Handbook of Smart Cities [1st ed. 2021] 3030696979, 9783030696979

Table of contents :
Preface
Contents
About the Editor
Contributors
Part I: Basic Concepts and Frameworks
1 Smart Cities: Fundamental Concepts
Introduction
Human Interaction
Fundamental Beginnings of the City
Qualitative and Quantitative Changes in Human Interactions Within the City
Data
Big Data
Information And Technology
Technology, Integrated Technology, and Responsive Technology
Architecture of a Technology Platform
Institutions
The Triple Helix
Institutional Logics Connecting Actors, Activities, and Roles
Climate and Energy
Introduction: The Green, Resilient Cosmo-Polity
The ``Old´´ Grid
The Smart Grid, Distributed Energy Resources, and the City
Conclusion
Summary
Glossary
References
2 Smart Cities Can Be More Humane and Sustainable Too
Introduction
More Humane and Sustainable Smart Cities
Live-Work-Play in the Same Area!
Sidewalks
Bike Lanes
Light-Engine Vehicles
Public Transport
Listen to Citizens´ Wishes, Interests, and Needs!
Deindustrialize your Mind!
9 a.m. to 5 p.m.
Schools
Tech Parks
Work and Employment
The Car
Cross Reference and Major Challenges
Demographics
Diversity and Priorities
Special Needs
Socialization
Conclusion
References
Further Reading
3 Smart Energy Frameworks for Smart Cities: The Need for Polycentrism
Introduction
Climate Change and Urban Energy Infrastructure
The Nature of the Challenge
Smart Grid and the Future of Smart Cities
Emerging Models for Urban Energy Transformation
Distributed Energy Resources
Energy Storage
Microgrids
Demand Response and Energy Management Systems
Smart Measuring Systems
Harvesting
Green Technologies
From Robustness to Resilience
A Polycentric Approach to Smart City Energy Governance
Conclusions
Cross-References
References
4 Urban Computing: The Technological Framework for Smart Cities
Introduction
The Sense-Analyze-Actuate Paradigm
Definition
Example
Optimizing the Use of Resources
Case Study: Optimizing Urban Energy
Sensing
Data Categories
Urban Sensing Modes
Networking
Internet of Things
Urban Platforms
Data Services
Integrated Urban Platforms
Analyzing: Intelligence
Prediction of Urban Resource Supply and Demand
Decision-Making
Control Theory
Exact Optimization Algorithms
Heuristic Algorithms and Artificial Intelligence
Ethical Implications
Actuating
Data Visualization
Human Interfaces
Consumer Interfaces
City Dashboards
Robotics/Autonomous Actuation
Conclusion
References
5 Smart Cities Data: Framework, Applications, and Challenges
Introduction
Smart Data Framework
Sensor Network Databases and Data Management
City Analytics
Deep Learning
Smart Visualization
GIS-Based Visualization
Quality and Veracity
General Data Protection Regulation (GDPR)
Smart Data Applications
Smart Government and Governance
Social Networks
Mobility and Transportation
Smart Environment
Smart Streetlights
Smart Homes and Smart Building
Smart Surveillance in Smart Cities
Privacy Challenges in Smart Cities
Security and Privacy Challenges
Privacy Threats
Privacy-Enhancing Technologies
Data Privacy in Data Sensing
Privacy and Availability
Conclusion
References
6 Smart Institutions: Concept, Index, and Framework Conditions
Introduction
Methodology
Literature Review
Selected Literature on Smart Cities
Smart Institutions in the Literature
A Working Definition of a Smart Institution
A Case Study on University Hospitals
General Considerations
A Review of the Case Study
An Index for Smart Institutions
Framework for Smart Institutions
Framework Conditions for UML and SSMU
Public Policy Supporting Innovations in Healthcare in Germany
Public Policy Supporting Innovations in Healthcare in Russia
Framework Conditions for UML in 2009 and 2017
Outstanding Academic Performance of UML
Openness of Academic Institutions for Collaborations
Willingness to Cooperate Across Disciplines
Support from Service Providers: The Leipzig Trade Fair
Other Framework Conditions of Relevance for a Smart Institution
Smart Institutions in Various Sectors of the Economy
Conclusion
Cross-References
References
Part II: Current Exemplary Smart Cities
7 Smart City Edmonton
Introduction
Definition: A Smart City is a Healthy City
Guiding Principles
Smart City Framework
Smart City Maturity Matrix
Smart City Ecosystem
Smart City Achievements
Edmonton´s Smart City Projects
Data and Technology
Data Accessibility and Sharing
Open Data
Shareable Solutions
Future-Proofing Technologies
Inclusive and Accessible Solutions
Data and Technology Partnerships
Standards for Data and Technology Solutions
Privacy, Security, and Ethics
Data Governance
Edmonton´s Open City Initiative
Security, Privacy, and Ethics Considerations
Resident and Community Engagement
Engagement Tools
Engagement Activities
Inclusive Engagement
Conclusion
8 From Invention City to Innovation City: The Case of Racine Wisconsin
Introduction
Racine, Wisconsin, Small Town USA
Advantages and Disadvantages of Smaller Urban Contexts
The Importance of Strategic Planning
Stakeholder Involvement
The Importance of a Middleman in Public Private Partnerships
Establishing City Priorities
Community Wide Connectivity
Energy and Sustainability
Smart Mobility and TF Century Transportation
Priority of Inclusivity
Conclusion
References
9 Urban Innovation Ecosystem and Humane and Sustainable Smart City: A Balanced Approach in Curitiba
Introduction
The Drivers for Smart Curitiba
Humane and Sustainable Smart City
Sustainable Development
Urban Innovation Ecosystem
Quadruple Helix as a Model to Bring Integration
Translating the Drivers into Policies and Strategies
Policy-Mix
Curitiba 2035 Strategic Plan
Translating the Strategies into Services and Projects
Smart Cities Institute
Curitiba Technopark and Vale do Pinhão
Startup Movement
ICITIES and Smart City Expo
Urban Projects
Conclusions
References
10 Holistic, Multifaceted, and Citizen-Centric Smart Taipei Strategies
The Strategy of the Taipei Smart City
Build a Smart City Ecosystem
Establishment of Smart City Management Office
Establish a Smart City Operation and Promotion Mechanism
Top-Down: Private Sector Operating Mechanism
Bottom-Up: Private Sector Operating Mechanism
Public-Private Partnership
Strengthening the Linkage of International Smart Cities
International Expositions
Exchange Visits
Cooperation Workshops
Taipei Smart City Achievements
Smart Government
Intelligent Road and Pipeline Management
Smart Streetlight
Pumping Station Automatic Monitored Control System
Feitsui Reservoir Smart Security Monitoring Control System
Taipei Free: Free Wi-Fi in Taipei Public Area
Data.Taipei Open Data Platform
Taipei Geographic Integration Platform
App.Taipei
Hello.Taipei - Taipei City Simple Petition System
Smart Social Housing
Smart Transportation
Smart Health and Care
Smart Education
Smart Campus
Innovative Education
Lifelong Education
Smart Payment
Smart Start-Up
The Future of Taipei Smart City
New Promotion Framework for Taipei Smart City with 1 Core+ 7 Key Directions
Continue to Promote Innovation Culture to Public Sector
Establish Sustainable Smart City Implementation Mechanism and Specification
Improve Public-Private Partnership
Strengthen PoC Effectiveness
Broaden Collaboration and Construction Scale
The Establishment of GO SMART
Conclusion
11 Smart City Transformation for Mid-Sized Cities: Case of Canakkale, Turkey
Introduction
A Mid-sized City: Canakkale, Turkey
A Smart City Transformation Initiative: ``Canakkale on My Mind´´ CASE
Visionary Leadership
Collaboration and the Role of the Private Sector
A Road Map to Smart City Transformation
Phase 1: Understanding
Phase 2: Vision
Phase 3: Strategy
Critical Success Factors and Challenges
Governance Models for Mid-sized Smart Cities
Successful Cases of Smart City Transformations
A Model for Turkish Mid-sized Cities: Case of Canakkale
Conclusion
References
12 Stockholm: Smart City
What Do We Consider a Smart City?
Plan for a Smart and Connected City
Developed in Cooperation
Brochure: Smart & Connected (https://international.stockholm.se/globalassets/ovriga-bilder-och-filer/smart-city/brochure-smart...
What Makes Stockholm a Super Smart City?
Extensive Fiber Network
E-Services
Examples of E-Services
Preschool Portal
Residents´ Parking Permits
Report Problems in Traffic and Outdoor Environment
Radon Reading Search
Heat Pump License Applications
Care Diary
Apply for a School
Apply for a Building Permit
Komet: Web-Based Parent Training
Online Applications to Art School
Open Data
Data per Area
Culture and Archive Data
Population Data
Traffic and Parking Data
Environmental Data
Activities and Satisfaction Surveys
Geodata
The Stockholm Open Award
Innovative Solutions and International Smart City Cooperation
Hammarby Sjöstad
Hammarby Sjöstad: A Neighborhood with Integrated Environmental Solutions
Stockholm Royal Seaport
The GrowSmarter Project, Smart Refurbishment
Conclusions
Cross-References
Reference
13 Smart City Wien: A Sustainable Future Starts Now
Introduction
Vienna Is on Its Way
Smart City Wien Framework Strategy 2014
Smart City Wien Monitoring Process
Smart City Governance Is the Key to Success
Smart City Wien Framework Strategy 2019-2050
Thematic Fields
Energy Supply
Mobility and Transport
Buildings
Digitalization
Economy and Employment
Water and Waste Management
Environment
Healthcare
Social Inclusion
Education
Science and Research
Participation
Projects
E_OS: Renewable Energy from Sewage Sludge
Neighborhood Oasis
Smarter Together
WAALTeR: Active, Healthy Ageing
Sag´s Wien App
Citizens´ Power Plants: Community-Funded Solar Energy
Auto Bus
Smart Traffic Lights
Vienna Provides Space: Digital Twin
BRISE
Werkstadt Junges Wien: Co-Creating a Child and Youth Strategy for Vienna
Conclusion and Outlook
References
14 NEOM Smart City: The City of Future (The Urban Oasis in Saudi Desert)
Introduction
Research Methodology
Literature Review
NEOM Case Study
Content Analysis (Articles and Blogs)
Content Analysis (Pictures and Videos)
NEOM Smart City
Internet of Things Technologies
Smart Economy (SE)
Smart Living (SL)
Smart Governance (SG)
Smart Environment (SE)
Smart Mobility (SM)
Smart People (SP)
Discussions
Conclusion
References
15 Tehran in the Path of Transition to a Smart City: Initiatives, Implementation, and Governance
Introduction
Background of Smart Cities
Experiences and Measures for Smartening Tehran
Smart Governance
Smart Governance Challenges in Tehran
Smart Environment
Tehran Challenges in Implementing a Smart Environment
Smart Infrastructure
Infrastructure Challenges in Tehran Smartening
Smart Life
Tehran Challenges in Implementing Smart Life
Smart Transportation
Tehran Challenges in Implementing Smart Transportation
Smart Economy
Tehran Challenges in Implementing Smart Economy
Smart City Application in Fighting the Covid-19 Pandemic in Tehran
Conclusion
Governance Challenges
Citizenship Challenges
Technological Challenges
Economic Challenges
References
16 Rebranding Umhlanga as an Intelligent City
Introduction
Conceptualizing Smart Cities
The Developmental Perspective of Post-apartheid South Africa
Tools for Post-1994 Spatial Restructuring
The Current Realities of the Post-apartheid City
Background to eThekwini Municipality
Methodology
The Planning Perspective of Umhlanga
Insight into Umhlanga
Umhlanga: Responding to the Tenets of New Urbanism
Transport Sustainability of Umhlanga
Umhlanga as a Communication Node
Umhlanga and the Non-place Urban Realm
Safety and Security
Housing and Quality of Life
PPPs: A Winning Card for Smart Cities
Concluding Remarks
References
17 Bandung Smart City: The Digital Revolution for a Sustainable Future
Introduction
Songdo, South Korea
Sejong, South Korea
Masdar, UAE
Amsterdam, The Netherlands
San Francisco, USA
Brisbane, Australia
The Concepts of Smart Cities
The Development of Smart Cities in Indonesia
The Concept of Bandung Smart City (BSC)
Features of Bandung Smart City
Bandung Command Center
LAPOR! (Layanan Aspirasi dan Pengaduan Online Rakyat/Community Online Complaint and Aspiration Service)
Single Number Emergency Call 112
Bandung Panic Button
Bandung Planning Gallery
Discussion: Challenges and Opportunities
Conclusion
References
Part III: Human Dimension
18 Social Inclusion in Smart Cities
Introduction
Social Inclusion and Smart Cities
ICT Standards as Tools for Social Inclusion in Smart Cities
Smart Mobility and Social Inclusion
Interconnected Public Spaces and Social Inclusion
Related Projects About Inclusion in Smart Cities
Inclusive Accessibility in Smart Cities
Melbourne Making Life Easy For The Disabled
Smart Cities Addressing Homelessness And Isolation
Opensidewalks
Alma Houses
5G Connectivity and Social Inclusion
One Atlanta
Yingtan: 5G-Enabled Digital Twin City
Apps and Inclusive Smart Cities
Blindsquare
Safe & The City (SatC) App
City4Age (Elderly Friendly City Services for Active and Healthy Aging)
Smart Cities and Women
Safetipin
Gender Smart Cities
Unmanned Kiosks: The Best Way to Join Citizens with Cities
Kiosks in Case of Emergencies
Kiosks Deployed as Smart Street Furniture
Interconnected Public Spaces (IP-Spaces)
Elders´ Demographic Facts and Their Connection to Smart Cities
Elders´ Oriented IP-Spaces
Elder Activities
Elder Activities in the Context of IP-Spaces
Technological Interfaces for IP-Spaces
Audiovisual Accessibility
Gestural Interfaces for IP-Spaces
Related Legislation with the Use of Technology in Interconnected Public Spaces
European Legislation
MUSA: An Inclusive Smart Bus Stop
MUSA Smart Bus Stop
MUSA Smart Bus Stop System Architecture
Sensorization of Buses
MUSA Transport Services
Interconnected Public Space Service in MUSA
MUSA Smart Stop and IP-Spaces Current Developments
Conclusions
References
19 Malaysia Smart City Framework: A Trusted Framework for Shaping Smart Malaysian Citizenship?
Introduction
Literature Review
Malaysia Smart City Framework and Citizenship
Citizenship and The Nations-of-Intent in Malaysia
Methodology
Findings and Discussions
General Frames of Malaysian Smart City Policies and Strategies
Malaysia´s Citizenship Framing in the MSCF
Overall Themes on the Relationship Between Smart City and Citizenship
Suggestions in Building Smart City and Smart Citizenship in Malaysia
Conclusion and Contributions
References
20 Making Smart Cities ``Smarter´´ Through ICT-Enabled Citizen Coproduction
Introduction
ICT-Enabled Coproduction
The Concept of Coproduction
The Adoption of ICT to Coproduce
Characteristics of ICT-Enabled Coproduction
The Process of ICT-Enabled Coproduction
Direct Interaction Between the Coproducing Actors
Motivated Coproducing Actors
Shared Resources
Joint Decision-Making Process
Potential Outcomes of ICT-Enabled Coproduction Through the Lenses of Public Values
Advantages of ICT-Enabled Coproduction
Challenges of ICT-Enabled Coproduction
ICT-Enabled Coproduction Initiatives
The Case of ``Leuven, Maak het Mee,´´ Belgium
The Case of ``SmartBike,´´ Belgium
Concluding Remarks
References
Part IV: Energy Dimension
21 Smart Cities and the Challenge of Cities´ Energy Autonomy
Introduction
The Concept of Smart City
Smart Energy-Autonomous Cities
Need for Energy Smart Cities
Urbanization
Challenges for the Transition to Smart Cities
Sociopolitical Challenges
Financial Challenges
Technological Challenges
Environmental Challenges
Smart City Planning
Transition to Energy Smart Cities
Methodologies and Tools in Buildings of Smart Cities
General Description
Energy Saving and Management Tools
Energy Management and Saving in the Building Sector
Decision Support Systems (DSS)
Existing Methodologies and Tools
Evaluation
The Greek Reality: Greek Legislation - Directive 2010/31/EU
Conclusions
References
Legislation
22 Energy Harvesting in Smart Cities
Introduction
Kinetic Energy Harvesting in Urban Environments
Kinetic Energy from Human Activities
Running Vehicles
In-Pipe Water Flow
Airflow
Structural Vibrations
Power Management for Energy Harvesting Systems
Voltage Converter
MPPT Circuit
Charge Management Circuit
Wireless Sensing and Communication
LoRa for Smart City Applications
Long-Range Connectivity
Low Power
Low Cost
Good Reliability and Robustness
High Scalability Potential
Example Applications
Building Monitoring
Urban Greenhouse Gas Monitoring
Bridge Condition Monitoring
Urban Water Meter Monitoring
Urban Environmental Monitoring
Human Surroundings Monitoring
Adoption of Energy Harvesting Powered LoRa
Conclusion
References
23 Greenhouse Gas Mitigation in Smart Cities: Political Economy and Strategic Mitigation Alliances
Introduction: The Smart City in the New Climate Regime
The Smart City, Renewables, and Greenhouse Gas Emissions
The Heterogeneity of Smart City GHG Emissions
Smart Cities, Electrical Power, and the Grid: Mitigation
Governance of the US Electrical Grid
Ohm´s Law
Governance: Capital Accumulation and the Hope Framework
The Contradiction of Renewable Technologies with Hope Framework
Electrical Utility Reconfigurations of the 1970s and 1990s
1970s: Economical Rationalization of Pricing
1990s: Regulating Market Discipline
City as Producer of Electrical Power
City as Consumer of Electric Power
Conclusion: The Smart City as Mitigator
References
Part V: Technology Dimension
24 Technology: Person Identification
Introduction
Technology for Identification in Smart Cities Cloud Services
The Identification Technologies in Systems of Internet of Things
Taxonomy of IoT Authentication Schemes
Authentication Schemes Survey
Smart Grids
RFID and NFC-Based Applications
Vehicular Networks
Smart Homes
Wireless Sensor Networks
Mobile Network and Applications
Identification Using Video Surveillance Systems of a Smart City
Centralized Architecture of Surveillance System
Decentralized Surveillance System Concept
Decentralized Surveillance Systems with Edge TPU
Algorithm of Searching Cells with Motion Detection
Face Detection
3-Tier Decentralized Surveillance Systems
Surveillance Systems Integration into ERP
Conclusions
References
25 User Interfaces in Smart Cities
Introduction
A Day in a Smart City
How to Read This Chapter
IoT, ICT, and a System of Systems
Characteristics of a Smart City
Public Utilities
Mobility
Public
Private
Health and Safety
Health
Public Safety Services
Communication and Amber Alert
Quality of Life
Playful
Social
People of a Smart City
Tech Literacy
Ability
Local Versus Global
Local
Global
Resident Versus Government
Resident
Government
Work Versus Leisure
Work
Leisure
Interface Trends for Smart Cities
Tangible User Interfaces (TUI)
Tactile Internet Systems
Urban Interaction Using TUI
Health/Global/Residents
Geospatial Tangible Urban Planning
Local/City Planning
Participative Design
Tangible Interfaces for Three-Dimensional Interaction
Ambient Interfaces
Ambient Intelligence
Context-Aware Middleware for Ambient Intelligence
Resident Employee
The Smart Bus Stop
In-Environment Interface (IEI)
Environment-Dependent Interfaces
Environment-Independent Interfaces
Ambient Actuation Through ShapeBots
Ambient Play
Ambient Surveillance
Ambient Intelligence in Healthcare
Health/Ability/Residents
Healthy Aging
Virtual Humans and Agents for an Aging Population
Environment Scale Interfaces
Parks and Recreation
Pervasive Games
LiftTiles
City as a Playground
Digitally Enhanced Communal Spaces
Mobile/Wearable
Smartphone as a Data Collection Mechanism for Security Resident
Government Concerns
Digital Democracy
Wearable Health Monitoring
Social Wearables
Co-creation Wearables
Wearable Interfaces for Ubiquitous Gaming
Extended Reality
Augmented Reality
Virtual Reality
Material Based
Sustainable Interfaces
Everyday Objects
Smart Material Interfaces
Data Representation and Physicalization
Healthcare Applications
Resident Engagement and Resident Participation
Government and Professionals
Nanotechnology for Health and Smart Cities
Conclusion
References
26 Vehicular Network Systems in Smart Cities
Introduction
Vehicular System Layered Architecture
Development Environment for Vehicular Networks
Vehicular WSN and WDSN System
VANETs
Vehicular Communication
IPv6
Wave
Routing
RPL
Network Technologies for Enabling Vehicular Communication in a Smart City
Network Classes
Short-Range Technologies
Long-Range Technologies
Security in Vehicular Networks
Vehicular Networking Applications
Challenges
Conclusions
References
27 How Technology Makes a Difference: Digital, Agile, and Design Thinking
Introduction
Where Technology Provides Lot of Opportunities
Where Technology Makes a Difference
How Technology Connects with Cities
What Are the Keys to Unlock Digital Transformation
IoT
Why There Is a Tremendous Growth in IoT
Why There Is an Acceleration of IoT (Especially in the Last Few Years)
Why IoT Has a Bright Future Ahead
IoT Means
How Does IoT Works
Differences Between ToI, M2M, and IoE
How to Manages the Environment
How to Builds an Eco-Friendly Environment
Applications of IoT in Different Industries
How Is IoT Technology Classified Into
How Should Be Enable IoT Growth
IoT Are Achievable in the Near Future
Growth of the Global IoT Market Over the Years and Expectations
Role of Government and Regulatory Authorities
Deployment and Adoption of IoT Will Be Different Across Geographical Regions
Value Chain: A New Ecosystem
Data Collection
Market Opportunities
Gartner Recommendation: Top 10 Technology Trends in Data and Analytics
Top 10 Digital Transformation Trends for Australia and New Zealand by IDC
Strategy
Why Digital Strategy Is Very Important for Companies
What Is Digital Transformation
Which Type of Strategy and When
One of the Hardest Things About Strategy
Digital Strategy Is Not Equal to IT Strategy
Is Digital Edge Different from Digital Automation
Challenges
Agile in Digital Transformation
How Design Thinking Helps Digital Transformation with Five Steps
Conclusion
References
28 Building Smart City Solutions with Focus on Health Care and GDPR
Introduction
Source of Data
Simplex and Complex Data Sources
Process of Data Collection
Data Publish Cycles and Thresholds
Data Model and Converters
Private and Public Data Solutions
Anonymization of the User Data
Anonymization Process
Data Mining Capabilities Over Anonymized Data
Geolocation Data Anonymization
Extensibility of the Architecture
Microservice Architecture
Scalability
Continuous Integration
Event Communication via Webhooks
Extend a Smart City with a Smart Health Care Solution
Med-i-hub System
Med-i-hub Sensor Layer
Med-i-hub Service Layer
Data Storage
HL7 Standard and FHIR Support in Med-i-hub System
Smart Personal Assistant
Measurement Data Classification
The Role of the Med-i-hub System in the Smart City Ecosystem
Conclusion
References
29 Smart Mobility Ontology: Current Trends and Future Directions
Introduction
Ontology Components
Ontology Classification
Ontology Developing Approaches
Direction of Taxonomy Hierarchy
Source Type of Ontology
Text Documents
Schemata
Ontology Languages
RDF Language
OWL Language
Ontology Design Procedure
Mobility Ontologies
Foundation Ontologies
Geospatial Ontology
Time Ontology
Weather Ontology
Units of Measure
Change
Household and Dwelling Ontology
Organization Ontology
Stakeholder Ontology
Trip
Transportation Physical Network Ontologies
Road Transportation Network
Railway Transportation Network
Cycling and Pedestrian Network
Transit System
Freight Transportation System
Road Service Area
Future Directions in Smart Mobility Ontology
Mobility Sensors
MaaS
Autonomous Robotics
Connected Roadways and Internet of Vehicles Technologies
Conclusion
References
Part VI: Data Dimension
30 Towards Autonomous Knowledge Creation from Big Data in Smart Cities
Introduction
Big Data in Smart Cities
Smart Cities and Big Data Challenges
The Vision of (Autonomous) Knowledge Creation
Example Scenarios
Anomaly Detection
Activity Recognition
Remaining Useful Life and Survivability
Desired Solution Properties
Wisdom of the Crowd Framework
Examples and Results
Fault Detection and Failure Prediction for a Fleet of City Buses
Transfer Learned Knowledge Across Diverse Fleets
SAFARI Framework for District Heating
Outlook and Conclusions
References
31 Interoperability Effect in Big Data
Introduction
Characterizing Data
Data in Vs
Data Structure
Characterizing Interactions
Smart Applications and Urban Computing
Integration, Interoperability, and Coupling
Application Interactions in Big Data Contexts
Interoperability Frameworks
The NIST Big Data Interoperability Framework
A Layered Interoperability Framework
Big Data Standards
Where Should Be Interoperability Headed?
Conclusion
References
32 Data Protection and Smart Cities
Introduction
From Privacy To Data Protection
Territorial Scope of The General Data Protection Regulation
General Data Protection Regulation
What Is An EU Regulation?
Structure of the Regulation
Definition of Personal Data and Processing of Personal Data
Choosing the Basis for Processing in the Context of Smart Cities
Rights of the Data Subject
Smart City Service Provider or Equipment Vendor as a Potential Controller or Processor
Data Protection Impact Assessment
Designation of the Data Protection Officer
Smart City, IoT, and Privacy
Compliance: Where Do We Start?
Conclusion
References
33 Multitier Intelligent Computing and Storage for IoT Sensor Data
Introduction
Applicability Use Cases
Healthcare and Telemedicine
Public Safety and Disaster Response
Smart Transportation
Smart Gardening
Multitier Reference Framework for IoT Data Processing in Smart Cities
Computing Continuum for IoT Data
Cloud Computing
Regional Cloud Computing
Edge/Fog Computing
Virtualization Techniques
Pre-virtualization
Hypervisor-Based Virtualization
System-Based Containerization
Application-Based Containerization
Intelligence for Smart Cities
Supervision in Machine Learning
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Training in Machine Learning
Online Training
Offline Training
Generalization in Learning
Instance-Based Learning
Model-Based Learning Reinforcement Learning
Federated Learning
Deep Learning
Learning in Different Tiers
Healthcare
RoboNet
Federated Learning on Multiple Tiers
Intelligent Drones
Data Management and Storage
Data Acquisition
Unified Access Platform
Bluetooth Low Energy
Multitier Storage
Device Tier
Fog Tier
Mashups
Data Analytics on Fog-Stored Data
Cloud Tier
Topic-Based IoT Storage
IoT Data Security in Motion and at Rest
Conclusion
References
34 Deep Learning for LiDAR-Based Autonomous Vehicles in Smart Cities
Introduction
Deep Learning for Object Detection
What Is Deep Learning?
Convolutional Neural Networks
What Is Object Detection?
Training a CNN for Object Detection
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Transfer Learning
Data Augmentation
Inference at the Edge
LiDAR in Autonomous Vehicles
Sensor Types in Autonomous Vehicles
Cameras
Radar
Ultrasonic
LiDAR
LiDAR Fundamentals
LiDAR´s Relevance in Industry
LiDAR and Deep Learning for Autonomous Vehicles
Autonomous Vehicles in the Smart City Ecosystem
LiDAR for Pedestrian Detection
Case Study: Creating a Deep Learning Model for LiDAR-Based Inference
LiDAR Selection
Parsing and Visualization
Sample Data and Labeling
Preprocessing
CNN Selection
Dataset Creation and Labeling
Training
System and Performance
Analysis
Conclusion
Future Research Directions
References
Part VII: Institutions Dimension
35 Corporate Social Responsibility (CSR): Governments, Institutions, Businesses, and the Public Within a Smart City Context
Introduction
Smart Cities: Promises from Business
Smart Cities: The Publics Role and Citizenship
Business and CSR Responsibility
CSR: A Single Organizational View
CSR: As Multi-Stakeholder Engagement
Meeting Community and Business Expectations
Conclusion
References
36 Social Emergence, Cornerstone of Smart City Governance as a Complex Citizen-Centric System
Introduction
The Smart City: A Collection of Smarties or a System?
How Smart Were the Cities of the Past?
Cities as Far from Equilibrium Adaptive Systems
What Makes a City Smart?
Why Do We Need Strong Citizen-Based Interactions Within the Urban System?
Rethinking the Smart City Concept from the Perspective of Citizens´ Bottom-Up Involvement: The Cases of Barcelona (Spain) and ...
The Case of Barcelona: Decidim.Barcelona
The Case of Medellín: ``City for Life´´
Social Emergence as a Proposed Lens for a Finer Grained Understanding of How Bottom-Up Dynamics Within Smart Cities Initiate a...
The Generative Emergence Model, as a Promising Way to Study Processes of Emergence in Socially Smart Communities
Conclusion
References
37 Exploiting Big Data for Smart Government: Facing the Challenges
Introduction
Big Data: Views and Usage
Existing Views on Big Data
Issues when Using Big Data
A Discerning Definition of BD
Databases
Inductive Reasoning
Models
In Summary
Challenges of Using Big Data in Practice
Data Quality Issues
Evolving Semantics
System Realities
Statistical Truths
Towards a Framework for Responsible Use of Big Data
Achieving Transparency
Dealing with Uncertainty
Illustrative Examples
Conclusion and Future Research
References
Part VIII: Smart Cities Infrastructure Ecosystem
38 Feeding a Smart City
Introduction
A Brief History of Urbanization
Agricultural Revolutions
Agriculture 1.0
Agriculture 2.0
Agriculture 3.0
Agriculture 4.0
Food and Employment
Food Regulation, Fraud, and Deception
Food and Religion
Judaism: Kosher
Christianity
Muslim: Halal
Hinduism
Sikhism
Buddhism
Quakers
Amish
Subsidies
Monoculture
Retail Practice, Shelf Life, Dates, and Food Processing
Shelf Life
Retail Practice
Processing of Food
Preserving Food
Meat
Extending the Shelf Life of Meat
Sheep/Lamb/Mutton
Cow/Beef
Pig: Pork, Ham, and Sausages
Blood and Offal (Internal Organs)
Alternatives: Bison, Camel, Deer (Venison), Goats, and Kangaroo
Poultry
More Than Food
Fish
Milk and Dairy Products
Milk Consumption Around the World
Nutritional Value of Milk
Dairy Products
Initial Milk Processing
Beyond Pasteurization
Eggs
Egg Regulation
Egg Preservation
Plant Based Foods: Varieties and Genetics
Legumes
Brassicas
Genetics
Seeds, Grains, Nuts, and Bread
Seeds as Food
Culinary Nuts
Bread
Animal Feed
Grass
Grain as Animal Feed
Food Security, Continuity, and Transparency of Supply
Endangered Crops
Vitamins, Allergies, Intolerances, and Deficiencies
Vitamins and Dietary Supplements
Plastics, Carbon, and the Future of Local Food
The New Local
Conclusion
References
39 IoT and Blockchain-Based Smart Agri-food Supply Chains
Introduction
Literature Review
The Architecture of Smart Agri-food Supply Chains
The IoT Architecture and Communications
RFID
LoRa
MQTT
The Key Components of Blockchain
Merkle Tree
Timestamp
Asymmetric Cryptography
Consensus Mechanism
Smart Contract
Key Data Flows
Product Flow
Information Flow
Finance Flow
Practical Applications
The Quality Assurance and Traceability of Agricultural Products
E-commerce of Agricultural Products
Credit Issues in Transactions of Agricultural Products
Agricultural Insurance
IBM Watson IoT and Blockchain Platform
Ant Group: A Pioneer of the Blockchain-Enabled Supply Chain in China
Challenges of Blockchain-Based Smart Agri-food Supply Chains
Legal Provisions Lag Behind Blockchain´s Evolution
The Cost of Devices and Maintenance
Security Concerns
Storage, Throughput, and Velocity
Conclusion
References
40 A Primer on Smart Contracts and Blockchains for Smart Cities
Introduction
Blockchain
Blockchain Networks
Consensus Algorithms
Consensus in Permissioned and Permissionless Networks
Hyperledger
Blockchain-as-a-Service (BaaS) Offering
Bitcoin
Ethereum
Corda
Iota
Developer Tools and Environments
Platform and Tools
Programming Languages
Popular Use Cases of Blockchain
Smart Contracts
Endorsement Phase
Validation
Calculating the Importance of a Smart Contract
Use Cases
Writing Smart Contracts
Using Basic Data Types
Smart Contract Functions
Add a New Service
Discussion
Pitfalls
Challenges
Advantages and Disadvantages of Smart Contracts
Conclusion
References
41 Technology-Led Disruptions and Innovations: The Trends Transforming Urban Mobility
Introduction
Global Transport Challenges
Rapid Urban Population Growth
Road Safety
Traffic Congestion
Ageing Infrastructure
Environmental Emissions
Transport Systems Resilience
Transport Energy Provision
Climate Change
Public Health and Pandemics
City Logistics and Urban Supply Chains
Limitations of Traditional Approaches
Emerging Trends
Technology-Led Opportunities and Innovations
Smart Cities: Context and Definitions
Early Developments in Intelligent Transport Systems
Advanced Traffic Management Systems
Advanced Traveler Information Systems
Advanced Vehicle Control Systems
Disruptive Mobility
Self-Driving Technologies
Levels of Vehicle Automation
Self-Driving Algorithms: The Key Differentiator
The Promise of Automated Vehicles: The Moral Imperative
Vehicle Electrification
Changing Consumer Attitudes
Broader Access to Charging Infrastructure
Stricter Regulatory Policies
Blockchain
Internet of Things
Mobile, Cloud and Fog Computation
The Sharing Economy and Collaborative Mobility
Autonomous on-Demand Shared Mobility Opportunities
Artificial Intelligence
Crowd Sourcing, Urban Sensing, and Smart City Logistics
Impact of Technology on Freight and Logistics Services
Benefits of Technology-Driven Transport Solutions
Conclusion
References
42 Advances on Urban Mobility Using Innovative Data-Driven Models
Introduction
Data Acquisition
Use Case: Curitiba, Brazil
Complex Networks
Application to Public Transportation Systems
Link Streams
Background on Link Streams
Methodology
Use Case: Curitiba, Brazil
Origin/Destination Matrix Estimation
Application to Public Transportation Systems
Application to Private Transportation Systems
Triple Helix Model
Use Case: Curitiba, Brazil
Conclusion and Future Research
References
43 Towards Interoperability of Data Platforms for Smart Cities
Introduction
Relevant Concepts and Research Approach
Smart City
Data Platform
Interoperability
Methodology
Smart City Examples from Practice
Smart City in Santander
Smart City in Munich
Dismantling Interoperability for Smart City Data Platforms
A Mix of Approaches Allows for Omitting Known Pitfalls
Analyzing Examples from Practice
Santander
Munich
Conclusion and Key Takeaways
References
44 Future Urban Smartness: Connectivity Zones with Disposable Identities
Introduction
Part 1: Limits of Smartness
Part 2: Zones of Connectivity
Cold Spots
Properties of Cold Spots
Proximity Unplugged
Ambient Privacy
Wilderness
Trust
Unprogrammability
Playfulness
Value Proposition
The Park
The Trust Framework
Art for SmArt
Hot Spots
The Cruise and Passenger Ships Hybrid-Spots
Part Three: Disposable Identities
Conclusion
References
45 Problem-Driven and Technology-Enabled Solutions for Safer Communities
Introduction
Philosophy: Public and Open-Source, Privacy Compliance, and Community Consultation
Deployment of a Regional, Open, and Free-to-Use LoRaWAN Network
LoRaWAN Network Topology and Deployment in the Illawarra
Design of an Interoperable IoT Architecture
AIoT and Edge Computing for Early Warning Systems
Culvert Blockage Detection
Estuaries and Lagoon Management
Environmental Monitoring: Quality Watch, Pollution Stop, and iOyster
Gross Pollutant Traps Monitor
Water Quality Monitor
Low-Cost (Physicochemical) Sensing of Water Quality
Conclusion and Future Work
Live Data Informing Simulation: FloodAware
GAMA Platform
Modeling Flooding Events
The FloodAware Model
Algorithms
Optimization of Rainfall
Flow Method
Data
Calibration
Future Work and Integrations
Conclusion
References
46 Crowdsourcing for Smart Cities That Realizes the Situation of Cities and Information Sharing
Introduction
Background
Smart Cities and Cyber-Physical Systems
Sensing and Monitoring of Situations in Town
Environmental Statuses
Human Activities
Crowdsourcing
Model of CPS with Crowdsourced Mobile Sensing
Service Platform
Mobile Applications for End Users
Applications for Civil Administration
Case Study 1: Collecting Traffic and Road Conditions with Crowdsourced Drive Recording App
Purpose
``Drive around-the-corner´´: A Drive Recorder Application
Map with Event Information
Posting Event
Settings
Sensing Functions
User Data
Onboard Location and Motion Sensors
Movies
Website
An Example
Survey
Analyzing Road Surface Conditions
Feature Extraction and Selection
Classification
Experimental Results and Discussion
Case Study 2: Collecting Traffic and Service Status of Public Transportation with Crowdsourced Mobile App
Methodology
Beacons
Onboard Beacon
Beacon at the Bus Stop
Mobile Application
User Functions
Logging Functions
Scenario 1: Grasping Bus Location and Estimating Arrival Time
Scenario 2: Grasping Waiting Passengers at the Bus Stop
Scenario 3: Notifying Getting On/Off
Discussions
Cost
Accuracy
Acquired Data
Service Quality
Evaluation Index
Data Blank
Target Route
Results
Case Study 3: Collecting the Atmosphere in Town with Social Service App
Nicott: An LBS for Explorers in Town
Service Description
User Functions
Event Information
Sharing Posted Contents
Posting Content
Map
Sensing Functions
User Data
Onboard Location and Motion Sensors
Facial Feature Points
Experiment
Subjects
Creating Training Data Set
Classifying Facial Expressions
Conclusion
References
47 Layer-Based Reference Model for Smart City Implementation
Introduction
Small Cities and Rural Communities
Lighthouse Smart Cities and Challenges of Small Cities
Layer-Based Reference Model for Smart City Implementation
Technology Layer (TL)
Service Layer (SL)
Business Layer (BL)
Smart City Shaping Selection Process
Application of the Smart City Shaping Selection Process
Shared Usage of Renewable Energy
Pollution of the Environment
Enhanced Mobility for Cars, Bikes, and Pedestrians
Intelligent Public and Shared Buildings
Conclusion
References
Part IX: Ethical Challenges
48 ``Eyes and Ears´´: Surveillance in the Indian Smart City
Introduction
Technologies and Data in the Smart City
Data Collection Tools
Data Collection and Utilization
Governance and Technocratic Firms
Emerging Smart Cities in the Global South
The Indian Case
Introduction to India´s Smart Cities Mission
Surveillance Technologies in India´s Smart Cities
The Legal Framework of Data Privacy in India
The Srikrishna Expert Committee Report
The Personal Data Protection Bill (2019)
Discussion
Absence of Guidelines, Policy, and Legislation
Data Accountability, Security, and Privacy
Big Data and Corporations: Public Data, Private Profit?
Policing, Monitoring, and Community Targeting
Conclusion
References
49 Reclaiming the Smart City: Toward a New Right to the City
Introduction
Ethical Issues of Smart Cities for Citizens
Lefebvre´s Right to the City
Contemporary Lenses to the Smart City
The Right to the Smart City
Conclusion and Reflection
References
50 Application of the General Data Protection Regulation for Social Robots in Smart Cities
Introduction
Development of the EU GDPR and Its Effects on Citizens of Smart Cities
Technological Developments and New Data Protection Challenges
Legal Issues of Social Robots in Smart Homes
Case Study Analysis: Social Robots for Smart Citizens
Conclusions
References
Web Links
Court Cases
Part X: Bottle Necks and Potential Enablers
51 Optimization Problems Under Uncertainty in Smart Cities
Introduction
Optimization in Smart Cities
Location/Allocation of Urban Services
IoT and Opportunistic IoT (oIoT)
OMAs and oIoT Development
Jointly Exploiting OMAs and oIoT
Routing Problems in Smart Cities
Comparative Analysis of OMAs with Alternative Approaches
Decision-Making Under Uncertainty
Sources of Uncertainty
Optimization Paradigms for Decision-Making Under Uncertainty
Robust Optimization (RO)
Stochastic Programming (SP)
Multistage SP Embedding Discrete Choice Problems (msSPDC)
Does Uncertainty Complicate the Problems?
Does Uncertainty Matter?
An Innovative Extreme Value Theory-Based Deterministic Approximation Approach (EVTDA)
Assumptions
The EVTDA for msSPDC Problems
The EVTDA in Practice
Applicability of the EVTDA
Parameters´ Calibration
Results Obtained from the EVTDA Applications in Smart Cities´ Problems
Conclusions
References
52 Information Technology Macro Trends Impacts on Cities: Guidelines for Urban Planners
Introduction
Innovation and Cities
Basic Technology Concepts
Information Systems
Information Systems Services
Service Requirements
Regulation
Opportunities, Recommendations, and Challenges
Recommendations
Specific Recommendations
Challenges
Examples of Approaches for Solving Challenges
Conclusion
References
53 Advanced Visualization of Neighborhood Carbon Metrics Using Virtual Reality: Improving Stakeholder Engagement
Introduction
Background
Background
ZEN Definitions, ZEN KPIs, ZEN Pilot Project, and Stakeholder Participation
Tools for Stakeholder Participation
ZEN Definition and ZEN Key Performance Indicators (KPIs)
ZEN Pilot Project
Advanced Visualization for ZEN Carbon Metrics
Concept for the ZEN Toolbox
Virtual Reality
Method
Research Method
Data Source and LCA Method
Building LCA Database-Tool (bLCAd-Tool)
User Study and Questionnaire
VR Visualization of Buildings BIM Files and GHG Emissions
Technical Details
Technology
Software Architecture and Code
Event-Driven Programming
3D Model
System Interaction
Results
Virtual Reality Application Overview
Full View
ZEN View
ZEB View
Results of User Tests and Questionnaires
Conclusions and Discussion
Further Work
Appendix 1 ZEN Assessment Criteria and Key Performance Indicators (Wiik et al. 2018a, 2019)
Appendix 2 ERD of the bLCAd-tool 3 (Løvhaug and Mathisen 2019)
Appendix 3 UML Diagram of ZENVR (Løvhaug and Mathisen 2019)
Appendix 4 Request for Participation in Research Project (Løvhaug and Mathisen 2019)
Appendix 5 Interview Guide (in Norwegian only) (Løvhaug and Mathisen 2019)
References
54 Smart City Needs a Smart Urban-Rural Interface: An Overview on Romanian Urban Transformations
Introduction
Towards a Smart Development of the Postsocialist Romanian Cities
Cities´ Dynamics During the Postsocialist Period
Spatial Reverberation of the Urban Changes on Urban-Rural Interface
Assessing of the Main Smart Results in the Current Urban Development
Defining a Smart Urban-Rural Interface in the Postsocialist Romania
Overview on the Dynamics of Postsocialist Cities and Their Urban-Rural Interfaces
Current Challenges of Cities and Urban-Rural Interfaces Accelerating Their Smart Development
Fundamental Role of Public Administration
Need of an Integrated Metropolitan Governance
Implications for Re-thinking Integrated Urban-Rural Planning
Conclusions
References
55 Journeys in the Age of Smart Cities: Some Fresh Perspectives
Introduction
Imagination Journeys
Introduction
Stories as Vehicles of the Imagination
Frameworks for the Imagination Journey
The Creative Innovation Development Process
A Smart City Application
Reflecting on the Journey
Entrepreneurial Journeys
The Age of Intelligence-Assisted Entrepreneurs
A Tale of Smart and Anti-smart Cities
Final Thoughts
Social Design Journeys with Future Customers
Cities as Complex Ecosystems
Social Design and Future Customers
Small-Scale Ecology Reactions on Large-Scale Challenges
Memory Journeys
Memory, Emotion, and Life
Current Research
Speculative Thoughts
Final Thoughts
Educational Journeys
Learning Experiences as Journeys
Example of Learning Journey Design in K-12 and Higher Education
The Future of Learning
Artificial Intelligence
Background
Smart Cities as Multidimensional Navigable Spaces
Smart City Data
Artificial Intelligence Solutions for Smart Cites
Final Thoughts
Internet of Things
Background
Challenges and Solutions
Final Thoughts
Wearables
Where We Are Now
An Illustrative Example of Current Research
Signposting the Future
Final Reflections
Smart Transport
Physical Journeys
Autonomous Systems
Vehicle Communication and Security
Challenges
Final Thoughts
Digital Twins
A Vision for the Future
Digital Twins
Future Directions and Challenges
Final Thoughts
Conclusion
References
56 Openness: A Key Factor for Smart Cities
Introduction
Openness and Smart Cities
Related Work
Defining Openness for Smart Cities
Dimensions: Transparency, Participation, Collaboration
Transparency
Participation
Collaboration
Intersections: Deep Participation, Citizen-Centric Services, Analytical Methods, and Tools
Deep Participation
Citizen-Centric Services
Analytical Methods and Tools
Implementing Openness: Some Examples
Deep Participation: Place and City Tool
Citizen-Centric Services: Open Data Portals
Analytical Methods and Tools: Participatory Air Quality Sensing
The Open City Toolkit (OCT)
Discussion
Digital Sovereignty
Balancing Interests
Harvesting Synergies
Conclusion
References
57 The Importance of Creative Practices in Designing More-Than-Human Cities
Introduction
A Brief History of City Visions
Resolving Tensions through Participatory Processes
New Methods for More-Than-Human Cities
Creativity, Creative Practice and Arts-Based Methods
Creativity in City Visions
Creative Practice in City Design
Arts-Based Methods in Practice
Empowering Youth to Express their Lived Experience
Designing Future Personas as Voice for the Voiceless
Conclusions
References
58 Influence of Smart Cities Sustainability on Citizen´s Quality of Life
Introduction
Smart Sustainable Cities and the Quality of Life in Smart Cities
Data and Method
Sample Selection
Research Methodology
Analysis of Results
Descriptive Statistics
Hypotheses Testing
Discussions and Conclusions
References
Part XI: Closing Words
59 Smart Cities: State of the Art and Future Challenges
Introduction
Part 1: Understanding the Basics and the Holistic Concept
Part 2: Understanding Its Components
Part 3 Understanding How to Evolve the Concept
Concluding Remarks
References
Index

Citation preview

Juan Carlos Augusto Editor

Handbook of Smart Cities

Handbook of Smart Cities

Juan Carlos Augusto Editor

Handbook of Smart Cities With 370 Figures and 91 Tables

Editor Juan Carlos Augusto Department of Computer Science Middlesex University London, UK

ISBN 978-3-030-69697-9 ISBN 978-3-030-69698-6 (eBook) ISBN 978-3-030-69699-3 (print and electronic bundle) https://doi.org/10.1007/978-3-030-69698-6 © Springer Nature Switzerland AG 2021 This work is subject to copyright. All rights are reserved 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 Switzerland AG. The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

“To Celeste and Axel, with the hope they live in a better world” London, December 2020

Preface

Our planet is transitioning through an exciting new phase in its rich evolutionary process. We humans have inhabited this place in the universe for some time and are among the most sophisticated living entities in existence, at least for now. A combination of powerful inner forces, some innate positive motivations, combined with some practical needs are fostering this new quest from humanity to upgrade these urban spaces called cities. Cities come in different sizes and each one is significantly different to each other. All are big and complex conglomerates of humans. However, each one has a different history, inhabited by a different mix of people, with different needs, with a different mix of available resources, existing under different governments that shape in some way or another which aspirations and actions are fostered or discouraged. Throw to the picture described above an increasingly sophisticated mix of technological tools. Cities have obtained in the last half century unprecedented access to information, knowledge, and connectivity. Technological diversity and its potential impact on society have been growing steadily. It is true many technological products and current areas of exploration are prematurely presented as more sophisticated that what they really are. Still, technological progress is continuously moving forward in an undulating manner. From time to time some area of exploration comes to a momentary halt, the vast majority makes their way to market and evolution, like fridges, TV sets, phones, cars, and planes did in the past. Step by step, model by model, year by year, getting gradually and permanently accepted by society and incorporated in our lives. Cities create a supra entity out of the synergies of those humans so intimately sharing space, time, and resources. This Major References Works project on Smart Cities considers from various fundamental perspectives this growing phenomenon at this stage of our civilization where technology is consciously considered at such a level that can be used to bring benefits at a massive scale. Although technology is one of the main enablers, we should keep in mind it is after all only a collection of tools to support human existence. The five broad frameworks we selected to structure this publication are Humans and Institutions as main recipients, Technology as enabler, and Energy and Data as fundamental resources.

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The project provides a forum for leading experts in this area to discuss fundamental concepts and applications, how the infrastructure both enables and adds dependability, how current services can be improved and new ones conceived, what are the current affordances and what needs to be developed, what do we know well, and what else we need to investigate deeper if we want to make progress. Diversity is an important part of this project and as such this forum is open to all because we understand everyone is a stakeholder. We encourage citizens from all regions of the planet, from diverse professions, cultural backgrounds, ages, genders, and any other significant dimension of society, to provide their views, expectations, needs, and preferences as, after all, this technology will only be considered a progress if it helps us humans to better experience life. This is a complex enterprise, one for which is difficult, to pinpoint a beginning and an end; as with most things in this world, this concept also flows, will grow in waves, and morph with other aspects of life on this planet as it progressively embeds in our civilization. This intends to be one of the first major organized landmarks in this important theme and we hope it contributes to a mature reflection on the subject and into a healthy use of technology for all. London, UK December 2020

Juan Carlos Augusto

Contents

Volume 1 Part I

Basic Concepts and Frameworks . . . . . . . . . . . . . . . . . . . . . . .

1

1

Smart Cities: Fundamental Concepts . . . . . . . . . . . . . . . . . . . . . . . Peggy James, Ross Astoria, Theresa Castor, Christopher Hudspeth, Denise Olstinske, and John Ward

3

2

Smart Cities Can Be More Humane and Sustainable Too . . . . . . . Eduardo M. Costa

35

3

Smart Energy Frameworks for Smart Cities: The Need for Polycentrism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Joseph Nyangon

55

Urban Computing: The Technological Framework for Smart Cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mélanie Bouroche and Ivana Dusparic

89

4

5

Smart Cities Data: Framework, Applications, and Challenges . . . Muhammad Bilal, Raja Sher Afgun Usmani, Muhammad Tayyab, Abdullahi Akibu Mahmoud, Reem Mohamed Abdalla, Mohsen Marjani, Thulasyammal Ramiah Pillai, and Ibrahim Abaker Targio Hashem

6

Smart Institutions: Concept, Index, and Framework Conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hans Wiesmeth, Dennis Häckl, and Christopher Schrey

Part II 7

Current Exemplary Smart Cities . . . . . . . . . . . . . . . . . . . . . .

Smart City Edmonton . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Katie Hayes, Soumya Ghosh, Wendy Gnenz, Janice Annett, and Mary Beth Bryne

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177 179

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8

9

10

11

From Invention City to Innovation City: The Case of Racine Wisconsin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Peggy James and William Martin

201

Urban Innovation Ecosystem and Humane and Sustainable Smart City: A Balanced Approach in Curitiba . . . . . . . . . . . . . . . Luiz Márcio Spinosa and Eduardo M. Costa

223

Holistic, Multifaceted, and Citizen-Centric Smart Taipei Strategies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chen-Yu Lee and Taipei Smart City Project Mangement Office (TPMO) Smart City Transformation for Mid-Sized Cities: Case of Canakkale, Turkey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Berrin Benli, Melih Gezer, and Ezgi Karakas

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12

Stockholm: Smart City . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Gustaf Landahl

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13

Smart City Wien: A Sustainable Future Starts Now . . . . . . . . . . . Thomas Madreiter, Angela Djuric, Nikolaus Summer, and Florian Woller

313

14

NEOM Smart City: The City of Future (The Urban Oasis in Saudi Desert) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Somayya Madakam and Pragya Bhawsar

15

Tehran in the Path of Transition to a Smart City: Initiatives, Implementation, and Governance . . . . . . . . . . . . . . . . . . . . . . . . . . Kiarash Fartash, Amirhadi Azizi, and Mohammadsadegh Khayatian Yazdi

16

Rebranding Umhlanga as an Intelligent City . . . . . . . . . . . . . . . . . C. Erwee, L. Chipungu, and H. Magidimisha-Chipungu

17

Bandung Smart City: The Digital Revolution for a Sustainable Future . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dody Arfiansyah and Hoon Han

Part III 18

Human Dimension

................................

Social Inclusion in Smart Cities . . . . . . . . . . . . . . . . . . . . . . . . . . . Víctor Manuel Padrón Nápoles, Diego Gachet Páez, José Luis Esteban Penelas, Olalla García Pérez, Fernando Martín de Pablos, and Rafael Muñoz Gil

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467 469

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Malaysia Smart City Framework: A Trusted Framework for Shaping Smart Malaysian Citizenship? . . . . . . . . . . . . . . . . . . . . . Seng Boon Lim, Jalaluddin Abdul Malek, Mohd Yusof Hussain, and Zurinah Tahir Making Smart Cities “Smarter” Through ICT-Enabled Citizen Coproduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Paula Rodriguez Müller

Part IV

Energy Dimension

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Smart Cities and the Challenge of Cities’ Energy Autonomy Vassiliki Meleti and Vasiliki Delitheou

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Energy Harvesting in Smart Cities . . . . . . . . . . . . . . . . . . . . . . . . . Zheng Jun Chew, Yang Kuang, Tingwen Ruan, and Meiling Zhu

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Greenhouse Gas Mitigation in Smart Cities: Political Economy and Strategic Mitigation Alliances . . . . . . . . . . . . . . . . . . . . . . . . . Ross Astoria

Part V

Technology Dimension . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Technology: Person Identification . . . . . . . . . . . . . . . . . . . . . . . . . . Igor Bezukladnikov, Anton Kamenskih, Aleksander Tur, Andrey Kokoulin, and Aleksander Yuzhakov

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User Interfaces in Smart Cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . Torin Hopkins, S. Sandra Bae, Julia Uhr, Clement Zheng, Amy Banić, and Ellen Yi-Luen Do

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Vehicular Network Systems in Smart Cities . . . . . . . . . . . . . . . . . . Edna Iliana Tamariz-Flores and Richard Torrealba-Meléndez

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How Technology Makes a Difference: Digital, Agile, and Design Thinking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Muni Prabaharan

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Building Smart City Solutions with Focus on Health Care and GDPR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Emirhan Enler, Istvan Pentek, and Attila Adamko

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Smart Mobility Ontology: Current Trends and Future Directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ali Yazdizadeh and Bilal Farooq

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Volume 2 Part VI 30

Data Dimension . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Towards Autonomous Knowledge Creation from Big Data in Smart Cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sławomir Nowaczyk, Thorsteinn Rögnvaldsson, Yuantao Fan, and Ece Calikus

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Interoperability Effect in Big Data . . . . . . . . . . . . . . . . . . . . . . . . . José Delgado

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Data Protection and Smart Cities . . . . . . . . . . . . . . . . . . . . . . . . . . Goran Vojković and Tihomir Katulić

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Multitier Intelligent Computing and Storage for IoT Sensor Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Osamah Ibrahiem Abdullaziz, Mahmoud M. Abouzeid, and Mohamed Faizal Abdul Rahman

34

Deep Learning for LiDAR-Based Autonomous Vehicles in Smart Cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Vinay Ponnaganti, Melody Moh, and Teng-Sheng Moh

Part VII 35

Institutions Dimension . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Corporate Social Responsibility (CSR): Governments, Institutions, Businesses, and the Public Within a Smart City Context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Andrew D. Roberts

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Social Emergence, Cornerstone of Smart City Governance as a Complex Citizen-Centric System . . . . . . . . . . . . . . . . . . . . . . . . . 1009 Claude Rochet and Amine Belemlih

37

Exploiting Big Data for Smart Government: Facing the Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1035 Sunil Choenni, Niels Netten, Mortaza S. Bargh, and Susan van den Braak

Part VIII

Smart Cities Infrastructure Ecosystem . . . . . . . . . . . . . . .

1059

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1061

38

Feeding a Smart City Jonathan Lodge

39

IoT and Blockchain-Based Smart Agri-food Supply Chains . . . . . 1109 Lehan Hou, Ruizhi Liao, and Qiqi Luo

Contents

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40

A Primer on Smart Contracts and Blockchains for Smart Cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1131 Srini Bhagavan, Praveen Rao, and Laurent Njilla

41

Technology-Led Disruptions and Innovations: The Trends Transforming Urban Mobility . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1163 Hussein Dia, Saeed Bagloee, and Hadi Ghaderi

42

Advances on Urban Mobility Using Innovative Data-Driven Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1199 Marcelo O. Rosa, Keiko V. O. Fonseca, Nádia P. Kozievitch, Anderson A. De-Bona, Jeferson L. Curzel, Luciano U. Pando, Olga M. Prestes, and Ricardo Lüders

43

Towards Interoperability of Data Platforms for Smart Cities . . . . 1237 Matthias Buchinger, Peter Kuhn, and Dian Balta

44

Future Urban Smartness: Connectivity Zones with Disposable Identities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1259 Rob van Kranenburg, Loretta Anania, Gaëlle Le Gars, Marta Arniani, Delfina Fantini van Ditmar, Mantalena Kaili, and Petros Kavassalis

45

Problem-Driven and Technology-Enabled Solutions for Safer Communities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1289 Johan Barthelemy, Mehrdad Amirghasemi, Bilal Arshad, Cormac Fay, Hugh Forehead, Nathanael Hutchison, Umair Iqbal, Yan Li, Yan Qian, and Pascal Perez

46

Crowdsourcing for Smart Cities That Realizes the Situation of Cities and Information Sharing . . . . . . . . . . . . . . . . . . . . . . . . . . . 1317 Kenro Aihara and Hajime Imura

47

Layer-Based Reference Model for Smart City Implementation . . . 1359 Patrick-Benjamin Bök and Ute Paukstadt

Part IX

Ethical Challenges

................................

1385

48

“Eyes and Ears”: Surveillance in the Indian Smart City . . . . . . . . 1387 Uttara Purandare and Khaliq Parkar

49

Reclaiming the Smart City: Toward a New Right to the City . . . . 1419 Maša Galič and Marc Schuilenburg

50

Application of the General Data Protection Regulation for Social Robots in Smart Cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1437 Gizem Gültekin-Várkonyi, Attila Kertész, and Szilvia Váradi

xiv

Contents

Part X

Bottle Necks and Potential Enablers . . . . . . . . . . . . . . . . . .

1463

51

Optimization Problems Under Uncertainty in Smart Cities . . . . . . 1465 Edoardo Fadda, Lohic Fotio Tiotsop, Daniele Manerba, and Roberto Tadei

52

Information Technology Macro Trends Impacts on Cities: Guidelines for Urban Planners . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1493 Keiko V. O. Fonseca, Nádia P. Kozievitch, Rita C. G. Berardi, and Oscar R. M. Schmeiske

53

Advanced Visualization of Neighborhood Carbon Metrics Using Virtual Reality: Improving Stakeholder Engagement . . . . . 1517 A. Houlihan Wiberg, Sondre Løvhaug, Mikael Mathisen, Benedikt Tschoerner, Eirik Resch, Marius Erdt, and Ekaterina Prasolova-Førland

54

Smart City Needs a Smart Urban-Rural Interface: An Overview on Romanian Urban Transformations . . . . . . . . . . . 1551 Ioan Ianoş, Andreea-Loreta Cercleux, Radu-Matei Cocheci, Cristian Tălângă, Florentina-Cristina Merciu, and Cosmina-Andreea Manea

55

Journeys in the Age of Smart Cities: Some Fresh Perspectives . . . 1571 V. Callaghan, J. Chin, F. Doctor, T. Kymäläinen, A. Peña-Rios, C. Phengdy, A. Reyes-Munoz, A. Tisan, M. Wang, H. Y. Wu, V. Zamudio, S. Zhang, and P. Zheng

56

Openness: A Key Factor for Smart Cities . . . . . . . . . . . . . . . . . . . 1611 Simge Özdal Oktay, Sergio Trilles Oliver, Albert Acedo, Fernando Benitez-Paez, Shivam Gupta, and Christian Kray

57

The Importance of Creative Practices in Designing MoreThan-Human Cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1643 Annika Wolff, Anne Pässilä, Antti Knutas, Teija Vainio, Joni Lautala, and Lasse Kantola

58

Influence of Smart Cities Sustainability on Citizen’s Quality of Life . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1665 Manuel Pedro Rodríguez Bolívar

Part XI 59

Closing Words . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1691

Smart Cities: State of the Art and Future Challenges . . . . . . . . . . 1693 Juan Carlos Augusto

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1703

About the Editor

Dr. Juan Carlos Augusto is professor of computer science at Middlesex University London, and head of the Research Group on Development of Intelligent Environments and of Smart Spaces Lab, which won the first prize at the 2019 edition of Machine Intelligence Competition that took place at the British Computer Society Headquarters. With a technical background in artificial intelligence, software engineering, and human–computer interfaces, his research interest lies in design and implementation of sensing systems that provide a practical benefit to humans. The application domain he most often explored has been ambient-assisted living, smart education, and smart cities. His interests intersect with several computer science areas, for example, ambient intelligence user-centered computing, context awareness, Internet of Things, and ubiquitous computing. Dr. Augusto has contributed to the research community with more than 260 publications, including several co-edited books on various types of smart systems. He has given more than a dozen invited talks and tutorials at international workshops and conferences and has also chaired numerous technical events. Dr. Augusto has been appointed co-editor-in-chief of the Journal on Ambient Intelligence and Smart Environments (IOS Press) and the Journal on Reliable Intelligent Environments (Springer), and he is the editorial board member of other international journals. He has led several UK-/EU-funded quadruple helix in style-innovation projects. He has advised several international funding bodies, including being external referee and monitoring expert for the European Commission.

xv

Contributors

Reem Mohamed Abdalla School of Hospitality and Tourism, Taylor’s University, Subang Jaya, Malaysia Jalaluddin Abdul Malek School of Social, Development and Environmental Studies, National University of Malaysia, Bangi, Selangor, Malaysia Mohamed Faizal Abdul Rahman International College of Semiconductor Technology, National Chiao Tung University, Taiwan, China Osamah Ibrahiem Abdullaziz Department of Electrical Engineering and Computer Science, National Chiao Tung University, Taiwan, China Mahmoud M. Abouzeid Department of Electrical Engineering and Computer Science, National Chiao Tung University, Taiwan, China Albert Acedo ITI/LARSyS, Instituto Superior Tcnico (IST), Universidade de Lisboa, Lisbon, Portugal Attila Adamko Department of Information Technology, University of Debrecen, Debrecen, Hungary Kenro Aihara Digital Content and Media Sciences Research Division, National Institute of Informatics, Tokyo, Japan Mehrdad Amirghasemi SMART Infrastructure Facility, University of Wollongong, Wollongong, NSW, Australia Loretta Anania European Commission, Brussels, Belgium Janice Annett Open City and Technology, City of Edmonton, Edmonton, AB, Canada Dody Arfiansyah School of Built Environment, University of New South Wales, Sydney, NSW, Australia Marta Arniani Futuribile, Nice/Milan, Italy Bilal Arshad SMART Infrastructure Facility, University of Wollongong, Wollongong, NSW, Australia xvii

xviii

Contributors

Ross Astoria Political Science, University of Wisconsin-Parkside, Kenosha, WI, USA Juan Carlos Augusto Department of Computer Science, Middlesex University, London, UK Amirhadi Azizi Institute for Science and Technology Studies, Shahid Beheshti University, Tehran, Iran S. Sandra Bae University of Colorado, Boulder, CO, USA Saeed Bagloee Department of Civil and Construction Engineering, Swinburne University of Technology, Melbourne, VIC, Australia Dian Balta fortiss GmbH, Munich, Germany Amy Banić University of Wyoming, Laramie, WY, USA Mortaza S. Bargh Research and Documentation Centre, Ministry of Justice and Security, The Hague, The Netherlands Research Center Creating 010, Rotterdam University of Applied Sciences, Rotterdam, The Netherlands Johan Barthelemy SMART Infrastructure Facility, University of Wollongong, Wollongong, NSW, Australia Amine Belemlih Paris Dauphine PSL University, Paris, France EM Lyon Casablanca Campus, Casablanca, Morocco Transilience Institute for Territory Resilience and Transformation, Casablanca, Morocco Fernando Benitez-Paez Institute of New Imaging Technologies (INIT), Universitat Jaume I, Castellón de la Plana, Spain Berrin Benli Novusens Smart City Institute, Kale Group, Turkish Informatics Foundation, Canakkale, Turkey Rita C. G. Berardi Department of Informatics, Federal University of Technology, Curitiba, PR, Brazil Igor Bezukladnikov Department of Automation and Remote Control, Perm National Research Polytechnic University, Perm, Russia Srini Bhagavan University of Missouri-Kansas City, Kansas City, MO, USA Pragya Bhawsar Strategic Management, Indian Institute of Management, Sirmaur, India Muhammad Bilal School of Computer Science and Engineering, Taylor’s University, Subang Jaya, Malaysia Patrick-Benjamin Bök HSPV NRW, Münster, Germany

Contributors

xix

Mélanie Bouroche School of Computer Science and Statistics, Trinity College, Dublin, Ireland Mary Beth Bryne Open City and Technology, City of Edmonton, Edmonton, AB, Canada Matthias Buchinger fortiss GmbH, Munich, Germany Ece Calikus Center for Applied Intelligent Systems Research, Halmstad University, Halmstad, Sweden V. Callaghan School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK The Business School, Canterbury Christ Church University, Canterbury, UK Theresa Castor Communication, University of Wisconsin-Parkside, Kenosha, WI, USA Andreea-Loreta Cercleux Department of Human and Economic Geography, Faculty of Geography, University of Bucharest, Bucharest, Romania Zheng Jun Chew University of Exeter, Exeter, UK J. Chin School of Computing Sciences, University of East Anglia, Norwich, UK L. Chipungu University of KwaZulu-Natal, SOBEDS, Durban, South Africa Sunil Choenni Research and Documentation Centre, Ministry of Justice and Security, The Hague, The Netherlands Research Center Creating 010, Rotterdam University of Applied Sciences, Rotterdam, The Netherlands Radu-Matei Cocheci Department of Urban Planning and Territorial Development, “Ion Mincu” University of Architecture and Urban Planning, Bucharest, Romania Eduardo M. Costa LabCHIS – Humane Smart City Lab, Federal University of Santa Catarina (BR), Florianópolis, Brazil Knowledge Engineering and Management Dept., Federal University of Santa Catarina (BR), Florianópolis, Brazil Jeferson L. Curzel Instituto Federal de Santa Catarina (IFSC), Joinville, Brazil Anderson A. De-Bona Centro Universitário Dinâmica das Cataratas (UDC), Foz do Iguacu, Brazil José Delgado Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal Vasiliki Delitheou Department of Economics and Regional Development, Panteion University of Social and Political Sciences, Athens, Greece Hussein Dia Department of Civil and Construction Engineering, Swinburne University of Technology, Melbourne, VIC, Australia

xx

Contributors

Angela Djuric Smart City Agency, UIV Urban Innovation Vienna GmbH, Wien, Austria Ellen Yi-Luen Do University of Colorado, Boulder, CO, USA F. Doctor School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK Ivana Dusparic School of Computer Science and Statistics, Trinity College, Dublin, Ireland Emirhan Enler Department of Information Technology, University of Debrecen, Debrecen, Hungary Marius Erdt Fraunhofer Singapore, Nanyang Technological University, Singapore, Singapore C. Erwee University of KwaZulu-Natal, SOBEDS, Durban, South Africa José Luis Esteban Penelas Universidad Europea de Madrid (Diseño, Arquitectura y Construcciones Civiles), Madrid, Spain Edoardo Fadda Department of Control and Computer Engineering, Politecnico di Torino, Torino, Italy Yuantao Fan Center for Applied Intelligent Systems Research, Halmstad University, Halmstad, Sweden Bilal Farooq Laboratory of Innovations in Transportation (LiTrans), Ryerson University, Toronto, ON, Canada Kiarash Fartash Institute for Science and Technology Studies, Shahid Beheshti University, Tehran, Iran Cormac Fay SMART Infrastructure Facility, University of Wollongong, Wollongong, NSW, Australia Keiko V. O. Fonseca Department of Informatics, Federal University of Technology, Curitiba, PR, Brazil Hugh Forehead SMART Infrastructure Facility, University of Wollongong, Wollongong, NSW, Australia Lohic Fotio Tiotsop Department of Control and Computer Engineering, Politecnico di Torino, Torino, Italy Diego Gachet Páez Universidad Europea de Madrid (Ciencias y Tecnología de la Información y las Comunicaciones), Madrid, Spain Maša Galič Department of Criminal Law and Criminology, VU University Amsterdam, Amsterdam, The Netherlands Olalla García Pérez Universidad Europea de Madrid (Ingeniería Industrial y Aeroespacial), Madrid, Spain

Contributors

xxi

Gaëlle Le Gars Brussels, Belgium Melih Gezer Novusens Smart City Institute, Kale Group, Turkish Informatics Foundation, Canakkale, Turkey Hadi Ghaderi Department of Business Technology and Entrepreneurship, Swinburne University of Technology, Melbourne, VIC, Australia Soumya Ghosh Open City and Technology, City of Edmonton, Edmonton, AB, Canada Wendy Gnenz Open City and Technology, City of Edmonton, Edmonton, AB, Canada Gizem Gültekin-Várkonyi Faculty of Law and Political Sciences, University of Szeged, Szeged, Hungary Shivam Gupta Bonn Alliance for Sustainability Research/Innovation Campus Bonn (ICB), Bonn, Germany Dennis Häckl WIG2 GmbH, Wissenschaftliches Institut für Gesundheitsökonomie und Gesundheitssystemforschung, Leipzig, Germany Hoon Han School of Built Environment, University of New South Wales, Sydney, NSW, Australia Katie Hayes Open City and Technology, City of Edmonton, Edmonton, AB, Canada Torin Hopkins University of Colorado, Boulder, CO, USA Lehan Hou School of Data Science, The Chinese University of Hong Kong, Shenzhen, China A. Houlihan Wiberg The Research Centre for Zero Emission Neighbourhoods in Smart Cities (ZEN), Department of Architecture and Technology, Norwegian University of Science and Technology, Trondheim, Norway The Belfast School of Architecture and the Built Environment, Ulster University, Belfast, UK Christopher Hudspeth Philosophy, University of Wisconsin-Parkside, Kenosha, WI, USA Mohd Yusof Hussain School of Social, Development and Environmental Studies, Faculty of Social Sciences and Humanities, National University of Malaysia, Bangi, Selangor, Malaysia Nathanael Hutchison SMART Infrastructure Facility, University of Wollongong, Wollongong, NSW, Australia Ioan Ianoş Interdisciplinary Centre for Advanced Research on Territorial Dynamics, University of Bucharest, Bucharest, Romania

xxii

Contributors

Hajime Imura Graduate School of Interdisciplinary Information Studies, The University of Tokyo, Tokyo, Japan Umair Iqbal SMART Infrastructure Facility, University of Wollongong, Wollongong, NSW, Australia Peggy James Political Science, College of Social Sciences and Professional Studies, University of Wisconsin-Parkside, Kenosha, WI, USA Mantalena Kaili European Law Observatory on New Technologies-ELONTech, Athens, Greece Anton Kamenskih Department of Automation and Remote Control, Perm National Research Polytechnic University, Perm, Russia Lasse Kantola Theatrum Olga, Diakonia College of Finland, Lahti, Finland Ezgi Karakas Novusens Smart City Institute, Kale Group, Turkish Informatics Foundation, Canakkale, Turkey Tihomir Katulić Faculty of Law, University of Zagreb, Zagreb, Croatia Petros Kavassalis University of the Aegean, Chios, Greece Attila Kertész Faculty of Law and Political Sciences, University of Szeged, Szeged, Hungary Mohammadsadegh Khayatian Yazdi Institute for Science and Technology Studies, Shahid Beheshti University, Tehran, Iran Antti Knutas LUT University, Lappeenranta, Finland Andrey Kokoulin Department of Automation and Remote Control, Perm National Research Polytechnic University, Perm, Russia Nádia P. Kozievitch Department of Informatics, Federal University of Technology, Curitiba, PR, Brazil Rob van Kranenburg IoT Council, Resonance Design BV, Gent, Belgium Christian Kray Institute for Geoinformatics (ifgi), University of Münster, Münster, Germany Yang Kuang University of Exeter, Exeter, UK Peter Kuhn fortiss GmbH, Munich, Germany T. Kymäläinen VTT Technical Research Centre of Finland Ltd, Tampere, Finland Gustaf Landahl Environment and Health Administration, City of Stockholm, Stockholm, Sweden Joni Lautala Theatrum Olga, Diakonia College of Finland, Lahti, Finland Chen-Yu Lee Taipei, Taiwan

Contributors

xxiii

Yan Li SMART Infrastructure Facility, University of Wollongong, Wollongong, NSW, Australia Ruizhi Liao School of Humanities and Social Science, The Chinese University of Hong Kong, Shenzhen, China Shenzhen Key Laboratory of IoT Intelligent Systems and Wireless Network Technology, Shenzhen, China Jonathan Lodge City Farm Systems Ltd, Slough, UK Sondre Løvhaug Department of Computer Science, Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology, Trondheim, Norway Ricardo Lüders Universidade Tecnológica Federal do Paraná (UTFPR), Curitiba, Brazil Qiqi Luo School of Management and Economics, The Chinese University of Hong Kong, Shenzhen, China Somayya Madakam Information Technology, FORE School of Management, New Delhi, India Thomas Madreiter Executive Group for Construction and Technology, City of Vienna, Vienna, Austria H. Magidimisha-Chipungu University of KwaZulu-Natal, SOBEDS, Durban, South Africa Abdullahi Akibu Mahmoud School of Computer Science and Engineering, Taylor’s University, Subang Jaya, Malaysia Cosmina-Andreea Manea “Simion Mehedinti – Nature and Sustainable Development” Doctoral School, Faculty of Geography, University of Bucharest, Bucharest, Romania Daniele Manerba Department of Information Engineering, Università degli Studi di Brescia, Brescia, Italy Mohsen Marjani School of Computer Science and Engineering, Taylor’s University, Subang Jaya, Malaysia William Martin City of Racine, Racine, WI, USA Fernando Martín de Pablos Universidad Europea de Madrid (Ciencias y Tecnología de la Información y las Comunicaciones), Madrid, Spain Mikael Mathisen Department of Computer Science, Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology, Trondheim, Norway

xxiv

Contributors

Vassiliki Meleti Department of Economics and Regional Development, Panteion University of Social and Political Sciences, Athens, Greece Florentina-Cristina Merciu Department of Human and Economic Geography, Faculty of Geography, University of Bucharest, Bucharest, Romania Melody Moh Department of Computer Science, San Jose State University, San Jose, CA, USA Teng-Sheng Moh San Jose State University, San Jose, CA, USA Rafael Muñoz Gil Universidad Europea de Madrid (Ciencias y Tecnología de la Información y las Comunicaciones), Madrid, Spain Niels Netten Research and Documentation Centre, Ministry of Justice and Security, The Hague, The Netherlands Research Center Creating 010, Rotterdam University of Applied Sciences, Rotterdam, The Netherlands Laurent Njilla Air Force Research Lab, Rome, NY, USA Sławomir Nowaczyk Center for Applied Intelligent Systems Research, Halmstad University, Halmstad, Sweden Joseph Nyangon Center for Energy and Environmental Policy (CEEP), University of Delaware, Newark, DE, USA Sergio Trilles Oliver Institute of New Imaging Technologies (INIT), Universitat Jaume I, Castellón de la Plana, Spain Denise Olstinske Applied Professional Studies, University of Wisconsin-Parkside, Kenosha, WI, USA Simge Özdal Oktay Institute for Geoinformatics (ifgi), University of Münster, Münster, Germany Víctor Manuel Padrón Nápoles Universidad Europea de Madrid (Ingeniería Industrial y Aeroespacial), Madrid, Spain Luciano U. Pando Instituto Federal do Paraná (IFPR), Campo Largo, Brazil Khaliq Parkar CESSMA, University of Paris, Paris, France Anne Pässilä LUT University, Lappeenranta, Finland Ute Paukstadt HSPV NRW, Münster, Germany A. Peña-Rios BT Research Labs, Adastral Park, Ipswich, UK Istvan Pentek Department of Information Technology, University of Debrecen, Debrecen, Hungary Pascal Perez SMART Infrastructure Facility, University of Wollongong, Wollongong, NSW, Australia

Contributors

xxv

C. Phengdy Learning Design and Technology, San Diego State University, San Diego, CA, USA Thulasyammal Ramiah Pillai School of Computer Science and Engineering, Taylor’s University, Subang Jaya, Malaysia Vinay Ponnaganti San Jose State University, San Jose, CA, USA Muni Prabaharan Chennai, India Ekaterina Prasolova-Førland Department of Education and Lifelong Learning, Norwegian University of Science and Technology, Trondheim, Norway Olga M. Prestes Instituto de Pesquisa e Planejamento Urbano de Curitiba (IPPUC), Curitiba, Brazil Uttara Purandare IITB-Monash Research Academy, Mumbai, India Yan Qian SMART Infrastructure Facility, University of Wollongong, Wollongong, NSW, Australia Praveen Rao University of Missouri-Columbia, Columbia, MO, USA Thorsteinn Rögnvaldsson Center for Applied Intelligent Systems Research, Halmstad University, Halmstad, Sweden Eirik Resch The Research Centre for Zero Emission Neighbourhoods in Smart Cities (ZEN), Department of Architecture and Technology, Norwegian University of Science and Technology, Trondheim, Norway A. Reyes-Munoz Telecommunications and Aerospace Engineering, Universitat Politècnica de Catalunya, Barcelona, Spain Andrew D. Roberts School of Business and Law, Central Queensland University, Melbourne, Australia Claude Rochet Paris Dauphine PSL University, Paris, France Fondation Robert de Sorbon, Institut Franco Allemand d’Etudes Européennes, Paris, France Manuel Pedro Rodríguez Bolívar Department of Accounting and Finance, University of Granada, Granada, Spain A. Paula Rodriguez Müller Public Governance Institute, KU Leuven, Leuven, Belgium Marcelo O. Rosa Universidade Tecnológica Federal do Paraná (UTFPR), Curitiba, Brazil Tingwen Ruan University of Exeter, Exeter, UK Oscar R. M. Schmeiske Department of Informatics, Federal University of Technology, Curitiba, PR, Brazil

xxvi

Contributors

Christopher Schrey WIG2 GmbH, Wissenschaftliches Institut für Gesundheitsökonomie und Gesundheitssystemforschung, Leipzig, Germany Marc Schuilenburg Department of Criminal Law and Criminology, VU University Amsterdam, Amsterdam, The Netherlands Seng Boon Lim School of Social, Development and Environmental Studies, Faculty of Social Sciences and Humanities, National University of Malaysia, Bangi, Selangor, Malaysia Luiz Márcio Spinosa LabCHIS / Federal University of Santa Catarina (BR), Triple Helix Association (IT), Curitiba, Brazil LabCHIS – Humane Smart City Lab, Federal University of Santa Catarina (BR), Florianópolis, Brazil Nikolaus Summer Smart City Agency, UIV Urban Innovation Vienna GmbH, Wien, Austria Roberto Tadei Department of Control and Computer Engineering, Politecnico di Torino, Torino, Italy Zurinah Tahir School of Social, Development and Environmental Studies, Faculty of Social Sciences and Humanities, National University of Malaysia, Bangi, Selangor, Malaysia Taipei Smart City Project Mangement Office (TPMO) Taipei, Taiwan Cristian Tălângă Interdisciplinary Centre for Advanced Research on Territorial Dynamics, University of Bucharest, Bucharest, Romania Edna Iliana Tamariz-Flores Faculty of Computer Sciences, Autonomous University of Puebla, Puebla, México Ibrahim Abaker Targio Hashem Future Technology Research Center, National Yunlin University of Science and Technology, Douliu, Taiwan Muhammad Tayyab School of Computer Science and Engineering, Taylor’s University, Subang Jaya, Malaysia A. Tisan Department of Electronic Engineering, Royal Holloway, University of London, Surrey, UK Richard Torrealba-Meléndez Faculty of Electronics Sciences, Autonomous University of Puebla, Puebla, México Benedikt Tschoerner Fraunhofer Singapore, Nanyang Technological University, Singapore, Singapore Aleksander Tur Department of Automation and Remote Control, Perm National Research Polytechnic University, Perm, Russia Julia Uhr University of Colorado, Boulder, CO, USA

Contributors

xxvii

Raja Sher Afgun Usmani School of Computer Science and Engineering, Taylor’s University, Subang Jaya, Malaysia Teija Vainio Aalto University, Espoo, Finland Susan van den Braak Research and Documentation Centre, Ministry of Justice and Security, The Hague, The Netherlands Delfina Fantini van Ditmar Royal College of Art, London, UK Szilvia Váradi Faculty of Law and Political Sciences, University of Szeged, Szeged, Hungary Goran Vojković Faculty of Transport and Traffic Sciences, University of Zagreb, Zagreb, Croatia M. Wang Learning Design and Technology, San Diego State University, San Diego, CA, USA John Ward Geography/GIS, University of Wisconsin-Parkside, Kenosha, WI, USA Hans Wiesmeth Graduate School of Economics and Management, Ural Federal University, Yekaterinburg, Russia Faculty of Business and Economics, TU Dresden, Dresden, Germany Annika Wolff LUT University, Lappeenranta, Finland Florian Woller Smart City Agency, UIV Urban Innovation Vienna GmbH, Wien, Austria H. Y. Wu National Taipei University of Technology, Taipei, Taiwan Ali Yazdizadeh Laboratory of Innovations in Transportation (LiTrans), Ryerson University, Toronto, ON, Canada Aleksander Yuzhakov Department of Automation and Remote Control, Perm National Research Polytechnic University, Perm, Russia V. Zamudio Division of Graduate Studies and Research, TecNM / Instituto Tecnológico de León, León, México S. Zhang Department of Computer Science, Shijiazhuang University, Shijiazhuang, PR China P. Zheng The Business School, Canterbury Christ Church University, Canterbury, UK Clement Zheng National University of Singapore, Singapore, Singapore Meiling Zhu University of Exeter, Exeter, UK

Part I Basic Concepts and Frameworks

1

Smart Cities: Fundamental Concepts Peggy James, Ross Astoria, Theresa Castor, Christopher Hudspeth, Denise Olstinske, and John Ward

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Human Interaction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fundamental Beginnings of the City . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Qualitative and Quantitative Changes in Human Interactions Within the City . . . . . . . . . . . . . . Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Big Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Information And Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Technology, Integrated Technology, and Responsive Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . Architecture of a Technology Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

4 7 7 8 10 10 13 13 14

P. James (*) Political Science, College of Social Sciences and Professional Studies, University of WisconsinParkside, Kenosha, WI, USA e-mail: [email protected] R. Astoria Political Science, University of Wisconsin-Parkside, Kenosha, WI, USA e-mail: [email protected] T. Castor Communication, University of Wisconsin-Parkside, Kenosha, WI, USA e-mail: [email protected] C. Hudspeth Philosophy, University of Wisconsin-Parkside, Kenosha, WI, USA e-mail: [email protected] D. Olstinske Applied Professional Studies, University of Wisconsin-Parkside, Kenosha, WI, USA e-mail: [email protected] J. Ward Geography/GIS, University of Wisconsin-Parkside, Kenosha, WI, USA e-mail: [email protected] © Springer Nature Switzerland AG 2021 J. C. Augusto (ed.), Handbook of Smart Cities, https://doi.org/10.1007/978-3-030-69698-6_2

3

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Institutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Triple Helix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Institutional Logics Connecting Actors, Activities, and Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Climate and Energy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Introduction: The Green, Resilient Cosmo-Polity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The “Old” Grid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Smart Grid, Distributed Energy Resources, and the City . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract

This introductory chapter identifies key fundamental charactistics of a smart city. First and foremost, however, a smart city is based on humans and human interaction for a common purpose. These interactions are often successfully and synergistically organized through university-industry-government-citizens collaborations. A further distinguishing feature of smart cities is the unprecedented amount and types of data that can now be collected and produced through digital technologies. Technology also allows the city to prioritize interactions and establish dynamic relationships, allowing continuous identification and resolution of challenges. Regardlesss of the evolution of cities, they continue to be political and social entities; technology must be developed and implemented in line with the needs of citizens so that the end result is an increase in well-being and responsiveness. The top four research themes in smart city research are: (1) Technology (29%), (2) The nature of smart cities (17%), (3) Models and frameworks (13%), and (4) Policy and strategy ( 8%). Smart city innovators may want to start at the end and work backward.

Introduction Popular attention turned to the smart cities concept in 2010 when IBM initiated the first smart cities challenge, donating $50 million in technology and services to 100 cities. In 2014, Songdo, South Korea, was declared to be the first smart city. Songdo was built from the ground up with an intentional technological foundation supported through Cisco Systems; in 2019, the technology works but the city inhabitants number only one third of the projected population. Technology without community. While there are more “new” cities like Songdo (King Abdullah Economic City, Saudi Arabia, Treasure Island, San Francisco Bay Area, Masdar City, and Abu Dhabi), the great majority of development is in existing cities. All cities need to be smarter, as 68% of the population in 2050 is expected to be living in urban areas. Development plans will increasingly be focused on the modification and modernization of infrastructure, services, and economic systems; the demands on physical space will require the expansion of digital space utilization. Add concerns for equity, inclusivity, resilience, and responsiveness, and there is a concomitant need to

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integrate and connect across all city activities, including the public and private sector, and the most important component – the citizens. In 2017, the largest market for smart city products and development was in Europe; however, spending on smart cities development is projected to grow by 37% in Asia-Pacific, 27% in Latin America, 23% in the ME and Africa, and 14% in North America by 2020 (Smart city strategies: A global review 2017. Catapult Future Cities). Smart city development has proceeded in two phases. Initially, private companies promoting technology dominated the activity, resulting in the insertion of isolated technological products that were not strategically embedded in the dynamics of city life (Townsend 2013). While this was an important first move to introduce, and gain acceptance for, the innovative changes that technology might offer, it became apparent that the technology needed to be more thoughtfully embedded into the larger governance relationship with its citizens. With this move to the social and political consideration of citizen engagement and wellbeing, more consideration was given to equity, inclusivity, resilience, and responsiveness in the second phase. A result of this realization was that technology needed to be considered with a larger urban development framework, accessible to citizens, and integrated/connected within an analytic process. Smart city applications have not necessarily followed this linear description. Mosco (2019) identified three drivers that can catalyze the development of a smart city: state-driven; private or corporate driven; and, citizendriven. Depending on the primary driver, the operational logic of a smart city may have implications not only for its general functionality but for the impact that the smart city network will have on citizens and the impact citizens will have on the city. Many cities remain focused on technology; others rely on a governance model; others focus on citizen wellbeing and engagement; still others focus on sustainability. It is due to this diversity of definitions and applications that various indices will identify different cities as being smart, according to different criteria. Table 1 lists the three top ranking smart cities according to three separate indices with variable Table 1 Select indices ranking smart cities, with top three cities Source Smart City Strategy Index (SCSI) (Roland Berger 2019) IESE Cities in Motion Index (2018)

Statista (2019)

Dimensions Action Fields; Policy & Infrastructure; Strategic Planning

First Vienna, Austria

Second London, UK

Third St. Albert, Canada

Human Capital; Social Cohesion; Economy; Governance; Environment; Mobility & Transportation; Urban Planning; International Outreach; Technology transport & mobility; sustainability; governance; innovation economy; digitalization; living standard; expert perception

New York City, US

London, UK

Paris, France

Gotheberg, Sweden

Bergen, Norway

Stockholm, Sweden

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dimensions of “smartness.” The multiplicity of indicators demonstrates that smart cities are diverse in their planning, applications, and values. A broad definition of a smart city is an urban environment where technology allows for an efficient relationship between data and its applications in order to provide an environment that is responsive, resilient, and healthy. The functional aspects of this relationship are that a smart city is more immediately responsive, predictive, adaptive, and is capable of learning. The outcomes of a smart city include sustainable and healthy lifestyles, economic efficiency, political and social inclusivity through equitable engagement, and ability for all public and private residents to flourish. These ideal outcomes must be balanced with the increasing possibility of surveillance, lack of control and consent in regards to privacy in data collection, and the profiling of “normal” populations (Sadowski and Pasquale 2015). The smart city concept may appear to be revolutionary, but upon close inspection is more evolutionary. Five areas will be introduced in this chapter, to be elaborated by others in future chapters: (1) Human Interaction, (2) Institutions, (3) Data, (4) Technology, and (5) Climate and Energy. Human interaction focuses on issues of privacy and security, media richness, attitudinal adaptation, and human flourishing in a technological environment. Individual interactions are taken to an institutional level, where the emergence of the triple helix model offers the ability to frame technological development within and between a partnership of universities, governments, and private interests. Data driven decision-making for individuals and organizations is a logical progression from the ability to process big data and to provide linkages between edge computing and deeper learning. Information Technology focuses on the foundation of IoT platforms, wireless connectivity and access, and the use of blockchains to manage information, records, and ownerships in a secure cloud setting. Finally, Climate and Energy considers the efficient and sustainable use of energy, through monitoring and adaptation systems, as well as the distributed energy resources (DER) available within a smart city environment, and serves as an illustrative application of previously discussed concepts. Cities are large human settlements (Kuper and Kuper 1996). Although scholars debate when the earliest cities were formed and have attempted to define criteria for identifying ancient large settlements as cities (Childe 2008), it is generally agreed that sometime around 5000 years ago humans first began to engage in agriculture and to settle in large population densities. Whether agriculture led to the development of cities, or cities led to the development of agriculture is still a matter of debate (Bairoch 1988). Regardless, settling together people created access to benefits and shared resources such as ideas, transportation networks, natural resources, markets, and cultural amenities all of which continue to spur the growth of cities. Thus, cities are social and political entities; technology must be developed and implemented in line with the needs of citizens so that the end result is an increase in well-being and efficiency. The top four most common research themes is smart city research are: (1) technology (29%), (2) nature of smart cities (17%), (3) model and

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frameworks (13%), and (4) policy and strategy (8%). Smart city innovators may want to start at the end with the fourth category and move backwards.

Human Interaction Fundamental Beginnings of the City In Plato’s Republic, Socrates gives an account of the perfect city. He suggests that cities are “the product, apparently, of our needs” (Plato 2000, p. 51). He then begins with the necessities – food, shelter, and clothing – and proceeds to the things necessary for those necessities – ploughs, tools, hides, and cloth. But meeting bare necessities is not enough because, as Glaucon notes, “we are not a city of pigs” (Plato 2000, p. 55). So, residents turn their attention to luxuries necessary for a city – incense, perfumes, and cakes. Thus, the underlying question that drives Plato’s account is “what helps human flourishing?” Anything that harms the well-being of citizens is excluded from Socrates’ city and anything that enhances their well-being is included. Although the world has become more complicated as technology has advanced, this question always remains. Perhaps it is masked by other questions but it always remains fundamental to the design and creation of a city. Should the city have a public water supply? Should the city have a sewer system? Should it have walls? Should it have open spaces? Should it have schools, police departments, and fire stations? Should it have roads, sidewalks, paths? Should it have public Wi-Fi and Internet? No matter how technology changes, the question of human flourishing remains the guiding thread of a city’s development. Politicians and developers that ignore this question do so at their peril. This question is carried forward into the Modern Era. For example, Jane Jacobs claims that “We need [diversity] so city life can work decently and constructively, and so the people of cities can sustain (and further develop) their society and civilization” (Jacobs 1992, p. 241). Working decently and constructively is not an end in itself; those are only means towards the ends of people developing their society and civilization. Jeff Speck argues that walkability answers the question “how can these typical cities provide their citizens a quality of life that makes them want to stay?” (Speck 2013, p. 4). In his account, improving the walkability of a city improves the well-being of the people that live there. And Charles Montgomery argues that “the city should strive to maximize joy and minimize hardship” (Montgomery 2013, p. 43). It is easy to get caught up in the flash and sparkle of new technology, to get lost in the marvel of innovation, but one must never lose sight of that fundamental, underlying goal: to help humans to flourish. Focusing on that goal will, of course, lead to another question: what does it mean to flourish as a human being? Although it may seem that no answer is possible to this question because of the complexity of human experience, the problem is not, in the broad strokes, that difficult – Plato has already given us a road-map for that.

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Food, shelter, clothing, and some things that raise existence above mere subsistence are necessary not merely for survival but also for well-being. In addition, in order to flourish, humans need to interact with others – humans are social animals and, so, cities must not only take that aspect into consideration but they must create spaces to encourage and support social lives. Humans like various forms of entertainment, which means that cities need to provide venues for games, music, theater, and other forms of expression. Humans also need to be productive. This does not mean that humans need to be a cog in some industrial machine but rather that humans like accomplishing things. Some fulfill that need through their jobs while others do it through their hobbies. A successful city, then, must also provide for the opportunity to engage in pursuits that let people feel that sense of accomplishment. So, while smart city technology and governance can provide more efficiency and responsiveness, it must not do so at the expense of the individual’s need to be unique, independent, and free of what Foucault calls “governmentality,” a system of modern government that overly shapes and affects the conduct of the population as a whole. Humans also have a need for privacy and security. These two are extremely important when considering Smart Cities because of the way technology challenges the relationship between these two. The first thing to note is that privacy and security are not synonymous. Privacy is about limiting access often to the point of being alone. Security is about being protected from danger or harm. The Hope diamond is not private but is very secure while a walk in the woods is not secure but private. For centuries, one of the surest ways to keep something secure was to keep it private – a Polaroid can only be seen by those it is shown it to but a jpeg can be posted on the Internet for the world to see. The interconnectivity of the myriad of household devices, when combined with the power of artificial intelligence will make public much of what citizens have been accustomed to be private. Information that had previously only been available to close associates is now collected and stored on remote servers in the hands of corporations, governments, and data brokers. Are the changes being wrought to conceptions of privacy and security – of which things are private and which things are secure – helping human beings to flourish? Would losing this or that bit of privacy enhance well-being? How can one promote happiness in Smart Cities knowing that by their nature, they fundamentally change which things are private and degree to which those things are private?

Qualitative and Quantitative Changes in Human Interactions Within the City Advanced technologies and Smart Cities alter forms of human interaction by changing how human interaction is mediated and through the inclusion of nonhuman entities as interactors. Because of the emphasis of Smart Cities on technology, attention to communication in smart cities has tended to focus on the use of communication technologies, information dissemination, and the use of data. Media scholar, Marshall McLuhan in the mid-twentieth century wrote of technological determinism, as epitomized through his phrase “the medium is the message”

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(1967). Through this concept, McLuhan highlighted how the very form of a given media shapes interaction through the type of physical cues that are available. For example, a basic telephone allows one access to sound (aural information) and to speak to another. However, video-conferencing now allows one access to sound as well as sight (visual information). While various technologies enable communication, there are ways that they constrain communication because of the limitations in media richness or number of available cues afforded by those technologies. There are four dimensions to this concept: personal focus, immediacy of feedback, conveyance of multiple cues, and variety of languages that can be conveyed. In one sense, the Smart City is an enabler of human interactions through increased technology use. For example, in the South Korean smart city of Songdo, educational instruction can take place through video conferencing (Tanaka 2012), and neighbors can talk to neighbors through video (Poon 2018). However, this ubiquity of technology has also earned Songdo the reputation of being a “lonely” city as neighborly contact often takes the form of video calls rather than face-to-face chats (Poon). Also, a consequence of over-emphasizing mediated interaction is decreased attention to those who are physically present, digital addiction, and decreased “immediacy” and closeness. Interaction in the digitally mediated city includes information, but it also includes other functions of communication such as relationship formation. Castells (2011) writes of the “rise of the network society” where networks of technologically mediated human interactions characterize the new economy and will hail radical changes in organizational structures. Social media and the mass sharing of data also allow for increased human interaction in the sharing of individual information on a mass scale. As this data is collected, however, it can have unintended consequences. For example, Strava is a fitness app that tracks one’s walking and running information. This information in turn can be uploaded and shared via social media to share one’s fitness progress with others for support and to be part of a fitness community. However, Strava data also had unintentional consequences when in 2017, Department of Defense officials realized that military base locations and configurations could be determined by identifying Strava running route heat maps generated by military personnel overseas (Kronisch 2019; also see Cooren 2019). Another area of change in human interaction in smart cities is that of humancomputer and human-machine interaction. Increasingly, humans will be interacting with computers, robots, cobots, machines, and artificial intelligence rather than solely with other humans. Guzman (2018) elaborates on this distinction by explaining that “in human-machine communication, technology is conceptualized as more than a channel or medium: it enters into the role of a communicator” (emphasis in original, p. 3). The distinction is one where interaction is not just about information dissemination but one of “meaning making.” Computers, machines, and other advanced technologies are not just tools but are interactants that affect how people make sense of the world. For example, some of the collective research findings in Guzman (2018) include identifying how robots are socially constructed through various marketing materials, how knowledge that one’s communication will be evaluated by a robot will cause more anxiety as compared to human evaluation,

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and how social robot persuasion can be just as effective as human-generated persuasion. In 2015, Boston Dynamics created an Internet stir when they released a video of a man kicking a robot dog (Parke 2015). While this video was intended to be a demonstration of robot dog Spot’s agility and maneuverability, viewers of the video expressed concern about “robot abuse” and the ethics of how robots may be treated. Within smart cities, there may be several opportunities for human-computer or human-machine interactions in areas such as transportation (e.g., taxi robots), service, delivery drones, and more. In addition to service areas, humans may interact with robots in the workplace. Each of these areas has their own advantages as well as concerns. Attitudes towards such technologies may range from positive due to assistance afforded through these technologies to concern or feeling threatened. The advanced technologies of smart cities are not just tools but are now a part of the equation of human interaction.

Data Cities require data in order to operate. Cities provide systems for housing, transportation, sanitation, safety, utilities, land use, and communication that provide benefits to people. On the other hand, large settlements also involve costs such as higher crime and pollution. Cities grow because the benefits of living in large settlements outweigh the costs. Whether it is deciding how much food to grow, where to build housing, how much to tax people, or which areas are impacted the most by crime, data has played an essential role in the operation of a city. Smart technologies enable smart cities to gather and process a greater variety of data at a scale and speed that hitherto has not been seen in human society.

Big Data In the past, data existed on paper. Today and in the future, an increasing amount of data used to manage cities is collected, stored, and processed over the Internet, and thus exists in cyberspace. The term “cyberspace” first appeared when Danish artists Susanne Ussing and Carsten Hoff began creating works of art under the moniker Atelier Cyberspace (Lillemose and Kryger 2015). However, in its modern usage, the term can be credited to William Gibson who described “cyberspace” in his 1982 short story “Burning Chrome” as “A graphic representation of data abstracted from banks of every computer in the human system” (Gibson 1982). Others credit John Perry Barlow with using “cyberspace” in its current definition (Barlow 1990). The Pentagon has formalized the definition for their own purposes (Shachtman 2008). The term “big data” first appeared sometime in the 1990s (Lohr 2013). “Big data” is a term with no solid definition. Big data refers to the large and increasing volume of data available (Hashem et al. 2015). In general, it refers to having an amount of

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data that surpasses the technological ability to store, manage, and process (Manyika et al. 2011). Big data is often in a nonhuman readable format and also requires large computational power for processing and analysis (Hashem et al. 2015) in order to translate the data into valuable insights (Hashem et al. 2015). The most important thing about data is that it informs decision-making (and can be used by machines to make decisions using artificial intelligence) and shapes the decision framework. In the cities of the future, people will be increasingly connected to the internet and will be both creating data as well as serving as entities about which data is collected. In addition, an Internet of Things will exist whereby smart devices such as vehicles and buildings will be connected to the Internet via sensors, software cameras, microphones, radio frequency identification, and wireless sensors that allow these devices to collect, exchange, and utilize data. This will produce a tremendous amount of data. The amount of data is almost unimaginable and necessitates a new paradigm for dealing with it. The volume of data in the world is predicted to grow 40% per year and 90% of the data has been created in the last 2 years (Waal-Montgomery 2016). Today it is estimated that the world produces about 2.5 quintillion bytes of data per day. The United States alone produces 2,657,700 gigabytes of data on the Internet every minute (Hale 2017). This presents many challenges for utilizing big data in decisionmaking. Current technology is not capable of dealing with this amount of data in terms of data capture, storage, analysis, searching and querying, sharing and transfer, visualization, updating, privacy, and security. Big data is often described by characteristics called the “Vs” that provide a framework for understanding and utilizing it. The “Vs” are (1) volume, (2) variety, (3) velocity, and (4) veracity (Marr 2014; Zikopoulos et al. 2012; Manyika et al. 2011). Volume refers to the quantity of data being collected and stored using various devices. Volume plays an important role in the utilization of data. Big data offers greater insight and has the potential to reveal hidden patterns during analysis, but also requires new technologies to collect, store, and process. Variety refers to the type and nature of the data being collected and stored from various devices. Examples of data types include text, video, images, audio, and data logs. About 80% of data is considered “unstructured” and therefore cannot easily be put into the type of database tables currently in use (Marr 2014) Velocity refers to the speed of data generation and transfer. Big data is often collected in real time and can be continuously produced creating a very large volume of data. Velocity can also refer to the processing and turn-around time for data utilization. Veracity refers to the quality and value of the data. This is sometimes called the “messiness” or “trustworthiness” of big data sets. Quality and value can vary quite a bit and directly affects the usefulness of data in analysis and predictive modeling. In addition to the characteristics described by the “Vs”, big data can be classified into categories based on five additional aspects (Hashem et al. 2015). These aspects are (1) data sources, (2) content format, (3) data stores, (4) data staging, and (5) data processing (Harshem et al 2015).

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Data sources include social media (data generated by URL to share or exchange information and ideas in virtual communities and networks), machine-generated data (automatically generated by computers without human intervention), sensing (using devices to measure physical quantities), transactions (financial and other data that require a time dimension), and Internet of Things (smart phones, cameras, other devices connected through the Internet that serve economic, environmental, and health needs). Content format refers to whether data is structured (numbers, words, dates that are easily formatted using current database technology), semi-structured (data that do not follow conventional database system design), and unstructured (text messages, videos, social media data, etc. that do not follow a specified format). Data stores refer to the structure of data storage. This includes document-oriented (storing documents in a database based on rows or records), column-oriented (storing data in rows/records and columns), graph database (storage in a graph model utilizing nodes, edges, properties), and key-value (utilizing row keys to store large datasets in relational databases). Data staging refers to the processes of cleaning (identifying incomplete or unreasonable data), transforming (changing data into a usable format), and normalizing (structuring data to minimize redundancy) data. Data processing refers to ways in which data is collected and utilized. This includes batch processing of large subsets of data, real-time collection, performing analysis using cloud computing, performing operations using the distributed power of decentralized networks, etc. In addition, smart cities will likely rely on a combination of centralized computers as well as a decentralized network. Whether it is the location of buildings, people, or data collection devices, location plays a major role in the management of cities. Geographic Information Systems (GIS) can play a major role in smart planning, smart policy-making, sustainable practices, smart management, smart services, and smart end-to-end solutions (Deogawanka 2016). In a future of smart cities, big data on the Internet will play a primary role. Data will be collected everywhere on everything. As the twenty-first century begins, some of the major concerns surrounding big data involve privacy and security. Data will continue to be collected not only on “who” people are and “what” people are doing but also “where” they are doing it. Crampton (2010) discussed this in the context of what he calls the “biopolitics of fear.” This involves the utilization of data for nefarious purposes in order to wield power over others. The three stages discussed are: (1) divide the population into groups of “us” and “them,” (2) engage in geosurveillant technologies, and (3) creating a risk-based society. Smart cities must create ways to keep the data they collect both private and secure. Yet this also requires experts to analyze the actual level of privacy and security provided by technology. Claims about increased security of data lead to an increase in the quantity of confidential data being communicated, yet in the absence of end to end encryption, data is not truly private or secure despite corporate claims (Dellinger 2019). This same concern about the true level of privacy and security applies to

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cellular network security as well (The Threat Lab 2019), a technology that will be used widely for data collection by smart cities. In addition, the accuracy and utilization of things like facial recognition (Ravani 2019) and aggression detectors (Gillum and Kao 2019), and the rise of surveillance capitalism and data brokers (MacMillan 2019), present challenges for smart cities, as does the application of algorithms in types of data analysis and decision-making such as predictive policing (Weisburd et al. 2004). At the same time, big data provides an opportunity for subjugated voices to be heard. As more and more people create data and have data collected about them, opportunities will be created for everyone to get their voice out. Smart cities need data, lots of data. What is called “big data.” This data will be collected, stored, and processed in order to aid in decision-making. However, big data is so large in volume that current technologies are limited in terms of its use. As technologies are developed to work with big data, it is of the utmost importance that measures are taken to ensure data privacy and security. In addition, it has been argued that at best big data provide information on the past and present but not the future. In order to effectively plan for smart city, development predictive models must be developed to work with big data.

Information And Technology Technology, Integrated Technology, and Responsive Technology Advances in technology in the last three decades have made smart cities possible. Even while technology enhances the ability to collect data, it is the advances in connecting, analyzing, and relating data, both in real time and in predictive analyses, that provide the foundation for smart cities. The relationship between smart devices and their potential to connect to the real world was coined in 1999 as “The Internet of Things” Ashton (2009). Statista estimates that there will be 75 billion devices in 2025 (https://www.statista.com/statistics/471264/iot-number-of-connected-devicesworldwide/). These devices are not only talking to us; they are talking to each other, which provides the power of the network, which is the foundation of a smart city. Smart cities are built upon the creation of technology relationships able to dynamically contextualize lots of data. This allows for dynamic decision-making at the point of collection and the technology infrastructure to increase efficiency in government services and activities. It also allows for long-term adaptations to create resiliency in the system. Both long- and short-term activities result in a city platform that is continuously responsive to the needs of its citizens. This capability does not emerge all at once; technologically speaking, there is an evolutionary pattern towards the smart city that allows movement from technology to integrated technology to responsive technology. Five stages can be identified: (1) Measurement technology allows sensors to be implemented to collect data that can be used to monitor operational status, (2) Networked technology connects these

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sensors allowing the exchange of data, (3) Managed systems provide for real time analysis of the information collected from sensor arrays, (4) Integrated systems make the data and analyses available across intra and intercity systems, (5) Smart technology systems provide software as a service (SaaS) so that individuals, businesses, and community organizations can access services, manage participation, and directly integrate with the platform.

Architecture of a Technology Platform A full smart city platform can be conceptualized as having four “layers”: a sensor array to be the gateway to edge computing; the fog as a decentralized distributed network to connect devices to a remote server; a cloud which is more centralized and offers the capability for analysis, prediction, and adaptation; and finally a neural network core where independent learning occurs without human initiated or managed intervention. Arguably the neural network is not separate from the cloud; indeed, as a connected system, none of the four layers are separable from each other. As a heuristic image, however, this provides some capability to separate according to primary functions. Sensors represent that level of technology that is closest to real time data creation. Sensors, themselves, are not new; the first sensor was a thermostat invented by Warren S Johnson in 1883. The first motion detecting sensor was invented in the 1950’s. However, the exponential link in sensor technology occurred with low cost– low power sensors that, in addition to triggering a local actuator, could transmit data to the cloud to be used for long term analysis, interpretation, and system adaptation. The maturation of sensor technology, in step with the connective capacity of 5G, has resulted in speculations that trillions of sensors could be implemented in the coming years. These embedded systems will be in light poles, buildings, cars, and potentially in humans (Maenaka 2016). In the foggy cloud, the most promising development for political, economic, and legal institutions is block chain technology. This peer to peer decentralized recordkeeping and transaction enabling system was developed as a foundation for the bitcoin trade invented in 2009. Blockchain offers significant changes for banking, trade, and currency management by providing transparency and security in an unalterable ledger system between peers. But it can do the same for the judicial system in the provision, interpretation, and verification of evidence and testimony. It can be used in the management of a smart electrical grid. And, it can be used for a transparent and secure voting process. And it does this without the need of any external management entity. As a peer to peer transactional system, it allows the participants to set their transactional rules, and removes the need for the middleman. Traditional banking institutions, voting systems, and any recordkeeping system based on the legitimacy of secure sites for holding currency, securities, information, etc., will no longer be needed. And, the removal of these participants as mediators will speed up the transaction process, regardless of spatial distance. As of 2019, blockchain voting apps have been piloted in West

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Virginia midterm elections and Denver’s municipal elections. San Francisco and New York City are among the cities using the most block chain applications in 2019. At the core of smart city platforms is the deep learning in neural networks. In a neural network, the algorithmic system prioritizes problems and works to solve them on its own. Neural networks have the ability to identify submerged and complex patterns, learn from their interpretation, and make decisions that reflect the existing data. Yet, because they are connected to the edge computing sensor devices and the processes in the cloud, they are constantly open to modification by the addition of new data. The deep neural network processes data by sifting and winnowing raw data, identifying high level features at various levels of analysis; this learning process is then stored in memory and accessed as needed in analyzing similar problems in the future. The random access does not prioritize the immediate past, and it provides a sample of experiences that might be different each time, thus allowing a more complete learning process. Deep learning permits the smart city to reassess policies and services, not in an immediate frame, but rather in a process that allows for large amounts of dynamically accessed data to be used to make decisions on major modifications or adaptations in city activities. Use cases for deep learning include water consumption and conservation, energy management, public health, and economic development (Mohammadi and Al-Fuqaha 2018). As cities become smarter and IoT devices more integrated, the need for reliable and rapid connectivity will increase dramatically. This is especially important for transportation systems, so that information communication and feedback can be consistent at all times. This area has yet to be completely solved, even with 5G technology. 5G allows for high density usage but can be expensive to install and, even though it provides more bandwidth and speed than its predecessors, has more difficulty travelling at long distances and around infrastructure. For cities, this means that uneven coverage can be expected, and certain areas might experience degraded signals. Cities can explore combinations of other technologies with 5G such as a mesh network and dynamic network slicing to partition the 5G network to customize the network according to various use cases.

Institutions The Triple Helix As was suggested earlier, technology enables smart cities, but it does not define them. All cities are political social and economic systems; smart cities utilize technology to improve governance, but also to introduce new dynamics into governance through integration and systems learning. Although there are many actors that can shape the development and implementation of technology in a smart city, there is a growing acceptance that the triple helix model of university-industry-government relationships (Etzkowitz and Leydesdorff 1995) can provide the balanced

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configuration needed for innovation and knowledge development. (Etzkowitz and Leydesdorff 2000) At the points of intersection of the three institutions in the helix, innovation is most likely. The complex coexistence within the helix also suggests that the institutional interactions differ from “business as usual” in that they are collaborative rather than competitive, parallel rather than hierarchical, and connected rather than separate. Citizens in a smart city desire collaboration and participation in accord with the culture of their communities. For the sustainability of smart cities, citizens must contribute to the formulation of policies and urban development. This contribution is in the form of direct citizen participation in urban policy decisions, not just as beneficiaries. As active citizens, they express their wishes and seek solutions. Within the triple helix model, the University, as a key actor, supports the growth of “Smart people” through educational advancement opportunities, attracting engaged citizens, and assisting in identifying entrepreneurial opportunity for citizen participation Giffinger et al. (2007). The intersection of Industry and Citizen interests is found in the iterative decision-making between producers and consumers, where each actor can take on either role. The triple helix model combines the academic resources of universities, industries, and governments of civil society. As the smart city movement evolves across the world, the progressive leaders are focusing on a new quadruple helix model where citizens are embraced as partners and are becoming an integral fourth agent. With citizens as partners, the silos of universities, industry, and government begin to break down and all dimensions within the quadruple helix work collaboratively to drive modernization and transformation for the betterment of the city as a whole. A smart city designation is not simply a city that has integrated more technology; the goal of a smart city is to enhance the quality of life of its citizens through advanced technology that allows for a bottom-up policy approach rather than a top-down approach dictating passive citizen engagement. The citizens are the knowledge base when it comes to characteristics and problems within areas of the city and must not be excluded from the policy decision-making process. The quadruple helix provides an elevated and macro level perspective on the actors in a smart city; when unpacked it demonstrates a complexity and variability within cities depending upon the urban culture. Smart cities are difficult to define because of this characteristic. Technology is neutral; different cities manifest different combinations of actors, agendas, capacities, and values which will affect the way in which the technology is prioritized, utilized, and integrated across services and policies. Raven et al. (2017) take an institutional perspective on three cities in Japan, Germany, and the Netherlands and find that they present very differently depending upon the degree to which public private partnerships exist, the involvement of citizens in decision-making, and the influence of national governments, among others. The institutional logics of these cities also represent varying levels of actor involvement and intersection, and the nature of the core organizing structures. So as smart cities mature, it becomes apparent that they are not unique social or political forms; rather, they represent the next stage of urban development in a larger framework of governance and engagement.

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Institutional Logics Connecting Actors, Activities, and Roles As technology becomes more ubiquitous, attention is increasingly turning towards the institutional and organizational patterns that affect its implementation and impact. Still, research into the political and social influences on smart city development is relatively recent. Pierce et al. (2017) provide a useful framework for understanding the political processes that can affect the amount, type, and sustainability of smart city developments. The framework is composed of five dimensions: actors, city subsystems (for example, water), activity layers, actor roles, and institutional logic. The five dimensions can be conceptualized as follows: within a specific city subsystem, select actors will adopt roles that are located at one or more of the activity layers of the subsystem. The ways in which the actors interact, adopt their roles and implement their agendas will be defined by an institutional logic, which consists of rules, regulation, and behavioral expectations. Pierce et al. identify three activity layers within a subsystem: (1) Service, (2) Digital, and (3) Environmental and the Roles are categorized as (1) Idea generation and development, (2) Creation and maintenance, (3) Analysis, and (4) Governance. See Fig. 1. The immediate takeaway from the organizational framework is that it has the capacity to represent a large number of combinations, even for each actor. As more actors become involved, the character and culture of the development and implementation of smart city technology becomes increasingly complex and unique to the particular case. Pierce et al. identify eight distinct institutional logics that may be present to regulate actor behavior within these configurations: innovation logic, bureaucratic logic, equality logic, environmental logic, commons logic, co-creation logic, predatory logic, and classic market logic. Because the last two forms describe behaviors that are representative of competition, control, and exploitation, they may not be appropriate for a smart cities framework. However, the other six logics can regulate smart city relations. Even while citizen engagement has been touted as one of the benefits of smart cities, less work has been done on the means by which citizens can be engaged and in what capacity. The quadruple helix suggests centricity of citizen involvement in smart city culture, but little work has been done on how well citizens are engaged Fig. 1 A one actor, one city subsystem, illustration of the Pierce, et al. model

Actor

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Idea generation & development

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into the smart city decision-making framework and how that process depends upon existing institutional logics and city culture. Castelnovo et al. (2016) provide a policy assessment framework on five dimensions intersecting to create four quadrants of agenda defining behavior for citizen engagement (Fig. 2). Using the logic structure of the proposed smart city’s governance holistic assessment framework for citizen engagement and governance, six of Pierce et al.’s logic models can be superimposed as to where they might be most descriptive as behavioral expectations and regulations for the inclusion of citizen engagement into the organizational fields of decision-making. Co-creation logic, representing data and knowledge sharing, collaboration, and participatory decision-making, is at the center of citizen participation, where citizens are both consumers and producers of products and services. Information can be dynamically and iteratively revised, and the leaderfollower roles are interchangeable. Depending upon the quadrants of policy-making defined by the dimensions above, one institutional logic can be more helpful than others.

Fig. 2 Super-imposition of Pierce et al.’s institutional logics on Castelnovo et al.’s model of decision-making content emerging from the intersection of policy dimensions. Responsibility for this combination is solely on the author

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The remaining institutional logics can all enable citizen engagement, albeit to different degrees and manners, reflecting again the complexity of smart city development as it reflects the city’s values and culture. Institutional logics need not be static; while they reflect a culture, they can also be used to change a culture towards a different direction and, as such, are instruments of change as well as descriptors of states. This approach was successfully accomplished in the Reno-Sparks region of Nevada. Reno-Sparks became a host for Tesla when the company opened production facilities in 2016. Reno-Sparks had little time to plan for the challenges posed by this opening in terms of transportation, housing, population density, and utilities. And, the region needed a cultural shift in its institutional logic. The actor that was most influential in shifting the logic was the Economic Development Authority of Western Nevada (EDAWN). EDAWN manifests qualities much like other economic development and workforce development associations throughout the United States, in that it has the capability of bridging private and public interests, and can initiate shifts in culture with less perceived bias than others more solidly located in the public and private sectors. Successful Public Private Partnerships (P3) in the United States have often benefited from the integrating work of a middleman organization to interpret, translate, and negotiate relations between traditionally isolated actors. In addition to the relationship characteristics of actors, exogenous factors such as economic capacity, experience with public private partnerships, and population characteristics are relevant factors. In the United States, emergent smart cities are large population centers with strong economies. In 2019, Racine, Wisconsin became the smallest city to receive a smart cities grant, signaling that smaller urban centers have the potential to use technology effectively. With the increasing interest in technologically enabled change, smart cities are being joined by smart regions, smart counties, and smart states. The diversity in the political units utilizing technology to become smarter suggests that the configuration of organizational fields and institutional logics require careful attention.

Climate and Energy Introduction: The Green, Resilient Cosmo-Polity Municipal governments are at the nexus of several ground-level climate governance challenges. Municipalities often manage the immediate harm of climate disasters and implement long-term clean-up and rebuilding efforts. They also have primary jurisdiction over various land-use policies which must anticipate the new climatic regime and buffer its citizens against its deleterious impacts. With respect to response, rebuilding, and anticipating, then, municipalities are responsible for incorporating biophysical resilience into their built environments. At the same time that global warming places physical structures under duress, humans are expected to continue their migration to urban environments, some because of climate-induced distress. Upper end estimates of climate-distressed dislocations approach 1 billion people by 2050, or about 1 of every 9 humans. The

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welcoming of this diversity of individuals into settled social patterns is expected to strain social cohesion, so much so that, some think the Westphalian nation-state shall not endure the new climate regime. In addition to biophysical resilience, global warming, then, calls upon municipalities to build social resilience into their political, economic, and social forms. According to the recent IPCC 1.5C report, humans must abate greenhouse gas emissions to zero by 2050 in order to avoid increasing global average temperature by more than 1.5C. Virtually all mitigation pathways which avoid 1.5C increase also require the extensive deployment of carbon dioxide removal (CDR) technologies and methods. Further, at the present rate of emissions, the carbon budget will be exhausted by 2030 (IPCC 1.5C). Globally, cities account for about 70% of all greenhouse gas emissions and municipalities have jurisdiction over zoning laws, building codes, and infrastructure decisions which “lock in” patterns of energy use and greenhouse can gas emissions for decades. Climate governance, then, also calls upon municipalities to configure the built and social urban environments so as to abate greenhouse gas emissions. Accelerated in reaction to the Trump Administration, municipalities around the world have pledged to meet international greenhouse gas mitigation obligations and are leading the development of climate policy and climate governance. Under the auspices of various compacts, such as the C40, these municipal pledges operate across national jurisdictions, imparting a trans-national, multilateral element to municipal management. An adequate response to global warming then requires of municipal governments that they respond to and anticipate climate-disasters, increase the total amount of energy resources available to their expanding constituents, sharply reduce both total and per capita greenhouse gas emissions, secure equitable access to the energy resources, and to do so in transnational dialogue and partnership. In the context of climate and energy, the emerging smart city ideal is the green, resilient cosmo-polity. Amongst other trends, individuals, utilities, and municipalities are imbricating new energy technologies into both the greater electrical grid and personal spaces. This smartening and greening of the use of energy technologies exemplifies in many ways the challenge cities confront when they attempt to become smart. New governance structures are required to support green energy technologies; those technologies generate big data which must be transitioned into management decisions, the privacy and security of the individual is endangered, and those technologies’ promise of inclusion could vanish into even more extreme social and economic divisions.

The “Old” Grid In the United States during the early twentieth century, two governance models competed for control of the urban electrical power industry. Under the now-prevailing model, investor owned utilities (IOUs) received a franchise monopoly from the state. The franchise monopoly preserves the utility’s return on investment while obligating it to provide service to all within its franchise territory.

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Wisconsin pioneered this model in 1907 and virtually every state adopted it shortly thereafter. Municipal “public power” was the main competitor to the IOU model. Under the municipal public power model, municipalities would establish and finance a utility to provide electrical power to its citizens. Wisconsin adopted the IOU model to settle the political conflict between public power advocates in Milwaukee, Wisconsin’s largest city, and the local electrical power company. In this struggle to control the benefits and cost of the emerging electrical power sector, municipalities largely lost their legal authority and administrative capacity to establish public power systems, which some cities are struggling to regain as they attempt to meet their climate governance responsibilities. The Wisconsin model supported large, centrally controlled fossil fuel plants which served geographically and jurisdictionally dispersed customers. Unique to the electrical power industry, generation and loads (supply and demand, in economic discourse) must be perpetually and instantaneously matched. Under the Wisconsin model, the utility had the primary responsibility and control over this matching, which it did by increasing generation in response to increasing loads. Utilities accomplished this matching with leading-edge (at the time) econometric modeling, sophisticated marketing, and pricing strategies to induce load building, and precision monitoring. The “flow” of electrical power was from utility to customer and the gird was “unidirectional.” Governments (but not utilities) largely took load (or customer behavior) as a natural manifestation of autonomous consumer behavior, and thus not a subject of governance. The emergence of renewable technologies, in conjunction with the urgency of mitigating greenhouse gases, has distressed the Wisconsin model and opened new opportunities for electrical power generation and use. The imbrication of these technologies into the energy system presents a variety of governance challenges to the green, resilient cosmo-polity.

The Smart Grid, Distributed Energy Resources, and the City Distributed energy resources (DER) disturb the grid’s unidirectionality. Rather than “production,” these new technologies are “resources” and rather than “central” they are “distributed” and operate at the “grid edge.” DER include, inter alia, fuel cells, demand response (receiving payment for curtailment of demand during peak hours), combined heat and power systems, thermostats, batteries, solar PV, micro-wind, electrical vehicles, smart meters, and a variety of devices in the built environment (such as LED light bulbs) which reduce energy consumption in the domicile or place of business. Especially given the constraints of the grid, DER manifest a paradigmatic big data, machine learning coordination problem. Besides having different spatial and temporal locations in the grid, DERs have different technological attributes and greenhouse gas emission profiles. Solar PV produces only when the sun shines, which may or may not be contemporaneous with the use of electrical power. Batteries are both load and production, and some DER are not also non- or low-carbon emitters. Coordinating the variegated spatial, temporal, environmental,

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and technological attributes of DERs to sustain grid reliability while meeting mitigation targets and maximizing capital efficiency is conducive to monitoring and machine learning (or perhaps surveillance and control). The transition to DER, then, is the physical and technological manifestation of the digitization of the economy generally, and of the electrical grid specifically. For instance, the New York Public Authority (NYPA) has partnered with GE to self-consciously become the “world’s first fully digital utility” (https://www. businesswire.com/news/home/20171025006089/en/New-York-Power-AuthorityNYPA-GE-Partner). The project will monitor all utility assets along a range of variables (e.g., temperature and vibration) and NYPA is constructing a facility specifically to analyze the data flow (https://www.utilitydive.com/news/nypa-con tinues-digital-transformation-as-new-york-funds-grid-upgrades/521259/). This monitoring of grid typology should allow for enhanced capital and technological efficiency, prevent blackouts, and increase grid resilience. Smaller systems, such as the micro grid at Gordon Bubolz Nature Preserve in Appleton, Wisconsin have tailored a general software package (SaaS) to coordinate the contributions of solar PV, a micro turbine, a battery, and hydrogen fuel cells so as to minimize power imports from the grid. As an example of the challenge of incorporating DER into the “old” Wisconsin model, due to the absence of state-level policies coupled with utility reticence, the Nature Preserve is able to take only partial economic advantage of its contributions to the grid and the greenhouse gas benefits of its micro grid remain uncredited. Blockchain is frequently mentioned as a software solution to coordinating and crediting the diverse DER contributions to the grid from entities with varied economic interests. Blockchain’s distributed and digital ledger allows for the trusted transfer of digital coin between unknown parties. It also allows for the creation of digital currency representative of a variety of underlying values. Hence, many have noticed that it can be used to create an exchange platform for digital coins representative of underlying energy values, such as a “renewable kilowatt,” which otherwise anonymous parties could exchange. As compared to a centralized, perhaps stateadministered clearinghouse, however, blockchain has yet to be recognized as the superior solution to this coordination challenge. Blockchain is itself energy intensive and the energy needed to maintain the digital ledger might vitiate the environmental benefits of the DER it supports. Incorporating DER into the grid offers a once a century opportunity to reallocate the ownership (and hence the economic benefits) of generative assets and make an important contribution to unwinding economic inequality. Many DER have convivial characteristics, empowering their owners and users toward participation and selfdirection (in contrast, for instance, to nuclear power). When accompanied by intentional and vigorous policy support to remove barriers to access, the local and convivial characteristics of some DER provide the opportunity for economic rejuvenation. Under a regime of energy democracy, the ownership of the DER will be widespread and diffused, allowing a large number of stakeholders to enjoy the benefits of the transition to the renewable energy economy. That ownership might be individual (rooftop solar), through a collaborative association (a coop or

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not-profit) or through the municipality. Various pilot projects have settled on the “neighborhood” as the optimal social unit from which to build up the green, resilient cosmo-polity. Oakland’s Ecoblock aims to enhance social and economic inclusion, while reducing its biophysical impacts to near zero (https://ced.berkeley.edu/eventsmedia/news/cities-of-the-future-harrison-frakers-ecoblock-project-to-make-oaklandneig). The neighborhood scale allows for the digital coordination of DER’s stochasticity over a variety of loads while taking advantage of economies of scale without surrendering the appropriation of the system’s surplus value to a third party. However, as yet another type of sensor, DER are data source which powerful actors can manipulate for their own benefit. According to Zuboff, for instance, the terms-of-service and end-user licensing agreements for the Alphabet-owned Nest “smart” thermostat “reveal oppressive privacy and security consequences in which sensitive information is shared with other devices, unnamed personnel, and third parties for the purpose of analysis and ultimately for trading in behavioral futures markets, an action that ricochets back to the owner in the form of targeted ads and message designed to push more products and services.” (Zuboff, p. 237) The convivial characteristics of DER, then, are only preserved against such intrusion when citizens use law and policy to restrain profit-maximizing actors. Moreover, if not accompanied by countervailing policies, the introduction of technologies will perpetuate or exacerbate existing social and economic inequalities at both the local and global scale. In the case of DER, the threat is of energy apartheid, under which presently favored economic classes invest in both DER and their enabling physical and social infrastructure. They “defect” from the grid and others are left dependent upon an aging and financially unstable fossil grid. The presently existing built environment is not designed to support DER and thus provides the inertial conditions for the development of energy apartheid. Roofs do not necessarily face towards the azimuth, renters do not have control over the structure of their dwelling, and while economically competitive on a 7–10 year time frame, the initial DER investment is substantial from the point of view of the individual household’s budget. The “neutral” market reflects this same bias. One study from Professor Reames’ Urban Energy Lab found that census tracts with low-income, low-mobility, and high percentages of minority groups lack access in the private market to the basic DER of LED lightbulbs. The image of the illuminated Goldman Sacks building amongst another otherwise blacked out Manhattan in the wake of Superstorm Sandy is a representative image of energy apartheid.

Conclusion The incorporation of DER, then, offers both promise and peril for smart cities aiming at a green, resilient cosmo-polity. At best, they can help meet local and global mitigation commitments while supporting energy democracy and convivial technology. At the other end of the spectrum, they threaten energy apartheid and intrusive surveillance. The green, resilient cosmo-polity transcends nation-states, but the successful incorporation of DER (and other smart technologies) into the

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municipality depends upon the reorientation at every level of governance towards greenhouse gas mitigation, equitable energy access, and the support of social and environmental conditions required for human welfare. Without this reorientation, the imbrication of digitally connected technology, such as DER, into the city will remain nonproductive, or even retrograde.

Summary What exactly a smart city looks like varies. This introductory chapter identifies key fundamental characteristics of a smart city. First and foremost, a smart city is based on humans and human interactions for a common purpose. These interactions coalesce in institutions with smart cities characterized by the synergies enabled through university-industry-government-citizen collaborations. A distinguishing feature of smart cities is the unprecedented amount and types of data and information that can now be collected through diverse types of digital technologies. Smart cities have a multi-faceted relationship to climate and environment: they rely on physical and energy resources from the environment; they also affect their environment. To thrive and flourish, smart cities must be designed so as to take into account societal changes hailed through climate change and human movement (migrations and immigration). This introductory chapter has been formatted in such a way as to indicate that the five areas of smart city research and applications might be considered in isolation from one another. The section on climate and energy has provided some linkages by applying significant and recurring themes within the energy framework. Finally, it may be useful to suggest thematic similarities between the areas themselves; these themes can be used to guide decision-making and planning in a variety of smart city developments. The cascading effect within systems is central to smart city development. A cascade occurs when actions taken in one venue will have a continuous impact on other venues. This can be positive or negative; due to the connectivity of the smart city platform, it is certain to occur. The ability to not only mitigate cascade effects but also to use them to advantage is characteristic of a resilient system. It seems useful to address smart city developments from a system thinking perspective, applicable not only to the IoT technology and network connections but to the connectivity between actors and across subsystems. Technology allows the city to prioritize interactions and establish dynamic relationships, allowing a continuous identification and resolution of decision-making challenges; the data collected from edge computing arrays creates an increase in information pathways with the potential for spontaneity. This is countered by the structuring of data through deep learning which allows for pattern creation and reduces the reactionary behavior at the edge to predictive planning for the future. From a transactional perspective between actors, responses in one interaction can lead to multiple ripple effects in other portions of the network. Smart city development necessarily involves new types of relationships across levels of communities, within communities, and across individual agents. The first

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case, the establishment of relationships across levels of communities, represents a new opportunity for explanatory and predictive analysis. Up until now the ecological inference fallacy, as the error of making inferences regarding one level taken from analyses at a different level, has limited predictive capacity. With big data that is structured and aggregated, one can combine both individual and group level data to enable us to make inferences that circumvent this fallacy. Within communities, technology and data can also permit us to create institutional relationships (as compared to institutional structures) that can dynamically organize change agents and stability providers as needed. Finally, recognizing that agents are not fixed in a smart city network, there is more incentive to use collaboration rather than competition. The latter process is divisive and isolating, contrary to the assumptions of a network. This has implications that immediately confront existing governance institutions, social forms, and moral boundaries. It is certain that there will be destabilization/reconfiguration of existing systems, with either good or bad impacts. For example, the intertwining of previously separate institutions, as represented by the helix model, presents a new relationship that will require different types of decision-making, levels of transparency, and balancing of individual versus group interests. Finally, there are two areas of thematic inquiry that are most directly related to the concept of flourishing introduced earlier in this chapter as an important consideration in the design of any smart city development project. They are the moral questions of privacy/security and the political questions of civic engagement and participation. A critical examination of the impact of smart city technology on individuals must not be ignored. Wellbeing can be considered through the efficient delivery of services, but it also must include issues of privacy, security, individuality, and objectification. Citizen participation, and consent, in data collection, technology use, and artificial intelligence must have priority in order to ensure the flourishing of society. Data as management threatens to become surveillance and the sculpting of behavior so as to become profitable to the public or private surveyor. A related concern addresses issues of equity and inclusivity. Technology can increase access for citizen engagement and participation in decision-making by reducing barriers generated by disparities in income, education, and digital literacy. However, technology is not an end, it is a means. Equity must not be promised to residents as equality of opportunity, which would introduce the potential ethical problem of financial burdens necessary to take advantage of the opportunity. Rather, it should ensure equality of condition so that any smart city development always includes citizens at the core.

Glossary 5G Fifth Generation Cellular technology that will enable faster and more reliable data and signal transmission at near real time. Considered a necessity for smart city development, 5G allows for connected vehicles and mass sensor deployment. Actuator A device that causes a machine or other device to operate.

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Artificial Intelligence Activity that mimics human thinking. A broad term which encompasses both machine and deep learning. The latter works most effectively with large amounts of data. Barriers to Access Physical, legal, or sociological structures which hinder access to otherwise appropriate technology and resources, especially for low-income individuals and communities. Batteries Used for Grid energy storage to store energy for use at peak demand. Big Data Large amounts of data that, due to size and complexity, is difficult to process using traditional methods. Using the cloud as a relational platform, new technologies, such as Apache Hadoop and NoSQL, can structure and analyze big data. Biophysical Resilience The biophysical approach to economy and society incorporates the first and second laws of thermodynamics into its inquires. Biophysical resilience is the ability of a social or biological form to return to that form after an exogenous perturbance. Bitcoin A type of digital currency in which a record of transactions is maintained and new units of currency are generated by the computational solution of mathematical problems and which operates independently of a central bank. Blockchain A data structure composed of cryptographically connected records. Transactions can be transparent, but it is virtually impossible to modify the data within a block. First used for bitcoin trade, it has expanded into global economic transactions and information exchange. Blockchain removes the need for a middleman in transactions and can make transactions near instantaneous across borders. Built Environments Human made space that includes buildings and greenspace. Capital Efficiency Also referred to as return on capital employed (ROCE), this measures the ratio of profit to capital invested. Because the collecting and analyzing of big data allows cities to target services more effectively, capital efficiency can be increased. The rate at which fixed, physical capital is in use as measured against its technologically optimal usage. Carbon Budget The amount of greenhouse gases (usually designated in tons CO2e) which may be emitted into the atmosphere before a biophysical threshold is exceeded, such as global average temperature or atmospheric concentration of CO2-e. As a policy instrument, a carbon budget can be allocated to jurisdictions. The jurisdiction must then undertake measures to ensure it does not exceed that budget. Many think that “carbon budget” should be replaced with “carbon debt” as humans most likely need to remove carbon dioxide from the atmosphere and are therefore “over budget.” Of the Carbon Neutral Alliance member cities (2019), 6 are located in Europe, 7 in the United States, 3 in Australia, 2 in Canada, 1 in Japan, and 1 in Brazil. Carbon Dioxide Removal A process which removes carbon dioxide directly from the atmosphere. In 2018 Tampere Finland was one of the first cities to use this process and use the CO for regional heating, thus becoming a carbon negative city.

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Centrally Controlled A business or governance structure under which processes and operations are under the direction of a single entity. Typically contrasted with a networked structure. Climate Disasters Disasters attributable to the new climate regime. Typically, a climate disaster appears as an ordinary “natural disaster” but differentiated by its increased intensity or increased frequency. Climate Governance The set of policies, strategies, and implementation processes responsive to global warming. Typically, these include mitigation, drawdown, adaptation, and, recently, damage and loses. Urban areas can be primary sources of threats to climate change, but the nature of the challenge is such that multilevel and multisector approaches are required to succeed in climate governance. The nonhierarchical multilevel approach is well suited to the regional and state institutions of Europe but may be more challenged in a federal system such as the United States and Canada. Cloud/Cloud Computing Cloud computing is an information technology (IT) model for enabling ubiquitous access to shared pools of data and computing resources, typically over the Internet. Cobots Collaborative robots specifically designed to work cooperatively with humans in a work environment. Combined Heat and Power Systems (CHP) The thermodynamic process of cogeneration by which “waste” heat is used to produce electrical power. Uses otherwise wasted heat generated through electricity production to provide thermal heat energy. Communication Technologies It includes all mediums that are used to process and communicate information. Used in combination with information technology (ICT). Convivial Technologies or Tools Originally developed by Ivan Illich, convivial tools empower individuals to work with independent efficiency. As developed by Adrea Vetter, convivial technologies enhance relatedness, adaptability, accessibility, bio-interaction, and appropriateness. Cyberspace Interconnected digital technology (internet) that allows for communication and information transfer. Data Quantitative or qualitative measures of variables in an unstructured format. Structured Data is information. Decentralized Network Distributed nodes or groups of nodes that can operate independently from each other, but can also share information. Typically contrasted with centrally controlled systems. Deep Learning An artificial intelligence function that imitates the workings of the human brain in processing data and creating patterns for use in decision-making. Demand Response An economic relationship between energy producers and consumers which pays consumers to reduce consumption during peak demand so as to enhance overall capital efficiency. Demand response is a distributed energy resource. Digital Addiction Increasingly dependent relationship with digital devices.

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Digitization The transfer of information into digital formats (computers and network). Digitization increases efficiency of information storage, transfer, and analysis. In political economy, digitization characterizes the economic trend of business revenues originating from the sale, use, and manipulation of digital data vis-a-vis the production of material goods. Distributed and Digital Ledger Database that is shared and synchronized across multiple sites, transparent, with no central manager or controller. Blockchain and Ethereum are distributed ledger systems. Distributed Energy Resources (DER) Energy resources that can be used individually or combined into the larger electricity grid. DER’s can be a combination of energy sources integrated or supporting the larger electrical grid when necessary. Dynamic Network Slicing software that optimizes 5G performance by partitioning portions of the 5G network according to dynamically changing use cases. Ecoblock: An urban community design which manages biophysical systems (energy, water, waste) so as to minimize impacts on the surrounding environment. Ecoblocks exist in Oakland California and Quingdao China. Edge Computing A distributed computing architecture in which data processing is performed on a network of devices or nodes know as edge or smart devices rather than taking place in a centralized location like a cloud or server. Edge computing allows for real time response, even while providing data to a central source. Electrical Vehicles A vehicle which uses one or more electric motors for propulsion. Energy apartheid The inequitable access to energy and energy resources which perpetuate and are an aspect of global or local exploitation. Typically contrasted with energy democracy. Energy Democracy Equitable decision-making and ownership of productive energy resources. Consumers are also producers, innovators, and decisionmakers in planning energy creation and distribution. Typically contrasted with energy apartheid. Equity/Equitable The absence of avoidable or remediable differences among groups of people, whether those groups are defined socially, economically, demographically, or geographically. An equality of condition, rather than opportunity. Smart city planners can use technology to include marginalized populations in areas such as public health, transportation, and biometric identity cards for the homeless. Flourish The ability to attain and achieve a complete and sufficient good. Fuel Cells A renewable and clean technology that produces energy outside of the main electrical grid but can be linked to the grid. Energy is produced from a supply of oxygen and hydrogen. Greenhouse Gas Emission Profiles A description and classification of an entity’s or technology’s greenhouse gas emissions. A greenhouse gas profile provides data about the origin of greenhouse gas emissions with the aim of identifying abatement policies. Greenhouse Gas Inventory A type of emission inventory developed for a variety of reasons. Greenhouse gas inventories typically use Global Warming Potential

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(GWP) values to combine emissions of various greenhouse gases into a single weighted value of emissions. Grid Resilience The degree to which an electrical grid can withstand disruptive threats to power distribution (either natural or man-made). Often used interchangeably with grid reliability or grid hardening. Grid Typology Grids are composed of generation, transmission, distribution, and distributed energy resources, each of which, besides their electrical power characteristics, have environmental and financial characteristics. Grid typology is the multidimensional description of these characteristics, sometimes visual. Human-Machine Interaction Design and use of computer technology for a cooperative and seamless relationship with users and machines. Inclusivity The practice or policy of including people who might otherwise be excluded or marginalized, such as those who have physical or mental disabilities and members of minority groups. Institutional Logics A core concept in sociological theory and organizational studies. It focuses on how broader belief systems shape cognition and behavior of actors. Internet of Things (IoT) The network of physical devices that are connected to the Internet and the communication that occurs between these objects and systems. Investor Owned Utilities (IOU): A for profit enterprise that acts as a public utility. IPCC 1.5C Report Intergovernmental Panel on Climate Change report (2018) on the impacts of global warming of 1.5C above pre-industrial levels https://www. ipcc.ch/sr15/ LED Light Bulbs Bulbs that utilize LEDs (light-emitting diodes) to produce light. LED light bulbs are a more environmentally friendly alternative to incandescent bulbs. Media Richness A quality of a communication medium that refers to its ability to use multiple opportunities to provide meaning to the users. Mesh Network A local network topology in which the infrastructure nodes (i.e., bridges, switches, and other infrastructure devices) connect directly, dynamically, and nonhierarchically to as many other nodes as possible and cooperate with one another to efficiently route data from/to clients. Microgrid A group of interconnected loads and distributed energy resources within clearly defined electrical boundaries that acts as a single controllable entity with respect to the grid. Micro-Wind Residential-based wind turbine that can link to a larger electrical grid or stand-alone off grid. Mitigation Pathways A depiction, typically graphic, of a future abatement curve for reducing greenhouse gases on a timeframe which will meet a particular carbon budget goal. Neural Network A set of algorithms, modeled loosely after the human brain that are designed to recognize patterns. They interpret sensory data through a kind of machine perception, labeling, or clustering raw input. Patterns are numerical, contained in vectors, into which all real-world data, be it images, sound, text, or time series, can be translated.

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Public Power Not for profit utilities operated by municipal state or national governments. Public Private Partnerships (P3) A cooperative arrangement between two or more public and private sectors, typically of a long-term nature. Related Concept in in Australia and the UK- Private finance initiative (PFI). Quadruple Helix A set of interactions between academia, industry, governments, and citizens to foster economic and social development. Resilience The ability of an organism or entity to recover to its original state after an external, disruptive impact. Robots A machine that can be programmed to perform a series of complex tasks. Sensor Array A sensor is an electronic component, module, or subsystem whose purpose is to detect events or changes in its environment. Smart City Urban development that integrates information and communication technology (ICT) and Internet of things (IoT) technology in a secure fashion to manage a city’s assets, deliver city services effectively, efficiently, and equitably. Smart city uses information and communications technology (ICT) to enhance livability, workability, and sustainability. Smart Meters An electrical meter which provides a temporally indexed record of two-way flows of electrical power, often transmitted to the central controller and aggregated for the purposes of optimizing the electrical power system of which it is a part. Typically, transmissions are done at least once a day, sometimes more often, enabling dynamic adaptation to energy production, consumption, and distribution needs. Social Cohesion A societal condition that works toward the well-being of all its members, fights exclusion and marginalization, creates a sense of belonging, promotes trust, and offers its members the opportunity of upward mobility (rising from a lower to a higher social class or status). Social Resilience The ability of a group or community to adapt to change, take advantage of opportunities, and become less vulnerable to disruptive events. In social resilience, this includes the ability to adapt, even while maintaining the core identities and values of the group. Socially Constructed The provision of meaning by society. Socially constructed meaning will vary depending upon region, culture, and organizational systems. And, an urban environment based on integrated technology can socially construct a different idea of “city.” Philosophically, the social reconstruction of the city and its citizens has implications for the objectification of an individual, and its functional relationship within a managed system that might be terms a city. Software as a Service (SaaS) A software distribution model in which a third-party provider hosts applications and makes them available to customers over the Internet. Solar PV A power system designed to supply usable solar power by means of photovoltaics (PV). Technological Determinism A theory that suggests technology can shape individual and societal values and behavior.

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The Fog A computing architecture that allows for the performance of intermediate computing, networking, and storage, connecting data centers and edge devices. Advantages of using the fog as an intermediate network are low latency (quicker response time) and less bandwidth (dynamically aggregated use of information sources). Thermostats A device for sensing the temperature of the surrounding environment and turning on a heating or cooling system to retain the surrounding environment at a designed temperature. Smart thermostats are connected to the Internet of Things so become a device for the sensing and digitization of events. Triple Helix A set of interactions between academia, industry, and governments, to foster economic and social development. Can contain Private Finance Initiatives (PFI) and Public Private Partnerships (P3s).

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Ravani, S. (2019). Oakland committee approves ban on facial recognition surveillance. San Francisco Chronicle. https://www.sfchronicle.com/crime/article/Oakland-committee-approvesban-on-facial-14050026.php. Accessed 8 July 2019. Raven, R., Sengers, F., Spaeth, P., Cheshmehznagi, A., & Xie, L. (2017). An institutional perspective on smart city experimentation: Comparing Ningbo, Hamburg and Amsterdam. Presentation at ECOCITY World Summit, 12–16 July 2017, Melbourne. Roland Berger. (2019). Navigating complexity: The smart city breakaway. Think: Act Magazine. https://www.rolandberger.com/publications/publication_pdf/roland_berger_smart_city_break away_1.pdf Sadowski, J., & Pasquale, F. (2015). The spectrum of control: A social theory of the smart city. First Monday, [S.l.], June 2015. ISSN 13960466. https://firstmonday.org/ojs/index.php/fm/article/ view/5903/4660. https://doi.org/10.5210/fm.v20i7.5903. Date accessed 1 July 2019. Shachtman, N. (2008). 26 years after Gibson, Pentagon defines ‘Cyberspace’. Wired. https://www. wired.com/2008/05/pentagon-define/ Speck, J. (2013). Walkable city: How downtown can save America, one step at a time. New York: North Point Press. Tanaka, W. (2012, April 10). Cities of the future: Songdo, South Korea-Education. https://news room.cisco.com/feature-content?articleId¼776668. Accessed 8 July 2019. The Threat Lab. (2019). The history of cellular network security doesn’t bode well for 5G. https://www.eff.org/deeplinks/2019/06/history-cellular-network-security-doesnt-bode-well-5 g?fbclid¼IwAR0RK0s_HtDaUXsR2Vcz0eFFdgEYI3fqhxxa6aHIPpTGbUOt8Ii4Z1ZWlsw. Accessed 8 July 2019. Townsend, A. M. (2013). Smart cities: Big data, civic hackers, and the quest for a new utopia. New York: WW Norton and Company. Waal-Montgomery, M. D. (2016). World’s data volume to grow 40% per year & 50times by 2020: Aureus. https://e27.co/worlds-data-volume-to-grow-40-per-year-50-times-by-2020aureus-20150115-2/. Accessed 8 July 2019 Weisburd, D., Mastrofski, S. D., Greenspan, R., & Willis, J. J. (2004). The growth of Compstat in American Policing. Washington, DC: Police Foundation Reports. https://www. policefoundation.org/publication/the-growth-of-compstat-in-american-policing/ Zikopoulos, P., Parasuraman, K., Deutsch, T., Giles, J., & Corrigan, D. (2012). Harness the power of big data the IBM big data platform. Emeryville: McGraw Hill Professional.

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Smart Cities Can Be More Humane and Sustainable Too Eduardo M. Costa

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . More Humane and Sustainable Smart Cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Live-Work-Play in the Same Area! . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sidewalks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bike Lanes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Light-Engine Vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Public Transport . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Listen to Citizens’ Wishes, Interests, and Needs! . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Deindustrialize your Mind! . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 a.m. to 5 p.m. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Schools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tech Parks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Work and Employment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Car . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cross Reference and Major Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Demographics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Diversity and Priorities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Special Needs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Socialization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract

The term “smart city” has been used extensively by technologists and the media to describe a place where modern technologies, mostly information and communications technologies (ICTs), are widely used by local governments, institutions, E. M. Costa (*) LabCHIS – Humane Smart City Lab, Federal University of Santa Catarina (BR), Florianópolis, Brazil Knowledge Engineering and Management Dept., Federal University of Santa Catarina (BR), Florianópolis, Brazil e-mail: [email protected] © Springer Nature Switzerland AG 2021 J. C. Augusto (ed.), Handbook of Smart Cities, https://doi.org/10.1007/978-3-030-69698-6_3

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and citizens. Technology is always seen as a way forward. “The more, the better. They will be beneficial to citizens eventually, in one way or another.” Or will they? Well, maybe, but not necessarily. This chapter argues that the humane side of urban planning should be taken into account first. Humane is not only one of the dimensions of a proposed solution. It is the dimension to guide all the others. In fact, every city project should start with a clear definition of what is the actual citizen’s problem that is being solved, and its results should be measured against that goal. For instance, a control center is a costly and useful tool to measure and orientate car traffic in the city. But its cost should be evaluated and compared against other solutions that improve mobility of citizens in town, not mobility of cars. There is a subtle difference here. Mobility of car drivers is not a measure of citizen’s mobility as a whole. A proper bike lane or an improvement in the public transportation system, for instance, may offer much better and cheaper solutions when the focus is changed from the car to the citizen. Once citizens’ wishes, interests, and needs are clearly identified, technology will be, of course, part of the solution. It is just a question of resetting priorities: people and the environment first; then, comes everything else.

Introduction Let us start in the medieval villages, in the fifteenth century. People lived in small areas, in villages (up to 10,000 people) or cities (more than 10,000 but less than 100,000). Cities were many times enclosed by a defensive wall, and most of the daily activities were conducted in the house (Gies and Gies 1969). Women bought foods and spices at the local stores, cooked and worked on household chores, sewed clothes, and raised children, with or without the help of house maids. Peasant men went out of the walls for crops and hunting. Military men left their villages for battles. And clergy and noble men ruled the place. Two characteristics of the medieval village are important for our study. First, the cities were small, up to an approximate circle of 1-mile radius, for the very simple reason that the water that was used for everything in the house came from the well, generally a single structure in the central square or commons. Water was carried in heavy buckets made of iron and steel, and, when loaded with water, these buckets could not be carried far. The second characteristic is that people lived, worked, and played in this small area and did not have to go anywhere. Except for merchants and people in battles, their whole life was lived in the city. Now let us fast forward to Paris in 1852. Louis Napoleon, a nephew of Napoleon I, had just become Emperor Napoleon III, after a meteoric rise from elected member of the National Assembly of the new republic in 1848; first president of the new republic in the same year; a coup d’état in 1851, which gave him a 10-year term as president; and a plebiscite in 1852, when the French voted to become an empire again, away from the short-lived first republic. The new emperor had had plans for

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some time for the new redeveloped capital of his empire but lacked someone to execute them. He then found the mayor of Bordeaux, Georges-Eugène Haussmann, a long-term political ally. The two men were responsible for the rebuilding of the city center, the opening of new boulevards and plazas, and the new residential and commercial buildings and for the development of new financial mechanisms (Kirkland 2013). In an amazingly short time, they transformed Paris into the most beautiful city in the world, starting with the 4 arrondissements (first, second, third, and 4th) in the city center and then expanding it to the 20 arrondissements that exist still today. One of the most striking characteristics of these boroughs is that, in each one of them, one could live, work, and play, as if they were back in the medieval times. And, coincidence or not, all of these boroughs have circa 1-mile radius each! Paris influenced the redesign of cities all over Europe and in other places in the world, including Rio de Janeiro, in Brazil, and Buenos Aires, in Argentina. Unfortunately, this period didn’t last long, as the industrial revolution, which had already started, had a profound impact on the urban planning of cities in Europe and elsewhere. The industrial revolution set up its factories in the city (Roberts 1980). But in a short time, they discovered that the industry was messy, dirty, and polluted the city with smoke. Henceforth, planners began to segregate the daily functions of living, working, and playing into separate portions of the city (Mumford 2018). This movement was later facilitated by the invention and popularization of the car (principally Ford Model T). People, at least those who had a car, could move from one function region to the other, using the new machine. Urban planners adapted quickly to the new idea, and the city became hostage to the presumption that what was good for the car (new and larger roads, viaducts, bridges, etc.) was also good for the citizen, an equivocal concept that is prevalent still today. Jane Jacobs (1961) was a lonely voice in the 1960s against the segregation of daily functions and an early apologist of the need for diversity in the city, and maybe that is the reason her book The Death and Life of Great American Cities became a classic reference. The initial phrase of the book sets the tone: “this book is an attack on current city planning and rebuilding.” But even with the repercussion of her ideas, the city-for-the-car planning became dominant all over the world. The results of this deliberate choice are there to be seen: traffic congestion everywhere, street violence, anxiety, depression, etc. The number of annual deaths in the world related to traffic accidents amounts to more than 1,350,000! It is by far the major cause of nonnatural deaths in the world. And we hear, in the nightly news everywhere, that there was an accident in a remote mountain that killed five people. We feel sorry for them and their families. However, traffic accidents, at the same time, kill circa 4,000 people a day! And what do we do about it? Maybe a campaign against drinking and driving (a major cause of accidents) here and there, and not much else. Besides this horrible death toll around the world, traffic accidents disable or injure another 50 million. Since these terrible numbers are concentrated in a few countries, they are equivalent to the death rates of civil wars. And we only pray not to be part of these gruesome statistics! It is high time we do something against this fact. Now.

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For instance, limit the speed within a city to, say, 40 miles/h, not a sign here and there or a bumper before a school crossing but everywhere in town. Another measure could be directed to car manufacturers: in many countries, the maximum speed limit in any road is 70 mph. How do manufacturers produce cars with a maximum speed of 110 mph? Isn’t this a violation of the law or, at least, the spirit of the law? In recent years, a myriad of tools and services to help in the city are being developed and offered in the market. They do help city managers to take care of their cities and also help connected citizens to move about and use city services. Some of them will be described in this book. Unfortunately, though, most tools and services are offered to help the driver. Cities are places for cars, right? So, tools and services should help drivers and traffic managers. It is only logical. It is also a mistake. Tools and services should help citizens to opt for mass transit, shared cars, biking and walking, and other active transportation alternatives, and not only help the ominous driver. This move requires a change in attitude, a cultural change – naturally difficult but has got to be tried. In the beginning of this century, a movement started in Europe to give more attention to the citizen instead of to the new technology. Many people in the world adopted the concept, including this author. They called their movement the Human or Humane Smart City (Costa and Oliveira 2017) and later coined by the author Humane and Sustainable Smart City (HSSC) (Costa and Pacheco 2020). The movement is not against technology, of course. It is just a reminder of the main focus we are all after: the citizen and the environment. There are successful projects to study such as in Bilbao (Azua 2006), Melbourne (Yigitcanlar et al. 2008), and Copenhagen (Gehl and Svarre 2013). They all have in common the citizen, the user, and the environment – not technology, or cars. A good way to start is to follow three basic concepts of the more HSSC: Livework-play in the same area, which means a new attitude toward urban planning; attend to the citizens’ wishes, interests, and needs, asking them to participate actively in the urban planning; and go through a process of mental deindustrialization. The following sections detail this action plan.

More Humane and Sustainable Smart Cities Smart cities and knowledge cities are quite well-known (Yigitcanlar and Lee 2011; Chang et al. 2018; Lara et al. 2016; Jones et al. 2019), and some of their characteristics are detailed in this book. Knowledge cities have been defined first, before the arrival of modern technology (Carrillo 2006, 2015). The concept reflects the need to characterize the place that is suitable for the development of the new society where knowledge – and therefore people – are the main means of production. Then came the idea of a creative city (Yencken 2013) that was capable of attracting the creative class of people (Florida 2014) who would develop the creative economy (Howkins 2001, 2013) of the future: a recognition of the powerful transformational characteristics of sectors such as advertising, gastronomy, architecture, design, fashion, video,

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Internet publications of all sorts, photography, computer games, music, performing arts, publishing, research and development, software, electronic publishing, arts and crafts, and entertainment as a whole. Another concept that is related to our studies is the resilient city (ICLEI 2019) that refers to the city that is flexible enough and prepares itself to face natural disasters, wars, or economic downturns (The Rockfeller Foundation 2013). Here we introduce the humane and sustainable dimensions that are to be added to the smart city concept in order to transform the city in a way that is relevant to the citizen, and not, for instance, to some smart city ranking (of which there are so many). In particular, one has to be careful of the already more than 100-year old phenomenon that Sam Schwartz baptized motordom: an unrelenting devotion to the kingdom of the private car (Schwartz 2015). Consider, for instance, that there is a travel book entitled “the ten widest freeways in the world to drive before you die.” Imagine the indescribable thrill of driving on Katy Freeway in Houston with its 26 lanes! I can hardly wait. A city that wants to be more humane and sustainable has to focus its urban planning, projects, and expansion on the citizen and the environment (Yigitcanlar et al. 2018). This is not to go against technology: it is to set priorities right. Technology is undeniably smart and useful if, and only if, we can use it in this right direction. A few examples might help explain the idea. Florianópolis, a state capital in the south of Brazil, is an island with circa 400 km2. It is connected to the continent by three bridges. The old bridge is being repaired, and the two more recent bridges carry the traffic across the 500 m channel. Traffic on both bridges is terrible with frequent congestions throughout the day (Yigitcanlar et al. 2018). Because of this, the city has been considering, for some time, where to build a fourth bridge, in order to alleviate the traffic congestion. It is only logical, isn’t it? Yes, if you answer the wrong question of how to improve the traffic conditions between the island and the continent. Wrong, if you answer the right question of how to improve the mobility of people across the channel. To this right question, the answer might be a passenger ferry boat service – several times cheaper! Janette Sadik-Khan, former transport secretary of New York under Mayor Bloomberg, planned to re-urbanize Broadway Avenue. Had she limited the scope of her project idea to the car traffic in the large avenue, she would be able to propose a very limited intervention (Sadik-Khan and Sollomonow 2017). Instead, she surveyed the daily movement of people in the avenue, including pedestrians. To everyone’s surprise, four times as many people used the sidewalks than the road! She then convinced the mayor (she recalled Bloomberg’s mantra in office “In God, we trust. Everybody else, bring data!”) that that precious public space should be occupied accordingly, giving more space to sidewalks and bike lanes. Bloomberg approved the plan in stages, changing a few blocks at a time. Today, Broadway is a boulevard with large sidewalks, bike lanes, and less space for the cars. And everyone loves it! Note that the change did not necessarily involve technology, but it helped transform the city into a more humane and sustainable place. Focus on the citizen, not on cars or technology, is clearly the lesson learned here.

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Live-Work-Play in the Same Area! Most of our cities grew enormously in the 1900s. The trouble is that the twentieth century was exactly the time when our planners embraced the wrong idea that the daily functions of living, working, and playing should be conducted in different parts of the city, segregated from one another. And transport from one segregated part to the other would be performed by car. It did not work, of course, firstly, for people who did not own a car. Later on, and still today, it does not work for anyone. It is probably better not to try and analyze why we went on that route. Let us just recognize that it did not work and that from now on we must do something different. Starting with the city code: in most cities, the segregation is frozen in the local regulation. For each space in the city, the code establishes what is the allowed use of the area: commercial, residential, industrial, entertainment, and such. One might think that some of this regulation is needed. For instance, you don’t want a bar cluster next to a secondary school or a hospital. But the trouble is that the code is much more divisive. For instance, who would not want a bakery on the corner of a residential block? City codes do not allow this arrangement in many places. “Residential area only” used to be a plus for a house value, within or outside a gated community. This means you need the car to buy a match box or a pain killer pill. Is that really desirable today? City codes should allow and even give incentives for residential, entertainment, and light work licenses in every region of the city (Glaeser 2011). A good area to limit a region is the 1-mile radius from medieval villages, a size that was followed in Paris many centuries later in their arrondissements (Deutschmann 2017). In each of these 1-mile radius regions in the city, people would be allowed to live, work, and play (Larson 2012). And how would they go about within the region and move from one region to the other? By foot, by bike, light-engine vehicles, and public transport. Let us take them in turn (Fig. 1).

Sidewalks Sidewalks are probably the most neglected urban equipment in most cities today. They were, at first, the citizen’s main means of transport. Then they lost space (and area) gradually to new car lanes and parking spaces. In many towns there are public parking places downtown that are used all day long by single cars. Each car takes one person only (in general) to work. Even so, and worst of all, some of these parking lots are free! While the cars sit there all day long, using public land, hundreds, maybe thousands, of people go about their businesses in very narrow sidewalks. It is simply insane! The existing sidewalks, besides being very narrow, are kept very poorly, provoking daily accidents, breaking limbs, and spraining ankles, especially for women in high heels. Since it is so bad, how does it go on and on? It is difficult to tell. My hunch is that sidewalks, in many towns, are to be used by poor people, so authorities are not that much concerned with their opinion.

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Fig. 1 Live, work, and play in the same area

Funny enough, tourists flock to Las Ramblas, in Barcelona, and to ChampsÉlysées, in Paris, and marvel at their width and beauty. But then they return home and approve the enlargement of an existing avenue in detriment of the existing sidewalk. This trend must be reversed. Several cities, even in North America, have put their streets on a diet to get thinner. One may think of Portland in the USA or Vancouver in Canada, well-known for their modern attitude toward urban planning. But what about Oklahoma City? It used to be the eighth most obese city in the country up to 2007, when Mayor Mick Cornett launched his re-urbanization plan that included 36 miles of new sidewalks and 35 miles of bike paths and walking trails. The active mobility plan was detailed in a website named thiscityisgoingonadiet.com. He even lost 40 pounds himself in order to set the example. In their studies of walkability, they identified that sidewalks are better used when they connect to good public transport, so the plan provided for a new modern streetcar system set on the grounds of a former road that run right through the city. One can also start small. A good guerrilla tactics to enlarge sidewalks in your city is the concept of a parklet. In the parklet, two car park spaces by the curb are occupied by a wooden or steel deck that is decorated with small flowerpots, benches, and tables and chairs, extending the serving area of a restaurant or a café. Once parklets are applied in a few places along a block, it is much easier to enlarge the sidewalk in the full extension of the block. The parklet idea, in use in many towns today, might require some change in the city code, but it is well worth the effort. The importance of good sidewalks cannot be over emphasized since they are so neglected today. Politicians like to inaugurate new and large avenues, viaducts, and bridges (named after someone they need to honor for some reason). But they don’t inaugurate a sidewalk (and they should!), so why bother?

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In order to start interventions of the street diet kind, it is important to bring merchants and shop owners on board. Their first impression is that if you reduce the number of car lanes in the street, their existing business would receive less people. It is exactly the opposite. It is not difficult to figure out that pedestrians and bikers are much more likely to stop by and buy something than passing cars. But it takes talking and convincing. Better show good examples and reliable data about the results of the interventions through simulation, a handy tool that is available to predict the ultimate results of change.

Bike Lanes Bikes are a wonderful means of transport for small distances. Bikers do exercise in the open air, leave their cars at home, and do not pollute the atmosphere. They depend on weather conditions, of course. But consider, for instance, Copenhagen: even with the severity of the winter there, 40% of the workers in town commute daily on their bikes. The same is true in Amsterdam. In the developing world, bikes used to be very important vehicles for daily transport as well, but unfortunately, bikes are being replaced by cars, a sign of modernity and of status. Cities should encourage bikers with the construction of a network of bike lanes everywhere in town. But the whole system in the city must be adapted so that bikes are more useful: there must be safe bike parking lots in town, showers in commercial buildings, mutual respect education campaigns in order to avoid collisions between cars and bikes and to increase safety for all, etc. And it is relatively cheap, compared to, for instance, a new viaduct or underpass, projects that are never questioned by the population, still in adoration of motordom. A smart idea was implemented in Paris and in many other towns in the world – the concept of a bike sharing system. You get the bike here and return it there, either in pre-assigned bike parking lots or, in some cases, anywhere within a defined delimited area. Bike sharing may be free to the user (maintained by the local administration) or may cost a small monthly subscription fee. In Barcelona, for instance, the largest cost of the system to the administration is to carry and redistribute the bikes at the end of the day, since Barcelonians that live in the hills take the bike in the morning going down and commute back by bus in the evening. One of the reasons the bike sharing system can be very cheap is the use of the actual bikes for advertisement of large corporations or specific marketing campaigns, in any case, a good system to implement anywhere. With the adoption of bikes by young professionals, many cities created bike lanes painting lines to limit them in existing streets. This clearly does not work! It is better not to do anything! Painted bike lanes are a constant source of collisions between cars and bikes and should be avoided. Bike lanes must be clearly segregated from the streets in order to make the arrangement safe for both bikes and cars. There are even cheap rubber block separators that may be used to segregate them, as used, for instance, in Barcelona.

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Light-Engine Vehicles In recent years, many cities all over the world are using light-engine vehicles for city transport. Scooters, electric bikes, electric skates, Segways, toy scooters, and all kinds of variations of these are in the streets today, mostly powered by electricity. They are wonderful means of short-distance transport, and many companies are renting them on a shared basis, charging monthly or by the hour. They are dangerous too, of course. And as they are already in service, they will require some kind of regulation, now, maybe even segregated lanes. Thsese new vehicles should not be demonized, banned, or dismissed, though, for being dangerous: we must regulate all vehicles according to their perilously. Otherwise, we would add an additional problem with the new vehicles to our already dangerous transport system.

Public Transport There is a chicken-and-egg dispute between cars and public transport. Some cities don’t invest in public transport because everyone, and their dog, has a car. The flip side is that people say they need a car because there are no, or very few, public transport options. This stalemate must be broken by the public authority. And it should be in favor of the public transport, by all means. There are so many possibilities of public transport today that one or many of them will be suitable to any given city – all sorts of buses, underground metro, light rail, BRTs (bus rapid transit), and such. As the new generation of the millennials takes over, it will be imperative for modern cities to provide good-quality transport in order to attract these youngsters. They are not so keen on cars (thank God!), and many don’t even bother to apply for a driving license – new world! In order to be more successful and attractive, cities must assume that they are in direct competition, with other cities in the same country or in other countries, in order to attract talent. The alternative is to face decay. A good way to sell the investment in transport idea to “old-timers,” even to the ones that live in the green suburbs, is that we need to prepare the city with good public transport for the new generation; otherwise our children and, God forbid, our grandchildren, will go live somewhere else.

Listen to Citizens’ Wishes, Interests, and Needs! Citizens, in this context, are not only the persons who live in the city but also people who work or go there on a regular basis, like weekday workers or regular tourists, for instance. In short, people who are direct users of the city. Think of Manhattan, for instance, where the number of workers that commute into town is the same as the number of actual residents (Morse and Qing 2012): it would make no sense to plan the city for its residents only.

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And no, it does not happen naturally. You hear all and every elected official say that they are there to serve the citizen (that is why they are called civil servants). But they don’t. Reasons are many. But once invested in a commanding post, they know what to do. Maybe because of a common mistake, we tend to think we know what other people want. It is even worse when you have things in common with that group. Let us say a graduate student thinks he knows what other students think and want; an elected official thinks he knows what is better for the town since he is also a resident. The popular project development tool called design thinking deals exactly with this problem. It helps developers put themselves in the users’ shoes. Walt Disney Jr., who created the Disney empire, is reported seen squatting several times in the internal roads and looking pensive during the construction of the first Disneyland Park: he was trying to look at the place from the perspective of and with the eyes of a child. We need to observe, study, measure, and feel what are citizens’ main wishes, interests, and needs. Frank Gehl, a Danish specialist in urban design, suggests several forms to study the city before doing anything (Gehl and Svarre 2013). Observers should watch the city in order to determine what the citizens do, how often, and at what times, and who they are. And the observer may assume different roles as she counts, analyzes, interprets, and tries to make sense of this complex system that a city is (Fig. 2). A common mistake is to develop a new city plan and then conduct a public hearing in order to invite the locals to contribute to the final details of the project. Eduardo Paes, former mayor of Rio de Janeiro, planned a new cable car for Rocinha (the largest slum in town, with more than 100,000 people) and went there for a public hearing: to his astonishment and dismay, people said they did not need or want a cable car there. They needed a sports facility, a new sewage system, a good school, and many other things first, and then, they would consider a new cable car. The lesson was learned by the administration, and, in their next major project the “Marvelous Port,” the redevelopment of the old harbor, they partnered with the population from the beginning, and the project was an astounding success. Fig. 2 Observe citizens’ wishes, interests, and needs. (This Photo by Unknown Author is licensed under CC BY)

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Mouraria (Moorish quarter) in Lisbon is a good example of a successful redevelopment of a place achieved in close proximity with the locals. Set at the back hill of Castelo São Jorge, a national monument and former royal residence, it was forgotten in the twentieth century and became a dangerous district with drug dealing and prostitution activities all day long. It was also a cheap place to live, so it housed many immigrants from Portugal’s colonial past. In the last two decades, with the direct involvement of the European Human Smart City movement (Oliveira and Campolargo 2015), it was transformed completely, building up from the strength of the local communities. And now, besides being the home of the fado (most traditional Portuguese music), it is a place for diversity in Lisbon with all different kinds of cuisine, art, and culture and a tourist attraction that is a testimony of the city’s rich historical and cultural tradition. In some cities all over the world, City Hall launched an initiative of a “participatory budget.” It is a good start. In the process, city officials talk to and listen to local neighborhoods’ demands and make them vote on the better use of the money (ultimately their money) originally allocated to that district. It evolved from Porto Alegre in Brazil and has shown interesting results all over the world. A recent World Bank report (United Nations 2019) describes these cases but also demands more participation of the very poor and the young in the process. One of the benefits of this talking and listening to the population directly (Nelson 2006) is a movement against clientelism and wrong decisions by the administration. For instance, some smart city solutions are easy to sell to city mayors with or without corruptive practices: new technology looks good, is shown in the media (“a new camera surveillance system is being installed in the walking precinct downtown. . .”), and makes the city top on comparative rankings, but it may be irrelevant for the citizen. China has pioneered a guideline toward a more sustainable environment making it compulsory for cities which want to get finance from the China Development Bank (Huang et al. 2015). The 12 Green Guidelines point to the relevant measurements to be taken into account in the city and clearly put technology as a tool to achieve better livability. This study followed the earlier eco-city idea from the green movement (Register 2006). Citizens are opinionated people. Give them a city topic in a local café, and they will have an opinion about it. Most of these opinions, though, are ill-informed, shortsighted, and mixed up with ideology. The antidote is data and good governance (Almeida et al. 2018). It is all too important to collect and make available reliable data about everything in town, or in the area of a proposed project (Aravena 2018). This is where technology can be of good use, as will be shown later in this book.

Deindustrialize your Mind! Yes, we have to face it! We are addicted to industry. We only think of private companies as if they were industries. We prepare our kids at school and at universities to work in industries. And we prepare our cities as if the daily commuters were going from the residential areas to some industry somewhere, with its rigid working

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Fig. 3 Deindustrialize your mind

hours. It is high time we deindustrialize our minds and recognize that we have lived for a long time already in a postindustrial era, the knowledge era. Let us analyze a few examples and relate them to our cities and our urban planning mindset (Fig. 3).

9 a.m. to 5 p.m. In a normal industry, every single worker of a typical assembly line must be there at the same time since they are part of a process where one worker depends upon the completion of the work of the previous worker in order to fulfill his/her task. At 5 p. m., the assembly line halts (or there might be another shift of workers), a loud whistle is blown, and everyone leaves the premises and goes home or elsewhere. There is nothing wrong with this routine except for the fact that only one in four or five workers in OECD countries still works in industry. And our cities are prepared and planned to tackle only this kind of commuter and this particular pattern. Even when you take the group of industrial workers, a large proportion is working in comfortable offices downtown, not in the assembly line outside the city. So, a question pops up: why do we all work from 9 to 5? Daylight might be an excuse, but even that would suggest a different pattern based upon the season and the latitude of the place. In fact, it is because of our industrial mentality which we should get rid of as soon as possible. The majority of our workers in the city work in the services and financial sectors and need not follow this rule. In many companies today, they do not have to work together or in the same place at all. This fact explains the success of the new co-working facilities that sprang up in every city. Workers there (one in five works for large companies) mingle, discuss, and enjoy meeting people from other companies, not their own mates. In this era, since knowledge actually grows when it is shared, co-working spaces are ideal to share experiences and grow professionally.

Schools Schools are in trouble. Students aren’t happy. There are interesting experiments here and there, but the old joke unfortunately is closer to reality today: an engineer, a surgeon, and a teacher were frozen 100 years ago and come back today. The first two

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cannot understand and grasp all the changes that happened; the teacher walks into the classroom and starts giving his lecture as usual. There are two major problems with this reactionary behavior in the schools. Firstly, the teacher is not the only holder of knowledge anymore, and it does not make sense to keep the one-to-many format of a classroom. Knowledge is also available in students’ hands on their smartphones. In many instances, they even challenge what the teacher says. Secondly, and this is much more serious, the schools prepare our children to work in the industrial society, and the education process is an assembly line! Sir Ken Livingston, a brilliant English educator, bashes our school systems in his popular TED talks, which have been seen by millions of people already. With a lot of humor and wit, Livingston points out that kids go through an assembly line, receiving the same components at every stage of the line. Parents who have two or more children are always surprised by how different their children are, even having been brought up in exactly the same way. But not in the schools. There, they are all the same and must behave likewise. Livingston compares the school system with a prison, where the inmates also have prescribed times for lunch, recreation, sunbathing, etc. and must follow strict rules of behavior. Any brakes in the code of conduct are punished severely. No wonder studies have shown that creativity, a basic soft skill for workers in the new knowledge era, actually diminishes with every year in the school system! A good start to change is to flip the classroom. Make the students work their way through the learning process. Transform the teacher into a facilitator that has some knowledge about the subject (not all) but that can help curate the content that is available online. Trouble is, of course, the teachers! Some are not prepared or willing to change the way they have taught the same subject for decades. And they might feel insecure with new technology.

Tech Parks Tech or science parks are a “must” in any modern city. It is a place where you congregate modern companies for the “new millennium.” Most of them are built by a roadside far away from the city, and companies which go there receive tax incentives from the local government. Most of them are also empty. Why? You guessed it. Our industrial mindset thinks of a tech park as a new modern version of an “industrial district,” a popular city project of the second half of the last century. In that case, it made sense to have industries gathered by a major road to receive and distribute supplies and products. And tax incentives were crucial for the established industries. But is it still relevant today? In the knowledge era, the main production means is, of course, knowledge. Knowledge, contrary to any other production means, actually grows when it is shared. So, tech parks or any other innovation hub should be established and planned with the objective of facilitating the exchange of ideas and the direct contact between workers from different companies. Modern tech parks should look more like a large

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co-working facility, compact, with lots of cafés and amenities for people to meet and talk to other people. And they should not be placed away from town. They should be placed downtown or as close to the city’s coolest areas and universities as possible. Let me just stress this point once more: Tech parks are not modern versions of industrial districts!

Work and Employment Almost every day we hear and read studies about the “end of work” and the millions of unemployed people that will follow this disruptive trend. A recent cartoon depicts a modern industry that has only two creatures inside the factory: a dog and his keeper. The keeper is there in order to feed the dog. The dog is there in order not to let anyone, including his keeper, mess up anything. So yes, industrial employment is decreasing. In fact, any menial job that can be executed by a machine or robot will eventually be so. As for the millions of unemployed? Not so sure. Employment contracts are indeed being transformed and will look different. Labor laws were enacted in the industrial era (again) and were meant to protect the “poor” industrial worker from the “greedy” capitalist owner of the factory that wanted to exploit him/her to the limit. These laws do not make any sense today and are being revised everywhere. Trade unionists march in protest, but the trade unions themselves do not make a lot of sense today. Think of a software startup company or an IT export company (Costa 2001). It has to install modern and cool facilities with jukeboxes, table tennis, and free lunch in order to attract the best possible talent (Florida 2005, 2014). Do these workers need a trade union? Do they even know that they belong to one (when it is compulsory)? Work will change, definitively. Employment with a contract will probably diminish also. But will that mean the end of work, or the end of work as we know it today? My hunch is the second. The United Nations study on future work estimates that half the students entering school today will work in their adult life in some profession or occupation that does not exist today. How then can we predict that work will end? In occupations that we do not know of, today?

The Car And yes, back to the poster child of the industrial era, the automobile or, simply, the car; we think of industry the whole time, as we have seen, but we just love the car. Cars are useful machines to take you from one place to another, at your own time and will. They are nice toys too (remember “the difference between men and boys is the price of their toys. . .”). And they are very pricy too – to the owner and to society. Let us first calculate the cost to the buyer. Car prices have been kept more or less constant through the decades. You get more stuff in today’s car obviously, but the final price to the buyer is more or less the same. The buyer, when making his purchase decision, decides if she will pay in cash, installments, or lease. If she can

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afford that, fine. She drives out the car shop door, onto the road. But in most cases, there was no calculation of what economists like to call TCO (total cost of ownership). Take this list of hidden (or forgotten at purchase time) costs of your car just sitting still, parked in your garage, no consideration of running costs, at this moment: insurance at, say, US$1500 a year (average in the USA); road tax at, say, 5%; depreciation at 10% a year; opportunity cost (what you would get if you invested the money instead of buying the car) at, say, 5%; and garage costs (if you own your garage, maybe you could rent it or do something else with it) at say US$1000 a year. If the purchase price is around US$25,000, our estimate of these hidden costs would amount to an additional US$7500 a year! That is a staggering 30% a year or circa double the original price in 3 years. And that figure doesn’t take into account the running cost of the car – fuel, tolls, maintenance, parking, cleaning, etc. – a very costly toy, if you ask me. Better use Uber or your other favorite app for shared transport. Nobody likes to see these numbers, because people love their cars. Or have we been brainwashed by marketing campaigns to love the car? Since the car industry is in deep trouble today, faced with what amounts to a “perfect storm” of unforeseen radical changes (electric, autonomous, and shared cars), they are pushing their wares even more strongly through all kinds of ads. In fact, there are so many car ads on television today that the ad campaigns lost creativity and became more or less standardized. Car ads go like this: a nice looking young professional enters her keyless car and drives happily on a nice city street (no traffic), to a winding road (sunny day), and then, suddenly, she turns into a tarmac road where the car performs nicely (seen from the outside, not if you are in), crosses a small river stream, drives through meadows and flowery fields, and ends up in a swirl that raises dust beautifully on top of a mountain during sunset. Have you seen it? Me too – several times and from different manufacturers. A car ad campaign in Brazil tried to break away from this homogeneousness and produced this novelty which managed to be even more stupid: a nice looking young professional enters his keyless car and drives happily on a nice city street (no traffic), to a winding road by the sea (sunny day), and then, suddenly, he sees a stunningly beautiful mermaid swimming in the distance. He parks the car, swims there, kisses the mermaid, and then poof, she drives away in his car leaving him there, astounded, in the water. This is a real ad; believe me! One wonders: what is the message? Buy this car, drive to the sea, and you will see a mermaid that will fool you and run away with your new car? Or is it buy this car that even a mermaid with a long tail is able to drive? Now let us study the cost of the car to society. Most cities have a traffic department that takes care of the flow of vehicles in town, mostly cars; a road and transport department that builds and maintains viaducts, streets, and roads; a public parking (sometimes free) facility; and many other operations related to the car. They are all very costly, but the services are free to their users, the car owners. For some reason, every infrastructure demanded by cars is free. Schwartz (2015) suggests there was an orchestrated lobby of car manufacturers and other interested parties (tire industries, insurance companies, etc.) in the USA in the beginning of the 1900s that

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set the scheme, but the fact is it is supposed to be free almost everywhere. Few cities have a bike and pedestrian department. It is as if our cities were built for the car. And its infrastructure cost is huge. Now think of the cost of car accidents. Besides the personal and family tragedies involved in these accidents, there is the additional cost of treatment for the more than 50 million people that are disabled or injured in the world every year. Some, perhaps most, of these costs are borne by society as a whole. Add to that the cost of air pollution and all sorts of illnesses caused by it, and also the cost of the disposal of tires and old parts, or even entire old vehicles, a large number that nobody talks about. And what is the actual solution? If we do deindustrialize our thinking and take the car not as a deity but as a means of transport, rational conclusions emerge. For instance, clarify the costs of cars to the owners through public campaigns. Charge the car owner the full cost of his piece of equipment. We charge air travelers an airport tax to use the infrastructure, don’t we? Why not charge the car for its use of the public roads, parking lots, and such? Some cities (Singapore, London) did just that, charging tolls for cars going to congested downtowns on weekdays. It is a good start.

Cross Reference and Major Challenges Change is always difficult – cultural changes, even more. In order to improve our cities, we must face many challenges, such as these depicted in the corresponding chapters of this book.

Demographics As the percentage of senior people in society grows, the city needs to prepare itself for the different needs of an aging population. There will be many active old people who will want to be in contact with other people, young and old, and not to be segregated away from society and into old people’s homes. And the city will need to tend to the special needs of old people in terms of mobility, exercise, and health care in and outside the home. Technology can certainly help here, and a number of startups are now focusing on this growing segment of the market.

Diversity and Priorities Cities that are more humane, sustainable, and smart value diversity in terms of the use of the space, in terms of gender, sexual orientation, and age and in terms of income levels. But this diversity also brings with it a new level of difficulty in setting up priorities. A new kind of governance must be in place, where citizens or citizens’ councils meet frequently to discuss and reach consensus on the best ideas and projects to be prioritized by the administration.

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Originally this challenge was mostly related to poor people in big cities of developing countries. But with the recent wave of lawful and unlawful immigration of all kinds of different people from poor countries to richer ones, the developed world and their major cities must now tackle this challenge head on.

Special Needs It is surprising the difference in cost to have a ramp installed in a building for wheelchairs when 1) it is done at first, during construction, or 2) it is done later as an afterthought or something determined by legislation. Inclusive design is this new trend where architects and engineers think of all kinds of possible users of a new facility before it is built. Many of the users will have special needs and must be accommodated in the project from its conception. This challenge is also related but not limited to the demographics challenge. It involves people who have some kind of disability but that want and deserve to have a life with as much activity and work opportunities as everybody else’s.

Socialization With the new media and social networks, it is much easier to be in electronic contact with lots of friends and acquaintances. Eventually, it is also much lonelier. The new tools are fine but there is still need for physical contact and face-to-face meetings. Technology may be a distraction but may also help in the organization of networks of people that resemble the old neighborhoods and invite them to interact in person as well. The European Union My Neighborhood Project (Oliveira and Campolargo 2015) presented several different experiments toward that goal. It is interesting to realize that some of the problems of the cities today (violence, pollution, no safe parks, segregation, etc.) compound with the excessive use of the social networks in order to isolate us and our kids (Harari 2015). But before we start planning for the next “tech detox spa,” let us use the power of technology for something that brings people closer, in the old village sense – a major challenge indeed, in an era when we tend to have lunch alone and with our smartphone next to the plate, which is even worse for one’s health than the old TV set on the kitchen table.

Conclusion The first step to improve livability in our cities is to recognize that the situation is not good. Even in the star cities that attract the best talent available in the world (New York, London, Hong Kong, Vancouver, Lisbon, San Francisco, etc.), there are vexing signs of instability: gentrification, unaffordability, segregation, and inequality, in the words of Florida (2017). Not only that, the situation is getting worse,

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particularly in the developing world. It is important to open people’s eyes to the possibilities of more humane interventions in their cities. From the amazing achievements of Emperor Napoleon III and Mayor Haussmann in Paris, during the nineteenth century, and its side effects in Europe and other cities in the world, we went through a series of mistakes in the twentieth century that led to the situation we are in today. And considering the dislocation of people from rural areas to cities that is happening now, and will continue in the future, in the developing world, past mistakes must now be corrected. China alone will move a large population, the size of one entire USA, to cities, in the next 12 years. It is just impossible (in economic terms) to do this with the model of “cities for cars.” There must be another way. In order to implement change, we must first go through a process of deindustrialization of our mindset, including seeing the car for what it is, not as a necessary key component of our daily life. Perhaps, in our process of deindustrialization, this is the most difficult step. But once we realize the amount of brainwashing about cars that we have been submitted to, it is doable. For the reader who gets interested in the subject and wants to do something in his/ her own neighborhood, we suggest a list of books for further reading. And as a way of starting immediately, we suggest an acupuncture intervention in his/her city. Choose a borough or region of the city that is rich in diversity that carries a history or narrative worth telling other people and that is significant to the city: start there, and the results will showcase the wonderful possibilities open to the whole city. Cities can be more humane and sustainable, using technology, being smarter. And it does not depend on the mayor, the local council, or anyone else. It depends upon you.

References Almeida, V. A. F., Doneda, D., & Costa, E. M. (2018). Humane smart cities: The need for governance. IEEE Internet Computing, 22(2), 91–95. https://doi.org/10.1109/MIC.2018.022 021671. Aravena, A. (2018). Interview to Arch Daily. Available at https://www.archdaily.com/906076/ alejandro-aravena-shares-the-foundational-philosophies-at-the-core-of-his-socially-consciouspractice. Accessed 1 Apr 2019. Azua, J. (2006). Bilbao: From the Guggenheim to the Knowledge City. In J. Carrillo (Ed.), Knowledge society: Approaches, experiences and perspectives (pp. 97–112). Oxford: Elsevier. Carrillo, J. (Ed.). (2006). Knowledge society: Approaches, experiences and perspectives. Oxford: Elsevier. Carrillo, J. (2015). Knowledge based development as a new economic culture. Journal of Open Innovation: Technology, Market, and Complexity, 1, 15. Chang, D. L., Marques, J. S., Costa, E. M., Selig, P. M., & Yigitcanlar, T. (2018). Knowledge-based, smart and sustainable cities: a provocation for a conceptual framework. Journal of Open Innovation: Technology, Market, and Complexity, 4(1), 5. Costa, E. M. (2001). Global e- commerce strategies for small businesses. Cambridge: The MIT Press.

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Costa, E. M., & Takahashi, E. (2020). Humane and sustainable smart cities. London: Elsevier. To be published. Deutschmann, M. (2017). The one mile radius. Advantage, Charleston, NC. Edition. Oxford: Oxford University Press. Florida, R. (2005). The flight of the creative class: The new global competition for talent. New York: Harper Collins. Florida, R. (2014). The rise of the creative class. New York: Basic Books. Florida, R. (2017). The new urban crisis. New York: Basic Books. Gehl, J., & Svarre, B. (2013). How to study public life. Washington, DC: Island Press. Gies, F., & Gies, J. (1969). Life in a medieval city. New York: Harper-Collins. Harari, Y. N. (2015). Sapiens – A brief history of humankind. New York: Harper-Collins. Howkins, J. (2001). Creative economy. New York: Penguin Books. Howkins, J. (2013). Creative economy: How to make money from ideas. New York: Penguin Books. Huang, C. C., Busch, C., Dongquan H., & Harvey, H. (2015). The 12 green guidelines. China Development Bank Capital. https://energyinnovation.org/wp-content/uploads/2015/12/12Green-Guidelines.pdf. Accessed 15 Mar 2019. ICLEI. (2019). Resilient cities report 2018. https://resilientcities2018.iclei.org/wp-content/uploads/ RC2018_Report.pdf. Accessed 19 Mar 2019. Jones, P., et al. (2019). Smart cities: Overview and glossary. In J. C. Augusto (Ed.), Handbook of smart cities. Cham: Springer. Kirkland, S. (2013). Paris reborn: Napoleón III, baron Haussmann and the quest to build a modern city. New York: St.Martin’s Press. Lara, A. P., Costa, E. M., Furlani, T. Z., & Yigitcanlar, T. (2016). Smartness that matters: towards a comprehensive and human-centered characterization of smart cities. Journal of Open Innovation: Technology, Market, and Complexity, 2(1), 1–13. Larson, K. (2012). Brilliant designs to fit more people in every city. TedX talk, Boston. Available at https://www.ted.com/talks/kent_larson_brilliant_designs_to_fit_more_people_in_every_city Morse, M. L., & Qing, C. (2012). The dynamic population of Manhattan. https://wagner.nyu.edu/ files/rudincenter/dynamic_pop_manhattan.pdf. Accessed 29 Mar 2019. Mumford, E. (2018). Designing the modern city: Urbanism since 1850. Oxford: BW&A Books. Nelson, A. C. (2006). Leadership in a new era: Comment on “Planning Leadership in a New Era”. Journal of the American Planning Association, 72(4), 393–409. https://doi.org/10.1080/ 01944360608976762. Oliveira, Á., & Campolargo, M. (2015). From smart cities to human smart cities. In 48th Hawaii international conference on system sciences, Kauai, HI (pp. 2336–2344). https://doi.org/10.1 109/HICSS.2015.281. Register, R. (2006). Eco-cities: Rebuilding cities in balance with nature. Canada: New Society Publishers. Roberts, J. M. (1980). The pelican history of the world. London: Penguin Books. The Rockfeller Foundation. (2013). 100 resilient cities. https://www.rockefellerfoundation.org/ourwork/initiatives/100-resilient-cities/. Accessed 19 Mar 2019. The United Nations. (2019). The future of work. Available at https://www.un.org/pga/73/event/thefuture-of-work/. Accessed 19 July 2014. Yencken, D. (2013). Creative cities. In Space, Place and Culture 2013. Victoria: FutureLeaders.com.au. Yigitcanlar, T., & Lee, S. (2011). Moving towards a Knowledge City?: Brisbane’s experience in knowledge-based urban development. International Journal of Knowledge-Based Organizations, 1. https://doi.org/10.4018/ijkbo.2011070102. Yigitcanlar, T., O’Connor, K., & Westerman, C. (2008). The making of knowledge cities: Melbourne’s knowledge-based urban development experience. Cities, 25(2), 63–72. Yigitcanlar, T., Marques, J. S., Lorenzi, C., Bernardinetti, N., Schreiner, T., Fachinelli, A., & Wittmann, T. (2018). Towards Smart Florianópolis: What does it take to transform a tourist island into an innovation capital? Energies, 11(12), 3265.

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Further Reading Costa, E. M., & Oliveira, Á. D. (2017). Humane smart cities. In R. Frodeman, J. T. Klein, & R. C. Pacheco (Eds.), The Oxford handbook of interdisciplinarity (2nd ed.). Oxford: Oxford University Press. Glaeser, E. L. (2011). Triumph of the city. New York: Penguim Press. Jacobs, J. (1961). The death and life of great American cities. New York: Vintage Books. Sadik-Khan, J., & Sollomonow, S. (2017). Street fight: Introduction for an urban revolution. New York: Penguin Books. Schwartz, S. I. (2015). Street smart: The rise of cities and the fall of cars. New York: PublicAffairs. Yigitcanlar, T., Kamruzzaman, M., Foth, M., Sabatini, J., Costa, E. M., & Ioppolo, G. (2018). Can cities become smart without being sustainable? A systematic review of the literature. Sustainable Cities and Society. https://doi.org/10.1016/j.scs.2018.11.033.

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Smart Energy Frameworks for Smart Cities: The Need for Polycentrism Joseph Nyangon

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Climate Change and Urban Energy Infrastructure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Nature of the Challenge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smart Grid and the Future of Smart Cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Emerging Models for Urban Energy Transformation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Distributed Energy Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Energy Storage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Microgrids . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Demand Response and Energy Management Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smart Measuring Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Harvesting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Green Technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . From Robustness to Resilience . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A Polycentric Approach to Smart City Energy Governance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cross-References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract

Rapid growth in megacities has prompted deep transformations intended to change sociotechnical systems, deep social and institutional practices, and scientific inquiries to better understand energy and material flows of cities. Typically, these processes are defined by sociotechnical experimentation and purposive reshaping of the synergies between jurisdictions, sectors, and technical solutions required to optimize resource management and improve institutional diversity and its configurations. This chapter studies features of smart energy frameworks

J. Nyangon (*) Center for Energy and Environmental Policy (CEEP), University of Delaware, Newark, DE, USA e-mail: [email protected] © Springer Nature Switzerland AG 2021 J. C. Augusto (ed.), Handbook of Smart Cities, https://doi.org/10.1007/978-3-030-69698-6_4

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for smart cities leadership in an attempt to ignite transformations in energy business models for sustainability systems from the bottom up. Following this polycentric approach, the chapter documents seven emerging models for smart city energy governance, namely distributed energy resources development, energy storage, microgrids, demand response and energy management systems, smart measuring systems, energy harvesting, and green technology innovations. One observation is that while Singapore and Shanghai are a product of advanced polycentric strategic planning, the urban developments around the greater Jakarta area is an outcome of gradual alignments and reconfigurations of urban design toward the polycentric goal. In addition, energy systems and utility business models are changing simultaneously in several cities with respect to institutional contexts, urban planning, and customer choice. A key message of this chapter is that capturing the impacts of these urban transformation across the quartiles of energy resource development, technological progress, and policy stringency requires the design and implementation processes that simultaneously promotes polycentric authority and contributes to informed understanding of the scale and consequences of these transitions.

Introduction Rapidly rising populations in cities, urbanization and economic development have prompted the emergence of megacities, i.e., urban agglomerations with populations exceeding 10 million inhabitants (Kennedy et al. 2015). Because of their sheer size and complexity, megacities present epic social, economic, and environmental challenges. In the last three decades, major cities in the United States and Europe have been prioritizing new forms of sustainable urban development, notably new urbanism, compact city models and smart urban growth through transit-oriented development to counteract these challenges (Ewing et al. 2017; Noland et al. 2017; Kim and Larsen 2017; Chhetri et al. 2013). Although these models have different origins and objectives, they generally seek to improve energy and material flows toward reduced energy- and water-use intensity, increased adoption of mass transit systems, controlled growth and expansion, mixed and diverse land-use development, and stronger urban sensibility. In the global south such as in many African and Asian cities like Cape Town, Bangalore, Bangkok, Beijing, Chennai, Guangzhou, Hong Kong, Lagos, Mexico City, Nairobi, Nanjing, and Shanghai, however, a variety of smart urban energy solutions have mainly focused on alleviating environmental pollution and the decreasing density (or, alternatively, urbanized area per capita), in part, owing to rapid population growth and rural-urban migration (Chiu 2012). In 2014, China reported the largest urban population globally of 758 million as well as six megacities, i.e., Shanghai, Beijing, Chongqing, Guangzhou, Tianjin, and Shenzhen, and is projected to add one more megacity (Chengdu) and six more large

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cities (i.e., Wuhan, Dongguan, Hong Kong, Nanjing, Foshan, and Shenyang) by 2030 (United Nations 2014). This remarkable rapid growth in the size and number of megacities upends a range of social-technical transitions, institutional change, economic innovations, and scientific inquiries. The frameworks adopted to understand energy and material flows of cities, in particular, the nexus between urbanization, resilience, and resource efficiency and related synergies between jurisdictions, sectors, and technical solutions required to optimize resource management and improve institutional frameworks for effective service delivery have focused on top-bottom strategies. (Clark et al. 2019; Sircar et al. 2013). As a result, these strategies have been criticized for being ineffective, inflexible, less transparent, and inadequate in mediating the effects of socio-environmental inequalities in cities. For instance, unlike most American cities, Chinese cities are high-density areas integrated via transit-oriented development (TOD) (with high levels of mixed-land use configurations around public transport stops), making them ideal for the compact city model. The new urbanism concept thus is not ideal for the progrowth ethos of most Chinese megacities and other densely populated cities in the developing world, but rather less populated European cities which tend to emphasize a more compact urban form and smart growth as an antidote to the ills associated with urban sprawl (Wey and Hsu 2014; MacLeod 2013). Transit-oriented development model, however, is more suitable for cities like Berlin, London, Madrid, Milan, New York, Paris and others, which have a long history of implementing mass transit systems, because it maximizes integrated access to residential, business and leisure activities within walking distance of near-excellent public transport (Noland et al. 2017). Smart, networked cities increasingly require polycentric governance of socio-technical systems that together form the elements of their energy frameworks in order to foster smart growth, accelerate low-carbon transitions, and lessen fragility concerns that emanate from a troika of rapid population growth, urbanization, and climate change challenges. With respect to climate change, the cost of urban infrastructure damage is rapidly rising. For instance, in August 2017, Hurricane Harvey cut a destructive path across Texas and the Gulf of Mexico leaving thousands without electricity, while in 2012, Hurricane Sandy caused extensive destruction and damage to energy infrastructure across several northeastern states (Maryland, Delaware, New Jersey, New York, Connecticut, Massachusetts, and Rhode Island) due to high wind and coastal storm surge. The U.S. National Oceanic and Atmospheric Administration (NOAA) estimates the total damage from Harvey and the 2005 Hurricane Katrina at $130 billion and US$168 billion (2019 consumer price index cost adjusted value), respectively, making these two events the costliest U.S. weather and climate disasters on record since 1980 (Smith 2019). As acknowledged by the Intergovernmental Panel on Climate Change (IPCC), energy infrastructure (as well as other high-quality urban infrastructure-based networked systems like transportation) is increasingly confronted with a series of grand challenges – rapid population growth, urbanization, and climate change (Revi et al. 2014). This situation is compounded

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by the fact that these critical urban infrastructure systems constitute the backbone of a networked city, are largely inflexible to changes in utilization and external conditions due to the spatial alignment and coevolution of their elements, are underfunded and often poorly maintained, and are increasingly complex and interconnected across several functions. (The problem is particularly acute in urban areas, where growing populations stress society’s support systems and natural disasters, accidents, and terrorist attacks threaten infrastructure safety and security.) Diversifying energy systems – through the interlinked mix of clean energy technologies, institutional change, user-centered design practices, and market and regulatory innovations – can help cities achieve their low-carbon objectives, promote greater energy security and electric grid stability, and improve access to modern energy services as new energy demand is projected to take place in rapidly urbanizing metropolitan regions and megacities (Taminiau et al. 2019; Nyangon and Byrne 2018; Byrne and Taminiau 2018; Taminiau et al. 2017). Therefore, the investment decisions cities make today in high-growth intensive sectors especially infrastructure such as roads, electrical power systems, sewers, and buildings will influence the evolution of urban spatial structure and their socio-environmental dynamics for several decades. Different types of urban systems, for example, compact, well-connected cities versus sprawling, car-dependent urban locations, can act as straightjackets for smart cities of the future by providing integrated and resilient energy frameworks and metrics for addressing known and unknown challenges. Such efforts entail increasing momentum of niche innovations, weakening existing legacy systems, and strengthening exogenous trends and developments, which when aligned can destabilize the existing system to create processes that yield breakthrough innovations (Nyangon 2017). While the potential benefits of smart energy networked cities exist in smart investments such as energy management services, energy storage, distributed energy resources, demand-side management, and automatic measurement and verification, poorly managed urban growth does have social and economic costs. This chapter proposes reorienting the principles and tenets of energy business models and frameworks toward a polycentric approach by focusing on six key imperatives: (a) stakeholder-driven approach, (b) enhanced accountability and legitimacy, (c) inclusivity and equitability, (d) adaptive management, (e) shared learning, and (f) continuous improvement to promote integrated energy governance and material flows in cities. Polycentricity can result from advanced planning or self-organization. For instance, while polycentric cities like Singapore (Field 1999) and Shanghai (Ziegler 2006) emerged from an advanced strategic planning, the urban development around the greater Jakarta area is a result of gradual alignments and modifications in planning to explore the potential of interactive technologies and systems, toward the polycentric objective (Hudalah and Firman 2012). Conversely, Shenzhen and Guangzhou are a product of special urban policy (Wu 1998), while London, Amsterdam, Paris, Frankfurt, and many European cities are a product of both self-development and planning (Hall and Pain 2006).

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Climate Change and Urban Energy Infrastructure “How does a lively neighborhood [city] evolve out of a disconnected association of shopkeepers, bartenders and real estate developers? How does a media event take on a life of its own? How will new software programs create an intelligent worldwide web?,” Steven Johnson writes in Emergence (Johnson 2001). According to Mumford (1938), the city is “a point of maximum concentration for the power and culture of a community.” Cities are shaped by policies, regulations, and formal written development plans as well as spontaneous, unpredictable self-organizing individual elements that give rise to intelligent and sophisticated systems working on their own prescribed logic. Consider control processes such as traffic lights and their control coordination mechanisms, or the socioeconomic characteristics and attributes of heating and cooling in buildings which influence household energy consumption, or even waste collection and management processes, every city model falls somewhere along a continuum. These self-organization phenomena involve large-scale systems where no single activity or individual exerts control over the processes. It also provides a fruitful source of inspiration for understanding the elements of smart networked cities: how they emerge, deliver societal functions such as personal mobility, and implement niche technological innovations such as piped water infrastructure, heated buildings, pedestrian streetscape facilities, as well as cultural, political, economic and behavioural changes, in a manner often known as “combinatorial innovation” – the ability to combine novel technologies together to create a new wave of discoveries (Youn et al. 2015). (Combinatorial innovation process exhibits two key characteristics: “exploitation” (i.e., continuous refinements of existing combinations of technologies) and “exploration” (i.e., the development of new technological combinations) (Youn et al. 2015).) Today, the narrative of the low-carbon transition puzzle in cities, the ascent of energy-related infrastructure challenges in metropolis, the demands for quality livability standards, and the entire unfolding urban sensibility is fundamentally intertwined with climate change. It is also embedded in politics, urban policy, and polycentric governance efforts. The rapid spread of the COVID-19 pandemic is a grim reminder of how global natural disasters exacerbated by unsustainable resource and energy use practices like climate change respect no boundaries.

The Nature of the Challenge Dynamism is an abiding feature of smart cities. The dynamic nature of cities is characterized by rising quest for accumulation, of creative destruction and of growth and dislocation spurred on by technological advances which have become symbolic with rapid urbanization and the ascent of megacities. However, climate change now threatens this dynamism and its configurations of urban sensibility and urban resilience. Often characterized as a “super wicked” problem, meaning that its impacts are global, complex, and urgent, climate change is, in part, “driven by policies and technologies that created a path-dependent reliance on high carbon

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fossil fuels” paradigm, implying that a robust climate solution for governing smart cities should nurture “countervailing policies that trigger path-dependent low-carbon trajectories” (Levin et al. 2012). The impacts of current and anticipated climate change on urban systems are substantial, disrupting energy provision, food distribution, water supply, waste removal, financial markets, and increased susceptibility to pandemics (Agbemabiese et al. 2018). For instance, solar photovoltaic (PV) cells generally work optimally at low temperatures. Climate-induced temperature increase affects the conversion efficiency of a PV cell (Emodi et al. 2019). Increased intensity and frequency of storms disrupt wind power generation, with higher waves lessening electricity production of offshore wind turbines (de Jong et al. 2019). Offshore oil and natural gas facilities and low-lying coastal infrastructure in port cities are equally vulnerable to climate-induced impacts as sea levels rise and wind storms increase in severity and frequency (de Jong et al. 2019; Emodi et al. 2019; Cortekar and Groth 2015). As urbanization spreads up, high unemployment and social unrests in cities, rising competition for resources such as water and food, widening inequality gap, and environmental degradation also threaten urban development. Climate change exacerbates these threats, forcing city authorities to explore explicit sociotechnical interventions to mitigate these threats as well as support economic growth and sustainable low-carbon development. Attention must thus be broadened toward interactions between climate, energy, and other socio-technical systems. Aging urban infrastructure increases severity of climate risks. Storm-related power outages and direct physical damages from climate-induced natural disasters increase operations and maintenance costs as well as capital investment in energy infrastructure (Markolf et al. 2019; Miller and Hutchins 2017). Furthermore, increased frequency and duration of extreme weather and storm-related power outages result in prohibitively expensive insurance premiums for cities and utilities. A case in point is California’s PG&E bankruptcy filing in 2019, citing billions of dollars in liabilities stemming from wildfires in 2017 and 2018 (Blunt and Gold 2019). Due to these geophysical and climatic disasters, cities are increasingly exploring a host of adaptation and mitigation strategies, especially adaptive smart policies, energy frameworks, user practices, programs, and technical solutions to actively phase out existing technologies and systems that lock in institutional and behavioral systems for decades (U.S. Department of Energy 2015). Table 1 summarizes direct physical impacts from extreme events on urban infrastructure systems and potential smart grid solutions.

Smart Grid and the Future of Smart Cities Given the centrality of technological innovations in supporting polycentric energy governance efforts related to climate change, water and wastewater management, mobility, economic competitiveness, and a range of other material flows, it is not surprising that cities are expending considerable capital in developing evolutionary business models for explaining innovation, consumer acceptance, and multi-level energy frameworks to better understand socio-technical regimes of

Water and sewerage systems, e.g., water supply, flood management, etc.

Transportation systems, e.g., roads, bridges, rail, airports, etc.

Urban infrastructure Energy systems, including energy storage (e.g., flywheels, compressed air, pumped hydro, and batteries); energy generation, transmission and distribution systems; energy efficiency (e. g., air-conditioning systems); smarter grids (e.g., smart street lighting, grid management, intelligent loads, and traffic signal)

Climate risks and vulnerabilities Reduced power plant efficiency Increased variability, power outages Reduced capacity utilization of energy assets Thermal expansion joints beyond design capacity Rising electricity demand structural instability of gas pipelines Reduced transmission and distribution capacity Flooding of railways, highways, and low-lying infrastructure Reduced structural integrity of gas pipelines, pavements, etc. Road and bridge washouts Cancelled or delayed flights Traffic disruptions, vehicle overheating, and tire deterioration Damage from increased thaw cycles More debris in stormwater management systems Rising operations and maintenance cost Smart measuring Alter design storm criteria Green infrastructure and technologies

Green infrastructure and technologies Resilience Relocation of roads and infrastructure

Potential smart solutions District heating and smart grids Microgrids Energy storage Harvesting Robustness and resilience

Table 1 Summary of city infrastructure disruptions related to extreme weather events

(continued)

Nyangon et al. (2017a), Hakelberg (2014), Bulkeley and Castán Broto (2013), and Bartos and Chester (2014)

Markolf et al. (2019), Clark et al. (2019), Underwood et al. (2017), Rattanachot et al. (2015), and Taylor and Philp (2015)

Sources Taminiau et al. 2019, de Jong et al. (2019), Burillo et al. (2019), Emodi et al. (2019), Nyangon and Byrne (2018), Agbemabiese et al. 2018, Nyangon (2017), Taminiau et al. 2017, and Cortekar and Groth (2015)

3 Smart Energy Frameworks for Smart Cities: The Need for Polycentrism 61

Green infrastructure systems

ICT infrastructure

Urban infrastructure Waste management and disposal systems

Table 1 (continued)

Climate risks and vulnerabilities Increased cost of repairs and maintenance Flooding of underground waste infrastructure systems Signaling and control disruptions Performance-reduced functionality of on-demand services Interaction complexity Need for security and amount of data Increased runoff due to vegetation loss Increased wildfires Rising energy costs Green stormwater infrastructure Permeable pavements Green roofs

Green infrastructure and technologies

Potential smart solutions Green infrastructure and technologies

Burillo et al. (2019), Clark et al. (2019), Agbemabiese et al. 2018, Nyangon et al. (2017b), and Nyangon (2014)

Maki et al. (2019) and Duncan (1995)

Sources Choudhary et al. (2019) and Miller and Hutchins (2017)

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transitions and the momentum for renewable energy innovations such as solar PV, wind, and bio-energy. In recent times, cities such as New York City, London, Shanghai, Philadelphia, San Francisco, and Tokyo have adopted smart energy roadmaps, including energy storage, demand response, and smart measurement and reporting, to accelerate their transformation toward clean energy economies. Besides climate-related concerns, energy systems are experiencing several challenges, including lagging investments, energy efficiency gap, diversification of energy generation assets, optimal deployment of expensive assets, and demandside management. Yet, majority of the urban electricity infrastructure is outmoded, stressed, aging, and incapable of responding to these critical issues (ASCE 2017; U.S. Department of Energy 2015; Fox-Penner 2010). In addition, the existing grid is unidirectional and hierarchical and consists of mostly centralized generation assets, meaning it converts only half of the fuel input into electricity without recovering the waste (heat). In this framework, transitioning to a smart grid system addresses this major shortcoming of the power grid, as well as optimizes energy asset utilization and operation efficiency while facilitating roll out of new energy products, services, and platforms. A smart grid is an “intelligent” electrical grid – uses digital, multidirectional communications; provides multiple customer choices to improve reliability of electricity supply, system operating efficiency, and energy services; and consists of mostly distributed generation assets which reduce operating costs while maintaining power grid flexibility and use of pricing models applications. The unfolding New York energy transition, for example, involved diversifying energy generation mix, through solar PV, wind and bioenergy technologies. The goal is to improve electricity choices for customers as well as enhance the resiliency and flexibility of the electricity, transportation, heating, and industrial systems against possible direct impacts from climate risks. Nyangon and Byrne (2018) used a combination of business model innovation, simulation, and Gary Hamel’s business concept innovation framework to study the ongoing reorganization of the New York energy market under the Reforming the Energy Vision (REV) process. Expanding on these concepts of diversified power generation mix and intelligent grid infrastructure solutions (DeRolph et al. 2019), this chapter proposes that incorporating elements of smart energy business models such as strategic resource management, revenue model, customer interface, and value propositions, in addition to flexibility and agility, may help animate high levels of reliability and resiliency of urban infrastructure systems, as illustrated in Fig. 1. Furthermore, as the existing urban energy infrastructure continues to age, a new window of opportunity for smart grid applications is emerging. Elements of this smart grid regime include technologies and strategies such as distributed energy resources, energy storage, microgrids, demand management technologies, smart measuring, harvesting, green technologies, and resilience (or robustness). Most cities in the developed world are already implementing a variation of these smart grid systems, through an assortment of technological innovations, principally by incorporating new technologies and assets into old operations and existing infrastructure. First, at its core, the smart grid implementation is a lateral integration and careful overhaul of the existing grid through information technology, circuit

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Fig. 1 Common interconnections of critical urban infrastructure systems. (Source: Author’s illustration)

infrastructure, and communication applications. However, because of the electric power sector’s tangled economic and regulatory structure, the implementation of smart grid in cities may take the form of “part destination” and “part vision” (U.S. Department of Energy 2015). As such, evolution of the smart grids will be dependent on several factors, notably innovation in technology, energy investment and market structures, policy, regulatory jurisdictions, and a city’s needs and requirements. Second, the rising demand for a decarbonized, distributed, and digitalized electricity landscape creates technical and business process challenges for power operators. These challenges include transitioning to a smart grid future, at the highest possible return on investments, as soon as possible, at the minimum cost, without endangering critical energy services in their jurisdictions. Utilities in the developing world have fewer legacy systems and have a clear advantage over their counterparts in the developed world (Farhangi 2010). This is because most cities in developing counties have minimal requirements for backward compactivity with their existing systems, and moving forward investment can be directed toward cleaner, sustainable energy alternatives. Furthermore, cities in the developed world make smart grid investments in a highly regulated environment compared to their counterparts in developing countries. As Fig. 2 demonstrates, a typical smart grid pyramid consists of several technologies, with asset management occupying the base of smart grid development. Decomposing smart energy systems into implementation components such as energy efficiency, demand-side management programs, energy storage, and microgrids provides cost-effective solution to mitigation and adaptation (Pallonetto et al. 2019; Oprea et al. 2018). Accomplishing each task requires deployment and

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Fig. 2 Components of smart grid pyramid. (Source: Author’s illustration)

integration of various technologies to climate-proof the electric grid. Which style of smart grid innovation is right for smart cities? Implementing the smart grid vision involves weaving an array of technologies into the city infrastructure, including predictive analytics, the Internet of Things, big data, and artificial intelligence. In terms of specific projects, examples of smart grid milestones include advanced metering infrastructure (AMI), advanced distribution operations (ADO), advanced transmission operations (ATO), and advanced asset management (AAM). The IBM Smarter Cities Challenge program, for example, promotes a systematic data collection approach and strengthens sustainability planning and urban governance (Alizadeh 2017). AMI comprises of smart meters, communications networks, and information management systems for processing vast amounts of new data. AMI networks enable utilities to collect meter data remotely, facilitate customer participation in demand response and energy-efficiency improvement, and support the evolution of tools and grid management technology that will drive the smart grid future, including outage restoration and integration of electric vehicles and distributed generation. Furthermore, AMI supports practical application of time-varying rates, resulting in peak demand reduction in household energy consumption, in

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certain cities, by almost 30% (Wang et al. 2019). While smart meters offer substantial benefits to utilities and consumers, new challenges are being upended such as the need for continuous improvement of interoperability and embedding of AMI architecture systems to address cybersecurity and privacy concerns (Lightner and Widergren 2010). In light of this, the AMI smart grid electricity infrastructure should be scalable, be capable of adapting to changes, and include open-standard technology architecture to enable interoperability among several applications in order to support a wide array of city operations. For distribution networks, implementing ADO, particularly distribution management system; automated fault detection, isolation, and restoration (FDIR); and distribution automation technologies – i.e., energy management system (EMS), supervisory control and data acquisition (SCADA), distribution management system (DMS), and outage management system (OMS) – could provide increased granularity of and access to smart control mechanisms needed for an adaptive and “selfhealing” distributed grid, improving reliability and climate resilience (Pérez-Arriaga and Knittel 2016). On the other hand, ATO improves transmission reliability and congestion management on the transmission system by integrating the distribution system with regional transmission organizations (RTO) and market applications. Finally, AAM improves the utilization of transmission and distribution assets at the operational level and supports effective management of these assets from a life cycle perspective. AAM includes equipment health monitors and synchrophasor systems – consisting of phasor measurement units, communications networks, and data visualization systems. On the grid network, a key distinction is that whereas transmission and consumption are essentially passive elements of the power grid, generation is dynamic. For cities, identifying and addressing decarbonization, decentralization, and digitalization challenges, require investment in a smart grid to facilitate systematic deployment of energy assets from the outset. Furthermore, characterizing the deployment challenges to establish if they are technological, behavioral, or structural provides a good starting point. In addition, integrated assessments, foresight, and scenario facilitate imagination of urban innovation futures, including diversified generation assets, economic transformation, and policy innovation. Therefore, the sequence of knitting in smart energy solutions might vary significantly across cities and regions, and utilities should approach this transition based on a holistic assessment of their assets and the existing regulatory environment. For instance, to advance decentralized energy governance approach, cities such as Songdo and Masdar have adopted a sequence that follows the following strategy to improve resilience and robustness: implement AMI to establish physical communication infrastructure to the energy generation assets, followed by ADO to assure selfhealing of the distribution system, and then ATO systems to address congestion concerns on the transmission system. Finally, implement AAM to support “smart” real-time transactions, with predictive asset-modeling capabilities built on real-time data (Lee et al. 2016). As James et al. (▶ Chap. 1, “Smart Cities: Fundamental Concepts”) highlight in the introduction chapter, climate change adaptation measures, including distributed

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energy resources, provide double dividend benefits such as emission reduction, energy savings, and operational improvement. In broader terms this enables cities to improve their energy security, grid reliability, and demand-side management. To realize this goal, technology, market, and policy-oriented strategies for smart grid such as energy storage, microgrids, demand response, and distributed energy resources are discussed to explain their role in optimizing existing energy assets and mitigate climatic extremes. In addition, the modernization of the electric power grid policy framework is increasingly not an optional add-on for utilities but an essential planning component of urban energy infrastructure.

Emerging Models for Urban Energy Transformation This section discusses some of the components and elements of a smart grid in Fig. 2.

Distributed Energy Resources Distributed energy resources (DERs) enable active participation by consumers in the power grid. DERs include PVs, small wind power plants, small natural gas-fired generation and combined heat and power (CHP) technologies, energy efficiency, electricity and thermal storage, demand response (DR), heat pumps, and electric vehicles (EVs). (While wind power systems are often connected at distribution voltages, they are a mature technology and rarely deployed at customer sites.) All of these technologies have unique characteristics and sometimes complex interactions with the distribution grid. For example, while rooftop and ground-mounted solar PVs and wind power systems are fueled by non-dispatchable sources of energy and therefore have variable energy output, electricity and thermal storage and fuel cells provide more flexibility and reliability to the grid (Nyangon 2017). On the other hand, energy efficiency, DR, EVs, and heat pumps are customer dependent and therefore behavior- or participation-centric. In this regard, city planners could assess the potential of the DERs from two main perspectives: accommodating DERs, which implies that their implementation may create adverse impacts on the electric distribution network, and integrating DERs, which means that they may mitigate grid constraints such as limited hosting capacity and unbalanced power flow (Trencher and van der Heijden 2019; Eid et al. 2016). The transformation from consumers to prosumers – active energy market participants who consume less bulk kilowatt-hours from the grid due to energyefficiency improvements while producing more through small-scale distributed generation – is one of the most exciting research areas of DERs and grid service development. (The term prosumer refers to energy consumers who are also producers.) For urban areas where conventional CHP plants are available, DER installations can be used to improve power systems restoration after power outages, improve frequency stability, and reduce blackouts. DER electricity development in

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cities, particularly solar PV and wind power systems, can be traced to favorable support policies and incentives by national and local governments, private sector financial investments, and technology and market improvement (Nyangon et al. 2017b). DER can help cities lower and stabilize household electricity costs, which are passed down to consumers, and improve grid flexibility because they are far more flexible to site and operate than fossil-based technologies. At the transmission level, flexible alternating current transmission system (FACTS), phasor measurement unit (PMU), fault current limiters, and synchronous switching devices provide instantaneous voltage support during extreme weather events and storms (FoxPenner 2010). In addition, they also enhance power quality, balance reactive power, and improve reliability and efficiency of bulk power shipment over long distances during power outages. At the distribution level, high-speed transfer switches and dynamic volt-amperes reactive (DVAR) support load isolation, improve grid reliability, and minimize power quality events. This makes a combination of DERs and smart grid investment a cost-effective strategy for improving grid flexibility to mitigate against climate-induced impacts. Furthermore, because DERs accommodate all generation sources particularly intermittent, non-dispatchable renewable energy sources, storage options, and low-carbon renewable natural gas-fired generation systems as well as cogeneration, they sustain a clean energy economy and urban infrastructure development. DERs also offer cities opportunities to reduce their near- and long-term greenhouse gas emissions through “solar city” strategy and economics (Byrne and Taminiau 2018), thereby mitigating climate impacts by reducing total GHG emissions. DERs perform twin functions: (1) adaptation and (2) mitigation of climate impacts. For example, combined cooling heat and power (CCHP) and cogeneration systems are a form of an integrated DER energy system, which delivers both heat and electricity, as well as improve system efficiency (Prinsloo et al. 2016; Eid et al. 2016). Such DER technologies can be paired with information communication technologies (ICTs) in cities to enable communication and control of the DER resource of interest. ICTs can also improve local system signaling and reliability of electrically constrained portions of the grid, thus providing critical system resiliency during widespread outages caused by extreme weather events and other disruptions.

Energy Storage Utility-sited energy storage provides the needed integration with variable renewable energy sources to mitigate supply-demand imbalances. Previously, pumped hydropower plants had been the only known grid-integrated technology for delivering significant flexibility to the power grid (Beires et al. 2018). Common electricity and thermal storage technologies include electrochemical or physical (e.g., compressed air) mediums, ice storage, molten salt storage, and others. In recent times, the growth of large- and small-scale battery storage has supported stabilization of power grids in cities. Electromobility is the main technology driver of the growth of battery

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storage, and as Hoarau and Perez (2019) show solar PVs, lithium-ion batteries, and electric vehicles (EVs) are emerging as the three disruptive innovations in power grids. Lithium-ion battery technology has very good power and energy density at high frequency and robustness which makes it suitable for consumer electronic devices (Nykvist et al. 2019). Another new factor is that advances in smart grid technologies make deployment of energy storage to integrate high amounts of renewable electricity systems possible. This necessitates maximizing locational value of DERs to deliver reliability services in locations where networks are frequently constrained (Burger et al. 2019). To minimize network supply-demand imbalances and integrate a growing share of variable power into the grid, investment in distribution network assets is necessary. Additionally, to improve electric grid resilience in cities, on-site renewable energy systems can be combined with energy storage (i.e., batteries, ultracapacitors, and flywheel energy storage), as well as other auxiliary equipment and services. When paired with energy storage, and facilitated by smart grids, these systems provide a reliable backup power in the event of a blackout, as well as ultra-clean power needed for sensitive industrial processes. Apart from batteries, utilities can deploy vehicle-to-grid (V2G) distributed storage devices to support grid balancing and enhance peak-shaving capability in cities. Noel et al. (2019) analyzed willingness-to-pay attributes for EVs in Norway, Iceland, Denmark, Sweden, and Finland and found that V2G capability is significantly positive. With V2G and smart grids, municipal utilities can flatten their daily consumption load curves, optimize grid management, and improve system flexibility (Pérez-Arriaga and Knittel 2016) with significant benefits to the environment, urban air quality, energy security, and ecological integrity.

Microgrids Microgrids are self-contained, self-sustaining grids, operating in a small geographical region, often powered by DERs, and can operate in both grid-tied and islanded modes (Hussain et al. 2019; Farzan et al. 2013). They are a potential solution to climate-induced power disruption events due to their islanding ability. Urban infrastructure such as hospitals, schools and universities, data centers, and municipal facilities and offices are examples of facilities that require unusually high levels of reliable electricity and can benefit from microgrid deployment, operating either as a stand-alone system or in conjunction with the municipal utility system, to guarantee proactive scheduling, feasible islanding, and outage management and reduce the impact of major disruptions. A notable microgrid project in the United States is a Hurricane Sandy star, the Princeton University campus. The Princeton microgrid consists of 15 MW CHP plant, a 5 MW PV array, and a load prioritization strategy during islanding. (Two years after Hurricane Sandy, recognition of Princeton’s microgrid still surges https://www.princeton.edu/news/2014/10/23/two-years-afterhurricane-sandy-recognition-princetons-microgrid-still-surges) Microgrids provide reliable onsite power supply with fewer outages, and self-healing power systems,

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through the use of digital information, automated control, and autonomous systems (Farzan et al. 2013). By enabling integration of multiple DERs with advanced controls and communication platforms, Washom et al. (2013) explain that microgrids offer significant system operational benefits and ameliorate constraints associated with the centralized electric grid.

Demand Response and Energy Management Systems Adaptation measures in the power sector, to implement climate change resilience, are best done at supra-local level by county and regional governments, because of their broad legislative powers. Near-term demand management measures (e.g., smart meters, new tariffs, and intelligent load management) provide cost-effective mitigation strategies as well as enhance flexibility and grid management solutions for reducing carbon intensity in the electricity sector. In this regard, demand management and demand response offer two strategies to even outpeak power demand: (1) load shedding and (2) load shifting. According to the Federal Energy Regulatory Commission (FERC), demand response refers to “changes in electric usage by demand-side resources from their normal consumption patterns in response to changes in the price of electricity over time, or to incentive payments designed to induce lower electricity use at times of high wholesale market prices or when system reliability is jeopardized.” (Federal Energy Regulatory Commission. Reports on demand response and advanced metering http://www.ferc.gov/ industries/electric/indus-act/demand-response/dem-res-adv-metering.asp) Demand response is a subset of end-use customer energy solutions known as demand-side management (DSM). Besides demand response, DSM includes energy efficiency programs. Various entities including transmission and distribution system operators, utility companies, and end-use consumers can all benefit from demand response, either in the form of price-driven or incentive-based demand response programs. Load shifting tries to smooth the power demand away from peak periods through price incentives thereby improving power efficiency and optimizing resource allocation to achieve efficient electricity use (Kuiken and Más 2019; Varma and Sushil 2019; Wang et al. 2018). Load shedding, on the other hand, is a form of targeted blackout where utilities enter into agreement with large electricity consumers such as industries or universities to reduce their consumption during peaking crises in return for discounted rates. This is a form of incentive-based demand response program and is triggered either by high electricity prices or a grid reliability problem. Dynamic pricing can dramatically reduce energy demand swings and increase overall generating efficiency. Demand response as a proactive measure can be implemented both manually and automatically. When fully automated, human intervention is removed and demand response is initiated at a home, building, or facility through receipt of an external communications signal that shifts the load, thus reducing peak and total energy demand (Fox-Penner 2010). The manual demand response entails controlling the use of certain appliances, for example, dishwashing machines, in different time

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periods of the same day. Semi-automated demand response, on the other hand, involves some form of human intervention whereby a pre-programmed demand response strategy is initiated through a centralized control system. The success of dynamic pricing methods nevertheless depends on consumer behavior. The current price-driven demand response programs include time-of-use (TOU) pricing, critical peak pricing (CPP), and real-time pricing (Yan et al. 2018; Faruqui and Leyshon 2017). Besides dynamic pricing, cities are engaging investorowned utilities (IOUs) in their territories to implement smart building automation and control solutions to improve provision of energy services. Advanced metering, time-varying rates, dynamic market-based prices, and energy management systems (EMSs) have the potential to reduce uncertainty in electricity prices than ever before. Without these smart controls, the problems of the grid will worsen, and critical operations in cities will be severely affected. Siemens has observed that investments in smart grids – resulting in increased usage of load shedding and load shifting – could reduce national electricity needs by nearly 10%. (Smart infrastructure business update: https://assets.new.siemens.com/siemens/assets/public.1557934458.69c056d92369-49ddba5e-1a519a71049e.dgcustomersummit2019.pdf)

Smart Measuring Systems Significant progress has been made in improving measurement, reporting and verification (MR&V) system for urban energy as well as other performances like air, environment, water, waste management, transport and mobility. However, the indicators used for measuring these performances and smartness rarely consider a holistic approach that goes beyond one component (economy, environment, mobility, people, governance). Additionally, a lack of standardized common metrics for MR&V adds complexity in governance and information management. Under these circumstances, energy performance measurement through data-driven powered insights and peer-city benchmarking strategies have emerged as viable solutions for improving the understanding of the complexity and dynamism of urban energy transition – from fossil fuels to renewable power (Hiremath et al. 2013). As far as the building energy dimension is concerned, polycentric frameworks based upon people-centric and smart measuring strategies, particularly common indicator approaches, could be deployed to improve both the measurement of energy supply and demand metrics in cities. In this framework, these efforts could focus on the linkages between multiple innovations and sociotechnical systems like integrated district heating systems in which electricity grids are coupled with gas networks, rooftop solar PV systems for electricifying residential buildings, V2G configurations, integrated urban planning and transport systems via TOD, and intermodal transport to facilitate efficiency of transport links, nodes, and the provision of services within and across cities (Pires et al. 2014). By adopting a common indicators approach, cities can also improve reporting of energy project performance on an ongoing basis. Smart, automated performance measurement and control opportunities will arise in many ways:

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• Intelligent monitoring and control of energy-consuming devices to reduce performance variations, engage energy users in new decisions and action that reduce energy, and improve energy savings guarantee. • Foster better understanding of the drivers of energy-efficiency improvement and changes in the social, natural, and built environments. • Use targeted incentives and rewards to increase participation and commitment to energy efficiency actions. • Any governmental program providing a subsidy for clean energy projects can require reporting as a condition. • Leverage technology to promote smart metering of generation resources, transportation system efficiency, appliance labeling, building codes, and energy savings performance contracts (ESPCs). Standardization of ICT interfaces for smart cities will also support the New Urban Agenda (Habitat 2019) and a specifically Urban Sustainable Development Goal (USDG) as part of the United Nations 2030 Agenda for Sustainable Development, encourage continuous learning and improvement, promote accountability, and identify performance gaps (d’Alençon et al. 2018). Under a common framework of MR&V, learning, cooperation, and emulation among different cities with common smart city objectives and characteristics can be enhanced to promote smart mobility, smart urban infrastructure, smart economy, smart energy, and smart urban governance. An example of a comprehensive framework for measuring economic, environmental, and social performance of cities is the release of the Sustainability Tools for Assessing and Rating (STAR). The STAR framework offers a menu-based system for enabling cities to build inclusive, equitable, and accountable development (STAR Communities 2017). It is a leading framework for assessing and promoting sustainability performance of cities at different scales. With the STAR framework, cities can evaluate their performance across different goal areas covering the built environment and climate and energy (STAR Communities 2015), discover “best practices” that move the needle toward the identified outcomes, and communicate their progress to the stakeholders. However, cities and their measurement metrics are network phenomena and cannot be studied in isolation. If the measurement indicators fail to improve or fully capture the resource and material flows in cities, the performance evaluation of the city’s assets could be considered incomplete or undervalued (Pires et al. 2014). In addition, without fully understanding the energy and material flows, systematic evaluation of the measurement indicators could be problematic thus undermining long-term planning and development goals. To address these concerns, a smart measurement framework should incorporate three main guiding principles: bottom-up stakeholder-driven approach, consensus-based process, and inclusivity. Stakeholder-driven process fosters synergy among different city agencies and entities, thereby improving decision-making process. Consensus-based process promotes transparency and accountability, offering immense opportunity for deeper engagement on smart development. Stakeholder-driven and consensus-based approaches together foster improved qualitative assessment of urban complexity

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and dynamics. On the other hand, inclusivity refers to the balance between rigor and comprehensiveness of the measurement metrics in order to address the full range of measurement, reporting, and verification systems.

Harvesting Rapid urbanization, population growth, climate change, and high cost of maintenance of urban infrastructure projects have pushed city planners, engineers, and scientists to look for alternative and sustainable methods of harvesting energy such as solar, wind, hydropower, and geothermal. Solar PV panels absorb sunlight as a source of energy to generate electricity in the form of direct current (DC). Solar cells are made of semiconductors, such as wafer-based crystalline silicon cells or thin-film silicon cells. Solar energy harvesting occurs when electrons in the PV cells are freed upon after being struck and ionized by photons from the sun to power electrical devices. Examples of harvesting include the following projects: • Solar and wind energy: harvesting both bulk and distributed solar and wind power, from sparsely populated regions with low electrical demand outside the city and transmitting it to urban areas where electricity is needed. • Biofuel or bioenergy: sustainably harvesting bioenergy from crop residues, crops, wood, or wood waste using methods that do not contribute to emissions. • Stormwater mitigation: promoting green infrastructure such as green roofs and urban rainwater harvesting for use in landscape irrigation or interior building applications, which would reduce water consumption. Rainwater harvesting also saves energy incurred in municipal networks for transportation and distribution.

Green Technologies Despite documented compelling benefits of green technologies – e.g., addresses climate change adaptation and mitigation, sustains economic growth and investment, improves energy utilization efficiency, and promotes substitution of fossil fuels with clean energy in production – pragmatic investments in these technology solutions remain limited, uncoordinated, and ineffectual relative to demand and the climate challenge (Weina et al. 2016). A review of heterogeneous effects of green technology innovations across cities with different income levels shows that these investments can stimulate total-factor carbon productivity as well as human capital (Du and Li 2019) in several ways. First, the applications of green technology innovations improve climate change adaptation and mitigation, thereby promoting health and well-being of existing residents. Second, green technologies advance energy innovations and utilization efficiency. Third, green technology innovations can promote the substitution of fossil fuels with low-carbon energy in production, supporting industry upgrade which in turn spurs economic growth. However, in practice,

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investments in green technology applications face barriers such as financing obstacles as well as market and institutional complexities. This is compounded by uncertainty and risks associated with new green technologies – smart street lighting, lithium-ion batteries for EVs, solar PVs, offshore wind power, biofuels and bioethanol, and cost-effective green vehicles – with respect to regulatory structure, financing barriers, and systemic institutional gaps (Kanger et al. 2019; Nykvist and Nilsson 2015; Lam et al. 2010). Unfortunately, this affects the diffusion of these technologies in cities. Second, difficulties in administering huge capital investments due to legal and administrative challenges, complex investment instruments, and lack of specialized expertise restrict the level of infrastructure-scale investments at the city-level because of higher initial cost. A key element of green technology innovations is that most investment decisions target a specific sector (e.g., energy, utilities, manufacturing, etc.), a specific asset class (e.g., fixed income, equities, infrastructure, etc.), or a specific city (e.g., cities in the developed world or the developing world). While there is significant investment potential in many of these areas – particularly in renewable energy, energy efficiency, and decarbonization technology – the growing investment gaps and the tightening of capital in the global banking sector mean that investors often weigh investment decisions against risk profiles of a city, and not merely on the merits of the green technology innovations alone. To generate new sources of revenues to fund green technologies, cities should expand the share of green financing, issue municipal-backed “green bonds” to promote socially responsible investments, pursue new international sources of climate funding, reduce taxes on green products, and promote new cooperation on green technologies with other cities through forums such as C40 Cities, Global Covenant of Mayors, Cities Alliance, and ICLEI – Local Governments for Sustainability. Table 2 summarizes risk factors focusing on the green technology aspects of a smart city programs.

From Robustness to Resilience Robustness and resilience are two complementary concepts applied in various energy studies, including energy security assessment (Martišauskas et al. 2018), building energy design and retrofits (Ascione et al. 2017), grading building energy performance (e.g., energy use intensity, total energy, peak power) (Papadopoulos and Kontokosta 2019), and energy installation (e.g., heating source, ventilation system, status of refurbishment) (Pasichnyi et al. 2019), to deal with the increasing uncertainties and meta-complexities that characterize energy systems in cities. Resilience refers to the ability of a system to “rebound” or withstand initial shock (interruptions) (Hughes 2015), while robustness is the capacity to maintain functions of a system (policy, political system, organization, or institution) in spite of uncertainty (Capano and Woo 2017). The nonlinearity and spillover effects associated with the complexity of climate hazards, urbanization, and unforeseen population and demographic shifts, combined with widespread and systemic environmental damage, aging infrastructure, pollution, and mounting health costs in cities, require a

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Table 2 Risk factors associated with smart city solutions Risk-tier Technical risk

Energy resource risk

Extreme weather risk

Construction risk

Offtake risk

Environmental risks

Risk events Performance of technology, technical availability, technical lifetime, equipment defect/degradation, grid connection failure, inability to fulfil warranties and guarantees, technological change, etc. Variability and quality of technical potential, e. g., solar irradiation data, wind speed data, simulation model Extreme wind conditions, heatwaves, flooding, thawing, and snowstorm Cost overruns, completion delay, noncompletion, project quality, abandonment, force majeure, natural disasters, political risk, land availability

Credit risk, large level of investment/long tenor of return, additional equity required later, commitment, misalignment of investors’ objectives Risks related to the location and surrounding environment of the project, impact on local residents, weather, and environmental opposition

Mitigation strategy Proven technology Quality components correctly dimensioned Manufacturer warranties and performance guarantees O&M guarantees Take-and-pay power purchase agreements (PPA) Use of proven databases with wellcorrelated theoretical and empirical data On-site measurements Use of technical protection measures Site selection

Risk taker Manufacturer, engineering procurement, and construction (EPC) contractor, O&M contractor

Fixed time and budget turnkey contract (EPC) completion guarantees Monitoring reports Performance reports Penalty clauses Cost contingency funds Long-term offtake agreement (PPA) takeand-pay, credit enhancement, accelerated taxes, regulations, etc.

Manufacturer, EPC contractor, sponsor

Environmental impact assessment Risk of incurring fees, fines, or withdrawal of license

Developer, sponsor

Developer, consultants

Designer, EPC contractor

Sponsor, offtaker

degree of flexibility in policy and governance systems. Indeed, it necessitates combinatorial innovations in technological exploitation and exploration (Youn et al. 2015). These adaptations also demand application of a resilience-based approach rather than robustness per se to address these complex, emergent, and

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evolving threats to urban energy systems (Nyangon et al. 2017b). Is robustness synonymous with resilience? How can cities advance flexibility, agility, and overall resilience to address technological and institutional “lock-in” effect inherent in our urban infrastructure systems? These are vital questions for city authorities and can be resolved by implementing incremental adaptations in public policy and policy design to mitigate potential risk of system lock-in effects for new technological solutions and corresponding institutional path dependency that can prevent these transitions from taking place. In addition to robustness, cities can address uncertainties and complexities of energy networks by integrating complex adaptive systems, dynamic planning, flexibility, and agility in order to advance greater resilience. As a result, contemporary urban studies are increasingly emphasizing resilience of the socioeconomic and built environment and technical functions of cities by including these additional elements in urban policy formulation in order to strengthen and fortify city assets in the face of increased risk of failure. As Spaans and Waterhout (2017) explain, “resilience includes not only the shocks (such as earthquakes, fires, and floods), but also the stresses that weaken the fabric of a city on a day-to-day or cyclical basis. By addressing both these shocks and stresses, a city becomes more responsive to adverse events, and is overall better equipped to deliver its functions in both good times and bad, to all populations.” Similarly, Davoudi (2012) explains that “resilience is defined not just according to how long it takes for the system to bounce back after a shock, but also how much disturbance it can take and remain within critical thresholds.” Notable methods for improving resilience in energy governance include diversifying energy supply (e.g., solar PV, wind, biomass, if available + fossil especially gas-fired generation and CHP) (Nyangon and Byrne 2018) and improving socioeconomic metrics through enhanced choices for electricity services for customers and developing cost-efficient mini- and microgrid networks. Such diversity allows smoothing of daily electricity load patterns and shifts electricity load to locations with greatest demand, thereby increasing cost-efficiency and network management. Bisello and Vettorato (2018) offer a seven-part multiple benefits approach as a paradigm for evaluating smart urban energy transition – smart economy, smart governance, smart built environment, smart mobility and connectivity, smart community, smart services, and smart natural environment – and its positive impacts on resilience of energy infrastructures, well-being, health, indoor comfort, property value increase, and competitive advantage on the smart city. Energy is the cornerstone of these components. With the rapid progression of climate change, advances in technological innovation, and urbanization shifts, energy systems, and by extension these components, will likely become more complex and interconnected. As a result, a better understanding of interconnectedness and the resulting indirect vulnerabilities of urban energy systems is necessary to mitigate increasing risks. In doing so, cities ought to advance a nexus thinking and integrated urban design, planning, and management rather than a sectoral line (“silos”) approach, meaning municipalities can appropriate synergistic benefits of better integrated resource management.

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Finally, recourse to moving beyond robustness (toward resilience) is an essential component of smart city energy networks. In particular, peer-city benchmarking is vital for understanding when robustness is particularly suitable and when it is not based on the complexity of smart cities. Regular peer-to-peer comparison and evaluation of the foundational measurement metrics (e.g., smart growth, environmental integrity, healthy communities, and social inclusion) are desired to gain a better idea of specific actions that can enhance flexibility, agility, responsiveness, and inclusive decision-making. Finally, quantitative and qualitative methods for benchmarking energy strategies and energy protocols toward smart resource use are needed to foster resilient urban energy frameworks for better decision-making in cities.

A Polycentric Approach to Smart City Energy Governance The above discussion highlights several key elements of the smart city energy governance: (a) networks (e.g., the existence of networks for facilitating mutual learning processes between cities to deliver quality urban services, promote effective urban governance, and improve management structures); (b) scales (e.g., connecting and aligning several scales, actors, and responsibilities rather than containing efforts to one scale;) (c) polycentric energy systems (e.g., thinking of solutions in context, notably developing numerous smaller, modular rooftop solar PV plants located closer to consumers); and (d) the common pool issues of energy access, rebound effects, energy justice, and inequality for greater acclimatization of the benefits of electricity decarbonization in cities. Polycentricity refers to decentralized governance systems encompassing several independent centers of decision-making often performing function in a coordinated fashion across sectors and scales (Aligica and Tarko 2012; Ostrom 2010). The emerging “polycentricity” paradigm and thinking or “adaptive regulatory framework” in smart city governance enables a salient conceptualization of citywide transformations. Notably, governance for transformations, governance of transformations, and transformations in governance (Burch et al. 2019) resulting in significant environmental improvements (such as waste reduction and air-pollution abatement), social progress (e.g., social relations and growth of green jobs), and economic benefits (e.g., reductions of energy costs). The nonlinearity and complexity of smart grid challenges, especially climate risks, urbanization, demographic shifts, systemic environmental change, and energy infrastructure investment deficit facing many cities, can be addressed by a “polycentric” strategy that incorporates shared learning, adaptive management, civil society strategies, and creative experimentation to support existing transformative innovations and empower local energy development. Initially proposed in the 1960s and 1970s (Aligica and Tarko 2012), polycentric strategy has been applied to evaluate “solar city” economics (Byrne and Taminiau 2018), climate justice (Fischedick et al. 2018; Martinez-Alier et al. 2016; Ostrom 2010), urban energy planning for 100% renewability in Frankfurt and Munich cities (Radzi 2018), alleviating urban traffic congestion (Li et al. 2019), assessing energy efficiency

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gap (Zou et al. 2019), and evaluating new capacities for transformative climate governance in New York City (the United States) and Rotterdam (the Netherlands) (Hölscher et al. 2019). In terms of smart energy infrastructure governance, polycentric policy underscores the elements summarized in Table 3 – transparency, inclusivity, accountability, and responsive network practices. Jones and Kammen (2014) posit that urban development and suburbanization have created key questions: what is the degree of change of each urban energy process, for example, in energy cost savings, mitigation of greenhouse gas emissions, and material flows in cities? How are cities reshaped by governance processes as they grow? Polycentricity offers a promising strategy for addressing these questions. It provides a viable strategy to rethink energy infrastructure investments with a view to implementing smart energy systems for residential homes, smart buildings, and increasing energy security through energy analytics and artificial intelligence applications. It also resonates with the concepts of regime complexity (Keohane and Victor 2011), institutional fragmentation (Zelli and Asselt 2013), and experimentalist governance (Jordan et al. 2015), allowing for flexibility, agility, social learning, systems-oriented approach, and changing course when new information becomes available. In essence, a polycentric approach engages multiple stakeholder groups in the design, implementation, and management of smart energy future in cities. As a result, a polycentric system spans across multiple scales. For example, both centralized and decentralized electricity networks serving a city’s jurisdiction such as the New York City metropolitan area or London metropolitan belt are a part of city or regional or national grids.

Table 3 Key elements of polycentric policy Elements Stakeholder-driven approach Enhanced accountability and legitimacy

All-inclusive and more equitability Adaptive governance system

Shared learning More robust

Polycentric discourse applications Polycentric policy designs emphasize community ties and collaboration among various agencies and civil societies Project sponsors of polycentric policy designs support internal and external transparency and accountability, for instance, in the planning and management of resources, to cater for the growing urban complexity and dynamics Polycentric systems address a full range of environmental, social, and economic issues as well as involve a diverse number of stakeholders “distributes decision-making powers across the system and ensures coordination through an overarching system of norms and rules that defines the logic of interactions between actors” (Biesbroek and Lesnikowski 2018) to encourage polycentric innovation across scale, place, and time Emphasize creative experimentation, trust building, and problem resolution Ability to address grand challenges of governance in megacities through continuous improvement and steady accumulation of marginal changes across sectors and scale – if one approach or domain fails, others can step in, hence, greater resiliency

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Many smart city developments already exhibit elements of a polycentric governance approach. For example, Ruhr in Germany, Stoke-on-Trent in the United Kingdom, and the San Francisco Bay Area in the United States all have complex coordination arrangements across sectors and scale, involving different stakeholders at multiple levels. Additionally, a number of cities are already implementing decentralized and “smart” infrastructure solutions, including rooftop solar technologies which provide parties with multiple simultaneous roles as both producers and consumers – prosumers of energy. In addition, these plans address unique challenges of the city, for instance, by establishing “smart” solutions that support short- and long-term operational risks, as well as incentives. Cities can formalize these requirements into (smart grid) technical standards in order to institutionalize the polycentric smart energy framework. Together, these plans and strategies, supported by “orchestration” of cooperation involving diverse actors and technologies, could phase in an integrated smart city framework (see Table 4) that is viewed much more as part of a long-term solution to pervasive global changes in human societies, including urbanization, climate change, urban inequality, and digitization agendas.

Conclusions What can a polycentric paradigm offer energy infrastructure governance in cities? In this chapter, a polycentric strategy, which connects and align scales, responsibilities, and actors, has been described and proposed as an alternative pathway for energy governance in smart cities Historically, cities were mostly centrally governed, but rising contemporary challenges highlighted in this chapter have upended the need for multiple domains of authority governing. This new mode of governance is characterized by adaptive management, new dynamics of techno-economic networks and multilevel, polycentric and multi-layered governance of energy decisions. A key message of this chapter was to analyze the significance of socio-technical systems for deep decarbonization in a way that simultaneously promotes polycentric authority. This approach promotes accountability, inclusivity, innovation, trustworthiness, bottom-up learning, adaptation, and multiple levels of cooperation across sectors and scales. It also embeds flexibility, agility, cultural discourse and social acceptance, systems-oriented design, and equity at multiple scales, all which are fundamental to operationalizing socio-technical energy transitions. While a polycentric perspective is a offered to rethink the governance of urban energy infrastructure, the existing energy systems and utility business models are changing simultaneously in several cities with respect to diversity, planning, and customer choices. As a result, this requires careful political attention to the technological, regulatory, infrastructure, user-design practices and markets, maintenance and supply networks, and social consequences of the energy transitions. Climate change, demand for modern energy services, outmoded and aging power grid, and rising cost of energy are some of the drivers of this transformation. As a major source of carbon emissions, the electricity sector is also facing regulatory pressure to transition in a manner that limits stranded assets and risk of locked-in technological

Power Communications infrastructure

Applications and technologies

Transmission Wide area network – backhaul network between the field assets and the utility

Utility control and load monitoring for EV and V2G applications Integration of utility systems into consumer business processes

Smart charging of EVs and V2G Customer solutions

Generation Local area network

Visibility and control systems for distributed assets

Peak load management and control AMI, MDM, CIS, outage detection, billing Visibility and control systems for DER assets

Utility system EMS, SCADA, DMS, and OMS

Microgrids

Distributed generation

Demand response AMI

Smart energy strategy Distribution automation

Table 4 Framework for the integrated smart grid

Application data flow to/from enduser energy and building management systems Substation/distribution Home area network (links load devices and appliances for greater utility and consumer control)

Application data flow for EVs and V2G devices

Application functionalities FDIR, remote switching, voltage control, substation automation, grid asset protection, power quality management, automated feeder configuration Acquisition of energy data; load forecasting and load shifting Remote meter reading, theft detection, customer prepay, and real-time pricing Monitoring, dispatch, and control of DER assets such as renewables, CHP, and energy storage devices Aggregation of supply and demand resources into a network that is either grid tied or islanded (e.g., microgrid)

Home or building Communications infrastructure

Home/building portals, online billing, and pay/prepay; TOU pricing data

Customer usage and revenue for demand response activities; customer and utility loads; impacts to peak and non-peak Vehicle load; storage capability

Data and visualization of energy enduse Power outages, consumption (voltage and current readings) DG load generation capacity and performance data

End-user data Point of consumption voltage

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systems. A polycentric smart energy governance promotes a bottom-up continuous incremental change, in which lock-in problems are reduced because outlived technologies are phased out in a manner that complements model-based analysis with socio-technical policy-oriented solutions. The approach to urban energy system governance described here acknowledges the role of smart grids in fostering climate resilience and robustness in cities. This strategy, in part, incorporates “hardening” of urban infrastructure to withstand extreme weather events as well as in certain circumstances the option to relocate certain infrastructure services to less vulnerable locations. Moreover, advances in smart grid networks and energy infrastructure systems, including smart meters, DER generation, EVs, demand response, energy storage, and V2G technologies as well as integration of these solutions across urban sectors and scale, mean that the number of actors involved is likely to increase, and so does the complexity of regulation and governance system. Such complex systems call for shared learning, experimentation, information sharing across scales and jurisdictions, greater accountability and transparency, and flexibility in order to deal with uncertain, unpredictable, and nonlinear forms of economic, social, and environmental interruptions. Additionally, adapting and developing smart grid systems in cities require reliable and state-ofthe-art energy supply and demand datasets and related infrastructure services – transportation, housing, water, mobility, and ICT services. Although full adaptation is an ongoing challenge for city management, public and private sector service providers, local businesses, residents, and other stakeholders, deepening and aligning polycentrism across sectors and scales underpins the development and implementation of smart energy frameworks that are sociopolitically acceptable, cost-effective, and coevolutionary with technologies and societal development.

Cross-References ▶ Smart Cities: Fundamental Concepts

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Urban Computing: The Technological Framework for Smart Cities Mélanie Bouroche and Ivana Dusparic

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Sense-Analyze-Actuate Paradigm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Optimizing the Use of Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Case Study: Optimizing Urban Energy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sensing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Data Categories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Urban Sensing Modes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Networking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Internet of Things . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Urban Platforms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Analyzing: Intelligence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Prediction of Urban Resource Supply and Demand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Decision-Making . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ethical Implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Actuating . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Data Visualization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Human Interfaces . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Consumer Interfaces . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Robotics/Autonomous Actuation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract

Increased urbanization is putting a strain on the limited shared urban resources, for example, road space, energy, and clean air and water. Smart cities leverage technology to manage such shared resources more efficiently, thereby improving citizens’ quality of life. This chapter introduces and discusses technical challenges in managing city-scale resource infrastructures and potential solutions. M. Bouroche (*) · I. Dusparic (*) School of Computer Science and Statistics, Trinity College, Dublin, Ireland e-mail: [email protected]; [email protected] © Springer Nature Switzerland AG 2021 J. C. Augusto (ed.), Handbook of Smart Cities, https://doi.org/10.1007/978-3-030-69698-6_5

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We frame the discussion within the Sense-Analyze-Actuate paradigm, a model leveraged by most smart city solutions. The Sense step entails gathering data from existing or newly deployed dedicated sensors, owned by public agencies and businesses, as well as contributed by citizens. In the Analyze step, these disparate and often unreliable sources of data are fused to improve the authenticity of data (improving detection, confidence, reliability, and reducing ambiguity) as well as extending its spatial and temporal coverage. In this step, optimization techniques, and artificial intelligence in particular, allow to reason on the data, making resource management decisions in a centralized or decentralized approach. In the Actuate step, the results of the analysis can either be presented to human operators, i.e., visualized for decisions support, or used to directly actuate the changes, adapted to the specific urban resource.

Introduction As the proportion of people living in cities increases, shared urban resources, such as road space, energy, and clean air and water, come under increasing strain. Smart cities leverage technology to manage these shared resources better (Manzoor et al. 2014). As cities are large-scale, loosely structured, dynamic systems of systems, such urban computing needs to adopt a multidisciplinary approach to consider the multifaceted impact of technology in cities and ensure that it ultimately improves citizens’ quality of life.

The Sense-Analyze-Actuate Paradigm Definition Urban computing has been defined as “a process of acquisition, integration, and analysis of big and heterogeneous data generated by diverse sources in urban spaces, such as sensors, devices, vehicles, buildings, and humans, to tackle the major issues that cities face (e.g., air pollution, increased energy consumption, and traffic congestion)” (Zheng et al. 2014). While this definition encompasses a lot of the concepts associated with urban computing, the acquisition, integration, and analysis of data is not in itself sufficient to address urban challenges. Indeed, some actuation, potentially in the form of visualization to support human decision-making, is required to close the feedback loop and effect changes in a city. This can be represented by the Sense-Analyze-Actuate paradigm (see Fig. 1). While this concept is very close to the Map-Analyze-Plan-Execute (MAPE) loop of autonomous computing (Jacob Fig. 1 The Sense-AnalyzeActuate paradigm

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Fig. 2 Smart streetlight in the Sense-Analyze-Actuate paradigm

et al. 2004), and even some older concepts such as Stability Augmentation Systems in control theory (Markland 1970), its simplicity and wide applicability make it particularly suitable to describing urban computing.

Example Consider, for example, a smart streetlight, a very simple urban computing application. In its simplest version, a smart traffic light would Sense the current light level, Analyze this by comparing it to a threshold, and Actuate by setting the light intensity level. A more elaborate version might Sense nearby pedestrians and road traffic in addition to current light level, in which case it might Analyze by also computing the expected paths of the actors (see Fig. 2).

Optimizing the Use of Resources Shared urban resources can be classified into several domains: mobility (road space), energy (electricity, gas), water, pollution (air quality), waste, health, etc. Each domain presents its own set of challenges. For example, mobility needs to address traffic congestion and the provision of appropriate transport means at the right place, at the right time, and for the right price, in terms of both public and shared transport. Similarly, the water domain investigates the provision of clean water when and where it is needed, at a sufficient pressure, despite potential leaks in the network. Traditionally, each type of urban resources has been managed independently, sometimes even within the same domain. Indeed, in many cities, buses are still operated independently of metros or tramways. Different resources, however, interact with each other, as buses, tramways, and cars often share some of the same road space, as well as potentially having partially overlapping demand. There are also some cross-domain interactions of resources, such as increased car traffic increasing pollution. These interactions can be positive (an increase in bus frequency might decrease private car traffic), negative (an increase in motorized traffic decreasing air quality), or mixed (an increase in the use of electric vehicle potentially improves air quality but also increases energy demand). For these reasons, smart cities need to break down existing application or domain silos and adopt a system of systems approach to integrate interdependent public and private systems (Naphade et al. 2011).

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Overall, optimizing the use of urban resources can be modelled as matching the supply and the demand of the resource, in real-time. Both the supply and demand of resources can vary over time. For example, in the mobility domain, the road traffic (demand) will vary over time. The road space (supply) typically only varies very slowly over time (as new roads or lanes are planned and built), with the exception of reversible lanes. Both the demand and supply of public transport vary over time (e.g., bus frequency and capacity). Matching supply and demand can be achieved by a combination of three approaches: (a) Adapting the supply to the demand: regulating the amount of the resource available, for example, scheduling more buses. (b) Demand-side management (or adapting the demand to the supply): incentives (positive or negative) can be used to modify the consumers’ demand, for example, by providing cheaper metro tickets at off-peak times. (c) Storage: some resources can be stored when the supply exceeds the demands and released when the demand exceeds the supply, for example, water can be stored in water towers.

Case Study: Optimizing Urban Energy This section illustrates the concepts presented above in the domain of urban energy. The energy demand of a given household varies over time depending on the time of the day, the weather, and the presence and occupation of its inhabitants. For example, a typical weekday usage of a household exhibits low demand during the night, with two peaks: the morning one when inhabitants are up and getting ready for work and the higher evening peak, when they arrive home from work. This pattern is illustrated in Fig. 3, which shows the daily usage of a typical Irish household recorded during Irish smart meter trials. The amplitude of these variations is exacerbated by electric vehicle uptake, as they tend to be plugged in at around the same time (when people come home from work) and draw a large amount of energy as soon as they are connected until they are fully charged. The supply of energy typically also varies significantly over time, especially with the use of renewable energy such as solar panels or wind turbines, the outputs of which are weather-dependent. In the case of urban energy, the three approaches to matching supply and demand correspond to: (a) Adapting the supply to demand: generators can be powered or shut down; hydroelectric dams can generate extra electricity by releasing water when required. (b) Demand-side management: financial incentives in the form of scheduled or realtime varying pricing are commonplace in the electricity market, for example. (c) Storage: energy can be stored both in consumer-owned devices, such as storage heaters or electric-vehicle batteries, and on the grid, for example, by pumping water into storage when the electricity supply exceeds the demand. These can be enabled in 15 s and recover about 80% of the energy.

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Fig. 3 A typical daily residential energy demand pattern

The remainder of this chapter includes a section for each of the Sense, Analyze, and Actuate steps.

Sensing The first step of the smart city loop relates to capturing and gathering urban data. A significant challenge is to find suitable data in terms of content, accuracy, and frequency. To address those, section “Data Categories” presents the different data categories available in smart cities, and section “Urban Sensing Modes” discusses the different sensing modes. Sections “Networking” and “Internet of Things” discuss the gathering of the data focusing on communication technologies and the Internet of Things, respectively, and section “Urban Platforms” urban platforms, which can store and combine this wide array of data.

Data Categories There is a wide range of data available in smart cities, including maps, listing of restaurants, traffic counts on specific roads, pollution levels, mobile phone traces, etc. This data can be classified in terms of its spatial and temporal properties. Spatially and temporally static data includes points, such as points of interests, e.g., cafes; lines, such as rivers or pipelines; and graphs such as road networks. Spatially static and temporally dynamic data relates to a specific point or area but varies over time. For example, the temperature at a given location or the traffic count in an area.

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Spatially and temporally dynamic data include moving objects and their trajectories, for example, plane or individual bus tracking.

Urban Sensing Modes Sensing in smart cities can be undertaken in four modes: • Traditional sensing uses sensors dedicated to a specific application, for example, a particulate matter monitor for air quality monitoring (e.g., Paprotny et al. 2010). • Passive crowdsensing leverages existing infrastructures to passively collect data generated by crowds, for example, using public transport ticketing information to monitor and predict people’s movement (Bagchi and White 2005). • Opportunistic sensing (or active crowdsensing) exploits information from users’ own sensors, in a purpose other than the one originally intended, such as their GPS data to estimate congestion when using a navigation app (e.g., D’Andrea and Marcelloni 2017). • Participatory sensing (or crowdsourcing) relies on users to generate information, for example, by taking a photo, pressing a button, or writing a report (e.g., Manzoor et al. 2014). In addition, such sensors can be static or mobile. Information can typically be obtained using several of the sensing modes. For example, the congestion levels in a given street could be assessed via traditional sensing using loop sensors buried in the road, by passive crowdsensing using mobile phone traces to estimate the number of cars and their velocity, by active crowdsensing using phones’ GPS readings, or by crowdsourcing written or photo reports from users. Each sensing mode has different advantages and drawbacks. Traditional sensing typically offers very accurate information, as sensors can be chosen specifically for the task at hand. It is, however, typically very costly to deploy and maintain such sensors (e.g., loop detectors get damaged during road works and the road needs to be dug up if they need to be repaired or replaced). Passive crowdsensing on the other hand does not require any additional investment but can only gather data for which sensors are available, and the resulting data is typically less complete and/or accurate. Similarly, opportunistic sensing can only use sensors that are already owned by users, and when these are mobile, extra sensing might have (battery or data) cost implications for users. In addition, privately owned sensors raise specific challenges both in terms of trust (Manzoor et al. 2012) and privacy (e.g., Qin et al. 2014), and citizens might need to be incentivized to cede their privacy (Connolly et al. 2018a, b).

Networking The wide array of sensed data often needs to be shared or gathered to extend its authenticity and availability. Smart cities can use a variety of communication technologies, which can be split according to their range.

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Short-range technologies can be based on: • The 802.11 standard, also known as Wi-Fi (range ~100 m) – thanks to its large bandwidth and low cost, it can be used both to transport sensed data (e.g., Kanhere 2011) and to monitor urban mobility (e.g., Chon et al. 2014). • The Bluetooth and Bluetooth Low Energy (BLE) standards (range 50–150 m) – present on a wide array of devices, they can be used, for example, to monitor mobility and interactions at large-scale events (Stopczynski et al. 2013). • The IEEE 802.15.4 standard, which specifies the physical layer and media access control for low-rate wireless personal area networks used by Zigbee (10–100 m), for example, its low energy consumption makes it particularly suitable for cheap, potentially remote, sensor platforms, such as precision agriculture (Morais et al. 2008) or environment monitoring (Haefke et al. 2011), potentially at very large scale (Tennina et al. 2011). • Near-field communication (NFC) – its very short range (4 cm) makes it suitable for applications where close contact is guaranteed, such as payment, ticketing, advertising, and indoor navigation (Pesonen and Horster 2012). • The 802.15.6 standard – its very short-range communication is used for body area networks (BANs) and wearable computing. Long-range technologies include: • Cellular network technology, including its fifth generation (5G) (~500 m range), which, thanks to its very high bandwidth, coverage, and reliability, can be used for a wide array of applications such as traffic management (Marinescu et al. 2012) and smart grids (Accenture 2017). • Low-power wide-area network (LPWAN), which is divided into licensed (LTE-M, NB-IoT, and EC-GSM) and unlicensed (Sigfox- and LoRa-based standards – up to 50 km) – their high range and low energy requirements make them suitable for large-scale monitoring applications such as river (Guibene et al. 2017) or building (Pasolini et al. 2018) monitoring. These technologies, however, often need to be combined depending on the range, bandwidth, and power consumption requirements, to ensure scalability (e.g., Morris et al. 2017).

Internet of Things The Internet of Things, or IoT, is the interconnection, using the networking technologies discussed above, of devices in our environment such that they can interact with each other without requiring human interaction. The Internet of Things has a wide array of applications, including in: • Consumer applications, such as in smart homes or for elderly care • Commercial applications, in healthcare and transportation, for example

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• Industrial applications, e.g., in manufacturing and agriculture • Infrastructure application, such as energy management or environmental monitoring The Internet of Things has been mostly characterized by its explosive growth and current scale: it is expected that by 2020, over 20 billion devices will be connected. Managing this scale is particularly challenging, in particular in relation to addressing, discovery, composition, standardization, architecture, interoperability and integration, management, etc. (Razzaque et al. 2015; Colakovic and Hadzialic 2018).

Urban Platforms Once the data has been gathered, it needs to be integrated in common platforms. These platforms can offer either data-only services or a full integrated platform.

Data Services Datastores and marketplaces provide access to urban data, typically in raw form, in a common, open publishing format, as an input into wider services innovation. A recent example is the London Datastore (https://data.london.gov.uk), established by the Greater London Authority. On top of that, a number of platforms offer urban data platform-as-a-service, providing a service for the sale, purchase, and sharing of a wide variety of data from multiple sources between citizens, city government, and businesses in cities. An example of this is the City Data Exchange, established within the City of Copenhagen (https://www.citydataexchange.com). Integrated Urban Platforms Fully fledged urban platform, also referred to as City Operating Systems, goes further than just providing data services, by • Being open and programmable, offering an open application development environment where third-party developers can create new smart city applications and services on the city’s ICT infrastructure • Being technology agnostic, supporting a wide variety of devices and technologies from Internet-of-Things (IoT) sensor to wireless, wired, and cloud computing infrastructure • Abstracting underlying resources, by hiding all the technology details of the underlying ICT infrastructure and providing simple programming interfaces • Integrating with legacy systems, already running in cities Such platforms can be: • Centralized, which is easier to manage but creates a potential bottleneck and single point of failure

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• Fully distributed, which is more resilient, offers potentially faster reaction times and higher privacy but is challenging to maintain and scale • Hybrid, based on co-locality, and exploiting edge computing to maintain privacy, reduce bandwidth requirements, and improve timeliness when required While a number of urban platform proposals exist, such as the open-source FIWARE (https://www.fiware.org), CityOS (https://cityos.io), and CityVerve (https://cityverve.org.uk), there is currently no standard platform, and fervent efforts are being put into research and deployment activities by academia and industry, fueled by the perspective of a global market of $755 million by 2027 (Navigant 2018).

Analyzing: Intelligence With the increased amount of information from sensors, an opportunity is arising to enable more fine-grained management of resources for greater efficiency. This requires novel algorithms for the management of large-scale resource networks, which are able to utilize this information and make use of it both for long-term planning and decision-making in real time. In this section, we present the main developments in the algorithms for management of smart city resources. We first discuss algorithms for prediction of demand, which can better inform decision-making algorithms, and then discuss resource-allocation algorithms, before finally discussing their ethical implications.

Prediction of Urban Resource Supply and Demand Abundant data gathered from traditional and IoT sensors can be analyzed to predict future resource demand and supply, with the two main aims: to improve long-term network capacity or plan network structure and for real-time resource allocation decision. For example, an open dataset containing New York City taxi trips (NYC Taxi 2019) has been used both to plan the size of the taxi fleet required (Yang et al. 2019) and to position the available vehicles in real time in the areas where higher demand is expected based on historical patterns (Gueriau and Dusparic 2018). For resources where, in addition to the demand, supply can also vary dynamically, data can be used to predict resource supply as well. For example, in balancing energy usage, the prediction of the demand, which varies based on the time of the day, season, or day of the week, is required as well as the prediction of renewable energy supply, which varies based on weather (Dusparic et al. 2015, 2017). The techniques used range from well-established traditional statistical techniques such as ARMA, ARIMA, neural networks (Marinescu et al. 2013), custom dynamic hybrid models (e.g., Marinescu et al. 2014) to novel approaches based on deep neural networks (DNNs), for example, DNNs for traffic flow prediction (Yi et al. 2017) and ridesharing demand (Wang et al. 2017). The type of techniques used varies based on

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numerous data characteristics, as well as the scale on which prediction is performed (Cavallo et al. 2015). Results of the prediction are then incorporated into decisionmaking processes, which are optimized for the predicted demand or supply at any point in time. Furthermore, they can monitor the resource usage in real time, and if the current demand differs from the predicted one, changes in the resource allocation algorithm are made in real-time to optimize it to the new demand pattern. Example applications of this synergy between prediction and dynamic real-time optimization in the smart city context can be found in demand-side energy management (Marinescu et al. 2017) or urban traffic control (Salkham and Cahill 2010).

Decision-Making Decision-making at city scale requires multi-objective optimization and coordination of a large number of heterogeneous geographically dispersed entities, based on historical, current, and predicted information about the demand and supply of a given resource. Take the example of optimizing urban traffic control; traffic lights need to optimize their own local throughput, waiting time, as well as prioritizing public vehicles and serving pedestrian requests. In addition, they need to coordinate with their upstream and downstream junctions to enable steady flows of vehicles, implement so-called green waves, and ensure city-wide traffic optimization. As discussed in section “Optimising the Use of Resources,” the supply of the resource (road space) can differ throughout the day (e.g., rush hour restrictions for private vehicles, road closures due to accidents) or between days (e.g., road maintenance). The demand on the road network also differs throughout the day, both spatially and temporally (e.g., rush hour vs nighttime), between days (e.g., weekend vs weekday), or depending on the weather or season (e.g., increased demand on rainy days). Similar patterns can be observed in domestic energy use (as discussed in the previous section), or in taxi/ride-sharing, where the demand for shared vehicles increases on a rainy day, at rush hour, or on weekend nights. To address all the requirements and constraints of optimization in such large-scale heterogeneous systems, a wide range of algorithms is being investigated. They can be broadly divided into centralized and decentralized approaches, depending on whether they are managed in a distributed fashion or a central management entity has a view of the overall system (Bennati et al. 2018), as well as into exact (guaranteed optimal) or heuristic algorithms. Due to the scale of the system, the complexity of exact centralized algorithms is often prohibitive in city scenarios, so a variety of decentralized artificial intelligence approaches are being explored, such as multiagent systems, game theory, and cooperative intelligent agents. In the remainder of the section, we provide an overview of the main types of approaches rather than a fully exhaustive list, but we do aim to present examples of approaches with different characteristics and those that currently have the most applications in smart city scenarios. We observe that a particular algorithm can be suitable for a range of smart city applications (due to their similarity in terms of characteristics and scale) but also that a particular smart city resource can be managed by a wide variety of

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algorithms, depending on the desired focus of the management strategy (e.g., privacy, fairness, multi-objective optimization, etc.).

Control Theory Control theory is concerned with regulating a system, while satisfying its operating constraints, and taking into account available resources (Crisostomi et al. 2016). This maps to the requirements of a city resource management, with a significant difference being that classical control theory is concerned with regulating a single system (and having a complete view of the system’s state), while at the city scale that is often not feasible. Numerous extensions to classical control theory approaches are being investigated to enable their applications in smart city domain. Model predictive control (MPC) has, for example, been used to manage urban drainage systems (Lund et al. 2018) to prevent flooding, thereby enabling protection of human life/ health, property, and environment. Another example of MPC application is in the energy domain, where it has been used for demand response in a multi-zone building to optimize energy consumption, cost, and comfort (Lauro et al. 2015). The fuzzy logic approach has been applied in urban traffic control, to model traffic flows and control the duration of the green signal at a particular traffic light (Eze et al. 2014), as well as in parking allocation to optimize travel time/distance to the parking location (Dahiru 2015). Proportional-integral-derivative (PID) controllers have been applied in smart city domain in energy management (Harris et al. 2014) and in autonomous vehicle tracking (Alonso et al. 2013) and control (Monteil et al. 2016). Exact Optimization Algorithms Exact optimization algorithms ensure the optimality of the solutions provided (i.e., guarantee finding the one that minimizes or maximizes a given objective function) but at the expense of high computational complexity for solving larger problems. Therefore, as already mentioned, such approaches are not feasible for city-scale realtime management, as decisions need to be made frequently and quickly. However, exact algorithms can still be utilized either to study a smaller computationally feasible subset of a problem and for longer-term planning. Examples of use of exact optimization algorithms are the use of the Dijkstra’s path finding algorithm in vehicle routing (Nha et al. 2012), integer linear programming for optimization of waste collection route (Bueno-Delgado et al. 2019), and classical AI planning for urban traffic management (McCluskey et al. 2017). Heuristic Algorithms and Artificial Intelligence In contrast to exact algorithms, heuristic methods do not provide guarantees of optimality but generate good enough (and sometimes optimal) solutions in a reasonable computational time, enabling their applications in real-time to city-wide dynamic systems. Often these systems are implemented as collections of intelligent agents that optimize their own performance but also cooperate and coordinate to ensure satisfactory overall system performance. While in isolation, optimal behavior of a single agent can often be guaranteed to converge to optimal performance, the lack of guarantees in multi-agent systems stems from agents operating and actuating

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in the same environment, thereby influencing each other’s performance. In the smart city resource management scenario, individual local resource managers, consumers, and providers are modelled by intelligent agents, and the overall system is modelled as a multi-agent system. In traffic management, agents can be individual vehicles or traffic lights; in ride-sharing scenarios, agents are again either vehicles or passengers/ consumers; and in energy demand response scenario, devices that use, store, or generate energy can be modelled as intelligent agents. There is a wide variety of algorithms that define the behavior of these intelligent agents as well as the techniques in which they cooperate (or compete, in the case of game theory) or achieve system-wide optimization. Ant Colony Optimization (ACO). This family of optimization algorithms is inspired by the behavior of ants in an ant colony. When searching for a food source, ants in a colony converge to moving over the shortest path, among different available paths, when moving between their nest and the food source. This behavior is realized by ants depositing a substance called a pheromone on the path which attracts further ants. Trips over shorter paths get completed more quickly, causing more trips to be made on those routes and therefore more pheromone to be deposited on them. This behavior is naturally suited to routing applications in smart cities, and as such has been applied, for example, in the optimization of traffic routes and achieving a citywide balance of vehicles (Rehman et al. 2018) or optimal path planning for waste collection (Bueno-Delgado et al. 2019). Particle Swarm Optimization (PSO). This approach is a self-organizing optimization technique inspired by the flocking behavior of birds. Each particle (a bird) in a swarm (a flock or a population) represents a potential solution and moves through the search space (possible set of solutions) seeking an optimal solution. Particles broadcast their current position (i.e., the quality of their current solution), and each particle then accelerates its movement toward a function of the best position it has found so far and the best position found by its neighbors. In the smart city domain, this approach has been applied to improve quality of riot video footage retrieval (Ramyam et al. 2017) and to optimize wind turbine energy performance (Abdullah et al. 2018), for example. Evolutionary Algorithms (EA). EAs are a family of optimization algorithms inspired by biological evolution. The initial population of solutions is created randomly, and through the evolutionary processes of selection, crossover, and mutation, the most suitable solutions are found after a number of generations. By mimicking the biological evolutionary process, EAs are able to self-adapt, i.e., they can evolve and tune their own parameters. EAs have also been applied in routing for waste collection (Karadimas et al. 2007), traffic assignment in traffic modelling (Bazzan et al. 2014), and device scheduling in energy demand-side management (Mellouk et al. 2018). Game Theory. Unlike in the other approaches presented, in game theory, multiple agents compete against each other while improving the overall system performance. Such approaches have been applied in traffic optimization, where game theory has been used to incentivize users to change travel routes and models to provide balance in the system (Mei et al. 2017) and to resolve water allocation conflicts (Jhawar et al. 2018).

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Reinforcement Learning (RL). RL is an unsupervised learning technique in which an intelligent agent learns how to meet its goal by receiving feedback on its actions from the environment in the form of a reward (e.g., a traffic light agent is “rewarded” by the low waiting time of the cars). Recently, RL has been combined with deep artificial neural networks, into deep RL, which is able to address more complex environment-state inputs than conventional RL, expanding its use in smart cities. Both conventional RL and deep RL have been extensively applied in the smart city domain, with the former, for example, applied in traffic management (Dusparic and Cahill 2016) and energy demand-side management (Reymond et al. 2018; Diddigi et al. 2017) and the latter in achieving zero-energy status in energy communities by energy sharing (Prasad and Dusparic 2019) and in optimizing ride-sharing request allocation (Al-Abbasi et al. 2019).

Ethical Implications With the increasing automation of decision-making in all spheres of our lives, including smart cities, there are growing concerns about the lack of transparency and accountability of AI-based algorithms. As AI-based decisions are often based on enormous amounts of data and simulation, the solutions provided are often “black box,” i.e., they offer no explanation as to why did an algorithm makes a particular decision. This opens up a potential for both unconscious and malicious introduction of bias and discrimination within AI-based applications (O’Neil 2016). For example, one of the most controversial AI-based technologies currently on trial in smart cities is the use of facial recognition, for policing in particular. There are growing concerns about the accuracy of such technology, arguing that its use is premature as it is not technically ready (Page 2018), as well as concerns about citizen privacy advocating that such technology should not be deployed regardless of its technical readiness. As a result, several cities have legislated against the use of facial recognition technologies, which as of this year is illegal in San Francisco and Oakland, California (Osborne 2019). Other examples of technologies the ethical implications of which are being particularly discussed are predictive policing (Asaro 2019), where algorithms are used to predict which individuals will re-offend; self-driving cars, which will, for example, need to make decisions on who to potentially hurt when accident is unavoidable (Borenstein et al. 2017); and in-home smart devices (Sivaraman et al. 2018) and assistants which have the ability to listen to inhabitants conversations and derive information about them from their voice. Ensuring ethical and legal implementations of technologies in cities is being addressed through legal frameworks (e.g., the German government is proposing rules on how autonomous vehicles should behave in the case of an accident (Lutge 2017)), technology solutions (Zambonelli et al. 2018), as well as proposing guidelines and standards that ethical, transparent, and accountable systems should follow. Some of the most prominent design standards proposed are developed by bodies such as Atomium – the European Institute for Science, Media and Democracy

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(AI4People 2018), the IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems (IEEE 2016), and the European Commission’s High-Level Expert Group on AI (EC 2019).

Actuating Cities effectively become smart when they act upon the data that they have sensed, gathered, and analyzed. Actuation can include humans in the loop (decision support) which requires the visualization of the data and features derived either on a screen or on other interfaces or be fully autonomous.

Data Visualization Data visualization is “the study of transforming data and information into interactive visual representations” (Liu et al. 2014). It is essential to enable humans to make sense of the large amount of data present in smart cities. The techniques depend on the category of the data represented (Zheng et al. 2016). Temporal data can be represented by line graphs (see Fig. 4). Spatial data can be represented by point-based visualization (see Fig. 5), but those do not scale well when the number of items to be represented increases. In this case, heatmap-based visualization (see Fig. 6) is more appropriate. Spatiotemporal data can be represented by space-time cubes (see Fig. 7) or by a combination of images (see Fig. 8) or an animation (Zheng et al. 2016).

Human Interfaces Smart cities should be ultimately at the service of their inhabitants, in their multiple roles, and this requires appropriate interfaces: Number of trips per time 150

Num Trips

125 100 75 50 25 0

21:33 May 01

01:20 May 03

05:06 May 04

08:53 May 05

Fig. 4 Taxi trips over time for three different areas (Ferreira et al. 2013)

12:40 May 06

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Fig. 5 Point-based visualization of taxi pickup and drop-off points (Ferreira et al. 2013)

Fig. 6 Heatmap-based visualization of number of vehicles at a given time (Liu et al. 2011)

• As consumers or residents of the city, for example, a smart home management system. • As information recipients, such a real-time passenger information for public transport. • As participants for example contributing data or participating in citizen science. • As testers, notably by providing feedback on new proposals, for example, in validation workshops. • As managers, by providing key insight on current urban conditions and supporting human decision.

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Fig. 7 Visualizing earthquake events in a spacetime cube (Gatalsky et al. 2004)

Fig. 8 Crime rates in each US states over the last 40 years (Andrienko et al. 2010)

• As makers/creators, for example, by designing and implementing community/ neighborhood apps, engaging in civic hacking, etc. The scope, nature, and stage of the interaction between citizens and urban projects are crucial to ensuring a meaningful participation of citizens in cities and realizing truly citizen-centric cities (Cardullo and Kitchin 2018). Indeed, new technologies such as smart phones and associated apps can contribute to the further individualization and liberalization of urban society, though they could also potentially be

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harvested to create an open, democratic “community of strangers” (de Waal 2014), with more collaborative models of smart city governance (“smart cities 2.0” see (Barns 2018)). This highlights the importance of designing appropriate interfaces for citizens, catering for their wide range of ages and abilities.

Consumer Interfaces A wide array of public-facing interfaces are available in smart cities. Some interfaces are available in public places, ranging from screens displaying the time of the next bus, for example, to more abstract ambient displays, potentially facilitating local engagement, such as results of votes on local issues (Koeman 2017). Other interfaces are available only in private spaces, such as smart home control systems, or even serve a single user at a time (e.g., a mobile app for travel support). These different types of interfaces exhibit different challenges, ranging from the ability to capture and maintain user interests for public displays, recognizing users, learning their preferences, and preferred modality of interaction for private space and devices interfaces (see also Augusto et al. 2013).

City Dashboards City dashboards are performance management tools, allowing quick access to key performance indicators via data visualizations and simple metrics. They typically focus on revealing data relevant to a city’s operation via simple data visualizations, widgets, and analytics. These provide dynamic and/or interactive graphics, maps, and 3D models to display information about the performance, structure, pattern, and trends of cities (Kitchin and McArdle 2016: 2) (see Fig. 9). Such dashboards can be designed and managed by the cities themselves or by other organizations, e.g., universities (e.g., see the University College London’s “City Dashboard” (Pettit et al. 2017)).

Robotics/Autonomous Actuation The development of smart cities is also marked by the more extensive deployment of different types of robots, with their use being extended from the home, hospital and factory use out into the cities and their integration with urban spaces. Smart cities are seeing the deployment of both mobile and stationary robots, ground, aerial and marine ones, and humanlike robots (Tiddi et al. 2019). Example uses include robot taxis, smart chairs, and social robots/translators (Fourtané 2018). Humanoid police officer robots are being trialed, with Dubai hoping to replace its police force with police robots by 2030 (Kovacic 2018). The European Robotics League (ERL 2019) is supporting the developments of urban robots by organizing yearly competitions for smart city robots, in which they compete in a set number of smart city tasks, such as serving products in a shop, delivering coffee shop orders, taking the elevator, opening the door, and delivering aerial emergency pill. Waste collection in urban

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Fig. 9 Four examples of urban data dashboards (Barns 2018)

environments has been demonstrated in the DustBot project, where robots were deployed to both autonomously clean pedestrian areas and perform door-to-door collection interacting with householders (Reggente et al. 2010). City traffic is being improved by the use of unmanned aerial vehicles (UAVs) for traffic management, such as accident reporting, flying speed cameras, flying dynamic traffic signals, flying speed cameras, etc. (Menouar et al. 2017), as well as for more efficient parking, in which a robot acquires, lifts, and transports vehicles to a parking spot in a garage structure (Smart City Robotics 2019). Health applications of robots in cities include an ambulance robot, which comes equipped with an AED to be used in cases of cardiac arrest (Samani and Zhu 2016). As all of these robots require city and citizen data for their correct operation, as well as to make automated decisions, they are subject to the same privacy, data protection, and ethical concerns discussed previously in section “Analyzing: Intelligence” (Torresen 2018).

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Conclusion This chapter provided an overview of the technological framework for smart cities. It first presented the Sense-Analyze-Actuate paradigm and how it can be used to optimize the use of shared resources in smart cities. The supply and demand of given resources can be matched by a combination of three potential approaches: adapting the supply to the demand, demand-side management, and resources storage. This was illustrated on a residential energy case study. The chapter then presented an overview of the challenges and existing work related to each of the Sense-Analyze-Actuate steps. Urban data can be arranged into three different categories, in terms of its spatial and temporal properties. This will affect how, where, and how often it should be collected. Four modes of sensing are available in smart cities: traditional, passive crowdsensing, opportunistic sensing (or active crowdsensing), and participatory sensing (or crowdsourcing). A wide range of networking technologies, both long and short range, can be exploited to gather the sensed data. The combination of connected artifacts makes the Internet of Things, the scale of which is growing exponentially. Finally, the data is typically connected to urban platforms either providing only data services (datastore or marketplaces) or a fully integrated platform. The Analyze step exploits algorithms to optimize the use of resources. These algorithms can be exploited not only to allocate resources, but also to predict both the supply and demand in real-time, as an input to such algorithms. Prediction algorithms include well-established traditional statistical techniques, custom dynamic hybrid models, and novel approaches based on deep neural networks. Resource allocation mechanisms can be centralized or decentralized and exact or heuristic. Automating decision-making raises a plethora of ethical questions, especially surrounding the lack of visibility as to how an outcome is reached. Actuation closes the feedback loop of the Sense-Analyze-Actuate paradigm. It can be undertaken via visualization to support human decision-making or visual analytics, via other smart city interfaces with its wide range of users, or directly, exploiting robotics, which have been applied to a wide variety of urban domains.

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Smart Cities Data: Framework, Applications, and Challenges Muhammad Bilal, Raja Sher Afgun Usmani, Muhammad Tayyab, Abdullahi Akibu Mahmoud, Reem Mohamed Abdalla, Mohsen Marjani, Thulasyammal Ramiah Pillai, and Ibrahim Abaker Targio Hashem

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smart Data Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sensor Network Databases and Data Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . City Analytics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smart Visualization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Quality and Veracity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . General Data Protection Regulation (GDPR) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smart Data Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smart Government and Governance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Social Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mobility and Transportation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smart Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Privacy Challenges in Smart Cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Security and Privacy Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Privacy Threats . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Privacy-Enhancing Technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Data Privacy in Data Sensing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Privacy and Availability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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M. Bilal (*) · R. S. A. Usmani · M. Tayyab · A. A. Mahmoud · M. Marjani · T. R. Pillai School of Computer Science and Engineering, Taylor’s University, Subang Jaya, Malaysia e-mail: [email protected] R. M. Abdalla School of Hospitality and Tourism, Taylor’s University, Subang Jaya, Malaysia I. A. Targio Hashem (*) Future Technology Research Center, National Yunlin University of Science and Technology, Douliu, Taiwan e-mail: [email protected] © Springer Nature Switzerland AG 2021 J. C. Augusto (ed.), Handbook of Smart Cities, https://doi.org/10.1007/978-3-030-69698-6_6

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Abstract

Recent technological developments and the availability of enormous amounts of real-time data have played a vital role in the expansion, evolution, and success of smart city projects. Smart data can be used in a variety of smart city applications, but difficulties in managing such data are pushing smart cities toward the adoption of data management frameworks. Many studies have brought into focus the importance of these frameworks as they combine data collection, processing, analysis, management, and visualization and provide privacy and security features for different smart city applications, i.e., transportation, to promote a better quality of life. This chapter highlights key components of the data management framework, reviews various smart city applications, and discusses privacy and security challenges associated with smart city data. From the perspective of data frameworks, it is seen that the data used in smart city applications is unstructured coming from heterogeneous sources, i.e., sensors and social media, besides others. Therefore, the collection, processing, analysis, management, and visualization of such data are challenging. To perform these tasks, recent technologies, i.e., Internet of Things (IoT), sensor networks, machine learning, etc., have been used. Moreover, the use of smart data for smart government and governance provides several facilities for the public and business. The smart data is revolutionizing the daily communication of users along with their mode of transportation by introducing Social IoT (SIoT) and autonomous vehicles. Lastly, the challenges related to privacy and security of the data in smart cities that needed to be addressed are highlighted. This chapter will guide academics and enterprises to progress in data management framework and its applications in smart cities in the near future.

Introduction Big data is a massive flow of data produced by the digital world such as the Internet of Things (IoT), multimedia, and social media that can be analyzed for more accurate business decisions and strategic moves. The organizations continuously capture this rapidly increasing volume of detailed data from the Internet of Things, multimedia, and social media. The total amount of big data is beyond imagination as it is increasing at a rapid pace around the globe. People are exchanging information, ideas, and data on web application all the time. Moreover, this big data has enormous potential in the utilization of services in smart cities. In smart cities, a significant role is played by information and communication technology (ICT) as it makes data available, which is collected from the digital city. The information and communication technology (ICT) is also known as the Internet of Things. Smart city associates a city with the digital city, and it links them via the Internet of Things. The smart cities sensors capture data through the IoT devices from various smart city gateways and resourcefully process it to execute it in a specific region. Data and smart cities have made life more comfortable around the globe by creating better cities. The smart city and IoT have helped the government of China

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in creating new traffic routes to avoid congestion. The smart city and IoT have cut cost in their road construction. Nanjing information center has installed one million sensors into private cars and 10,000 into taxis and 7000 into busses to collect and analyze traffic data. After processing the data, they send updates via smartphones to the commuters. In Italy, sensors are installed in the trains, and the major rail operators get the real-time messages about the mechanical condition of each train. This data has helped the officials to prepare a course of action for any unfortunate event by providing a process for better maintenance predictions. The systems and services are reliable due to this innovative technology and prevent cities from major interruptions. Los Angeles (LA), USA, is switching to new light-emitting diodes (LEDs) and replacing approximately 4500 miles of streetlights. These LEDs are connected with the smart devices that will update the officials about the status of each bulb in the city. This data will help the team to repair any malfunction in the LED. LA is planning to use these lights as a signal to warn citizens about the precarious conditions in the future. The city is thinking of changing the colors of the LED or maybe making them blink which will solve the problem. The population of urban areas and smart cities are ever rising. Smart city sensors monitor almost everything. Such innovation will not stop until they can monitor each and everything from sources of energies, to road constructions, to trash cans and streetlights. This data comes with challenges like effective management of data so it can be accessed, analyzed, combined, and used across departments and organizations. A smart city should have the ability to share data in real time so that the private and public sectors can work seamlessly together, which poses a challenge of integration between these sectors. Furthermore, smart cities deploy different types of sensors, and each sensor usually requires a new database, triggering a procurement process. The high cost of storing big data reflects on the cost of the smart city, adding to the financial backing needed upfront (Deren et al. 2015). Moreover, it is difficult to do knowledge mining in big data as big data contains rules and knowledge associated with data. These data rules and knowledge are obtained by conducting in-depth data mining and analysis. However, the fundamental properties of big data automatically make it difficult to process and to analyze the smart city data especially dataset containing spatial information (Li et al. 2001). This chapter consists of five sections. The section “Smart Data Framework” gives an overview of key components of data management frameworks for smart cities, and the section “Smart Data Applications” describes the application of smart data in smart cities. The section “Privacy Challenges in Smart Cities” discusses the privacy and security challenges regarding smart city data, and lastly, section “Conclusion” concludes this chapter.

Smart Data Framework In this section, each component of a data management framework for smart cities are discussed. Figure 1 gives a graphical illustration of the data framework for data management in smart cities.

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Fig. 1 Smart cities data management framework

Sensor Network Databases and Data Management Sensor network databases involve a blend of sensor and stored data. Sensor network is a set of sensor devices (nodes) with resources, which are connected to each other wirelessly and installed in an area to collect environmental elements such as humidity, temperature, light, gas density, motion, pressure, and so on (Plageras et al. 2018) and process the data to store them in database (Changbai et al. 2008). Sensor network allows the user to remotely monitor the physical information of the environment (Küçükkeçeci and Yazıcı 2018). Sometimes sensor database is identified as in-network sensor query processing systems (ISQPS), which had been designed to collect, process, and aggregate data from sensor network/wireless sensor network (Luo and Wu 2007). In recent years, with the rapid development of information and communication technologies, the sensor network is collecting a massive amount of environmental data based on query processing construct. The challenge nowadays is how to reduce the volume of data collection and to transfer them from the node to the base location. Due to this issue, (Changbai et al. 2008) developed a new query language construct called SNQL, for dealing with large sensor database. The result indicated that the developed SNGL database increases the efficiency of the network and query flexibility. A study by (Plageras et al. 2018) has proposed a sensor management system for collecting tremendous data generated from sensors installed in the smart buildings. This proposed system found to be a solution for accumulating and handling sensor’s data in a smart city. The sensor network is one of the advanced technologies for smart buildings. It collects several types of data from various sources. It is essential to consider the reduction of energy consumption and operating expenses. Owing to these reasons, (Azri et al. 2019) carried out research and proposed 3D geo-clustering techniques/algorithm that assists in organizing information of sensor network stored

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in a database. The result shows that the algorithm can stabilize the node energy utilization and extension of the network. Network power consumption needs to be reduced when the database was queried, which aided in decreasing the traffic. The research conducted by (Tsiftes and Dunkels 2011) proposed an Antelope database management system for network sensor devices. This result shows the proposed system enables it to reduce congestion for network power consumption. In order to improve power consumption, it is vital to understand the physical data and query processing between network layers and applications. The research was carried out by (Sudha and Nagesh 2018) that listed the most important query, which includes queries on multidimensional ranges, queries on historical data, long-time continuous queries, snapshot queries, and event-related queries. Similarly, investigation of the query layer design for sensor network and interaction between the query layer and in-network aggregation was studied by (Yong Yao 2012). Incremental time slot algorithm was proposed to explore how to record the data transmission between nodes. The result showed the successful performance of the proposed algorithm. The SINS database system was developed by (Dekkers et al. 2017) using a network sensor, which was installed in five different rooms to capture daily activities in a home environment for 1 week which included 16 activities. The sensor network comprises of 13 nodes. The purpose of the study was to investigate the activities carried out in the house using the benchmark system. The normalized confusion matrix was used for analysis. The result showed that the best performance was found in the hall and the worst in the bedroom.

City Analytics The world is rapidly urbanizing, with future global population growth projected to occur mostly in cities and towns, and the environmental impression of cities encompasses beyond what is sustainable (Moglia et al. 2018). These developments provide some innovations such as economy creativities, the transformation of economy innovations into solutions, and a safe haven for the functional development of urban cities. Moreover, information and communications technologies (ICTs) are major tools that facilitate the developments and configuration of urban devices (Valls et al. 2018). Users must know how to use and operate the technologies in an urban environment, which will aid their adaptability to the environment. With the expansions in smart computing and mobile technologies, the collection of datasets in urban cities is improving geometrically that capture the pulse of urban life (Galbrun et al. 2016). These publicly available datasets have created opportunities for both the government and other authorities to make use of them to improve the quality of life for the people living in the city. With recent trends and development of low-cost sensors, miniaturization of computing and electronics, actuation and control systems, nanotechnology, and wireless communication have contributed to emerging research areas in urban computing with overlapping themes and challenges. These technologies enable urban computing research to be deployed in the wild, in the real context of cities

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as the living laboratory, situated within public spaces, facilitating open interactions with individuals, groups, and communities. Recently, the nature of pervasive computing technologies has made our lives to revolve around smart objects/things that are always connected to the Internet, which have changed the way we live, communicate, and work in a smart urban environment (Salim and Haque 2015). However, this has created both technological and interactional opportunities for citizens in smart cities through urban-computerized projects. Studies by (Krieg et al. 2018) have shown that urban data gathered can be used to develop a parking system to reduce traffic congestion in cities. In this regard, the authors developed and implemented SmartPark in San Francisco cities. The system depends on pervasive Wi-Fi and cellular infrastructure, which is capable of providing drivers with real-time parking availability information. In the city of Montreal, (Malandra et al. 2018) used LTE embedded into a web-based application to support a huge amount of machine-to-machine (M2M) traffic communication model. The model provides the precise location of different sets of machines such as traffic lights, smart meters, bus stops, etc. It also enables the study of the traffic produced by realistic M2M components in smart cities environments. Recently, (Honarvar and Sami 2019) used real urban dataset collected and extracted from multiple sources in the city of Aarhus, Denmark, to develop a prediction of particulate matter model in the city. The data collected are related to urban buildings, road traffic, air pollution, weathercasts, and points of interest (POI). The model is based on transfer learning and is validated using RMSE and MAE. The urban big data supported by the IoT are progressively becoming related entirely through regularly and automatically sensed data, especially in smart sustainable cities. The IoT and ICT tools are used for generating the datasets using routine and automatic sensing, which replaces the conventional approach (Bibri 2018). Besides, ubiquitous sensing is the main feature of smart sustainable cities of the future, which typically rely on the fulfillment of several ICT visions of ubiquitous computing, particularly the IoT. A smart, low-cost, static, acoustic sensing device based around consumer hardware was implemented by (Mydlarz et al. 2017) in New York City (NYC) using microelectromechanical systems (MEMS) microphone in order to generate consistent decibel levels. The NYC is known as an urban sound environment having the following characteristics loud, disturbing, exciting, and dynamic. The urban sound environment has an intense influence on the quality of life of the city’s inhabitants.

Deep Learning Bu, Wang, and Gao (2019), the authors, presented a multi-projection deep computation model (MPDCM) to generalize DPDCM for smart data in the Internet of Things (Bu et al. 2019). MPDCM maps the input data into multiple nonlinear subspaces to learn the interacted features of IoT big data by substituting each hidden layer with a multi-projection layer. The used learning algorithm is based on backpropagation and gradient descent that are designed to train the parameters of the presented model. Finally, the authors conduct an extensive experiment based on the

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two representative datasets, i.e., Animal-20 and NUS-WIDE-14, to verify the presented model by comparing with DPDCM. Chenhui, Shuodong, Zhuo, and Peng (2019) presented a deep learning model for potentially diagnosing gallbladder stone with big data from the medical Internet of Things (Yao et al. 2019). Gallstones can be classified into four types, i.e., cholesterol stones, bile pigment stones, mixed stones, and other rare stones, based on the chemical composition of gallstones convolutional neural network to learn the features of the collected data. The authors used a convolutional neural network model to learn the features of the collected imaging data of the gallstones. Moreover, the authors have described an effective learning approach for training the developed convolutional neural network. Leyi et al. claimed to accurately predict protein subcellular locations (Wei et al. 2018). The authors have proposed a deep learning-based predictor called DeepPSL by using stacked autoencoder (SAE) networks. The authors claimed that the predictor automatically learns high-level and abstract feature representations of proteins by exploring nonlinear relations among diverse subcellular locations. Experimental results evaluated with threefold cross-validation show that the proposed DeepPSL outperforms traditional machine learning-based methods. It is expected that DeepPSL, as the first predictor in the field of PSL prediction, has great potential to be a powerful computational method complementary to existing tools. The authors initially used an unsupervised approach to automatically learn the high-level latent feature representations in the input data and initialize parameters and then use a supervised approach to optimize these parameters with the backpropagation algorithm. Using the computational power of graphical processing units (GPUs) and CUDA, they have trained the deep networks efficiently. The authors also considered two well-known feature representation methods. The first one is based on physicochemical properties of proteins, while the other is based on adaptive skip dipeptide composition. Both features have been proven effective in multiple bioinformatics problems. Finally, the authors claim that by fusing the above two feature types, they yielded a total of 588 features (¼ 188 + 400) as the input of deep network (Wei et al. 2018). The proposed DeepPSL achieved satisfactory overall performance, obtaining 37.4% in terms of overall accuracy (OA) for the ten-class subcellular localization prediction. Sannino and De (2018) have proposed a novel deep learning approach for ECG beat classification (Sannino and De Pietro 2018). The proposed approach has been developed using the TensorFlow framework, the deep learning library from Google, in the Python programming language. A deep learning technique is introduced in this work to meet the challenges faced by classifying the ECG beats. The authors used the dataset for each subject of the MIT–BIH database; they have computed four preprocessing steps. Furthermore, they have removed from the dataset the last 14,828 items. Additionally, the authors claimed it was necessary in order to balance the dataset due to the fact that the classes were imbalanced, namely, we had too many normal beats (N) compared to abnormal ones (V, S, and F). In fact, in these cases, conventional algorithms are often biased toward the majority class because their loss functions attempt to optimize quantities such as error rate, not taking into

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consideration the data distribution. In the worst case, minority examples are treated as outliers of the majority class and are ignored, the learning algorithm simply generating a classifier that classifies every example as the majority class. To avoid these problems, the authors decided to select only 2288 items representing the normal beats class (N) from the initial 66,750, randomly selected from all the subjects. Therefore, the final dataset was composed of a total of 4576 items, 2288 representing the normal beats class (N) and 2288 representing the abnormal beats class (A).

Smart Visualization Visualization of data is the process in which the data can be visualized, and more information can be shown with the help of pictures, graph, and charts. This technique helps in deciding on challenging issues in the data visually. When it comes to big data visualization, it becomes more challenging because of its characteristics. Application of big data in smart cities makes it more complex and challenging as the data is coming from several sources, and it also has a significant impact on decisionmaking. However, with the development of virtual reality (VR), augmented reality (AR), mixed reality (MR), and Google Maps have changed the practical efficiency of smart city application (Hashem et al. 2016). In smart cities, data is collected with the help of different sensors automatically, and then this data can be used for long-term analysis. In order to make long-term decision depending upon the data, several tools can be used. Usually, in smart cities, a benchmark approach is used aimed to have a better result while comparing the performance and data usage between cities (Osman 2019). Similarly, data now in smart cities is available to the habitant of the city via the Internet using different visualization methods like dashboard, etc.; this smart dashboard may be a compromise of fact and figures in the form of chart and statistics that explain how much affects are there on citizens from such policies (Osman 2019). For example, noise value can explain the effects of noise pollution on citizen and others as well. Visualization of data via dashboard can have multiple view categories like data streams, format of data, and resources of data that involved key challenges and processes (Ben Sta 2017). In order to display the data in a specific format, first data should be in a standardized format that includes visualization and understanding (Lim et al. 2018; Rathore et al. 2016). Visualization and understanding can also be made easy with the involvement of graphs and plots and different pictures. While in a phase of development of the dashboard, there may have several data resources that have to be considered. In the smart city project, data may come from different (1) experimental setup like temperature, sound, barometer, and others and (2) third-party data like project partner. Firstly, data is fully controlled by the project manager and member of the project and then send it to the database in a particular format. These preprocessing methods are easier as the data is under a controlled environment and continuously under critical monitoring phase so that all the bugs and other mistakes can be fixed at this stage (Lee et al. 2014; Pan et al. 2016).

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Preprocessing will guarantee that the values sent to the database must be correct and dependable. Secondly, for privacy reasons, there exist some degree of uncertainty in the data as it is hard to know what the team member has implemented on his behalf. For example, one may use a different file like JSON file with values and time stamp, but on the other hand, the team members may have the complex format of CSV file with time stamp and other parameters. Special care may be needed for external and legal data. For this reason, first, the data need more care for retrieval from sources to be processed and converted to a standard format so that it can be inserted into the database. The data is now ready for visualization with the help of frontend. Concerning the frontend, one of the major challenges was to settle the color palettes because it helps the user while navigating the database. Chart, graph, and other visual basic can be done with the help of JavaScript add-on called Chart.js, which is an open-source and free to use package. This package helps to build smooth, simple, and very informative charts. Visualization of data is always a challenging task, and it depends upon the data and constraints.

GIS-Based Visualization Geographic information system (GIS)-based visualization is now widely used for analyzing and decision-making for spatial data. It has earned a high level of popularity in urban planning, traffic data monitoring, environmental decision, and modern mode of transportation. Visualization in a smart city context is challenging as it provides an interactive and easy-to-use environmental tool for users (Hashem et al. 2016; Pan et al. 2016). Such an environment can integrate 3D touch screen integration with smart city application. These integrations can enable policy-maker to translate data into knowledge or information, which is the most critical in quick response or fast decision-making platform (Hashem et al. 2016). The information extracted from different platform and environment will be used to represent information based on the requirement of the user. GIS-based visualization will create efficient and flexible devices for smart city toward realizing the vision of a smart environment.

Quality and Veracity In modern time, cities are expanding more and more, and almost half of the world’s population lives in developed cities according to the environmental statistics with more than six devices connected (Habibzadeh et al. 2019) to the Internet. This concludes that there exist billions of devices connected to the Internet, namely, smart light, traffic road signals, pedestrian management system, smart security cameras, smart monitoring room, and control rooms and smart healthcare. Furthermore, smart homes and the devices connected to them are also part of smart cities (Habibzadeh et al. 2018a, 2019). Moreover, application connected to the smart cities has benefits for both citizens and the underlying environment (Habibzadeh et al. 2018a). Similarly, smart cities include smart economy, smart governance, smart

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people, smart mobility, smart environment, smart security system, smart surveillance, and smart living standards (Appio et al. 2019). With all the advantages of smart cities, one of the integral components of real-world smart cities includes data management. Data management consists of data acquisition and processing, and it relies on quality and veracity of data. A smart city collects data from heterogeneous IoT devices, such as pollution, noise, weather, and traffic among others. The quality of smart city data depends on three factors, i.e., (1) precision of collection devices or measurement errors, (2) quality of data communication and environmental noise, and (3) level of detail of the measurements and observations in temporal and spatial dimensions (Barnaghi et al. 2015). The quality of information issues become more prominent when different data with varying quality has to be integrated for use in smart city applications that use data with high dynamicity, velocity, and volume. Smart city applications include futuristic applications such as Ambient Assisted Living (AAL), which helps the elderly to live independently for as long as possible (McNaull et al. 2012); smart parking, which helps drivers to find empty parking spaces; smart environment, which help in conserving energy by adjusting temperature in fully automated workplaces and homes; and smart transportation, which issues bad traffic condition warnings to drivers (Habibzadeh et al. 2018b). These applications are reliant on the veracity of smart city data. The veracity of smart city data depends on the precision of data collection devices, measurement errors, quality of communication, and environmental noise. The veracity of smart city data can be assured by using trustable resources or using a combination of resources to verify the data.

General Data Protection Regulation (GDPR) In 2016, the European Union (EU) introduced a law to protect the data and privacy of all individual citizens of EU and European Economic Area (EEA). This law is known as the General Data Protection Regulation (GDPR). The primary goal of GDPR is to give its citizens the control to their own data (Team 2017). As discussed in previous sections, smart cities collect and use an overwhelming amount of personal data. As smart cities collect more and more data of the citizens, the concerns about the security of smart city data protection measures become more noticeable, especially prominent in cases of data breaches in private companies like Yahoo (Trautman and Ormerod 2017). These privacy flaws are highlighted by the EU’s GDPR as it helps in securing the huge amount of data collected and stored by smart city technologies. Many smart cities were unprepared for privacy practices introduced with GDPR, for they are not looking for solutions in the private sector. The major sticking point of GDPR for businesses and organizations is the requirement for the appointment of a data protection officer, whose role requires dual skills in data protection laws and IT. Apart from the data protection officer, the compulsory implementation of a local cyber security plan is also a big concern to smart city data protection operators. Local cyber security plan is in place to secure the storage of data, but it will increase the

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cost of smart cities initiatives. Hence, in the implementation of GDPR, a substantially stricter form of personal data protection in the EU and EEA, the question arises: Will GDPR slow down the development of smart cities? GDPR will certainly affect the development of smart cities, but it shouldn’t be seen as a hurdle in its development as it will help in building trust with the citizens as they will reduce the fear of possible abuse and they will have control over their information and privacy in smart city models (Vojkovic 2018).

Smart Data Applications Smart Government and Governance In the public sector, the promising transformation has been observed over the past few years. Cities are being turned into smart cities by governments around the globe to address challenges (Allwinkle and Cruickshank 2011). This gave birth to a new phenomenon known as “smart government.” Previously, the term smart government was used to refer to a government that is aware of its social roles and performing its tasks effectively by using its capabilities (Kliksberg 2000). Development projects like public administration and e-government were initiated to meet the needs of individuals and companies which also motivates governments to become smart (Schedler et al. 2004; Schedler and Proeller 2010). The smart government can be viewed as an effort of using the latest digital innovations to achieve promises that have not yet been achieved in previous development initiatives, i.e., e-government (Guenduez et al. 2017). In addition to few new features, i.e., data-based decision, creativity, resilience, etc., most of the features, i.e., sustainability, integration, effectiveness, efficiency, public administration, etc., known from the literature of e-government were also listed as the smart government features (Schedler et al. 2004; Gil-Garcia et al. 2016). The smart government is still a fuzzy concept as in the literature there are only a few definitions of smart government, and none of them are widely accepted (Harsh and Ichalkaranje 2015; Mellouli et al. 2014; Scholl and Scholl 2014; von Lucke 2016). This makes it difficult for the implementation and governance of smart government initiative. The smart government can be referred to as “using advanced technologies to improve the effectiveness of public services, establish a commercial setting for companies and start-ups, and reduce both expenditure and energy utilization.” Emerging technologies such as the Internet of Things (IoT), machine learning, cloud computing, and sensor networks have enabled objects to connect, interact, exchange, and process data in smart cities (Schedler 2018; Paola and Rosenthal-Sabroux 2014). Thus, smart cities tend to enhance financial and political effectiveness, enable sociocultural- and industry-driven growth, and solve social, financial, and environmental issues (Hollands 2008; Townsend 2013). IoT also offers unique possibilities for people to engage and impact smart city policies, create, and test them (Viale Pereira et al. 2017). IoT-enabled artificial intelligence-based solutions are being used as key areas of smart government to enhance governance

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effectiveness and the living standards, i.e., energy management (Chatterjee et al. 2018; Axelsson and Granath 2018). It can be assumed that a smart government will generate cooperative environments and foster cooperation between government and nongovernment organizations (NGO) besides citizens (Nam and Pardo 2014). Whereas, smart governance is usually described as the ability to use digital technologies and smart information processing and policy-making practices (Scholl and Alawadhi 2016). Rational, political, cultural, and institutional perspectives have been used for understanding smart governance. The rational view perceives governance as the results of the rational study. The political view takes governance as the consequence of a tradeoff between various significant values. The key idea of the cultural perspective is that governance is primarily meaningful among stakeholders. Whereas, the outcomes form the combination of past practices, principles, standards, procedures, and conventions form the institutional perspective (Meijer and Thaens 2018). A study also differentiates smart city governance perspectives including smart government, smart decision-making, smart management, and smart communication (Meijer et al. 2016). The notion of smart government has a vital role in the increasing smart city debate and grows alongside other smart city aspects including smart environment, smart economy, smart mobility, smart living, and smart people (Pereira et al. 2018). Smartness in these fields occurs in the domain-specific assessment and by combining huge volumes of structured and unstructured data. This allows self-learning algorithms to produce more accurate predictions about certain facts, communities, and individuals which enables a far more efficient and user-friendly way of automating or executing certain tasks (Guenduez et al. 2019). Governments and authorities lack thorough knowledge of success factors for smart government, i.e., in Switzerland, numerous municipality governments are adopting a smart city strategy, some are in their inception, whereas others are very developed (Hollands 2008). This study has demonstrated on how smart public projects can be introduced by a recent study (Guenduez et al. 2017). They concentrated on technology, big data, algorithms, and individual participation. However, in smart cities, public authorities are still at the initial stage of the journey to the smart government (Mettler 2019). Currently, the most serious challenge in exploring the potential of emerging technologies in smart cities is probably not realizing what needs to be done for smart governments (Praharaj et al. 2018). The important success factors for smart government projects were reported as institutional, organizational, and leadership (Guenduez et al. 2018). There are already many successful examples of this transformation toward smart cities. Artificial intelligent bots in France are helping and advising the individual in searching for jobs. Analysis of traffic data in Los Angeles improves road safety. Big data-based surveillance of fishing quotas paves the way for evidence-based decisions in Germany. Automatic data retrieval in Sweden is saving user’s time. Moreover, government agencies and real-time data make rapid, focused, and even preventive police operations possible in Estonia (Kankanhalli et al. 2019; Ruhlandt 2018). Initiatives for real-time monitoring of water quality and flood detection using sensor networks

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can be used address prevailing situations of water crisis (Bilal et al. 2019a). The change toward smart government is not easy as the present institutional, organizational, financial, and technical barriers present significant difficulties for government authorities (Schedler et al. 2017).

Social Networks A “social network” is a digital space where people share opinions and ideas, connect and communicate with individuals, and create a sense of virtual community (Clemons et al. 2007). Online social networks (OSNs), i.e., Facebook, Twitter, Pinterest, and Instagram, are extremely popular, and more and more people are using the OSNs to connect with their friends and acquaintances. OSNs altered the means by which individuals connect and triggered a lively debate on whether the affordances of such OSNs will also change the means by which individuals communicate (Kumar et al. 2010). Social networking platforms produce an enormous quantity of information on a regular basis, and the social network analysis research is increasing exponentially due to the diversity, volume, and complexity of data (Eirinaki et al. 2018). Social networking sites have provided individuals with access to the huge source of data with little or no restrictions (Pang and Lee 2008). The OSNs have become a major source for the acquisition and distribution of data in various fields such as commerce (Beier and Wagner 2016), entertainment (Shen et al. 2016), technology (Chen 2016), and contingency planning (Stieglitz et al. 2018). Online social media data has the ability to predict user profile attributes (Kosinski et al. 2013). It is essential to provide the consumer with the information they are searching because of the rapidly growing data. In order to profile user interests, social recommendation systems have been implemented (Jamali and Ester 2010; Tang et al. 2012). Researchers have been using social media data to predict the outcome of the elections, political debates and its influence on the individual, and perspectives into reactions to health and disease outbreaks and spread of news via social networks (Bilal et al. 2019b; Nawaz et al. 2017; Hermida et al. 2012). Social network analysis (SNA) incorporates network and graph theory methods to study and explore social interactions. Within social networking sites, people, users, items, or objects are regarded as nodes, while relationship, interaction, and association are depicted as edges (Otte and Rousseau 2002). Web 2.0 has empowered people to interact efficiently, establishing networks with mutual interests, sharing the information, and posting huge volumes of valuable, user-generated content (Tan et al. 2011). Moreover, the application program interface (API) provided by social media platforms can be used to crawl and retrieve data. “Data analytics” can be used to derive perceptions, relations, patterns or behaviors, and insights from these tremendous amounts of data from OSNs (Bendoly 2016). SNA is considered as the most common and very well-established data analysis sub-domain. It provides a broad range of tools, techniques, methods, and principles for collecting, processing, and analyzing huge amounts of social media data that contain valuable information (Wasserman and Faust 1994; Gandomi and Haider 2015). Machine

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learning (ML), natural language processing (NLP), and text mining are among the most popular methods for sentiment analysis, SNA, and data mining (Stieglitz and Dang-Xuan 2013). It is a common perception that the social relationship usually influences communication between smart devices. This indicates that social networking theory can be utilized to boost the quality of service for those social connections. Furthermore, the key concepts of social networks, i.e., centrality and community, were studied in order to efficiently understand the recent architectures of the wireless network. A study provides a detailed overview of social networks and reviews their applications in wireless communication (Jameel et al. 2018). Presently, the focus of researchers on SNA is rapidly increasing, and several studies have explored various characteristics of social networks. The application of social networks in the wireless network has been widely studied. The social network can aid healthcare services by providing location-based facilities and monitoring individual behavior (Falk 2011). A study used one of the renowned social networking websites Vk.com to search and collect public data related to the user in a systematic manner (Bagretsov et al. 2017). An SNA-based rising star forecasting model was reported to produce the best results when compared with baseline models based on other approaches (Ning et al. 2017). On the basis of social media profile and social relationships, one can accurately predict an individual’s personality. A number of studies had explored language variation from the perspective of demographic and psychographic characteristics (Bamman et al. 2014). Various recommendation systems, such as recommendations for movies, recommendations for friends, etc., use enormous social media data to find trending topics and friends recommendation (Jiang et al. 2016). Different features extracted from social media data were used to develop machine learning algorithms to identify the missing link between individuals (Fire et al. 2013). Content-based fitness and health assessment were carried out by using Twitter data (Kendall et al. 2011). A proposed system introduced comprehensive geographical features based on topics discussed on Twitter and maps consumers’ geographical preferences (Vosecky et al. 2013). The social network of influential individuals and their followers can be identified with SNA. SNA is also used to study user behavior to assess its causal relationships on the network as a whole. Social media firms have acknowledged the significance and role of influencers in the purchase or replacement of products. The content of social networks has been used in the information systems to identify and to analyze information dissemination (Zhang et al. 2016). The businesses use data from social media to target audiences, to identify preferences of clients, and to get feedback of product or services. Social networks have many advantages along with some disadvantages (Wendling et al. 2013). It is also necessary to study and to analyze events such as spreading fake news and rumors along with the reputation of the user across social networking sites (Qin et al. 2015). With the increasing user-generated content across social networks, it has become increasingly important to profile consumers by extracting information shared on OSNs. Social profiling is an emerging approach to address the challenges faced in meeting user demands. A study reviewed and classified social profiling research, describing methods, sources of data, limitations, and open challenges (Bilal et al. 2019c).

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Social media analytics is generally described as a complex process. Therefore, the entire method and steps involved need to be standardized. Recently, the Internet of Things (IoT) has been seen as an efficient technique for improving asset management (Lee et al. 2015). The IoT connects computers, devices, sensors, and individuals, and it is anticipated that this technology will connect billions of devices in the near future. However, it is very difficult to use traditional techniques for integrating and maintaining a huge network of these devices. Social networks are capable to connect and maintain communication with billions of people using social interactions. This leads to an emerging field of Social IoT (SIoT) that connects and maintains billions of devices in IoT networks using principles of social networks (Thangavel et al. 2019). The concept of SIoT resulting from the integration of social media in IoT has been introduced in fields such as management of product lifecycle, vehicle tracking, and employee assistance (Cai et al. 2014; Schurgot et al. 2012; Kranz et al. 2010). Twitter has provided users with an API that can be used by users and applications to post messages and manage the user account. The efficient communication between devices can be achieved using such APIs, and this leads to the rapid implementation of IoT solutions. Twitter can assist the devices in the IoT network to interact and communicate with inter-network devices, intra-network devices, and people, thereby increasing the strength of the IoT as a whole (Ortiz et al. 2014). But with such a broad user network and their related information, Twitter also attracts spam or fraudulent users who foster their illegal activities or attempt to deceive users and influence the feelings of specific social communities (Schulz et al. 2017).

Mobility and Transportation One of the twentieth-century significant socioeconomic transitions was the widespread use of automobiles (Geels 2012). There is an ongoing global discussion on how new technologies, e.g., automated cars, communication applications, and IoT, will improve mobility for individuals and groups. Furthermore, it is stated that “smart mobility” transformation, which combines these emerging technologies to improve the organization and operation of the transportation system, has already started. Like any socio-technical transformation, the questions of how the change will be handled and how the advantages and disadvantages will be managed are important (Docherty et al. 2018). Smart mobility is mostly described as a transition of equal reach with respect to automobility, centered on a variety of positive developments in how individuals travel. The smart mobility was described as a future vision in which transportation will be presented as a service accessible on request, with people having immediate access to a clean, renewable, effective, and convenient transportation system (Wockatz and Schartau 2015). Followed by the extensive adoption of integrated and autonomous vehicles, it was argued that somehow the smart mobility will offer potential benefits in safety and fewer travel expenses by efficient use of transport infrastructure and automobiles. These new frameworks with shared ownership of mobility resources and real-time data

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integration will also reduce the hold of big companies on transport supplies (Fagnant and Kockelman 2015). The capacity for autonomous cars to decrease journey times for a diverse range of trips will have a much greater impact on individuals and the economy rather than only saving time (Wadud et al. 2016). There are some key characteristics of smart mobility that are being discussed and common among all feature perspectives (Kuosa 2016). Mobility is taken as a service in which companies replace individual ownership of automobiles, i.e., the capacity of the individual to buy transport facilities package operated by certain operators. This is supported by the embedded aggregator and payment systems capable of processing huge volumes of real-time data to meet user demands (Thakuriah et al. 2016). The user’s decisions of mobility and non-mobility are being influenced by real-time crowdsourced user-generated content (Toole et al. 2015). Smart infrastructure, such as connected automobiles, uses individual operational data and gives realtime feedback to monitor the behavior of travellers and enhance system efficiency (Alam et al. 2016). Nowadays, automobiles are being electrified using renewable energy from batteries, hybrid, and other new technologies. The use of smart power grids in electric cars can be emission-free as well as provide a solution for the use of renewable energy (Bakker et al. 2014). The autonomous vehicles enable all passengers in a vehicle to perform their task during travelling where no user is required to drive the vehicle (Fagnant and Kockelman 2015). The recent trend toward the installation and use of vehicle automation and communication systems (VACS) in automobiles is due to significant advances in ICT and sensor networks. VACS provided users with comfort and safety along with controlling traffic and emissions for connected automobiles (Diakaki et al. 2015). The volume of VACS-equipped connected autonomous cars will rise quickly over the next decade. In addition, human-driven vehicles still retain their place in global markets. However, the roads will soon be shared by both human-driven and autonomous vehicles (Levin and Boyles 2016). It is essential that we know how users adapt to the existing smart transportation system, given the advantages of a connected environment. Vehicular social networks (VSNs) inherited features make it difficult to enable smart mobility and efficiency in data transfer. The Internet of Vehicles (IoVs) has emerged where automobiles operate as sensing terminals to collect data of in-vehicle and smartphone devices and then release it to users. VSN is a new framework that attracts scholarly and industrial attention, but the integration of social networks with IoVs is in its early stages. Incorporating smart controllers and connectivity techniques in smart cities create a whole new domain for IoVs as automobiles have considerably transformed (Rahim et al. 2018). There are still many obstacles, i.e., the distribution of messages and analysis of big trajectory data, trust, security, and anonymity, to use VSN for enabling smart mobility. The literature related to overcoming these challenges faced by VSNs is very limited until now. Therefore, it is necessary to develop social trust-based techniques for secure and reliable connections in VSNs (Rahim et al. 2018). There are many examples of smart mobility in various smart cities throughout the globe. London ranked second in the world’s smartest city ranking, according to the Cities in Motion Index (CIMI). London’s transport system integrated number plate

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identification for managing traffic flow to effectively reduce traffic jams during rush hours. It also involves Wi-Fi accessibility, smart roads, and bike share programs (Berrone et al. 2016). The green city index of the United States and Canada recognized San Francisco as the greenest city. The San Francisco transport authority adopted the approach to substitute single-occupant automobiles with shared electric, connected, and automated vehicles which solved many problems with the timeconsuming and costly transport (Silva et al. 2018). One of the potential problems faced by the upcoming transportation systems is developing smart mobility governance techniques that use wireless communication to accomplish worldwide roaming. In addition, it is necessary to integrate and interoperate advanced mobility governance techniques in heterogeneous networks to integrate prospective wireless systems in smart cities (Yaqoob et al. 2017). With the evolution of smart mobility, the key system components will be reconfigured resulting in different outcomes of mobility, i.e., patterns of land use, jobs, housing, etc. (Kim et al. 2015).

Smart Environment Smart environment means the living standard, living style, and the things around the smart city, not the actual environment of the city. It aims to provide the basic necessity of life and provide a better interaction between the citizen and their surroundings. The smart environment is provided with the help of artificial intelligence and machine learning, thus creating a responsible, adaptable machine into the environment (Jain and Nagarajan 2016; Augusto et al. 2013), for example, data collection from different microphone sensor and cameras located in the city and applying different machine learning algorithms to detect emotions and gesture recognitions, especially in case of smart classrooms, where the instructor and students both adjust their learning with the help of visual aids. In a smart environment, data can be from different nodes with different format because of the diverse nature of devices connected in a particular environment, which further generates the ranges of senses like communication, data, and security of data (Jain and Nagarajan 2016; Sheu et al. 2016).

Smart Streetlights Smart streetlight is one of the most adaptable applications in smart cities. Smart streetlights can help in energy consumption optimization. This can be done with the help of sensor nodes as well as monitoring with smart cameras. The saved energy can be supplied to that area where the energy is needed. Sensor nodes are efficiently deployed to monitor the streetlights. These nodes also have a camera for the visual insight of the location so that action can be taken as per need. The smart light system can also be found in the literature review, like Veena et al. (Gharaibeh et al. 2017) has proposed the smart streetlight system for smart city, in which the hardware application is capable of taking video as an input with the help of a camera and detect the movement of vehicles and pedestrian to switch on or off streetlight. This feature will optimize energy as well as the consumption of energy in an efficient manner. Sheu

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et al. (Sheu et al. 2016) have introduced the light-emitting diode (LED) for streetlight with multiple colors, using high-power integrated circuit (IC) and high-quality image processing for accurate decision. In case there is fog or high rain, the system immediately generates the alert to activate the LEDs with the help of power IC so that with the help of multicolor, pedestrian and drivers can recognize the exact path.

Smart Homes and Smart Building Inside the smart environment, there are two different applications named as smart homes and smart buildings. These applications can ensemble different sensors and actuators that are deployed in homes and buildings to improve the energy efficiency (Bellido-Outeiriño et al. 2016; Collotta and Pau 2015) and consumption of utilities (Crowther et al. 2012; Daher et al. 2017) and ensure security (Zeng et al. 2017), which connect smart homes to homes and overall smart applications like smart grid (Zhang et al. 2015) and smart health management system (Zhang et al. 2015) for citizens. Smart Surveillance in Smart Cities It is the most challenging part of the smart city application for the past recent years, mainly due to the improvement and advancement in image processing and its application. In previous, IBM Db2 (van Zoonen 2016; White 2001) and IBM WebSphere (White 2001), IBM smart Surveillance Systems (S3) can generate the system alert and security alert with the extraction of information and detection of a vulnerability in the system.

Privacy Challenges in Smart Cities Smart cities are developed that reforms the society and quality of life through several features like digital connectivity, digital transport system, smart health management, and increased inefficiency and accessible in cities. Similarly, the interest of smart cities has been increased up to a certain threshold with the deployment of information and communication technology (ICT). Long-term objectives of smart cities are organized in order to enhance the quality of services provided to the citizen, and that will ultimately improve the lifestyle up to mark (Khatoun 2017). One of the basic features of smart cities is the development of infrastructure, construction of road, and introduction of smart health. Without an efficient transport system, the concept of the smart city will not be fulfilled. Intelligent transport system (ITS) has been known as one of the primary building blocks for a smart city. Indeed, road infrastructures have been benefiting from ICT for a decade (Menouar et al. 2017). Although the advanced level of ITS has been deployed to update, the technology is continuously evolving. By the symmetry of continuous inventions, next-generation ITS technologies like smart health card, smart vehicles, either finished or about to complete toward largescale worldwide deployment. The concept of smart cities will be demolished if the citizens are reluctant to participate and involve in the construction of smart cities. However, by incorporating benefits, on the other hand, it also opens for security and

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privacy challenges in smart cities, along with the people living in these cities (Menouar et al. 2017; Braun et al. 2018). Maintaining user privacy and ensuring data security are one of the challenge tasks in smart cities, especially for those scenarios where the public is involved directly like the health system, transport system, communication system, and other critical systems. These challenges may include privacy preservation with high-dimensional data, securing a network with the large surface attack, establishing reliable data sharing practices, properly utilizing artificial intelligence, and mitigating failure cascading through the smart network (Braun et al. 2018).

Security and Privacy Challenges As a core concept, security is not absolute, but, in fact, it is dynamic, a basic and phenomenal method to prevent attacks on smart cities and its inhabitants. These can be directly or indirectly related to the citizen through digital or physical connections. So, security challenges will always be the most abundant opportunities for security risks in a smart city environment. Specifically, while taking privacy into account, Elmaghraby and Lasovio’s are the first two principles that help regarding the preservation of privacy and cyber privacy. These principles state as (1) “activities within the home have the greatest level of protection” and (2) “activities that extend outside of the home depend on reasonable expectations of privacy” (Elmaghraby and Losavio 2014).

Privacy Threats Due to the nature of the interconnectivity of smart cities, data will be manipulated throughout the processes, with multi-access among multiparties. This property makes the data open for the vulnerabilities. An attacker can get access from any point of entry into the system and get the most secret information of the citizen. Furthermore, since every stakeholder of the smart city have different priorities and will have exits gaps between intermediate and other stakeholder’s privacy standards. Privacy threats are prevalent in public sector organizations, such as hospitals and transportation authorities that provide essential services to citizens (Braun et al. 2018; Ijaz et al. 2016). Unlike the private sector, public authorities will have more scope to ensure the privacy protections as it should not be like its funding, and livelihood may be affected while achieving its goals. In smart cities, this gap in the protection of privacy will have a higher stake as compared to other cities. Roughly, the health system may involve public and private partnership while achieving the desired goals in terms of maintaining privacy and security (Khatoun 2017). While in public sector, hospitals may be on administer care and be under central decisionmaking authority so that the distribution of patient and medicines can be achieved efficiently through public and private partnership (van Zoonen 2016; Elmaghraby and Losavio 2014; Khokhar et al. 2016). In this way, the critical and sensitive

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information can be taken by the respective authorities for a decision regarding the transferring of patients, treatment schedule, and home address.

Privacy-Enhancing Technologies Indeed, cities are developing to become “smart cities,” where the applications suffer a severe concern regarding security and privacy of user’s data. In the paradigm of new information and networking, a smart city should have the following properties to maintain or to be declared as a smart city. These are the properties like information from unauthorized resources, disclosure, modification, inspection, disruption, and annihilation. The most common security properties that the smart city should have in order to provide secure information, communication, and physical world are confidentiality, integrity, non-repudiation, availability, scalability, access control, and privacy (Salas Mccluskey 1988). Besides all the general and basic concern, still smart cities are facing numbers of security challenges. Smart cities, on the one hand, collects sensitive data and information directly from lives of citizens and manipulates the collected data in respective scenarios and influence citizens accordingly. This unique characteristic of data opens many security loops.

Data Privacy in Data Sensing The data is processed after it is successfully collected and transmitted over the network; therefore, it creates loopholes for an attacker to inject vulnerabilities into the data to manipulate and misuse the data. This privacy concern in the smart city may be compromises of user’s identity, location, health reports in the healthcare system, lifestyle inferred from intelligence, smart energy, home, and society even in the community so on and so forth. It would be a very large security damage if such information can be stolen from the smart city system. To overcome and address this issue of privacy and security of data, some off-the-shelf security and privacy techniques can be applied. These techniques may include encryption, anonymity, and access control (Khokhar et al. 2016; Salas Mccluskey 1988). Martinez et al. have proposed a set of privacy and security concept for general privacy requirements for smart cities and their applications. This privacy may include the identity, query, location, and biometric prints like footprints. After that, the owner is identified and provides the basic idea to overcome the general problem. However, still, there exists some portion of private information leakage that can be treated as a strong concern in this era (Jones et al. 2015). Similarly, in smart cities, especially in the smart home, a surveillance camera is used to detect abnormal behavior or theft. This act of taking information from home may acquire secret information of smart home, and it is prejudicial to the privacy of home (Elmaghraby and Losavio 2014; Salas Mccluskey 1988). To overcome such intruders, many application or existing security and privacy protection measures are taken into account. However, the potential attackers like an agent, a security guard, and an employee of the smart home who can have

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access to the surveillance record or security record may take the private information and leave it to the attacker (Cherdantseva et al. 2016). Moreover, the data in a smart city are on the highly granular scale as it comes from diverse types like the privacy requirements which may differ for different types (de Bruijn and Janssen 2017). It is still a challenge to develop a phenomenal mechanism that can balance between the efficiency and privacy of smart cities.

Privacy and Availability The smart cities have comprehensive and remarkable benefits of using the cloud server to provide services to the citizen as well as data storage and information. It creates a security threat for smart cities due to the untrustworthy nature of cloud servers. In case, if the data is not protected and it is saved in plaintext into the cloud, then it can quickly reveal to many attackers especially if the cloud admin itself revealed the data (van Zoonen 2016; Braun et al. 2018). To overcome this problem, the other way to save user data is to encrypt the data and save it in the form of ciphertext so that server admin can see user data (Baig et al. 2017; Amin et al. 2014; Aldairi and Tawalbeh 2017). In this, the cloud server admin can see the encrypted data and cannot perform any kind of operation over encrypted data of applications of smart cities. Furthermore, the use of a fully homomorphic encryption scheme to protect the data in the cloud can improve the security of data in the cloud. However, on the other hand, this method also allows operation on encrypted data like summation and comparison. So, this also has opened a new way for researchers to dig it out more, and still it is a challenging work for researchers, especially in smart cities where there is already massive data. Similarly, data sharing and access control are also a challenging issues in smart cities, where the data is being shared to another point for a particular operation such as in healthcare, the patient data is shared with a doctor for analysis or in traffic data where the data is collected from a smartphone, a surveillance camera or GPS in a crowdsourcing way. For all over the globe, yet it is a challenging and security risk to define the common policy for data sharing and access control (van Zoonen 2016; Martinez-Balleste et al. 2013; Lacinák and Ristvej 2017). Data sharing and access control policy for homomorphic encryption are still open for research.

Conclusion With the increasing research, advancement in technology, and attempts toward the transition of cities into smart cities, the concept of smart cities is becoming more complex and ambiguous. To overcome this challenge and grasp the various characteristics of smart cities, this research organized the current literature from the perspectives of smart data framework, applications, and challenges. The increasing volumes of data being generated by the sensors network have been collected, processed, and organized in a variety of ways using deep learning techniques and

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smart network databases. The datasets generated form IoT, and ICT tools are used for routine and automatic sensing, which replaces the traditional approaches. However, the communication of user-rich and private data over public networks also leads to several challenges, i.e., privacy and security. The proper and secure use and analysis of this rich data collected by using advanced technologies, i.e., IoT, WSNs, VSNs, etc., lead to a wide range of services. The most prominent applications of using smart data include smart government and governance of smart cities, smart mobility, and smart communication. However, the change toward smart government is not easy as the present institutional, organizational, financial, and technical barriers make it difficult. From the perspective of social networks, SIoT is an emerging field as it connects and maintains billions of devices in IoT networks using principles of social networks. Similarly, several solutions have been proposed for smart mobility and transportation, which introduce mobility as a service in which a user can request a transport service provided by different companies. The introduction of autonomous vehicles is also revolutionizing the domain of smart mobility to a great extent. The open challenges faced by the current research on the provision of services in smart cities and data management are also highlighted. This study will help practitioners and researchers to grasp concepts, characteristics, and current state of literature for smart cities data and how emerging technologies are being used to manage such huge volumes of data in real time.

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Smart Institutions: Concept, Index, and Framework Conditions Hans Wiesmeth, Dennis Häckl, and Christopher Schrey

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Selected Literature on Smart Cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smart Institutions in the Literature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A Working Definition of a Smart Institution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A Case Study on University Hospitals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . General Considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A Review of the Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . An Index for Smart Institutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Framework for Smart Institutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Framework Conditions for UML and SSMU . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Framework Conditions for UML in 2009 and 2017 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Other Framework Conditions of Relevance for a Smart Institution . . . . . . . . . . . . . . . . . . . . . . . . Smart Institutions in Various Sectors of the Economy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cross-References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract

For a city to turn into a smart city, suitable framework conditions have to enable and enhance the required creativity of the inhabitants. If new technologies are meant to establish fast connections between citizens, also in order to H. Wiesmeth (*) Graduate School of Economics and Management, Ural Federal University, Yekaterinburg, Russia Faculty of Business and Economics, TU Dresden, Dresden, Germany e-mail: [email protected] D. Häckl · C. Schrey WIG2 GmbH, Wissenschaftliches Institut für Gesundheitsökonomie und Gesundheitssystemforschung, Leipzig, Germany e-mail: [email protected]; [email protected] © Springer Nature Switzerland AG 2021 J. C. Augusto (ed.), Handbook of Smart Cities, https://doi.org/10.1007/978-3-030-69698-6_7

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strengthen channels for innovative activities, then smart institutions should operate under framework conditions, which integrate the employees to allow an optimal functioning of these institutions with respect to the societal goals of a smart city. This chapter introduces a concept and an indicator of smart institutions, focusing on research-oriented public institutions. These concepts are discussed by means of case studies involving institutions providing maximum medical supply and access to high-performance medicine. Framework conditions, raising the degree of smartness, are analyzed as well. Some remarks on smart government and smart institutions in other sectors of the economy are included as well.

Introduction The concept of a “smart city” has gained increasing attention over the last years. More and more cities from countries from all over the world consider themselves “smart” in one way or the other and use this seeming mega trend as a marketing device: the “Smart City Strategy Index 2019” (SCSI 2019) identifies 153 cities around the world with an official smart city strategy. A closer look, however, shows that there is no clear and unique definition. This is understandable, if one accepts that each city faces its own challenges on its way towards sustainability, a concept which is often closely related with a smart city (cf., e.g., Hollands 2008; Barrionuevo et al. 2012; Mori and Christodoulou 2012; Turcu 2013; MarsalLlacuna et al. 2015; Yigitcanlar and Lee 2014; Albino et al. 2015; Huston et al. 2015; Hajduk 2016; Ahvenniemi et al. 2017; Martin et al. 2018; Jones et al. 2019) and which is itself a concept depending substantially on various local and regional framework conditions (cf., e.g., Holman 2009; Turcu 2013 for the context considered here). In fact, according to SCSI 2019, smart cities “tend to have one thing in common – a sound strategic approach” (SCSI 2019, p. 6), and Jones et al. concede “. . . that smart cities are diverse in their planning, applications, and values” (Jones et al. 2019, p. 2). Such a situation inspires and requires reviews of the literature, and there exist quite a few of them on smart cities and related concepts. Albino et al. (2015) provide an interesting survey on the various definitions of a smart city, which have been proposed in the literature in recent years. Anthopoulos, among other issues, discusses smart city conceptual models (Anthopoulos 2017, p. 9f), and Neirotti et al. (2014) explore the “diffusion of smart city initiatives” in order to understand the role various variables have in planning a smart city, and Wilhelm and Ruhlandt (2018) mention in their literature review that research on smart cities lacks a systematic understanding of the different components of smart city governance. Thus, it seems that not too much has changed since Hollands (2008) complained about the sparse knowledge on smart cities. Interestingly, important concepts refer to, as already indicated, sustainability and connect the development towards a smart city in some sense to a sustainable development (Barrionuevo et al. 2012; Kourtit et al. 2012; Thuzar 2011).

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In view of this literature, applications of new and advanced technologies, in particular, information and communication technologies (ICT: cf. Glossary in Jones et al. 2019), to cities are still important for a smart city (cf. Frost and Sullivan 2019; Jones et al. 2019), but the concept itself seems to go far beyond technical and technological issues. It is understood that a smart city is obviously dependent on its inhabitants, in particular on their willingness to accept these new framework conditions, on their willingness to bring themselves in with their creativity, to contribute towards the goals of a smart city, a sustainable development, for example. This points then to the creativity of a city, possibly to the existence of a “creative class,” and then also, of course, to “enablers” (SCSI 2019, p. 7), to the framework conditions enabling and enhancing this required creativity, which is recognized as a key driver of a smart city (Albino et al. 2015; Komninos 2011; Thuzar 2011). It is here that the concept of a smart city touches or, maybe, even comprises the concept of creativity: implementing a smart city necessitates a climate for establishing a creative class (Florida 2005, 2012), without, however, disempowering and marginalizing other citizens (cf. Martin et al. 2018). There is, thus, an interesting constellation: according to quite a few core publications, the concept of a smart city refers first of all to sustainability, which is also the ultimate goal of a circular economy (Korhonen et al. 2018). Nevertheless, Martin et al. (2018) point to certain tensions between smart city visions and the goals of a sustainable development, and Ahvenniemi et al. (2017) study differences between smart and sustainable cities. Next, there is the relationship of a smart city to creativity – with a creative class considered necessary for preparing the path towards a smart city. In conclusion, a smart city, sustainability, a circular economy, and the existence of a creative class are – in the literature – to some extent considered related, dependent on each other, enhancing one another. The important question that remains is to get this process, the development towards a smart city, started. One should not expect that there is a natural evolvement of all relevant societal issues towards such an outstanding state of the society, also characterized by public goods, nor should one expect that the provision of advanced digital technologies alone will initiate a revolutionary development. No doubt, they are important, and they can surely accelerate the required development, but more needs to be done, in particular regarding fundamental changes in the mind-set of the people. Without support from a tangible part of a city’s inhabitants, the intended sustainable development risks to fail. In particular, in view of the important role of ICT for smart cities, privacy and security issues must not be forgotten (cf. Jones et al. 2019, p. 7). This issue will be briefly addressed in section “Smart Institutions in Various Sectors of the Economy.” In SCSI 2019, the role of the “stakeholders” is indicated pointing to “citizen acceptance” and “partnerships” (SCSI 2019, p. 7). A weight of 7.5% assigned to the stakeholders in the Smart City Development Index seems, however, little, given the importance of a support of a smart city strategy through the citizens. A recent study, conducted by ATG Access, found that 68% of UK respondents do not know what a smart city is or how the concept can benefit urban residents (cf. https://www. governmenteuropa.eu/smart-city-investment-report/92975/). This points to the

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necessity of a more careful integration of the citizens, including the companies and institutions. That’s exactly the point, where “smart institutions” come into the picture. If new technologies are meant to establish and enable fast and reliable connections between the inhabitants of a city, also in order to strengthen existing and open additional channels for innovative activities, institutions, or, rather, smart institutions, should operate under framework conditions, which integrate the employees to allow an optimal functioning of these institutions with respect to the societal goals of a smart city, perhaps including a sustainable development and a development towards a circular economy. Smart institutions, to be more precise, thus motivate employees and enable the creative class and top researchers in universities, companies, and other institutions, to fully employ their capabilities – for their own goals – but thereby also raising the welfare of the society. These remarks provide a first characterization of smart institutions and help to prepare the working definition in section “A Working Definition of a Smart Institution.” Accordingly, a smart institution should react upon changing framework conditions, thereby encouraging innovative activities in support of developing a smart city. Obviously, research-oriented public institutions with malleable framework conditions seem to be perfect candidates for smart institutions. These considerations point to a in this sense perfect integration of relevant stakeholders. Clearly, a smart city depends on its smart institutions, and, vice versa, smart institutions are dependent on a creative framework, which flourishes best in a smart city. With smart institutions, the chapter also addresses contexts, which are often related to topics discussed in the literature on smart cities, namely, the careful utilization of relevant pieces of infrastructure for an optimal provision of services to the people in the city (Hall 2000; Harrison et al. 2010; Kourtit and Nijkamp 2012; Mardacany 2014; Marsal-Llacuna et al. 2015; Kumar et al. 2018). Some authors also consider the “built environment” (cf. Glossary in Jones et al. 2019) as a foundation of the smart city infrastructure in order to facilitate connectivity and provide networking across the community (Mardacany 2014). This chapter attempts to go beyond the perception of a smart city as a high-tech intensive city connecting people and thereby promoting a sustainable development (cf., e.g., Bakıcı et al. 2012) or “to deliver the new services in an efficient, responsive and sustainable manner for a large population” (Kumar et al. 2018). As already indicated, other aspects or framework conditions, incorporated in the concept of a smart institution, in particular creativity, are of relevance “to leverage the collective intelligence of the city” (Harrison et al. 2010). This emphasizes once again the conclusion reached above: smart institutions, occupying the built environment, are important for smart cities, not to say that smart institutions and smart cities are dependent on each other. It is the aim of this chapter to provide a working definition of a smart institution; also to introduce an index, which allows a comparison between smart institutions operating in related fields; and to investigate framework conditions of relevance for institutions to this regard. As the concept of a smart institution will, for good reason,

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focus on research-oriented public institutions, the relevance of smart institutions in the private sector and the government will also be addressed. After all, making better use of the creative potential of an institution is an issue for all sectors of an economy. This then points to the “quadruple helix model” (Jones et al. 2019, p. 20, Glossary), extending the “triple helix model” of university-industry-government relationships (Etzkowitz and Leydesdorff 1995, 2000) by additionally including the citizens as stakeholders of relevance for the developing a smart city. The analysis makes in particular use of case studies involving university hospitals and university medicine in Leipzig, Germany, and in Tomsk, Russia, on the one hand, and university medicine in Leipzig in two different periods of time, on the other. These cases provide sufficient insight into the intrinsic nature of smart (public) institutions, in particular the role of identifying and adequately integrating important stakeholders. The chapter is structured as follows: a short methodology section helps to explain the scientific procedure adopted in this chapter. The review of the literature in the next section turns first to publications considering certain aspects of smart cities, which are closely related to the concept of a smart institution adopted here. This review points to some characteristics, peculiarities, and framework conditions attributed to and associated with smart institutions. The next subsection addresses the literature on smart institutions, which is still manageable, probably due to the stronger focus on infrastructure in the literature in the context of smart cities. This stage introduces also some literature on stakeholder integration, which proves to be decisive for the concept of a smart city and which then allows, together with the other aspects collected, a working definition of a smart institution. A case study involving the medical universities in Leipzig and Tomsk mentioned above, demonstrates how different framework conditions can affect certain output parameters of such institutions, can affect the “smartness” of such institutions. These examples help to clarify the concept and to introduce an index of a smart (public and research-oriented) institution. This index is related to various existing indices in the context of smart cities, and its properties are briefly analyzed. By means of this index, it is possible to compare institutions, in particular those operating in the same field – as the university hospitals in Leipzig and Tomsk, for example. In a first approach, these differences will be related to differences in certain framework conditions – to ones which are exogenously given and to ones which are endogenously and which can be affected internally through the management of the institution or externally through public policy. Differences in innovation strategies of the participating countries will thereby play a role, revealing varying degrees of stakeholder integration. This latter aspect points to the relevance of the public administration in this context and, in particular, to “smart government.” Another case study referring to the university medicine in Leipzig will provide a more detailed insight into the relevance of certain endogenous framework conditions by analyzing the value of the index for two different periods of time: 2009 and 2017. This investigation demonstrates important steps of this institution on its way towards a smart institution. Moreover, it also points to possibilities for modifications of the internal framework conditions and the public policy to accelerate the development

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towards a smart institution. Again, the issue of choosing framework conditions to appropriately integrating interesting stakeholders to enable the creative potential of an institution will dominate the analysis. Thereafter smart institutions in other sectors will be briefly investigated. Also, the question of alternative indices for smart institutions, especially rankings, will be briefly addressed. Some final remarks conclude the chapter.

Methodology The extensive review of the relevant literature in the next section will motivate a working definition of the concept of a smart institution. According to the classifications of case study research designs, this design can be classified “no theory first” (Ridder 2017, p. 286), to capture the richness of observations without being limited by a theory. This research design will also play a role with respect to the introduction and definition of an index for smart institutions. Again, relevant properties for comparable indices are sampled from the literature and then combined with insight gained from the case studies, one of them taken from Wiesmeth et al. (2018). These case studies result from projects analyzing economic effects of the Leipzig University Medicine (UML) carried out by the authors in 2011 and 2019 with data from 2009 and 2017. The first case study helps to explain relevant features of these concepts. As already mentioned, the second case study involves only UML – with data from different years in order to investigate the changes from one period to the other. The research designs of these concrete case studies can be classified “social construction of reality,” with the case being itself of interest, not theory-building (Ridder 2017, p. 288). The formal methodology used directly in the case studies is characterized by an incidence analysis and specifies the Keynesian multiplier analysis in order to provide a framework for discovering and quantifying several regional economic effects. The quantitative analysis shows the importance of these institutions for regional economic development. Differences regarding the size of the various multipliers result from differences in relevant framework conditions, thus providing room for policy implications to be investigated thereafter.

Literature Review There is, for sure, a vast literature on smart cities, which will, for the purposes of this chapter, only selectively be reviewed in the following subsection. The focus is thereby on the role of certain components of smart cities, such as the physical infrastructure in its relation to the human capital, pointing to some aspects of smart institutions, in particular the requirement of an adequate integration of the population. The literature on indices measuring and aggregating various dimensions of smart cities will only be touched briefly.

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The literature on smart institutions and related concepts, including stakeholder integration, will be reviewed thereafter. It will also prove interesting to connect the concept of a smart institution with the “theory of institutions,” originating already in the 1960s.

Selected Literature on Smart Cities As already indicated, Hollands (2008), Nam and Pardo (2011), Neirotti et al. (2014), Albino et al. (2015), Hajduk (2016), Anthopoulos (2017), Fernandez-Anez et al. (2018), Yigitcanlar et al. (2018), and Jones et al. (2019), among others, provide extensive surveys on concepts or discuss relevant aspects of a smart city. Whereas Hall (2000) aims “to provide a preliminary critical polemic against some of the more rhetorical aspects of cities labelled as smart,” Nam and Pardo (2011) identify “a set of the common multidimensional components underlying the smart city concept.” Neirotti et al. (2014) strive to develop an integrated definition of the concept and explore “the diffusion of smart initiatives” by means of an empirical study, and Albino et al. (2015) aim to clarify the meaning of “smart” in the context of cities “through an approach based on an in-depth literature review.” Hajduk (2016) addresses urban planning as a crucial factor in urban management and refers to “adequate intellectual resources and proper institutions as well as developed infrastructure” as relevant for smart cities. Fernandez-Anez et al. (2018) turn to the issue of implementing a smart city, thereby addressing the importance of governance and stakeholders “in developing smart city initiatives and their capacity to face urban challenges.” Finally, Yigitcanlar et al. (2018) propose “identifying and linking the key drivers” for a smart city, thereby focusing on the literature aimed at a conceptual development and providing an empirical base. On the other hand, the literature referring to the role of institutions in the context of smart cities in particular is not very extensive. There is, of course, as already indicated, a vast literature emphasizing the role of the infrastructure and the role of the people, pointing to the necessity of connecting people, communities, and the industry (cf., e.g., Nam and Pardo 2011; Kourtit and Nijkamp 2012; Hajduk 2016). In particular Hajduk mentions trends of smart city initiatives referring to a different focus, also with regard to the physical infrastructure or the creative human capital: there is the “digital city,” the “green city,” and the “knowledge city” (cf. Hajduk 2016, Table 1). A smart city “exploits ICT to optimize the performance and effectiveness of serviceable and needful city processes, activities and services typically by joining up diverse components and actors into a more or less seamlessly interactive intelligent system” (Hajduk 2016, p. 37). There are various publications introducing, analyzing, and applying indices of smart cities. The approach of Holman (2009) is of particular interest in this context: the incorporation of governance into indicators in order to “aid the evaluation of policy.” Mori and Christodoulou (2012) discuss conceptual requirements for a “City Sustainability Index,” and also Turcu (2013) and Tran (2016) address various aspects of sustainability indicators. Lützkendorf and Balouktsi (2017) discuss

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“flexible indicator systems supporting the process of sustainable development,” whereas in SCSI 2019, an assessment of smart city initiatives is provided, based on 3 smart city dimensions, 12 criteria, and 31 sub-criteria. The resulting “Smart City Strategy Index (SCSI)” is then applied to reveal the leading positions of Vienna, Austria (cf. also Fernandez-Anez et al. 2018), London, the UK, and St. Albert, Canada. In the context of introducing a simple but nevertheless appropriate index of a smart institution, properties of these indicators and indices will be reconsidered. SCSI 2019 mentions 153 cities around the world, which “have published an official Smart City strategy.” However, out of these 153 cities, only 15 “have plans that demonstrate a comprehensive strategic approach,” and only 8 of these 15 “are at an advanced stage of implementation” (SCSI 2019, p. 2). This points to the decisive challenge of implementing a smart city. The crucial issue in this context seems to be to motivate citizens to accept the new environment, in particular the technological changes associated with a smart city, and to respond to changes in the environment. Again, the careful integration of the citizens, the integration of the relevant stakeholders, is necessary to leverage the creative potential. ICT can, of course, help to achieve this goal. There is a vast literature on stakeholder theory and managing stakeholders, originating with foundational publications of Freeman (1994) and others. Since then, stakeholder engagement has been addressed in a multitude of papers covering a broad range of areas. Interestingly, sustainability issues as well as issues of corporate social responsibility are often brought together with stakeholder engagement. Thus, Amor-Esteban et al. (2018) provide useful information for stakeholder engagement by proposing an industrial corporate social responsibility practices index. Reed et al. (2018) investigate the challenges associated with diverse and conflicting stakeholder and public priorities in environmental management, and Quan et al., while addressing a firm’s sustainable development, investigate the role of the government as the most influential stakeholder (Quan et al. 2018, p. 148). Moreover, Wiesmeth (2018) investigates stakeholder engagement and stakeholder integration in the context of environmental innovations, which are also of relevance for a smart city (cf., e.g., SCSI 2019, p. 7). In conclusion of this review of part of the literature on smart cities, what seems to be missing is a more extensive study of issues pertaining to the implementation of a smart city. In particular, the question arises to what extent the theory of stakeholder integration, originating and emerging in the management literature, could be of use in this context and, thus, also in the context of smart institutions. So far, in view of the literature, an analysis of framework conditions, which allow, for example, an efficient interaction of the physical infrastructure with the people working in it and with it, is not yet available. Such an analysis would add importance to the role infrastructure plays in a smart city. As smart institutions are considered an integral part of a smart city, implementing smart institutions is consequently of relevance to this regard and seems to be one of the first steps towards implementing a smart city. In addition, it allows a somewhat separate investigation of a critical part of a smart city, beyond the technical infrastructure, and the framework conditions supporting its operations within a smart city.

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Smart Institutions in the Literature As already indicated, there are only a few publications so far, referring directly to the concept of a “smart institution.” There are, however, various papers addressing smart institutions predominantly in a technical, less research-oriented context. Moreover, the review will also be extended to cover some aspects of the “theory of institutions” originating with the seminal publications of Coase (1937, 1960). To begin with some technical applications, Murphy et al. (2000) discuss the design of “smart” water market institutions, in order to integrate local climatic conditions into water markets. The idea thereby is to evaluate “proposed institutional changes to help facilitate a more rapid and smooth adoption of changes in the water system.” Similarly, the concept of a “smart” home is usually associated with the goal to “lead us to water and waste management, green building, safe and healthy living environment” (Gosh 2018). Moreover, Sai Sachin et al. (2018) identify “smart institutions,” which make use of a sophisticated energy management system. Their analysis refers to higher-educational institutions, which can keep the rising electricity bills in limits “by proper management of electricity distribution among various sections in the campus.” With his paper on “Smart Libraries,” Schöpfel (2018) connects smart cities with libraries. “How does the smart city impact the libraries as cultural and scientific assets? and how can libraries contribute to the development of the smart city?” are some of the questions addressed in his paper. The resulting concept of a smart library can then be characterized by smart services, smart people, smart place, and smart governance – with a particular emphasis on smart governance. Regarding these publications, in particular those of Gosh (2018), Sai Sachin et al. (2018), and Schöpfel (2018), it is important to understand the necessary and thoughtful integration of a physical infrastructure, even a building, and the people, associated with this infrastructure, using this infrastructure in one way or the other. Changes in the infrastructure, in the home, and in the library, for example, are meant to induce behavioral changes, of importance also for the implementation of a smart city. Referring again to Murphy et al. (2000), they explicitly stress a somewhat different context: in their case, institutional changes, not only changes in the physical infrastructure, should help to achieve a certain goal, of relevance first of all for water markets, but also for the design of markets for other environmental commodities. Especially this aspect is of interest with respect to the approach regarding smart institutions adopted here: institutional changes or policy changes should motivate and bring about behavioral changes supporting the development of a smart institution and a smart city. It is important to note that this literature brings stakeholder integration, addressed already above, clearer into the picture. Thus, as a first approach towards a definition, a smart institution should allow for framework conditions, which integrate relevant stakeholders in order to make optimal use of the existing physical and organizational infrastructure to the benefit of society. As already indicated, for a public institution, this refers to the “quadruple helix model” accentuated in Jones et al. (2019).

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It remains to clarify what is understood by “optimal use” of the given physical and organizational infrastructure. This issue will be addressed after the following review of another thread of literature on institutions and smart institutions. Additional and valuable input to these thoughts is provided through the extensive literature on “institutions,” originating with the seminal work of Coase (1937, 1960; cf. also Medema 1995), initiating with his ideas on the “nature of the firm,” on “property rights,” on “transaction costs,” and on various other issues on a broad theory of institutions, which has since then attracted and continues to attract many researchers. Consequently, a vast literature has helped to investigate and explain aspects of the economic role of institutions. This literature includes the well-known contributions of North (1990), Williamson (2009), and Ostrom (2010), among many others. According to North (1990), “institutions affect the performance of economies,” and institutions also “affect the differential performance of economies over time.” Williamson (2009) investigates the role of transaction costs in the context of institutions, and Ostrom (2010) argues that “a core goal of public policy should be to facilitate the development of institutions that bring out the best in humans.” Although investigating economic effects of certain institutions, the chapter does not directly address the issues of transaction costs, property rights, etc., which assume an important role in the literature on institutions. This contribution therefore continues in this framework with more or less straightforward economic effects of (smart) institutions. Nevertheless, the quote above taken from Ostrom (2010) characterizes a smart institution in the context of this approach quite well. Interestingly, Goorha and Mohan (2016) present an analytical approach regarding institutions, which “draws inspiration from control process engineering in the physical sciences.” In this context, they come up with the following characterization of “smart institutions,” which makes them different from “traditional” or “generic” institutions: they are assumed to be context sensitive, they are forward-looking in their operation, and they emphasize the role of information. Therefore, smart institutions “can be considered to operate in a closed control loop,” and they “envisage the management of smart institutions to involve feedback . . . to be drawn from members of the society . . .” (Goorha and Mohan 2016, p. 3f). This interpretation of a smart institution stresses in particular the importance of a feedback: a smart institution reacts in a certain way to changes in its environments and tries to adapt to new or changing framework conditions. Thus, in view of this literature, a smart institution is considered a living organism, which can be adjusted to respond optimally to changes in its environment or which reacts optimally upon changes in its environment. The questions that remain have to be asked with respect to the initiator of the changes or with respect to directions of the changes: where do the changes come from, respectively, what is the direction of the changes, and what does “optimal” mean in this context? These questions are, for example, addressed in Wiesmeth et al. (2018) for the case of research-oriented public institutions, for university hospitals operating in different countries under obviously different framework conditions. The focus is then on

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identifying the role of the varying framework conditions with respect to the general economic performance of these hospitals. Thereafter, changes in some of these conditions could be applied to improve this performance, of course. The above considerations lead, in the following subsection, to the working definition of a smart institution derived from and based on various aspects in the literature, thus mirroring the case study research design “no theory first.”

A Working Definition of a Smart Institution Wiesmeth et al. (2018) characterize smart institutions in a particular context, based on the results of a case study involving two university hospitals, one in Germany and one in Russia, as already indicated. In order to arrive at a working definition for a smart institution, the procedure in this case study is combined with various proposals provided in the literature reviewed above. From the literature on institutions, the basic view that “institutions affect the performance of economies” (North 1990) is important. Institutions are therefore of utmost importance for economies, in particular for highly developed economic systems. Part of the literature on smart cities addresses the role of the physical infrastructure in order to facilitate connectivity and provide networking across the community (Mardacany 2014) or “to deliver the new services in an efficient, responsive and sustainable manner for a large population” (Kumar et al. 2018). Turned somewhat differently, these opinions point to the integration of stakeholders by means of the built environment, thus opening a gate towards the literature on stakeholder engagement and stakeholder integration (cf., e.g., Wiesmeth 2018): for an optimal performance, a smart institution takes care of the adequate integration of the relevant groups of stakeholders. Moreover, respecting the societal goal “to leverage the collective intelligence of the city” (Harrison et al. 2010) means also stressing the role of creativity for a smart institution and the development of a smart city (Florida 2012). Goorha and Mohan (2016) point out that smart institutions are context-sensitive, forward-looking in their operation, emphasizing the role of information. Finally, Ostrom (2010) emphasizes that “a core goal of public policy should be to facilitate the development of institutions that bring out the best in humans.” Accordingly, these hints from the literature propose to introduce a working concept of a smart institution in the following way, stressing once again the relationship between a smart institution and a smart city: Definition: Facilitated through appropriate internal and external framework conditions, a smart institution adequately integrates relevant stakeholders to leverage the collective intelligence of the institution to bring out optimal results for society in all possible dimensions. Employment opportunities resulting directly or indirectly from the operations of an institution are an indicator of these benefits. After all, they point to the provision of goods and services for which there is demand in the society.

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Observe that this definition does not question the existence of a particular institution, nor does it refer to any plans establishing a particular institution. Rather, the definition focusses on an existing institution, investigating its operations and the framework conditions, which affect these operations. In particular, this definition postulates the context-sensitive development of an institution “to bring out the best in humans” (Ostrom 2010), to function optimally, by carefully adjusting the framework conditions. “Optimality” then refers to additional employment opportunities resulting from the operations of the institution. And, in view of the introductory remarks and the literature review, the definition seems to point to public institutions with research activities. The following sections and subsections discuss relevant aspects of this definition. In particular, the case study in Wiesmeth et al. (2018) will first of all help to explain the concept of a smart institution in more detail and then allow to introduce an index characterizing the level of “smartness” of an institution. Moreover, by means of this case study, it will be possible to at least partially reveal the effects of relevant and changing framework conditions, thereby also illustrating the role of the public policy – pointing to “smart government.” The relevance of smart institutions in other sectors of the economy beyond research-oriented public institutions will be discussed in section “Smart Institutions in Various Sectors of the Economy.”

A Case Study on University Hospitals This section discusses an example of research-oriented public institutions, which any major city must have: a hospital offering a maximum supply and access to highperformance medicine. With such an institution, the question arises whether existing framework conditions guarantee an optimal performance – not only regarding medical practices but also regarding research activities with further economic effects. The ensuing question is then whether modified framework conditions, resulting from public policy or managerial efforts, can gradually turn these institutions into smart institutions – given the above definition.

General Considerations Scientific institutions, such as university hospitals or, more generally, public institutions comprising the university medicine, can have significant impacts on the development and growth of regions. These impacts include economic and social impacts ranging from the offer of employments and trainee positions to the economy’s supply side with a qualified labor force, the provision of information and transfer of knowledge and technology, as well as cultural opportunities. This holds in particular for university hospitals with their wealth of different disciplines extending into other academic fields and the potential of attracting additional research institutes for intense collaboration and, in the end, for additional

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jobs. Considering this situation leads immediately to the question, how to make best or “smart” use of an institution, such as a university hospital, that a larger city needs anyway? In view of the definition provided above, the term “smart” refers to the benefits this institution is generating for society, measured through additional employment. However, before it is possible to provide a more detailed answer to this question, the various potential economic effects of such an institution on society have to be classified. There are, first of all, the so-called demand effects, pointing to resources the institution consumes, because it employs medical and administrative personnel, because it teaches and trains medical students, because it needs a large variety of medical supplies, and because it constantly needs to repair equipment and buildings or invest in new ones. At a first glance, the so-called supply effects seem to be more difficult to analyze. They refer in particular to the attractiveness of the institution – due to its research activities, or due to the quality of the students leaving the institution with an academic degree, and to other public or private research institutes settling in the neighborhood of the university hospital (“knowledge spillover”). However, most of these supply effects, for sure those which refer to the collaboration with other research institutes or other organizations or to third-party funded research projects, generate again demand effects through investments into the infrastructure or consumption activities of the employees. The analysis focuses therefore on the demand effects at large including those originating from these supply effects. By comparing these effects for university hospitals in different regions or countries, it is possible to get some insight into the framework conditions, which are of relevance for strong supply and, thus, additional demand effects. In this context, the required conditions for an “optimal” regional impact with outstanding innovation activities and substantial employment effects have to be investigated. This should motivate efforts or changes in the relevant framework conditions, allowing “smart” use of these university hospitals.

A Review of the Case Study The following example, taken from Wiesmeth et al. (2018), allows a more detailed view on the working concept of a smart institution. The analysis investigates the university hospitals or, rather, the university medicine, in Leipzig (UML) in Germany, and the Siberian State Medical University (SSMU) in Tomsk, Russia. Both institutions offer maximum medical supply and access to high-performance medicine and have a long history as research institutions. UML is larger in terms of the number of employees and the number of students; however SSMU serves a much larger area than UML. Moreover, these areas are different regarding climatic and geographic conditions and regarding the density of the population. In addition to that, access to these medical institutions is regulated differently in the two countries – with the possibilities offered by public and private health insurances playing an important role.

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Following the multiplier approach briefly discussed in the methodology section, a special impact model is adopted – an incidence analysis, which observes the flows of expenditures and their distributing impacts and which helps to determine direct income, consumption, and employment effects. As explained above, the model will be applied to study the demand effects at large of UML in Germany and SSMU in Russia – with data collected in close contact with the management of the hospitals. The respective institution is considered a consumer of various inputs in the model. These inputs consist of the expenses for construction, material, personnel, etc. within 1 year. The goal is then the analysis of the regional economic impact of these expenses. The calculated demand effects can be divided into direct, indirect, and induced effects. Although there are no consistent definitions in the literature, direct effects usually refer to primary income, consumption, and employment effects originating from the analyzed institution. Indirect effects arise from the university’s material and construction expenditures. Moreover, in order to capture the demand effects at large, the term is meant to comprise demand effects of people employed through third-party funds, in outsourced institutions, in research centers, and in spinoffs as well as students and visitors of medical fairs. Induced effects, for example, in form of increasing employment, result from this people’s consumption expenditures and the related demand of goods and services. Income effects describe direct, indirect, and induced incomes resulting from the existence of the analyzed institution. Consumption effects describe this people’s consumption expenditures separated into different sectors (cf. Wiesmeth et al. 2018 for further details). In the end, indirect and induced effects result again in demand effects, and direct, indirect, and induced effects comprise total demand effects, associated with this institution. In order to focus on the aspects of relevance for a smart institution, this analysis considers in particular “employment effects” that are again composed of direct employees (university hospital staff), indirect employees (university hospital staff paid by third-party funds, staff in supply firms, as well as staff in research centers and spin-offs), and induced employees, the latter being employed because of the staff’s consumption expenditures (cf. again Wiesmeth et al. 2018 for more details). The results based on total demand effects show that UML reveals an employment multiplier of 1.93, and SSMU of 1.56, implying that each full-time position in the hospitals leads approximately to an additional full-time position in the vicinity of UML and to an additional half-time position in the vicinity of SSMU. A more careful analysis shows that UML succeeds in attracting more additional research institutions, although SSMU supports more employees in the supplier industries. These core results are presented in Figs. 1 and 2 (cf. again Wiesmeth et al. 2018 for more details regarding this case study, in particular regarding the calculation of the multiplier). Summarizing, the analysis of these example institutions points to significant differences regarding supply effects and demand effects originating from and associated with these university hospitals. Thus, in view of the introductory remarks to this chapter, it should be the concern of “smart” cities to make “smart” use of their institutions, such as university hospitals, but also other institutions, which are

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Fig. 1 Employment multiplier and further results of the case study for UML. (Source: Own drawing after Wiesmeth et al. 2018)

Fig. 2 Employment multiplier and further results of the case study for SSMU. (Source: Own drawing after Wiesmeth et al. 2018)

dependent on modifiable framework conditions to leverage the creative potential. This is, by the way, the motivation for the particular case study approach adopted in Wiesmeth et al. (2018). Of course, a more detailed analysis regarding the reasons for the observed differences is required to provide further insight into the relevant framework conditions.

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Before the following short analysis of the framework conditions, an indicator for a smart institution is proposed. In the sense of the “no theory first” research design, this working definition is based on the results of the above case study, but also on various proposals in the literature. Thus, this index is applicable to other researchoriented public institutions. It mainly allows a comparison of institutions operating in more or less identical fields. The question of an appropriate indicator for smart institutions in other sectors will be addressed in section “Smart Institutions in Various Sectors of the Economy.”

An Index for Smart Institutions Mori and Christodoulou (2012) discuss “conceptual requirements” for a City Sustainability Index (CSI), thereby reviewing some existing indicators in this context. They refer in particular to the – regarding sustainability – necessary consideration of environmental, economic, and social aspects, of external impacts on areas beyond the city, and of applicability of the index both in developed and developing countries. Other contributions, just to mention two of them, propose a method for selecting a set of sustainable development indicators (Tran 2016) or explore “flexible” indicator systems for a sustainable development in cities (Lützkendorf and Balouktsi 2017). This literature on indicators for a sustainable development is certainly of relevance for smart cities in view of their sustainability goals. For this reason, these indicators might also be considered for this case of smart institutions. However, taking into account the somewhat smaller range of a smart institution and its specific tasks, a simpler index or indicator might do as well. As the “optimal” functioning of such an institution is of interest, the employment multiplier presented in the last section will be used as an indicator characterizing a smart institution. This implies, however, that this index is to some extent dependent on the context of public research-oriented institutions with a certain number of employees financed by the public administration of the city, the region, or the country. It remains to be seen how far this definition can be extended to include other smart institutions (cf. section “Smart Institutions in Various Sectors of the Economy”). Definition: The employment multiplier derived for a certain research-oriented (public) institution indicates in particular to what extent this institution succeeds – under the given framework conditions – to generate additional employment opportunities in the institution, in research centers and spin-offs, and in the supplier industries. This indicator is characterized by the following properties: first of all, although it is not really possible to use this index to compare fundamentally different organizations – the employment multipliers may fundamentally differ across economic branches – it allows the comparison of institutions, which provide similar services

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or goods. This is the result of the case analyzed in the last section: obviously different exogenous and endogenous framework conditions lead to different levels of the employment multiplier for UML in Leipzig and SSMU in Tomsk. Moreover, this index clearly focuses on economic aspects, and environmental and social aspects might, however, be indirectly included: if the activities of an institution spill over to environmental organizations, which profit from a corresponding collaboration and react with additional jobs, then this environmental aspect is also mirrored in the index. UML, for example, collaborates with the Helmholtz Centre for Environmental Research – UFZ (cf. also section “Framework for Smart Institutions” for more details). A similar consideration holds for social aspects: as soon as spillovers of an institution to the social agenda lead to jobs, they are “captured” by the index. In the context of the university hospitals, a cooperation with special care homes in the vicinity could yield such a result, for example. Also, the quality of services, for example, medical services, provided by an institution, is mirrored in this indicator: excellent services tend to attract thirdparty funds from the industry or invite research centers for collaboration – with further employment opportunities. Another issue addresses the regional impact, which is of relevance for the size of the index. The definition of this index refers, of course, to a certain region. In the case of the university hospitals investigated in the last section, the regions under study were the Free State of Saxony for UML and the Region of Tomsk for SSMU. However, different geographic delimitations of the areas under study are possible, to the greater city areas, for example, or to Central Germany instead of the Free State of Saxony, in order to better understand the economic importance of an institution for a city or the extended region. And, finally, the index can be applied to similar institutions both in the developed and the developing world, thereby possibly allowing interesting insights into the dependence of the index on the level of economic development. Once again, the results of the last section point exactly to such a situation: the differences regarding the employment multiplier for UML and SSMU are, likely, also a consequence of the differences in economic welfare – despite the fact that both hospitals provide maximum medical supply and access to high-performance medicine. This aspect deserves indeed some further analysis and is left for future research. In summary, this index is certainly not an index measuring sustainability of a (smart) city with all its pillars. Despite of its simple structure, it nevertheless allows interesting insights into the “smartness” of a public institution. It also allows, at least in principle, an investigation of the level of smartness depending on the relevant framework conditions. In this context, for example, it would certainly be interesting to understand in more detail the economic background of the different levels of the index for UML and SSMU discussed in the last section. It is also important that this index is incorporated into structures of local governance and that it therefore helps “to aid the evaluation of policy” (Holman 2009). A thorough comparison of the indices for various institutions enables policy makers internal and external to the institutions to adjust or redirect framework conditions –

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thereby attempting to raise the level of smartness of an institution. Consequently, this index allows a feedback loop in the sense of Goorha and Mohan (2016). This aspect will now be investigated in the following section. First, relevant framework conditions will be analyzed briefly in the context of the above case study, before a more recent case study with data for UML from 2017 will be investigated.

Framework for Smart Institutions In this section the effect of relevant framework conditions on the smartness of an institution measured by the index, the employment multiplier, will be investigated. The ultimate goal of this analysis is to understand these effects, also their strength, and their dependence on other economic variables, for example, GDP per capita. Some of these framework conditions are exogenously given, such as the density of the population; others, such as structural properties of the system of higher education, or the level of innovation activities in research institutions and companies, can, however, be modified, at least in the long run. This provides policy makers with some tools to raise the level of smartness of certain institutions and thereby that of the (smart) city in consideration. The following analysis first returns to the university hospitals UML in Leipzig, Germany, and SSMU in Tomsk, Russia, in order to gain some preliminary insight into this important context. These considerations help to learn more about how to make best use of this index. Thereafter, based on this additional information, the indices for UML for 2 different years, 2009 and 2017, will be investigated and compared, and relevant changes analyzed. The influence of completely exogenous factors, such as geography, demography, or modalities of access to the institutions, can thereby be neglected. Consequently, the focus remains on aspects of stakeholder integration by means of appropriately adjusting framework conditions.

Framework Conditions for UML and SSMU The “smartness” index reveals substantially different values for UML and SSMU. What are possible reasons? Which framework effects are particularly strong and induce these differences? Of course, owing to the high loss of information, the employment multiplier is only partially applicable as measure to evaluate the economic effects of an institution. In order to extract some additional information, in order to get a feeling for the differences regarding the values for the two institutions, it is meaningful to consider some aspects, which play a role in calculating the indices. This applies, for example, to characteristics of the affiliated research institutions, to the third-party funded research contracts, and to the service providers. In the analysis, the employment multipliers turn out 1.93 for UML and 1.56 for SSMU. The higher employment multiplier for UML likely points to a higher attractiveness of UML for public and private research institutions outside the

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university, for example, for the establishment of the Leipzig Heart Center. Although SSMU is responsible for a significantly higher number of indirect employees in the supplier industry due to large expenses for material and investments, the total number of indirect employments is lower. By additionally considering the thirdparty funded positions, UML is responsible for a larger share of indirect employment. Regarding induced employment, the support of the Leipzig Trade Fair through organizing medical conferences and congresses has to be mentioned. All effects together then yield a higher employment multiplier for UML (cf. Fig. 1 and Wiesmeth et al. 2018). Therefore, the question arises: what makes UML comparatively more attractive to third-party research funding and to outside research centers and spin-offs? Possible answers to this fundamental question could perhaps first be found in geographic differences: the index for SSMU is lower, although the area under study, the Region of Tomsk, is substantially larger, with a much lower density of the population in comparison to the situation of relevance for UML. However, as the economic effects of UML are restricted to the Free State of Saxony, considering a larger region, Central Germany, for example, would rather raise the employment multiplier of UML. Other potential framework conditions inducing this result could refer, for example, to the number of people treated in these hospitals in relation to the populations in the areas under study. However, one should expect that due to the fact that both hospitals are offering maximum medical supply, differences regarding these numbers should not have much consequences for the attractiveness to collaboration and outside research interests. In view of Ostrom (2010), there thus remains an analysis of the relevant framework conditions, which are meant to foster innovations in the medical sector, which, in general, “facilitate the development of institutions that bring out the best in humans.” A brief glance at the framework conditions, which affect supply of innovative commodities in the medical sector in Germany and Russia, will reveal some crucial differences between Germany and Russia. They are briefly documented in the following subsections, again pointing to the issue of a “smart government.”

Public Policy Supporting Innovations in Healthcare in Germany With its “High-Tech Strategy” (HTS), the German government wants to focus “research and innovation policy activities on certain priority task areas and key topics of relevance to society” (BMBF 2018, p. 11). The goal thereby “is not merely to generate technological innovations, but also to set processes of social change in motion, at the same time developing and spreading service innovations and social innovations” (BMBF 2018, p. 13). “Healthy living” is one of the current priority tasks. The government considers personalized medicine and digital networking as key drivers of progress in patient care, with the digitalization of healthcare constituting “one of the greatest challenges facing the healthcare sector in the years ahead” (BMBF 2018, p. 18). The telematics infrastructure shall be extended, and nursing care shall be updated to make more efficient use of information and communication

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technologies. To accelerate deployment of new knowledge and innovative products, scientists from university and non-university research facilities shall collaborate with other research institutions in the area of healthcare. Moreover, a specialized program on medical technology shall help the highly SME-based sector developing viable innovations for the benefit of patients. Thus, this policy of the federal government is certainly meant to further develop institutions such as university hospitals in this sense of smart institutions. The main goal of these activities is to integrate all relevant stakeholders in an appropriate way: researchers at universities and in other research institutions, the industry, and, in particular, the SMEs focusing on innovations in medical technology. This, again, is in agreement with various recommendations to governments and city planners on how to develop a smart city: think integrated, and involve all stakeholders (cf., e.g., SCSI 2019, p. 16). As already indicated, this allows to talk about “smart government” as a facilitator through adjustments of the regulatory framework (Gil-Garcia and Aldama-Nalda 2013). BMBF (2018) considers this approach as a means to attract “brilliant minds” and “creative thinkers” and “to open up new creative forms of collaboration to spur the transfer of ideas into innovations and the implementation of research findings into applications” (BMBF 2018, p. 1). It remains to be seen whether this approach is visible in the development of UML between the years 2009 and 2017.

Public Policy Supporting Innovations in Healthcare in Russia The percentage distribution of gross domestic expenditure on R&D by sector of performance in 2014 shows that in Russia the government sector accounts for 30.5%, in comparison to 15.1% in Germany. The business sector in Russia is with 59.6% not too far below that of Germany with 66.9%. A larger difference results for the higher education sector with 9.8% in Russia compared to 18% in Germany (Russia 2016, p. 254f). In addition, there are various large-scale research institutes such as ROSNANO, ROSATOM, and the Federal Space Agency in Russia, which receive a substantial part of the federal research funding (http://minfin.ru/ru/budget/federal_budget/index. php). These data point to one essential difference between the public research policies of Russia and Germany: universities with their large number of researchers play a more important role in Germany than in Russia. Moreover, SMEs seem to be better integrated into the research activities in Germany, which results, of course, also from the – due to historical reasons – larger share of SMEs in Germany. Therefore, in view of the important aspect of stakeholder integration, relevant stakeholders, in particular researchers at the universities and SMEs, are of higher relevance in innovation policies in Germany. In conclusion, this stronger “integrating” policy in Germany might help to explain the significant number of research contracts and collaborations of UML, in addition to the research proximity of the Leipzig Heart Center. The following section explores in more detail the development of UML on its way towards a smart institution.

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Framework Conditions for UML in 2009 and 2017 The comparable analysis of UML with data from 2017 leads to the following main results graphically represented in Fig. 3 (corresponding to Fig. 1) and Fig. 4. First of all, Fig. 4 reveals with 2.18 a significantly higher employment multiplier for UML in 2017 in comparison to 1.93 in 2009. A closer inspection of the results for the 2 years shows, in particular, that there is, first of all, an increase in the number of direct employees between 2009 and 2017. This larger number of direct employees in 2017 is associated with an even stronger increase regarding the numbers of indirect employees. Accordingly, UML continued to attract additional research activities – first through a larger number of third-party funded research projects. Also, various research centers in the vicinity of UML responded with an increase in the number of employees. This is true for the Leipzig Heart Center, in particular. In addition to that, the number of medical conferences and congresses at the Leipzig Trade Fair has increased substantially between 2009 and 2017, raising consumption effects, thereby inducing further employment effects. What are major reasons for this development? Some of the changes in relevant framework conditions since 2009 will be addressed in the following subsections.

Outstanding Academic Performance of UML Between 2009 and 2017, the number of direct employees (full-time equivalents) increased from 3.604 to 4.139. The more than 500 new positions comprise more than 110 additional physicians, who help to extend the research activities of UML and attract new research projects. In particular, the establishment of the Clinic of Angiology and the Leipzig University Cancer Center has attracted additional renowned researchers.

Fig. 3 Employment multiplier and further results of the case study for UML in 2009. (Source: Own drawing after Wiesmeth et al. 2018)

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Fig. 4 Employment multiplier and further results of the case study for UML in 2017. (Source: Own calculations and drawing)

Various reasons were, of course, responsible for this increase in the number of direct employees at UML. However, one of them was the extension of the medical supply, at least for the years to come, into important and promising fields, such as angiology and cancer research. Consequently, the number of third-party funded positions in UML increased as well from 339 in 2009 to 462 in 2017. The number of scientific publications in renowned journals increased similarly and accompanied the institutional development. This number increased from 1.326 contributions in 2009 to 1.572 in 2017, which is well above the average within the university medicine in Germany (cf. [in German] http://www.landkartehochschulmedizin.de). Of course, this positive development was fueled by the outstanding reputation, UML gained in the scientific community over the last decades. Thus, not surprisingly, an excellent academic performance, also resulting from a corresponding hiring policy, is one of the most important drivers or framework conditions for a successful further development of an academic institution towards a smart institution.

Openness of Academic Institutions for Collaborations Since 2009, UML has extended its relations and collaborations with various academic institutions in its vicinity, thereby again integrating additional stakeholders into its activities. The following information on selected collaborative research projects shows that the “openness” of the research institutions for an intense collaboration is crucial for this extension of the range of scientific activities: (a) Innovation Centre Computer Assisted Surgery (ICCAS): this is a research initiative at the University of Leipzig. It integrates researcher and surgeons from UML and further computer scientists and engineers from the University

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of Leipzig and the Leipzig University of Applied Sciences (https://www.iccas. de/?lang¼en). (b) Leipzig Research Center for Civilization Diseases (LIFE): there is a large number of internal and external research institutions linked together in the LIFE-network, among them are the Leipzig Heart Center, the Helmholtz Centre for Environmental Research – UFZ in Leipzig, the Max Planck Institute for Human Cognitive and Brain Sciences in Leipzig, and various institutes of the University of Leipzig and the Leipzig University of Applied Sciences (http://life. uni-leipzig.de/en/). (c) Interdisciplinary Centre for Bioinformatics (IZBI): this institution is an interfaculty research unit of the University of Leipzig integrating researchers from UML and from other parts of the University of Leipzig and the Max Planck Institute for Mathematics in the Sciences in Leipzig (http://www.izbi.unileipzig.de). (d) Bio City Leipzig (BBZ): professors at UML are involved in research at the Bio City, supporting also young researchers (https://bio-city-leipzig.de/welcome). This continuously extended cooperation of UML with other research institutions, in particular with those located in Leipzig, induced additional research positions outside UML, but nevertheless closely associated with it. Moreover, younger scientists are integrated into these activities by means of Junior Research Groups, and further projects, such as the Helmholtz Institute for Metabolic, Obesity and Vascular Research (HI-MAG), founded in 2018, will in near future contribute towards an even more intense collaboration of UML with other research institutions – in the region and beyond (https://www.helmholtz-muenchen.de/hi-mag/index.html). These positive results are a consequence of the “openness” of the research institutions enabling the integration of relevant stakeholders – thus a very important framework condition for the development of a smart institution.

Willingness to Cooperate Across Disciplines Clearly, the cooperation across disciplines in larger projects is often facilitated through personal contacts of researchers from different disciplines. Thus, an academic institution, a university, for example, should provide for appropriate framework conditions. This applies, in particular, to university medicine, which is often considered to engage more in applied research in comparison to the faculties in natural sciences, which are more often involved in basic research projects. This is true for UML, where third-party funding of (applied) research is dominated by the German Federal Ministry of Education and Research with approximately 28% of third-party funds in 2017 (UKL 2017). So far, UML collaborates with researchers from other parts of the university in various research centers, some of them characterized above. In addition, the Centre for Obesity Research integrates researchers of UML and University of Leipzig. This is a positive observation although there seem to be possibilities for intensifying these contacts. As it includes a further interesting potential for collaborative research activities across disciplines, this deserves a closer attention in the near

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future. Joint activities with researchers from biology, biochemistry, and bioinformatics seem to play a dominating role. Appropriate framework conditions could help to encourage further cross-disciplinary collaboration and help to exploit this potential for a smart institution. Thus, the German High-Tech Strategy (cf. section “Public Policy Supporting Innovations in Healthcare in Germany”) did not yet satisfactorily address this issue.

Support from Service Providers: The Leipzig Trade Fair Leipzig has always been known for its location at the crossroads of Via Regia and Via Imperii, two medieval roads with a large economic significance for interregional trade. This location helped to establish the traditional Leipzig Trade Fair some 850 years ago. After the German reunification, the fair gradually assumed its former role, meanwhile providing services for all kind of fairs. The medical sector has gained special importance in recent years, based on a closer and closer cooperation with UML. For this reason, expenses of exhibitors, guests, and visitors of medical conferences and congresses have almost doubled between 2009 and 2017, adding up to more than 14 million € in 2017 (cf. also UKL 2009, 2017 for more information on medical conferences and fairs). Moreover, the fact that many international conferences related to medical aspects, such as Arab Health (January 2019), Russian Forum on Prosthetics and Orthotics (June 2019), and Beauty and Health (October 2019), just to name a few, are scheduled each year encourages perhaps sustainable contacts between researchers from UML and from many other parts of the world. Thus, the increasing cooperation between Leipzig Trade Fair and UML helped, on the one hand, to develop an excellent infrastructure and perfect services and raised, on the other hand, not only the level of awareness for UML in Germany and abroad but probably also the attractivity of UML for researchers elsewhere. Consequently, the Leipzig Trade Fair has to be counted – as an important provider of services – among the important framework conditions with a positive effect on UML as a smart institution.

Other Framework Conditions of Relevance for a Smart Institution The above analysis of the development of UML from 2009 to 2017 has shown that this institution is on a successful track towards a smart institution. There are, however, as indicated above, a few issues, which need to be taken care of. Regarding the internal organization of UML, framework conditions should be adjusted to: (a) Encourage more direct cooperation between researchers at UML and researchers from other faculties of the University of Leipzig and other academic institutions. (b) Increase the share of third-party funding for projects with a stronger focus on basic research.

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Both issues will tend to raise the employment multiplier: in the first case, crossdisciplinary cooperation will eventually induce larger, externally funded research projects, and in the second case, the additional knowledge and competencies acquired through basic research will help to get additional funding for more applied projects. Regarding the external environment, the financial means for the Medical Faculty from public sources have to be considered more carefully. In 2010, the Medical Faculty received a grant from the Free State of Saxony of approximately 47.0 Mio. Euro (after 53.0 Mio. Euro in 2009) to cover running expenses. This grant was raised to 60.6 Mio. Euro in 2017. However, taking into account the rate of inflation (consumption price index), the Medical Faculty received only 55.4 Mio. Euro in 2017 – with the purchasing power of 2010 Euros. If one uses the rate of growth of the GDP of the Free State of Saxony as a deflator, then the grant provided in 2017 corresponds to the grant provided in 2010. Thus, between 2010 and 2017, the public grant did not rise faster than GDP. In view of the results obtained and discussed above, UML should be treated as a serious economic factor with a significant rate of return, measured by means of the employment multiplier. The final framework condition to be introduced refers therefore to finances from the public administration, acting as “smart government”: in the current situation of UML, additional public funds for attractive medical fields at UML will likely increase all kinds of research activities with a significant return for the medical sciences and for the medical supply of UML, but also for the economy of the Free State of Saxony. But, of course, the question where to put available, additional funds is also a matter of opportunity costs and cannot be answered without a detailed analysis of the regional economy.

Smart Institutions in Various Sectors of the Economy The empirical analysis regarding smart institutions so far focused on university medicine and on public institutions with research activities. This focus allowed the introduction and consideration of an index of smartness, the employment multiplier, with properties postulated in the literature. Nevertheless, smart institutions, operating under malleable framework conditions with a research-orientation and the goal to enable the creative potential of the employees, are also of relevance and importance in other sectors of the economy. This section briefly addresses smart institutions in the educational and health sectors, in industry, and in business, in addition to the important role the government is assigned in this context, pointing to the potentially increased vulnerability of (smart) cities through acts of terrorism and cyberattacks. Moreover, other potential indicators of smartness, such as rankings or any indicators based on profits of private business companies in a market economy, are investigated. In view of this more

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introductory approach, a more careful analysis of these and related questions pertaining to smart institutions in other sectors is left for further research. (a) Education: For public and research-oriented institutions in the educational sphere, the approach adopted here can be applied immediately. As all these institutions are dependent on framework conditions, which can be affected by the public administration, a feedback loop “to facilitate the development of institutions that bring out the best in humans” (Ostrom 2010), characteristic of a smart institution, is guaranteed. In case the institution is private, but not for profit, then the methodology remains in principle applicable, as long as there is a basic funding for personnel, material costs, and investments from the shareholders. However, it remains open, to what extent the shareholders are capable and willing to adjust the framework conditions to develop the institution towards a smart institution. Peculiarities of educational, research-oriented institutions, such as excellent performances in teaching and research, are respected in the smartness indicator, as they tend to attract further research institutes in the vicinity and/or the collaboration with other research institutes and the industry and third-party funding. The situation with private, for-profit institutions in education will be addressed below. (b) Health: Similarly, for public, research-oriented institutions providing health services, the procedure chosen here is again immediately applicable. Adjustable framework conditions allow the feedback loop, which is characteristic for a smart institution. Private, not-for-profit institutions can also be adequately respected, if the shareholders are willing to steer the institution towards a smart institution. An outstanding record regarding the provision of health services or research activities will affect the smartness indicator: other service providers such as special care homes or retirement homes will settle in the vicinity, or industry will look for collaboration. Again, the situation with private, for-profit institutions in education will be addressed below. (c) Industry and Business: In the assumed context of a market economy, the situation becomes somewhat different. For industry and business companies, also in the educational fields and the healthcare sector, profit orientation changes the picture. Of course, these institutions can react and do react upon framework conditions, and adjustments to changing market conditions are frequent. However, that’s the crucial difference to public institutions: these adjustments are typically initiated and governed by the motif to collect additional profit. This is, of course, absolutely justified in the context of a market economy, where business companies are free to choose their production activities and the public administration has only limited possibilities to affect the development of a particular institution towards a smart institution. However, as public goods tend to play role for a smart city, there might be a discrepancy between the operations of a private, for-profit institution and the focus of a smart institution.

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The consequence might be that business companies, on the one hand, offer their products and services for the development of a smart city, for example, in the fields of ICT, and claim even to be decisive in this context, but do not seem to get too much involved as a “smart institution.” Indeed, according to Frost and Sullivan (2019), who consider a smart city from a technological point of view, the smart city market is estimated to be at 1.56 trillion US $ by 2025. Smart metering, wireless sensor networks, and high-speed broadband, are, among others, the key building blocks of a smart city. Thus, it seems that the main task of industry and business consists in supporting the development of smart institutions and smart cities by means of technological innovations. There is, however, one interesting observation based on the brief analysis of public policy supporting innovations in healthcare (cf. sections “Public Policy Supporting Innovations in Healthcare in Germany” and “Public Policy Supporting Innovations in Healthcare in Russia”). Although in a free market economy the government can typically not interfere with the operations of a single private company, it can nevertheless attempt to steer a whole branch of the private economy in a certain direction. Among the prominent examples are healthcare and renewable energies. Thus, it seems to be possible to develop research-oriented private business into smart institutions. This requires again further analysis. The question regarding an appropriate indicator of smartness also remains to be investigated. Some remarks on indicators based on profit will be made below. (d) Government: As indicated occasionally throughout the text, public administration and government have an important role regarding the development of smart institutions. After all, it is their task to adjust the framework conditions for those institutions, which operate with public funds. Therefore, if they act as a facilitator through adjustments of the regulatory framework (Gil-Garcia and Aldama-Nalda 2013), thereby bringing out the best in humans (Ostrom 2010), they could be considered “smart government” or a “smart government institution.” There is, however, another aspect, which a “smart government” has to tend to: the increasing vulnerability of smart cities regarding terrorism and cyberattacks – also as a result of large investments in advanced infrastructure, in particular ICT. Urban security has to become top priority, although spending on public safety varies considerably among countries with the USA leading in terms of per-capita expenditure (Audier et al. 2017, p. 3). What needs to be done seems to resemble, even match the approach taken here: relevant stakeholders have to get involved to foster network collaboration. The government must promote collaboration among private security companies, business companies, and citizens “through information sharing, integrated tools (notably, to support command and control), incentives, and clearly defined roles” (Audier et al. 2017, p. 7). Thus, the government is obliged to make the required changes to the framework conditions in order to provide optimal protection, also through the expected reaction of the stakeholders upon these changes. This corresponds perfectly to the ideal of a “smart government.”

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The question of an appropriate indicator for the degree of smartness of an institution has already been addressed: for public, research-oriented institutions, the employment multiplier can be applied to measure smartness. However, for institutions such as universities in general, and research institutes in particular, there is also a multitude of rankings, evaluating their performance, in order to allow for national and international comparisons. Regarding universities, such rankings usually take into account performance indicators such as teaching, research, knowledge transfer, and international outlook with the weights for the various subcategories usually determined exogenously. Occasionally, also the opinion of peers is respected. Of course, these rankings measure important aspects of the performance of the institutions. Nevertheless, they do not seem to capture all aspects of relevance for a smart institution in the sense of the approach chosen here. For example, the methodology of the World University Rankings 2019 measures knowledge transfer mainly through industry income. Thus, research institutions settling in the vicinity of a university to profit from the proximity are not directly respected (cf. https:// www.timeshighereducation.com/world-university-rankings/methodology-world-uni versity-rankings-2019), although these “clusters” result from the research activities of the university and are of utmost importance of a smart institution. But, again, these rankings have a different focus and different goals and should, therefore, not be expected to function also as a primary indicator of a smart educational institution. As for-profit institutions operating in a competitive environment do not a priori count among smart institutions due to the feedback loop in a free market context focusing on profits and not directly on the requirements of a smart city, any indicator based on profits seems not appropriate as an indicator of smartness. To repeat and to emphasize, this does not mean that private business companies are not important for the development of a smart city or that the profit goal is not of relevance for these companies; it rather means that the profile of a smart city goes beyond private business interests. However, as indicated above, if public policy affects a whole branch of the private sector, then the question of an appropriate indicator comes up again and should be investigated. These are some preliminary reflections on smart institutions in various sectors of the economy. These thoughts clearly deserve and require further analysis.

Conclusion In this chapter, the economic performance of institutions, in particular researchoriented public institutions, was considered a significant cornerstone of a development of a city towards a smart city. The reason is that a smart city is dependent on the support from its citizens, in particular, from the creative class. This motivates the consideration of smart institutions, of institutions, which operate optimally under appropriate framework conditions. Smart institutions

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motivate employees and enable, in particular, researchers in universities, companies, and other institutions to fully employ their capabilities to the benefit of the society. Consequently, the chapter considers smart institutions in the context of a smart city. In view of the literature, these institutions adequately integrate – through appropriate framework conditions – relevant stakeholders to leverage the collective intelligence of the institution for the benefit of society. This characterization points first of all to public, research-oriented institutions. In order to allow a more careful analysis, case studies support and illustrate the theoretical concept of a smart institution and a corresponding indicator based on the employment multiplier. The case studies refer to particularly important institutions: university hospitals and medical faculties of universities offering maximum medical supply and access to high-performance medicine. The first case study compares UML with SSMU and allows some insight into the effects of differing framework conditions – some of them exogenous. The second case study considers UML at different periods of time. The ensuing analysis illustrates the role of various internal and external framework conditions in the context of a smart institution. Relevant, malleable framework conditions point to incentives for further collaborations, for integrating further researchers – internally and externally – requiring thus a certain openness of these institutions. Of importance is also the support of external service providers, the Leipzig Trade Fair in the case considered here. And finally, financing from public sources should take into account the economic impact of these institutions, which are often only considered as cost factors: they should provide their core services at minimum costs to the taxpayer. Finally, the context of institutions in various sectors of the economy is considered, regarding the potential for smart institutions. Whereas public, research-oriented institutions pose no problem, private companies in a market economy require special attention. The goal to generate profit need not always be in line with the provision of public goods also characterizing smart institutions and smart cities. A public policy addressing branches of the economy may, however, help to direct these institutions towards smart institutions. A brief discussion illustrated that indicators such as rankings or those based on profits are not really applicable as indicators of the smartness of an institution – they serve different goals. In summary, this chapter introduces smart institutions and demonstrates the dependence of the level of the smartness of an institution on framework conditions, which can be adjusted to increase economic performance. This fits seamlessly into the concept of a smart city. Further research should be directed to smart government and smart institutions in other sectors of the economy, especially to institutions in the private sector. Moreover, there is a need to introduce a more general indicator for smart institutions, which should, optimally, be compatible with the employment multiplier proposed here.

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Cross-References ▶ Corporate Social Responsibility (CSR): Governments, Institutions, Businesses, and the Public within a Smart City Context ▶ Smart Cities Can Be More Humane and Sustainable Too ▶ Smart Cities: Fundamental Concepts ▶ Smart City Wien: A Sustainable Future Starts Now ▶ Smart Energy Frameworks for Smart Cities: The Need for Polycentrism

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UKL. (2017). Jahresbericht 2017. Wertschätzung [in German]. https://www.uniklinikum-leipzig. de/Seiten/jahres%2D%2Dund-qualitaetsberichte.aspx. Accessed 14 May 2019. Wiesmeth, H. (2018). Stakeholder engagement for environmental innovations. Journal of Business Research. First online published December 24, 2018. https://doi.org/10.1016/j. jbusres.2018.12.054. Wiesmeth, H., Fiala, O., Stegareva, E., Häckl, D., & Weinhold, I. (2018). Smart institutions for smart cities. IOP Conference Series: Earth and Environmental Science (Vol. 177). https://doi. org/10.1088/1755-1315/177/1/012003. Wilhelm, R., & Ruhlandt, S. (2018). The governance of smart cities: A systematic literature review. Cities, 81, 1–23. https://doi.org/10.1016/j.cities.2018.02.014. Williamson, O. E. (2009). Pragmatic methodology: A sketch, with applications to transaction cost economics. Journal of Economic Methodology, 16, 145–157. https://doi.org/10.1080/ 13501780902940729. Yigitcanlar, T., & Lee, S. H. (2014). Korean ubiquitous-eco-city: A smart-sustainable urban form or a branding hoax? Technological Forecasting and Social Change, 89, 100–114. https://doi.org/ 10.1016/j.techfore.2013.08.034. Yigitcanlar, T., Kamruzzaman, Md., Buys, L., Ioppolo, G., Sabatini-Marques, J., Moreira da Costa, E., & Yun, J. J. (2018). Understanding ‘smart cities’: Intertwining development drivers with desired outcomes in a multidimensional framework. Cities, 81, 145–160. https://doi.org/ 10.1016/j.cities.2018.04.003.

Part II Current Exemplary Smart Cities

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Smart City Edmonton Katie Hayes, Soumya Ghosh, Wendy Gnenz, Janice Annett, and Mary Beth Bryne

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Definition: A Smart City is a Healthy City . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Guiding Principles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smart City Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smart City Maturity Matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smart City Ecosystem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smart City Achievements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Edmonton’s Smart City Projects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Data and Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Data Accessibility and Sharing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shareable Solutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Future-Proofing Technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Inclusive and Accessible Solutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Data and Technology Partnerships . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Standards for Data and Technology Solutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Privacy, Security, and Ethics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Data Governance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Edmonton’s Open City Initiative . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Security, Privacy, and Ethics Considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Resident and Community Engagement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Engagement Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Engagement Activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Inclusive Engagement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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K. Hayes (*) · S. Ghosh · W. Gnenz · J. Annett · M. B. Bryne Open City and Technology, City of Edmonton, Edmonton, AB, Canada e-mail: [email protected] © Springer Nature Switzerland AG 2021 J. C. Augusto (ed.), Handbook of Smart Cities, https://doi.org/10.1007/978-3-030-69698-6_17

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Abstract

As a world leader in leveraging data, technology, and innovation, the City of Edmonton (the City) recognizes the need for an approach that is communitydriven, evidence-based, and delivered through partnerships in order to achieve sustainable solutions. As a global leader in open data, open government, digital innovation, and being a Smart City, Edmonton demonstrates an unwavering commitment to being a progressive and collaborative learning organization. The City envisions a community that thrives and is united, not divided, by data, information, and digital technologies. Edmonton is a city for all, connected, and healthy. In order to achieve this, the City works with partners to break down traditional barriers to improving the development and delivery of policies, programs, and services.

Introduction Modern municipalities operate in a period marked by a rapidly changing business environment, a call for more open and interactive government, and an everincreasing need to work collaboratively to address the complex challenges of today and tomorrow. A smart city approach aims to achieve meaningful outcomes for residents by leveraging the fundamental benefits of data and technology.

Definition: A Smart City is a Healthy City Edmonton is currently undergoing a resident-led digital transformation. To continue to provide value to residents, the City recognizes it must be a nimble organization – continuously evaluating and embracing the endless possibilities that accompany change. As a result, the City created the Business Technology Strategy, the first of its kind in Canada, to guide the use of data, business solutions, and diverse technologies to improve life in Edmonton. As a digital city, Edmonton is embracing new ways of delivering programs and services to address the challenges of the day with residents at the core. Complementing the Business Technology Strategy is Edmonton’s Smart City Strategy – an innovation ecosystem of government, academia, residents, and industry – that follows the International Organization for Standardization Standard 37106 (2018). It is not just about the administration of municipal programs and services; it is about Edmonton as a thriving community. Edmonton is a creative community of changemakers and social innovators – where residents are engaged with their community and lead the charge for a better future. The City of Edmonton addresses today’s challenges and creates tomorrow’s opportunities through collaboration and innovation.

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For Edmonton, a Smart City is not just about technology. It is about creating and nurturing a resilient, livable, and workable city through the use of technology, data, and social innovation. According to the World Health Organization, urban populations experience some of the world’s most prominent health disparities. Residents are faced with increasing urban health hazards resulting from inadequate housing, transportation, food, and environmental systems including air pollution, unhealthy diets, physical inactivity, and isolation. Now, in the midst of the digital revolution that is transforming how individuals interact, communicate, and connect, cities are also faced with understanding the technological challenges affecting the health of residents and how to lessen the impact of the digital divide. Although technology is an integral part of building a smart and connected city, there are several nontechnical components that work together to complete a Smart City ecosystem and become catalysts for innovation. These components range from the creation of public spaces where residents can come together to gain a sense of community belonging to the partnerships that will continue to drive the transformation of today’s urban physical and digital environments. The City of Edmonton actively creates opportunities for diverse input and participation by inviting residents to play a larger role in shaping their community to enable social and economic growth and impact environmental and health outcomes. The City of Edmonton proposes that municipal-level interventions guided by residents will have a significant impact on building healthier cities and will improve the quality of life for residents today and into the future. Edmonton’s innovative Smart City approach to improve health through preventative measures addresses the true needs of the community through a collaboration between public and private sector organizations and residents. This approach, enabled by technology, analytics, and data, will ensure Edmonton is a place where all residents have equitable opportunity for healthy, safe, and joyful lives.

Guiding Principles The principles shown in Fig. 1 guide the ongoing development of Edmonton’s Smart City program. These principles emphasize the City of Edmonton’s commitment to building a healthier, more connected city with residents and partners, inciting innovation within the region and beyond. These guiding principles are directly aligned with the foundational principles of the City of Edmonton’s Business Technology Strategy – a City Council-approved strategy that enables a fully integrated approach to managing information, data, and technology. The City has a significant amount of valuable data, business solutions, and diverse technologies. To better leverage these assets, the application of the Business Technology Strategy increases internal and external data sharing, optimizes processes, and delivers quality service while managing costs effectively – all in partnership with stakeholders and residents.

Fig. 1 Smart City Guilding Principles

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Smart City Framework Edmonton’s Smart City Framework is a holistic approach to working collaboratively with residents and partners to optimize the use of data and technology and influence the development of policies, programs, services, and innovative funding models. Working with residents and partners, the City of Edmonton developed this approach to leveraging data, technology, and innovation in order to provide an exceptional quality of life for residents. This framework is the foundation for Edmonton’s phased approach to the building of a healthier, more connected city. This framework is the foundation for Edmonton’s Smart City approach to building a healthy, more connected city (Fig. 2).

Smart City Maturity Matrix The Smart City Program uses the following maturity matrix as a self-assessment to understand the current state of the organization and establish tangible goals for development (Fig. 3).

Smart City Ecosystem A city of the future – a Smart City – is a Healthy City. It is one where residents, industries, academic sectors, and government work collaboratively to learn about the

Fig. 2 Smart City Framework

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Fig. 3 Smart City Maturity Matrix

challenges the city is facing and create, test, and scale sustainable solutions. A Smart City identifies the transformational shifts required to boldly challenge the status quo and build an inclusive and digitally enabled community. Together with residents and partners, a Smart City creates and nurtures a resilient, livable, and workable community that rises to the challenges being faced today, enhances the vibrancy and diversity of the city, and embraces the opportunities of tomorrow. Cities are in the unique position of working directly with residents and the local built environment to use technology and innovation to revolutionize the urban setting and improve the health of residents. A city of the future – a Smart, Healthy City – recognizes this incredible opportunity to identify and intentionally advance transformative priorities with residents, not for them. Through established

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Fig. 4 Innovation Ecosystem

mechanisms and community partners, the City is uniquely positioned to understand the health of its residents and quickly test interventions at the neighborhood level, measuring outcomes and reporting results to inform decision-making in order to scale solutions effectively. The City of Edmonton continues to advance the development of the Smart City Ecosystem. This ecosystem comprises public sector organizations, private sector organizations, academic institutions, and residents. The ecosystem works collaboratively to improve the capacity of all partners while developing efficient and effective ways to provide meaningful services to residents. The growth of the ecosystem will continue by identifying partnerships, opportunities for innovation, and the means by which to improve the efficacy of programs and services (Fig. 4).

Smart City Achievements The City of Edmonton is a world leader in leveraging data, technology, and innovation to improve quality of life for residents. In addition to being named Canada’s Most Open City and a Top 7 Intelligent Community of the Year, Edmonton is the most recent winner of the Gold WeGo Smart Sustainable City Award, the first Canadian city to win the IBM Smarter Cities Challenge Award, and the first Canadian pilot of Johns Hopkins University’s Center for Government Excellence What Works Cities initiative. The following achievements and initiatives demonstrate the City of Edmonton’s readiness to work in partnership with community to continue to lead as a Smart City:

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• The City has led the country in understanding the value of combining open data, public engagement, and analytics. The Open City Initiative is a complex program of work streams and projects spanning all 30+ internal business areas and extending to external organizations through outreach and partnerships. The accomplishments of this initiative are internationally recognized. • With the development and implementation of Canada’s first measurable Open Data Strategy, Edmonton has shown its commitment to transparency and openness. Edmonton’s Open Data Portal was launched in 2010 and has grown to over 2,000 assets with more than 50 million annual transactions. Edmonton’s City Council was also the first in the United States and Canada to adopt the International Open Data Charter. This adoption again demonstrates the unprecedented commitment to accessibility and transparency by City of Edmonton leaders. • The City of Edmonton’s Analytics Center of Excellence (ACE) is worldrenowned for delivering complex and multidisciplinary projects. ACE has completed projects of global significance, including a contextual analysis of crime, development of a human trafficking identification tool, and an optimization model for snow plowing routes. The optimization and analytics models developed through these projects are made available to other municipalities under the Creative Commons license and open-source code. • The City was a finalist in the $50 million category of Infrastructure Canada’s Smart Cities Challenge, a competition open to all municipalities, local and regional governments, and Indigenous communities across Canada to define their future, using a resident-driven, smart city approach. The challenge was based on four guiding principles of openness, integration, transferability, and collaboration. • Edmonton was also the first Canadian city to win the Smart Cities Council of North America Readiness Challenge. More than 100 cities throughout the United States, Canada, and Mexico applied detailing the smart cities projects planned in their communities. Winners were selected based on inclusiveness, sustainability, and impact of their current and planned work. The City of Edmonton continues to deliver complex, multi-stakeholder, and multidimensional projects in partnership with all levels of government, industry, and residents – continuing to use proven mechanisms and processes for project excellence while demonstrating an unwavering commitment to Smart City growth, collaboration, and leadership.

Edmonton’s Smart City Projects Utilizing an extensive body of national and global subject matter experts, academic research, and thought leaders as well as ongoing, focused engagement with residents and stakeholders, the City of Edmonton identified two outcomes that would be the focus for Edmonton’s participation in Infrastructure Canada’s Smart Cities

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Challenge. The outcomes listed below remain relevant as the City continues to work toward achieving Vision 2050 and the goals outlined in ConnectEdmonton, Edmonton’s Strategic Plan 2019–2028. 1. To improve quality of life for residents 2. To transform how municipalities across Canada work with residents and partners to achieve excellence in data and technology Edmonton has made significant progress in achieving both outcomes, as shown through the project work described in Tables 1 and 2.

Table 1 Projects focused on improving quality of life for residents Project or initiative Health Hack Competition – March 2018

Grow with Google – September 2018

HackED 2019 – January 2019

You Can Benefit – ongoing

RECOVER – ongoing

Deliverable The Health Hack competition brought together the civic tech community to build solutions for a healthier city. Five finalists were selected on March 16, 2018, and their ideas included a buddy bench extension program, a cannabis ecosystem, a fitness app for nonathletes, an urban design and mental health app, and a wheelchair accessibility tracker Grow with Google is a series of community events that help Canadians develop the skills they need to prepare for a job, find a job, or grow their business. In this collaboration with the City of Edmonton, Google provided training to local educators, business owners, aspiring technology professionals, and entrepreneurs. Over 400 individuals were able to build community partnerships, learn valuable digital skills, and enhance their career potential through this initiative The City of Edmonton sponsored this student-led Faculty of Engineering initiative at the University of Alberta. Through this sponsorship and engagement at the event, the City of Edmonton raised awareness to 450 attendees for open data and Smart City initiatives. Participants in the hackathon created teams to design and build smart, innovative projects to solve problems important to the community. It is an excellent opportunity for learning through collaboration that leads to positive sociological and psychological outcomes The You Can Benefit online tool helps residents in Edmonton easily access information on municipal, provincial, and federal benefits. You Can Benefit provides Edmontonians access to more than 28 programs and 120 community services in one place, such as the City of Edmonton Leisure Access Program and Ride Transit Program, the Alberta Child Care Subsidy, and the Alberta Seniors Benefit. Several iterations have been introduced to provide better reliability and results RECOVER is a community wellness program developed for the City’s most vulnerable populations. Using a phased approach, the program aims to develop a new, fully integrated approach across a continuum of pre-crisis, crisis intervention, postcrisis, and transitional services

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Table 2 Projects focused on transforming how municipalities work with residents and partners to achieve excellence in data and technology Project or initiative Open Data Citizen Advisory Group – March 2018

Canadian Open Data Summit 2018 – Wendy Gnenz, Canadian Open Data Leader Award – November 2018

City Park Usage – Pedestrian Counter – ongoing

Open Science Partnership with the University of Alberta – ongoing

MetroLab Network partnership with the University of Alberta – ongoing

Deliverable The City, in partnership with the Open Data Citizen Advisory Group, shared insights and feedback on the functionality of the Open Data Portal. The group provided feedback on the look and feel of the tool and file structure allowing the City to make user-centric improvements The Canadian Open Data Summit jury recognized Edmonton’s successes and leadership in the Open Data movement in Canada under the strategic leadership of the City’s chief information officer, Wendy Gnenz. Wendy was the driving force behind advancing the Open City Policy, adopting the International Open Data Charter, and winning Most Open City in the Open Cities Index 3 years in a row Through the Smart Cities program, a prototype was developed for a park pedestrian counter. The prototype uses thermal sensing and image recognition to understand how parks or attractions are utilized. The sensors use a wireless data sharing network to transfer data. The pedestrian counter has gone through a variety of iterations to improve accuracy, and the code was shared through an open-source platform with municipalities globally The City of Edmonton works with researchers at postsecondary institutions to actively promote open data for research purposes. Edmonton’s Open Data Portal is regularly referenced as a source in academic publications. As an example, a University of Alberta professor in Earth and Atmospheric Sciences directed an entire class of graduate students to perform geospatial analysis using Edmonton’s open data. The students’ final work was presented at City Hall with viewers from City Planning Committees, City employees, and the public. The students contributed diverse research and analysis as well as requests for new datasets to be included in the Open Data Portal The MetroLab Network between municipalities and universities focuses on bringing data, analytics, and innovation to local government. These institutions partner together to tackle problems and share solutions and best practices for economic development, resiliency, social equity, transportation, and governance. This initiative aims to positively impact Edmontonians and strengthen the reputation of (continued)

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Table 2 (continued) Project or initiative

Developing Shareable Solutions – ongoing

Deliverable the City as a partner in innovative city-building. Edmonton is the first participating Canadian municipality in the MetroLab Network The City of Edmonton continuously works with partners in developing technology solutions to improve the lives of residents. These solutions are shareable with communities and can be applied utilizing local partnerships and data. Solutions include the Optimized Safe Needle Response and Emergency Operations Demand Dashboard

Data and Technology Progressive organizations around the world continually reimagine themselves through innovative digital tools, systems, and processes. In today’s dynamic environment, it is imperative for municipalities to understand the role technology plays in building smart, sustainable cities and addressing complex societal challenges in a collaborative setting. Urban planning needs to embrace the digital opportunities that contribute to the vibrancy and sustainability of places and refrain from taking a siloed approach to managing investments in connected technology and physical infrastructure. Through the use of data and connected technology, the City of Edmonton is rethinking the planning and development of urban landscapes and the delivery of services in order to avoid the inefficiencies of today and build a healthier, more connected city of the future. The built environment will influence health outcomes and impact the way residents feel, both physically and mentally. Data and connected technology will be used within the Smart City Ecosystem to improve well-being in Edmonton by creating spaces and solutions that are accessible, vibrant, and inclusive, that celebrate the unique features of the City and its residents, and that increase security and reduce isolation. Residents’ relationship with technology is constantly evolving. To meet the diverse and dynamic needs of residents and community, cities must build and strengthen internal and external collaboration and better leverage the data, business solutions, and diverse technologies that exist. Edmonton’s Business Technology Strategy provides a strategic framework to connect all of these pieces in order to transform Edmonton and the region it occupies into a place that meets the expectations of the modern world. The Business Technology Strategy is the foundation from which data and technology decisions for Smart City initiatives are made. Edmonton’s Smart City program also follows ISO 37106: 2018 Guidance on Establishing Smart City Operating Models for Sustainable Communities. The

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premise of the ISO framework is to lead the transformation of the traditional municipal operating model to empower the community through data. It will also break down silos that inhibit truly resident-centric service delivery and enable digital inclusion in ways that are not achievable through traditional technology approaches.

Data Accessibility and Sharing A foundational element of Edmonton’s Smart City program is to increase the capacity for data accessibility and sharing within the Smart City Ecosystem and in municipalities across Canada. Through this increased capacity, the use of data can be optimized to enhance the development and delivery of programs and services for residents, as well as enhance and animate the physical spaces they occupy. This consolidation of disparate data through partnerships also increases the ability for organizations and municipalities to identify gaps and biases and work together to resolve them. Information and data may be shared between partners; however, a formalized data sharing, information management, or research agreement must be in place prior to this occurring and a Privacy Impact Assessment complete, if required. In this event, the data is shared, retained, and refreshed at the source.

Open Data The City of Edmonton currently has over 2,000 data assets in the Open Data Portal. All of this data is publicly available and has been obtained and published under an established set of management controls and approval processes. The Open Data Portal also makes available certain datasets that are external to the City of Edmonton. The Edmonton Police Service, Edmonton Public Library, Alberta Environment and Parks, and EPCOR have all shared data to be used in this tool for residents and community. This data is anonymized and was made available to the Open Data Portal through data sharing agreements with the City of Edmonton.

Shareable Solutions As the data and technology landscape evolves, so does the ability for Canadian municipalities and governments to realize innovative opportunities through the sharing of their experiences and solutions. This begins with sharing code and best practices as municipalities are often striving for similar outcomes through building and procuring the same solutions. However, the truly transformative nature of data and technology is not achieved by simply sharing open-source code. It is done through building a network of cities and their respective innovation ecosystems that will extend and sustain the new digital products that residents expect. This is a new business model opportunity where the efforts of each city and their community partners are multiplied, rather than duplicated. The work of Edmonton’s Smart City

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program includes active engagement of the network and community around the analytic and digital products that are created through all areas of the program. Through the development of shareable solutions, the City of Edmonton ensures transferability and scalability of knowledge, experiences, processes, and solutions across municipalities. As such, solutions and learnings are open, integrated, transferable, and collaborative beyond the traditional municipal boundary and span of control (Table 3).

Future-Proofing Technologies As cities continue to embrace disruptive innovation and technologies, the challenge of future-proofing becomes increasingly complex. The City of Edmonton has developed effective processes to work with the community to identify and test new technologies to advance municipal programs and services. Through the City’s broad partnership base, extensive subject matter expert network, and ongoing engagement with residents, Edmonton has the capacity to recognize potential future-proofing issues and proactively make adjustments to the program in order to ensure ongoing success. With a focus on data sharing enabled through service-oriented and microservices architecture, the City of Edmonton will address the challenge of future-proofing. This approach places a priority on how the interface between systems is created rather than how the specific technology is being used. Systems will evolve and change, but the value created through data sharing is sustained.

Inclusive and Accessible Solutions Smart cities are inclusive and accessible. They develop and use innovative technologies to benefit all residents and create equitable opportunities to live healthy, safe, and joyful lives. The City of Edmonton prioritizes being inclusive and accessible. In 2016, the City of Edmonton’s Open Data Portal was awarded the Canadian Open Data Award for Accessibility by the Open Data Society of British Columbia and Open North. Throughout the development and implementation of technology solutions for Smart City initiatives, the City engages key stakeholders and subject matter experts to offer their perspectives regarding content, accessibility, and usability. The City also works directly with residents to ensure the identification and creation of inclusive technology solutions that are responsive to evolving needs. Understanding and mitigating data biases and gaps also contribute to the accessibility and usability of technology solutions. The City of Edmonton works with subject matter experts to understand how biases in data can be verified and what mechanisms can be put in place to overcome negative effects.

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Table 3 Shareable solutions Project You Can Benefit

Emergency Operations Demand Dashboard

Transit Security Deployment Model

Optimized Needle Response

Description You Can Benefit, a made-inEdmonton web tool, provides individuals, families, and community workers with information on available municipal, provincial, and federal benefits. Built using open-source content, You Can Benefit can be shared with other organizations and municipalities nationwide The wildfire that forced 88,000 people in Northern Alberta to flee their homes in 2016 required a municipal response that was nimble and adaptive. In an effort to support the evacuation efforts, City of Edmonton staff developed and deployed an analytic dashboard to monitor the ever-evolving demands. By consolidating real-time evacuee service reporting, the dashboard empowered municipal decisionmakers with the information necessary to make critical service delivery decisions in uncertain and ever-changing times Edmonton Transit Security adopted the Transit Security Deployment Model, an approach that optimizes the deployment of transit peace officers to trouble locations in a timely manner along the transit network. The Transit Security Deployment Model uses cuttingedge data mining algorithms to automatically analyze current transit incident data to deploy officers where they are needed the most. Since its introduction, Edmonton Transit Security has seen its number of proactive incidents go up by 159 percent, while reactive events have gone down by 52 percent The Optimized Needle Response solution overhauled the municipal strategy that was in place to manage discarded needles by way of leveraging data to forecast anticipated resident complaints and

Ease of replicability/transferability (5 (high) to 1 (low)) 5 – Municipalities who use this code require foundational open data and analytics. A strong and collaborative working relationship with benefit providers is an additional requirement in order to acquire the necessary data

5 – Municipalities who use this code require foundational open data and analytics

3 – Municipalities who use this code must be confident in their open data and analytics maturity

3 – Municipalities who use this code must be confident in their open data and analytics maturity

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Table 3 (continued) Project

Safety Code Inspector

Description incorporate route, needle box, and shift optimization. In addition to increased operational efficiencies, the Optimized Needle Response has resulted in a near $200,000 cost avoidance per year The City of Edmonton performs over 50,000 inspections a year on newly built houses. In order to reduce the burden of this workload while upholding the integrity of the inspections, the City developed a predictive analytics software. This solution is able to identify low-risk safety code inspections, freeing up resources to concentrate on higherrisk safety code inspections. Currently being piloted in Edmonton, the goal is to reduce annual inspections by 10% (5,000) per year

Ease of replicability/transferability (5 (high) to 1 (low))

2 – Municipalities who use this code must be advanced in their open data and analytics maturity

Data and Technology Partnerships As evidenced in Edmonton’s Business Technology Strategy, the City of Edmonton views partnerships as being critical to the advancement of a modern municipal corporation. Specifically, the City has developed strategic partnerships with technology collaborators who are essential to the continued growth of Edmonton as a Smart City. As the regulatory environment for data and technology evolves, the City of Edmonton expects data and technology partners to assist and respond to changing regulations.

Standards for Data and Technology Solutions The City of Edmonton recognizes the importance of understanding and incorporating standards into the development and implementation of data and technology solutions. This section outlines standards and strategies the City of Edmonton uses to ensure interoperability and replicability of all data and technology assets. Data and technology solutions are evaluated based on conformity to the following ISO standards: • ISO 37106 Guidance on Establishing Smart City Operating Models for Sustainable Communities • ISO 27001 Managing Information Risks

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• ISO 27017 Controlling Cloud-Based Information Security • ISO 27018 Protecting Personal Data In addition, the following standards, toolkits, and strategies are also considered as technology solutions are developed. This ensures continued interoperability between the technologies and other existing community systems and services. It also increases the opportunities for infrastructure replicability and scalability. • Report to the Clerk of the Privy Council: A Data Strategy Roadmap for the Federal Public Service • Government of Canada Digital Standards • Canada’s Spatial Data Infrastructure • Canada’s Digital Geospatial Metadata • Canadian Standards for Big Data Analytics • Cyber Security – Government of Canada • CIO Strategy Council • Smart Cities for All

Privacy, Security, and Ethics The quality, reliability, and integrity of information are critical to effective decisionmaking at the City of Edmonton. The City is committed to ensuring compliance with privacy and security standards for obtaining and using data as well as having mitigating controls in place to minimize risk. In addition, the City not only ensures compliance with controls but also prioritizes the ethical use of data. This section outlines how data is governed at the City of Edmonton and provides the framework for how data is managed throughout the implementation of technology innovation and Smart City projects. As an Open City, Edmonton is working to build new ways to share information with residents, find new opportunities for dialogue, and make programs and services easier to access – continuously enhancing the quality and increasing the quantity of information available through open data. By provisioning, delivering, consuming, and crowdsourcing data, the City, along with residents and partners, enhances services, stimulates economic opportunities, encourages innovation, and unlocks new social values. It is this approach that not only positions Edmonton as a leader in open government but allows the City to work collaboratively with other municipalities and communities to share resources and experiences that transform how governments interact with residents and partners.

Data Governance Data governance is a fundamental pillar in the success of digital transformation. The City is a recognized leader in the use of data as a strategic asset, and, from the awardwinning Open Data Portal to the innovative work in the Analytics Centre of

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Excellence, Edmonton has set the bar for municipal data governance considerably high. In recognition of these efforts, Edmonton was selected as the first Canadian pilot city for the What Works Cities Initiative, a program delivered by the Center for Government Excellence (GovEx) at Johns Hopkins University. As a result of this partnership with What Works Cities, the City of Edmonton developed a comprehensive Data Governance Roadmap to guide the work that will enhance the City’s ability to treat data as a strategic asset and lay the foundation for advanced data practices. This roadmap lists major milestones and associated deliverables, the majority of which are underway by a team of individuals dedicated to improving data management practices across the organization. This includes work in the areas of data quality and standards, prioritization for release, privacy and security, and data retention. Edmonton’s progressive data governance practices continue to support the advancement of the City’s open government initiative and leadership as a Smart City.

Edmonton’s Open City Initiative An Open City is a connected city. Edmonton is building an open and connected city, in which residents have the opportunity to collaboratively design, develop, and deliver innovative, inclusive, and efficient public programs, services, and policies. The City’s Open Data Portal was launched in 2010 and was followed by the Open City Initiative – a municipal perspective on the philosophy of open government – in June of 2014. The Open City Initiative guided the development of the Open City Policy which was adopted by Edmonton’s City Council on April 14, 2015. Since that time, the City has continued to progress in its open government journey. The basis of the City’s award-winning Open Data Portal and other Open City projects is that the City’s information is a public asset – consistent with privacy legislation, it exists readily in a portal that Edmontonians can easily find and use in ways that will help improve their quality of life. The City has established an Open Data Advisory Group with representatives from diverse business areas, including privacy advisors, legal advisors, and data stewards. The City has also established an Open Data Citizen Advisory Group where residents are engaged to provide their ongoing feedback and ideas. As an operational body, the Open Data Advisory Group also manages the open data lifecycle through robust data quality review and release mechanisms. In addition, Edmonton’s Open Data program established the Smart City Steering Committee with executive representation from across the City of Edmonton. The Committee oversees and supports the Open Data program as it achieves its goals and vision. By providing leadership support to the Open Data program, the Committee ensures value realization through an annual performance audit. As an Open City, the entire City of Edmonton organization is working to build new ways to share information with residents, find new opportunities for dialogue, and make services easier to access. Under the governance of the Open City Initiative and Edmonton’s Open Data Strategy, and with adherence to privacy and security standards that meet the expectations of regulatory bodies and residents, the

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application of the Smart City program will continue to demonstrate Edmonton’s leadership in the practice of open government and commitment to building a city of the future alongside residents and partners.

Security, Privacy, and Ethics Considerations The City of Edmonton recognizes the need for community and residents to retain control over sensitive and personal data and for them to understand how this information is being protected. The City is committed to ensuring compliance with privacy and security standards for obtaining and using data as well as having mitigating controls in place to minimize risk. Ongoing efforts are made to integrate security and privacy considerations raised by users, residents, and partners throughout project implementation. Individual project plans will have a privacy and security component that will be developed through ongoing consultation with residents and stakeholders to ensure their expectations are met and to further the collective understanding of ethical privacy and security measures. In addition, the City of Edmonton recognizes the importance of privacy to residents as technology advances and the use of big data increases. As such, the City is prioritizing the development of mitigating controls in partnership with residents as this field evolves. The City is focusing on assessing the ethical considerations that go beyond current legislation related to data usage and analytics. Researching and applying leading practices is prioritized, including referencing the Information Accountability Foundation’s Essential Elements of Accountability, the United Nations Global Pulse Risks, Harm and Benefits Assessment Tool, and the Open Data Charter 2019 Strategy: Bringing Power into the Open. To accommodate development and growth of Edmonton’s Smart City program, privacy and security are being approached through an ongoing, cyclical process. When new projects are identified or a change in direction of an existing project or initiative is deemed necessary, the project will be evaluated for privacy and security implications prior to any action being taken. Privacy and security will be considered throughout the lifecycle of all projects, and any new ideas, data, or changes in approach will be analyzed through a standard privacy and security assessment. Ongoing reviews of existing projects and initiatives will ensure the Smart City program is continuously meeting the privacy and security needs of residents and partners in the Smart City Ecosystem. This includes the completion of Privacy Impact Assessments and receiving their acceptance from the Office of the Information and Privacy Commissioner of Alberta.

Resident and Community Engagement Public engagement is a critical component of all decision-making, and the City of Edmonton has robust processes and standards to ensure engagement activities are meaningful and accessible. Edmonton is a city that enables and values the

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participation of residents to define and achieve a better quality of life. The City is committed to seeking diverse opinions, experiences, and information through inclusive public engagement practices. Active, engaged Edmontonians make for a more vibrant and connected city as they are contributing to the enhancement of the City’s policies, programs, projects, and services. The City of Edmonton’s Public Engagement Framework is part of the City’s overall commitment to open government – Edmonton is an open, innovative, inclusive, and engaged city. Building such a city takes foresight, planning, and active participation by residents. An Open City creates opportunities for diverse input and participation, inviting residents to play a larger role in shaping their community and enabling social and economic growth. The City of Edmonton, by applying diverse methods of engagement throughout the implementation of Smart City projects, will continue to ensure ongoing alignment between the program’s outcomes and the concerns and needs of residents and stakeholders. As projects are identified, engagement plans will be built in collaboration with residents and partners. This will allow for residents and partners to shape the activities to best suit the outcome of the project and to apply learnings from previous engagement activities. A component of the engagement plan for each project will include a change management approach – the steps that will be taken to gain acceptance and onboard residents and stakeholders throughout the project implementation and beyond. It will also include a comprehensive communications plan that identifies how residents and stakeholders will be informed of how their input influenced the development, implementation, and sustainability of the project. Whenever possible, the City will work internally to identify opportunities to collaborate on engagement activities with other projects and programs that seek similar outcomes, so as not to overwhelm residents and stakeholders with multiple activities or events on very similar topics. The City will also work with partners to identify other similar opportunities for collaborative engagement activities.

Engagement Tools Building relationships with diverse communities through public engagement is a priority for the City of Edmonton. In collaboration with residents, community leaders, and service providers, the City develops engagement activities to best suit the needs of residents and makes use of a diverse array of engagement tools to ensure a meaningful connection with residents and stakeholders. These tools can be adapted to target different population groups in order to encourage high participation. A sample of these tools is provided in Fig. 5.

Engagement Activities The development of Edmonton’s Smart Cities Challenge Proposal was informed by 16 months of intense, focused engagement with stakeholders to understand what

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Fig. 5 Engagement Tools

makes Edmonton a Smart and Healthy City. This specific engagement was built upon 11 years of previous engagement that shaped Edmonton’s 2009–2018 Strategic Plan and its subsequent initiatives, as well as the recent work that was done with the community to revise the plan for 2019–2028. To provide transparency to the development of this approach and encourage participation, the City: • Advertised in print media and through posters in libraries, community centers, social agencies, safe houses, and shelters • Used Twitter and Facebook to update progress and solicit ideas • Gathered 260 distinct viewpoints from more than 1,000 individuals in the newcomer, Indigenous, low-income, homeless, vulnerable youth, seniors, and LGBTQ2S+ communities through communication vehicles reflecting their preferences, including in-person workshops, interviews, and paper and electronic surveys

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Fig. 6 Results of engagement activities conducted as part of Edmonton’s final proposal in Infrastructure Canada’s Smart Cities Challenge

Figure 6 provides a summary of the activities completed between July 2018 and February 2019 in which residents and stakeholders shared their stories and ideas. It highlights the success achieved in applying this approach to resident and community engagement during the finalist phase of Edmonton’s participation in Infrastructure Canada’s Smart Cities Challenge. Throughout the course of the public engagement activities for the Smart Cities Challenge, the City of Edmonton received overwhelmingly positive responses from residents, service providers, academic institutions, and the private sector regarding the pursuit of building a smarter, healthier, and more connected city. Residents and service providers will be invited to provide ongoing input into the development and expansion of engagement activities as Smart City Edmonton progresses. The City will continue to engage with residents using approaches that are meaningful to them to facilitate and encourage broad participation so that Edmonton continues to be a community in which residents lead the development of the City’s long-term strategic priorities.

Inclusive Engagement The City of Edmonton is committed to ensuring all residents have the opportunity to participate in civic life. To help formulate the strategy for inclusive engagement, the City of Edmonton worked with the nonprofit and academic sectors to learn about meaningful engagement activities for newcomers to Canada; the urban Indigenous

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population, seniors, children, and youth; and those with experience living in poverty and homelessness. The City also met with subject matter experts in the nonprofit sector, government, and academia to discuss opportunities for future collaborations on engagement activities and potential partnership opportunities within the Smart City Ecosystem. All engagement activities and plans will continue to be developed by applying a diversity and inclusion lens through consultation with subject matter experts, community leaders, and service providers. As the program evolves and projects grow, engagement processes will be modified based on feedback from the community to ensure they remain relevant and reflective of the diverse needs and aspirations of residents. In order to mitigate the potential for unintentional effects or bias toward certain population groups to arise as a result of engagement, the City will work with community leaders to understand the diverse needs of individual groups and design plans and activities collaboratively. The City will ensure the community retains ownership over the information gathered throughout the engagement process and remains informed as to how the information is being used to inform, enhance, or build projects.

Conclusion Smart Cities hold the promise to create healthier urban environments where residents can live their best lives. An open, inclusive, and collaborative community is foundational to success. Edmonton, as an Open City, learns from and integrates aspects of other open government initiatives. The City is evolving to collect and share data that will influence how public services are designed and delivered globally. Through this mindset of continuous learning and evolution, the City of Edmonton is a collaborator and contributor to how other communities can increase their capacity for open government. This means reducing socioeconomic, physical, and technical barriers and creating accessible channels for delivery of effective programs and services. Smart and connected cities have vibrant public spaces, creative and diverse residents, opportunities for economic development, and smart technologies. Connected cities have inclusive and innovative spirits that challenge the status quo and overcome barriers collaboratively. They are the cities that are transforming the regions they occupy and influencing community development at a national and global level. Edmonton is one of those cities and recognizes the importance connected communities play in building connected regions and ultimately a connected nation. Edmonton is Canada’s Most Open City (Public Sector Digest, 2015, 2016, and 2017) and a Top 7 Intelligent Community (Intelligent Community Forum, 2017). It is a place where the community leverages data and connected technology to become more engaged with one another through social interactions such as volunteering, celebrating, or just being together in shared spaces. In Edmonton, progress is linked to and driven by community for community. Connecting with others – across cultures, age groups, geography, and communities of interest – is seen as essential for creating a vibrant, connected, engaged, and healthy community for all.

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From Invention City to Innovation City: The Case of Racine Wisconsin Peggy James and William Martin

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Racine, Wisconsin, Small Town USA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Advantages and Disadvantages of Smaller Urban Contexts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Importance of Strategic Planning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Importance of a Middleman in Public Private Partnerships . . . . . . . . . . . . . . . . . . . . . . . . . . . Establishing City Priorities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Community Wide Connectivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Energy and Sustainability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smart Mobility and TF Century Transportation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Priority of Inclusivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract

Racine, Wisconsin, formally declared itself to be on the smart city track in 2018. Only 2 years in, this is an examination of the political, economic, and social challenges that have faced the city to date, and the significant accomplishments it has logged along the way. And, as we narrate the process, the overwhelming objective reasoning (the social good) of the citizens will be highlighted as a dominant and leading force in the smart city imaginary of Racine.

P. James (*) Political Science, College of Social Sciences and Professional Studies, University of WisconsinParkside, Kenosha, WI, USA e-mail: [email protected] W. Martin (*) City of Racine, Racine, WI, USA e-mail: william.martin@widiversified.com © Springer Nature Switzerland AG 2021 J. C. Augusto (ed.), Handbook of Smart Cities, https://doi.org/10.1007/978-3-030-69698-6_39

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Introduction As is evident throughout this book, smart cities can manifest in a variety of ways, depending upon the empirical evidence of the integration of technology and policy, and their inevitable interaction with the values of city policy makers. It has been suggested that the smart city is a sociotechnical imaginary (Sadowski and Bendor 2018) following the theoretical construct of Lacan, as it has a dynamic tension between subjective reason, represented by technology and its agents, and objective reason, the social good. Those who present this conceptualization critique the smart city as one that is dominated by the subjective, or individual interests, of an elite group (Cugurullo 2018; Sadowski and Bendor 2018). However, the theoretical conceptualization of the smart city imaginary is challenged by the empirical practices of its development. Taylor Buck and While (2015) argue “. . . the smart city discourse (including its critiques) is often rooted in the expectation of transformational systemic change that overlooks the roll out of the smart city through multiple incremental and smaller scale changes.” We interpret this as an acknowledgment of the pulling and hauling of the political decision-making process, where the decision context (actors, agendas, time constraints, learned behaviors, etc.) has as much to do with the final outcome as the original objectives. That is, the smart city imaginary can have many outcomes, and in order to understand how it got there, we need to be cognizant of the pulling and hauling throughout the process. With this in mind, this case study of Racine Wisconsin offers an insight into the very beginning of the smart city political process. Little work has been done on the political challenges that cities face at the zero point before they begin their smart city journey. At this zero point, there is no smart technology or information and communications technology (ICT) that dominates the decision-making agenda, only a goal to transform the urban landscape.

Racine, Wisconsin, Small Town USA Racine, Wisconsin, is a city transitioning from heavy traditional manufacturing to a postindustrial urban area. Situated on Lake Michigan between Chicago, Illinois, and Milwaukee, Wisconsin, it has seen declining economic development and population loss as many traditional factories have either closed their doors or moved away from the city into the more available farmlands to the west. Since 1970, the population has been slowly declining from a high of 95,162 in 1970 to 77,432 in 2018 (http:// worldpopulationreview.com/us-cities/racine-wi-population/). The population of the surrounding Racine County has seen modest increases in those years. With a poverty rate in 2017 of 20%, and a mean income of $31,111 in the same year, it has faced the daunting task of increasing the quality of life for its citizens by ensuring that those who live in the city are an employable workforce within new economic development. When compared to the notable smart cities in the United States, such as New York City; Boston, Massachusetts; Chicago, Illinois; Columbus, Ohio; Las Vegas,

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Table 1 Comparison of Racine to leading smart cities in the United States City Racine, WI New York, NY Chicago, IL Boston, MA Austin, TX Las Vegas, NV Columbus, OH

College graduate 16.1 27.4 38.4 48.5 50.4 23.9 35.7

Retail sales per capita 7944 11,067 8335 12,389 17,491 14,335 16,194

Poverty 2017 20 19 20.6 20.5 15.4 14.5 20.8

Population 77,432 8,398,748 2,705,994 694,583 964,254 644,644 892,533

Median family income 47,431 68,353 64,441 76,603 87,200 57,490 61,094

Source: US Census, https://www.census.gov/quickfacts/fact/table/US/PST045219

Nevada; and Austin, Texas (https://www.americancityandcounty.com/2019/05/08/ the-smartest-cities-in-the-u-s/), it appears to be an unlikely candidate for smart city development (Table 1). Easily the smallest city in terms of population, it also ranks the lowest in college graduate, retail sales per capita, and median family income. And, while the 20% poverty is slightly lower than three of the smartest cities, it is not balanced by any other income measure. Given that infrastructure (human and physical) and capital are two of the basic requirements to build a smart city, this puts Racine, and other cities like it, at an immediate disadvantage (Albanese 2018). Yet, with the estimate that 87% of the US population will live in urban areas by 2050, and the fact that only 349 of cities in the United States have a population of 100,000 or more (http://worldpopulationreview.com/countries/united-states-population/cit ies/), we need to be prepared for the urbanization of smaller towns and cities, with the accompanying need for efficiency and efficacy of services. Racine, and other towns like it, can be role models for the smaller urban areas. Chelsea Collier (2018) states: Some of the advantages that come with a smaller city include the ability to be more nimble. Overcoming the gridlock and regulatory red tape that plague many large metropolitan areas is easier when there are fewer people to slow down the process. Personal connections also come into play in a community where people know each other beyond titles. Being able to pick up the phone and discuss a new idea or project with someone you know often eases the difficulty of getting things done. Also in a less populated metropolitan area, it is easier to connect with the city’s residents. When designing solutions for friends and neighbors, it becomes more clear [sic] that smart cities is about people.

Racine, with a smaller population struggling with deindustrialization, can be considered a bellwether for many cities in the Midwest and across the country for the potential of successfully developing a smart city that couples strong economic development with public inclusivity and equity. Because of the qualitative relationship differences identified by Collier, there needs to be a communication strategy at the foundation of becoming a smart city. Racine Mayor Cory Mason began promoting his smart city vision with two basic messages: (1) an emphasis on a specific interpretation of a smart city as one that is inclusively responsive, equitable, efficacious, and organic

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with the objective of well-being for all citizens, and (2) connect the future vision of Racine with its past so as to demystify the smart city idea. The first message meant that all citizens, regardless of income, race, ethnicity, or education, could expect to benefit from a smart future. The second resulted in an intentional connection between innovation and invention. Racine is known as Invention City, due to the amount of products that emerged from the Racine in the first half of the twentieth century. Most of these inventions were directly related to the manufacturing industry and, as that declined, so did the reputation (Buenker et al. 1998). Tying the smart technological innovation to that past makes the step into the future more believable and acceptable to many of the long-term residents. Without certain events, however, it is unlikely that Racine would have stepped into this challenge. Economic activity in the area prior to 2017 was characterized by a struggling revitalization effort to recover from the 1980s when it lost 1000 jobs per year and the exodus of many downtown retailers (http://www.city-data.com/uscities/The-Midwest/Racine-Economy.html). But in 2017 Foxconn Corporation announced it would build its first facility in the United States, promising a 10 billion dollar investment over 3 years and the eventual creation of 13,000 jobs (https://www. theverge.com/2017/7/26/16034394/apple-iphone-manufacturing-foxconn-wisconsinplant-donald-trump). This announcement immediately generated a cluster effect of economic development in Racine County. Other corporations moved or expanded their activities in Racine and surrounding areas. Anticipating the needs of an increased population, roads were improved, health facilities were built, and housing developments began. The scene was remarkably similar to that which had occurred in the Reno-Sparks, Nevada community with the arrival of Tesla in 2014. Mike Kazmierski, president and CEO of the Economic Development Authority of Western Nevada (EDAWN), made these comments to Racine in 2018: • • • • •

Like Nothing You’ve Ever Experienced Before Unexpected 2nd Or 3rd Order Effects Massive Jobs Multiplier Effect – 2.84! Great New Secondary Job Opportunities May Feel Some Indifference: By The Rest Of The State: Many Not Actively Engaged; Some Will See This As Your Problem; Some Will Resent Your Success And Visibility • Surprising How Difficult It Is To Address Serious Issues Even After They Reach Crisis Mode And, one of the most important lessons was to recognize the importance of responding, rather than reacting; the need to develop technology that was not connected ONLY to the needs of the corporation. This is especially important in a smaller urban environment that can easily be overshadowed by corporate friendly technology to the point where the city loses its identity and the ability to follow its own agenda.

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Advantages and Disadvantages of Smaller Urban Contexts When we speak of the flexibility and adaptability of smaller urban contexts such as Racine, we are really talking about resilience (Bec et al. 2016; Zautra et al. 2008). Still, Bec and Moyle (2018) also find that there is a negative relationship between resilience and perceptions of change, suggesting that conservative reluctance to change the status quo can negatively impact the ability of a city to dynamically work with change. And, resilience is most effective in a smart city future when the model being used is based on a community city model. As James et al. explain in an earlier chapter, smart cities can follow a variety of pathways in terms of their relationship to technology. Small, postindustrial cities like Racine are best served by following a community pathway driven by social innovation that is geared toward meeting the already existing needs of its citizens. There are postindustrial cities in the United States that are struggling to meet the needs of their citizens. “Perhaps the most important common factor that many highperforming legacy cities share is an eye toward the future. Instead of trying to revive the industries that built them, the most successful cities are finding creative ways to reinvent themselves” (Boone 2017). For Racine, the priority of inclusive innovation in economic, political, and social development requires access, and collaboration. The groundwork for this has already been laid through an S.C. Johnson Foundation convening on Resilient Communities. S.C. Johnson and Son, Inc. was founded in Racine in 1886, and is currently in its fifth generation of family leadership. The Johnson Foundation, a philanthropic nonprofit trust established in 1928, is an educational center devoted to the facilitation of constructive and purposeful ideas. A forward thinking company, it removed chlorofluorcarbons (CFCs) from its products 12 years before the Montreal Protocol. Small urban contexts do have disadvantages, however, especially in the United States. Nationally, the United States is behind in smart city initiatives spending, accounting for only 4% of smart cities globally. Only four cities (New York, Los Angeles, Washington, D.C., and Chicago) are forecast to spend more than $300 million on smart city programs in 2020 (https://www.planetizen.com/blogs/105424us-falling-behind-smart-city-deployments-and-key-21st-century-infrastructure). The United States is also behind in 5G, considered as the backbone of any sustained and coherent smart city development. One of the significant obstacles to smart city advancement in the United States is the legal action filed in 2019 by over 80 cities against the Federal Communications Commission’s Accelerating Wireless Broadband Deployment by Removing Barriers to Infrastructure Investment rules (https://docs.fcc.gov/public/attachments/DOC-353962A1.pdf) which largely favored corporations over the local governments by removing decision making regarding the deployment of 5G cells. Small cities are even more vulnerable to this threat to local power, since they can be largely outmatched by corporate investors and business interests.

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Outside of the national context, local governments of small cities face the challenge of underdeveloped infrastructure and a lack of funds to invest in technology. This is especially true of the legacy cities, who are trying to recover from decades of a declining tax base, aging infrastructure, and governing that is largely based on crisis management rather than long-term strategic planning. Racine is a textbook case of just such a city. Henken and Amenta (2018) described the fiscal health of Racine: Its operations are heavily dependent on only two revenue sources – property taxes and intergovernmental revenues – both of which are severely constrained by external factors and neither of which has grown at the pace of inflation. . ..Overall, the City’s constrained revenue portfolio poses a significant challenge to its ability to maintain service levels and invest in quality-of-life and economic development initiatives.

The Importance of Strategic Planning Strategic planning to assign priorities of develop is vital in order to navigate the challenges small cities face and maximize opportunities. However, because of the dynamic shifting nature of opportunities and the fast pace of innovation, a master plan that is resistant to change may result in an inability to take advantage of shortterm developments. The City of Racine consciously eschewed a grand strategic plan in order to enable it to move quickly when the opportunity arose. Instead, the City encouraged the adoption of guiding principles that included (1) a realistic set of priorities, (2) a commitment to involve all stakeholders, including the private sector and the local universities, (3) prioritizing a 5G infrastructure, (4) creatively rethinking transportation. (Beyond the Hype Factory: 7 Steps to Make Cities Smarter. https://www.iotworldtoday.com/2017/10/05/7-smart-city-strategies-cities-across-world/)

Stakeholder Involvement Stakeholder involvement, so important in a small city, began with the successful collaborative effort to become the smallest designated smart city by the Smart City Council in 2019. In acknowledging the award, Mayor Mason recognized the important contributors to the proposal as Gateway Technical College, University of Wisconsin-Parkside, Racine County Economic Development Corporation, Racine County, and Foxconn Technology Group (https://journaltimes.com/news/local/govtand-politics/smart-cities-council-picks-racine/article_3654d5f6-4b4b-55c6-bdba-50 83083971e3.html). This initial core team represented private business, government, and university sectors, a combination that has long been considered essential to smart city planning and development. As stated earlier, Foxconn Technology Group was the stimulus to the smart city initiative, and remains a partner in the effort. The City of Racine continues to work closely with Foxconn Technology Group, which is investing $10 billion to build in the region the first-of-its-kind advanced manufacturing and research facilities and is opening an Innovation Center and other related facilities in the City’s downtown. However, it is important to note that Foxconn is not the sole driver of the private

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sector contribution. As a postindustrial city with a legacy of family-owned companies that still dominate the economic landscape, Racine welcomed the contributions of a number of corporations such as SC Johnson, CNHi, Twin Disc, and Insinkerator. Although this is a short list of private corporations and businesses, they are mentioned here because of their long-term participation in the Racine economy. SC Johnson and CNHI were founded in Racine in the nineteenth century, while Twin Disc and Insinkerator were founded in the early part of the twentieth century. As such, they represent an important and continuous link between past manufacturing and the future of Racine. Cluster development is also occurring; Foxconn suppliers in Racine County have been awarded 370 million dollars for construction work (Anderson 2020), and 23 new businesses in Racine’s downtown district in 2018 represented a 255% increase from 2016. Market rate housing with 850 units and an annual economic impact of 7.6 million dollars indicate that the city is benefitting from the Foxconn buzz as well as the smart city commitment from the Mayor’s office (Kruse 2019; Racine County’s 2020 budget is 167.8 million, https://www.racinecounty.com/government/finance/ finance-reports/executive-budgets, and the City of Racine’s budget is 215 million). The City of Racine is not the only government player in the smart city strategy; Racine County is a significant partner not only for the economic development potential but for its networking infrastructure that can be connected to the city. One thing that smart cities find to be challenging is the need to transcend traditional geographic borders using collaborative governance in the investment and application of technology (Soe 2018; Meijer and Rodríguez Bolívar 2016). ICT cannot do this automatically, especially in the historical context of smaller cities accustomed to competing with other local municipalities for dwindling funds from the state, federal government as well as a declining tax base. In 2019, Racine began leveraging state programs in the form of opportunity zones. In order to encourage economic investment and growth in distressed urban areas, the US 2017 Tax Cuts and Jobs Act established opportunity zones in all states (Congressional Research Service 2019). There are three tax incentives for investment in these zones: (1) temporary deferral of capital gains taxes on investments, (2) step up in basis for investments after 5 years, (3) exclusion of capital gains taxes after 10 years. In cooperation with Legacy Redevelopment Corporation, Racine is the first Wisconsin community to set up an opportunity fund to take advantage of the three zones in Racine. Investors can contribute to the fund with a minimum of $25,000.00 and the fund can be used for a variety of startups, development projects, or businesses. As stated earlier, no city government has the ability to engage in smart development, especially in ICT, without help from other sectors, and this is especially true of the smaller cities. Institutions of higher education (IHEs) and citizen groups are vital to support the process in the City of Racine. Even though it is a small city, Racine has the benefit of three IHEs within or on the edge of its boundaries. Two are 4 year Baccalaureate granting institutions, while the third is a 2 year technical college. The role of these institutions in the smart city process has not been the usual one many assume when thinking of universities as flagship research centers. These

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institutions have contributed to the functional needs of planning, workforce training, and citizen buy in which were so vital for the region. The relationship between the IHE’s government and the private sector has been through a process of knowledge creation, diffusion, and implementation. Carayannis and Grigoroudis (2016) provide an excellent conceptual and strategic discussion of how smart specialization can take place in this type of functional relationship, using a dynamic six-step process first described by the European Commission. The relationship is based on collaboration, planning, and functional feedback, rather than a series of discrete, possibly unrelated activities. An example of this collaboration is found in the development of a 2 year Associate Degree program by Gateway Technical College in Advanced Manufacturing, followed by a graduate certificate in Smart City Policy offered by the University of Wisconsin-Parkside. These complementary programs serve the needs of workers who need to upskill for industry 4.0 in robotics, machine interaction, networking, and intelligent automation; the graduate certificate provides the skills for the next level of company or government management by focusing on public private partnerships, smart city policy, and civic technology. Recall that one of the Mayor’s goals was to ensure that city residents could take advantage of smart city employment opportunities. These IHEs, smaller than flagship universities, had the ability to quickly develop and offer practical education packages that would help him achieve that goal. The fourth stakeholder in smart city development is the citizens. Often ignored in early smart city planning, we have learned that the citizens are vital for both success and sustainability. They are the group that transforms the triple helix into the quadruple helix. As we increasingly concentrate on inclusive access, it is not surprising that, once given access, citizens wish to participate in the decision-making process. Without their input, and their buy in, the city of Racine will not be able to sustain the smart city focus. Visioning Greater Racine is an example of such a group, tasking themselves to be a “networked-community initiative using the proven VISIONING process with the goal of transforming Racine into a flourishing place we are all proud to call home by 2030” (https://www.visioningagreaterracine.org/). Although formed prior to the smart city vision of Racine, they have embraced it as a necessary step toward accomplishing their mission. Members represent government, businesses, nonprofit organizations, and universities. As such is a thread that interweaves the quadruple helix. All of the above stakeholders, and more, were brought together in the “Smart City September” events in 2019 (https://racinesmartcity.com/#about). Three events were held. The first two allowed participants to work toward an understanding of smart cities, and engaged them in the practical applications of these concepts within the local context. Each of these events engaged over 400 participants, including leaders from the local universities, government, businesses, and nonprofit organizations. The events were absolutely essential for the involvement of citizens, and the collaborative efforts that were needed. Interestingly, these events would not have been nearly as impactful in a larger urban context, they would have been impossible to organize in a short period of time, and they also would not have been as needed.

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This is a smart city tactic for a small urban setting. The lesson here is that planning and politics is essential for successful launching in this type of urban environment. The third event was a celebration of Racine as an Innovation City, providing a connection to the city’s past, so as to make the future less daunting. The Racine Innovation & Technology Gala brought together the business community, government officials, educators, and nonprofit organizations to celebrate nominees for the Racine Innovation Awards and demonstrate how Racine is still shaping tomorrow. A second way to engage stakeholders, and to create new ones, was the use of tech prizes and competitions. Foxconn, partnering with the University of Wisconsin System, the Wisconsin Association of Independent Colleges and Universities, and the Wisconsin Technical College System, has sponsored two smart futures competitions. “The ‘Smart Cities-Smart Futures’ contest, a three-year competition that Foxconn said awards up to $1 million in cash and technical support to Wisconsin students, faculty and staff, last year drew 325 submissions representing 24 universities and colleges across Wisconsin, resulting in 12 final round winners” (https://www.jsonline.com/story/money/business/2019/09/10/foxconn-launches-sec ond-year-smart-cities-smart-futures-competition-wisconsin/2276843001/). In 2020, Visioning Greater Racine announced its own tech-prize competition (https://journal times.com/business/local/tech-contest-aims-to-make-racine-into-invention-city-again/ article_89b3e02b-1f62-51ca-8f65-9919bf444874.html).

The Importance of a Middleman in Public Private Partnerships Experience demonstrates that Public Private Partnerships (P3s) work best with the involvement of a mediator that has interests and connections in both the private and public sector. Dameri et al. (2016) cite the problem of coherence even within the triple helix partnership of government, private orgs, and universities. Without a central direction, coordinating the interests of all the key actors with the stakeholders expectations and needs, the smart city will remain an interesting innovative laboratory, but failing in creating public and private value for all in the long term. (Dameri et al. 2016, p. 2980)

As the triple helix model continues to evolve into a more collaborative, creative, and innovative partnership based on smart specialization, the role of the middleman becomes even more crucial (Poppen and Decker 2018). For small cities like Racine, which need to maintain decision-making autonomy in the face of private sector innovation, it is critical (Bielak et al. 2008). Middlemen can ensure that the partnership is a tool for better risk and cost allocation, not only a way to fill budget gaps, which is an ever present temptation for small city budget offices. As a nation, the United States lags behind most countries in the number of public private partnerships; enabling legislation in the states is lacking or incomplete, and there is little recognition of the need for a specific unit to ensure a successful partnership (Istrate and Puentes 2011). To compound the issue, in states with

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narrow enabling legislation, local municipalities have less experience structuring successful partnerships, since they have less ability to work within the enabled areas, usually transportation. In Wisconsin, legislation authorizes the state Department of Transportation to enter into build-operate-lease or transfer agreements with private entities for construction of transportation projects and for maintenance or operation of projects that are not purchased by the state upon their completion. An agreement may not be entered into unless the DOT determines that it advances the public interest and the private entity meets certain criteria. There is some good news, however; the National Law Review (2020) reports that 2019 was a banner year for P3s with a sharp increase in broader enabling legislation. And, with more cities pursuing this option, we can learn from the experience of others in this area. (Julie Kim and Mike Bennon (2017) have written an excellent case study and review of the Public Private Partnership process and structure. See P3 Project Structuring Guidelines for Local Governments.) Even though Racine is limited in formal arrangements, the City needed to establish a middleman to make the connections between all stakeholders, not only the private corporations. The City appointed its first Chief Innovation Officer in early January 2019 to facilitate and support Smart Cities initiatives, coordinate with partners, support the cross-functional team, work with stakeholders to develop a Smart Cities vision, goals and objectives, and action plan, and coordinate across the implementation of initiatives. Later that year, the City promoted the creation of Wisconsin Development Investments, LLC. The Innovation officer shifted his responsibilities to emphasize his role in the LLC as Executive Director. In this way a nexus was created between the city and private interests in economic development. The City of Racine has developed a number of informal public-private partnerships. At their core, those partnerships are based on several factors: shared public-private goals and objectives, unique private-sector capabilities or resources offered to enhance city services and outcomes for community residents and businesses, partner roles and responsibilities, delineation of public-private investments in initiative(s) and community, timeline or duration of the Memorandum of Understanding. These alliances represent a public private collaboration toward increasing economic competitiveness by leveraging the members’ assets as well as the city’s digital infrastructure.

Establishing City Priorities In keeping with the recommendation to keep strategy flexible and adaptive, Racine established priority areas, rather than develop a full strategic plan. There were problems to avoid, notably the intransigence of legacy governments, enclave development, and isolated projects. The last two were contrary to the city’s values of inclusivity, based on the practicality of ensuring that all residents could benefit from smart policies. As such, they were problems in the present and future; legacy governments represented the challenge of the past. Legacy governments represent an inheritance of particular behaviors and policies that worked well in the past, but may not be effective or equitable in the current day. The difficulty is that many

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residents, and politicians, still prefer to work within those behaviors, which can be characterized by power building, competitiveness, and opaque political dealings. None of these behaviors are useful in a smart city environment – worse they hinder technological progress. How does a city meet these challenges? In Racine, it began with a call for citizen engagement and broad stakeholder inclusion, short- and long-term planning, and an intimate understanding of the community needs and values (Heiskanen and Acharya 2017). Initiatives that appear to be complementary from a planning standpoint may not be compatible with public needs and will result in a rejection of technological improvements. For example, in Racine, public transportation is an identified challenge, yet public transportation and multimodal transportation, which is often in the private sector, are not easily integrated. Transportation is also one area where an enclave economy, the development of a business sector completely isolated from the local regional economy, will have a profound impact. Corporations setting up business on the fringe of an urban area may influence transportation lines that benefit them, but result in asymmetrical service provision for most of the population. This is a local concern in Racine, as many larger corporations seek to facilitate employee travel, but may be less concerned with the impact on Racine proper. Citizen engagement and stakeholder participation are important considerations in governmental decision making; in Racine, these actors also influenced the strategic choices made by the city and the agenda for technological development. Smaller cities need to take opportunities as they arise, even while maintaining their own decision-making autonomy. For Racine, three priorities emerged in 2019: (1) Community-wide Connectivity, (2) Smart Mobility and 21st Century Transportation, and (3) Energy and Sustainability.

Community Wide Connectivity Some efforts at smart city development have tended to focus on technology embedded in projects that have had high visibility, yet very little contribution to the overall well-being of the citizens. Any project undertaken must be assessed on two levels – its immediate value, and its contribution to the process, emphasizing characteristics related to its long-term vision, integrative capabilities, and the impact it will have on the population (Van den Bergh et al. 2018; Bilbil 2017; Letaifa 2015; Chatfield and Reddick 2016; Ramaprasad et al. 2017). Applying these principles to the prioritization of projects, many cities need to concentrate first on needed energy and digital infrastructure to support projects in the short and long term (Batty et al. 2012; Bresciani et al. 2017). And, infrastructure development should have the priority of avoiding enclaves, such as innovation districts, or urban islands, that are not connected to the rest of the urban population. As we noted earlier in this chapter, inclusivity is a priority of Racine, and is vital for citizen buy in, a population where education income and employment gaps are wide. Unfortunately, digital infrastructure is not a flashy attention getter, and the Mayor’s announcement of a partnership with US Cellular to install 5G capacity in

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Racine had a short-term public relations boost, but not a really lasting impact. Many in Racine are not sure what 5G capacity means; many do not have the ability to take advantage of a 5G connectivity. Still, a long-term vision required that this be a major priority early in the digitalization process. All City of Racine traffic signals and street lights are equipped with municipal Wi-Fi. Over 250 city-operated cameras are controlled through that system. Currently, the City of Racine has established 27.5 miles of conduit and fiber optic cabling. The conduit and the cabling are currently underutilized and can be a source of leasing income for the city. With limited additional resources, all cities need to be more cognizant of the resources that they do have that can be leveraged within public private partnerships. The City has also budgeted to expand the existing network, and has developed a plan to install additional conduit and fiber to this public infrastructure as the Department of Public Works makes road repairs and improvements. One of the functions of the expanded network will be to support one of the City’s primary Smart City initiatives – autonomous vehicle transportation testing, operation, and evaluation. The City’s fiber optic network will be integrated with small cells to enable low latency high-speed 5G wireless technology. The ultimate goal is to make this powerful new public infrastructure ubiquitous and establish communitywide connectivity. Municipal leaders are working closely with technology and higher education partners, including Foxconn Technology Group, University of WisconsinMadison College of Engineering – a leader in autonomous vehicle research and development, and Gateway Technical College – the nation’s first publicly funded institution for technical education, to define hardware needed and mitigate potential risks such as technological obsolescence. The Connecting Our Community project is vital to the future of the City of Racine and its economy. The City of Racine project aims to accomplish a number of important objectives: advance equity and inclusion; enhance the competitiveness of its human talent across all socioeconomic backgrounds and age cohorts; make city services more accessible to residents, businesses, and visitors; and accelerate the community’s economic growth by embracing high-speed ubiquitous 5G wireless technology installation and encouraging rapid adoption. The City’s goal is to extend that network throughout the municipality and integrate with it small cells, which will be mounted to its light poles and other available assets. Single units can provide service for upwards of 30 access points in high-density areas, and can extend service to access points anywhere from 10 m to over a kilometer away. Through a collaboration with the University of Wisconsin Parkside’s GIS Factory, maps indicated infrastructure suitable for providing the City of Racine citywide 5G Wi-Fi and identified gaps or areas beyond a potential coverage area provided by this infrastructure. As is often the case, private companies and public institutions have their own connectivity framework; the challenge is to be able to integrate their capacity into that of the city, and create a seamless network. In Racine’s case, connectivity sources included the City and County of Racine, Racine Unified School District, Midwest Fiber Optics, University of Wisconsin-System, WE Energies, and surrounding municipalities of Mt. Pleasant, Caledonia, Sturtevant, Franksville.

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The addition of small cells to the City’s existing public infrastructure will offer ubiquitous connectivity, creating the foundation for, and making possible, publicprivate Internet of Things (IoT) projects that cannot be imagined today. This new 5Genabling public infrastructure will support a new era of innovation in Racine, attracting those interested in leveraging these new tools of technology to innovate and advance quality of life for many more. The public infrastructure combining its fiber optic network with 5G-enabling small cells will be launched along the lakefront/downtown area to support the testing, operation, and evaluation of the City’s autonomous vehicle project. The plan is to expand that combined infrastructure into additional areas of the community to coincide with the expansion of the autonomous vehicle transportation service delivery routes. The City will work with Foxconn Technology Group, University of Wisconsin-Madison College of Engineering, and Gateway Technical College to ensure the small cell units procured meet optimal specifications for the testing and operations for autonomous vehicle transportation. The City also will work closely with the telecom industry and other sector leaders to determine devices needed to launch, operate, and maintain an effective 5G-enabled network. As Racine works to expand its digital infrastructure, it has also entered into a 5G expansion partnership with US Cellular. Enabling legislation was enacted by the State of Wisconsin in July 2019. “The bill creates a regulatory framework for the state and local governments for the deployment of wireless equipment and facilities, including in rights-of-way; the permitting process by wireless companies; regulation for access to government structures by wireless companies; and allows local governments to impose setback requirements for mobile support structures” (https:// www.bizjournals.com/milwaukee/news/2019/07/10/wisconsin-ranks-low-in-mobileinternet-speed-new.html). This can be considered a catch-up process for both the state and the city of Racine, since the push to provide this legislation and to enter into a partnership came from private sector needs for digital infrastructure to support AI, high definition resolution, and advanced manufacturing technology. In neighboring Milwaukee, which is part of the partnership with US Cellular, the impetus came from the Democratic National Convention, the nomination event for the party’s presidential candidate, coming to Milwaukee in summer of 2020. We think the external pushes are important to identify, as the process is not always about strategic planning – sometimes it is about seizing opportunities. The Cellular Telecommunications Industry Association estimates that installation of 5G-enabling technology and adoption of high-speed Internet access will result in an additional $118 million in GDP and 724 jobs for the City of Racine. The association estimates the increase in GDP to be $1.18 billion across Southeastern Wisconsin; over 7,200 jobs are expected to be created, all because of the integration and adoption of this powerful new tool (retrieved February 10, 2019, from: https:// www.ctia.org/the-wireless-industry/the-race-to-5g, CTIA, 5G Economic Impact by State: Wisconsin). Accenture, a multinational professional services company, offers similar estimates, indicating that small-medium-sized cities with population of 30,000–100,000 could see 300–1,000 jobs created as a result of embracing 5G technology (retrieved February 9, 2019, from: https://www.ctia.org/news/accenturesmart-cities-how-5g-can-help-municipalities-become-vibrant-smart).

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Energy and Sustainability An energy and fleet audit of Racine’s vehicles and city-owned buildings revealed 29,600 tons of carbon dioxide were emitted into the atmosphere in 2018. With this baseline, the city has prioritized opportunities for reducing its carbon footprint. One of the tactics to accomplish this has been to transition to electric public vehicles and buses. Illustrating the value of collaboration and partnership, Racine is a member of the Climate Mayors Electrical Vehicle Purchasing Collaborative, joining over 100 cities in 38 states. The Collaborative provides technical expertise to purchasers and a program that reduces the costs and barriers to electrifying fleets. In 2020, the City will partner with WE Energies on their SolarNow program, which will actually generate revenue for the City by creating solar panel arrays within the City limits. The energy company owns the solar panels and leases the city property; in return, the city will earn slightly over $2000.00 per month for 30 years. Aside from the monetary value, the arrays will encourage solar power in the city, as part of the Sustainability and Equitable Climate Action plan to implement an energy independence plan. Economic development in the downtown area includes a collaborative requirement to incorporate smart technologies, prioritize environmental sustainability, and LEED certification. (Leadership in Energy and Environmental Design (LEED) is a certification based on five areas: Building Design and Construction, Interior Design and Construction, Operations and Maintenance, Homes, Neighborhood Development. There are four ranking based on a point system – certification, silver, gold, and platinum.) In 2019, the Mayor created the Sustainability Task Force composed of representatives from IHE’s, local corporations, nonprofit sustainability groups, and city units. The task force provides continuity and coherence to sustainability projects, and is supported by the Coordinator of Sustainability and Conservation from the Mayor’s office. Originally the group was tasked with assessing energy usage of municipal buildings, but quickly extended its role to develop an assessment plan for the entire community. This will precede work on a sustainable action plan for the city, potentially using Detroit’s Sustainability Action Agenda as a template. Detroit involved multiple stakeholders in the development of the agenda, with 6800 citizens engaging in the process. The agenda holds the health and well-being of citizens at the center, focusing on equity and inclusivity as the core of sustainability, mirroring the goals of Racine. See Table 2 for a listing of Detroit’s Agenda Items.

Smart Mobility and TF Century Transportation Increasing mobility is a common priority for many cities in the United States. According to the 2019 Deloitte Mobility Index (https://www2.deloitte.com/us/en/ insights/focus/future-of-mobility/deloitte-urban-mobility-index-for-cities.html) all eight top performing cities for public transit supply were in Europe; four of the bottom seven cities for supply were in the United States. Why is this? Cities developed differently in the United States than in many other places across the globe.

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Table 2 Detroit Sustainability Action Agenda framework goalsa Healthy Thriving People

1. Increase access to healthy food, green space, and recreational opportunities 2. Improve air quality and reduce exposure to pollution 3. Advance equity in access to economic opportunity Affordable Quality Housing 4. Reduce the total cost of housing, including utilities 5. Improve the health and safety of existing and new housing Clean Connected 6. Transform vacant lots into safe, productive, and sustainable Neighbourhoods space 7. Reduce waste sent to landfills 8. Make it easier and safer to get around Detroit without a personal vehicle Equitable Green City 9. Enhance infrastructure and operations to improve resilience to climate impacts 10. Reduce municipal and citywide greenhouse gas emissions a

Source: Detroit Sustainability Action Agenda, https://detroitmi.gov/government/mayors-office/ office-sustainability/sustainability-action-agenda

Urban sprawl is much more pronounced resulting in lower density. The continued preference for driving one’s own car, and the affordability of doing so, results in less investment in public transport. And, even in those municipalities that create or maintain public transport, the systems are not integrated with population centers, meaning that many need to find transportation to the transportation. Racine has had a public transportation system since 1928 that has not been able to keep up with development patterns. Most of the service is close to the downtown, while residential and business development has increasingly used the space further west of the city. In 2013 a Southeast Wisconsin Regional Planning Commission, which included the County and City of Racine, evaluated the bus system and found unpredictable service, long waiting times, and, most importantly, a lack of access to many neighborhoods and businesses (see Map 1). From 2017 to 2018 all municipalities in Racine County increased in population with the exception of the City of Racine. According to the US Census, the 2018 combined population of these municipalities was 119,152 – all with little or no public transportation. Foxconn, along with dozens of other industries, is located outside the public transportation system, suggesting that employment opportunities provided by new or existing businesses are not available to Racine residents with no access to independent transportation. Many lower-income city residents do not have the personal transportation to reach the high-job areas outside the city’s corporate limits. Staffed bus routes to job growth pockets dispersed outside the city have proven cost prohibitive for relatively small ridership. Both the county and city of Racine are prioritizing a corridor to the larger industries to the west, but need to plan how this can be a platform for growing the transportation corridor to include other initiatives. Representatives from the Racine area, including government, university, and private sector members, worked with Kansas City to learn from their streetcar project. Kansas City put in a free 2.2 Mile streetcar supported by Wi-Fi along the

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Map 1 Public transportation coverage in the City and County of Racine

route, info on services and amenities in Kiosks, and integrated to other transit routes. They replaced water and sewer lines simultaneously and put in sensors for water management. The entire project incorporated citizen engagement; one of the consequences of this engagement was that each stop was moved at least once during the project, according to the input from the residents. Currently Kansas City is planning expansion of this route in two directions, linking to the University of Missouri-

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Kansas City and to the Riverfront neighborhood, long dormant and isolated, even though geographically close to downtown. Racine is learning from the Kansas City experience, ensuring that any transportation project takes advantage of the opportunities to include infrastructure improvements and embedded technology. Racine has largely rejected the expansion of public transport in terms of simply adding new routes, since that would be cost prohibitive, as well as continue the inefficiency of service. The development and expansion of an autonomous fleet of shuttles would support more flexible transit solutions; reduce the cost by operating small, driverless vehicles; attract new corporate partners desiring to participate in testing driverless technologies; and allow more city residents to gain those manufacturing job opportunities. City resident employment, earnings, and benefit coverage would increase, which would translate into greater economic impact and activity in the City of Racine. More families would be able to afford to buy and improve a home, rather than rent. In addition to increasing mobility and employment, and helping meet regional employers’ demand for the labor needed to continue competing and growing, such a project will augment the City of Racine fiber optic network. Just as roads, rail, ports, and utilities laid the foundation for future economic growth in earlier periods, the fiber optic network is the new public infrastructure that will fuel economic growth in the digital age. The City of Racine is soliciting corporate partners interested in leasing the expanded fiber optic network and/or attracted to expanding their business in the City of Racine because of the introduction of Smart Cities initiatives, such as the autonomous vehicle transportation and City-supported 5G integration. Besides the financial hurdles faced by this development, the city also must be a groundbreaker in the state of Wisconsin to promote enabling legislation that would permit a transportation partnership and allow testing on public roads. Part of the proposed test routing includes not only public city roads, but also state highways traversing through downtown Racine. The State of Wisconsin has been an early proponent of testing and advancing autonomous vehicle transportation, and the City of Racine is reaching out to the Department’s leadership in order to develop a partnership for the project. State statutes are silent with regard to the integration and implementation of autonomous vehicle operations. The City of Racine will need to take proactive steps to adopt new city ordinances permitting the operations of autonomous vehicles on public roads with the municipality’s corporate limits, and to integrate those vehicles with the county government.

Priority of Inclusivity The reader may have noted that all of the three priorities identified by Racine have been founded on the principle of inclusivity. Mayor Mason said at the very beginning of this effort that it was not worth doing if everyone in Racine would not benefit from the introduction of technology to the city environment. Technology is a neutral agent, and can be used to reduce gaps in opportunities or to widen them, depending upon the thoughtfulness of the politics behind the planning.

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When it comes to racial equity, the State of Wisconsin ranks at the very bottom (https://wallethub.com/edu/states-with-the-most-and-least-racial-progress/18428/). Significant gaps exist across racial and economic groups in education, income, and health. Residents have identified transportation, access to services, and a lack of information as barriers to decreasing that gap (Center for Urban Population Health 2015; Hess 2018). For city residents to benefit from a smart city, an infrastructure (physical and digital) needs to be in place to ensure equitable access to programs and opportunities. The National League of Cities (2017) identified action-oriented infrastructure development as one of the key tactics in a strategy to increase equity, efficiency, and community resilience. The logic is plain – equity is a relational and systemic policy demand; cities cannot address this through isolated, albeit well meaning, programs. Racine has made smart policy decisions to provide opportunities for an educated, mobile, and healthy workforce, integrating efforts of the private sector, universities, and government. Gateway Technical College, a 2 year institution, has initiated a degree in advanced manufacturing to enable graduates to increase employability in technology-based industries. The University of Wisconsin-Parkside has a Smart City Policy Graduate Certificate to train individuals in private public partnerships, civic technology, and smart policy making. Students will earn a graduate certificate emphasizing smart city policy making, the first of its kind in the United States. With smart cities more than doubling from 2018 to 2025, and smart city spending increasing to over 34 billion in 2020, effective city managers will need to manage the decision making and policy requirements of urban innovation. One of the ways to increase access to health care is to geographically decentralize service provision in both primary care and emergency services. Local, neighborhood-based services are more likely to be utilized as they are easier to get to, thus increasing the likelihood that residents increase their health literacy and practice healthy behaviors. In a move that builds upon Racine’s introduction of two community schools in the last 4 years, the city and county partnered with the private health sector, the Unified School District, and an IHE to establish a community health center at one of those community-based schools. “Instead of a family wondering, ‘Can I get to a hospital?’ Wondering, ‘Can I afford to set up an appointment for my child to get a physical?’ Now, I can walk from my house to the school,” says Shebaniah Muhammad, president of the Racine Community Health Clinic board of directors (Kraemer 2020). These equity targeted solutions, although they are not dominated by a technology component, are still smart city solutions. However, there are ways in which the digital infrastructure can contribute. The Smart Cities Council has optimistically suggested that the smart infrastructure might be more prominent at the nation level in 2020, with President Trump and the leading democratic presidential candidates all identifying it as a policy priority. Democratic candidates pledge to spend 1 trillion on infrastructure, as compared to the most recent expenditure of 459 billion in 2010 (Bane 2020). For example, Advocate Aurora Health and Foxconn Health Technology Business Group are partnering to transform health care in Racine by “enhancing preventive

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care and employer-based wellness programs; building a ‘smart city’ connectivity infrastructure; and investing in precision medicine and transformational training programs for a clinical team of the future” (Aurora HealthCare 2018). Increased connectivity between all levels of care will enable both providers and patients to benefit from more efficiency, allowing edge decision making throughout the sites, possibly enabling patients to make health-care decisions on their own. Besides increasing access across time and space, Foxconn’s technology will allow Advocate Aurora to utilize a predictive modeling platform that will enable more preventive care, reducing emergency room visits. The City government is also utilizing ICT to make its decision making more transparent and accessible to residents through a program called Connecting our Community, and to enable a more preventive approach to service needs. The City of Racine is making steady progress in this area, integrating multiple new technologies and making increasing use of existing platforms. For instance, the City of Racine has adopted CitySourced, a citizen engagement mobile application allowing residents to contact the City about a number of service needs, from garbage collection to code enforcement issues. CitySourced is an application specifically designed to support city-citizen interaction with data collection, analysis, and reporting as well as email and push notification functionality all built within the platform. The Racine Water Utility is currently working with the private sector to launch a mobile application that will allow residential and corporate customers to access data regarding their water usage. CitySourced will be included into the already existing Cityworks, which acts as a system of record and provides an ability to manage and schedule a wide range of city services. These Citizen Relation management tools increase responsiveness and communication with residents; the next step is to use the data collected at the edge to create a deeper understanding of community needs, and to predict where needs will emerge in the future. To that end, Racine is working with the national technology nonprofit, DataKind, as one of only six American cities to be awarded volunteer big data/data science services. DataKind is partnering with the City to analyze its neighborhood-based service utilization, which will inform internally developed, alternative service delivery recommendations so that the city can better serve or residents. Using AI pattern recognition methods to explore the spatial, temporal, and causal relations between city services, resources can be prepositioned and allocated more effectively with some problems being preempted altogether. Given the objective of increasing equitable and inclusive participation in Racine, government transparency and access is especially important to allow citizens to participate in the decision-making process. This is more than simple access to data or one-way communication; it involves the inclusion of the citizen voice in actual decision making (Kummitha and Crutzen 2018). This same consideration is important in providing ubiquitous access to 5G networks; citizens need to be able to access the network meaningfully, and defining that term within an equity framework may mean that different consumers participate at different capacities. For instance, civic technology can be provided, but citizens might not see the value of the technology for their own needs. Rolling out ICT needs to be accompanied by an information and engagement campaign to empower its users.

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The first step that Racine is taking toward that possibility is the use of electronic poll books in the 2020 elections. Only a handful of polling jurisdictions used e-books in 2012, the percentage increased to nearly 50% in 2016. E-books allow for shorter lines, reduce errors by updating voting records at the polling place, and can assist voters who may not be at the appropriate polling place in their jurisdiction. To do some or all of this, the software needs to be minimally connected to the election board’s network.

Conclusion As we said in the introduction, it is pretty much all about politics, and politics is based on values. Values often are battered by competing agendas, financial and regulatory constraints, and the fluidity of who is in power. Small municipalities are more vulnerable to these contexts than the larger megacities, so there exists an incentive to seize opportunities when they happen, resulting in what may look like a haphazard agenda. This is why it is preferable to speak about smart city priorities rather than a strategic plan. Still, due to the changing political landscape at local, state, and federal levels there is a concomitant need to institutionalize as much as possible, either through the political/legal system or through citizen support. No city can do become smart on its own – a small city will find the task impossible. Diverse and dynamic partnerships are essential. Especially in a small city environment, it is wise to not overly rely on any one source. Finally, we hope that this chapter will give some insight into the unique factors of an American City, as well as a look into the incremental decisions that need to take place at the very start of this transformation.

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Urban Innovation Ecosystem and Humane and Sustainable Smart City: A Balanced Approach in Curitiba Luiz Márcio Spinosa and Eduardo M. Costa

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Drivers for Smart Curitiba . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Humane and Sustainable Smart City . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sustainable Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Urban Innovation Ecosystem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Quadruple Helix as a Model to Bring Integration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Translating the Drivers into Policies and Strategies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Policy-Mix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Curitiba 2035 Strategic Plan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Translating the Strategies into Services and Projects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smart Cities Institute . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Curitiba Technopark and Vale do Pinhão . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Startup Movement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ICITIES and Smart City Expo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Urban Projects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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L. M. Spinosa (*) LabCHIS / Federal University of Santa Catarina (BR), Triple Helix Association (IT), Curitiba, Brazil LabCHIS – Humane Smart City Lab, Federal University of Santa Catarina (BR), Florianópolis, Brazil e-mail: [email protected] E. M. Costa LabCHIS – Humane Smart City Lab, Federal University of Santa Catarina (BR), Florianópolis, Brazil Knowledge Engineering and Management Dept., Federal University of Santa Catarina (BR), Florianópolis, Brazil e-mail: [email protected] © Springer Nature Switzerland AG 2021 J. C. Augusto (ed.), Handbook of Smart Cities, https://doi.org/10.1007/978-3-030-69698-6_15

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Abstract

The need to foster more Humane and sustainable smart cities (HSSC) is a challenge in many cities all over the world. It is crucial for urban planners to take notice and to connect their projects to the HSSC concepts and also to the Sustainable Development Goals for 2030. This work explores a balanced approach observed in the city of Curitiba in Brazil, involving three leading components: (i) main conceptual drivers, (ii) a policy and strategic plan, and (iii) several projects under execution and already in place. A descriptive framework emerged from a triangulation method to organize the components. The main conclusions are: (i) there is a symbiosis between the urban innovation ecosystem and the HSSC implementation mainly involving the ICTs, (ii) there is a positive mindset for innovation in the city, (iii) the participation of the stakeholders in the innovation ecosystem and in the HSSC decisions facilitates the development of actions, (iv) the organized civil society plays a major role, and (v) co-creation and co-management based on a triple helix approach provide stability and reduce vulnerability. At the end, this paper presents some considerations about the framework to support the decision-making processes of innovation managers and urban planners.

Introduction The development of public policies and strategies driving the cities and regions to more satisfactory levels of intelligence and sustainability is a constant and imperative challenge for public managers and urban planners. This chapter focuses on this challenge and explores a balanced approach between an urban innovation ecosystem and a vision of a more Humane and sustainable smart city (HSSC). A HSSC is a foundation that joins concepts from citizen-centric urban development, smart cities, and sustainable development (Giffinger et al. 2007; Costa and Oliveira 2017). Innovation ecosystems inserted in the urban context are a great help for HSSC development, providing new solutions for common urban problems. To explain this balanced approach, this paper explores a set of initiatives occurring in Curitiba, a city in the South of Brazil. See Fig. 1 and Table 1. There is no official policy to transform Curitiba into a HSSC. Nevertheless, a descriptive framework arises from the current application of several concepts and models, the adoption of development strategies, and the execution of several actions. See Fig. 2. This framework emerged from an exploratory and descriptive research, combining triangulation methods (Minayo et al. 2016; Marcondes and Brisola 2014) with traditional approaches to social research (Bhattacherjee 2012; Gerring 2012). Three main levels describe the framework: (i) The conceptual drivers level, which introduces a theoretic background inspired by the areas of humane smart city, sustainable development, urban innovation ecosystem, and quadruple helix model,;

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Fig. 1 Curitiba’s location. Source: Google Maps(R).

(ii) The policy and strategic level, which translates the conceptual drivers into a policy-mix proposition and a strategic plan called Curitiba 2035 (2017), specifically designed by the Observatory of the Paraná Industry Federation (extract from http://www.fiepr.org.br/observatorios/) (iii) The implementation level, which translates the policy and strategies into information and communication technology (ICT)-based services and a portfolio of projects.

The Drivers for Smart Curitiba Humane and Sustainable Smart City The European Union approaches smart cities by six fields of study: smart living, smart people, smart governance, smart mobility, smart environment, and smart economy (Giffinger et al. 2007). Costa and Oliveira (2017) added to the list two more fields to apply the concept into emerging countries: smart social inclusion and smart safety place. It tackles poverty in cities, segregated in slums and ghettos, and the problems associated with rapid unplanned growth and geographical expansion.

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Table 1 Curitiba’s general information. Source: Curitiba 2035 (2017).

Territory Altitude Installation date Elected authority (2017–2020) Latitude Longitude Territorial area 2016 Population density 2016 Degree of urbanization 2010 Population Population 2016 Number of voters 2016 Geometric growth rate 2010 Senior index 2010 Dependency ratio 2010 Sex ratio 2010 Aging rate 2010 Human development index – HDI 2010 Education Nursery enrollment 2015 Preschool enrollment 2015 Primary school enrollment 2015 High school enrollment 2015 Higher education enrollment 2015 Illiteracy rate 2010 Health Health facilities 2015 Hospital beds 2015 Fertility rate 2010 Gross Christmas rate 2015 Mortality rate 2015 Public services Households 2010 Piped water households 2010 Household with bathroom or toilet 2010 Households with waste collected 2010 Electricity households 2010 Water consumption 2016 Electric power consumption 2015 Economy Establishments 2015

Curitiba

Curitiba’s participation Region Paraná

934 m 03/29/1963 Rafael Greca 25
250 4000 S 49
160 2300 W 435 km2 4.320 hab/km2 100%

– – – – – 5% – 94%

– – – – – 0,20% – 85%

1.893.997 1.289.215 1% 38% 38% 91% 8% 0,823

56% 59% – – – – – –

17% 16% – – – – – –

39.250 28.631 221.952 78.815 130.582 2,13%

67% 47% 51% 57% 93% –

22% 12% 15% 17% 33% –

5664 5580 1,58 children/woman 13% 6%

84% 68% – – –

26% 21% – – –

576.190 575.598 575.630 575.635 576.057 125.736.770 m3 4.733.290 Mwh

60% 60% 60% 60% 60% 63% 54%

17% 18% 18% 19% 18% 22% 16%

61.574

70%

20% (continued)

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Table 1 (continued)

Employment 2015 Establishments in activity characteristics of tourism 2015 Active age population 2010 Economically active population 2010 Occupied population 2010 Occupancy rate 2010 Gross domestic product – GDP 2014 GDP per capita 2014 Gross value added – VAB 2014 GVA agriculture and livestock 2014 GVA industry 2014 GVA trade and services 2014 Municipal revenue 2016 Municipal expenses 2016 ICMS 2015

Curitiba 914.006 5065

Curitiba’s participation Region Paraná 74% 29% 75% 25%

1.531.838 995.543 947.195 95% R$ 78.8 billions R$ 42.315 R$ 6.3 billions R$ 8.2 millions R$ 14.8 billions R$ 48.9 billions R$ 7.0 billions R$ 6.8 billions R$ 8.8 billions

58% 59% 59% – 59% – 59% 1% 50% 64% 68% 69% 57%

17% 18% 18% – 23% – 21% 0% 20% 25% 24% 24% 36%

These added dimensions are unfortunately becoming more important in European cities as well, as they face the influx of large immigrant populations. In these eight fields, there are good and bad examples to learn from, and cities are organizing themselves to exchange knowledge and share their experiences. Solutions to cities’ problems are inevitably interdisciplinary in nature. They involve the social sciences, with studies on people’s behavior in communities, urban studies of spatial distribution of people and functions, and studies of social networks and their use in the context of cities. These solutions also involve studies of computer technology, sensor technology and high-speed connections, electronic and participatory government, and big data and business intelligence.

Sustainable Development According to Van Bellen (2007), “despite the large amount of concepts and definitions, or perhaps exactly because of these, the exact meaning of the term sustainable development is unknown.” It appeared during a summit in 1987 with the publication of Our Common Future or the Brundtland Report (UN-WCED 1987). The Rio 92 Conference defined sustainable development as a policy that meets the needs of present generations, without compromising the ability of future generations to meet their own needs (UN-SD 1992; IUCN 1996). Sustainable development is an ongoing process, an evolution in which people act toward a development that satisfies sustainability requirements. Missimer et al.

Sustainable Development

Quadruple Helix

Fig. 2 The framework

Conceptual drivers

Urban Innovation Ecosystem

Humane Sustainable Smart City

Urban Planning and Management,

Safety

Policy and strategic

Health and Quality of Life,

Mobility and Transportation,

Socioeconomic Development,

Environment and Biodiversity,

Curitiba 2035

Policy-mix

Coexistence in a Global City,

Governance

City of Education and Knowledge

Implementation

Urban Projects

Curitiba Technopark and Vale do ~ Pinha o

Smart Cities Institute

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(2010) argue that “sustainability is a state, and sustainable development points at processes towards or within that state of being.” Sustainable development refers to meeting needs without overwhelming the rest of nature and society (Challenger 2013) and is the maintenance of certain desired and necessary characteristics of people, their communities and organizations, and the surrounding ecosystem for a long period (Hardi and Zdan 1997). Sustainability aims to maintain or increase human well-being. It is a balanced management of the relationships between people and the world around them. The idea is to foster the attention to the needs of people without undermining the world’s ecosystem. The Rio Conference also produced the Agenda 21 program (UN-SD 1992), which introduced a wide range of assumptions and recommendations on how nations should act to change their development and match acceptable sustainability levels. Agenda 21 is an action plan that covers the global, national, and local scopes adopted by governments and society. It embraces all areas where human actions affect the environment and seeks to guide a new pattern of development. This research assumes that sustainability and sustainable development involve a global awareness to preserve finite natural resources, reduce the emission of pollutants, search for social equality, and foster economic growth. All these efforts need to take place without degrading the environment.

Urban Innovation Ecosystem Scholars in the field of entrepreneurship have dedicated increasing attention to understanding innovation (Wright 2008; World Bank 2010; Kraemer-Mbula and Wamae 2010) and innovation ecosystems (IEs) (Gomes et al. n.d.; Nambisan and Baron 2013; Shaw and Allen 2016; EU-CoR 2016; Adner 2006; Hwang and Horowitt 2012; Jackson 2011). The term has partly replaced the “innovation regions,” “milieu innovateur” (innovative environments), and “clusters” established by Porter in the 1990s. Engel (2014) extended the concept of a cluster, incorporating relevant actors in a mature ecosystem to better represent IEs through their components and behavior. IEs are strongly associated with the “knowledge economy” and “knowledge society” and enable sustainable entrepreneurship and innovation (Lawlor 2014). According to Autio and Thomas (2014), an IE is a network of interconnected entities structured around an organization, which incorporates both production and user side participants and creates value through innovation. The term urban innovation ecosystem (UIE) derives from the previous definitions and adds an urban scope (Spinosa et al. 2015). The UIE describes the function or role of independent factors that act jointly, but randomly and spontaneously, to enable the actions of entrepreneurs and innovators to allow innovation to occur according to a sustainable process in a given territory. They are “competitive assets in the knowledge economy integrated with the urban and regional environment” (Spinosa et al. 2018). Studies on knowledge-based urban development (KBUD) (Knight 2008; Spinosa and Krama 2017; Yigitcanlar and Velibeyoglu 2008) seizes the main aspects of the

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urban dimension of the UIE. KBUD is urban planners’ response to the changes produced in the urban environment through technological, economic, and social advancements. KBUD intends to improve the city’s ability to attract, generate, retain, and foster creativity, knowledge, and innovation. The city is viewed as an integrated arrangement that combines physical and institutional science park functions with civic and residential functions, thus offering an effective paradigm for sustainable cities of the future (Yigitcanlar 2011).

Quadruple Helix as a Model to Bring Integration The quadruple helix model proposed by Carayannis and Campbell (2009) is based on the triple helix model initially developed by Etzkowitz and Leydesdorff (1995) and Leydesdorff (2013). They argue the need for an integrated effort of the academic, private, and government sectors to build innovation environments. The quadruple model adds a fourth dimension, the civil society, to consider a tighter interaction with the local community involved in the innovation process. Most actions in Curitiba devoted to foster innovation within the city consider the involvement of these four sectors. More, the quadruple helix model adds some advantages compared to the triple helix model: (i) enhances innovation processes based on cocreation, involving the four kind of stakeholders, (ii) highlightes the dynamics of open innovation, and (iii) solutions are designed considering regional and local contexts rather than external practices (McAdam and Debackere 2017). All those issues are suitable for urban planning.

Translating the Drivers into Policies and Strategies Policy-Mix Based on studies carried out in Curitiba (Spinosa and Krama 2017), a more harmonious option to turn around the current scenario is to adopt an extended policy-mix (Borrás 2008; Borrás et al. 2009; Borrás and Edquist 2013). The term policy-mix usually refers to the balance and interactions between monetary and fiscal policies. The extended notion adds a social development dimension, which gains significance in Brazil due to the need to include the lower-income population into the country’s development process. Also, this extended notion covers the need for a more equitable regional and urban development. A policy-mix essentially highlights the interdependence of policies and a more holistic perspective to understand the scenario that will change. Any intervention aimed at improving performance or change in behavior should be based on an understanding of how they will interact with existing agreements – for example, if they are complementary, neutral, or conflicting (OECD 2010a, b, 2011). The holistic

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perspective must cover the National and Regional Innovation Systems and consider them as innovation ecosystems as previously defined with a high degree of mutual interaction. The dynamics of the actors and factors of the ecosystems must be considered actual components of the innovation performance.

Curitiba 2035 Strategic Plan Curitiba 2035 (2017) is a project developed by the Curitiba’s local society to build long-term guidelines that will frame the city’s development policies in the next 20 years. The project is a prospective study to indicate a way to position Curitiba as one of the main innovative cities in the world. This prospective study was based on an organized process of collective reflection of diverse segments of society It prioritizes the themes and actions to the desired local transformation. The priority themes are (Curitiba 2035 2017): (i) city of education and knowledge, (ii) socioeconomic development, (iii) mobility and transportation, (iv) health and quality of life, (v) environment and biodiversity, (vi) coexistence in a global city, (vii) urban planning and management, (viii) safety, and (ix) governance. Education and knowledge are widely debated in the reengineering of the future of cities. In the context of Curitiba 2035, the this theme proposes a discussion about the city’s performance on the development of its people, so that they can understand, react, and intervene on the reality of the world around them. It also highlights the processes of creation, sharing and the use of knowledge within the municipality. Within this thematic area, issues such as management of education, new teachinglearning models, technologies and methods in education, knowledge, and others are addressed. Socioeconomic development is a field with a broad conceptual repertoire, involving the association of economic growth with the improvement of society’s quality of life. It proposes a new approach for the improvement of the living conditions of the population through the provision of new jobs and better income, as well as by the increase of productive capacity and circulation of wealth. It suggests a qualified development, with allocation of resources in different sectors of the economy, which has a positive impact on social indicators and the quality of life of the population in a balanced way with the growing of the economic indicators. Mobility and transport issues profoundly influence the planning processes of cities, giving rise to relevant debates on the present conditions and unfolding a series of challenges for the future, among which the environmental, economic, and social rights. Looking for answers to the environmental challenges, there is a tendency to strengthen collective public transport and non-motorized transportation, aiming at reducing greenhouse gas emissions. From the economic point of view, there is a search for a financial balance of costs, with appropriate, clear, and transparent political values. In the social logic, studies indicate a diversity of trends, such as the universalization of access, the adoption of vehicle sharing, and the greater security awareness.

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Health and quality of life are concepts with profound variations in the contemporary debate. In the scope of Curitiba 2035, the health theme proposes the discussion about services, management models, and technologies oriented to the prevention, promotion, and treatment of health-disease processes. The theme of quality of life covers issues such as humanization and modernization of urban spaces for the well-being of individuals, associated with aspects such as culture, sports, leisure, and other conditions. All efforts intend to the improvement of people’s lives in the physical, mental, social, and spiritual perspectives. Environment and biodiversity address the discussion about the relationship between society and nature in the urban environment, contemplating processes related to the conscious use of natural resources and biodiversity It emphasizes practices of preservation, mitigation, and environmental treatment, with the objective of reaching sustainable levels. The management of the urban-social-environmental system is an essential procedure to ensure that the next generations have adequate conditions of life in the long term. In this sense, measures to control, scrutinize, and protect the environment must be consolidated, along with programs that include basic sanitation, recycling and reuse of solid waste, correct treatment of waste, reduction of greenhouse gas emissions, and management of resources, among others. Coexistence in a global city proposes to discuss social interactions in a city that grows in the number and diversity of people. The theme has its relevance extended due to the simultaneity of cultural, relational, and identity phenomena that characterize the contemporary social structure of the city. The thematic area requires integrated thinking about social relations and urban space, with special focus on axes such as multiculturalism, diversity, equity, inclusion, vulnerability, and ethics. The theme urban planning and management has a wide interpretation in literature, covering a diverse set of meanings. In the context of Curitiba 2035, the discussion is about the social action impact over the city production, whether it is proposed by institutional action or by interventions made by the population. Regarding planning, the assessment refers to the future, seeking to elaborate plans or programs with the objective to coordinate preventive or necessary actions in the urban context. In the case of management, it refers to the present, aiming at the systematization of management practices related to the interventions of different agents in the city. Security is a field with a vast conceptual repertoire, commonly involving the set of political and legal processes aimed at guaranteeing public order and coexistence of individuals in society. The security theme addresses the issue of preserving or restoring social harmony in the city environment, allowing the individuals to enjoy their rights, exercise their duties, and live without disturbance or fear. Urban governance can be defined as the sum of the spheres in which citizens and public and private institutions plan and manage common land issues. The concept encompasses the many ways in which institutions and individuals organize city management, as well as the processes used to realize a short- to long-term development agenda.

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Table 2 Curitiba’s education, research, and innovation ecosystem. Souce: Curitiba 2035 (2017). Education, research, and innovation ecosystem – 2016 Establishments Search and development Education Total Employment Management and direction in search and development Researchers Engineers Total Actors Startups Coworking spaces Local movements Higher education institutions Investors (organized groups) Acceleration programs Supporting entities Incubators and technology hotels Total

Curitiba 6% 94% 100%

Participation of Curitiba PR Brazil 43% 2% 27% 1% 28% 1%

5% 7% 88% 100%

41% 54% 46% 46%

75% 5% 5% 4% 3% 3% 3% 2% 100%

36% 44% 22% 21% 86% 100% 18% 19%

2% 2% 3% 3%

For the achievement of all these themes, Curitiba 2035 specifies that the city holds three main assets: (i) the education, research, and innovation ecosystem (see Table 2), (ii) the public governance (see Table 3), and (iii) the citizenship profile (see Fig. 3).

Translating the Strategies into Services and Projects Smart Cities Institute The Smart Cities Institute (ICI – Instituto das Cidades Inteligentes, extract from https://www.ici.curitiba.org.br) is the main provider of services based on information and communication technologies (ICTs) to the city of Curitiba. The role of ICTbased services in a smart city is a well-known key point. The ICI is a nonprofit organization that operates close to the city hall, aiming at the integration, development, and implementation of solutions for public management. It attends more than 5000 daily calls or other demands from citizens and 9000 technical service desk calls. Some of the solutions provided by ICI are2:

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Table 3 Curitiba’s public governance. Source: Curitiba 2035 (2017). Public governance Transparency index – 2015

Tax management index – 2013

Municipal efficiency ranking – 2016

Position 4
7
12
18
20
23
41
80
84
94
99
138
181
16
36
53
77
86
121
146
196
239
275
346
350
– – 40
130
342
349
439
818
1.060
1.212
1.275
1.330
2.107
2.215


Capitals Brasília Curitiba João Pessoa Recife Rio Branco São Paulo São Luís Vitória Rio de Janeiro Fortaleza Palmas Campo Grande Porto Alegre Rio de Janeiro São Paulo Porto Velho Recife Rio Branco Campo Grande Fortaleza Belém Curitiba Porto Alegre Manaus Boa Vista Capitals mean Cities mean Vitória Florianópolis João Pessoa Aracaju Belo Horizonte Teresina Fortaleza Curitiba Recife São Paulo Salvador Rio de Janeiro

Grade 10 10 10 10 10 10 9,58 8,75 8,61 8,19 8,19 6,81 5,83 0,8169 0,7744 0,7579 0,7452 0,7399 0,7212 0,7126 0,6976 0,6877 0,6795 0,664 0,6636 0,6449 0,4545 0,597 0,576 0,55 0,549 0,542 0,52 0,509 0,503 0,501 0,499 0,473 0,468

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• The management+ administrative and financial set of services aims at meeting the needs and concerns of the public manager regarding the control of financial transactions, accounting entries, administrative routines, purchasing and bidding processes, transport fleet, and assets. This product line also offers the publication of official acts through an official electronic journal and the use of multifunctional smart cards. • Smart card integrates in a single card the functionalities that facilitate the management of city’s human resources, such as access control, frequency, transportation, and credit, among others. • Electronic buying provides to the city hall the administrative purchase processes in electronic auctions and price surveys; it enables the reduction of operational costs and manages the supplier registry. It allows the consultation, disclosure, and issuance of documents in public tenders, such as notices, regulatory laws and decrees, minutes, results, etc. It offers access to three types of users, according to the current legislation, restricting or releasing information and functionality according to each profile: general public, vendor, or administrator. • Urban maintenance manages the activities of preservation of the spaces and public services, offering better planning and administration, control, and saving of time and financial resources; the urban maintenance solution presents integrated and dynamic modules that offer the public manager more quality and dynamism to take strategic actions in the municipality. • The management + citizen provides a communication platform between the public administrator and the citizen. In order to carry out the management of a municipality, it is necessary to know the demands of the population and their expectations from public services, besides having reliable data to support decision-making. The platform addresses these needs and offers a comprehensive service including: 1. Virtual channel, a citizen assistance tool via automated or human chat to avoid waiting queues: it supplies services like generation and reception of documents from the citizens, detection of problems in public services, sending the demands to the responsible departments, and evaluation of the perception of the population about the quality of municipal services. 2. Business intelligence, which provides performance indicators related to the demand for services rendered to the community: managers have access to situations that require more attention. 3. Relationship center, which publicizes public administration campaigns and projects; monitors the degree of knowledge and approval of actions of the city hall; invites citizens to participate in inaugurations, debates, and public hearings; and builds a network of volunteers for social campaigns. • The management + education integrates the information of the municipal education network, providing the school manager with analytics to support decision-making. It automates the activities of the units and the school sectors,

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L. M. Spinosa and E. M. Costa Channels of social participation (3) - 2015 84% Social Accounts

78% 66% 63% 63%

General ombudsman 29% 10% Real time online service

5% 5% 57%

Online Reporting

51% 27% Municipalites+ 500 thousand

Capitals

Total

Municipalities' adhesion to social networks (3) - 2015 23% Blog

18% 9% 72% 70%

Twitter 13% 60% YouTube

73% 17% 84%

Facebook

78% 62% Municipalites+ 500 thousand

Capitals

Total

Forms of public consultaiton (3) - 2015

Online voting

20% 11% 8% 31%

Online Forums

24%

10%

28% 31%

Online public consultation 11%

63% Survery

36% 18% Municipalites+ 500 thousand

Capitals

Fig. 3 Curitiba’s citizenship profile. Source: Curitiba 2035 (2017).

Total

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organizing and expediting the attendance to the daily demands of the school routine. It organizes administrative and academic processes, generating specific and managerial reports for the global monitoring of the educational scenario. Some functions include registration of students, release of grades and absences, and supervision of school activities. The solution meets the needs of elementary and secondary education, special classes, accelerated classes, and early childhood education. It also supports the school administrators in the fulfillment of legal duties required by the Federal Ministry (legal and control office of the municipality). • The management + mobility offers to the public manager the Operations Center, through which is possible to manage traditional and electric-powered buses in order to provide urban mobility in an integrated way. The Operations Center was developed in partnership with the Center for Excellence and Innovation in the Automobile Industry (CEIIA, www.ceiia.com) of Portugal. It involves online monitoring and real-time dashboards for visualization of fuel consumption, remote intervention of the vehicle at any time, simulation of traffic scenarios, and so forth.

Curitiba Technopark and Vale do Pinhão Curitiba Technopark and Vale do Pinhão (extract from www.valedopinhao. agenciacuritiba.com.br) are the main projects of the city to consolidate its innovation ecosystem. They were created to organize urban infrastructure and services to foster entrepreneurship based on a national policy (Zouain and Plonski 2006; Zouain et al. 2007; Miranda and Negreiros 2011). The innovation ecosystem was created in 2008 by the municipal government to stimulate the development of the high-tech sectors. It is totally integrated into the urban environment and is not fully deployed in a single lot or plot. The innovation facilities are spread in several different city neighborhoods. The innovation ecosystem is composed of the typical and main quadruple helix stakeholders and additionally of the accelerators, incubators, investment funds, research and development centers, startups, cultural and creative movements, etc. In addition to the City Hall of Curitiba, other institutions also foster the ecosystem, among them the Paraná Micro and Small Business Support Service (SEBRAE-PR), the Federation of Industries of the State of Paraná (FIEP), and the Federation of Commerce, Services and Tourism of Paraná (FECOMERCIOPR). In the beginning, the total area of the Technopark was 90.000 m2, concentrating efforts to induce an innovation environment, knowledge transfer, and development of technology-based activities. Later on it spread through the city covering larger areas:

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(i) Logistics Ring: where the two main Universities’ campuses are located, the Federal University of Paraná (UFPR) and the Pontifical Catholic University of Paraná (PUCPR), besides Institute of Technology for Development (LACTEC) and the Federation of Industries of the State of Paraná (FIEP); (ii) Rebouças Sector: containing the Federal Technological University of Paraná (UTFPR) and a convention center; (iii) CIC North Sector: where a Software Park is located; (iv) CIC South Sector: where the Institute of Technology of Paraná (TECPAR) is located. Curitiba Technopark now covers the entire city and currently has several companies engaged in the innovation process in areas such as telecommunications systems, computer hardware: hardware and peripherals, computer services, and research and development; design; laboratories of quality tests; precision instruments and industrial automation; and new technologies (biotechnology, nanotechnology, health, new materials, and environmental technologies). Vale do Pinhão is a more recent development, which started in 2017 to promote Curitiba as a smart city. Its charter states that Curitiba should become an “Intelligent City that develops its economy while increasing the quality of life of its citizen and generating efficiency in urban operations. The program involves all the municipal secretariats and the innovation ecosystem of Curitiba. The Vale do Pinhão focuses on startup creation and support.

Startup Movement The precise date of the beginning of the startup fostering movement in Curitiba is uncertain. However, the movement became official with the advent of the Curitiba Technopark and intensified with the implementation of the Vale do Pinhão. The movement comprises a number of universities, incubators (UFPR Innovation Agency, Fiep System, Jupter, UTFPR Incubator, Hotmilk PUCPR, IBQP, Intec TECPAR) and accelerators (District, Fiep System, ACE, Hotmilk, Founder Institute, Orbital, Jupter, and Isae), mentors, mutual funds, and institutions that help to foster entrepreneurship. In 2018, the city won the first place in the Connected Smart Cities ranking (https://www.connectedsmartcities.com.br/o-que-e-o-ranking-connectedsmart-cities/), which mapped out the cities with the highest potential for technological development in Brazil. In 2018 and according to the 100 Open Startups Movement (https://www. openstartups.net), 10 among the 100 most attractive startups in Brazil are located in Curitiba. The startups and their ranking are: GoEpik (4th), Loox Studios (15th), Eruga (38th), Pipefy (41th), Beenoculus (43th), Vidya Technology (48th), Ubivis (51th), Send4 (68th), 33 Robotics (77th), and the Pollen (98th). Other internationally projected startups are MadeiraMadeira, Contabilizei, anothe startups, and Olist. Curitiba is currently organizing itself to publicize worldwide as a city that houses a startup ecosystem focused on smart city solutions. This is a brand strategy that

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takes advantage of the international projection that the city had during the term in office of former mayor Jaime Lerner. There are several events dedicated to promote the startups throughout the city, organized by the efforts of Vale do Pinhão.

ICITIES and Smart City Expo ICITIES is an initiative to develop culture and solutions for smart cities in Curitiba. It is undertaken by a group of entrepreneurs (a startup) who created the ICITIES company (http://icities.com.br). ICITIES is one of the first groups in Brazil to be organized around the subject of smart cities and is one of the most active startup in the city. ICITIES has developed a business based on connecting six major axes: entrepreneurship of high impact (startups), creative economy, sustainability, clean energy, technology, and connectivity. ICITIES are: (i) Smart City Brasil, which involves consulting for the construction of smart cities (ii) ICITIES KIDS ICITIES Kids, which promotes the culture of smart cities for children, simulating the environment of a smart city, where the children participate in ludic workshops about renewable energy, smart mobility, robotics, conscious consumption, and recycling (iii) Smart Neighborhoods, which involves implementing smart solutions in delimited areas of the cities to test new technologies and measure public acceptance Another action of the same group is the realization of the Brazilian version of the Smart Cities Expo (https://www.smartcityexpocuritiba.com), one of the most important events in the smartcity area worldwide.

Urban Projects Curitiba is internationally recognized for being a pioneer in the implementation of sustainable urban planning. Much of this recognition is attributed to the architect Jaime Lerner (https://pt.wikipedia.org/wiki/Jaime_Lerner), who was mayor of Curitiba in three periods (1971–1975, 1979–1984, and 1989–1992). Some urban projects implemented by Lerner and others are the following: • The Rua das Flores (Street of the Flowers) is the first pedestrian-only street in Brazil inaugurated in 1972. The street is in the center of the city and has 3.300 m of extension. Approximately 100 thousand people walk on the street per day. The street is defined by centenary buildings and townhouses, tourist bars, and flower beds throughout the pathway. Street performers such as clowns interacting with passersby, musicians, and statue people are attractions in this urban space. Recently, Rua das Flores has been converted to a smart street (https://www. curitiba.pr.gov.br/noticias/calcadao-da-xv-de-novembro-e-a-segunda-rua-inter

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•

•

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ativade-curitiba-conecte-se/48766). The work of a startup called MCities promotes interaction between the street and the people by two main technologies: (i) several buildings and facilities received QR code panels, which provide street and event information, suggestions of tours and experiences throughout the street and (ii) tiny location technology devices – called beacons – which transmit information about services and commerce on site to smartphones via Bluetooth. The bus rapid transit (BRT) (http://www.brtbrasil.org.br) is implemented by means of the project called Rede Integrada de Transporte in 1974. The BRT is largely documented and is a relevant solution to improve the mobility in large cities. BRT involves urban changes, with the premise of permanently replacing individual traffic with public transport, reducing CO2 emissions, and reducing traffic jams. BRT has become attractive due to its cost-effectiveness for public managers and urban planners. The Ruas da Cidadania (Citizenship Streets) were created by the Curitiba City Hall, aiming at decentralizing public agencies and facilitating the population’s access to various public services. There are currently ten Citizenship Streets around the city: Bairro Novo, Boa Vista, Carmo/Boqueirão (the first), Cajuru, CIC, Praça Rui Barbosa, Pinheirinho, Fazendinha, Santa Felicidade, and Tatuquara. Each street has a main office with approximately 20.000 m2 attached to a bus terminal, to which many bus lines converge. The citizens have access to services concerning health, justice, police, education, sports, housing, environment, urbanism, social service, supply, among others. The parks of Curitiba are composed of a set of urban planning solutions balancing environmental protection, leisure, tourism, and mainly civil security. The parks are a system to protect the city from high rainfall indices and storms. Some of them act to drain the water excess. Today there are 29 parks spread in the city, and the best known are Parque Tanguá (the most beautiful), Parque Barigui (the most known by citizens), Passeio Público (the oldest), Parque Tinguí (a tribute to Ukrainian immigrants), Bosque do Papa (a tribute to the Pope visit), Bosque do Alemão (a tribute to the German immigrants), and the Jardim Bot^anico (a botanical conservation unit and one of the most beautiful), among others. The Unilivre or Free University of the Environment is a university which stated Curitiba as the first city in the world to have a space for studies and transfer of knowledge about the environment and ecology to the population. Unilivre was inaugurated in 1992, with the presence of oceanographer Jacques Cousteau. It is installed in a 874 m2 building built with eucalyptus logs (from reforestation), surrounded by the Bosque Zaninelli, which has 37.000 m2 of dense native forest, housing several bird species. The Ópera de Arame (Wire Opera) is a theater made of an impressive array of steel pipes and metal structures, covered with transparent polycarbonate plates, in a circular shape. The theater is also in a park and surrounded by an artificial lake, so access to the auditorium is via a walkway over the water.

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Conclusions This chapter briefly presented the framework guiding Curitiba City toward a HSSC, based on an exploratory and descriptive research methodology. The main finding is a balanced approach between Curitiba’s urban innovation ecosystem and Curitiba’s strategies and efforts to transform the city in a HSSC. The main reasons for this balance are the following: (i) There is a symbiosis between the urban innovation ecosystem and the set of actions toward the HSSC implementation, mainly those concerning the ICTs. The urban innovation ecosystem is a platform for the development and adoption of urban technologies. In fact, several of the identified technologies came from the need to implement the HSSC. The implementation carries out experimentation by the citizens that in turn provides new demands for the urban innovation ecosystem. New technology-based businesses are nurtured and oriented to solve urban problems. (ii) There is a positive mindset for innovation in the city. Curitiba’s citizens are proud to consider the city as a locus for innovative urban solutions. In fact, there is a constant appeal for innovation to the public planners and managers. This mindset also fosters the development of the urban innovation ecosystem. A common belief of citizens and stakeholders is that Curitiba is a living lab for urban innovations. (iii) Several stakeholders participate at the same time in the urban innovation ecosystem community and in the smart city community. Such context allows the perception of common problems and thus facilitates cooperation and coordination. Despite the inexistence of official governance for the entire process, the participation of stakeholders in both sides helped in the decisionmaking about what needs to be done. The construction of Curitiba 2035 is a major example. (iv) The balanced approach is mostly obtained by a development agenda supported by the organized civil society of Curitiba. There is no official statement or legal structures comprising the whole approach. However, there are specific laws and projects in the city hall focused on some components of the urban context, which help the balance. (v) For almost all the stakeholders, the development agenda for the city must be cocreated and co-managed (at least) by the government, academy, enterprises, and nongovernmental organizations. This perception guarantees more stability of the agenda and reduces the vulnerability that naturally comes when the substitution of public managers occurs due to new mayors or change of public policies. The three main components of the framework that structure the balance are organized in a deployment sequence that requires an analysis from effectiveness viewpoint. The main considerations are:

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(i) Despite the existence of the conceptual drivers inspired from the areas of humane smart city, sustainable development, urban innovation ecosystem, and quadruple helix model, there is no tight relationship among them. The apprehension of the drivers by the stakeholders is mainly tacit, and explicit references are few. The common sense among stakeholders is more important than formal references. A shared vision on how the city needs to be in the future is more powerful than documents. The common sense and the shared vision observed in Curitiba have been enough to provide the basis for policy and strategic definitions. (ii) The unfolding of the conceptual drivers in a policy-mix is also tacit. One of the main guidelines of the policy-mix concerns the fostering of innovation in all dimensions of sustainable development. Again, the common sense among the stakeholders allows enough security and stability for the transformation actions. The policy-mix and the high common sense came about in well-defined and detailed action plan entitled Curitiba 2035. This plan is explicit and cocreated, involving stakeholders, policy makers, users, and several other representatives of the city. (iii) Curitiba 2035 led the perception of delegates from the quadruple helix approach in a coherent way. Two present challenges are defying the implementation of the Curitiba 2035 plan. First is the difficulty to establish a governance system to handle the plan. This imposes several additional actions (workshops) to the planners to get the engagement of leaders from the city hall, industry, and academies. Second, federal economic policies in Brazil aren’t yet clear enough to support the investments. The city planners are working with public-private partnership models to replace the lack of investments. This research was only possible thanks to the financial and institutional support of the Higher Education Personnel Improvement Coordination (CAPES) of the Brazilian Ministry of Education, through the process N. BEX 6555/14-4, Senior Internship.

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Contents The Strategy of the Taipei Smart City . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Build a Smart City Ecosystem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Establishment of Smart City Management Office . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Establish a Smart City Operation and Promotion Mechanism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Strengthening the Linkage of International Smart Cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Taipei Smart City Achievements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smart Government . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smart Social Housing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smart Transportation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smart Health and Care . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smart Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smart Payment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smart Start-Up . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Future of Taipei Smart City . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . New Promotion Framework for Taipei Smart City with 1 Core+ 7 Key Directions . . . . . . . Continue to Promote Innovation Culture to Public Sector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Establish Sustainable Smart City Implementation Mechanism and Specification . . . . . . . . . Improve Public-Private Partnership . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Strengthen PoC Effectiveness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Broaden Collaboration and Construction Scale . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Establishment of GO SMART . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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C.-Y. Lee · Taipei Smart City Project Mangement Office (TPMO) (*) Taipei, Taiwan e-mail: [email protected]; [email protected] © Springer Nature Switzerland AG 2021 J. C. Augusto (ed.), Handbook of Smart Cities, https://doi.org/10.1007/978-3-030-69698-6_22

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Abstract

TPMO, as the bridge between city government and industry to improve Smart Taipei. Taipei City has a vision of livability and takes promoting smart city as a driving force. Therefore, “Smart City” is not a noun but a verb and is seen as a method to solve city challenges, meet the needs of citizens, and improve the quality of municipal services, to make Taipei City develop toward a livable city. The promotion of the smart city in Taipei City is in accordance with the policies and objectives of the city’s governance strategy. Through the innovation of mechanism, with citizens as the main part and combining the power of citizens and communities, together with the opportunities opened up by the public sector, information technology and innovation will be brought into the public sector, so that departments of Taipei City, private enterprises, start-ups, academic institutions, communities, and citizens can participate together and keep communicating with stakeholders and choose appropriate technology service solutions. Taipei City therefore will be enabled to make continuous progress under rapid environmental changes (Fig. 1).

The Strategy of the Taipei Smart City In the strategy of promoting smart city, both the local development and linking up with the world are taken into account. Taipei City takes “from public to private, from internal to external” as the policy and sees “the government as a smart city platform, the city as a living lab” as the main spirit of strategy. It is expected to facilitate the public, private, and people to form a partnership, cooperate in the promotion of the smart city, and improve the “Smart City Ecosystem” through engaging, encouraging, enabling, empowering, etc. Thus, Taipei City proposes three main development strategies, including establishing a smart city management office, setting up a smart city operation and promotion mechanism, and reinforcing the cohesion between smart cities globally. When promoting smart city, Taipei City not only considers the background of its population, geographical environment, historical context, etc.; it also carries out related work in line with international standards. Following the standardization framework developed by the international standardization organizations in the field of smart city, Taipei City hopes to interact with other international smart cities in a common language to address the same city issues and to think and develop technology solutions together. The concept of promoting smart city in Taipei refers to PAS 181 of British Standards Institute and ISO 37106 of International Organization for Standardization. Taipei City believes that smart city is people-centered, digital, open, and cooperative and that smart city services innovation and transformation are led and promoted by stakeholders. Therefore, in 2015 Taipei City launches promotion framework of creativity, innovation, and entrepreneurship based on “Open Government,” “Citizen

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Fig. 1 Taipei at a Glance. (Source: TPMO)

Participation,” “Public-Private Partnership” and seen as the core spirit, and further promoted the two major development axes of “Open Matching Platform” and “Service Innovation & Transformation.” The government is a smart city platform to promote public-private partnership, implement Smart City solutions, and drive the transformation for industries. In promoting smart city, Taipei City combines the vision of sustainable development with the UN Sustainable Development Goals and ISO 37120 Sustainable Development of Communities. ISO 37120 is divided into 17 themes, including Economy, Education, Energy, Environment, Finance, Fire and Emergency Response, Governance, Health, Recreation, Safety, Shelter, Solid Waste, Telecommunication and Innovation, Transportation, Urban Planning, Wastewater, Water, and Sanitation, about 2 to 10 indicators per theme, with a total of 100 indicators, which measures the performance of city services and quality of life and presents the city’s social, economic, and environmental development and performance. In 2015, Taipei City joins World Council on City Data, WCCD, and receives the Platinum certification in 2016 and 2017. Taipei City uses ISO 37120 as the KPI of Taipei City Strategy Map and sustainable development indicators. Taipei City uses indicators to observe the impact of information technology on citizen’s lives and social development and therefore to conduct expert assessments to help the department review implementation strategies. Taipei City continues to make connection with the industry through the Department of Information Technology of Taipei City Government to help other departments better use the information communication technologies to accelerate and innovate the promotion of city policies.

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Build a Smart City Ecosystem Instead of the government-led way of promoting the smart city, the Taipei City, on the other hand, promoted it through engaging, encouraging, enabling, empowering (4E), etc., to facilitate the public sector, the private sector, and people to form a partnership (4P). All three parties jointly participate in the promotion of the smart city and build a “4P smart city ecosystem driven by 4E.” The Taipei City opens up the experiment fields by public sector, in order to provide opportunities for the private sector to verify innovative smart city solutions, facilitate the partnership between each party, and develop a smart city innovation model. In the process of promotion, Taipei City encourages and engages to change the culture of the public sector. By enabling the public officials to take risks, they are willing to accept innovation projects which are brought up by the private sector. Furthermore, by the help from the departments of the city government to empower cooperated private sectors, strong and firm support will be provided for the innovation projects. All projects are expected to bring more smart services to the public; therefore, adjustments will be made through the feedback after the experience. After that, competent authorities will evaluate the projects and try to convert proof-of-concept (PoC) projects into policy’s direction.

Establishment of Smart City Management Office The fields of smart city are diversified, and the development of smart city must integrate industry, government, academia, and research institutes. Therefore, the “Smart City Committee” has been established by the Taipei City Government since 2015. The mayor serves as the chairman and appoints professional leaders in industry, politics, and academia as the committee members. The committee acts as a platform for the communication of policies between the private sector and the government and to assist public sector and private enterprises become smarter and more efficient. In 2016, the Department of Information Technology (DoIT) of the Taipei City Government establishes the “Taipei Smart City Project Management Office (TPMO),” which serves as the bridge between the public and the private sector and carries out the concept of “the government as a smart city platform, the city as a living lab.” On the one hand, with TPMO’s expertise in the field of information communication technology (ICT), TPMO provides consultation and suggestions on smart city issues for the government as the reference of “Top-Down” policy planning; on the other hand, TPMO adopts the “Bottom-Up” model and serves as a communication and matching platform for the public and private sector and promotes the Public-Private-People Partnership (4P); the government therefore can introduce innovation and resources of the private sector through the program named “Taipei Smart City Industrial Field Pilot Program.” Smart technology application solutions from tech companies or start-ups can become a part of the policy, truly solving city problems. By doing so, Taipei City

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Fig. 2 Taipei Smart City Project Management Office LOGO. (Source: TPMO)

becomes a “living lab” of the smart city and improves the quality of living for the citizens (Fig. 2).

Establish a Smart City Operation and Promotion Mechanism Under the existing Top-Down mechanism, the Taipei City Government promotes the smart city in the direction of the municipal development; meanwhile, it emphasizes the driving force of Bottom-Up mechanism, allowing all departments of Taipei City Government, private enterprises, academia, and citizens to participate. In the promoting practice, the Top-Down and Bottom-Up mechanisms do not operate separately but will be integrated into the process and then developed into a different collaboration mechanism. At this stage, the promotion of smart city has been gradually moved from the phase “establishment” to the phase “rolling-wave planning.” It is hoped to promote the smart city through the “Public-Private Partnership” method, letting the departments of Taipei City proactively cooperate across different units, and the practical solutions for cities can be implemented worldwide.

Top-Down: Private Sector Operating Mechanism The Top-Down mechanism can be divided into four steps according to the development of the city government policy: filtering, drafting, scenario setting, and implementing. In the filtering stage, Taipei City starts from the strategy map and assigns the department which should be responsible for it and introduces appropriate PoC projects into the public sector. Next, in the drafting stage, DoIT and TPMO assist departments of Taipei City Government to formulate and recommend “Smart Elements,” to applicate of innovative technologies such as IoT, AI, etc. Then, in the scenario setting stage, DoIT, TPMO, and the authority concerned jointly develop a “smart city plan and proposal” based on the actual demand and the feasibility of the solution provided by the industry. Finally, the PoC project will step into the implementing stage. Promoting smart city needs the participation of all departments of the Taipei City Government to formulate smart city-related policies with their expertise and resources. In order to make each department understand its role as a smart citypromoting unit and be willing to get involved into the collaboration mechanism,

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DoIT and TPMO organize several educational training courses and workshops with the help of international think tanks and academia so that the public officials can have a clear understanding regarding the connection between their own business and smart city in a practical experience lectured by professionals. In addition, through these educational training courses, each department can be able to recognize what assistance DoIT and TPMO can provide while implementing smart city policies. Therefore, when facing new Top-Down projects in the future, each unit can fully judge whether the projects can connect to other resources in the government. The core function of the Top-Down mechanism is to assist departments in “Smart Elements” designing and planning. When conducting policy planning on smart city, departments often have doubts about which Smart Elements or services should be included and hence consult DoIT and TPMO. With abundant case studies and international resources, TPMO first prepares a draft of Smart Elements or services that can be imported for the project based on the development of the trend of smart cities. Then, DoIT not only provides service content suggestions regarding technic and standardization, to assist departments in policy planning, but also acts as an ICT consultant for the follow-up project implementation. In the past, the government frequently faces the lack of industry information when drawing up the service requirement, which makes it difficult to carry out the project smoothly. Through TPMO’s close connection with the private sector, smart city solutions, technical standards, and other information can be easily obtained. As a consequence, DoIT and TPMO can be able to assist other departments in updating their industry and technology knowledge, provide commercial feasibility suggestions, and optimize the design of project services (Fig. 3).

Bottom-Up: Private Sector Operating Mechanism In response to the policy of “from public to private, from internal to external,” the Bottom-Up mechanism enables innovative technology from the private sector to be demonstrated in the public sector’s experiment field, which accumulates experiences by continuously reviewing and revising; the innovative technology eventually becomes a solution suitable to solve city problems, so as to improve municipal services and meet the citizens’ needs. For the purpose of letting the private sector have an opportunity to cooperate with the public sector in making innovative ideas and shaping a new type of smart services, the Taipei City Government launches “Taipei Smart City Industrial Field Pilot Program,” providing opportunity for entrepreneurs. Therefore, each stakeholder can follow the regulation as a basis to conduct PoC projects with the Taipei City Government. Once TPMO accepts the proposal from the private sector, the Bottom-Up mechanism will be initiated. The proposal then will be reviewed based on innovation, viability, the extent of public welfare, and legibility, and TPMO will have an overall discussion with the proposer. The proposal with the potential to develop will enter the scenario planning stage. At this stage TPMO discusses with the proposer regarding the application scenario of the solution, the design of scenario from the aspect of stakeholder, and the appropriate public experiment field. Then, with

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Fig. 3 Top-Down Projects. (Source: TPMO)

the assistance of the DoIT and legal advisors of TPMO, the relevant stakeholders who are responsible will sign a memorandum of understanding (MOU) of the pilot project and step into the implementing stage. The Taipei City Government will provide necessary administrative support during the period, and it is hoped that the project can be copied to other places with similar demands in Taiwan, or even be exported to cities abroad. Therefore, the city government will work with the central government in order to help in the business model development and promotion problems in the process of paradigm reproduction and marketing (Fig. 4).

Public-Private Partnership From the perspective of demand, the Top-Down approach comes from policy-driven needs, and the Bottom-Up approach comes from industries. Some innovation plans are in line with the Top-Down policy requirement and the Bottom-Up proposal, so the Public-Private Partnership is formulated. The partnership mechanism includes matching departments’ demands and private sector’s solutions and opening departments’ experiment fields. The former is that the department has a clear need to seek innovative solutions from the industry. The latter is the way to shape policies through demonstration in the experiment fields, and usually, those projects are more forward-looking or integrated smart services. At present, the themes of opened experiment fields include smart wards, autonomous vehicles, smart parking, smart street lightening, etc. The development of related smart services involves emerging technologies and covers issues such as hardware equipment, software application, and maintenance. Thus, it is necessary to coordinate companies from various industries. Through the departments’ opening up the fields actively and investing relevant expertise, the partnership can be seen as a platform for cultivating innovative public services for citizens and allows industries,

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Fig. 4 Bottom-Up Projects. (Source: TPMO)

academia, and research institutes to develop their own solutions, so as to lead the direction of smart city policy services in the future (Figs. 5 and 6).

Strengthening the Linkage of International Smart Cities Taipei City is actively linking up with global smart cities, by participating in international expositions, exchanging visits, holding cooperation workshops and video conferences, and establishing cross-city PoC project exchange platform. Taipei City strengthens the link between Taipei City and the international community and creates opportunity to cooperate with each other, so as to create commercial opportunities for smart city-related industries. Currently, Taipei City has contacted up to 15 countries, with 27 cities worldwide, including Amsterdam, Eindhoven, Boston, Kansas City, Barcelona, Greenwich, Fukuoka, Seoul, Selangor, Singapore, Tampere, etc.

International Expositions Nowadays, all countries are committed to the development of smart cities and their applications. In addition to planning domestic smart services, plenty of smart cityrelated expositions have also been developed, hoping to drive interaction between cities. Therefore, participating in relevant expositions is one of the measures that Taipei City promotes itself to other cities. In recent years, the Taipei City Government has successfully promoted its achievement through exhibitions. Aside from the Smart City Summit & Expo, which is held in Taipei every year, the government also proactively participates in important expositions, such as the Smart City Expo World Congress, which is held in Barcelona every November, in

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Fig. 5 Smart City Operation and Promotion Mechanism. (Source: TPMO)

Fig. 6 Taipei City Strengthening the Linkage of International Smart Cities. (Source: TPMO)

order to showcase the achievements of Taipei smart city and lead cooperate companies to promote Taipei City.

Exchange Visits The development of smart cities is not the same in every country; Taipei City visits their relevant applications of smart cities to achieve the goal of interacting with the world. In recent years, when the Smart City Summit & Expo is held in Taipei, Taipei City will invite foreign guests to visit the city’s attractions to truly experience how a

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smart city works. Furthermore, Taipei City also plans to have international visits occasionally and exchange practical experiences or projects of city planning and development with other cities. The information then will become an essential reference resource for Taipei City Government.

Cooperation Workshops While linking with other cities, having workshops is a way to deepen the benefit of exchange and brainstorming. For instance, Taipei City Government had cooperated with City Exchange Lab from Amsterdam and discussed the problems that Taipei might face and the possible smart city solutions in a workshop.

Taipei Smart City Achievements The fields of the smart city are diversified. Following on the principle of “from public to private, from internal to external,” Taipei City begins building a smart city from building a smart government. Therefore, Taipei City continues to invest in intellectual infrastructures and information constructions, and under the premise of information security, smart transportation, smart social housing, smart education, smart health and care, and smart payment are the five major promotion categories. Together with the start-up ecosystem, the 5+N layout of Taipei Smart City is formed (Fig. 7).

Fig. 7 5+N layout of Taipei Smart City. (Source: TPMO)

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Smart Government In terms of the intellectual infrastructure construction, Taipei City has implemented intelligent road management, intelligent street lamp, pumping station automatic monitored control system, Feitsui Reservoir smart security monitoring network, etc. Through the introduction of intelligent technology, the infrastructure and management efficiency are enhanced.

Intelligent Road and Pipeline Management The underground pipelines in Taipei City are intricate. When city renewal and basic maintenance projects are carried out, it is often difficult to confirm the pipeline location which causes construction obstacles. Taipei City therefore sets up Road & Pipeline Information Center (RPIC) to provide and monitor real-time road construction information. RPIC combines with GIS system and cloud technology and builds 3D pipeline maps, so that Taipei City can quickly and accurately grasp the information of underground pipelines when dealing with disasters or planning city renewal. Smart Streetlight In addition to continuously updating LED streetlight and improving energy-saving effects, Taipei City also introduces functions such as automatic failure report, automatic adjustment of light, automatic measurement of power data, remote control, etc., so that the energy consumption and maintenance situation of streetlight in Taipei City can be effectively grasped through the control center (Fig. 8). Pumping Station Automatic Monitored Control System Taipei City has built an automatic monitoring system for 87 pumping stations in the city, a system that automates and computerizes the operation of the pumping station

Fig. 8 Smart City Operation and Promotion Mechanism. (Source: TPMO)

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and can remotely control the pumping machine and flood control facilities to instantly send back real-time running images and information to ensure the safety of Taipei City.

Feitsui Reservoir Smart Security Monitoring Control System Feitsui Reservoir is located in a mountainous area with pool signal and insufficient power infrastructure. The long-distance and low-power LoRa (Long Range) which adopts new Internet of Things technology is used to construct a wide-area network environment. It is applied to reservoir monitoring equipment, including weather stations, hydrological stations, dam monitoring instruments, and other equipment to achieve the purpose of real-time monitoring and backup of data. As for security monitoring, the vehicle and ship movements are instantly grasped through the location tracker which combines with access control system such as physical and virtual electronic fence to ensure the security of Feitsui Reservoir (Fig. 9). As for the digital infrastructure, the Department of Information Technology (DoIT) of the Taipei City Government is responsible for the overall information infrastructure, assisting departments of Taipei City Government to improve their information capabilities and convenience of municipal services. At present, DoIT has provided smart government-related services to the public from the aspect of Internet infrastructure, open data, and municipal services. Regarding Internet infrastructure, DoIT has launched Taipei Free Wi-Fi service to meet the need of the public; in the open data aspect, the Data.Taipei open data platform and Taipei geographic integration platform are constructed to integrate government data and provide for the public use.

Fig. 9 Feitsui Reservoir Smart Security Network. (Source: TPMO)

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Taipei Free: Free Wi-Fi in Taipei Public Area In 2004, Taipei City implements “Taipei Wireless Broadband Construction Plan.” Since 2006, it has provided wireless broadband with the Public-Private Partnership method. In 2011, Taipei City’s wireless broadband infrastructure is further utilized, and Taipei Free, free Wi-Fi in public area, is launched in July. In 2015, DoIT promoted the “Taipei Wireless Network Alliance.” With the concept of “connective,” it cooperates with local stores to expand the number of free Wi-Fi hotspots without additional budget. Data.Taipei Open Data Platform Taipei City adheres to the principles of openness, transparency, participation, and innovation. Taipei City continues to promote information services that facilitate the public and support the industry. The establishment of “Data.Taipei” integrates the open data of the departments of Taipei City to a single portal, providing online service, file download, and API interface, and continuously updates the database. At present, the public has been able to query the visualized environmental information such as soil liquefaction potential area and rainfall flooding simulation on the Internet, burglary and theft of bicycles and cars, etc. In addition to the open of information, Taipei City has launched the plan of establishing a big data analysis platform that integrates the data shared by departments of Taipei City, so that the information is transformed into meaningful figures for public officials to make decisions. Taipei Geographic Integration Platform In 2015, DoIT introduces the GIS ArcGIS electronic map core engine to optimize the effectiveness of the electronic map service and provides instant municipal and life information. Taipei Geographic Integration Platform also continuously integrates information of various geospatial data in Taipei City and shares with the public. In the municipal service, DoIT promotes “App.Taipei,” “Hello.Taipei – Taipei City Simple Petition System,” and other services and systems, to provide the public with integrated municipal information and services. App.Taipei In 2012, Taipei City launches “App.Taipei” application service portal to integrate and promote its own mobile application software so as to facilitate citizens to quickly obtain various apps of Taipei City. The portal site contains information such as function introduction, operating screen pictures, download links, and other information to strengthen the Taipei City Government app promotion and usage. Hello.Taipei – Taipei City Simple Petition System In 2016, Taipei City establishes “Hello.Taipei” Simple Petition System which integrated the Taipei Public Hotline 1999, the municipal mailbox, and the petition counters of various departments, together with functions of GIS positioning, instant video uploading, quick response, etc. “Hello Taipei” provides a number of personalized services to make it easier for the public to grasp the progress of the case and

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the city’s response. It also introduces an automatic case assignment system, which effectively saves manpower of Taipei City. In addition, the big data platform integrates information of “Hello.Taipei” and applies big data analysis to create value and provide municipal governance.

Smart Social Housing The purpose of building social housing is not only to realize living justice and fulfill city aesthetics but also to build a high-quality social housing that is smart, energysaving, shock-resistant, and accessible and create a new residential operation model to make social housing an industrial experiment field of smart city. “The Taipei Public Housing Smart Community Implementation Plan” has built 32 social housing units by the end of 2018, with a total number of 12,000 units. The plan is expected to fully promote the intelligentization of social housing. Through the application of smart home technologies, residents can have more timely and comprehensive care in terms of safety, health, and comfort. In accordance with the characteristics of social housing, Taipei City has set different smart themes and provided multi-intelligent services, in addition to setting up Advanced Metering Infrastructure “Smart Watt-hour Meter,” “Smart Water Meter,” and “Smart Gas Meter,” and used additional 3% to 5% of construction funding to implement smart social housing equipment and service systems, which include energy-saving smart grid, community security, intelligent parking management, smart management cloud, and related smart services (Fig. 10). In addition to the intelligentization of social housing, Taipei City has also proposed “Smart Eco-Community” project to create smart ecological demonstration community in public facilities and public buildings in the overall development

Fig. 10 Smart Social Housing. (Source: TPMO)

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area with five dimensions including transportation, tourism, ecological environment, green energy, safety and disaster prevention, and healthy living. The smart ecocommunity forms community operating methods which are innovative, energysaving, low-carbon, environmental-friendly, sustainable, age-friendly, and peopleoriented through the establishment of green building, renewable energy systems, ecological recycling agriculture, etc.

Smart Transportation The transportation development of Taipei City takes sustainability, mobility, accessibility, responsiveness, and trustworthiness as core values with a vision of green energy, sharing, security, and electronic. At present, the development of Taipei City mainly focuses on the integration of both various application services and service systems such as multi-electronic ticket payment and smart parking management system. Considering expandability and flexibility, the ultimate goal is to develop a fully intelligent road management system in the medium and long term so as to drive the development of Internet of Vehicle (IoV), Driverless Vehicle, and Autonomous Vehicle. On the other hand, it is expected to minimize traffic accidents, road congestion, and carbon emission (Fig. 11). In addition, Mobility as a Service (MaaS) is looking to create a seamless, door-todoor multi-transport integration system that increases usage of green transportation and lift-sharing services and reduces the use of private vehicles. Taipei City has promoted relevant value-added services in recent years such as the public transport monthly pass and “Taipei Easy Go” app, which are the first plans for the introduction of MaaS. In the future, after the integration of transportation and finance sector, the completion of multi-transportation integration platform, and the

Fig. 11 4U Green & Share Transportation. (Source: TPMO)

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establishment of business model, there will be enough driving force. According to the traffic application field, the smart transportation-related plans at this stage can be divided into smart station, smart bus, smart parking, smart shared transport, friendly transportation service, and smart transportation system planning (Figs. 12 and 13). Taipei City also launched 4U green & share transportation plan: (i) Shared Bicycle – YouBike: the construction of 400 rental stations in Taipei City has been completed, allowing the public to walk to the rental station in only 5–10 min.

Fig. 12 4U Green & Share Transportation. (Source: TPMO)

Fig. 13 Smart Parking Billing System. (Source: TPMO)

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(ii) Shared Electric Motorcycle – U-Motor: built by the private sector which has provided 2,000 electric motors in Taipei City; Taipei City also grants parking fee discounts. (iii) Shared Electric Car – U-Car: the private sector has provided 100 shared electric cars in Taipei City and aims to promote the shared electric car to whole Taipei City. (iv) Smart Parking – U-parking: enhances the immediacy of parking information through the Internet of Things technology to reduce the time for people to seek car detours and improves the efficiency of parking management for Taipei City and operating units. Through intelligent facilities and apps, the goal of automating billing and payment process is achievable, and the convenience of parking service is further improved, and operating costs are reduced.

Smart Health and Care The population structure of Taipei City is continually aging, which increases the demand for health and medical care. Based on the mission of providing citizens with a healthy and complete living environment, Taipei City integrates healthy cloud services and health information management to promote public health, improve physical age and quality of life, and create a smart city where “Healthy Taipei, Care with No Distance.” Taipei City hopes to establish a smart care and medical system by introducing intelligent technology and services. The Department of Health of Taipei City develops and integrates different management platform functions of community health management, medical care, and life care, and to solve the problem that the information generated by various service teams is incomplete and inconsistent. In addition, in order to improve the physical age of the elder and build an environment that promotes the health and integrates the medical care so as to lower the social health-care cost, the Department of Health establishes a “people-oriented” smart health care integrate management platform in 2018, connecting and integrating various information on health and welfare services, to develop a friendly and intelligent health and care management services, so that Taipei City can collaborate and integrate intelligent information on various health and welfare services and develop a friendly and intelligent health and care management services, then achieves the vision of Taipei City smart care and medical care. In the past, information of long-term care was scattered everywhere, and there was no one-stop service. In 2018, the related long-term care information is integrated into a long-term care integration management system. Taipei City develops a longterm care platform for the public and provides a one-stop service which allows online applications, online information enquiries, bulletin boards system, health information, etc. Taipei City also integrates service units and hospitals and establishes a long-term data analysis platform which uses big data analysis for future policy reference.

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At the same time, in order to enhance Taipei City’s public service and city competiveness in the field of smart health and medical care, Taipei City Hospital establishes an information platform that intelligently manages all medical conferences from patient admission to discharge and plans the home medical services after discharge. It saves the medical team’s manual work time, provides people-oriented care from hospital to home, and displays smart medical treatment. Through the intelligent conference management system, users not only can sort out patients’ basic information, select meeting members, and take meeting minutes online but also can conclude the meeting online and transfer the conclusion to the next process. It is the first intelligent management system that digitizes the medical communication process in the country. Taipei City also proposes “Taipei City Hospital Smart Ward Pilot Project” to identify the existing internal service process needed to be improved in branches of different medical specialties, and according to these medical specialties, Taipei City Hospital allows companies to use information technology to assist in the intelligentization of medical services, so that they not only provide intelligent services but also provide more mobilized, intelligent clinical care, community care, and administrative work environment for medical service teams (Fig. 14).

Smart Education The main axis of Taipei City’s education development is based on reverse thinking, innovative experiments, and early deployment of future competitiveness. With a prospective vision, Taipei City develops students’ literacy in all aspects such as knowledge, ability, attitude, etc. The promotion of education in Taipei City focuses on the breakthrough of innovation through technology, creating a high-quality and

Fig. 14 Taipei City Hospital Smart Ward Pilot Project. (Source: TPMO)

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smart education through forward-looking and innovative teaching mode, and integrating diverse and rich digital education resources to create an open education platform, to practice the philosophy of “Education Equity.” Therefore, Taipei City has launched various smart education software and hardware integration service procurement, to improve the teaching environment of smart education and to coordinate with the curriculum guidelines of 12-year basic education general guidelines, infrastructure construction, teacher cultivation, teaching resources sharing, and many other forward-looking aspects. Through various projects to integrate cross-domain teaching and professional resources, and to support online and offline multi-situation teaching, and extend to the field of lifelong learning and develop smart campus (iCampus), in order to achieve a simplified and upgrade ministration (Fig. 15).

Smart Campus In building smart campus, Taipei City starts from network infrastructure, hardware equipment procurement, software content development, and teaching resource integration and gradually builds the smart campus (iCampus); iCampus integrates the concept of School Social Network (SSN) and cloud and Internet technology. iCampus provides comprehensive smart education services for teachers and students and develops six areas of smart learning, smart administration, smart management, smart health care, smart green energy, and smart community. It builds a “Personal Teacher and Student Platform” which is constructed by virtualized technology as the core of the system and integrates the clod architecture as the basic of the platform. Innovative Education With the core concept of “Mobile Learning and Smart Teaching,” “Computer Programming Education and Computational Thinking,” and “Maker Education,”

Fig. 15 Smart Education Promotion Mechanism. (Source: TPMO)

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Taipei City’s Smart Education applies information and communication technology such as interactive digital learning equipment, personal teacher and student platform, mobile device, 3R technology, and AI robots to various learning courses to enhance students’ STEM+ cross-domain literacy. Besides, with the COOC Cloud, students can arrange learning progress through digital textbook and online videos compiled by teachers, so as to achieve the goal of “Flipped Classroom.”

Lifelong Education In order to provide a quality lifelong learning environment, Taipei City provides high-quality digital learning services through the “Using Taipei e-Campus for Digital Learning” project. Taipei City promotes the learning needs of citizens in a smart, systematic, and digital way. The Taipei e-Campus platform provides a rich and high-quality digital curriculum for the public to promote mobile learning and implement the concept of learning anytime and anywhere.

Smart Payment In order to create a convenient mobile payment environment so as to move toward a cashless city, the Taipei City Government launches the smart payment platform “Pay.Taipei” in 2017, which is convenient for people to inquire and pay various fees anytime and anywhere and improves the convenience of online account verification and inquiry while reducing the city’s expenditure on collection fees. “Pay. Taipei” integrates various fees of the Taipei City Government; services are provided by existing payment methods (such as APP). In the future, “Pay.Taipei” will continue to include the Taipei City’s expenditure items and collaborate with external services, gradually moving from open payment to open identity verification and open services. Through the Taipei Card 3.0 service promoted by the Taipei City Government, people who apply online and complete certification can enjoy various online and offline integration services launched by the Taipei City Government and use ID to combine innovative payment applications (Fig. 16).

Smart Start-Up In the path of promoting smart city, innovation is the key gene for city development and a key factor for keeping the city open in the promoting process. The Taipei City Government encourages private sectors to propose innovative solution, and the Taipei Smart City Project Management Office (TPMO) serves as a platform for matching innovation and technology and citizen. TPMO discusses the application scenarios of products or services with the private sector and to consider situation design from the perspective of stakeholders. TPMO then discusses the suitable experimental field with the public sector for the verification of products or services and coordinates the Taipei City Government to open the experiment field. After 3 years of hard work, the Bottom-Up project has more than 170 cases. There are a

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Fig. 16 Smart Payment Promotion Mechanism. (Source: TPMO)

total of 168 private companies cooperating, among which 61 companies are startups. Through the three aspects of start-up resources support, product/service verification, and business model testing, the Taipei City Government provides interested companies with innovative solutions to verify in Taipei City, fully demonstrating the concept of promotion that Taipei City becomes a Living Lab.

The Future of Taipei Smart City Taipei takes the lead in setting up the Taipei Smart City Project Management Office, TPMO. TPMO is independent of the Taipei City Government structure; it assists the interdepartmental communication and coordination, introduces industrial innovation through the Bottom-Up mechanism, prompts the PoC cases to gradually form policy directions, simultaneously reverses the public sector culture, and assists the Top-Down policy which allows the public sector to have more opportunities to communicate with the industry. Adhering to the philosophy of “from internal to external, from public to private,” the Taipei City has promoted the Top-Down project in the fields of smart social housing, smart transportation, smart medical care, smart education, and smart payment for more than 2 years. Taipei City engages in more than 400 industry, education, and research units, assists in more than 170 Bottom-Up PoC projects, and cooperates with 33 departments of Taipei City Government to establish the Taipei Smart City ecosystem. Therefore, the smart city promotion mechanism in Taipei City has also been internationally recognized. In 2017, Taipei City claimed gold in the Cooperative City category of the WeGO Smart Sustainable City Awards from “World e-Governments Organization of Cities and Local Governments (WEGO). In 2018, in the list of the world’s top 50 smart city governments published by the Eden Strategy Institute in

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Singapore, Taipei City ranks 16th among the 140 smart cities in the world, ranking 5th in Asia. In 2019, IMD World Competitiveness Center in partnership with Singapore University of Technology and Design presents the IMD Smart City Index 2019, and Taipei City ranks 7th among the 102 smart cities in the world. Taipei Smart City has been well-known internationally. Looking into the future, how can Taipei City strengthen the overall efficiency through the continuous rolling adjustment of the mechanism and ensure the sound development of various mechanisms and how to deepen the connection, the share of resources, and the exchange of experiences between domestic and international communities to promote smart cities will be the focus of the next stage of Taipei City.

New Promotion Framework for Taipei Smart City with 1 Core+ 7 Key Directions The Taipei Smart City promotion framework has been implemented for more than 3 years. In response to the evolution of IoT technology and changing needs of city development, DoIT and TPMO review the implementation of various fields in 2019 and refer to the promotion of advanced cities in smart cities to re-examine and revise Taipei Smart City promotion framework. Based on the current smart city promotion framework, Taipei City will take “Smart Government” as core and “Cyber Security” and “Information Infrastructure” as the two main domains to promote “Smart Building,” “Smart Transportation,” “Smart Education,” “Smart Health,” “Smart Environment,” “Smart Safety,” and “Smart Economy,” which form a “1 Core, 2 Domains, 7 Directions” promotion framework. The framework combines “Open Government,” “Citizen Participation,” “Open Data,” and “International Linkage” to promote the strategy of “from public to private, from internal to external” (Fig. 17).

Fig. 17 1 Core+ 7 Key Directions of Smart Taipei. (Source: TPMO)

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Continue to Promote Innovation Culture to Public Sector Promoting smart city requires not only investing in high technology but, more importantly, whether the public sector can face innovation and change with an open mind. Therefore, how to establish a culture of “not afraid of failure, be brave to innovate” will enable government officials to accept new technologies and new practices, so that more innovation can be implemented in the public sector, and it will become the goal that Taipei strive to achieve during the process of promoting smart city. In the future, Taipei City will continue to expand and promote the change of the culture of the public sector. In addition to deepening the acceptance of the smart city innovation mechanism through education training and case study, Taipei City will also evaluate the arrangement of full-time responsible staff of each department to speed up the establishment of a culture of innovation as well as to enable the bureaus to have a more comprehensive understanding of Bottom-Up innovation cases.

Establish Sustainable Smart City Implementation Mechanism and Specification The sustainable development of smart cities has attracted the attention of all countries in recent years, and all governments have set sustainable development as the main axis of development. In the process of promoting smart cities, Taipei City also listed sharing vehicle, renewable energy, and smart grid as key targets. In the future, Taipei City can establish the evaluation mechanism for service procurement and investment promotion, so as to define the product or bid type suitable for adopting this model, and evaluate the adjustment of accounting standards or improve the flexibility of budget project modification so that the related department can quickly respond. The adjustment of the specifications provides sufficient incentives to increase the willingness of private enterprises to cooperate. At present, the central government’s subsidy program has also gradually shifted from the model of subsidizing the purchase of hardware to service procurement. It is very helpful for Taipei City to promote service procurement. In the future, we will continue to discuss with the central government the subsidies that meet local needs, so as to meet overall development trends and promote development of sustainable smart city.

Improve Public-Private Partnership At this stage, the public-private partnership includes two parts: match the needs of departments and open experiment field of the department. The needs match is based on the estimate projects and seeks innovative solution from the industry. Because of the clear requirements, it is usually to quickly move from PoC stage to the procurement stage. However, there is no institutionalized matching model at the current stage. In the future Taipei City will evaluate the promotion projects of the department

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in the next 2 or 3 years through systematic arrangement. The department defines the importance and urgency of these projects and guides the private enterprises to propose innovative solutions, so that the department has sufficient time for the trial of PoC verification and finding a feasible innovation model. By coordinating the policy promotion projects, the PoC cases can successfully obtain the support of the department and even the funding and also greatly raise the possibility of forming bids for PoC cases. By doing so, Taipei City can meet the two benefits of solving municipal problems and introducing innovative solutions, as well as achieving the goal of solving city issues with smart city promotions.

Strengthen PoC Effectiveness The Bottom-Up mechanism promoted by Taipei City has led many opportunities for innovative ideas to be implemented, and Taipei City has stood out from many international organizations’ ranking and has been internationally recognized. During the promotion process of more than 2 years, Taipei City finds out that some PoC cases proved successful may be difficult to continue or spread due to insufficient procurement fund in the current year, making it difficult for some innovation ideas to be verified and implemented. In the future, in the aspect of strengthening the subsequent spread effect of PoC, Taipei City will evaluate to include the bureau’s participation to jointly assist in assessing whether to allocate corresponding budgets for service procurement for some cases such as meeting the urgent needs of Taipei City or relevant areas of key policies and provide flexible funds after the successful verification of PoC cases so that these innovative solutions can be implemented and spread in the current year to achieve immediate results. Taipei City will also strengthen the internal horizontal linkage mechanism of the Taipei City, so that the results of the verification case can be effectively spread to various departments so as to provide more information on the smart service as a reference for service promotion.

Broaden Collaboration and Construction Scale In the process of various constructions of Taipei City, Taipei City Government adopts a step-by-step gradual expansion method. In addition to the joint discussion and construction planning by departments of Taipei City, the private enterprise is invited through the PoC mechanism to small-scale verification of feasible solutions. In the future, the Top-Down mechanism will be strengthened to tie in with the strategic themes and goals of the Taipei City Government Strategy Map and will organize policies which support the promotion and expansion of smart city construction projects. It is expected that these projects which have completed smallscale implementation verifications and ruled out regulatory doubts will gradually expand implementation in a planned manner. It is also expected that Taipei City will be scaled up by the development of proven projects, providing not only a wider

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range of smart services for citizens but also an opportunity to enhance the international visibility of Taipei City. As for strengthening participation of all sectors, Taipei City introduces suitable smart innovation application which is designed for the needs of the public through the promotion of experiment fields planning. In order to realize the needs of the public, Taipei City first integrates policies of the departments which are relevant to the experiment fields, then surveys community opinion leaders and related organizations by in-depth interviews, and design field-related questionnaires, hold civic participation activities, and further focus on the needs of experiment field and the public (Fig. 18). With such a citizen participation mechanism, Taipei City can clearly understand the needs of the public. In addition to online and offline activities, Taipei City also gathers the needs of the public through existing online platform. For example, the 1999 and i-Voting are excellent channels for citizen to participate. In the future, Taipei City will continue to strengthen the communication with the public, promote the concept and application of smart city, and link the Smart City Council with this citizen participation mechanism to create an effective, bilateral communication model. For example, the Taipei City Government establishes a citizen-participating ad hoc group to collaborate with NGOs and NPOs to design regular communication activities in various administrative districts and to conduct collected and inductive opinions in an open and professional discussion process. These opinions will be sorted out and voted through the citizen participation mechanism such as i-Voting and thus become the basis of policy promotion. In this way, citizen’s opinions and needs can be incorporated into the key direction of smart city promotion, and therefore seek innovation proposals to create public services that fit the needs of the public.

Fig. 18 Citizen Participation Mechanism. (Source: TPMO)

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The Establishment of GO SMART Global Organization of Smart Cities, GO SMART, is officially established during 2019 Smart City Summit & Expo; it aims to bring together smart city energies, build a platform for exchange and collaboration, and solve global city problems. GO SMART uses innovative models to drive communication between local government and industry and develops substantive cooperation relationships with international cities to jointly promote Inter-City PoC. In addition to the regular events, including membership meetings, forums, workshops, exhibitions, etc., GO SMART also assists members to participate in international exposure and marketing; the most important thing is to conduct Inter-City PoC cooperation program through GO SMART by exchanging smart innovative solutions to solve city issues and improve the quality of life (Fig. 19). At present GO SMART has been supported by other 5 cities and 17 smart cityrelated companies in Taiwan, and more than 21 cities coming from 4 continents have joined to discuss the problem faced by each city, the current status of smart city promotion, and the future operation mode and vision of GO SMART. In the future, we will continue to strengthen linkage with international cities and combine the strength of domestic cities with international cities to build a global communication network and promote international cooperation. The smart city innovation solutions will be experimented in Taipei City, implemented in Taiwan, and exported to the international cities, to promote “Taipei City = Smart City” brand (Fig. 20).

Fig. 19 Global Organization of Smart Cities. (Source: TPMO)

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Fig. 20 GO SMART Members. (Source: TPMO)

Conclusion Promoting smart city cannot rely solely on the Taipei City Government’s own efforts. It must gather energy, creativity, and resources from all aspects to jointly achieve the goal of solving city problems and building a livable city. In the future, Taipei City will continue to adhere to the philosophy of “Open Government,” “Public Engagement,” and “Public-Private Partnership.” Through participation, encouragement, empowerment, etc., the government, the industry, and citizen will form partnership to participate in the promotion of smart city and improve the “smart city ecosystem.” Taipei City will also continue to improve the smart city promotion mechanism, expand the scale of construction and collaboration, and strengthen the link with other international cities. In addition, Taipei City will strengthen its contacts with international smart city assessment agencies and provide relevant information of Taipei City to enhance the performance of international competitions and also enhance the positive image of Taipei Cities and make Taipei City as an international model of smart city.

Smart City Transformation for Mid-Sized Cities: Case of Canakkale, Turkey

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Berrin Benli, Melih Gezer, and Ezgi Karakas

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A Mid-sized City: Canakkale, Turkey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A Smart City Transformation Initiative: “Canakkale on My Mind” CASE . . . . . . . . . . . . . . . . . . . Visionary Leadership . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Collaboration and the Role of the Private Sector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A Road Map to Smart City Transformation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Critical Success Factors and Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Governance Models for Mid-sized Smart Cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Successful Cases of Smart City Transformations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A Model for Turkish Mid-sized Cities: Case of Canakkale . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract

The urban areas where half of the world’s population live is expected double in just 15 years from now. According to the 2017 World Population Prospects report, the world’s population is expected to increase to 8.6 billion in 2030, 9.8 billion in 2050, and 11.2 billion in 2100. The World Bank’s data reveals that around 72% of Turkey’s population, reaching 80 million, live in urban areas, and predictions show that this rate will rise to 80% in 2030 with a population surging to approximately 88 million. This staggering surge in urban populations facing the world and especially Turkey makes it imperative to use limited resources efficiently starting immediately. The Çanakkale on My Mind project aims to define the steps needed to transform Çanakkale, situated in the heart of the region dubbed the “golden circle” along the Istanbul-Izmir axis, into a smart city and design a road map together with the participation of relevant stakeholders. Born in Çanakkale’s B. Benli (*) · M. Gezer · E. Karakas Novusens Smart City Institute, Kale Group, Turkish Informatics Foundation, Canakkale, Turkey e-mail: [email protected]; [email protected]; [email protected] © Springer Nature Switzerland AG 2021 J. C. Augusto (ed.), Handbook of Smart Cities, https://doi.org/10.1007/978-3-030-69698-6_23

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district Çan, Kale Group joined forces with the Turkish Informatics Foundation (TBV) and Novusens Smart City Institute and launched the Çanakkale On My Mind project on February 1, 2017.

Introduction The world population, rapidly increasing over the last few centuries, has reached 7.7 billion. This number is expected to reach 10 billion by 2050 and 11.2 billion by 2100. Also, the urban population has a higher rate of increase compared to the world population. The urban population is growing around 76 million per year due to overmigration from rural areas to cities as well as enhanced employment opportunities and the urban quality of life. Today, the urban population has reached 4.2 billion. In other words, 55% of the world population live in cities around the world. By 2050, the urban population is expected to reach 6 billion (70%). In Turkey, the urban population rate is much higher than worldwide. 92.3% of the population of Turkey live in cities (Karakas et al. 2019). Due to the rapid increase in the world population, 1.7 times our Earth’s resources are now needed to support the demand on natural resources. This is called as ecological footprint, and it means that humanity’s current demands are 1.7 times faster than the amount of the planet’s available natural resources. In other words, it is emphasized that the natural resources will be inadequate after a while. In addition to that, the increased level of carbon emission, global warming, and environmental pollution occur. It is possible to say that the main source of all these problems are urban areas because of the population density in cities. Therefore, creating more liveable and sustainable word will be possible only if more livable and sustainable cities are created. Moreover, the urban population density causes many other problems such as housing, transportation, security, and underemployment. As a result of all these factors, new and innovative approaches are needed to minimize both the ecological dimension and the negative quality of life impacts of the population growth. Today, the latest information and communication technologies are utilized as part of the solutions. Thus, the concept of smart city has emerged (Karakas et al. 2019; Boes et al. 2016; Lazaroiu and Roscia 2012). Canakkale on My Mind Project is a smart city transformation initiative to improve the urban quality of life and to ensure sustainable environment while providing a competitive advantage to Canakkale on a global scale. It is aimed to determine the necessary steps Canakkale should take during smart city transformation in collaboration with all local stakeholders to create a smart city transformation road map. The project was launched on 1 February 2017 under the leadership of Kale Group (a pioneer in the Turkish industry specialized in ceramic tiles, born in Çan district of Canakkale and celebrating its 62nd anniversary today), in collaboration with Turkish Informatics Foundation (working for the transformation of Turkey to the information society) and Novusens Smart City Institute as the implementing partner (Benli and Gezer 2017).

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Fig. 1 The road map of Canakkale on My Mind Project. (Source: Benli and Gezer 2017)

The main point of the project is to ensure the participation of both local authorities and local residents in all phases of the smart city transformation of Canakkale. Therefore, it is crucial to ensure sustainable cooperation among local government, public and private sectors, university, and NGOs for the successful implementation of the project. Moreover, within the scope of the project, technology is adopted as a facilitator for the smart city transformation of Canakkale. In other words, technology is accepted as a tool instead of a goal during the smart city transformation (Benli and Gezer 2017). The project consists of five phases as depicted below (Benli and Gezer 2017; Fig. 1). The first phase of the project was completed on 31 May 2017 and the second phase was completed on 31 December 2018. The third phase of the project has been completed by the end of December 2019.

A Mid-sized City: Canakkale, Turkey Two great wars in history, Battle of Gallipoli and Trojan War, which have been the story of the international movies, took place in the territory of Canakkale. In other words, Canakkale has two important destinations of historical value: Gallipoli Peninsula and The Ancient City of Troy. Moreover, Canakkale have many natural and cultural heritage values in the world as Mount Ida, Dardanelles, Tenedos, Imbros, and Assos Ancient City (Karakas et al. 2019). Canakkale is a strategically important city located in north-western Anatolia in Turkey since it is a bridge between Asia and Europe continents and is located in the center of Istanbul–Izmir axis corridor which is now called as the golden circle (Benli and Gezer 2017). Today, Çanakkale is also a trade and peace route which is open to international sea traffic. Moreover, Canakkale will become a significant gateway providing connection to Europe and Asia roads after the completion of the bridge on Dardanelles. These external factors increase the current importance of Canakkale on a national and international level. With an area of 1,016 km2 and a population of around 545,000 residents (Canakkale Governor’s Office 2019), Canakkale has three main characteristics: being a university town, being an agro-industry town, and being a tourist city (GMKA 2016). There is Canakkale Onsekiz Mart University in the city and the current number of the university students (52,915) is approximately 30% of the population of Canakkale Center (COMU 2019). Therefore, the university population is important for the sustainable development of Canakkale. On the other hand, it is

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also a quite important to make Canakkale attractive by becoming a social and technology entrepreneurship hub for the young people that graduate from university and to ensure that they stay here (Cobanoglu 2019). Moreover, Canakkale has irrigable land above the country percentage and wide range of agricultural products as well as high potential for aquaculture (GMKA 2016). Lastly, the number of foreign tourists visiting Canakkale in June 2019 is 5319 which is an important number (TURSAB 2019). As a result, maximizing the life quality for all individuals and city attractiveness as well as improving the city image and sustainability for natural and cultural resources have crucial importance for Canakkale. These steps will be made possible through smart city transformation process with Canakkale on My Mind Project.

A Smart City Transformation Initiative: “Canakkale on My Mind” CASE Visionary Leadership Visionary leadership in smart cities is one of the key factors in the success of smart city transformation. Visionary leaders understand the potential benefits of smart city transformation. By a smart city, we mean a city that invests in information and communication technologies to use its limited resources more effectively and efficiently, generates savings as a result of those investments, improves its services and the quality of life thanks to those savings, reduces its carbon footprint, respects the environment and natural resources, and does all these in an innovative and sustainable manner (as defined by Faruk Eczacibasi, Chairman of Turkey Informatics Foundation). Countries no longer compete; now cities compete in the world! Zeynep Bodur Okyay, President and CEO of Kale Group

Such transformation enables cities not only to save costs but also improves the quality of life of their citizens and improves their city’s competitiveness to attract both new citizens and new businesses. As Zeynep Bodur Okyay, President and CEO of Kale Group, said “It is no longer countries that are in competition, but cities. Every city will have to gain a competitive edge to differentiate itself from the rest. Flexible and agile cities that can diversify their resources and offer economic, social and cultural opportunities to their citizens will survive. The cities that are best equipped to produce innovative, inclusive and ethical solutions in the face of multiplying risks and threats will emerge as leaders. Cities will compete and collaborate globally as interdependent entities and will drive the future.” (Bodur Okyay 2018). This was part of the reason why Kale Group, a conglomerate of 62 years old decided to take up smart city transformation of Çanakkale, the city it was born in Turkey. This constitutes an exemplary case for organizations such as Kale Group that

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has its roots and lands in a particular place such as Çanakkale and should make it part of their DNA to give back. We strongly believe that to make such efforts more valuable and sustainable, one needs to take a holistic and systematic approach and be open to collaboration. The notion of cities competing globally rather than nations has led to the “Çanakkale on My Mind” initiative in 2016 which aims to step up Canakkale’s transformation into a smart city and contribute to improving the quality of life (Benli and Gezer 2018). The visionary approach that sets the tone in “Canakkale on My Mind” Project is outlined explicitly in the words of Zeynep Bodur Okyay. “As Kale Group we have always strived to be part of every project that does justice to Canakkale’s potential and is carried out in keeping with the city’s spirit. The economic and social investments we have made in Çanakkale, even when the smart city concept had not yet been coined, is no secret. Kale Group is a group of companies, that is designoriented, grounded in innovation, and invests in cutting-edge technologies. However, they are not a technology company. And that is why the project is named Çanakkale on My Mind and not Smart City Çanakkale. This was an intentional choice. Because it’s people that are at the very heart of the project. And that’s what’s going to make all the difference. Projects, where the ultimate aim is the happiness and welfare of its people and technology is a means to an end, are bound to succeed and will last for many years.”

Collaboration and the Role of the Private Sector Research indicates that “co-operation between organizations” is among the top critical success factors for smart city transformation, and the situation is no different for Çanakkale where it has been cited as the top critical success factor (Karakas et al. 2019). This is precisely why the city of Çanakkale has embarked on this transformation journey together with all of the relevant organizations and all local stakeholders in an inclusive manner building on collective wisdom and constructive partnership. By employing a strategic plan, a systematic approach, a common vision, good governance, financial productivity, and sustainability throughout the initiative, the chances of success for such large transformation projects are increased considerably (Benli and Gezer 2017). As collaboration is one of the cornerstones of such a holistic initiative and smart city transformation, Kale Group teamed up with the Turkish Informatics Foundation (TBV) led by Faruk Eczacibasi who continues to deliver invaluable work for Turkey’s transformation into an information society, and with Novusens Smart City Institute as the expert organization with extensive experience in smart cities in Turkey. Kale Group joined forces with a think tank organization that is supported by one of Kale Group’s competitors in the Turkish market and TBV did not hesitate either. As Faruk Eczacibasi puts it, “we need to use our minds and ideas so that Çanakkale makes a leap. No city will become smart without the minds and ideas of its citizens.”

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Moreover, with the help of project partner Novusens, a bottom-up and participative approach has been adapted to make innovation and sustainable development possible. Such exemplary partnerships are required in order to make a difference in the world. To succeed, international organizations, national governments, municipalities, and local partners from civil society, academia, and the private sector need to join forces. As stated in Zeynep Bodur Okyay’s words: “I believe in dreaming big and setting aspirational, yet realistic, goals. For me, a city has to inspire. We aspire to make Canakkale a role model, setting a precedent for other cities of similar scale in Turkey and around the world. We stand ready to act as a facilitator, catalyzer and systems integrator, bringing together every stakeholder that shares the same vision and helping them work effectively. We are happy to pave the way for national and international partnerships” (Bodur Okyay 2018). It is well known that change starts locally. Initiatives that are locally driven and that include solutions taking into account the city’s dynamics create a lasting impact. Therefore, in addition to nationwide collaboration mentioned above, engagement of local government institutions, universities, private companies, and NGOs have been sought in the “Çanakkale on Their Mind” initiative. Including citizens in this process and keeping the lines of communication and dialogue open are critical elements to this end leading to collective wisdom and good governance practices (Benli and Gezer 2017). ‘Getting together is the beginning; being together is the development; working together is the success’ – Henry Ford

In today’s world, as cities are competing with each other instead of countries, cooperation becomes a precondition to gain a competitive advantage. As Henry Ford indicated, working together is the key to success. Cooperation among local government institutions, public sector, private sector, NGOs, and academy is the backbone of smart cities (Benli and Gezer 2018). By employing a strategic plan through this project, Kale Group wants to support the prioritization and implementation of services and practices that touch people and improve life for citizens. Furthermore, a common vision, good governance, financial productivity, and sustainability are key to making sure they succeed on this journey. Even though Kale Group has initiated and steered the Çanakkale on my Mind project, public private partnerships (PPP) play a quite significant role to ensure the continuity of smart city transformation. As a result of organized seminars and trainings, stakeholders can understand the gains of a smart city and may want to invest in the smart city transformation process. In this context, entrepreneurs can also be encouraged to open ICT-based workplaces. Thus, the city becomes attractive for young and talented individuals as well as new employment opportunities are created in the city. Moreover, the city also benefits from technology provider companies during the smart city transformation. It is worthwhile to try new approaches to known problems, and Çanakkale can set an example for global mid-sized cities to transform into smart cities and people into information society for sustainable development and inclusive welfare.

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A Road Map to Smart City Transformation Canakkale on My Mind Project consists of five phases as indicated earlier. The project is currently in its third phase which was planned to be completed by the end of December 2019 (Benli and Gezer 2017).

Phase 1: Understanding In the first phase of the project, it is aimed to analyze the current situation of Canakkale and to create a smart city vision as well as to determine a road map for the project. The first phase consists of the five steps (Benli and Gezer 2017). Literature review: The related reports (the strategy and operating reports prepared by local government, public and private sectors; the investment plans; the demographic reports prepared by Turkish Statistical Institute, etc.) are examined. Also, meetings are arranged with the related individuals. Lastly, literature is reviewed to examine the successful smart cities which have similar characteristics with Canakkale. Field visit: Firstly, the local stakeholders are identified. Then, the meetings are arranged with them. Canakkale smart city survey: A smart city survey has been developed for the participant organizations and then applied to 40 people from 17 institutes. Smart city seminars: Comprehensive seminars related to both the smart city concept and Canakkale on My Mind Project are organized with the participation of all local stakeholders to raise awareness about the smart city vision. Collective intelligence workshops: Two collective intelligence workshops are organized. First for public and private sectors, universities, and NGOs, and second workshop organized for Canakkale Youth Association members. Based on this process, the existing smart city applications of Canakkale are firstly identified (Table 1). Secondly, the priority areas which Canakkale should focus during the smart city transformation and their priority levels are determined. According to the findings, smart environment (22%) and smart transportation (21%) are the priority areas for Canakkale. Please see Fig. 2 for details. At the same time, critical success factors and challenges have been evaluated. Moreover, based on the feedbacks of the collective intelligence workshops’ participants, the solution suggestions (Tables 2 and 3) and the vision suggestions (Table 4) are identified. As most of the solutions may require significant financial sources and time, a list of quick win project suggestions was also prepared (Table 5). Lastly, the project road map is prepared in this phase (Table 6). Phase 2: Vision In the second phase of the project, the seminars and workshops have continued to improve the citizens’ smart city awareness and their innovative approach. While the second phase of Canakkale on My Mind was in progress, 2018 was declared “Year of Troy” in culture and tourism to celebrate the 20th anniversary of Troy’s addition to the UNESCO World Heritage List. In this regard, Canakkale hosted a series of events including a UNESCO conference, International Council of Museums

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Table 1 The existing smart city applications of Canakkale Smart stop Smart junction

Canakkale Kentkart CABIS

E-Municipality Smart city information system Municipality call center

Municipality of Canakkale green local government and cultural center building Municipality of Canakkale biological wastewater treatment facility

UEDAS SCADA system

Showing passenger’s arrival time of buses 30 smart stop boards are available Managing traffic lights depending on traffic density via sensors and cameras Available at Canakkale Cuma Street market junction The automated fare collection system for buses Smart bike rental system 14 stations, 92 bicycles, 120 parks, and 4 mobile stations Online municipal services Online integration between city map, population, zoning status, etc. Enabling citizens to reach local government via call center, mobile applications, social media, WhatsApp Under construction. Produces its own energy via solar power, wind power, etc. Physical purification, advanced biological treatment, sludge dewatering, ultraviolet disinfection The required energy for biological wastewater treatment facility will be provided by solar energy plant newly founded. Remote access for the measurement, the monitoring, and the control of energy distribution systems

Source: Benli and Gezer (2017).

meetings, and a World Tourism Forums which are quite important for the smart city and smart tourism transformation of Canakkale. Moreover, Troy and Canakkale have been promoted worldwide through international visits such as the participation in Korea’s largest tourism meeting, Korea World Travel Fair (KOFTA 2018) (www. troya2018.com, 2019a). Within the scope of 2018 the Year of Troy, one of the main objectives was to improve tourism and cultural infrastructure of Troy National Park and its surroundings. A proposal has been made to establish Troy as a sustainable tourism destination and part of a cultural route. One of the most important achievements can be shown as the opening of the Troy Museum, being one of the most important cultural projects of Turkey and having international awards. Moreover, the infrastructural improvements of villages (Tevfikiye Archeo-Village) in the region; new sightseeing and tour routes; attractions; the culture routes such as Troy Culture Route, St. Paul Route, Aeneas route; cycling and culture paths for the alternative tourism activities (www. troya2018.com, 2019b) will all make a big contribution to Canakkale’s smart city transformation.

17% 15% 12% 10% 8% 8% 7% 6% Safety Healthy life Art and culture Housing

Smart Living 29% 28% 23% 20%

29% 25% 25% 20%

21% 20% 17% 17% 16% 10%

Smart People Online training opportunities for citizens E-participation applications ICT supported work places Self-internet access centre

Smart Government Online services Integrated services managed with real time data Transparent open state Internet infrastructure Use of sensors Electronic payment systems

31% 30% 25% 14%

Fig. 2 Canakkale smart city applications and importance levels. Source: Adapted from Karakas et al. (2019) and Benli and Gezer (2017)

Smart Mobility Traffic monitoring systems Smart junctions Advanced passenger information system Smart bus stops Charging stations Smart bike rental system Smart payment systems Smart parameters Fleet tracking, maintenance, and geolocation 5%

Smart Environment Smart grids 16% Smart buildings 16% Early warning systems for natural disasters 14% Automated trouble shooting & preventive maintenance 13% Automated environmental pollution control 12% Smart electricity / water / gas meter 11% Smart payment systems 7%

Smart Economy The technology initiatives using new business models The works increasing productivity in manufacturing industry E-commerce applications Electronic payment systems

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Table 2 Solution suggestions of corporate participants Public transport using renewable energy Increasing electric vehicles Increase bicycle stations and rental bikes as well as reducing rental price Build rail systems and encourage public transportation Build cable car line for university students Separate lane on roads for student buses Pedestrianize bazaar area in downtown Open Sarıcay (the river in city center) to traffic Increase parking lots Underground parking and solid waste management should be required for new buildings Raise the awareness of public about transportation Establish traffic control center and share real-time data via mobile applications Establish disaster recovery center Gathering noise data via sensors Increase smart water management systems and smart irrigation systems Introduce treatment of wastewater, the use of wastewater in agriculture Raise the awareness and increase the sensitivity of public about environment Establish institutions providing training for software and improve human resource strategies to meet the need for semiskilled workers Increase the use of technology Increase the citizens’ participation to City Council, Neighborhood Council, etc. Move public institutions out of the city center and green these areas as well as give priority to pedestrians Use solar power for streetlights, establish solar power system above Cuma street market and encourage the proper buildings to utilize solar power Source: Benli and Gezer (2017). Table 3 Solution suggestions of Canakkale Youth Association Build charging units working with solar power Increase and generalize the use of solar power by the municipality and public institutes Increase use of smart traffic lamps Bury electrical wires underground Build safer bicycle roads Build Esenler–Kepez tramway line Establish Gondola line for Sarıcay Free wireless and fiber internet infrastructure throughout the city Abolish fair usage quota policy for internet Establish new buildings producing self-energy and having parking lots Mobile application for CABIS Using sensors for waste management and waste bins An application to convey the wastes at home to the Municipality Renovate the old buildings and to use them as cultural center, art, and science buildings Have city library open 24 h a day and build joint study areas in the library Source: Benli and Gezer (2017).

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Table 4 Vision suggestions Corporate participants SMART CITY VISION 1 A ÇANAKKALE that is focused on education, culture, tourism, and ecology, is technologyenabled, offers a high quality of life, is integrated with the international community, uses energy efficiently, is responsive to natural disasters, and embraces a participatory approach and tolerance. Canakkale Youth Association SMART CITY VISION 1 A ÇANAKKALE – capital of happiness and peace – that embraces diversity, offers high quality of life through its natural and historic beauties and entrepreneurial and innovative approaches.

Corporate participants SMART CITY VISION 2 A ÇANAKKALE where the city contributes to its citizens and citizens contribute to their city, raises environmentally friendly younger generations, and upholds peace above all.

Canakkale Youth Association SMART CITY VISION 2 A ÇANAKKALE that upholds production over consumption, can generate its own energy, and makes its voice heard across the globe through an entrepreneurial and innovative society.

Source: Benli and Gezer (2017).

Table 5 Quick win project suggestions Develop high speed wireless access in the priority areas (parks, streets, museums, buses, etc.) of city center Mobile application to show Wi-Fi hotspots in both Turkish and English languages Build solar powered or city network charging units in the priority areas (parks, streets, etc.) of the city center Smart Garbage Collection Management and sensor applications for garbage containers Public internet access center to increase the internet usage capacity of individuals Build living labs providing an environment to young people to find solutions or to produce ideas to the problems of the city Technology supported healthy and independent wellness center: monitoring the health status of the citizens through wearable technology Smart parking lot guidance system to show empty parking spaces Smart street lighting systems with LED lighting, Wi-Fi hotspots, and air and noise pollution sensors Automated air, water, and noise pollution control and monitor with sensors Traffic monitoring application to show real-time traffic data, road maps, and important traffic notifications GESTAS passenger information system to show occupancy rates of ships and real-time locations of ships, etc. E-participation applications to involve citizens in the decisions about the city Create Canakkale open data portal City tourism mobile applications or smart kiosks to provide local and foreign tourists with the upto-date data about the city Source: Benli and Gezer (2017).

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Table 6 Canakkale on My Mind road map 1

Understanding

2

Vision

3

Strategy

4

Action plan

5

Implementation and monitoring

Identifying priority needs of the city Determination of local stakeholders: Municipality of Canakkale, Governor’s office, Provincial Directorate of Culture and Tourism, Canakkale Onsekiz Mart University, CTSO, CASIAD, CATOD, GMKA, public and private institutions, Special Provincial Administration, Environment and Urban Planning Provincial Directorate, Commodity Exchange, City Council, Consulate of Australia, volunteers, and youth associations. Forming a smart city collective intelligence platform among stakeholders as international examples The smart city collective intelligence platform should be managed by a nonprofit mindset and transparent team who treats equally all stakeholders Creating a platform management office Identifying necessary smart city applications (on the axis of economic growth, economic benefit, and social benefit) Determining the selection criteria by which smart projects will be implemented Identification of necessary technology facilitators and innovation accelerators Determination of short- and medium-term objectives for Canakkale, creation of the marketing plan for the city Development of quick win project suggestions Determination of necessary resources for the large projects to be implemented Creation of financial resources: inclusion in annual plans of relevant institutions; creating a city fund; opening smart city support funds and applying European Union funds or related projects Determining potential town twinning or university twinning options; developing international cooperation; joining Horizon 2020 projects; becoming a member of important smart city platforms and organizations like Covenant of Mayors; creating a smart city master plan; organizing events to raise smart city awareness of the city Following the process of the project and making necessary corrections by smart city collective intelligence platform Identifying key performance indicators and creating a continuous assessment process

Source: Adapted from Karakas et al. (2019) and Benli and Gezer (2017)

Phase 3: Strategy In the strategy phase of the project, an international event named “Sustainable and Smart: Çanakkale on My Mind” Conference has been organized on 19 September 2019. In the conference, all stakeholders and the participants of the project took an active role and the steps for ensuring a successful smart city transformation for Çanakkale were discussed. A total of 7 sessions hosted 33 speakers who are the experts on sustainability and smart city took the stage. Furthermore, with the

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leadership of Kale Group, Çanakkale Municipality partnered with TBV from Turkey and from Spain the City Council of Tarragona and Tarragona Smart Mediterranean Region Foundation within the scope of the Town Twinning Action between Turkey and EU Grant Scheme. The duration of the town twinning project between Canakkale and Tarragona is 12 months with a total project budget of 144,188.92 Euros where 90% of the budget is granted. The project has three main objectives. The first one is identified as to develop cooperation between the two municipalities within the framework of the smart city partnership and to launch Canakkale smart city platform based on the examination of the example of Tarragona. Secondly, long-term cooperation is aimed to further the advancement of smart city transformations of both cities. Last but not least, it is purposed to design joint projects within the scope of smart city transformation (www.yereldeab.org.tr, 2019). Within the scope of the project, an opening and strategy development meeting is held with the participation of the project stakeholders from both Spain and Canakkale such as Deputy Mayor of Tarragona, General Manager of Tarragona Smart City Platform, Mayor of Canakkale, TBV, Novusens in Canakkale on 12 March 2019. Thus, possible joint strategies as well as preparations for a memorandum of understanding between the two municipalities and a road map for cooperation have been discussed. A visit was made to Tarragona the week of 8 July 2019 by a delegation from Çanakkale Municipality. The visit aimed to observe smart city applications that contribute to the production of economic services and formation of sustainable cities with the support of technology. A project development workshop was held with the participation of 20 experts from Canakkale and Tarragona municipalities, Tarragona Smart Mediterranean Region Foundation, TBV, and Novusens to focus on the proposals and plans for the potential smart city projects in which both municipalities can invest in the future. Currently, the project website is prepared (Please check http://canakkaleonmymind.org/). As a follow-up activity, a study visit to Brussels have been done together with the participation of the project partners to allow mutual knowledge and experience sharing and joint project development opportunities. Based on the findings of the visit, a joint strategy paper and a project website have been prepared (http://smartroas.com/).

Critical Success Factors and Challenges The participants of smart city surveys and collective intelligence workshops emphasize cooperation among organizations as the most critical success factor of the smart city transformation. Also, innovative approach is the identified as another important success factor. Moreover, other important success factors are identified as financial competence, expertise in information and communication technologies, and citizen’s involvement. Turkey Smart Cities Evaluation Report in 2016 ranks cooperation among organizations as fourth in terms of critical success factors whereas it is ranked first in a similar study done for Canakkale, showing the city’s awareness of the importance of

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cooperation. Financial competence and citizen’s involvement and adaptation are shown as the other significant potential challenges. Both cooperation of organizations and citizen inclusion and adaptation are more strongly emphasized in Canakkale compared to Turkey Smart Cities Evaluation Report in 2016 (Benli and Gezer 2017; Tables 7 and 8).

Table 7 Critical success factors for Smart City Applications in Canakkale

Source: Benli and Gezer (2017).

Table 8 Key challenges for Smart City Applications in Canakkale

Source: Benli and Gezer (2017).

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Governance Models for Mid-sized Smart Cities In the literature, the components of the smart city are clearly stated. According to Boyd Cohen, one of the well accepted smart city experts worldwide and Assistant Professor of Entrepreneurship, Sustainability and Smart Cities at Universidad del Desarrollo University, there are six smart city components namely smart economy, smart environment, smart mobility, smart people, smart living, and smart government (please see Fig. 3 for the details). A critical point during the implementation of a smart city transformation is the integration of all different components of a smart city, removing the silo-structures in the organizations.

Successful Cases of Smart City Transformations A smart city initiative should be able to create a framework for the description of how a city is designed and how the potential challenges can be managed. For a successful smart city initiative, organizational, technology, and policy factors followed by natural environment, infrastructure, economy, society, and government

Fig. 3 Smart Cities Wheel. Source: Cohen (2012)

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related factors need to be managed well. In this sense, visionary leadership is one of the most important elements impacting all the factors and affecting success of a smart city initiative (Karakas et al. 2019; Chourabi et al. 2012; Buhalis and Amaranggana 2014; Letaifa 2015; Nabben et al. 2016; Kumar et al. 2020). Expertise of the project team is quite significant while implementing the smart city transformation. Also, technically and socially skilled ICT leadership is essential. These conditions are fulfilled on the Canakkale on My Mind initiative in different ways. This project has been led and managed by Kale Holding, in cooperation with Turkish Informatics Foundation and Novusens Smart City Institute, who also has extensive expertise in smart cities. Moreover, good communication with citizens and all stakeholders; identifying clear and realistic objectives; planning and improving a road map; developing strategies for the needs of all stakeholders without focusing on a single area; and regulating political obstacles are the other important steps for a successful smart city initiative (Karakas et al. 2019; Chourabi et al. 2012; Buhalis and Amaranggana 2014; Letaifa 2015; Nabben et al. 2016; Kumar et al. 2020). In the case of Canakkale on My Mind Project, all these points are handled through different mechanisms whose details are described in the following sections. Above all, a smart city initiative should have a smart city vision. In other words, a smart city initiative should create an enthusiasm and have ambitions, like smart city’s contribution to environmental, economic, and social sustainability, as well as the quality of life. Therefore, it would be possible to convince citizens and stakeholders to support and contribute to smart city transformation. Thus, the citizens could have awareness of the smart city initiative as well as environmental and sustainable approach it is envisioning. That would also be a possible common ground for stakeholders. Since smart city transformation is a continuous process, the citizens should adopt this as a lifestyle observe and support the efforts through behavioral changes. All these factors would require a smart city initiative with a visionary leadership which is also addressed by the Canakkale on My Mind Project. The last point to focus on is the role of technology in the smart city transformation journey. Technology needs to have an enabler role for a smart city which means technology is certainly not a smart city goal, but rather it is a tool for the smart city. Internet of things, big data, cloud computing, virtual reality, augmented reality, robotic technologies, blockchain, mobile technologies, 5G and fiber optic infrastructure, and SCADA are the technological advances that are frequently utilized in smart city applications (Benli and Gezer 2018).To sum up, the critical success factors and the potential difficulties for a smart city initiative are summarized based on the literature review in Table 9. Based on the smart city model of Boyd Cohen, a smart city has had three evolutions. The Smart City 1.0 model is a technology-centered model dominated by large/multinational technology leading companies. Since first smart city examples in the world are based on the Smart City 1.0 model, the smart city concept has always been paired with the concept of technology. However, technology should be adopted only as a tool for improving the urban quality of life. According to the Smart City 2.0 model, smart cities should be run by the local authorities of the city instead

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Table 9 The success factors for a smart city initiative Organization

Potential difficulties Project volume; the attitude and behaviors of managers, differences among stakeholders, lack of cooperation, incompatible objectives, resistance to change, conflicts

Policy

Potential political pressures affecting ICT initiatives (the decisions of city council, municipality, etc.), political instability

Technology

Lack of employees having integration skills, lack of cooperation among the ICT firms or their departments, unclear ICT vision

Economy

Lack of innovation, productivity, employment level and experts Inadequate resources and high cost, lack of integration in the urban systems, the existing software or applications which are not suitable for smart city infrastructure, threats to security and privacy Not having a clear vision for the city, focusing on only technology during the smart city transformation process

Infrastructure

Management

Nature

Increasing the importance of social, economic, and technological resources of the city without considering the nature, deconstruction of natural environment

Necessary strategies Expertise of the project team, socially and technically skilled ICT leader, clear and realistic objectives, measurable project outputs, identifying relevant stakeholders, ensuring citizen participation, planning and creating road map, good communication, trainings for smart city, finding sufficient and innovative funds, examination of best practices, creating living labs or workplaces for citizens to create solutions together The cooperation among city council, municipality, governance, etc., preparation to remove the relevant legal barriers, providing appropriate political condition allowing the minimization of urban problems Organizing ICT trainings; considering the city’s technological capacity, resource availability, institutional willingness and changing cultural habits before the ICT infrastructure changes, determination of necessary technologies Creating opportunities for industrial development Ensuring integration among urban systems Finding the necessary economic resources

Transparent management approach; understanding the needs of each stakeholders; focusing on a clear vision and making long-term plans; determining the characteristic of the city, especially the strengths in order to prioritize smart city applications based on that and then gain a more powerful competitive advantage Protection of natural resources and the relevant infrastructure

(continued)

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Table 9 (continued) Society

Potential difficulties The education level of individuals, the creativity levels of individuals, participatory structures, conflicts among citizens or societies

Necessary strategies Trainings about the smart city, equal treatment while considering and meeting the needs of different communities, adopting multidisciplinary approach and including different actors, utilizing collective intelligence

Source: Karakas et al. (2019), Chourabi et al. (2012), Buhalis and Amaranggana (2014), Letaifa (2015), Nabben et al. (2016), Kumar et al. (2020).

of technology providers. Based on the Smart City 2.0, municipalities mostly determine what the future of their city should be together with the visionary mayors and city managers. In this model, city managers focus on technology support to improve the urban quality of life. Barcelona, Singapore, and Rio are the successful examples implementing this model in the world. Lastly, Smart City 3.0 model has been improved with a citizen participation approach. Thus, successful smart cities such as Amsterdam and Seoul have begun to implement the technology-supported and citizen-centered smart city model in order to manage next generation smart cities. Boyd Cohen predicts that the integrated version of Smart City 2.0 and Smart City 3.0 models will be the best model of the future. In other words, the best smart city transformation should be managed by the local authorities with citizen participation as well as the support of the technology. Canakkale on My Mind initiative is based on the combination of the Smart City 2.0 and the Smart City 3.0 (Benli and Gezer 2018).

A Model for Turkish Mid-sized Cities: Case of Canakkale Canakkale on My Mind initiative is the first smart city project in Turkey which started with the visionary leadership of a conglomerate born in that city, Canakkale, which collaborated with a prominent NGO aiming to contribute to Turkey becoming an information society that was matched with the enthusiasm of local stakeholders. The president of TBV (Turkish Informatics Foundation 2019), Faruk Eczacibasi summarizes this situation as follows: “We start out to make this exceptional city a real smart city by creating a collective mind together with local government and citizens, public and private sectors, academics and NGOs. The solution proposals provided here about smart urbanization will be example for all other provinces of Turkey.” Moreover, Zeynep Bodur, President and CEO of Kale Group, also supports Eczacibasi with these words: “We know very well that change starts locally. Projects that are locally driven and that include solutions taking into account the city’s dynamics create a lasting impact. Our goal is to make Çanakkale an example to other cities in Turkey and, in fact, make Çanakkale one of the success stories in the

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world. There is no reason that Çanakkale should not be cited in international literature as an innovation model in the near future?” To sum up; Canakkale on My Mind initiative is a first in its formation and approach in the sense that it can be considered as a give back initiative that uses technology as an enabler for the welfare of the city’s citizens, while taking a systematic and holistic approach through participation of all its stakeholders and using a smart city transformation methodology.

Conclusion Based on the researches of the United Nations, about half of the world’s population exceeding 7.5 billion lives in cities now. Moreover, this ratio is estimated to increase 70% in 2050 and Turkey has already passed this foresight. Therefore, there is a growing need for cities that invest in information and communication technologies to use limited natural resources more effectively and more efficiently; saves as a result of these investments; improve the quality of life with these savings; reduce to carbon footprint left in nature; respect the environment and natural resources; and implement all these with innovative and sustainable methods, or shortly smart cities. In this context, Canakkale on My Mind initiative aimed to identify the necessary steps for the smart city transformation journey with all the local stakeholders and to create a transformation road map for Canakkale, which is located in the heart of Istanbul–Izmir axis called the golden circle. Canakkale on My Mind Project is based on the integration of Boyd Cohen’s Smart City 2.0 and the Smart City 3.0. In other words, the visionary leadership of Kale Group and Zeynep Bodur Okyay, combined with citizen participation, engagement of local authorities, and the utilization of technology as a tool are the main characteristics of Canakkale’s smart city transformation. Moreover, on a tactical level, field visits, smart city surveys, smart city trainings and seminars, collective intelligence workshops were used to support the essential ingredients of smart city projects, visionary leadership, citizen participation, and cooperation between organizations. The importance of visionary leadership, citizen participation, and cooperation are evident in successful smart city examples in the world such as Amsterdam, Barcelona, Copenhagen, Vienna, and Montreal. In almost all smart city applications, the local government organizations lead the transformation process while there is effective cooperation among the public sector, private sector, NGOs, and academia. Moreover, the smart city platforms are managed in such a way that all stakeholders are treated equally; objectively; in a transparent manner; with the aim of creating social benefits. Continuing the smart city transformation process initiative will contribute to the livability and sustainability of Canakkale in a world where cities compete with each other rather than countries. Moreover, the city’s natural and cultural resources supported by the physical infrastructure and smart technologies will help Canakkale become a social, human, and creative hub of the region (Benli and Gezer 2017).

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References 2018 Troya Yili Canakkale. (2019a). 2018 Troya Yili Kapsaminda Dünya’da Troya Etkinlikleri Devam Ediyor. http://www.troya2018.com/2018-troya-yili-kapsaminda-dunyada-troyaetkinlikleri-devam-ediyor/. 25 Nov 2019. 2018 Troya Yili Canakkale. (2019b). Troya ve Civari. http://www.troya2018.com/category/genel/. 25 Nov 2019. Benli, B., & Gezer, M. (2017). Canakkale’s Roadmap to becoming a smart city. TBV-Novusens Smart City Institute, commissioned by Kale Group. http://canakkaleonmymind.org/wp-content/ uploads/2019/09/Kale-Group_TBV_Canakkale-On-My-Mind_Report.pdf. 1 Nov 2019. Benli, B., & Gezer, M. (2018). Smart City transformation-smart municipalities conference. Novusens Smart City Institute Bodur Okyay, Z. (2018). This is what a smart city should do for its people. https://www.weforum. org/agenda/2018/10/smart-city-people-canakkale-connected-iot-urban/. 8 Nov 2019. Boes, K., Buhalis, D., & Inversini, A. (2016). Smart tourism destinations: Ecosystems for tourism destination competitiveness. International Journal of Tourism Cities, 2(2), 108–124. Buhalis, D., & Amaranggana, A. (2014). Smart tourism destinations enhancing tourism experience through personalisation of services. In Z. Xiang & I. Tussyadiah (Eds.), Information and communication technologies in tourism (pp. 553–564). Cham: Springer. Chourabi, H., Nam, T., Walker, S., Gil-Garcia, J. R., Mellouli, S., Nahon, K., Pardo, T. A., & Scholl, H. J. (2012). Understanding smart cities: An integrative framework. In 45th Hawaii international conference on system sciences. Cohen, B. (2012). “What Exactly is a Smart City”. Fastcoexist.com. Retrieved 2012. COMU. (2019). Annual number of students. http://ogrenciisleri.comu.edu.tr/istatistikler/yillaragore-ogrenci-sayilari.html. 2 Nov 2019. GMKA. (2016). Çanakkale investment guide for agriculture and livestock. https://www.gmka.gov. tr/dokumanlar/yayinlar/Canakkale-Tarim-Hayvancilik-Rehberi.pdf. 2 Nov 2019. Karakas, E., Atay, L., & Cobanoglu, C. (2019, April 18–19). Digitalization of cities and smart city project: Çanakkale on my mind implementation. In International congress on digital transformation (pp. 43–64). Kumar, H., Singh, M. K., Gupta, M. P., & Madaan, J. (2020). Moving towards smart cities: Solutions that lead to the Smart City Transformation Framework (vol. 153(C)). Technological Forecasting and Social Change, Elsevier. Lazaroiu, G. C., & Roscia, M. (2012). Definition methodology for the smart cities model. Energy, 47(1), 326–332. Letaifa, S. B. (2015). How to strategize smart cities: Revealing the SMART model. Journal of Business Research, 68, 1414–1419. Nabben, A., Wetzel, E., Oldani, E., Huyeng, J., Boel, M., & Fan, Z. (2016). Smart technologies in tourism: Case study on the influence of iBeacons on customer experience during the 2015 SAIL Amsterdam event. Holland: NHTV Breda University of Applied Sciences. Turkish Informatics Foundation. (2019). Aklım Fikrim Canakkale. http://www.tbv.org.tr/aklimfikrim-canakkale,DP-1126.html. 25 Nov 2019. TURSAB. (2019). Tourist numbers and tourism income. https://www.tursab.org.tr/istatistikler. 2 Nov 2019. Yerelde AB. (2019). Gelecegin Akilli Sehirleri için Ortaklik Projesi. https://www.yereldeab.org.tr/ sehireslestirme/Haberler/TabId/450/ArtMID/3640/ArticleID/4890/Geleceğin-Akıllı-Şehirleriİ231in-Ortaklık-Projesi.aspx. 25 Nov 2019.

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Contents What Do We Consider a Smart City? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Plan for a Smart and Connected City . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Developed in Cooperation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Brochure: Smart & Connected (https://international.stockholm.se/globalassets/ovrigabilder-och-filer/smart-city/brochure-smart-and-connected.pdf) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . What Makes Stockholm a Super Smart City? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Extensive Fiber Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . E-Services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Examples of E-Services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Preschool Portal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Residents’ Parking Permits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Report Problems in Traffic and Outdoor Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Radon Reading Search . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Heat Pump License Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Care Diary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Apply for a School . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Apply for a Building Permit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Komet: Web-Based Parent Training . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Online Applications to Art School . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Open Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Data per Area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Stockholm Open Award . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Innovative Solutions and International Smart City Cooperation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hammarby Sjöstad . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hammarby Sjöstad: A Neighborhood with Integrated Environmental Solutions . . . . . . . . . . Stockholm Royal Seaport . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The GrowSmarter Project, Smart Refurbishment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 312 Cross-References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 312 Reference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 312

Abstract

For Stockholm a smart city is quite simply a city that utilizes digitalization and new technology to simplify and improve the life of its residents, visitors, and businesses and to offer the highest quality of life and the best environment for business. The way forward to make Stockholm a smart and connected city is to, via innovation, openness, and connectivity, make the city more economically, ecologically, democratically, and socially sustainable. A smart city is also a sustainable city reaching the city’s goals to be sustainable from different aspects such as being fossil fuel-free by 2040 and reducing greenhouse gas emissions. In 2019, the city received the Smart City Award for its GrowSmarter project at the Smart City Expo World Congress in Barcelona. The Swedish capital was commended for its “innovation, openness, and connectivity” and efforts to improve living conditions for residents.

What Do We Consider a Smart City? For Stockholm a smart city is quite simply a city that utilizes digitalization and new technology to simplify and improve the life of its residents, visitors, and businesses and also to offer the highest quality of life and the best environment for business. The way forward to make Stockholm a smart and connected city is to, via innovation, openness, and connectivity, make the city more economically, ecologically, democratically, and socially sustainable. A smart city is also a sustainable city. One of the citty’s goals is to be fossil fuelfree by 2040 as a way of reducing greenhouse gas emissions. In 2019 the city received the Smart City Award for its GrowSmarter project at the “Smart City Expo World Congress” in Barcelona. The Swedish capital was commended for its “innovation, openness, and connectivity” and efforts to improve living conditions for residents. Stockholm has a long history of being a leader in information and communications technology with many prominent companies and startups as well as established multinationals. Swedes are known for innovation: companies like Ericsson, Electrolux, Volvo, IKEA, and H&M set the standard, building world-leading international corporations. For 20 years ago, the city decided to invest heavily in an open fiber network. This turned out to be a brilliant move that today has generated billions in returns and fiber access to 100% of businesses and 95% of homes. The company is owned by the City of Stockholm itself, and private corporations are able to lease fiber on equal terms with service providers (Fig. 1).

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Fig. 1 The City of Stockholm is a globally renowned smart city

Plan for a Smart and Connected City On April 3, 2017, the City Council adopted a strategy to further develop the smart city through coordination of the City’s work on digitalization. In order to reach its vision of becoming a smart city, Stockholm decided to stimulate, guide, and coordinate different digitalization projects. The strategy for Stockholm as a smart and connected city, together with the City’s upcoming digitalization program, describes how this should be done. All new investments should be based on the needs of the people who live or work in the city – and also those just visiting.

Developed in Cooperation The strategy to become a smart and connected city was developed together with residents, academia, business, and analysis of global developments. • The City invited inhabitants of all ages to a direct dialogue at the Stockholm City Hall. • Dialogues through social media gathered more than 3350 people who provided feedback. Here, they expressed their views on the vision to become a smart and connected city and evaluated the city’s current digital interfaces as well as made suggestions for solutions that could be part of the smart city.

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• Meetings were held with employees of the City of Stockholm, as well as with representatives from startups, academia, and business. • The City of Stockholm, in cooperation with the Royal Institute of Technology, Ericsson, Vattenfall, ABB, Skanska, and Scania, established the innovation arena Digital Demo Stockholm. This arena runs projects to develop sustainable, innovative, digital solutions that contribute to improving quality of life for the people of Stockholm. Another partnership is Urban ICT Arena in Kista Science City, where the City together with the industry and universities test new technologies and services. • The city took part of experiences of other countries and cities. Selected initiatives in other smart cities have been used as inspiration. • An active exchange of best practice was also done with other cities that have made progress in their efforts to become smart cities.

Brochure: Smart & Connected (https://international.stockholm.se/ globalassets/ovriga-bilder-och-filer/smart-city/brochure-smart-andconnected.pdf) Summary of the strategy for Stockholm as a smart and connected city (Fig. 2) (https:// international.stockholm.se/globalassets/ovriga-bilder-och-filer/smart-city/summaryof-the-strategy-for-stockholm-as-a-smart-and-connected-city.pdf)

Fig. 2 The City of Stockholm’s strategy for a smart and connected city builds on all aspects of sustainability to support the highest quality of life for its citizens and the best environment for business

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What Makes Stockholm a Super Smart City? Extensive Fiber Network One important part of Stockholm’s modern ICT history is the company Stokab (http://www.stokab.se/In-english/), founded by the City of Stockholm in 1994. The deregulation of the telecom market, which had taken place the year before, was the reason for establishing the company. Despite proposals by a number of national parliamentary parties to divide up what was then the Telecommunications Administration into an independent, neutral infrastructure organization and a service organization, it remained intact and instead became the company Telia. Stockholm’s politicians believed that a neutral stakeholder was needed who could provide basic IT infrastructure to all on equal terms in order to generate competition, diversity, and a range of choice within telecommunications and data. To achieve this, the IT infrastructure company Stokab (http://www.stokab.se/Documents/Nyheter% 20bilagor/Stokab_eng.pdf) was formed through a political consensus. The company’s mission is to build, lease, and maintain a passive fiber-optic network to help foster favorable conditions for IT development and the positive development of the Stockholm region. Because Sweden was among the first EU countries to open its telecom market to competition, it was difficult to simply copy others’ solutions. Other countries, however, have since copied a number of creative institutional solutions generated in Sweden during these years. One of these is known as the Stokab Model, that is, the way in which the fiber-optic-based municipal network in Stockholm is organized. This model’s organization of the new network industry departed radically from the traditional approach to organizing such industries. The Stokab Model was based on two important insights: • The first was that a dynamic development of the new markets opened up by the Internet and broadband required competition between operators with a free right of establishment. • The second was that the high fixed costs of building networks in the city. It was neither desirable nor possible to justify the cost of digging up streets and running cable or pipes to properties multiple times for multiple suppliers (http://www. stokab.se/Documents/Nyheter%20bilagor/Stokab_eng.pdf). Since Stokab started in 1994, the goal of the municipally owned company has been to build a competition-neutral infrastructure capable of meeting future communication needs and spur economic activity, diversity, and freedom of choice, as well as minimize disruption to the city’s streets. Stokab leases fiber-optic networks that telecom operators, businesses, local authorities, and organizations use for digital communications. Leasing agreements are structured on favorable terms to encourage IT development and strong growth in the Stockholm region.

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In addition to fiber optics, Stokab (http://video.stockholm.se/video/9432050/ fibre-network-in-stockholm) provides space in nodes/hubs where customers can install communication equipment needed to connect their own networks to others’ networks. Over the years, Stockholm’s network has grown. In 2015, more than 90% of households and nearly 100% of businesses in the City of Stockholm are able to connect to the network. The fiber-optic network was also extended to cover Stockholm’s archipelago, so that all its larger, inhabited islands are connected. The network has also been extended via Mälarringen, which connects separate municipal networks around the Mälardalen region. The network is used by more than 100 telecom operators and 500 companies in Stockholm. Since the company is selffunded, it does so at no expense for public finances and benefits to end users include low commercial offers and flexibility of services supporting the city’s competitiveness and innovation capacity (Fig. 3). According to research institute (Forzati & Mattson 2013), Stokab’s network has generated a national economic profit of at least SEK 16 billion. This profit takes the form of more jobs, increased property values, and lower broadband prices. The network has also allowed for the extension of the 4G mobile networks, with four operators. It also creates conditions conducive to developing services, including

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Fig. 3 The Stokab fiber-network now incorporates most separate municipal networks in the Mälardalen region

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cloud services, smart e-services, and other innovations. Thanks to its well-developed open fiber network, Stockholm is well equipped to meet today’s challenges and that tomorrow’s. In 2015 the City of Stockholm was awarded the European Broadband Award from the European Commission with the jury saying that “Stokab has been a European pioneering model for municipal broadband development.”

E-Services The City of Stockholm’s e-services play an important part in the mission to offer fast, easy, and top-class service to Stockholmers, based on individual choice and preference. To achieve this objective, the City of Stockholm prepared an e-strategy (https:// international.stockholm.se/globalassets/ovriga-bilder-och-filer/e-strategy-city-of-sto ckholm.pdf) describing the road ahead. The strategy points out that efficient public services are key factors in a thriving city, and they are characterized by a common desire to prioritize citizens’ different needs and desires. The city provides support and facilitates everyday life. Examples are: to easily apply for permits; finding your way around the town; being able to run errands around-the-clock. As part of this goal, the City of Stockholm offers e-services that make it more convenient than ever to be a Stockholm resident.

Examples of E-Services Below are some of the numerous e-services (https://international.stockholm.se/gov ernance/e-governance/, https://international.stockholm.se/globalassets/ovriga-bild er-och-filer/e-tjanster_broschyr-16-sid_4.pdf) offered by the City of Stockholm. The list of services totals several hundreds (https://www.stockholm.se/-/Omwebbplatsen/Alla-e-tjanster/#index_A).

Preschool Portal One of the first services in the e-service program was the ability to apply for preschool. The service is available both as an open application and as one requiring login with digital identification. Applicants log in using their digital ID to accept or reject an offer. With the new version of this, e-service improves services for parents while also making administration and information easier for preschools. Both staff and parents can find out about day-to-day information, such as activities and what food is served for lunch. They can also make bookings and report absence. Preschool staff can use the portal to find and manage important information quickly and easily. The service makes administration easier when it comes to pupil withdrawals and charges. Parents will also be able to monitor their children’s creative output securely online.

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Residents’ Parking Permits Another service relates to residents’ parking permits. To get a permit, you need to own a car and live at an address where residents’ parking applies. It is also possible to record a payment for a period of time. With their digital ID, users can log in and register a permanent or temporary change of vehicle or service suspension. The service is available to around 60,000 citizens. In its benefit estimate, the Traffic and Waste Management Administration believes that around 75% of these will be using the service within 3 years of its launch, which corresponds to four full-time jobs.

Report Problems in Traffic and Outdoor Environment File a complaint or leave your point of view on Stockholm’s traffic and outdoor environment. You can choose between praise, error report, question, idea, and complaint. This service is available both as an app for smart phones and as an eservice. When used as an app, the service is very simple. If a person finds, for example, graffiti or broken park benches, the person can simply with the app report the location by GPS and send a photo and file under a heading. The maintenance teams then easily can prioritize repair. The app received more than on hundred thousand reports already in 2017 (Fig. 4).

Fig. 4 The application “Tyck till” or “give your opinion” has made it possible for the citizens to easily report faults to the maintenance teams, facillitating and speeding up repair

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Radon Reading Search One more basic e-service is the radon reading search, which was developed by the Environment and Health Administration. Radon readings in Stockholm’s residential areas are collected in a database, which can be searched by a building name and/or street address. The Environment and Health Administration previously used an external phone service for enquiries about radon, for instance. Now that the radon e-service is property-based, administrators can focus on informing property owners what they can do to reduce their radon levels, for example.

Heat Pump License Applications The e-service application for a heat pump license makes life far easier for property owners while at the same time ensuring that the Environment and Health Administration receives correct applications directly in its operations system. To make it possible to provide an e-service, all 14,000 borehole licenses were digitalized to create a map view. Around 60% of all the applications are received via the eservice, and every day building owners and heat pump suppliers alike use the site to find out how to apply and how the situation stands in the vicinity of their properties (Fig. 5).

Fig. 5 In the heat pump application, citizens that want to apply for a permit to drill a hole in the bedrock for a heat pump can easily site it and get quicker feedback about the permission

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Care Diary Elderly people themselves can use the Care Diary as an easy way of keeping track of decisions and documentation, for example, personal details, implementation plans, and day-to-day records of measures that have been taken. With their consent, close friends and relatives can also access this documentation. Records are taken from the City of Stockholm operations system for elderly care. A digital ID is required to log in and view the information.

Apply for a School This service deals with applications for school and makes it easier for parents to choose a school for their children. Parents can also track where their application is in the school selection process. The service covers both local authority and independent schools. It also means that head teachers have more time to plan ahead of the new school year, which makes staff planning far easier.

Apply for a Building Permit The City Planning Administration deals with around 9,000 planning issues a year. The e-service is divided into several parts and informs people how to apply for a building permit, where to find current plans, and how to interpret them. You can also order maps ahead of an application and track your application through the process. It is also possible to register when construction work begins.

Komet: Web-Based Parent Training Stockholm’s district councils train parents in using the Komet method to help improve communication with their children. School and preschool teachers are also trained in using the Komet program, which helps ensure calmer, more secure pupils and children’s groups. A version of the targeted parents’ program has been developed, and an e-service project enables parents to come into contact with the Komet program.

Online Applications to Art School The Stockholm School of Arts educates 15,000 children and young people aged 6–22 in art and design, dance, music, and theater skills. Its activities cover the whole city, in 20 or so of its own premises and something like 80 schools. The 14,000 course applications are received as forms, which are then registered. Residents are able to apply, supplement, and amend their applications and see where they are in the queue. This e-service will streamline planning and administration while also improving the level of service.

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One of the City’s goals is to make services and information more accessible and to help citizens, businesses, and staff communicate more easily by digital means: information and information management are therefore strategically important, something which is emphasized by the City setting aside major resources for this purpose. Part of this venture is the Stockholm City e-archive, which files all digital document transactions automatically. Two things are required to meet the City’s objectives: • The people of Stockholm must be able to find information via a single search portal, no matter where it is stored. • The information must be stored in a common e-archive as soon as transactions are made.

Open Data Since 2011, Stockholm City has published open data in several areas. The goal is to promote innovation and openness; the City is working actively to provide open data through the portal Open Stockholm (https://international.stockholm.se/governance/ smart-and-connected-city/open-data/). Public information is to be used not only by authorities but also by businesses and the public to create new services. The data is open to anyone (free) and can be accessed digitally via APIs/Web services. The City of Stockholm provides one third of all open data from the Swedish public sector with information from more than 100 open data sources.

Data per Area Culture and Archive Data Among the city’s cultural and archival data, there are links to the City’s building registers. Here is also available 35,000 pictures and documents from Stockholm’s history and associated metadata. Population Data The City of Stockholm collects population statistics as a basis for planning the service that is under the responsibility of the municipality, such as childcare, school, planning of social services activities, and forecasts for tax revenues. Traffic and Parking Data The traffic data contains road- and traffic-related data that Stockholm City collects for traffic planning, maintenance, and project planning. Environmental Data The environmental data contains maps and measurement data that the City of Stockholm has produced in order to describe the environmental situation within the municipality. There are also data from regional and national environmental monitoring.

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Activities and Satisfaction Surveys The unit database contains information about all of Stockholm’s business locations and is also the basis for Compare Service on stockholm.se. Geodata Stockholm City is responsible for maintaining basic geographical data across the city. Among our geographic data are different types of maps and aerial photos.

The Stockholm Open Award The City of Stockholm also organizes the Open Stockholm Award competition to stimulate new ideas and increase accessibility to the city’s service through the development of mobile services and apps based on the city’s open data. The ambition of the competition is that a winning project can be completed when the competition is completed.

Innovative Solutions and International Smart City Cooperation The City of Stockholm is aware of the great potential that smart city solutions can have on reducing the city’s impact on the environment. Since 1995 the city has reduced its greenhouse gas emissions per capita by 60%. Much of this has been accomplished by the transition from single oil furnaces in buildings to more district heating with cogeneration of heat using renewable fuels. Now the city needs new smart solutions to go further in reducing its emissions. These solutions need to better engage the inhabitants themselves who stand for most of the emission by the way they use energy for transport and heat/hot water.

Hammarby Sjöstad Hammarby Sjöstad: A Neighborhood with Integrated Environmental Solutions Stockholm City has put tough environmental demands on buildings, technical installations, and traffic environments in Hammarby Sjöstad. The urban area received its own environmental program with the aim of reducing the total environmental impact by half compared to an area built in the early 1990s. Hammarby Sjöstad also received its own cycle, the Hammarby model, which describes the environmental solutions for energy, water and sewage, and waste. Sweden’s export council has developed a model for the sustainable city – SymbioCity – which is based on experiences from Hammarby Sjöstad (Fig. 6).

Stockholm: Smart City

Fig. 6 The Hammarby model illustrates the circular model on how water, waste, and energy are produced and used in Stockholm

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Stockholm Royal Seaport In 2009, the Stockholm City Council decided that Stockholm Royal Seaport would be designated an area with an environmental profile with the mandate to determine what is possible in the current situation and push the boundaries where possible, to become a model of sustainable urban development. The built environment is robust over time which requires that buildings and facilities are designed with high quality. Materials, water, and energy are resources that must be used efficiently by, for example, creating eco-cycles. Using nonhazardous materials reduces impacts on human health and the environment. The generation and use of renewable energy are encouraged to make the area fossil fuel-free. Key Figures 2017 • The energy consumption is 40% lower than the n requirements. • One hundred percent of the properties are connected to a vacuum waste collection system, and 100% of kitchens have a waste disposal unit. • The amount of residual waste in 2017, 215 kg/apart./year compared with 242 kg/apart./year in 2015. • Twenty-two percent of the soil has been remediated so far, equating to 40 football fields. • Twenty-eight percent fill material has been reused in 2017 (Fig. 7).

Fig. 7 The Royal Seaport is the follower of Hammarby Sjöstad. This area with more than 15,000 new dwellings is Stockholm’s largest new development area, testing smart solutions and using farreaching environmental technologies

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The GrowSmarter Project, Smart Refurbishment GrowSmarter (http://www.grow-smarter.eu/solutions/) was 5-year project (2015–2019) supported by the European Commission led by the City of Stockholm. It focused on implementing smart solutions in refurbishment areas. This is extra important as refurbishment is often neglected compared to the flashy newly built areas in cities. In Europe, more than 200 million inhabitants live in areas buildt from the 1960s to 1970s. These residential areas are now in need for renovation. There is an enormous potential for building energy-efficient and smart solutions both in these buildings and in the neighborhoods. The GrowSmarter project included three Lighthouse cities, Stockholm, Cologne, and Barcelona, as well as five Follower cities, Valletta, Suceava, Porto, Cork, and Graz. GrowSmarter received funding from the European Commission’s Smart Cities and Communities Horizon 2020 research and innovation program. The scope of the project was to: • Demonstrated and validated 12 economically and environmentally sustainable integrated smart solutions in the three Lighthouse cities • Fostered collaboration between cities, businesses, and academia to transform the smart solutions into business models to be rolled out across Europe • Improved the quality of life for European citizens, reduce environmental impact, and create sustainable economic development GrowSmarter took a holistic approach to sustainable growth. The demonstrations in the Lighthouse cities were not the primary aim, but a means to contribute to solving city challenges and create validated business cases to initiate a market roll out of the smart solutions to Follower cities and the rest of the European market, thus helping Europe Grow Smarter. The 12 solutions were designed to meet the three pillars of sustainability. The main goal of the project is to demonstrate and market 12 smart solutions for: • Low-energy districts – More than 120,000 m2 of building space were renovated with an improved energy efficiency by 60%. – Smart energy-saving by providing information on real-time energy usage and waste levels to tenants is a key tool to help them see and reduce their own environmental footprint. – Smart local electricity management reduced grid fluctuations and saved electricity. • Integrated infrastructures – Smart streetlights – New business models for district heating and cooling – Smart waste collection, turning waste into energy – Big open data platform

Fig. 8 The GrowSmarter project demonstrated 12 smart solutions for smarter cities in Europe. The practical demonstrations were replicated in many other cities and helped create a market for solutions that both reduce greenhouse gas emissions and creates new jobs, thus helping Europe GrowSmarter

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• Sustainable urban mobility by improved the logistics for goods and also reduced the need for private cars in cities – Smart building logistic and alternative-fuelled vehicles – Sustainable delivery – Smart traffic management – Alternative fuel-driven vehicles for decarbonizing and better air quality – Smart mobility solutions (Fig. 8) GrowSmarter evaluated these solutions against targets related to climate change, energy usage, transport emissions, and jobs. The demonstrations both helped induce replication both in the participating cities and in other international cities as well. Introducing innovations is often regarded with skepticism. Why introduce smart streetlights with sensors when the existing system with one switch turning them all on at dusk and off at dawn has worked for over 100 years? One demonstrated and evaluated it was shown that these solutions worked and saved half the energy needed for convention streetlights. After that the city has decided to exchange 24,000 of them.

Fig. 9 In Stockholm the GrowSmarter project has renovated several buildings from the 1960s into energyefficient and smart buildings

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This is just one out of numerous examples of how the project has helped overcome the anxiety for innovations and change. The GrowSmarter project is an example of how Stockholm shares its experiences outside of its borders and together with other cities puts effort to help cities Grow Smarter stimulating economic growth and simultaneously reducing greenhouse gas emission (Fig. 9).

Conclusions The City of Stockholm is in many ways a smart city. The city utilizes digitalization and new technologies to simplify and improve the life of its residents, visitors, and businesses. As for all large organizations, testing new ideas through pilot projects has also been a successful method to get past the reluctance for change. Testing new technologies and methodologies before introducing them in a larger scale has helped avoid backlashes and help replicate the smart measures, as good examples are easily taken up by others once shown. Several reports and city comparisons have pointed out Stockholm as a leading smart city. For the City of Stockholm the challenging work continues and is developed further to meet the city’s goals to both be fossil fuel-free and to be the world’s smartest city by the year 2040.

Cross-References ▶ Smart Energy Frameworks for Smart Cities: The Need for Polycentrism

Reference Forzati and Mattson (2013). Acreo Rapport acr055698. https://www.ssnf.org/globalassets/sverigesstadsnat/fakta-och-statistik/rapporter-av-andra/acreo-stokab—en-samhallsekonomisk-analysacr055698sv.pdf

Smart City Wien: A Sustainable Future Starts Now

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Thomas Madreiter, Angela Djuric, Nikolaus Summer, and Florian Woller

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Vienna Is on Its Way . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smart City Wien Framework Strategy 2014 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smart City Wien Monitoring Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smart City Governance Is the Key to Success . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smart City Wien Framework Strategy 2019–2050 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Thematic Fields . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Energy Supply . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mobility and Transport . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Buildings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Digitalization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Economy and Employment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Water and Waste Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Healthcare . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Social Inclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Science and Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Participation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Projects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . E_OS: Renewable Energy from Sewage Sludge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Neighborhood Oasis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smarter Together . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WAALTeR: Active, Healthy Ageing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sag’s Wien App . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Citizens’ Power Plants: Community-Funded Solar Energy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Auto Bus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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T. Madreiter (*) Executive Group for Construction and Technology, City of Vienna, Vienna, Austria e-mail: [email protected] A. Djuric · N. Summer · F. Woller Smart City Agency, UIV Urban Innovation Vienna GmbH, Wien, Austria © Springer Nature Switzerland AG 2021 J. C. Augusto (ed.), Handbook of Smart Cities, https://doi.org/10.1007/978-3-030-69698-6_9

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Smart Traffic Lights . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Vienna Provides Space: Digital Twin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . BRISE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Werkstadt Junges Wien: Co-Creating a Child and Youth Strategy for Vienna . . . . . . . . . . . . . Conclusion and Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract

Vienna is among the most successful cities worldwide where quality of life, infrastructure, and innovation are concerned. Numerous studies and rankings place Vienna in top positions in terms of competitiveness, with the promise of further dynamic development. However, the City of Vienna is facing challenging times: the population in the federal capital is growing and will reach the two million mark over the course of the next decade. This development goes hand in hand with a rising demand for energy, demand for affordable and functional housing, and a need for strong mobility concepts. At the same time, climate change and a severe shortage of natural resources, especially fossil fuels, represent the big global challenges of the coming decades. This is where Vienna’s Smart City strategy comes into play. Smart City Wien is a long-term initiative by the City of Vienna to improve the design, development, and perception of the federal capital. The initiative looks at a cross-section of the city, covering all areas of life and includes everything from infrastructure, energy, and mobility to all aspects of urban development. It has set itself the task of modernizing the city in order to reduce energy consumption and emissions significantly without having to forego any aspects of consumption or mobility. To achieve this, the city government has adopted a framework strategy to attain its key objective for 2050: High quality of life for everyone in Vienna through social and technical innovation in all areas, while maximizing conservation of resources. This chapter first gives an overview of the history of framework strategies with an emphasis, among other things, on monitoring, which is essential to highlight successful areas as well as those which require more work. The second chapter describes the thematic fields of the Smart City Wien Framework Strategy 2019– 2050. The last chapter deals with various projects that have already been implemented or are in the process of being implemented and provides practical examples.

Introduction Making the transformation into Smart City Wien a reality is an enormous endeavor that will last for decades to come. The tasks associated with this transformation have to be borne by many shoulders. Policy-makers and administrators as well as local companies, the scientific community, culture and the arts, and – last but not least – every single citizen are all required to rise to the challenge. The City of Vienna can already look back on a decade of experience regarding this transformation and draw

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conclusions about what worked well and what did not. By applying this governance knowledge, Vienna hopes to set standards and meet the goals of Smart City Wien.

Vienna Is on Its Way From the beginning, Smart City Wien has shown commitment to Europe’s development goals up to 2050. Guided by the EU Strategic Energy Technology Plan (SET Plan), former Mayor Dr. Michael Häupl initiated Vienna’s transformation into a more sustainable urban living space in 2011. A project funded by the Austrian Climate and Energy Fund gave stakeholders inside and outside the city administration the opportunity to team up for collaboration. From 2011 to 2013, expert policymakers and administrators as well as representatives of the scientific and business communities and civil society worked out a strategic basis for the Smart City Wien Framework Strategy. In 2014, Vienna City Council adopted an initial draft document incorporating visionary concepts and action plans. On June 25, 2014, legislators at the local level made the so-called Smart City Wien Framework Strategy the legally binding foundation for Smart City Wien.

Smart City Wien Framework Strategy 2014 The first Smart City Wien Framework Strategy envisaged a smart transformation as the development of a city that assigns priority to and interlinks the issues of energy, mobility, buildings and infrastructure. In this, the following premises applied: radical conservation of resources, the development and productive use of innovation and new technologies, as well as a high and socially balanced quality of life (Vienna Municipal Administration 2014). The underlying goal was to safeguard the city’s ability to tackle future challenges in an integrated manner. The signature characteristic of the Smart City Wien Framework Strategy that set it apart from approaches taken by many other cities was its holistic approach and focus on social components. Social inclusion was integrated as an essential element of the strategy, with improving the everyday life of Vienna’s citizens being assigned the same importance as climate-related and ecological objectives. The City of Vienna consciously chose this approach, asserting that a city is only smart if all of its people have equal opportunities, enjoy a high quality of life and have access to the same degree of participation. The first Framework Strategy was built on three pillars - resource conservation, quality of life and innovation. It formulated objectives in numerous thematic fields ranging from education, economy and healthcare to energy, mobility and infrastructure. Objectives were long-term and inextricably linked to existing initiatives, plans and specialized sectoral strategies of the City of Vienna. As a superordinate framework, Vienna’s Smart City strategy is intended to provide orientation and ensure the coherence of all activities. Far-sighted, intelligent decisions in the past have made Vienna the city with the highest quality of life worldwide. However, to maintain these high standards, it

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proved necessary to strive for constant assessment and new and innovative solutions as climate change and increasingly scarce resources call for a new global approach and continuous innovation. Thus, a truly smart city must keep pace with the pulse of the times, cleverly adapting to changes.

Smart City Wien Monitoring Process Smart City Wien is a complex endeavor that touches upon every sphere of urban life. As one can only manage what one can measure, serious monitoring of transformational progress quickly turned out to be indispensable. An evaluation mechanism was therefore developed in 2016. Smart City Wien regularly reviews the extent to which the city is on track to achieve the goals of its framework strategy. This monitoring methodology is not only a basis for controlling the process, but also creates a platform to improve dialogue and collaboration between all stakeholders involved. It acts as a catalyst for city-wide governance and serves as a tool for communication and mobilization. The first monitoring cycle was carried out in 2017. Special attention was paid to extensive cooperation and building upon existing data, evaluations and reports. All steps in the monitoring process involved municipal departments as well as associated organizations and enterprises. Focus groups, interviews and workshops were used for detailed discussions about content and procedural questions. The rationale was that a well-coordinated approach would elicit maximum support (ownership) from all actors. Vienna’s first monitoring report in 2017 painted a positive overall picture: for 23 out of 51 individual objectives Vienna was fully on track, while the attainment of another eleven objectives was rated as being largely on track. However, the monitoring process also drew attention to shortcomings that will require increased efforts in the coming years. Assessments also showed that it is crucial to identify interrelated topics and potentially conflicting objectives in order to ensure a coherent implementation of the Smart City concept in Vienna. As many conflicting objectives became apparent, the need for proper management of complex highly interrelated urban systems (by measuring, addressing, discussing and prioritizing) will challenge traditional methods of government: growth versus conservation of resources, housing versus green spaces, affordable housing versus high ecological building standards, greening of roof surfaces versus solar collectors, etc. to name just a few (City of Vienna 2018). However, a particular effect of the Framework Strategy with its integrated approach is that the individual thematic fields und objectives should and will become more closely interlinked so that synergies emerge. It thus becomes clearly evident how activities in one area also produce a positive impact elsewhere, for example, when eco-friendly forms of mobility simultaneously improve traffic safety, reduce noise pollution and encourage healthy physical exercise. At the same time, seeing the whole picture also highlights conflicting objectives and allows an open debate on what should take priority.

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But the evaluation also flagged up thematic fields which were insufficiently covered by the Smart City Wien Framework Strategy in 2014 and/or which have not yet been linked to concrete objectives. This includes e.g., digitalization, adjustment to climate change, social innovation and the necessity of a joint approach throughout the entire metropolitan region: a professional dialogue and exchange of up-to-date data across municipal institutions and administrative bodies were seen to be particularly important. Due to the lack of a central data management system integrating all reports and data, coordination and cooperation across organizational units, tiers and sectors is in great need of improvement.

Smart City Governance Is the Key to Success Open innovation – aka sourcing innovation from distant fields and markets – frequently involving citizens as early as possible via participatory processes and ensuring a coherent implementation of the strategy beyond organizational and departmental boundaries are key challenges of Smart City governance. Across the world, cities are increasingly becoming the focus of policy-making on innovation and around climate and energy issues. By forming alliances cities can join forces to advocate their common agendas, for instance with regard to safeguarding the principles of general public interest and provision of public services, or ensuring that their thematic priorities are incorporated in EU funding programs. Unsurprisingly, two mindsets, above all, are important for Vienna’s future as a Smart City: enabling steady and constant evolution and creating space for the new. The new – be it services, corporate plans and business models, forms of mobility, modes of social interaction or cultural expression – hardly ever fits into established structures and remits. A few innovations are easy to integrate into tried-and-tested mechanisms and quickly produce positive results. Others start out as a challenge for the existing set-up. Making a commitment to the Smart City also means that the management structures of the city, in particular, will be repeatedly put to the test and so must be ready to be very adaptable. The innovation focus of Smart City Wien calls for new tools and approaches in the design and delivery of municipal services. In this respect the municipal administration is setting the bar very high: the quality of services is to be maintained at the same high level while taking even greater account of the various needs of everyone living in Vienna, thus demonstrating how modernization can be used to uphold and enhance quality of life. The full potential of the Smart City approach can only be realized if tasks and challenges are viewed from a more interdisciplinary, inter-agency perspective, cutting across the boundaries between remits and working together on joint solutions. It is often local action – supported or facilitated by appropriate measures – that succeeds in overcoming these boundaries. Non-cooperation, on the other hand, incurs high costs due to inconsistencies, duplications or gaps that then require readjustments later on.

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Overall management and coordination of the strategy is affected via a governance structure that reflects the complexity and breadth of content of the undertaking. In particular, the Smart City governance structure is designed to perform the following functions: (1) orientation for sectoral strategies and packages of measures, (2) initiation of key cross-departmental projects, (3) addressing stakeholders outside the public sector and (4) strategic and quality management through monitoring. In order to perform these functions, Smart City Wien utilizes personnel and financial resources on multiple levels. The responsibility of the policy-making level is to define a clear policy line for Smart City Wien. It issues policy instructions, approves planned measures and makes available the required resources. The level of the Chief Executive Office of the City of Vienna ensures the strategic coordination of Vienna’s Smart City initiative. Among other things, this also includes ensuring that sectoral strategies are aligned with the Smart City goals, initiating cross-cutting projects and measures, evaluating the monitoring results, discussing strategic courses of action and resolving conflicting objectives. It also guarantees the regular exchange of knowledge within the municipal administration at the operational level and promotes the development of suitable measures and projects on priority issues. Moreover, civil society, and particularly representatives of the scientific and business communities, is to be assigned an even greater role. All activities of the City of Vienna are supported by a Smart City Agency. The main task of this multidisciplinary team is to be a neutral innovation broker initiating and coordinating projects, advising and supporting municipal actors, managing stakeholder enquiries as a point of liaison for new partnerships, communication and networking activities and providing support to the Smart City governance structure. It is important to stress that Vienna’s evolution into Smart City Wien can only succeed if the goals and targets receive widespread support far beyond the city’s policy-making and administrative capacities, with a broad spectrum of stakeholders participating via interdisciplinary beacon projects, public-private partnerships, pilots, living labs and research partnerships/joint ventures, as well as participatory formats and alliances of all kinds. For instance, close coordination and collaboration with Vienna’s neighboring federal provinces and the local authorities within the Smart Region is essential. The Platform for Energy and Climate Action (Smart Region) under the auspices of Planungsgemeinschaft Ost, the joint planning organization of the three federal provinces of Vienna, Lower Austria and Burgenland, is one springboard for cooperative strategies and measures across the administrative boundaries. Federal-local collaboration and city partnerships will be equally important.

Smart City Wien Framework Strategy 2019–2050 Even though another three decades lie ahead until 2050, the coming years are of decisive importance. Vienna’s population continues to grow (Fig. 1) and the consequences of the climate crisis are becoming more and more evident – in the form of

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Fig. 1 Population trends 2009–2019; comparison of European cities. (City of Vienna, Statistics Vienna 2020)

extreme weather events such as torrential rainfall, droughts and heatwaves. The climate crisis is one of the major challenges of the twenty-first century, and one that will have a far-reaching impact on everyone’s life – in cities as well as in the countryside. For that reason, it is even more important that major cities take their future into their own hands rather than letting events take their course. By implementing its Smart City Wien Framework Strategy Vienna intends to do precisely that. However, as monitoring has shown, a long-term-strategy of this kind needs to be subject to adaptation and adjustment. This is particularly the case if a city is eager to tackle the consequences of the climate crisis and disruptive technological innovation holistically. The revised and updated version builds on the strategic guidelines, goals and objectives of the 2014 Smart City Wien Framework Strategy and develops them further. Close cooperation between representatives of the municipal administration and its associated enterprises and organizations, plus involvement of external experts from the fields of academia, business and representative bodies, was of decisive importance in the revision and update process. During the process, all of the thematic fields were subjected to critical examination. Consequently, existing objectives were tightened up and new ones defined (Vienna Municipal Administration 2019). For instance, “Digitalization” and “Participation” were incorporated into the strategy as new thematic fields. Moreover, a stronger focus was placed on current developments and challenges - especially on interplays between thematic fields. In addition, a materiality analysis was performed on all 17 Sustainable Development Goals of the UN 2030 Agenda and their 169 targets, and the results taken as a basis for the revision process.

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Thematic fields are no longer assigned to only one of the three dimensions Quality of Life, Resources and Innovation. Instead, each thematic field now refers to all of these headline goals, i.e., radical conservation of resources, contributing to quality of life and social inclusion, and the focus on innovation and digitalization as the key instruments for viable sustainable development. New issues such as adapting to the consequences of climate change, establishing a circular economy and drastic reductions in raw material consumption are addressed by the revised strategy. Target values for CO2 emissions and underlying calculation methods have also been adjusted: until now, the greenhouse gas reduction targets of the Climate Protection Programme of the City of Vienna (KliP) and the Smart City Wien Framework Strategy were always defined in relation to 1990 as the baseline year. From now on, 2005 will be taken as the baseline year. 2005 is the EU-wide standard baseline year for all CO2 emissions targets (comprising emissions inside and outside the ETS - Emissions Trading System). In parallel, monitoring indicators had to be re-examined and partially re-defined as well as adjusted to updated strategic objectives. The findings of the first monitoring cycle in 2017 were of vital importance to refine strategic adjustments. Collaboration has been key for strategy formulation and methodological set-ups. Likewise, partnership between municipal entities, associated enterprises in the public sector and external stakeholders from the private sector and civil society will play an essential role in bringing the strategy to life.

Thematic Fields The Smart City Wien Framework Strategy provides guidelines for the city’s transformation into a more livable and sustainable habitat up to the year 2050, touching upon almost every sphere of urban life. In 2017, a monitoring report flagged up those areas in which approaches required significant adjustments in order to mitigate conflicting objectives, incorporate new goals and respective requirements and keep Vienna’s focus on sustainable urban development as the main agenda. With digitalization and participation being integrated as new priorities, the Smart City Wien Framework Strategy now addresses 12 thematic fields which will be outlined hereafter (Vienna Municipal Administration 2019).

Energy Supply A secure, affordable, environmentally sound, needs-based energy supply is and remains one of the most important prerequisites for Vienna’s high quality of life and economic development. Therefore, one of the central objectives in the field of energy supply is to maintain Vienna’s high level of energy security. In order to concurrently meet the CO2 emission reduction targets, the city’s energy system requires radical transformation. This radical transformation will, among other things,

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be achieved by investing in the development of smart grids. The Smart City Wien Framework Strategy furthermore envisions a clear reduction in energy consumption combined with a step-by-step conversion to renewables. This requires an interplay of different factors: in future, per capita energy consumption for heating, hot water, and air-conditioning in buildings is to be drastically reduced. At the same time, the share of renewables is to be increased through a gradual conversion from gas and oil-powered heating systems to district heating, solar energy, or heat pumps. In the transport sector, a shift to eco-friendly modes of transport and an electrification of motorized private and goods transport is needed. The new demand for electricity in the transport sector should of course be satisfied from renewable sources. Other energy consumption, such as electricity for lighting or electronic devices and the energy consumed by trade and industry, is expected to decline only slightly. The shift to renewable sources in these sectors is therefore indispensable. In order to be more independent on the energy market, Vienna intends to extensively increase renewable energy production within the municipal boundaries. Apart from these technical measures, comprehensive awareness-raising, information and education campaigns are required in order to achieve appropriate changes in the consumption behavior of the Viennese population.

Mobility and Transport Mobility and transport are of pivotal importance to almost every city. They have a decisive impact on the daily routine of its citizens and are a major driver of a city’s success as a business location. At the same time, in many cities transport is the sector responsible for the greatest share of total greenhouse gas emissions. In Vienna, close to a third of final energy consumption is attributable to transport. In order to become a truly smart city, Vienna envisions convenient, safe, barrier-free, affordable mobility for all, whether or not they have their own car. Radical conservation of resources and preventing traffic-related carbon emissions means reducing the need to travel wherever possible, shifting journeys to efficient modes of transport, and making the transition from fossil fuels to carbon-free propulsion systems for all vehicles. Thanks to digitalization, virtual mobility can already replace physical mobility to some extent. The design of urban neighborhoods is another important lever to reduce traffic-related carbon emissions. By ensuring an attractive local mix of functions – housing, education, employment, shopping and leisure – within a short distance, Vienna enables its citizens to bike and walk as they go about their daily lives (Fig. 2). Shifting the transport sector towards cycling, walking, and the already wellestablished public transport system frees up public space, which to date has been primarily geared to the needs of car traffic. This allows for a more equitable urban layout that meets the needs of the citizens. New mobility options such as automated vehicles, perhaps in combination with sharing schemes, have the potential to help

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Fig. 2 Modal split 1993–2019. (City of Vienna, Statistics Vienna 2020)

further reduce the number of private motor vehicles in future. The City of Vienna will work with operators to ensure that such options are designed sustainably and safely for all road users. For all these considerations a close cooperation with Vienna’s neighboring local and regional authorities is essential in order to manage the traffic volumes crossing the city boundaries.

Buildings The combination of historic built fabric and numerous new buildings both shapes Vienna’s cityscape and underpins its unique atmosphere. Some 90% of the 170,000 or so buildings are used for housing. In view of the forecast population growth, at least 75,000 additional dwellings will be needed by 2030. A sufficient supply of affordable, high-quality housing thus has to be provided while simultaneously substantially reducing consumption of energy and resources and carbon emissions. This requires new buildings to be constructed to near-zero-energy standard, existing buildings to be fully reinsulated, and heating and energy systems to be gradually converted to non-fossil fuels. In terms of design, the future planning of buildings has to promote the use of eco-friendly building materials which are used as efficiently as possible and can be largely reused or recycled at the end of the building’s useful life. In line with the concept of a city of short distances, a good functional mix is striven for within neighborhoods and, where possible, also within buildings. The use of eco-friendly modes of transport can be supported by provision of attractive, easily accessible parking facilities for bikes, scooters, etc., either inside the building or close by. Further potential to make buildings more sustainable can be tapped by using building information modelling tools, which allow all parties involved in planning, operating and refurbishing a building to access comprehensive information across the entire life cycle of a building. Last but not least, a greater emphasis will be placed on protecting residents – particularly vulnerable groups such as socially isolated elderly people – from the heatwaves caused by the climate crisis. To this end, suitable installations to contain the threat of heatwaves are required, such as external sun blinds, shading, external water cooling, façade greening or rainwater circulation systems.

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Digitalization Digitalization and automation are major forces that drive the transformation of cities and societies. They permeate economic life, working life and community life and are transforming urban infrastructures. The City of Vienna strives to actively manage digitalization in all these spheres, providing up-to-date infrastructure, supporting stakeholders and keeping the municipal administration and its associated enterprises fit for the future. In doing so, the City of Vienna focuses on the rights and needs of everyone living in Vienna: Vienna does not view digitalization efforts as an end in themselves, but leverages the new technological possibilities to create equality of opportunity and an inclusive urban society. The municipal administration provides low-threshold access to digital information, public services and barrier-free participation and engagement for all social groups and puts special emphasis on social innovation processes. The basis for this is digital education and targeted training to equip everyone with the necessary digital skills, accompanied by special efforts to close the digital divide with regard to sex, age, ethnicity and people with special needs. At the same time, in the interests of equal opportunities and resilience the municipal administration will continue to provide services and information via non-digital channels. Over the course of the next decades, the City of Vienna will thus use digital data, tools and artificial intelligence in applications that help to conserve resources and maintain the city’s high quality of life, which will result in a modern, needs-based digital infrastructure benefitting Vienna’s citizens and its municipal administration. The data being mined will moreover be used to support decision-making, for realtime management of urban systems, and will be made available as open government data, especially for scientific, academic and educational use.

Economy and Employment A diverse economic structure, a well-trained workforce, a pronounced capacity for innovation, social calm and the maintenance and expansion of modern, fit-forpurpose infrastructure are prerequisites and conducive factors for a competitive and resilient metropolis. In Vienna, knowledge-based and technology-led services account for by far the largest share of regional added value. At the same time, the city boasts a highly productive industrial base. Conducive conditions and a supply of attractive sites and premises are to ensure the continued presence of both sectors. Unusually for a major city, Vienna also has a thriving agricultural sector within the municipal boundaries. This agricultural production is also to be safeguarded for the future and geared towards maximum resource efficiency and environmental sustainability. The opportunity for productive participation in the labor market is a decisive factor in terms of high quality of life. The growing city must provide low-threshold, non-discriminatory access to the labor market for all. As a location

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for smart businesses, Vienna itself becomes a driver of jobs the more the public and private sectors invest in eco-friendly technologies and services and the circular economy. Another factor essential to a city’s quality of life and economic attractiveness is the provision of public infrastructure and services which are affordable, of high quality, and available to all citizens and companies. Digitalization will help make these services still more easily and widely accessible. Vienna’s evolution into an environmentally and socially sustainable location will entail profound structural changes. The central guiding principle here is the transformation from the linear economic model to a circular economy, in which all stakeholders have to play their part. Public and private companies are required to develop new resource-efficient, circular processes, products, and business models. The municipal policy-makers and administration have to ensure transparent rules to create a reliable framework for business as well as providing incentives through subsidy schemes, collaborative partnerships and as a consumer. Last but not least, the forward-looking consumer behavior of Vienna’s citizens ultimately makes the transformation possible.

Water and Waste Management Ensuring the city has a secure supply of high-quality drinking water and reliable, environmentally sound disposal of all waste, waste water and sewage are key urban services and major factors in Vienna’s high quality of life, which is why the City of Vienna relies on self-sufficiency in these areas. With its spring water mains from protected mountain springs, Vienna has sufficient water supply capacity for the long term, even with ongoing population growth and the growing incidence of heatwaves due to climate change. Nevertheless, rainwater management measures are being intensified to benefit from sustainable urban water cycles. The extensive modern sewer network and the main sewage treatment plant ensure the eco-friendly disposal of municipal waste water and sewage. The high-quality standards in water management are guaranteed for the long term through consistent maintenance, refurbishment and needs-based expansion of infrastructure, coupled with optimum operational management. Waste management plays a key role in the transformation to a circular economy; therefore Vienna promotes waste prevention measures and aims to enable an increase in the proportion of waste recycled or reused as secondary raw material through its waste collection systems.

Environment Even today, climate change is already giving rise to an increasing number of very hot days (when the maximum temperature exceeds 30 °C) and very warm nights (when the temperature does not fall below 20 °C), which impair people’s health and quality

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Fig. 3 Temperature in °C; average temperatures were above the long-term average in eleven out of twelve months 2019. (City of Vienna, Statistics Vienna 2020)

Fig. 4 Land use in Vienna 2018. (City of Vienna, Statistics Vienna 2020)

of life and have a high cost to the economy (Fig. 3). Far-sighted planning and timely prevention and protective measures are therefore urgently required. Minimal environmental pollution and intact ecosystems are essential for healthy living conditions and a high quality of life in cities. Prevention and reduction of air, water and soil pollution and of heat and noise are thus central pillars of Smart City Wien, alongside the preservation and expansion of green spaces, soil functions and biodiversity and a healthy, sustainable diet and food production (Fig. 4). This can be supported by environmentally aware mobility habits and responsible consumption patterns. Close collaboration with the federal government and the EU is required here, especially with a view to meeting the environmental goals of the Smart City Wien Framework Strategy.

Healthcare Good health is seen by many as the most important commodity and is thus essential to individual well-being. The city’s health policy activities therefore focus on

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maintaining, promoting, improving and, where necessary, restoring the health of the Viennese population. The basis for this are Vienna’s Healthcare Goals, which is one of many sectoral programs that concretizes the Smart City Wien Framework Strategy and thus supports its implementation. Vienna’s Healthcare Goals cover living and working conditions as well as the urban environment. Key principles include equality of opportunity for all sectors of the population – including socially disadvantaged groups – and taking due account of the specific needs and health risks of women. A particular challenge in terms of health are the changed environmental conditions brought on by climate change, most notably the growing incidence of extreme weather events such as unusually long periods of hot weather, drought and torrential rainfall. The related changes in vegetation zones mean that allergenic plants and disease-carrying insects which were previously absent or rare in Vienna will become more widespread in the city and surrounding region. Furthermore, the impact of airborne pollutants is growing. A comprehensive package of measures for climate action and adaptation to climate change in all thematic fields of the Smart City Framework Strategy is therefore of prime importance if Vienna is to achieve its healthcare objectives, which include the prolongation of healthy life expectancy, the continued provision of high-quality medical care for everyone, as well as the promotion of active ageing and health literacy.

Social Inclusion Technological developments, digitalization and automation, and above all advancing climate change, affect everybody �