. Furthermore, the successful application of the Xiongan blockchain platform [13] fully proved that blockchain can be well integrated with the CSC. However, the application of any new technology will generate more unknown risks due to the increase of uncertainties [14]. Therefore, it is particularly important to identify and evaluate the potential risks of blockchain applications before blockchain becomes popular. Previous studies have paid little attention to the risks arising from the introduction of blockchain in the CSC. Most researchers only analyzed the risks of the application of blockchain from a macro perspective in the architecture, engineering, and construction (AEC) industry [15, 16]. However, the application of blockchain in different scenarios in the construction industry will produce different effects. Studying the risk of blockchain application in CSC can provide practitioners with a risk measurement in the early stage of application, thereby promoting the popularization of blockchain in CSC in the future. Therefore, the purpose of this study is to analyze the risks of blockchain in the CSC application with four main objectives: (1) establish a scenario model of CSC under the blockchain; (2) identify the risks of applying blockchain in CSC; (3) determine key risks in the application of blockchain in CSC; (4) find risk response strategies for the identified key risks.

2 Model Development 2.1 Theoretical Foundation In order to build a blockchain-based CSC model, the characteristics of blockchain and the operation mechanism of CSC were considered synthetically. The relevant theories are introduced in Sects. 2.1.1 and 2.1.2.

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2.1.1 Construction Supply Chain The CSC is not essentially a linear chain, but an integrated network, including upstream cash flow, downstream material flow, and bidirectional information flow [17]. A typical CSC stage generally starts from the owner generating the project requirements and includes the consultant communicating the project requirements, the designer carrying out the design and calculation, the supplier providing the building materials, the contractor and the subcontractor completing the construction, the supervisor finishing the acceptance, and the operator’s final operation [18]. From the structural aspect, the CSC is temporary, complex, and multi-subject. These characteristics make some problems in the supply chain increasingly prominent. Jing and Li [19] proposed that there were information barriers and communication barriers in the traditional CSC. Since there was a competitive relationship between CSC participants, information concealment had become the norm. Li et al. [20] stated that CSC management had many deep-rooted problems, such as lack of trust, fragmentation, and discontinuity, which resulted in risk aversion, widespread claims, less flexibility, high cost, and so on [21]. In order to solve these problems, some researchers proposed to use BIM to realize information sharing in CSC [22, 23]. However, the integration of BIM with the CSC alone is not enough to achieve data security, information transparency, openness, and mutual trust between participants [24]. 2.1.2 Blockchain As a decentralized database, the core technologies of blockchain include cryptography, smart contracts, hash algorithms, consensus algorithms, and digital signatures. Blockchain’s ability to protect data privacy and security relies heavily on cryptography. Encryption technology protects data from attacks by encoding data in a way that no one but the recipient can decrypt it [25]. The smart contract is a set of digitally defined promises, a program that automatically executes a contract when certain conditions are met [12]. Once a smart contract is deployed on the blockchain, all participating nodes on the chain will strictly follow the smart contract [26]. Hash algorithms guarantee the “tamper-evident” nature of data on the blockchain, and any input length can be hashed to obtain a fixed length output. Nofer et al. [27] argued that the hash value was unique and that any change in the data would result in a change in the hash value, so it was very difficult to tamper with the data. The consensus algorithms determine how the order and content of transactions are agreed between cluster nodes and ensure the consistency of node ledger data [28]. Digital signatures are algorithms that achieve a similar effect to traditional physical signatures. In the process of communication between two parties, the sender encrypts the information to be transmitted with his own private key to form a digital signature and sends it to the receiver together with the original messages. Then, the receiver uses the sender’s public key to verify the signature to confirm that the information received is the one sent by the sender [29]. The purpose of the CSC is to realize the sharing of information resources among multiple subjects, while blockchain uses distributed subjects to accomplish this together, which is characterized by “decentralization”. That means CSC and blockchain coincide

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in terms of themes. Therefore, it is perfectly feasible to integrate blockchain with the whole process of the CSC. 2.2 Conceptual Model This research synthesized concepts from two different research fields, critical problems in the CSC and blockchain applications, to develop a conceptual model. The CSC was divided into four main stages. And the potential application of blockchain in each stage is mined as follows. 2.2.1 Planning and Design Phase In the planning and design phase, timely and accurate information feedback is required, due to the existence of complicated collaborative approval stages, numerous design scheme adjustments, and multiple administrative supervision roles. Making use of the decentralized characteristics of blockchain to link the whole process of planning and design can greatly improve the efficiency of collaborative approval. In addition, blockchain can provide design model data to various parties in real time, which solves the problem of opaque design. Specifically, the consultant prepares a project proposal according to the owner’s needs and then uploads it to the blockchain. The designer accesses the information on the chain and then uploads the planning and design plan to the blockchain. Each supervision and approval unit accesses the data on the blockchain through his own key for examination and approval and supervision. Moreover, in this process, the contracts signed by all the participating parties are uploaded to the blockchain in the form of smart contracts. 2.2.2 Production and Transport Phase The production and transport phase is the CSC stage where a “broken chain” is most likely to occur. However, with the addition of blockchain, the manufacturer can view the construction progress information in real time. It means the manufacturer can adjust the production plan of building materials according to the project progress, and upload the 3D information model of building materials to the blockchain. During the transport process, suppliers upload the location of building materials and other information to the blockchain in real time. After each arrival and qualified acceptance of building materials, the smart contract will be launched to conduct fund settlement between trading parties. Using the traceability of blockchain, the building materials can be traced and the quality of materials can be guaranteed. At the same time, the process of building materials transport is traceable, and the flow of funds is transparent. 2.2.3 Construction Phase In the construction process, the coordination of personnel and equipment is very important. The blockchain can enable real-time monitoring of the production team of each sub-project, the equipment used by each production team, the construction mix ratio of each group of concrete, and other information. Monitoring results are used to make

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timely adjustments to the construction plan, and construction data is uploaded to the chain for permanent storage. In case of completion and acceptance, once there is a problem with project quality, the responsibility will be pursued according to the data on the chain; if the acceptance is passed, the smart contract will be activated and the fund transaction will be completed automatically without the participation of a third party. The application of blockchain in the construction phase fundamentally controls irregularities in the construction process. Accurate and efficient information flow enhances construction efficiency, meanwhile, clear and transparent capital flow ensures transaction security. 2.2.4 Operation and Maintenance Phase The operation and maintenance phase has a longer time span than other phases of the CSC, so the data needs to be stored for a long period of time and be readily available for viewing. The data saved by blockchain can not only be saved permanently, but also the security is guaranteed by the existence of encryption algorithms. Operators can schedule repairers, repair times, and repair costs based on repair requests sent to the chain by owners. Furthermore, using the transparency of blockchain, owners can check the status of building maintenance on the chain at any time. According to the above-mentioned integration of blockchain and the whole process of CSC, a blockchain-based CSC platform was built in Fig. 1. Among them, the collaborative relationship between various participants based on smart contracts constitutes the upstream blockchain platform of the CSC. In addition, the downstream blockchain platform of the CSC is composed of data collection, management monitoring, etc. Last but not least, the information platform that contains all the data information is the key core part of the whole. The upstream cash flow platform, the downstream material flow platform, and the two-way information flow platform together constitute a blockchain-based CSC platform.

Fig. 1. Blockchain-based CSC platform

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3 Risk Analysis Based on the blockchain-based CSC platform above, this study carried out risk analysis (including risk identification, risk assessment, and risk response) according to the methods described below. In order to comprehensively identify the risks of blockchain in CSC application scenarios, a systematic literature review was implemented. First, a comprehensive keyword search was conducted on research questions through CNKI and Scopus databases. Then, the risk identification results obtained in the first step were complemented by searching the application risks of blockchain to supply chains in other industries. Third, in order to ensure that no omissions occurred in the identification process, this research carried out a step-by-step detailed search of the planning and design phases, production and transport phases, construction phases, and the operation and maintenance phases of the CSC. Finally, a total of 26 risk factors were identified, including seven types of risks: technical risk, industry risk, social risk, cost risk, management risk, legal risk, and force majeure risk. In the risk assessment process, risk relevance was taken into account. Because the risk points generated in the blockchain can be transmitted to any node of the blockchain in a divergent manner through this interconnection between peer nodes, thereby jeopardizing the security of the entire blockchain system. Moreover, attributed to the integration of blockchain and CSC application scenarios, insider risks of blockchain affected the outside risks of the blockchain system. Therefore, it was particularly important to evaluate the correlation between risk factors after completing the risk identification of blockchain in the CSC. This research used content analysis to identify connections in the blockchain application risk network, i.e. to determine if there was a connection between two risks. The content analysis method was selected because it helped determine the relationship between research objects and understand the ideas in complex models through in-depth analysis (Table 1). In addition, due to the immature application of blockchain in the construction industry and the lack of experts who had experience with blockchain adoption in the CSC, the expert-based investigation was not applicable to this research. Through the collection of multi-party panel data, including blockchain research articles, industry reports, and practical cases of blockchain applications, it was finally determined that there were 131 connections between the above 26 risks, as shown in Fig. 2. The identified risks were mapped to N nodes in the network, and the arrows indicated the interrelationship between risks. Netminer 4.0 was utilized to realize the visualization of the risk network. This research analyzed the importance of risky nodes in the network in terms of network centrality, and nodes with high centrality were considered to have a lot of power in the network. These nodes occupied the central position for controlling the flow of resources and information. There were various metrics for network centrality analysis, such as degree centrality (DC), betweenness centrality (BC), and closeness centrality (CC). Their descriptions and meanings are shown in Fig. 2.

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Table 1. List of Risks of Blockchain Application in CSC. CODE

RISK

EXPLANATION

CATEGORY

R1

Public attitude

Blockchain is not Social risk trusted by the owners

R2

Conceptual cognitive

The concept of blockchain is blurred and one-sided

[26, 31]

R3

Cultural differences

Cultural differences among CSC participants

[26, 31]

R4

Lack of standards

Lack of industry standards for blockchain applications

R5

Design mistakes

Designers put too much trust in blockchain

[33]

R6

Technical suitability

Poor applicability Technical risk of blockchain to specific projects

[26, 34]

R7

Technology integration

Large files such as BIM models degrade blockchain performance

[34]

R8

Information interruption

Information Force interruptions due Majeure risk to uncertainties in shipping

R9

Sourcing costs

Blockchain has high capital requirements

R10

Change of plan Smart contracts cannot be changed or interrupted

Industry risk

STAGE

REFERENCES

Planning and [26, 30, 34] Design Phase

[32]

Production [35] and Transport Phase

Cost risk

[35, 36]

Technical risk

[34]

(continued)

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CODE

RISK

EXPLANATION

CATEGORY

STAGE

REFERENCES

R11

Information asymmetry

Wide variation in discourse among blockchain participants

Industry risk

Construction Phase

[36]

R12

Acceptance time

Blockchain arithmetic power and computing time are not controllable

Technical risk

R13

Engineering changes

Smart contracts cannot be changed or interrupted

R14

Multi platform

Multiple Industry risk operating projects cannot form an integrated platform

R15

High long-term Running a Cost risk costs blockchain platform for the long term requires high costs

[39]

R16

Malicious attacks

Encryption algorithms are under computational attack

[31]

R17

Security vulnerabilities

Smart contracts can’t fix security vulnerabilities

R18

Lost encryption Loss of key keys causes leakage of private data

R19

Legal gaps

Blockchain’s legislation is largely blank

[14, 26, 37]

[34]

Operation and Maintenance Phase

Technical risk Life-cycle Phase

[40]

[14, 31, 38]

[31]

Legal risk

[25, 31, 38]

(continued)

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Table 1. (continued) CODE

RISK

EXPLANATION

CATEGORY

R20

Conflict of laws

Decentralization conflicts with existing relevant laws

[31]

R21

Fraudulent behavior

False information or data uploaded to the blockchain

[30]

R22

Lack of talents

Lack of expertise in applying blockchain

R23

Organizational resistance

Organizing multiple participants on the blockchain is difficult

[30, 37]

R24

Eliminate the middleman

Elimination of intermediaries leads to disagreements

[26]

R25

System integration

Multiple Industry risk blockchain systems for the same construction project

[34]

R26

Increased costs The introduction of new technologies results in huge costs

Management risk

Cost risk

STAGE

REFERENCES

[31, 32, 35]

[34, 36]

4 Results and Discussion The results of degree centrality, betweenness centrality, and closeness centrality of the risk network nodes were calculated as shown in Fig. 2. It can be seen from the network parameter calculation table in Fig. 2 that R6 (Technical suitability) had the highest degree centrality and close centrality, which were 0.72 and 0.89, respectively. The technical risk referred to the risks inherent in the blockchain itself. As blockchain evolved at the core of the CSC, the technical risks associated with blockchain itself became increasingly prominent. In addition, technical risks included malicious attacks caused by cryptographic algorithms, security vulnerabilities caused by smart contracts, and the difficulty of determining the computation time caused by the decentralization of blockchain and multiple blocks. The existence of malicious attacks

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Social risk Industry risk Technical risk Cost risk Legal risk Management risk Force Majeure risk

Fig. 2. Blockchain-based CSC Risk Network Model and Parameter Calculation Results

and security vulnerabilities could bring out massive leakage of data and information on the chain, thus causing disruptions in the information flow of the CSC, and the flow of funds is no longer safe and reliable. At the same time, due to the huge volume of engineering data, blockchain computing was under great pressure. The calculation time was difficult to control, and additionally, too long calculation time would affect the ontime delivery of the project. Moreover, it could be noticed that technical risk had the most number of risk nodes in the network. Therefore solving the technical difficulties of blockchain itself was the foundation of applying blockchain. The second risk that needed to be concerned about was R1 (Public attitude). This risk belonged to social risk, which mainly occurred in the early stage of project planning and design. At this phase, project participants would decide whether to adopt blockchain, so the existence of social risks was inevitable. However, any new technology entering the industry must go through a process of cognition, acceptance, and trust. Blockchain was no exception, and its social cognition faced the risks of conceptual ambiguity, blind use, and extreme one-sidedness. Due to the differences in the cultural level and trust level of the participants in the CSC, the initial application of the blockchain would be difficult. The following risk that needed to be paid attention to was R11 (Information asymmetry), which had the highest betweenness centrality of 0.359. R11 was one of the industry risks, which were generated during the integration of blockchain and CSC. Although the two were well integrated according to the features of blockchain and CSC, there were some aspects that did not fit well. For example, the traditional CSC was a network with the general contractor as the central node, but the blockchain had the characteristics of a decentralized network. Thus with the addition of blockchain, the central position of the general contractor would be threatened. Furthermore, due to the

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complexity and multi-stage nature of engineering projects, it was difficult to integrate all stages and all work into one blockchain platform. Other risks were less severe and less likely to occur than the above key risks. But some still required attention: (1) realizing the application of blockchain in the CSC scenario was a challenging task, but there were few talents to tackle this challenge. The lack of highly skilled personnel was an important factor affecting the implementation of blockchain applications. It was also difficult for project managers to organize, as blockchain operations may require bringing together multiple participants [30]. Because blockchain had the potential to eliminate intermediaries, it may make the CSC management mode more complex and increase the possibility of differences; (2) as the application of blockchain in the supply chain was still in its infancy, running a blockchain system required high costs. The multi-stage nature of the CSC often required the existence of multiple blockchain subsystems, so the cost was higher. For general construction projects, despite the large amount of capital flow, many construction companies still had difficulty in capital turnover, and it was difficult to pay the cost of introducing blockchain at some stages; (3) at present, it was basically a legislative blank at the legal level because blockchain was still in the development stage. The ownership of data on the blockchain, access rights, accountability, and other issues have not yet been clearly stipulated by law. Therefore, it was difficult to deal with illegal data and fake data on the blockchain. The “code as law” of blockchain was not rigorous and may conflict with existing laws.

5 Conclusions and Recommendations The potential benefits of blockchain have attracted more and more researchers to explore its applications in the construction industry. Much evidence has shown that blockchain can support the construction of an integrated CSC platform through its own value characteristics, and participate in the cash flow, material flow, and information flow of the CSC. Based on the full cycle and perspective of the CSC, this research analyzed the integration of blockchain and CSC, and established a blockchain-based CSC platform. The relevant risks of blockchain in the CSC were identified through a systematic literature review. Then, a risk network was set up to analyze risk node importance by considering risk associations. Results showed that “Technical suitability” “Public attitude”, and “Information asymmetry” were perceived to be the critical risks. Finally, this research gave corresponding risk countermeasures for the identified critical risks. The relevant recommendations are stated as follows: First, for the social level of cognitive risk. Relevant participants in the CSC need to clarify the concept of blockchain and form a scientific blockchain concept guidance. Relevant laws and regulations must be formulated and improved closely with the pace of technological development, and a strict blockchain supervision system must be constructed. Second, for the more serious technical risks. There is a need to increase the research and development of blockchain and improve the security risk response capability of blockchain. The government needs to encourage blockchain companies to actively participate in global blockchain development and rule-making. Third, for the high-cost problem caused by the introduction of blockchain, the government should encourage multiple parties to invest in projects led by blockchain to ease the financial pressure.

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As an emerging technology, blockchain has a huge initial investment, which can easily bring excessive financial pressure to one party in the supply chain. Therefore, in order to avoid financial risks, it is necessary to increase investment in research and development in the upstream and downstream of the industrial chain in the initial stage of development, and promote the formation of alliances in the upstream and downstream of the CSC. Fourth, for talent shortage in blockchain applications. Colleges and universities should be encouraged to open relevant cross-disciplines, strengthen technical training, train professionals, and reserve sufficient backup for the application of blockchain on the ground. At the same time, construction project managers need to actively learn new knowledge and skills to expand their own cognitive and practical levels. They should be able to adjust the management mode in a timely manner under the addition of new technology, so as to avoid the newly generated management risks. Future research work can continue to refine the application platform by building the application framework of blockchain in the CSC. For example, starting from the architecture of blockchain, the integration of blockchain and CSC can be studied from the perspectives of the data layer, network layer, consensus layer, incentive layer, contract layer, and application layer. And on these bases, relevant risks can be further identified and analyzed.

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Predicting the Extended Life Cycle Energy Consumption of Building Based on Deep Learning LSTM Model Lei Liu, Vivian W. Y. Tam(B) , Khoa N. Le, and Laura Almeida School of Engineering Design and Built Environment, Western Sydney University, Sydney, Australia [email protected]

Abstract. Sustainable developments have been one of the main social forces worldwide, especially in the building sector. As the current biggest energy consumption industry of 35%, it is urgent to solve severe energy issues through advanced energy-saving technologies. Energy consumption prediction, as one of the important building energy management tools, can evaluate energy conservation policies and services timely. Unfortunately, there is still a significant difference between actual and predicted values. A consensus about the life cycle energy boundaries for buildings is being challenged. Some studies believed that the mobile energy related to building location can be accounted, except for traditional embodied and operational energies. Besides, deep learning was regarded as a method better than other simulation models in time series forecasts. To fill the gap between actual and predicted energy consumption values, this paper proposes to extend the life cycle energy boundaries of buildings and choose the Long Shortterm memory model (LSTM)) to predict the building energy consumption in China from 2020 to 2029, which is based on the historical data collected from 2005 to 2019. Results show that there was a remarkable increase in the past 15 years for the total life cycle energy consumption of buildings, but afterwards it will fluctuate at around 1,050 Mtce because of potential influencing factors such as recyclable concrete and prefabricated process applied into an increasing number of newly built buildings. Mobile energy consumption accounted for 24% share of total energy consumption, but it is expected to fall significantly in the next decade. Overall, this study provides a pathway to help reduce building energy consumption prediction errors. Keywords: Energy consumption prediction · Life cycle energy · Mobile energy · LSTM model

1 Introduction With the available natural resources decreasing and extreme disasters occurring frequently, sustainable developments have been proposed worldwide as an important solution. Huge fossil energy consumption was regarded as one of the arch-criminals of global © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 1737–1746, 2023. https://doi.org/10.1007/978-981-99-3626-7_135

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warming. According to the statistics of the top 3 carbon emissions countries of the US, China, and India, It was seen that their total natural disasters increased significantly with the growth of energy consumption in the past five decades [1]. At the 75th anniversary of the United Nations, 3 of 17 global sustainable development goals (SDGs) were related to energy issues [2]. They indicated the significance of primary energy on human survival and social development. Therefore, energy conservation actions were being carried out in different industries all over the world, especially in the building sector. As one of the biggest contributors, the construction industry has accounted for about 35% of energy consumption and 38% of carbon emissions globally [3, 4]. Most advanced technologies focused on fields like green building, recycling, and clean energy. To better evaluate their sustainable environmental benefits, energy consumption prediction is being regarded as an effective mean, which can help not only governments update policies but also practitioners adjust energy services, that is to say the firm bedrock of advanced building energy management [3]. However, current prediction technologies in China cannot meet the increasing accuracy requirements, and there is still an obvious gap between actual and predicted values. Based on the current energy conservation efficiency, Du et al. compared the energy consumption for urban and rural residential buildings in China, and concluded that real energy savings only made up 47–55% of the predicted value in urban areas, and a lower proportion in rural areas [5] (Fig. 1).

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In fact, traditional statistics methods like multiple learning regression simplified the internal relationship of objects and can only handle linear problems [6]. Machine learning methods like Random Forest (RF) then were widely used for time series prediction by creating non-linear models, but it cannot reflect the long-term sequence dependencies among input variables [4]. Further, ensemble methods like Genetic Algorithm-Neural Network (GA-NN) were developed only aiming at some specific single buildings according to their unique characteristics [7]. Besides, a consensus about the life cycle energy consumption prediction scope of buildings, including embodied and operational energies, is being suspected recently since some studies added building-related mobile energy as one of the dominant energy categories [8–10]. In their opinion, partly transportation energy activities related to building structure, location, and occupants’ travel attitude in the whole life cycle of buildings like materials transport and daily commuting should

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be recognized as indirect building energy consumption. Thus, to predict the building energy consumption more accurately and fill the error gap, this paper decided to extend the system boundaries for the life cycle energy of building and explore advanced prediction methods, taking China as an empirical study. It will provide a new pathway to improve energy consumption prediction accuracy at the national level, and help the Chinese relevant sectors find out the potential energy conservation issues, thereby adjusting policies and services to achieve energy-saving goals.

2 Literature Review Forecasting building energy consumption has become an important means to reflect the adaptability of energy conservation technologies and measures on current building systems [11]. One of the most popular methods is time series prediction, which is by mining the internal relationship and regular patterns from the historical data to predict future energy consumption [12]. The main development process of prediction methods involves four phases: statistical methods, machine learning, deep learning, and ensemble methods, as shown under Fig. 2. Traditionally, statistical methods were dominant in time series prediction, which usually refers to simplifying the complex internal relationships between variables (x, y) into linearity [3]. However, due to higher accuracy requirements in practice, it is inevitable to make sense of the complexity, irregularity, randomness, and non-linearity of data. Therefore, traditional statistical methods were myopic. Machine learning then was widely used for predicting building energy consumption. It depends on the high-speed electronic calculator to simulate the human brain and behavior, thereby obtaining more accurate prediction results [11]. An important advantage is that it can find out non-linear relationships among a large amount of historical data [4]. Subsequently, some studies found that it is difficult for them to process the sequential dependencies between input variables, and limited the prediction effectiveness to some extent [6]. Deep learning was then developed on this basis and achieved great success. It can deeply learn the representation of raw data and extract meaningful and non-redundant features from raw datasets through creating multiple processing hidden layers [4, 7]. Recently, with research more complex and specific, ensemble methods are becoming dominant. For example, after considering the building characteristics of each component, Karijadi and Chou combined the RF with LSTM to predict building energy consumption [3]. They focused on short-time scales like hours-head time steps in single buildings to explore specific building characteristics, but it did not suit the macro national level’s statistics and prediction proposed in this study. Based on the property of national level and accessibility of raw data, this study decided to develop a LSTM model for predicting the life cycle energy consumption of buildings in China. LSTM is considered as one of the state-of-the-art methods to predict macro level data. it not only handles the complex non-linear relationships, but also create long-term dependencies among historical data by adding some multi-threshold gates [13]. Compared with traditional machine learning models, it adds the hidden memory layer, which can remember and absorb the important memory from historical data. After randomly allocating the weight of characteristics in the input layer, data is filtered in the process layer to determine what data is left, and then plus the memory data to get

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outcomes in the output layer, as shown under Fig. 3. The specific process formulas are shown below: Firstly, there are a series of random weights generated by the computer, and then are combined with the original statistics covering multiple features (x1 , x2 , …, xn ) as input layer: W = (W 1 , W 2 , . . . , W n )

X˜ t = (xt1 W 1 , xt2 W 2 , . . . , xtn W n )

There are three gate thresholds in the process and memory layers: forget gate (ft ), input gate (it ), and output gate (ot ). They decided whether the processed and memory data is passed or not. ft = σ (Wxf X˜ t + Whf ht−1 + bf ) it = σ (Wxi X˜ t + Whi ht−1 + bi )

ot = σ (Wxo X˜ t + Who ht−1 + bo )

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Fig. 3. The long short-term memory (LSTM) model principle.

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3 Methodology 3.1 Life Cycle Energy Boundary of Buildings In general, the first step in the energy consumption prediction process is to define the system boundaries for the life cycle energy of buildings, which has to account for all direct and indirect energy input of buildings in their life span. Referred to the attributional life cycle assessment (ALCA) which utilizes the existing statistical data to assess the share of environmental and economic benefits of a product, this paper determined the energy boundary of “cradle to beyond life”, from which includes three energy consumption categories: embodied, operational, and mobile energies, as shown under Fig. 4. Embodied energy (EE) is used for building structures and materials like envelop, columns, and windows. It involves five phases: raw materials mining and manufacturing (EE P ), construction and demolition (EE c ), recurrent retrofit (EE r ), and recycling/reuse (rE). Operational energy (OE) refers to that input from outside in the usage process of buildings, including those used for maintaining the building environment (heating, cooling, ventilation, air conditioning, and light) and various activities (working, equipment, lift, and water heating) [14]. It usually included three parts: daily fuels (OE f ), electricity (OE e ), and centralized heating (OE h ). Mobile energy (ME) is exactly a part of transportation energy which is related to the building environment such as building location and occupants’ behavior. It mainly comprises two parts: materials transportation (ME t ) in the life of buildings, and individual commuting and trips (ME c ). To facilitate the statistics and conversion among energy variables above, the energy consumption unit is unified as a million ton of standard coal equivalent (Mtce), which is a popular conversion index for quantifying energy consumption according to the calorific value of standard coal. Operation

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3.2 LSTM Model Analysis Firstly, it is to collect the historical data. Given the accessibility and authority of raw energy consumption data as well as the characteristic of ME, this paper decided to collect

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that on urban residential buildings in China from 2005 to 2019. The sources of virgin data and processing were shown under Fig. 5. Firstly, most of the EE and OE data were obtained from China Building Energy Consumption Research Report 2016–2021 [15– 20]. It was the national EE P , EE c , and OE. For other data required, it has to be estimated and modified. The coefficient of 42% was used for exchanging between national and urban residential levels [21]. EE r accounted for 0.5% of EE P , which should be excluded from EE [22]. Besides, rE was defined as 5% of EE P according to the systematic review of Zhang et al. [23]. For the missing of OE f , OE e , and OE h from 2005 to 2009, 3% for centralized heating use and 8% for electricity consumption annually are adopted to estimate the missing data [15]. In terms of the estimation of ME t , it is necessary to collect the material intensity for different building structures [24], the urban residential building floor area of different structures [25], the transport vehicles volume of each material [24], and the average truck diesel consumption [26]. Also, ME c involved the annual private vehicle ownership of different types, and the unit energy consumption of different private vehicles [27]. The specific estimation formulas required for the above can refer to by Tam et al.[8] .

Fig. 5. Energy consumption data sources and processing

Secondly, it is to develop a LSTM prediction model. The intact and repeatable training code is shown under Fig. 6. Firstly, it should load sequence data from 2005 to 2019. For a better fit and to prevent the training from diverging, standardizing the training data was conducted. The responses were specified as the training sequences with values shifted by a one-time step. Then, it has to define the input, LSTM, and output layers to create an LSTM regression network. Meanwhile, training options have to be specified, such as using Adam optimization, training for 200 epochs, and so on. Subsequently, it started to train the neural network with the specified training options using the train Network function. Further, to evaluate the accuracy, it has to calculate the root mean squared error (RMSE) between the predictions and the target to test the network. The RMSE is 0.0239, which indicated the prediction accuracy is high. Finally, the next 10-time steps were forecasted.

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Fig. 6. Training code and model of LSTM.

4 Results and Discussion 4.1 Life Cycle Energy Consumption from 2005 to 2019 Based on the collected raw data above, the cumulative life cycle energy consumption of urban residential buildings in China was obtained. As shown under Fig. 6, it was clearly seen that there was a moderate increase in the total energy consumption from 390 Mtce in 2005 to 1089.3 Mtce in 2019. Meanwhile, three dominant energy categories (EE, OE, & ME) achieved a significant growth, but EE tended to be stable these days. Besides, the ME accounted for 24% of total energy consumption in 2019, four times higher than that in 2005 (Fig. 7). Overall, it indicated that with China’s economy and the real estate flourishing, building energy consumption increased inevitably, although some advanced energy-saving technologies were applied. Because the working intensity and living standard of modern citizens are jumping, ME has to be one of the most urgent energy issues to be solved (Fig. 8). 4.2 Life Cycle Energy Consumption from 2020 to 2029 After multiple iterations and modifications of the LSTM model, the predicted values of total and mobile energy consumption by 2029 were obtained, as shown in Figs. 9 and 10. It was seen that total building energy consumption will remain at around 1,100 Mtce from 2019 to 2022, and then slightly decrease to 1,000 Mtce by 2029. There are a few possible reasons such as the constant covid-19 pandemic, birth rate reduction, and green building promotion in cities, which all will reduce the housing purchase intention. Moreover, since the Chinese real estate rapid developed in the past few years, old building stock has reached saturation. Therefore, the total amount of life cycle energy consumption

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of buildings in China will not increase significantly as expected. Besides, the mobile energy consumption related to building location significantly fell from 281.38 Mtce in 2023 to 206.30 Mtce in 2029. It indicates that with city infrastructure improvement and residential buildings being denser, citizens in China will significantly increase public transportation travel such as metro and high-speed trains. Further, the specific change reasons should be discussed later.

Fig. 9. Total energy consumption prediction by 2029

Fig. 10. Sub-category (ME) prediction by 2029

5 Conclusion Given the current limitation of system boundary and prediction methods for building energy consumption, this paper proposed an extended life cycle energy scope of buildings (including embodied, operational, and mobile energies), and adopted the cutting-edge deep learning method-LSTM to predict the next energy consumption of urban residential buildings in China by 2029, which is based on the historical data collected and estimated from 2005 to 2019. The results show that the total building energy consumption will fluctuate around 1100 Mtce, and the mobile energy consumption fell significantly

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from 281.38 to 206.30 Mtce. Overall, this paper will provide a useful reference to help government evaluate current energy-saving policies and services.

References 1. British Petroleum. Statistical Review of World Energy 2020 (2020) 2. United Nations. Sustainable Development goals (2022). https://www.un.org/sustainabledeve lopment/sustainable-development-goals/ 3. Karijadi, I., Chou, S.-Y.: A hybrid RF-LSTM based on CEEMDAN for improving the accuracy of building energy consumption prediction. Energy Build. 259, 111908 (2022) 4. Khalil, M., et al.: Machine learning, deep learning and statistical analysis for forecasting building energy consumption—a systematic review. Eng. Appl. Artif. Intell. 115, 105287 (2022) 5. Du, Q., et al.: The energy rebound effect of residential buildings: evidence from urban and rural areas in China. Energy Policy 153, 112235 (2021) 6. Li, Y., et al.: EA-LSTM: evolutionary attention-based LSTM for time series prediction. Knowl.-Based Syst. 181, 104785 (2019) 7. Dara, S., Tumma, P.: Feature extraction by using deep learning: a survey. In: 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA) (2018) 8. Tam, V.W., Liu, L., Le, K.N.: Modelling and quantitation of embodied, operational and mobile energies of buildings: a holistic review from 2012 to 2021. Eng. Constr. Archit. Manage. (2022) 9. Fenner, A.E., et al.: Embodied, operation, and commuting emissions: a case study comparing the carbon hotspots of an educational building. J. Clean. Prod. 268, 122081 (2020) 10. Yu, M., Wiedmann, T., Langdon, S.: Assessing the greenhouse gas mitigation potential of urban precincts with hybrid life cycle assessment. J. Clean. Prod. 279, 123731 (2021) 11. Al-Shargabi, A.A., et al.: Buildings’ energy consumption prediction models based on buildings’ characteristics: Research trends, taxonomy, and performance measures. J. Build. Eng. 54, 104577 (2022) 12. Jing, W., et al.: A prediction model for building energy consumption in a shopping mall based on Chaos theory. Energy Rep. 8, 5305–5312 (2022) 13. Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014) 14. China’s Ministry of Housing and Urban-Rural Development. Standard for energy consumption of building GB/T51161-2016 (2016) 15. CABEE. China Building Energy Consumption Annual Report 2021 (2021). http://www.199it. com/archives/1369165.html 16. CABEE. China building energy consumption annual report 2020. J. BEE 49(2) (2021) 17. CABEE. China Building Energy Consumption Annual Report 2019. Industry 7, 30–39 (2019) 18. CABEE. China Building Energy Consumption Annual Report 2018. Industry 2, 26–31 (2018) 19. CABEE. China Building Energy Consumption Annual Report 2017 (2017). https://mp.wei xin.qq.com/s/CW2BRjAYS0nJbKgDiuG-3g 20. CABEE. China Building Energy Consumption Annual Report 2016 (2016) 21. THUBERC. China building energy efficiency annual development research report 2020. China Architecture & Building Press, China (2020) 22. Dixit, M.K.: Life cycle recurrent embodied energy calculation of buildings: a review. J. Clean. Prod. 209, 731–754 (2019)

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23. Zhang, Y., et al.: China’s energy consumption in the building sector: a life cycle approach. Energy Build. 94, 240–251 (2015) 24. Geng, J., et al.: Quantification of the carbon emission of urban residential buildings: the case of the Greater Bay Area cities in China. Environ. Impact Assess. Rev. 95 (2022) 25. National Bureau of Statistics of China. China statistical yearbook. CHINA: China Statistics Press (2005–2019) 26. Pääkkönen, A., et al.: The potential of biomethane in replacing fossil fuels in heavy transport— a case study on Finland. Sustainability 11(17) (2019) 27. National Bureau of Statistics. National data (private vehicle ownership). China (2005–2019)

Research on Constraints and Countermeasures for the Development of New Energy Vehicles in China Ziwei Chen and Liyin Shen(B) School of Spatial Planning, Hangzhou City University, Hangzhou 310015, China [email protected]

Abstract. The growing global concern about climate change due to greenhouse gas emissions from vehicles coupled with the depletion of natural resources is driving the adoption of alternative fuel technologies in the global economy. Electric vehicles are positioned as a green, clean alternative technology with the potential to enable an efficient transition to sustainable low-carbon emission transport systems and to conserve natural resources. New energy vehicles represented by electric vehicles have less pollution, low noise and high energy efficiency, which is the direction of future automobile development. Despite the announcement of preferential policy measures to encourage the adoption of electric vehicles, multiple potential barriers that interact with each other have hindered the penetration of electric vehicles in some countries. While researchers have identified some restricting factors in European and American countries, the question is “There is a lack of empirical research on the restricting factors of EV promotion in the Chinese context, and how do they interact with each other?” A two-phased AHP-FCE tools are applied. Firstly, Analytic Hierarchy Process (AHP) method is used in ranking and prioritizing the important restricting factors/sub-factors. Then, multilevel Fuzzy Comprehensive Evaluation (FCE) method is applied to obtain the final evaluation score. Our research contributes to a better understanding of the multifaceted nature of EV restricting factors and their interdependencies in policy and decision making. This paper provides a valuable reference for the construction of low-carbon cities and urban sustainable development. Keywords: Electric vehicle · New energy vehicle · Low-carbon city · Sustainable development

1 Introduction The transport sector is a major consumer of fossil resources and a main contributor to greenhouse gas (GHG) emissions (Hassouna and Al-Sahili, 2020), leading to a series of urban resource and environmental problems. Statistics show that by 2050 there will be three times as many cars in the world as there are on the roads today. In this context, developing electric vehicles (EVs) is an inevitable trend for countries to promote energy conservation and emission reduction, and it has become a global consensus (Park et al., © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 1747–1757, 2023. https://doi.org/10.1007/978-981-99-3626-7_136

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2022). Numerous automobile-manufactures rush to introduce new models to meet the tight regulations of the countries they operate in. On October 9, 2020, the State Council of China issued the New Energy Vehicle Industry Plan (2021–2035), which was a strategic measure to promote country’s transformation from a big automobile country to a powerful automobile country, also a strategic plan to deal with climate change and promote green development. However, the total number of EVs on the road is still a small share in the total car stock (Ahmadi, 2019). In Europe, overall market shares for electromobility remain low, where Germany, France and the United Kingdom all posted market shares lower than 4% in 2019 (Schulz and Rode, 2022). Even though China is leading the way with a global electric vehicles market share of almost 50%1 , the overall domestic popularity is not very ideal. According to statistics from the Ministry of Public Security of PRC, the national EVs ownership reached 8.104 million, only accounting for about 2.61% of the total number of automobiles by the end of June 2022 in China2 . Thus, China’s current promotion of EVs has not yet achieved the desired target, and there are still challenging issues in the promotion and diffusion of EVs. China is now the largest emitter of CO2 emissions worldwide, producing roughly twice the CO2 emissions of the United States each year3 . The international society generally believes that China’s vigorous development of electric vehicles can achieve a large amount of carbon emission reduction, which is of great significance to global energy conservation and sustainable development. Therefore, it is necessary to identify and understand the restricting factors for the development of electric vehicles and take countermeasures. Previous studies have done a lot of work on the restricting factors for developing electric vehicles in different countries and regions. In Asia, Tarei et al. (2021) identified the barriers to the adoption of EVs, including technology, infrastructure, finance, behavior and external factors in India. They found that EV barriers such as performance and range, the total cost of ownership, shortage of charging infrastructure, lack of consumer awareness about EV technology are critically influential in driving EV adoption. Patyal et al. (2021) also raised 13 barriers to EV adoption in India, which involved standardization and codes, recharge time, battery range, charging infrastructure, safety, lack of awareness, high cost, limited acceptance, sustainability of fuel source, resource limitation, electric grid, resale anxiety, and government policies. In Pakistan, Asghar et al. (2021) indicated that state financial subsidies, market prices, technical malignancy and social drawbacks as restricting factors for EV adoption. In Europe, Biresselioglu et al. (2018) mapped the electric mobility barriers contained a lack of charging infrastructure, economic restrictions and cost concerns, technical and operational restrictions, lack of trust, lack of information and knowledge, limited supply of electricity and raw materials, and practicability concern. In Italy, Giansoldati 1 Data from Statista. https://www.statista.com/study/49240/emobility---market-insights-and-

data-analysis/. 2 Data from the Ministry of Public Security of PRC: https://app.mps.gov.cn/gdnps/pc/content.

jsp?id=8577652. 3 Data from Statista. https://www.statista.com/statistics/239093/co2-emissions-in-china/#statis

ticContainer.

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et al. (2020) suggested that insufficient density of fast-charging stations and high initial purchase price were the two most important barriers to EV diffusion. Focusing on the freight transport, Melander et al. (2022) revealed the barriers that hinder the adoption of electric freight vehicle (EFVs) in Stockholm, Sweden, which can be categorized as internal, external and governmental barriers. They were (1) internal barriers: limitations in charging infrastructure and grid capacity problems, costs, loading capacity, the limited range of EFVs and new ways of managing distribution. (2) external barriers: limitations in charging infrastructure and grid capacity problems, problems charging outside of premises, few options on the market and alternative sustainable solutions. (3) governmental barriers: changing political directives. In the UK, Berkeley et al. (2018) summarized 19 headline barriers to purchase of EVs, including availability of public charging stations, length of time it takes to charge a EV, limited vehicle driving range, concerns over durability of the battery, concern that driving behavior and using vehicle features will diminish driving range, uncertainty concerning the process of home/public charging, expectation that EV technology will improve in the future so are delaying purchase, dwelling would be unsuitable for home charging, belief that EVs are an inferior/unreliable technology, expectation that improvements in internal combustion engine (ICE) will continue thereby offsetting environmental benefits of EVs, high purchase price, length of time to offset higher purchase price through savings made in fuel and taxation, anxiety over the re-sale value, uncertainty over maintenance, service and repair infrastructure, lack of choice and availability in the EV market, difficulties in understanding how to calculate fuel costs and potential savings of EVs, vehicle design/aesthetics are inferior compared to market for ICE vehicles, concern over the real environmental impact of EVs, a lack of general understanding of the benefits of driving EVs. In the Americas, the cost premium, range limitations, and recharging time of plug-in electric vehicle (PEVs) are all perceived as disadvantages and are significantly associated with decreased intent to purchase in the United States (Carley et al., 2013). Moreover, plug-in hybrids and fuel cell vehicles have never gained significant market share as existing tax incentives do not appear to be sufficient to overcome barriers to market acceptance in California. (Greene et al., 2014). Besides, driving range, refueling infrastructure, charging time duration and price are believed to most influence PEVs adoption in the U.S. (Tuttle and Baldick, 2015). From the review and analysis of U.S. state EVs policy, there is lack of policy coherence, without considering the new policy instruments potential interaction with each other and existing federal and state policies (Hayashida et al., 2021). However, it appears that little research has been conducted about EVs restricting factors in the context of China. On the one hand, China is the world’s largest carbon emitter and has great potential to contribute to energy conservation and emission reduction by promoting EVs. On the other hand, the national conditions of China and Western countries are different, so it is necessary to find a suitable way for the development and diffusion of EVs in China. Therefore, studies on the restricting factors for the development of electric vehicles in China are largely missing. This study thus aims at contributing to bridging these gaps in the literature.

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2 Methodology The methodology for conducting this research can be designed in the following research roadmap (See Fig. 1) and it includes four research procedures: 1) to identify the restricting factors for promoting electric vehicles (RFPEV); 2) to classify the RFPEV; 3) to establish the weighting values of the RFPEV; 4) to generate the key RFPEV.

Fig. 1. Research roadmap

In the first research procedure, the restricting factors for promoting the development of electric vehicle will be discerned through comprehensive literature review. Then, the RFPEV will be revised and simplified through expert interviews.

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In the second step, based on the extensive and up-to-date review of the extant literatures on RFPEV, with the main aim of classifying and mapping the restricting factors for the promotion of EVs in China, the typical research works which have explored the restricting factors for promoting EVs as follows. For example, The limited capacity and lifespan of batteries have been modeled (Guo et al., 2019), and the long charging time of batteries and security concerns have been estimated (Fraiji et al., 2022), as well as disposal and recycling of batteries also have been concerned (Malik et al., 2016). Moreover, driving route and range of EVs have received extensive scientific attention (Lam et al., 2022; Ricardo et al., 2021). And some companies like Tesla which are trying to solve these problems. Besides, some scholars have investigated EVs’ issues during its supply chain. For example, the high research and development (R&D) risks, initial production costs and market uncertainty were believed that made the new energy vehicle (NEV) industry take higher risks than the traditional vehicle industry (Li et al., 2021; Wang et al., 2022). In addition to, the capital constraint for suppliers is also a financial challenge that affecting the development of the NEV supply chain, especially after the decline of state subsidy policies (Meng et al., 2022). On the one hand, the energy security restricting factors cannot be ignored. In the short term, it also suggests that environmental pressure is greater than energy security, and energy security will make us feel more stressed from 2022 (Du et al., 2019). On the other hand, insufficient charging infrastructures were extensively discussed, which affects the diffusion of the electric vehicles (Casini et al., 2021; Greene et al., 2020; Kullman et al., 2021; Schulz and Rode, 2022). But few studies have paid attention to whether the distribution of charging infrastructure is reasonable. By reviewing these studies, the RFPEV can be classified into five categories, including technical factors, infrastructural factors, economic factors, governmental factors and public factors. Then, the 19 indicators library of RFPEV will be given. Thirdly, following the indicator library of the RFPEV, the weighting values of the RFPEV will be established by the analytic hierarchy process model. This task will be implemented through conducting the Analytic Hierarchy Process (AHP) method. Assessing the importance of restricting factors categories with corresponding restricting factors by selected experts. The panel of experts identified for this study were selected from four major areas, a) university and academic/research institutes, b) automotive vehicle companies manufacturing EV, c) EV industry representative bodies and d) government energy department. List of experts were selected based on their more than 10 years of relevant expertise in relation to EV. We contacted 16 such experts to conduct the study while 10 of them ultimately agreed to participate resulting in about 62.5% response success rate. The list of identified EV restricting factors categories and restricting factors were discussed by experts for ranking and prioritization following the AHP steps. Next, to express the quantitative results of evaluation indicators clearly and systematically, it is necessary to introduce the multi-level fuzzy comprehensive evaluation (FCE) method to obtain the final evaluation score. We need construct the fuzzy relation matrix ∼

M of each subset, and then calculate fuzzy comprehensive evaluation result vector Ci of each subset by combining the weight vector W = (w1 , w2 , ...wi ). Then, we set comment value V = (100, 80, 60, 40, 20), and the fuzzy comprehensive evaluation scores of single factor Fi , the fuzzy comprehensive evaluation scores of each

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subset Ci and the fuzzy comprehensive evaluation scores of target layer A were obtained. Finally, the evaluation results of importance for the RFPEV will be showed and we can identify the key factors.

3 Results 3.1 AHP Method Results: Establishment of Weights for EV Restricting Factors and Sub-factors Following the Analytic Hierarchy Process (AHP) methodology described above, the judgment matrix of each level is scored by 1–9 scale method, and then the scoring results are collected and summarized. After that, the average value is calculated and the judgment matrix is constructed. In this paper, Asymptotic Normalization Coefficient (ANC) method is used to calculate the maximum eigenvalue and eigenvector of judgment matrix. In order to avoid the error of calculation result caused by the consistency index of judgment matrix beyond the normal range, the research based on AHP needs to test the consistency of judgment matrix. Firstly, the consistency index CI of the judgment matrix should be calculated, where n is the order of judgement matrix. And then, we check the value of RI. Then the consistency ratio CR of the judgment matrix is calculated. It is generally believed that when CR < 0.1, it passes the consistency test; otherwise, the matrix needs to be adjusted. According to the above calculation steps, the judgment matrix of criterion layer relative to target layer is constructed, and the index weight is obtained. Firstly, the largest eigenvalue λmax = 5.356 of the judgment matrix is calculated. Then the consistency index CI = 0.089 of the judgment matrix is calculated. As RI = 1.120. The consistency ratio CR of the judgment matrix is 0.080, which is less than 0.1. So, this judgment matrix passes the consistency test. Therefore, the technical factor (C1), infrastructural factor (C2), economic factor (C3), governmental factors (C4) and public factors (C5) indicators of weight value are 0.4414, 0.2718, 0.1291, 0.1103 and 0.0475 respectively. Similarly, the judgment matrix and calculation weights of technical factors, infrastructural factors, economic factors, governmental factors and public factors can be obtained, and the maximum eigenvalue λmax and CI values are calculated, and they all passed the consistency test. In the technical factor judgement matrix, the largest eigenvalue λmax = 5.205 of the judgment matrix is calculated. And the consistency index CI = 0.051 of this judgment matrix is calculated. As RI = 1.120. The consistency ratio CR of this judgment matrix is 0.046, which is less than 0.1. So, the technical factor judgment matrix passes the consistency test. Thus, limited battery durability (F1), lengthy charging time (F2), limited driving range (F3), security concerns (F4), disposal and recycling (F5) indicators of weight value are 0.2915, 0.1874, 0.2368, 0.2319 and 0.0525 respectively. In the infrastructural factor judgement matrix, the largest eigenvalue λmax = 4.100 of the judgment matrix is calculated. And the consistency index CI = 0.033 of this judgment matrix is calculated. As RI = 0.890. The consistency ratio CR of this judgment matrix is 0.037, which is less than 0.1. So, the infrastructural factor judgment matrix passes the consistency test. Therefore, shortage of charging stations (F6), unreasonable distribution of charging stations (F7), lack of sharing between charging piles (F8), uncertainty on

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maintenance, service, and repair infrastructure (F9) indicators of weight value are 0.4301, 0.2888, 0.1807 and 0.1005 respectively. In the economic factor judgement matrix, the largest eigenvalue λmax = 4.130 of the judgment matrix is calculated. And the consistency index CI = 0.043 of this judgment matrix is calculated. As RI = 0.890. The consistency ratio CR of this judgment matrix is 0.049, which is less than 0.1. So, the economic factor judgment matrix passes the consistency test. Thus, high purchase price (F10), market uncertainty (F11), anxiety over re-sale (F12), supplier capital constraints (F13) indicators of weight value are 0.5138, 0.1896, 0.1963 and 0.1002 respectively. In the governmental factor judgement matrix, the largest eigenvalue λmax = 4.045 of the judgment matrix is calculated. And the consistency index CI = 0.015 of this judgment matrix is calculated. As in RI = 0.890. The consistency ratio CR of this judgment matrix is 0.017, which is less than 0.1. So, the governmental factor judgment matrix passes the consistency test. Therefore, subsidy reduction (F14), local protectionism (F15), incomplete policies and regulations (F16), limited energy resource (F17) indicators of weight value are 0.4652, 0.1875, 0.2594 and 0.0879 respectively. In the public factor judgement matrix, the largest eigenvalue λmax = 2 of the judgment matrix is calculated. And the consistency index CI = 0.000 of this judgment matrix is calculated. As RI = 0.000. The consistency ratio CR of this judgment matrix cannot be calculated. But the second-order data all meet the consistency test, so the final calculated weights of public factor judgment matrix pass the consistency test. Therefore, lack of awareness (F18), scepticism on safety and reliability (F19) indicators of weight value are 0.3919 and 0.6081 respectively. 3.2 Multi-level Fuzzy Comprehensive Evaluation: Calculate the Score Result To quantify and display the scores of evaluation indicators at each level systematically and clearly, we introduce the Fuzzy Comprehensive Evaluation (FCF) method, which uses the Membership Theory in fuzzy mathematics to transform the qualitative index evaluation into the quantitative index evaluation. The data in this part were collected by questionnaire survey. We invited 243 experts in automobile and new energy vehicle industry to evaluate the constraint degree of the index. A total of 235 valid questionnaires were collected, with effective recovery of 96.7%. After reliability statistical analysis, the Cronbach’s Alpha value was 0.811, and the Cronbach’s Alpha value based on standardized items was 0.810. Therefore, the analyzed questionnaire data had high internal consistency and strong reliability. Since the evaluated object in this study is multi-level, it is necessary to evaluate the object layer by layer starting from the index factors at the bottom. After obtaining the result vector of fuzzy comprehensive evaluation respectively, we will build the fuzzy relation matrix at the upper level, and then obtain the comprehensive evaluation score at the highest level. Firstly, according to the research model, the evaluation index set is divided into 5 subsets Ci , each of which contains multiple second-level evaluation factors Fi . Then, according to the statistical data of the expert questionnaire and the weight vector W = (w1 , w2 , ...wi ), the membership degree mi of the single factor evaluation index in each sub-set was determined and normalized.

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Then, we construct the fuzzy relation matrix of each subset, and calculate the fuzzy comprehensive evaluation result vector of each subset by combining the weight vector W = (w1 , w2 , ...wi ). ∼

Next, building the fuzzy relation matrix C of the target layer, and the fuzzy comprehensive evaluation result vector A of the target layer is calculated by combining the weight vector W = (0.4414, 0.2718, 0.1291, 0.1103, 0.0475). Finally, we obtain the fuzzy comprehensive evaluation scores of single factor Fi , the fuzzy comprehensive evaluation scores of each subset Ci and the fuzzy comprehensive evaluation scores of target layer A. The fuzzy comprehensive evaluation score of restricting factors for the promotion of EVs in China is 77.820. In the five comment sets, the High weight value is the highest (0.348). Combined with the maximum membership rule, the final comprehensive evaluation result is High.

4 Discussions and Conclusion This paper sought to explore restricting factors for promoting EVs. A two-phased AHPFCE tools are applied in this research to arrive at results. The Analytic Hierarchy Process (AHP) is used to give weight to EV category restricting factors and corresponding subfactors, followed by the Fuzzy Comprehensive Evaluation (FCE) to score and rank each restricting factor. The results clearly show that technical factor is a very important restricting reason for promoting EVs, but also that infrastructural aspects function as main constraints. Besides, economic, governmental and public factors also have restrictive effect on the promotion of EVs. Technical restricting factor is ranked first based on the expert view and data analysis. Within the main category of Technical restricting factors, the Security concerns plays a critical role among the sub-factors. Many researchers highlight the safety concerns as a key obstacle to EVs promotion (Fraiji et al., 2022). Notably, the Limited driving range is the second-highest and the Lengthy charging time is the third-highest within the Technical restricting factors’ category. Besides, the importance of Limited battery durability is also mentioned. These observation strengthens the research findings of Tuttle and Baldick (2015) and Berkeley et al. (2018), who emphasized concerns on driving range, charge time duration and battery life in the diffusion of PEVs. Our study corroborates that importance of technology as a key obstacle to EVs promotion is emphasized by researchers for many regions, countries around the world (Fraiji et al., 2022), such as India (Patyal et al., 2021; Tarei et al., 2021), Sweden (Melander et al., 2022), U.K. (Berkeley et al., 2018) and U.S.(Greene et al., 2014). Many countries are growingly encouraging the transition to EVs for combating to global climate change and building low-carbon cities as well as sustainable development, because the ability of EVs to dramatically reduce transport-related greenhouse gas emissions and dependence on fossil fuels when charged with renewable sources of energy (Coffman et al., 2017; McLaren et al., 2016; Woo et al., 2017). China is the world’s largest carbon emitter. If China can promote the use of more EVs, it will be an important contribution to global carbon reduction. Extant studies have done a lot of

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work around the barriers to EVs adoption in western countries from multi-dimensional framework. However, it has not received much empirical research attention in China. Our research contributes to the ongoing discussion about promoting EVs (Asghar et al., 2021; Melander et al., 2022; Schulz and Rode, 2022). The promotion of EVs is largely dependent on overcoming the multiple barriers, which are categorized as technical, infrastructural, economic, governmental and public based on the source of their generation with corresponding sub-factors. Our findings confirm those from previous studies of EVs, which indicate that security concerns (Fraiji et al., 2022), limited driving range (Lam et al., 2022), lengthy charging time (Patyal et al., 2021), unreasonable distribution of charging stations (Gholami et al., 2022), lack of sharing between charging piles (Huang, 2020), limited battery durability (Berkeley et al., 2018) and shortage of charging stations (Biresselioglu et al., 2018) are most important restricting factors for promoting the EVs. We add to the literature and expert advice by exploring these restricting factors in detail. Our research attempts to fill the apparent gap and provides a comprehensive view and framework of addressing the EVs restricting factors for promotion. Thus, the paper also contributes to the field of studies aimed at sustainable transport development and low carbon city construction in the future. This study provides a lot of policy implications related to the promotion of EVs. China has suitable context for promoting EVs as it has strong ambitions for more sustainable road transport. However, despite clear plan and goal, there is still some uncertainty considering the constraints of different aspect factors. The Chinese government should strengthen the technological research and development of EVs, improving the EVs’ safety, endurance and charging problems which are widely concerned at present. At the same time, the government should increase the construction of supporting infrastructure of EVs, such as charging, energy storage. Particularly optimizing the spatial layout planning of charging piles and promoting the EVs market to realize charging sharing, which makes them more reasonable laid out and convenient for using. Besides, the government should improve the relevant policies and regulations for promoting EVs, such as gradually lifting restrictions on the purchase of EVs in various regions. And promoting the implementation of support policies such as free from driving restriction. Moreover, the government also should establish a healthy EVs related legislations to effectively protect the rights and interests of the public.

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Biresselioglu, M.E., Demirbag Kaplan, M., Yilmaz, B.K.: Electric mobility in Europe: a comprehensive review of motivators and barriers in decision making processes. Transp. Res. Part A: Policy Pract. 109, 1–13 (2018). https://doi.org/10.1016/j.tra.2018.01.017 Carley, S., Krause, R.M., Lane, B.W., Graham, J.D.: Intent to purchase a plug-in electric vehicle: a survey of early impressions in large US cites. Transp. Res. Part D: Transp. Environ. 18, 39–45 (2013). https://doi.org/10.1016/j.trd.2012.09.007 Casini, M., Vicino, A., Zanvettor, G.G.: A chance constraint approach to peak mitigation in electric vehicle charging stations. Automatica 131 (2021). https://doi.org/10.1016/j.automatica.2021. 109746 Coffman, M., Bernstein, P., Wee, S.: Integrating electric vehicles and residential solar PV. Transp. Policy 53, 30–38 (2017). https://doi.org/10.1016/j.tranpol.2016.08.008 Fraiji, Y., Ben Azzouz, L., Trojet, W., Hoblos, G., Azouz Saidane, L.: Context-aware security for the intra-electric vehicle network under energy constraints. Comput. Electr. Eng. 97 (2022). https://doi.org/10.1016/j.compeleceng.2021.107517 Gholami, K., Karimi, S., Anvari-Moghaddam, A.: Multi-objective stochastic planning of electric vehicle charging stations in unbalanced distribution networks supported by smart photovoltaic inverters. Sustain. Cities Soc. 84 (2022). https://doi.org/10.1016/j.scs.2022.104029 Giansoldati, M., Monte, A., Scorrano, M.: Barriers to the adoption of electric cars: evidence from an Italian survey. Energy Policy 146 (2020). https://doi.org/10.1016/j.enpol.2020.111812 Greene, D.L., Kontou, E., Borlaug, B., Brooker, A., Muratori, M.: Public charging infrastructure for plug-in electric vehicles: what is it worth? Transp. Res. Part D: Transp. Environ. 78 (2020). https://doi.org/10.1016/j.trd.2019.11.011 Greene, D.L., Park, S., Liu, C.: Analyzing the transition to electric drive vehicles in the U.S. Futures 58, 34–52 (2014). https://doi.org/10.1016/j.futures.2013.07.003 Guo, H., Wang, X., Li, L.: State-of-charge-constraint-based energy management strategy of plugin hybrid electric vehicle with bus route. Energy Convers. Manage. 199 (2019). https://doi.org/ 10.1016/j.enconman.2019.111972 Hassouna, F.M.A., Al-Sahili, K.: Environmental impact assessment of the transportation sector and hybrid vehicle implications in palestine. Sustainability 12(19) (2020). https://doi.org/10. 3390/su12197878 Hayashida, S., La Croix, S., Coffman, M.: Understanding changes in electric vehicle policies in the U.S. States, 2010–2018. Transp. Policy 103, 211–223 (2021). https://doi.org/10.1016/j.tra npol.2021.01.001 Huang, Q.: Insights for global energy interconnection from China renewable energy development. Glob. Energy Interconn. 3(1), 1–11 (2020). https://doi.org/10.1016/j.gloei.2020.03.006 Kullman, N.D., Goodson, J.C., Mendoza, J.E.: Electric vehicle routing with public charging stations. Transp. Sci. 55(3), 637–659 (2021) Lam, E., Desaulniers, G., Stuckey, P.J.: Branch-and-cut-and-price for the electric vehicle routing problem with time windows, piecewise-linear recharging and capacitated recharging stations. Comput. Oper. Res. 145 (2022). https://doi.org/10.1016/j.cor.2022.105870 Li, Q., Wang, M., Xiangli, L.: Do government subsidies promote new-energy firms’ innovation? Evidence from dynamic and threshold models. J. Clean. Prod. 286 (2021). https://doi.org/10. 1016/j.jclepro.2020.124992 Malik, M., Dincer, I., Rosen, M.A.: Review on use of phase change materials in battery thermal management for electric and hybrid electric vehicles. Int. J. Energy Res. 40(8), 1011–1031 (2016) McLaren, J., Miller, J., O’Shaughnessy, E., Wood, E., Shapiro, E.: CO2 emissions associated with electric vehicle charging: the impact of electricity generation mix, charging infrastructure availability and vehicle type. Electr. J. 29(5), 72–88 (2016). https://doi.org/10.1016/j.tej.2016. 06.005

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Analysing Impacts of Landfill Charge on Recycling Rate Based on a System Dynamics System Model Mingxue Ma, Vivian W. Y. Tam(B) , Khoa N. Le, and Robert Osei-Kyei School of Engineering, Design and Built Environment, Western Sydney University, Penrith, NSW 2751, Australia [email protected]

Abstract. In China, construction and demolition (C&D) waste is one of the largest waste streams. Majority of the waste is improperly managed and disposed in dumping sites. Therefore, there is a pressing need to properly manage C&D waste for a sustainable future. Legislative measures could be adopted to stimulate C&D waste recycling. It is found that increase in landfill charge could decrease reliance on landfill. This study develops a system dynamics model to evaluate impacts of landfill charge and its combination policy with development of recycling centres on recycling rate of C&D waste. The model is developed through Vensim PLE software, and urban area of Chongqing, China is selected as a case study. Findings indicate that landfill charges of 10 yuan/ton (or US $1.47/ton) and 30 yuan/ton (or US $4.42/ton) have limited impacts on recycling rate. Landfill charge of 50 yuan/ton (or US $7.36/ton) can make some differences. Impacts of combination policies are obvious and recycling rate in this scenario is higher than that in base scenario. Findings of this paper could help improve the efficiency of C&D waste management and adopt market for recycled aggregate. Keywords: C&D waste · waste management · recycling

1 Introduction Construction industry has become the pillar industry of the world economy [1]. Continuous construction, renovation and demolition activities have resulted in a large quantity of construction and demolition (C&D) waste which needs to be managed [2]. In China, C&D waste is one of the largest waste streams. Majority of the waste is improperly managed and disposed in dumping sites [3]. C&D waste brings negative implications on society and environment, including land deterioration [4]. Therefore, there is a pressing need to properly manage C&D waste for a sustainable future [5]. Much of the C&D waste has recycling potentials and could be produced into recycled aggregate [6]. Increasing recycling rate could not only increase the economic value of C&D waste, but also reduce dependence on landfills [4]. In recent years, it has become urgent for virgin aggregate to be replaced with recycled aggregate, because acquiring virgin aggregate involves considerable amount of © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 1758–1766, 2023. https://doi.org/10.1007/978-981-99-3626-7_137

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energy and carbon dioxide emissions [7]. Recycled aggregate could be applied in various engineering works, such as backfills, road pavement and production of concrete [8]. Improving recycling rate of C&D waste could increase availability of recycled aggregate in supply. Legislative measures could be adopted to stimulate C&D waste recycling. Specifically, it is found that increase in landfill charge could decrease reliance on landfill [9]. A great collection of previous studies focused on C&D waste recycling. Wu, Zuo [5] summarized these studies into two categories: (1) identification of major topics and future trends; and (2) analysis of specific aspects, including quantification of C&D waste. Recently, academic attention was paid to the properties and applications of recycled aggregate as a substitution of virgin aggregate [10–14]. Few studies assessed C&D waste management from the economic perspective [15, 16]. This study develops a system dynamics model to evaluate impacts of landfill charge and its combination policy with development of recycling centres on recycling rate of C&D waste. Findings of this paper could help improve the efficiency of C&D waste management and adopt market for recycled aggregate.

2 Research Method System dynamics model was founded in 1950, which was originally used to solve managerial problems. It is used to study complex feedback systems, investigate relationship between variables and simulate dynamic behaviours of systems [17]. System dynamics model was widely used in waste management studies [18]. This article develops a system dynamics model to analyse recycling of C&D waste and the supply of recycled aggregate, using Vensim PLE software. 2.1 Model Description Three subsystems are contained in this model: (1) waste production, (2) waste treatments, and (3) supply and demand of recycled aggregate. In waste production subsystem, amount of C&D waste is assumed to increase with population grows. According to Lin, Xie [19], one person could generate 0.6 tons of C&D waste annually. Recycling and landfilling are considered as the main following treatments of C&D waste. Since waste producers are driven by profits, cost minimisation opportunities could dominate their behaviours [20]. Recycling and landfill cost are compared in this subsystem. If recycling cost is lower than landfill cost, recycling would be the preferred method to handle the C&D waste. Specifically, transportation costs of C&D waste to landfill site or recycling centres are considered. Two variables, ‘transportation distance to recycling plants’ and ‘transportation distance to landfill’ are assumed to be inside a RANDOM NORMAL function, which vary within a range. In supply and demand of recycled aggregate subsystem, concept of and cross elasticity of demand is applied. Cross elasticity of demand measures the change in quantity demand of one good after the change in the price of its substitution. It could describe change in quantity demand of recycled aggregate when the price of virgin aggregate varies. Cross elasticity of demand formula is defined as

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following: Ec =

QA QA PB PB

=

QA PB · PB QA

where QA is the quantity of product A; PB is price of product B; Q is the change in the quantity of product A; and P is the change in the price of product B. 2.2 Casual Loop Diagram and Stock Flow Diagram Figure 1 presents casual loop diagram and Fig. 2 presents stock-flow diagram of this study. Casual loop diagram and stock-flow diagram are two different versions of same model [16]. While casual loop diagram is described in words and arrows, a stock-flow diagram is described in equations and computer code [16].

demand of recycled aggregate

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+

+

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+ amount of waste landfilled

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Fig. 1. Casual loop diagram change in price of virgin aggregate

reuse percentage

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increment of population

population

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waste production rate unit check

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landfill cost

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inflation rate

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increased price

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minimal cost transportation distance to landfill

cross-price elasticity of recycled aggregate demand

demand of recycled aggregate

landfill percentage



previous year demand recycled aggregate

ratio of concrete waste to recycled aggregate

+ unit transportation cost

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Fig. 2. Stock-flow diagram

transportation distance to recycling plants

Policy 2: development of recycling centers

cross price elasticity Ec

price of virgin aggregate

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2.3 Data Collection This article selects Chongqing, China as a case study. The urban area of this city has a population of 3.4 million in 2021 and comprises of nine administrative regions. However, Chongqing heavily depends on landfilling, and C&D waste is poorly handled [21]. Several sources of data are used in this study: interviews with two experts and two managers from recycling centres, previous literatures, municipal government documents and Chongqing statistical yearbook (Yearbook is an annual statistical publication which covers comprehensive data related to society and economy). 2.4 Model Testing The model is simulated for a period of 15 years from 2015 to 2030, the time step is 1 year. Table 1 compares simulated population and their historical values. According to Table 1, simulated errors are within 10%, which presents the effectiveness of the model [22]. Table 1. Simulated errors of population (ten thousand persons) Year GDP

2015

2016

2017

2018

2019

2020

Simulated values

912.36

950.679

982.052

998.746

1019.72

1036.04

Historical values

912.36

950.76

982.46

999.47

1019.96

1036.26

Error (%)

0%

0.009%

0.04%

0.07%

0.02%

0.02%

3 Simulation Results 3.1 Scenario Design Two scenarios are developed to evaluate the impacts of policies on the recycling of C&D waste. These two scenarios are illustrated in Table 2. Table 2. Two scenarios developed in this study Scenarios

Description

Base scenario

This scenario presents the results when no policies are introduced

Scenario 1

This scenario assesses impacts of landfill charge

Scenario 2

This scenario assesses impacts of combination policies (landfill charge and development of recycling centres

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3.2 Recycling Percentage of C&D Waste Recycling percentage is assumed to be 0.15 in 2015. Three charging schemes (from low to high) are investigated in this study. The landfill charge was 12.5 yuan/ton (or US $1.71/ton) in March 2022 and was rounded to 10 yuan/ton (or US $1.47/ton) in this study. While high landfill charge could minimize dependence on landfilling, maximum charge should not exceed 250% and 400% of original landfill and public fill charge respectively [23]. Therefore, the three charging schemes are 10 yuan/ton, 30 yuan/ton and 50 yuan/ton (or US $1.47/ton, $4.42/ton and $7.36/ton separately). Figure 3 presents the impacts of two scenarios on recycling percentage of C&D waste. Specifically, “Dmnl” in Fig. 3 standards for dimensionless, which means the variable “recycling percentage” has no unit. In base scenario, recycling percentage is deceased to 0.1 in 2016 and remains 0.1 in following years. When landfill charge equals to 10 yuan/ton (or US $1.47/ton), the distribution of recycling percentage exactly overlaps with that in base scenario ( Fig. 3a). Landfill charge of 30 yuan/ton (or US $4.42/ton) can make some differences. Compared to base scenario, there is no change in recycling percentage during the period from 2015 to 2028. In 2029, the recycling percentage is expected to reach 0.15, but it might fall back to 0.1 in 2030. When landfill charge equals to 50 yuan/ton (or US $7.36/ton), recycling percentage is 0.15 in 2020, 2021 and 2029, while the number is 0.1 in other years. In scenario 2 (Fig. 3b), landfill charge is combined with the development of recycling plants. Impacts of combination policies are obvious and recycling rate in this scenario is higher than that in base scenario. When landfill charge equals to 10 yuan/ton (or US $1.47/ton), recycling percentage reaches 0.2 in 2017, while it is 0.1 in 2023. In other years, the recycling percentage remains at 0.15. Distributions of recycling percentage are observed to be same, when landfill charge equals to 30 yuan/ton (or US $4.42/ton) or 50 yuan/ton (or US $7.36/ton). The recycling percentage is constant at 0.2 during the period from 2017 to 2030.

(a) Scenario 1

(b) Scenario 2

Fig. 3. Impacts of policies on recycling rate

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4 Discussion In this study, change in recycling percentage of C&D waste is determined by two factors: gap in supply and demand of recycled aggregate and recycling cost. When demand is greater than supply, prices of recycled aggregate tend to rise, which could attract more recyclers enter the industry. In addition, when landfill cost exceeds recycling cost, recycling might become a preferred method to handle the C&D waste. Table 3 presents comparison results of landfill and recycling costs under two scenarios and numbers in red are situations when minimal costs equal to recycling costs. In base scenario, landfill costs are lower than recycling costs. Impacts of landfill charges of 10 or 30 yuan/ton (US $1.47 or $4.42/ton) in scenario 1 on waste holders’ behaviours could be negligible. When landfill charge is increased to 50 yuan/ton (or US $7.36/ton), landfill costs exceed recycling costs in 2019, 2020 and 2028. In the remaining years, landfill is still a common treatment to deal with the waste. Impacts of combination policies are significant (scenario 3). Combination policies of development of recycling plants with 30 or 50 yuan/ton (or US $1.47 or $7.36/ton) landfill charge could simultaneously increase landfill costs and decrease recycling costs, and therefore make landfill cost be higher than recycling cost and stimulate recycling. These findings are consistent with Fig. 3b. Combination policies of 10 yuan/ton (or US $1.47/ton) landfill charge could decrease reliance on landfilling. Transportation cost plays an important role in recycling and landfill cost. Based on the market investigation, unit cost for recycling is between 10 yuan–12.5 yuan/ton (or US $1.47-$1.84/ton). However, transportation distance to recycling centres could be very long. Currently, transportation distance between construction site with recycling centres ranges from 27.7 to 60 km, which would result in high transportation cost. Average transportation distance to landfill site is around 20.3 km. Development of recycling plants could reduce transportation distance of C&D waste. Figure 4 presents change in transportation distance. In 2021, Chongqing Municipal People’s Government released a management plan on C&D waste in urban area, aiming to achieve recycling rate of 85% in 2035 [24]. Ten additional recycling centres would be established before 2035 and each administrative region has at least one recycling centre. After development of recycling centres, transportation distances range from 5 to 43.8 km, with average distance of 16.46 km. This study has some limitations. Firstly, this study only considered the impacts of landfill charge (i.e., landfill charge and its combination policy with development of recycling centres). The impacts of other policies and their combinations could be investigated in future studies. Secondly, high landfill charge does not indicate high recycling rate. In scenario 2, when landfill charge equals to 30 yuan/ton (or US $4.42/ton) or 50 yuan/ton (or US $7.36/ton), their impacts on recycling percentage and minimal costs are observed to be same. Future research efforts could be spent on optimized landfill charge. Thirdly, unavailability of official data might cause unprecise results. For instance, data related to annual production amount of C&D waste, recycling percentage and demand of recycled aggregate are unable to be collected from government website. Amount of C&D waste is calculated by waste production ratio multiplying population. The calculated amount of C&D waste could be significantly different from the real amount. Recycling and landfill percentages were collected from interviews with managers of recycling centres. These

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Year

Base scenario

Scenario 1 Charge = 10

Charge = 30

Scenario 2

2015

44.1199

54.1199

74.1199

2016

53.0612

63.0612

83.0612

2017

37.2174

47.2174

67.2174

87.2174

2018

45.6874

55.6874

75.6874

2019

58.0369

68.0369

88.0369

2020

63.1348

73.1348

93.1348

2021

31.1428

41.1428

61.1428

2022

21.9252

31.9252

51.9252

2023

55.6413

65.6413

85.6413

2024

30.2807

40.2807

60.2807

2025

41.7416

51.7416

71.7416

2026

60.9178

70.9178

90.9178

110.918

2027

58.1847

68.1847

88.1847

108.185

2028

56.8206

66.8206

80.5464

2029

40.2775

50.2775

70.2775

2030

52.9051

62.9051

82.9051

Charge = 50

Charge = 10

Charge = 30, 50

51.2969

51.2969

59.0935

59.0935

47.2174

59.1496

95.6874

37.3073

37.3073

90.0167

27.3674

27.3674

44.2041

44.2041

81.1428

24.0768

24.0768

71.9252

31.9252

37.1371

49.7871

49.7871

80.2807

32.9496

32.9496

91.7416

41.5749

41.5749

56.3333

56.3333

56.4007

56.4007

80.5464

33.9838

33.9838

90.2775

47.9738

47.9738

38.3707

38.3707

94.1199 103.061

110.613

105.641

102.905

Fig. 4. Transportation distance to recycling plants

numbers are round guess. Market equilibrium was assumed to be achieved in 2015. The system dynamics model could be further adjusted if more data is obtained.

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5 Conclusion A great collection of previous studies focused on C&D waste recycling. Recently, academic attention was paid to the properties and applications of recycled aggregate as a substitution of virgin aggregate. Few studies assessed C&D waste management from the economic perspective. This study developed a system dynamics model to evaluate impacts of landfill charge and its combination policy with development of recycling centres on recycling rate of C&D waste, using Vensim PLE software. Three subsystems were contained in this model: (1) waste production, (2) waste treatments, and (3) supply and demand of recycled aggregate. Concept of and cross elasticity of demand was applied. Urban area of Chongqing, China was selected as a case study. The model was simulated for a period of 15 years from 2015 to 2030, the time step was 1 year. Simulated errors were within 10%, which presents the effectiveness of the model. Two scenarios were developed to evaluate the impacts of policies on the recycling of C&D waste. Findings indicate that landfill charges of 10 yuan/ton (or US $1.47/ton) and 30 yuan/ton (or US $4.42/ton) have limited impacts on recycling rate. While landfill charge of 50 yuan/ton (or US $7.36/ton) could make landfill costs exceed recycling costs in 2019, 2020 and 2028, landfill is still a common treatment to deal with the waste in remaining years. Impacts of combination policies were obvious and recycling rate in this scenario was higher than that in base scenario. When landfill charge equals to 10 yuan/ton (or US $1.47/ton), recycling percentage reaches 0.2 in 2017, while it is 0.1 in 2023. In other years, the recycling percentage remains at 0.15. When landfill charge equals to 30 yuan/ton (or US $4.42/ton) or 50 yuan/ton (or US $7.36/ton). The recycling percentage is constant at 0.2 during the period from 2017 to 2030. Compared to single landfill policy, combining landfill charge and development of recycling centres is more efficient. The findings of this study could contribute to practice of C&D waste management and help government to formulate proper policies to increase recycling rate. Future studies could investigate the impacts of other policies and their combinations and the optimized landfill charge.

References 1. Ma, L., Zhang, L.: Evolutionary game analysis of construction waste recycling management in China. Resour. Conserv. Recycl. 161, 104863 (2020) 2. Bernardo, M., Gomes, M.C., de Brito, J.: Demolition waste generation for development of a regional management chain model. Waste Manage. (Elmsford) 49, 156–169 (2016) 3. Duan, H., Wang, J., Huang, Q.: Encouraging the environmentally sound management of C&D waste in China: an integrative review and research agenda. Renew. Sustain. Energy Rev. 43, 611–620 (2015) 4. Yuan, H., et al.: A dissipative structure theory-based investigation of a construction and demolition waste minimization system in China. J. Environ. Plan. Manage. 65(3), 514–535 (2022) 5. Wu, H., et al.: Construction and demolition waste research: a bibliometric analysis. Archit. Sci. Rev. 62(4), 354–365 (2019) 6. Wu, H., et al.: Status quo and future directions of construction and demolition waste research: a critical review. J. Clean. Prod. 240, 118163 (2019)

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7. Kisku, N., et al.: A critical review and assessment for usage of recycled aggregate as sustainable construction material. Constr. Build. Mater. 131, 721–740 (2017) 8. Tam, V.W.Y., Soomro, M., Evangelista, A.C.J.: A review of recycled aggregate in concrete applications (2000–2017). Constr. Build. Mater. 172, 272–292 (2018) 9. Toši´c, N., et al.: Multicriteria optimization of natural and recycled aggregate concrete for structural use. J. Clean. Prod. 87, 766–776 (2015) 10. Bai, G., et al.: An evaluation of the recycled aggregate characteristics and the recycled aggregate concrete mechanical properties. Constr. Build. Mater. 240, 117978 (2020) 11. Fonseca, N., de Brito, J., Evangelista, L.: The influence of curing conditions on the mechanical performance of concrete made with recycled concrete waste. Cement Concr. Compos. 33(6), 637–643 (2011) 12. Martínez-Lage, I., Vázquez-Burgo, P., Velay-Lizancos, M.: Sustainability evaluation of concretes with mixed recycled aggregate based on holistic approach: technical, economic and environmental analysis. Waste Manage. (Elmsford) 104, 9–19 (2020) 13. Abdel-Hay, A.S.: Properties of recycled concrete aggregate under different curing conditions. HBRC J. 13(3), 271–276 (2017) 14. Zhang, J., et al.: Recycled aggregates from construction and demolition wastes as alternative filling materials for highway subgrades in China. J. Clean. Prod. 255, 120223 (2020) 15. Au, L.S., Ahn, S., Kim, T.W.: System dynamic analysis of impacts of government charges on disposal of construction and demolition waste: a Hong Kong case study. Sustainability 10(4), 1077 (2018) 16. Yuan, H.P., et al.: A model for cost–benefit analysis of construction and demolition waste management throughout the waste chain. Resour. Conserv. Recycl. 55(6), 604–612 (2011) 17. Liu, J., Liu, Y., Wang, X.: An environmental assessment model of construction and demolition waste based on system dynamics: a case study in Guangzhou. Environ. Sci. Pollut. Res. 27(30), 37237–37259 (2019). https://doi.org/10.1007/s11356-019-07107-5 18. Marzouk, M., Azab, S.: Environmental and economic impact assessment of construction and demolition waste disposal using system dynamics. Resour. Conserv. Recycl. 82, 41–49 (2014) 19. Lin, Z., et al.: Towards a robust facility location model for construction and demolition waste transfer stations under uncertain environment: the case of Chongqing. Waste Manage. (Elmsford) 105, 73–83 (2020) 20. Zhao, W., Ren, H., Rotter, V.S.: A system dynamics model for evaluating the alternative of type in construction and demolition waste recycling center – the case of Chongqing, China. Resour. Conserv. Recycl. 55(11), 933–944 (2011) 21. Ma, M., et al.: Challenges in current construction and demolition waste recycling: a China study. Waste Manage. 118, 610–625 (2020) 22. Liu, J., Teng, Y., Wang, D., Gong, E.: System dynamic analysis of construction waste recycling industry chain in China. Environ. Sci. Pollut. Res. 27(30), 37260–37277 (2019). https://doi. org/10.1007/s11356-019-06739-x 23. Mak, T.M.W., et al.: A system dynamics approach to determine construction waste disposal charge in Hong Kong. J. Clean. Prod. 241, 118309 (2019) 24. Chongqing Municipal People’s Government, Management plan for construction and demolition waste in urban area of Chongqing (2021–2035), Chongqing Municipal People’s Government, Editor (2021)

Overcoming the Effect of Young Workers’ Rebellious Psychology on Unsafe Behavior in Construction Patrick X. W. Zou1,2(B) and Ruili Wang1,2(B) 1 School of Economics and Management, Chang’an University, Xi’an, Shaanxi, China

[email protected], [email protected] 2 Engineering Research Center of Highway Infrastructure Digitalization, Ministry of Education,

Chang’an University, Xi’an 710064, Shaanxi, China

Abstract. Workers unsafe behavior, particularly of those of young workers, is a main cause of accidents in the construction industry, partly because of their rebellious psychology. How young workers’ rebellious psychology affects unsafe behavior remains unknown. This paper explains the phenomenon and answers this question from the perspectives of equity theory and social psychology theory, and puts forward intervention measures according to the internal and external factors. The measures include understanding young workers’ needs, improve organizational fairness, providing safety training and learning; implementing technology-enabled psychological measures and creating a strong safety culture. Finally, the paper discusses future research directions which includes adapting new research methods for data collection and analysis by developing and applying neuro-technology enabled experimental design. Keywords: Unsafe behavior · Construction safety · Young workers · Rebellious psychology · Equity theory · Social psychology

1 Introduction and Research Aim Construction safety is about the safety of people’s lives and property, which is a sign of coordinated and healthy economic and social development. According to The Ministry of Housing and Urban-Rural Development of the People’s Republic of China [1], as of May 2021, there were 689 construction accidents and 794 deaths nationwide. Construction organizations’ safety performance level is generally low, employees’ safety awareness is weak. The governments have put forward policies to improve the situation, for example, “Party and government share the same safety responsibility, one post with two responsibilities, and make concerted efforts to improve safety”. At the enterprise level, it is also important to promote safe construction, actively strengthen self-management. It is not only the organization, but also the workers need to eliminate potential safety hazards from the source and implement safety responsibilities. There is research focused on unsafe behaviors of construction workers. Based on cognitive and social psychology theories and existing accident causation models, Fang, © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 1767–1782, 2023. https://doi.org/10.1007/978-981-99-3626-7_138

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Zhao and Zhang developed a cognitive model of construction workers unsafe behavior [3]. In the five-stage cognitive model, taking action, with obtaining information and selecting response are the two key stages. Obtaining information elaborates construction workers’ observations and processing of hazard information on sites by themselves, and selecting response adopts the theory of planned behavior to reflect various factors of influence when workers take unsafe behavior. Zhang and Fang analyzed the cognitive mechanism of unsafe behavior based on theory of planned behavior, pointed out from the psychological point of view that the failure of “selective response” was the main cause of unsafe behavior [4]. Ye, Duan and Wang [5] used interpretive structural modeling to analyze construction workers’ unsafe behavior from four aspects: individual, organization and management, production and social environment, and developed a three-level hierarchical structural model of the factors. It was found that workers were more likely to have psychological imbalance due to various factors. In addition, physical and emotional stress can also directly affect the unsafe behavior of construction workers [6, 7]. It is found that 13 factors can induce unsafe behaviors in the new generation of construction workers in China, which includes weak safety awareness and unsafe psychology [8]. Recent research by Xu and Zou has found that most construction accidents happened in the age group of 20–30 years old young workers [2]. Furthermore, in the recent interviews with the general manager and safety manager of a major construction company, it is also found that young workers are more likely to take risks and behave unsafely on construction sites, and this problems need to be solved urgently. These highlight young workers’ unsafe behavior is a major problem causing construction accidents. It is necessary to investigate why young workers are more likely to take risks and being injured. Generally speaking, unsafe behavior of young construction workers can’t be attributed to a single cause, it is the result of multiple psychological factors. When facing young construction workers, rebellious psychology may be a major cause for conscious unsafe behaviors. Currently, there is little research focused on this problem. This paper aims to theoretically analyze and explain the unsafe behavior of young construction workers due to rebellious psychology and propose suggestions to change the situation and improve safe behavior. In other words, the authors attempt to answer the following questions: What are the main causes of rebellious psychology among young construction workers that leads to unsafe behavior and how to solve this problem? The paper first classifies unsafe behavior, then identifies factors influencing or leading to unsafe behavior, and analyses these factors from intrinsic and extrinsic perspectives. The paper then explores the extend, the causes and the effects of young construction workers rebellious psychology and theoretically explain the situation using equity theory and social psychology theory. Based on these, the paper provides six suggestions for overcoming the effect of young workers’ rebellious psychology on unsafe behavior, and concluded by providing a summary and future research directions.

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2 Unsafe Behavior and Influencing Factors 2.1 Types of Unsafe Behavior Numerous researches have been carried out to investigate unsafe behaviors. Burdettb, Starkey and Chalton [9] pointed out that unsafe behavior can directly or indirectly lead to the occurrence of safety accidents, where direct unsafe behavior is unsafe behavior that directly leads to accidents and indirect unsafe behavior is unsafe behavior that indirectly leads to accidents [10]. Zhang and Fang [4] pointed out that unsafe behaviors are mainly divided into unsafe behaviors that directly lead to accidents and unsafe behaviors that indirectly lead to accidents. They further pointed out that the former mainly refers to construction workers’ behaviors that do not do a good job of preventing safety hazards and generating safety hazards, for example, not wearing helmets and safety belts when working, while not listening to site command is a behavior that generates safety hazards; the latter mainly includes not attending safety training, not actively communicating with managers about project safety, etc. Synthesizing the relevant literature, unsafe behavior refers to intentionally or unintentionally, directly or indirectly behavior that may lead to safety accidents, and unsafe behavior can be classified into four categories, namely, habit-deviant unsafe behaviors, perception-deviant unsafe behaviors, procedural-deviant unsafe behaviors, and skill-deviant unsafe behaviors [11] as summarized in Fig. 1.

Fig. 1. Types of unsafe behavior

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2.2 Influencing Factors Different research has looked into factors that may lead to unsafe behavior. For example, Dodool and Al-Samarraie [12] undertook a review on factors leading to unsafe behavior, with data drawn from seven sectors across construction, healthcare, informal sector enterprises, manufacturing, mining, energy, agriculture, and multidimensional context, and found the following are key common factors: 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19.

Lack of adequate knowledge on OSH (human factor) Culture of risk acceptance (human factor) Risky behaviors of co-workers (human factor) Over confidence (human factor) Intentional violation of safety rules (human factor) Work pressure (human factor) Stress (human factor) Poor health conditions (human factor) Sleep disorders (human factor) Sedentary lifestyle/eating habits (human factor) Non-use of PPE (human factor) Negligence (human factor) Improper safety equipment/discomfort (equipment factor) Incongruent job roles (management factors) Job insecurity (management factors) Low management commitment to safety or poor supervision (management factors) Violence/aggression from clients (management factors) Long working hours (management factors) Poor work environment (environment factor)

Their study also summarized other research findings of factors leading to unsafe behavior, including: lack of adequate knowledge on safety and non-compliance with the established work procedures as the major causes of injuries among workers; the nature of construction work (physical and dangerous) influence workers’ risk perception and their safety behavior, workers viewed risk as a necessary part of their job, influenced their perspective about safety procedures, poor and unsafe work environment; pressure to meet deadlines. Perceived ‘tough guy’ attitude associated with construction work, stress and pressure. These factors are also included in the list above. In the above list, 11 out of 19 are human factors, four are management factors, one environment factor and one equipment factor. Their research concluded that there was an increase in empirical research studies on unsafe workers behaviors from 2008 to date (2019). Yet, studies are still lacking on exposing context specific problems and solutions. A summary of factors leading to unsafe behavior is as shown in Fig. 2. 2.3 Factor Analysis This paper discusses the factors influencing unsafe behavior into two types -- extrinsic and intrinsic factors.

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Fig. 2. Factors that may lead to unsafe behavior

2.3.1 Intrinsic Factors The intrinsic factors refer to the individual factors of construction workers, including unstable psychological state and insecure psychology. (1) Unstable psychological state Unstable psychological state may lead to development of unsafe worker behaviors, such as fatigue, stress, bad mood. Negative cognitive state of workers, such as mental lethargy and distraction, may lead to slower responses to warnings and unsafe behaviors during high-volume construction activities [13]. Passive emotional state and high level of mental stress may reduce productivity [7]. There are many factors that cause fatigue in workers, all of them reduce their ability to think. Qi, Zhou, and Ye., et al. noted that stress can trigger unsafe behavior and contribute to construction accidents by negatively affecting cognitive factors [6]. There are many causes of stress, such as sleep problems, emotional stress, and so on. These can lead to a decrease in workers’ concentration and awareness of safety hazards, as well as the possibility of ignoring safety knowledge in a stressful state, lead to safety accidents. Workers are aware of the importance of safety, but under the pressure of production or their own stress, there are still unsafe behaviors. (2) Insecure psychology Insecure psychology refers to the psychological state that has a negative impact on people’s behavior and adverse consequences for people’s safe production activities. Research has demonstrated that insecure psychology, such as paralysis, fluke,

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shortcut, rebellion, herding and bravado, has a significant impact on unsafe behavior [14]. Similarly, herding, fluke, shortcut, boredom, paralysis, rebellion, and risk-taking bravado significant affect construction workers’ unsafe behaviors [15]. 2.3.2 Extrinsic Factors The extrinsic factors are the factors that can influence unsafe behavior of workers, including environment and organization. (1) Physical and social environment The environment includes the physical environment (e.g., weather, temperature, etc.) and the social environment (e.g., worker influence, organizational climate, etc.). The physical environment causes unsafe behaviors by influencing the workers’ operating environment and operation methods; the social environment includes national governance and supervision, social norm and supervision and family persuasion, etc., and studies have shown that all these factors have an impact on workers’ unsafe behaviors [5]. (2) Organizational safety climate Safety climate refers to the perceived relative importance of safety by organizational members [16]. Social exchange theory suggests that when there is a positive safety climate in an organization, employees feel obligated to give back to the organization and thus exhibit safe construction behaviors [17], in contrast, if construction workers are in a poor safety climate for a long time, it will cause a series of unsafe behaviors. Safety climate has a subjective normative influence on employees through their perceptions, feelings, and behaviors. In order to avoid unsafe behaviors, organizations should aim to build a positive safety climate. (3) Organizational safety training Organizational safety training has important impact on workers safety attitude, knowledge and behavior. Safety training can influence different aspects of the construction site safety and improve safety in a more profound way than passive and supportive actions [18], and effective safety training can greatly reduce the occurrence of unsafe behaviors and improve the skill level of workers. How to conduct effective safety training is something that managers should think about, whether safety training is just a formality or a procedure, whether the responsibility for training is cascaded, whether safety investment is made as a corporate planning, and whether increasing safety investment is an important guarantee to improve safety management of the company, which includes investing in safety training.

3 Young Workers’ Rebellious Psychology 3.1 Concept of Rebellious Psychology Rebellious psychology is referred to as a psychological state in which people take opposite attitudes and words and deeds to each other’s demands in order to maintain their selfesteem. It is a universal psychological phenomenon, especially among young workers, and its main feature is blind behavior tendency, that is, the things around us are arbitrary

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and lack of thinking process, which may lead to all kinds of safety accidents [19]. It is far from enough to attribute rebellious behavior to cognitive problems, because the cognitive process is a psychological process of human beings, not an isolated existence, but closely related to emotions and consciousness [20]. Further, rebellious psychology is different from “reverse thinking”. Reverse thinking reflects the dialectical understanding of both sides of the contradiction, which is a positive and negative thinking or pioneering thinking, while the way of thinking of rebellious psychology is a simple turn-back phenomenon of one-way and linear thinking, and it is a negative and resistant thinking movement [21]. 3.2 Causes of Rebellious Psychology Rebellious psychology is a complicated psychological state, which is affected by various factors, such as organizational environment, social environment, personal characteristics, etc. (1) Psychological gap Psychological gap means that employees are making “horizontal comparison, that is, self-comparison with others” or “vertical comparison between themselves and themselves” to judge the fairness of their remuneration, which will affect their efforts in work. For example, if the current situation is compared with the previous situation, if the ratio of current remuneration to current investment is less than that of past remuneration and investment, then employees will feel unfair and their enthusiasm for work will decline. In the “horizontal” comparison, if employees feel unfair, they will even automatically reduce their efforts to achieve some kind of psychological balance. Once the reality deviates too much from the idealization before employment, workers will have a psychological gap, which will lead to a rebellious attitude towards leaders and even the company. (2) Work environment The environmental psychology theory explains the influence of the environment on individual behavior and psychology. Studies have shown that front-line management, as the most frequent contact with construction workers, is responsible for the implementation of almost all organizational policies as well as authority [18]. In construction, there exist front-line managers who do not comply with the rules and regulations, but used only to restrain the workers, in this situation, the workers do not feel respected and are prone to rebelliousness. There also exists the phenomenon that workers’ rights and interests are not protected, i.e. for some reasons the workers’ legal rights and legitimate interests are not protected, their practical problems in life are not solved, or unreasonable rewards, or punishments and rules and regulations are imposed beyond any reason. As an emerging group, young construction workers are psychologically and mentally active, and it is difficult for them to maintain a permanent balance in the face of heavy workload and various sources of danger on construction sites. Studies have shown that young workers with less than 1 year of work experience are more prone to accidents [22].

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3.3 Consequences of Rebellious Psychology Unstable or insecure occupational psychology and working environment will have a significant impact on the unsafe behavior of construction workers [23]. For example, paranoid psychology will have a direct impact on the unsafe behaviors of construction workers, and improper safety motivation will also have a significant impact on unsafe behaviors [24]. Rebellious psychology, as one kind of unsafe psychology of construction workers, will have an impact on the unsafe behavior of construction workers. Individual behavior in an organization will have an impact on the whole organization. Because of the existence of informal organizations, young workers are more likely to communicate with each other and strengthen each other, thus evolving into a collective rebellious mentality, intensifying contradictions and spreading liberal tendencies, such as not paying attention to their own work and not thinking about potential safety hazards. The unsafe behavior manifested by the rebellious psychology is diverse, for example: (1) working at height without wearing helmets, not standardized wearing safety-belts, etc.; (2) technical personnel work without license, (3) unauthorized access to restrict construction sites areas, (4) non-implementation of mandatory standards. Lack of adequate knowledge about safety and health, violation of safety rules, work stress and non-use of protective equipment are the main factors of unsafe behavior [12], and rebelliousness plays a role in it.

4 Theoretical Explanation of Rebellious Psychology 4.1 From Equity Theory Perspective Equity theory was put forward by American psychologist Adams in 1965, which holds the view that employees’ satisfaction with wages can affect their work enthusiasm, and satisfaction depends on a comparative process. They are concerned with their absolute remunerations as well as relative remuneration. They need to maintain a sense of fairness in distribution. Some enterprises take all kinds of measures to make employees maintain a subjective sense of fairness, for example, giving bonuses privately, etc., so as to arouse the enthusiasm of employees. When employees feel unfair, they will probably take one or more of the six ways to resist: Change their input; Change their output; Distort selfcognition; Distort others’ cognition; Select other reference objects; Resign and leave [25]. Regarding young construction workers, if hard work is not rewarded with fair treatment, it is easy to produce rebellious psychology, and the price paid is enormous for all. Therefore, managers should guide them to form a correct sense of fairness, and further curb unsafe behaviors. 4.2 From Social Psychology Perspective According to the American social psychologist G.W. Allport (1954), social psychology tries to understand and explain how individuals’ thoughts, feelings, and behaviors are influenced by the real, imagined, and implied presence of others. This kind of effect is

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caused by the micro-social environment (e.g., the interpersonal interactions of construction workers in this paper, the media to which construction workers are exposed, the management leadership to which construction workers are exposed, etc.) in which individuals and groups live., i.e., the range of interactions to which individuals are directly exposed. Similarly, the macro environment (e.g., national system, ideology, cultural background, etc.) also has an impact on individual behavior, i.e., the range of interactions to which individuals are indirectly exposed. Individual or group behavior is always influenced by the micro and macro environment, and in this way develops into their behavioral responses. 4.2.1 Theory of Social Cognition Psychology Social cognition is the way people select, understand, store, and recall information about the social behavior of other people (including themselves), i.e., social cognition studies how people use social information to make judgments and decisions [26]. Since people are limited in their ability to process the information they receive, social cognition views people as cognitive miser, so people take shortcuts to process information and do not want to spend a lot of time and mental effort to think about it. Construction workers make decisions to take unsafe behaviors under the role of rebelliousness, shortcut mentality and energy-saving mentality [15]. Gilbert (1989) divided thinking into automatic and intentional. Cognitive processes also include the involvement of motivation and emotion. Goals, desires, and emotions influence the way people understand social situations [26], and poor motivation and unsafe psychology (e.g., rebelliousness) play a role in construction workers’ decisions about unsafe behavior. Young construction workers with rebellious psychology may make wrong decisions when facing safety hazards [14]. People produced social behaviors based on social cognition, and environment and genetics are factors that influence social behaviors, and both work together to determine human psychology and behavior [27]. Biological theory suggests that any behavior has its genetic basis, which explains the influence of emotions on behavior, and emotions are largely expressed in physical behaviors, such as the safety accidents caused by construction workers who take unsafe behaviors under the effect of bad emotions [8]. The cognitive state of construction workers can significantly affect their physical and mental health, decision-making processes and behaviors, which can further affect their job performance, such as occupational safety and health and productivity [28]. 4.2.2 Attitude Formation and Development Attitude is the central issue in social psychology. Attitude is an individual’s solid psychological disposition toward a particular thing, idea or other person, consisting of three components: cognitive, affective and behavioral tendencies [27]. The relationship between the three components is shown in the Fig. 2. The three components of attitudes are interdependent, and having a behavioral tendency does not necessarily produce actual behavior, as behavior is determined by more than just attitudes, but is also related to the environment in which the individual lives, and the environment referred to here includes

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the social environment (e.g., organizational environment, work group environment, etc.) and physical environment (weather, temperature, etc.). The poor environment can cause working fatigue among construction workers, which has an impact on their physiology and psychology, thus causing unsafe behavioral decisions. For example, the pressure caused by the social environment can prompt young workers to align themselves with group norms, which can cause the spread of unsafe behaviors and eventually develop into unsafe behavioral habits [29].

Cognitive components: Understanding Perception Information

Emotional

Behavioral

component:

propensity

Judgement

component:

Emotion

Intention

Pressure

Preference

......

Tendency

Situation ......

Externalizing behavior

......

Fig. 3. Components and relationships of attitudes [27]

Attitude formation is predicated on the presence of motivation and the person’s activity situation or external environment. The formation and development of attitudes involves interpersonal influences and personality influence: (1) interpersonal influences, including peers, work groups and social groups, social institutions, etc. and the influence of peers and work groups is great, people often compare their attitudes and beliefs with others, and when they differ with peers, they may be subject to intangible pressures such as blows and ridicule. Young construction workers, the subjects of this paper, will be influenced by the common action standards of older groups in the face of intangible pressures such as work experience and work informal groups, once a state of disharmony is created, psychological pressure will arise, and if in this state for a long time, it would likely breed rebelliousness. In the construction context, workmate influence has a direct impact on workers’ behavior [18]. (2) Personality influence, personality often affects the individual’s attitude towards things. The personality of young construction workers is generally more flamboyant and carefree than older workers, in the face of construction safety hazards is rational (insist on safety-first, don’t do unsure behavior) dominant, or irrational (bravado, self-confidence, rebellious psychology) prevails, bringing different consequences. 4.2.3 From Attitude to Action The construction environment could be complex, dangerous and characterized by heavy workload, this is one factor that makes it difficult for young construction workers to

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maintain a psychological balance. Whether there is rebelliousness, blind self-confidence, bravado, whether there is bad emotional pressure and self-restraint ability and finally produce behavioral tendency to decide what behavior to choose in the face of safety hazards (e.g., whether to wear safety PPEs strictly as required, whether to act according to automatic cognitive thinking), the organization environment (such as level of compliance with regulations) and physical environment play an important role in choosing, as shown in the Fig. 3:

Fig. 4. Decision-making process of construction workers based on three components of attitude [26]

5 Suggestions for Dealing with Rebellious Psychology to Improve Safety Performance Based on the above discussion, for the young construction workers who have the above problems, it is necessary to implement not only the traditional management methods such as rewards and punishments, performance appraisal, criticism and praise, but also new measures, as shown in Fig. 4 (Fig. 5). 5.1 Understand Young Workers’ Needs It is necessary to understand the urgency of certain needs of young workers to effectively motivate them, such as the needs of daily life, needs for sense of belongings, needs of opportunities of self-esteem and improve their job satisfaction and quality of life. It is also necessary to understand that young workers rebellious psychology is a kind of temporary instinct reaction to external environment. It is also necessary to understand that safety is a low-level need and the most basic need. Only based on understanding of the needs and expectations, can trust be built and consent achieved. Maslow’s hierarchy of needs tells us that everyone is an animal in need, and his/her needs depend on what he/she hasn’t got yet, such as respect, self-realization and other advanced needs. At the same time, what people need at different stages is also different.

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Understand young workers’ needs Enhance young workers’ safety training and learning

Improve organizaonal fairness

Foster strong safety culture Intervene with technologyenabled measures

Implement psychological intervenon measures

Fig. 5. Suggestions for dealing with rebellious psychology to combat unsafe behavior

5.2 Improve Organizational Fairness Organizational distribution fairness, procedural fairness and interaction fairness have a significant positive impact on employee satisfaction. Organizations should establish a scientific and fair salary performance incentive mechanism, optimize human resource allocation, achieve personnel and job matching, build a fair organizational culture and working atmosphere, and improve the sense of fairness in employee interaction [31]. Managers can also improve workers’ job satisfaction to some extent to promote their sense of identity and satisfaction, and reduce unsafe behaviors caused by insecurity. In addition, frontline managers are the leaders who have the most frequent contact with construction workers, and organizational fairness plays a positive mediating role between managers and workers’ behavior [30]. Managers gain organizational recognition by improving safety performance, which in turn promotes positive emotions among workers and reduces unsafe behaviors. 5.3 Enhance Young Workers’ Safety Training and Learning It is necessary to carrying out organizational training frequently, find ways suitable for young workers to learn safety knowledge and skills and develop right attitude towards safety. By doing so, proficiency can be improved, safe construction capacity can be improved, and unsafe behavior can be reduced. It should be noted that when training and learning is initiated by learners, it is the most effective and can achieve the desired

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results. For example, workers can exchange experiences with others. The role of the organization is to provide a supportive learning environment for learners with the goal of not only improving productivity and performance, but also work safety. The learning curve showed that with the increase of task experience, the time for workers to perform tasks and the number of mistakes decrease at a decreasing rate [32]. Safety training and learning is also an important part of attitude formation and development. As discussed in the previous section, attitude is a precursor of action and behavior, and attitude has three components – cognitive component, emotional component and behavior propensity component as shown Fig. 3 – all need to be fully considered in design and delivery of safety training. Furthermore, the design and implementation of safety training sessions also need to consider the human factors identified in Fig. 2. 5.4 Implement Psychological Intervention Measures It is necessary to intervene from the psychological perspective. Organizations should treat rebellious psychology correctly, make use of the influence of informal organizations, convey a healthy organizational culture, promote its development in the right direction, do a good job in ideological and political work, take due responsibility, and not suppress it excessively. In addition, the working environment should be optimized, such as reasonable shift arrangements, regular change of safety signs, due respect and rights protection for young construction workers, and regular safety talks and meetings. In the intervention process, it is also necessary to understand the different types of unsafe behavior and their characteristics as shown in Fig. 1 and develop and promote measure to prevent them from happening. These include habit, perception, skill and procedure related unsafe behavior. 5.5 Intervene with Technology-Enabled Measures Human behavior is externalized by the psychology, and physiological changes can reflect the psychological state. The Research Report on Artificial Intelligence-based Intelligent Highway Application Technology [33] proposes to introduce intelligent wearable devices into the work site to achieve dynamic real-time monitoring and early warning of workers. Such technology-based measures can strengthen workers’ sense of safety and understand employees’ feelings, and help them better understand their own bodies health situation, such as heart rate, stress level and other health information. The technology-enabled measures can also integrate intelligent wearable devices with GPS technology to track workers’ positions and implement rescue on time when they are in danger or accidents. By combining modern information technology, the bad physiology or psychology of the workers can be monitored to reduce unsafe behaviors. 5.6 Forster a Strong Safety Culture and Environment The importance of fostering a strong safety culture cannot be emphasized enough. Only by having positive strong culture, environment and safety policies, attitude and behavior can be developed and maintained. The organizational leadership should develop

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“five-in-places” and “five-implementations”. The five-in-places are: safety responsibility, safety investment, safety training, safety management and emergency rescue. The five-implementations are to implement “the same responsibility of the party and government”, “one post and two responsibilities” for safe construction, safe construction organization, safe management enforcement and safe construction reporting system. Abided by the national Work Safety Law of China, work safety should be people-oriented, adhere to safe development, and establish and improve safety policy and mechanism. For accident investigation, adhere to the principle of “four points”, that is, don’t stop until the root causes of the accident is found out, don’t stop until the person responsible for the accident has received the punishment, don’t stop until the person responsible for the accident and the masses have been educated, don’t stop until the accident has specified practical corrective measures. By implementing the above discussed “five-in-places”, “five-implementations” and “four points”, plus the other suggestions discussed in the Sects. 5.1 to 5.5, a strong and healthy safety culture can be fostered which will help improve young workers positive attitude, knowledge and behavior towards construction safety.

6 Summary and Future Research 6.1 Summary The safety accidents faced by the construction industry are major problems. This paper discussed and explained the causal factors of unsafe behavior in construction sites from theoretical points of view, including equity theory and social psychology theories, at social, environmental, organizational, group and personal levels. This paper specifically discussed how rebellious psychology may lead to unsafe behavior, analyzed the causes and consequences of rebellious psychology in the context of young construction workers, and provided suggestions to solve these problems, from the aspects of young construction workers, organizations and technology. These include understanding young workers’ needs, improve organizational fairness, improving young workers learning and providing safety training, implementing psychological intervention measures, intervening with technology-enabled measures and fostering strong safety culture. It should be noted that this paper mainly explains the research problems together with solutions from a theoretical point of view. The proposed solutions and the specific practical implications are to be analyzed in future empirical studies, discussed in the next section. 6.2 Future Research Based on the above discussion and the unfolded causes and consequences of the rebellious psychology of young workers on unsafe behavior, in the future, a survey could be conducted with young construction workers to further study the extend of the problem. In addition, future research could use information technologies such as wearable Electroencephalogram (EEG) technology to detect the young construction workers’ psychological state, fatigue degree, emotional stress; Eye tracking technology can also be used to monitor young construction workers’ ability to identify potential safety hazards when they

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are in a rebellious state. Although rebellious state belongs to a relatively obscure state of mind, it can be monitored according to the physiological indicators of people reflected by it, such as fatigue, emotion, stress, etc., which can be monitored by technology and play a preventive role. These technologies are expected to be applied to construction sites, reduce the occurrence of unsafe behaviors, and improve the performance of construction safety.

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Socio-economic Drivers of Energy Consumption: Evidence from Three Urban Agglomerations in the Yangtze River Economic Belt Mengxue Li, Yu Zhang(B) , Xi Cai, Liudan Jiao, and Xiaosen Huo School of Economics and Management, Chongqing Jiaotong University, Chongqing, China [email protected]

Abstract. This study explores the Socio-economic drivers of energy consumption in three Urban Agglomerations and cities in the Yangtze River Economic Belt during the 2011–2020 period. Using the logarithmic mean Divisia index (LMDI) method decompose the change in energy consumption into five factors: energy intensity effect (ΔCEI ), per urban population GDP effect (ΔCPG ), urbanization rate effect (ΔCUR ), investment population support coefficient effect (ΔCIP ) and investment effect (ΔCI ). The main results showed the following: (1) ΔCI ranks the first most important factor in three Urban Agglomerations and in whole cities from 2011–2020 period; (2) The impact of ΔCUR on the reduction of energy consumption is negative in three Urban Agglomerations and in whole cities. (3) ΔCEI has a strong impact on swelling energy consumption in three Urban Agglomerations and in 68 cities during the study period. The most of the cities mainly focusing on Urban Agglomeration in the middle reaches of the Yangtze River as well as Yangtze River Delta Urban Agglomeration. (4) ΔCIP has a most powerful force to reduce energy consumption in three Urban Agglomerations and in whole cities over the whole study period. (5) The role of ΔCPG in the increase of energy consumption cannot be disregarded in three Urban Agglomerations as well as in cities particularly showing in Shanghai, Nanjing. Therefore, decision-makers should balance the social and economic implications of energy usage in addition to developing suitable fixed asset investment strategies. Keywords: Energy Consumption · three Urban Agglomerations · LMDI · Socio-economic Drivers · the Yangtze River Economic Belt

1 Introduction With speedy the economy advance, China has caused a range of problems with regard to the use and availability of environmental resources, especially concerning energy consumption [1]. In 2010, China passing Japan has become the world’s second-largest economy [2]. Data from 2018 shows that China’s energy consumption is 24 percent of the global total, accounting for 34 percent of global energy consumption growth. [3]. At present, with the acceleration of China’s urbanization process, the value of energy consumption will continually swell. It is expected that by 2025, China’s urbanization rate © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 1783–1796, 2023. https://doi.org/10.1007/978-981-99-3626-7_139

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will reach 65.5%, and it is conservatively estimated that the new rural migrant population will be more than 80 million [4]. Therefore, growing energy consumption could pose a great challenge to sustainable economic advance, resource and environmental balance in urban ecosystems. And it is indeed required to study the mechanism of the impact of urbanization on energy consumption. Energy consumption in urban agglomerations of the Yangtze River Economic Belt as the research object is selected for the sake of its total population and economic aggregate. The Yangtze River Economic Belt has the broadest hinterland and development space in China, accounting for 42.9% of the country’s total population and 46.2% of the country’s total economic output as stated in its development report in 2019. There is no doubt that it is under greater pressure of ecological environment than other regions in China. Therefore, by analyzing the evolution of energy consumption in urban agglomerations and cities of the Yangtze River Economic Belt from 2011 to 2020, this paper finds out the driving factors affecting energy consumption and their modes of action, so that policymakers can better formulate energy policies to promote the sustainable development of population, resources, and environment. The rest of the article is structured as follows: Sect. 2 provides a concise literature review. Sections 3 and 4 describe the methodology and data sources. The empirical results and analysis are respectively described in Sect. 5. Finally, this study is concluded in Sect. 6.

2 Literature Review A significant purpose of energy consumption research is to make targeted recommendations for energy policy [5, 6]. There are many ways to study energy consumption in the research of predecessors such as the decomposition method [7], Granger causality test [8], life-cycle analysis [9], the machine learning algorithm [10], etc. Generally, most previous studies used LMDI to investigate energy consumption, as LMDI can help identify key indicators that drive changes in energy consumption. The logarithmic mean Divisia index method (LMDI) [11] is the utmost popular one in previous studies because of its merits like adaptability, easy of application as well as zero robustness. Table 1 shows that previous studies using LMDI to analyze energy consumption can be sorted into two groups: one is energy consumption in different sectors [12, 13] such as transportation energy consumption [14, 15], and carbon dioxide emissions in the building sector [16]. And the other is different types of energy consumption such as carbon dioxide emissions [16], the renewable energy [17], and electricity consumption [18]. The results of the decomposition of two categories are mainly the effect of economic, industrial structure, energy structure and energy intensity. The LMDI method can be used to scientifically draw recommendations for sustainable policies [19] and low-carbon policies [20]. In summary, existing studies provide a lot of evidence for the decomposition of factors of energy consumption. However, some deficiencies still exist and this study tries to solve. First of all, previous studies mainly focused on other industries or sectors while few studies have decomposed energy consumption factors in the context of urbanization. Next, research samples of previous studies mainly focused on cities, provinces or

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Table 1. The main conclusions from previous studies using LMDI. Literature

Research objective

Factor

Main Conclusions

[12]

Energy consumption in metallurgical industry

a) b) c) d)

The regional output The industrial structure Technological progress The technical efficiency change effect e) The capital-energy change effect f) The labor-energy change effect g) Energy structure

a) The technological progress effect has the utmost influence on the decline in energy intensity; b) The labor-energy change is the biggest obstacle to reducing energy intensity

[13]

Agricultural carbon emissions

a) The agricultural production efficiency b) The industrial structural effect c) Agricultural economic development-level effect d) The agricultural labor effect

a) Production efficiency, agricultural structure, and labor are the main factors to decrease agricultural carbon emissions b) Economic level is the most important factor leading to an increase in the short term

[14]

Transportation sector CO2 emissions

a) Intensity of transportation modes b) The effect of population change c) The fuel usage use intensity of each transportation mode d) The ratio of each transportation mode to total transportation services e) Per capita economic activity effect f) The transportation intensity

a) Carbon dioxide emissions from the transport industry are mainly caused by economic growth; b) The transport intensity has a significant reduction effect.

[15]

Transportation sector CO2 emissions

a) b) c) d) e)

Energy emission intensity Energy structure Energy intensity The transportation intensity Technological progress

a) The capital input effect is the key factor driving carbon emissions; b) The technology state effect is the key factor in limiting carbon emissions

[16]

Residential building carbon emissions

a) b) c) d)

The carbon emission factor Energy intensity Residential income level Residential population density

Residential energy intensity has a major contributing role on the decoupling

[17]

Renewable energy

a) b) c) d) e) f)

Carbon trade intensity The trade of fossil fuels effect Energy intensity Energy production efficiency Electricity financial power effect Economic growth

Energy production efficiency and Economic growth in renewable electricity generation per GDP account for the total and negative changes of Carbon dioxide emissions

[18]

Electricity consumption in China

a) b) c) d)

Economic growth Technological progress Economic structure Energy intensity

a) The effect of economic growth on electricity consumption is positive, but the effect of technological progress on electricity consumption is negative; b) There are clear differences in economic structure and energy intensity among eastern, central and western regions

countries. But few studies have sampled multiple representative urban agglomerations. Therefore, this study is expected to fill these gaps and more fully explore the drivers

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of energy consumption in the Yangtze River Economic Belt’s urban agglomerations and their including cities in 2011–2020 adopting the LMDI model. Figure 1 shows that the study area map contains three urban agglomerations, namely Chengdu-Chongqing Urban Agglomeration, Urban Agglomeration in the middle reaches of the Yangtze River and Yangtze River Delta Urban Agglomeration, as well as cities they contain, for a total of 68 cities. At last, the results of this paper can provide an empirical basis for the Chinese government to understand Socio-economic drivers of energy consumption at the city level and the urban agglomeration level.

Fig. 1. Urban agglomerations and their including cities in Yangtze River Economic Belt

3 Methodology The LMDI method can be represented as an extended Kaya identity, first proposed by Kaya [21], who related energy consumption to population, economic advance and energy intensity. Combining the context of urbanization and the previous literature [22, 23], we rewrite the Kaya identity as Eq. (1). Ct Gt UP t Pt × × × × It Gt UP t Pt It t t t t = CEI × CPG × CUR × CIP × CIt

Ct =

(1)

where t represents the time. C t represents the energy consumption in year t; G t represents the Gross Domestic Production(GDP) in year t; UP t represents the annual urban population; P t represents the annual population; I t represents the annual fixed asset investment; t = C t /G t is the energy intensity in year t; C t = G t /UP t is the per urban populaCEI PG t = UP t /P t is the urbanization rate in year t; C t = P t /I t is the tion GDP in year t; CUR IP investment population support coefficient in year t; CIt = I t is the fixed asset investment in year t. Table 2 presents the variables’ definitions in Eq. (1).

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The energy consumption value (ΔC t,0 ) between a base year (C 0 ) and the target year can be expressed as Eq. (2).

(C t )

ΔC t,0 = C t − C

(2)

Using LMDI in its additive formula, the energy consumption value (ΔC t,0 ) can be expanded as Eq. (3). t t t t ΔC t,0 = C t − C 0 = ΔCEI + ΔCPG + ΔCUR + ΔCIP + ΔCIt

(3)

t is the annual energy intensity effect, ΔC t where ΔCEI PG is the per urban population t is the annual urbanization rate effect, ΔC t is the annual investment GDP effect, ΔCUR IP population support coefficient effect, and ΔCIt is the effect of investment. The additive LMDI method is accepted to decompose each factor as follows (Eqs. 4– 8) [24]:   Ct Ct − C0 t × ln( EI ΔCEI = ) (4) 0 t 0 ln C − ln C CEI   t CPG Ct − C0 t × ln( ) (5) ΔCPG = 0 ln C t − ln C 0 CPG   t CUR Ct − C0 t × ln( = ) (6) ΔCUR 0 ln C t − ln C 0 CUR   t CIP Ct − C0 t × ln( ΔCIP = ) (7) 0 ln C t − ln C 0 CIP   CIt Ct − C0 t × ln( ) (8) ΔCI = ln C t − ln C 0 CI0

To visibly mirror the contributions of each effect, the contribution ratios are defined as Eq. (9). γEI =

t t t t ΔCPG ΔCEI ΔCUR ΔCIP ΔCIt , γ = , γ = , γ = , γ = PG UR IP I ΔC t,0 ΔC t,0 ΔC t,0 ΔC t,0 ΔC t,0

(9)

4 Data Source Raw datasets are from China Urban Statistical Yearbook (2012–2021), including investment in fixed assets (I t ), urban population (UPt ), population of long-term residents (Pt ), GDP (Gross domestic product, Gt ), and energy consumption (C t ). GDP data are converted into 2011 constant prices. The energy sources considered in this study include three categories: electricity, natural gas, and liquefied petroleum gas. The total energy consumption is converted into standard coal equivalent according to the China Energy Statistical Yearbook.

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Variables

Definitions

Ct

Energy consumption of cities/urban agglomerations

Gt

Gross Domestic Production of cities/urban agglomerations

UP t

The urban population

Pt

Population of long-term residents

It

The fixed asset investment

t CEI t CPG t CUR t CIP CIt

The energy intensity The per urban population GDP The urbanization rate The investment population support coefficient The fixed asset investment

5 Results and Discussion 5.1 Analysis of Decomposition Results in Urban Agglomerations 5.1.1 Chengdu-Chongqing Urban Agglomeration In Table 3, the decomposition findings of energy consumption in Chengdu-Chongqing Urban Agglomeration are displayed, and Fig. 2 displays the trend from 2011 to 2020. During the studied periods, ΔCI accounting for 99.41% of the total contributions, plays a leading part in the growth of energy consumption. ΔCEI (34.05%), ΔCUR (30.47%) and ΔCPG (28.18%) respectively rank the second, the third and the fourth role., respectively. In contrast, ΔCIP has negative effect on energy consumption, accounting for a contribution share of − 92.11%. ΔCIP denotes the efficiency of the unit fixed asset investment supporting population growth. It has a most strong influence on reducing energy consumption from 2011– 2020 period, with the decomposition result of -79.96 million tce as shown in Table 3. This shows that rising the investment population support coefficient could be able to effectively control the total energy consumption. As is shown in Table 3, it fell to the highest value (-5.61 million tce) in 2016–2017, then it decreased to the lowest value (-10.95 million tce) in 2017–2018. It is not difficult to see that the fluctuation during the studied period may be related to the urbanization development and the administration policy, which exactly affects the scale of the fixed asset investment.

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Energy consumption and GDP are combined to form ΔCEI , which measures how much energy is used to produce one unit of GDP. ΔCEI has a dominant influence on increasing energy consumption, with the decomposition result of 29.55 million tce over the whole study period as shown in Table 3. The highest value is 38.08 million tce in 2016–2017 period, and the lowest value is -8.53 million tce during 2019–2020 period as shown in Table 3. ΔCI plays the most dominant part in the increase of energy consumption, with the decomposition result of 86.30 million tce over the whole study period as shown in Table 3. During whole study period the contribution ratio is 99.41% as shown in Fig. 2. From the annual perspective, the effect of investment was positive in all time periods. During 2019–2020 period, the effect of investment in energy consumption is lowest value of 6.29 million tce, and the effect of investment in energy consumption is highest value of 11.57 million tce during 2017–2018 period as shown in Table 3. The role of ΔCPG in the growth of energy consumption cannot be disregarded. During whole study period the contribution ratio is 28.18% as shown in Fig. 2. From the annual perspective, except for the 2011–2012 and 2019–2020 periods, ΔCPG in other periods is positive. During these two time periods, ΔCPG decreased energy consumption by 0.07 million tce and 3.23 million tce, respectively. The impact of ΔCUR on the reduction of energy consumption is negative. The decomposition analysis indicates that the value of ΔCUR is 26.45 million tce (Table 3) and its contribution share to total energy consumption is 30.47% (Fig. 2). As shown in Table 3, ΔCUR will definitely contribute to the value of energy consumption. Before 2019, ΔCUR changed in a relatively mild way. From the annual perspective, ΔCUR in all periods is positive. During 2013–2014 period, ΔCUR in energy consumption is lowest value of 1.71 million tce, and ΔCUR in energy consumption is highest value of 7.23 million tce during 2019–2020 period as shown in Table 3. Table 3. The decomposition results of the energy consumption in Chengdu-Chongqing Urban Agglomeration. Time

Effect (10,000 tce)

Contribution ratio

ΔC

ΔCEI

ΔCPG

ΔCUR

ΔCIP

ΔCI

γI

γG

γUR

γIP

γI

2011–2012

44.20

−196.34

−7.31

219.35

1099.66

−4.4415

−0.1653

4.9621

609.73

103.66

292.53

175.08

1022.13

0.1700

0.4798

0.2871

2013–2014

9.44

375.72

171.19

896.77

18.1419

475.17

293.28

179.24

865.17

−60.9954 −0.1052

39.8176

2014–2015

−575.55 −50.00

0.6172

0.3772

2015–2016

785.20

178.37

343.17

201.56

889.06

0.2272

0.4370

0.2567

2016–2017

4402.54

3808.20

264.86

261.73

628.80

0.8650

0.0602

0.0594

2017–2018

1562.96

1087.56

67.57

345.50

1157.49

0.6958

0.0432

0.2211

2018–2019

1043.09

1139.36

367.94

946.64

−0.5241

1.0923

0.3527

−24.2318 −1.6133 −91.0013 −1.7100 −1.0532 −0.1274 −0.7007 −0.8284

24.8765

2012–2013

−322.86

723.21

1123.99

3.3936

1.2838

−2.8756

3.6675

−4.4692

2446.30

2644.79

−1071.16 −983.67 −858.69 −812.52 −826.96 −561.04 −1095.15 −864.11 −922.36 −7995.67

8629.71

0.3405

0.2818

0.3047

−0.9211

0.9941

2019–2020

−251.49

−546.72 −853.47

Total

8680.85

2955.72

1.6764 95.0371 1.8208 1.1323 0.1428 0.7406 0.9075

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Fig. 2. The contribution ratio of each effect in Chengdu-Chongqing Urban Agglomeration from 2011 to 2020.

5.1.2 Urban Agglomeration in the Middle Reaches of the Yangtze River The decomposition results of energy consumption in the urban agglomeration in the middle reaches of the Yangtze River are shown in Table 4 and Fig. 3. During 2011– 2020 period we find that the energy consumption in Urban Agglomeration in the middle reaches of the Yangtze River increased by around 123.95 million tce. ΔC I played a leading role in increasing the energy consumption, whereas ΔC IP played the dominant role in falling. ΔC EI was the second largest contributor to increased energy consumption, while ΔC PG and ΔC UR had slight effects in the changes in energy consumption. ΔC IP denotes the efficiency of the unit fixed asset investment supporting population development. It has a most great influence on reducing energy consumption during the whole study period, with a contribution share of − 80.55% as shown in Fig. 3. This shows that rising ΔC IP could be able to constraint the total energy consumption in an operative approach. As is shown in Table 4, ΔC IP fluctuated slightly from 2011 to 2018. Then, it decreased to the lowest value (−17.27 million tce) in 2018–2019, then it fell to the highest value (1.35 million tce) in 2019–2020. This suggests that raising ΔC IP may be able to effectively reduce overall energy use. For the growth of energy consumption, ΔC EI is positive throughout the study period, accounting for a contribution share of 67.43% as shown in Fig. 3. As shown in Table 4, from 2011 to 2020, ΔC EI augmented energy consumption by around 83.58 million tce. ΔC I plays a foremost part in the growth of energy consumption. During whole study period the contribution ratio is 81.82% in Fig. 3. From the annual perspective, the effect of investment was positive for most of the time, with the exception of the 2019–2020 period. During 2019–2020 period, the effect of investment reduced energy consumption by 2.29 million tce as shown in Table 4.

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The role of ΔC PG in the growth of energy consumption can be noticed. During whole study period the contribution ratio is 12.16% as shown in Fig. 3. From the annual perspective, except for the 2017–2018 and 2019–2020 periods, ΔC PG in most periods is positive. During these two time periods, ΔC PG decreased energy consumption by 33.87 million tce and 3.36 million tce, respectively. The effect of ΔC UR on the reduction of energy consumption is negative. The decomposition result specifies that the value of ΔC UR is 23.72 million tce (Table 4) and its contribution share to total energy consumption is 19.14% (Fig. 3). In Table 3, ΔC UR will confidently contribute to the change in the energy consumption. Previously 2016, ΔC UR varied in a quite mild way while it changed intensely after 2017. Table 4. The decomposition results of the energy consumption in Urban Agglomerations in the middle reaches of the Yangtze River. Time

Effect (10,000 tce)

Contribution ratio

ΔC

ΔCEI

ΔCPG

ΔCUR

ΔCIP

ΔCI

γEI

γPG

γUR

γIP

2011–2012

317.62

80.48

17.88

201.51

1441.48

0.2534

0.0563

0.6344

2012–2013

543.77

419.44

180.77

1520.74

0.3324

39.16

422.07

177.38

10.7771

4.5292

2014–2015

339.74

310.44

212.57

0.9137

0.6257

2015–2016

524.58

325.40

217.60

401.05

−0.1339 −14.8567 −0.6166 −0.0920

0.7714

2013–2014

−72.83 −581.84 −209.47 −48.29

0.6203

0.4148

2016–2017

7132.70

4063.41

2713.06

318.01

1168.98

0.5697

0.3804

0.0446

2017–2018

1643.14

4654.24

−3387.44

344.45

1634.80

2.8325

−2.0616

0.2096

2018–2019

1985.81

555.44

1021.51

338.94

−1423.74 −1504.35 −1238.72 −1120.68 −371.18 −1130.75 −1602.91 −1726.56

1796.48

0.2797

0.5144

0.1707

2019–2020

−131.67

−82.83

−335.55

381.25

134.94

−229.47

0.6291

2.5485

−2.8955

Total

12394.85

8358.32

1506.80

2372.47

−9983.95

10141.21

0.6743

0.1216

0.1914

−4.4825 −2.7665 −31.6294 −3.2986 −0.7076 −0.1585 −0.9755 −0.8694 −1.0248 −0.8055

1260.27 1146.88

γI 4.5383 2.7967 32.1798 3.3757 0.7645 0.1639 0.9949 0.9047 1.7428 0.8182

Fig. 3. The contribution ratio of each effect in Urban Agglomerations in the middle reaches of the Yangtze River from 2011 to 2020.

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5.1.3 Yangtze River Delta Urban Agglomeration Results of Yangtze River Delta urban agglomeration attained from decomposition analysis by LMDI are demonstrated in Table 5 and Fig. 4. During the studied periods, ΔC I accounting for 70.21% of the total contributions, plays the preponderant part in the growth of energy consumption. ΔC EI (49.37%), ΔC PG (22.30%) and ΔC UR (14.88%) rank the second, the third and the fourth class, respectively. In contrast, ΔC IP has the negative influence on energy consumption, accounting for a contribution share of − 56.76%. ΔC IP has a most robust effect on reducing energy consumption during 2011–2020 period. This shows that improving ΔC IP could be able to curb the total energy consumption in an operative way. From the annual perspective, the investment population support coefficient was negative in most time periods. As is shown in Table 5, the highest value is −6.21 million tce in 2017–2018 period, and the lowest value is −33.69 million tce in 2011–2012 period. ΔC EI has a great influence on increasing energy consumption during the whole study period, and the value of decomposition result is around 16.72 million tce in Table 5. The highest value is 161.50 million tce in 2016–2017 period, and the lowest value is −11.83 million tce in 2013–2014 period. ΔC I plays the most dominant part in the growth of energy consumption. During whole study period the contribution ratio is 70.21% as shown in Fig. 4. From the annual perspective, ΔC I was positive in each time period, and the highest value as well as the lowest value respectively correspond to 37.35 million tce in 2018–2019 period and 13.30 million tce in 2016–2017 period as shown in Table 5. The role of ΔC PG in the growth of energy consumption cannot be unnoticed. From the annual perspective, except for the 2011–2012 and 2017–2018 periods, ΔC PG in most periods is positive. During these two time periods, ΔC PG decreased energy consumption by 4.88 million tce and 2.18 million tce, respectively. The impact of ΔC UR on the reduction of energy consumption is negative. The decomposition result shows that the value of ΔC UR is 50.37 million tce (Table 5). In Table 4, ΔC UR will definitely contribute to the variation of the energy consumption. Previously 2018, ΔC UR changed in a rather mild way, but it changed severely after 2019. And Table 5. The decomposition results of the energy consumption in Yangtze River Delta urban agglomeration. Time

Effect (10,000 tce)

Contribution ratio

ΔC

ΔCEI

ΔCPG

ΔCUR

ΔCIP

ΔCI

γEI

γPG

γUR

γIP

γI

2011–2012

1738.15

1804.66

−488.36

308.80

3482.39

1.0383

−0.2810

0.1777

2617.44

1216.11

925.84

351.11

3083.74

46.4618

0.3537

0.1341

2013–2014

537.36

1211.87

418.46

3164.15

0.7787

1254.46

1256.35

326.14

1.0015

0.2600

2015–2016

1758.74

1492.02

433.17

1999.63

−2.2022 −0.3046 −0.1669

2.2552

2014–2015

−1183.39 −382.08 −293.59

0.8484

0.2463

2016–2017

18412.14

16150.45

1562.86

483.68

1330.19

0.8772

8.4882

2.6270

2017–2018

3179.55

1133.75

−218.28

453.39

2431.23

0.3566

−6.8652

0.1426

2018–2019

3637.75

1335.66

3735.09

0.3672

384.87

926.16

2175.72

−0.2125 −1.3216

0.3911

723.75

−773.21 −956.49

1422.68

2019–2020

0.5318

1.2797

Total

33859.35

16716.22

7549.85

5036.58

23774.02

0.4937

0.2230

0.1488

−1.9385 −1.1306 −5.7200 −1.8477 −1.0647 −0.0606 −0.1952 −0.5725 −2.4960 −0.5676

2.0035

2012–2013

−3369.34 −2959.35 −3073.73 −2317.82 −1872.50 −1115.04 −620.54 −2082.48 −1806.51 −19217.31

2371.88

1.1782 5.8883 1.8907 1.1370 0.0722 0.7646 1.0268 3.0062 0.7021

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the decomposition result directs that the value of ΔC UR is from 4.53 million tce in 2017–2018 period to 13.36 million tce in 2018–2019 period.

Fig. 4. The contribution ratio of each effect in Yangtze River Delta urban agglomeration from 2011 to 2020.

5.2 Analysis of Decomposition Results in Cities In Fig. 5, the contribution ratio of each effect in 68 cities of Yangtze River Economic Belt attained from decomposition result by LMDI are showed during 2011–2020 period. ΔC EI has a robust effect on increasing energy consumption in the most of cities mainly focusing on Urban Agglomeration in the middle reaches of the Yangtze River and Yangtze River Delta Urban Agglomeration, which is particularly evident in 8 cities, including Ziyang, Xianning, Yichun, Huanggang, Zhenjiang, Suzhou, Jiaxing and Jinhua as shown in Fig. 5. The contribution proportion of 8 cities are 147.40%, 74.07%, 190.23%, 89.28%, 76.84%, 71.96%, 70.82% and 81.00%, respectively. ΔC PG in the growth of energy consumption cannot be unnoticed mainly focusing on cities of Yangtze River Delta Urban Agglomeration, particularly showing in Shanghai, Nanjing in Fig. 5. The contribution proportion of two cities are 177.59% and 71.14%, respectively. The influence of ΔC UR on the reduction of energy consumption is negative in whole cities. The highest contribution proportion is Zigong (666.87%), and the lowest contribution proportion is Shanghai (0.31%). ΔC IP has a most robust effect on reducing energy consumption in all cities over the whole study period. The highest contribution share is Zhenjiang (–8.71%), and the lowest contribution proportion is Zigong (3251.13%).

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ΔC I is the most important factor in whole cities over the whole study period. The influence of the investment effect on the growth of energy consumption is positive in most of cities, except for Suzhou (–1.27%).

Fig. 5. The contribution ratio of each effect of cities in the Yangtze River Economic Belt from 2011 to 2020.

6 Conclusion This study applies the LMDI additive decomposition method to recognize the main factors driving energy consumption in three Urban Agglomerations and cities of the Yangtze River Economic Belt during the 2011–2020 period. The variation of energy consumption is decomposed into five factors: energy intensity, per urban population GDP, urbanization rate, investment population support coefficient and investment effect. The main conclusions may be summarized as follows: (1) In three Urban Agglomerations, the effect of energy intensity has a strong influence on increasing energy consumption during 2011–2020 period. The effect of energy intensity has a robust effect on increasing energy consumption in the most of cities mainly focusing on the urban agglomeration in the middle reaches of the Yangtze River and Yangtze River Delta Urban Agglomeration. It is particularly evident in 8 cities, including Ziyang, Xianning, Yichun, Huang gang, Zhenjiang, Suzhou, Jiaxing and Jinhua.

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(2) The role of the effect of per urban population GDP in the increase of energy consumption cannot be disregarded in three Urban Agglomerations. The effect of per urban population GDP mainly focuses on cities of Yangtze River Delta Urban Agglomeration, particularly showing in Shanghai, Nanjing. (3) The impact of the urbanization rate effect on the reduction of energy consumption is negative in three Urban Agglomerations and in whole cities. The change accounts for government formulated more attractive property policies to attract a great deal of populations to cities. (4) The investment population support coefficient has a most great impact on reducing energy consumption in three Urban Agglomerations and in whole cities over the whole study period. This points to that rising the investment population support coefficient could be capable of effectively restraining the growth of energy consumption. (5) The effect of investment ranks the first most important factor in three Urban Agglomerations and in whole cities over the whole study period. The influence of the investment effect on the growth of energy consumption is positive in most of cities, except for Suzhou (−1.27%). Overall, based on our findings, policymakers should pay greater attention to their economy, population, and fixed asset investment policies given that these variables will have an impact on energy consumption. Acknowledgments. This work is supported by National Natural Science Foundation of China (Grant No.72204033), Humanities and Social Science project of Ministry of Education of China (Grant No. 21YJC630169), China Postdoctoral Science Foundation (Grant No. 2022M711457), Natural Science Foundation of Chongqing (Grant No. Cstc2021jcyj-msxmX1010), Social Science Planning Project of Chongqing, (Grant No. 2020QNGL25), Science and Technology Research Project of Chongqing Education Commission (Grant No. KJQN202000724), and Humanities and Social Science Research Project of Chongqing Education Commission (Grant No. 21SKJD072).

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The Evolution of Employment Spatial Structure in Shenzhen Based on Mobile Phone Signaling Data Chunmei Chen and Yani Lai(B) Department of Construction Management and Real Estate, Shenzhen University, Shenzhen, China [email protected]

Abstract. Urban spatial structure and its spatial evolution is of great significance for optimizing urban spatial structure and formulating urban spatial planning. Numerous studies have showed that many megacities in the world are moving towards polycentric urban spatial structure, but few studies have considered the role of urban polycentric planning in this process. Based on mobile phone signaling data of Shenzhen in 2012 and 2016, this paper explored the evolutionary characteristics of employment spatial structure in Shenzhen and evaluated the role of urban polycentric planning in the process. Firstly, this study found that Shenzhen is also moving towards a polycentric urban spatial structure and the differences among employment centers are shrinking. In 2012, six employment centers were identified and mainly distributed in Futian District and Luohu District, indicating that the monocentric spatial structure of Shenzhen remained prominent until 2012. By 2016, this study identified ten new employment centers. Except for Futian District and Luohu District, Nanshan District, Longhua District and Longgang District have emerged new employment centers. Secondly, this study confirmed that the polycentric development is a trend in the evolution of Shenzhen’s employment spatial structure, but employment centers identified in 2012 and 2016 have a relatively limited influence on the employment population distribution, especially employment centers outside the Futian District, Luohu District and Nanshan District. Finally, this paper found that the urban master plan has played a significant guiding role in the formation of employment centers in the central city and the central region of Shenzhen, while the planned subcenters, located in the eastern and northwestern of Shenzhen, have not yet been formed and still need to be cultivated and further guided. Keywords: Polycentric spatial structure · Employment center · Urban master plan · Mobile phone signaling data

1 Introduction Urban space decentralization and polycentricity caused by population expansion has become a universal phenomenon in metropolitan cities [1, 2]. To promote sustainable development, polycentric urban planning is becoming an essential spatial strategy for © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 1797–1810, 2023. https://doi.org/10.1007/978-981-99-3626-7_140

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megacities [5]. It has been shown that the employment population plays a more significant role in urban spatial structure than the residential population [3]. The concentration of the employed population can generate an agglomeration economy and have an essential role in land value, population density and other economic activities. Therefore, the emergence of employment centers is considered an essential sign of urban polycentric spatial structure, and existing studies generally measure urban polycentric spatial structure in terms of the employed population [4]. For example, based on economic census data, Sun (2012) identified that Beijing had two employment centers and five employment subcenters in 2008, and the results showed that Beijing has the characteristics of urban polycentric spatial structure [6]. Based on economic census data, Sun (2014) analyzed the evolutionary characteristics of employment spatial structure in Shanghai from 1996 to 2008 and found that Shanghai’s employment space is evolving towards a polycentric spatial structure [7]. Many empirical studies have shown that urban planning plays a leading role in the formation of urban polycentric spatial structure in China [8], but the effects of its planning implementation needs to be further explored. Meanwhile, the development of modern information technology and the emergence of spatial big data, such as mobile phone signaling data, POI (Point of Interests) and night lighting data, provides new possibilities for identifying urban spatial structure from a finer scale. Compared with traditional survey data, spatial big data has higher accuracy, and how to use spatial big data to identify urban spatial structure has received extensive attention. This paper aims to explore the evolutionary characteristics of employment spatial structure in Shenzhen and analyze the role of urban polycentric planning in this process. Firstly, based on mobile phone signaling data of Shenzhen in 2012 and 2016, this research explore the characteristics of employment distribution in Shenzhen using Exploratory Spatial Data Analytical (ESDA) method. Secondly, the threshold method is used to identify the employment centers in 2012 and 2016, and this study analyzed the impact of identified employment centers and test the polycentricity of the employment spatial structure through polycentric model. Finally, according to the identified employment centers, this paper analyzed the effectiveness of the master plan of Shenzhen. The remainder of this paper is structured as follows. Section Two reviews the related concepts and theories on urban spatial structure and the progress of existing empirical studies. Section Three introduces the research data and methods. Section Four analyzes the characteristics of employment spatial structure in Shenzhen and the effectiveness of urban polycentric planning. Section Five draws conclusions of this paper.

2 Urban Spatial Structure Since the 1960s, scholars have started to study urban space and proposed a conceptual framework of urban spatial structure. Foley (1964) argued that urban spatial structure includes static spatial forms and dynamic functional linkage [9]. Based on the conceptual framework proposed by Foley, Webber (1964) argued that urban spatial structure contains both form and process and divided urban form into static (land, population, jobs, etc.) and dynamic (human flow, logistics, information flow, etc.) aspects [10]. Bourne (1971), on the other hand, proposed a more comprehensive and scientific conceptual framework, stating that urban spatial structure should contain three aspects: urban form, action

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process and the inner mechanism between them, thus the subsystems of the city are connected into a whole urban system [11]. Synthesizing the definitions of the urban spatial structure by various scholars, the definition of the urban spatial structure by Bourne (1971) is more comprehensive and scientific, and urban spatial structure should contain both spatial forms and functional links. The employment spatial structure in this study focuses on the spatial distribution of urban elements and defined as the degree of spatial aggregation as well as the spatial distribution state of employed population. The urban center is the basic node in the composition of urban spatial structure and has received much attention. The urban center contains two forms: morphological center and functional center [12]. The morphological center reflects the spatial distribution characteristics of elements such as population, employment, and land use and emphasizes the physical spatial distribution form of the city. Functional centers reflect the flow and connection of economics, information, and other elements between regions, emphasizing the degree of information flow and functional interdependence. However, the definition of urban centers is still controversial, and it has been generally agreed that urban centers are related to employment [3, 13]. For example, Giuliano (1991) considers urban centers as agglomerations of population and employment and defines employment centers as areas where the density of jobs exceeds a certain threshold [3]. Cladera (2009) defines an urban center as a place where employment density is significantly large and have a significant impacts on the overall employment spatial structure [13]. Synthesizing the definitions of various scholars and related studies, the employment center studied in this paper focus on the morphological perspective and have two characteristics: (1) the employment density is significantly higher than the surrounding areas; (2) the employment center has large scale and can have a significant impact on the urban spatial structure. The city’s spatial structure undergoes complex changes and can be presented as monocentric, polycentric, and decentralized [1, 14]. The change in urban spatial structure in China is mainly influenced by the level of economic development and government interventions [2]. The government can influence urban spatial structure through urban planning, land ownership and rental market, immigration control, preferential policies and other approaches. Meanwhile, the government can also adopt polycentric spatial development strategy to promote urban development. In terms of socioeconomic development, economic decentralization and marketization are helpful to promote the polycentric development. In general, urban planning plays a leading role in the formation of urban polycentric spatial structure in China. For example, Huang (2017) found that employment centers in Beijing grew from 6 to 16 from 1996 to 2010 and concluded that urban planning can facilitates the transfer of population to the suburban areas, thus promoting the formation of employment subcenters [5]. Liu (2018) examined the changes in the employment spatial structure in Beijing from 2001 to 2010, and the study showed that the urban polycentric planning has an important role in the transformation of urban form in the Beijing metropolitan area [2]. However, some studies have also pointed out that the urban polycentric spatial structure does not always develop as the urban planning [15]. Whether the urban spatial structure has the characteristics of polycentric and whether the urban polycentric planning has an important role in the formation of muti-center spatial structure still needs further discussion.

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3 Data and Methodology 3.1 Study Area Shenzhen is located in the southeastern part of Guangdong Province, and is one of the megacities in China. In 1980s, Shenzhen first introduced the idea of polycentric planning and became China’s first Special Economic Zone (SEZ), including Luohu, Nanshan, Futian and Yantian districts. In 2010, the SEZ covers the whole city, including six additional districts: Bao’an, Longhua, Longgang, Guangming, Pingshan and Dapeng New Districts. Meanwhile, Shenzhen put forward the Master Plan of Shenzhen (2010– 2020), which proposed to establish a polycentric spatial structure. Since 2012, urban renewal has formally become the main channel of construction land supply in Shenzhen, and the spatial structure of the city may change in different ways. The research area of this paper covers all the administrative regions of Shenzhen, and the basic unit is 250 m × 250 m grid. The areas located within the basic ecological control line, orange line, purple line, historical buildings and historical features areas are restricted areas for demolition and reconstruction, so the grids located within the ecological control line are removed. The research area includes 16902 grids with an area of 1056.375 km2 , accounting for 52.9% of the whole city’s area after removing the above grids (according to the Shenzhen Statistical Yearbook 2017, the total land area of Shenzhen is 1997.27 km2 ). The research area is shown in Fig. 1.

Fig. 1. Study area

3.2 Data Sources This study utilized mobile phone signaling data from Unicom operators in Shenzhen during March 2012 and November 2016. To ensure the sample size was comprehensive

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and the research findings were representative, the study focused solely on individuals who are employed, work 8 h a day, and have a fixed workplace. The study selected specific time periods during the workday, including 9:00, 10:00, 11:00, 14:00, and 15:00, to identify characteristic workplace behaviors of employed individuals. It is important to note that the maximum coverage radius of a mobile phone base station is approximately 1000m. Therefore, if a mobile phone user connects to the same base station or a nearby base station within 1000m three or more times per day, the base station is considered a potential workplace for the user that day. If the same base station is identified as a potential workplace for a user on 3 or more work days during 5 consecutive work days, it is identified as the user’s workplace. Subsequently, the study utilized Kernel Density analysis to allocate mobile phone users identified at each base station to 250 m * 250 m grids. 3.3 Methodology 3.3.1 Exploratory Spatial Data Analytical (ESDA) This study investigate the spatial distribution characteristics of the urban employed population using the Exploratory Spatial Data Analytical (ESDA) technique based on relevant studies [16, 17], including global spatial autocorrelation and local spatial autocorrelation. To study the overall spatial agglomeration characteristics of the employed population in the study area, this research first used global spatial autocorrelation to identify the employment distribution pattern, and the global Moran’s I formula is shown as follows:   n n i=1 j=1 wij (xi − x) xj − x  Moran s I =   S2 ni=1 nj=1 wij   In the formula, S 2 = 1n ni=1 (xi − x)2 ; x = 1n ni=1 xi , where xi is the observed value of spatial unit i; n  is the  total number of spatial units; wij is a spatial weight matrix of spatial unit i and j; ni=1 nj=1 wij is the sum of all spatial weights. The value of Moran’s I can range from -1 to 1. The closer the absolute value is to 0, the less relevant the spatial units are, indicate the spatial distribution is random. When the Moran’s I value is close to 1, it indicate that there is a positive relationship between spatial units and show the characteristics of ‘high-high’ or ‘low-low’ aggregation. When the Moran’s I value is close to -1, it indicate that there is a negative relationship between spatial units and show the characteristics of ‘high-low’ or ‘low-high’ aggregation. The global spatial autocorrelation cannot identify the specific clustering category and the location of clustering. Therefore, this paper used the local spatial autocorrelation analysis to further explore the agglomeration characteristics of the employed population. This paper selected the Getis-Ord index (Gi∗ ) to test the local spatial autocorrelation, which can identify hot spots (i.e. ‘high-high’ clusters) and cold spots (i.e. ‘low-low’ clusters). The calculation formula is shown as follows:  j wij xi ∗ Gi =  k xk

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In the formula, wij is a spatial weight matrix of spatial unit i and j; xi is the observed value of spatial unit i; j wij is the sum of the spatial weights of row i. If the high values in the spatial unit area are clustered together, the Gi∗ is larger; if the low values are clustered together, the Gi∗ is smaller. 3.3.2 Identification of Employment Centers This paper can identify the potential employment centers with the characteristic of ‘high-high’ clusters based on the local spatial autocorrelation analysis. According to the definition of employment center in this paper, the threshold method is used to judge whether the potential employment centers have a significant effect on the employment spatial structure, so as to determine the boundary of employment center. With reference to the threshold used by Huang (2017) [5] and Liu (2018) [2], this study identified employment centers with the population for more than 0.5% of the total population of the research area. The formula is shown as follows: Ei ≥ 0.5% Et Et is the total number of employed population in Shenzhen, and Ei is the total number of employed population in the ‘high-high’ cluster areas (the area is composed of grids with common edges or corners). After identifying employment centers, this paper used the existing multi-center model to analyze the impact of Shenzhen’s employment centers on the distribution of the employed population as well as test the polycentricity of the employment spatial structure. The polycentric model formula is as follows:  βj dj + ε ln Di = α + j

Di is the employment density of the observation grid (for all the grids have the same area, the employment population in the grid is used to replace the employment density of the observation grid), βj is the coefficient of the j th employment center, and dj is the distance from the observation grid to the j th employment center. If the expected value of βj is negative, which means that the closer to the employment center, the greater the employment density is. If the expected value of βj is positive, indicating that two employment centers are adjacent.

4 The Evolution of Employment Spatial Structure of Shenzhen 4.1 Characteristics of Employment Distribution in Shenzhen In general, the total number of employed population in Shenzhen increased from 10960807 to 12525679 from 2012 to 2016, with an increase of 14.28%. From the perspective of employment share, the employment distribution inside and outside the Special Economic Zone (SEZ) has not changed much, and the employment scale outside the SEZ is larger than that inside the SEZ, accounting for 66% of the total employment

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in the city. From the perspective of employment density, the average employment density inside and outside the SEZ in 2012 was 16157 persons/km2 and 8795 people/km2 respectively, and both of them increased to 18421 people/km2 and 10062 people/km2 in 2016 respectively. The employment density increase outside the SEZ (14.41%) is slightly higher than that inside the SEZ (14.01%), while the employment density inside the SEZ is higher than that outside the SEZ, as the employment density outside the SEZ was only 54.62% of that inside the SEZ in 2016. As seen in Fig. 2, the number of employment population in Nanshan District, Longhua District and Longgang District increased significantly from 2012 to 2016, and the employment growth rate was 39.15%, 46.62% and 33.86% respectively. In contrast, the employment growth rate of Futian District and Luohu District was slow, which was 5.95% and 3.30% respectively. The number of employed population in the remaining seven administrative regions decreased, among which the employment of Dapeng New District and Yantian District decreased significantly by 61.30% and 23.97% respectively. And Bao’an District had the largest decline in employment number, which was 117855.

Fig. 2. Number of employees in Shenzhen administrative districts

4.2 The Evolution of Employment Spatial Structure of Shenzhen From the distribution map of employment density (Fig. 3), the evolution of employment spatial structure of Shenzhen shows the characteristics of polycentric development from 2012 to 2016. Although the polycentric structure was determined in the urban master plan in 1996, the monocentric spatial structure of Shenzhen was still prominent until 2012. The high employment density areas were mainly distributed in Futian District and Luohu District, while there were many middle-high density areas in Nanshan District, Longhua District and Bao’an District. By 2016, Shenzhen has the characteristics of polycentric spatial structure. In addition to Futian District and Luohu District, Nanshan District, Longhua District and Longgang District have emerged new employment highlands, indicating that the urban master plan in Shenzhen has a significant guiding effect on the formation of employment centers in the central city and the central region of Shenzhen.

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Fig. 3. Distribution of employment density in Shenzhen

The results of global spatial autocorrelation analysis are shown in Fig. 4. The Moran index of Shenzhen’s employment in 2012 and 2016 were 0.535 and 0.491 respectively, with p values both less than 0.01, showing a significant positive spatial autocorrelation, which means that the employment of Shenzhen in 2012 and 2016 had obvious spatial agglomeration. The Moran index of employment decreased from 2012 to 2016, which shows that the degree of employment cluster has declined. From Moran’I scatter diagram, the difference among the high-value cluster areas of Shenzhen is narrowing, which means that the balanced development trend of Shenzhen’s employment clusters has appeared.

Fig. 4. Global Moran’I scatter diagram

In 2012, only 6 employment centers were identified in Luohu District and Futian District (Fig. 5a), including 2 first-level centers, 2 s-level centers and 2 third-level centers. The first-level centers consist of the Luohu center (Center 1) and Huaqiang North center (center 2). Luohu center includes four streets: Dongmen, Nanhu, Guiyuan and Huangbei, with an area of 4.88 km2 and an employment density of 81950 people/km2 . The total number of employed population in Luohu center was 399511, with an employment share of 3.64%. Huaqiang North center includes Futian, Nanyuan and Huaqiang North streets,

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with a total area of 3.38km2 and an employment density of 104395 people/km2 . The total number of employed population was 352335, with an employment share of 3.21%.

Fig. 5. The employment centers in Shenzhen (a: centers in 2012; b: centers in 2016)

By 2016, 10 new employment centers were identified in Shenzhen (Fig. 5b), and the spatial structure of Shenzhen presents the trend of multi-centered development. In Nanshan District, a new first-level center-High-Tech Park center (Center 7) was identified, which only includes Yuehai street, with an area of 3.13km2 and an employment density of 1264980 people/km2 , and the number of employed population was 395308, with an employment share of 3.16% in 2016. Outside the SEZ, a second-level center and 5 third-level centers were identified in the central region, and two third-level center were identified respectively in the western coastal region and the eastern region. From the perspective of employment share, the power of the original 5 employment centers has declined from 2012 to 2016, except Yuanling center. The emerging High-Tech Park center has become the most influential employment center, and the embryonic form of multi-centered spatial structure dominated by two main centers has appeared (Fig. 6). Meanwhile, the difference between the employment centers in Shenzhen is narrowing. In 2012 and 2016, the range of employment shares of employment centers was 2.91% and 2.63% respectively, and the variance was 1.28%2 and 0.74%2 respectively, which means that the balanced development trend of Shenzhen employment centers appeared. A multi-center model was used to fit the parameters, the results are shown in Fig. 7. From the significance test results, except for center 4 (Futian center) and center 12 (Longhua center), there is a significant correlation (above 95% confidence level) between the distance to the center (independent variable) and the logarithm of the employed population (dependent variable). Among them, the confidence level of center 2 (Huaqiang North center) increased from 90% to 95% from 2012 to 2016. In terms of the results of parameter fitting, in 2012, the βj of center 1 (Luohu center) and center 5 (Xinzhou center) was positive, which may be caused by the existence of employment centers with higher employment density near center 1 and center 5, such as center 2 (Huaqian North center) next to center 1 (Luohu center), and center 4 (Futian center) and central 6 (Chegongmiao center) on both sides of center 5 (Xinzhou center). By 2016, the employment concentration degree of center 2 (Huaqiang North center) and center 3 (Yuanling center) increased, and the Center 7 (High-Tech Park center) in

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Fig. 6. Employment shares of employment centers in Shenzhen

Fig. 7. Parameter fitting results of multi-center model

Nanshan District and center 14 (Guanlan center) in Longhua District identified in 2016 had a significant impact on the employment distribution. Among the five third-level employment centers located in the central region, only the βj of center 10 (Foxconn center) was negative, the βj of the other four third-level centers, center 9 (Wuhe center), center 11 (Bantian center), center 12 (Longhua center), and center 13 (Qinghu center) was positive. Two new third-level centers in Bao’an District and Longgang District, center 15 (Gushu center) and center 16 (Baolong center) had little impact on the overall employment distribution of Shenzhen. The adjusted R2 increased from 0.139 to 0.207 from 2012 to 2016, indicating that the polycentric development is a trend in the evolution of Shenzhen’s employment spatial structure. However, the adjusted R2 value is low, indicating that the impact of employment centers on the overall employment distribution of Shenzhen is relatively limited, especially for the employment centers outside the SEZ. In 2016, the area of employment centers inside and outside the SEZ were 16.63 km2 and 7.01 km2 respectively, with corresponding employment shares of 13.44% and 5.22% respectively. Therefore, compared with the employment centers inside the SEZ, the employment centers outside the SEZ were still at a lower level in terms of scale and cluster degree. 4.3 A Comparison with the Urban Spatial Structure in Shenzhen Master Plan The Master Plan of Shenzhen (2010–2020) proposed to establish a three-level polycentric spatial structure, as shown in Fig. 8, including two main urban centers, namely FutianLuohu center and Qianhai center; five subcenters, namely Longgang center, Longhua

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center, Guangming center, Pingshan center, and Yantian center; and eight cluster centers, namely Aviation City, Shajing, Songgang, Guanlan, Pinghu, Buji, Henggang, and Kui Chong.

Fig. 8. The master plan of Shenzhen (2010–2020)

The employment spatial structure of Shenzhen develops along the master plan of Shenzhen. The first-level centers identified in the central area of Shenzhen are consistent with the planned main urban centers. In 2012, six employment centers were identified in Shenzhen. Among them, two first-level centers, center 1 (Luohu center) and center 2 (Huaqiang North center), were consistent with the scope of ‘Futian-Luohu’ center in the master plan, and the other four employment centers were also located in the central area of Shenzhen, thus the employment spatial structure of Shenzhen is still monocentric. By 2016, Futian-Luohu center and one first-level center—High-Tech Park center identified in Qianhai center have appeared, indicating that monocentric urban spatial structure in Shenzhen is moving towards polycentric spatial structure. Outside the SEZ, the improvements of urban rail transit plays a guiding role in the evolution of the urban spatial structure of Shenzhen. As the Longhua line and Shenzhen North railway station put into operation, employment centers growth along the urban rail transit in the central area of Shenzhen. In 2016, this study identified five third-level centers and one second-level center in the planned centers (Longhua center and Guanlan cluster center). However, no employment center was identified in the other two planned cluster centers (Buji and Pinghu cluster centers). Compared with the master plan of Shenzhen, the development of employment space was in the initial stage in the eastern region and western coastal region, and only one third-level center was identified in Longgang center and Aviation City cluster center, respectively. Moreover, no employment centers appeared in the eastern coastal region, indicating that the implementation of the master plan of Shenzhen is not effective here.

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Generally speaking, the employment centers identified in 2012 and 2016 were basically consistent with the planned centers. Shenzhen’s employment spatial structure is developing along the ‘2 + 5 + 8’ muti-center spatial structure in the master plan of Shenzhen. In 2016, Shenzhen’s employment spatial structure changed from a monocentric structure to a ‘2 + 1 + 2’ polycentric structure, that is, a polycentric spatial structure composed of two main urban centers ( Futian-Luohu center and Qianhai center), one subcenter (Longhua center) and two cluster centers (Guanlan center and Aviation City center). The embryonic of a polycentric spatial structure are emerging, which shows that the master plan of Shenzhen plays a guiding role in the evolution of Shenzhen’s employment spatial structure. Although center 16 (Baolong center) has been identified in Longgang center, its scale and grade were not consistent with the planned subcenters, which means that Longgang center still needs to grow. Yantian center, Pingshan center, Guangming Center, and six cluster centers including Shajing, Songgang, Pinghu, Buji, Henggang and Kuichong are still to be cultivated.

5 Conclusions and Discussions Based on the mobile phone signaling data of Shenzhen in 2012 and 2016, this paper analyzed the evolutionary characteristics of employment spatial structure in Shenzhen from 2012 to 2016, and evaluates the role of Shenzhen’s urban master plan in the formation process of polycentric spatial structure of Shenzhen. This study shows that the employment spatial structure of Shenzhen presents the characteristics of polycentric development and the differences among employment centers are shrinking. In 2012, the employment centers in Shenzhen were mainly concentrated in the SEZ. By 2016, 10 new employment centers were identified in both SEZ and non-SEZ, indicating that Shenzhen’s employment spatial structure has transformed from monocentric to polycentric. Meanwhile, the range and variance of employment shares of employment centers have decreased from 2012 to 2016, which means that the balanced development trend of Shenzhen employment centers appeared. In generally, according to the multi-center model, the impact of employment centers on the overall employment distribution of Shenzhen is relatively limited, especially employment centers in non-SEZ, indicating that the employment centers outside the SEZ were still at a lower level. Finally, this paper found that the urban master plan in Shenzhen has a essential role in the formation of employment centers and most of planned urban centers evolved to real centers. In 2016, in addition to Futian-Luohu Center, Qianhai Center is also identified as main center, indicating that Shenzhen’s original main center is losing its power. Except for the employment centers identified in real, the others that not formed in planned centers need to be further guided. In general, this study reveals the evolutionary characteristics of employment spatial structure of Shenzhen from the perspective of morphology, which can provide a better understanding of the employment spatial structure in Shenzhen, and also provide ideas for other relevant studies. In fact, the government’s polycentric urban planning is only one aspect that affects the polycentricity development. The government’s governance structure, the government intervention and other factors can affect the formation of urban polycentric spatial structure. Therefore, this paper only examined the effectiveness of

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the urban master plan, the interaction between the role of the government and the market economy is also should be taken into account. This study also found that employment centers in the SEZ and the central region of Shenzhen is unbalanced, and relevant policies should be developed by region to promote the balanced development of employment centers. The method proposed in this study for identifying employment centers can help to assess the effectiveness of urban polycentric planning. Most studies rely on economic census data, which may not provide precise information on urban spatial structure, leading to research that is either too broad or lacks accuracy in its findings. In contrast, this study utilizes mobile phone signaling data to track people’s movements in the city, resulting in a vast amount of data and more precise measurements. While the residential population is a crucial factor in urban spatial structure, this study only uses employment population data due to data limitations. However, given the growing job-housing imbalance in many megacities, it is equally essential to measure urban spatial structure from the residential population to determine whether residential and employment centers overlap and improve the match degree between population and employment. Of course, this study has some limitations that point to the directions for the future research. First, the mobile phone signaling data used in this paper contains only 2012 and 2016, which can only reflect the stage status of the master plan of Shenzhen, and future studies can evaluate the implementation effects of the master plan by add data for 2020. Meanwhile, this study did not examine the specific influencing factors affecting the employment spatial structure and fails to measure the employment centers from the perspective of functional connection. Future research can combine multiple space big data, such as POI data, night lighting data, etc., and identify urban centers from both morphological and functional perspectives. Meanwhile, factors that affect the urban spatial structure, such as urban rail transit, land use policy, accessibility and other factors, should be considered in future studies.

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9. Foley, D.L.: An approach to metropolitan spatial structure. University of Pennsylvania Press, Philadelphia (1964) 10. Webber, M.M.: The urban place and the nonplace urban realm. University of Pennsylvania Press, Philadelphia (1964) 11. Bourne, L.S.: Internal structure of the city: readings on space and environment. Oxford University Press (1971) 12. Green, N.: Functional polycentricity: a formal definition in terms of social network analysis. Urban Stud. 44, 2077–2103 (2007) 13. Roca Cladera, J., Marmolejo Duarte, C.R., Moix, M.: Urban structure and polycentrism: towards a redefinition of the sub-centre concept. Urban Stud. 46(13), 2841–2868 (2009) 14. Liu, K., Murayama, Y., Ichinose, T.: Using a new approach for revealing the spatiotemporal patterns of functional urban polycentricity: a case study in the Tokyo metropolitan area. Sustain. Cities Soc. 59, 102176 (2020) 15. Sun, B., Shi, W., Ning, Y.: An empirical study on the polycentric urban structure of Shanghai and strategies in future. In: Urban Planning Forum, pp. 58–63 (2010) 16. Vasanen, A.: Functional polycentricity: examining metropolitan spatial structure through the connectivity of urban sub-centres. Urban Stud. 49(16), 3627–3644 (2012) 17. Xinyi, N., Liang, D., Xiaodong, S.: Understanding urban spatial structure of shanghai central city based on mobile phone data. China City Plan. Rev. 24(3) (2015)

Author Index

A Ababio, Benjamin Kwaku 1301 Almeida, Laura 1737 Ampratwum, Godslove 811 An, Dongyang 1109 B Bai, Jing 85 Bai, Ju 938 Bao, Jianqiu 856, 1553 Bian, Shiyu 1615 Bin, Wei 164 Bo, Qiushi 1538 Bu, Zengwen 466 C Cai, Xi 484, 1783 Chan, Albert P. C. 353 Chan, Daniel W. M. 1396 Chang, Xingyu 786 Chen, Bin 1651 Chen, Chunmei 1797 Chen, Haotian 840 Chen, Hongyu 265 Chen, Jiawei 765 Chen, Jindao 1585 Chen, Junjie 1, 125 Chen, Kaiwen 765 Chen, Ke 280, 431 Chen, Limei 1147 Chen, Penglu 561 Chen, Qiwen 1262 Chen, Run 300, 366 Chen, Siwei 1357 Chen, Songchun 11 Chen, Yajun 501 Chen, Yang 235, 1682 Chen, Yuzhe 1214 Chen, Zhi 1027 Chen, Zhiwei 938

Chen, Zilin 1673 Chen, Ziwei 1747 Cheng, Baoquan 96 Cheng, Dahao 96 Cheng, Jack C. P. 1692 Chi, Cheryl Shu-Fang 1701 Chong, Dan 104, 1278 D Dai, Mingsen 440, 998 Das, Moumita 1692 Deng, Hui 1109 Deng, Xinran 911 Deng, Yichuan 1109 Di, Keyi 801 Ding, Zhikun 57, 823 Dong, Kexin 1419 Dong, Yuanyuan 501 Dong, Zhiming 125 Duan, Huabo 85 F Fan, Jiamin 776 Feng, Shuang 1123 Figueiredo, Karoline 1462 Fu, Yonglin 1 G Gao, Wenjun 340, 379 Gao, Wenmin 265 Ghansah, Frank Ato 927, 1301 Goh, Cheng Siew 405 Gong, Xiaoqiang 466 Gong, Xingbo 1692 Gu, Haijun 727, 998 Guo, Bao 911 Guo, Hongling 11 Guo, Zhenchao 1163 Guo, Ziyang 11

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Li et al. (Eds.): CRIOCM 2022, LNOR, pp. 1811–1815, 2023. https://doi.org/10.1007/978-981-99-3626-7

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H Haddad, Assed 1462 Han, Qi 1382 Han, Yilong 1701 Hao, Jian Li 1328 Hao, Tong 1134 He, Feng 85 He, Hongman 1123 He, Hui 353 He, Qinghua 353, 1419 He, Xin 765 Hong, Jingke 235 Horan, Peter 669 Hu, Bin 501 Hu, Xiaowei 988 Hu, Xin 1239 Huang, Guanying 1448 Huang, Yihao 104 Huang, Yuang 856, 1553 Huang, Yukuan 885 Huang, Zhi-yu 1055 Huang, Zhiyu 199 Huangfu, Dongmei 74 Huo, Tao 938 Huo, Xiaosen 484, 602, 786, 960, 1134, 1315, 1783 I Ibrahim, Abubakar Sadiq 1176 Ibrahim, Muhammad Nasir 1176 J Jiang, Jiannan 951 Jiao, Liudan 484, 602, 786, 960, 1097, 1134, 1315, 1522, 1783 Jing, Wei 1011 K Kan, Hongsheng 501 Kentie, Nick 1382 L Lai, Tianxin 235 Lai, Yani 633, 706, 1797 Laovisutthichai, Vikrom 533 Lau, Stephen Siu Yu 533 Le, Khoa N. 1328, 1737, 1758 Leng, Rong 1538 Leung, Charissa Chi Yan 1682

Author Index

Li, Chen 431 Li, Dezhi 1448 Li, Guijun 649 Li, Gunjun 574 Li, Hanlin 431 Li, Haotian 150 Li, Haoxiang 998 Li, Hongxia 199 Li, Hongyang 104 Li, Huili 134 Li, Jiawen 1419 Li, Jiayu 313, 547 Li, Long 867, 1198 Li, Mengxue 484, 1783 Li, Miao 720 Li, Qi-li 1055 Li, Qili 199 Li, Qin 1370 Li, Shenghan 561, 1147, 1214, 1602, 1638 Li, Shengnan 960 Li, Shuo 1163 Li, Xiangyu 1660 Li, Xiao 173, 1027 Li, Xiaomeng 776 Li, Yan 1055 Li, Yanmin 466 Li, Yinbo 1701 Li, Yuxuan 1673 Liang, Dong 1231 Liang, Qi 389, 1370 Liang, Yingzi 695 Liao, Peiyi 1278 Liao, Shiju 313 Liao, Zhaoqian 1248, 1723 Lin, Xiao 11 Liu, Boyang 951 Liu, Chang 291 Liu, Chunlu 669 Liu, Guo 1239 Liu, Hao 1405 Liu, Jian 85 Liu, Lei 1737 Liu, Lu 189 Liu, Rui 253, 1055 Liu, Tongfei 324 Liu, Xinqi 452 Liu, Yan 679 Liu, Ye 199, 1055 Liu, Ying 1522

Author Index

Liu, Yongqi 856, 1553 Liu, Yuhan 1692 Liu, Yuyang 1538 Lou, Jinfeng 340, 927, 1301 Lu, HongLin 189 Lu, Qian 1499 Lu, Weisheng 1, 125, 340, 379, 533, 927, 1186, 1301, 1437 Luan, Haiying 867, 1198 Luo, Chunxi 898 Luo, Hanbin 431 Luo, Jungang 720, 1222 Luo, Lan 1538 Luo, Weijia 1673 Luo, Xiaowei 96 Luther, Mark B. 669 Lv, Ping 615 Lv, Xiaoyue 1723 M Ma, Junjie 1673 Ma, Mingxue 1758 Ma, Songling 40 Ma, Xiaoteng 649 Ma, Xiaozhi 164 Ma, Xinyao 173 Mackee, Jamie 1627 Mao, Chao 173 Mao, Peng 1262 Matthews, Jane 669 Mei, Lin 389 Meng, Donghan 649 N Nawaz, Ahsan 1176 Nguyen, Nhung T. T. 475 Nie, Zhenjun 225 Niu, Yongning 856 O Osei-Kyei, Robert 811, 1758 P Palaco, Angela 1710 Pan, Wei 452, 516, 1357 Pan, Xing 988 Pan, Yipeng 1 Pang, Ben 253 Peng, Peng 911

1813

Peng, Ziyu 1186 Poon, Chi Sun 1357 Q Qiu, Jun 615 Qiu, Yuanyuan 389 Qu, Xinru 1576 R Rong, Lihui 74 Ruan, Yanling 199 S Sang, Meiyue 313, 547 Santoso, Djoen San 720, 1222 Shan, Yan 739 Shao, Lijia 1248 Shao, Yixiao 431 Shen, Chen 1660 Shen, Liyin 313, 1747 Shen, Luoxin 988 Shen, Wenxin 1615 Shi, Shuai 24 Sing, Michael 1627 Siqi, Jia 975 Song, Dianwei 561 Song, Hecai 720 Song, Yanqiu 574 Su, Hao 96 Su, Xing 776, 1176, 1566, 1710 Su, Yang 749 Sun, Chengshuang 951 Sun, Hui 695 T Taibao, Sun 749 Tam, K. L. 533 Tam, Vivian W. Y. 475, 811, 1328, 1462, 1737, 1758 Tan, Liyue 1615 Tan, Tan 280 Tan, Yi 561, 1147, 1214, 1602, 1638 Tang, Liyaning Maggie 1627 Tang, Llewellyn 150 Tang, Xu 1186 Tao, Xingyu 1692 Teng, Yue 235, 1357 Thomas Ng, S. 1448 Tian, Jun 988

1814

Tian, Qian 695 Tran, Cuong N. N. 475 W Wan, Kangda 1615 Wang, Chen 1475 Wang, Deliang 1222 Wang, Haoyu 1475 Wang, Hey Yee 405 Wang, Hongdi 1084 Wang, Jiajia 1651 Wang, Jiayuan 366 Wang, Jinyu 739 Wang, Jun 1328 Wang, Lina 1396 Wang, Qianqian 727 Wang, Ruili 1767 Wang, Ruiyang 24 Wang, Shuqi 867 Wang, Tengteng 1198 Wang, Xinrui 57 Wang, Xinyu 1315 Wang, Yuhong 1682 Wang, Zheng 669 Wang, Zihao 1291 Webster, Chris 1186 Wei, Guanglan 74 Wen, Li 1069 Wen, Shu 1163 Wen, Xinping 823 Wong, Peter Kok-Yiu 1692 Wong, Saika 1566 Wu, Chengke 173, 1027 Wu, Chunlin 898 Wu, Fan 1673 Wu, Huanyu 300, 366, 856, 1553 Wu, Huicang 695 Wu, Liu 1097, 1522 Wu, Liupengfei 340, 379, 927 Wu, Weidong 911 Wu, Ya 1097, 1315, 1522 Wu, Yifei 633 Wu, Yuwei 324 X Xing, Weiqi 1328 Xiong, Zhongwei 574 Xiu, Dapeng 1011 Xu, Haoran 1042

Author Index

Xu, Jinying 280 Xu, Na 801 Xu, Qianqian 440, 998 xu, Wenyu 1638 Xu, Xin 765 Xu, Yiwen 1109 Xu, Yue 280 Xu, Zhuo 1239 Xue, Fan 1231, 1651 Xue, Yanan 1084

Y Yan, Diya 213 Yan, Xiaoli 265, 1069 Yang, Delei 1419 Yang, Jianxiong 1566 Yang, Lin 1248, 1723 Yang, Qing 1084 Yang, Xiaodong 134 Yang, Yi 679 Yang, Zhongze 1437 Yao, Fan 324 Yao, Jiayu 134 Ye, Wenyu 1123 Yi, Xiaoyue 150 Yi, Ziwei 1198 Yin, Mengtian 150 Yong, Qiaoqiao 300, 366 Yu, Bo 300, 602 Yu, Jingxiao 501 Yu, Qingyi 1163 Yu, Xu 593 Yuan, Chunbao 823 Yuan, Hongping 164 Yuan, Jingfeng 253 Yuan, Lei 466 Yuan, Lili 57 Yuhong, Wang 975

Z Zeng, Deheng 739 Zeng, Feiping 466 Zeng, Hui 324 Zeng, Wenhua 801 Zhan, Dongyue 417 Zhang, Beibei 417, 1405 Zhang, Bing 1499 Zhang, Bo 801

Author Index

Zhang, Guangtao 1485 Zhang, Haotian 679 Zhang, Lingyu 547 Zhang, Qian 291 Zhang, Qiqi 516 Zhang, Rui 1222 Zhang, Shang 440, 727, 998, 1042, 1291, 1475 Zhang, Shengxi 867 Zhang, Shuang 1627 Zhang, Shuhai 1011 Zhang, Xinyu 1097 Zhang, Xun 189 Zhang, Yang 1357 Zhang, Yaning 1027 Zhang, Yaolin 1602 Zhang, Yu 484, 602, 786, 1783 Zhang, Yuesong 1011 Zhao, Jinxian 501 Zhao, Lilin 998 Zhao, Weishu 911 Zhao, Wencheng 801 Zhao, Xianbo 213

1815

Zhao, Xiaojing 1576 Zheng, Chunmo 1566 Zheng, Lang 340 Zheng, Qi 280 Zheng, Sheng 840, 885 Zheng, Xian 898, 938 Zhong, Botao 988 Zhong, Qiqing 1042 Zhong, Xueyan 988 Zhou, Chenghao 225 Zhou, Hao 440 Zhou, Jingwen 765 Zhou, Junhong 633, 706 Zhou, Qianyun 1651 Zhou, Shanjing 1337 Zhou, Yang 1370 Zhou, Yao 85 Zhu, Jun 1419 Zhu, Weina 951 Zhu, Yi 1291 Zhuo, Jihuan 225 Zou, Patrick X. W. 40, 57, 1767 Zuo, Jian 57