Carbon Emission Calculation Methods for Highway Tunnel Construction 9811653070, 9789811653070

Table of contents :
Preface
Contents
1 Introduction
1.1 Research Background and Significance
1.1.1 China’s Emission Reduction Targets and Actions
1.1.2 The Development Status of Highway Tunnel Industry in China
1.1.3 Research Backgrounds of the Tunnel Emission Reduction
1.2 Research Status and Review
1.2.1 Research Progress on Carbon Emissions from Tunnels
1.2.2 LCA Framework for the Carbon Emissions of Tunnels
1.2.3 Research Progress on LCA Uncertainty
1.3 Existing Problems
1.4 Main Research Content
References
2 Carbon Emission Quantification Theory and Modular LCA Method
2.1 Introduction
2.2 Basic Knowledge of Carbon Emissions
2.2.1 Greenhouse Gases
2.2.2 Carbon Exchange Pathway
2.3 Basic Methods for Quantifying Carbon Emissions
2.3.1 Direct Measurement Method
2.3.2 The Process Analysis Method
2.3.3 The Input–Output Method
2.3.4 The Hybrid Method
2.4 Overview of LCA
2.4.1 Objectives and Steps
2.4.2 Functional Unit and Reference Flow
2.4.3 Product System and Unit Process
2.4.4 System Boundary
2.4.5 Data Collection and Calculation
2.4.6 Environmental Effects and Assessment Methods
2.4.7 Main Features of LCA
2.5 Overview of Product Modularization
2.5.1 Concept of Modularization
2.5.2 Modular LCA Method
2.6 Conclusions
References
3 Carbon Emission Prediction Method for Tunnel Construction
3.1 Introduction
3.2 Method
3.2.1 Potential Factors Affecting Tunnel Construction Emissions
3.2.2 Overview of the Tunnels
3.2.3 Calculation Method for Tunnel Construction Emissions
3.2.4 Common Prediction Models
3.2.5 Data Analysis Method
3.3 Results
3.3.1 Carbon Emissions of Tunnel Construction
3.3.2 Factors Influencing Carbon Emissions from Tunnel Construction
3.3.3 Models Predicting Carbon Emissions from Tunnel Construction
3.4 Discussion
3.5 Conclusions
References
4 A Modular Calculation Method for the Carbon Emissions of Highway Tunnel Construction Based on the Chinese Standard Quota
4.1 Introduction
4.2 Methods and Data
4.2.1 Goal and Scope
4.2.2 Inventory Analysis
4.2.3 Sensitivity Analysis of Settings for Material Transportation, Collection, and Processing
4.3 Results and Discussion
4.3.1 Inventories of Input and Carbon Emissions for Primitives
4.3.2 Case Study
4.3.3 Sensitivity Analysis
4.4 Conclusion
References
5 Uncertainty Analysis of Carbon Emissions from Highway Tunnel Construction
5.1 Introduction
5.2 Uncertainty Analysis Method
5.2.1 Data Quality Indicator
5.2.2 Parameter Probability Distribution
5.2.3 Monte Carlo Simulation
5.2.4 Maximum Likelihood Estimation
5.2.5 Scenario Analysis
5.3 Uncertainty Analysis of Carbon Emissions of Unit Engineering Quantity
5.3.1 Parameter Value
5.3.2 Sample Size
5.3.3 Uncertainty Analysis of Primitives’ Carbon Emissions
5.3.4 Primitive Carbon Emission Fitting
5.4 Uncertainty Analysis of Carbon Emissions from Lining Construction
5.4.1 Parameter Value
5.4.2 Model Uncertainty Analysis
5.4.3 Parameter Uncertainty Analysis
5.4.4 Analysis of Scenario Uncertainty
5.5 Conclusion
References
6 Carbon Emission Transition of Highway Tunnel Construction
6.1 Introduction
6.2 Methods and Materials
6.2.1 Rock Mass Grades and Lining Designs
6.2.2 System Boundary
6.2.3 Inventory Data
6.2.4 Evaluation Indexes for Tunnel Emission Increment
6.3 Results and Discussion
6.3.1 Carbon Emission Evaluation
6.3.2 Transition Path Analysis of the Tunnel Construction Emission
6.4 Conclusion
References
7 Influence of Tunnel Lining Design Parameters on Construction Carbon Emissions
7.1 Introduction
7.2 Carbon Emission Variation Characteristics of Two-Lane Highway Tunnel Lining
7.2.1 Two-Lane Highway Tunnel Lining Design Specifications and Case Design Parameters
7.2.2 Typical Two-Lane Highway Tunnel Support Model
7.2.3 Effect of Change of Lining Design Parameters on Carbon Emissions of Two-Lane Tunnels
7.3 Changes in Carbon Emissions from Excavation and Support of Three-Lane Highway Tunnels
7.3.1 Carbon Emission Characteristics of Three-Lane Highway Tunnel Lining
7.3.2 A Calculation Model for Concrete Lining of a Three-Lane Tunnel
7.3.3 Influence of Changes in Lining Parameters of a Three-Lane Tunnel on Carbon Emissions
7.4 Discussion
7.5 Conclusion
References
8 Carbon Emission Characteristics of Inclined Shaft Construction in Highway Tunnel
8.1 Introduction
8.2 Project Profile
8.3 Calculation Method and Inventory Data
8.4 Carbon Emission from Inclined Shaft Construction
8.4.1 Energy Carbon Emissions from Various Materials
8.4.2 Carbon Emissions from Construction Process
8.5 Effect of Inclined Shaft Length and Slope on Carbon Emission from Excavation and Slagging
8.5.1 Influencing Factors and Inventory Data
8.5.2 Influence of Inclined Shaft Slope on Carbon Emissions of Construction Machinery in Excavation Process
8.5.3 Effect of Slope of Inclined Shaft on Carbon Emissions from Slagging
8.5.4 Influence of Inclined Shaft Length on Carbon Emissions from Excavation and Slagging
8.6 Conclusion
References

Citation preview

Chun Guo Jianfeng Xu

Carbon Emission Calculation Methods for Highway Tunnel Construction

Carbon Emission Calculation Methods for Highway Tunnel Construction

Chun Guo · Jianfeng Xu

Carbon Emission Calculation Methods for Highway Tunnel Construction

Chun Guo School of Civil Engineering Southwest Jiaotong University Chengdu, Sichuan, China

Jianfeng Xu State Grid Chengdu Power Supply Company Chengdu, Sichuan, China

ISBN 978-981-16-5307-0 ISBN 978-981-16-5308-7 (eBook) https://doi.org/10.1007/978-981-16-5308-7 Jointly published with Southwest Jiaotong University Press The print edition is not for sale in China Mainland. Customers from China Mainland please order the print book from: Southwest Jiaotong University Press. © Southwest Jiaotong University Press 2022 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of 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 publishers, 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 publishers nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publishers remain neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Preface

A tunnel is a transportation facility buried in the ground. Surrounding rock conditions are the critical basis for tunnel design and essential variables that affect carbon emissions from tunnel construction. The complex and changeable surrounding rock conditions require different design schemes, resulting in different tunnel construction emission levels. With surrounding rock conditions taken into consideration, this book is striving to establish accurate calculation models to analyze the carbon emission hotspots for tunnel construction and clarify differences in tunnel construction carbon emissions under different design parameters. Also, the tunnel’s emission reduction potentials under changes in design parameters and emission factors are explored, which can provide scientific theoretical support for low-carbon design and scientific emission reduction in China’s tunnel industry. This book is mainly available to the engineering and technical personnel in the construction industry, especially tunnel and underground engineering, involving design engineers, construction engineers, scholars, tunnel owners, management departments. This book consists of Introduction, Part I, and Part II. • Introduction (Chap. 1) covers the research background and significance of carbon emissions in the tunnel industry. This chapter systematically reviews the research progresses of tunnel carbon emissions, life cycle assessment (LCA) framework, and uncertainty research progress. • Chap. 2 through 5 focus on the carbon emission calculation method: – Chapter 2 introduces the concepts of greenhouse gases and emission calculation methods. – Chapter 3 explores the factors influencing carbon emissions from tunnel construction and determines the models predicting the carbon emissions. – Chapter 4 proposes a modular carbon emission calculation method for highway tunnel construction. – Chapter 5 analyzes the parameter uncertainty, model uncertainty, and scenario uncertainty of the carbon emissions from tunnel construction by the Monte Carlo method. v

vi

Preface

• Chap. 6 through 8 demonstrate the application of the carbon emission calculation method in exploring the emission characteristics and carbon emission reduction potential of highway tunnels. – Chapter 6 evaluates the effect of surrounding rock conditions on carbon emissions and ascertains the transition paths of carbon emissions. – Chapter 7 explores the relationship between tunnel design and carbon emissions and determines the marginal carbon emissions caused by the change of design parameters of tunnel lining. – Chapter 8 studies the emission characteristics of highway inclined shaft construction. Chengdu, China April 2021

Chun Guo

Acknowledgments This book is funded by the Thirteen Five National Key Research and Development Program of China (2019YFC0605104). We would like to express our gratitude to many people for their assistance in the preparation, translation, language editing, and typesetting of this book. Lu Yang, Huaquan Fu, and Guolin Zhang have done the language editing work. Yalin Guo, Kaitian Long, and Yixiang Wang have done considerable translation and typesetting work. Without their tremendous support and outstanding contribution, the completion of this book would have been impossible.

Contents

1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 Research Background and Significance . . . . . . . . . . . . . . . . . . . . . . . . 1.1.1 China’s Emission Reduction Targets and Actions . . . . . . . . . 1.1.2 The Development Status of Highway Tunnel Industry in China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1.3 Research Backgrounds of the Tunnel Emission Reduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Research Status and Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2.1 Research Progress on Carbon Emissions from Tunnels . . . . 1.2.2 LCA Framework for the Carbon Emissions of Tunnels . . . . 1.2.3 Research Progress on LCA Uncertainty . . . . . . . . . . . . . . . . . 1.3 Existing Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4 Main Research Content . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1 1 1

5 6 6 10 15 19 20 22

2 Carbon Emission Quantification Theory and Modular LCA Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Basic Knowledge of Carbon Emissions . . . . . . . . . . . . . . . . . . . . . . . . 2.2.1 Greenhouse Gases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.2 Carbon Exchange Pathway . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Basic Methods for Quantifying Carbon Emissions . . . . . . . . . . . . . . . 2.3.1 Direct Measurement Method . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.2 The Process Analysis Method . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.3 The Input–Output Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.4 The Hybrid Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4 Overview of LCA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.1 Objectives and Steps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.2 Functional Unit and Reference Flow . . . . . . . . . . . . . . . . . . . . 2.4.3 Product System and Unit Process . . . . . . . . . . . . . . . . . . . . . . . 2.4.4 System Boundary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.5 Data Collection and Calculation . . . . . . . . . . . . . . . . . . . . . . . .

29 29 29 29 30 30 30 31 32 39 41 42 42 43 44 44

3

vii

viii

Contents

2.4.6 Environmental Effects and Assessment Methods . . . . . . . . . . 2.4.7 Main Features of LCA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5 Overview of Product Modularization . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5.1 Concept of Modularization . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5.2 Modular LCA Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

45 45 46 46 47 52 52

3 Carbon Emission Prediction Method for Tunnel Construction . . . . . . 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.1 Potential Factors Affecting Tunnel Construction Emissions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.2 Overview of the Tunnels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.3 Calculation Method for Tunnel Construction Emissions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.4 Common Prediction Models . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.5 Data Analysis Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.1 Carbon Emissions of Tunnel Construction . . . . . . . . . . . . . . . 3.3.2 Factors Influencing Carbon Emissions from Tunnel Construction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.3 Models Predicting Carbon Emissions from Tunnel Construction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

55 55 56 56 57 58 61 63 65 65 70 74 76 79 79

4 A Modular Calculation Method for the Carbon Emissions of Highway Tunnel Construction Based on the Chinese Standard Quota . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 4.2 Methods and Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 4.2.1 Goal and Scope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 4.2.2 Inventory Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 4.2.3 Sensitivity Analysis of Settings for Material Transportation, Collection, and Processing . . . . . . . . . . . . . . . 91 4.3 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 4.3.1 Inventories of Input and Carbon Emissions for Primitives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 4.3.2 Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 4.3.3 Sensitivity Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 4.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104

Contents

5 Uncertainty Analysis of Carbon Emissions from Highway Tunnel Construction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Uncertainty Analysis Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.1 Data Quality Indicator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.2 Parameter Probability Distribution . . . . . . . . . . . . . . . . . . . . . . 5.2.3 Monte Carlo Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.4 Maximum Likelihood Estimation . . . . . . . . . . . . . . . . . . . . . . . 5.2.5 Scenario Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3 Uncertainty Analysis of Carbon Emissions of Unit Engineering Quantity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.1 Parameter Value . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.2 Sample Size . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.3 Uncertainty Analysis of Primitives’ Carbon Emissions . . . . 5.3.4 Primitive Carbon Emission Fitting . . . . . . . . . . . . . . . . . . . . . . 5.4 Uncertainty Analysis of Carbon Emissions from Lining Construction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4.1 Parameter Value . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4.2 Model Uncertainty Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4.3 Parameter Uncertainty Analysis . . . . . . . . . . . . . . . . . . . . . . . . 5.4.4 Analysis of Scenario Uncertainty . . . . . . . . . . . . . . . . . . . . . . . 5.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Carbon Emission Transition of Highway Tunnel Construction . . . . . . 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2 Methods and Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.1 Rock Mass Grades and Lining Designs . . . . . . . . . . . . . . . . . . 6.2.2 System Boundary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.3 Inventory Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.4 Evaluation Indexes for Tunnel Emission Increment . . . . . . . 6.3 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.1 Carbon Emission Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.2 Transition Path Analysis of the Tunnel Construction Emission . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Influence of Tunnel Lining Design Parameters on Construction Carbon Emissions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2 Carbon Emission Variation Characteristics of Two-Lane Highway Tunnel Lining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2.1 Two-Lane Highway Tunnel Lining Design Specifications and Case Design Parameters . . . . . . . . . . . . . . 7.2.2 Typical Two-Lane Highway Tunnel Support Model . . . . . . .

ix

107 107 108 109 112 114 117 117 118 119 122 127 134 142 148 150 152 154 158 159 165 165 166 166 167 172 175 176 177 179 183 183 185 185 186 186 187

x

Contents

7.2.3 Effect of Change of Lining Design Parameters on Carbon Emissions of Two-Lane Tunnels . . . . . . . . . . . . . . 7.3 Changes in Carbon Emissions from Excavation and Support of Three-Lane Highway Tunnels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3.1 Carbon Emission Characteristics of Three-Lane Highway Tunnel Lining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3.2 A Calculation Model for Concrete Lining of a Three-Lane Tunnel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3.3 Influence of Changes in Lining Parameters of a Three-Lane Tunnel on Carbon Emissions . . . . . . . . . . . . 7.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 Carbon Emission Characteristics of Inclined Shaft Construction in Highway Tunnel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2 Project Profile . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.3 Calculation Method and Inventory Data . . . . . . . . . . . . . . . . . . . . . . . . 8.4 Carbon Emission from Inclined Shaft Construction . . . . . . . . . . . . . . 8.4.1 Energy Carbon Emissions from Various Materials . . . . . . . . 8.4.2 Carbon Emissions from Construction Process . . . . . . . . . . . . 8.5 Effect of Inclined Shaft Length and Slope on Carbon Emission from Excavation and Slagging . . . . . . . . . . . . . . . . . . . . . . . 8.5.1 Influencing Factors and Inventory Data . . . . . . . . . . . . . . . . . . 8.5.2 Influence of Inclined Shaft Slope on Carbon Emissions of Construction Machinery in Excavation Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.5.3 Effect of Slope of Inclined Shaft on Carbon Emissions from Slagging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.5.4 Influence of Inclined Shaft Length on Carbon Emissions from Excavation and Slagging . . . . . . . . . . . . . . . . 8.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

198 203 203 206 212 218 219 221 223 223 223 224 225 225 225 230 230

230 232 233 234 234

Chapter 1

Introduction

1.1 Research Background and Significance 1.1.1 China’s Emission Reduction Targets and Actions With the large-scale utilization of natural resources and fossil energy by humans, the fossil energy buried in the ground is rapidly consumed. The carbon sequestered in solids enters the air environment with combustion activities, causing carbon dioxide concentration in the global atmosphere to increase dramatically. The large-scale use of fossil fuels is considered the main reason for the increase in global temperature [123]. In response to the impact of global warming on the ecological environment and human life, major countries worldwide have jointly signed the “Paris Agreement”, committed to controlling the global average temperature rise within 2 °C in this century. In 2009, China proposed that by 2020 carbon dioxide emissions per unit of GDP would be reduced by 40–45% compared to that of 2005; non-fossil energy would account for about 15% of primary energy consumption, and forest areas and forest stock would increase by 40 million ha and 1.3 billion m3 respectively compared with those in 2005. In 2015, the national independent contribution target was further proposed: carbon dioxide emissions would reach the peak around 2030 as soon as possible; carbon intensity would be reduced by 60–65% in comparison with that in 2005; the proportion of non-fossil energy in primary energy consumption would reach 20%; forest stock would increase by approximately 4.5 billion m3 compared with that in 2005. In 2020, the Chinese government pledged to strive for the peak of carbon dioxide emissions by 2030 and achieve carbon neutrality by 2060, which indicated China’s commitment for the road emission reduction. To achieve its emission reduction targets, China regards adaptation to the climate change as an essential national strategy to respond actively to the climate change. China has constantly strengthened meteorological disaster risk management and the construction of climate resilience infrastructure for farmland water conservancy and improved ecosystem services such as forests, grasslands, and wetlands to strengthen © Southwest Jiaotong University Press 2022 C. Guo and J. Xu, Carbon Emission Calculation Methods for Highway Tunnel Construction, https://doi.org/10.1007/978-981-16-5308-7_1

1

2

1 Introduction

Table 1.1 China’s action forecasts in reducing carbon emission intensity and developing non-fossil energy Items

2005–2020 2020–30 2030–40 2040–50

Average annual decline rate of CO2 intensity per unit of GDP (%)

3.9

4.4

6.3

9.2

Annual average new non-fossil energy installed power (GW)

41.5

62.8

9.6

90.1

Wind energy (GW)

13.9

23.0

31.0

35.0

Solar power (GW)

7.0

24.5

33.0

40.8

Nuclear power (GW)

3.4

9.0

9.3

10.5

Source INDC scenario results calculated by PECE model of China Climate Strategy Center and Renmin University of China

continuously adaptation actions and practices. Besides, China has taken several measures to bring together the majestic forces to achieve the goals of independent contribution: adjusting the industrial structure; optimizing the energy structure to save energy and resources; improving the efficiency of energy resource utilization; developing non-fossil power to restore the natural ecological environment; conserving forests to increase carbon; and developing the carbon market. Table 1.1 lists China’s action forecasts on energy conservation and emission reduction. Establishing a unified carbon emission trading market in China is an essential institutional innovation that controls and reduces greenhouse gas emissions and promotes the green and low-carbon economic development transformation with market mechanism. Since the launch of China’s carbon emissions trading market at the end of 2017, it has had a wide range of influences in the international community and attracted much attention. China’s first carbon emissions trading market was established in Shenzhen city. The carbon emissions trading system covers 40% of the city’s total carbon emissions. The cumulative trading volume of allowances is 18.07 million tons, and the cumulative trading volume is 596 million yuan. In 2016, the management and control scope expanded to 881 companies, which is currently one of the pilot regions with the largest number of companies, the most active transactions, and the most significant emission reduction effect. According to statistics, compared with the leading domestic groups of similar types in China, the carbon emission intensity of coal power in Shenzhen has dropped by 2.5%, while the gas power has fallen sharply by 8.9% and the overall carbon emission intensity of the power sector has decreased by about 10%. In the same period, the average carbon intensity of manufacturing enterprises controlled by the carbon emissions trading system, decreased with a percentage of 34.8%, from 0.43 tons of CO2 /10,000 CNY to 0.29 tons of CO2 /10,000 CNY. Through pilot projects, the rate of reduction in carbon emissions per unit of GDP and the rate of economic growth in China were increased constantly in Hubei

1.1 Research Background and Significance

3

Province. 90% of emission control companies have established functional departments such as carbon asset management to pledge loans to banks for more than 150 million yuan with carbon emission allowances. Besides, the pilot has also played a role in green poverty alleviation in poverty-stricken areas. The household biogas project group in the old district of Hong’an County was developed as a compensation mechanism to trade in the carbon market and gained a profit of 13 million yuan after passing the China Certified Emission Reduction (CCER) certification. In 2020, to promote the construction of China’s carbon emissions trading market, the Ministry of Ecology and Environment of the PRC issued the “Measures for the Administration of Carbon Emissions Trading (for Trial Implementation)” and “Measures for Administration of National Carbon Emissions Registration, Transaction and Settlement (for Trial Implementation)”, and solicited opinions publicly on the two documents. This was the first time that systematic rules had been issued at the national level since the launch of the national carbon emission trading market in 2017. Based on the documents, companies or other economic organizations with annual greenhouse gas emissions of 26,000 tons of carbon dioxide equivalent (CO2eq ) (i.e. the total energy consumption of about 10,000 tons of standard coal and above) were regarded as key emission units. In December 2020, the State Council of China issued the white paper “China’s Energy Development in the New Era.” The white paper shows that since 2012, China’s energy production and utilization methods have undergone significant changes and formed a multi-wheel-driven energy stable supply system, with an average annual growth of 2.8% in energy consumption supporting an average yearly growth of 7% in the national economy. Clean energy accounted for 23.4% of total energy consumption, increasing 8.9 percentage points from 2012. China’s cumulative installed capacity of hydropower, wind power, and solar power ranks first globally. The green development of energy has played an essential role in the reduction of carbon emission intensity. In 2019, China’s carbon emission intensity reduced by 48.1% compared with that in 2005 so that the target of reducing carbon emission intensity by 40–45% proposed in 2015 was achieved ahead of schedule.

1.1.2 The Development Status of Highway Tunnel Industry in China Since the beginning of the twenty-first century, China has successively issued the “National Highway Network Planning” and the subsequent “National Highway Network Planning (2013–2030)”. The implementation of the western development strategy and the “One Belt And One Road” initiative has brought a rare historical opportunity to construct China’s transportation facilities. According to the Ministry of Transport of China, between 2010 and 2015, China’s investment in transportation infrastructure totaled 13.4 trillion Yuan.

4

1 Introduction

Fig. 1.1 The mileage of highway tunnels in China from 2000 to 2018

With the continuous extension of China’s highway network to the western mountainous areas and the offshore deep-water areas, the total number and construction scale of highway tunnels continue to increase. Since 2011, the average annual growth of road tunnels in China has exceeded 1000 km. By the end of 2018, road tunnels’ total operating mileage has reached 17,236 km [59]. The road tunnel mileage growth in recent years is shown in Fig. 1.1. In China, the total length of 1058 extra-long highway tunnels and 4315 long highway tunnels are 4706.6 km and 7421.8 km respectively. Statistics from the International Tunneling Association and the Underground Space Association (ITA) indicate that China has already been a tunnel power with the largest number of tunnels, the biggest construction scale, and the fastest development speed for the length of under-construction tunnels in China accounting for about 50% of those globally in recent years. At present, China has fully mastered the complete set of technologies for highway tunnel construction, including drilling and blasting, shield tunneling, and immersed tube construction. Representative projects are the Qinling Zhongnanshan Tunnel (drilling and blasting method, 18.02 km) and the Shanghai Yangtze River Tunnel (shield method), the Hong Kong-Zhuhai-Macao Undersea Tunnel (immersed tube method), the Zhuhai Gongbei Tunnel (tube-curtain freezing method). World-class projects under construction include the deep-medium channel (immersed tube method) and the Xinjiang Tianshan Shengli Tunnel (drilling and blasting method, 22.035 km). In the past 40 years, China’s highway tunnels have gradually developed from single-hole 2-lane tunnels to double-hole 4-lane, 6-lane, and 8-lane tunnels. The world’s largest 8-lane road tunnel group has been built on the Southeast Second Ring Road of Jinan, Shandong, among which the Jiangshuiquan Tunnel (a total length of 3101 m) is the world’s longest double-hole 8-lane road tunnel. By 2020, the Hong Kong-Zhuhai-Macao Bridge subsea tunnel (6-lane, 5.6 km long) is the

1.1 Research Background and Significance

5

world’s longest highway immersed tube tunnel and the only deep-buried immersed tube tunnel, as well as China’s first offshore immersed tube tunnel. The submarine part is about 5664 m, composed of 33 huge immersed tubes and a final joint of a closed section, with the maximum installation depth exceeding 40 m.

1.1.3 Research Backgrounds of the Tunnel Emission Reduction Relevant studies have shown that tunnels are the transportation facilities with the highest material and energy density. The carbon emissions generated by tunnel construction is more important than those of ordinary buildings [80]. American scholars have measured the carbon emissions from the construction phase of the San Francisco-Anaheim section of the California high-speed railway and found that the tunnel only accounts for 15% of the length of the entire line, but the carbon emissions during the construction process account for 60% [21]. In the domestic cases in China, the 2.5-km long Chongqing Jinyunshan Highway Tunnel emitted 526,000 t CO2eq during the construction period, which shows that the carbon emissions during the tunnel construction period are considerable. With complex structure, numerous construction procedures and dozens of unit construction procedures, tunnels consume various upstream building materials and energy products to form a cross emission source. Therefore, a detailed list of building materials and energy consumption is the key to calculating carbon emissions for tunnels. Based on the historical data regression, statistics, and budget quota methods, tunnel inventory data in the tunnel design stage can be obtained. Among them, the widely use of budget quota method in China’s carbon emission calculations can help researchers to estimate effectively the material and mechanical input of the unit engineering quantity through a sophisticated budget system. However, the process of using the budget quota is cumbersome and involves many products and machinery. The greatest weakness is that the inventory data calculation process needs repeating in different tunnels, which consumes a lot of time and energy. Fast and efficient calculation and assessment methods for carbon emissions are still lacking in the current studies. A tunnel is a transportation facility buried in the ground. The surrounding rock conditions are critical for tunnel design and are essential variables affecting carbon emissions from tunnel construction. The complex and changeable surrounding rock conditions require different design schemes, resulting in different tunnel construction emission levels. Owing to China’s vast territory and varied geological conditions, the existing studies which analyze Chinese tunnels’ carbon emission levels based on single case are one-sided. Carbon emission calculations of tunnels need to be closely integrated with tunnel designs. With the surrounding rock conditions taken into consideration, accurate calculation models should be established to analyze the carbon emission hotspots for tunnel construction and clarify the differences in tunnel

6

1 Introduction

construction carbon emissions under different design parameters. Additionally, the tunnel’s emission reduction potential with the changes of the design parameters and the emission factors should be explored, which can provide scientific theoretical support for the low-carbon design and the scientific emission reduction in China’s tunnel industry.

1.2 Research Status and Review In the past five years, some studies have been carried out on the tunnel carbon emission levels, through which the calculation models of carbon emissions for tunnels were established and the key processes and emission sources affecting tunnel emissions were clarified. Life-cycle assessment (LCA), as an environmental management tool, is widely used in the environmental impact assessment of the entire life cycle of a product or facility. The calculation models of carbon emissions and the corresponding inventory data based on the LCA method have developed rapidly in recent years.

1.2.1 Research Progress on Carbon Emissions from Tunnels Greenhouse gases (GHG) include carbon dioxide (CO2 ), nitrous oxide (N2 O), methane (CH2 ), sulfur hexafluoride (SF6 ), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), etc. According to the contributions of different gases to the greenhouse effect, all GHG emission units are normalized to CO2eq . Carbon emissions of tunnels contain the direct carbon emissions and the indirect carbon emissions [16]. The former reports the emissions from the sources owned or controlled by the entity which cover the emissions from various types of fossil fuel combustion at the tunnel construction site; the latter reports the activity result of the entity and the emissions from the sources owned or controlled by another entity, specifically the carbon emissions from the upstream material production, the processing, transportation, and the use of purchased electricity and heat [117]. Few studies only consider carbon emissions from energy consumption [67, 69]; while most studies still full consider the indirect carbon emissions from the upstream building material production and transportation [41, 52, 53, 86, 115]. Resources and energy consumption are the driving force to produce building materials. Either a mechanical operation or a steel smelting consumes a lot of energy. Taking cement production as an example, each ton of cement produced will exert 891.71 kg of carbon dioxide, i.e., the emission factor of cement products is 891.71 kg/t [70]. The emission factors here include the direct emissions from the production of building materials and the indirect emissions from the upstream products. With the clarification of the input list of materials and energy in the tunnel construction, the consumption of various materials and energy is multiplied by the corresponding

1.2 Research Status and Review

7

emission factors and added up to obtain the tunnel’s overall carbon emission level. This calculation method based on the emission factors and the material energy input inventory is called the emission factor method [2, 116]. The existing cases indicate that the production and transportation of the building materials upstream of the tunnel contributed more than 60% of the tunnel construction’s carbon emissions. Figure 1.2a, b show the ratios of a tunnel construction process and material energy emissions respectively (Cement, electricity, and steel carbon emissions accounting for a significant proportion) [43]. Therefore, only considering the direct carbon emissions will underestimate severely the carbon emissions from tunnel construction. Existing tunnel carbon emission studies involve part or all of the carbon emissions of facilities during the production, transportation, construction, demolition, and operation phases of building materials. The emission calculation ranges of different studies are quite different, which reduces the contrast of different studies. Huang et al. [52, 53] analyzed the environmental impact of the tunnel drilling and blasting excavation process without considering the discharge of surrounding rock support and lining. Xu et al. [117] emphasized that the production of upstream building materials is an essential source of the tunnel’s overall carbon emission by analyzing the emission pathways of the tunnel materialization stage. Guo et al. [42] focused on the tunnel construction ventilation system and subdivided the sources of construction ventilation equipment and upstream building materials carbon emissions. Emissions during tunnel operation generally involve tunnel ventilation and lighting, while tunnel

Fig. 1.2 Carbon emission proportions of tunnel construction process and sources [43] a emission proportion of procedures, b emission proportion of materials and energy

8

1 Introduction

Fig. 1.3 Calculation boundary for carbon emissions during tunnel construction

maintenance refers to lining replacement and road maintenance. Some researchers have considered the carbon emissions generated by vehicle operation to calculate emissions during the operation period of road tunnels [22]. Figure 1.3 shows the calculation boundary of carbon emissions during tunnel construction. There are many studies on carbon emissions from highway tunnels, but few on those from railway tunnels. However, studies on carbon emissions from subway facilities, especially shield tunnels, show an increasing trend [99, 110]. Studies on subway shields mainly adopt the emission coefficient method, combined with the quota, to calculate the carbon emissions from shield tunnels. Xiao and Ma [114] used the emission coefficient method to study the emission level of the electrical energy consumption in shield tunnels without considering the carbon emissions from the production and transportation of building materials. Wang et al. [108] carried out research on carbon emissions during the materialization stage of shield tunnels in subway construction projects, which covered the production, transportation, and installation of building materials and prefabricated components within the carbon emission boundary based on the comprehensive consideration of the civil construction facilities and the corresponding supporting facilities in shield tunnels. Dong [31] calculated the carbon emissions from a shield tunnel with compiling quotas. Pi [88] established a carbon emission calculation model for shield tunnel construction using the life cycle method. Some exploratory work on carbon emission mechanisms in tunnel construction has been done by the authors’ team. Guo et al. [41] found that the emission levels of tunnels with different rock mass grades vary greatly by analyzing the influence of the surrounding rock grade and the pavement type on the carbon emissions from tunnels. Xu et al. [115] used the emission coefficient method to analyze five tunnels’ GHG emissions with different surrounding rock conditions. By comparing the same

1.2 Research Status and Review

9

construction process with other surrounding rock conditions, it is found that with the increase of the surrounding rock grade, GHG emissions from tunnel construction increase rapidly. The grade of the surrounding rock is an essential criterion for judging the quality of the surrounding rock. As the mechanical indicators of the surrounding rock deteriorate, the grade of the surrounding rock gradually increases. According to China’s surrounding rock classification standards, Grade III–V surrounding rock is between medium-quality and poor-quality, accounting for the highest proportion of tunnel surrounding rock. The case study shows that the carbon emissions from surrounding rock Grade V and IV are equivalent to 283 and 188% of that of Grade III. However, the increase in GHG emissions from tunnel construction with low surrounding rock quality under the same surrounding rock level is still considerable. The influence of surrounding rock conditions on emissions is significant. The essence of tunnel engineering is to excavate and support caverns in the ground. For tunnels excavated by drilling and blasting method, without adequate support after the excavation of the tunnel, the surrounding rock may be squeezed, deformed, or destroyed, which may cause the failure of the tunnel structure. Therefore, the tunnel designer will adopt strong support measures to maintain the stability of the tunnel structure. With the deterioration of surrounding rock conditions, the consumption of steel, cement, sand, and other materials has increased greatly, and emissions during the tunnel construction period have grown up dramatically. Therefore, great importance should be attached to the effect of surrounding rock conditions on tunnel construction emissions. The influence of surrounding rock conditions on GHG emissions is also reflected in the changes of tunnel construction procedures. At present, drilling and blasting method is widely used in highway tunnel constructions. Key tunnel construction processes in China include advanced support, excavation, rock support, molded lining, pavement, decoration, ventilation, and lighting. As shown in Table 1.2, with the weakening of surrounding rock conditions, cement grouting is added to the advanced supporting process and the amount of steel frames increases in the rock support processes. However, the thickness of concrete in the molding and lining process increases, and the concrete becomes reinforced concrete, thereby enhancing the supporting strength of the tunnel structure. The carbon emissions of surrounding rock support and molded lining have grown remarkably with the deterioration of surrounding rock conditions [115]. Study on energy saving and emission reduction of tunnel ventilation and lighting is abundant [39, 40, 82], while research on carbon emission reduction of tunnel lining structure is scarce. Due to the complicated tunnel construction process and varied geological conditions, the surrounding rock has a significant influence on the tunnel design and engineering volume. In theory, if all the structural design parameters of tunnels take the minimum value, the carbon emissions can be controlled to the lowest level. Since the tunnel structure’s safety is decided by the joint action of various supports, the support strength should be comprehensively considered according to the surrounding rock conditions. It is unclear how tunnel design parameters will affect the carbon emissions of tunnel linings, and low-carbon tunnel design lacks a useful

10

1 Introduction

Table 1.2 Construction procedures for different lining types [115] Procedure

Sub-procedure

T1

T2

T3

T4

T5

Grouting

–

–

–

–

x

Rock bolt

–

x

x

x

–

x

x

x

x

x

Steel frame

–

x

x

x

x

Rock bolt

x

x

x

x

x

Metal mesh

x

x

x

x

x

Shotcrete

x

x

x

x

x

Advance support

Tunnel excavation Rock support

Casting and lining Concrete arch wall

x

x

x

x

–

Concrete inverted arch

–

x

x

–

–

Reinforced concrete arch wall

–

–

–

–

x

Reinforced concrete inverted arch

–

–

–

–

x

x

x

x

x

x

Ventilation and lighting

Note T1 is of rock mass Grade III; T2, T3, T4 are of rock mass Grade IV; T5 is of rock mass Grade V

entry point. Focusing on case studies of two-lane tunnels, current carbon emissions calculations lack carbon emissions studies of large-section three-lane tunnels.

1.2.2 LCA Framework for the Carbon Emissions of Tunnels LCA consists of four parts: goal and scope, inventory analysis, impact assessment, and interpretation [5]. This section describes the technical framework of carbon emission research for tunnels, focusing on how to calculate carbon emissions for tunnels. The calculation boundary of carbon emissions for tunnels is clarified to obtain the inventory data of carbon emissions for tunnels. Finally, various emission values through the emission coefficient method are calculated and added up to gain the total value of carbon emissions.

1.2.2.1

Goal and Scope

The research goal and scope explain the reasons for carrying out this research and point out the direction for the applied research results [49]. The research scope includes the definitions of tunnel’s life cycle functional units, system boundaries, data requirements, assumptions, and constraints, etc. [33]. The tunnel’s life cycle involves

1.2 Research Status and Review

11

raw material mining, material processing, transportation, and use, maintenance, and abandonment. The functional unit is defined as a “quantified product system function used as a benchmark unit” [19]. The functional unit determines the research object, so all subsequent analysis and the input and output in the life cycle inventory analysis (LCI) are related to it. The goal scope and functional units vary in different studies, which is not conducive to a mutual comparison between various studies [73]. Table 1.3 lists the functional units of multiple studies, which often covers the production and transportation of building materials and energy. Guo et al. [41] used five functional unit settings to compare the effects of multiple pavement types and rock mass grades on tunnels’ GHG emissions, and reported multifunctional units in road LCA research [96, 109]. Loijos et al. used 12 different functional units to analyze 12 types of concrete pavements, making LCA’s conclusions more robust [76]. Carbon emissions from tunnel construction involve the direct emissions from the energy consumption of construction machinery and emissions from the production and transportation of the building materials and energy. The tunnel construction process contains advanced support, excavation, rock support, molded lining, waterproof and drainage, pavement, decoration, construction ventilation, and lighting. Materials or energy are input in each process, emitting GHG into the environment. Table 1.3 The goal scope and functional units of some tunnel LCA Country

References

Functional unit

C

O

M

T

W

Switzerland

Miliutenko et al. [80]

–

x

–

–

x

x

Switzerland

Miliutenko et al. [80]

–

x

x

x

x

x

Norway

Huang et al. [52]

Tunnel excavation per linear meter with drilling and blasting

x

–

–

x

x

Norway

Huang et al. [53]

A typical Norwegian tunnel in 100 years

x

x

x

x

x

Norway

Kalvå [61]

A road system simulating x annual average daily traffic over a 20-year time horizon

x

x

x

–

China

Guo et al. [41]

1-km highway tunnel in 100 years

x

x

x

x

x

China

Xu et al. [115]

Advance support, excavation, rock support, lining and ventilation and lighting of 1 km long highway tunnel

x

–

–

x

x

China

Guo et al. [42]

–

x

–

–

x

–

US

Ahn et al. [2]

–

x

x

–

x

–

Note C is the construction phase; O is the operation phase; M is the maintenance phase; T is the transportation; M is the maintenance phase; W is the waste treatment

12

1 Introduction

The system boundary also covers the choice of process. In terms of tunnel construction methods, there are huge differences in working procedures and material usage between the Norwegian Method of Tunneling (NMT) and New Austrian Tunneling Method (NATM). The NATM uses a composite lining in the tunnel support system, which often requires both shotcrete and molded concrete [84]. The NMT advocates the use of a single lining system, which will save a lot of cement and reinforcement by removing the molded concrete [9]. Basic assumptions and settings are essential for LCA research [62]. In establishing the LCA model, especially when carrying out the LCA of the tunnel facility in the early stage of the project, it is essential to adopt necessary assumptions to deal with data deficiency. There are many types of building materials and machinery involved in the construction of tunnels. The specific LCA often excludes some material inputs and energy inputs that exert little influence on the research conclusions to reduce data processing workload [8]. It’s necessary to evaluate the rationality of removing the unit process or material from the system boundary from the material quality, energy, and environmental importance. Miliutenko et al. [80] made some research on equipment preparation, control, and monitoring processes, excluding the materials with a mass percentage of less than 0.01% and the tiny GHG emissions generated by the life of tunnel construction crew. Waste treatment includes waste classification, transportation, storage, treatment, and final disposal [90]. The current tunnel LCA research is not comprehensive for waste treatment, only considering the solid waste rock produced by tunnel excavation. The waste rock is large in quantity and complex in composition, often containing mineral components. The construction party transports the blasted soil and rock outside the tunnel for centralized treatment and crushes some of the soil and rock with rock crushers. Building materials such as concrete aggregates, sand, and stone chips, and gravel are recycled and put into the tunnel construction. The remaining earthwork may be sold to generate more economic benefits.

1.2.2.2

Inventory Data

Inventory analysis is a quantitative analysis of resources, energy consumption, and environmental emissions during products’ life cycle, which consists of data collection, calculation, and quantification of each unit process’s input and output. In addition to the construction site’s engineering data, the system boundary also covers the emissions of upstream building materials during production and transportation. This part of the data is scattered or difficult to obtain, so emission factors are usually used to characterize the carbon emissions of building materials and fuels per unit mass or volume [97, 121]. The inventory data is divided into two parts: foreground data and background data. The foreground data refers to the consumption of the materials and energy, usually coming from bidding documents, design data, technical manuals, or statistical data from related organizations. There are three methods for obtaining foreground data in the current tunnel LCA studies.

1.2 Research Status and Review

13

The first method is based upon survey and design data and project budget quota data. The engineering quantity of each meter of the tunnel is obtained from the survey and design data, and the calculated data of materials and mechanical shifts per meter of tunnel construction is gained in accordance with Highway Engineering Budget Quota [103]. The budget quota is the quota adopted in the construction drawing stage, and the engineering quantity is calculated through the construction drawings and the engineering quantity calculation rule. The project budget quota covers labor, materials, and machinery, including road engineering, tunnel engineering, material collection, processing, and transportation. After obtaining mechanical shift data, the fuel consumption per shift of construction machinery and the fuel consumption of various machinery types are obtained through the Highway Engineering Machinery Shift Cost Quota. With the emission coefficient method, the total emissions of materials and energy sources during the tunnel’s life cycle are gained. The second method uses the empirical data in the construction bidding documents to provide fuel consumption data of various construction machinery through the database when specific data is missing [44]. Huang et al. [53] provided the life cycle inventory data of each meter of the tunnel, which are based on the experience data of tunnel construction in Norway from 2004 to 2011. For example, Norwegian University of Science and Technology used its own Tunsim database to evaluate the number of blasting materials during tunnel construction. Fuel and electricity consumption is another important sector of emissions from tunnels. Because the types of tunnel construction machinery are usually different, the types of tunnel construction machinery and material and fuel consumption parameters may be assumed through experience data. The fuel consumption and fuel types of various machinery types are obtained from the Tunsim database, related research, and material suppliers. The third method uses a simplified LCA model to evaluate tunnel facilities’ lifecycle emissions during the planning stage of the tunnel project. The LICCER model provides a simple model based on excel tools, which can determine the energy utilization and GHG emissions of various transportation facilities, including tunnels, in the life cycle [14]. The LICCER model tunnel is predefined according to the default consumption of earthwork, tunnel wall components, concrete tunnel entrances, tunnel vault lining, and pavement. The calculation of the tunnel’s earthwork and material usage comes from a simple regression function, which is derived from the data of different tunnel categories in Norway [15]. Based on the rock’s volume and quality obtained by blasting in the tunnel, the number of resources needed for the tunnel can be estimated. The background data originates from the carbon emission factors provided by various databases, government organizations, professional institutions, and related Refs. [29, 95, 119]. Commonly used LCA databases involve the United Nations Intergovernmental Panel on Climate Change (IPCC) database, Ecoinvent, China Life Cycle Database (CLCD), GaBi database, etc., as shown in Table 1.4. At present, there are many LCA databases, so suitable databases are selected for different research needs. The energy structure, emission levels, and production processes differ in other regions, so huge differences may exist in the same material’s emission factors in different areas. When carrying out LCA research, the local authoritative database

14

1 Introduction

Table 1.4 Existing life cycle database Country

Database

Boundary

EU

European platform on life cycle assessment

Europe

Sweden

SPINE CPM LCA database

World

Denmark

EDIP

Denmark

LCA food

Denmark

IVAM LCA data

Netherlands

Dutch input output

Netherlands

Franklin US LCI

US

Ecoinvent

World

Netherlands

Switzerland

BUWAL 250

Switzerland

Swiss agricultural life cycle assessment database

Switzerland

Germany

German network on life cycle inventory data

Germany

Thailand

Thailand LCI database project

Thailand

China

CLCD

China

ITRI database

Taiwan Province

Japan

Japan national LCA project

Japan

Australia

Australian life cycle inventory data project

Australia

Canada

Canadian raw materials database

Canada

US

US LCI database project

US

should be the first choice, which can better represent the region’s actual emission level to ensure the accuracy and comparability of the data. If it cannot meet the needs, databases from other regions may be alternatives. In addition to the above-mentioned professional databases, there are many studies on carbon emission factors in worldwide literature. Ju and Chen [60] and Zhang et al. [120] from Tongji University collected emission factors of different energy and building materials, respectively. Based on CLCD and IPCC, Gao [35] of Tsinghua University used input–output analysis to calculate the carbon emission factors of upstream products of construction projects. Notte et al. on the basis of the Italian regional GHG emission inventory, studied how to reduce the uncertainty of GHG emission estimation for road transportation and improved the GHG emission calculation accuracy by enhancing the quality of emission data [64]. Balaguer reviewed the environmental impact of traditional and alternative materials in road construction, pointing out that the most common materials are recycled asphalt, fly ash, and polymers [7]. Table 1.5 lists the carbon emission factors collected by the research team in previous studies.

1.2 Research Status and Review Table 1.5 Emission factors of various materials and energy

1.2.2.3

15 Materials and energy

Emission factor

Unit

Wood

146.3 [1]

kg CO2eq /m3

Steel

2.309 [72]

t CO2eq /t

High density polyethylene

2.850 [1]

t CO2eq t

Cement

0.702 [98]

t CO2eq /t

Explosive

0.263 [122]

t CO2eq /t

Alphalt

0.248 [111]

t CO2eq /t

Ceramics

1.400 [120]

t CO2eq /t

Sand

0.004 [68]

t CO2eq /t

Gravel

0.003 [68]

t CO2eq /t

Mine power

0.133 [118]

t CO2eq /t

Polypropylene

3.280 [118]

t CO2eq /t

Electricity

0.972 [41]

t/MWh

Gasoline

3.943 [70]

t CO2eq /t

Diesel

4.369 [70]

t CO2eq /t

Emission Coefficient Method

Three sources of carbon emissions from tunnel construction are: • Building materials. The production of building materials is mostly completed outside the construction site. Common materials are steel, cement, explosives, sand, gravel, water, asphalt, ceramic tiles, etc. • Transport machinery. The Mobile machinery used in tunnel construction refers to dump trucks, loaders, concrete mixer trucks, etc. • Construction machinery. For example, air compressors, concrete mixing plants, excavators, rock drilling rigs, concrete sprayers, AC welding machines, tunnel fans, etc. The total emissions of tunnel construction are calculated by adding up the carbon emissions from the material production, fuel consumption for transportation, and construction machinery, as shown in Table 1.6.

1.2.3 Research Progress on LCA Uncertainty Given that the tunnel structure is a complex entity assembled from a variety of building materials, its building materials and energy are provided by supply chains distributed around the world. Therefore, when LCA is used to analyze the environmental impact of tunnel construction, the research results inevitably show some uncertainty. As early as 1989, the U.S. Environmental Protection Agency [104] proposed the role and influence of uncertainty and variability in LCA modeling. In

16

1 Introduction

Table 1.6 Calculation equations for GHG emissions Source Material Em

Equation  E m = (e f i × m i ) i

Construction machinery Eo

Eo =



Et =



i—machinery type i; efi —emission factors of the fuel for machinery i; vi —fuel consumption in unit time for machinery i; ni —working time of machinery i

(e f i × νi × n i )

i—machinery type i; efi —emission factors of the fuel for machinery i; vi —fuel consumption in unit time for machinery; ni —working time of machinery i

i

Total

i—material type I; ef i —emission factor of material i; mi —consumption of material i

(e f i × νi × n i )

i

Transport machinery Et

Comment

E total = E m + E o + E t

1992, the American Society for Environmental Toxicology and Chemistry (SETAC) discussed the quality of LCA data at a seminar. After recognizing the importance of merging the uncertainty of LCA, the SETAC LCA Data Availability and Data Quality Working Group were established. In 2007, Lloyd and Ries [74] once again emphasized the importance of LCA uncertainty analysis and believed that the existing LCA research lacked the application of uncertainty modeling. Despite this, most LCA studies in the field of civil engineering still fail to assess the inherent uncertainty, and LCA uncertainty studies in the tunnel field are particularly rare.

1.2.3.1

LCA Sources of Uncertainty

Traditionally, LCA studies contain only a single specific value in the results. However, a single value without the range of uncertainty cannot represent the true environmental impact situation because there is uncertainty in every measurement. Therefore, uncertainty and variability are essential for LCA research, which will enhance the reliability and usefulness of LCA research results [54]. Otherwise, wrong or one-sided research results may appear, thereby misleading relevant decision-making [30]. There are two types of uncertainty. One is random uncertainty, which is an inherent characteristic of things, making researchers lack sufficient information about the true value of data [12]; the other is cognitive uncertainty, which stems from the researcher’s subjective choice [20]. Random uncertainty has always been the focus of LCA research, while few studies are concerned with cognitive uncertainty [36, 65]. The current academic circles have not yet formed a unified understanding of the source of LCA uncertainty. Huijbregts et al. [57] divided LCA uncertainty into parameter uncertainty, model uncertainty, and choice uncertainty, and divided LCA variability into geographic variation, time variation, and variation between objects

1.2 Research Status and Review

17

and sources. On this basis, Björklund [12] expanded the types and source classification of uncertainties, including (1) inaccurate data; (2) missing data; (3) lack of representative data; (4) model uncertainty; (5) choice uncertainty; (6) spatial variation; (7) time variation; (8) variation between object and source; (9) epistemological uncertainty; (10) error; (11) uncertainty estimation. The classification of the uncertainty sources mentioned above is relatively cumbersome. Lloyd and Ries [74] proposed a straightforward uncertainty classification method, which divided LCA uncertainty into three main types: • Parameter uncertainty. It refers to the input uncertainty in parameter values, containing the original data in the unit process or reference flow and influencing characterization factors, such as the fuel consumption of construction machinery. • Scenario uncertainty. It refers to models that generate emissions and characterizing factors, such as the next 20 years of China’s energy structure and tunnel construction technology. • Model uncertainty. For instance, the radiative forcing model of GHG is used to calculate GHG’s global warming potential in the future. Besides, scenario uncertainty should also be paid attention to, such as functional units, time horizons, geographic scales, natural background, allocation procedures, waste treatment options, environmental thresholds, and expected technology choices. According to Lloyd and Ries’s [74] 24 research statistics including LCA uncertainty analysis, the parameter uncertainty is more targeted than the model and scenario uncertainty. The measurement errors in the input data, the choice of system boundaries, potential assumptions, and the incompleteness of the model all contribute to the reliability and accuracy of the LCA results [26, 27]. In particular, the LCI phase and the life cycle impact assessment (LCIA) phase of LCA, both of which are dataand computationally intensive steps, involve many models and data assumptions that may lead to incorrect LCA results [55, 79].

1.2.3.2

LCA Uncertainty Analysis Method

Parameter and scenario uncertainties are the most considered factors in LCA uncertainty, which is usually combined with sensitivity analysis to explore each parameter’s impact or scenario on the result. However, in some cases, there seems to be some confusion between sensitivity and uncertainty analysis, which may be confused as synonyms [93]. Sensitivity analysis aims at the influence of input parameters on the final output result, while uncertainty analysis stresses the result’s variability. The mathematical theories and techniques currently used in the LCA literature to analyze the uncertainty of results are: • • • •

Possibility theory, as in Ref. [3]. Fuzzy theory, as in Refs. [11, 32, 46]. Taylor series expansion, as in Ref. [51]. Data Quality Index (DQI), as in Refs. [106, 113].

18

1 Introduction

• Expert judgment, as in Refs. [66, 101, 105]. • Practitioners’ beliefs, as in Ref. [10]. • Two or more technologies used in some studies, as in Refs. [28, 75]. The DQI method based on the qualitative evaluation of data quality has been widely used in LCA [63, 89, 107]. DQI methods can be divided into qualitative methods and semi-quantitative methods. Qualitative methods evaluate data quality based on qualitative descriptions, such as dividing data quality into three levels: good, fair, and poor. The qualitative method is a more subjective data quality assessment method, which is often used as an auxiliary method, and the more commonly used is the qualitative and semi-quantitative method [112]. The semi-quantitative method uses a numerical scoring system to process the assigned data quality scores to obtain a single data quality score based on probability distribution [6, 83]. Monte Carlo simulation is the most common among all stochastic modeling methods, which is the most widely used method when analyzing the uncertainty of different products, facilities, and industrial sectors [13, 18, 24, 25, 37, 50, 56, 81, 85, 100, 102]. Relying on a pre-defined probability distribution, Monte Carlo simulation runs the model enough times to analyze the simulation results. There are usually three steps: • Extract the distribution function of the original data, that is, the data on the intermediate flow and the reference flow of the unit process; • Create random samples based on the probability distribution of the original data; • Perform an iterative process and collect sample results. With the advancement of computer hardware and software, Monte Carlo simulation of large data sets has become feasible [77]. Many kinds of professional LCA software, such as SimaPro and OpenLCA, can now use Monte Carlo simulation to perform uncertainty analysis on sampled foreground and background LCI data. For example, the most widely used data source—Ecoinvent database contains 90% of unit process data [91].

1.2.3.3

Challenges Faced by LCA Uncertainty Analysis

The usefulness of Monte Carlo simulation hypothesizes that enough experiments have been conducted to clarify data distribution and uncertainty. However, stochastic modeling and uncertainty analysis may become too complicated for general researchers to accept due to some reasons. The specific reasons are summarized as follows: • The data volume is highly required [23, 48, 81, 92]; and there is no explicit agreement on the number of iterations needed, which increases the length (i.e., calculation time) and complexity of the calculation [74]; • Monte Carlo simulation methods and procedures are too complicated for some researchers and scholars [17].

1.2 Research Status and Review

19

In some studies, with the parameters’ uncertainty taken into account, an utterly dependent sampling method is adopted to carry out the Monte Carlo simulation. The running time of the simulation varies according to the number of unit processes included in the study and the computer’s computing power. Imbeault-Tetreault et al. [58] conducted a Monte Carlo simulation study on LCA cases, using nearly 900 unit processes that relied entirely on sampling, and the whole process took several hours. Henriksson et al. [47] performed 1000 times Monte Carlo simulations that entirely relied on sampling on the comparative LCA of Asian aquatic products, which also took a long time. The above researches show that Monte Carlo simulation’s computational cost increases substantially with the increase of model complexity [47]. While still maintaining the computational accuracy, some researchers are committed to significantly reduce the computational power required for Monte Carlo simulation [87] or compare different error propagation calculation methods, such as sampling techniques [45]. However, the above work did not help Monte Carlo simulation to be more widely adopted. In contrast, other methods such as sensitivity and scenario analysis [4, 38, 78] are more commonly used to quantify uncertainty. When using Monte Carlo simulation, the distribution shape of integrated LCI results is an essential factor affecting data storage. In the study of waste incinerators by Sonnemann et al. [101], the distribution of integrated LCI obtained by Monte Carlo simulation is approximately lognormal. Some studies suggest that the lognormal distribution may be suitable for LCA inventory data, impact assessment, and path analysis because it can avoid negative emissions and impacts [34]. Therefore, many LCA studies use the lognormal distribution to analyze the LCI results [45, 48, 58, 94]. In Ref. [71], Limpert et al. proved that the product of lognormally distributed data will generate lognormal distribution. However, LCA data can be presented as lognormal distribution and other distribution types, such as normal distribution and triangular distribution, whose distribution type of the product of its probability distribution cannot be determined analytically.

1.3 Existing Problems In general, researches on carbon emissions of tunnel engineering mostly are based on specific cases, in which certain stages in the building material production, transportation, construction, operation, and maintenance (or certain types of materials and energy) are selected to evaluate the carbon emission of a single tunnel or per linear meter. Due to the different functional units and system boundaries used in various studies, LCA studies’ results are difficult to compare, convert, or transplant [74, 104]. 20 important reason is that the tunnel is a non-standardized underground structure. Even if the lining construction activity per linear meter is used as the functional unit, the engineering quantity per linear meter still differs under different geological conditions, which makes the functional unit’s input–output calculation data unable to reuse, sharply increasing the workload of data collection and processing.

20

1 Introduction

Current researches mostly focus on engineering case calculations, in which the data of tunnel construction engineering volume are obtained from the designer or management department for subsequent carbon emission calculations without considering the relationship between tunnel design and emissions. The engineering volume data is only one of the tunnel design results and closely related to influencing factors such as lining design parameters, geological conditions, and construction technology. Since tunnels are transportation facilities constructed in the stratum, various design schemes are required under complex and changeable geological conditions. It is difficult to provide sufficient support for tunnel low-carbon design by depending on a limited number of carbon emission calculation cases. So it is necessary to conduct an in-depth exploration of the relationship between tunnel design and emissions, to clarify the laws and critical points of tunnel construction emission reduction at the design stage. Moreover, LCA researchers in tunnels and even civil engineering seldom study the uncertainty of calculation results. Some important uncertain factors, such as parameter uncertainties, model uncertainties, and scenario uncertainties, have not yet received full attention from experts and scholars, which makes the reliability and stability of existing LCA research results doubtful and is unable to ensure the conclusions representative and effective.

1.4 Main Research Content This book proposes a calculation path for carbon emissions based on unit engineering volume and a modular calculation method for carbon emissions from tunnel construction. It also analyzes the carbon emission characteristics of typical highway tunnel construction based on design specifications and actual cases. And then, it explores the prediction methods and models of carbon emissions from tunnel construction. Besides, it uses stochastic simulation modeling to analyze how the uncertainty of parameters, models, and scenarios impacts carbon emissions from tunnel construction. The emission transition paths of tunnel construction under different surrounding rock conditions are clarified. The influence law of lining design parameters on carbon emission are determined. The influence of the length and scope of the inclined shaft on carbon emission of slag transportation are analyzed. The main contents of each chapter are as follows: This chapter introduces the research background and significance of carbon emission calculation for tunnels. The system boundary, inventory data, and calculation method of carbon emission for tunnels are reviewed in detail from the research progress of carbon emissions and tunnel LCA research framework. The research hotspots, main progress, and shortcomings of carbon emissions research from tunnels are also comprehensively reviewed. Chapter 2: Research is carried out around the main methods of carbon emission quantification and the basic concepts of tunnel life cycle assessment, starting from the basic idea of carbon emissions, clarifying the concepts and theories of LCA

1.4 Main Research Content

21

and modular LCA. This chapter provides the basis for subsequent carbon emission calculation methods and research on emission reduction strategies. Chapter 3: To elucidate the mechanism of carbon emissions during the construction of highway tunnels, this chapter expands the relationship of the geological conditions and construction parameters with carbon emissions. 49 lining designs and geological conditions for eight real tunnels in southwestern China are studied. The LCA method is used to calculate the emissions from 49 tunnels. Relevant analyses are conducted to identify the key indicators affecting tunnel construction emissions. The linear regression models for carbon emissions from tunnel construction are established. Chapter 4: A modular calculation method of carbon emissions from tunnel construction is proposed based on existing technology methods. This method clarifies the data flow correspondence between different tunnel construction procedures and transportation and material processing. It can thoroughly and quickly call the input and emission data of the unit engineering volume in tunnel construction, avoiding double calculation of tunnel inventory data due to changes in engineering quantity. Chapter 5: Researches on carbon emissions from tunnel construction involve a lot of assumptions and data. The existing tunnel LCA research does not analyze the uncertainty of carbon emissions, which has a severe adverse effect on the research results’ credibility. With the full consideration of the uncertainty of parameters and scenarios, based on the method and principle of uncertainty analysis, this chapter analyzes the carbon emission range of the unit engineering volume for tunnel construction. It also considers the development scenario of future low-carbon tunnel lining design based on the engineering volume of the existing project case and the engineering volume calculation model, which further clarifies the uncertainty of carbon emissions per linear meter of tunnel construction. Chapter 6: In order to evaluate the effect of surrounding rock conditions on carbon emissions, this chapter expounds the relationship between tunnel designs and rock mass classifications. Besides, five different surrounding rock conditions and tunnel lining designs of a real tunnel in China are introduced in detail. LCA is used to analyze the carbon emissions in five tunnels with different surrounding rock. The tunnels with worse rock conditions generate more carbon emissions in construction. The importance of surrounding rock conditions to carbon emissions in tunnels is clarified. Based on the defined relative contribution indexes, transition paths of carbon emission are ascertained. Chapter 7: To realize the low-carbon design of tunnel construction, the relationship between tunnel design and carbon emissions is explored in this chapter. Based on the lining design specifications and engineering design cases, this chapter puts forward the typical design models of Chinese highway tunnels. The marginal carbon emissions caused by the change of design parameters of tunnel lining is proposed. Chapter 8: In order to promote energy saving and emission reduction of highway tunnel inclined shaft construction, the emission characteristics of highway inclined shaft construction are studied in this chapter. On grounds of the inclined shaft lining design of surrounding rock of Grade IV and V, combined with the Chinese standard quota system, the material energy inventory data for the construction of inclined

22

1 Introduction

shafts is clarified. Furthermore, the emission coefficient method is adopted to calculate the carbon emissions of different processes and materials and energy. The influence of the slope and length of the inclined shaft on the carbon emissions of excavation and slagging are analyzed.

References 1. 2006 IPCC guidelines for national greenhouse gas inventories (2006) Institute for Global Environmental Strategies, Hayama 2. Ahn C, Xie H, Lee SH et al (2010) Carbon footprints analysis for tunnel construction processes in the preplanning phase using collaborative simulation. In: Construction research congress 2010: innovation for reshaping construction practice—proceedings of the 2010 construction research congress 3. André JCS, Lopes DR (2012) On the use of possibility theory in uncertainty analysis of life cycle inventory. Int J Life Cycle Assess 17. https://doi.org/10.1007/s11367-011-0364-9 4. Andrianandraina H, Ventura A, Senga Kiesse T et al (2015) Sensitivity analysis of environmental process modeling in a life cycle context: a case study of hemp crop production. J Ind Ecol 19. https://doi.org/10.1111/jiec.12228 5. Azarijafari H, Yahia A, Ben Amor M (2016) Life cycle assessment of pavements: reviewing research challenges and opportunities. J Clean Prod 112 6. Baek CY, Tahara K, Park KH (2018) Parameter uncertainty analysis of the life cycle inventory database: application to greenhouse gas emissions from brown rice production in IDEA. Sustain 10. https://doi.org/10.3390/su10040922 7. Balaguera A, Carvajal GI, Albertí J, Fullana-i-Palmer P (2018) Life cycle assessment of road construction alternative materials: a literature review. Resour Conserv Recycl 132 8. Barandica JM, Fernández-Sánchez G, Berzosa Á et al (2013) Applying life cycle thinking to reduce greenhouse gas emissions from road projects. J Clean Prod 57. https://doi.org/10. 1016/j.jclepro.2013.05.036 9. Barton N (2017) Minimizing the use of concrete in tunnels and caverns: comparing NATM and NMT. Innov Infrastruct Solut 2. https://doi.org/10.1007/s41062-017-0071-x 10. Benetto E, Dujet C, Rousseaux P (2006) Possibility theory: a new approach to uncertainty analysis? Int J Life Cycle Assess 11. https://doi.org/10.1065/lca2005.06.212 11. Benetto E, Dujet C, Rousseaux P (2008) Integrating fuzzy multicriteria analysis and uncertainty evaluation in life cycle assessment. Environ Model Softw 23. https://doi.org/10.1016/ j.envsoft.2008.04.008 12. Björklund AE (2002) Survey of approaches to improve reliability in LCA. Int J Life Cycle Assess 7 13. Bojacá CR, Schrevens E (2010) Parameter uncertainty in LCA: stochastic sampling under correlation. Int J Life Cycle Assess 15. https://doi.org/10.1007/s11367-010-0150-0 14. Brattebø H, Birgisdotter H, Potting J et al (2013a) LICCER model technical report: account of technical backgrounds of the LICCER model. Report nr. 4.2. ed.: ERA-NET Road 15. Brattebø H, Birgisdotter H, Potting J et al (2013b) LICCER final report. ERA-NET Road, Stockholm 16. Buyle M, Braet J, Audenaert A (2013) Life cycle assessment in the construction sector: a review. Renew Sustain Energy Rev 26 17. Cambridge University Built Environment Sustainability (CUBES) (2016) Focus group on ‘risk and uncertainty in embodied carbon assessment’. Facilitator: Francesco Pomponi. Paper presented at Cambridge 18. Canter KG, Kennedy DJ, Montgomery DC et al (2002) Screening stochastic life cycle assessment inventory models. Int J Life Cycle Assess 7. https://doi.org/10.1007/BF02978906

References

23

19. Cass D, Mukherjee A (2011) Calculation of greenhouse gas emissions for highway construction operations by using a hybrid life-cycle assessment approach: case study for pavement operations. J Constr Eng Manag 137. https://doi.org/10.1061/(asce)co.1943-7862.0000349 20. Cellura M, Longo S, Mistretta M (2011) Sensitivity analysis to quantify uncertainty in life cycle assessment: the case study of an Italian tile. Renew Sustain Energy Rev 15 21. Chang B (2009) Initial greenhouse gas emissions from the construction of the California high speed rail infrastructure: a preliminary estimate. Dissertation, University of California 22. 陈灵均. 公路隧道交通碳排放特性与影响机制研究. 重庆: 重庆交通大学, 2017. Chen L (2017) Study on characteristics and influence mechanism of traffic carbon emission in highway tunnel. Dissertation, Chongqing Jiaotong University 23. Chevalier JL, Le Téno JF (1996) Life cycle analysis with ill-defined data and its application to building products. Int J Life Cycle Assess 1. https://doi.org/10.1007/BF02978652 24. Chou JS, Yeh KC (2015) Life cycle carbon dioxide emissions simulation and environmental cost analysis for building construction. J Clean Prod 101. https://doi.org/10.1016/j.jclepro. 2015.04.001 25. Ciroth A, Fleischer G, Steinbach J (2004) Uncertainty calculation in life cycle assessments. Int J Life Cycle Assess 9. https://doi.org/10.1007/bf02978597 26. Clavreul J, Guyonnet D, Christensen TH (2012) Quantifying uncertainty in LCA-modelling of waste management systems. Waste Manag 32. https://doi.org/10.1016/j.wasman.2012.07.008 27. Clavreul J, Guyonnet D, Tonini D, Christensen TH (2013) Stochastic and epistemic uncertainty propagation in LCA. Int J Life Cycle Assess 18. https://doi.org/10.1007/s11367-013-0572-6 28. Coulon R, Camobreco V, Teulon H, Besnainou J (1997) Data quality and uncertainty in LCI. Int J Life Cycle Assess 2. https://doi.org/10.1007/BF02978816 29. 崔鹏. 建筑物生命周期碳排放因子库构建及应用研究. 东南大学, 2015. Cui P (2015) Research on construction and application of building life cycle carbon emission factor database. Dissertation, Southeast University 30. Di Maria F, Micale C, Contini S (2016) A novel approach for uncertainty propagation applied to two different bio-waste management options. Int J Life Cycle Assess 21. https://doi.org/ 10.1007/s11367-016-1101-1 31. 董红明. 盾构施工碳排放定额编制方法硏究-从横琴新区马驢洲交通隧道工程为例. 广 州工业大学, 2016. Dong H (2016) A study on the compilation method of carbon emission quota for shield construction—a case study of the Maluzhou traffic tunnel project in Hengqin New District. Dissertation, Guangdong University of Technology 32. Egilmez G, Gumus S, Kucukvar M, Tatari O (2016) A fuzzy data envelopment analysis framework for dealing with uncertainty impacts of input-output life cycle assessment models on eco-efficiency assessment. J Clean Prod 129. https://doi.org/10.1016/j.jclepro.2016.03.111 33. Finkbeiner M, Inaba A, Tan RBH et al (2006) The new international standards for life cycle assessment: ISO 14040 and ISO 14044. Int J Life Cycle Assess 11 34. Frischknecht R, Jungbluth N, Althaus HJ et al (2005) The ecoinvent database: overview and methodological framework. Int J Life Cycle Assess 10 35. 高源雪. 建筑产品物化阶段碳足迹评价方法与实证研究. 北京: 清华大学, 2007. Gao Y (2007) Evaluation method and empirical research on carbon footprint of building products in the phase of materialization. Dissertation, Tsinghua University 36. Gavankar S, Suh S (2014) Fusion of conflicting information for improving representativeness of data used in LCAs. Int J Life Cycle Assess 19. https://doi.org/10.1007/s11367-013-0673-2 37. Geisler G, Hellweg S, Hungerbühler K (2005) Uncertainty analysis in life cycle assessment (LCA): case study on plant-protection products and implications for decision making. Int J Life Cycle Assess 38. Gregory JR, Noshadravan A, Olivetti EA, Kirchain RE (2016) A methodology for robust comparative life cycle assessments incorporating uncertainty. Environ Sci Technol 50. https:// doi.org/10.1021/acs.est.5b04969 39. Guo C, Wang M, Yang L et al (2016) A review of energy consumption and saving in extra-long tunnel operation ventilation in China. Renew Sustain Energy Rev 53

24

1 Introduction

40. Guo C, Xu J, Yang L et al (2017) Energy-saving network ventilation technology of extra-long tunnel in climate separation zone. Appl Sci 7. https://doi.org/10.3390/app7050454 41. Guo C, Xu J, Yang L et al (2019) Life cycle evaluation of greenhouse gas emissions of a highway tunnel: a case study in China. J Clean Prod 211. https://doi.org/10.1016/j.jclepro. 2018.11.249 42. 郭春, 郭雄, 徐建峰, 等. 隧道施工通风系统碳排放边界研究. 2016 中国隧道与地下工程 大会 (CTUC) 暨中国土木工程学会隧道及地下工程分会第十九届年会论文集. 成都: 现 代隧道技术, 2016. Guo C, Guo X, Xu J et al (2016) Study on the carbon emission boundary of ventilation system in tunnel construction. Paper presented at the proceedings for 2016 China tunnel and underground engineering conference (CTUC) and the 19th annual conference of the tunnel and underground engineering branch of the Chinese civil engineering society. Modern Tunnelling Technology, Chengdu 43. 郭春, 徐建峰, 张佳鹏. 隧道建设碳排放计算方法及预测模型. 隧道建设, 2020, 40(8): 140. Guo C, Xu J, Zhang J (2020) Calculation method and prediction model of carbon emission from tunnel construction. Tunnel Constr 40(8):140 44. Hammervold J (2014) Towards greener infrastructure. Dissertation, Norwegian University of Science and Technology 45. Heijungs R, Lenzen M (2014) Error propagation methods for LCA—a comparison. Int J Life Cycle Assess 19. https://doi.org/10.1007/s11367-014-0751-0 46. Heijungs R, Tan RR (2010) Rigorous proof of fuzzy error propagation with matrix-based LCI. Int J Life Cycle Assess 15. https://doi.org/10.1007/s11367-010-0229-7 47. Henriksson PJG, Rico A, Zhang W et al (2015) Comparison of Asian aquaculture products by use of statistically supported life cycle assessment. Environ Sci Technol 49. https://doi. org/10.1021/acs.est.5b04634 48. Hong J, Shaked S, Rosenbaum RK, Jolliet O (2010) Analytical uncertainty propagation in life cycle inventory and impact assessment: application to an automobile front panel. Int J Life Cycle Assess 15. https://doi.org/10.1007/s11367-010-0175-4 49. Hong J, Shen GQ, Feng Y et al (2015) Greenhouse gas emissions during the construction phase of a building: a case study in China. J Clean Prod 103. https://doi.org/10.1016/j.jclepro. 2014.11.023 50. Hong J, Shen GQ, Peng Y et al (2016) Uncertainty analysis for measuring greenhouse gas emissions in the building construction phase: a case study in China. J Clean Prod 129. https:// doi.org/10.1016/j.jclepro.2016.04.085 51. Hoxha E, Habert G, Chevalier J et al (2014) Method to analyse the contribution of material’s sensitivity in buildings’ environmental impact. J Clean Prod 66. https://doi.org/10.1016/j.jcl epro.2013.10.056 52. Huang L, Bohne RA, Bruland A et al (2015) Environmental impact of drill and blast tunnelling: life cycle assessment. J Clean Prod 86. https://doi.org/10.1016/j.jclepro.2014.08.083 53. Huang L, Bohne RA, Bruland A et al (2015) Life cycle assessment of Norwegian road tunnel. Int J Life Cycle Assess 20. https://doi.org/10.1007/s11367-014-0823-1 54. Huijbregts M (2002) Uncertainty and variability in environmental life-cycle assessment. Int J Life Cycle Assess 7 55. Huijbregts MAJ (1998) Application of uncertainty and variability in LCA. Int J Life Cycle Assess 3. https://doi.org/10.1007/bf02979835 56. Huijbregts MAJ (1998) Application of uncertainty and variability in LCA: part II: dealing with parameter uncertainty and uncertainty due to choices in life cycle assessment. Int J Life Cycle Assess 3. https://doi.org/10.1007/BF02979345 57. Huijbregts MAJ, Norris G, Bretz R et al (2001) Framework for modelling data uncertainty in life cycle inventories. Int J Life Cycle Assess 6. https://doi.org/10.1007/BF02978728 58. Imbeault-Tétreault H, Jolliet O, Deschênes L, Rosenbaum RK (2013) Analytical propagation of uncertainty in life cycle assessment using matrix formulation. J Ind Ecol 17. https://doi. org/10.1111/jiec.12001 59. 蒋树屏, 林志, 王少飞. 2018 年中国公路隧道发展. 隧道建设 (中英文), 2019 (7), 1217– 1220. Jiang S, Lin Z, Wang S (2019) China’s road tunnel development in 2018. Tunnel Constr 7:1217–1220

References

25

60. 鞠颖, 陈易. 建筑运营阶段的碳排放计算-基于碳排放因子的排放系数法研究. 四川建筑 科学研究, 2015, 41(3): 175–179. Ju Y, Chen Y (2015) Calculation of carbon emissions in building operation phase—research on emission coefficient method based on carbon emission factor. Sichuan Build Sci 41(3):175–179 61. Kalvå POF (2015) Life cycle assessment of the Byåsen tunnel in Trondheim, Norway— assessing emissions from traffic and infrastructure. Dissertation, Norwegian University of Science and Technology 62. Karlsson R, Carlson A (2010) Estimation of energy consumption and carbon dioxide emissions during construction, maintenance and operation of roads. J Leadersh Stud 1:23–26. https:// doi.org/10.1002/jls.20030 63. Kennedy DJ, Montgomery DC, Rollier DA, Keats JB (1997) Data quality: assessing input data uncertainty in life cycle assessment inventory models. Int J Life Cycle Assess 2. https:// doi.org/10.1007/BF02978420 64. La Notte A, Tonin S, Lucaroni G (2018) Assessing direct and indirect emissions of greenhouse gases in road transportation, taking into account the role of uncertainty in the emissions inventory. Environ Impact Assess Rev 69. https://doi.org/10.1016/j.eiar.2017.11.008 65. Laner D, Rechberger H, Astrup T (2014) Systematic evaluation of uncertainty in material flow analysis. J Ind Ecol 18. https://doi.org/10.1111/jiec.12143 66. Lasvaux S, Schiopu N, Habert G et al (2014) Influence of simplification of life cycle inventories on the accuracy of impact assessment: application to construction products. J Clean Prod 79. https://doi.org/10.1016/j.jclepro.2014.06.003 67. 李乔松, 白云, 李林. 盾构隧道建造阶段低碳化影响因子与措施研究. 现代隧道技术, 2015, 52(3), 1–7. https://doi.org/10.13807/j.cnki.mtt.2015.03.001. Li Q, Bai Y, Li L (2015) Research on influencing factors and measures of low carbonization in shield tunnel construction. Mod Tunnel Technol 52(3):1–7. https://doi.org/10.13807/j.cnki.mtt.2015.03.001 68. 李思堂, 李惠强. 住宅建筑施工初始能耗定量计算. 土木工程与管理学报, 2005, 22(4): 54. Li S, Li H (2005) Quantitative calculation of initial energy consumption for residential building construction. J Civ Eng Manage 22(4):54. https://doi.org/CNKI:SUN:WHCJ.0.2005-04-014 69. Li X, Liu J, Xu H et al (2011) Calculation of endogenous carbon dioxide emission during highway tunnel construction: a case study. In: ISWREP 2011—proceedings of 2011 international symposium on water resource and environmental protection 70. Li X, Ou X, Zhang X et al (2013) Life-cycle fossil energy consumption and greenhouse gas emission intensity of dominant secondary energy pathways of China in 2010. Energy 50. https://doi.org/10.1016/j.energy.2012.12.020 71. Limpert E, Stahel WA, Abbt M (2001) Log-normal distributions across the sciences: keys and clues. Bioscience 51 72. 刘宏强, 付建勋, 刘思雨, 等. 钢铁生产过程二氧化碳排放计算方法与实践. 钢铁, 2016, 51(4): 74–82. Liu H, Fu J, Liu S et al (2016) Calculation method and practice of carbon dioxide emission in steel production process. Steel 51(4):74–82 73. Liu Y, Wang Y, Li D (2017) Estimation and uncertainty analysis on carbon dioxide emissions from construction phase of real highway projects in China. J Clean Prod 144. https://doi.org/ 10.1016/j.jclepro.2017.01.015 74. Lloyd SM, Ries R (2007) Characterizing, propagating, and analyzing uncertainty in life-cycle assessment: a survey of quantitative approaches. J Ind Ecol 11. https://doi.org/10.1162/jiec. 2007.1136 75. Lo SC, Ma HW, Lo SL (2005) Quantifying and reducing uncertainty in life cycle assessment using the Bayesian Monte Carlo method. Sci Total Environ 340. https://doi.org/10.1016/j.sci totenv.2004.08.020 76. Loijos A, Santero N, Ochsendorf J (2013) Life cycle climate impacts of the US concrete pavement network. Resour Conserv Recycl 72. https://doi.org/10.1016/j.resconrec.2012. 12.014 77. Maronna R (2006) Random number generation and Monte Carlo methods. Stat Pap. https:// doi.org/10.1137/1.9781611970081.ch10

26

1 Introduction

78. Marvinney E, Kendall A, Brodt S (2015) Life cycle-based assessment of energy use and greenhouse gas emissions in almond production, part II: uncertainty analysis through sensitivity analysis and scenario testing. J Ind Ecol 19. https://doi.org/10.1111/jiec.12333 79. Mendoza Beltran A, Prado V, Font Vivanco D et al (2018) Quantified uncertainties in comparative life cycle assessment: what can be concluded? Environ Sci Technol 52. https://doi.org/ 10.1021/acs.est.7b06365 80. Miliutenko S, Åkerman J, Björklund A (2011) Energy use and greenhouse gas emissions during the life cycle stages of a road tunnel—the Swedish case norra länken. Eur J Transp Infrastruct Res 12. https://doi.org/10.18757/ejtir.2012.12.1.2948 81. Miller SA, Moysey S, Sharp B, Alfaro J (2013) A stochastic approach to model dynamic systems in life cycle assessment. J Ind Ecol 17. https://doi.org/10.1111/j.1530-9290.2012. 00531.x 82. Moretti L, Cantisani G, Di Mascio P (2016) Management of road tunnels: construction, maintenance and lighting costs. Tunn Undergr Space Technol 51. https://doi.org/10.1016/j. tust.2015.10.027 83. Muller S, Lesage P, Ciroth A et al (2016) The application of the pedigree approach to the distributions foreseen in ecoinvent v3. Int J Life Cycle Assess 21. https://doi.org/10.1007/s11 367-014-0759-5 84. Niedbalski Z, Małkowski P, Majcherczyk T (2018) Application of the NATM method in the road tunneling works in difficult geological conditions—the Carpathian flysch. Tunn Undergr Space Technol 74. https://doi.org/10.1016/j.tust.2018.01.003 85. Niero M, Pizzol M, Bruun HG, Thomsen M (2014) Comparative life cycle assessment of wastewater treatment in Denmark including sensitivity and uncertainty analysis. J Clean Prod 68. https://doi.org/10.1016/j.jclepro.2013.12.051 86. O’born RJ, Brattebø H, Iversen O MK et al (2016) Quantifying energy demand and greenhouse gas emissions of road infrastructure projects: an LCA case study of the Oslo Fjord crossing in Norway. Eur J Transp Infrastruct Res 16(3):445–466. https://doi.org/10.18757/ejtir.2016. 16.3.3152 87. Peters GP (2007) Efficient algorithms for life cycle assessment, input-output analysis, and Monte-Carlo analysis. Int J Life Cycle Assess 12. https://doi.org/10.1065/lca2006.06.254 88. 皮膺海. 盾构隧道施工碳排放测评研究. 南昌大学, 2016. Pi Y (2016) Research on carbon emission measurement and evaluation of shield tunnel construction. Dissertation, Nanchang University. https://doi.org/10.7666/d.D01055981 89. Pomponi F, D’Amico B, Moncaster AM (2017) A method to facilitate uncertainty analysis in lcas of buildings. Energies 10. https://doi.org/10.3390/en10040524 90. Powell JT, Townsend TG, Zimmerman JB (2016) Estimates of solid waste disposal rates and reduction targets for landfill gas emissions. Nat Clim Change 6. https://doi.org/10.1038/ncl imate2804 91. Qin Y, Suh S (2017) What distribution function do life cycle inventories follow? Int J Life Cycle Assess 22. https://doi.org/10.1007/s11367-016-1224-4 92. Reap J, Roman F, Duncan S, Bras B (2008) A survey of unresolved problems in life cycle assessment. Int J Life Cycle Assess 13. https://doi.org/10.1007/s11367-008-0009-9 93. Röder M, Whittaker C, Thornley P (2014) How certain are greenhouse gas reductions from bioenergy? Life cycle assessment and uncertainty analysis of wood pellet-to-electricity supply chains from forest residues. Biomass Bioenergy 79. https://doi.org/10.1016/j.biombioe.2015. 03.030 94. Rosenbaum RK, Pennington DW, Jolliet O (2004) An implemented approach for estimating uncertainties for toxicological impact characterisation. In: Complexity and integrated resources management, transactions of the 2nd biennial meeting of the international environmental modelling and software society, iEMSs 95. Santero N, Loijos A, Akbarian M, Ochsendorf J (2011) Methods, impacts, and opportunities in the concrete pavement life cycle 96. Santos J, Bryce J, Flintsch G et al (2015) A life cycle assessment of in-place recycling and conventional pavement construction and maintenance practices. Struct Infrastruct Eng 11. https://doi.org/10.1080/15732479.2014.945095

References

27

97. Seto KE, Panesar DK, Churchill CJ (2017) Criteria for the evaluation of life cycle assessment software packages and life cycle inventory data with application to concrete. Int J Life Cycle Assess 22. https://doi.org/10.1007/s11367-016-1060-6 98. 沈镭, 赵建安, 王礼茂, 等. 中国水泥生产过程碳排放因子测算与评估. 科学通报, 2016, 61(26): 2926. https://doi.org/10.1360/N972016-00037. Shen L, Zhao J, Wang L et al (2016) Calculation and evaluation of carbon emission factors in China’s cement production process. Chin Sci Bull 61(26):2929. https://doi.org/10.1360/N972016-00037 99. Sihabuddin S, Ariaratnam ST (2009) Quantification of carbon footprint on underground utility projects. In: Building a sustainable future—proceedings of the 2009 construction research congress 100. Sills DL, Paramita V, Franke MJ et al (2013) Quantitative uncertainty analysis of life cycle assessment for algal biofuel production. Environ Sci Technol 47. https://doi.org/10.1021/es3 029236 101. Sonnemann GW, Schuhmacher M, Castells F (2003) Uncertainty assessment by a Monte Carlo simulation in a life cycle inventory of electricity produced by a waste incinerator. J Clean Prod 11. https://doi.org/10.1016/S0959-6526(02)00028-8 102. Su X, Luo Z, Li Y, Huang C (2016) Life cycle inventory comparison of different building insulation materials and uncertainty analysis. J Clean Prod 112. https://doi.org/10.1016/j.jcl epro.2015.08.113 103. 交通公路工程定额站. JTGT B06-02-2007 公路工程预算定额. 北京: 人民交通出版社, 2007. Traffic and highway engineering quota station (2007) JTGT B06-02-2007 Highway engineering budget quota. China Communications Press, Beijing 104. United States Environmental Protection Agency (1989) Exposure factors handbook. Report EPA/600/8-89/043. United States Environmental Protection Agency, Washington, DC 105. Von Bahr B, Steen B (2004) Reducing epistemological uncertainty in life cycle inventory. J Clean Prod 12. https://doi.org/10.1016/S0959-6526(02)00197-X 106. Wang E, Shen Z (2013) A hybrid data quality indicator and statistical method for improving uncertainty analysis in LCA of complex system-application to the whole-building embodied energy analysis. J Clean Prod 43. https://doi.org/10.1016/j.jclepro.2012.12.010 107. Wang E, Shen Z, Neal J et al (2012) An AHP-weighted aggregated data quality indicator (AWADQI) approach for estimating embodied energy of building materials. Int J Life Cycle Assess 17. https://doi.org/10.1007/s11367-012-0417-8 108. 王幼松, 黄旭辉, 闫辉. 地铁盾构区间物化阶段碳排放计量分析. 土木工程与管理学报, 2019 (3): 12–18. Wang Y, Huang X, Yan H (2019) Measurement and analysis of carbon emission in physico-chemical stage of metro shield tunnel. J Civ Eng Manage 3:12–18. https://doi.org/CNKI:SUN:WHCJ.0.2019-03-003 109. Wang T, Lee IS, Kendall A et al (2012) Life cycle energy consumption and GHG emission from pavement rehabilitation with different rolling resistance. J Clean Prod 33. https://doi. org/10.1016/j.jclepro.2012.05.001 110. 王成武, 马振东. 建设工程施工碳排放定额估算法及应用展望 建筑经济 2016 (4), 59–61. Wang C, Ma Z (2016) Estimation method of carbon emission quota for construction project and its application prospect. Constr Econ 4:59–61. https://doi.org/10.14181/j.cnki.1002-851x. 201604059 111. Wang X, Duan Z, Wu L, Yang D (2015) Estimation of carbon dioxide emission in highway construction: a case study in southwest region of China. J Clean Prod 103. https://doi.org/10. 1016/j.jclepro.2014.10.030 112. Weidema BP (1998) Multi-user test of the data quality matrix for product life cycle inventory data. Int J Life Cycle Assess 3. https://doi.org/10.1007/BF02979832 113. Weidema BP, Wesnæs MS (1996) Data quality management for life cycle inventories—an example of using data quality indicators. J Clean Prod 4. https://doi.org/10.1016/S0959-652 6(96)00043-1 114. 肖时辉, 马振东. 建设工程施工碳排放计算方法在盾构施工中的应用. 建筑经济 2018 (1), 36–42. Xiao S, Ma Z (2018) Application of construction carbon emission calculation method in shield construction. Constr Econ 1:36–42

28

1 Introduction

115. Xu J, Guo C, Chen X et al (2019) Emission transition of greenhouse gases with the surrounding rock weakened—a case study of tunnel construction. J Clean Prod 209. https://doi.org/10. 1016/j.jclepro.2018.10.224 116. Xu J, Guo C, Yu L (2019) Factors influencing and methods of predicting greenhouse gas emissions from highway tunnel construction in southwestern China. J Clean Prod 229. https:// doi.org/10.1016/j.jclepro.2019.04.260 117. 徐建峰, 郭春, 郭雄, 等. 隧道物化阶段碳排放计算模型研究. 2016 中国隧道与地下工程 大会 (CTUC) 暨中国土木工程学会隧道及地下工程分会第十九届年会论文集. 成都: 现 代隧道技术, 2016. Xu J, Guo C, Guo X (2016) Research on calculation model of carbon emission in tunnel materialization stage. Paper presented at the proceedings for 2016 China tunnel and underground engineering conference (CTUC) and the 19th annual conference of the tunnel and underground engineering branch of the Chinese civil engineering society. Modern Tunnelling Technology, Chengdu 118. 俞海勇, 曾杰, 胡晓珍, 等. 基于 LCA 的化学建材生产碳排放量研究分析. 化工新型材料, 2015 (2), 218. Yu H, Zeng J, Hu X et al (2015) Research and analysis of carbon emissions from chemical building materials production based on LCA. New Chem Mater 2:218 119. 张春霞, 章蓓蓓, 黄有亮, 等. 建筑物能源碳排放因子选择方法研究. 建筑经济, 2010 (10): 108–111. Zhang C, Zhang P, Huang Y et al (2010) Research on the selection method of building energy carbon emission factors. Constr Econ 10:108–111 120. 张涛, 姜裕华, 黄有亮, 等. 建筑中常用的能源与材料的碳排放因子. 中国建设信息, 2010 (23): 58–59. Zhang T, Jiang Y, Huang Y et al (2010) Carbon emission factors of energy and materials commonly used in buildings. Inf China Constr 23:58–59 121. Zhang YR, Wu WJ, Wang YF (2016) Bridge life cycle assessment with data uncertainty. Int J Life Cycle Assess 21. https://doi.org/10.1007/s11367-016-1035-7 122. 张振芳. 露天煤矿碳排放量核算及碳减排途径研究. 北京: 中国矿业大学, 2013. Zhang Z (2013) Research on carbon emissions accounting and carbon emission reduction approaches in open-pit coal mines. Dissertation, China University of Mining and Technology 123. Zimov SA, Schuur EAG, Stuart Chapin F (2006) Permafrost and the global carbon budget. Science 80:312

Chapter 2

Carbon Emission Quantification Theory and Modular LCA Method Chun Guo

2.1 Introduction With the ever-increasing greenhouse effect, the issue of carbon emissions has received extensive attention globally. This chapter starts with the basic concepts and exchange channels of GHG emissions, focuses on the main methods of quantifying GHG emissions and the basic concepts of LCA, and introduces the concept of product modularity and the modular LCA method. Moreover, it provides the basis for the subsequent study of carbon emission calculation methods and emission reduction strategies for tunnels.

2.2 Basic Knowledge of Carbon Emissions 2.2.1 Greenhouse Gases The so-called “carbon emissions” is a collective term for the emissions of all greenhouse gases, not just carbon dioxide emissions. According to the Montreal Protocol, GHGs are mainly divided into the following six categories: • • • • • •

Carbon dioxide (CO2 ); Methane (CH4 ); Nitrous oxide (N2 O); Hydrofluorocarbons (HFCs), such as CHF3 ; Perfluorocarbon (PFCS), such as CF4 , Cn F2n+2 ; Sulfur hexafluoride (SF6 ), nitrogen fluoride (NF3 ), halogenated ether, etc.

Given the significant differences in the ability to cause the greenhouse effect among various GHGs, when quantifying the overall effect, researchers generally use the equivalent conversion method to evaluate the equivalent calculation values. Since CO2 is the most common GHG with the largest emission amount, its equivalent © Southwest Jiaotong University Press 2022 C. Guo and J. Xu, Carbon Emission Calculation Methods for Highway Tunnel Construction, https://doi.org/10.1007/978-981-16-5308-7_2

29

30

C. Guo

emission amount is taken as the standard for the measurement of GHGs emissions, which is referred to as “Carbon emissions” and expressed as “CO2eq ”. GHG emissions can be converted based on global warming potential (GWP) and global temperature potential (GTP), which are separately related to cumulative radiation intensity and temperature reflection at a specific time. In the studies of carbon emissions during the life cycles of buildings, CO2 , CH4 , and N2 O are usually the research focuses. And the corresponding conversion coefficients can be determined according to the IPCC AR5 report. Normally, a carbon emissions research has a research base period of 100 years, so the corresponding conversion coefficient is either CO2 : CH4 : N2 O = 1: 28: 265 (GWP) or 1: 4: 234 (GTP). Considering the particularity of emission sources, the effectiveness of data, and the cumulative effects, other GHGs are usually ignored. It should also be noted that apart from the six categories listed above, water vapor (H2 O) and ozone (O3 ) can also cause the greenhouse effect. However, due to their rapid temporal and spatial distribution changes, it’s not easy to describe them, so they are generally not used as control items.

2.2.2 Carbon Exchange Pathway In human production activities, the pathways of carbon exchange are complex and diverse, but in general, they can be summarized into the following aspects: • Carbon emissions from the combustion of fossil fuels (such as coal, oil, and natural gas); • Carbon emissions from industrial production activities, such as fossil energy mining, limestone calcination and decomposition, etc.; • Carbon emissions or biological carbon sequestration from agricultural activities, such as grain planting and nitrogen fertilizer application, intestinal fermentation, manure, and the growth of living tree reserves, etc.; • Carbon emissions generated during landfill, composting, or incineration; • Carbon reduction achieved indirectly through replacing traditional fossil energy with clean energy such as biomass; • Carbon storage realized through carbon dioxide capture and storage technology.

2.3 Basic Methods for Quantifying Carbon Emissions 2.3.1 Direct Measurement Method The direct measurement method is the most basic carbon emission quantification method, which refers to directly monitoring carbon emission sources by using standard measurement tools and experimental methods to obtain corresponding data.

2 Carbon Emission Quantification Theory and Modular LCA Method

31

Theoretically, its result can be reliable by representing the actual carbon emission level. However, due to many constraints in terms of monitoring conditions, measuring instruments, and cost input, the direct measurement method is challenged to be widely used in general carbon emission analysis. This method can be mainly used for regional time-based CO2 concentration monitoring from a macro level, which, at the microlevel, can be mainly used to measure the carbon emission coefficient during specific production processes, such as the combustion of fossil energy and the chemical reaction process of carbon-containing compounds. The carbon emission coefficient of resource and energy obtained by the direct measurement method is the primary data for the quantitative analysis of carbon emissions, directly affecting the accuracy of other quantitative method. Therefore, it is of great significance to improve the measurement accuracy of the direct measurement method through technical means.

2.3.2 The Process Analysis Method The process analysis method quantifies carbon emissions based on the activity data of carbon emission sources and the emission coefficients of the corresponding processes. Its basic concept can be expressed as Eq. 2.1. Specifically, the process analysis method is to split specific production procedure according to the process flow. The carbon emissions of each production process are expressed by the product of the measured carbon emission coefficient and the corresponding activity data. Subsequently, the total carbon emissions (E) of the whole procedure can be calculated based on the sum of carbon emissions of each process. E=



(ε × Q)

(2.1)

where, ε and Q are the emission coefficient and activity data of each production process, respectively. It should be noted that due to the materials or energy sources that interact in the process flow, the above formula will generate a cycle calculation. Suh and Huppes [11] took steel production as an example. Steel production needs to use steam as its heat source, and the steam production, in turn, consumes steel. In other words, during this cyclical process of steel production, steam is consumed directly and steel itself indirectly. For such cases, it is more accurate and convenient to use the coefficient matrix to calculate. Firstly, define the technology matrix A˜ = a˜ i j . a˜ i j represents the amount of the its product consumed or produced by process j. And, the relationship between each process’s duration and the net production volume is shown in Eq. 2.2. A˜ · x˜ = y˜

(2.2)

vector Q˜ net =  the next step is to define the product’s net output column  Then,   ˜  Q net, j  and row vector of carbon emission coefficient ε˜ = ε˜ j , where ε˜ j represents

32

C. Guo

the carbon emissions of the jth process per unit time. Supposing the technology matrix A˜ is a non-singular matrix, the total carbon emissions can be expressed as Eq. 2.3. E = ε˜ · A˜ −1 · Q˜ net

(2.3)

This process analyzing method above is based on the carbon emission coefficient, usually called the emission coefficient method, which has been put into a wide range of application in carbon emission quantification because of its simplicity of the concept, its convenience in calculation, and its function of detailed process splitting and analyzing carbon emissions. It should also be noted that during the carbon emission process splitting, due to the constraints of objective conditions and calculation costs, it is inevitably necessary to ignore certain secondary processes. As a result, the definition of the system boundary of the calculation will be incomplete, which brings truncation errors to the result of the process analysis [7, 15]. For example, in calculating the carbon emissions from cement production, this method would consider ore mining, raw material calcination, grinding and other processes based on the measured emission coefficients of energy use and limestone decomposition. However, it is challenging to include carbon emissions from upstream processes, such as plant construction and equipment loss, resulting in calculation errors.

2.3.3 The Input–Output Method 2.3.3.1

Basic Concepts

Leontief proposed the input–output method in 1970. The input–output table is established based on the idealized quantity model of “input = output”, which is aimed to comprehensively studies the quantitative dependence relationship between various national economy sectors and various production processes. The input–output method satisfies the following basic assumptions: • Pure sector assumption: Each industrial sector only produces a specific homogeneous product, has a single input structure, and adopts a single production technique. • The stability assumption: The direct consumption coefficient is fixed during the tabulation period, ignoring the impact of production technology progress and labor efficiency improvement. • The proportionality assumption: Sectoral input and output are in a positive relationship, namely, as output increases, all kinds of consumption (inputs) required increase proportionally. In recent years, through the introduction of energy or environmental flows into the input–output model, this method can be applied to industry-level energy and environmental problem analysis. The input–output analysis method considers the

2 Carbon Emission Quantification Theory and Modular LCA Method

33

production links between various sectors according to the input–output table, thus can capture carbon emission flow of the entire production chain and avoid the truncation error caused in the method of process analysis [5]. However, limited by the “pure sector assumption” and the number of sector divisions, this method can only estimate the carbon emissions of specific production processes with the average level among sectors [8], bringing a rough analysis result for micro issues. For example, in China’s input–output table, all plastic product production is attributed to the “plastic products industry”, making it impossible to conduct independent research on various plastic products’ carbon emissions.

2.3.3.2

Basic Theory

A typical economic input–output table is shown in Table 2.1. In the table, X ij represents the amount of sector i products directly consumed in the production of sector j products. The value-form input–output table shows a row balanced relationship “intermediate product + final product = total product”, expressed as Eq. 2.4. n 

X i j + Yi = X i (i = 1, 2, . . . , n)

(2.4)

j=1

Simultaneously, there is a column balanced relationship “intermediate investment + initial investment = total investment”, as shown in Eq. 2.5. n 

X i j + N j = X j ( j = 1, 2, . . . , n)

(2.5)

i=1

ai j represents the direct consumption of the product of sector i in each unit product production of sector j, as shown in Eq. 2.6. Table 2.1 Economic input–output table Input

Intermediate demand (Xij )

Final product (Y)

Total product (X)

X1n

Y1

X1

X2n

Y2

X2

…

…

…

…

…

Xnn

Yn

Xn

N2

…

Nn

–

–

X2

…

Xn

–

–

Sector 1

Sector 2

…

Sector n

Sector 1

X11

X12

…

Sector 2

X21

X22

…

…

…

…

Sector 3

Xn1

Xn2

Sector 4

N1

Sector 5

X1

34

C. Guo

ai j = X i j / X j (i, j = 1, 2, . . . , n)

(2.6)

  ai j , 0 ≤ ai j < Define value-form direct consumption coefficient matrix A = n ai j < 1. Substitute the matrix into the row balance relation (Eq. 2.4), and 1, i=1 organize it into a matrix form expressed as Eq. 2.7. X = (I − A)−1 · Y

(2.7)

where, X and Y are the total product and final product column vector, respectively; (I − A)−1 is the Leontief inverse matrix (L), also known as the total demand coefficient matrix.

2.3.3.3

Carbon Emission Input–Output Model

The carbon emission input–output analysis is based on the value-form input–output model. The carbon emission coefficient matrix is introduced to study the flow of carbon emissions accompanying economic activities. The input–output analysis of carbon emissions needs to meet the general assumptions of the traditional input– output model. Moreover, it is believed that the carbon emission coefficients of sectoral products are relatively stable. That is, during a certain period of research, the carbon emissions of producing unit sector products are averaged and relatively constant. The basic structure of the carbon emission input–output table is shown in Table 2.2. The direct carbon emissions Di corresponding to the total products of sector i can be calculated as Eq. 2.8. Di =

m1  

m2     EC pi · f p + E Nqi · f q

p=1

(2.8)

q=1

Table 2.2 Carbon emission input and output table Input Economic investment

Carbon emissions input

Sector

Intermediate demand Sector 1

Sector 2

…

Sector n

Final product

Total product

Sector 1

X11

X12

…

X1n

Y1

X1

Sector 2

X21

X22

…

X2n

Y2

X2

…

…

…

…

…

…

…

Sector n

Xn1

Xn2

…

Xnn

Yn

Xn

Sector 1

d11

d12

…

d1n

F1

D1

Sector 2

d21

d22

…

d2n

F2

D2

…

…

…

…

…

…

…

Sector n

dn1

dn2

…

dnn

Fn

Dn

2 Carbon Emission Quantification Theory and Modular LCA Method

35

where, m1 and m2 are the number of energy and non-energy carbon emission types in sector i, respectively, ECpi is the consumption of pth energy in sector I, ENqi is the total amount of the qth industrial production process, fp is the carbon emission intensity of the pth energy, fq is the carbon emission intensity of the qth industrial production process. From the row balance relationship of the carbon emission input–output table, we can get Eq. 2.9. n 

di j + Fi = Di (i = 1, 2, . . . , n)

(2.9)

j=1

where, Di , Fi , and dij can be expressed as Eqs. 2.10–2.12, respectively. Di = εi · X i

(2.10)

Fi = εi · Yi

(2.11)

di j = X i j εi = ai j · D j /ε j · εi = φi j · D j

(2.12)

If we define the diagonalmatrix  as εˆ = diag[ε1 , ε2 , . . . , εn ] and the dimensionless coefficient matrix as  = φi j n×n , by substituting the above formula into the row balance relationship, we can get Eq. 2.13. D = (I − φ)−1 · εˆ · Y = G · Y

(2.13)

The formula above clearly describes the relationship between the sector’s direct carbon emissions and the final products, where, the coefficient matrix G is called the coefficient matrix of total carbon emission demand, representing the carbon emission flow between sectors in the production process; Element gij represents the implicit carbon emissions produced by sector i to obtain the final product of sector j. Besides, the coefficient matrix  can be expressed as  = εˆ · A · εˆ −1 , then (I − )−1 = (I − εˆ · A · εˆ −1 )−1 = εˆ · L · εˆ −1 , so the coefficient matrix G is expressed as Eq. 2.14. G = (I − )−1 · εˆ = εˆ · L

(2.14)

For further calculation, if C j represents the total carbon emission demand (implicit carbon emission) of the final product of sector j, it is shown in Eq. 2.15. Cj =

n  i=1

(εi · L i j Y j ) =

n  i=1

gi j Y j

(2.15)

36

C. Guo

Correspondingly, the implicit carbon emission intensity of the products of sector j can be expressed as Eq. 2.16.   Cj = (εi · L i j ) = gi j Yj i=1 i=1 n

ςj =

n

(2.16)

Alternatively, if ε = [ε1 , ε2 , . . . , εn ] and ς = [ς1 , ς2 , . . . , ςn ] represents direct carbon emission and implicit carbon emission intensity, respectively, and the above relationship can be rewritten into the following matrix form as shown in Eq. 2.17. ς =ε·L

(2.17)

Therefore, according to the economic value Costj of the sector’s products and the implicit carbon emission intensity ς j in the above formula, the corresponding input–output carbon emissions EIO is shown in Eq. 2.18. E IO, j = ς j · Cost j

2.3.3.4

(2.18)

Impact of Interregional Product Flow

Considering the flow of products between regions, the carbon attribution of imported and exported products directly affects regional carbon emission measurement values and emission reduction strategies. At present, three ways are adopted to deal with the carbon attribution of imported and exported products, namely: • Attribution Mode 1: the exporter is responsible [2, 9]; • Attribution Mode 2: the importer is responsible for the carbon emissions according to the actual amount [10, 12]; • Attribution Mode 3: the corresponding importer is responsible for the carbon emissions according to the replacement amount, which, refers to the carbon emissions that would occur supposing the importer produces the product [1]. The following subscript “D” indicates the local products, and the subscript “I” indicates the imported products, and then, the relationship between regional products is shown in Fig. 2.1. According to the product composition relationship between the regions shown in Fig. 2.1, we can obtain the balance formula represented by the following vector for local products A D · X D + Y D = X D , namely: X D = (I − A D )−1 · Y D = L D · Y D

(2.19)

For imported product A I · X D + Y I = X I , by introducing the above formula, we get Eq. 2.20.

2 Carbon Emission Quantification Theory and Modular LCA Method

37

Fig. 2.1 Product composition relationship between regions

X I = A I · L D · YD + YI

(2.20)

To facilitate the analysis, the final product column    the authors diagonalize vectors, that is, Yˆ D = diag Y D,1 , Y D,2 , . . . , Y D,n , YˆC = diag YC,1 , YC,2 , . . . , YC,n ,     Yˆ F = diag Y F,1 , Y F,2, . . . , Y F,n , Yˆ E = diag Y E,1 , Y E,2 , . . . , Y E,n , Yˆ I = diag Y I,1 , Y I,2 , . . . , Y I,n . According to the basic definitions of the three models, it is easy to derive the attribution relationship of carbon emissions as shown in Table 2.3. It should be noted that ς1 in Mode 3 should be replaced by ς D . With the row vectors ςnom and C R representing the nominal implicit carbon emission intensity and the carbon emission attribution amount, then ςnom is expressed as Eq. 2.21. ςnom = C R · Yˆ R−1

(2.21)

Table 2.3 The attribution relationship of carbon emissions in three modes Row vector of total carbon emissions (Ctotal ) Carbon emissions input of local product (CD )

Splitting of carbon emissions For consumption ε D · L D · YˆC For accumulation ε D · L D · Yˆ F

Carbon emissions input of imported product (C1 )

Carbon attribution row vector (C R ) Mode 1 √

Mode 2 √

Mode 3 √

√

√

√

× √

× √

√

√

×

×

For export ε D · L D · Yˆ E For direct use ς I · Yˆ I

√

For intermediate input

ς I · A I · L D · YˆC + Yˆ F

×

For processing trade ς I · A I · L D · Yˆ E

×

×

38

C. Guo

where, the local consumption of the final product Yˆ R is equal to the total sum of YˆC , Yˆ F , and Yˆ I . A new variable κ E(I ) is introduced and expressed as Eq. 2.22. κ E(I ) = Yˆ E(I ) · Yˆ R−1

(2.22)

Attribution mode 1 does not consider import implicit carbon emissions, so we get:

C R,1 = ε D · L D · YˆC + Yˆ F + Yˆ E = C D

(2.23)

ςnom,1 = ε D · L D · (I + κ E − κ I )

(2.24)

Attribution mode 2 considers the implicit carbon emissions of imported products for intermediate input and final use, so:

C R,2 = (ε D + ς I · A I ) · L D · YˆC + Yˆ F + ς I · Yˆ I

(2.25)

ςnom,2 = (ε D + ς I · A I ) · L D · (I − κ I ) + ς I · κ I

(2.26)

For attribution mode 3, we move ς I · κ I in mode 2 to the left, and replace ς I with ς D , and then we get:   ςnom,3 = ε D + ςnom,3 · A I · L D

(2.27)

Combine items containing ςnom,3 and simplify the formula as: ςnom,3 = ς D = ε D · L

(2.28)

Based on the above quantitative relationship and formula derivation, a couple of general conclusions can be drawn as follows: • Mode 1 is effective and accurate in accounting in the regional actual total carbon emissions, but certain regions can achieve “carbon transference” (that is, increasing κ I or decreasing L D ) by transferring high-carbon emitting products or setting up production plants in other regions to avoid the responsibility of reducing emissions, which is not conducive to implementing the carbon tax mechanism and fulfilling the emission reduction targets in various regions. In extreme cases, it can be seen from Eq. 2.24 that if all products in a particular area are imported, then we get ςnom = 0. On the contrary, if all the products are exported, then we get ςnom → ∞. • Mode 2 is effective and accurate when calculating the actual total carbon emissions of the entire system, in which, ςnom is the actual implicit carbon emissions of local consumer goods. However, when the carbon emission level of products in

2 Carbon Emission Quantification Theory and Modular LCA Method

39

the region is higher than that of the imported products (i.e., ς I < ςnom ), the region will tend to reduce its own emission reduction responsibilities through importing products. In this sense, ςnom does not effectively reflect the actual local production level. • Mode 3 is inaccurate in accounting the actual total carbon emissions either within the area or the entire system. However, the indicators of product carbon emission intensity are consistent with the regional actual production levels. Among the above ones, Mode 1 is more suitable for calculating the regional actual carbon emissions to assess the environmental quality, while Mode 2 is more suitable for accounting and controlling the entire system’s actual carbon emissions to balance the amount of carbon transference between regions and the environmental carrying capacity, and Mode 3 is more suitable as an objective reflection of each region’s actual production level and correspondingly serves as a reference for formulating emission reduction strategies. It is worth noting that in Mode 3, ςnom can be determined by using existing data, whereas in Modes 1 and 2, the calculations turn out to be much more complicated considering that the ςnom is related to the input ratio of imported products and the corresponding producer emission factors.

2.3.4 The Hybrid Method The process analysis method can be used for a detailed evaluation of the specific carbon emission process. The results obtained are relatively more accurate, and it is more convenient to update the database. However, there is usually a truncation error due to its limited system boundary. The input–output analysis method uses economic value and input–output tables to calculate, and enjoys a better system boundary at the macro level. However, the accuracy of the result aimed at a specific carbon emission process is compromised to some extents. Combining the advantages of the two methods, researchers have widely used the hybrid method in quantifying carbon emissions in recent years [3]. According to the different composition structures of the hybrid method, it can fall into three types: the tiered hybrid method (TH), the hybrid method based on input–output analysis (IOH), and the integrated hybrid method (IH) [11].

2.3.4.1

TH Method

The TH method uses process analysis to study the primary production or application processes and uses input–output data to estimate the carbon emissions in other processes. And the sum of the two shows the total amount of carbon emissions. The fundamental concept of the TH method can be expressed in the following algebra, matrix, or block matrix form Eqs. 2.29, 2.30 and 2.31.

40

C. Guo

ET H =

 ε p · Q p + ς I O,t − ς I O, p · Costt



E T H = ε˜ · A˜ −1 · Q˜ + ε · (I − A)−1 · Q˜ I O  ET H =

ε˜ 0 0ε



A˜ 0 0 (I − A)

−1 

Q˜ ˜ QIO

(2.29) (2.30)

(2.31)

Here, ς I O,t , ς I O, p are respectively the carbon emission intensities of the overall and process p calculated by the input–output method, E T H is the amount of carbon emissions calculated according to the TH method, Q p is the material quantity of the main production process p, Q˜ is the material quantity of the main process represented by the vector, Q˜ I O is the value of the other processes expressed in vectors. The TH method extends the system boundary of the original process analysis method to a certain extent. It can be obtained according to the linear superposition result of the process analysis method and the input–output analysis method. Therefore, it has a clear concept and requires a comparatively smaller calculation amount, serving as the most commonly used hybrid analysis method. It should be noted that the TH method is supposed to exclude the internal connection between the process analysis system and the input–output analysis system, which is prone to problems such as unclear boundary division and double counting.

2.3.4.2

The IOH Method

The IOH method uses more detailed sectoral production data and consumption data to split the industrial sectors in the input–output table to improve the accuracy of the calculation results. For example, according to calculation needs, sector j is divided into r sub-sectors according to the ratio of energy consumption to output, and then the original nth order direct consumption coefficient matrix An×n is extended to the (n + r − 1)-order coefficient matrix A . −1  E  = ε  · I − A · Q˜ I O

(2.32)

Restricted by the carbon emission input–output table’s structure, the abovementioned departmental adjustments are generally considered only in the production process analysis, and the application and disposal processes are still calculated according to the process analysis method. Therefore, this type of hybrid method can be summarized in the following matrix or block matrix form as shown in Eqs. 2.33 and 2.24.  −1 · Q˜ I O E I O H = ε˜ · A˜ −1 · Q˜ + ε · I − A

(2.33)

2 Carbon Emission Quantification Theory and Modular LCA Method

 EI OH =

ε˜ 0 0 ε



A˜  0  0 I − A

−1 

Q˜ ˜ Q I O

41

(2.34)

Based on detailed product and environmental flow data, the IOH method provides more detailed data compared to the original input–output method through sectoral dismantling. However, the calculation amount has increased significantly, and part of the process analysis results are mixed, making it challenging to clarify the internal relationship and redundancy of the analysis system. Besides, because the departmental dismantling is highly dependent on the level of detail and accuracy of the additional flow data, it is difficult to evaluate the reliability of the calculation results of this method without sufficient data or known accuracy.

2.3.4.3

The IH Method

The IH method was proposed by Suh and Huppes [11]. This method adopts the process analysis method to calculate the overall carbon emissions and uses the input–output method for additional and downstream analysis. The upstream  upstream    C and the downstream truncation error matrix = truncation error matrix C u u,i j   Cd = Cd,i j  were defined. Where, Cu,i j represents the value quantity of the input– output product i ignored in the process analysis of product j in unit time. Cd,i j represents the process analysis material quantity of product i that is ignored in the input–output analysis of product j in unit time. The process analysis and the input– output analysis models are integrated according to the block matrix, then Eq. 2.35 is obtained. 

EI H

ε˜ 0 = 0ε



A˜ Cd Cu (I − A)

−1 

Q˜ 0

(2.35)

Comparing the three methods, the TH method and the IOH method supplement the system boundary through external additional carbon emissions from Q˜ I O and partial Q˜ I O . In contrast, the IH method forms a unified technology matrix for carbon emission quantification by integrating the coefficient matrix of process analysis and input– output analysis, which, has more complete internal structure and system boundaries but higher calculation cost, variety of additional assumptions and more difficult operation.

2.4 Overview of LCA Life Cycle Assessment (LCA) is a means to evaluate the overall environmental impact of a product or a type of facility from “cradle to grave”, and to examine the problem from the perspective of regional, national, and even global scope and

42

C. Guo

with the perspective of sustainable development. As early as 1969, the Coca-Cola Company used LCA for the first time to analyze the environmental impact of product packaging to enhance its recyclability, portability, durability and economy. With the improvement of its connotation and the development of methodology, LCA has been applied to various industries apart from the manufacturing one. To facilitate the development of LCA in various industries, the International Organization for Standardization (ISO) has successively issued ISO 14000 and ISO 14044 series of standards, namely the principles and framework of life cycle assessment and the requirements and criteria of life cycle assessment. To make the technical connotation of LCA better understand, the basic concepts of LCA will be introduced as follows.

2.4.1 Objectives and Steps Different goals of the product life cycle analysis: 1. 2. 3. 4. 5.

to acquire environmental information and integrate ecological knowledge; to determine the optimization potential of environmental performance; to reduce the use of materials and the impact on the environment; to create sustainable products; to combine economic and environmental advantages. LCA is divided into four steps according to the ISO 14040/14044 standard:

• Definition of objectives, system boundaries, and scope: Which products should be analyzed and compared under certain conditions and what are these conditions specifically? • Life cycle inventory: What material and energy flow is there in the life cycle of the product? • Impact assessment: How should the impact of the life cycle inventory results on the environment be assessed? • Interpretation: How should the results of the life cycle inventory and impact its assessment be interpreted?

2.4.2 Functional Unit and Reference Flow The functional unit is the basis for quantifying the input and output of specific functions of LCA products. When comparing different systems with the same function, the functional unit ensures that the comparison is based on a common foundation. To achieve specific functions, different systems need to consume a certain number of products. To take the sand screening of tunnel waste slag as an example, manual screening and mechanical screening, as two systems, should take the same number of screening heaps as their common functional unit, and the corresponding reference

2 Carbon Emission Quantification Theory and Modular LCA Method

43

flow is the number of manual working days and mechanical shift required for sand screening of the same heaps.

2.4.3 Product System and Unit Process LCA treats the life cycle of a product as a specific product system. The primary performance of this system depends on its function. Figure 2.2 shows an LCA example of a product system, where there are multiple unit processes that are interrelated, with

Fig. 2.2 Product system example

Fig. 2.3 Product system unit process example

44

C. Guo

intermediate product flow and to-be-treated waste flow as their media. Figure 2.3 shows an example of a product system unit process. With various purposes of research, LCA sets the unit process boundaries with different detailed levels.

2.4.4 System Boundary The system boundary defines the unit process which can be incorporated into the system. Ideally, the input and output of the product system model boundary should be elementary streams. In real conditions, some inputs and outputs have slight influence on the overall research results, so that some unit processes can be deleted. In general, the following life cycle stages, unit processes, and flows should be included in the research boundary: 1. 2. 3. 4. 5. 6. 7.

Material acquisition; Input and output in the main process of manufacturing and processing; Distribution and transportation; Production and consumption of fuel, electricity, and heat; Disposal of process waste and products; Use and maintenance of products; Product recycling after use.

2.4.5 Data Collection and Calculation The inventory analysis includes data collection and calculation to quantify the input and output of the system. In each unit process, the data could be categorized in terms of the following items: • • • •

Energy input, raw material input, auxiliary input, and other physical input; Products, symbiotic products, and waste; Emissions to air, water, and soil; Other environmental factors. After data collecting, the calculation steps include:

1. 2. 3.

Validating the data; Identifying the correlation between the data and the unit process; Determining the correlation between the data and the reference flow of the functional unit. According to each unit process and its functional unit, the result of the inventory is obtained.

2 Carbon Emission Quantification Theory and Modular LCA Method

45

2.4.6 Environmental Effects and Assessment Methods Generally, it is necessary to assess all the related potentially harmful effects on soil, air, and water with the consideration of the use of resources and materials on the one hand and the emission of pollutants on the other. The ISO 14044 standard describes a great variety of possible environmental impacts, among which the most famous are: • • • •

Global warming potential/carbon footprint (GWP) Acidification potential (AP) Potential eutrophication (EP) Human toxicity.

One of the big challenges for LCA is to summarize various environmental impacts (such as the greenhouse effect or eutrophication). Different assessment methods, have differences in their procedures, such as weights. Some methods only consider selected environmental areas, such as accumulated energy demand or carbon footprint. Other fully aggregated methods, such as ecological indicators, combine various environmental impacts into one key figure. Among them, the most well-known methods are: • • • • • • •

CML2001; Accumulated energy needs; Ecological indicators 99; Ecological footprint; Ecological scarcity approaches; Impact World+; Recipe 2016.

The decisive factor in determining the appropriate impact categories and assessment methods should always be the product being analyzed and the individual facts.

2.4.7 Main Features of LCA According to specific research purposes and scope, LCA conducts a systematic evaluation the product system’s environmental factors and impacts. LCA is a relative method based on functional units, and all subsequent researches, the input and output in LCI, and the results of LCIA correspond to functional units. Therefore, there is no unified model for LCA. Although LCA focuses on the environmental impacts, it does not provide absolute or precise predictions of the environmental impacts for the following reasons: 1.

LCA conducts research based on reference unit, which is a relative description of environmental impacts;

46

2. 3. 4.

C. Guo

The environmental data of LCA is subject to space and time; There are uncertainties in the environmental impacts itself; Certain environmental impacts refer to the future impacts.

2.5 Overview of Product Modularization 2.5.1 Concept of Modularization From the perspective of products, a module is a collection of components [13]. The module assumes independent functions in the product and does not depend on other functions. It has a series of product interfaces and can quickly form a product with other modules to realize its various functions. Besides, the module also has the following characteristics: 1.

2.

3. 4.

5.

6.

7.

8.

It is the component unit of the product. As the products of product breakdown, modules are capable of being combined into new products, and can also be separated, disassembled and replaced from original products in the form of units. It is a relatively independent functional unit. Although the module is an integral part of the product, it shows definitely specific functions which are independent of other functions of the product. It has interfaces for composing the product, easily composes and helps to improve the function of the product. It is standardized. Modules are decomposed through the analysis of the functions and structures of similar products. They use typical common units that are derived from simplification and unification, which are parts of standardization. Therefore, the modules must be standardized and can be used in different products. It is serialized. To meet the requirements for a certain type of modular product to the maximum in an economical and reasonable way, a corresponding serialized module of this specific type of product must be designed to be put in common use within a specific range. It is versatile. To overcome the limitations of the serialized modules which are typically aimed at specific types of products, the modules are designed to be commonly applicable and thus cost-saving. It is hierarchical. The module itself can be composed of several sub-modules, which can be further divided if necessary. A product composed of modules can be considered as a module of the upper-level system. It is interchangeable. A similar module can replace a module in a product after a malfunction without affecting its performance, which, can dramatically reduce the time for the maintenance of the product.

2 Carbon Emission Quantification Theory and Modular LCA Method

47

Modularization refers to the concept or process of clustering product parts into several logically independent units. This process is carried out following a specific purpose (or a driving force) and rules (algorithms). Back in the 1960s, people tried to combine some parts of the product into modules to facilitate production, assembly, and debugging. Traditionally, modules are organized to realize functions, to parallelize the processing, assembling, and monitoring, and to facilitate their maintenance. With the development of the green economy, the environmental friendliness of modules in the product life cycle has been highly valued. Besides, the convenience of the product maintenance, the disassembly, and the reuse are also taken into consideration. The traditional function-oriented modular definition cannot meet the needs of modern modular designs. Currently, in product modularization, it is necessary not only to consider the traditional issues such as functions and parallel processing and assembly, but also to consider the entire life cycle issues such as maintainability, the possibility of upgrading, recycling, and reutilization. Such modularization is called the modularization of the entire life cycle. At present, some mature manufacturers of modular products can request component suppliers for industrial combinations of product modules. For example, when Fuji Xerox in Japan manufactures its copiers, its subsidiary suppliers provide various modules such as automatic feeders, image input unit, laser image scanning output device, customer replaceable unit, machine shell, paper tray, low-voltage power supply, high-voltage power supply, which also called subsystems. The company is responsible for designing the copier frames and assemble modules into them. Through this move, Fuji Xerox managed to optimize the copier’s functions and achieved significant performance in its maintenance, disassembly, material recovery, and recycling. Fuji Xerox claimed that 99.7% of its products can be returned to the material cycle after being discarded, and the emissions to the environment (emissions after discarding) are down to almost zero. It shows the excellent advantages of modularization in reducing the burden on the environment. Nowadays, modularization has become a widely used design method, but without a unified definition. Scholars like Yu and Tao [14] believe that modularization is the concept or process of clustering products into several independent units. Jia [6] holds that a modular design of products is based on the analysis of product functions, so different modules are designed according to specific product functions, and different products are composed through the selection and combination of modules to meet different design needs. In this sense, modularization is planning and organizing products or systems, [4] which is often carried out following specific purposes and rules. Differences in the purposes and results of modularization account for the lack of a universal one.

2.5.2 Modular LCA Method Modular division considers functional factors and considers the product maintenance, disassembly, reuse, environmental characteristics, and other issues throughout the life

48

C. Guo

cycle. The versatility and relative independence of the modules equip the modular LCA whit apparent advantages. Through data integration, the input and output data are modularized to realize the reuse of inventory data and reduce the workload of LCA data collection and calculation. In addition, the relative independence of the modules makes different LCA modules independent of each other, which makes it possible for researchers to modify a single module, and significantly enhances the LCA method’s extrapolation, that is, to estimate the LCA of the new product based on the existing product LCA.

2.5.2.1

Module Reuse in LCA

This section does not discuss the division and combination of modules but the component-levelled data reuse in LCA implementation achieved with the relative independence and versatility of modules. There are two module reuse levels: direct reuse and modification reuse. (1)

Direct Reuse

Direct reuse of a module means that the type and consumption of process data such as raw materials, energy, and processing technology contained in the original module are not changed when the new product is evaluated for LCA. Direct reuse must meet two requirements simultaneously: the material and energy types remain unchanged; the consumption amount remains unchanged. In the designing of serial products, many modules of one product can be used by other products without modification. For example, the same type of compressor can be used in refrigerators of different models. This introduction of compressor into refrigerators can be regarded as direct reuse, which normally requires the common standard modules or modules standardized in the same series of products. Besides, a direct introduction of non-module standard parts, such as bolts, nuts, and other standard parts are also considered as direct reuse. (2)

Modification Reuse

Modification reuse of a module means that a certain module can be applied to a new product only after some of its materials, energy data types, and consumption values are modified. When one of the following conditions is met, it is called modification reuse: 1. 2. 3.

Part of materials and energy types have changed; The amount of consumption has changed with the types of materials and energy remain the same; The factors mentioned above have changed at the same time.

Only when the type or value of the module’s energy or materials is partially changed, the modification reuse method is adopted. With the types and consumption of energy and materials entirely changed, we get two completely different modules, which, therefore, cannot be called modification reuse.

2 Carbon Emission Quantification Theory and Modular LCA Method

2.5.2.2

49

Module Database Development

Data reuse is essential in LCA research. To achieve data reuse and reduce the amount of collection and calculation of LCA data, many countries and organizations have developed related databases, which is of great significance to the development of LCA. However, all of their data is production data of primary materials or energy, which is a single piece of data, and this may cause low efficiency when the data is reused. One of the most outstanding merits of modular design lies in the versatility of the module. In terms of the product’s natural attributes, the reuse of modules is to reuse the raw materials and energy consumption data contained in the modules, that is, the overall reuse of a set of LCI data. Modular reuse can significantly improve the level and efficiency of LCI data reuse, which is achieved by establishing an LCI database. Correspondingly, the concept of establishing a modular database is proposed here to achieve the purpose of module-level data reuse. The LCI database is process-oriented, storing the material and energy consumption data of the unit process. However, one module contains several processes, and the data in the module comes from the LCI database but cannot replace the LCI database. The module database and the LCI database are compared in Fig. 2.4. It can be seen from Fig. 2.4:

Fig. 2.4 Module database and LCI database comparison

50

C. Guo

Table 2.4 Module database and LCI database comparison

LCI database

Module database

Storage unit

Process

Module (a set of processes)

Feature

Relative independence

Relative independence

Interchangeability

Interchangeability

Versatility

Versatility

Data exchange comparison

Module exchange comparison

LCA implementation

Product design and LCA implementation

Application

1.

2.

3.

4. 5.

The basic unit stored in the LCI database is the process, that is, the primary raw material and energy data, while the primary unit stored in the module database is the module, which can be regarded as the union of the processes; The module database and the LCI database have the same low level in the LCA, and both are the basic units for establishing a product system and independently conducting environmental impact assessment; The process data in the module database comes from LCI data, which is still the foundation of the LCA software. The module database is built to facilitate product modeling and improve the efficiency of data reuse, but is not able to replace the LCI database; The LCI database can be regarded as a module database of the smallest unit containing only a single piece of data; Standard parts can be regarded as a module (such as bolts and nuts), which is beneficial to promoting the application of standardization in LCA and improving the evaluation efficiency of LCA.

The similarities between the module database and the existing LCI database are shown in Table 2.4. From above it can be seen that the establishment of the LCA module database is of great significance. It offers a supplement to LCA theory by effectively applying modularization theory and makes it easier to integrate modular design and evaluation. This section will preliminarily explore the fundamental issues of module database establishment. Before discussing module database development, the data archiving format should be focused on just like what is done with LCI database establishment. (1)

Data Archiving Format of the Database

According to the definition of ISO14048, the data archiving format refers to the structure of the data archiving, including data entries, data entry groups, and the relationship between them. As mentioned above, to reduce the workload of data collection and provide data universality, each country has launched its own LCI database. However, the differences in their data archiving formats have brought

2 Carbon Emission Quantification Theory and Modular LCA Method

51

inconvenience to data exchange, especially to data exchange and application based on computer programs. To unify the LCI data format, the International Organization for Standardization has done much work and finally specified the ISO14048 data format standard based on the existing data format. In the module database proposed in this section, its storage unit, namely the module data, is also designed for data versatility. Wherever data is reused, it is essential to ensure the transparency of data and guarantee its good applicability and quality when referenced. Therefore, the archiving format of the modular data should be an important consideration in the design of a modular database. The archiving format of module data needs to be considered from the following three aspects: • Module data shares the nature of LCI data and is the basic unit that constitutes the product system. Therefore, the relevant content of the LCI data archiving format should be taken for reference; • The module data shares the nature of a product system, and it is a combination of a set of processes. Therefore, the relevant requirements in product evaluation should be also considered in the archiving of module data; • Modules are not random, but are divided and combined according to specific principles. For a single product, with different division principles, the structure of a module can be different. Therefore, the archiving format of the module must specify the principle on which the module is divided. Here, software MPT-LCA is taken as an example to illustrate the data archiving format framework. The data archiving format adopted by MPT-LCA is designed based on the ISO14040 series of standards and through the combination of existing data formats such as SPINE, SPOLD, etc. The data archiving format adopted by the MPT-LCA module database is shown in Fig. 2.5. It can be seen from Fig. 2.5 that the data archiving format framework of the module can be divided into four parts, which are respectively module description, process input, data quality requirements, and management information. This framework is formulated following the ISO14048 standard and combining the attributes of the

Fig. 2.5 Data archiving format in module library

52

C. Guo

module itself, which is featured with concise data format and is able to ensure the transparency of module information and the sufficiency of data quality information.

2.6 Conclusions This chapter introduces the basic concepts of GHG emissions, summarizes the direct measurement method, process analysis method, input–output analysis method, and hybrid method for carbon emission quantification. Moreover, the basic concept of LCA and the modular LCA method are introduced and help to lay a foundation for the calculation and research of carbon emission in the following chapters.

References 1. Chen GQ, Chen ZM (2010) Carbon emissions and resources use by Chinese economy 2007: a 135-sector inventory and input-output embodiment. Commun Nonlinear Sci Numer Simul 15. https://doi.org/10.1016/j.cnsns.2009.12.024 2. Davis SJ, Caldeira K (2010) Consumption-based accounting of CO2 emissions. Proc Natl Acad Sci USA 107. https://doi.org/10.1073/pnas.0906974107 3. Guan J, Zhang Z, Chu C (2016) Quantification of building embodied energy in China using an input-output-based hybrid LCA model. Energy Build 110. https://doi.org/10.1016/j.enbuild. 2015.11.032 4. 侯亮, 唐任仲, 徐燕申. 产品模块化设计理论、技术与应用研究进展. 机械工程学报, 2004 (01): 56–61. Hou L, Tang R, Xu Y (2004) Research progress of product modular design theory, technology and application. J Mech Eng 01:56–61 5. Huang YA, Weber CL, Matthews HS (2009) Categorization of scope 3 emissions for streamlined enterprise carbon footprinting. Environ Sci Technol 43. https://doi.org/10.1021/es9 01643a 6. 贾延林. 模块化设计. 北京: 机械工业出版社, 1993. Jia Y (1993) Modular design. Machinery Industry Press, Beijing 7. Lenzen M (2000) Errors in conventional and input-output-based life-cycle inventories. J Ind Ecol 4. https://doi.org/10.1162/10881980052541981 8. Onat NC, Kucukvar M, Tatari O (2014) Scope-based carbon footprint analysis of U.S. residential and commercial buildings: an input-output hybrid life cycle assessment approach. Build Environ 72. https://doi.org/10.1016/j.buildenv.2013.10.009 9. Peters GP, Hertwich EG (2008) CO2 embodied in international trade with implications for global climate policy. Environ Sci Technol 42. https://doi.org/10.1021/es072023k 10. Su B, Ang BW (2011) Multi-region input-output analysis of CO2 emissions embodied in trade: the feedback effects. Ecol Econ 71. https://doi.org/10.1016/j.ecolecon.2011.08.024 11. Suh S, Huppes G (2005) Methods for life cycle inventory of a product. J Clean Prod 13. https:// doi.org/10.1016/j.jclepro.2003.04.001 12. Tian X, Chang M, Lin C, Tanikawa H (2014) China’s carbon footprint: a regional perspective on the effect of transitions in consumption and production patterns. Appl Energy 123. https:// doi.org/10.1016/j.apenergy.2014.02.016 13. 杨鸣. 机电产品模块化生命周期评价方法研究及其软件开发. 上海交通大学, 2011. Yang M (2011) Research on modular life cycle assessment method of mechanical and electrical products and its software development. Shanghai Jiaotong University

2 Carbon Emission Quantification Theory and Modular LCA Method

53

14. 于随然, 陶璟. 产品全生命周期设计与评价. 科学出版社, 2012: 133–138. Yu S, Tao J (2012) Product life cycle design and evaluation. Science Press, pp 133–138 15. Zabalza Bribián I, Valero Capilla A, Aranda Usón A (2011) Life cycle assessment of building materials: comparative analysis of energy and environmental impacts and evaluation of the ecoefficiency improvement potential. Build Environ 46. https://doi.org/10.1016/j.buildenv.2010. 12.002

Chapter 3

Carbon Emission Prediction Method for Tunnel Construction Chun Guo

3.1 Introduction Carbon emission prediction, a hot topic in the construction industry and transportation industry, in which (BP) neural network model, STIRPAT model, system dynamics model and grey prediction model have been widely used [2, 8, 10, 15, 20]. Hao and Gao [12] combined multi-objective genetic algorithm to train a BP neural network prediction model of building carbon emissions. Chen et al. [3] used STIRPAT model and grey forecast model to predict the future carbon emissions of the construction industry. Liu and Zhao [14] used a system dynamics model to analyze the carbon emission impacts of buildings, and divided the building’s carbon emissions system into four subsystems: economy, management, population, and environment. The carbon emissions of the construction industry were predicted through gray correlation analysis, support vector machine and standard cuckoo search algorithm. Gao et al. [7] selected total population, GDP per capita, vehicle ownership, carbon emission intensity, urbanization rate, passenger turnover and cargo turnover as the influencing factors of urban transportation carbon emissions based on the STIRPAT model, and compared the predicting effects of multiple models. Different from ordinary above-ground buildings, tunnels are underground structures in various geological environments, composed of the surrounding geological body and the tunnel supporting structure from a structural point of view. The surrounding rock of the tunnel is not only the main source of material for the overall structure, but also an important load carrier and main source of load. The tunnel support is designed to improve the bearing capacity and the load-bearing conditions of the surrounding rock, so the surrounding rock can play structural good role in the structure. Since the impact of tunnel carbon emissions must include the geological conditions and design parameters around the tunnel, the carbon emission prediction method of traditional ground construction is not suitable for tunnel engineering. It is necessary to reanalyze the potential influencing factors of tunnel construction and establish a carbon emission prediction model for tunnel construction. Besides, the current case studies have different functional units, and the inconsistency in the © Southwest Jiaotong University Press 2022 C. Guo and J. Xu, Carbon Emission Calculation Methods for Highway Tunnel Construction, https://doi.org/10.1007/978-981-16-5308-7_3

55

56

C. Guo

research scopes makes it challenging to compare various studies [4]. Having evaluated the emissions of numerous tunnels based on identical system boundary and functional unit, this study focused on the following two questions: • What are the key factors affecting tunnel construction emissions? • What methods can be used to predict tunnel construction emissions? Section 3.2 introduces the research framework, the emission calculation and data analysis methods. Section 3.3 presents the main research findings. Section 3.4 expands the significance of the findings and weaknesses of this research, and Sect. 3.5 summarizes the conclusions.

3.2 Method The method used in the research involves: 1. 2.

3.

proposing the categories and definitions of the potential influence factors; summarizing the calculation method of carbon emissions for tunnel construction, given that the authors elaborated the calculation method in one existing literature [18]; providing the data analysis method which was used to ascertain the factors influencing and models predicting the carbon emissions from tunnel construction.

3.2.1 Potential Factors Affecting Tunnel Construction Emissions A tunnel is an underground structure buried in a geological environment. A sectional view of the tunnels in the rock mass is shown in Fig. 3.1. From a structural point of view, the structural system of tunnels consists of the surrounding rock mass and the artificial supporting structure. The purpose of tunnel design is to improve the bearing capacity of the surrounding rock. In this study, the rock mass grade, buried depth, tunnel bias, rock mass quality, net distance between tubes, excavation method, and excavation area are selected as potential factors affecting the carbon emissions of tunnels, which are closely related to tunnel construction and easily obtained from survey and design files. Explanations and categories of the six classification indicators are depicted in Table 3.1 where the excavation area refers to a quantitative indicator representing the excavation size of the tunnel section. In accordance with the previous study, there is a strong correlation between the carbon emissions of tunnels and the total mass of materials in the lifecycle [9]. Therefore, the effect of the total mass of materials on the carbon emissions of tunnel construction was further evaluated in this study.

3 Carbon Emission Prediction Method for Tunnel Construction

57

Fig. 3.1 Sectional view of the tunnels in the rock mass [19]

Table 3.1 Potential classified influence factors for tunnel emissions [19]

Potential influencing factor

Categories of the factors

Rock mass grade

1. Grade III 2. Grade IV 3. Grade V

Buried depth

1. Deep-buried

Tunnel bias

1. Unbiased

2. Shallow-buried 2. Biased Rock mass quality

1. Poor 2. Moderate 3. Good

Net distance between tubes

1. Neighborhood

Excavation method

1. Full-face excavation method

2. Non-neighborhood 2. Step method 3. Benching method

3.2.2 Overview of the Tunnels Eight highway twin tunnels have been constructed in Sichuan Province, Guizhou Province, Yunnan Province, and Chongqing Municipality in Southwest China through the drilling and blasting method. The basic characteristics of the eight tunnels

58

C. Guo

Table 3.2 Basic parameters of the eight tunnels [19] Tunnel name

Location

Tunnel length (unit: m) (left line/right line)

The Xinerlangshan Tunnel

Sichuan Province 13,459/13,406

The Lancangjiang Tunnel

Yunnan Province 3852/3894

11

The Dayingshan Tunnel

Yunnan Province 3795/3720

10

The Yufengshan Tunnel Chongqing Municipality

3702/3671

The Zuoshanzhai Tunnel

Guizhou Province

405/446

The Maoli Tunnel

Guizhou Province

428/422

The Xinzhaiyao Tunnel Guizhou Province

696/716

The Baizhao Tunnel

Yunnan Province 1235/1233

Number of lining designs 7

5 11

5

are specified in Table 3.2, while the geological condition parameters and construction parameters of 49 types of tunnel lining designs for the eight tunnels are listed in Table 3.3. Based on the eight tunnels, the authors attempted to separate the different lining sections of one tunnel and assumed that there were 49 twin tunnels corresponding to the 49 lining design parameters from Table 3.3. Each assumed tunnel has coherent geological conditions and a tunnel lining design that allows for visual comparison between them.

3.2.3 Calculation Method for Tunnel Construction Emissions A method for calculating the carbon emissions of tunnel construction with different rock mass grades was proposed [20]. This method was used to calculate the carbon emissions of several construction procedures: advance support, tunnel excavation, rock support, casting and lining, and ventilation and lighting. A brief introduction to the key points of the calculation method is as follows. There are two main types of life cycle emissions in tunnel construction: the direct emissions from construction machinery’s energy consumption, and the indirect emissions from the production and transportation of energy and materials. The functional unit is the “construction activities (containing advanced support, tunnel excavation, rock support, casting and lining, and ventilation and lighting) of a one-km tunnel in Southwest China”. The energy consumption and materials during the construction stage are calculated on the basis of the Highway Engineering Budget Quota [16],

3 Carbon Emission Prediction Method for Tunnel Construction

59

Table 3.3 Geological conditions and construction parameters for the tunnel lining designs [19] No.

Rock mass grade

Buried deptha

Tunnel biasb

Rock mass qualityc

Net distance between tubesd

Excavation methode

Excavation area (m2 )

1

V

2

2

2

2

3

113.47

2

V

2

1

2

2

3

109.84

3

V

1

1

1

2

3

108

4

IV

1

1

3

2

2

105.11

5

IV

1

1

2

2

2

105.08

6

IV

1

1

1

2

2

95.42

7

III

1

1

2

2

1

91.53

8

V

2

2

2

1

3

148.49

9

V

2

1

2

1

3

145.31

10

IV

2

1

3

1

3

138.45

11

IV

1

1

2

1

3

138.45

12

V

1

1

3

2

3

106.2

13

V

2

2

2

2

3

106.2

14

V

1

1

2

2

3

104.34

15

V

1

1

3

1

3

106.2

16

V

1

1

2

1

3

104.34

17

V

1

1

2

2

3

110.75

18

IV

2

1

2

2

2

101.71

19

IV

1

1

3

2

2

96.75

20

IV

1

1

3

2

2

85.9

21

III

1

1

2

2

1

83.79

22

V

2

1

2

2

3

106.2

23

V

1

2

2

2

3

106.2

24

V

1

1

2

2

3

104.34

25

V

1

1

3

1

3

106.2

26

V

1

1

2

1

3

101.66

27

IV

2

1

2

2

2

101.71

28

IV

1

1

3

2

2

96.75

29

IV

1

1

1

2

2

85.57

30

IV

1

1

2

1

2

101.71

31

III

1

1

2

2

1

83.65

32

III

1

1

3

2

1

85.52

33

V

2

1

2

2

3

175.55

34

V

2

1

2

2

3

177.1 (continued)

60

C. Guo

Table 3.3 (continued) No.

Rock mass grade

Buried deptha

Tunnel biasb

Rock mass qualityc

Net distance between tubesd

Excavation methode

Excavation area (m2 )

35

V

1

1

2

2

3

169.15

36

IV

2

1

3

2

3

168.84

37

III

1

1

2

2

2

131.7

38

V

2

1

2

2

3

163.99

39

V

1

1

2

2

3

163.06

40

V

1

1

3

2

3

168.7

41

IV

2

1

2

2

2

168.84

42

IV

1

1

2

2

2

131.7

43

V

2

1

3

2

3

112

44

V

2

2

2

2

3

114.46

45

V

2

1

2

2

3

107.16

46

V

1

1

2

2

3

101.72

47

IV

1

1

2

2

2

97.48

48

III

1

1

2

2

1

80.49

49

III

1

1

2

2

1

78.83

a 1.

b 1.

c 1.

Deep-buried, 2. Shallow-buried; Unbiased, 2. Biased; Good, 2. Moderate, 3. Poor; d 1. e Neighborhood, 2. Non-neighborhood; 1. Full-face excavation method, 2. Step method, 3. Benching method

tender, and Chinese National Unified Machinery Shift Cost [17]. The project cost of one-m tunnel is directly obtained from the tender. The materials and mechanical shifts consumed by one-km tunnel construction are calculated according to the Highway Engineering Budget Quota. The electricity consumption or hourly fuel of various types of machinery is presented in Chinese National Unified Machinery Shift Cost. The life-cycle carbon emissions from various energy sources and materials are calculated with the emission factor method listed in previous literature [18]. The GWP-100 method normalizes different GHGs to the carbon dioxide equivalent (CO2eq ) considering the greenhouse effect. It should be noted that, although the 49 tunnels in this study were assumed as twin tunnels, the GHG emissions were set only for a single tube calculation, which is beneficial to explore the excavation area’s influence on carbon emissions. The excavation area, a potential factor that influences emissions, refers to a single tube’s excavation area. Finally, the calculated carbon emissions with specific excavation area are for a single tube.

3 Carbon Emission Prediction Method for Tunnel Construction

61

3.2.4 Common Prediction Models 3.2.4.1

Trend Extrapolation Forecasting Method

The trend extrapolation forecasting method is used to seek the law of the development and change of things over time based on the historical and realistic data of things, so as to speculate on their future conditions. The assumptions of the trend extrapolation method are: 1. 2.

There is no jumping change in the development process of things, i.e., the development and change of things are gradual. The structure and function of the researched system remain basically unchanged, that is, the trend extrapolation model established in light of the historical data can be suitable for the future trend changes.

From the above two assumptions, it can be seen that the trend extrapolation forecasting method is a statistical forecasting method for the gradual development of things. In short, a mathematical model is used to fit a trend line to extrapolate and predict the future development of things in the future. The trend extrapolation forecasting method mainly uses the scatter diagram drawing (graph recognition) and the calculation of the difference method for model selection. Due to the merits of revealing the future development of things and quantitatively evaluating their functional characteristics, the trend extrapolation forecasting method, requiring at least five years’ data, is quite suitable for medium and long-term new product forecasting.

3.2.4.2

Regression Prediction Method

The regression prediction method is determined by the correlation between the independent variable and the dependent variable. According to the number of independent variables, the regression prediction method can be divided into univariate regression prediction (one variable) and multiple regression prediction (multiple variables). Based on the correlation between the independent variable and the dependent variable, it falls into two types—linear regression prediction method and nonlinear regression method. The key of regression prediction lies in function fitting which enables a function curve to fit well with the known data and predict the unknown data.

3.2.4.3

Kalman Filter Prediction Model

Kalman filter is a set of recursive estimation models taking the minimum mean square error as the best estimation criterion to seek for. Basically, Kalman filter adopts the state space model of signal and noise, using the estimated value at the previous

62

C. Guo

moment and the observed value at the present moment to update the estimation of the state variables, so as to attain the estimated value of the occurrence time. The Kalman filter prediction model, consisting of prediction step, estimation step, and forward step, is suitable for real-time processing and computer operations. In the prediction step, the estimation of the state at time t depends on all the information up to time t-one. In the estimation step, after the status is updated, the estimation needs to be compared with the actual observation at time t. The updated status is a combination of earlier calculations and new observations. The weight placed on each component is determined by “Kalmangain”, which depends on the noise w and v. The smaller the noise, the higher the credibility of the new observation and the greater the weight, and vice versa. The forward step means that the previous “new” observation becomes the “old” observation when preparing for the next round of estimates. Any length of prediction can be made at any time through advance state transition. The main advantage of the adaptive Kalman filter is that only a small amount of data is needed to get the starting point of the prediction although a little more data will make the result a little better. It can self-adjust and automatically set parameters from continuous observations. The disadvantage is that the ability to consider complexity is limited, and sometimes the convergence is slow or no convergence occurs, which can be judged by formal standard.

3.2.4.4

Combination Forecasting Model

The combined forecasting method uses multiple forecasting methods for the same problem. The main purpose of the combination is to comprehensively utilize the information provided by various methods to improve the prediction accuracy as much as possible. There are two basic forms of combined predictions, with one being equal weight combination (the prediction values of each prediction method are combined with the same weight group to synthesize new predicted values), the other being unequal weight combination (the predicted values of different prediction methods are given different weights). The principles and application methods of these two forms are exactly the same, but the weights are different. From experience, it’s accepted that the combination forecasting method using unequal weight combination is more accurate.

3.2.4.5

BP Neural Network Prediction Model

Back-ProPagation Network, also called back propagation neural network, by constantly modifying the network weights and thresholds, enables the error function to drop (approaching the expected output) along the negative gradient direction through the training of sample data. It is a widely used neural network model, which is mostly applied in function approximation, model recognition and classification, data compression and time series forecasting.

3 Carbon Emission Prediction Method for Tunnel Construction

63

Based on the above analysis, it’s believed that the regression prediction method is simple and can be used to explore the influencing factors related to the carbon emission of tunnel construction. The author will try to utilize regression prediction methods to establish a prediction model of carbon emissions from tunnel construction.

3.2.5 Data Analysis Method Relevant analysis was conducted to obtain the correlation between tunnel construction and carbon emissions, while regression analysis was carried out to obtain the methods for forecasting construction emissions. The correlation and regression analysis in this research were primarily carried out using IBM SPSS Statistics 20.0 (IBM), which has the basic functions of data management, statistical analysis, chart analysis, and output management. Among the study subjects, several categorization data require value assignment before they can be applied in regression analyses and correlation. Table 3.4 indicates the values assigned to different variables and Fig. 3.2 depicts the flow of data analysis in this study. First, the bivariate correlation analysis method was used to analyze the correlation between carbon emissions and various potential influencing factors, to be specific, Pearson product difference correlation, Kendall rank correlation coefficient and Spearman rank correlation. The Person product-difference correlation requires Table 3.4 Value assignment for the different parameters [19]

Parameter

Variation of the parameter

Value assigned to the parameter

Rock mass grade Grade III

3

Grade IV

4

Buried depth Rock mass quality

Grade V

5

Shallow

1

Deep

2

Poor

0

Moderate

1

Good

2

Net distance between tubes

Non-neighborhood

0

Neighborhood

1

Excavation method

Full-face excavation method

1

Step method

2

Tunnel bias

Benching method

3

Unbiased

0

Biased

1

64

C. Guo

Fig. 3.2 Flow of data analysis [19]

the distribution of the original variables to be bivariate normal distribution, while the Spearman rank correlation method which has weaker statistical power but a wider range of applications than Pearson correlation coefficient uses the rank size of the two variables for linear correlation analysis. Kendall rank correlation coefficient, a kind of rank correlation coefficient, is used to reflect the correlation of categorical variables and suitable for correlation analysis of dual categorical variables. Several factors that are significantly related to carbon emissions are obtained through bivariate analysis. Partial correlation analysis was then conducted for further analysis. Certain factors to be fixed were controlled, and the impact of the other factors on carbon emissions from tunnel construction was then considered. Finally, linear regression was conducted to assess the interdependence of two or more variables. The goodness of fit, collinearity of the independent variables, and residual sequence correlation of the regression models were verified. If the regression models had multiple independent variables, SPSS provided a t-test function to aid in the identification of some independent variables that have a weak impact on carbon emissions. In unary linear regression, the correlation coefficient is used to judge the significance of the regression. In multiple regression, the modified coefficient of determination (Adj. R2 ) is used to represent the goodness of fit of the regression model. Adj. R2 (between zero–one) represents the regression effect of the regression function. The closer Adj. R2 is to 1, the stronger the independent variable’s ability to explain the dependent variable and the better the equation’s goodness of fit. Multicollinearity refers to the existence of a certain degree of linear correlation between the independent variables. That is, an independent variable can be described by a linear combination of other independent variables. A severe collinearity trend between the independent variables should be avoided. Common judgment indicators consist of the variance inflation factor (VIF), Eigenvalue, and conditional index (CI).

3 Carbon Emission Prediction Method for Tunnel Construction

65

If VIF > 10, Eigen value < 0.01, or CI > 100, the independent variable may have multiple collinearity issues. Residual sequence correlation has a significant impact on the estimated parameters’ validity. Sequence correlation refers to autocorrelation’s occurrence between various residual terms in the regression formula. In the case of residual sequence correlation or the failure of the D.W test, E-views 10.0 software can resolve such problems. First, the Lagrange multiplier (LM) test was conducted to determine residual sequence correlation’s order. Durbin’s two-step method was then followed to resolve the residual sequence correlation problem [5, 13]. In this research, the screening of regression equations was separated into two stages, as shown in Fig. 3.2. The first stage was selecting the regression models with a high goodness of fit (Adj. R2 ≥ 0.8) and independent noncollinearity ((VIF) < 10, Eigenvalue > 0.01, (CI) < 100). The second stage was to verify the randomness of the regression equation’s residual sequence. If the D.W test fails or the residuals are correlated, the issue related to the regression model residual sequence is resolved in EViews 10.0. Upon searching for standard residuals with values larger than three, the outlier samples were deleted to improve the model’s predictive effect.

3.3 Results 3.3.1 Carbon Emissions of Tunnel Construction Table 3.5 presents the inputs of materials and energies of 49 tunnels. The carbon emissions of the construction of the tunnels were calculated, as shown in Table 3.6. The emissions ranged from 5.08 × 103 to 52.63 × 103 t CO2eq , with an average of 20.37 × 103 t CO2eq . The carbon emissions with different rock mass grades and excavation areas are shown in Fig. 3.3. The emissions from tunnel construction are relatively scattered. It was expected that the average tunnel emissions would be higher with poor surrounding rock conditions. The average carbon emissions with Grade V, Grade IV, and Grade III rock masses were 26.37 × 103 , 16.29 × 103 , and 7.43 × 103 t CO2eq , respectively. However, the tunnel emissions with high-grade surrounding rock mass are not strictly higher than those with low-grade surrounding rock mass. As shown in Fig. 3.3, the emissions with Grade IV rock mass and smaller excavation areas may be even higher than those with Grade V rock mass. Figure 3.4 shows the carbon emissions from tunnel construction with different total masses of materials. As the material input increases, the tunnel emissions appeared to increase.

120

123

115

118

103

123

102

125

89

80

66

65

10

11

12

13

14

15

16

17

18

19

20

21

79

6

149

84

5

9

84

4

74

100

3

158

112

2

8

116

1

7

Wood (m3 )

No.

250

908

964

2842

5095

3444

4279

3481

4245

3878

1820

3230

5472

5349

229

775

880

1178

2516

3136

4299

Steel (t)

5460

6560

7729

10,276

15,839

11,462

13,877

12,949

13,826

13,799

13,067

16,077

18,869

21,036

5977

8018

9180

10,400

12,179

13,149

15,256

Cement (t)

83

66

74

78

34

32

32

32

32

32

106

106

44

45

90

73

81

81

33

34

35

Explosive (t)

Table 3.5 Inputs of materials and energy of the tunnels [19]

11,815

13,792

16,141

21,083

29,928

21,059

25,683

23,789

25,621

25,588

26,793

26,822

33,939

36,975

12,724

16,670

19,253

19,283

22,221

24,017

27,815

Sand (t)

14,662

16,632

19,600

25,056

35,766

25,387

30,349

28,126

30,349

30,349

32,950

32,950

40,442

44,077

15,905

20,225

23,638

23,671

26,984

29,242

32,926

Gravel (t)

16

21

24

33

58

42

54

47

52

52

39

47

66

75

18

26

30

37

43

47

55

Water (103 × m3 ) Electricity (MWh)

1867

1960

2338

2642

3924

3403

3995

3373

3826

3706

3566

3705

5078

5304

1998

2215

2481

2568

3360

3677

3878

Gasoline (t)

0

7

7

12

36

21

31

21

30

26

14

19

38

57

0

6

7

10

15

19

34

Diesel (t)

(continued)

131

149

166

202

246

197

225

218

225

225

248

252

303

319

140

168

184

186

203

210

245

66 C. Guo

Wood (m3 )

123

129

112

134

113

90

80

70

94

64

67

200

228

181

174

107

215

149

178

141

131

No.

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42

3321

5228

8426

6770

7394

385

3831

3285

3438

4366

797

270

3427

1054

1078

2948

3440

4277

3440

4276

3881

Steel (t)

Table 3.5 (continued)

20,987

25,848

40,903

28,172

29,678

10,032

20,967

23,384

26,330

24,073

6623

4778

10,249

6175

7704

10,243

13,058

14,410

13,032

14,361

14,017

Cement (t)

118

123

51

50

50

130

130

52

54

54

84

82

78

66

74

78

31

32

32

32

32

Explosive (t)

44,383

50,774

59,505

53,950

55,300

22,337

42,021

43,308

49,297

47,786

14,021

10,164

21,140

12,725

16,138

21,087

23,925

25,805

23,890

25,745

25,681

Sand (t)

56,416

63,383

73,980

67,193

68,647

28,140

51,009

53,253

60,230

57,972

16,945

12,582

25,066

15,101

19,600

25,056

28,136

30,349

28,126

30,349

30,349

Gravel (t)

73

88

119

96

95

30

78

77

93

92

21

14

33

19

24

33

50

58

50

57

54

Water (103 × m3 ) Electricity (MWh)

3919

4288

6398

5106

5053

3011

5077

5507

6509

6337

1966

1865

2969

2170

2419

2718

3650

4368

3638

4207

3970

Gasoline (t)

16

26

51

40

49

0

20

13

13

23

6

0

17

7

7

13

21

31

21

31

26

Diesel (t)

(continued)

315

363

415

381

392

215

347

352

381

375

149

125

203

147

167

203

215

226

218

226

225

3 Carbon Emission Prediction Method for Tunnel Construction 67

Wood (m3 )

186

119

97

90

84

35

31

No.

43

44

45

46

47

48

49

162

566

1000

2949

4009

5727

6961

Steel (t)

Table 3.5 (continued)

5097

5808

8183

9994

12,661

15,952

15,409

Cement (t)

8

8

75

31

33

35

34

Explosive (t)

10,794

12,079

17,088

20,370

25,545

32,573

30,547

Sand (t)

13,410

14,662

20,975

24,722

30,425

39,236

35,754

Gravel (t)

16

19

27

39

47

58

81

Water (103 × m3 ) Electricity (MWh)

1029

1268

2476

3192

3336

3852

7340

Gasoline (t)

0

4

7

14

27

41

33

Diesel (t)

47

57

167

190

228

260

252

68 C. Guo

3 Carbon Emission Prediction Method for Tunnel Construction Table 3.6 The carbon emissions and total mass of materials of the tunnels [19]

69

No.

Carbon emissions (103 t CO2eq )

Total mass of materials (103 t)

1

23.65

135.90

2

19.59

116.77

3

17.40

107.48

4

12.82

91.43

5

11.30

82.98

6

9.96

71.73

7

7.15

53.14

8

31.44

182.44

9

29.78

164.49

10

22.05

126.54

11

17.13

113.42

12

21.56

125.43

13

22.39

126.58

14

19.85

115.92

15

22.65

128.09

16

18.66

103.47

17

25.61

144.47

18

16.00

92.60

19

10.22

68.80

20

8.86

58.63

21

6.66

48.72

22

21.96

128.43

23

23.18

131.91

24

20.08

118.29

25

23.37

133.36

26

20.10

118.93

27

16.25

91.98

28

10.49

68.19

29

9.04

54.44

30

17.40

92.55

31

6.19

41.82

32

8.71

59.43

33

32.84

226.75

34

32.85

232.91

35

29.41

200.29

36

28.36

196.13 (continued)

70 Table 3.6 (continued)

C. Guo No.

Carbon emissions (103 t CO2eq )

Total mass of materials (103 t)

37

11.60

91.35

38

41.45

255.97

39

39.18

252.40

40

52.63

302.04

41

33.78

233.84

42

26.19

197.95

43

31.99

170.09

44

26.89

151.92

45

20.67

120.24

46

16.45

96.69

47

10.74

74.77

48

6.62

52.44

49

5.08

45.38

Fig. 3.3 Carbon emissions with different rock mass grades and excavation areas [19]

3.3.2 Factors Influencing Carbon Emissions from Tunnel Construction The correlation between the various influence factors and carbon emissions was analyzed, as shown in Table 3.7. The excavation methods, excavation area, rock mass grade, buried depth, and total mass of materials were significantly related

3 Carbon Emission Prediction Method for Tunnel Construction

71

Fig. 3.4 Carbon emissions with different total masses of materials [19]

Table 3.7 Correlation analysis of potential influencing factors and carbon emissions [19]

Potential influence factor

Correlation index

Correlation coefficient

Sig.

Rock mass grade Spearman

0.730

0.000**

Buried depth

Spearman

0.512

0.000**

Excavation method

Spearman

0.760

0.000**

Excavation area

Pearson

0.833

0.000**

Total mass of materials

Pearson

0.975

0.000**

Tunnel bias

Spearman

0.257

0.074

Rock mass quality

Spearman

0.099

0.497

Net distance between tubes

Spearman

0.155

0.289

** Indicates

that the correlation between the potential influence factor and carbon emissions is significant (Sig. < 0.001)

to carbon emissions. The correlation coefficient with the total mass of materials is the highest, at 0.975. This is followed by the excavation area. Excluding these factors, the excavation method, rock mass grade, and buried depth are all classification indicators. The tunnel emissions of the above three classification indicators are shown in Fig. 3.5. As shown in Fig. 3.5a, c, the emissions with different excavation methods and rock mass grades show diverse distribution ranges. In Fig. 3.5b, the emissions of deep-buried tunnels seem to be more scattered than those of shallow-buried. The

72

C. Guo

differences in the statistics of each classification indicator are listed in Table 3.8. The average emissions of deep-buried tunnels are smaller than those of shallowburied. The benching method has the highest average emissions, followed by the step method, and then the full-face excavation method.

Fig. 3.5 Carbon emissions with different classifications. a Excavation method, b buried depth, c rock mass grade [19]

3 Carbon Emission Prediction Method for Tunnel Construction

73

Fig. 3.5 (continued) Table 3.8 Comparison of the average carbon emissions with different rock mass grades, buried depths, and excavation methods [19] Influence factor

Classification

Number of samples

Mean value

Standard deviation

Mean standard error

Rock mass grade**

Grade III

7

7.43

2.14

0.81

Grade IV

16

16.29

7.62

1.90

Grade V

26

26.37

8.44

1.66

Buried depth**

Shallow

17

26.58

7.02

1.70

Deep

32

17.07

10.27

1.81

Excavation method**

Full-face excavation method

6

6.74

1.19

0.49

Step method

14

14.62

7.20

1.92

Benching method

29

25.97

8.20

1.52

** Indicates that the difference in the average values of different classifications is significant (Sig. < 0.001). The authors further analyzed the partial correlation characteristics of these factors, and used the rock mass grade, buried depth, and excavation method as control variables to compare the correlations between other influencing factors and carbon emissions, and the results are shown in Table 3.9. Excluding the rock mass grade and excavation methods, the remaining influence factors are significantly correlated with the carbon emissions as controlling variables. Further analysis indicated that there is a significant correlation between the rock mass grade and excavation method, with a correlation coefficient of 0.914 (P = 0.000** )

74 Table 3.9 Partial correlation analysis of the correlated factors [19]

C. Guo Controlling factor

Correlated factor Correlation coefficient

Rock mass grade

Buried depth

Buried depth

Excavation method

−0.328

Sig. 0.023*

Excavation method

0.232

0.113

Excavation area

0.837

0.000**

Total mass of materials

0.970

0.000**

Excavation method

0.642

0.000**

Excavation area

0.793

0.000**

Rock mass grade

0.620

0.000**

Total mass of materials

0.970

0.000**

Rock mass grade −0.141

0.339

−0.284

0.05*

Buried depth Excavation area

0.764

0.000**

Total mass of materials

0.966

0.000**

*, ** Indicate

that the correlation between the correlated factor and carbon emissions is significant

3.3.3 Models Predicting Carbon Emissions from Tunnel Construction The regression equations with single, double, and triple independent variables were used to fit the carbon emissions of tunnel construction. There was one regression equation with a single independent variable, two regression equations with double independent variables, and five regression equations with triple independent variables. The fitting results are shown in Table 3.10. According to the data analysis method described in Fig. 3.2, the regression equations were first evaluated in terms of the goodness of fit and multicollinearity of each equation. The fitting effect of regression Equation No. 1 is the best, with an Adj. R2 value of 0.95. Meanwhile, the fitting effect of Equation No. 3 is the worst, with an Adj. R2 smaller than 0.8. For the remaining equations, the Adj. R2 values were between 0.83 and 0.85. Equations No. 4, 6, and 8 had three independent variables, but the goodness of fit was not better than that of Equation No. 2, with two independent variables. In addition to the above equations, there may be more regression equations with more independent variables. The contributions of the non-collinear variables to the fitting of carbon emissions were analyzed, and the rock mass grade, tunnel bias, excavation area, buried depth, rock mass quality, and net distance between tubes are

3 Carbon Emission Prediction Method for Tunnel Construction

75

Table 3.10 Regression equations for the carbon emissions from tunnel construction [19] No. Independent variable Unit 1

M

2

W S

3

4

103 t GHG = 0.16M + 0.083

S

I

1.386 >0.01

10.569

1.229 >0.01

6.175

GHG = 0.266S + 10.313W −5.349M − 42.636

0.847

GHG = 0.243S + 5.688W +1.430P − 32.976

0.833

GHG = 0.238S + 5.972W −2.023P − 31.864

0.842

GHG = 0.241S + 5.971W −0.890I − 33.721

0.832

m2

m2

m2

W S

8.732

0.832

Q 8

1.386 >0.01

GHG = 0.237S + 5.814W −0.624D − 31.701

W S

14.953

m2

P 7

1.190 >0.01 0.777

W S

9.725

GHG = 0.233S + 5.181M −19.546

M 6

1.190 >0.01

m2

W S

4.328

0.835

D 5

1.000 >0.01

GHG = 0.241S + 5.886W −33.495

W

m2

Eigenvalue CI

0.950

m2

M S

Regression equation Adj. R2 VIF (unit: 103 t CO2eq )

1.358 >0.01

13.030

1.272 0.01

9.969

1.504 >0.01

11.216

8.947 0.01

1.918

1.102 >0.01

10.028

1.204 >0.01

15.952

1.193 >0.01

3.922

1.197 >0.01

11.144

1.006 >0.01

16.835

1.222 >0.01

2.083

1.190 >0.01

10.143

1.035 >0.01

15.763

Note M total mass of materials, W rock mass grade, S excavation area, D buried depth, P tunnel bias, Q rock mass quality, I net distance between tubes

included in the regression equation. The regression coefficients and t-test data are shown in Table 3.11. Only the regression coefficients of the excavation area and rock mass grade were significant (P = 0.000). Considering various factors, Equations No. 1 and 2 were selected for the analysis of the second round of regression equations. Durbin’s two-step method was followed to eliminate the correlations of the residuals of Equation No. 1. The case diagnosis function provided by SPSS was used to elucidate the outliers of the regression equations, and the two regression equations had a shared set of outliers (standard residual greater than three). The outliers were deleted and the regression equations were refitted. The residual characteristics of the new regression equations were then tested, and the results are shown in Table 3.11. The fitting effects of the new regression equations are shown in Fig. 3.6. The geological conditions of the outlier case are

76

C. Guo

Table 3.11 Regression coefficients and significance levels Independent variables

Regression equation (unit: 103 t CO2eq )

Adj. R2

W

GHG = 0.239S + 5.839W −1.258I − 0.245D −2.263Q + 1.697P −30.670

0.837

P S D Q I

Standard parameter

T

Sig

0.416

6.110

0.000

0.051

0.781

0.439

0.658

9.437

0.000

−0.011

−0.166

0.869

−0.116

−1.957

0.057

−0.798

−0.798

0.43

Note W rock mass grade, P tunnel bias, S excavation area, D buried depth, Q rock mass quality, I net distance between tubes

special in that the tunnel was crossing a fault rupture zone. Although the fault is not included in the influencing factors of this study, the existence of the fault resulted in lower emission prediction values than the actual emission value (Table 3.12).

3.4 Discussion Previous studies on carbon emissions rarely considered the construction parameters of highway tunnels, excluding the research conducted by Huang et al. [11], who found that the carbon emissions and excavation area of tunnels constructed through the drilling and blasting method are approximately linear under the same geological conditions. A larger excavation area results in higher material consumption and mechanical use, which aids in understanding the increase in carbon emissions with a larger excavation area, as shown in Fig. 3.3. Xu et al. [18] found that poorer rock mass conditions generate more carbon emissions during tunnel construction, which is consistent with the result of the correlation analysis for rock mass grade in this study. Under weak geological conditions, the construction difficulty increases, and more material input is required to ensure the safety of tunnel construction. Some meaningful findings were provided by the correlation analysis in this study. The variables correlated with tunnel emissions significantly included the buried depth, excavation method, excavation area, rock mass grade, and total mass of materials. Further analysis found a strong correlation between the rock mass grade and excavation method. It is recognized that the rock mass grade is a core indicator for selecting the tunnel construction method [6]. The buried depth is a categorization index in this research, and, according to the comparison of the mean values in Table 3.7, there are significant differences between the emissions of deep-buried and shallow-buried tunnels. Shallow-buried tunnels are usually located in weak, scattered and unstable surrounding rocks requiring strong construction support. It’s crucial to choose appropriate support strategies and construction methods to prevent tunnel collapse and ground subsidence. Since the full-face excavation method

3 Carbon Emission Prediction Method for Tunnel Construction

77

Fig. 3.6 Fitting effect of the new regression equations for the carbon emissions of tunnel construction. a Excavation area and rock mass grade as the independent variables, b total mass of materials as the independent variable [19]

may cause severe disturbance to the surrounding rocks and loosen the rock mass, the full-face excavation method in shallow-buried tunnels is prohibited. The design codes of highway tunnels of China stipulate that the surrounding rock stress of deep- and shallow-buried tunnels should be calculated by different methods. Shallow-buried tunnels’ construction considers the surrounding rock’s loose pressure only, while

78

C. Guo

Table 3.12 New regression equations for the carbon emissions of tunnel construction [19] No.

Independent variable

Regression equation (unit: 103 t CO2eq )

Adj. R2

D.W

Standard residual

LM test

1

M

GHG = 0.147M + 1.8

0.934

2.15

(−1.8, 2.7)

–

2

W

0.864 GHG = 0.218S + 5.828W −30.874

1.581

(−1.6, 2.7)

Random sequence

S

Note M total mass of materials, W rock mass grade, S excavation area

that of deep-buried tunnels must take into account the surrounding rock’s additional deformation pressure, resulting in differences in support structures’ material inputs [1]. The influence of materials’ total mass is more straightforward, as the higher the quantity of invested materials, the higher the carbon emissions. This study proposed two theoretical prediction formulas for construction emissions. Both prediction formulas are linear regression models, and are simple and practical. The first prediction formula takes the excavation area and rock mass grade as the dependent variables. The advantage of this is that the independent variable parameters are easily obtained, although the prediction accuracy is not particularly high. The second prediction method has high prediction accuracy when the total mass of materials in tunnel construction is available. The flexible use of the two prediction methods is conducive to estimating the emissions from tunnel construction. The above two approaches to forecasting carbon emissions from tunnel construction are currently applicable to Southwest China, but there are weaknesses in its applicability to other areas. For instance, the second prediction method merely concerns materials, total mass without paying attention to the inputs for electricity and other energy. The emission levels of six major regional grids in China are different, which will affect the regression equation’s predictive effects to some extent. Besides, due to the limitation of the accuracy in the survey and design data of each tunnel, some indicators in this study, which are classification indicators, will reduce the regression models’ ability to predict tunnel emissions. It should be pointed out that even if the indicators that are significantly related to tunnel emissions are obtained, it is still difficult for researchers to compare the tunnel construction emissions from the difference of a single variable. Table 3.8 shows the average value and standard deviation of tunnel construction emissions under different surrounding rock levels, burial depths and excavation methods. As for the level of surrounding rock, the average emissions from the tunnels of surrounding rock Grade III are less than one-third of that of surrounding rock Grade V. By and large, the emissions of tunnels tend to climb with the expansion of the excavation area, but obviously the excavation area is not sufficient to predict the emission values of tunnel construction with different surrounding rock levels. In particular, for tunnels of surrounding rock Grade V, huge differences still exist in the carbon emissions of

3 Carbon Emission Prediction Method for Tunnel Construction

79

tunnels with similar excavation areas. Even if the emissions of tunnels tend to increase with the total mass of the material, there is no guarantee that a higher material quality will correspond to a larger emission value of tunnels. This study provides studies of tunnel construction emissions with a novel idea: in the unified functional unit and research boundary, various factors affecting tunnel construction emissions were evaluated based on several case calculation results. With the assistance of the related theories and statistical software, the regression models forecasting the tunnel construction emissions have been built successfully. In the future, the potential factors affecting tunnel construction emissions will be explored with more sample data obtained, and thus, theoretical prediction equations suitable for larger regions will be established.

3.5 Conclusions This study provided an innovative analytical method to find out the factors influencing carbon emissions. The statistical method is effective when exploring the emission mechanism with numerous tunnel samples. In calculating carbon emissions, complicated calculation and huge amounts of data are needed for LCA method. Therefore, it may be not simple enough for engineers to estimate the emission level of a tunnel. This study proposes two theoretical models predicting the carbon emissions from tunnel construction. The models are simple and provide emission references at the design stage of tunnel projects. The conclusions are listed as follows: 1. 2.

In this study, the carbon emissions of a single tube in 49 tunnels ranged from 5.08 × 103 to 52.63 × 103 t CO2eq , with an average of 20.37 × 103 t CO2eq . The influencing factors correlated with carbon emissions are the total mass of materials, excavation area, excavation method, rock mass grade, and buried depth, from highest to lowest. The rock mass grade and excavation method are significantly correlated.

References 1. 招商局重庆交通科研设计院有限公司. JTG D-70-2010 公路隧道设计细则. 人民交通出版 社, 2010. China Merchants Chongqing Communications Technology Research & Design Institute Co. LTD (2010) Standards of the People’s Republic of China JTG D70-2010 road tunnel design rules. People’s Communication Press, Beijing (in Chinese) 2. 陈冲. 基于 LCA 的建筑碳排放控制与预测研究. 华中科技大学, 2013. Chen C (2013) Research on building carbon emission control and forecast based on LCA. Huazhong University of Science and Technology 3. 陈雨欣, 陈建国, 王雪青, 等. 建筑业碳排放预测与减排策略研究. 建筑经济, 2016 (10): 14– 18. Chen Y, Chen J, Wang X et al (2016) Research on carbon emission forecast and emission reduction strategy of construction industry. Constr Econ 10:14–18

80

C. Guo

4. Chau CK, Leung TM, Ng WY (2015) A review on life cycle assessment, life cycle energy assessment and life cycle carbon emissions assessment on buildings. Appl Energy 143 5. Durbin J (1970) Testing for serial correlation in least-squares regression when some of the regressors are lagged dependent variables. Econometrica 38. https://doi.org/10.2307/1909547 6. 关宝树. 隧道工程施工要点集. 人民交通出版社, 第二版, 2011. Guan B (2011) Key point in tunnel construction, 2nd ed. People’s Communications Press, Beijing 7. 高金贺, 黄伟玲, 蒋浩鹏. 城市交通碳排放预测的多模型对比分析. 重庆交通大学学报 (自 然科学版), 2020, 39(07): 33–39. Gao J, Huang W, Jiang H (2020) Multi-model comparative analysis of urban traffic carbon emission prediction. J Chongqing Jiaotong Univ (Nat Sci) 39(07):33–39 8. Guo C, Xu J, Yang L et al (2017) Energy-saving network ventilation technology of extra-long tunnel in climate separation zone. Appl Sci 7. https://doi.org/10.3390/app7050454 9. Guo C, Xu J, Yang L et al (2019) Life cycle evaluation of greenhouse gas emissions of a highway tunnel: a case study in China. J Clean Prod 211. https://doi.org/10.1016/j.jclepro. 2018.11.249 10. 何涛. 基于低碳化发展的区域交通碳排放影响因素分析及预测研究. 河北工业大学, 2017. He T (2017) Analysis and forecast of influencing factors of regional transportation carbon emissions based on low-carbon development. Hebei University of Technology 11. Huang L, Bohne RA, Bruland A et al (2015) Environmental impact of drill and blast tunnelling: life cycle assessment. J Clean Prod 86. https://doi.org/10.1016/j.jclepro.2014.08.083 12. 郝佳莹, 高健. 基于 NSGA-II 改进 BP 神经网络的建筑碳排放—碳减排预测模型. 建筑 节能, 2018 (44): 122–124. Hao J, Gao J (2018) Building carbon emission-carbon emission reduction prediction model based on NSGA-II improved BP neural network. J Build Energy Effic 44:122–144 13. 李子奈. 计量经济学. 高等教育出版社, 第二版, 2010. Li Z (2010) Econometrics, 2nd ed. Higher Education Press, Beijing 14. 刘菁, 赵静云. 基于系统动力学的建筑碳排放预测研究. 科技管理研究, 2018 (9): 219–226. Liu J, Zhao J (2018) Research on prediction of building carbon emissions based on system dynamics. Sci Technol Manage Res 9:219–226 15. 潘秀. 我国交通运输业碳排放影响因素及预测研究.中国矿业大学, 2018. Pan X (2018) Research on influencing factors and prediction of carbon emissions from transportation. China University of Mining 16. 交通公路工程定额站. JTGT B06-02-2007 公路工程预算定额. 北京: 人民交通出版社. Quota station for highway engineering. MOT (2007) JTGT B06-02-2007 highway engineering budget quota. China Standard Press, Beijing 17. 中国住建部. 全国统一施工机械台班费用定额. 中国计划出版社, 2011. The PRC MOHURD (2011) The drafting standard for the cost budget of the national unified construction machine team. China Planning Press, Beijing 18. Xu J, Guo C, Chen X et al (2019a) Emission transition of greenhouse gases with the surrounding rock weakened—a case study of tunnel construction. J Clean Prod 209 19. Xu J, Guo C, Yu L (2019b) Factors influencing and methods of predicting greenhouse gas emissions from highway tunnel construction in southwestern China. J Clean Prod 229. https:// doi.org/10.1016/j.jclepro.2019.04.260 20. 徐勇戈, 宋伟雪. 基于 FCS-SVM 的建筑业碳排放预测研究. 生态经济, 2019, 35(11): 37– 41. Xu Y, Song W (2019) Research on carbon emission prediction of construction industry based on FCS-SVM. Ecol Econ 35(11):37–41

Chapter 4

A Modular Calculation Method for the Carbon Emissions of Highway Tunnel Construction Based on the Chinese Standard Quota Jianfeng Xu

4.1 Introduction A clear inventory of building materials and energy usage is the key to calculating the carbon emissions from tunnel construction. The budget quota method has been widely used in the calculation of carbon emissions in China. He [7] used metro quotas to measure carbon emissions during the construction of open-cut stations. Wang [18] subdivided the construction process and established a carbon emission calculation model for the construction process through the National Unified Construction Engineering Basic Quota and the energy consumption of mechanical shifts. Researchers can effectively estimate the material and mechanical input of the unit engineering quantity through a perfect budget system. However, the process of using the budget quota is cumbersome and involves a large number of products and machinery [13]. Furthermore, the most critical defect is that existing studies need to repeat the inventory data calculation process when dealing with different tunnels, which consumes a lot of time and energy. In order to solve the problem of large workload and low reuse rate of traditional LCA inventory data processing, modular LCA began to be applied to product’s life cycle design. Modular design refers to the design of a series of functional modules based on the function, performance and specifications of the product, and different products are formed through the selection and combination of modules. Baldwin and Clark [2] believe that the core of modularity is the use of relatively independent smaller systems to form a complex product or process. To put it simply, modular design organically combines certain elements of the product to form a series of subsystems with specific functions, and uses the subsystem as a universal module to combine with other product elements to form a new system and produce a series of products with the same or different functions [23]. By applying the idea of modularity, Zhang [24] quantified the carbon emissions of the product’s functional modules and established a carbon emissions mapping model for products. Yu and Tao [22] introduced LCA modularization methods and cases for products in Product Life Cycle Design and Evaluation. © Southwest Jiaotong University Press 2022 C. Guo and J. Xu, Carbon Emission Calculation Methods for Highway Tunnel Construction, https://doi.org/10.1007/978-981-16-5308-7_4

81

82

J. Xu

Currently, the modular LCA method has been applied in agriculture, chemical, and construction industry. Jungbluth et al. [11] divided vegetable purchase into five modules according to the determinants of environmental effects and corresponding product characteristics, i.e., vegetable production, transportation, processing and distribution, packaging and consumption. Roches et al. [14] proposed a modular extrapolation method for agricultural LCA by dividing the existing crop inventory into nine modules corresponding to the main field and post-receiving activities, where each module was associated with key agricultural inputs. Such modular extrapolation method reduced the complexity of the inventory, saved the data processing time for the Life Cycle Inventory (LCI) by concentrating data on 9 key inputs, created common data sets, and determined the level of variability. Lee et al. [12] used the synthesis of a certain product as an example to compare the environmental performance of batch processing and continuous processing, which included the seven modules of production, washing, transportation, waste treatment, processing, washing, and facility. Hafner and Riiter [9] proposed a method for evaluating the environmental impact of wooden buildings. According to the European standards EN15978 and EN15804, LCA is divided into four modules based on various stages, i.e., the production stage, use stage, end-of-life stage, and external load of the system boundary. Traditional tunnel LCA research divides the life cycle into material production, construction, use and maintenance, and abandonment, and calculates the input and emissions of each phase. However, waste treatment, recycling and transportation often run through the various processes of the whole tunnel construction. Therefore, the input and emissions generated by the above process need to be further allocated to different stages, which increases the workload of inventory data collection and calculation. In order to solve the above problems, this research uses the LCA concept and calculation method to propose a modular calculation method for the carbon emissions of tunnel excavation and support. This method clarifies the correspondence of data flow between different tunnel construction procedures and transportation and material processing. It can fully and quickly call the unit engineering quantity’s input and emission data of the tunnel construction, and avoid the repeated calculation of the tunnel inventory data due to the change of the engineering quantity. After obtaining the tunnel engineering volume data, the input and emission data of each module can be directly called, which can help quickly complete calculating the carbon emissions from the tunnel construction. Section 4.2 of this chapter introduces the framework of the modular method and inventory data. The concepts of tunnel construction LCA modules and primitives are defined, and the method calculating the input and emission of each module is given. Section 4.3 carries out sensitivity analysis according to engineering examples, and defines the impact on carbon emissions of market-to-tunnel transport distance, transport vehicle types, sand and gravel recovery ratio, slag discharge transport distance and slag discharge vehicle load. Section 4.4 gives a summary of this chapter.

4 A Modular Calculation Method for the Carbon Emissions …

83

4.2 Methods and Data 4.2.1 Goal and Scope The purpose of this research is to provide a modular calculation method for evaluating material energy input and carbon emission during tunnel construction, and production and transportation of upstream products. Generally, a product’s complete life cycle refers to a series of stages from material production to the final dismantling and recycling, including material production, construction, operation, maintenance, dismantling, and recycling. This study’s system boundary consists of four parts: 1. 2. 3. 4.

Upstream material production, Tunnel site construction, Material transportation, Material collection and processing.

The drilling and blasting method is widely used in the excavation of highway tunnels in China. On-site lining construction generally includes the assembly and installation of steel mesh, bolt, steel frame, and molded lining. Besides, material collection and processing are also necessary. Sand and gravel, essential ingredients of concrete, can be recycled from tunnel slag, reducing the purchase cost of construction companies and the environmental pressure of mining. Material transportation activities are divided into two parts, i.e., transportation from the market to the tunnel construction site and transportation within the tunnel site. In this study, the functional unit was set as “excavation and lining construction per meter of the tunnel.” However, it isn’t easy to estimate the transportation data of construction machinery between different construction sites. Therefore, the installation, monitoring, and dismantling costs of various equipment were not covered within this study. The emissions at the operation, maintenance and abandonment stages are excluded, while the input and emissions of blasting and slagging is classified in the tunnel excavation procedure. In material collection and processing, the recycling of sand, crushed gravel, concrete mixing, and on-site water extraction are considered. Figure 4.1 shows a simplified flow chart of the lining construction. As shown in Fig. 4.1, the flow of materials and energy required for tunnel construction is relatively complicated. However, there is a close connection between on-site construction, material production, transportation, and material processing. Thus, it seems inefficient to divide input and carbon emissions only based on life cycles. Additionally, variation in the input of a single process will have a massive impact on the downstream material flow and energy flow, so it is not suitable to divide the modules based on life cycles either. Hence, the authors propose a process modular method calculating the carbon emissions for tunnel construction. However, tunnel construction is not a standardized product. Tunnel construction can adopt different construction procedures and designs based on the surrounding

84

J. Xu

Fig. 4.1 Flow chart of tunnel lining construction in the system boundary

rock grades. As shown in Fig. 4.2, the tunnel construction processes, which are independent of each other, are versatile in different tunnel projects, thereby conforming to the characteristics of modularity. With the tunnel lining process as the core, the upstream material production, transportation, and material processing are integrated vertically to obtain the module’s overall input and emissions. The emission calculation steps of each module are summarized as follows:

Fig. 4.2 Composition of the modules

4 A Modular Calculation Method for the Carbon Emissions …

1.

2.

3.

4.

5.

6.

85

Calculate the input of materials and mechanical shifts for on-site construction in Module N based on the Highway Engineering Budget Quota. Convert the machine-team into fuel consumption to obtain the on-site input of material, energy, and carbon emissions. Specify the source of materials required for on-site construction in Module N, i.e., dividing the source of materials into two types: direct market purchase, and on-site collection and processing. Determine the material types to be collected and processed on-site in Module N. Then, calculate the material energy input and emissions for material collection and processing based on the amount of material input in Step 1. Divide materials into two parts. One is the materials bought from the market, as to which it is to calculate the energy input and carbon emissions from the transportation between the market and tunnel site and internal transportation on the site. The other is the materials collected on-site, as to which it is to calculate the energy input and carbon emissions of transporting materials within the tunnel site. Accumulate the input and emissions of on-site construction, material transportation, material collection and processing, and material production. The cumulated sum is the input and emission value of Module N. Calculate the input and emission of Module N + 1. Repeat Steps one to five until the calculations of input and emissions of all modules are completed.

4.2.2 Inventory Analysis 4.2.2.1

Sources of Inventory Data

The inventory data contains the foreground and background data. Foreground data is the quantitative value of a unit process or an activity obtained from direct measurement or a calculation based on direct measurement at its source [17]. Engineering quantity is an essential part of foreground data and generally derived from design data, technical manuals, or statistical data from related organizations. The engineering quantity data were converted into labor, materials, and mechanical shifts consumed during tunnel construction based on JTG/T 3832-2018 Highway Engineering Budget Quota [21]. The energy consumption per unit shift was obtained from JTG/T 3833-2018 Highway Engineering Machinery Shift Cost Quota [16], which converted the mechanical shifts into fuel consumption, where mechanical shift refers to the efficiency of unit machinery in eight hours. Background data are indirectly measured, calculated, or obtained quantified values of a unit process [17]. Emission factors are a type of background data, and they can be obtained from Intergovernmental Panel on Climate Change, life cycle databases, existing documents and specifications.

86

4.2.2.2

J. Xu

Modular Calculation Methods for Input and Carbon Emissions

Carbon emissions of a module contains three parts: site construction and material production, material transportation, and material collection and processing. For a certain module, the engineering quantity is set to be u. The input and carbon emissions for on-site construction and material production are given (Eqs. 4.1 and 4.2). Iu =



Muj +

j

Gu =





Cuk × Fk

(4.1)

Cuk × Fk × E k

(4.2)

k

Muj × E j +

j

 k

where, I u is the input of materials and energy required for on-site construction and material manufacture, Gu is the carbon emissions of on-site construction and material manufacture, M uj is the input of Type-J material, E j is the emission factor of Type-J material, C uk is the shift amount of Type-K construction machinery, F k is the energy consumption of Type-K construction machinery for a unit shift, E k is the emission factor of the energy used by the Type-K machinery. Highway Engineering Budget Quota provides the values and units of M uj and C uk , whereas Highway Engineering Machinery Shift Cost Quota provides the values and units of F k . The values of E j and E k can be obtained from Table 1.5. Equations 4.1 and 4.2 calculate the input and carbon emissions of upstream material production and on-site construction processes. However, it does not consider material transportation and processing. Transportation activity is a vital part of tunnel construction and its modular inputs and emissions from the market to the construction site are express as Eqs. 4.3 and 4.4. Im1 = G m1 =





m × Dc × Fc

(4.3)

m × Dc × Fc × E c

(4.4)

where, I m1 is the energy input for transporting materials from the market to the tunnel site, Gm1 is the carbon emissions of transporting materials from the market to the tunnel site, m is the load capacity of the Type-C carrier, Dc is the transportation distance between the market and tunnel site, F c is the energy consumed for transporting one-t material by one-kilometer using the Type-C carrier, E c is the emission factor of the energy used by the Type-C carrier. The m and F c values of different vehicles, obtained from the carbon emission calculation method standard [5], are listed in Table 4.1. For transporting materials within the construction site, the corresponding input and emissions are express as Eqs. 4.5 and 4.6.

4 A Modular Calculation Method for the Carbon Emissions … Table 4.1 The energy consumption of long-distance transportation

Im2 =

Carrier

m/t

Fc

Energy type

Heavy diesel truck

10

0.075 kg/(t km)

Diesel

Heavy diesel truck

18

0.059 kg/(t km)

Diesel

Heavy diesel truck

30

0.036 kg/(t km)

Diesel

Heavy diesel truck

46

0.026 kg/(t km)

Diesel

Electric locomotive

/

0.010 kWh/(t km)

Electricity

 c

G m2 =

87

 c

(Ccm1 + k × Ccma ) × Fc

(4.5)

(Ccm1 + k × Ccma ) × Fc × E c

(4.6)

m

m

where, I m2 is the energy input for transporting materials on the construction site, Gm2 is the carbon emissions on the construction site, C cm1 is the shift amount for transporting Type-M materials by a basic distance such as one kilometer for a Type-C carrier, C cma is the shift amount for transporting Type-M material by an additional distance a for a Type-C carrier, k is the amount of additional transportation distance a, F c is the fuel consumption amount for the Type-C carrier in one shift, E c is the emission factor of the energy used by the Type-C carrier. The values and units of C cm1 , C cma , and a was obtained from the Highway Engineering Budget Quota, whereas F c was obtained from the Highway Engineering Machinery Shift Cost Quota. Several types of vehicles and transportation materials were involved in this study. Table 4.2 lists the emission E c of material transportation obtained from Table 1.5. Under actual conditions, not all construction materials were purchased from the market. The contractor can use the waste soil and rock excavated from the tunnel as material resources for sand and gravel. Additionally, because the tunnel was far from the city, the concrete was often mixed on-site with cement, sand, and gravel. The input and emissions of processing materials were given (Eqs. 4.7 and 4.8). Ir =



Mrj +

j

Gr =

 j

Mrj × E j +



Crk × Fk

(4.7)

Crk × Fk × E k

(4.8)

k

 k

where, I r is the energy input for recycling materials, Gr is the carbon emissions for recycling materials, M rj is the input for Type-J materials, E j is the emission factor for Type-J materials, C rk is the shift amount for Type-K construction machinery, F k is the energy consumption per shift for Type-K construction machinery, E k is the emission factor for the energy used by Type-K construction machinery.

88

J. Xu

Table 4.2 The energy consumption and shift amount for the carriers Carrier

Load

Material

Unit freight volume

Shift amount for the initial 1-km-transportation

Shift amount for one additional 1-km-transportation

Lorry

8t

Wood

100 m3

1.63

0.13

Steel

100 t

1.45

0.1

Cement

100 t

1.75

0.09

Wood

100 m3

1.36

0.11

Steel

100 t

1.23

0.08

Cement

100 t

1.48

0.08

Wood

100 m3

0.91

0.07

Steel

100 t

0.83

0.05

Cement

100 t

1.01

0.05

Wood

100 m3

0.65

0.05

Steel

100 t

0.6

0.04

Cement

100 t

0.72

0.04

Soil, sand

100 m3

0.62

0.13

Gravel

100 m3

0.66

0.14

Stone

100 m3

0.81

0.17

Soil, sand

100 m3

0.53

0.11

Gravel

100 m3

0.57

0.11

Stone

100 m3

0.7

0.13

Soil, sand

100 m3

0.43

0.09

Gravel

100 m3

0.5

0.11

Stone

100 m3

0.57

0.12

Soil, sand

100 m3

0.38

0.08

Gravel

100 m3

0.39

0.08

Stone

100 m3

0.48

0.11

Soil, sand

100 m3

0.28

0.07

Gravel

100 m3

0.3

0.06

Stone

100 m3

0.37

0.09

Soil, sand, gravel

100 m3

0.26

/

Stone

100 m3

0.38

/

Soil, sand, gravel

100 m3

0.15

/

Stone

100 m3

0.22

/

Soil, sand, gravel

100 m3

0.12

/

Stone

100 m3

0.17

/

Concrete

100 m3

1.176

0.07

10 t

15 t

20 t

Dump truck

8t

10 t

12 t

15 t

20 t

Loader

1 m3

2 m3

3 m3

Transit mixer truck

8t

Diesel consumption for one shift/kg 44.95

50.29

61.72

81.14

49.45

55.32

61.6

67.89

77.11

49.03

92.86

115.15

100.57

4 A Modular Calculation Method for the Carbon Emissions … Table 4.3 Mechanical input for sieving sand and crushed gravel per 100 m3 pile

Procedure

89

Machinery type

Tunnel waste slag screening

Gravel mining

m3

1 wheel loader

Crk

Fk

1.2

49.03 kg diesel

Drum screening 5.8 machine

12.98 kWh

250 × 400 mm jaw crusher

35.7 kWh

3.42

Drum screening 3.48 machine

12.98 kWh

The Highway Engineering Budget Quota provides the values and units of M rj and C rk , whereas Table 4.3 lists the number of mechanical shifts and fuel consumption per unit volume of waste slag screening and gravel collection. The Highway Engineering Machinery Shift Cost Quota provides the value and unit of F k , whereas the values of E j and E k can be obtained from Table 4.1. In summary, Eqs. 4.1–4.8 provide the calculation methods of input and carbon emissions for the unit processes of tunnel construction, material transportation, and material collection and processing. Suppose tunnel construction contains n modules, and each module corresponds to a tunnel construction unit process, several material transportation processes, and processing unit processes. In that case, the overall input and emissions of the site construction, material transportation, and processing of unit processes are the module’s input and emissions, express as Eqs. 4.9 and 4.10. I = G=

 n



Ij =

n

Gj =

 n



Itu +

n

G tu +



 n

Icm1 +

n

G cm1 +



Icm2 +

n

 n

G cm2 +



Ir m

(4.9)

Gr m

(4.10)

n

 n

where, I is the overall input of materials and energy for tunnel construction, G is the overall carbon emissions of tunnel construction, I j is the input of materials and energy for Module J, Gj is the carbon emissions for Module J.

4.2.2.3

Calculation Pathway for Unit Engineering Quantity

Tunnel construction involves various materials and machinery, which requires large amounts of data calculation. Traditional emission calculations of tunnels emphasize inventory and background data, where researchers obtained tunnel foreground data from the engineering volume, budget quota, and energy consumption of shifts, and then calculated the emissions, as shown in Fig. 4.3. The engineering quantity of different tunnels is a variable that changes with the changes in tunnel design parameters. Even if the quotas’ data have not changed in Highway Engineering Budget

90

J. Xu

Fig. 4.3 Traditional pathways of carbon emission calculation for tunnel construction

Fig. 4.4 Calculation pathway of carbon emissions for unit engineering quantity

Quota and Highway Engineering Machinery Shift Cost Quota, the foreground data still needs to be calculated repetitively, which consumes a lot of time and energy. In this study, the authors improved the calculation method, by introducing the tunnel engineering volume in the last step of the calculation, so as to establish a callable module input and emission database of specific engineering volume, avoiding the introduction of variables in the early calculation period, accelerating the calculation speed, and increasing the utilization rate of the database. The calculation path is shown in Fig. 4.4. Under certain working conditions, the input–output list of unit engineering quantities is constant and can be reused as the subsequent list of data calculation. The primitive is defined as a module with unit engineering quantity of on-site tunnel construction. The input and carbon emissions of an individual primitive can be predetermined (determined value or interval value). Then researchers specify the number of primitives in each module. For Module J, the number of primitives is aj . And the total input and carbon emissions are given using Eqs. 4.11 and 4.12. I =

n 

EI j × a j

(4.11)

EG j × a j

(4.12)

j

G=

n  j

4 A Modular Calculation Method for the Carbon Emissions …

91

where, I is the overall input of materials and energy for tunnel construction, G is the overall carbon emissions from tunnel construction, aj is the number of primitives in Module J, E Ij is the input of materials and energy for the primitives of Module J, E Gj is the carbon emissions for the primitives of Module J.

4.2.2.4

Calculation Example of Input and Carbon Emissions for a Primitive

The shotcrete process was considered an example to illustrate the specific method for calculating the input and emissions of primitives. Shotcrete is a unit process of the surrounding rock support. Regardless of transportation and material collection and processing, every 10 m3 of concrete sprayed requires 0.01 m3 of wood, 5.628 t of cement, 24 m3 of water, 7.2 t of medium-coarse sand, 6.84 m3 of gravel, 1.29 shifts of concrete sprayers, and 0.78 shift of 20 m3 /min electric air compressors. These data were obtained from the Highway Engineering Budget Quota. Each shift of concrete spraying machine consumes 43.01 kWh of electricity, and each shift of 20 m3 /min electric air compressor consumes 601.34 kWh of electricity. The mechanical energy consumption data were obtained from the Highway Engineering Machinery Shift Cost Quota. To summarize, 10 m3 shotcrete requires 0.01 m3 of wood, 5.628 t of cement, 24 m3 of water, 7.2 t of medium-coarse sand, 6.84 m3 of gravel, and 524.53 kWh of electricity. According to Eq. 4.2, using the emission factor data from Table 1.5, the site construction of 10 m3 of shotcrete and material production emits 4.48 t CO2eq . Some specific settings were offered for calculating the emissions from material transportation, collection, and processing. Material loss considered, 10 m3 of shotcrete needs 12 m3 of concrete. Moreover, 50% medium-coarse sand and 100% crushed stone were obtained through material collection and processing. The sand and gravel collected from tunnel slag were sent to the concrete mix station, 500 m away from the tunnel portal. The tunnel was 1 km long. The waste disposal site and market were 10 and 500 km away from the tunnel portal. Wood is used for installing lining structures, and its density is 750 kg/m3 . Table 4.4 lists the unit energy consumption of machinery required for material transportation and processing. According to Eqs. 4.2–4.5, the input and carbon emissions of one cubic metre of shotcrete is calculated, as shown in Table 4.5, and the total carbon emission is 504.147 kg CO2eq .

4.2.3 Sensitivity Analysis of Settings for Material Transportation, Collection, and Processing LCA researches require specific assumptions and settings to reduce the research difficulty. The difference in basic assumptions increases the difficulty in comparing

92

J. Xu

Table 4.4 Unit energy consumption of machinery in material transportation and processing [6] Process

Material

Machinery

Energy Energy Energy consumption’s consumption’s consumption’s value unit fuel

Transportation Wood, Heavy fuel from market to cement, steel, truck (30 t) the tunnel site sand, explosive

0.036

kg/(t km)

Transportation Wood inside the Steel tunnel site Sand

Lorry (15 t)

0.604

kg/m3

Diesel

Lorry (15 t)

0.543

kg/t

Diesel

Dump truck (20 t) Wheel loader (3 m3 )

0.796

kg/m3

Diesel

Gravel

Dump truck (20 t) Wheel loader (3 m3 )

0.749

kg/m3

Diesel

Waste rocks

Dump truck (20 t) Wheel loader (3 m3 )

0.850

kg/m3

Diesel

Concrete

Concrete mixer truck (6 m3 )

1.242

kg/m3

Diesel

Explosive

Lorry (15 t)

0.765

kg/t

Diesel

Concrete mixing station (60 m3 /h)

2.756

kWh/m3

Electricity

Water

Pump

0.607

kWh/m3

Electricity

Gravel

Electric jaw 1.672 crusher (250 × 400 mm) Drum screening machine

kWh/m3

Electricity

Sand

Wheel loader (1 m3 ) Drum screening machine

0.588

kg/m3

Diesel

0.753

kWh/m3

Electricity

Material Concrete transportation and processing

Diesel

4 A Modular Calculation Method for the Carbon Emissions … Table 4.5 Input and carbon emissions for one cubic meter shotcrete

93

Materials or energy

Value

Unit

Carbon emissions/kg CO2eq

Wood

0.001

m3

0.146

Cement

562.800

kg

395.283

Sand

514.800

kg

1.030

Water

2.400

m3

14.16

Electricity

57.706

kWh

56.113

Diesel

11.486

kg

24.936

different studies. The input of the unit engineering quantity of on-site construction was fixed. Therefore, the input and emissions of this part can be used directly in other studies. However, transportation and material collection face many influencing factors and there may appear significant differences based on different on-site construction conditions. Therefore, it is necessary to analyze how the parameters of material transportation, collection, and processing influence module emissions. Sensitivity analysis studies the impact of the change on several factors on related key indicators from the perspective of quantitative analysis [4]. The essence is to explain the law of how key indicators are affected by the variation in these factors by changing the values of related variables in turn. This study adopts a method of controlling a single variable to analyze the impact on carbon emissions during tunnel construction of the transportation distance from the market to the tunnel site, type of transport vehicle, recycling ratio, slag transportation distance, and slag transportation vehicle load. Group zero is set as the control condition to facilitate the comparison, whereas Groups one–five are set as the test conditions. The parameter settings of each group of working conditions are listed in Table 4.6. The selected road tunnel in China is 1012 m long and has a two-way four-lane design for a designed speed of 80 km/h. The tunnel was constructed using the borehole-blasting method. The rock masses of Grade V, Grade IV, and Grade III were 53 m, 720 m, and 239 m, respectively. The engineering quantities for various surrounding rock grades are shown in Table 4.7.

4.3 Results and Discussion 4.3.1 Inventories of Input and Carbon Emissions for Primitives Material transportation, collection, and processing require considering factors, such as material recovery ratio and transportation distance. The assumptions of transportation, collection, and processing for the case study are shown in Table 4.8. Suppose the conditions of material transportation, collection, and processing remain constant.

94

J. Xu

Table 4.6 Parameter settings of each group Group

Distance between market and tunnel site/km

Types of Recycling vehicles ratio of transporting sand/% materials from market to tunnel

Recycling ratio of gravel/%

Transportation Slag distance of transportation tunnel slag/km vehicle load/t

0

500

Diesel truck (30 t load)

50

100

10

20

1

100

Diesel truck (30 t load)

50

100

10

20

200

Diesel truck (30 t load)

50

100

10

20

300

Diesel truck (30 t load)

50

100

10

20

400

Diesel truck (30 t load)

50

100

10

20

500

Diesel truck (30 t load)

50

100

10

20

500

Diesel truck (10 t load)

50

100

10

20

500

Diesel truck (18 t load)

50

100

10

20

500

Diesel truck (30 t load)

50

100

10

20

500

Diesel truck (46 t load)

50

100

10

20

500

Electric locomotive transportation

50

100

10

20

500

Diesel truck (30 t load)

0

0

10

20

500

Diesel truck (30 t load)

20

20

10

20

500

Diesel truck (30 t load)

40

40

10

20

500

Diesel truck (30 t load)

60

60

10

20

500

Diesel truck (30 t load)

80

80

10

20

500

Diesel truck (30 t load)

100

100

10

20

500

Diesel truck (30 t load)

50

100

3

20

2

3

4

(continued)

4 A Modular Calculation Method for the Carbon Emissions …

95

Table 4.6 (continued) Group

5

Distance between market and tunnel site/km

Types of Recycling vehicles ratio of transporting sand/% materials from market to tunnel

Recycling ratio of gravel/%

Transportation Slag distance of transportation tunnel slag/km vehicle load/t

500

Diesel truck (30 t load)

50

100

6

20

500

Diesel truck (30 t load)

50

100

9

20

500

Diesel truck (30 t load)

50

100

12

20

500

Diesel truck (30 t load)

50

100

15

20

500

Diesel truck (30 t load)

50

100

10

8

500

Diesel truck (30 t load)

50

100

10

10

500

Diesel truck (30 t load)

50

100

10

12

500

Diesel truck (30 t load)

50

100

10

15

500

Diesel truck (30 t load)

50

100

10

20

Table 4.7 Engineering quantities for the tunnel case Process

Rock mass grade III

Rock mass grade IV

Rock mass grade V

Tunnel excavation (m3 )

200,425

513,065

5530

Shotcrete

(m3 )

5617

25,826

469

Steel frame (kg)

0

2,282,517

66,833

Connecting bars (kg)

0

579,141

7490

Mortar bolt (kg)

244,825

1,208,926

23,959

Steel mesh (kg)

223,465

504,633

4997

Secondary lining (arch and wall) (m3 )

18,977

45,235

588

Secondary lining (inverted arch) (m3 )

0

17,553

226

Rebar (kg)

0

0

80,042

96

J. Xu

Table 4.8 Assumptions for transportation, collection, and processing Basic parameters and assumptions On-site transportation of waste soil and rock

The waste soil and rock are collected at ten km from the entrance of the cave, and a 20-ton dump truck is used to load and unload the soil and rock with a wheel loader

Material collection and processing

50% of sand and 100% of crushed gravel are recovered from tunnel slag

On-site transportation of materials

A 15-ton truck is used to transport wood, steel and blasting materials. A 20-ton dump truck is used to transport soil, sand, stone chips, and gravel. The material accumulation site and the concrete mixing station are 1 km away from the entrance of the cave. The length of the tunnel is 1 km. Use concrete trucks to transport concrete, with an average transport distance of 1.5 km

Transport of materials from the market to the tunnel

The transportation distance of building materials is set to be 500 km, and heavy-duty diesel trucks are used for transportation (30 t load)

In that case, the input and emissions of each primitive are fixed and can be directly used in other studies on highway tunnels in China. The percentage of carbon emissions from material collection and processing, onsite transportation, market-to-tunnel transportation, and material production is calculated, as shown in Fig. 4.5. Emissions from material production and on-site construction, material collection and processing, and market-to-tunnel material transportation accounted for more than 70%, less than 1.6% (weak impact), and 0.2–10% of total emissions, respectively. Regarding the excavation processes, carbon emissions from

Fig. 4.5 Proportion of each module’s carbon emissions

4 A Modular Calculation Method for the Carbon Emissions …

97

material transportation on the tunnel site accounted for more than 19% of modular emissions (primarily from the slagging of the tunnel). For non-slagging processes, the transport emissions accounted for less than 1.8%, which was a significant drop. From the perspective of modules, material transportation and processing emissions account for 20–30% of excavation and arch wall concrete emissions, whereas in other processes, material transportation and processing emissions account for less than 10% of emissions. The primitive inputs that exclude material transportation and processing are shown in Table 4.9, whereas the assumed primitive inputs based on Table 4.8 are shown in Table 4.10. If the assumptions for the transportation and processing of different materials are provided, the corresponding primitive inputs and emissions can be quantified. The most important is that the input and carbon emissions of primitives will not be affected due to the variation in engineering quantities. Different from standard industrial products, tunnel designs are affected by actual conditions, construction requirements, and geological conditions, and their design parameters vary [15]. So, this research provides a method calculating the input and carbon emissions of unit engineering quantity from the perspective of unit processes. Since the primitive inventory data are obtained from the Chinese national standard quotas, the calculation method of this study is applicable to all drilling and blasting tunnels in China [19, 25]. The input and carbon emissions can represent the current clean production level of drilling and blasting construction in China. Besides, the carbon emission factors of this study are mainly from local literature [19, 20], so this calculation method is extremely representative and usable.

4.3.2 Case Study Based on Tables 1.5, 4.7, and 4.10, the input and carbon emissions of each module is calculated, as shown in Table 4.11. The carbon emissions from tunnel construction were presented as a Sankey diagram, as shown in Fig. 4.6. The materials and energy with the highest emissions on the left side are cement, steel, electricity, and diesel, and the modules with the highest emissions on the right side are the secondary lining (arch wall), shotcrete, tunnel excavation, and steel frame. The results are consistent with existing literature [8, 17, 19]. By using modular inventory data, researchers can not only calculate the emissions of each module, but also specify the source and proportion of carbon emissions of each module. Considering the secondary lining (arch wall) as an example, the main emission sources are cement, diesel, and steel.

4.3.3 Sensitivity Analysis According to the parameter settings listed in Table 4.6, the material transportation distance between the market and tunnel site, transportation vehicle types, sand and gravel recovery ratio, slag discharge distance, and slag discharge vehicle load are used as single variables to analyze the influence of the aforementioned factors on

Wood/m3

0.001

0.001

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.001

0.011

0.000

0.000

Primitive

E1

E2

E3

E4

E5

E6

E7

E8

E9

E10

E11

E12

E13

E14

1.028

2.540

10.703

0.000

1.026

1.045

1.020

1.150

1.075

0.179

0.209

0.284

0.326

0.368

Steel/kg

0.000

386.90

386.90

562.80

0.000

0.347

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

Cement/kg

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.305

0.767

0.985

1.038

1.091

Explosive/kg

0.000

862.290

862.290

1029.60

0.000

0.343

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

Sand/kg

0.000

1138.500

1138.500

1026.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

Gravel/kg

0.000

0.600

0.600

2.400

0.000

0.013

0.000

0.000

0.000

0.250

0.250

0.250

0.350

0.350

Water/m3

Table 4.9 Material energy input of primitives (excluding material transportation and processing) Electricity/kWh

0.041

2.505

5.037

52.453

0.128

1.407

0.000

0.289

0.067

9.658

7.919

8.332

12.845

14.186

Gasoline/kg

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.018

0.018

0.051

0.051

0.051

0.051

0.051

Diesel/kg

0.000

0.000

0.000

0.000

0.000

0.003

0.000

0.000

0.000

0.015

0.015

0.015

0.015

0.022

98 J. Xu

Wood/m3

0.0006

0.0005

0.0004

0.0004

0.0004

0.000

0.000

0.000

0.000

0.000

0.001

0.011

0.000

0.000

Primitive

E1

E2

E3

E4

E5

E6

E7

E8

E9

E10

E11

E12

E13

E14

1.028

2.540

10.703

0.000

1.026

1.045

1.020

1.150

1.075

0.179

0.209

0.284

0.326

0.368

Steel/kg

0.000

386.900

386.900

562.800

0.000

0.347

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

Cement/kg

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.305

0.767

0.985

1.038

1.091

Explosive/kg

Table 4.10 Primitive input (based on the assumptions in Table 4.8)

0.000

431.145

431.145

514.800

0.000

0.172

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

Sand/kg

0.000

0.600

0.600

2.400

0.000

0.013

0.000

0.000

0.000

0.250

0.250

0.250

0.350

0.350

Water/m3 Electricity/kWh

0.041

7.487

10.020

57.706

0.128

1.407

0.000

0.289

0.067

9.658

7.919

8.332

12.845

14.186

Gasoline/kg

0.000

0.000

0.000

0.000

0.000

0.000 v

0.000

0.018

0.018

0.051

0.051

0.051

0.051

0.051

0.043

14.932

36.964

20.727

0.043

0.114

0.043

0.048

0.045

1.663

1.666

1.671

1.870

2.077

Diesel/kg

4 A Modular Calculation Method for the Carbon Emissions … 99

100 Table 4.11 Input and carbon emissions of tunnel lining

J. Xu Material/energy

Input value

Input unit

Wood

333.64

m3

48,813

Steel

1,023,128.50

kg

2,362,402

Cement

6,976,884.79

kg

4,897,773

Explosive

74,857.78

kg

19,680

Sand

7,217,815.57

kg

28,871

Water

44,293.27

m3

26,134

Electricity

1,489,570.56

kWh

1,448,468

Gasoline

11,638.10

kg

22,352

Diesel

663,880.04

kg

1,441,284

Total

–

–

10,295,777

Fig. 4.6 Emission flow between emission sources and modules (unit: kg CO2eq )

Carbon emissions/kg CO2eq

4 A Modular Calculation Method for the Carbon Emissions …

101

tunnel excavation and support emissions. Figure 4.7 shows the sensitivity analysis results for different factors. Material transportation from the market to the tunnel site and the collection and processing of materials have a significant impact on carbon emission calculation results, whereas the impact of material transportation within the tunnel site is weak. Existing researches did not analyze how setting and assumptions influence LCA results of tunnels [1, 3, 10, 19], so it is not available to compare the results of different studies.

Fig. 4.7 Sensitivity analysis of carbon emissions from tunnel excavation and support. a Material transportation distance between market and the tunnel site, b transportation vehicle types, c sand and gravel recovery ratio, d slag discharge distance, e slag discharge vehicle load capacity

102

Fig. 4.7 (continued)

J. Xu

4 A Modular Calculation Method for the Carbon Emissions …

103

When a diesel truck (30 t load) was used as the vehicle, the emissions increased by 128.24 t CO2eq for every 100 km increase in the haul distance. The maximum emission fluctuation range was 4.91% compared to the original operating conditions. The largest emission gap between different vehicles was 1729.78 t CO2eq , and the corresponding fluctuation range was 16.54%. For every 20% increase in the proportion of sand and gravel recycling, the tunnel construction emissions increased by 257.59 t CO2eq , and the maximum emission fluctuation range was 12.32%. When the slag tapping distance increased by three kilometers, the tunnel slag transportation emission increased by 67 t CO2eq , which accounted for less than 2.6% of the total emissions. The slag transport vehicle’s weight has a minor impact (not exceeding one percentage) on the overall emissions of the tunnel. Considering that the on-site transportation quantity of other materials is smaller than that of the slagging process, it can be determined that on-site transportation has a smaller impact on construction emissions. The innovations and advantages of this study lie in the following aspects: 1.

2.

Existing LCA research focuses on the main processes of tunnel construction and ignores the influence of actual conditions among different tunnels on the research results. However, this study considers the actual conditions of different tunnels. By considering material transportation, collection, and processing as the research objects, the key roles of the material transportation distance between the market and tunnel site, transportation vehicle, and recycling ratio of slag and stone on the carbon emissions of tunnel construction are analyzed, thereby providing a scientific basis for energy conservation and pollution reduction in subsequent tunnel constructions. This study provides the calculation methods of input and carbon emissions for modules and primitives, thereby providing good versatility and ability to overcome difficulties in reusing the research results caused by the difference in LCA assumptions. Simultaneously, based on the results of sensitivity research, the influence of transportation inside the tunnel site on the overall carbon emissions of tunnel construction can be ignored, which simplifies the procedure of calling after module modification.

Given the huge size of Chinese market, materials with different production processes can be used in different regions [21], and, as a result, the emission factors of materials and energy may be different from those discussed in this article. Furthermore, this research provides the input data of materials and energy so that other researchers can obtain the basic primitive input and emission database that satisfy the requirements in actual projects according to the actual emission factors. The merits of the process modular method should be highlighted. As the process modules and stage modules share identical unit processes and input data, the calculation result will show no difference. However, the process module has a significant advantage: it provides the input and carbon emission inventory of unit engineering quantity. Tenders provide only the engineering quantity of one-meter tunnel lining construction. The engineering quantity can be converted to input data of materials and energy within budget quota systems. As a result, the process module method

104

J. Xu

will be far more convenient to calculate the total carbon emissions. When the engineering quantity data or lining design changes, researchers will not be required to recalculate the emissions from unit processes. The calculation method focuses on the input and carbon emissions of unit engineering quantity for on-site construction. The engineers who have little knowledge about LCA can use the method to calculate the carbon emissions from tunnel construction, bringing huge convenience in emission accounting.

4.4 Conclusion This study proposes a modular calculation method for carbon emissions during tunnel excavation and lining construction and expounded the modular system boundary theory of tunnel construction. The calculation formulas for input and carbon emissions of each module are provided in Eqs. 4.1–4.10. Additionally, the concept of primitives is firstly proposed as a supplement to the module. The novelty lies in the application of unit engineering quantity in carbon emission calculation. For the same construction process of different tunnels, the material and energy input and carbon emission data of the same primitive can be reused, thereby reducing the workload of carbon emission calculation and increasing the tunnel LCA’s utilization rate database. The input and carbon emissions of the on-site construction of each primitive were fixed, but the inputs of material transportation, collection, and processing parts were affected by the assumed conditions. This study provides a list of input and carbon emissions of each module under given settings. Furthermore, a sensitivity analysis was performed based on engineering cases to specify the emission flow relationship between material energy and modules. The authors explored the key factors influencing material transportation, collection, and processing, i.e., the distance between the market and tunnel site, type of vehicle, and material recovery proportion. Slag transportation on the tunnel construction site had a minor impact on carbon emissions.

References 1. Buyle M, Braet J, Audenaert A (2013) Life cycle assessment in the construction sector: a review. Renew Sustain Energy Rev 26 2. Baldwin CY, Clark KB (1997) Managing in an age of modularity. Harv Bus Rev 75 3. Chang B (2009) Initial greenhouse gas emissions from the construction of the California high speed rail infrastructure: a preliminary estimate. University of California, Davis, Ann Arbor 4. 陈灵均. 公路隧道交通碳排放特性与影响机制研究. 重庆: 重庆交通大学, 2017. Chen L (2017) Research on carbon emission characteristics and influencing mechanism of highway tunnel traffic. Chongqing Jiaotong University 5. 郭春, 郭雄, 徐建峰, et al 隧道施工通风系统碳排放边界研究. 2016 中国隧道与地下工程 大会 (CTUC) 暨中国土木工程学会隧道及地下工程分会第十九届年会论文集. 成都: 现 代隧道技术, 2016. Guo C, Guo X, Xu J et al (2016) Study on the carbon emission boundary of ventilation system in tunnel construction. Paper presented at the proceedings for 2016 China

4 A Modular Calculation Method for the Carbon Emissions …

6.

7.

8. 9.

10.

11.

12.

13.

14.

15.

16.

17.

18.

19.

20.

21.

105

tunnel and underground engineering conference (CTUC) and the 19th annual conference of the tunnel and underground engineering branch of the Chinese civil engineering society. Modern Tunnelling Technology, Chengdu 郭春, 徐建峰, 张佳鹏. 隧道建设碳排放计算方法及预测模型. 隧道建设, 2020, 40(8): 140. Guo C, Xu J, Zhang J (2020) Calculation method and prediction model of carbon emission from tunnel construction. Tunnel Constr 40(8):140 贺晓彤. 城市轨道交通明挖车站建设碳排放计算及主要影响因素分析. 北京: 北京交通大 学, 2015. He X (2015) Calculation of carbon emissions and analysis of main influencing factors in the construction of open-cut stations. Beijing Jiaotong University Huang L, Bohne RA, Bruland A et al (2015) Environmental impact of drill and blast tunnelling: life cycle assessment. J Clean Prod 86. https://doi.org/10.1016/j.jclepro.2014.08.083 Hafner A, Rüter S (2018) Method for assessing the national implications of environmental impacts from timber buildings—an exemplary study for residential buildings in Germany. Wood Fiber Sci 50. https://doi.org/10.22382/wfs-2018-047 蒋树屏, 林志, 王少飞. 2018 年中国公路隧道发展. 隧道建设 (中英文), 2019 (7), 1217– 1220. Jiang S, Lin Z, Wang S (2018) China’s road tunnel development in 2018. Tunnel Constr (7):1217–1220 Jungbluth N, Tietje O, Scholz RW (2000) Food purchases: impacts from the consumers’ point of view investigated with a modular LCA. Int J Life Cycle Assess 5. https://doi.org/10.1007/ BF02978609 Lee CK, Khoo HH, Tan RBH (2016) Life cycle assessment based environmental performance comparison of batch and continuous processing: a case of 4-D-erythronolactone synthesis. Org Process Res Dev 20. https://doi.org/10.1021/acs.oprd.6b00275 Miliutenko S, Åkerman J, Björklund A (2011) Energy use and greenhouse gas emissions during the life cycle stages of a road tunnel—the Swedish case norra länken. Eur J Transp Infrastruct Res 12. https://doi.org/10.18757/ejtir.2012.12.1.2948 Roches A, Nemecek T, Gaillard G et al (2010) MEXALCA: a modular method for the extrapolation of crop LCA. Int J Life Cycle Assess 15. https://doi.org/10.1007/s11367-0100209-y Sihabuddin S, Ariaratnam ST (2009) Quantification of carbon footprint on underground utility projects. In: Building a sustainable future—proceedings of the 2009 construction research congress 王幼松, 黄旭辉, 闫辉. 地铁盾构区间物化阶段碳排放计量分析. 土木工程与管理学报, 2019 (3): 12–18. Wang Y, Huang X, Yan H (2019) Carbon emission measurement and analysis of metro shield tunnel in the materialization stage. J Civ Eng Manage (3):12–18 王成武, 马振东. 建设工程施工碳排放定额估算法及应用展望. 建筑经济 2016 (4), 59–61. Wang C, Ma Z (2016) Estimation method of carbon emission quota for construction project and its application prospect. Constr Econ (4):59–61 王建军, 赵伟, 王世亮. 建筑物建造过程碳排放计算方法研究. 建筑科学, 2014, 030(002): 8–12. Wang J, Zhao W, Wang S (2014) Research on calculation method of carbon emission during building construction. Build Sci 030(002):8–12 徐建峰, 郭春, 郭雄, et al. 隧道物化阶段碳排放计算模型研究. 2016 中国隧道与地下工程 大会 (CTUC) 暨中国土木工程学会隧道及地下工程分会第十九届年会论文集. 成都: 现 代隧道技术, 2016. Xu J, Guo C, Guo X (2016) Research on calculation model of carbon emission in tunnel materialization stage. Paper presented at the proceedings for 2016 China tunnel and underground engineering conference (CTUC) and the 19th annual conference of the tunnel and underground engineering branch of the Chinese civil engineering society. Modern Tunnelling Technology, Chengdu Xu J, Guo C, Yu L (2019) Factors influencing and methods of predicting greenhouse gas emissions from highway tunnel construction in southwestern China. J Clean Prod 229. https:// doi.org/10.1016/j.jclepro.2019.04.260 肖时辉, 马振东. 建设工程施工碳排放计算方法在盾构施工中的应用. 建筑经济 2018 (1), 36–42. Xiao S, Ma Z (2018) Application of calculation method of construction carbon emission in shield construction. Constr Econ (1):36–42

106

J. Xu

22. 于随然, 陶璟. 产品全生命周期设计与评价. 科学出版社, 2012: 133–138. Yu S, Tao J (2012) Product life cycle design and evaluation. Science Press, pp 133–138 23. 尹建华, 王兆华. 模块化理论的国内外研究述评. 科研管理, 2008 (03): 187–191. Yi J, Wang Z (2008) Review of the research on modularity theory at home and abroad. Sci Res Manage (03):187–191 24. 张伟伟. 面向客户需求的产品碳排放映射关键技术研究. 合肥工业大学, 硕士论文. 2017: 38–40. Zhang W (2017) Research on key technologies of product carbon emission mapping for customer demand. Thesis, Hefei University of Technology 25. Zimov SA, Schuur EAG, Stuart Chapin F (2006) Permafrost and the global carbon budget. Science 80:312

Chapter 5

Uncertainty Analysis of Carbon Emissions from Highway Tunnel Construction Jianfeng Xu

5.1 Introduction Different LCA studies may lead to different evaluation results for the same services or products, indicating their uncertainties, which come from various sources [5, 62]. Huijbregts et al. [24] believes that LCA uncertainties originate not only from the input data, but also from the normative selections and mathematical models. Therefore, people should quantify the uncertainties of parameters, scenarios, and models at the same time. Parameter uncertainty is the most discussed factors in general LCA uncertainty analyses, where random analysis methods are often adopted to compare the calculated results with the deterministic results [28]. The uncertainties of scenarios and models can be assessed by scenario analyses and sensitivity analyses [9]. At present, most of the domestic literatures about carbon emissions apply the Monte Carlo method to the uncertainty analysis. Huang [22] used the Long-range Energy Alternative Planning System (LEAP) model to conduct a scenario analysis of China’s power demand for the next three decades. By setting strong, weak and benchmark electrification scenarios, using normal distribution, minimum extreme distribution, and maximum extreme distribution, he evaluated the uncertainty of the power demand in the baseline situation, and clarified the key source of the uncertainty. Gao and his teamworkers [13] used the Monte Carlo simulation method to compile a coal carbon emission inventory based on the data of China’s coal mining, transportation, and utilization in 2011. Then they conducted an uncertainty analysis to clarify the main factors of carbon emissions and the key paths to emission reduction in the mining and selection process, and came up with the ranges of carbon emissions uncertainty in coal mining process. Zhai and his team members [69] studied the uncertainty of the carbon emission calculation and emission reduction cost in the Clean Development Mechanism (CDM) project, and pointed out the key impact of the carbon emission factor of power generation and the methane emission factor of coal mining on the CDM project. Wang and Zhang [58] used maximum likelihood estimation to fit the distribution types of various daily activities of urban dwellers and © Southwest Jiaotong University Press 2022 C. Guo and J. Xu, Carbon Emission Calculation Methods for Highway Tunnel Construction, https://doi.org/10.1007/978-981-16-5308-7_5

107

108

J. Xu

then used Monte Carlo simulation to study the emission levels of daily life activities of Chengdu residents. In most uncertain analyses, it is assumed that the input variables conform to specific parameter distributions, including lognormal, normal, triangular, and trapezoidal distributions. For example, Zhang et al. [73] assumed that the carbon emission factors of gasoline, diesel, and electricity and the number of shifts consumed by unit engineering quantity were normally distributed when studying carbon emissions during bridge construction. Such assumption lacks a valid basis, which adds difficulty to the application of the Monte Carlo method. In actual researches, the maximum likelihood estimation is often used to determine the distribution types of parameters. In order to solve the problem of changes in the covariance value of related variables in random sampling, Min et al. [75] proposed a combination of Monte Carlo simulation and non-parametric block sampling, and used the variance contribution rate method to identify the key factors affecting carbon emissions. Park et al. [44] developed a set of input variable selection programs, which selected variables with a large variance contribution rate for data substitution to reduce the variance and uncertainty of the model output. Huang et al. [23] proposed a quantitative evaluation method for LCA data quality based on LCA raw data and inventory data algorithms, so as to control data quality (Ref. [75]). Researches on carbon emissions in tunnel construction involve a large number of assumptions and data, but the existing tunnel LCA researches does not analyze the uncertainty of carbon emissions, which has a severe adverse impact on the research results’ credibility. This study analyzes the carbon emissions’ uncertainty for tunnel construction from such perspectives as parameters, scenarios, and model uncertainty. This chapter is divided into four sections. Section 5.2 introduces the methods and principles of uncertainty analysis. Section 5.3 analyzes the uncertainty of primitive carbon emissions and presents fitting formulas for primitive carbon emissions’ inclusion interval. Section 5.4 analyzes the uncertainty of overall carbon emissions per linear meter of lining construction. Section 5.5 is the summary of this chapter.

5.2 Uncertainty Analysis Method The first step to assessing data uncertainty is to evaluate the quality of data, that is, to use the data quality indicator (DQI) method. DQI contains a standard quality assessment matrix, including five types of data indicators. The aggregate data quality index (ADQI) is then calculated, and the deviation of the input data is determined. The contents of the DQI method are described in Sect. 5.2.1. After data quality information is obtained, preparation for the Monte Carlo simulation begins. Among all preparations, obtaining information about the distribution of input data is critical. The common parameter distributions of input variables include lognormal distribution, triangular distribution, and uniform distribution. This part will be introduced in Sect. 5.2.2.

5 Uncertainty Analysis of Carbon Emissions from Highway Tunnel …

109

Monte Carlo simulation is one of the most used stochastic analysis methods, and its calculation principles, steps, and relevant knowledge will be introduced in Sect. 5.2.3. The discrete sampling values and the estimated values of the outputs can be obtained through Monte Carlo simulation. However, the relevant information about the probability distribution of the outputs is still lacking. In this case, repeated simulation and maximum likelihood estimation methods can be used to complete the distribution analysis of the output, which will be introduced in Sect. 5.2.4. Scenario analysis is based on the current development status and related phenomena and trends. It considers different results that may occur in the future, analyzes the impact and driving forces of their occurrence, etc., to help decisionmakers carry out strategic planning. Traditionally, scenario analysis is an intuitive qualitative forecasting method, which can be effectively combined with uncertainty analysis to enhance its applicability and compatibility. The methods and steps of scenario analysis will be introduced in Sect. 5.2.5.

5.2.1 Data Quality Indicator In 1990, Funtowicz and Ravetz [12] introduced the pedigree matrix into uncertainty analysis and converted qualitative expert judgments of the “pedigree criterion” of a specific set of problems into numerical scales. The judgment criteria were used as the columns of the table, and the numerical codes as the row, and as the language description of the value in each cell in the table. The pedigree matrix’s goal is to transform the qualitative description of the relevant aspects of the research object into a quantitative digital evaluation. Therefore, it can be considered that the pedigree matrix is a tool to quantify the qualitative evaluation description. According to research needs, the evaluation scales and standards can be selected flexibly. Currently, there are no further formal requirements for the structure of the matrix. Weidema and Wesnæs [61] transferred the pedigree matrix to LCA; their matrix is square, with a scale from one to five and five criteria. In 1998, Weidema released a slightly modified version of a multi-user test based on the initial matrix [60], which has been widely recognized. An important example of its application is the Ecoinvent database. The pedigree matrix used in this book, shown in Table 5.1, includes five data quality indicators: reliability, completeness, time range, geographic scope, and technical scope [61]. A semi-quantitative evaluation of input data is performed, and each DQI is scored from 1 to 5 [18, 29]. According to the pedigree matrix’s general method [61], DQI = 1 means the highest data quality rating, and DQI = 5 means the lowest. This rating method has been applied in many literature Refs. [1, 2, 7, 39]. It is worth noting that the method of data quality rating is not consistent. In some studies [4, 27], DQI = 5 refers to the highest data quality rating, while DQI = 1 refers to the lowest. The data quality analysis (DQA) method is used at the unit process level to simplify parameter uncertainty analysis. The materialization stage of the tunnel includes the production, transportation, and processing of various materials. The inventory data source is complex, and the

110

J. Xu

Table 5.1 Pedigree criterion DQI DQI = 1

DQI = 2

DQI = 3

R

Data verified and based on measurements

Verified data based partly on assumptions, or unverified based on measurements

Unverified Precise estimation data based partly on assumptions

Inaccurate estimation

C

Representative data taken from the appropriate sample and time range

Data collected from a smaller sample, but within a reasonable period

Data taken from an appropriate sample, but not in the right time range

Representative data, but from a very small sample

Data not representative from a very small sample or it is unknown

TR

Deviation up to Deviation up to Deviation 3 years 6 years up to 10 years

Deviation up to 15 years

Deviation over 15 years or data age unknown

GS

Local scope

National scope Continental range

Global scope

Data of unknown origin

TS

Data on the analyzed process and enterprise

Data on the analyzed process and technology, but from a different source

Data on similar processes/products with the same technology

Data on similar processes/products with different technology

Data on the analyzed process, but with different technology

DQI = 4

DQI = 5

Note R reliability, C completeness, TR time range, GS geographical scope, TS technology scope

emission of building materials and energy is significantly related to geography, time, and technology. It should be noted that the source of building materials and the energy consumption is often a single source, such as design data and quota data. DQI is set as 2 to maintain the data integrity according to “time interval reasonable but collected from a smaller sample” in Table 5.1. This chapter uses JTG/T 3832-2018 Highway Engineering Budget Quota and JTG/T 3833-2018 Highway Engineering Machinery Shift Cost Quota to calculate the material consumption and energy consumption of the unit engineering quantity. Although the above quotas are national standards and are quite representative, they have not been verified. Similarly, the sources of local carbon emission factors are limited, and values are often integrated from related research. Therefore, DQI is set as 3 to ensure the reliability of the quota data and emission factors according to “partially based on assumptions and not verified” in Table 5.1. According to Table 5.1, data in the same unit has five DQIs, which, can effectively reflect the data’s reliability, but are too redundant, and further processing is needed to enhance the representativeness of the uncertainty and reduce the workload of the

5 Uncertainty Analysis of Carbon Emissions from Highway Tunnel …

111

uncertainty analysis. This study uses the ADQI to solve the above problems. The socalled ADQI is a comprehensive index obtained by characterizing and normalizing five DQIs [39]. Researchers need to assign weights to the five DQI indexes to obtain a comprehensive quality index. In the simplest case, all DQI weights can be set to 0.2, and then ADQI is the average of the five DQI values. In fact, different DQI weight values are often not equal [2, 39, 57]. For example, in the study of Maurice et al. [3, 39], the weights related to the geographic and technical scope is 0.25, and the weight related to the other three DQIs is 0.167. Given the wide range of material sources for tunnel construction, the diversity of geography and technology may increase the data’s uncertainty. Therefore, this study adopts the weights proposed by Maurice et al. [39]. Finally, the DQI values and the weights of each DQI are added together to obtain a comprehensive data quality index. The next stage of DQA is to link the data quality index obtained by the pedigree matrix with the probability distribution [27, 57]. Kennedy et al. [27] and Wang et al. [57] established the so-called transformation matrix. In addition, Kennedy et al. [26] proposed a four-parameter Beta function whose probability density distribution function is shown in Eq. 5.1. The function uses shape parameters (α, β) and position parameters (a, b) to control the probability distribution characteristics, which can flexibly adapt to various distribution functions. f (x; α, β, a, b) =

(x − a)(b − x) (α + β) ; (a ≤ x ≤ b) (b − a) (α)(β)

(5.1)

The conversion relationship between the distribution parameters and the comprehensive score of data quality is relatively complicated and is generally obtained from expert experience. According to the literature [71], the conversion relationship can be simplified to Eqs. 5.2 and 5.3. (α, β) = max[int(2 AD Q I ) − 5,1] · (1,1)

(5.2)

(a, b) = μ[0.4 + 0.05int(2 AD Q I ), 1.6 − 0.05int(2 AD Q I )]

(5.3)

where, μ is the data’s representative value, such as the average value, log likelihood value. It is worth noting that for point data or a collection with a small amount of data (such as quota data), it is often difficult to determine the reliable mean or standard deviation. Given that the probability density function of the normal distribution requires a clear expectation or standard deviation value, the application of the normal distribution in DQA may be limited, so it is necessary to consider the distributions based on other parameters, such as triangular distribution, continuous uniform distribution, or Beta distribution etc. [2, 27], among which triangle and continuous uniform distribution are based on two parameters: minimum and maximum [3]. In this study, a triangular distribution is used to convert the ADQI result into the deviation level of the process’s input value, as depicted in Table 5.2. This method

112

J. Xu

Table 5.2 Deviations corresponding to different ADQI values ADQI

Deviation/%

ADQI

Deviation/%

ADQI

Deviation/%

1.0

10

2.4

24

3.8

38

1.2

12

2.6

26

4.0

40

1.4

14

2.8

28

4.2

42

1.6

16

3.0

30

4.4

44

1.8

18

3.2

32

4.6

46

2.0

20

3.4

34

4.8

48

2.2

22

3.6

36

5.0

50

has also been applied in the other study [3]. If the ADQI score is 1.0, the allowable standard deviation of the data is 10%. As the ADQI value increases, the deviation gradually increases. When the ADQI value is small, the probability density function image appears as a narrow triangular distribution, with a large concentration around the central value and a high probability. Therefore, when implementing LCA uncertainty analysis, researchers may need to input either accurate inventory data or an interval range. Taking electric energy as an example, suppose the electricity consumption is 6 kWh, and the ADQI value is 1, and the deviation is 10%, the electric energy input can be expressed as [5.4 kWh, 6.6 kWh].

5.2.2 Parameter Probability Distribution The distribution type of LCA inventory data can generally be determined by experts judgement or depending on the sample’s statistical analysis results. Common distribution types include normal distribution, uniform distribution, and triangular distribution.

5.2.2.1

Triangular Distribution

In existing studies, triangular distributions were often adopted to simulate energy consumption intensity and energy emission factors. Although there are upper and lower limits on the values of emission factors due to technological limitations, the closest values can be obtained through collecting, sorting, and statistically analyzing the existing parameters. With the advancement of technology and clean energy in the future, emission factor values will decline further. But the most likely value still exists within a certain period. Therefore, the characteristics of emission factors conform to the triangular distribution. Additionally, the material and energy input corresponding to the unit engineering quantity at the current stage is given in the quota, which is the most likely value, and a specific deviation value range is set as 10–50% (see Sect. 5.2.1

5 Uncertainty Analysis of Carbon Emissions from Highway Tunnel …

113

for details). The low and high values of the unit engineering quantity input can be obtained. Therefore, the quota data can be described by triangular distributions. The characteristics of the triangular distribution have been briefly described at the end of Sect. 5.2.1. From a mathematical point of view, the low value of the triangular distribution is a, the Mode is c, and the high value is b. It is a continuous probability distribution, and its probability density function is expressed in Eq. 5.4.  f (x|a, b, c ) =

2(x−a) , (b−a)(c−a) 2(b−x) , (b−a)(b−c)

c≥x ≥a b≥x >c

(5.4)

The cumulative distribution function is expressed in Eq. 5.5.  f (x|a, b, c ) =

5.2.2.2

(x−a)2 , (b−a)(c−a) (b−x)2 1 − (b−a)(b−c) ,

c≥x ≥a b≥x >c

(5.5)

Uniform Distribution

In the modular emission calculation model established in Chap. 4, the proportion of sand and gravel recycled from the waste soil and the transportation distance from the market to the tunnel are random variables that follow continuous distributions, with low and high values. However, unlike the triangular distribution, these random variables do not have the most probable value. Therefore, the distribution type of these random variables can be assumed to be uniform. If the random variable X obeys a uniform distribution, X’s probability density function is expressed in Eq. 5.6.  f (x|a, b ) =

0, 1 , b−a

x < a or x > b b≥x ≥a

(5.6)

The cumulative distribution function is expressed in Eq. 5.7.

F(x|a, b ) =

5.2.2.3

⎧ ⎨ 0, ⎩

x−a , b−a

1,

x b

(5.7)

Norm Distribution

The normal distribution is a “bell-shaped” probability distribution with “low at both ends” and “high in the middle”. It has been widely used in the fields of mathematics and engineering and has an important position in statistics. If the random variable

114

J. Xu

X obeys a normal distribution, the expectation is μ, and the variance is σ2 , which is counted as X ~ N (μ, σ2 ). The probability density function of X is shown in Eq. 5.8.   (x − μ)2 f (x) = √ exp − 2σ 2 2π σ 1

(5.8)

The cumulative distribution function cannot be obtained directly through integration, and is generally expressed in the form of an error function as shown in Eq. 5.9. (z) =



 1 z−μ 1 + erf √ 2 σ 2

(5.9)

5.2.3 Monte Carlo Simulation 5.2.3.1

Method Introduction

Monte Carlo simulation is based on probability and statistical theory and methods often used to generate random numbers or pseudo-random numbers of a certain probability model to obtain approximate solutions to real problems. It is also called the random sampling method [6]. The specification JJF 1059.2-2012 Monte Carlo Method for Evaluation of Measurement Uncertainty defines that the Monte Carlo method is a method using a random sampling of the probability distribution to carry out distribution propagation [74]. This method was proposed by Ulam and Von Neumann in the 1940s and can be traced back to the French Buffon’s pi needle test in the eighteenth century. According to the Bayesian School, an unknown parameter can be regarded as a random variable, and probability is used to understand an unknown variable’s change. When an event has a specific probability, the event’s frequency can be obtained by simulating the occurrence of the event. When the sample size is large enough, the frequency of events is the probability. Therefore, the essence of the Monte Carlo method is to use a probability model to describe the result of an event. Suppose that a function contains n random variables, the input quantity X i ’s probability density function (i = 1, 2, 3, …, N) are available. By discrete sampling of the probability density function of the input quantity X i , researchers can calculate the discrete sampling value of the output quantity Y’s probability density function to obtain the best estimate of the output quantity, the standard uncertainty, and the containment interval. As mentioned earlier, the Monte Carlo method is one that relies on sampling, and an increase in the number of samples will enhance the credibility of the results.

5 Uncertainty Analysis of Carbon Emissions from Highway Tunnel …

5.2.3.2 (1)

Implementation Step

Determine the Monte Carlo simulation input 1. 2. 3.

Define the output Y; Determine the relevant input values X 1 , X 2 …, X N ; Create the functional relationship model between Y and input values X 1 , X 2 …, X N , which is shown in Eq. 5.10; Y = f (X 1 , X 2 , . . . , X N )

4.

5.

(2)

2.

Equation 5.10 is also known as the measurement model. Capital letters represent the quantity, and f is the measurement function. According to the existing information, set the probability density function for each input quantity, such as normal distribution, uniform distribution, etc.; Set the sample size M, which is related to the distribution shape of the probability density function and the inclusion probability p. The value of M must be at least 104 times greater than 1/(1 − p). Under normal circumstances, M = 106 will provide a 95% inclusive interval for the output.

Extract M sample values x i, r (i = 1, 2, …, N, r = 1, 2, …, M) from the probability density function of the input quantity X i ; For each sample vector (x 1,r , x 2,r , …, x N,r ), calculate the model value of the corresponding Y (Eq. 5.11).  yr = f x1,r , x2,r , . . . , x N ,r , r = 1, 2, . . . , M

(4)

(5.10)

Determine the probability distribution of the output according to the probability distribution of the relevant input 1.

(3)

115

(5.11)

Monte Carlo simulation output These M model values are arranged in ascending order, and the discrete value G of the distribution function of the output Y is obtained. Report results 1. 2.

Calculate the estimated value y of Y and the standard uncertainty u(y) of y from G; Calculate the inclusion interval [ylow , yhigh ] of Y when the inclusion probability p is given by G.

The mean value of the output is calculated as Eq. 5.12. M 1 y˜ = yr M r =1

(5.12)

116

J. Xu

The standard deviation is calculated as Eq. 5.13.    u( y˜ ) = 

5.2.3.3

1 (yr − y˜ )2 M − 1 r =1 M

(5.13)

Implementation Platform

Python is an object-oriented high-level programming language with dynamic data type characteristics. Guido van Rossum invented Python in 1989. The language itself is also developed based on related languages, including ABC, C, C++, Unix shell, etc. [48]. At present, Python has become one of the mainstream programming languages and has been widely used in scientific computing and statistics, artificial intelligence, web crawlers, software development and other fields. This research uses Python to carry out a Monte Carlo simulation. The version number is Python 3.7.1 64-bit. The running platform is Visual Studio Code, which is an excellent integrated development environment with the characteristics of opensource, cross-platform, modularity, and rich plug-ins. The Python standard library covers regular expressions, the Internet, web browsers, GUIs, databases, and text. In addition to the Python standard library, this research also uses SciPy, NumPy, and Matplotlib extension libraries [40]. It is believed that the cooperative performance of SciPy, NumPy, and Matplotlib can be comparable to MATLAB software. Here is a brief introduction to the three extension libraries: • NumPy is a software package that uses scientific computing, including powerful N-dimensional array objects. NumPy has powerful linear algebra, Fourier transform and random number functions, and is able to store and process large matrices [56]. The random number is usually generated by the random module, which is essential for Monte Carlo simulation. • SciPy is a commonly used scientific computing software package that can handle interpolation, integration, optimization, image processing, and ordinary differential solution [41]. SciPy can calculate NumPy matrices and achieve collaborative work, which can significantly improve computational efficiency thus providing a variety of probability density functions required by Monte Carlo simulation. • Matplotlib is a graphical data tool for Python, which has been widely applied in the field of Python 2D plotting. Matplotlib can generate graphs such as histograms, bar graphs, error graphs and scatter plots with only a tiny amount of code [43]. Matplotlib is not directly involved in the random simulation process, but is used to visualize the simulation results.

5 Uncertainty Analysis of Carbon Emissions from Highway Tunnel …

117

5.2.4 Maximum Likelihood Estimation Maximum likelihood estimation is a parameter statistical method used to obtain a sample set’s correlation probability density function. For a specific random variable, researchers can obtain the random variable’s sampling value with a specific probability density function by Monte Carlo simulation method. However, the probability density function that the random variable obeys is still not certain [67]. At this time, researchers can repeatedly sample the sample set and use the sample set data to estimate the probability density function. Specifically, the maximum likelihood estimation method is adopted to clarify the distribution types and parameters that the sample set obeys.

5.2.5 Scenario Analysis Sections 5.2.1 to 5.2.3 introduced semi-quantitative and quantitative processing methods for dealing with parameter uncertainties. But as Lloyd and Ries [36] stated, in addition to parameter uncertainty, the uncertainty of scenarios should also be given full attention to. As the future is full of uncertainties, the future development route of a country, region, or even the world is not unique; therefore, policymakers and scholars need some research tools and methods that can enable them to analyze future uncertainties so as to deal with upcoming problems and crises. A scenario is not a prediction of the future but a description of how the future will change [50]. The scenario describes some possibilities, which may not be very high though. By demonstrating the scope and types of future possibilities, scenarios provide support for people to take wise actions, and at the same time, illustrate the role of human activities in shaping the future, as well as the relationship between environmental changes and human behaviors. The scenario first officially appeared in the Second World War and was used for war strategy analysis. Soon, people discovered the value of scenarios and applied them to a series of strategic planning. Now, the scenario has been widely used in various strategic behaviors, including strategic planning, policy analysis, decision management and even global environmental assessment, etc., which explicitly involves the environmental economy, low-carbon development, energy economy and other fields [10]. Therefore, scenario analysis has become a popular way to analyze the future. Thanks to this analysis tool, some essential international organizations have produced significant research results, including Global Environment Outlook 4: Environment for Development and IPCC Summary for Policymakers [20, 37, 47]. Sustainable Energy Scenario Analysis of China in 2020 issued by the Energy Institute of the National Development and Reform Commission of China is also a result of the application of the scenario analysis method. Hundreds of diversified scenarios have been developed in the past three decades, including the prospects of specific countries, regions and even the world’s activities. Also, UNEP has published the

118

J. Xu

Integrated Environmental Assessment and Reporting training manual for reference [47].

5.3 Uncertainty Analysis of Carbon Emissions of Unit Engineering Quantity The analysis process in this section is shown in Fig. 5.1. According to the general steps and methods of Monte Carlo simulation in Sect. 5.2.3, the foreground and background data of the tunnel construction unit processes are collected. In terms of the deviation range of the project quota determined by semi-quantitative DQI method and the value range of carbon emission factors based on the existing literature, the triangular distributions and uniform distributions are set according to the characteristics of the input data. After completing the above work, the Python-based Monte Carlo simulation algorithm is written on the Visual Studio Code platform to process the input data. It is worth noting that this section only analyzes the uncertainty of primitives’ carbon emissions. The collecting principles and methods of inventory data follow the provisions in Sect. 4.3.2.

Fig. 5.1 Uncertainty analysis process of carbon emission of primitives

5 Uncertainty Analysis of Carbon Emissions from Highway Tunnel …

119

5.3.1 Parameter Value 5.3.1.1

Engineering Quota

The activity level data in this study comes from the Chinese national standard budget quotas. As mentioned above, the budget quotas are the national engineering construction pricing standard, which is relatively complete and regularly updated according to the development of the construction level, resulting in its fine representativeness in time range and geographic range. For JTG/T 3832-2018 Highway Engineering Budget Quota and JTG/T 3833-2018 Highway Engineering Machinery Shift Cost Quota, its reliability (DQI = 3), completeness (DQI = 2), time range (DQI = 1), geographical scope (DQI = 2), technical scope (DQI = 2) are set; according to the weight setting of different DQI in Sect. 5.2.1 and the calculated comprehensive data quality index (ADQI = 2), the activity data deviation in the above two quotas is taken as 20%. Due to a large amount of activity level data in the quota, only the calculation examples will be made in Sect. 5.3.2.

5.3.1.2

Carbon Emission Factor

Emission factors represent the emission levels of energy or products in the production and flow process. According to the differences in the study areas, commodity types, and time, emission factors in different studies vary a lot from each other, bringing huge uncertainties to carbon emission research [31]. According to the IPCC’s proposal, countries should use their own peer-reviewed openly published literature to reflect the national practices accurately [46]. In the absence of relevant literature, IPCC default factors or emission factor values of other countries or regions can be used. This study’s emission factors are mainly derived from relevant domestic documents, as shown in Table 1.5. There is a lot of research on emission factors, and further collection and summary are needed to reflect their minimum and maximum emission values. According to the calculation tool of power system emission factor issued by the Executive Council of the Clean Development Mechanism under the United Nations Framework Convention on Climate Change, the Ministry of Ecology and Environment of China calculated the marginal emission factors of electricity. Table 5.3 collects the carbon emission factors of China’s different regional power grids. The carbon emission factor is a dynamically changing value and is closely related to the regions. In this chapter, the emission factor interval of electricity is 0.837–1.094 kg CO2eq /kWh. The net calorific value of gasoline and diesel is 43.0 GJ/t given in Volume II of the IPCC Guidelines for National Greenhouse Gas Inventories. According to Feng [11], the lower limit of diesel’s life-cycle carbon emission coefficient is 76.4 g CO2eq /MJ, and the upper limit is 102.4 g CO2eq /MJ. The lower limit of gasoline’s carbon emission coefficient is 76.3 g CO2eq /MJ, and the upper limit is 98.9 g CO2eq /MJ. The

120

J. Xu

Table 5.3 Carbon emission coefficient of regional power grid terminal electricity consumption from 2012 to 2017 (unit: t CO2eq /MWh) Power grid

2012

2013

2014

2015

2016

2017

Northern China

1.0021

1.0302

1.0580

1.0416

1.0000

0.9680

Northeastern China

1.0935

1.1120

1.1281

1.1291

1.1171

1.1082

Eastern China

0.8244

0.8100

0.8095

0.8112

0.8086

0.8046

Central China

0.9944

0.9779

0.9724

0.9515

0.9229

0.9014

Northwestern China

0.9913

0.9720

0.9578

0.9457

0.9316

0.9155

Southern China

0.9344

0.9223

0.9183

0.8959

0.8676

0.8367

recommended values of diesel and gasoline emission factors in the GBT 51366-2019 Building Carbon Emission Calculation Standard are 72.59, and 67.91 g CO2eq /MJ, slightly lower than the emission ranges provided by Feng [11]. Ou et al. [42] calculated the life cycle carbon emissions of diesel and gasoline to be 101.6 and 91.7 g CO2eq /MJ, which are in line with the above emission ranges. Based on relevant literature, diesel’s carbon emission coefficient is 72.59–102.4 g CO2eq /MJ, and the carbon emission coefficient of gasoline is 67.91–98.9 g CO2eq /MJ. Combining the IPCC net calorific value, the calculated carbon emission factor interval of diesel is 3.121–4.403 kg CO2eq /kg, and the emission factor of gasoline is 2.920–4.253 kg CO2eq /kg. Water is an important resource for on-site construction, and the emission factor values in current research are relatively small. For example, the Ecoinvent database uses 0.42 kg CO2eq /t, and carbon emission coefficients in some researches are 0.2– 0.3 kg CO2eq /t for water production [14, 19, 38, 59]. Considering that most mountain tunnels are in remote areas and the difficulty of taking water is greater than that of ordinary urban buildings, this study adopts 0.59 kg CO2eq /t based on the calculation of the energy consumption of water taking equipment in the quota. Sand and gravel are essential building materials for concrete. Because the tunnel site is often far away from the city, the construction enterprises usually use cast-inplace concrete. Research on relevant literature shows that the carbon emissions of sand and gravel is mainly concentrated between 2.0 and 24.0 kg CO2eq /t [71]. The gravel emission value is set to be between 1.4 and 24 kg CO2eq /t, and the emission value of sand is between 1.5 and 24 kg CO2eq /t. Wood is one of the commonly used building materials in the construction industry. Although the amount of timber used in tunnels is small, its carbon sequestration effect during growth and emissions during the felling stage is complicated. This study does not consider the carbon sequestration effect of wood during the growing process and only analyzes the carbon emission during the cutting and processing process. According to Ref. [71], the emission factor interval for common wood is 60–1288 kg CO2eq /m3 . Shen et al. [53] surveyed and sampled the cement production lines in 22 provinces of China. They obtained the activity level data of 359 cement production lines and calculated the cement’s comprehensive emission factor. The comprehensive emission

5 Uncertainty Analysis of Carbon Emissions from Highway Tunnel … Table 5.4 Uncertainty and deviation of emission factors

121

Material/energy

Value

Unit

Min–Max

Wood

146.3

kg CO2 eq/t

60–1288

Steel

2.309

t CO2 eq/t

2.013–2.309

Cement

0.702

t CO2 eq/t

0.626–0.811

Explosive

0.263

t CO2 eq/t

0.202–0.324

Water

0.59

kg CO2 eq/t

0.21–0.59

Sand

4.0

kg CO2 eq/t

1.5–24.0

Gravel

3.0

kg CO2 eq/t

1.4–24.0

Electricity

0.972

kg CO2 eq/kWh

0.837–1.094

Gasoline

3.943

t CO2 eq/t

2.920–4.253

Diesel

4.369

t CO2 eq/t

3.121–4.403

factor of cement products is 0.702 t CO2eq /t, and the 95% confidence interval is 0.626– 0.811 t CO2eq /t, which are adopted as the value interval of cement’s carbon emission factor in this chapter. There exist several calculation methods for steel carbon emissions, including the Provincial Greenhouse Gas Inventory Compilation Guidelines, Steel Carbon Emission Guidelines, and carbon footprint research methods based on ISO standards. According to the above calculation method, Liu et al. [34] found that the carbon emissions per ton of steel produced were 2.116, 2.013, and 2.309 t CO2eq /t. The average carbon emission of carbon steel given in the GBT 51366-2019 Building Carbon Emission Calculation Standard is 2.05 t CO2eq /t. In this chapter, the interval of 2.013–2.309 t CO2eq /t is taken. Zhang [72] used the B-Wilson method to calculate the carbon emission factor of explosives commonly used in coal mines. The emission factor of ammonium nitrate explosives was 0.263t CO2eq /t. The number of relevant domestic studies on ammonium nitrate explosives is relatively small, and the ratio of different ingredients may vary. In this book, according to the DQI classification method in Table 5.1, each DQI is taken as 3, 1, 4, 2 and 2 respectively, the ADQI value is calculated to be 2.33, the deviation is ±23.3%, and the interval is 0.202–0.324 t CO2eq /t. Based on the above investigation, the recommended values and value ranges of different emission factors are given, as shown in Table 5.4.

5.3.1.3

Material Recycling and Transportation

According to the sensitivity analysis in Sect. 4.5.2, the recycled proportion of sand and gravel in the waste soil and rock, the transportation distance from the market to the tunnel, and the type of vehicles have an important impact on the carbon emissions of tunnel construction; while the carbon emissions of transportation in the tunnel account for very small ratio, and the impact is weak. This study will consider the uncertainty of the material transportation from the market to the tunnel and material

122

J. Xu

Table 5.5 Uncertainties and deviations in material recycling and transportation Factor

Value

Unit

Energy

Recycled proportion of sand and gravel

0–100

%

–

Transportation distance from the market to the tunnel

0–500

km

–

Diesel consumption from the transportation between the market and the tunnel

0.013–0.037

kg/(t km)

Diesel

recycling ratio. According to the characteristics of data distribution, they are assumed to obey continuous distributions. The values and deviations are shown in Table 5.5.

5.3.2 Sample Size 5.3.2.1

Trial Calculation

Sample size has an essential influence on Monte Carlo simulation. When the sample size is too small, the output result is difficult to achieve stability. However, the excessive number of tests undoubtedly increases the computation cost and reduces the test efficiency of Monte Carlo simulation [51]. No exact sampling number or sample size have been clearly suggested in current academic circles. Monte Carlo Method for Evaluation of Measurement Uncertainty presents a practical guidance: a sample size of 106 will make the output cover a 95% inclusive interval, and the sample size should not be less than 104 . Furthermore, this guidance provides an adaptive Monte Carlo test method. By increasing the number of tests, twice the standard deviation of each result is less than the standard uncertainty’s numerical tolerance. Although this method provides theoretical guidance for Monte Carlo simulation, its shortcomings are obvious: it requires continuous iterative calculations, gradually increasing the number of samples from small to large, so that the estimated value of the output result, the standard uncertainty and the endpoint of the inclusion interval achieve statistical stability, which makes the operation process cumbersome and the efficiency low. To calculate the appropriate test times suitable for this study, in this section, with the 10 m3 of shotcrete process in the tunnel quota as the research object, and the mean and standard deviation as the evaluation indicators, the stability of output results under different sample sizes are analyzed, using the algorithm written by Python language on VS Code platform. Besides, to verify the compatibility of the algorithm with different distribution types, two cases where the carbon emission factor obeys the triangular distribution and the uniform distribution are considered. The uniform distribution is suitable for obtaining only the minimum and maximum values of random variables. It is similar to triangular distribution but lacks the information of the most likely value. This section will discuss the appropriate sample size under these two types of distributions [16, 73].

5 Uncertainty Analysis of Carbon Emissions from Highway Tunnel …

123

As for the tunnel part of the JTG/T 3832-2018 Highway Engineering Budget Quota, for every 10 m3 of C25 concrete sprayed, 0.01 m3 of wood, 5.628 t of cement, 24 m3 of water, 7.2 m3 of medium and coarse sand, 6.84 m3 of gravel, 1.29 units of concrete sprayers, 0.78 machine-team of 20 m3 /min electric air compressor are required. These data come from the JTG/T 3832-2018 Highway Engineering Budget Quota, whose uncertainty range is shown in Table 5.6. Moreover, each machine-team of concrete spraying machine consumes 43.01 kWh of electricity, and each machineteam of 20 m3 /min electric air compressor consumes 601.34 kWh of electricity. These data come from the highway construction machinery cost rate, and the value ranges of the relevant parameters are shown in Table 5.7. Based on the emission factor data in Table 1.5, the calculated emission is 4.48 t CO2eq . This section only takes the tunnel’s quota as an example to determine reasonable sample size for the Monte Carlo simulation. While the emissions are incomplete and do not include the emissions during material handling and transportation. The technical characteristics of the Monte Carlo algorithm used in this study are as follows: The number of samples is 10i (i = 1, 2, 3 … 7). Also, the random values generated by this algorithm are not pre-allocated or registered; they will be regenerated every time they are sampled or iterated. This algorithm’s maximum sampling frequency is 107 , which is much higher than the recommended value of 106 given in JJF 1059.2-2012 Monte Carlo Method for Evaluation of Measurement Uncertainty to ensure that various possible differences can be collected in random sampling operations. Based on the Python extension libraries like SciPy, NumPy and Matplotlib, the mean and standard deviation of carbon emissions under different sample sizes and emission factor distribution types are obtained, as shown in Fig. 5.2. When the sample size reaches 105 or more times, the mean and standard deviation of Table 5.6 Uncertainty and deviation of materials and shifts in the JTG/T 3832-2018 Highway Engineering Budget Quota Material/machinery

Value

Unit

Deviation (%)

Ranges

Wood

0.01

m3

20

0.008–0.012

Cement

5.628

t

20

4.50–6.754

Water

24

m3

20

19.2–28.8

Sand

7.2

t

20

5.76–8.64

Gravel

6.84

m3

20

5.47–8.21

Concrete spraying machine

1.29

Machine-team

20

1.032–1.548

Electric air compressor

0.78

Machine-team

20

0.62–0.94

Table 5.7 Uncertainty and deviation of energy consumption of construction machinery Machinery

Value

Unit

Deviation (%)

Range

Concrete spraying machine

43.01

kWh

20

34.41–51.61

Electric air compressor

5.628

kWh

20

4.50–6.75

124

J. Xu

Fig. 5.2 Simulation results under different sample sizes. a Mean value, b standard deviation

the simulation results have reached a relatively stable state for uniform distribution and triangular distribution. The research results are consistent with the literature [49].

5.3.2.2

Sample Size Verification

Section 5.3.2.1, taking shotcrete in the quota of tunnels as an example, calculates the carbon emissions of 10 m3 shotcrete, but does not cover material transportation and collection and processing. Furthermore, taking the carbon emissions of 1 m3 of shotcrete module as an example, Monte Carlo simulation is carried out, with the iteration number of 1 and the sample size ranging from 101 to 106 . Figure 5.3 shows the calculation results of a single iteration. When the sample size is small, the data is

5 Uncertainty Analysis of Carbon Emissions from Highway Tunnel …

125

Fig. 5.3 Frequency distribution histogram of carbon emissions of single sampling. a Sample size N = 10, b sample size N = 102 , c sample size N = 103 , d sample size N = 104 , e sample size N = 105 , f sample size N = 106

126

Fig. 5.3 (continued)

J. Xu

5 Uncertainty Analysis of Carbon Emissions from Highway Tunnel …

127

more scattered, and as the sample size increases, the average carbon emission is about 523 kg CO2eq , and the emission interval is 350–840 kg CO2eq . It should be noted that the ordinate of Fig. 5.3 uses the ratio of frequency to the class interval, which can be approximated as a probability density. Through normalization, the cumulative product of interval width and frequency is equal to 1. Specifically, ordinate = number of intervals/(total number × interval width). Furthermore, the stability of the sample size under multiple iterations is analyzed. The iterative calculation is set to ten times, and the simulation results are shown in Fig. 5.4. As the sample size increases, the mean values, standard deviation, 2.5 percentile and 97.5 percentile of the carbon emissions’ simulation results gradually stabilized. Comprehensively considering the simulation convergence effect and calculation cost, the sample size (N = 106 ) can meet the accuracy requirements of this study.

5.3.3 Uncertainty Analysis of Primitives’ Carbon Emissions According to the method in Sect. 5.2, the sample size N is 106 , and the carbon emission uncertainty of each primitive is determined by Monte Carlo simulation, as shown in Fig. 5.5. The simulation results show that the sample distribution presents prominent bell-shaped characteristics: low at front and back, and high in the middle. Although the graph distribution is not entirely symmetrical, it implies that the normal distribution can be used to fit the carbon emission distributions of the primitives, which is a follow-up simulation. This study provides probability distribution parameters for carbon emissions from tunnel construction with different engineering quantities. Figure 5.5 shows the probability densities corresponding to different carbon emission distributions, but the relevant distribution parameters are still lacking. In a statistical simulation, parameters such as mean, standard deviation, minimum and maximum values can well describe the distribution characteristics of a sequence of numbers. Table 5.8 gives the uncertainty description of each primitive’s carbon emissions and compares the emission ranges with the determined carbon emission values of primitives. The results indicate that the determined values of primitives’ carbon emissions are all within the ranges of samples’ carbon emissions. Moreover, there is a slight deviation between the samples’ mean values and the determined values, i.e. the determined values obtained according to the assumptions in Table 4.8 may not be the most likely emission values. Judging from the numerical values in Table 5.8, the deviations between the samples’ mean values and the determined values are between −5% and +6%, which are mainly derived from the value differences of the input parameters. In this study, the project quota data and carbon emission factors adopt triangular distributions, and the material transportation and recycling ratio parameters are set to be uniformly distributed. Triangular distribution and uniform distribution are bilaterally symmetrical, so suppose the parameter values in the deterministic study are kept near the

128

J. Xu

Fig. 5.4 Calculation results of 10 iterations. a Mean values, b standard deviation, c 2.5 percentile, d 97.5 percentile

5 Uncertainty Analysis of Carbon Emissions from Highway Tunnel …

129

Fig. 5.4 (continued)

mid-range of the interval, it will help to reduce and eliminate the deviation between the samples’ mean and the definite value. For example, the engineering quota’s value is based on the quality of semi-quantitative data, and the original data is the median value of the interval, which eliminates the difference between deterministic research and the sample mean. However, some parameters in deterministic research are not the median values of Monte Carlo simulation. The material transportation parameters are set to be uniformly distributed, and the transportation distance from the market to the tunnel is 0–500 km. If it is close to the sample mean value, the determined transportation distance should be close to the median values, that is, 250 km. However, according to Table 4.8, in calculating the determined value of primitives’ emissions, the transportation distance is 500 km, which means the market transportation carbon emissions in the deterministic study have doubled the sample average. Similarly, the carbon emission factors took larger values in the deterministic study, which directly impacted carbon emissions. For example, the steel’s carbon emission factor is between 2.013 and 2.309 kg/kg CO2eq . Correspondingly, the steel’s emission factor in the deterministic study is 2.309 kg/kg CO2eq , which is the maximum value of the range. Due to the extensive use of steel in steel support and lining reinforcement, the deterministic carbon emissions of the above processes are higher than the sample average, that is, the deviation in Table 5.8 is negative. In Table 5.8, the deviations of the secondary lining arch wall, secondary lining inverted arch, and the shotcrete take positive values, recycling sand and gravel considered, including the transportation, treatment, and recycling of waste slag in the tunnel. According to the sampling value setting, the recovery ratio is 0–100%, and the average value is 50%, much smaller than those in the deterministic study. Therefore, more raw materials need to be purchased from the market, generating more emissions during production and transportation. Besides, since the above three primitives increase the sand and gravel recycling process compared with other primitives, it brings greater uncertainty and leads to a substantial increase in standard deviation.

130 Fig. 5.5 Frequency distribution histogram of carbon emissions of primitives. a 1 m3 of Grade I surrounding rock excavation and slag, b 1 m3 of Grade II surrounding rock excavation and slag, c 1 m3 of Grade III surrounding rock excavation and slag, d 1 m3 of Grade IV surrounding rock excavation and slag, e 1 m3 of Grade V surrounding rock excavation and slag, f 1 kg of section steel frame, g 1 kg of grid steel frame, h 1 kg of connecting reinforcement, i 1 kg of mortar bolt, j 1 m of cartridge bolt, k 1 m of hollow bolt, l 1 kg of steel mesh, m 1 m3 of shotcrete, n 1 m3 of concrete of secondary lining wall, o 1 m3 of concrete of secondary lining inverted arch, p 1 kg of rebar, q 1 m of small pipe, r 1 m3 of injecting cement

J. Xu

5 Uncertainty Analysis of Carbon Emissions from Highway Tunnel … Fig. 5.5 (continued)

131

132 Fig. 5.5 (continued)

J. Xu

5 Uncertainty Analysis of Carbon Emissions from Highway Tunnel … Fig. 5.5 (continued)

133

134

J. Xu

Fig. 5.5 (continued)

5.3.4 Primitive Carbon Emission Fitting 5.3.4.1

Skewed Distribution Processing and Fitting Method

The skewed distribution is a kind of probability distribution of continuous random variables, corresponding to the normal distribution. It has the characteristics of an asymmetrical distribution curve, and the concentrated position is biased to one side. According to the concentrated position’s relative position, the skewness distribution can be divided into two forms: positive skewness and negative skewness. The former data set position is biased to the smaller value side, while the latter data set position is biased to the larger value side, as shown in Fig. 5.6. In order to describe the degree of asymmetry of the skewed distribution, a skewness coefficient is introduced. When the distribution is symmetrical, the skewness coefficient is 0. When the skewness coefficient is greater than 0, the peak shifts to the left and the distribution is positively skewed; when the it is less than 0, the peak shifts to the right and the distribution is negative. The larger the absolute value of the skewness coefficient, the more pronounced the asymmetry of the distribution. It is generally believed that when the absolute value of the skewness coefficient is greater than 1, it is highly skewed. When the absolute value of the skewness coefficient is between 0.5 and 1, it is moderately skewed. When the absolute value is between 0 and 0.5, it is slightly skewed.

5 Uncertainty Analysis of Carbon Emissions from Highway Tunnel …

135

Table 5.8 Uncertainty analysis of carbon emissions of primitives Primitive

Mean valuea

Standard deviationa

Emission intervalsa (95% probability)

Deviation*/%

1 m3 of Grade I surrounding rock excavation and slag

19.005

1.815

15.691–22.753

−1.62

1 m3 of Grade II surrounding rock excavation and slag

17.562

1.652

14.538–20.957

−1.60

1 m3 of Grade III surrounding rock excavation and slag

12.938

1.119

10.861–15.22

−2.49

1 m3 of Grade IV surrounding rock excavation and slag

12.301

1.07

10.323–14.488

−2.56

1 m3 of Grade V surrounding rock excavation and slag

13.782

1.271

11.44–16.391

−2.28

1 kg of section steel frame

2.544

0.194

2.177–2.921

−4.47

1 kg of grid steel frame

2.925

0.201

2.544–3.317

−4.22

1 kg of connecting reinforcement

2.413

0.203

2.029–2.806

−4.32

1 kg of mortar bolt

4.009

0.27

3.492–4.543

−2.65

1 m of cartridge bolt

11.861

0.793

10.349–13.438

−2.79

1 m of hollow bolt

16.823

1.115

14.698–19.022

−2.63

0.203

2.043–2.821

−4.30

1 kg of steel mesh

2.427

1 m3 of shotcrete

523.54

59.795

423.131–658.308

3.77

1 m3 concrete of secondary lining wall

425.63

56.012

335.13–555.964

5.79

1 m3 concrete of secondary lining inverted arch

367.331

49.629

286.882–482.791

5.96

1 kg of rebar 1 m of ϕ42 small pipe grouting 1 m3 injecting cement

2.345

0.202

1.964–2.738

−4.48

13.543

0.982

11.68–15.496

−3.03

1057.903

106.482

861.727–1273.054

0.33

Note a Unit is kg CO2eq *The deviation is defined as (mean value − determined value)/determined value × 100

136

J. Xu

Fig. 5.6 Skewed distribution. a Positive skewness, b negative skewness

The sample data of some primitives’ carbon emissions in this study showed prominent asymmetric skew distribution traits, such as carbon emissions of shotcrete in Fig. 5.6. Compared with the skewed distribution, the probability density function of the normal distribution is easier to obtain, effectively reducing the difficulty in subsequent data processing. Therefore, some preprocessing methods are needed to convert the data from skewed distribution to normal distribution. When the data is positively biased, the commonly used processing methods include logarithmic transformation, square root transformation, arcsine transformation and reciprocal transformation. When the data shows a negative bias, researchers can take the opposite number to convert the data to a positive bias and then process it according to the positive bias processing method. The initially skewed data set can be transformed into a sample close to the normal distribution through data preprocessing. The mean and standard deviation of the data can be obtained after the deviation adjustment by statistical methods, and then the corresponding normal distribution probability density function can be gained, which can fit the adjusted data set, obtain the fitting sample data set of original skew distribution through reverse processing, and compare with the skew distribution data to verify the fitting effect. The above data processing flow is shown in Fig. 5.7. It is necessary to note that when the skewness coefficient is small, say, less than 0.1, the

5 Uncertainty Analysis of Carbon Emissions from Highway Tunnel …

137

Fig. 5.7 Data correction and fitting process

sample’s asymmetry is weak. If the accuracy requirements are met, there is no need for data correction processing.

5.3.4.2

Rectification and Fitting of Primitive Emission Sample Data

Based on the NumPy library, the skewness coefficients of the carbon emission sample data of each primitive in Fig. 5.6 were calculated, and the results are shown in Table 5.9. The skewness coefficients of shotcrete, secondary lining arch wall concrete, and inverted arch are between 0.58 and 0.72, indicating a high degree of skewness and may need to be processed by measures such as logarithmic transformation or square root transformation. The skewness coefficient of the surrounding rock excavation slag is about 0.20 and the skewness coefficient of the remaining primitives is less than 0.08, which is close to normal distribution. According to the magnitude of the skewness coefficient, three types of processing are performed on the sample data of primitive emissions: • Two logarithmic transformation processing: suitable for shotcrete, secondary lining arch wall and inverted arch. When primitive emission values are relatively large, such as the average carbon emissions of 1 m3 of shotcrete being 523.153 kg CO2eq , it is often necessary to multiply the carbon emission samples by a small coefficient before logarithmic transformation so as to speed up the tail data reduction. Moreover, according to the needs of skewness adjustment, a second logarithmic transformation is performed. The base of logarithmic transformation can be natural logarithm e or 10. The fitting effect of the normal distribution after logarithm processing is shown in Fig. 5.8a–c; • One logarithmic transformation processing: suitable for excavation and slag of surrounding rock of Grade I, II, III, IV, and V. The primitive emission value is not extensive, and the base of the logarithmic transformation can be the natural logarithm e. Although cement grouting has an enormous carbon emission value per unit volume, it can be multiplied by a small coefficient and then subjected to logarithmic transformation to obtain a good correction effect. The fitting effect of the normal distribution after logarithm processing is shown in Fig. 5.8d–i; • Direct obtainment of samples’ means and standard deviation: suitable for section steel frames, grid steel frames, connecting steel bars, mortar bolts, cartridge bolts,

138

J. Xu

Table 5.9 Skewness coefficient of primitive carbon emission sample data Primitive

Skewness coefficient

Primitive

Skewness coefficient

1 m3 of Grade I surrounding rock excavation and slag

0.22

1 m of cartridge bolt

0.070

1 m3 of Grade II surrounding rock excavation and slag

0.21

1 m of hollow bolt

0.062

1 m3 of Grade III surrounding rock excavation and slag

0.20

1 kg of steel mesh

0.046

1 m3 of Grade IV surrounding rock excavation and slag

0.19

1 m3 of shotcrete

0.58

1 m3 of Grade V surrounding rock excavation and slag

0.20

1 m3 of concrete for secondary lining arch wall

0.71

1 kg of section steel frame

0.036

1 m3 of concrete for secondary lining inverted arch

0.72

1 kg of grid steel frame

0.029

1 kg of rebar

0.039

1 kg of connecting steel bar

0.039

1 m of ϕ42 small pipe 0.084 grouting

1 kg of mortar bolt

0.079

1 m3 of injecting cement

0.18

hollow bolts, steel meshes, reinforcement rebar and grouting small pipes. The skewness coefficient is less than 0.09, so the mean and standard deviation of samples are calculated without correction transformation, which can be directly used as the normal distribution fitting parameters. After correction, the distribution of each primitive in Fig. 5.8 meets X ~ N(μ, σ 2 ), and the mean μ and standard deviation σ are shown in Table 5.10. Based on the normal distribution data in Table 5.10, the equations of carbon emission of each primitive sample are established, as shown in Eqs. 5.14–5.22. Y1 = 100 × 10e

X 1 −0.336

(5.14)

where, Y1 stands for the carbon emissions from 1 m3 of shotcrete, kg CO2eq ; Y2 = 150 × 10e

X 2 −0.808

(5.15)

where, Y2 stands for the carbon emissions from 1 m3 of concrete of secondary lining wall, kg CO2eq ;

5 Uncertainty Analysis of Carbon Emissions from Highway Tunnel … Fig. 5.8 Normal distribution fitting after data correction. a 1 m3 of shotcrete; b 1 m3 of concrete of secondary lining arch wall; c 1 m3 of concrete of secondary lining inverted arch; d excavation and slag of 1 m3 Grade I surrounding rock; e excavation and slag of 1 m3 Grade II surrounding rock; f excavation and slag of 1 m3 Grade III surrounding rock; g excavation and slag of 1 m3 Grade IV surrounding rock; h excavation and slag of 1 m3 Grade V surrounding rock; i 1 m3 injecting cement

139

140 Fig. 5.8 (continued)

J. Xu

5 Uncertainty Analysis of Carbon Emissions from Highway Tunnel …

141

Fig. 5.8 (continued)

Table 5.10 Normal distribution fitting parameters after data correction processing Variable

μ

Primitive m3

of shotcrete

σ

X1

1

0

0.068

X2

1 m3 concrete of secondary lining arch wall

0

0.124

X3

1 m3 concrete of secondary lining inverted arch

0

0.102

m3

X4

Excavation and slag of 1

0

0.095

X5

Excavation and slag of 1 m3 Grade II surrounding rock

0

0.094

X6

Excavation and slag of 1 m3 Grade III surrounding rock

0

0.086

m3

Grade I surrounding rock

X7

Excavation and slag of 1

0

0.087

X8

Excavation and slag of 1 m3 Grade V surrounding rock

Grade IV surrounding rock

0

0.092

X9

1 m3 injecting cement

0

0.044

Y3 = 100 × 10e

X 3 −0.583

(5.16)

where, Y3 stands for the carbon emissions from 1 m3 of concrete of secondary lining inverted arch, kg CO2eq ; Y4 = 30 × e X 4 −0.461

(5.17)

where, Y4 stands for the carbon emissions from the excavation and slag of 1 m3 Grade I surrounding rock, kg CO2eq ; Y5 = 30 × e X 5 −0.54

(5.18)

where, Y5 stands for the carbon emissions from the excavation and slag of 1 m3 Grade II surrounding rock, kg CO2eq ; Y6 = 25 × e X 6 −0.663

(5.19)

where, Y6 stands for the carbon emissions from the excavation and slag of 1 m3 Grade II surrounding rock, kg CO2eq ;

142

J. Xu

Y7 = 25 × e X 7 −0.713

(5.20)

where, Y7 stands for the carbon emissions from the excavation and slag of 1 m3 Grade IV surrounding rock, kg CO2eq ; Y8 = 25 × e X 8 −0.6

(5.21)

where, Y8 stands for the carbon emissions from the excavation and slag of 1 m3 Grade V surrounding rock, kg CO2eq ; Y9 = 100 × 10 X 8 +1.022

(5.22)

where, Y9 stands for the carbon emissions from 1 m3 of injecting cement, kg CO2eq . In terms of Eqs. 5.14–5.22, the skew distribution curve is fitted. Compared with the frequency distributions of the original samples (Fig. 5.6), it can be seen the fitting curve fits well with the original samples, as depicted in Fig. 5.9. The above primitive sample data with larger skewness coefficients have been processed by logarithmic transformation. The remaining primitives include section steel framework, grid steel framework, connecting steel bars, mortar bolts, cartridge bolts, hollow bolts, steel mesh, rebar and grouting small pipes, whose skewness coefficients are less than 0.09, close to normal distributions, which can be directly fitted to get the normal distribution. The calculated mean μ and standard deviation σ of the above primitives are shown in Table 5.11. After fitting the normal distribution curve according to the distribution parameters in Table 5.11 and comparing it with the frequency distribution of the original samples (Fig. 5.6), the result indicates the fitting curve fits well with the original samples, as shown in Fig. 5.10.

5.4 Uncertainty Analysis of Carbon Emissions from Lining Construction Section 5.3 presents inventory data for random modelling of primitives’ carbon emissions. On this basis, a fitting model for the overall carbon emissions from the tunnel construction can be further established. The specific method is to multiply the engineering quantity of each module and the primitives’ carbon emissions to obtain the module’s carbon emissions and add the module’s carbon emission value to obtain the overall carbon emissions. However, the fitting model regards different primitives as mutually independent random variables, different from the actual situation. For example, different construction processes share the same set of transportation machinery, the same source of fuel and building materials. In order to clarify the deviation of carbon emissions calculated by the fitting model, a stochastic analysis calculation model including all unit processes of tunnel lining

5 Uncertainty Analysis of Carbon Emissions from Highway Tunnel … Fig. 5.9 Comparison of the fitting skewed distribution with the original samples. a 1 m3 of shotcrete, b 1 m3 concrete of secondary lining arch wall, c 1 m3 concrete of secondary lining inverted arch, d excavation and slag of 1 m3 Grade I surrounding rock, e excavation and slag of 1 m3 Grade II surrounding rock, f excavation and slag of 1 m3 Grade III surrounding rock, g excavation and slag of 1 m3 Grade IV surrounding rock, h excavation and slag of 1 m3 Grade V surrounding rock, i 1 m3 of injecting cement

143

144 Fig. 5.9 (continued)

J. Xu

5 Uncertainty Analysis of Carbon Emissions from Highway Tunnel …

145

Fig. 5.9 (continued)

Table 5.11 Fitting parameters of normal distributions of primitives with small skewness

μ

σ

Variable

Primitive

X10

1 kg of section steel framework

2.543

0.194

X11

1 kg of grid steel framework

2.926

0.201

X12

1 kg of connecting steel bars

2.412

0.202

X13

1 kg of mortar bolts

4.007

0.27

X14

1 m of cartridge bolts

11.859

0.791

X15

1 m of hollow bolts

16.821

1.112

X16

1 kg of steel mesh

2.426

0.202

X17

1 kg of rebar

2.345

0.203

X18

1 m of grouting small pipes

13.541

0.983

construction will be established in this study, referred to as the overall LCI model of the tunnel. This model will be used to analyze the carbon emission changes in tunnel construction under different scenarios. Subsequently, the tunnel’s overall LCI model is used to analyze the carbon emission uncertainty of different tunnel construction cases, including key indicators such as the mean values, standard deviations, and 95% inclusion intervals, and in-depth analysis of the uncertainty of each module. Finally, the impact of scenario uncertainty on tunnel construction’s carbon emissions is analyzed. Besides, the impact of the decrease of engineering quantity caused by future design optimization and the reduction of emission factors brought about by technological progress on the uncertainty of tunnel lining’s emissions is considered. The overall analysis flow chart is shown in Fig. 5.11.

146 Fig. 5.10 Comparison and verification of normal distribution fitting and original sample. a 1 kg of section steel framework, b 1 kg of grid steel framework, c 1 kg of connecting steel bars, d 1 kg of mortar bolts, e 1 m of cartridge bolts, f 1 m of hollow bolts, g 1 kg of steel mesh, h 1 kg of rebar, i 1 m of grouting small pipes

J. Xu

5 Uncertainty Analysis of Carbon Emissions from Highway Tunnel … Fig. 5.10 (continued)

147

148

J. Xu

Fig. 5.10 (continued)

Fig. 5.11 Uncertainty analysis process of overall carbon emissions

5.4.1 Parameter Value 5.4.1.1

Engineering Quota

The engineering quantity per linear meter of the tunnel lining can be acquired from the tunnel’s survey and design drawings. However, such materials are commercial technical achievements and are challenging to obtain in large quantities. Therefore, it is not easy to obtain many tunnel engineering quantities per linear meter by the census. As an alternative method, the population can be estimated through partial tunnel design data samples. For example, the self-sampling method is used to generate a series of pseudo samples through Bootstrap. These samples are all sampled with replacement, and then the statistics of the pseudo samples are calculated to obtain

5 Uncertainty Analysis of Carbon Emissions from Highway Tunnel …

149

the corresponding confidence interval [8]. However, only when the number of initial samples is large enough can the distribution of self-sampling approach the population. The length of road tunnels in China is tens of thousands of kilometers, and the overall scale is enormous. However, the number of samples in this study is relatively limited, and it is not easy to accurately reflect the overall distribution characteristics. In summary, at this stage, it is difficult to accurately obtain the distribution probability of lining construction engineering quantities through direct statistics or indirect estimation methods. According to Table 3.1 in Sect. 3.2, the highway tunnels’ lining design samples collected include rock mass of Grade III, IV, and V, shallow and deep buried tunnels, with good, poor, and general surrounding rock quality, displaying good geological conditions and representativeness of surrounding rock grades. Given that the semiempirical engineering analogy method is still widely used in the current road tunnel designs, the designs of the same section scale and surrounding rock conditions are referred to in the tunnel design. Therefore, the tunnel samples in this study can reflect the design characteristics of specific tunnels in a certain period of time, and the quantities in Table 3.2 are still used as the data sources for uncertainty analysis.

5.4.1.2

Scenario Analysis Setting

In 2019, the power industry’s carbon emissions accounted for more than 40% of China’s carbon emissions, which placed tremendous pressure on the indirect carbon emissions from upstream building materials and the direct carbon emissions from construction sites. In order to lead the continued low-carbon development of the power industry, the National Development and Reform Commission and the Ministry of Ecology and Environment of China have successively issued the “Plan for Building the National Carbon Emission Trading Market (Power Generation Industry)” and the “Implementation Plan for Distributing Carbon Dioxide Emission Allowances in the Power Generation Industry”. Clean energy represented by hydropower is developing rapidly. In 2019, China’s hydropower industry’s cumulative installed capacity was approximately 358 million kWh, an increase of 1.55% over 2018. According to relevant plans, China will vigorously implement the “West-to-east Power Transmission” strategy in the next 30 years, focusing on constructing large hydropower bases in the Yangtze River’s upper reaches, Jinsha River, the Dadu River and the Lancang River. It is foreseeable that the carbon emission baseline of the power industry will continue to decrease. At present, the tunnel supporting structure’s design mainly adopts the engineering analogy method and empirical method, and the supporting parameter designs do not entirely match the actual engineering requirements [30, 64, 65, 70]. For example, after field measurement, Tian et al. [55] found that the axial force of bolts, the compressive stress of shotcrete and the tensile stress of steel frame in a single-track tunnel were far lower than the limit values. The result indicates that there is still room for optimization of tunnel support design parameters. Chinese researchers have studied the optimization of bolt length, bolt spacing, shotcrete thickness, and

150

J. Xu

Table 5.12 Analysis scenario setting Scenario

No.

Description

Baseline scenario

BS01

The deviation range of the quota data is given based on DQI. The distribution form of the input parameters is set as triangular distribution and uniform distribution. The values of the input parameters are shown in Sects. 5.3.1 and 5.4.1

Low carbon emission factor scenario

SS01

Carbon emission factors of steel, cement, diesel, and electricity are reduced by 15%

Low input scenario

SS02

Shotcrete’s work volume dropped by 10%

SS03

Molded concrete’s work volume dropped by 15%

SS04

Bolt’s work volume decreased by 15%

SS05

Steel framework’s work volume decreased by 15%

molded concrete thickness [17, 32, 33, 45, 52, 54, 63]. Through force and deformation analysis, practical design of bolt length, spacing, and configuration range, they are able to reduce construction costs [21, 35, 68]. According to the optimization practice of mechanized construction support for the Zhengzhou-Wanzhou high-speed railway, the molded concrete for surrounding rock of Grade IV and V reduces by 5–10 cm (10–20% reduction). The steel framework spacing of the rock mass of Grade IV has been adjusted from 1.2 to 1.4 m (About 15% reduction), while the steel framework spacing for rock mass of Grade V increased from 1.0 to 1.2 m (about 17% reduction). The concrete thickness decreased by 1–2 cm (about 10% reduction) [25]. With the development of information technology, dynamic adjustment based on actual stratum excavation will improve design parameters’ rationality based on pre-design. Xu [66] applied the dynamic design method to the jointed rock tunnels, which reduced bolts’ cost by 53% and the cost of steel frameworks by 30%. As a result, the amount of building materials and energy used during tunnel construction will witness a decrease. In summary, this study will consider the technological progress factors based on the baseline emission scenario, mainly focusing on low emission factor scenarios for electricity and building materials and low input scenarios for design optimization, as depicted in Table 5.12. In this chapter, the “expert judgment” method, which mainly comes from literature, is adopted to set the investment reduction ratio of tunnel construction optimization [11, 15, 21, 32, 33, 45, 52, 54, 63, 68].

5.4.2 Model Uncertainty Analysis First, two algorithms calculating the tunnel’s overall carbon emission are established based on the aforementioned primitive LCI list, namely, the fitting carbon emission

5 Uncertainty Analysis of Carbon Emissions from Highway Tunnel …

151

algorithm and the overall LCI algorithm for tunnel construction. The former uses the primitives’ carbon emission fitting formula, combined with each process’s engineering quantities, to calculate the overall carbon emissions. Its advantage is that the code is simple and easy to use. The disadvantage is that it assumes that each module is independent of the others and has a certain error with the actual working conditions. Also, the formula calculating carbon emissions of the primitive are given, and scenario analysis cannot be carried out on this basis. The latter establishes an LCI model that includes all unit processes. Although the calculation process takes a longer time, it has the advantage of accuracy, being able to adjust primitive inventory data to analyze the uncertainty of carbon emissions under different scenarios. Take engineering case 1 in Table 3.2 as an example to compare the calculation results of the two algorithms. As shown in Fig. 5.12, the 95% inclusion interval obtained by the fitting carbon emission algorithm is 23.403–29.926 t CO2eq , and the 95% inclusion interval obtained by the overall LCI of tunnel construction is 24.109– 28.414 t CO2eq . By comparison, it is found that the distribution range obtained by the fitting carbon emission algorithm is relatively narrow; the probability density is high. And it also contains the most likely values of the actual samples’ carbon emissions, that is, the emission values at the peak. In Fig. 5.12, the 95% inclusive interval’s endpoints are marked, that is, the 2.5 percentile value and the 97.5 percentile value. When the sample size N is 106 , the 95% inclusive interval endpoint values are converged, and the calculation is repeated ten times. The maximum difference between the four endpoints is only 0.024 t CO2eq . Overall, the 95% inclusion interval obtained by the fitting carbon emission algorithm is within the overall LCI algorithm’s inclusion interval, indicating the result of the fitting calculation is quite representative.

Fig. 5.12 Comparison of the calculation results of the fitting carbon emission algorithm and the overall LCI algorithm of the tunnel

152

J. Xu

5.4.3 Parameter Uncertainty Analysis The parameter uncertainty analysis adopts the benchmark scenario to analyze the uncertainty of the overall carbon emission per linear meter of tunnel construction and the uncertainty of the carbon emission of each module. The average and standard deviation of carbon emissions are calculated using the overall LCI algorithm for tunnel lining construction, and the results are shown in Fig. 5.13. Among them, rock mass of Grade V has the highest carbon emission level, and the corresponding standard deviation value is also larger, followed by those of Grade IV surrounding rock, and the lowest carbon emission level and standard deviation is from Grade III surrounding rock. Further analysis of the uncertainty deviations of different linings is conducted. The 95% inclusion interval length of the carbon emissions of surrounding rocks Grade III, Grade IV and Grade V are 2.252–2.810 t CO2eq , 2.798–4.88 t CO2eq and Fig. 5.13 Carbon emissions of benchmark scenario BS01. a Average emission values, b 95% inclusion interval

5 Uncertainty Analysis of Carbon Emissions from Highway Tunnel …

153

Fig. 5.14 Carbon emission uncertainty of modules in the baseline scenario BS01

4.623–7.618 t CO2eq , respectively. Overall, as the surrounding rock grades increase, the carbon emission uncertainty from lining construction also increases. The mean and standard deviation of carbon emissions for different modules are calculated with the fitting carbon emission algorithm, as shown in Fig. 5.14. Based on the specific modules, the emission distribution ranges of shotcrete, hollow bolts, section steel frames, and concrete of the secondary lining arch wall are relatively wide, with greater uncertainty. Since the uncertainty of the modules mainly comes from the uncertainty of primitives, the greater the engineering quantity of the process, the higher the uncertainty of its emissions. Taking the steel framework process as an example, the maximum mass of the steel framework per linear meter is 2881 kg, and the minimum mass is 0 kg. The emission uncertainty of 1 kg section steel is relatively fixed, and its 95% emission range is 2.177–2.921 kg CO2eq . Therefore, the greater the steel frame’s mass per linear meter, the longer the error bar in Fig. 5.14. Another example is that the 95% carbon emission range of 1 m3 of injecting cement is 861.727–1273.054 kg CO2eq , with high uncertainty of unit engineering quantity. However, its engineering quantity is small, with a maximum value of only 0.58 m3 , making the module’s emission uncertainty weak.

154

J. Xu

5.4.4 Analysis of Scenario Uncertainty For comparison, the authors counted the frequency distribution of the average carbon emission per linear meter of tunnel lining construction under six scenarios (Fig. 5.15). Scenarios BS01 and SS01 correspond to the baseline scenario and the emission factor reduction scenario respectively. Scenarios SS02, SS03, SS04 and SS05 correspond to the engineering quantity reduction scenario of shotcrete, molded concrete, bolts, and steel frames. Overall, the distribution range of the average emission values of Scenario SS01 has changed from 6–30 to 4–26 t CO2eq , indicating that Scenario SS01 has sound emission reduction effects for different tunnel cases. Similarly, the distribution range of the mean emission values of Scenario SS03 changes from 6–30 to 4–30 t CO2eq . In the baseline scenario, there are five cases where the emission average falls within the 26–28 t CO2eq range. Correspondingly, there is no case in the SS03 scenario where the emission average falls within the 26–28 t CO2eq range, indicating that this scenario has a good emission reduction effect on high-emission tunnels. However, the frequency distributions of the mean emission values of the scenarios SS02, SS04 and SS05 are similar, and the overall emission reduction effect is not evident. The energy consumption from the production of some primary building materials in China is higher than that of developed countries. For example, the comprehensive energy consumption of cement production in China is 24.3% higher than that in Japan [71]. To accelerate the pace of technological upgrading and low-carbon development in key domestic industries, the Ministry of Ecology and Environment requires key sectors such as steel and cement to accelerate their inclusion in the national carbon emission trading market during the “14th Five-Year Plan” period. The Ministry encourages technological advancement of advanced enterprises and reduces outdated production capacity. With the continuous improvement of China’s production technology, the production of key materials and the level of electric energy emissions will continue to decline. According to the scenario SS01 setting, when the carbon emission factor reduces, the tunnel construction’s average carbon emissions decrease by 0.9–4.328 t CO2eq , which drops by 14.53–14.70% compared to the baseline emission scenario. Shotcrete is a vital support component after tunnel excavation, and it is also a vital component of the tunnel’s permanent lining. The unique advantage of shotcrete is that it is the only supporting body that contacts the surrounding rock in a large area, so it is difficult to be replaced by other supporting structures [15]. Therefore, the shotcrete of sufficient thickness plays an essential role in ensuring the stability of the tunnel. However, related studies show that if the thickness of shotcrete reaches a certain value, the control of the increase in thickness on the tunnel deformation will no longer be evident [32, 54], which leads to rethinking and exploring the optimization of suitable shotcrete thickness. While the vital role of shotcrete in surrounding rock support and with the lining optimization cases in existing projects taken into consideration, the reduction value of shotcrete engineering quantity is set to 10% (1–2 cm), so that the

5 Uncertainty Analysis of Carbon Emissions from Highway Tunnel … Fig. 5.15 Average frequency of carbon emissions from tunnel construction under different scenarios. a Scenario BS01, b Scenario SS01, c Scenario SS02, d Scenario SS03, e Scenario SS04, f Scenario SS05

155

156 Fig. 5.15 (continued)

J. Xu

5 Uncertainty Analysis of Carbon Emissions from Highway Tunnel …

157

average carbon emissions per linear meter decrease by 0.123–0.551 t CO2eq , which drops by 1.51–2.79% compared to the baseline emission scenario. As early as 2001, Peng et al. [45] proposed an optimization model for molded concrete based on the Lagrangian operator method theory. It is not uncommon to study the thickness optimization of molded concrete, and there is a delicate balance between rebar and concrete engineering quantity. When the thickness of molded concrete is lower than the optimal value, to meet the structural strength requirements, the increase in the lining’s rebar ratio should lead to a rise in the amount of reinforcement. And that should be valued in the optimization of molded concrete. This study lays special stress on analyzing the impact of the decline in the amount of molded concrete on the overall carbon emissions. According to scenario SS03, the average carbon emissions per linear meter reduce by 0.508–1.408 t CO2eq , which drops by 3.97–8.96% compared to the baseline emission scenario. The current tunnel designs in China mainly adopt the New Austrian Tunnelling Method, and the bolts are often designed with full-section uniformity. The advantage is to increase the construction safety, but it may cause material waste [68]. Besides, some studies have found that there is an optimal interval for the length of the bolt. When the bolt reaches a certain length, its control effect on the surrounding rock’s disturbance and deformation increases insignificantly [30]. For Scenario SS04, by optimizing the length spacing and layout range of bolts, when the bolt quantity reduces by 15%, the average carbon emissions per linear meter reduce by 0.053– 0.621 t CO2eq , which drops by 0.54–2.35% compared to the baseline emission scenario. Shotcrete has low strength at the beginning of spraying, and the steel frame bears the formation pressure to control the deformation of the surrounding rock. Currently, section steel frameworks and grid steel frameworks are widely used in the initial support of tunnels, both of which have different advantages. The former has high rigidity and can be loaded quickly, but the cost is high; the latter has light weight and good supporting effect. However, the unit cost of the steel frame is relatively high. Under the premise of ensuring the support strength, the project’s economy can be enhanced by increasing the spacing of the steel frame, and the project progress can be accelerated [15, 54]. According to Scenario SS05, by optimizing the steel frame spacing and other methods, when the engineering quantity of the steel frame reduces by 15%, the average carbon emissions per linear meter reduce by 0–1.10 t CO2eq , which drops by 0–3.75% compared to the baseline emission scenario. Further comparison is carried out of the impact of various scenarios on the carbon emissions from tunnel construction with different rock mass grades. The average carbon emissions from tunnel construction are averaged according to rock mass grades, and the results are shown in Fig. 5.16a. Figure 5.16b shows the average differences of carbon emissions in different scenarios relative to the baseline scenario. As the grades of the surrounding rock rise, the amount of lining construction per linear meter increases. Hence when the amount of a particular process decreases by a percentage according to the scenario setting, the average emission value change gradually increases. However, the emission reduction effects of different scenarios on different surrounding rock grades are different. For example, under SS04 and SS06,

158

J. Xu

Fig. 5.16 Mean difference of average carbon emissions of tunnel cases. a Mean of average carbon emissions, b mean difference of average carbon emissions

the surrounding rock grades’ average emission values vary unevenly. The emission reduction effect of the rock mass of Grade V is much higher than that of the rock mass of Grade III. It indicates that this scenario has a specific emission reduction effect on the rock mass of Grade V, but the impact of reducing emissions on rock mass of Grade III is very weak. Correspondingly, under scenarios SS02 and SS03, there are specific emission reduction effects for different surrounding rock grades.

5.5 Conclusion The conclusions are as follows: 1.

The sample size has an essential influence on the convergence of the stochastic modeling calculation results and should be selected carefully. Once the sample size is too tiny, the accurate carbon emission data cannot be obtained. According

5 Uncertainty Analysis of Carbon Emissions from Highway Tunnel …

2.

3.

4.

5.

159

to the convergence analysis of iterative calculation, the sample size is determined to be 106 . The convergent sample’ means, standard deviation, and 95% inclusive interval can be obtained. Modelling the simulation results of the primitives’ carbon emissions, according to the skewness of the output sample distribution, a skewness adjustment method for the primitives’ carbon emission distributions is proposed and the fitting models of different primitives are obtained. Applying the fitting models to the calculation of the tunnel’s overall carbon emissions, the obtained 95% inclusive interval of carbon emissions is narrow and has a high probability, included in the 95% inclusive interval of the overall LCI model of the tunnel, thus verifying the fitting model’s accuracy and reliability. As the grades of surrounding rock increase, the uncertainty of carbon emission from lining construction will increase. In engineering cases, the lengths of 95% inclusive intervals of carbon emissions from the surrounding rocks of Grade III, Grade IV, and Grade V are 2.252–2.810 t CO2eq , 2.798–4.88 t CO2eq and 4.623–7.618 t CO2eq , respectively. From the modules’ perspective, the emission distribution ranges of shotcrete, hollow bolts, steel framework and concrete of the second lining arch wall are relatively wide, with more significant uncertainty. Based on the expert judgment data obtained from the literature survey, multiple analysis scenarios for the consumption reduction of energy and building materials and the optimization of tunnel design parameters are proposed. The calculation found that the emission reduction effects of different scenarios are quite different. The reduction of carbon emission factors of important emission sources represented by steel, cement, diesel, and electricity has a prominent effect on the emission reduction of tunnel construction. For shotcrete, molded concrete, bolts, and steel frames, after reducing the specified proportion of the engineering quantity, the average carbon emissions per linear meter decreased by 0.123–0.551 t CO2eq , 0.508–1.408 t CO2eq , 0.053–0.621 t CO2eq and 0–1.10 t CO2eq , respectively It can be seen that the optimization of molded concrete design brings the best emission reduction effect.

References 1. Baek CY, Park KH, Tahara K, Chun YY (2017) Data quality assessment of the uncertainty analysis applied to the greenhouse gas emissions of a dairy cow system. Sustainability 9. https://doi.org/10.3390/su9101676 2. Baek CY, Tahara K, Park KH (2018) Parameter uncertainty analysis of the life cycle inventory database: application to greenhouse gas emissions from brown rice production in IDEA. Sustainability 10. https://doi.org/10.3390/su10040922 3. Bałdowska-Witos P, Piotrowska K, Kruszelnicka W et al (2020) Managing the uncertainty and accuracy of life cycle assessment results for the process of beverage bottle moulding. Polymers (Basel) 12. https://doi.org/10.3390/polym12061320

160

J. Xu

4. Canter KG, Kennedy DJ, Montgomery DC et al (2002) Screening stochastic life cycle assessment inventory models. Int J Life Cycle Assess 7. https://doi.org/10.1007/BF02978906 5. Cellura M, Longo S, Mistretta M (2011) Sensitivity analysis to quantify uncertainty in life cycle assessment: the case study of an Italian tile. Renew Sustain Energy Rev 15:4697–4705. https://doi.org/10.1016/j.rser.2011.07.082 6. 陈国汉. 蒙特卡洛模拟及其 Stata 应用实现. 经济科学出版社, 2015. Chen G (2015) Monte Carlo simulation and its Stata application realization. Economic Science Press, Beijing 7. Ciroth A, Fleischer G, Steinbach J (2004) Uncertainty calculation in life cycle assessments. Int J Life Cycle Assess 9. https://doi.org/10.1007/bf02978597 8. Davison AC, Hinkley DV (1997) Bootstrap methods and their application 9. Di Lullo G, Zhang H, Kumar A (2016) Evaluation of uncertainty in the well-to-tank and combustion greenhouse gas emissions of various transportation fuels. Appl Energy 184. https:// doi.org/10.1016/j.apenergy.2016.10.027 10. Erdmann L, Hilty LM (2010) Scenario analysis. J Ind Ecol 14(5):826–843. https://doi.org/10. 1111/j.1530-9290.2010.00277.x 11. 冯旭杰. 基于生命周期的高速铁路能源消耗和碳排放建模方法. 北京交通大学, 2014. Feng X (2014) Modeling life cycle energy consumption and greenhouse gas emissions for high-speed railways. Dissertation, Beijing Jiaotong University 12. Funtowicz SO, Ravetz JR (1990) Uncertainty and quality in science for policy 13. 高俊莲, 徐向阳, 郑凤琴, 霍冉. 基于全生命周期的煤炭碳排放清单计算与不确定性分析. 中国煤炭, 2017, 43(06): 22–26. Gao J, Xu X, Zheng F et al (2017) Coal carbon emission inventory calculation and uncertainties analysis based on lifecycle analysis. Chin Coal 43(6):22–26 14. 高源雪. 建筑产品物化阶段碳足迹评价方法与实证研究. 北京: 清华大学, 2007. Gao Y (2007) Assessment methodology and empirical analysis of embodied carbon foot-print of building construction. Dissertation, Tsinghua University 15. 关宝树. 隧道工程施工要点集. 人民交通出版社, 第二版, 2011. Guan B (2011) Essentials of tunnel construction, 2nd edn. People’s Communications Press, Beijing 16. 郭家丰, 卢川, 毛辉辉, 孙中宁, 王建军, 王晓烈. 新型不确定性分析容忍限估计方法. 核 动力工程. https://doi.org/10.13832/j.jnpe.2020.06.0131. Guo J, Lu C, Mao H et al (2020) A new uncertainty analysis method for tolerance limit evaluation. Nucl Power Eng. https://doi. org/10.13832/j.jnpe.2020.06.0131 17. 何成滔. 软岩隧道支护设计优化研究. 西安科技大学, 2011. He C (2011) Study on optimization of the support design of the rock tunnel. Dissertation, Xi’an University of Science and Technology 18. Hedbrant J, Sörme L (2001) Data vagueness and uncertainties in urban heavy-metal data collection. Water Air Soil Pollut Focus 1 19. Hong J, Shen GQ, Feng Y et al (2015) Greenhouse gas emissions during the construction phase of a building: a case study in China. J Clean Prod 103. https://doi.org/10.1016/j.jclepro. 2014.11.023 20. 胡春迪, 杨崧. 研究表明 IPCC-决策者摘要的相对可读性变得更低. 科学通报, 2015, 60(33): 128. Hu C, Yang S (2015) Research shows that the relative readability of the IPCC-decision maker summary has become lower. Chin Sci Bull 60(33):128 21. 黄波, 杨小礼. 浅埋偏压隧道锚杆加固参数的优化研究. 矿业工程, 2009, 7(02): 67–69. Huang B, Yang X (2009) Optimized design of rockbolts for shallow embedded bias tunnels. Min Eng 7(2):67–69 22. 黄建. 基于 LEAP 的中国电力需求情景及其不确定性分析. 资源科学, 2012, 34(11): 2124– 2132. Huang J (2012) Scenario analysis of Chinese power demands and uncertainty assessment based on the LEAP mode. Resour Sci 34(11):2124–2132 23. 黄娜, 王洪涛, 范辞冬, 周杉财, 侯萍, 杨洁. 基于不确定度和敏感度分析的 LCA 数据质量 评估与控制方法. 环境科学学报, 2012, 32(06): 1529–1536. Huang N, Wang H, Fan C et al (2012) LCA data quality assessment and control based on uncertainty and sensitivity analysis. Acta Sci Circum 32(6):1529–1536

5 Uncertainty Analysis of Carbon Emissions from Highway Tunnel …

161

24. Huijbregts MAJ, Gilijamse W, Ragas AMJ, Reijnders L (2003) Evaluating uncertainty in environmental life-cycle assessment. A case study comparing two insulation options for a Dutch one-family dwelling. Environ Sci Technol 37. https://doi.org/10.1021/es020971+ 25. 金强国. 郑万高铁隧道大型机械化施工支护优化. 隧道建设 (中英文), 2018, 38(08): 1324– 1333. Jin Q (2018) Optimization of support design of large-scale machanized construction of Zhengzhou-Wanzhou high-speed railway tunnel. Tunnel Constr 38(8):1324–1333 26. Kennedy DJ, Montgomery DC, Quay BH (1996) Data quality: stochastic environmental life cycle assessment modeling: a probabilistic approach to incorporating variable input data quality. Int J Life Cycle Assess 1. https://doi.org/10.1007/bf02978693 27. Kennedy DJ, Montgomery DC, Rollier DA, Keats JB (1997) Data quality: assessing input data uncertainty in life cycle assessment inventory models. Int J Life Cycle Assess 2. https://doi. org/10.1007/BF02978420 28. Lee MH, Lee JS, Lee JY et al (2017) Uncertainty analysis of a GHG emission model output using the block bootstrap and Monte Carlo simulation. Sustainability 9. https://doi.org/10. 3390/su9091522 29. Lewandowska A (2011) Environmental life cycle assessment as a tool for identification and assessment of environmental aspects in environmental management systems (EMS) part 1: methodology. Int J Life Cycle Assess 16. https://doi.org/10.1007/s11367-011-0253-2 30. 李建, 谭忠盛. 大断面黄土隧道初期支护与围岩相互作用机理研究. 现代隧道技术, 2013, 50(3); 79–86. Li J, Tan Z (2013) On the interaction mechanism of the primary support and rock mass in a loess tunnel with a large section. Mod Tunnel Technol 50(3):79–86 31. Li X, Yang F, Zhu Y, Gao Y (2014) An assessment framework for analyzing the embodied carbon impacts of residential buildings in China. Energy Build 85. https://doi.org/10.1016/j. enbuild.2014.09.051 32. 李沿宗, 高攀, 邹翀等. 木寨岭隧道变形分析及初期支护参数优化研究. 隧道建设, 2011, 31(3): 320–324. Li Y, Gao P, Zou C et al (2011) Deformation analysis and primary support parameter optimization: case study on Muzhailing tunnel. Tunnel Constr 31(3):320–324 33. 刘光明, 彭立敏, 施成华等. 基于结构安全性的偏压隧道初期支护参数优化. 铁道科学与 工程学报, 2012, 9(4): 79–93. Liu G, Peng L, Shi C et al (2012) Research on the optimization design of primary support parameters of unsymmetrical loading tunnel based on the structure safety. J Railw Sci Eng 9(4):79–93 34. 刘宏强, 付建勋, 刘思雨 et al 钢铁生产过程二氧化碳排放计算方法与实践. 钢铁, 2016, 51(4): 74–82. Liu H, Fu J, Liu S et al (2016) Calculation methods and application of carbon dioxide emission during steel-making process. Iron Steel 51(4):74–82 35. 刘杰. 裂隙岩体隧道支护受力变形分析及锚杆参数设计建议. 公路交通科技 (应用技术 版), 2017, 13(01): 104–106. Liu J (2017) Analysis of stress and deformation of tunnel support in fractured rock mass and suggestions for bolt parameter design. J Highw Transp Res Dev 13(1):104–106 36. Lloyd SM, Ries R (2007) Characterizing, propagating, and analyzing uncertainty in life-cycle assessment: a survey of quantitative approaches. J Ind Ecol 11. https://doi.org/10.1162/jiec. 2007.1136 37. 娄伟. 情景分析理论与方法. 社会科学文献出版社, 2012. Lou W (2012) Scenario analysis: theory and method. Social Sciences Academic Press (China), Beijing 38. Mao C, Shen Q, Shen L, Tang L (2013) Comparative study of greenhouse gas emissions between off-site prefabrication and conventional construction methods: two case studies of residential projects. Energy Build 66. https://doi.org/10.1016/j.enbuild.2013.07.033 39. Maurice B, Frischknecht R, Coelho-Schwirtz V, Hungerbühler K (2000) Uncertainty analysis in life cycle inventory. Application to the production of electricity with French coal power plants. J Clean Prod 8. https://doi.org/10.1016/S0959-6526(99)00324-8 40. Oliphant TE (2007) Python for scientific computing. Comput Sci Eng 9. https://doi.org/10. 1109/MCSE.2007.58 41. Olivier BG, Rohwer JM, Hofmeyr JHS (2002) Modelling cellular processes with Python and Scipy. Mol Biol Rep 29. https://doi.org/10.1023/A:1020346417223

162

J. Xu

42. 欧训民, 张希良. 中国终端能源的全生命周期化石能耗及碳强度分析. 第七届中国软科 学学术年会, 2009: 208–214. Ou X, Zhang X (2009) Fossil energy consumption and GHG emission of final energy by LCA in China. Paper presented at the seventh academic annual meeting of China soft science, Beijing 43. Pajankar A (2017) Matplotlib. Apress, Berkeley 44. Park YS, Yeon SM, Lee GY, Park KH (2019) Proposed consecutive uncertainty analysis procedure of the greenhouse gas emission model output for products. Sustainability 11. https:// doi.org/10.3390/su11092712 45. 彭立敏, 施成华, 刘小兵. 隧道钢筋混凝土结构的优化设计模型及应用. 中国公路学报, 2001, 14(2): 71–74. Peng L, Shi C, Liu X (2001) Optimization design model and application about the reinforced concrete structure of tunnel. Chin J Highw Transp 14(2):71–74 46. Penman J, Gytarsky M, Hiraishi T et al (2006) 2006 IPCC—guidelines for national greenhouse gas inventories 47. Pintér L (2007) GEO resource book: a training manual on integrated environmental assessment and reporting (module overviews). Winnipeg 48. Politz JG, Martinez A, Milano M et al (2013) Python. ACM SIGPLAN Not 48(10):217–231 49. Pomponi F, D’Amico B, Moncaster AM (2017) A method to facilitate uncertainty analysis in LCAs of buildings. Energies 10. https://doi.org/10.3390/en10040524 50. Programme UNE (2003) UNEP in 2002. Environ Policy Collect 3:79–82 51. Qin Y (2019) Characterizing uncertainties in life cycle assessment. Dissertation, University of California 52. 沈才华, 童立元, 刘松玉. 卵砾石层浅埋公路隧道支护方案的优化研究. 土木工程学报, 2008 (07): 71–75. Shen C, Tong L, Liu S (2008) Optimization design of support scheme for a shallow tunnel in gravel deposits. Chin Civ Eng J 41(7):71–75 53. 沈镭, 赵建安, 王礼茂 et al 中国水泥生产过程碳排放因子测算与评估. 科学通报, 2016, 61(26): 2926. Shen L, Zhao J, Wang L et al (2016) Calculation and evaluation on carbon emission factor of cement production in China. Chin Sci Bull 61(26):2926–2938 54. 孙水泉. 铁路隧道施工初期支护参数优化研究. 西南交通大学, 2015. Sun S (2015) Optimization study on the initial of railway tunnel. Dissertation, Southwest Jiaotong University 55. 田明杰, 仇文革, 朱旺, 牟智恒, 黄海昀. 单线铁路隧道施工量测信息分析与初期支护优化 研究. 铁道建筑, 2018, 58(11): 78–82+90. Tian M, Qiu W, Zhu W et al (2018) Measurement information analysis and initial support optimization research of single track railway tunnel construction. Railw Eng 58(11):78–82+90 56. Van Der Walt S, Colbert SC, Varoquaux G (2011) The NumPy array: a structure for efficient numerical computation. Comput Sci Eng 13. https://doi.org/10.1109/MCSE.2011.37 57. Wang E, Shen Z, Neal J et al (2012) An AHP-weighted aggregated data quality indicator (AWADQI) approach for estimating embodied energy of building materials. Int J Life Cycle Assess 17. https://doi.org/10.1007/s11367-012-0417-8 58. 王婧婕, 张凯山. 成都市居民生活方式碳排放的不确定性分析. 环境工程, 2016, 34(S1): 926–930. Wang J, Zhang K (2016) Uncertainty analysis of carbon emissions resulting from daily activities of residents of Chengdu. Environ Eng 34(S1):926–930 59. 王帅. 商品混凝土生命周期环境影响评价研究. 北京: 清华大学, 2009. Wang S (2009) Research on the EIA of ready-mixed concrete during the life cycle. Dissertation, Tsinghua University 60. Weidema BP (1998) Multi-user test of the data quality matrix for product life cycle inventory data. Int J Life Cycle Assess 3. https://doi.org/10.1007/BF02979832 61. Weidema BP, Wesnæs MS (1996) Data quality management for life cycle inventories—an example of using data quality indicators. J Clean Prod 4. https://doi.org/10.1016/S0959-652 6(96)00043-1 62. Williams ED, Weber CL, Hawkins TR (2009) Hybrid framework for managing uncertainty in life cycle inventories. J Ind Ecol 13. https://doi.org/10.1111/j.1530-9290.2009.00170.x 63. 伍国军, 陈卫忠, 戴永浩, 郭小红, 曹传林. 浅埋大跨公路隧道施工过程和支护优化的研 究. 岩土工程学报, 2006 (09): 1118–1123. Wu G, Chen W, Dai Y et al (2006) Study on excavation sequences and support optimization for shallow-buried and large-spanned tunnels. Chin J Geotech Eng 28(9):1118–1123

5 Uncertainty Analysis of Carbon Emissions from Highway Tunnel …

163

64. 伍文国. 隧道支护优化及变形破坏概率分析. 湖南大学, 2010. Wu W (2010) Optimization for tunnel support structure and failure probability analysis for deformation. Dissertation, Hunan University 65. 谢伟华. 浅埋偏压小净距隧道施工顺序及初期支护参数优化研究. 长沙理工大学, 2016. Xie W (2016) Study on construction sequence and initial support parameter of the shallow buried tunnel with small interval and unsymmetrical loading. Dissertation, Changsha University of Science & Technology 66. 徐建平, 桑运龙, 刘学增, 杨志峰. 节理发育岩体隧道支护的动态设计方法与应用. 地下 空间与工程学报, 2017, 13(02): 416–421. Xu J, Sang Y, Liu X et al (2017) Observational method and its application for tunnel supporting in jointed rock. Chin J Undergr Space Eng 13(2):416–421 67. 徐建军, 谭鲜明, 张润楚. 种惩罚最大似然方法估计混合回归模型. 中国科学: 数学, 2019, 049(008): 1159–1182. Xu J, Tan X, Zhang R (2019) A penalized maximum likelihood approach for estimating mixture of regression models. Sci Sin Math 49(8):1159–1182 68. 杨晓辉. 地质顺层偏压隧道锚杆支护参数优化及施工技术研究. 铁道建筑技术, 2020 (09): 65–69+79. Yang X (2020) Study on the optimum design of rockbolt parameters for tunnels in geologically inclined bedding strata. Railw Constr Technol (09):65–69+79 69. 翟青, 刘星, 杨玉峰. CDM 项目温室气体减排成本的不确定性分析. 环境科学学报, 2004 (04): 649–654. Zhai Q, Liu X, Yang Y (2004) Uncertainty analysis on greenhouse gases reduction cost in CDM project. Acta Sci Circum 24(4):649–654 70. 张涛. 地铁隧道初期支护参数优化研究. 隧道建设, 2014, 34(10): 926–930. Zhang T (2014) Study on optimization of parameters of primary support of metro tunnel. Tunnel Constr 34(10):926–930 71. 张孝存. 建筑碳排放量化分析计算与低碳建筑结构评价方法研究. 哈尔滨工业大学, 2018. Zhang X (2018) Research on the quantitative analysis of building carbon emission and assessment methods for low-carbon buildings and structures. Dissertation, Harbin Institute of Technology 72. 张振芳. 露天煤矿碳排放量核算及碳减排途径研究. 北京: 中国矿业大学, 2013. Zhang Z (2013) Study on carbon emissions accounting and carbon emission reduction approach of surface coal mine. Dissertation, China University of Mining and Technology 73. 张振浩, 刘敏涵, 陈威. 桥梁建设期间碳排放量的不确定性分析. 交通科学与工程, 2019, 35(04): 57–62. Zhang Z, Liu M, Chen W (2019) Analysis on the uncertainty of carbon emission during the bridge construction. J Transp Sci Eng 35(4):57–62 74. 周桃庚. 用蒙特卡洛法评定测量不确定度. 中国质检出版社, 2013. Zhou T (2013) Using Monte Carlo method to evaluate the uncertainty of measurement. Standards Press of China, Beijing 75. Min LEE, Jong LEE, Joo LEE, Yoon KIM, Yoo PARK, Kun LEE et al (2017) Uncertainty Analysis of a GHG Emission Model Output Using the Block Bootstrap and Monte Carlo Simulation. Sustainability 9(9): 1522-10.3390/su9091522

Chapter 6

Carbon Emission Transition of Highway Tunnel Construction Chun Guo

Abstract Different from ground engineering, tunnel is highly influenced by surrounding rock. However, surrounding rock has never been mentioned in previous published studies on carbon emissions in tunnels. In order to evaluate the effect of surrounding rock conditions on carbon emissions, this study expounded the relationship between tunnel designs and rock mass classifications. Besides, five different surrounding rock conditions and tunnel lining designs of a real tunnel in China were introduced in detail. LCA was used to analyze the carbon emissions in five tunnels with different surrounding rock. The tunnels with worse rock conditions generate more carbon emissions in construction, while the emissions are between 6,220 t– 17,010 t CO2 eq . More than 60% of the carbon emissions are from materials. The importance of surrounding rock conditions to carbon emissions in tunnels was clarified in this study. Emissions increased sharply in many cases after comparing the same construction for different surrounding rock conditions. Based on the defined relative contribution indexes, transition paths of carbon emission were ascertained.

6.1 Introduction LCA method provides a means of assessing a facility’s overall environmental effect from cradle to grave. The existing LCA studies demonstrated that the tunnel construction stage played a significant role in carbon emissions [4, 8, 10]. All these studies, however, may overlook a significant factor in the tunnel construction: the surrounding rock conditions. What must be noticed is that tunnels are underground. Good surrounding rock conditions provide strong support and help stabilize the tunnel structure after excavation. Thus tunnels with good surrounding rock conditions require few material inputs. In contrast, the consumption of the energy and materials rise greatly in weak surrounding rocks. In fact, tunnels’ surrounding rock conditions are subtle and changeable. Construction departments normally take varying design schemes according to the actual rock conditions. It therefore is of great significance to explore the influence of surrounding rock conditions. This study intends to analyze the impact of the surrounding rock conditions on tunnel carbon emissions and focuses on the following two questions:

© Southwest Jiaotong University Press 2022 C. Guo and J. Xu, Carbon Emission Calculation Methods for Highway Tunnel Construction, https://doi.org/10.1007/978-981-16-5308-7_6

165

166

1. 2.

C. Guo

What are the differences in the carbon emissions from tunnel construction with varying surrounding rock conditions? What is the carbon emissions’ changing mechanism during tunnel construction?

The emission transition pathways were determined under varying surrounding rock conditions. The new findings in the study offered a novel perspective on how to reduce the carbon emissions and energy consumption in tunnel construction by focusing on the major inputs of the materials and energy in critical emissions processes. The first section (Sect. 6.1) analyzed the tunnel construction’s emissions in varying surrounding rock conditions, and then explored the tunnel emissions’ changing mechanism. The second section (Sect. 6.2) introduced the methods, evaluation indicators and research framework. The third section (Sect. 6.3) showed the major findings and clarified its significance. The fourth section (Sect. 6.4) summarized the findings of the study and presented its deficiency as well.

6.2 Methods and Materials 6.2.1 Rock Mass Grades and Lining Designs The environmental conditions in underground projects are completely unlike those in ground projects. Tunnels are located in various kinds of geological environments and considerably affected by their surrounding environments [7]. The surrounding rock conditions are the basis to select construction techniques and underground engineering designs. Among the plenty of factors that affect the stability of engineering rock mass, the strength of rocks and the rock mass integrity are rock mass’ basic characteristics, which serve as the commonality of diverse rock engineering types. According to the rock mass integrity and rock strength, the surrounding rock is classed into six grades. Besides, groundwater, high initial stress, and the unfavorable soft faces are all prejudicial to the surrounding rocks’ stability. Rock lithology is another consideration when determining the rock mass conditions. The surrounding rock grading has been described in detail in Road Tunnel Design Rules JTG TD70-2010, Standard for Engineering Classification of Rock Mass GB/T 50218–2014 [1, 11]. On the basis of an actual tunnel in Sichuan Province, China, the surrounding rock parameters were all acquired from the design documents and survey, as shown in Table 6.1. Five varying lining designs and engineering quantity data per meter were acquired from the tender. To conduct a quantitative analysis of the varying surrounding rock conditions’ impact on tunnel discharges, five tunnels of the same length were supposed in this research and their lining designs. Tunnel lining designs are shown in Fig. 6.1. Table 6.2 describes the tunnels’ basic parameters.

6 Carbon Emission Transition of Highway Tunnel Construction

167

Table 6.1 Surrounding rock parameters of the tunnels [16] Rock Lining Surrounding Structure mass type rock state grade III

IV

V

Diagram of Deformation Poisson’s Allowable surrounding modulus ratio bearing rocks (GPa) capacity (kPa)

Z3

Andesite

Fractured, blocky

10

0.23

2.5

Z4w

Granite

Fractured, blocky inset

3

0.32

1.2

Z4

Shale

Layered masonry

3

0.32

1.0

Z4j

Mudstone

Layered masonry

2

0.35

0.8

Z5

Dust

Loose

0.3

0.4

0.35

6.2.2 System Boundary The carbon emissions of the Chinese highway tunnel during construction stage were assessed according to ISO 14040/14044 [2]. Both the indirect emissions and the direct emissions were covered in the study. The direct emissions came from energy consumption by construction machinery while the indirect emissions resulted from transportation and manufacture of materials. The system boundary is shown in Fig. 6.2. The drilling and blasting method was applied in the construction of the five tunnels. The fundamental construction processes included: advance support, tunneling, rock support, casting and lining and ventilation and lighting. The construction processes that are corresponding to varying surrounding rock conditions are listed in Table 6.3. In this study, the loading work was completed by loaders; the material transportation was finished by 15t trucks and a 12t dump trucks; the waste disposals were solely about the blasted rocks, which were transported 10 km from the tunnel portal to get crushed, from which 100% of the stone chips and gravel and 50% of the sand were recycled separately; Other stuffs were transported from the market to the tunnel construction site (the transportation distance is assumed to be 100 km); emissions from the production, mending and maintenance of machinery were not considered to avoid double counting; the carbon emissions generated by people’s living and working in the tunnel sites were of small amount and thus not considered; also, the control, monitoring, and preparation processes were not included.

168

C. Guo

Fig. 6.1 Lining designs of different tunnels. a Z3 is suitable for the surrounding rock of Grade III affected by geological structures, b Z4w is suitable for the surrounding rock of Grade IV with hard rocks at the bottom of the tunnel, c Z4 is suitable for the tunnel deep-buried in the surrounding rock of Grade IV, d Z4j is suitable for tunnel buried in the weak surrounding rocks of Grade IV, e Z5 is suitable for general surrounding rocks of Grade V. Note The unit of measure of dimension is centimeter (cm) [16]

6 Carbon Emission Transition of Highway Tunnel Construction

Fig. 6.1 (continued)

169

170

C. Guo

Fig. 6.1 (continued)

Table 6.2 Basic parameters of tunnels [16] Excavation area (m2 )

Tunnel

Rock mass grade

Tunnel design

Tunnel length (km)

T1

III

Z3

78.83

1

T2

IV

Z4w

82.66

1

T3

IV

Z4

97.48

1

T4

IV

Z4j

98.94

1

T5

V

Z5

101.72

1

The functional unit was “1-km Chinese highway tunnel’s construction activities (including advance support, tunneling, rock support, casting and lining, and ventilation and lighting)”. The greenhouse effect of varying GHG was evaluated by the characterization factor at the midpoint level. Recipe 2016 gave the global warming potential (GWP) for varying GHG [5]. This study took the GWP-100 method, where all GHG emission units were normalized to CO2 eq .

6 Carbon Emission Transition of Highway Tunnel Construction

171

Fig. 6.2 System boundary [16] Table 6.3 Construction procedures for different lining types [16] Construction procedure

Z3

Z4j

Z4

Z4w

Z5

Advance support Grouting

x

Rock bolt Tunneling

x

x

x

x

x

x

x

x x

Rock support Steel shotcrete

x

x

x

Rock bolt

x

x

x

x

x

Metal mesh

x

x

x

x

x

Shotcrete

x

x

x

x

x

C30 concrete arch wall

x

x

x

x

x

x

Casting and lining C30 concrete inverted arch

Ventilation and lighting

C30 reinforced concrete arch wall

x

C30 reinforced concrete inverted arch

x x

x

x

x

x

172

C. Guo

6.2.3 Inventory Data The kinds of materials, transportation vehicles and construction machinery consumed in varying construction processes are shown in Table 6.4. A sum of six types of materials, six types of construction machinery and seven types of transport machinery are included. The project quantity over per meter tunnel was given directly by the tender, substituted into the Highway Engineering Budget Quota, and calculated the fuel consumption and material for tunnel construction was calculated [13]. Highway Engineering Budget Quota is the project quota for China’s highway, which is based on the normal construction conditions and reasonable construction organization. The quota is represented by the consumption of materials, mechanical work shifts and labor, including pavement works, tunneling works, material collecting and processing, and transportation. The carbon emission factor is a key parameter for assessing tunnel emissions. The emission factor data of all materials were obtained from the IPCC Guidelines and the Chinese domestic literature [3, 6, 9, 12, 14, 17]. The cement was considered the NSP cement. The power system data were from the emission factors of the regional grid baseline that was issued by the Chinese government. The Chinese power grid consists of six regional ones according to the regional distribution. The emission data of Central China Power Grid were used by this study in 2016. The emission factors of fuels and materials are shown in Table 1.5. The unit fuel consumption per hour was obtained through the quota of Chinese unified machinery shift costs, as shown in Table 6.5 [15]. The construction machinery levels were the same for the five tunnels. The quota for mechanical shift costs is based on the unit of a mechanical shift, which stipulates the quantity standards of manhours, fuel and expenses it consumes. The costs are convertible into a fixed amount in the form of money. As shown in Table 6.6, the total amount of carbon emissions Table 6.4 Types of materials, transportation vehicles and construction machinery [16] Process

Material inputs

Transportation vehicle

Off-road machinery

Advance support

Wood, steel, cement

Lorry, dump truck, powered tipper

Electric air compressor

Tunneling

Wood, steel, explosive

Wheeled loader, dump truck

Electric air compressor, excavator

Rock support

Wood, steel, cement, sand, water, gravel

Lorry, powered tipper

Concrete jet, Ac arc welder, Electric air compressor, concrete mixing plant

Casting and lining

Wood, steel, cement, sand, water, gravel

Concrete mixing carrier, concrete pump

Ac arc welder, concrete mixing plant

Ventilation and lighting

Axial fan, lamp

Diesel Electricity Electricity Electricity

9 m3 /min

20 m3 /min

100 kW

60 m3

Powered air compressor

Electric air compressor

Axial fan

Concrete mixing plant

Diesel

Diesel

3 m3

60 m3

12 t

4t

15 t

Concrete mixing carrier

Concrete pump

Dump truck

Lorry

Lorry

Diesel

Gasoline

Diesel

Diesel

2 m3

Wheeled loader

Transportation vehicle

Electricity

32 kVA

Ac arc welder

Electricity

Fuel

5 m3 /h

Specification

Concrete jet

Off-road machinery

Construction machine

Table 6.5 Parameters of on-site construction equipment [16]

7.09 kg

3.18 kg

5.78 kg

9.12 kg

6.88 kg

8.15 kg

82.69 kWh

67.2 kWh

87.36 kWh

6.45 kg

12.07 kWh

1.93 kWh

Energy consumption per hour

863

0

22,037

891

1,240

4,369

340

10,160

9,666

2,313

218

2,692

T1 (hours)

1,678

3,543

26,349

1,316

1,738

4,752

568

12,752

18,417

5,251

6,972

6,112

T2 (hours)

1,513

2,326

25,813

1,317

1,739

4,672

536

12,563

16,548

4,265

17,444

4,964

T3 (hours)

1,289

2,037

21,785

1,013

1,262

3,955

439

10,654

15,051

4,255

15,919

4,953

T4 (hours)

(continued)

2,113

4,242

27,433

1,488

1,965

4,908

637

13,115

22,449

5,797

17,202

6,748

T5 (hours)

6 Carbon Emission Transition of Highway Tunnel Construction 173

Powered tipper

Construction machine

Table 6.5 (continued)

1t

Specification Diesel

Fuel 0.75 kg

Energy consumption per hour 112

T1 (hours) 1,813

T2 (hours) 1,442

T3 (hours) 1,442

T4 (hours) 2,604

T5 (hours)

174 C. Guo

6 Carbon Emission Transition of Highway Tunnel Construction

175

Table 6.6 Calculation formulas of carbon emission in tunnel construction [16] Source Material E m

Formula  E m = (e f i × m i ) i

Off-road machinery E o

Eo =

 i

Comment i—material type; efi —emission factor of material i; mi —consumption of material i

(e f i × vi × n i ) i— machinery type; efi —emission factor of fuel i; vi —consumption amount of fuel i per hour, ni — working hours for equipment i;

Transportation vehicle E t E =  (e f × v × n ) i—vehicle type; efi —emission factor of t i i i fuel I; vi —consumption amount of fuel i i per hour; ni —working hours for vehicle i Total

E total = E m + E o + E t

from fuel that was consumed by transportation, construction machinery and material production was the whole discharges during the tunnel construction.

6.2.4 Evaluation Indexes for Tunnel Emission Increment With the change of rock conditions, the emissions of various construction processes and emission sources may change. These changes in emissions can be expressed in increments, which are the emission differences from the same construction processes or from the same source for two different tunnels. The change of surrounding rock conditions can directly result in the increment of tunnel emissions, the increment of construction process emission and the discharge increment of emission sources. In this chapter, we compared the emissions of construction processes or emission sources in adjacent surrounding rock conditions. The comparison between the discharge of tunnel Ti and tunnel Tj was expressed as aTi,Tj . i, j ∈ {1, 2, 3, 4, 5}, i = j + 1. The comparison matrix among tunnels was as Eq. 6.1. ⎡

⎤

φ

⎢a ⎢ T 2,T 1 φ ⎢ A=⎢ aT 3,T 2 φ ⎢ ⎣ aT 4,T 3

φ aT 5,T 4 φ

⎥ ⎥ ⎥ ⎥ ⎥ ⎦

(6.1)

This chapter defines ‘the relative distribution of construction processes in emission increment’ (Eq. 6.2). aT i,T j, p =

E T i, p − E T j, p × 100% ET i − ET j

(6.2)

176

C. Guo

where, aTi,Tj,p represents the ratio of the emission difference during process p to the total emission difference, ETi,p is the carbon emission of tunnel Ti during construction process p, ETj,p is the carbon emission of tunnel Tj during construction process p, ETi is the total carbon emission of tunnel Ti, ETj is the total carbon emission of tunnel Tj. i, j ∈ {1, 2, 3, 4, 5}, i = j + 1 p ∈ { p1 , p2 , p3 , p4 , p5 ,}

advance suppor t, tunneling, r ock suppor t, = casting and lining, ventilation and lighting Carbon emissions of tunnel construction were from the materials, transport vehicles and construction machinery. The transport vehicles and construction machinery consumed diesel, gasoline and electricity. Therefore, the emission sources of tunnel construction were divided into materials and fuels. ‘The relative distribution of material/fuel emissions in emission increment’ was defined as aTi,Tj,r (Eq. 6.3). aT i,T j,r =

E T i,r − E T j,r × 100% ET i − ET j

(6.3)

where aTi,Tj,r represents the ratio of the emission difference for material/fuel r to the total emission difference, ETi,r is the carbon emission of material/fuel r for tunnel Ti, ETj,r is the carbon emission of material/fuel r for tunnel Tj, ETi is the total carbon emission of tunnel Ti, ETj is the total carbon emission of tunnel Tj. i, j ∈ {1, 2, 3, 4, 5}, i = j + 1 r ∈ {r 1, r 2, r 3, r 4, r 5, r 6, r 7, r 8, r 9, r 10} = {wood, steel, cement, ex plosive, sand, gravel, water, electricit y, gasoline, diesel}

6.3 Results and Discussion In terms of total emission amount, as the surrounding rock grade rose, the corresponding carbon emissions from tunnel construction rose dramatically. Figure 6.3 shows the compositions of carbon emissions of different tunnels. For instance, the rock mass grades of T1, T3, and T5 are Grade III, IV, and V respectively. The carbon emission of T1 was in sound accordance with the data in literature [4]. The carbon emissions of T3 and T5 were equal to 273% and 184% of T1. With the same surrounding rock grade, carbon emissions still rose considerably. The emissions of

6 Carbon Emission Transition of Highway Tunnel Construction

177

Fig. 6.3 Carbon emission compositions of different tunnels [16]

T4 and T3 increased by 15% and 28% in comparison with T2 respectively. Materials contributed greatly to the carbon emissions, with off-road machinery’s emissions ranging from 18 to 25%, and transportation vehicles accounting for less than 12% of total emissions. The emission proportions of material production were in line with the existing literature [4, 8, 10]

6.3.1 Carbon Emission Evaluation Figure 6.4 shows different construction processes’ emission data. For advance support, the surrounding rock of T1 belonged to Grade III and did not require advance support due to good surrounding rock conditions. The surrounding rocks of T3, T4 and T2 were all Grade IV. The same advance support design was applied in the three tunnels, so they produced the same emissions in advance support. The surrounding rocks of T5 were more fractured, which required grouting construction and raised construction emissions. In the tunneling process, the carbon emission amount from excavation of T2 was lower than T1. In fact, the excavation area of T2 was a bit larger than T1, while the surrounding rock of T1 was more solid, bringing about more excavation emissions and material consumption. The excavation area of T3 was 23% larger than T1 with an emission amount only 9% larger than T1. It is shown that excavation per unit area would cause more emissions, although better tunnel conditions required smaller excavation areas.

178

C. Guo

Fig. 6.4 Carbon emissions and proportions in different construction processes. a Carbon emissions in different construction processes, b stacked carbon emissions from different tunnels. Note The surrounding rocks of T2, T3 and T4 are of Grade IV, while T1 and T5 are of Grade III and Grade V, respectively [16]

6 Carbon Emission Transition of Highway Tunnel Construction

179

Unexpectedly, there was a considerable leap in the emissions from the rock support of the surrounding rock of Grade IV (T2) compared to that of Grade III (T1) (Fig. 6.4a). The emissions from the rock support of T2 were 3,018 t CO2eq higher than that of T1, which was equivalent to 48% of the sum of all the construction emissions of T1. For convenience, we called these huge incremental emissions “steps”. It seemed that the tunnel emission leaped from a low level to a high level along these “steps”. We referred to this steep rise in emissions as “Step A” (Fig. 6.4a), while a similar leap in emissions during the casting and lining as “Step B”. T5 (the surrounding rock of Grade V) emitted 2,991 t CO2eq more than T4 (the surrounding rock of Grade IV) during the casting and lining process, which was equivalent to 69% of the emission from the casting and lining of T4. Rock support was the highest-emission process in T4 and T2, as shown in Fig. 6.4b. For T1, T3, T5, the highest-emission process was casting and lining. The emission from tunneling was less than from casting and lining or rock support. The amount of emission of ventilation and lighting or advance support was not large and the total of their proportions in the tunnel’s total emissions remained at between 7 and 11%. Figure 6.5a shows the contributions of the emission sources in different tunnels to the carbon emissions. “Step 1” refers to the leap in the emissions from the steel of T1 to that of T2 (i.e., the emissions from the steel of T2 is 1,721 t CO2eq higher than those of T1); “Step 3” refers to the leap in the emissions of the cement of T1 to that of T2 (the emissions from the cement of T2 is 1,217 t CO2eq higher than those of T1). The two emission leaps were equal to 28% and 20% of the total discharge of T1. Cement of T5 emitted 2,911 t CO2eq more than that of T4, which approximated to one quarter of the total emissions from T4. This increment was named “Step 2” accordingly. The discharge from steel in T1 accounted for only 3.26% of the overall discharge of the tunnel. By contrast, the relative contributions of steel from T3, T4 and T2 were between 17 and 22%, while that of T5 tunnel occupied 33%. Among all the emission sources in tunnel construction, the emissions from cement were the highest, which accounted for more than 41% of the total. The proportions of diesel emission and electricity emission showed declining trends if the surrounding rock conditions were weaker. The former proportion ranged from 17 to 23% and the latter ranged from 7 to 14%. Emissions from wood, explosives, gravel, sand, gasoline and water contributed very little, accounting for up to 2.2% of the aggregate, so elaborate analysis were no longer carried out.

6.3.2 Transition Path Analysis of the Tunnel Construction Emission With the weakening of surrounding rock conditions, the sums of tunnel construction emissions rose aggressively. In particular, as rock mass grades altered, researchers

180

C. Guo

Fig. 6.5 Sources of carbon emissions. a Emissions of materials and fuels; b emissions of materials and fuels from different tunnels [16]

identified “step” emission data for sources and construction processes. For convenience, we proposed the concept of “emission transition pathways forward". Along these important sources or processes, tunnel construction’s emissions leaped from the low level to the high level. Based on the emission increment ratio, this section clarified the sources or processes with sharp emission rises as rock conditions varied.

6 Carbon Emission Transition of Highway Tunnel Construction

181

Table 6.7 Relative emission distribution of construction processes aTi,Tj,p [16] aTi,Tj

p1

p2

p3

p4

p5

aT2,T1

10

-3

81*

11

1

aT3,T2

0

16

7

69*

8

aT4,T3

0

2

97*

0

1

aT5,T4

3

6

21

69*

1

Note * Marks the highest relative distribution in emission

As shown in Table 6.7, relative contribution of each construction process was calculated using Eq. 2.2 in Sect. 2.4. Rock support contributed an 82% increment (3,018 t CO2eq ), ie, “Step A” in Fig. 6.4a, when the surrounding rock grade changed from Grade III (T1) to Grade IV (T2). Rock support contributed 21% increment when the rock mass grade switched from Grade IV (T4) to Grade V (T5), while the incremental proportion of casting and lining was 69% (2,991 t CO2eq ), namely, “Step B” in Fig. 6.4a. In the cases of rock mass of Grade IV, emission increment was low. Just 1,000 t CO2eq could lead to a relative incremental contribution of more than 70%. The emission rise between T2 and T3 was concentrated in the casting and lining process, with a contribution rate of 69% (1,030 t CO2eq ). Tunneling contributed 16% (245 t CO2eq ). Rock support process contributed 97% (1,222t CO2eq ) of the emission increment between T3 and T4. The relative contributions of different sources were calculated with Eq. 6.3, as shown in Table 6.8. A single kind of material or fuel did not contribute more than 70% to the tunnel emissions. Emission increases were mainly distributed in steel, cement and electricity. When the rock mass grade changed from Grade III (T1) to Grade IV (T2), the contributions of steel and cement were 46% (1,721 t CO2eq ) and 33% (1,217 t CO2eq ), respectively, namely “Step 1” and “Step 3” in Fig. 6.5a. When the surrounding rock conditions changed from Grade IV (T4) to Grade V (T5), steel contributed 67% (2,911 t CO2eq ) of emission increment, which is the “step 2” in Fig. 6.5a. According to the “Step” data in Sect. 3.1 and the tunnel construction emission path analysis in Sect. 3.2, the transition path of carbon emission from tunnel construction was drawn in Fig. 6.6. Table 6.8 Relative emission distribution of material/fuel aTi,Tj,r [16] aTi,Tj

r1

r2

r3

r4

r5

r6

r7

r8

r9

r10

aT2,T1

0

46*

33

0

0

0

0

18

1

2

aT3,T2

0

5

64*

0

1

1

0

18

0

11

aT4, T3

0

57*

33

0

0

0

0

4

1

4

aT5,T4

0

67*

20

0

0

0

0

11

0

2

Note * Marks the highest relative distribution in emission

182

C. Guo

Fig. 6.6 Carbon emission transition paths for tunnel construction. Unit: t CO2 eq . Note The radiuses of concentric circles (arcs) are proportional to the emissions in different construction processes. Red arrows indicate the main construction processes (relative contribution > 65%) of the emission increments [16]

So far, three characteristics of tunnel carbon emissions have been summarized in terms of the surrounding rock conditions: 1.

2. 3.

Correlation of the surrounding rock conditions: The varieties of surrounding rock conditions can greatly change tunnel construction emission. The differences of surrounding rock conditions determine the difference of tunnel designs, and the emissions of tunnels will also change. Non-continuity of emissions: The change of surrounding rock conditions is discontinuous, so the tunnel construction emission is also non-continuous; Non-consistency of emission transition path: With the weakening of the surrounding rock conditions, carbon emissions from tunnel construction will increase, but the paths from low emission levels to higher levels are usually inconsistent.

6 Carbon Emission Transition of Highway Tunnel Construction

183

6.4 Conclusion The innovation of the study lies in considering the rock mass conditions in the carbon emissions of tunnel construction. This study proposed that construction emissions with weak surrounding rock showed considerable rises when compared to sound surrounding rock conditions. What’s more meaningful is that the study for the first time determined the emission transition pathways when the surrounding rock conditions varied. The change in rock mass grade resulted in prominent emission transitions, indicating that any changes in the surrounding rock conditions must be taken into account when evaluating tunnels’ emissions. Besides, tunnel designers and engineers are encouraged to control major inputs of materials and energies in key construction processes. Due to limitation of the current research cases, there was only one tunnel lining design for rock mass of Grade III and Grade V in this study, and tunnel emissions were not much considered. The main findings of this study are listed as follows: 1.

2.

3.

4.

The tunnels with weaker rock conditions produce more carbon emissions in construction, with five 1-km highway tunnels’ construction emissions counted as between 6,220–17,010 t CO2 eq . Materials contributed most of the carbon emissions, with off-road machinery’s emission ranging from 18 to 25% and transportation vehicles’ emission less than 12% of overall emissions. Rock support and casting and lining were the major emission transition processes when the rock mass conditions got weak. In the terms of the emission sources, the emission rises were mainly distributed in cement, electricity and steel, with over 69% of the emission increment from steel and cement. Three emission characteristics of tunnel construction emission that was influenced by surrounding rock were summarized as: correlation of surrounding rock conditions, non-continuity of emission and non-consistency of emission transition pathway.

References 1. 招商局重庆交通科研设计院有限公司.JTG D70-2010 公路隧道设计细则. 人民交通出版 社, 2010. China Merchants Chongqing Communications Technology Research & Design Institute Co. Ltd. (2010) Standards of the People’s Republic of China JTG D70-2010 Road Tunnel Design Rules. People’s Communication Press, Beijing 2. Finkbeiner M, Inaba A, Tan R et al (2006) The new international standards for life cycle assessment: ISO 14040 and ISO 14044. Int J Life Cycle Assess 11(2):80–85 3. 高源雪. 建筑产品物化阶段碳足迹评价方法与实证研究. 清华大学. Gao Y (2012) Assessment methodology and empirical analysis of embodied carbon footprint of building construction. Master Sci. Tsinghua University 4. Huang L, Bohne RA, Bruland A et al (2015) Life cycle assessment of Norwegian road tunnel. Int J Life Cycle Assess 20. https://doi.org/10.1007/s11367-014-0823-1

184

C. Guo

5. Huijbregts M, Steinmann ZJN, Elshout PMFM et al (2016) ReCiPe 2016—a harmonized life cycle impact assessment method at midpoint and endpoint level. Report I: Characterization. Natl Inst Public Heal Environ 6. IPCC (2006) IPCC Guidelines for National Greenhouse Gas Inventories. 3:1–40 7. 蒋树屏. 中国公路隧道数据统计. 隧道建设, 2017(05):643-644. Jiang S (2017) Data statistic of Chinese highway tunnels. Tunn Constr 37:643–644 8. Kalvå POF (2015) Life cycle assessment of the Byåsen tunnel in Trondheim, Norway— assessing emissions from traffic and infrastructure. Dissertation, Norwegian University of Science and Technology, Trondheim, Norway 9. 李思堂, 李惠强. 住宅建筑施工初始能耗定量计算. 土木工程与管理学报, 2005, 22(4):54. Li S, Li H (2005) Quantitative calculation of construction initial energy use in residential building. J HUST (Urban Sci Ed) 22(4):54–57 10. Miliutenko S, Åkerman J, Björklund A (2012) Energy use and green-house gas emissions during the Life Cycle stages of a road tunnel—the Swedish case norra länken. Eur J Transp Infrastruct Res 12:39–62 11. 中国住建部. GB/T 50218-2014工程岩体分级标准. 中国计划出版社, 2014. Ministry of Water Resource PRC (2014) Standard for Engineering Classification of Rock Mass GB/T 50218-2014. China Planning Press, Beijing 12. 国家发改委. 2016年中国区域电网排放因子[EB/OL]. http://www.ndrc.gov.cn/yjzq/201704/ t20170414_847850.htm (2017-05-01). National Development and Reform Commission of Climate Change (2017) Chinese regional power grid baseline emission factors in 2016. http:// www.ndrc.gov.cn/yjzq/201704/t20170414_847850.html. Accessed 1 May 2017 13. 交通公路工程定额站. JTGT B06-02-2007公路工程预算定额. 北京: 人民交通出版社, 2007. Quota station for highway engineering, MOT (2007) JTGT B06-02-2007 Highway Engineering Budget Quot. China Standard Press, Beijing 14. 沈镭, 赵建安, 王礼茂, et al. 中国水泥生产过程碳排放因子测算与评估. 科学通报, 2016, 61(26):2926. Shen L, Zhao J, Wang L et al (2016) Calculation and evaluation on carbon emission factor of cement production in China. Chin Sci Bull 61:2926–2938 15. 中国住建部. 全国统一施工机械台班费用定额. 中国计划出版社, 2011. The PRC MOHURD (2011) The drafting standard for the cost budget of the national unified construction machine team. China Planning Press, Beijing 16. Xu J, Guo C, Chen X et al (2019) Emission transition of greenhouse gases with the surrounding rock weakened—a case study of tunnel construction. J Clean Prod 209. https://doi.org/10.1016/ j.jclepro.2018.10.224 17. 张振芳. 露天煤矿碳排放量核算及碳减排途径研究. 北京: 中国矿业大学, 2013. Zhang Z (2013) Study on carbon emissions accounting and carbon emission reduction approach of surface coal mine. Ph.D. Dissertation, Chinese University of Mining and Technology

Chapter 7

Influence of Tunnel Lining Design Parameters on Construction Carbon Emissions

7.1 Introduction The surrounding rock conditions of the tunnel are complex and changeable. Limited by the accuracy of survey methods and economic costs, it is difficult for designers to obtain precise and accurate geological conditions before construction. In addition, the traditional engineering analogy method has large support safety reserves and poor economy. In this regard, global researchers have been exploring the dynamic designs of tunnel engineering and information-based construction methods, and divided the tunnel design into two stages: pre-design and information feedback design [10]. The dynamic design method, which has been well applied in the actual engineering, corrects the design or optimizes the design parameters according to the geological conditions and variation phenomena in the construction. Relying on the ZhengzhouWanzhou high-speed railway tunnel project, Jin [8] determined the optimization plan of supporting parameters through on-site measurement and finite element verification. Xu et al. [14] clarified the steps and implementation of the dynamic design method for tunnel support in rock mass with joints, and achieved good economic results in practical engineering applications. Huang [6] proposed the optimization designs and related parameters of the oil and gas pipeline shield tunnel shaft and segment based on the Xijiang Mud Water Shield Tunnel and the Nantan Sea Shield Tunnel. The above optimization researches of tunnel design have brought significant economic benefits by reducing the amount of engineering and material input, but have failed to consider the corresponding benefits of carbon emission reduction. The power consumption of the ventilation and lighting of highway tunnels accounts for about 80% of the total, and that is why the current research on energy saving and emission reduction of tunnels is concentrated in the field of ventilation and lighting during the operation [4]. Chao et al. [5] believe that the effective use of natural wind can significantly reduce the energy consumption of mechanical ventilation equipment. Wang et al. [11] believe that the key factors for energy saving and emission reduction in highway tunnels include civil structure, electromechanical facilities, traffic characteristics, economic capacity and conditions. The horizontal © Southwest Jiaotong University Press 2022 C. Guo and J. Xu, Carbon Emission Calculation Methods for Highway Tunnel Construction, https://doi.org/10.1007/978-981-16-5308-7_7

185

186

7 Influence of Tunnel Lining Design Parameters on Construction …

and vertical line shape, section size, and portal characteristics of the tunnel have important influences on the total amount of civil engineering and the configuration of mechanical and electrical facilities. At present, the research on emission reduction of tunnel structures, especially lining structures, is relatively rare. During the construction process, tunnel design is one of the decisive factors for tunnel construction investment. The existing literature has not yet explored the impact of design parameter changes on the carbon emissions of tunnel construction. The designers do not understand the emission reduction potential of each process, and it is difficult to effectively evaluate the emission reduction effect of tunnel dynamic design. Therefore, it is necessary to further clarify the effect of design parameter changes on carbon emissions and provide an effective entry point for low-carbon tunnel design. Based on the lining design specifications and engineering design cases, this research establishes a tunnel engineering quantity model. The modular carbon emission calculation method is used to analyzes the impact of changes in highway tunnel lining design parameters on carbon emissions. The processes that have huge impact on the emissions of the tunnel are clarified, which provides a reference for the low-carbon design of the tunnel. This chapter is divided into three sections. The second section establishes a design model of a typical two-lane highway tunnel, and analyzes the changes of the emissions of the lining of two-lane highway tunnels with the changes of design parameters. The third section analyzes the carbon emission characteristics of the three-lane tunnel lining based on case analysis and modeling. The fourth section gives a summary of this chapter.

7.2 Carbon Emission Variation Characteristics of Two-Lane Highway Tunnel Lining Based on China’s highway tunnel design specifications and existing cases, a two-lane highway tunnel design model is established, the supporting parameters of the tunnel model of different surrounding rock grades are determined, and the calculation model of highway tunnel engineering quantity is obtained. Using the primitives’ emission values in Table 4.9, the authors multiply the engineering quantity and the primitives’ emissions respectively to obtain the carbon emissions of each module.

7.2.1 Two-Lane Highway Tunnel Lining Design Specifications and Case Design Parameters Compared with the surrounding rocks of Grade III, IV, and V, Grade I and II surrounding rocks are of better quality. However, since there are fewer tunnel design cases for Grade I and II surrounding rocks, this study only considers the surrounding

7.2 Carbon Emission Variation Characteristics of Two-Lane …

187

rocks of Grade III, IV, and V. The support parameters of the two-lane highway tunnel are shown in Table 7.1, and the values are from the Guidelines for Design of Highway Tunnel and the Specifications for Design of Highway Tunnels. The authors summarize the design parameters and relevant normative values of multiple 80 km/h highway tunnels, and the design parameters of tunnels with different surrounding rock grades are shown in Table 7.2.

7.2.2 Typical Two-Lane Highway Tunnel Support Model There is no design standard drawing for highway tunnels in China, and the tunnel sections of different design units are different. Furthermore, geological conditions have a great impact on tunnel design, which may result in huge differences in the design parameters of tunnels with the same surrounding rock grade. In order to facilitate the development of research, this chapter obeys the following settings: • The tunnel design does not consider special geological conditions. And the design speed is 80 km/h; • The inner contour of the tunnel refers to JTG3370.1 2018 Specifications for Design of Highway Tunnels; • According to the designs with or without inverted arch, two lining section types are used respectively; • The design parameters of the surrounding rock lining of all grades are taken from the recommended values in JTG3370.1 2018 Specifications for Design of Highway Tunnels; • This chapter does not discuss the impact of changes in lining reinforcement parameters on carbon emissions. The Specifications does not give the reference value of lining reinforcement, and the design of lining reinforcement needs to be calculated according to the internal force of the lining under the actual stratum load or according to the analogy of similar projects. And it is inconvenient to carry out detailed analysis without specific stratum and material parameters. The lining design parameters of some tunnel cases are shown in Table 7.2. 7.2.2.1

Parameters of Tunnel Lining Design Model

The design parameters of surrounding rock tunnels of all grades are summarized in Table 7.3. The tunnel lining design drawing and the internal outline drawing of JTG3370.1 2018 Specifications for Design of Highway Tunnels is referred to, which helps obtain the typical highway tunnel lining in Fig. 7.1. The corresponding lining design parameters refer to design specifications and examples, and specific descriptions are shown in Table 7.4. Shotcrete, casting and lining, steel mesh, and system bolts are suitable for all surrounding rock grades, while steel frames are suitable for Grade IV and V Grade surrounding rock tunnels.

3–4

3–3.5

3–3.5

15–25

18–28

20–25

2.5–3

15–22

0.6–1

0.6–1

0.8–1.2

0.8–1.2

0.8–1.2

1–1.2

1–1.5

1–1.2

1–1.5

Bolt spacing (m)

Note A: Guidelines for Design of Highway Tunnel (JTG D70-2004) B: Specifications for Design of Highway Tunnels (JTG3370.1 2018) C: Guidelines for Design of Highway Tunnel (JTG TD 70-2010)

Grade V

2.5–3

12–20

2.5–3

2.5–3

8–12

12–15

2–3

8–12

Grade IV

2–3

8–12

Grade III

Bolt length (m)

Shotcrete thickness (cm)

Surrounding rock grade

20*20

20*20

20*20

25*25

25*25

25*25

part@25*25

part@25*25

part@25*25

Steel mesh spacing (cm)

Table 7.1 Parameters from Chinese highway tunnel design specifications

0.6–1

Arch wall, inverted arch 0.6–1

Arch, wall, inverted arch

1–1.5 or part

0.8–1.2

Arch, wall

–

–

–

Longitudinal distance of steel frame (m)

45

35–50

45

35–40

35–40

35

35

30–35

35

Secondary lining arch wall thickness (cm)

45

0 or 35–50

45

0 or 40

0 or 35–40

35

–

–

–

Secondary lining inverted arch thickness (cm)

C

B

A

C

B

A

C

B

A

Source

188 7 Influence of Tunnel Lining Design Parameters on Construction …

Shotcrete arch and wall (cm)

12

10

10

10

20

20

20

20

C20 18

24

24

No.

1

2

3

4

5

6

7

8

9

10

11

24

24

0

0

0

0

0

0

0

0

0

Shotcrete inverted arch (cm)

Table 7.2 Lining design parameters

C

C

A

A

A

A

A

A

A

A

A

Bolt type

3.5

3

3

3

3.0

2.5

3.0

2.5

2.5

2.5

2.5

Bolt length

I14/1 I14/1

8 20 × 20 6.5 25 × 25 6.5 25 × 25 8 20 × 20 8 20 × 20 8 20 × 20 8 20 × 20 6.5 25 × 25 8 20 × 20 8 20 × 20

0.6 × 1.2 0.6 × 1.2 0.6 × 1.2 0.75 × 1 0.75 *1 0.6 × 1 0.6 × 1 0.6 × 1 0.75 × 0.75 0.5 × 0.75

I18/0.75

I18/0.75

Grid/1

I14/1

I14/1

/

/

/

/

8 20 × 20

0.6 × 1.2

Steel frame/ vertical spacing (m)

Steel mesh spacing (cm)

Bolt spacing L h × Lz *

45(RC)

45(RC)

40

35

35

35

35

35

35

35

35

45(RC)

45(RC)

40

0

35

0

/

/

/

/

/

Secondary Secondary lining arch and lining wall (cm) inverted arch (cm)

(continued)

Grade V

Grade V

Grade IV

Grade IV

Grade IV

Grade IV

Grade IV

Grade III

Grade III

Grade III

Grade III

Rock mass grade

7.2 Carbon Emission Variation Characteristics of Two-Lane … 189

24

24

24

12

13

14

0

0

0

Shotcrete inverted arch (cm)

C

C

C

Bolt type

3.5

3.5

3

Bolt length

Steel mesh spacing (cm)

8 20 × 20 8 20 × 20 6.5 20 × 20

Bolt spacing L h × Lz *

0.5 × 0.8 0.6 × 1.0 0.6 × 0.8

Note Lh × Lz : horizontal spacing × vertical spacing; A: cartridge bolt; C: hollow grouting bolt

Shotcrete arch and wall (cm)

No.

Table 7.2 (continued)

I18/0.8

I18/0.6

I18/0.8

Steel frame/ vertical spacing (m)

45(RC)

45(RC)

45(RC)

45(RC)

45(RC)

45(RC)

Secondary Secondary lining arch and lining wall (cm) inverted arch (cm)

Grade V

Grade V

Grade V

Rock mass grade

190 7 Influence of Tunnel Lining Design Parameters on Construction …

7.2 Carbon Emission Variation Characteristics of Two-Lane …

191

Table 7.3 Tunnel design parameters Symbol

Variable

Unit

Grade III surrounding rock

Grade IV surrounding rock

Grade V surrounding rock

n

Thickness of secondary lining

m

0.30–0.35

0.35–0.40

0.35–0.50

k

Thickness of shotcrete

m

0.08–0.12

0.12–0.2

0.18–0.28

Lh

Horizontal spacing of bolt

m

0.6

0.6

0.6

Lz

Longitudinal spacing of bolt

m

1.2

0.8–1.2

0.6–1

Lb

Length of single system bolt

m

2.5

2.5–3

3–3.5

a

Longitudinal spacing of two adjacent steel frames

m

–

0.8–1.2

0.6–1

w

Ring spacing of m rebars for steel frame connection

–

0.6–1

0.6–1

wt

Steel mesh spacing

0.25

0.25

0.2

mt

Weight per unit kg/m length of 8 rebar

0.395

Ss

Area of contour m2 in tunnel

73.95

7.2.2.2

m

Tunnel Model Engineering Quantity

A coordinate axis with the position of point O1 is established in Fig. 7.1 as the origin. The center line of the tunnel is the y-axis, and the straight line where points O1 and O2 are located is the x-axis. Table 7.5 lists the equations of each section on the left side of the tunnel in Fig. 7.1. The volume of tunnel excavation, molded concrete and shotcrete per linear meter can be calculated from the section equations in Table 7.5. The area measurement and angle measurement functions in AutoCAD software are used for irregular graphics. The concrete volume of the secondary lining arch wall of the Grade III surrounding rock tunnel per linear meter is expressed by Eq. 7.1. S1 =

 π (5.55 + n)2 − 5.552 + 2.35 × 2n = 1.57n 2 + 21.98n 2

(7.1)

192

7 Influence of Tunnel Lining Design Parameters on Construction …

Fig. 7.1 Design drawing of surrounding rock tunnel lining (bolts not shown). a without inverted arch, b with inverted arch

The concrete volume of the secondary lining arch wall per linear meter of Grade IV or V surrounding rock tunnel is expressed by Eq. 7.2. πn × [90 × (n + 11) + 9.706 × (n + 17) + 62.38 × (n + 2.4)] 180 = 2.83n 2 + 22.79n (7.2)

S1 =

7.2 Carbon Emission Variation Characteristics of Two-Lane …

193

Table 7.4 Tunnel excavation and support design Process

Description of design

Tunnel excavation

The actual excavation area is calculated according to the outer contour of the tunnel, including the area of the cross section, initial support, secondary lining, and the excavation of the tunnel floor. Among them, there is no inverted arch for Grade III surrounding rocks, and inverted arches for Grade IV and V surrounding rocks

Casting and lining

Grade III surrounding rock uses C30 concrete without reinforcement for arch wall; Grade IV surrounding rock uses C30 concrete for arch wall and inverted arch. The arch wall and inverted arch have the same thickness; Grade V surrounding rock uses reinforced concrete for arch wall and inverted arch, the thickness of arch wall and inverted arch concrete are the same

Shotcrete

After the tunnel is excavated, the operation of injecting concrete is carried out at the arch wall position. A 4-cm thick protective layer is formed on the surface of the surrounding rock when the concrete is first sprayed, and the steel mesh, bolt and steel frame are arranged. And the remaining thickness of concrete is sprayed at the end

Steel mesh

Grade III surrounding rock usually adopts local steel mesh layout, arranged within the 180° of the circumferential arch. For Grade IV and V surrounding rocks, metal mesh is placed on the arch wall. The steel mesh is of single layer

System bolts

25 hollow grouting bolt or 22 cartridge bolt is used. Bolt layout adopts plum-shaped layout. Grade III surrounding rock adopts local bolts, arranged in the 180° range of the circumferential arch. For Grade IV and V surrounding rocks, system bolts are arranged in the arch wall range

Steel frame and connected rebar The thickness of the concrete protective layer between the steel frame and the surrounding rock is four centimeters, and the thickness of the concrete protective layer on the side adjacent to the air is two centimeters. The thickness of shotcrete used for I18, I16 and I14 I-steels should not be less than 24 cm, 22 cm and 20 cm. In the actual tunnel design case, the Grade III surrounding rock generally has no steel frame support, and the Grade IV surrounding rock usually uses I14 and I16 steel frames. The I18 steel frame is commonly used in Grade V surrounding rock, and the quality of connected rebars and connected steel plates are selected from actual tunnel design cases. For Grade IV surrounding rock, the mass of I14 section steel frame per meter is 16.89 kg/m, the longitudinal distance is 0.8–1.2 m, the mass of steel plates per meter of tunnel connection is 31.64/a kg, the diameter of connection rebars is 22 mm, and the mass of steel frame connection rebars per meter is 71.52/w kg. I16 steel frame has a mass of 20.513 kg/m, a longitudinal distance of 0.8–1.2 m. The mass of tunnel connection steel plate is 80.34/a kg per meter, the diameter of connection rebar is 22 mm, and the mass of a steel frame connection rebar is 72.71/w kg per meter For Grade V surrounding rock, the longitudinal distance of the steel frame is 0.6–1 m, and the section height is 14-22 cm. Ordinary Grade V surrounding rock can choose I18 I-steel, the total steel plate per meter of tunnel connection is 99.48/a kg, the diameter of connection rebar is 22 mm, the weight is 2.98 kg/m, and the mass of steel frame connection rebar per meter is 72.71/w kg The feet-lock bolt adopts 25 cartridge bolt, each steel frame adopts 4 bolts, and each bolt is 3 m long

The volume of the secondary lining inverted arch concrete per linear meter of Grade IV or V surrounding rock tunnels is expressed by Eq. 7.3. S2 =

πn × 17.92 × (n + 30)=0.31n 2 + 9.38n 180

The volume of shotcrete per linear meter tunnel is expressed by Eq. 7.4.

(7.3)

194

7 Influence of Tunnel Lining Design Parameters on Construction …

Table 7.5 Section equation of tunnel lining Lining type

Section type

Equation

Without inverted arch

Internal section of secondary lining

x 2 + y 2 =5552

90°

(x − 295) +

–

External section of secondary lining

Internal section of shotcrete

External section of shotcrete

Included angle 2

y 2 =8502

x 2 + y 2 = (555 + 100n)2 (x − 295) + 100n)2 2

y2=

(850 + –

x 2 + y 2 = (555 + 100n)2 (x − 295) + 100n)2 2

y2=

90°

90°

(850 + –

x 2 + y 2 = (555 + 100n + 90° 100k)2 (x − 295)2 + y 2 = (850 + – 100n + 100k)2

With inverted arch

Internal section of secondary lining

x 2 + y 2 =5552

90°

(x − 295)2 + y 2 =8502

9.706°

(x + 425) + (y + 123)2 =1202

62.38°

2

x 2 + (y − 1190)2 =15002 17.92° External section of secondary lining

Internal section of shotcrete

External section of shotcrete

x2 + y 2 = (555 + 100n)2 (x − 295) + 100n)2 2

y2=

90°

(850 + 9.706°

(x + 425)2 + (y + 123)2 = (120 + 100n)2

62.38°

x2 + (y − 1190)2 = (1500 + 100n)2

17.92°

x 2 + y 2 = (555 + 100n)2

90°

(x − 295)2 + y 2 = (850 + – 100n)2 x 2 + y 2 = (555 + 100n + 90° 100k)2 (x − 295)2 + y 2 = (850 + – 100n + 100k)2

7.2 Carbon Emission Variation Characteristics of Two-Lane …

S3 =

195

 π (5.55 + n + k)2 − (5.55 + n)2 + 2.35 × 2k = 22.14k + 3.14kn + 1.57k 2 2 (7.4)

where, k is the thickness of shotcrete, m; n is the thickness of molded concrete, m. The tunnel design cases are referred to, and a single-layer steel mesh is used for the tunnel model. Local steel mesh is used for the Grade III surrounding rock. And the steel mesh at the location of the arch wall is used for the Grade IV and Grade V surrounding rocks. In actual operation, it is often used to spray four centimeters of concrete first, and then install steel meshes and bolts. The circumferential length of the Grade III surrounding rock steel mesh or system bolt arrangement area is expressed by Eq. 7.5. L = π(5.55 + k + n − 0.04) = 17.31 + 3.14k + 3.14n

(7.5)

The circumferential length of the Grade IV or V surrounding rock steel mesh or system bolt arrangement area is expressed by Eq. 7.6. L = π(5.55 + k + n − 0.04) + 2 ×

16π × 8.5 = 22.07 + 3.14k + 3.14n (7.6) 180

The total length of the system bolt per linear meter of Grade III surrounding rock is expressed by Eq. 7.7. L bt1 = L b

π(5.55 + k + n) 36.297 + 6.54k + 6.54n = 2L h L z Lz

(7.7)

The total length of the system bolt per linear meter of Grade IV or V surrounding rock is expressed by Eq. 7.8. L bt1 =

Lb Lb (22.07 + 3.14k + 3.14n) = (18.39 + 2.62k + 2.62n) 2L h L z Lz

(7.8)

where L h is Bolt circumferential distance, m; L z is longitudinal distance between bolts, m; L b is the length of a single bolt, m. The mass of the steel mesh per linear meter of the tunnel for Grade III and IV/V surrounding rocks are expressed by Eqs. 7.9 and 7.10. 

 2L 34.62 + 6.28k + 6.28n + 1 mt = mt + mt wt wt   2L 44.11 + 6.28k + 6.28n Mt = + 1 mt = mt + mt wt wt

Mt =

(7.9) (7.10)

196

7 Influence of Tunnel Lining Design Parameters on Construction …

Fig. 7.2 Steel frame layout

where M t is the quality of the metal mesh, kg; wt is the arrangement space of metal mesh, m; mt is the mass of rebar per unit length, kg. Figure 7.2 is a schematic diagram of the steel frame. The angle β between the bottom of the steel frame and the straight line O1 O2 can be calculated by Eq. 7.11. For Grade IV surrounding rock, 0.35 m ≤ n ≤ 0.4 m, so 15.01° ≤ β ≤ 15.12°. For Grade V surrounding rock, 0.35 m ≤ n ≤ 0.5 m, so 14.86° ≤ β ≤ 15.12°. To simplify the calculation, β is calculated as an approximate value of 15°. β= arcsin

2.35 8.66 + n

(7.11)

The circumferential length of the steel frame is expressed by Eq. 7.12. L s = π(5.57 + n + 0.01i) + 2π ×

β × (8.52 + n + 0.01i) 180

= 3.925n + 21.2 + 0.03i

(7.12)

where i is the height of the steel frame, cm; L s is the circumferential length of steel frame, m. The total masses of steel frame and connection rebars per linear meter are expressed by Eqs. 7.13 and 7.14. Mf =

m f Ls mb + a a

(7.13)

7.2 Carbon Emission Variation Characteristics of Two-Lane … Table 7.6 Steel frame and connecting rebar parameters

197

Steel frame type

mf (kg)

Ls (m)

I14

16.89

23.10

31.64

I16

20.513

23.15

80.34

I18

24.143

23.40

99.48

Ml =

mb (kg)

2.98L s w

(7.14)

where mf is unit mass per meter of section steel, kg; a is Longitudinal spacing of steel frame, m; mb is the mass of the steel plate connected to each steel frame, kg; w is Circumferential spacing of connecting rebars, m. The parameters in Eqs. 7.13 and 7.14 are taken from Table 7.6, and the rest of the parameters are taken from Table 7.3. The mass of each steel frame connecting steel plate refers to the existing design value. The total length of the feet-lock bolt per linear meter is expressed by Eq. 7.15. L bt2 =

12 a

(7.15)

According to the tunnel lining model and the above settings, a calculation model of highway tunnel engineering quantity is established. Table 7.7 shows the engineering quantity interval of the tunnel models. In Table 7.7, the steel frame of Grade IV surrounding rock adopts I14 steel frame, and the steel frame of Grade V surrounding rock adopts I18 steel frame. Table 7.7 Engineering quantity range of surrounding rock Process

Unit

Grade III surrounding rock

Grade IV surrounding rock

Grade V surrounding rock

Secondary lining arch wall

m3

6.735–7.885

8.323–9.569

9.820–12.103

Secondary lining inverted arch

m3

–

3.321–3.802

3.321–4.768

Shotcrete

m3

1.857–2.811

2.811–4.742

4.234–6.762

Steel mesh

kg

58.865–59.758

74.752–76.042

94.087–97.190

System bolt

m

32.332–32.823

73.581–74.856

115.375–119.191

Steel frame

kg

–

351.500–527.250

664.43–1107.383

Connecting rebar

kg

–

68.840–114.733

69.730–116.217

Feet-lock bolt

m

–

10.000–20.000

10.000–20.000

198

7 Influence of Tunnel Lining Design Parameters on Construction …

7.2.3 Effect of Change of Lining Design Parameters on Carbon Emissions of Two-Lane Tunnels According to the above calculation formula, combined with the values of different parameters in Tables 7.2 and 7.6, the modular emission calculation method is used to analyze the influence of design parameter changes on the carbon emissions of shotcrete.

7.2.3.1

Secondary Lining

The secondary lining is divided into two parts: arch wall and inverted arch. For surrounding rock of Grades III, IV and V, for every one centimeter increase in the thickness of the arch wall concrete, carbon emissions increase by 92.54 (±0.25), 100.23 (±0.46) and 101.94 (±1.02) kg CO2eq , as shown in Fig. 7.3a. Grade III surrounding rock adopts the design with no inverted arch, while Grade IV and V surrounding rocks use the inverted arch structure, which increases the volume of the concrete arch wall per linear meter of tunnel, so the emissions in Fig. 7.3a increase sharply. The engineering quantity of the secondary lining inverted arch increases approximately linearly with the increase in thickness. For every one-centimeter increase in thickness of the inverted arch, carbon emissions increase by about 30 kg CO2eq , as shown in Fig. 7.3b.

7.2.3.2

Shotcrete

It can be seen from Fig. 7.4 that as the thickness of the shotcrete increases, the emissions generated by the shotcrete increase rapidly and approximately linearly, and the increment in carbon emissions remains at 117–121 kg CO2eq per centimeter of shotcrete. In addition to the shotcrete thickness, the change in the second lining thickness will also cause a slight change in the discharge of shotcrete.

7.2.3.3

Bolt

The carbon emissions of the system bolts are affected by the thickness of the concrete, the type of bolts, the length of the bolts and the distance between the bolts. When the thickness of the tunnel lining increases, the carbon emission of bolts increases slightly. For example, for every one-centimeter increase in the thickness of the composite lining of Grade III surrounding rock, the emission of the cartridge bolt increases by 0.66 kg CO2eq . The 25 hollow grouting bolt generates 1.42 times the carbon emissions of the 22 cartridge bolt with the same length. The engineering quantity of bolt is directly proportional to the length of the bolt and inversely proportional to the distance between the bolts. Therefore, the longer the single bolt and the

7.2 Carbon Emission Variation Characteristics of Two-Lane … Fig. 7.3 Carbon emissions of secondary lining. a Arch wall, b inverted arch

Fig. 7.4 Carbon emissions of shotcrete

199

200

7 Influence of Tunnel Lining Design Parameters on Construction …

smaller the distance between the bolts, the more carbon emissions are produced by the bolt. Taking the bolt spacing and concrete thickness as variables, the authors calculate the emissions of system bolt for the tunnel models with Grade III, Grade IV, and Grade V surrounding rock, as shown in Fig. 7.5. The emission range of the system bolts of the Grade III surrounding rock tunnel is 394–401 kg CO2eq . Although the concrete thickness has increased from 0.38 m to 0.47 m, the change in carbon emissions per linear meter of cartridge bolt is less than 7 kg CO2eq , which indicates that the concrete thickness has a weak influence on the carbon emissions of the bolt. In contrast, the distance between bolts has a significant impact on emissions. The smaller the bolt spacing, the higher the corresponding carbon emissions, and the faster the growth rate. Take Fig. 7.5b as an example, when selecting the Grade IV surrounding rock and the bolt with a length of three meters, the product of the bolt spacing is taken as 0.48, 0.56, 0.64 and 0.72 m2 , respectively. The average carbon emission values of bolts are 905.54, 776.18, 679.15 and 603.69 kg CO2eq . When the bolt spacing product increases by 0.08 m2 , the carbon emissions of bolts decrease by 129.36, 90.02 and 75.46 kg CO2eq , respectively. In Fig. 7.5, the bolt types are all cartridge bolt. When the length of the bolt and the type of bolt change, the discharge value can be converted according to the proportional relationship. The feet-lock bolt is a part of the bolt system. Each steel frame adopts a 25 cartridge bolt with a total length of 12 m. The discharge range of the feet-lock bolt is 172.77–345.54 kg CO2eq , and the discharge increases as the longitudinal spacing of the steel frame decreases.

7.2.3.4

Steel Frame and Longitudinal Connecting Rebars

Figure 7.6a shows the carbon emissions of steel frames at different spacings. As the longitudinal spacing increases, the carbon emissions of the steel frame decrease. The distance between the steel frame of the Grade V surrounding rock is 0.6–1 m, and the distance of the steel frame of the Grade IV surrounding rock is 0.8–1.2 m, and then the emissions of the three steel frames are compared with the steel frame spacing as a variable. When the steel frame spacing is the same, the carbon emission ratios of various steel frames remain stable. The carbon emission ratio of I18 and I16 steel frames is 1.197, and that of I16 and I14 steel frames is 1.316. Figure 7.6b shows the carbon emissions of the connecting rebars of different spacing steel frames, and the emission range of connecting rebars is 173.62–293.10 kg CO2eq .

7.2.3.5

Steel Mesh

Figure 7.7 shows the carbon emissions of the steel mesh of different surrounding rock grades. Due to the relatively small work amount of steel mesh, the carbon emissions per linear meter of steel mesh is between 152.296 and 162.561 kg CO2eq , which has a slight impact on the carbon emissions of the tunnel.

7.2 Carbon Emission Variation Characteristics of Two-Lane …

201

Fig. 7.5 Carbon emissions of cartridge bolt. a Grade III surrounding rock Lb = 2.5 m, b Grade IV surrounding rock, Lb = 3 m, c Grade V surrounding rock, Lb = 3.5 m

202

7 Influence of Tunnel Lining Design Parameters on Construction …

Fig. 7.6 Carbon emissions of steel frame and connecting rebars. a Steel frame, b Connecting rebar

Fig. 7.7 Carbon emissions of steel mesh

7.3 Changes in Carbon Emissions from Excavation and Support …

203

7.3 Changes in Carbon Emissions from Excavation and Support of Three-Lane Highway Tunnels A three-lane tunnel has a large cross-sectional area, and its amount of supporting works is generally higher than that of a two-lane tunnel. The current carbon emission calculation researches focus on single-hole two-lane tunnels. Therefore, carbon emission studies for three-lane tunnels are relatively rare, and the emission characteristics of different construction procedures are not clear. In this section, the authors will carry out research on the emission characteristics of tunnel composite lining based on engineering cases, and analyze the emission level of three-lane composite lining construction. According to the existing specifications, a concrete quantity model of lining design with shotcrete at the inverted arch position was established to analyze the influence of design parameters on lining carbon emission.

7.3.1 Carbon Emission Characteristics of Three-Lane Highway Tunnel Lining 7.3.1.1

Project Overview

The length of a tunnel is 2,468 m, the design speed is 100 km/h, the net width of the tunnel construction boundary is 14.5 m, and the net height is 5 m. The surrounding rock in the tunnel area is dominated by strongly and moderately weathered sandstone, argillaceous siltstone, shale, basalt and limestone. The thickness of the weathered layer is large, and the surrounding rock is classified as Grade IV2 -V2 . This tunnel involves five different lining designs, whose design parameters are shown in Table 7.8. The engineering quantity data of different linings obtained by surveying design data are shown in Table 7.9.

7.3.1.2

Carbon Emission Level of Excavation and Support for the Three-Lane Highway Tunnel

The cumulative emissions of various types of lining are calculated as shown in Fig. 7.8. The emission levels of sf4a, sf4b, sf5a, sf5b and sf5c per linear meter of lining are 32.211 t, 21.46 t, 39.112 t, 36.463 t and 54.75 t CO2eq , respectively. The average emission levels of Grade IV and V surrounding rock linings are 26.83 t and 43.44 t CO2eq , respectively, with significant differences. When the grade of surrounding rock is the same, the lining emission of deep-buried tunnels is lower than that of shallow-buried tunnels. The carbon emissions of the fractured section of the Grade V surrounding rock are the highest, which increases by 15.64 t CO2eq compared with the shallow-buried section of the Grade V surrounding rock.

Grade IV deep 25 buried

Grade V 29 shallow buried

Grade V deep buried

Grade V fault fracture

sf4b

sf5a

sf5b

sf5c

29

27

Grade IV 25 shallow buried

sf4a

Shotcrete (arch wall) thickness (cm)

Surrounding rock types

Lining type

29

27

29

–

25

Shotcrete (inverted arch) thickness (cm)

Table 7.8 Lining design parameters of the case tunnel

4.5

4

3.5

3.5

3.5

Bolt length (m)

Steel mesh diameter, spacing (cm)

8 @150 × 150 Double arch wall 8 @ 200 × 200 Single arch wall 8 @150 × 150 Double arch wall 8 @150 × 150 Double arch wall 8 @150 × 150 Double arch wall

Bolt spacing (m)

1 × 0.8

1×1

1 × 0.6

1 × 0.7

1 × 0.6

I22b 60

I20b 70

I22b 60

I18 100

I18 80

Steel frame spacing (cm)

70 RC

60 RC

60 RC

50 RC

55 RC

Secondary lining (arch wall) thickness (cm)

70 RC

60 RC

60 RC

50 RC

55 RC

Secondary lining (inverted arch) thickness (cm)

204 7 Influence of Tunnel Lining Design Parameters on Construction …

7.3 Changes in Carbon Emissions from Excavation and Support …

205

Table 7.9 The engineering quantity per linear meter of the tunnel lining Project name

Unit

sf4a

sf4b

sf5a

sf5b

sf5c

Excavation

m3

159.84

153.82

164

163.1

168.7

Shotcrete

m3

12.5

8.14

14.4

13.47

14.55

Steel mesh

kg

333.65

123.06

338.4

337.1

343.3

25 Hollow bolt

m

59.06

36

78.75

77.14

56.25

22 Mortar bolt

kg

140.81

–

–

–

–

Small pipe

m

36

–

36

36

106.5

Cement grouting

m3

1.59

–

1.59

1.59

13.74

Steel frame

kg

1424

740

2895

2107

2924

Connecting bars

kg

94

86

97

95

100

Concrete arch wall

m3

16.96

15.6

18.58

18.58

21.84

Concrete invert arch

m3

7.12

6.46

7.77

7.77

9.09

Rebar

kg

2421.31

1537.31

2858.42

2845.62

2949.31

Fig. 7.8 Carbon emissions of different lining types

Further comparison of the emission levels of different modules indicates that the module with the lowest carbon emissions is the steel mesh, and the carbon emissions per linear meter are less than 0.9 t CO2eq . The module with the highest emissions is the bolt/small pipe/grouting in the fault fragmentation section of the Grade V surrounding rock, which reaches 16.946 t CO2eq . The length of the small pipe and the engineering quantity of grouting work in the Grade V surrounding rock fragmentation section (sf5c) have been greatly increased, and so have the level of carbon emissions. Various

206

7 Influence of Tunnel Lining Design Parameters on Construction …

types of surrounding rocks use steel frames with different models and longitudinal spacing, and the emission values of steel frames are significantly different.

7.3.2 A Calculation Model for Concrete Lining of a Three-Lane Tunnel Section 7.2 analyzes the emission levels of different modules of a three-lane tunnel under the conditions of Grade IV and V surrounding rock. In order to make the research in this chapter more representative, an engineering quantity model of tunnel lining concrete is established to analyze the impact of concrete thickness changes on the engineering quantity of shotcrete and secondary lining, and to clarify the potential of carbon emission reduction brought by changes in concrete thickness.

7.3.2.1

Lining Design Basis of Three-Lane Highway Tunnel

The design parameters of the composite lining can be selected according to the engineering analogy method. The Specifications for Design of Highway Tunnels (2018) has offered the recommended values of the three-lane tunnel support parameters, as shown in Table 7.10.

7.3.2.2

Engineering Quantity Model of Tunnel Lining Concrete

According to the case tunnel lining design, a tunnel lining model is established, as shown in Fig. 7.9. Among them, k and n represent the thickness of shotcrete and molded concrete, unit being m. Type I lining is suitable for sf5a, sf5b, sf5c, sf4a. And Type II lining is suitable for sf4b. On the basis of Fig. 7.9, the shotcrete and molded concrete are subdivided into several areas, and Fig. 7.10 is obtained, in which only one side of the shotcrete and molded concrete is marked. The engineering quantity per linear meter of molded concrete area S1 for Type-I lining is expressed as Eq. 7.16. S1 =

π π [(8.65 + n)2 − 8.652 ] = (17.3n + n 2 ) 8 8

(7.16)

The engineering quantity per linear meter of Type-I lining molded concrete area S2 is expressed as Eq. 7.17. S2 =

π π [(5.35 + n)2 − 5.352 ] = (10.7n + n 2 ) 8 8

(7.17)

16–24

20–30

IV

V

–

– 3.5–4

3–3.5

3–3.5

12–20

III

–

Shotcrete (arch Shotcrete Bolt length wall) thickness (inverted arch) (cm) thickness (cm)

Surrounding rock grade

Table 7.10 Three-lane tunnel lining support parameters

0.5–1

0.8–1.2

1–1.2

Bolt spacing

Arch wall @20 × 20

Arch wall @20 × 20

Partial part @25 × 25

Steel mesh spacing (cm)

16–20

0 or 14–16

Steel frame section height (cm)

Arch wall 18–22 invert 0.6–1

Arch wall 0.8–1.2

Arch wall 1–1.2

Steel frame spacing (m)

50–60(RC)

40–50(RC)

35–45

Secondary lining (arch wall) thickness (cm)

0 or 50–60(RC)

0 or 40–50

–

Secondary lining (inverted arch) thickness (cm)

7.3 Changes in Carbon Emissions from Excavation and Support … 207

208

7 Influence of Tunnel Lining Design Parameters on Construction …

Fig. 7.9 Lining design model of three-lane tunnel. a Type I: with shotcrete inverted arch, b Type II: without shotcrete inverted arch

The engineering quantity per linear meter of Type-I lining molded concrete area S3 is expressed as Eq. 7.18. S3 =

20.01π π [(5.35 + n)2 − 5.352 ] = (10.7n + n 2 ) 360 18

(7.18)

The engineering quantity per linear meter of Type-I lining molded concrete area S4 is expressed as Eq. 7.19.

7.3 Changes in Carbon Emissions from Excavation and Support …

209

Fig. 7.10 Area division of shotcrete and molded concrete. a Type I: with shotcrete inverted arch, b Type II: without shotcrete inverted arch

210

7 Influence of Tunnel Lining Design Parameters on Construction …

S4 =

55.3π [(1.51 + n)2 − 1.512 ] = 0.153π(3.02n + n 2 ) 360

(7.19)

According to AutoCAD software, the engineering quantity per linear meter of Type-I lining molded concrete area S5 is 0.18–0.19 m3 , and the approximate value is 0.19 m3 . The engineering quantity per linear meter of Type-I lining molded concrete area S6 is expressed as Eq. 7.20. S6 =

14.68π [(25 + n)2 − 252 ] = 0.0407π(50n + n 2 ) 360

(7.20)

The engineering quantity per linear meter of Type-I lining molded concrete area S7 is expressed as Eq. 7.21. S7 =

π π [(8.65 + k + n)2 − (8.65 + n)2 ] = (17.3k + 2kn + k 2 ) 8 8

(7.21)

The engineering quantity per linear meter of Type-I lining molded concrete area S8 is expressed as Eq. 7.22. S8 =

π π [(5.35 + k + n)2 − (5.35 + n)2 ] = (10.7k + 2kn + k 2 ) 8 8

(7.22)

The engineering quantity per linear meter of Type-I lining molded concrete area S9 is expressed as Eq. 7.23. S9 = 3.433k

(7.23)

The engineering quantity per linear meter of shotcrete area S10 of Type I lining is taken as an approximate value of 0.73 m3 . The engineering quantity per linear meter of shotcrete area S11 for Type I lining is expressed as Eq. 7.24. S11 =

14.68π [(25 + k + n)2 − (25 + n)2 ] = 0.0407π(50k + 2kn + k 2 ) (7.24) 360

The engineering quantity per linear meter of the concrete area S1 , S2 , S3 and S4 of Type II lining molded concrete is calculated according to Eqs. 7.16–7.19, respectively. Type II lining molded concrete areas S6 , S7 , S8 , and S9 are calculated according to Eqs. 7.20–7.23 per linear meter. According to AutoCAD software calculations, the engineering quantity per linear meter of the concrete area S5 and S10 of the Type II lining molded concrete is approximately 0.29 and 0.24 m3 . The engineering quantity per linear meter of the concrete area S9 of Type II lining is expressed as Eq. 7.25. S9 = [3.25 − 0.657(0.5 − n)]k = 2.922k + 0.657nk

(7.25)

7.3 Changes in Carbon Emissions from Excavation and Support …

211

In summary, the engineering quantity of shotcrete per linear meter of Type I lining is expressed as Eq. 7.26. Ssl = 2 × (S7 + S8 + S9 + S10 + S11 ) = 41.738k + 3.657kn + 1.829k 2 + 1.46

(7.26)

The engineering quantity of molded concrete arch wall of Type I lining per linear meter is expressed as Eq. 7.27. Scl = 2 × (S1 + S2 + S3 + S4 + S5 ) = 28.59n + 2.88n 2 + 0.38

(7.27)

The engineering quantity of molded concrete inverted arch of Type I lining per linear meter is expressed as Eq. 7.28. Scyl = 2S6 = 12.786n + 0.256n 2

(7.28)

The engineering quantity of shotcrete of Type II lining per linear meter is expressed as Eq. 7.29. Ssll = 2 × (S7 + S8 + S9 + S10 ) = 27.834k + 4.456kn + 1.571k 2 + 0.48 (7.29) The engineering quantity of secondary lining arch wall per linear meter of type II lining is expressed as Eq. 7.30. Scll = 2 × (S1 + S2 + S3 + S4 + S5 ) = 28.59n + 2.88n 2 + 0.58

(7.30)

The engineering quantity of secondary lining inverted arch per linear meter of Type II lining is expressed as Eq. 7.31. Scyl = 2S6 = 12.786n + 0.256n 2

7.3.2.3

(7.31)

Validation of Lining Concrete Engineering Quantity Model

Section 3.2.2 gives the calculation formulas for the engineering quantity per linear meter of shotcrete, secondary lining arch wall and inverted arch. The actual engineering quantities in Table 7.9 is compared to verify the validity of the lining engineering quantity model, as shown in Table 7.11. Compared with the actual value of concrete engineering quantity of each lining, the predicted values of the model have very little error, indicating that the model can effectively predict the engineering quantity corresponding to different thicknesses of concrete.

212

7 Influence of Tunnel Lining Design Parameters on Construction …

Table 7.11 Comparison of predicted value of lining concrete engineering quantity model and actual value Lining type

Concrete type

Actual value (m3 )

Predictive value (m3 )

Difference (m3 )

sf4a

Shotcrete

12.5

12.51

0.01

Arch wall reinforced concrete

16.96

16.98

0.01

Invert reinforced concrete

7.12

7.11

0.01

Shotcrete

8.14

8.10

0.04

15.605

0.005

6.457

0.003

sf4b

Arch wall reinforced concrete Invert reinforced concrete sf5a

14.4

14.36

0.04

Arch wall reinforced concrete

18.58

18.57

0.01

7.77

7.76

0.01

Shotcrete

13.47

13.46

0.01

Arch wall reinforced concrete

18.58

18.57

0.01

7.77

7.76

0.01

Shotcrete

14.55

14.46

0.01

Arch wall reinforced concrete

21.84

21.80

0.04

9.09

9.08

0.01

Invert reinforced concrete sf5c

6.46

Shotcrete

Invert reinforced concrete sf5b

15.6

Invert reinforced concrete

7.3.3 Influence of Changes in Lining Parameters of a Three-Lane Tunnel on Carbon Emissions As mentioned earlier, the carbon emissions of tunnel excavation, connecting steel bars, and steel mesh are low, and their impact on the emissions of tunnel lining construction is small. This section consists five parts: shotcrete, secondary lining concrete, steel frame, steel bar, bolt and small pipe grouting, and clarifies the influence of the above design parameter changes on the emission of the three-lane tunnel.

7.3 Changes in Carbon Emissions from Excavation and Support …

7.3.3.1

213

Shotcrete

According to Eq. 7.26 and Table 2.9, the carbon emissions produced by shotcrete of different thicknesses is calculated, as shown in Fig. 7.11. As the thickness of shotcrete increases, the carbon emissions of shotcrete increase approximately linearly. For Type

Fig. 7.11 Carbon emission of shotcrete under different thickness. a Type I lining, b Type II lining

214

7 Influence of Tunnel Lining Design Parameters on Construction …

Fig. 7.12 Carbon emission of molded concrete under different thicknesses

I lining, every additional 1 cm of sprayed concrete will increase carbon emissions by 0.223–0.227 t CO2eq . For Type II lining, every additional 1 cm of sprayed concrete will increase carbon emissions by 0.152–0.155 t CO2eq . Comparing the two cases when n is 0.5 m and n is 0.6 m, the difference in the emission of shotcrete per linear meter of tunnel is 0.03–0.06 t CO2eq , which indicates that the thickness of the molded concrete has a weak effect on the carbon emission of shotcrete.

7.3.3.2

Secondary Lining Concrete

According to Eqs. 7.27, 7.28, 7.30, 7.31 and Table 2.9, the concrete emission under different thicknesses of the second lining is calculated, as shown in Fig. 7.12. As the thickness of the secondary lining increases, the carbon emissions of the arch wall and invert concrete increase approximately linearly. For every one-centimeter increase in the thickness of the molded concrete, the carbon emissions of the arch walls and inverted arch increase by approximately 0.12 t CO2eq and 0.045 t CO2eq , respectively.

7.3.3.3

Steel Frame

Table 7.12 compares the types of different surrounding rock steel frames and the mass of rebar. The four linings of sf4a, sf5a, sf5b and sf5c adopt closed steel frame, and the total length of I-beam is between 47 and 48 m. The difference in the total mass of different steel frames mainly comes from the types and the longitudinal spacings of I-beam.

7.3 Changes in Carbon Emissions from Excavation and Support …

215

Table 7.12 The mass of each steel frame Lining type

Steel frame model

Total length (m)

Steel frame quality (kg)

Longitudinal connection rebar mass (kg)

sf4a

I18

47.2

1139.52

74.95

sf4b

I18

30.67

740.47

85.7

sf5a

I22b

47.56

1737.1

58.43

sf5b

I20b

47.47

1474.83

66.55

sf5c

I22b

48.03

1754.27

60.2

Under the condition of Grade V surrounding rock, the steel frame of the three-lane tunnels can choose I18, I20b and I22b, and the longitudinal spacing is 0.6–1 m. The total circumferential length of the closed steel frame I18, I20b and I22b is 47.5 m, and the mass of each steel frame is 1,147, 1,476 and 1,735 kg. The total length of the non-closed steel frame is 30.67 m, the mass of each steel frame is 740 kg, and the longitudinal spacing is 0.8–1.2 m. The emission level of the steel frame under different I-beam types and longitudinal spacings is calculated, as shown in Fig. 7.13. The closed steel frames I18, I20b, and I22b emit 3.05–5.09 t CO2eq , 3.93–6.55 t CO2eq , and 4.62–7.70 t CO2eq per linear meter, respectively. Under the same steel frame spacing conditions, the carbon emission ratio of I22b to I20b steel frames is 1.175, and the carbon emission ratio of I20b to I18 steel frames is 1.287. For the non-closed steel frame, the carbon emission per linear meter of I18 steel frame is 1.64–2.46 t CO2eq within the range of 0.8–1.2 m in the longitudinal distance.

Fig. 7.13 Carbon emission of steel frames with different longitudinal spacing

216

7.3.3.4

7 Influence of Tunnel Lining Design Parameters on Construction …

Rebar

The configuration of the rebar needs to meet the design specifications of the tunnel lining and the strength of the surrounding rock support. This chapter does not involve the calculation details of the rebar, but carries out the discussion based on the case tunnel lining reinforcement data, and only the influence of the change of the main reinforcement rebar type on the steel input is considered. Table 7.13 presents the amount of rebar per meter for the four rebar designs. The thickness of the lining varies from 0.5 to 0.7 m. The main reinforcement adopts C25 or C22 rebar. C16 rebar are used for the distribution bars, and A10 rebars are used for the bracing. Figure 7.14 shows the emission levels of rebar under four lining thicknesses. The C25 main reinforcement emissions of sf5a and sf5c are 5.86 and 5.90 t CO2eq , respectively. The emissions of distributed reinforcements per linear meter are between 0.86 and 1.76 t CO2eq , and the emission range of tie bars is 0.38–1.15 t CO2eq . Comparing sf5a and sf4a, it shows that the emission difference of rebar mainly comes from the main reinforcement of the tunnel, for the length of the two main bars is similar, which suggests that the change of the rebar types has an important impact on the carbon emissions of rebar. The rebars of sf4a and sf4b have the same type, and the length of the rebars and carbon emissions have increased significantly, which suggests that the type of rebars is not the only factor that affects the emission of rebars. From the perspective of burial depth, sf4a, sf4b, and sf5a, sf5b are combinations of shallow and deep burial under Grade IV and V surrounding rock, respectively. There is a huge emission gap between sf4a and sf4b rebars. However, the engineering quantity and emissions of sf5a and sf5b rebars are the same, so the buried depth may not be an important factor affecting the emission of rebars. In addition, the increase in the thickness of the lining Table 7.13 Weight of rebar Lining type

Lining thickness (m)

Rebar specifications

Total length (m)

Gross weight (kg)

Total weight (kg)

sf5a, sf5b

0.6

C25

434.79

2388.92

2858.42

C16

452

713.25

A10

760.94

469.5

C25

438.01

2404.49

C16

456

716.41

A10

883.02

544.82

C22

433.19

2002.72

C16

450

710.1

A10

701.76

432.98

C22

345.23

1040.15

C16

222

350.32

A10

254.18

156.84

sf5c

sf4a

sf4b

0.7

0.55

0.5

2949.31

2421.31

1537.31

7.3 Changes in Carbon Emissions from Excavation and Support …

217

Fig. 7.14 Carbon emissions of rebar under different lining thickness

will increase the carbon emission of rebars, but this increase will not be linear. When the lining thickness is between 0.50 m and 0.55 m, the carbon emissions of rebars increases sharply, and it goes up from 0.6 m to 0.7 m, the increase of rebar emissions was small.

7.3.3.5

Bolt and Small Pipe Grouting

Table 7.14 lists the hollow grouting bolt parameters of sf5a and sf5b. For different bolt spacings and bolt lengths, the emission amount of the system bolt per linear meter is shown in Fig. 7.15. It can be seen from the figure that as the distance between bolts decreases, the carbon emissions of the system bolts increase significantly, in inverse proportion. But the carbon emission of the system bolt is directly proportional to the length of a single bolt. When the space of the bolt and the length of a single bolt remain unchanged, the total emissions of the system bolts are proportional to the number of bolts per ring. Table 7.14 Bolt parameters of Grade V surrounding rock system Lining type

Single bolt length (m)

Circumferential spacing × longitudinal spacing (m)

Circumferential bolt (root)

Total length of bolt (m)

bolt carbon emissions (t CO2eq )

sf5a

3.5

1 × 0.6

13.5

78.75

1.36

sf5b

4

1 × 0.7

13.5

77.14

1.33

218

7 Influence of Tunnel Lining Design Parameters on Construction …

Fig. 7.15 Carbon emissions per linear meter of tunnel system bolt

When the number of bolts per ring of the tunnel changes, the discharge value of the corresponding parameter can be read in Fig. 7.15 and multiplied by the corresponding ratio to obtain the total emissions of the system bolts. Small pipe grouting is suitable for surrounding rock support in the area of fault rupture zone. The length of small pipe used for sf5c surrounding rock support is 4.5 m, and the grouting volume per linear meter is 11.35 m3 . However, 36 m long lock-foot small pipes and 1.36 m3 of cement slurry are used for each steel frame of the sf5a and sf5b linings. According to the carbon emission data in Table 4.9, the emission value of cement slurry per cubic meter is as high as 1,054.376 kg CO2eq . Once a large amount of cement grouting is used in tunnel support, the emission level of the tunnel will increase drastically as shown in Fig. 7.9.

7.4 Discussion Existing studies have confirmed that surrounding rock support and secondary lining produce a large amount of carbon emissions during tunnel construction [1, 3, 7]. From the perspective of the unit process, the discharge of shotcrete, steel frame and secondary concrete is very prominent [12, 13]. In addition, the emission level of these processes is also closely related to factors such as the surrounding rock grade, the buried depth of the tunnel, and the excavation area of the tunnel section [12, 13]. The above research helps to understand the emission level of tunnel construction. However, just knowing which are the main emission processes is not enough. A further step is that we need to use existing knowledge in engineering practice, and

7.4 Discussion

219

guide our engineering designs from the perspective of emission reduction. Unfortunately, the existing research is still in the case calculation stage, and scholars rarely discuss the carbon emissions in tunnel construction from the perspective of design. For tunnel designers, the link between lining design parameters and carbon emissions from construction is still unclear [9]. According to the Specifications for Design of Highway Tunnels, even if the surrounding rock is of Grade I, 30 cm thick molded concrete is still used in the design. The tunnel designs reserve space for the deformation of the surrounding rock after the initial support, and the lining ring is constructed after the deformation of the surrounding rock is stable. Therefore, the lining with strong supporting capacity may not be weighted, and it is only used as a supporting reserve, which greatly increases the construction cost and energy consumption of the tunnel. Compared with the construction of the Norwegian tunnel with single-layer lining, the lowcarbon design of the composite lining has huge room for development in the future [2]. This chapter provides practical support for the optimization of tunnel design parameters and a practical tool for tunnel designers to reduce carbon emissions during the design stage. Even tunnel designers who are not familiar with LCA can get inspiration or suggestions for low-carbon design in this research. It must be pointed out that the highway tunnel design models proposed in this chapter is derived from engineering cases, and the geological conditions of the engineering cases are mostly deep-buried tunnels with ordinary rock masses, which limits the application scope of the design models of this study. In complex geological conditions, parameters of rock mass mechanics vary widely, which makes the tunnel lining design more complicated. In addition, currently in China, there is not a unified standard drawing for road tunnel lining design, which means that in the face of complex geological conditions and extremely weak surrounding rocks, the design schemes given by different design organizations are often not uniform. It is difficult to obtain a representative tunnel design model under complex surrounding rock conditions. However, this study fully refers to the Chinese tunnel design specifications and related design cases. The supporting measures adopted in the lining model strictly comply with the design specifications and can represent the design standards of Chinese highway tunnels. The lining models are suitable for calculating the carbon emissions for the excavation and support of tunnels of Grade III, IV and V of general surrounding rock quality. This study will help tunnel designers analyze the influence of lining design parameters on tunnel emissions.

7.5 Conclusion This chapter establishes calculation models for the lining construction engineering quantity of a typical two-lane highway tunnel, and uses the modular carbon emission calculation model to clarify the influence of the changes in tunnel design parameters on the carbon emission of the two-lane tunnel lining construction. Relying on engineering cases to carry out the calculation of carbon emissions from tunnel

220

7 Influence of Tunnel Lining Design Parameters on Construction …

construction, this chapter displays the key processes affecting carbon emissions of three-lane tunnels, and establishes a model calculating the concrete volume of the three-lane tunnel, and clarifies the impact of the lining design parameters on carbon emissions. The research content in this chapter can be used to analyze the emission reduction potential brought by the optimization of design parameters and provide a reference for low-carbon design schemes. The main conclusions are as follows: 1.

2. 3.

4.

5.

Under the existing tunnel design specifications, changes in tunnel design parameters have little impact on the emission of steel mesh and tunnel excavation. The most influential construction procedures include shotcrete, secondary lining arch walls, bolt/grouting, steel frame, and rebar. In addition, emissions from cement grouting per cubic meter are relatively high, and a large-scale use will cause the tunnel’s carbon emissions to grow rapidly. As the concrete thickness increases, the carbon emissions of shotcrete and arch wall and invert arche of the secondary lining increase approximately linearly. The carbon emissions of the system bolts are affected by the lining thickness, the type of bolts, the length of the bolts and the distance between the bolts. As the thickness of the lining increases, the emissions of the bolts exhibit a slight linear increase. As the pitch of the bolts decreases, the carbon emissions of the system bolts increase significantly. The carbon emissions of the system bolts are proportional to the length of a single bolt. The carbon emissions of steel frame is inversely proportional to the longitudinal spacing yet proportional to the mass of the steel frame. Under the conditions of the same steel frame spacing and similar steel frame circumferential length, the carbon emission ratios of various steel frames remain stable. The carbon emission ratios of steel frames I22b to I20b, I20b to I18, I20b to I18, and I18 to I16 are 1.175, 1.287, 1.197, and 1.316, respectively. The rebar type and the length of the circumferential steel bar are important factors that affect the carbon emission of the rebar. The carbon emission of rebar may increase with the increase of lining thickness, but it is non-linear, and its influence characteristics need more in-depth study.

In order to achieve low-carbon lining design, it is recommended to adopt different control strategies for different design parameters: 1.

2.

3.

For the types with large fluctuations in carbon emissions, carbon emissions are more sensitive to changes in design parameters and have a greater potential for emission reduction, which should be the control focus; For the types with limited emission fluctuations, carbon emissions are not sensitive to subtle changes in design parameters, but large changes in the values of design parameters will still result in huge cumulative emissions; For the low-emission types, the carbon emissions of the modules are small, and the emission reduction potential is also small.

References

221

References 1. Ahn C, Xie H, Lee SH et al (2010) Carbon footprints analysis for tunnel construction processes in the preplanning phase using collaborative simulation. In: Construction research congress 2010: innovation for reshaping construction practice—proceedings of the 2010 construction research congress 2. Barton N (2017) Minimizing the use of concrete in tunnels and caverns: comparing NATM and NMT. Innov Infrastruct Solut 2. https://doi.org/10.1007/s41062-017-0071-x 3. Chen L (2017) Research on carbon emission characteristics and influencing mechanism of highway tunnel traffic. Chongqing Jiaotong University (in Chinese) 4. 储诚赞, 刘玉新, 燕凌. 公路隧道节能方式探究[J]. 现代隧道技术, 2016,53(01):23–27. Chu C, Liu Y, Yan L (2016) Research on energy-saving ways of highway tunnels. Mod Tunnelling Technol 53(01):23–27 5. 晁峰, 王明年, 于丽, 郭春. 特长公路隧道自然风计算方法和节能研究[J]. 现代隧道技术, 2016, 53(01):111–118+126. Chao F, Wang M, Yu L et al (2016) Study on natural wind calculation method and energy saving of extra-long highway tunnel. Mod Tunnelling Technol 53(01):111–118+126 6. 黄寒. 油气管道越江盾构隧道结构设计参数优化分析[D]. 西南交通大学, 2017. Huang H (2017) Optimal analysis of structural design parameters of oil and gas pipeline cross-river shield tunnel. Southwest Jiaotong University 7. Huang L, Bohne RA, Bruland A et al (2015) Life cycle assessment of Norwegian road tunnel. Int J Life Cycle Assess 20. https://doi.org/10.1007/s11367-014-0823-1 8. 金强国. 郑万高铁隧道大型机械化施工支护优化[J]. 隧道建设(中英文), 2018, 38(08):1324–1333. Jin Q (2018) Optimization of large-scale mechanized construction support for Zhengwan high-speed railway tunnel. Tunnel Constr 38(08):1324–1333 9. Li X, Liu J, Xu H, et al (2011) Calculation of endogenous carbon dioxide emission during highway tunnel construction: A case study. In: ISWREP 2011 - Proceedings of 2011 International Symposium on Water Resource and Environmental Protection 10. 李志业, 曾艳华. 地下结构设计原理与方法[M]. 成都: 西南交通大学出版社, 2003. Li Z, Zeng Y (2003) Principles and methods of underground structure design. Southwest Jiaotong University Press, Chengdu 11. 王少飞, 涂耘, 邓欣. 公路隧道节能减排对策[J]. 铁道工程学报, 2011, 28(05):71–75+114. Wang S, Tu G, Deng X (2011) Countermeasures for energy conservation and emission reduction in highway tunnels. J Railw Eng Soc 28(05):71–75+114 12. Xu J, Guo C, Chen X et al (2019) Emission transition of greenhouse gases with the surrounding rock weakened—a case study of tunnel construction. J Clean Prod 209. https://doi.org/10.1016/ j.jclepro.2018.10.224 13. Xu J, Guo C, Yu L (2019) Factors influencing and methods of predicting greenhouse gas emissions from highway tunnel construction in southwestern China. J Clean Prod 229. https:// doi.org/10.1016/j.jclepro.2019.04.260 14. 徐建平, 桑运龙, 刘学增, 杨志峰. 节理发育岩体隧道支护的动态设计方法与应用[J]. 地下 空间与工程学报, 2017, 13(02):416–421. Xu J, Sang Y, Liu X et al (2017) Dynamic design method and application of tunnel support in jointed rock mass. Chin J Underground Space Eng 13(02):416–421

Chapter 8

Carbon Emission Characteristics of Inclined Shaft Construction in Highway Tunnel

8.1 Introduction Although the existing studies have conducted a lot of analysis on the carbon emission characteristics of the main tube of the tunnel [1, 4, 5], there are few literature reports on the carbon emission characteristics of the inclined shaft of the tunnel. The inclined shaft is an important auxiliary tunnel for the construction of a long tunnel, which plays an important role in shortening the construction period of the project and speeding up the construction progress. Relying on engineering cases, this project analyzes the carbon emission characteristics during the construction of inclined shafts of highway tunnels, clarifies the carbon emission levels during the different construction processes, and explores the influence law of inclined shaft slope and length on the carbon emissions during excavation and slagging.

8.2 Project Profile A two-way six-lane tunnel, located in the Qinling Mountains, has the total length of 7,018 m for its main body in both directions and the design speed is 80 km/h. As channels for construction entry and channels for later rescue and escape, two inclined shafts, are designed for permanent support. The inclined shaft has a section area of 61.02 m2 , a width of 10.0 m, a slope of 9°, and a length of 1,500 m. The surrounding rock grades of the inclined shaft are mainly Grade IV and V, which are excavated by the drilling and blasting method. In this study, the construction processes for inclined shaft involve advanced support, tunnel excavation, initial support and secondary lining. The engineering quantity data per linear meter of inclined shafts of surrounding rock Grade IV and V are shown in Tables 8.1 and 8.2.

© Southwest Jiaotong University Press 2022 C. Guo and J. Xu, Carbon Emission Calculation Methods for Highway Tunnel Construction, https://doi.org/10.1007/978-981-16-5308-7_8

223

224

8 Carbon Emission Characteristics of Inclined Shaft Construction …

Table 8.1 The engineering quantity of the inclined shaft of rock mass Grade IV

Table 8.2 The engineering quantity of the inclined shaft of rock mass Grade V

Item

Unit

Excavation

m3

Quantity 80.5

22 mortar bolt

m

224.5

Steel mesh

kg

83.7

I18 steel

kg

504.4

Longitudinal connection steel bar

kg

80.9

Steel plate

kg

113.7

25 mortar anchor

m

44.4

Sprayed concrete

m3

5.2

C30 arch wall concrete

m3

8.2

C30 inverted arch concrete

m3

4.4

Rebar

kg

829.8

C15 plain concrete

m3

7.2

Item

Unit

Excavation

m3

87.8

42 lead conduit

m

48.4

Cement mortar

m3

1.94

22 early strength mortar bolt

m

224.5

Steel mesh

kg

132.7

I20a steel

kg

1539.4

Longitudinal connection steel bar

kg

169.4

Steel plate

kg

264.9

Spray coagulation clean soil

m3

8.8

C30 arch wall concrete

m3

10.2

C30 inverted arch concrete

m3

5.6

Rebar

kg

1069

C15 plain concrete

m3

7.3

Quantity

8.3 Calculation Method and Inventory Data Carbon emissions from construction consist of direct carbon emissions and indirect carbon emissions. The former includes carbon emissions from the consumption of fossil fuels at the construction site, and the latter covers carbon emissions from the production and transportation of building materials as well as that from the consumption of electricity during construction [2]. This chapter takes “inclined shaft construction per linear meter” as the functional unit to calculate its carbon emissions. Carbon emissions during the production, installation, and dismantling of construction equipment are outside the scope of the study.

8.3 Calculation Method and Inventory Data

225

The inventory data of carbon emissions from inclined shaft construction is composed of two parts: foreground data and background data [3]. Foreground data refers to the consumption of various materials and energy. The material and machinery input quantity corresponding to the engineering volume can be gained by substituting the engineering quantity data per linear meter of inclined shaft into the JTG/T3832-2018 Highway Engineering Budget Quota. At the same time, JTG/T3833-2018 Highway Engineering Machinery Shift Cost Quota covers the energy consumption data of various construction machinery units and shifts, and then the materials and energy quantity corresponding to the engineering quantity can be obtained. Background data refers to the emission factors of materials or energy. With the emission factor data listed in reference [3], the emission coefficient method is used to multiply and accumulate the background data and the inventory data to obtain the carbon emission values per linear meter of inclined shaft construction. Table 8.3 contains some basic parameters and assumptions for carbon emission calculation, which is convenient for obtaining inventory data in unit processes (Tables 8.4 and 8.5).

8.4 Carbon Emission from Inclined Shaft Construction 8.4.1 Energy Carbon Emissions from Various Materials Figure 8.1 shows the carbon emission values of different materials and energy sources. The carbon emissions from the inclined shaft construction of Grade IV and V surrounding rock are 18.13 t CO2eq and 27.14 t CO2eq , respectively, which shows a big gap between the carbon emissions. However, the proportion of emissions from materials and energy is relatively close. Among them, emissions from cement account for more than 40%, emissions from steel account for more than 32%, emissions from electricity account for more than 13% and emissions from diesel account for more than 7%. The total energy emissions of the above materials exceed 90%, which has a controlling effect on carbon emissions from the construction of inclined shafts.

8.4.2 Carbon Emissions from Construction Process The carbon emission levels of each process of the inclined shaft construction are clarified (see Fig. 8.2). For Grade IV surrounding rocks, the three processes with the highest carbon emissions are mortar bolts (3.59 t CO2eq ), secondary lining arch walls (3.06 t CO2eq ) and rebar (2.08 t CO2eq ), while those of Grade V surrounding rocks are shotcrete (4.22 t CO2eq ), section steel frame (4.12 t CO2eq ) and secondary

226

8 Carbon Emission Characteristics of Inclined Shaft Construction …

Table 8.3 Assumptions for material transportation, collection and processing Item

Basic parameters and assumptions

Outdoor transport of waste rock

The waste rock recovery distance is 10 km from the entrance of the cave. A 20-ton dump truck is used for transportation from the entrance to the rock recovery site. And the transportation of 100 m3 of rock consumes 0.91 shifts, the corresponding diesel consumption per shift is 77.12 kg, and the carbon emissions are 152.34 kg CO2eq . When rail transportation is used in the inclined shaft, a wheel loader is also required. Loading and unloading 100 m3 of rock consumes 0.12 shifts, the fuel consumption per shift is 123.29 kg, and the carbon emission is 32.12 kg CO2eq

Materials acquisition and processing

Material collection and processing consider the recovery of sand and gravel. Using tunnel waste slag to screen sand, the production of 100 m3 piles consumes 1.2 shifts of 1m3 tire wheel loaders and 5.8 shifts of drum screeners. Correspondingly, 209.3 kg of diesel and 52.2 kWh of electrical energy are consumed, and 254.29 kg CO2eq of carbon emissions are produced. A 250 mm × 400 mm electric jaw crusher and drum screen are used to produce crushed gravel. The energy consumptions are 35.7 kWh per shift and 12.98 kWh per shift, respectively. The carbon emissions of 100 m3 crushed gravel is 200.94 kg CO2eq . It is assumed that 50% of sand and 100% of crushed gravel are provided through on-site rock recovery in the tunnel site area

Slag transportation in inclined shaft

For the inclined shaft with a slope of 9°, a 3-m3 tire loader and a 20-t dump truck are used to transport the slag. For every 100 m3 of rock consumed, 0.4 shifts of loaders and 1.25 shifts of dump trucks are used to generate carbon emissions of 316.32 kg CO2eq

Material from market to tunnel

The transportation distance of building materials is 500 km, and it is transported by heavy-duty diesel trucks (with a load of 30 t), and the carbon emission factor is 0.078 kg CO2eq /(t km)

lining arch wall (3.81 t CO2eq ). The carbon emissions of steel mesh and connecting steel bars is the lowest, less than 0.4 t CO2eq . Compared with Grade IV surrounding rock, most of the carbon emissions in the construction processes of Grade V surrounding rock have increased. The growth in carbon emissions from section steel frame is the biggest, from 1.35 t CO2eq to 4.12 t CO2eq , with an increase of 205%. The increase in carbon emissions of shotcrete is 1.72 t CO2eq . In addition, carbon emissions in processes such as the secondary lining arch wall, inverted arch, rebar, and excavation and slagging have all grown up to some extent. The changes in carbon emissions in each process correspond to the engineering quantities in Tables 8.1 and 8.2. However, the carbon emission from

853.10

0.00

0.01

0.00

0.01

0.09

0.00

0.00

Steel mesh

Shotcrete

Arch wall concrete

Inverted arch concrete

Rebar

Concrete-filled 0.00

11.18

87.76

0.00

85.87

178.64

2403.36

0.00

1702.36

3172.58

2926.56

0.00

59.32

232.15

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

6321.74

0.00

3794.08

7070.78

5353.92

0.00

58.67

229.60

0.00

8424.0

0.00

5009.4

9335.7

5335.2

0.00

0.00

0.00

0.00

7.92

0.00

2.64

4.92

12.48

0.00

2.22

8.70

0.00

0.00

0.00

34.10

29.64

75.99

292.98

10.75

240.55

941.46

0.00

33.69

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

9.17

35.29

35.39

22.81

223.07

48.39

3.56

18.86

73.83

3.42

22.49

240.12

25 mortar bolt

699.13

0.00

0.00

2.76

0.02

0.00

0.00

1428.03

22 mortar bolt

82.52

0.00

20.13

0.00

0.00

0.00

Connection rebar

542.23

0.00

0.00

62.71

Steel support

0.00

0.03

Excavation of Grade IV surrounding rock

16.64

Wood (m3 ) Steel (kg) Cement (kg) Explosive (kg) Sand (kg) Gravel (kg) Water (m3 ) Electricity Gasoline (kg) Diesel (kg) (kWh)

Process

Table 8.4 The consumption of materials and energy in the construction of 1-m inclined shaft of rock mass Grade IV

8.4 Carbon Emission from Inclined Shaft Construction 227

1099.02

0.00

0.02

0.00

0.01

0.11

0.00

0.00

22 mortar bolt

Steel mesh

Shotcrete

Arch wall concrete

Inverted arch concrete

Rebar

Concrete-filled 0.00

14.22

109.17

0.00

136.14

699.13

172.79

2436.74

0.00

2166.64

3946.38

4952.64

0.00

232.15

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

6409.55

0.00

4828.82

8795.36

9060.48

0.00

229.60

0.00

0.00

8541.00

0.00

6375.60

11,612.7

9028.80

0.00

0.00

0.00

0.00

0.00

8.03

0.00

3.36

6.12

21.12

0.00

8.70

0.00

0.00

21.95

0.00

43.93

37.72

94.53

495.81

17.04

941.46

0.00

102.81

1900.7

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

27.98

3.01

2.26

35.78

45.59

29.03

277.48

81.89

5.65

73.83

7.17

68.64

251.98

34.64

8.09

0.00

0.00

0.00

1.55

0.00

Connection rebar

1654.86

33.4

0.00

267.76

0.00

0.00

0.00

4.36

Section steel frame

15.56

0.00

0.00

0.04

2745.88

0.00

Excavation of Grade V surrounding rock

0.00

0.00

0.00

Grouting

0.00

0.00

Small pipe

174.09

Wood (m3 ) Steel (kg) Cement (kg) Explosive (kg) Sand (kg) Gravel (kg) Water (m3 ) Electricity Gasoline (kg) Diesel (kg) (kWh)

Process

Table 8.5 The consumption of materials and energy in the construction of 1-m inclined shaft of rock mass Grade V

228 8 Carbon Emission Characteristics of Inclined Shaft Construction …

8.4 Carbon Emission from Inclined Shaft Construction

229

Fig. 8.1 Carbon emissions from different materials and energy

Fig. 8.2 Carbon emissions from different procedures

mortar bolts of Grade IV surrounding rock is higher because it involves the carbon emissions from mortar bolts used for advanced support and initial support.

230

8 Carbon Emission Characteristics of Inclined Shaft Construction …

Table 8.6 Number of the mechanical shifts for the excavation of 1-m inclined shaft Item

Slope 7° IV

Slope 9° V

IV

Slope 12° V

IV

Slope 25° V

IV

V

Crawler hydraulic 0.024 single bucket excavator (1 m3 )

0.018 0.024

0.026 0.032

0.035 0.048

0.079

Air-legged pneumatic rock drill

7.342

8.429 7.342

8.429 7.342

8.429 7.342

8.429

Truck (3 t)

0.081

0.088 0.081

0.088 0.105

0.114 0.000

0.000

Electric air compressor (20 m3 /min)

2.294

3.020 2.375

3.161 3.091

4.065 3.027

3.328

8.5 Effect of Inclined Shaft Length and Slope on Carbon Emission from Excavation and Slagging 8.5.1 Influencing Factors and Inventory Data The actual length and slope of the inclined shaft often change. How it influences the carbon emissions from excavation and slagging will be further analyzed in this study. The slopes of the inclined shafts are 7°, 9°, 12°, and 25°, and the lengths of the inclined shafts are 1,700 m, 1,900 m, 2,100 m, 2,300 m, and 2,500 m. When the inclined shaft is 1,500 m long, the number of mechanical shifts per meter of inclined shaft excavation is shown in Table 8.6. Following the Highway Engineering Budget Quota, when the inclined shaft is more than 1,500 m long, for every 100 m increase in the length of the inclined shaft, the number of additional mechanical shifts consumed by the excavation of the inclined shaft per linear meter is shown in Table 8.7. The mechanical shifts and transportation methods for slagging discharge from inclined shafts with different slopes will also change. As shown in Table 8.8, when the slope is 7°, 9° and 12°, the slagging discharged from the inclined shaft is transported by flexible transport, and when the slope is 25°, it is transported by track. Table 8.9 lists the energy consumption data per unit shift for different construction machinery.

8.5.2 Influence of Inclined Shaft Slope on Carbon Emissions of Construction Machinery in Excavation Process The length of the inclined shaft is set to 1500 m, and the carbon emissions of machinery in the excavation of the tunnel with different inclined shaft slope are

8.5 Effect of Inclined Shaft Length and Slope on Carbon Emission …

231

Table 8.7 Number of the additional mechanical shifts for the excavation of 1-m inclined shaft Item

Slope 7° IV

Slope 9° V

IV

Slope 12° V

IV

Slope 25° V

IV

V

Crawler hydraulic 0.008 single bucket excavator (1 m3 )

0.009 0.008

0.009 0.008

0.009 0.008

0.009

Air-legged pneumatic rock drill

0.813

0.887 1.030

1.124 1.344

1.449 1.312

1.466

Truck (3 t)

0.008

0.009 0.008

0.009 0.008

0.009 0.000

0.000

Electric air compressor (20 m3 /min)

0.250

0.272 0.266

0.281 0.338

0.360 0.330

0.334

Table 8.8 Number of mechanical shifts for the slagging-out inside the inclined shaft Item

Slope 7° IV

Crawler hydraulic 0.000 single bucket excavator (1 m3 )

Slope 9° V

IV

Slope 12° V

0.000 0.000

IV

0.000 0.000

Slope 25° V

IV

V

0.000 0.330

0.342

Tire loader (3 m3 ) 0.242

0.263 0.322

0.334 0.403

0.439 0.000

0.000

Dump truck (20 t) 0.700

0.764 1.006

1.001 1.224

1.335 0.000

0.000

Shuttle mine car (8 m3 )

0.000

0.000 0.000

0.000 0.000

0.000 2.037

2.063

Single Cylinder Winch (2 × 1.5 m)

0.000

0.000 0.000

0.000 0.000

0.000 0.137

0.105

Twin Cylinder Winch (2 × 1.5 m)

0.000

0.000 0.000

0.000 0.000

0.000 0.998

1.027

Table 8.9 Energy consumption per mechanical shift of different machinery Mechanical types Crawler hydraulic single bucket excavator (1

Types of energy m3 )

Unit

Value

Diesel

kg

74.91

Air-legged pneumatic rock drill

–

–

–

Truck (3 t)

Gasoline

kg

26.12

Electric air compressor (20 m3 /min)

Electricity

kWh

601.34

Tire loader (3 m3 )

Diesel

kg

123.29

Dump truck (20 t)

Diesel

kg

81.14

Shuttle mine car (8 m3 )

Electricity

kWh

67.30

Single Cylinder Winch (2 × 1.5 m)

Electricity

kWh

52.83

Twin Cylinder Winch (2 × 1.5 m)

Electricity

kWh

149.03

232

8 Carbon Emission Characteristics of Inclined Shaft Construction …

Fig. 8.3 Carbon emissions from the machines in the excavation of the inclined shaft

calculated, as shown in Fig. 8.3. When the slope is 7°, 9° and 12°, the carbon emissions of machinery increase with the increase of the slope. When the slope changes from 12° to 25°, the emission intensity of the inclined shaft construction machinery decreases. From Table 8.9, the energy consumption level of electric air compressors is much higher than that of other machinery. Combined with Table 8.6, it can be seen that the quantity change of mechanical shifts of electric air compressors (20 m3 /min) has an important impact on the emission level of inclined shaft excavation.

8.5.3 Effect of Slope of Inclined Shaft on Carbon Emissions from Slagging Figure 8.4 shows the carbon emission value of slagging per linear meter of inclined shaft, which is between 0.18 t and 0.35 t CO2eq . The carbon emission value of Fig. 8.4 Carbon emissions from slagging

8.5 Effect of Inclined Shaft Length and Slope on Carbon Emission …

233

slag transportation in the shaft of Grade V surrounding rock is slightly higher than that of the Grade IV surrounding rock. Similar to the carbon emissions from the excavation process of the inclined shaft, as the slope increases from 7° to 12°, the carbon emissions from slagging show an increasing trend. When the slope of the inclined shaft changes to 25°, the carbon emission of Grade IV surrounding rock rises slightly, and the carbon emission of Grade V surrounding rock declines mildly. In general, the level of carbon emissions from slagging in the inclined shaft is relatively low, and the impact on construction carbon emissions is limited.

8.5.4 Influence of Inclined Shaft Length on Carbon Emissions from Excavation and Slagging The carbon emissions from excavation and slagging of inclined shafts with different lengths are calculated, as shown in Fig. 8.5. The corresponding carbon emissions are Fig. 8.5 Carbon emissions from the excavation and slagging of the inclined shaft. a rock mass Grade IV, b rock mass Grade V

234

8 Carbon Emission Characteristics of Inclined Shaft Construction …

directly proportional to the length of the inclined shaft. For every 100 m increase in the length of the inclined shaft, the carbon emissions from excavation and slagging increase by 0.15 t–0.2 t CO2eq .

8.6 Conclusion The main conclusions are listed as follows: • The surrounding rock grade is a key factor affecting carbon emissions from inclined shaft construction. For Grade IV and V surrounding rocks with excavation areas of 80.5 m2 and 87.8 m2 , the carbon emissions from inclined shaft construction are 18.13 t CO2eq and 27.14 t CO2eq , respectively. • For Grade IV surrounding rocks, the three processes with the highest carbon emissions are mortar bolts (3.59 t CO2eq ), secondary lining arch wall (3.06 t CO2eq ) and rebar (2.08 t CO2eq ), while for Grade V surrounding rocks, the three processes with the highest emissions are shotcrete (4.22 t CO2eq ), section steel (4.12 t CO2eq ) and secondary lining arch wall (3.81 t CO2eq ). • Among all kinds of materials and energy, the emissions of cement, steel, electricity and diesel account for more than 40%, 32%, 13% and 7%. The above-mentioned materials and energy have a controlling effect on carbon emissions from inclined shaft construction. • When the length of the inclined shaft exceeds 1,500 m, for every 100 m increase in the length of the inclined shaft, the carbon emissions during the slag and slagging process of the inclined shaft will increase by 0.15 t–0.21 t CO2eq . The carbon emission value of slagging per meter of inclined shaft is relatively small, ranging from 0.18 t to 0.35t CO2eq , which has a limited impact on carbon emissions from inclined shaft construction. • As the level of surrounding rock rises, the increase in emissions of steel frames and shotcrete is particularly significant. The material input can be reduced by optimizing the design of the sectional steel frame, using high-performance shotcrete, and reducing the thickness of shotcrete. In the construction of inclined shafts, the energy consumption of some machinery units per shift is relatively large, and the change in the number of mechanical shifts has a greater impact on the carbon emissions of the inclined shaft construction machinery. It is recommended that the management system of high-power construction machinery should be established and improved to shorten the unnecessary operation time of machinery.

References 1. Ahn C, Xie H, Lee SH et al (2010) Carbon footprints analysis for tunnel construction processes in the preplanning phase using collaborative simulation. In: Construction research congress 2010:

References

2. 3.

4.

5.

235

innovation for reshaping construction practice—proceedings of the 2010 construction research congress Buyle M, Braet J, Audenaert A (2013) Life cycle assessment in the construction sector: a review. Renew Sustain Energy Rev 26 郭春, 徐建峰, 张佳鹏. 隧道建设碳排放计算方法及预测模型[J]. 隧道建设, 2020, 40(8):140. Guo C, Xu J, Zhang J (2020) Calculation methods and prediction models of carbon emission from tunnel construction. Tunnel Constr 40(8):140 李乔松, 白云, 李林. 盾构隧道建造阶段低碳化影响因子与措施研究[J]. 现代隧道技术, 2015, 52(3):1–7. Li Q, Bai Y, Li L (2015) Study of influential factors and measures for low carbonization during the construction of shield tunnels. Mod Tunn Technol 52(3):1–7 Li X, Liu J, Xu H et al (2011) Calculation of endogenous carbon dioxide emission during highway tunnel construction: a case study. In: ISWREP 2011—Proceedings of 2011 international symposium on water resource and environmental protection