Railways and Sustainable Low-Carbon Mobility in China [1st ed.] 9789811590801, 9789811590818

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Linna Li

Railways and Sustainable Low-Carbon Mobility in China

Railways and Sustainable Low-Carbon Mobility in China

Linna Li

Railways and Sustainable Low-Carbon Mobility in China

123

Linna Li Faculty of Geographical Science Beijing Normal University Beijing, China

ISBN 978-981-15-9080-1 ISBN 978-981-15-9081-8 https://doi.org/10.1007/978-981-15-9081-8

(eBook)

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Foreword

Today's world has entered a great era full of innovation and the promotion of global cooperation. Modern information technology and a highly developed transportation system have facilitated economic contact and social communication. Sustainable transport is a vital component of sustainable development, which contributes to multiple goals of the United Nations 2030 Agenda for Sustainable Development, including good health and well-being, affordable and clean energy, sustainable cities and communities, and climate action. In particular, developing a low-carbon transport system is significant for minimizing energy consumption, greenhouse gas emissions, and atmospheric pollution. Facing the 2030 Global Sustainable Development Goals (SDGs) and coping with the huge challenges of global environmental changes and human sustainable development, sustainable transport, especially low-carbon transport, has become an important research issue. China, as a developing country, has experienced rapid development of its transport infrastructure and motorization during the last 40 years of reform and opening-up, which has brought huge social and economic effects as well as negative environmental impacts. In 2008, the Beijing-Tianjin high-speed rail began operations, marking China’s entrance into the high-speed railway age. The rapid development of high-speed rail has provided opportunities for developing low-carbon mobility in China by changing passengers’ travel modes and making modal shifts in transport systems. Studying the role of railways in sustainable low-carbon mobility can support national strategies in China, including realizing ecological civilization, building national strength in transportation, and mitigating global climate change. However, among the substantial studies about railway transport in China, most geographers have focused on its network evolution and socioeconomic impact, and few studies have comprehensively examined the evolutionary role of railways in low-carbon mobility, especially taking high-speed rail into consideration.

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This book is based on the author’s theoretical research about sustainable transport as well as intensive fieldwork in Shanghai Hongqiao Transport Hub, Pearl River Delta Intercity Railway, and Hong Kong International Airport over the last decade. It provides a systematic and multidisciplinary perspective for understanding the role of railways in low-carbon mobility. Based on the principle of “retrospect of the past, analysis of the current situation, and prospects for the future”, this book adopts multiple methods, including a spatial-temporal analysis, observational survey, interviews, questionnaires, and geostatistics method, to comprehensively analyze the competition, cooperation, and integration between railways and other transport modes, which makes the book a valuable contribution to the existing literature regarding railways and low-carbon transport in China. In particular, Chapters 3 and 4 provide an overview of the contribution of railways to low-carbon emissions in China in the past few decades at both the national and regional levels. Chapters 5 and 6 focused on the current competition, cooperation, and integration situation between railways and other transport modes as well as their contribution to low-carbon emissions. Chapter 7 addressed the institutional barriers and provides policy implications for developing low-carbon mobility in the future. Based on these studies, this book provides a comprehensive understanding of the role of railways in low-carbon mobility, the competition, cooperation, and integration between different transport modes and the barriers for integrated transport in China. In sum, this book has great value for both academic research on transport geography and policymaking for transport system development in China. This book not only contributes to the research about sustainable low-carbon transport from a geographical perspective but also provides feasible strategies for improving low-carbon mobility in China, which is of great interest to readers who are interested in sustainable transport and a valuable reference for students, academics, planners, and policymakers.

Yansin Lin Fellow, The World Academy of Sciences (TWAS) Chair, IGU Commission on Agricultural Geography and Land Engineering (IGU-AGLE) Professor, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences (CAS), Bejing, China Director general, Key Laboratory of Regional Sustainable Development Modeling, CAS, Bejing, China

Preface

Over the past several decades, the travel behavior of Chinese residents has undergone a notable transformation due to rapid urbanization and industrialization, with travel demands increasing and travel modes changing. On the one hand, it has made travel more affordable, convenient, and efficient; on the other hand, various pollution caused by traffic has placed significant pressure on the environment. Indeed, transport development is a complex system that involves multiple aspects, such as the population, economy, resources, and environment. In China, it is particularly important to explore a pathway towards sustainable development that can reach a balance between mobility and the environment. Among the kinds of transport modes, railways have characteristics such as mass capacity, high safety, and low-carbon emissions, which is appropriate for regions with a high population density and flat terrain. However, as the transport system has evolved, the role of railways has decreased and railways have been replaced by automobiles and airplanes with higher energy consumption and carbon emissions. In recent years, high-speed rail technology has given railways an opportunity to make a comeback and shifted the modal share of transport systems, which was further influenced the sustainable development of transport systems. Railways and sustainable transport have been themes of my research since 2008, when my master studies focused on the accessibility effects of intercity-railways in the Pearl River Delta. The last decade has seen a rapid development of railways in China, including railway network extension, railway electrification, and high-speed railway development. My research has focused on the relationship between railways and low-carbon emissions, land use change, and social equity. This book, entitled Railways and Sustainable Low-Carbon Mobility in China, has been divided into three parts. Part I Retrospect of the Past: Modal Shift is Important tries to estimate how much railways have contributed to low-carbon transport in China over the past few decades at both the national and regional levels in comparison with other transport modes and emphasizes the contribution of this modal shift to increases in transport carbon emissions in China. Part II Current Situation: Competition and Integration Between Railways and Other Transport Modes analyzes the competition and integration between railways and other transport modes vii

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using several case studies. Part III Prospects for the Future: Breaking Institutional Barriers determines the barriers to modal shifts from other transport modes back to railways and further integration of different transport modes from both the infrastructure and institutional perspectives. It also proposes cooperation as a strategy to promote sustainable low-carbon transport systems in China. From the perspective of system analysis, this book explores the relationship between railways and sustainable low-carbon transport from multiple temporal and spatial scales and proposes a pathway to build a sustainable transportation system in China through railways and integrated transport. It is hoped that this book provides references for scholars and decision-makers of relevant departments, as well as helps the public understand the relevant issues of sustainable mobility in China. Beijing, China

Linna Li

Acknowledgements

This book is based on my Ph.D. dissertation at the University of Hong Kong and my following studies at Beijing Normal University. My interest in sustainable transport started from the impact of railways on economic development, carbon emissions, and social equity in my master’s and Ph.D. research, and then broadened to the relationship between transport development and land use change, rural poverty and urban-rural spatial restructuring. This book would not be possible without the support and encouragement from everyone that helped me during my research. I would like to take this opportunity to express my genuine gratitude to all of them. My greatest appreciation goes to my supervisors, Professor Becky P.Y. Loo (Ph.D. supervisor) at the University of Hong Kong and Professor Xiaoshu Cao (master supervisor) at Sun Yat-sen University, for their intellectual guidance and endless encouragement in both my research and everyday life. Their hard-working, rigorous scholarship, and international academic interaction have continued to influence my research advancement, career development, and personal enrichment. My sincere gratitude also goes to the experts that have provided many constructive suggestions and comments on my research. In particular, Professor Yansui Liu, fellow of The World Academy of Science (TWAS) and professor at the Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences (CAS), who has inspired me and guided my research after my Ph.D.. His academic insights about the human-earth relationship and rural regional system have guided my research directions. I also want to thank Professor Claude Comtois at the University of Montreal for his constructive suggestions and comments on my Ph.D., and Dr. Liu Wujun at Shanghai Airport Authority for his kind help with my investigation of the Shanghai Hongqiao Transport Hub. In addition, I would like to thank the academic staff and my friends at the Department of Geography, the University of Hong Kong, and my colleagues at Beijing Normal University, who have shared their research experiences with me. I also want to thank my family. Without their understanding and support, I would not have been able to finish this book.

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Acknowledgements

Finally, I would like to thank the National Natural Science Foundation of China (Grant No. 41701119) and the Hong Kong Research Grant Council (Grant No. 748408H) for their grant support. Additionally, I want to thank the publishers of several journals including Transportation, Energy Policy, Cities, and Journal of Regional Science for granting me the permission to reuse my published materials in this book.

Contents

1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Railways and Low-Carbon Mobility . . . . . . . . . . . . . . . . . . . . . 2.1 Railway Development: Make a Comeback? . . . . . . . . . . . . . 2.2 Railways and Sustainable Transport . . . . . . . . . . . . . . . . . . . 2.2.1 Railways and Economically Sustainable Transport . . . 2.2.2 Railways and Socially Sustainable Transport . . . . . . . 2.2.3 Railways and Environmentally Sustainable Transport . 2.3 Intermodal Relationship Related to Railways . . . . . . . . . . . . 2.3.1 Competition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.2 Cooperation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.3 Integration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4 Intermodal Relationship and Sustainable Transport . . . . . . . . 2.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Part I

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Retrospect of the Past: Modal Shift is Important

3 Railways and National Carbon Emissions from Passenger Travel in China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 China’s Background . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.1 Travel Trends in China . . . . . . . . . . . . . . . . . . 3.1.2 Railway Development in China . . . . . . . . . . . . 3.2 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 National CO2 Emissions from Passenger Travel . . . . . . 3.3.1 Distance-Based Method . . . . . . . . . . . . . . . . . . 3.3.2 Fuel-Based Method . . . . . . . . . . . . . . . . . . . . . 3.3.3 Insights from the Two Methods . . . . . . . . . . . . 3.4 Provincial CO2 Emissions from Passenger Transport . . 3.5 The Role of Railways in CO2 Emission Reduction . . . .

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3.6 The Role of Modal Shift in CO2 Emission Reduction . . . . . . . . . . 3.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Railways and Regional Carbon Emissions from Passenger Travel in China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.1 Estimation Method . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.2 Decomposition Analysis . . . . . . . . . . . . . . . . . . . . . . 4.3 CO2 Emissions from Transport Sector in Three Metropolitan Regions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.1 CO2 Emissions from Transport Sector in JJJ . . . . . . . 4.3.2 CO2 Emissions from Transport Sector in YRD . . . . . 4.3.3 CO2 Emissions from Transport Sector in PRD . . . . . 4.3.4 Comparison Between Three Metropolitan Regions . . . 4.4 Factors Influencing CO2 Emission from Transport Sector in Three Metropolitan Regions . . . . . . . . . . . . . . . . . . . . . . 4.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Part II

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Current Situation: Competition and Integration Between Railways and Other Transport Modes

5 Competition Between Railways and Other Transport Modes . . . 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Background of China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.1 Conventional Railway Improvement . . . . . . . . . . . . . . 5.2.2 High-Speed Rail Development . . . . . . . . . . . . . . . . . . 5.3 The Relevance of Railway Improvement to Air Transport . . . . 5.4 The Relevance of Railway Improvement to Air Flight Patronage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4.1 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5 The Relevance of HSR Development to Air Transport Supply 5.6 Implications for Low Carbon Transport . . . . . . . . . . . . . . . . . 5.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Cooperation Between Railways and Other Transport Modes 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2 Integration Between Railways and Urban Transport . . . . . 6.3 Integration Between Railways and Air Transport . . . . . . . 6.4 Case study: Shanghai Hongqiao Comprehensive Transport Hub . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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6.4.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143 6.4.2 Interchange Service in Hongqiao . . . . . . . . . . . . . . . . . . . 146 6.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164 Part III

Prospects for the Future: Breaking Institutional Barriers

7 Breaking Institutional Barriers in China . . . . . . . . . . . . . . . . 7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2 Institutional Barriers of Integrated Transport in China . . . . . 7.2.1 Organization of Different Transport Modes in China 7.2.2 Ownership of Different Transport Modes in China . . 7.2.3 Operation of Different Transport Modes in China . . 7.3 Implications for Policy Making . . . . . . . . . . . . . . . . . . . . . 7.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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8 Towards Sustainable Low-Carbon Mobility in China . . . . . . . . . . 8.1 Summary of the Book . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.1.1 Retrospect of the Past: Modal Shift is Important . . . . . . 8.1.2 Current Situation: Competition and Integration Between Railways and Other Transport Modes . . . . . . . . . . . . . . 8.1.3 Prospects for the Future: Breaking Institutional Barriers . 8.2 Contributions of the Book . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2.1 Major Theoretical Contributions . . . . . . . . . . . . . . . . . . 8.2.2 Major Practical Contributions . . . . . . . . . . . . . . . . . . . . 8.3 Suggestions for Further Studies . . . . . . . . . . . . . . . . . . . . . . . .

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Chapter 1

Introduction

Railways, combined with road, air and water transport, are the four main transport modes of passenger transport system (Rodrigue et al. 2013). Since their first development in England in the early 19th Century, railways have played an important role in passenger travel. Meanwhile, the technology of railways has evolved over time, from the steam trains, to diesel trains and electric trains. However, in the early to middle 20th Century, passengers began to choose automobiles and air transport instead of railways in North America and Europe (Shaw and Docherty 2009), therefore, the role of railways in passenger travel declined and railways were out of the academic spotlight in comparison to other transport modes (Loo 2014). Moreover, despite the dependence of passenger travel on railways in some developing countries, such as China and India, the shift from railways to other transport modes also occurred in these countries. According to Owen (1987), in the early stage of mobility, when activities are confined in the regional dimension, railways carry most traffic volume; when the activity scope of people is widened to the national and international dimensions, alternative modes like airplanes become more important than railways; motor car also becomes an important mode at sub-regional level because of its door-to-door service. It implies that the increase of mobility demand following economic development requires faster and more convenient transport modes, thereby promoting the shift from railways to road and air transport (Ausubel et al. 1998; Schafer et al. 2009). However, the modal shift from railways to road and air transport is like to be a doubleedged sword. On one hand, it may increase the mobility level of human beings; on the other hand, it may put more pressure on fossil fuels and climate change, due to the higher emission intensities of automobiles and airplanes than that of trains (Givoni et al. 2009). Moreover, according to the IEA (2010), the transport system accounted for 22% of global emission in 2008 and is one of the few sectors with emission still growing. Therefore, the evolution of the transport system is in a dilemma of high mobility and low carbon emissions.

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 L. Li, Railways and Sustainable Low-Carbon Mobility in China, https://doi.org/10.1007/978-981-15-9081-8_1

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Recently, the renaissance of railways for passenger travel in the form of HSR brings opportunities for reversing the modal shift trend from railways to road and air transport, which could possibly strike a balance between increasing mobility needs and reducing environmental impacts. HSR is defined as “new lines designed for speeds above 250 km/h and upgraded lines for speeds up to 200 or even 220 km/h” by UIC (2010). It not only has the environmental advantages of conventional railways, but also has the ability to increase capacity, reduce travel time and provide high level of service (Givoni 2006). Many experiences show that HSR can attract passenger travel from both air and road transport. For instance, in European routes Paris-Lyon and Madrid-Seville, the operation of HSR has attracted air passengers and increased the railway market share (European Commission and Directorate General Transport 1998). Meanwhile, HSR can promote the operation and integration between railways and other transport modes, especially air transport. In some European airports, such as Frankfurt airport, Paris Charles de Gaulle airport and London Heathrow airport, HSRs are connected to these airports and air-rail integrated service is provided to passengers (Steer Davies Gleave 2006). Currently, HSR has been developed in many developed countries, such as Japan, France, Germany, Spain, UK and Italy (Eastham 1998). It is expected to change the modal shift back from road and air transport to railways through promoting cooperation and integration between different transport modes. Many developing countries, with rapid growth rate of passenger travel demand, are also developing HSRs, especially China. Although China is a newcomer in developing HSRs with its first HSR opened in 2008, currently China has built the largest HSR network in the world (UIC 2013). Therefore, it is interesting to explore the new role of railways in the passenger transport system as HSR develops, and also their contribution to sustainable transport, especially in the developing countries. Sustainable transport is a concept derived from sustainable development, the definition of which is mostly quoted as “development that meets the needs of the present without compromising the ability of future generations to meet their own needs” (United Nations 1987). Accordingly, sustainable transport can be generally defined as transport system that meets the needs of the present without compromising the ability of future generations to meet their own needs (Richardson 2005). This broad definition concerns not only socioeconomic but also environmental impacts of transport. On one hand, transport is closely related to socioeconomic development by changing the mobility of people and freight (Rodrigue et al. 2013); on the other hand, transport may cause diverse and widespread environmental impacts, including air and water pollution, solid waste generation, noise pollution and habitat disturbance (Greene 1997). It is asserted that only a transport system that can generate more or the same socioeconomic benefits but a lower environmental cost can be called sustainable (Givoni and Banister 2010). Therefore, sustainable transport is not a single goal but numerous competing goals and it has to balance various tradeoffs among different desirable goals, such as increasing the mobility and protecting the environment (Loo 2008). To achieve this balance, several strategies including reducing the need to travel by substitution of Information and Communications Technology (ICT), encouraging modal shifts by transport policy, reducing travel lengths by land-use policy, and

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improving efficiency by technological innovation have been recommended (Banister 2008). However, the possibility of alternative solutions for sustainable transport (such as the substitution of transport by ICT and the wide use of alternative fuel vehicles) may not work in the near future. On one hand, people are not making fewer trips over time despite the substitution of transport by ICT, and the trips tend to be longer distances and duration (Banister 1999); on the other hand, the widespread application of alternative fuel vehicles is hindered by various technical, economic, and policy factors (Li and Loo 2014a), and the fossil fuels will remain the dominant sources of energy for transport at least up to 2030 (Shafiee and Topal 2009). Thus, railway development has been seen as a promising strategy for achieving sustainable transport in different societies (Loo 2014). However, it is still under debate since the railway projects are financially risky in light of the high capital and operating costs (Loo and Cheng 2010), and may only be appropriate for regions with high passenger demand or undeveloped areas. Nevertheless, the role of railways in sustainable transport should be studied in a comprehensive transport system, because without considering the interaction between different transport modes, the research problem could not be well understood. Much research has been done about the sustainability of railways and the intermodal relationship between railways and other transport modes. However, there are still some gaps. Firstly, it is necessary to analyze the role of railways in sustainable transport from analyzing the relationship between different modes, because in the future, not only competition, but also cooperation and integration will determine the modal share of railways and the overall sustainability level of the transport system. Secondly, most studies about intermodal relationship between railways and other transport modes focus on HSR, while conventional railways are neglected. Thus, to review the role of conventional railways from a historical perspective is necessary because it may throw light on the impacts of newly developed HSR in developing countries. Thirdly, when studying the competition between railways and other transport modes, most are limited to disaggregate model for a single city pair, therefore an analysis based on multiple city pairs will be needed for understanding this issue more comprehensively. Fourthly, most studies about integration are about the planning aspect and few are related to institutional aspect, and there is still a need to study the institution variable of integrating railways and other transport modes. Moreover, most research is about developed countries, which have different socioeconomic background from developing countries, especially China. In China, passenger travel demand grows rapidly due to economic development, and the infrastructure of both railways and other transport modes experiences rapid development; therefore, the role of railways in sustainable transport may not be the same as the developed countries. This book tries to analyze the evolutionary role of railways in sustainable lowcarbon mobility by analyzing the intermodal relationship between railways and other transport modes, the idea of which is applicable for other developing countries. By studying the case of China, it can elaborate the theme more comprehensively. From geographical and governance perspectives, this book will explore the variables in competition, cooperation and integration of railways and other transport modes, and then guides the sustainable transport development in the future. The analysis is based

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on the assumption that the overall mobility, including passengers travel frequency and travel distance, is relatively stable, and the main focus is therefore put on the modal split of different transport modes. It firstly examines the contribution of railways to environmental sustainability, especially low carbon emissions in China in the past few decades. Then, the current competition, cooperation, and integration between railways and other transport modes as well as the impact of their relationship to climate change are studied. In particular, the competition between railways and other transport modes may change the patronage between city pairs and transport carbon emission; the cooperation and integration between railways and other transport modes may improve the travel experience of passengers and reduce carbon emission. Finally, some implications for future sustainable transport development are discussed. Based on the aims and objectives, three research hypotheses are proposed to test the proposition of this book, which is promoting sustainable transport by developing railways in China. Hypothesis 1: Modal shift has significant implications on total CO2 emissions from passenger transport in China from 1949 to 2009. It intends to estimate the historical evolution of CO2 emissions from four passenger transport modes at the national level in China since 1949. Accordingly, the main factors that influence transport CO2 emissions are examined, such as population, income, travel activity, modal shift, fuel mix, and emission factor. Although there are profound institutional changes in China from 1949 to 2009, institutional variable is not examined independently in this book. Their impacts, however, are pervasive and, hence, reflected in changes of other directly measurable variables, such as population, income and travel activity. By comparing the emissions from railways and other transport modes, the role of conventional railways in sustainable transport is revealed. Hypothesis 2: Levels of air patronage between city pairs in China is correlated with the changes in the railway sector. Air transport is chosen as the transport mode to study the competition with railways. Using a time series approach, it aims to find out the competition between railways, including both conventional railways and HSRs, and air passenger transport at the city level. It is hypothesized that railway improvement and air flight patronage change between city pairs are correlated. In particular, the correlation may differ by different kinds of railway improvement (extension and acceleration) and different hauls (short, medium, and long haul). This hypobook is based on the assumption that the competition between air transport and highway transport in China is limited due to their different travel distances in China (Zhang and Zhao 2012). Hypothesis 3: Progress towards better integration of railways with other transport modes has been happening in China. This hypothesis can be tested by analyzing the current situation of cooperation and integration between railways and other transport modes at the station level in China and exploring the institutional coordination mechanism behind the cooperation. The progress refers to the integration level between railways and other transport

1 Introduction

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modes in China. In general, an integration of infrastructure (i.e. co-location only) is considered as a low level of integration; an integration of multimodal information and services is considered as a medium level of integration; and the full integration of ticketing and management is considered as the highest level of integration. This book will try to explore whether higher level of integration between railways and other transport modes has been realized in practice or emphasized in policies. Meanwhile, the analysis does not neglect the integration barriers of infrastructure and institution, the separate institutional models of railways and other transport modes, and the strategies for further promoting their coordination. Three periods are covered in this book. In the past few decades, it is needed to understand better how much railways contribute to sustainable transport, in comparison with other transport modes, i.e. road, air, and water transport. Using carbon dioxide emissions (CO2 ) as an indicator, both the total emissions at the national level and provincial level and the historical emission intensities for each mode will be estimated in a comparative analysis. Meanwhile, the contribution of modal shift to CO2 emission change will be explored. Currently, the competition between railways and other transport modes needs to be modeled to find out whether the railway improvement, especially the HSR development is correlated with the patronage level of other transport modes and transport carbon emission. Meanwhile, the cooperation between railways and other transport modes, as a strategy to promote the whole transport system, will be investigated to reveal the recent progress of integration between railways and other transport modes. The cooperation issue is as important as competition, because it impacts both the competitiveness of railways as a public transport mode and transport carbon emission. In the future, the barriers of modal shift from other transport modes back to railways and further integration between railways and other transport modes need to be discussed from both infrastructural and institutional perspectives. Some implications for future development of railways in the transport system are discussed in order to maximize the potential benefits of railways in future sustainable transport development. The book is composed of eight chapters. The contents of each chapter are illustrated as follows. This chapter provides an introduction about the research problem. Three hypotheses are proposed to specify the research problem. Also, the research significance and organization of the book are presented. Chapter 2 outlines the conceptual framework and makes the literature review. The literature review is organized around the three themes: railway development and sustainable transport; intermodal relationship related to railways (competition, cooperation, and integration); intermodal relationship and sustainable transport. Based on the analysis, the research gaps are identified. Chapter 3 is the national-level analysis tracing the historical evolution and spatial disparity of CO2 emissions from passenger transport in China. The general trends of CO2 emissions from four passenger transport modes are estimated by both the distance-based and fuel-based methods. It tries to prove that emissions from rail

6

1 Introduction

transport have lower emission intensity than road and air transport. Moreover, the decomposition analysis is used to explore the factor of modal shifts influencing the growth of passenger transport CO2 emissions in China since 1949. Some implications to encourage modal shifts towards sustainable transport modes are also discussed. Chapter 4 is the regional-level analysis, which focuses on three metropolitan regions of China, including Jing-Jin-Ji (JJJ), Yangtze River Delta (YRD) and Pearl River Delta (PRD), are the most developed regions of China. It tries to estimate and analyze the structure and influencing factors of CO2 emissions from transport sector, including passenger and freight transport, simultaneously covering road, rail, and water transport. Chapters 5 and 6 are about the current situation. Chapter 5 is the city-level analysis focusing on the issue of competition between railways and other transport modes in China, in particular between railways and air transport. It firstly adopts a timeseries approach and analyzes the interrelationship between railway improvement and air flight patronage, and then analyzes the association between HSR development and air flight service change. It tested the hypobook that the level of air patronage between city pairs in China was correlated with changes in the railway sector, and the relationship differs by railway extension and railway acceleration, and also differs by different hauls. Chapter 6 is the station-level analysis examining the cooperation between railways and other transport modes (including urban transport, highway and air transport) at the railway station hub. First, the location of conventional railway stations, high speed railway stations, coach terminals and airports in China is analyzed to show the spatial differentiation of different transport mode terminals. Then, Shanghai Hongqiao Transport Hub is chosen as a study case. By observational survey and questionnaires with passengers, the performance of intermodal interchange between railways and other transport modes are evaluated by a set of indicators. Chapter 7 further analyzes how to promote sustainable transport by railway development and an integrated transport system in the future. It identifies the barriers of seamless integration from the perspective of planning. Furthermore, the institutional framework of different transport modes in China is analyzed to emphasize the significance of institutional cooperation for future integration between railways and other transport modes. Finally, some implications for promoting seamless transport integration are made. Chapter 8 is the conclusion and discussion. The major conclusions are presented in the chapter. The wider implications of this research for both theory and practice are discussed. Lastly, the future research directions are figured out based on this book.

Chapter 2

Railways and Low-Carbon Mobility

Railways are transport means of conveying freight and passenger by moving wheeled vehicles on rail tracks (Loo and Li 2014). There are different forms of railways for passenger travel: conventional intercity railway and high-speed railway (HSR) that connect major urban areas; commuter rail, light rail, metros, and trams that provide services within and near urban areas. Although railway development has been seen as a promising strategy for achieving sustainable transport, especially low carbon emissions (Åkerman and Höjer 2006; Han and Hayashi 2008; Schiess 2006; Skinner et al. 2010), the role of railways in sustainable transport has not been systematically investigated with consideration of intermodal relationship. This book aims to analyze the relationship between railway development and sustainable transport by discussing the intermodal relationship related to railways. As shown in Fig. 2.1, assuming that the overall transport mobility is stable, as both conventional railway and HSR develop, their competition, cooperation, and integration with air and road transport may change the role of railways in the transport system, and further promote environmentally sustainable transport because railways perform much better in carbon emission than air and road transport. In other words, the carbon emission advantage of railways compared with road and air transport may make railways important in sustainable transport; however, the contribution of railways to sustainable transport depends on the modal share of each transport mode, which is determined by the interrelationship between railways and other transport modes, including competition, cooperation, and integration. Thus, analyzing different kinds of intermodal relationship is helpful for understanding the railways’ impacts on sustainable transport. Carbon emission is chosen as the single most representative indicator to evaluate the sustainability of different transport modes, because of its great importance. These three concepts are analyzed in the following parts with an extensive review of literature.

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 L. Li, Railways and Sustainable Low-Carbon Mobility in China, https://doi.org/10.1007/978-981-15-9081-8_2

7

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2 Railways and Low-Carbon Mobility

Railway Development  Conventional railway improvement  High-speed rail development

Carbon emission  Rail  Road  Water  Air

Competition  Level of patronage  Service supply

Cooperation  Infrastructure  Service

Intermodal Relationship/ Modal Shift

Integration  Institution  Political implication

Sustainable Transport  Low carbon emissions

Legend

Impact

Progression

Fig. 2.1 The conceptual framework

2.1 Railway Development: Make a Comeback? As early as the early 19th century, steam trains had been successfully developed in England. Although electric trains, trams and rapid transit systems had been introduced in the 1880s, it was not until the Second World War that the motive power of rail transport largely evolved from steam to diesel to electricity. However, the role of rail in passenger transport was still largely replaced by automobiles and aviation after the Second World War, especially in North America. The reason lies in that for short distances, automobiles are faster and more convenient than rail transport; for long distance, air transport is much faster than most rail transport. So, in areas where automobiles and air transport are available and well-developed, such as the United State of America (USA), passengers tend to choose these other transport modes instead of railways. In much of the developing world, such as China and India, railways are still dominant for passenger travel. However, although different kinds of strategies were adopted, such as increasing travel speed, providing air conditioning, night berths and restaurant services (O’dell and Richards 1971), the decline trend of railways in many countries was not reversed (Loo and Li 2014).

2.1 Railway Development: Make a Comeback?

9

Since the HSR technology was introduced to passenger travel in Japan in the 1960s, many other countries have developed HSR systems, such as France, Germany, Spain, UK, Italy, and China (Eastham 1998). Hall and Banister (1994) asserted that HSR is the reason for railways to make a comeback: “Transport technologies seldom make a comeback, save in nostalgia trips for well-heeled tourists…. But there is a spectacular exception: railways, written off thirty years ago as a Victorian anachronism destined to atrophy before the steady growth of motorway traffic, have suddenly become one of the basic technologies of the twenty-first century.” The comeback of railways may represent in the HSR network expansion around the world. Based on the statistics of UIC (2013), there is currently a total of 21,472 km HSR in operation, 13,964 km under construction, and 16,347 km planned in the world (Table 2.1). The length of HSR under construction and planned is more than that of the current HSR in operation, which implies the future rapid development of HSR. However, most of the HSRs are in Asia and Europe, while America only accounts for 1.7% of the total HSR in operation and 7.9% of the total HSR planned. Compared with the dense HSR network in the Europe, Japan and China, there is only one HSR line in the USA, between Boston and Washington, D.C. It means that the comeback of railways has its special geographical distribution, mainly in the Europe and Asia instead of the North America. Most of these countries are developed countries, while China as a developing country has the longest HSR in the world, with 9,867 km in operation. There are many reasons for the comeback of railways in these regions. First, the negative environmental, economic, and social impacts of automobiles and air transport, such as local air pollution and traffic congestion as well as global warming, gradually manifest themselves and received worldwide concerns as the passenger traffic volume increases (Shaw and Docherty 2009). Second, the technological improvement of rail transport makes it not only environmentally sustainable but also more competitive to automobiles and air transport because of the higher speed and greater comfort. Third, since the possibility of alternative solutions for sustainable transport will not work in the near future, such as transport by telecommunication and alternative fuel vehicles, railways that are more mature technologically, received wide interest and widely used in different societies (Loo and Li 2014). Last but not least, railways are appropriate for transporting large volumes of passengers over travel Table 2.1 Length of HSR in different regions by November 2013 Unit: km

Region

In operation

Under construction

Planned

Europe

7,378

2,565

8,321

Asia

13,732

11,199

6,258

America

362

–

1,288

Africa

–

200

480

Total

21,472

13,964

16,347

Data source UIC (2013)

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distances from 100 to 1200 km (European Commission and Directorate General Transport 1998), with characteristics of both safety and efficiency (Asian Development Bank 2008); while higher passenger volume is usually supported in areas with higher density of population and development because these are favorable factors for railway operation.

2.2 Railways and Sustainable Transport “Rail is a low carbon transport mode, and railway operators are working hard to continually improve their environmental performance. Rail plays a positive role in society, by providing millions of green jobs worldwide and offering access to employment and leisure opportunities. Rail also benefits the global economy by enabling congestion-free access to employment and facilitating freight deliveries.” (UIC 2011) Sustainable transport is a comprehensive concept, which concerns the economic, social and environmental impacts of transport (Givoni and Banister 2010; Loo 2008). Each dimension of sustainability has its own specific meaning: economic sustainability relates to the affordability, equity and efficiency of transport as well as the growth of regional economy and creation of employment; social sustainability emphasizes the basic accessibility of individuals, the safety requirement, and poverty reduction, as well as equity within and between generations; environmental sustainability emphasizes the protection of the global climate, ecosystems, natural resources, and human health (Dalkmann and Sakamoto 2011). It is asserted that railways have the potential to promote sustainable development, social equity, and community livability (Loo 2014), since they can support economic development (Banister and Berechman 1999), vitalize local communities (Cervero 1995), equalize individual mobility (Litman 2012), and minimize environmental impacts (Givoni et al. 2009).

2.2.1 Railways and Economically Sustainable Transport According to the definition of sustainable transport by the European Union (2001), the economically sustainable transport system “is affordable, operates fairly and efficiently, offers choice of transport mode, and supports a competitive economy, as well as balanced regional development”, with social and environmental sustainability realized at the same time. In a narrow sense, the economically sustainable transport emphasizes the financial viability and efficiency, which requires the transport system to be efficient in both investment and operation (Gwilliam 1997). For railways, there are still some debates about its economic sustainability, especially for the financial efficiency. However, a wider discussion is on their indirect impacts on the regional employment, industrial location and station-area development.

2.2 Railways and Sustainable Transport

11

a. Realizing financial efficiency As an infrastructure providing public service, railway projects usually need large investment for construction and operation, and tend to be unprofitable. However, there are some ways to improve the financial efficiency. For instance, despite the higher construction cost due of tunnels and bridges, some parts of Shinkansen in Japan still obtained high revenue because of higher population density, closer stops, and higher frequency, as well as lower price compared with airfares (Lee 2007). In 2004, the daily passenger volume per km is 319 in Japan, much higher than that of France (38 passengers per km) and Germany (79 passengers per km). In France, some of the TGV system has much higher financial efficiency than other countries due to lower construction cost. Its lower construction costs are related to advanced technology, urban form and terrain of France, the eschewal of mixed use (affecting gradient and curve parameters), and the use of classic track to access the urban centers avoiding expensive urban construction. For every 100 million US dollars cost, the TGV can transport 6.7 passengers, higher than that of Shinkansen (5.5 passengers) (Lee 2007). These two cases have shown that both high traffic demand and low construction cost are important for the financial efficiency of railways. It is supported by the argument of Vickerman (1997) that a HSR between two city centers requires the passenger demand to reach between 12 and 15 million per year so that it can compensate the cost. The construction and development model of railways can also influence their financial efficiency. For instance, the MTR of Hong Kong is commercially operated, unlike many railway systems in the world, which are operated by the public sector. By “Rail and Property” development model, the MTR Corporation gets more than half of its income from property development and supports its rail transit operation (Cervero and Murakami 2009). Non-fare income from the property above the metro station keeps the metro system financially sustainable without government subsidy and provides the citizens with high quality services at affordable fares. Its experience may enlighten the operation models for other railway systems around the world, especially the railway development in cities with high density and rapid growth (Tang et al. 2004). Nevertheless, the direct economic revenue from railways is limited, more expectations of railways are on their indirect impacts on the economic development, such as job generation and urban revitalization (Vickerman 2009; Loukaitou-Sideris et al. 2013). b. Increasing regional employment There are several studies about railways and regional economic development, especially the employment growth. However, the evidences vary case by case, and there is no agreement until now (Givoni 2006). As Vickerman (2009) argues, the indirect and wider economic impacts of railways on regional economic development depend on various factors: the competition in the affected regions, the ability to gain benefits from the agglomeration effect, and the labor market. In general, the introduction of railways, even HSR, may not guarantee the employment and economic

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growth in the region, due to the complicated mechanism of economic development. Nevertheless, empirical case studies still help to understand the issue. Shinkansen in Japan could be the most frequently studied case. A study found that Shinkansen had on average increased the employment in the regions with direct railway services compared to those regions without, especially in the economic sectors of retail, industrial, construction, and wholesale (Haynes 1997). Meanwhile, the employment growth rate in the regions along the Shinkansen is positively associated with the incidence of industries such as business services, banking services, real estate, opportunities for higher education, and good accessibility of the stations, whereas negatively associated with the share of manufacturing and the share of the elderly population (Sands 1993; Givoni 2006). Similarly, although many intermediate areas were bypassed (TGV as more of an airplane than a train), some other regions along TGV in France, such as Le Mans, Nantes and Vendom, have experienced high growth in employment; however, it is argued that these regions were cities that had already got good local economic conditions before the HSR opened (Banister and Berechman 1999). It seems that the benefits from railways tend to be attained by the regions with economic advantages, especially the central cities, while the smaller and intermediate cities are less likely to get the opportunities introduced by railways, which depends on three criteria including the city size, location in the railway network and distance from central cities (Yin et al. 2014). For instance, Cervero and Bernick (1996) showed that the Shinkansen had decreased the economic opportunities of intermediate cities along the Tokyo-Osaka line. While, Chen and Hall (2011) found that cities within 1 h HSR distance from London had got some spill-over effects from London to increase their employment in value-added activities; for the cities with 1–2 h travel distance from London, they also gained some economic advantages compared to the cities without HSR; however, the positive effects diminished for the cities with more than 2 h travel distance from London. c. Influencing industrial location By changing the accessibility of different cities and promoting the interaction of passengers, freight, and service between cities, the railways can influence industrial location and change the regional spatial structure (Fröidh 2005). There are various factors influencing the impacts of railways on regional industrial distribution, i.e. the competition between the affected regions, the ability to gain benefits from the agglomeration effect, and the labor market (Vickerman 2009). Therefore, the impacts of railways on specific industrial location also vary case by case. In general, the economic sectors that need face-to-face interaction are more easily influenced by the railways, especially HSRs, which include business services, R&D, tourism, education, congress, and convention activities (Yin et al. 2014). For instance, a study about the HSR between Perpignan in France and Barcelona in Spain found that HSR may reinforce the tourism agglomeration around Barcelona but weaken the tourism in Perpignan, because of reduced transport costs and increased competition of the tourism destinations between Barcelona and Perpignan (Masson and Petiot 2009). Similarly, the industries that are easily influenced by the railways tend to concentrate

2.2 Railways and Sustainable Transport

13

in some central cities, based on the agglomeration effects, especially for the financial service sectors (Graham 2007). However, for the economic sectors such as manufacturing, retail, and commercial activities, the impact of railways, especially HSR, on their location change is not so significant (Yin et al. 2014). For one thing, people mostly travel for business and leisure purposes, instead for retail and wholesale; for another, the HSR does not transport freight and may not influence manufacturing to a large extent (Puga 2002). Moreover, by influencing the industrial location, the railways may also lead to convergence or divergence in economic performance. A case study in Europe shows that there is a general trend of convergence following the introduction of HSR, which means the growing similarity in the economic structure among different cities, while the case study of Pearl River Delta in China shows that there is more of a divergence of economic structure in the region under the influence of HSR (Cheng et al. 2013). d. Promoting station-area development The primary development area of railway station usually refers to the area within 5 to 10 min accessibility of the station, where the development effects of railway are most likely to occur (Yin et al. 2014). There are evidences about the impact of railways on the station-area development. In Japan, a case study about Tokyo shows that railway service may bring more economic activities, such as real estate, retail and commerce around the stations (Calimente 2012). However, it is argued that the development around the stations, especially the newly built ones, is closely related to their accessibilities to the city center (Sands 1993). In Germany, the railway station Kassel-Wilhelmshohe has got much more demand for hotel, office and retail around it from the railway (Sands 1993). By adopting the strategy of transit-oriented development (TOD), the land use structure change around the railway stations is usually accelerated (Cervero and Landis 1997), and the land and property value increases (McDonald and Osuji 1995). However, studies show that not every catchment area of railway station gets more development. For instance, the Le Creusot and HautePicardie stations along the TGV in France did not attract more business activities, mainly due to their low passenger demand and service frequency (Yin et al. 2014). Therefore, the passenger demand and accessibility of the station is important for its catchment development. Among the different kinds of economic activities, the location and prices for office around the station area is most widely discussed. One study about the Netherlands concluded that for different types of railway stations, their attractions for office location are different. The stations with international HSR services can attract international offices around the stations; however, the domestic railway stations are less likely to attract offices, because of the short domestic distances in Netherlands (Willigers and Van Wee 2011). Another study identifies several factors influencing the office prices in the area around railway stations, which include regional economy, image, regional rail accessibility and urban car accessibility, land development structure, public support, national accessibility and “clustering” effect (de Jong 2007). Actually, the property price change around the railway station could be complicated

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and hard to predict, because it fluctuates by multiple factors. Some studies showed that the effect of railway stations can make the commercial property prices increase within a short range, and make the residential property prices increase within a long distance, especially for the commuter railway stations (Debrezion et al. 2007). However, some other studies did not find any significant effect about the impact of railway on the house price, such as in Tainan of Taiwan (Andersson et al. 2010).

2.2.2 Railways and Socially Sustainable Transport According to the Center of Sustainable Transportation in Toronto, the socially sustainable transportation refers to a transport system that: “(a) allows the basic access and development needs of individuals, companies, and societies to be met safely and in a manner consistent with human and ecosystem health, and promotes equity within and between successive generations; (b) is affordable, operates fairly and efficiently, offers choice of transport mode, and supports a competitive economy, as well as balanced regional development.” (Goldman and Gorham 2006) Based on this definition, the contribution of railways to social sustainability mainly depends on its ability to promote social equity, create employment options, integrate with peripheral regions, and provide safe travel services (Li and Loo 2014b). e. Promoting social equity Social equity refers to the equitable distribution of benefits and burdens in society, and it is related to transport (Beyazit 2010; Litman and Brenman 2012). Usually, the transport disadvantaged people include zero-car households, low-income people who may own a car, children, youngsters, women, the elderly, the disabled, and ethnic minorities (Litman 2003). Because of transport disadvantages, it is more difficult for them to access either public or private sector facilities, including education, employment, key services and affordable goods; thus a combination of social exclusion problems happens to them. Although the role of public transport in reducing social exclusion has been widely recognized by policy makers around the world (Lethbridge 2008), there is limited recognition about the impact of railway development on social equity. Will railways aggravate or alleviate the inequity? It is still a question of great debate. Compared with private vehicles, railways have the characteristics of public transport, which are more affordable and available for socially disadvantaged groups. However, some argue that railways are less socially sustainable than public buses. On one hand, railways tend to serve a higher-income population than bus services. In the USA, rail transit only contributes about a tenth of the trips of the poor, and the majority of transit riders are not poor (Pucher 1981). On the other hand, the construction or extension of a railway line may increase the property value and make it difficult for the low income or minorities to afford living in convenient locations (Sanchez

2.2 Railways and Sustainable Transport

15

et al. 2003). Indeed, railways and buses each have different advantages and shortages, and they should be applied in different situations and coordinated to promote social equity. Railways can provide faster, longer and more comfortable services than buses. They are usually appropriate for high density transport corridors, particularly useful for commuters (Light Rail Now 2000). Also, they can promote multi-modal transport and transit-oriented development around stations (Litman 2012). Without railway services, the transport disadvantaged may get less transport options and be restricted in their mobility (Shaw et al. 2003). There are several case studies about the role of railways in promoting social equity, which have some differences in developing and developed countries. In developing countries, railways are usually viewed as a measure to reduce poverty. A case in Namibia shows that railways allowed poor people to get access to goods and services that were affordable for them, and the development around stations promoted employment and increases incomes for the poor communities nearby, especially in the rural area (Nyambe et al. 2009). In developed countries, TOD has become a strategy for improving the livability of different communities. For instance, the Twin Cities region of Minnesota and Saint Paul in the USA is constructing a Central Corridor Light Rail Line (CCLRT). It is an 11-mile transit corridor with diverse racial communities. Along this corridor, 28% of the population were black or African Americans, 16% were Asians or Pacific Islanders, 7% Latinos, 4% two or more races, and 1% American Indians. The poverty rate was 27% in 2009, much higher than in the city and more than a quarter of the population were foreign-born from Southeast Asia, Eastern Africa and Latin America. By health impact assessment (HIA), it is predicted that this new railway line will create job opportunities for residents and promote local business development. For the low income and disabled, access to social opportunities will be increased and coordination of railway with bus, bicycle and pedestrian infrastructure will be promoted (Malekafzali and Bergstrom 2011). Another example is the San Diego Trolley’s East Line, which was built in the late 1980s. It mainly serves the city’s low income district in the east of the city, attracting many more riders than bus services. In the 1990s, this line extended to the suburbs and attracts people from higher income class, but the low income ridership still gets increased and the poor population can get access to employment in the suburb areas (Light Rail Now 2000). Therefore, railways can be a good measure to promote the livability of poor communities, which are easily social excluded. However, more strategies should be adopted to keep the poor from displacement of higher income population in the communities around the railway stations. f. Integrating peripheral regions In regional theories, it is still a debate whether transport improvement would strengthen the core-periphery system of a region or promote regional integration. The outcome of a transport system connecting the peripheral regions with the core would either improve the accessibility of peripheral regions and accelerate their growth rate, or otherwise increase the agglomerative advantage of the core and attract the labor and capital away from peripheral regions (Gauthier 1970). The interrelationship

16

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between transport development and regional development could be very complicated; however, railways are often built in the hope of improving accessibility of peripheral regions and providing more opportunities for them. Among different kinds of transport infrastructure, railways have long been constructed to promote regional integration. In Great Britain, since the 1850s, railways have been constructed to connect England with peripheral regions (Hechter 1971). Modern railways connecting with peripheral regions can be better planned from the perspective of spatial equity. For instance, the European high speed railway (HSR) was expected to link peripheral regions to its geographic, political and economic center. Some scholars argue that this system is increasing concentration into the main metropolitan centers and caused inequality to some small towns and rural areas, which could become mere transit areas (Vickerman 1997; Ross 1994). Despite the polarization trend, some small and intermediate cities still benefited from the HSR by overcoming their isolation location because of the HSR connectivity. For example, Lyon and Lille in France had experienced great economic growth induced by the TGV (Hall 2009); small and intermediate cities with HSR stations in Spain had much higher growth rate and housing investment than other local cities (Garmendia, Ribalaygua et al. 2012; Garmendia, Romero et al. 2012); the cities within a 1 h HSR travel distance from London in the UK were also found to attract some economic activities from London (Chen and Hall 2011). At the city level, rapid rail development seems a good measure to integrate the peripheral area with the city center. In Sweden, the Svealand line was opened in 1997 between Eskilstuna and Stockholm. It is found that it has significantly increased the accessibility of the peripheral area of Stockholm and more workplaces in the center can be reached by the labor in peripheral regions through the Svealand line (Fröidh 2005). g. Creating employment options As a transport project, the construction of railway itself can generate some job opportunities for the local people in the short term. In the long term, employment can be created by the regional development promoted by railways. Some studies have found that commuter transport is closely related to employment. For the employees, difficulties in travel and high travel cost may limit their job options, especially for the poor; for the employers, they may feel that people who have to travel long distances or depend on public transport are not so reliable (Wilson 2003). However, with urban sprawl, spatial mismatch between jobs and housing has become pervasive. Many job opportunities are increasingly located away from low-wage workers, so transport becomes extremely important. What workers need is a relatively fast and affordable transport mode. Light rail systems are generally adopted in cities as a measure to connect the housing and the workplaces (Lau 2011). For instance, the Hiawatha light rail line between downtown Minneapolis and its southern suburb in the USA, which opened in 2004, has some impacts on job accessibility. The newly constructed light rail has greatly increased the number of jobs for low income workers because of improved accessibility. Improved accessibility mainly comes from the fast speed of light rail compared to traditional bus services and better integration of rail systems

2.2 Railways and Sustainable Transport

17

with its bus connections. Statistics of districts along the Hiawatha light rail line showed that, the number of low-wage jobs accessible within 30 min of transit travel increased by 14,000 jobs in rail station areas and an additional 400 jobs by bus connections. After the opening of this light rail, many low-wage workers and lowwage jobs also relocated near the station areas (Center for Transportation Studies 2010). So, the Hiawatha light rail line has improved transport equity in Minneapolis greatly. However, apart from the accessibility improvement brought by the construction of the rail system, many other strategies are also important for the success of the railway system in creating employment. They include subsidies towards the socially targeted populations, integrating the rail system with other transit modes and related land use policies. h. Providing safe travel services The issue of safety mainly causes injury and property destruction, which is closely related to social sustainability. Rail transport is generally safer than other land transport modes because trains operate on exclusive tracks. In Europe, the risk of fatality per billion passenger-km for rail transport from 2004 to 2011 was only 0.15, much lower than that of cars (3.8 fatalities per billion passenger-km) (Allianz pro Schiene 2013). The safety level of rail transport is almost closed to that of air transport: the comparative ratio of accident rate between the railway and air transport was nearly 1:0.8 (Ellwanger 1990). When contrasting safety of different transport modes by the indicator of fatalities per billion passenger travel hours, rail transport is the safest, which was about 0.2 in Europe in 2001, much lower than that of air transport 16 and road transport 18 (European Transport Safety Council 2003). Considering that currently the total cost of road crashes is even higher than congestion and population, at least in Europe, and road transport is pervasive around the world, railways may contribute more to safe individual travel as alternatives to cars. Meanwhile, since the risks to air passengers are mainly in the take-off and landing phases, the shorter flights have higher accident risks than long flights and rail travel (European Transport Safety Council 2003). It is also recommended to replace these short flights by railways to keep passenger travel safe.

2.2.3 Railways and Environmentally Sustainable Transport According to OECD (2002a), environmentally sustainable transport is defined as: “Transport does not endanger public health or ecosystems and meets needs for access consistent with a) use of renewable resources below their rates of regeneration, and b) use of non-renewable resources below the rates of development of renewable substitutes.” Based on the definition, six criteria are proposed, including noise, air quality, acidification and eutrophication, ground-level ozone, climate change, and land use. The environmentally sustainable transport concerns the total environmental impact

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of the whole transport system; therefore, restraining the environmentally damaging transport modes and promoting the environmentally friendly transport modes could be one pathway to environmentally sustainable transport (OECD 2002a). In general, railways have relatively low impacts on environment because of low energy consumption, low air pollution, less noise pollution, less landscape impact, and low greenhouse gas emissions, in comparison with other transpiration modes such as highway and aviation. In the following part, the performance of railways is compared with other transport modes in several aspects of the environment. i. Energy consumption There are several studies showing that railways are more sustainable on energy consumption comparing with other transport modes. For conventional railways, the energy consumption of railways in Germany was only 0.6 PJ/billion pkm, while the energy consumption of road transport was 2.1 PJ/billion pkm (Ellwanger 1990). Meanwhile, the energy consumption of conventional railways keeps decreasing. In the UK, the energy consumption for railways decreased by about 25% from 1995 to 2005 for both diesel and electric trains (UIC 2008). For HSRs, the energy consumption is even lower than conventional railways, although they have much higher speed. The HSR in Germany reached only 0.3 PJ/billion pkm at a speed of 250 km per hour (Ellwanger 1990). According to van Essen et al. (2003), except for coach, railways are better than any other transport mode in energy consumption for long distance (more than 250 km) travel in Europe. However, there is still argument that the energy consumption of HSR was worse than conventional rail, and the advantage of HSR over aircraft depends on the load factor (de Rus and Nash 2007). Nevertheless, the HSRs have great potential to contribute to energy conservation in the future. On one hand, the traction energy of HSR is electricity, which could use renewable resources such as nuclear energy, solar energy and hydroelectric power while highway and aviation mainly depend on petroleum that is non-renewable. On the other hand, the diverted passengers from highway and aviation to HSR would make the energy consumption of the road and air transport decrease. For instance, both the routes of Paris-Lyon and Madrid-Seville had a much lower modal share of road and air transport after the HSR opened (de Rus and Nash 2007). j. Air pollution The air pollution of transport is closely related to energy consumption. For conventional railways, most types of emissions are significantly lower than road and air transport. For instance, in Germany, except for soot, all other types of emissions of railways were lower than road transport: Carbon Monoxide (CO) of railway was 0.06 g/pkm, while that of road transport is 9.3 g/pkm; Nitrogen Oxide (NOx ) of railway was 0.43 g/pkm, much lower than that of road transport (1.70 g/pkm); Hydrocarbons (CH) of railway was 0.03 g/pkm, also lower than that of road transport (1.10 g/pkm) (Ellwanger 1990). In Japan, the railways also have much lower pollutant emissions per pkm (Ishida and Iwakura 1998). In addition, as the technology improves, the emissions per passenger-km of railways decrease over time. For HSR,

2.2 Railways and Sustainable Transport

19

it has a potential to further decrease the emissions by changing the source of electricity to renewable energy sources. For instance, traveling from Berlin to Frankfurt by railways may emit about 26 kg CO2 and 25 g NOx , while by car it emits about 98 kg CO2 , 350 g NOx and 20 g PM10 , by air it emits about 85 kg CO2 , 280 g NOx and 5 g PM10 (UIC 2008). However, HSR has higher emissions of SO2 per pkm than domestic air transport (Watkiss et al. 2001). In conclusion, the railways have significant advantages in air pollution. k. Noise pollution Railways have some noise pollution around terminals and along railway lines. The main source of noise is the air resistance and the contact between wheel and rail. Compared with other transport modes, railways perform relatively well, because the annoyance levels of railways on the same day-night average sound level (Ldn) is much lower than that of air and road transport (Miedema and Vos 1998). It means that the subjective annoyance effects of noise from railways are much lower than that of air and road transport. l. Landscape impact The construction, operation, and maintenance of railways needs land use, mainly for rights of way and terminals, which can have impacts on the local landscape. Compared with road transport, railways have an advantage of land use and make less landscape impact. Assuming carrying the same amount of passengers, the land taken by a double-track electric railway line is less than a half of land used by a fourlane highway (Ellwanger 1990). It is also found that the capacity of the railways per meter of railway width is 9000 persons per hour, much larger than that of that of cars (200 persons per hour) and buses (1500 persons per hour) (UIC 2008). However, it is argued that the lines of railways actually consume less land comparing with the terminals. Also, their construction and operation may contribute to barrier effects, which mean that they cut through natural resource areas and cause disturbance to animal migratory paths and ecosystems (van der Ree et al. 2007). Compared with air transport, railways usually require more land per passenger trip (Chester and Ryerson 2013; Rus 2011), which is one disadvantage of railways. However, there are already some strategies to mitigate the impacts, such as situating the railways on pillars. Nevertheless, the mass capacity of railways makes the railways more land use efficient and less landscape impact, at least compared with road transport. m. Carbon dioxide emissions Among the different kinds of environmental impacts of transport, climate change plays an important role and is a key issue of sustainable transport. In Europe, the cost of climate change accounts for 7–29% of total external cost of environmental impacts from passenger transport, just lower than accidents in the high scenario, and lower than accidents, air pollution and up- and downstream in the low scenario. Meanwhile, there are many discussions emphasizing the environmental advantages of railways by comparing the environmental performance of railways and other transport modes,

20

2 Railways and Low-Carbon Mobility

especially automobiles and airplanes (Cai et al. 2011; Schipper et al. 2011; Scholl et al. 1996; Uehara 2009). In these studies, carbon dioxide emission is probably the most frequently considered issue because carbon dioxide (CO2 ) contributes most among greenhouse gases (GHG) to climate change due to its large quantity and long residence time (Lashof and Ahuja 1990). Achieving the goal of low carbon emissions, not only means to reduce the global warming effect, but also means to conserve fossil fuel resources (Chapman 2007). It is worth noting that there are some other indicators of sustainable transport. For instance, current discussions on road emissions are focusing much more on particulates, which have implications for diesel versus electric traction, plus where and how the electricity is generated. However, for consistency of different transport modes and the emphasis on climate change, this book chooses CO2 emissions as a measurement of sustainable transport. Compared with other GHG pollutants, CO2 emissions are emitted in much larger quantities, and the data for CO2 emissions are usually available (Givoni et al. 2009). Also, CO2 emissions can reflect the change of GHG, which means that as CO2 emissions grow, the overall GHG emissions also tend to grow. Therefore, CO2 emissions are chosen as the single most representative indicator for analyzing the impact on climate change. Generally, CO2 intensity, measured in grams of CO2 released per passenger-km, are adopted as the comparison index. It depends on various factors, including the energy source, type of engines, distance and load factor, thus differs much between different countries and regions (Intergovernmental Panel on Climate Change 1999). The Intergovernmental Panel on Climate Change (1990) has summarized the CO2 intensity of different passenger transport modes based on several studies around the world (Centre for Energy Conservation and Environmental Technology 1997; Faiz et al. 1996; OECD 1997; TEST 1991; Whitelegg et al. 1993). It shows that the CO2 intensity of trains ranges from 0 to 50 g C per passenger-km (pkm), CO2 intensity for buses or trams are between 3 and 30 g/pkm, while CO2 intensity for cars and light trucks are between 20 and 100 g/pkm, CO2 intensity for air travel are between 30 and 100 g/pkm. If the electric trains are using coal source electricity, its CO2 emission may be higher than buses, two-occupant small automobiles, even long haul airplanes, which suggests the comparison should be analyzed according to specific regional background. Table 2.2 summarizes the results of CO2 intensities of transport modes from different studies about different countries and regions for comparison. In general, the average CO2 emission intensity of railways is usually lower than that of road and air transport in the same country. The only mode that has a comparable CO2 emission intensity with railways is bus; however, the emission intensity of buses in most countries gets increased by time, while the intensity of railways generally becomes lower. For instance, the reduction of CO2 intensity of railways may attribute to the locomotive improvement, because the CO2 intensity of electric train is 22% lower than diesel train in the UK (ATOC 2007; Defra 2009). When comparing the CO2 intensities of railways in different countries, the USA has a much higher CO2 intensity than EU countries and Japan. However, the studies about developing countries, especially China, are rare, which needs further research. Indeed, the limited existing research about developing countries has already showed much different situation from the developed countries. For instance, the CO2 intensity of electric train is higher than

2.2 Railways and Sustainable Transport

21

Table 2.2 CO2 intensities of different transport modes in different countries Unit: g/pkm Country/Region

Year

Air transport

Railways

Road transport

Reference

Germany

1987

180

79

180

Whitelegg et al. (1993)

USA

1973

330

50

210 (cars) 60 (buses)

Scholl et al. (1996)

1990

180

40

180 (cars) 60 (buses)

1973

240

50

170 (cars) 40 (buses)

1990

150

40

180 (cars) 50 (buses)

1973

320

40

110 (cars) 50 (buses)

1990

150

20

110 (cars) 60 (buses)

1973

300

80

150 (cars) 40 (buses)

1990

220

60

160 (cars) 50 (buses)

1973

440

50

100 (cars) 40 (buses)

1990

360

50

100 (cars) 40 (buses)

1973

310

130

140 (cars) 50 (buses)

1990

150

40

130 (cars) 80 (buses)

1973

–

20

110 (cars) 60 (buses)

1990

220

30

120 (cars) 110 (buses)

1973

280

30

140 (cars) 80 (buses)

1990

240

10

150 (cars) 70 (buses)

1973

260

100

110 (cars) 30 (buses)

1990

110

80

110 (cars) 40 (buses)

1994

218

135 (steam) 17 (diesel) 25 (electric)

21

Japan

France

Germany

Italy

UK

Norway

Sweden

Denmark

India

Ramanathan and Parikh (1999) (continued)

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2 Railways and Low-Carbon Mobility

Table 2.2 (continued) Country/Region

Year

Air transport

Railways

Road transport

Reference

EU

2000

140 ( 2000 km)

25

75 (cars) 18 (buses)

Gössling et al. (2005)

UK

1995

214

61

106

ATOC (2007)

2005

227

60

104

Germany

2006

99

22 (long distance) 18 (high speed)

48

China

2007

–

15 (long distance) 17 (high speed)

26 (buses)

Japan

1990

115

17

138

2007

109

19

159

Finland

2007

257 (domestic short haul) 177 (domestic long haul)

76 (diesel) 15 (intercity electric) 24 (fast electric)

105 (in 2009)

VTT Technical Research Centre of Finland (2009)

USA

1960

356

176

170 (cars) 42 (buses)

Schipper et al. (2011)

1973

368

130

214 (cars) 56 (buses)

1990

223

116

182 (cars) 62 (buses)

2008

154

94

160 (cars) 46 (buses)

IFEU (2008)

Uehara (2009)

diesel train and road transport in India, which is probably due to the high proportion of electricity generated from coal, and the high load factor and low level of service of road transport. Besides comparisons at the country or regional level, there are also some studies that concentrate on comparisons at the city pair level, especially for HSR. Some results are presented in Table 2.3. For a single route, railways generally perform better than air and road transport. However, the performance may be influenced by the HSR technical characteristics (speed, acceleration patterns, gradients and route’s tunnels characteristics) (Network Rail 2010), stops on the route and the distance Table 2.3 CO2 intensities of different transport modes in selected routes Unit: g/pkm City pair

Air transport

Railways

Road transport

Reference

Berlin-Frankfurt

156

48

180 (cars)

UIC (2008)

London-Manchester

210

26.6

160

Miyoshi and Givoni (2013)

2.2 Railways and Sustainable Transport

23

between them (Givoni et al. 2009), type of engine, electricity generation sources (Miyoshi and Givoni 2013), and load factor (Chester and Ryerson 2014), which will be discussed in the following part. Despite the debate about the low carbon emission characteristics of railways, the development of modern railways has posted some challenges to low carbon emissions. Because of the higher speeds, better acceleration, better accessibility, more safety provisions, more comfortable environment, and provision of air conditioning, there may be an increase of consumption per passenger for railways (Palacin and Kemp 2005). Moreover, the cost of constructing railways is huge; therefore, its potential to reduce greenhouse gas emissions is still under great debate, especially in the USA. Some argue that the HSR can remove only a little part of CO2 emissions, however with high costs (Cox and Vranich 2008). Some others believe that HSR has a great potential to reduce CO2 emissions because of its lower energy efficiencies compared with airplanes and automobiles as well as the predicted large modal shift from airplanes and automobiles to HSR (American Public Transportation Association 2012). In practice, railways may not always be sustainable everywhere under all circumstances, and there are still many challenges for railways to perform sustainably (Loo 2014). The first challenge comes from the large embedded CO2 emissions of railway construction and maintenance (de Rus and Nash 2007; Smith 2003). It is doubted whether the railway operation advantages over other transport modes could offset the embedded emissions (Westin and Kågeson 2012). Accordingly, recent studies tend to use lifecycle analysis to estimate the CO2 intensities of railways, instead of traditional end use analysis. The difference is that the end use analysis only takes the fuel combustion emissions during vehicle operation into consideration, while the lifecycle analysis also considers the emissions during fuel extraction, processing, distribution, and vehicle manufacturing (Scholl et al. 1996). It is found that the end use emission for cars accounts for 72% of its lifecycle emission, while another 17– 18% of emissions are fuel extraction, processing and distribution, and 10% from vehicle manufacturing (IEA 1993). Similarly, for railways, the HSR system ICE in Germany generates 64% of its CO2 emissions from traction of trains, which is the end use emission, 16% from passenger journey overhead (trips between the train stations and the origin or destination by car), 8% from infrastructure construction, 4% from board energy consumption for air conditioning, light, and train restaurant, 3% from train trips to and from maintenance and repair works, 3% from infrastructure use like rail tunnels, other 2.4% from material transports, construction site and soil excavation (von Rozycki et al. 2003). Another study suggests that the HSR along the London-Manchester route has 69% of its CO2 emissions produced by the railway operation (Miyoshi and Givoni 2013). Therefore, the embedded CO2 emissions of railways may account for more than 30% of the lifecycle emission of railways. For different technologies of railways, their lifecycle emissions are different (Table 2.4). When comparing the lifecycle emissions of railways with automobiles and airplanes, it is argued that only when the traffic volumes are large enough, which is at least 10 million annual one-way trips, the railways can balance the construction emissions and keep their advantages in CO2 emissions (Westin and Kågeson 2012). However,

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Table 2.4 Characteristics and emissions of different kinds of railways Train

Year

Service Speed (km/h)

Class 91 IC225

1989

200

536

Class 390 Pendolino

2003

200

439

High speed rail Class 373 Eurostar

1993

300

750

12

0.22

TGV Reseau

1992

300

377

15

0.23

TGV Duplex

1995

300

545

15

0.16

AVE S103 Velaro

2004

300

404

15

0.24

Shikansen 700 series

1998

270

1323

15

0.11

Conventional rail

Capacity

Typical lifetime train-km (million)

Emissions over lifetime (g CO2 eq/seat-km)

12

0.26 0.29

Source Network Rail (2010)

some scholars assert that although the construction emissions of HSR are higher than that of highway, the longer service life of HSR compared with highway could make its annual average CO2 emission much lower (Fu et al. 2013). In this book, due to data limitations about the construction emissions of HSR and conventional railways, as well as other vehicles in China, it will only take the operation emissions into account. Considering that most of the lifecycle emissions of transport vehicles come from operation (IEA 1993; von Rozycki et al. 2003), the exclusion of construction emission won’t influence much of the results. Second, the load factor matters for carbon emissions of railways. Railways may have high emission per passenger-km when operating with low load factor (Westin and Kågeson 2012). According to the study of California, USA by Chester and Horvath (2010), a HSR with 873 passengers is GHG emission-equivalent to a car with 3.2 passengers and heavy rail transit with 191 passengers, and aircraft with 120 passengers, which means when the HSR carries less than 873 passengers would produce more GHG emissions per passenger-km than the car carries more than 3.2 passengers and aircraft with more than 120 passengers. Therefore, railways may not always be more low carbon and climate friendly than automobiles and aircrafts, and improving the load factors of railways is important for keeping its low carbon emissions. Third, for railways depending on electricity, including HSRs and electric trains, the generation sources of electricity are important for reducing CO2 emissions. In developed countries, where the electricity production is cleaner, the railways may have lower carbon emissions; however, in developing countries, where the electricity

2.2 Railways and Sustainable Transport

25

production depends more on fossil fuels, the railways may be less ‘climate friendly’ than automobiles (FREng 2004). However, the developing countries may lower the carbon emissions of railways by increasing the load factors, and they have more potential to reduce the carbon emissions of railways by changing its energy structure in the future. The disparity of energy structure also leads to the differences of regional disparity of railway and sustainable development, which needs more attention. Fourth, if taking door-to-door effects into account, since HSR is spatially inflexible and passengers need to take other transport mode to get to the HSR station, it may increase the CO2 emissions of the total journey. In contrast, cars have the flexibility of achieving door-to-door travel. Therefore, HSR may not have as low carbon emissions as expected, especially for very short distance, when the access and egress journey to and from the HSR is relatively long (Givoni 2007). It is argued that even over the 400 km distances that HSR may have at best very small environmental benefits, if taking the door-to-door travel and the effects of the construction of railways (Button 2012). In addition, the debate relates to the concerns about the longer route of HSR over aircraft, the frequency of HSR and airline (Givoni 2007). It was also argued that the long lifecycle of railway, about 30 to 40 years, makes its technological development slow, while the cars are more easily to promote new technology of improving emission efficiency, therefore in the future, the gap of CO2 emission between railways and cars will be smaller (Smith 2003). Based on the analysis, we may conclude that except for technical and operational strategies such as the lower CO2 content of electricity supply, fewer stops and improved aerodynamic design, the most important strategy for railways to keep a low CO2 intensity is increasing its load factor. According to the four-stage model of transport, if the trip generation and trip distribution is unchanged, then modal split determines the travel demand of railways on a particular route (Dickey. 1983). Therefore, the modal split among railways and other transport modes is very important for railways in sustainable transport.

2.3 Intermodal Relationship Related to Railways The definition of “intermodal” in transport usually refers to a single journey with the movement of passengers or freight from one transport mode to another (Rodrigue et al. 2013), and higher level of intermodality means more cooperation and integration between different transport modes (European Commission 1997). In this book, intermodal relationship refers to the interaction between different transport modes, which is much broader than its traditional definition. Actually, because of the different characteristics of different transport modes, they may either be complementary or substitutes, from an economic perspective. For instance, different transport modes have different appropriate travel distance and also have some distance intervals overlapped; thereby they can substitute each other in the overlapped distance intervals and be complementary in other distance intervals (Fig. 2.2). “When the successive

26

2 Railways and Low-Carbon Mobility

Air

Car

Distance [km]

Train

200

400

600

800

1000

1200

1400

1600

1800

2000

Fig. 2.2 Distance intervals for the principal market of different modes of transport. Source European Commission and Directorate General Transport (1998)

utilization of two transport modes are necessary or preferred than the utilization of a single transport mode, these two modes are complementary” (European Commission and Directorate General Transport 1998); while, when only one of two transport modes can be used by the passengers for a journey, these two modes are substitutes (Givoni and Banister 2006). The complementarity or substitution between different modes further leads to the competition, cooperation and integration between different modes with different operators. According to Givoni and Banister (2006), when the substituted transport modes are operated by different operators, competition occurs; when complementary transport modes are operated by different operators, cooperation and integration may occur. Therefore, the intermodal relationship can be divided into three types: competition, cooperation and integration. Actually, all these three types of intermodal relationship are interlinked (Brons et al. 2009). For example, integration between railways and urban transport may help the railways to compete with air and road transport in the same route (Brons et al. 2009). From a traditional point of view, competition related to railways occurs between railways and road transport, water and air transport, and cooperation is between railways and water transport or road and air transport; nowadays, the competition, cooperation and even integration between railways and air transport become important (Stubbs and Jegede 1998). The competition, cooperation or integration may cause a modal shift from road and air transport to railways. Furthermore, it is predicted that when the annual mobility level reaches more than 10000 pkm per person, high speed transport, including air transport and HSR would be dominant in passengers’ travel (Schafer et al. 2009). Thus, HSR will play a more important role in the future by changing the modal share of transport system through competing, cooperating and integrating with air transport.

2.3.1 Competition Modal competition—“When one mode is directly competing with another or with different firms of the same mode, which is often a zero sum game. Competition can take place over cost, time, reliability and niche markets. Each corridor has a

2.3 Intermodal Relationship Related to Railways

27

passengers and freight balance reflecting their respective competitiveness level.” (Rodrigue et al. 2013) The competition related to railways may happen between conventional railways and road or air transport, or between HSR and road or air transport, even between conventional railways and HSR. First, for the competition between conventional railways and road or air transport, studies are limited, and the general trend is that conventional railways are replaced by road and air transport (Loo and Li 2014). Bel (1997) explored the changes in road travel on the demand for intercity railways, and found that journey time by road coach has a positive correlation relationship with the demand of railways, while the flight frequency of air transport has a negative correlation relationship with the demand of railways. A few other studies have discussed the effect of conventional railway improvement on the road or air transport, which is under great debate. For instance, some study shows that in China, its railway speed-up programs had little impact on the rapid passenger demand growth of air transport (Fu 2005). However, another study shows that the speed-up of railways in China between 1997 and 2009 had a negative impact on air patronage growth, especially for the air patronage at regional airports (Wang and Yip 2013). Second, for the competition between HSR and road or air transport, there are quite a few studies, especially about the competition between HSR and air transport. Case studies in European city pairs show that HSR often dominates the travel market within 350 km travel distance; from 350 km to 1000 km, HSR and air transport would compete with each other; when the distance is over 1000 km, air transport would dominate (European Commission and Directorate General Transport,1998). Another study about Europe shows similar observation by taking conventional railway into account, however differs from the journey distance, which suggests the conventional rail dominates the travel distance less than 400 km instead of air transport; HSR has the potential to shift air transport below 800 km; air transport dominates the journey distance above 800 km (Committee on Climate Change 2009). In Korea, the HSR dominates in the journey distance less than 500 km (Park and Ha 2006). Actually, the travel time, instead of distance matters in the competition between HSR and air transport. By establishing a model including HSR/Air transport market share and travel time, it shows that as travel time increases, the HSR/Air transport market share decreases (Sinclair Knight Merz 2010). Another study argues that the rail transport takes much less time than air transport considering the access/egress to and from the airport or rail station, baggage and check-in time, which may be called “wasted time”. The average shortened “wasted time” of rail transport compared to air transport is about 56 min, which is about 750 km by aircraft (Cokasova 2003). Thus, HSR has a relative large substitution effect on short-haul air transport (Watkiss et al. 2001); however, different geographical background may represent different situations of competition. Third, for the competition between conventional railways and HSR, they are usually neglected, except for a few studies (Cascetta et al. 2011; Hsu and Chung 1997; Hsu et al. 2010; Raturi et al. 2013). It is found that HSRs can best serve medium to long distance passenger travel markets, while conventional railways can

28

2 Railways and Low-Carbon Mobility

best serve commuter travel and as feeders for the HSRs (Hsu and Chung 1997). Another study shows that the high value of time of passengers, which means that the passengers are willing to pay a high price for the same amount of time, determines the share of HSR compared with other transport modes in a transport corridor, including conventional rail and bus (Raturi et al. 2013). However, another study about the route between Rome and Naples found that the operation of HSR did not change the conventional intercity trains; however, the use of HSR increased greatly because of induced new trips (Cascetta et al. 2011). a. Models analyzing the competition When analyzing the competition that relates to railways, most studies adopt models from the demand instead of the supply side. Meanwhile, most models are based on disaggregate data for a single transport corridor (Bonnafous 1987; de Rus and Inglada 1997; Hensher 1997; Park and Ha 2006; Roman et al. 2009), instead of aggregate data for multiple city pairs (Sinclair Knight Merz 2010). From the demand side, the modal split model following the traditional four-step transport planning model (McNally 2000) is adopted to analyze the modal share of railways and other transport modes in a transport corridor. These modal split models are basically logit model based on the utility maximization theory, most of which are used to predict the modal split (ex ante studies) instead of analyzing the observed modal split (ex post studies) among modes (Dobruszkes 2011). Actually, the use of real observed modal split data is rare due to the business data confidentiality of different transport modes, while the prediction of modal split can be based on stated preference (SP) survey or revealed preference (RP) survey information of passengers. SP refers to the data collection techniques that require the decision maker to evaluate and make trade-offs between some hypothetical alternatives which have different attributes; RP refers to the actual choice of the decision maker after considering a set of alternatives (Wardman et al. 2002). These models play an important role in supporting the decision making of rail infrastructure. The main models include binary choice logit, multinomial logit, hierarchical logit and heteroscedastic extreme value model (Wardman et al. 2002). The binary logit model is appropriate for the competition between only two transport modes; the multinomial logit model is the most common used for competition between more than two transport modes; the heteroscedastic extreme value model takes more different variances of different transport modes (Capon et al. 2003). For instance, in Italy, Nijkamp et al. (1996) adopt both the traditional logit model, which is based on the utility maximization theory, and the (feed-forward) neural network model, which considers the passengers’ modal choice as a result of the network’s adaption, to analyze the modal split of HSR and road transport, and found that the Neural Network model is better for prediction. In Germany, Mandel et al. (1997) apply the Box-Cox logit mode choice model to forecast the intercity passenger travel, with consideration of factors including travel cost, travel time, frequency, number of transfers in the network, passengers’ socioeconomic characteristics, as well as trip purpose, either business or private travel. In Spain, Martín and Nombela

2.3 Intermodal Relationship Related to Railways

29

(2007) adopt the multi-nominal logit model to predict the passenger travel demand of HSR, with consideration of distance, population and transport infrastructure investment; Roman et al. (2009) use disaggregated nested logit model based on mixed RP and SP surveys from travelers in Madrid-Zaragoza and Madrid-Barcelona corridor, with consideration of travel times, travel costs, access/egress times, headway, reliability and comfort. They found that the HSR could divert very limited passengers from air transport in the Madrid-Barcelona corridor. In South Korea, Kim et al. (1998) adopt the neural network model and predicts that the HSR would lead to the demand of the Seoul-Busan airline to decrease by 69.5% and the demand of the Seoul-Daegu airline to decrease by 59.0%; Chang and Chang (2004) establish a static traffic assignment model to predict the competition between HSR and other transport modes, with consideration of supply and demand constraints, travel time, and fare of each transport mode; Park and Ha (2006) adopts logit model to analyze the SP survey, and found that the opening of HSR leads to a large reduction of domestic aviation demand, and the main factors influencing modal choice are access time, toll fees and operation frequency. In China, Zhang et al. (2012) establish a binary logit model with SP survey data, and explore the relationship between the competition of HSR and air transport and ticket price discount of air transport in the WuhanGuangzhou corridor. For the London-Paris corridor, Behrens and Pels (2011) adopt nested and mixed multinomial logit models based on the RP survey, and found that HSR is competitive for both conventional and low-cost airlines. Besides the logit models, some models from the game theory are applied to the analysis of competition between railways and other transport modes. For instance, Hsu et al. (2010) uses the heuristic algorithm to find the optimizing model about best decisions of conventional railways and HSR, with consideration of fare, access/egress cost and time. Raturi et al. (2013) establish a game theory model to analyze the competition between HSR and other transport modes under different entry strategy of HSR and different response strategies of existing modes. However, studies from game theory perspective are very limited. From the supply side, which means the supply of seats and frequency of railways and other transport modes, studies are also rare. Dobruszkes (2011) analyzes five European city-pairs, with consideration of travel frequency and fares of HSR and air transport. He found that the introduction of HSR services led to the complete discontinuation of air service between Paris-Metz/Nancy, near-complete discontinuation between Paris and Brussels, major decline between Brussels and London, more significant decline of number of seats than number of flights between Paris and Marseilles, and not prevented an increase in air service between Cologne and Munich due to the relative long rail travel time and availability of low-cost airlines. Based on the above analysis, more studies analyzing the competition related to railways are needed, especially on the supply side (supply of seats and frequency) using an aggregate, ex post, and multi-disciplinary perspective. Meanwhile, most existing models use fixed coefficients from pervious modal choice studies and simply change the relevant variables such as time, frequency, etc., assuming that responsiveness remains the same. As the assumption is not realistic, more studies about how HSR as a new mode of transport affect the behavior of users are needed. However, due

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to the short history of HSR in China, it is not feasible for this book to analyze the impact of HSR as a new transport mode on passengers’ travel behavior separately. b. Factors influencing competition From the above, the competition between railways and other transport modes may correlate with many factors. Travel time and cost are the two most commonly used factors, followed by frequency, accessibility, comfort, trip distance, number of transfers, environmental impact, reliability, delay and hour of departure (Capon et al. 2003; Wardman et al. 2002). According to Steer Davies Gleave (2006) and Sinclair Knight Merz (2010), the rail journey time was believed to be the most important factor influencing the market share between HSR and air transport, and can explain most of the modal share variations. Other important factors included fare, service frequency, service quality, access/egress times, and reliability, low-cost airline fares, environmental transport taxation, limits on slots, new airport capacity, and lower rail operating costs (Steer Davies Gleave 2006). Travel time is a significant factor that determines the modal share between railways and other transport modes, especially HSR and air transport. Figure 2.3 shows that the share of railways is closely related to rail journey time. The share of railways decreases as the journey time increases, because more passengers choose air transport

Fig. 2.3 Relationship between rail journey time and rail share. Source adapted from Wardman et al. (2002)

2.3 Intermodal Relationship Related to Railways

31

instead. When the rail journey time is less than 3 h, the market share of railways are more than 70%; when the journey time increases to 5 h, the share still stays at more than 40%; however, for the journey time more than 5 h, the market share decreases rapidly. The influence of journey time to the modal split is due to the value of times, which depends on income, age, journey length, trip purpose and mode (Derek Halden Consultancy 2003). Passengers may choose different transport modes and make trade-offs between the value of time and other costs, such as fare and level of service. Generally, for inter-city travel, the journey time simply refers to the time from terminal to terminal. However, the total journey time should include access time, check-in time, waiting time at the terminal, transfer time, baggage pickup time and egress time (Dobruszkes 2011; Pagliara et al. 2012). Since railways do not need to check-in, it could be save at least 30–45 min for the in-vehicle travel time compared with air transport (Steer Davies Gleave 2009). Also, passengers tend to feel more onerous about the out-of-vehicle time than the in-vehicle travel time, which may influence the modal choice of passengers (Capon et al. 2003). The fare is considered as one of the most important factors, besides travel time (Pagliara et al. 2012). However, the influence of fare differs by the journey purposes and passenger types. Leisure passengers are more sensitive to fare than business passengers in modal choice (Steer Davies Gleave 2009). Therefore, for the route with a high proportion of business travelers, the influence of fare will be omitted while for the route with a high proportion of leisure passengers, the factor of fare will be important (Pagliara et al. 2012). Also, for passengers with different income, the influence of fare is also different. Some studies introduce the type of worker to the competition model in order to understand the influence of fare. Generally, full time salaried workers are expected to have higher values of time than part time salaried workers (Capon et al. 2003). Moreover, when the airlines provide low cost service, the competition between railways and air transport may be changed, which needs further research (Dobruszkes 2010; Duarte et al. 2008). Frequency is almost as important as fare, when determining the modal share of railways (Pagliara et al. 2012). It is defined as the departures by time interval (Capon et al. 2003), which impacts the attractiveness of transport schedules offered by each operator for passenger travels (Duarte et al. 2008). As Steer Davies Gleave (2009) suggested, an improvement of frequency from every two hours to every one hour is equal to 20 min of travel time reduction. In the London-Paris corridor, for instance, the frequency is as important as travel time in determining the travel choice of passengers (Behrens and Pels 2011). Service quality of transport usually refers to the on-board catering, check-in facilities, seating offered and terminal facilities. It is supposed that the increase of service quality without increase in fare may affect the model share; however, the studies in Europe do not show that the service quality has significant influence on the modal share between railways and air transport, mainly because their service quality is almost the same (Steer Davies Gleave 2009). However, compared with road transport, the HSR is more comfortable and convenient, with wider seat space (Pagliara et al. 2012), which may influence its competition with road transport (Duarte et al. 2008).

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Access/egress time is also an important factor. Usually, railway stations are located in the city center and easier to access or egress than airports. However, it is not always this case for newly constructed railway stations, and it is asserted that access/egress time can challenge the utility of railways (Lane 2012). A study about London shows that the passengers with their journey origins significantly closer to the station than the airport are more likely to choose railways for all routes. The modal share of railways for these passengers near railway stations is at least 10% higher than those passengers closer to the airport (Steer Davies Gleave 2009). In China, based on questionnaire surveys, it is also found that the long access/egress time to the HSR stations has negative influence on the effectiveness of the HSR system (Wang et al. 2013). In addition, some other studies recommend improving the access services to railway stations in order to encourage the use of railways (Brons et al. 2009). Reliability refers to the difference between the timetable and actual travel times (DETR 2000), and low reliability is often caused by the disruption of services (Duarte et al. 2008). Although railways are thought to be more reliable than air transport (Pagliara et al. 2012), the influence of reliability to the modal share of railways depends on the route (Steer Davies Gleave 2009). By comparing the HSRs and conventional railways in the UK, the former have a higher reliability than the latter; there are no significant differences in the modal share of conventional railways before and after the HSR operates (Steer Davies Gleave 2009). However, in South Korea between Seoul and Daegu, the unreliability of HSR operation in the early stage has led to a lower share of HSR (Park and Ha 2006). In particular, reliability is important for business travelers (den Boer et al. 2011; Capon et al. 2003).

2.3.2 Cooperation Cooperation related to railways generally covers two forms: cooperation between railways and air transport, cooperation between railways and urban transport. Cooperation of railways and air transport is often perceived as railways providing a mode to access the airport or as a feeder to hub airport. Stubbs and Jegede (1998) summarized five principal forms of railways linking with airports: metro line, special line, spur line, branch line and main line, and argues that the branch line and main line can provide the basis for integration between railways and air transport. From the airport’s perspective, Doganis and Dennis (1989) classified the cooperation models based on ‘hinterland’ and ‘hourglass’ hubs. The former adopts short-haul railways to connect hub airport and transfer passengers to long-haul flight, while the latter is feeding regional hub airports from cities in the regions. Gösling (1997) proposed three alternative strategies to improve the cooperation between airport and railways: new or upgraded rail links to the airport; off-airport terminals located in the city center; and ground transport centers consolidating public transport and connecting to the airport by automated passenger movers. Basically, cooperation concentrates more at the infrastructural level, often no cooperation at the operational and institutional levels is realized. From the market’s perspective, it is still far from free mobility

2.3 Intermodal Relationship Related to Railways

33

and sustainable transport. Based on the competition between HSR and air transport, the HSR can be used to replace airline service up to 750 km (Cokasova 2003). For the cooperation between railways and urban transport, the connection has already been established. Therefore, integration, as a high level of cooperation, will be discussed in the following part.

2.3.3 Integration Modal integration—“An intermodal system is one in which the individual modes are linked, governed, and managed in a manner that creates a seamless and sustainable transportation system. Such a system should be economically efficient, environmentally sound, safe and secure and ethically based” (Givoni and Banister 2010). As transport system develops, the integration between different transport modes becomes a significant issue of sustainable transport, which can provide convenient, rapid, efficient, safe and seamless individual travel, thereby reduce the interchange cost (Bhattacharyay 2012; European Commission 2004; Grimme 2007; Winn 1995). The expected vision for an integrated transport system is that every transport mode plays its role “at its best scale and operation” and cooperates with each other (Reis et al. 2012). Different studies have proposed different key elements of integration for transport system (Table 2.5). Most of them emphasize the integration at both physical and institutional levels, which are different levels in the “integration ladder”, with integration of infrastructure lowest, integration of information and service moderate, and integration of fares, management and pricing highest (Preston 2012). Basically, there are two forms of integration related to railways, one is between railways and urban transport, the other is between railways and intercity transport. For the integration between railways and urban transport, it belongs to the issue of integration of public transport, which is defined as “the organizational process through which elements of the passenger transport system (network and infrastructure, tariffs and ticketing, information and marketing) are, across modes and operators, brought Table 2.5 Elements of integrated transport in literature Literature

Key elements

Preston (2012) Information

Services

Ticketing

Infrastructure Management provision

Reis et al. (2012)

Logical interaction

Contractual interaction

Financial interaction

Relational interaction

Institutional connectivity

People-to-people connectivity Baggage handling

Promotion of Intermodality

Physical interaction

Bhattacharyay Physical (2012) connectivity European Comission (2004)

Networks Door-to-door Tariffs and and information ticketing: interchanges

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into closer and more efficient interaction, resulting in an overall positive enhancement to the overall state and quality of the services linked to the individual travel components” (Consortium led by NEA Transport Research and Training 2003). Indeed, the interchange of public transport inside the city is more related to railways than other transport modes. As study shown, about 65% of rail journeys consist of interchange in London and 80% of commuter rail trips involve interchange in New York (Guo and Wilson 2011; Hine et al. 2003). The interchange usually occurs in an intermodal terminal, and there are several frameworks to analyze the attributes related to integration in public transport (Table 2.6), which could give some insights to the integration between railways and urban transport. However, these factors seem to be rigid, with rare consideration about the variation with different passenger and trip attributes. Moreover, there are few studies on developing countries. Because of the HSR development, the integration of railways with other intercity transport modes, especially air transport, receives more attention. Currently, there are more than 130 airports with a direct link to railway network or HSR network in the world (Chiambaretto and Decker 2012). An efficient air-rail integration system should have several characteristics, including seamless integration, ticket system transparency, easy access information, terminal accessiblility, security, timetable coordination, reliability and punctuality, common booking, check-in integration, attractive price and short total travel time (EUROCONTROL 2004). Some other scholars summarize the requirements of integration between railways and air transport in three aspects: infrastructure, including rail track into airport terminal, airport terminal station, baggage trolleys, ramps, lifts, covered walkways; Table 2.6 Attributes of integration in public transport Coccia (1999)

Terzis and Last (2000) Institute of Logistics and Transport (2000)

NSW Ministry of Transport (2008)

• • • • • •

• Overall design and layout • Accessibility and linkages with the surrounding urban area • Facilities • Image • Information • Signage • Personal security • Operational safety

• Actual and perceived security and safety • Punctual services • Well maintained and clean interchange facilities • A pleasant and comfortable environment • Clear service and timetable information • Way-finding and directional signage

Visibility Way-finding Shelter Security Accessibility Service information • Facilities

• A hierarchy of point to point access • Minimizing interchange • Facilitating interchange • Through ticketing • Regard all railway stations as points of interchange • Location of interchange • Routing and circulation plan • Meet basic requirements of facilities • Environment • Information

2.3 Intermodal Relationship Related to Railways

35

train services, including high frequency, serving variety of large population centers, time coordination to serve all flights; operational efficiency, including baggage check in at railway stations, air/rail information available in station and terminal, through rail-air ticketing (Stubbs and Jegede 1998). So, there are different levels of integration based on the elements: weak, with linked but separate facilities; moderate, combined purchasing and physical integration; strong, code-sharing of trains as flights and automated baggage transfer; peak, seamless passenger connection and train/car operated by the airline (Suski 2011). In practice, different regions have developed different levels of integration between railways and air transport. In the USA, the connection between railways and air transport is preliminary, mainly by dedicated access, or public access connecting the airports and the rail system; meanwhile, there are no airports in the USA with direct national or regional railway connection; however, airports with local rail systems are common (Goetz and Vowles 2009). In Europe, the integration development is much more mature, and has been divided into three types, i.e. low level as interlinking agreements, moderate level as code-share agreements, and high level as joint-venture cooperation. There are several benefits for integrated transport. For the integration between railways and urban transport, it may reduce the cost of interchange, because the interchange may produce a penalty caused by the additional time spent on interchanging and waiting, and the cost may influence the demand of both railways and urban transport, especially for commuters and business travelers (Hine and Scott 2000). However, the interchange cost also depends on the personal and trip characteristics, such as age, gender, income, trip purpose, travel distance and mode, while the related studies are still limited (Hine et al. 2003). There are several benefits for integrated transport. For the integration between railway and air transport, it brings different kinds of benefits such as releasing runway and air traffic capacity resources, offering immediate relief to congestion, reducing negative environmental impacts, and improving ground access to airports (Cokasova 2003). For the airports, their motivation to integrate with railways may come from increasing catchment, enabling growth, alleviating congestion, and attracting target customers (Vespermann and Wald 2011). However, it is suggested that not every airport needs to be integrated with railways, or even have a railway connection, because only airports with congestion and a large market share of distance less than 500 miles will benefit from the integration (Suski 2011). By cost-benefit analysis, Widmer and Hidber (2000) concludes that it is efficient to integrate railways with hub airports, but not efficient to integrate with medium-sized airports. Koło´s et al. (2012) further proposes the requirement of passenger traffic volume of airport for the railway and air transport integration, which shows that the global and long haul international airport with annual passengers more than 2.8 million need railway connection; other airports with annual passengers more than 1 million only need metro or tram connection; smaller airport only need bus services. Besides the airport size, the distance to the airport by rail is also important, because it determines the benefits about time and cost saving to passengers (Widmer and Hidber 2000). Based on the estimations of social welfare, Jiang and Zhang (2014) further point out that the

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modal substitutability between railways and air transport in the market is important for the benefits: if the modal substitutability is low, the integration improves welfare no matter whether the airport is congested not, while if the modal substitutability is high, only when the airport is capacity-constrained, the integration can increase the social welfare. However, in practice, the air-rail integration sometimes only has a very limited impact to release the capacity of hub airport. For instance, Grimme (2007) finds that the Al-Rail service only released 2–3 daily slot pairs for alternative use. In addition, most of the studies focus on a single airport connecting the city center by railways; for multiple airport cities, the challenge is linking the multiple airports together, which is possibly realized by railways (Koło´s et al. 2012). Integration of the transport system, based on the cooperation between different transport modes, is recommended by many scholars as a transport development strategy (Givoni and Banister 2007). However, the implication of integration always faces quite a few obstacles. From the passengers’ perspective, the survey of MIMIC (Coccia 1999) shows seven types of interchange barriers: logistical and operational barrier, psychological barrier, institutional and organizational barrier, physical design barrier, local planning and land use barrier, economic and social barrier as well as information barrier. For specific group of people, such as the youth and elderly, women, disabled people, cyclists, and commuters, they also face some specific barriers. All these barriers faced by passengers actually stem from the operation and governance (Grimme 2009), such as extensive modal orientation and modal separation tradition, regulatory barriers between multi-stakeholder, significant funding shortfalls and insufficient staff for intermodal management (European Commission 2004; Winn 1995). For instance, Macario (2007) constructs a regulatory framework for integration and argues the determinant role of institutions in integration. Therefore, it needs interaction among different transport modes and operators through the organizational process: establishing common objectives, communicating and reaching institutional and financial integration. In this process, it is recommended to set up a regulatory and institutional framework as the ‘rule of game’ and clearly divide the roles between different stakeholders, coordinate the integration by the authority, increase the awareness about integration level, refine score indexes as performances comparison, make active policies to coordinate different concessions, use integration in award criteria, and strive towards compatibility of services (Consortium led by NEA Transport Research and Training 2003). For the coordination between different stakeholders, Sorensen and Longva (2011) suggest four distinctive mechanisms for integration: organizational, contractual, partnership and discursive cooperation. Besides the coordination mechanisms, considering the public characteristics of railways, the role of public authorities is very important, especially when setting up the institutional and regulatory framework (Aparicio 2011).

2.4 Intermodal Relationship and Sustainable Transport

37

2.4 Intermodal Relationship and Sustainable Transport Traditional transport system is usually operated and managed by different modes, and this modal separation tradition also influences the transport research. However, isolation of the analysis of each transport mode can do little help to understand sustainable transport in a holistic manner. As a system, different transport modes need to be put together to make a comprehensive analysis, which is especially important for the environmentally sustainable transport study (Schiess 2006). Currently, studying sustainable transport from the multi-modal perspective has become a trend; however, more studies are still needed in this direction. As discussed above, the intermodal relationship including competition, cooperation and integration may change the modal share of transport system, and impact the sustainable transport development. The consequence of competition between railways and other transport modes is modal shift, which occurs “when one mode develops better advantages over existing modes and captures a share (or the totality) of their traffic” (Rodrigue et al. 2013). Many case studies have provided evidence to show that that substituting an aircraft or car seat with a railway seat is beneficial (Givoni 2007; Jamin et al. 2004). Considering that HSR is competitive in short-haul route with air transport, modal shift from air transport to HSR becomes a recommended way to sustainable transport (Asian Development Bank 2008). In Europe, it is targeted that by 2050, most medium-haul passenger travel would be shifted to railways (European Environment Agency 2011) and the total air transport emission would reduce by 10% until 2020 (Sostenibile 2010). Another study shows that the conventional railway and HSR have the potential to reduce CO2 emissions by 2–11 Mt and 14–18 Mt, respectively, compared with total transport emission in Europe in 2020 of 773 Mt (den Boer et al. 2011). In China, it is also predicted that with the 9% annual growth rate of traffic turnover volume by 2020, a reduction of CO2 emissions by 26–32% will be achieved by developing railway network and controlling highway and air transport (Han and Hayashi 2008). The cooperation and integration may also lead to modal shift, the impact of which to sustainable transport is rarely examined. Zanin et al. (2012) analyzes the integration of HSR and air transport at Madrid Barajas Airport in Spain, and shows that passengers are changing from air travel and private car to the railways, which leads to 5 kg CO2 reduction per passenger and 10% of emissions reduction on the corridor. However, there are still some debates about the modal shift to low carbon emissions, which need more consideration. For one thing, if the freed airport capacity is used for more flights, the overall environmental impact would be increased (Givoni 2006; Williams and Noland 2006). For the other, many studies show that railways can only shift a very small percentage of air transport, and thereby reduce very limited CO2 emission. For instance, the modal shift from air and road to railways on the London–Manchester route may reduce CO2 emission by 0.1 Mt per annum in 2033, less than 1% of total transport emissions in the UK in 2007, which is mainly because the modal shift is very small and the electricity source heavily relies on fossil fuels (Miyoshi and Givoni 2013). At the national level, in the USA, it is predicted that

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only 1% reduction of CO2 emission from transport will be brought by HSR in the USA compared with the scenario without HSR in 2050 (Joint Transport Research Center 2008; Kosinski et al. 2011); in the UK, it is predicted that the modal shift from aviation to railways would reduce the CO2 emissions by 0.4 Mt to 2.2 Mt in 2050, which is relatively small compared with the 81 Mt total aviation emissions (Committee on Climate Change 2009). The limited contribution of HSR to lower carbon emissions is mainly due to the small share of short-distance air travel (Jamin et al. 2004) and road travel that is about to shift to HSR (Kosinski et al. 2011). It is even argued that making railways to solve the problem of climate change is wrong (Kageson 2009). However, all these predictions can only prove that modal shift is difficult, but it is still possible to achieve CO2 emission reduction by railways, by promoting the modal shift, increasing the load factor and improving the energy efficiency of railways (Givoni et al. 2009). Besides the modal shift, there may be some induced travel due to railway improvement, which is usually neglected in the analysis. Induced travel is defined as travel demand, in the form of new trips or long trips, which in response to the travel time saving or travel cost reduction due to a new transport facility (Bailly 2000). The term is generated from the observations of highway improvements, and most studies are accordingly about highway and related elasticity estimates (Noland and Lem 2002). There are a few studies about the induced travel estimation about HSR. King (1996) suggests that HSR in France and Japan has produced induced traffic about 35%, which is larger than 30% of diverted traffic; Hensher (1997) estimates that the HSR between Sydney and Canberra would derive 26% of induced travel; Yao et al. (2003) predict that in Japan, an HSR linking Tokyo, Nagoya, and Osaka will induce about 1.1% of travel demand at the national level, and the effect of induced travel is stronger in the metropolitan regions along the line, accounting for 14.5% of total induced travel. The induced travel may eliminate some benefits of carbon emission mitigation effects from modal shift, which is worth noting. Actually, based on the argument of Chester and Ryerson (2014), there are many challenges for estimating the environmental impact of HSR. First, there is a spatial incompatibility between HSR and other transport modes; second, the environmental review process often excludes modal alternatives; third, the consideration of environmental impacts rarely combines with the consideration of social and economic outcomes; fourth, there is little data about the environmental performance of future railways and aircrafts; fifth, the understanding about transport and land use which leads to secondary impacts is poor. Therefore, efforts should be made in the aspects of methodology, policy and data collection; most importantly, the integration of the HSR system to the “complementary and competitive configurations of transport services”. Although the analysis is based on the background of the USA, it is also applicable to Europe and Asia, which shows that many uncertainties exist in measuring the role of railways in sustainable transport, especially from the perspective of intermodal relationship.

2.5 Summary

39

2.5 Summary After reviewing the interaction between the three key concepts of the conceptual framework, i.e. railway development, intermodal relationship, and sustainable transport, it is suggested that existing studies have done much about the sustainability of railways and the intermodal relationship between railways and other transport modes, respectively, however, more work needs to be done to help understanding the role of railways in sustainable transport. Firstly, it is necessary to analyze the role of railways in sustainable transport from a multi-modal perspective. Due to the modal isolation tradition in transport research, most previous studies preclude other transport modes when discussing the sustainability of railways. However, more and more evidences show that transport is a system and all transport modes need to be integrated in the analytical framework of sustainable transport. Also, the modal shift caused by intermodal competition, cooperation and integration may greatly determine the environmental performance of the railway system, which is especially important in studying the railways and sustainable transport. Secondly, most research about railways concentrates on HSR and adopts forecast models to study their role in sustainable transport. However, many uncertainties exist in these studies, even in the developed countries with abundant data and policy support (Chester and Ryerson 2014). Therefore, more studies about conventional railways from a historical perspective are needed as a basis for the forecast studies, especially in the developing countries with HSR newly developed. Thirdly, when studying the competition between railways and other transport modes, most are limited to disaggregate models for a single city pair. Therefore, it is crucial to establish some aggregate models based on multiple city pairs, which can not only take different regional characteristics into consideration, but also give a more comprehensive understanding about competition and modal shift. Fourth, most studies about integration have proposed some factors and framework about seamless integration, however, few of them have analyzed the factors about seamless integration according to the attributes of passengers and trips. Meanwhile, most are about planning aspects and few are related to institutional aspects. Therefore, more studies should be conducted to investigate passengers’ perception about seamless integration and the institutional barrier of integrating railways and other transport modes. Finally, most existing studies about the present topic are from the perspective of engineering or economics; however, transport research is an interdisciplinary issue, geography as a comprehensive discipline may strengthen the study about railways and sustainable transport from the spatial, temporal, people-oriented and institutional perspectives. Moreover, most research is about developed countries, which have different socioeconomic background from the developing countries. Making a case study in a developing country, especially China, with both railways and other transport modes rapidly developing, can help to understand the role of railways in sustainable transport.

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In light of the above research gaps, this book tries to explore the role of railways in a multi-modal environment, by analyzing its competition, cooperation and integration with other transport modes. Meanwhile, it takes both conventional railways and HSR into consideration, by tracing the historical development of railways in China. When studying the competition between railways and other transport modes, aggregate models based on multiple city pairs are used; when studying the integration between railways and other transport modes, an evaluation framework is constructed. In sum, the book is based on the discipline of transport geography, which represents the integration of spatial, temporal, and sustainable perspectives (Rodrigue et al. 2013).

Part I

Retrospect of the Past: Modal Shift is Important

Chapter 3

Railways and National Carbon Emissions from Passenger Travel in China

3.1 China’s Background As reviewed in Chap. 2, most studies focused on the developed countries to study the role of railways in sustainable transport, while this book chooses China as a case of developing countries. In this book, China refers to the mainland China, which excludes Taiwan, Hong Kong, and Macau, mainly due to their different transport and statistical systems. With more than 13.5 billion population and 9.6 million km2 land (National Bureau of Statistics of China 2013), there is a huge travel demand in China, which greatly impacts its economy, society and environment. Meanwhile, China is experiencing rapid development, with great transitions in its economic structure, urban system, population distribution and governance (Friedmann 2005). Therefore, the special socioeconomic background of China requires specific study about the issue of sustainable transport, rather than simply following the experiences of the developed countries. It is worth noting that China is a highly populated country, which makes the results of the book less applicable to other jurisdictions of low density. However, the findings may still be generalized to these jurisdictions at the city level and the station level, because railways may serve for the city pairs in these regions with relatively high density, and the seamless integration between railways and other transport modes at station level is also important for sustainable transport development. Moreover, density can be increased by policies such as smart growth.

3.1.1 Travel Trends in China The travel demand of China has grown from 15.5 billion passenger-kilometer (pkm) in 1949 (National Bureau of Statistics of China 1999) to 3,338 billion pkm in 2012 (National Bureau of Statistics of China 2013) (Fig. 3.1). It is predicted that the travel

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 L. Li, Railways and Sustainable Low-Carbon Mobility in China, https://doi.org/10.1007/978-981-15-9081-8_3

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3 Railways and National Carbon Emissions from Passenger Travel in China

4,000 3,500

billion pkm

3,000 2,500 2,000 1,500 1,000 500 2009

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1967

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0

Passenger turnover volume Fig. 3.1 National passenger turnover volume in China (1949–2012) Source National Bureau of Statistics of China (1999, 2013)

demand will keep growing with a similar growth rate from 1978 to 2012, about 9% per annum, and reach 6,600 billion pkm in 2020 (Han and Hayashi 2008). Meanwhile, the transport modal split changed a lot in the past few decades (Fig. 3.2). The share of rail transport keeps dramatically decreasing, especially from 1970 to 1997, while the share of both road and air transport keeps increasing. In 2012, the share of rail, road, water and air transport is 29.4%, 55.3%, 0.2% and 15.1%, respectively, which shows that the current transport system of China is dominated by road transport. In contrast, the modal split structure by rail, road, water and air transport in 1949 and 1978 was 83.9: 5.1: 9.8: 1.2 and 62.7: 29.9: 5.8: 1.6, respectively, both of which were dominated by railways. It means that road and air transport modes have been having much higher growth rates in terms of passenger volume than railways, and the general trend of modal shift in China was from railways to road and air transport, with the total traffic volume growing rapidly. It is different from the situation in developed countries, which experienced a modal shift with a more stable traffic volume. There are several reasons for the modal shift. First, the capacity constraint of rail network has limited the passengers’ choice of railways (Fu et al. 2012). With only 6% of the world’s railway length, China’s railway undertakes about 25% of world’s railway traffic, with the world’s highest railway traffic intensity. Due to the capacity constraint, the railways in China could only provide 2.76 million seats per day for passengers on average in 2008, but the traffic demand per day was more than 3.72 million passengers in that year. Therefore, it is always very difficult for

3.1 China’s Background

45

% 100 90 80 70 60

Air

50

Water Road

40

Rail

30 20 10 0 1949 1954 1959 1964 1969 1974 1979 1984 1989 1994 1999 2004 2009

Fig. 3.2 Modal share of passenger transport in China (1949–2012) Source National Bureau of Statistics of China (1999, 2013)

the passengers to buy one ticket during the peak period of railway traffic, especially during the spring festival (China News Press 2008). Second, the infrastructure of both road and air transport expands rapidly in China, especially after the “reform and opening up” policy adopted in 1978. For the road transport, China began to develop the expressway network in the late 1980s, and has a total length of 96,200 km expressway in 2012 (National Bureau of Statistics of China 2013). For the air transport, the civil aviation has a profound change in its governance since 1978, including becoming independent from military control, separating the operation of airlines and airports from the central government, and changing airlines to profit-driven business corporations (Jin et al. 2004). Meanwhile, the infrastructure of air transport develops, for instance, the number of civil airports doubled, from 77 in 1978 to 180 in 2012 (National Bureau of Statistics of China 2013). However, the railway network develops much slower than road and air transport. The annual average growth rate from 1978 to 2012 of railway length is 1.9%, while the growth rate of highway length and regular civil aviation route length is 4.7% and 9.5%, respectively (National Bureau of Statistics of China 2013). Third, as the income grows in China, passengers tend to choose individual travel mode (such as automobiles) for short trips and high speed travel mode (such as aircrafts) for long trips, since the automobiles and airplane tickets become affordable (Skeer and Wang 2007). In China, the possession number of private passenger cars increased from 0.02 million in 1985 to 764 million in 2012; meanwhile, the annual passenger number of air transport increased from 2.3 million in 1978 to 319.4 million

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3 Railways and National Carbon Emissions from Passenger Travel in China

in 2012 (National Bureau of Statistics of China 2013). Actually, the modal share changing towards automobile and aircrafts is a world trend (Schafer and Victor 1999). Due to fixed travel time and travel money budget, which means that the travel time of people spending on travel is stable and the travel expenses keep a fixed share of personal expenditures, passengers may increase their travel expenses and mobility as their income grows, and chooses more flexible and faster transport mode (Schafer 1998). Based on the travel demand growth and modal split structure in China, it is shown that the transport system in China demonstrates a large and increasing travel demand, which is dominated by road transport and shifted away from the railways. The trend is similar to the experiences of developed countries; however, whether China will duplicate the same pathway as the developed countries, it is still uncertain and needs exploration.

3.1.2 Railway Development in China

10000

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1949 1952 1955 1958 1961 1964 1967 1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000 2003 2006 2009

10000 km

The first railway of China was opened in 1876, and there is nearly 140 year history of railway development in China. In the early stage, the railway network was primarily constructed in the Northeast China by Japan, aiming to exploit China’s natural resources by railways. Since 1949, the railway network was expanded (Fig. 3.3), with

3.1 China’s Background

47

the aim of an integration of national railway system (Leung 1980). However, before 1978, the railway construction was concentrated in the northwest and southwest, mainly the inland areas; after 1978 with economic reform and opening up policy, the construction of railways began to shift to the coastal area in the east. Until the early 1990s, about 2/3 of China’s area had been covered by the railway network. Then, the focus of railway development changed from new line construction to existing line electrification and acceleration (Wang et al. 2009). It was represented by six rounds of railway “speed-up” campaigns during 1997 and 2007. However, the capacity of the railways is still in shortage and the dominant role of railways in passenger travel in China was gradually replaced by road and air transport. Since 2008 when China opens its first HSR between Beijing and Tianjin, it has built the world’s largest HSR network (UIC 2013). As planned in Mid-to-Long Term Railway Network Plan of China (revised in 2008), the total length of HSR will reach above 16000 km in 2020, including four horizontal HSR lines connecting the east and west, and four vertical HSR lines connecting the north and south, as well as nine regional intercity HSR systems (Ministry of Railway 2008). It is expected to solve the problem of railway capacity limitation and reverse the modal shift from road and air transport to railways. The development of HSR has already shown some impacts on other transport modes. On one hand, the construction of HSR attracts some passengers from air transport. For example, air flights from Nanjing to Wuhan have been suspended after the operation of HSR, and flights from Wuhan to Changsha have lost their passengers since the operation of HSR (Xu et al. 2010). On the other hand, there are some examples of cooperation between railways and air transport. For instance, the Hongqiao Comprehensive Transport Hub in Shanghai has concentrated all transport modes, including HSR, air transport, coach, metro, and bus, to promote the transport integration based on the railway station. However, due to the short history of HSR in China, it is too early to make a conclusion whether the HSR can guide the transport system to sustainability. Moreover, the cooperation between HSR and air transport is still very limited in China at present and there seems not to be any policy about strengthening the cooperation between the railway and air transport in the future. Based on the above background in China, this book intends to explore the role of railways in sustainable transport. To reiterate, it firstly examines the contribution of railways to carbon emission reduction since 1949 at national level. Then, the competition between railways and air transport is studied at the city level; the cooperation and integration between railways and other transport modes is studied at the station level. Finally, some implications for future sustainable transport development are discussed. The following part will introduce the methodology of this book. Railways, as an important transport mode, have played important role in sustainable development, including promoting economic development, supporting social equity, and reducing environmental impacts. This chapter focuses on the environmental aspect of sustainability, especially the carbon dioxide (CO2 ) emissions, and tries to examine the role of railways in sustainable transport. As a primary greenhouse gas, CO2 emissions exist in a large amount and have a long residence time in the atmosphere (Lashof and Ahuja 1990). The concentration of CO2 and other

48

3 Railways and National Carbon Emissions from Passenger Travel in China

greenhouse gases in the Earth’s atmosphere have caused the Earth’s surface temperature to rise, due to the increasing human activities (Oreskes 2004). Therefore, the reduction of CO2 emissions has become a central theme in sustainable development. Compared with other transport modes, railways are considered to have low CO2 emission per passenger-km carried because of their large capacity. This issue is especially important for the developing countries, which are experiencing great transition in transport and energy. For instance, in China, although the transport sector contributed only about 7% of the national CO2 emissions (IEA 2010), its high reliance on coal, rapid travel demand growth and motorization makes low carbon emissions very challenging. The Chinese economy relies heavily on coal (67% of the total primary energy supply came from coal), which is low in energy efficiency and is more polluting (Li et al. 2011). Meanwhile, China’s passenger turnover volume has risen more than 160 times from 15.5 billion passenger-kilometers (pkm) in 1949 to 3,338 billion pkm in 2012 (National Bureau of Statistics of China 1982–2013). In addition, China has undergone rapid motorization in recent years, making the modal share of railways decreasing (Loo et al. 2011). Therefore, it is significant to review the role of railways in low carbon emission before exploring strategies for sustainable transport. However, the role of railways to low carbon emissions has rarely been quantified, especially in China. Although there are some official data and research (He and Li 2010; He et al. 2005; Zhang et al. 2011) about China’s transport CO2 emissions in China (International Transport Forum 2011; National Development and Reform Commission 2004), little attention was put on passenger transport only. In addition, spatial disparity and historical analysis is also limited (Cai et al. 2011). This chapter tries to estimate how much railways have contributed to carbon emissions in China, from both temporal and spatial perspectives, in comparison with all other three kinds of passenger transport modes (road, air and water transport). In particular, it tries to analyze whether railways performed better than other passenger transport modes such as air and road transport in China from 1949 to 2009, and whether the phenomenon of modal shift from railways to other transport modes changed the role of railways in CO2 reduction of China from 1949 to 2009. The structure of this chapter is as follows. The next Sect. 3.2 describes the methodology adopted in this chapter. Then, estimation results of CO2 emissions from four kinds of passenger transport modes (road, rail, air and water) since 1949 are presented both temporally and spatially in Sects. 3.3 and 3.4. Based on the estimation, Sect. 3.5 quantifies the role of railways in low carbon emission in China by comparing with other transport modes; Sect. 3.6 investigates the contribution of modal shift to CO2 emission change. The final part Sect. 3.7 provides the summary of this chapter and makes some suggestions for promoting CO2 mitigation in China by railway development.

3.2 Methodology

49

3.2 Methodology To estimate CO2 emissions from the transport sector, generally two approaches are used: the distance-based method and fuel-based method (Greenhouse Gas Protocol 2005). Using the distance-based method, distance or travel activity data are multiplied by CO2 emission intensity. Either the aggregate or disaggregate approach can be adopted. The aggregate approach considers each passenger transport mode as a whole, while the disaggregate approach distinguishes different vehicles (e.g. highway buses, taxis, city buses, private cars, institutional vehicles and motorcycles in road transport) and makes estimation for each sub-mode respectively. Using the fuelbased method, either the “bottom-up” or “top-down” approach can be applied. The “bottom-up” approach measures CO2 emissions by considering changing components of the transport system that affect CO2 emissions, such as transport activity, fuels and vehicles (Schipper et al. 2009). The most frequently used “bottom-up” approach is the ASIF approach, which considers travel activity (A) by passenger kilometers, the modal share (S) by ratios of passenger kilometers, the fuel intensity (I) by liters per passenger kilometer and the fuel emission factor (F) by CO2 emissions per liter of fuel. Theoretically, vehicle type, travel distance and fuel type can all be taken into consideration in the estimation. However, due to severe data limitation, the “top-down” approach is more widely used in China (He and Li 2010; He et al. 2005). The “top-down” approach, also called the reference approach by IEA and the United Nations Framework Convention on Climate Change (UNFCC), estimates CO2 emissions based on the total amount of fuel consumption or fuel sales (Gössling et al. 2005). It multiplies fuel consumption by the CO2 emission factor for each fuel type respectively and sums them up. In this chapter, both the disaggregate distance-based method and the “top-down” fuel-based method are used to estimate the CO2 emissions from passenger transport in China. The formula of the distance-based method is as follows:   P Ti,k,t × E Ii,k,t (3.1) G i,t = k

where Gi,t represents CO2 emissions of passenger transport mode i in year t, PTi,k,t represents passenger turnover volume (in pkm) of sub-mode k in mode i, EIi,k,t is CO2 emission intensity (g/pkm) of sub-mode k in mode i. The PTi,k,t data of rail, air and water transport are all obtained from the China Statistical Yearbook from 1981 to 2010. Because statistics of road passenger transport in China only include highway buses (Schipper et al. 2009), the passenger turnover volumes from other sub-modes, including taxis, city buses, private cars, danwei (institutional) vehicles and motorcycles, have to be estimated. The passenger turnover volume from each kind of vehicles is estimated by multiplying the vehicle number (Fig. 3.4) with the annual average mileage and average load (Table 3.1). Numbers of taxis, public buses, private cars (including private small vehicles and private mini vehicles) and motorcycles as well as total civil passenger motor

50

3 Railways and National Carbon Emissions from Passenger Travel in China

10000 9000 8000 10000 Units

7000 Taxis

6000

City buses

5000

Institutional vehicles

4000

Private cars

3000

Motorcycles

2000 1000 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008

0

Fig. 3.4 Road vehicle numbers in China (1980–2009)

Table 3.1 Average annual mileage and average load of vehicles in China Taxis

City buses

Private cars and institutional vehicles

Motorcycles

Average annual mileages (km)

1980–1995: 121545 1996–2009: 71175

1980–2000: 35000 2001–2009: 34000

1980–1998: 16000 1999–2009: 18000

1980–1998: 9000 1999–2009: 10000

Average load (persons)

1.1

16

1.3

1.2

Average gasoline consumption (km/kg)

15.07

3.91

15.07

50.23

Source He et al. (2005), Wang et al. (2006), IFEU (2008)

vehicles are obtained from the China Statistical Yearbook and China Automotive Industry Yearbook. However, there are no official data about danwei (institutional) vehicles. So, they are estimated by subtracting the number of all other motor vehicles (public business vehicles, taxis, inner city public buses and private vehicles) from the total civil passenger motor vehicles. Moreover, EIi,k,t data are summarized from several relevant studies of other administrations because time series sub-modal emission intensity data are unavailable in China. If there are no modal intensity inputs, one cannot estimate the CO2 emissions based on the distance-based approach (there are too many unknowns). Hence, international data were used in the estimations. Data from eight European countries, three Asian-Pacific countries and one North American country, as well as the

3.2 Methodology

51

regions of Taiwan and the European Union, are obtained both from official reports and published research papers. The international data, covering the period from 1960 to 2010, included Denmark (Scholl et al. 1996), Finland (VTT Technical Research Centre of Finland 2009), France (Scholl et al. 1996), Germany (Scholl et al. 1996), India (Ramanathan and Parikh 1999; Singh 2006), Italy (Scholl et al. 1996), Japan (Uehara 2009), Norway (Scholl et al. 1996), Sweden (Scholl et al. 1996), the UK (ATOC 2007; Defra 2009), the USA (Schipper et al. 2011), New Zealand (Becken and Patterson 2006), Taiwan (Lin 2010) and the European Union (Gössling et al. 2005). Yet, it is hard to determine which EIi,k,t data are the most applicable to China in different periods because the emission intensity of transport is influenced by many factors. They include the technological features of vehicles, such as, fuel efficiency, vehicle weight, vehicle air conditioning and type of fuel used, and the load factor, which, in turn, is determined by various socioeconomic factors (Scholl et al. 1996). Since the purpose of this paper is to study the general growth trend, three scenarios are chosen for estimation: the low, average and high (Table 3.2). Due to the lack of data in the 1950s and 1980s, the EIi,k,t data are replaced by those of 1960s and 1990s respectively. Also, for highway buses and motorcycles, their EIi,k,t data in 1980s and 1990s are replaced by those of 2000s. For road and water transport, only emissions from the 1980s to 2000s can be estimated by this method. From Table 3.2, the average emission intensity figures for different modes have been getting lower over time. In other words, CO2 emissions per pkm of different transport modes have generally declined over the past few decades. The fuel-based method of estimating CO2 emissions of passenger travel mode i in year t is basically obtained by multiplying fuel consumption of a fuel type used in the mode (kilograms) and the CO2 emission factor of the fuel type (g/kilogram). If more than one fuel type is used for a mode, the sum of the CO2 emission of different fuel types used in the mode is calculated. For railways, the fuel types include coal and diesel. Electricity consumption is transformed into coal because it is the most commonly used fuel to generate electricity in China. For air transport, fuel types include jet kerosene and aviation gasoline. For road transport, gasoline and diesel are the main fuel types. For water transport, fuel type is mainly diesel. The fuel consumption data of rail, water and air transport are from Yearbook of China Transportation and Communications and Compile of China Aviation Statistics 1949–2000, which contain the fuel consumption of both freight and passenger transport. To get the fuel consumption of passenger transport in these modes, the percentage of passenger turnover volume in total converted ton-kilometers of freight and passenger transport is multiplied for each transport mode, where passenger turnover volume is converted into ton-kilometer according to the rate of conversion in China Statistical Yearbook. For road transport, the fuel consumption includes two parts: the public business vehicle fuel consumption data are from Zhang et al. (2011); and the fuel consumption data of other vehicles are estimated by multiplying vehicle numbers, average annual mileage and average gasoline consumption (Table 3.1). Indeed, the emissions of both road and rail transport can be affected by congestion and the failure of capacity, and there are differences between transport capacity and passenger demand. For instance, the highway length increased by an annual growth rate of 5% from 1980

–

176

356

–

Motorcycles

Rail

Air

Water

–

356

176

–

–

–

–

–

–

356

176

–

–

–

–

–

–

240

20

–

–

–

–

–

–

315

64

–

–

–

–

–

Avg.

–

440

130

–

–

–

–

–

High

1990s

42

110

10

–

37

15

113

–

Low

44

182

55

–

130

57

165

–

Avg.

45

360

116

–

182

110

317

–

High

2000s

41

109

19

54

37

15

104

18

Low

49

129

52

56

112

59

145

33

Avg.

115

154

94

58

168

104

388

52

High

Note Data from Denmark, Finland, France, Germany, India, Italy, Japan, Norway, Sweden, the UK, the USA and New Zealand, as well as the regions of Taiwan and the European Union, are used

–

–

Private cars and institutional vehicles

–

Taxis

City buses

–

Highway buses

Road

1970s Low

High

Low

Avg.

1960s

Table 3.2 CO2 emissions per pkm based on international experience (g/pkm)

52 3 Railways and National Carbon Emissions from Passenger Travel in China

3.2 Methodology

53

Table 3.3 Derived CO2 emission factor by type of transport fuel Fuel type

CO2 emission factor default (kg/TJ)

Net calorific value default (TJ/Gg)

Emission factor (kg CO2 /ton fuel)

Coal

96100

18.9

1816.29

Jet kerosene

71500

44.1

3153.15

Aviation gasoline

70000

44.3

3101.00

Motor Gasoline

69300

44.3

3069.99

Diesel

74100

43.0

3186.30

to 2009, while the highway passenger volume increased by an annual growth rate of 9% in the same period (National Bureau of Statistics of China 2010). However, due to data limitations, it is not possible to consider the factors of congestion, and failure of road or rail capacity separately in this book. Emission factor data are derived from IPCC Guidelines for National Greenhouse Gas Inventories (2006) (Table 3.3). Next, based on the national CO2 emission estimation, CO2 emissions at the provincial level is calculated by multiplying passenger turnover volume and national CO2 emission intensity of the respective sub-mode of each transport mode and summing them up. Passenger turnover volume data of rail transport, water transport and public buses on highway of road transport in provinces are obtained from the China Compendium of Statistics 1949–2008 and China Statistical Yearbook 2010. Because passenger turnover volumes from other vehicles in each province are not available in official statistics, they have to be estimated by the methods discussed above at the national level. Vehicle numbers are obtained from the China Statistical Yearbook, China Automotive Industry Yearbook and Yearbook of China Transportation and Communications; annual average mileage and average load factor are the same as shown in Table 3.1. Air transport is not included at the provincial level analysis because in China, the air passenger turnover volume data in each province is based on airline companies registered in the province instead of reflecting the real air transport activities happening in the province. Moreover, due to data limitations, CO2 emission intensity is derived from the fuel-based estimation. Since the vehicle number data in provinces, especially the motorcycle number data, can only be obtained since the late 1980s, the provincial level analysis is limited from 1988 to 2009. Both coefficients of variation (CV) and the maps are used to show the regional disparity dynamics. CV is a statistical measure to measure data dispersion and is defined as the ratio of standard deviation to the mean of the data series. Then, by estimating the national and provincial CO2 emissions from the four different transport modes of passenger travel, the overall performance of railways to CO2 emission reduction is contrasted with other transport modes based on their shares of passenger turnover and CO2 emissions. Also, alternative scenarios assuming that all railway passengers had all shifted to either road transport or air transport are built, to estimate the total CO2 emissions from passenger travel over the years, at both national and provincial levels. The alternative scenarios are contrasted with

54

3 Railways and National Carbon Emissions from Passenger Travel in China

the current scenario to further analyze the significance of railways in low carbon emissions in China. Finally, main factors contributing to CO2 emission change are analyzed by the Logarithmic Mean Divisia Index (LMDI) approach (Ang 2005), to investigate the contribution of modal shift to CO2 emissions change. Based on the Kaya Identity, the total emissions (G) is a product of seven factors, including the population factor (P), income factor (I), travel propensity factor (TP), travel activity mix factor (TM), modal energy factor (ME), fuel mix factor (FM) and emission factor (EF). These seven factors are grouped under three effects. Firstly, factors of P, I and TP represent the travel activity effect. These factors determine the absolute levels of travel activities in a society and, hence, the emission levels. Then, TM represents the structural effect, that is, the modal shift between different transport modes. Lastly, the energy intensity effect is captured by ME, FM and EF. These are factors affecting the CO2 emission level per unit of travel activity (pkm). Mathematically, the relationship between total emissions and the seven factors is expressed as Eq. (3.2). It is worth noting that although there are profound institutional changes in China during the study period, the institutional factor is not analyzed as an independent variable in the model of the book. This is mainly because of the difficulty of quantifying multiple institutional changes and the potential problem of double-counting because major institutional changes would have affected other variables (such as population, travel volume and income) included in our analysis already. G=

 i, j

=

P×



Fi, j G i, j T Ti Fi E × × × × × P E T Ti Fi Fi, j

P × I × T P × T Mi × M E i × F Mi, j × E Fi, j

(3.2)

i, j

where i denotes the passenger transport mode, j denotes the fuel type, and P, E, T, F, G denote national population, national GDP at constant price, passenger turnover volume, amount of fuel consumption and amount of CO2 emission respectively. Then, following Ang (2005), multiplicative and additive decomposition indices of each factor are calculated. The LMDI formulae used for calculating the indices are shown in Table 3.4. Two periods are examined: 1949–1979 and 1980–2009. Data for CO2 emissions (G, Gi,j ) are based on the fuel-based estimation. However, data of water transport emissions before 1975 and road transport before 1980 are not available. They are estimated by multiplying passenger turnover volume by water CO2 emission intensity in 1975 and road CO2 emission intensity in 1980 respectively. As a result, energy intensity effect of water and road transport cannot be included in the decomposition analysis of 1949–1979. Moreover, there is no data on changes in the emission factors of specific fuel types (EFj ) in China over time. Hence, the fact that the same fuel type might have a lower CO2 emission intensity over time cannot be analyzed in the decomposition analysis. As discussed in this section, the data required for this chapter are very intensive and not all of them are available from official sources. Hence, estimations based both

3.2 Methodology

55

Table 3.4 The LMDI formulae for each factor in the decomposition analysis Factor

Multiplicative decomposition

Population D P =  (P)  exp i, j Income (I) D I =   exp i, j Travel DT P = propensity  (TP) exp i, j Travel activity mix (TM)

DT M =  exp i, j

Modal energy factor (ME)

D M E =  exp i, j

Fuel mix (FM)

DF  M =  exp i, j

Emission factor (EF)

DE  F =  exp i, j















   0 / ln G T − ln G 0 G i,T j − G i, j i, j i, j   (G T − G 0 )/ ln G T − ln G 0    0 / ln G T − ln G 0 G i,T j − G i, j i, j i, j   (G T − G 0 )/ ln G T − ln G 0    0 / ln G T − ln G 0 G i,T j − G i, j i, j i, j   (G T − G 0 )/ ln G T − ln G 0    0 / ln G T − ln G 0 G i,T j − G i, j i, j i, j   (G T − G 0 )/ ln G T − ln G 0    0 / ln G T − ln G 0 G i,T j − G i, j i, j i, j   (G T − G 0 )/ ln G T − ln G 0    0 / ln G T − ln G 0 G i,T j − G i, j i, j i, j   (G T − G 0 )/ ln G T − ln G 0    0 / ln G T − ln G 0 G i,T j − G i, j i, j i, j   (G T − G 0 )/ ln G T − ln G 0

Additive decomposition  ln

 ln

 ln

ln

ln

ln

IT I0





G P = 0  G i,T j − G i, j i, j ln G T − ln G 0 i, j i, j



T PT T P0

G I = 0  G i,T j − G i, j



i, j ln G T − ln G 0 i, j i, j



T M iT T M i0

M E iT M E i0



0 E F i, j

G T P = 0  G i,T j − G i, j

G T M = 0  G i,T j − G i, j i, j ln G T − ln G 0 i, j i, j



F M i,T j

E F i,T j



i, j ln G T − ln G 0 i, j i, j

0 F M i, j

ln

PT P0

G M E = 0  G i,T j − G i, j i, j ln G T − ln G 0 i, j i, j

G =   F M G T − G 0 i, j i, j i, j

ln G i,T j

0 − ln G i, j

G =   E F G T − G 0 i, j i, j i, j ln G T − ln G 0 i, j i, j

 ln

 ln

 ln

ln

ln

ln

IT I0





T PT T P0



T M iT T M i0

M E iT M E i0





F M i,T j



0 F M i, j

ln

PT P0

E F i,T j



0 E F i, j

from international experience and local knowledge is required for many of the data. Table 3.5 provides a summary list of the key data required, their sources and ways of estimation (if necessary) to facilitate better understanding.

3.3 National CO2 Emissions from Passenger Travel 3.3.1 Distance-Based Method From 1980 to 2009, CO2 emissions from passenger transport increased dramatically. Figures 3.5, 3.6, 3.7, 3.8 and 3.9 show the distance-based CO2 emissions from the road, rail, air and water transport respectively. The three scenarios of low, average and high modal carbon emission (Gi,t ) are shown as three curves in each figure. The general trend of CO2 emissions from road, rail and air transport was rising

56

3 Railways and National Carbon Emissions from Passenger Travel in China

Table 3.5 List of key data required, their sources and ways of estimation (if necessary) Data required

Model

Available from official sources (Y/Partial/N)

Ways of estimation, if necessary

Modal and sub-modal Distance-based passenger-kilometers method, fuel-based method, decomposition analysis

Partial

National road transport sub-modal pkm not available Mainly estimated by vehicle number, annual average mileage and average load

Modal and sub-modal Distance-based emission intensity method

N

Summarized from relevant studies of other overseas administrations. Three scenarios of low, average and high modal carbon emission intensity were estimated

Modal ton-kilometers

Fuel-based method

Y

Modal conversion factor between passenger-km and ton-km

Fuel-based method

Y

Modal fuel consumption

Fuel-based method

Partial

Emission factor by fuel type

Fuel-based method

Y

Provincial modal passenger-kilometers

Provincial level Partial emission estimation

Fuel consumption of road transport not available Mainly estimated by vehicle number, average annual mileage and average gasoline consumption

Provincial road transport pkm not available Mainly estimated by provincial vehicle number, annual average mileage and average load Air transport pkm not reflecting provincial air activities and was excluded from the estimation (continued)

3.3 National CO2 Emissions from Passenger Travel

57

Table 3.5 (continued) Data required

Model

Available from official sources (Y/Partial/N)

Ways of estimation, if necessary

Provincial modal and sub-modal emission intensity

Provincial level N emission estimation

Adopted the modal and sub-modal emission intensity at the national level from fuel-based estimation

Provincial motor vehicle number

Provincial level Partial emission estimation

Provincial institutional vehicle number estimated by the number of total civil passenger motor vehicles and other motor vehicles in the provinces

National population

Decomposition analysis

Y

National GDP at constant price

Decomposition analysis

Y

400 350

CO2 emission (Mt)

300 250 200 150 100 50

1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

0

Low

Average

Fig. 3.5 CO2 emissions from road passenger transport

High

1950 1952 1954 1956 1958 1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008

CO2 emission (Mt)

1950 1952 1954 1956 1958 1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008

CO2 emission (Mt)

58 3 Railways and National Carbon Emissions from Passenger Travel in China

80

70

60

50

40

30

20

10

0

Low

Low

Average

Average

Fig. 3.7 CO2 emissions from air passenger transport

High

Fig. 3.6 CO2 emissions from rail passenger transport

60

50

40

30

20

10

0

High

3.3 National CO2 Emissions from Passenger Travel

59

1.4

CO2 emission (Mt)

1.2 1.0 0.8 0.6 0.4 0.2 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

0.0

Low

Average

High

Fig. 3.8 CO2 emissions from water passenger transport

600

CO2 emission (Mt)

500 400 300 200 100 0 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 Low

Average

High

Fig. 3.9 Total CO2 emissions from all passenger transport in China

over the years, while the CO2 emissions from water transport were relatively stable. However, due to the data limitation of emission intensities for respective transport mode over the years, which were generalized by international experience (as shown in Table 3.2), there was a significant drop of CO2 emission from rail transport in 1970. Nevertheless, this limitation doesn’t influence the overall growing trend of

60

3 Railways and National Carbon Emissions from Passenger Travel in China

CO2 emissions. Figure 3.6 show the national CO2 emissions for the period. Overall, the national CO2 emissions from passenger transport have increased dramatically, especially since 1980. The low, average and high CO2 emissions from passenger transport stood at 4 Mt, 13 Mt and 25 Mt respectively in 1980. By 2009, the respective figures were 187 Mt, 333 Mt and 488 Mt. The analysis also clearly shows that road transport was the leading contributor of CO2 emissions from passenger transport in China, especially after 1980. In 1980, road transport produced less than 7 Mt of CO2 emissions but it produced at least 135 Mt (low scenario) and up to about 361 Mt (high scenario) of CO2 emissions in 2009. Rail transport shows more variations by the three scenarios. Under the low scenario, its emissions tumbled in 1970 and stabilized at less than 5 Mt of CO2 emissions until 2000. Under the average and high scenarios, CO2 emissions from rail transport grew more rapidly. Nonetheless, as railway has the lowest emission intensity, CO2 emissions from railway were still relatively low at about 74 Mt in 2009 under the high scenario. The trend for air transport CO2 emissions demonstrates a very high growth rate, especially after 1980. It emitted less than 2 Mt of CO2 in 1980 under all three scenarios. In 2009, it emitted about 37 Mt (low scenario) to 52 Mt (high scenario) of CO2 . Comparatively speaking, water transport emissions were the most stable. Under all three scenarios, water transport emitted less than 1 Mt CO2 for most years between 1980 and 2009.

3.3.2 Fuel-Based Method Figure 3.10 shows the results of passenger transport CO2 emission estimates by the fuel-based method. Similar to the results of the distance-based method, road transport was the largest contributor of passenger transport CO2 emission in China. It emitted about 270.9 Mt CO2 in 2009, almost 87 times as much as the figure in 1980 (3.1 Mt). Air transport was the second largest contributor in 2009. The CO2 emissions from air transport increased dramatically after 1980, rising from 0.8 Mt in 1980 to 29.3 Mt in 2009. Rail and water transport were relatively stable in CO2 emissions. Rail transport emitted 1.8 Mt CO2 in 1949, 9.5 Mt in 1988, and only 7.6 Mt in 2009. Water transport emitted 0.07 Mt in 2009, a level even lower than its emissions in 1980 (0.18 Mt). In 2009, the shares of CO2 emissions by road, rail, air and water transport were 87.99%, 2.48%, 9.51% and 0.02% respectively. The respective shares in 1980 were 28.05%, 63.46%, 6.91% and 1.59%. Figure 3.11 shows the CO2 emissions per pkm from passenger travel based on fuel consumption. Both rail and water transport demonstrated low and falling emission intensity. In other words, the energy efficiency of these two modes has improved over time. Air transport had the highest CO2 emission intensity, but it decreased after 1973. This was probably due to the fuel change from aviation gasoline to jet kerosene. Road transport has the second highest emission intensity and it increased from 32 g/pkm in 1980 to 71 g/pkm in 2009.

3.3 National CO2 Emissions from Passenger Travel

61

300

CO2 emission (Mt)

250 200 Air 150

Rail Road

100

Water

50

1949 1952 1955 1958 1961 1964 1967 1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000 2003 2006 2009

0

Fig. 3.10 CO2 emissions from passenger travel based on fuel consumption

450

CO2 emission per pkm (g)

400 350 300 Air

250

Rail

200

Road

150

Water

100 50 1949 1952 1955 1958 1961 1964 1967 1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000 2003 2006 2009

0

Fig. 3.11 CO2 emissions per pkm from passenger transport based on fuel consumption

3.3.3 Insights from the Two Methods Figure 3.12 juxtaposes the different estimates under the distance-based and fuelbased methods. In general, the fuel-based estimates of national CO2 emissions from China’s passenger transport were between the low and average scenarios of

62

3 Railways and National Carbon Emissions from Passenger Travel in China

600

CO2 emission (Mt)

500 400 Low 300 200

Average High Fuel-based

100 0

Fig. 3.12 Contrast of distance-based and fuel-based estimations

the distance-based estimates. To recall, the high, average and low scenarios of the distance-based method reflect the different levels of empirical carbon emission intensity in different parts of the world. Hence, the overall CO2 emission intensity for passenger transport in China was probably lower than the average situation. However, it was not as low as that demonstrated to be achievable elsewhere in the world under the low scenario. While results of the distance-based method (using modal carbon intensities based on wide ranges taken from other administrations) do not give much insights about the Chinese context, these estimates do give us some indications about the CO2 emission intensity achievable (the low scenario) by other overseas administrations at the same period. Moreover, with the estimates from the fuel-based method, one can get some insights about the relative performance of the passenger transport sector in China. How about the specific modal CO2 emission performance per pkm? As explained earlier, there is no other feasible way a prior in estimating the modal CO2 emission intensity (g/pkm) in China based on the distance-based method. Nonetheless, with both sets of estimates, additional insights can be obtained. With the modal CO2 emissions (Gi,t ) estimated by the fuel-based method, one can derive the modal carbon intensities per pkm from Eq. (3.1) in an ex post manner. The results, together with the international data in different periods (see Table 3.2), are displayed in Fig. 3.13. Due to various technological factors and load factors, the CO2 emission intensity (g/pkm) of different countries can differ much even for the same mode. During the study period, the road CO2 emissions per pkm in China were lower than the levels in Japan, the UK and Finland but higher than India. Nonetheless, the level has been rising continuously since 1990. Generally, road CO2 emissions per pkm are lower in

3.3 National CO2 Emissions from Passenger Travel

CO2 emission (g/pkm)

Road

180 160 140 120 100 80 60 40 20 0 1985

UK Japan India Finland China 1990

1995

2000

2005

2010

Year

CO2 emission(g/pkm)

Rail

UK

200 180 160 140 120 100 80 60 40 20 0 1950

Japan US France Germany Italy Norway Sweden Denmark EU 1970

1990

2010

China

Year

CO2 emission (g/pkm)

A

500 450 400 350 300 250 200 150 100 50 0 1950

US EU Japan India France Germany Italy Sweden Denmark 1970

1990

2010

China

Year

Water

140

CO2 emission (g/pkm)

Fig. 3.13 Passenger transport CO2 emission intensity of China compared with overseas administrations

63

120 100 80

UK

60

EU

40

China

20 0 1985

1990

1995

2000

Year

2005

2010

64

3 Railways and National Carbon Emissions from Passenger Travel in China

developing countries with lower use of private automobiles and more public transport, which resulted in higher passenger load per vehicle. For rail CO2 emissions per pkm, the performance of Japan was consistently better than the European countries. USA has the highest emission intensity in rail transport. While the rail CO2 emission intensity of China was only about average in the 1970s, it has been falling consistently. Moreover, the rail emission intensity in China was much better than the USA throughout the study period. This was probably due to the much higher passenger and freight intensity per km of railway tracks in China, especially when compared to the USA (Loo and Liu 2005). For air CO2 emissions per pkm, the fuel change from aviation gasoline to jet kerosene around the 1980s has greatly lowered the emission intensity of air transport in China. Overall, significant progress has been achieved for rail and air transport. For water transport, China’s water emission intensity was lower than that of European Union and the UK. Generally, China’s passenger transport sector compared quite well internationally in terms of CO2 emissions per pkm for all four major transport modes. However, load factors, rather than better energy efficiency or transport fleet, were likely to be the major reasons.

3.4 Provincial CO2 Emissions from Passenger Transport From 1988 to 2009, CO2 emissions from passenger transport increased in all provinces. Table 3.6 summarizes the situations in 1988, 1998 and 2009. It is noteworthy that the provincial totals do not necessarily tally with the national totals because provincial data were incomplete and some passenger transport activities were listed under the central government, of which provincial breakdowns are not provided. Nevertheless, one can see that great regional disparity persisted. The regional disparity of CO2 emissions over time can be examined by changes in CVs for each transport mode. The higher the CV, the higher the regional disparity. During the study period, the CV of total passenger transport emissions in 1988, 1998 and 2009 is 0.63, 0.72 and 0.78 respectively. It means that the overall trend was for the CO2 emissions of different provinces to diverge from 1988 to 2008. For rail transport emissions, the regional disparity did not change much, with the CVs for 1988, 1998 and 2009 being 0.77, 0.75 and 0.78 respectively. Nonetheless, the regional disparity of water transport emissions has reduced since 1998 with the CV falling from 1.59 then to 1.13 in 2009. However, for road transport, its distribution among provinces has become much more uneven from 1988 to 2008. This was due to the rapidly increasing CO2 emissions from leading economic provinces or municipalities like Guangdong, Shandong, Jiangsu and Zhejiang. The amount of CO2 road emissions in these four provinces increased by as much as 27.69 Mt (from 1.32 to 29.01 Mt), 24.06 Mt (from 0.69 to 24.75 Mt), 20.55 Mt (from 0.68 to 21.23 Mt) and 17.78 Mt (from 0.47 to 18.25 Mt) respectively between 1988 and 2009 (Table 3.6). By 2009, Guangdong (29.01 Mt), Shandong (24.75 Mt) and Jiangsu (21.23 Mt) were emitting more than 20 Mt of CO2 annually from road passenger transport alone. Moreover, road passenger transport emitted over 10 Mt of CO2 annually in five other provinces

79,007

135,365

271,667

526,179

849,428

335,134

559,757

241,602

Fujian

Jiangxi

Shandong

Henan

Hubei

Hunan

Guangdong

430,305

Jiangsu

272,604

93,181

Shanghai

387,857

38,481

Heilongjiang 509,375

Anhui

198,049

409,140

Jilin

Zhejiang

145,743

959,324

Liaoning

90,223

198,564

396,448

188,640

413,090

231,911

232,390

76,748

208,763

137,048

177,687

358,828

88,555

225,977

218,065

396,062

Inner Mongolia

916,993

Hebei

Shanxi

197,456

Tianjin

397,005

611,799

362,515

678,181

381,869

494,744

100,371

398,521

282,295

301,871

49,542

231,673

191,596

468,623

156,586

137,390

651,612

119,274

90,657

2009

1,317,953

415,905

441,854

563,383

692,453

224,674

356,814

286,624

474,684

675,100

564,978

503,606

334,632

766,514

235,016

278,522

560,848

194,218

681,351

6,815,740

1,811,210

2,180,115

2,939,682

4,116,138

944,946

1,559,750

1,537,909

2,441,167

3,837,400

1,765,871

1,702,673

1,524,313

2,813,152

1,176,663

1,435,188

3,183,718

1,287,952

3,154,823

1998

1988

65,757

1998

1988

172,397

Road emissions (tons)

Rail emissions (tons)

Beijing

Provinces

Table 3.6 Provincial transport CO2 emissions from 1988 to 2009

29,014,915

8,250,162

8,668,327

13,944,151

24,747,556

5,668,028

8,266,708

8,094,499

18,253,928

21,232,637

5,847,302

5,635,820

5,419,858

8,905,860

5,550,628

7,067,279

14,414,816

4,926,469

14,150,299

2009

22,394

6,100

38,807

369

359

1,435

3,914

7,125

17,472

8,107

48,773

649

29

7,438

–

–

–

–

–

1988

968

2,380

500

9,534

377

1,746

211

7,120

1,229

6,016

323

254

6,728

–

–

–

165

–

2009

13,387 6,748

3,654

4,894

216

5,162

1,447

1,672

1,601

9,897

1,177

4,111

27

76

2,452

–

30

–

219

–

1998

Water emissions (tons)

1,581,949

981,762

815,795

1,413,180

1,218,991

497,776

496,093

681,606

764,760

1,113,512

706,932

1,013,630

743,801

1,733,276

453,081

504,499

1,477,841

391,674

853,748

1988

7,027,691

2,211,312

2,373,649

3,352,988

4,353,211

1,178,783

1,638,170

1,748,273

2,588,112

4,016,264

1,808,463

1,900,749

1,670,132

3,174,432

1,265,218

1,525,441

3,579,780

1,367,178

3,220,580

1998

(continued)

29,418,668

8,862,929

9,033,222

14,622,832

25,138,959

6,163,149

8,368,825

8,493,231

18,543,343

21,535,737

5,902,860

5,867,816

5,611,708

9,381,211

5,707,214

7,204,669

15,066,428

5,045,908

14,240,956

2009

Sub-total of rail, road and water emissions (tons)

3.4 Provincial CO2 Emissions from Passenger Transport 65

2009

18,238

Ningxia

326,199

251,578

0.77

Average

Standard deviation

Coefficient of variation

0.75

116,091

154,575

72,395

15,771

9,147

174,186

280,177

0.78

191,410

246,301

130,002

29,743

38,642

0.68

267,885

396,016

201,958

67,189

62,795

185,055

331,836

19,775

248,442

213,536

559,125

517,073

0.75

1,384,272

1,842,521

1,161,427

228,916

248,499

624,677

1,164,676

97,234

1,368,145

711,748

2,421,095

752,515

1,721,619

0.80

6,953,346

8,742,976

3,943,097

1,238,270

799,094

2,478,344

6,098,946

378,523

7,914,498

3,638,815

13,120,284

3,530,177

1.40

14,291

10,172

–

–

–

–

85

–

569

329

31,289

–

1,068

7,128

1988 2,292

1,626

2009

1.59

5,675

3,568

–

–

–

158

333

–

607

691

3,142

1.13

3,132

2,760

–

–

51

196

354

–

1,484

3,909

2,139

25,474 9,901

2,507

2,695

1998

0.63

454,174

718,123

327,428

85,427

90,254

459,649

705,990

19,775

319,006

399,143

1,006,375

–

118,028

568,712

1988

0.72

1,445,243

1,994,872

1,233,822

244,687

257,646

799,021

1,358,366

97,234

1,402,415

839,324

2,564,742

823,383

519,881

1,700,084

1998

0.78

7,048,748

8,991,414

4,073,099

1,268,013

837,787

2,758,717

6,431,279

386,520

7,986,108

3,800,117

13,346,381

3,635,427

1,725,609

8,275,111

2009

125,470

27,459

Qinghai

331,979

7,997

70,126

157,393

223,958

–

115,057

8,111,343

2009

9,459,783 4,637,241 7,635,330 11,880,472 57,118,162 271,032,250 203,439 85,629 66,251 21,543,694 61,841,032 278,733,831

274,594

Gansu

193,357

–

33,663

126,885

140,505

95,349

1,698

1,593,746

1998

Sub-total of rail, road and water emissions (tons)

Provincial Total*

374,069

Shaanxi

301

45,394

306,574

Water emissions (tons)

Xinjiang

69,995

185,278

Guizhou

–

415,961

Sichuan

Tibet

–

Chongqing

Yunnan

1,903

Hainan

162,142

1988

103,643

1998

1988

255,010

Road emissions (tons)

Rail emissions (tons)

Guangxi

Provinces

Table 3.6 (continued)

66 3 Railways and National Carbon Emissions from Passenger Travel in China

3.4 Provincial CO2 Emissions from Passenger Transport

67

or municipalities, that is, Zhejiang (18.25 Mt), Hebei (14.41 Mt), Beijing (14.15 Mt), Henan (13.94 Mt) and Sichuan (13.12 Mt) (Table 3.6). The modal emission intensity of road increased in all provinces except Tibet; and it varied considerably depending on how the road pkm were realized in different provinces. Generally, the higher is the share of private automobiles within road transport, the higher is the carbon emission intensity of the mode, ceteris paribus. Thus, Beijing, Tianjin and Shanghai with high motorization rates had the highest road transport emission intensities over the years. In 2009, their emission intensities were 115 g/pkm, 105 g/pkm and 99 g/pkm respectively, much higher than the national average level 71 g/pkm. Figure 3.14 shows the spatial distribution of CO2 emissions from passenger transport over time. It is clear that the annual CO2 emissions from passenger transport were highly concentrated in a few eastern provinces, such as Guangdong, Beijing, Shandong and Jiangsu, while the western region, including Tibet, Qinghai, Gansu and Ningxia, kept having much lower CO2 emissions in all these years. In 1988, there was not much difference between provinces in the eastern and in the central part of

Fig. 3.14 Regional disparity of CO2 emissions from passenger transport in China

68

3 Railways and National Carbon Emissions from Passenger Travel in China

China; however, in 1998 and 2009, eastern provinces such as Guangdong, Jiangsu and Shandong were having much larger CO2 emissions than the inland provinces.

3.5 The Role of Railways in CO2 Emission Reduction Table 3.7 shows the overall modal share of both passenger transport turnover and respective CO2 emissions for each transport mode in China from 1980 to 2009, since the road transport data can be only attained after 1980. In this period, railways have undertaken 23.7% of passenger transport turnover in China, producing only 8.7% of CO2 emissions. Its quotient of CO2 emissions share and transport turnover share (0.37) is much lower than that of air transport (1.90) and road transport (1.15), which means that the railways contribute much less CO2 emissions for the same amount of passenger travel. The average CO2 emissions per passenger-km for railways over the years were only 18.4 grams, also much lower than air transport (94.9 grams) and road transport (57.8 grams), also showing that railways performed better than other passenger transport modes such as air and road transport in China. However, the modal share of railways (23.7%) is much lower than road transport (70.1%) in terms of passenger transport turnover in China since 1980, which greatly limit the role of railways in low carbon emissions. It is worth noting that railways here mainly refer to conventional railways, instead of HSR, because the HSR only develops after 2008. If without railways in China, almost all the passengers would be undertaken by either road or air transport, considering the limited usages of water transport. Alternative scenarios of substituting railways for road transport and air transport are constructed. Although the scenarios seem to be unrealistic, the purpose is to estimate the upper and lower bounds, instead of making a forecast. Assuming that the all rail passengers had shifted to road transport, the total CO2 emission from passenger travel in China from 1980 to 2009 would increase from 2469 Mt to 2869 Mt; while assuming that all rail passengers had shifted to air transport, the total CO2 emission from passenger travel in China would increase to 3458 Mt (Fig. 3.15). Therefore, the railways have led to 400–989 Mt CO2 emission reduction in China since 1980. However, its modal share was decreasing, which makes its contribution declined in Table 3.7 Overall modal shares of passenger travel and CO2 emissions of different transport modes from 1980 to 2009 Rail

Road

23.7%

70.1%

5.4%

0.8%

Share of CO2 emissions from passenger travel

8.7%

80.9%

10.3%

0.1%

Quotient of CO2 emissions share and passenger transport turnover share

0.37

1.15

1.90

0.18

Share of passenger transport turnover

Average CO2 emission per passenger-km (grams)

18.4

57.8

Air

94.9

Water

9.1

3.5 The Role of Railways in CO2 Emission Reduction

69

400.00 350.00

CO2 emissions (Mt)

300.00 250.00 200.00 150.00 100.00 50.00 0.00 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 Current scenario Alternative scenario (road transport dominant) Alternative scenario (air transport dominant) Alternative scenario (rail transport dominant) Fig. 3.15 Contrast of alternative scenarios and current scenario Note road or air transport dominant means that all the rail passengers shift to road or air transport; rail transport dominant means that all the road and air passengers shift to rail transport

recent years, which will be discussed in the following part (Sect. 3.6). In contrast, assuming that all the road and air transport would be undertaken by railways, CO2 emission from passenger travel in China from 1980 to 2009 would decrease to only 738 Mt. Therefore, the railways have greatly mitigated the CO2 emissions from transport in China over the years, comparing with road and air transport. From the spatial perspective, excluding air transport, if the railways passengers had shifted to road transport, the sub-total CO2 emissions of rail, road and water transport in these provinces would increase; while if the road transport passengers shifted to railways, the sub-total CO2 emissions of rail, road and water transport in these provinces would decrease (Table 3.8). On average, the modal shift from rail to road would make the CO2 emissions from passenger travel increase 12%, 24%, and 13% in year 1988, 1998, and 2009, respectively; while the modal shift from road to rail would make the CO2 emissions from passenger travel decrease 16%, 69%, and 83% in year 1988, 1998, and 2009, respectively. It means that the contribution of railways to low carbon emissions in these provinces generally increased from 1988 to 1998, but decreased from 1998 to 2009; however, the railways could contribute more if they can attract more road transport passengers.

70

3 Railways and National Carbon Emissions from Passenger Travel in China

Table 3.8 Contrast of alternative scenarios and current scenario in provinces Provinces

Alternative scenario (Road transport dominant: all rail transport replaced by road)

Alternative scenario (Rail transport dominant: all road transport replaced by rail)

1988 (%)

1998 (%)

1988 (%)

1998 (%)

2009 (%)

Beijing

13

14

4

−16

−72

−85

Tianjin

21

31

15

−10

−67

−83

Hebei

20

32

22

−10

−67

−82

2009 (%)

Shanxi

14

20

11

−15

−70

−84

Inner Mongolia

17

24

13

−13

−69

−84

Liaoning

20

46

25

−10

−64

−81

Jilin

20

37

15

−11

−66

−83

Heilongjiang

20

34

20

−11

−67

−82

8

11

5

−16

−72

−85

Jiangsu

10

11

6

−18

−72

−85

Zhejiang

9

12

7

−18

−72

−85

15

31

14

−14

−67

−83

Shanghai

Anhui Fujian

6

11

5

−20

−72

−85

Jiangxi

14

49

29

−15

−63

−80

Shandong

13

17

6

−15

−71

−85

Henan

16

37

18

−13

−66

−83

Hubei

12

21

14

−16

−70

−83

Hunan

15

45

24

−14

−64

−81

5

8

6

−21

−73

−85

Guangxi

12

14

6

−16

−72

−85

Hainan

0

0

0

−25

−75

−86

Guangdong

0

13

9

−25

−70

−84

Sichuan

11

15

7

−17

−71

−85

Guizhou

15

40

16

−15

−65

−83

Yunnan

6

8

4

−21

−73

−86

Tibet

0

0

9

−25

−75

−84

Shaanxi

17

45

22

−13

−64

−82

Gansu

19

72

38

−11

−57

−79

Qinghai

11

14

21

−17

−71

−82

Ningxia

6

20

10

−21

−70

−84

Xinjiang

12

18

12

−17

−71

−84

Average

12

24

13

−16

−69

−83

Chongqing

3.6 The Role of Modal Shift in CO2 Emission Reduction

71

3.6 The Role of Modal Shift in CO2 Emission Reduction Figures 3.16 and 3.17 summarize the results of decomposition analysis of passenger transport CO2 emissions in China. From the decomposition analysis, it is possible to estimate the extent to which each factor contributed to the total CO2 emission change in China during the study period. To reiterate, the three types of effects affecting CO2 emissions are the travel activity effect, structural effect and energy intensity effect. The two periods examined are 1949–1979 and 1980–2009. Both periods are examined by the LMDI decomposition method with multiplicative and additive indexes. To recall, these results are based on Eqs. (3.2) and the LMDI formulae in Table 3.4, conceptualizing carbon emission changes as caused by the seven factors of population (P), income (I), travel propensity (TP), travel activity mix (TM), modal energy (ME), fuel mix (FM) and emission factor (EF). The first three factors represent the travel activity effect. TM represents the structural effect, and ME, FM and EF represent the energy intensity effect. Figures 3.16 and 3.17 suggest that most factors considered have been driving the CO2 emission amount up. In particular, income (I) was the leading factor contributing to the increase in passenger transport CO2 emissions in both periods. Income in China (measured by real GDP per capita) has increased by almost 34-fold over the last sixty years. Next, the increased travel propensity (TP) from 1949 to 1979 was the second important factor leading to the increasing CO2 emissions. The increased travel intensity (measured by the level of passenger transport activities per income generated) was mainly due to rising employment rate, rapid urbanization, improving living standard, and increasing motorization (Rout et al. 2011). In 1949–1979, the travel activity mix factor (TM) and modal energy factor (ME) were contributing to CO2 emission reduction. These factors were having multiplicative indexes below 1

Fuel mix

Population 7 6 5 4 3 2 1 0

Modal energy factor

Income 1949-1979 1980-2009 Travel propensity

Travel activity mix Fig. 3.16 LMDI multiplicative decomposition results of passenger transport CO2 emissions in China

72 200 180 160 140 120 100 80 60 40 20 0 -20

3 Railways and National Carbon Emissions from Passenger Travel in China

1949-1979 1980-2009

Population

Income

Travel Travel propensity activity mix

Modal energy factor

Fuel mix

Fig. 3.17 LMDI additive decomposition results of passenger transport CO2 emissions in China

and additive indexes below 0. However, TM became the second important factor for increase in passenger transport CO2 emission from 1980 to 2009. From 1949 to 1979, the passenger modal shift in China was in favor of low carbon emissions. In sharp contrast, the passenger modal shift in China from 1980 to 2009 directly contributed to higher carbon emissions. As discussed in this chapter, the modal split of passenger travel changed over time in China from rail-dependence to road- dependence in the last few decades, especially after 1980. Both road and air transport are passenger modes having high CO2 emission intensities. In contrast, water transport with low CO2 emission intensity has been declining. Rail transport was increasingly replaced by road transport since 1998.

3.7 Summary First, in China, the emission of CO2 from rail passenger transport has been relatively stable over the years, partly because of the improvements in emission efficiency and partly because of the fall in passenger volume until 1998. In contrast, the emissions from road transport and air transport are the largest, both of which are increasing over time; water transport is also a low transport emission mode, however with low share of passenger travel. The average emission intensities of rail, road, air and water transport from 1980 to 2009 are 18.4, 57.8, 94.9 and 9.1 grams per passenger-km. By alternative scenarios analysis, if railways shift to road or air transport, the CO2 emissions from passenger travel in China from 1980 to 2009 would have increased 400–989 Mt. Thus, railways performed much better than other passenger transport modes such as air and road transport in China. In order to reduce the carbon dioxide emissions, it is important to maintain the railways as major transport modes instead of road or air transport.

3.7 Summary

73

Second, the spatial analysis further suggests that the emission of CO2 from road transport has actually become more concentrated at the eastern coastal region. Generally, most emission of CO2 from passenger transport was concentrated in the highly developed provinces, such as Beijing, Guangdong, Jiangsu and Shandong. The west region has lower CO2 emissions. Although detailed breakdowns of CO2 emissions from air transport at the provincial level are not available, it is likely that air transport activities are also highly concentrated at the developed coastal region. Considering the highly concentration of road and air transport in the eastern region and the flat terrain in the eastern region, railways could play more important role in eastern coastal region than the west. Third, the modal shift has changed from an emission reduction factor for 1940 and 1979 to the second most important factor leading to rising CO2 emissions for 1980 to 2009. Therefore, besides policies about reducing energy intensity and applying alternative fuels, strategies to encourage modal shifts toward low-carbon passenger modes, such as railways, should also be actively adopted. Further analysis about modal shift from air and road transport to railways in China will be discussed in the next three chapters.

Chapter 4

Railways and Regional Carbon Emissions from Passenger Travel in China

4.1 Introduction Carbon dioxide (CO2 ) emission from energy consumption has become a worldwide concern due to its great pressure on climate change (Intergovernmental Panel on Climate Change 2006). Among the various sources of global anthropogenic CO2 emission, the transport sector accounted for 23% in 2013 (IEA 2014), which is however very difficult to decarbonize. On the one hand, current transport sector relies greatly on fossil fuels; on the other hand, the demand of both passenger and freight transport keeps growing (Schwanen 2015). China, as the world’s second largest economy and a developing country, its challenge to reduce CO2 emissions from transport sector is even greater. Since 1990, the passenger turnover volume in China has grown about 4 times to 2,575.17 billion passenger-km in 2013, and the freight turnover volume has grown more than 6 times to 168,014 billion ton-km in 2013 (National Bureau of Statistics of China 2014). Moreover, most transport energy consumption in China came from fossil fuel, the share of which was 71% in 1990 and above 90% in 2013 (IEA 2014). It is worth noting that there is great regional disparity of CO2 emissions from transport sector in China, mainly concentrating in a few eastern developed regions (Loo and Li 2012; Tian et al. 2014). Thus, estimating and analyzing the CO2 emissions from transport sector in these developed regions is significant for policy making about energy conservation and low carbon emissions of the transport sector in China. There are three major metropolitan regions in China, which are called Jing-Jin-Ji (JJJ) region, Yangtze River Delta (YRD) region and Pearl River Delta (PRD) region. As shown in Fiure 4.1, JJJ region includes Beijing, Tianjin and Hebei province in North China; YRD region includes Shanghai, Jiangsu province and Zhejiang province in Eastern China; PRD region includes nine cities of Guangdong province in South China. These three regions only accounted for 5% of the national land, but concentrated 24% of the national population and 45% of the national GDP in 2013 (National Bureau of Statistics of China 2014; Statistical Bureau of Guangdong © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 L. Li, Railways and Sustainable Low-Carbon Mobility in China, https://doi.org/10.1007/978-981-15-9081-8_4

75

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4 Railways and Regional Carbon Emissions from Passenger Travel in China

2014). Meanwhile, they have been undergoing rapid urbanization and motorization in recent decades (Gu et al. 2011), with relative high growth rate of urban population (Haas and Ban 2014) and vehicle ownership (Liu et al. 2013), both of which drive the transport CO2 emissions to grow. In addition, most national policies about energy conservation and low carbon emission have highlighted these metropolitan regions with more advanced technologies and stricter energy efficiency standards than other regions (General Office of the State Council 2014). Thus, the three metropolitan regions are expected to play a leadership role in promoting low carbon transport in China. However, CO2 emissions from transport sector in these regions lack sufficient understanding. Most existing studies focus on CO2 emissions from transport sector at national level (He et al. 2005; Wang et al. 2011; Yang et al. 2015), provincial level (Cai et al. 2012; Hao et al. 2014) or city level (Peng et al. 2015; Wang et al. 2013; Liu et al. 2013). The studies about the metropolitan region as a whole (Wu et al. 2012) are still limited. To promote the sustainable transport development in metropolitan regions and solve the transport and energy problems in megacities, it is necessary to understand the spatial-temporal patterns and structure of CO2 emissions from transport sector in metropolitan regions, as well as the influencing factors. Furthermore, among the substantial studies about CO2 emissions from transport sector, the freight transport has not got as much attention as passenger transport, and the long-distance transport has not got as much attention as short-distance travel, especially at the regional level (Schwanen 2015). Thus, it is necessary to estimate the CO2 emissions from transport sector in these three metropolitan regions, which cover both passenger and freight transport and intercity and urban transport in the metropolitan regions. Meanwhile, more studies about analyzing the influencing factors of the CO2 emissions growth in each metropolitan region are needed (Fig. 4.1). This chapter tries to estimate the CO2 emission from both intercity and urban passenger and freight transport in three major metropolitan regions of China, simultaneously covering rail, water, and road transport modes. It further analyzes the influencing factors of CO2 emission in each metropolitan region by decomposition analysis. The structure is as follows. It firstly introduces the methodology and data sources of the estimation. Then, it estimates the CO2 emissions from transport sector in the three metropolitan regions, i.e. JJJ, YRD, and PRD since 2000. Based on the estimation and comparison of amount, modal structure and regional disparity of CO2 emission from transport sector in these three regions, influencing factors such as population, economic welfare, transport modal structure, and energy efficiency are analyzed by decomposition analysis. Finally, some conclusion and discussion about policy implications is made.

4.2 Methodology

77

Fig. 4.1 The location of three major metropolitan regions in China

4.2 Methodology 4.2.1 Estimation Method In general, the transport system of a metropolitan region includes intercity and urban passenger and freight transport, simultaneously covering rail, road, water and air transport. However, in China, due to the limitation of statistical scope, the urban passenger transport is not included in the statistics of passenger transport volume and usually neglected when estimating the regional CO2 emissions from transport sector. Meanwhile, the statistics of provincial air transport are based on airline companies registered in each province instead of reflecting the actual air transport activities in that province. Thus, to overcome these limitations, this paper tries to incorporate urban passenger transport in parallel with intercity passenger transport, by estimating the CO2 emissions from urban passenger transport based on the data of vehicle

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4 Railways and Regional Carbon Emissions from Passenger Travel in China

population, vehicle kilometer travelled each year, fuel consumption rate, and CO2 emission factor of fuel used for each vehicle type (Liu et al. 2013). Moreover, the air transport was excluded from the estimation of CO2 emissions at regional level. According to the Intergovernmental Panel on Climate Change (2006), the CO2 emissions from mobile combustion can be estimated by either “top-down” approach based on fuel sales or “bottom-up” approach based on transport activity. Some studies use “top-down” approach that relies on fuel sale to estimate CO2 emissions from transport in China, but the official fuel sale data based on the China Energy Statistical Yearbook covers not only transport sector, but also the storage and post, and communication sectors (Liu et al. 2012; Guo et al. 2014). This study adopts the most frequently used “bottom-up” approach, i.e. ASIF, with consideration of travel activity (A), modal share (S), fuel intensity (I), and fuel emission factor (F). In other words, the CO2 emissions are estimated by multiplying the total regional transport turnover volume by the energy consumption per amount of transport turnover volume for each vehicle type and CO2 emission factor for each fuel type. The formula is as follows: Gt =

 i, j

T Vi, j,t × E Ii, j,t × E Fi, j,t

(4.1)

where Gt refers to total CO2 emissions of one metropolitan region in year t, TV i,j,t refers to the total converted regional transport turnover volume (in ton-km) of vehicle type j in transport mode i, EI i,j,t refers to the energy consumption per ton-km (in joule/ton-km) of vehicle type j in transport mode i, EF i,j,t refers to the CO2 emissions factor for the energy type (in kg CO2 per Joule) for vehicle type j in transport mode i. All vehicle types and fuel types of different transport modes are listed in Table 4.1. Table 4.1 Transport modes, vehicle types and energy types in metropolitan region Regional transport

Transport modes Vehicle types

Freight transport & Intercity passenger Rail transport

Energy types

Steam locomotive

Coal

Diesel locomotive

Diesel

Electric locomotive Electricity Road Water Urban passenger transport

Gasoline vehicle

Gasoline

Diesel vehicle

Diesel

Inland ship

Diesel

Coastal ship

Diesel

Rail

Metro

Electricity

Road

Urban public buses

Gasoline

Taxis

Gasoline

Private LDVs

Gasoline

Business LDVs

Gasoline

Motorcycles

Gasoline

4.2 Methodology

79

The data of both freight turnover volume and intercity passenger turnover volume of each transport mode in all provinces and cities of each metropolitan region are obtained from China Statistical Yearbook (National Bureau of Statistics of China 2003–2013), Yearbook of China Transportation and Communications (Yearbook House of China Transportation and Communications 2001–2014), Guangdong Statistical Yearbook (Statistical Bureau of Guangdong 2001–2014) and several local statistical yearbooks of cities in PRD. It is worth noting that the statistical scale about road and water has changed in China since 2008, which makes the freight turnover volume and intercity passenger turnover volume of intercity road transport and water transport in the years after 2007 slightly different from the years before 2007. Nevertheless, the disparity in the sub-periods, i.e. 2000–2007 and 2008–2013 and the disparity between three metropolitan regions as well as the general trend of CO2 emissions from transport sector over the years can still be found based on the data. All intercity passenger turnover volumes (in passenger-km) are converted to freight turnover volume (in ton-km) by converted coefficient provided in China Statistical Yearbook (1 for rail transport, 10 for road transport, and 3 for water transport). The energy consumption per ton-km is estimated by the national fuel consumption and national total converted transport turnover volume of each transport mode from Yearbook of China Transportation and Communication (Yearbook House of China Transportation and Communications 2001–2014). It is assumed that the energy consumption per ton-km of each transport mode in all provinces and cities is the same as the national level, since the data about transport energy intensity in each province or each city are either unavailable or lack uniform statistics standard. The CO2 emission factor of different fuel types is derived from the guideline of the Intergovernmental Panel on Climate Change (2006). The electricity used in the railways is transformed into coal, according to the statistic about electricity generation in China. Since the data about passenger turnover volume of urban transport are not available in the statistic yearbook, its CO2 emission is estimated by multiplying the vehicle population by the VKT, fuel consumption rate, and CO2 emission factor of the fuel for each vehicle type used for urban passenger travel (Liu et al. 2013). The main vehicles considered in this paper include urban public buses, taxis, private light-duty passenger vehicles (LDVs), business LDVs, motorcycles, and metros. G t,up =

 i, j

V Pi, j,t × V K Ti, j,t × F E i, j,t × E Fi, j,t

(4.2)

where Gt,up refers to CO2 emissions from the urban passenger transport in one metropolitan region in year t, VPi,j,t refers to the vehicle population (in unit) of vehicle type j in transport mode i, VKT i,j,t refers to the vehicle kilometer travelled (in km/unit) each year for vehicle type j in transport mode i, FE i,j,t refers to fuel consumption rate (in L/km) for vehicle type j in transport mode i, EF i,j,t refers to the CO2 emissions factor (in tons/L).

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4 Railways and Regional Carbon Emissions from Passenger Travel in China

The vehicle population of each vehicle type in the provinces and cities of each metropolitan region are from China Statistical Yearbook (National Bureau of Statistics of China 2001–2014), China Automotive Industry Yearbook (China Automotive Technology & Research Center, China Association of Automotive Manufactures 2001–2014), Guangdong Statistical Yearbook (Statistical Bureau of Guangdong 2001–2014) and several local statistical yearbooks of cities in PRD. The numbers of road vehicles in these three metropolitan regions are shown in Fig. 4.2. The VKT of different vehicles during the past decade are attained from Huo et al. (2012). The fuel consumption rates of different vehicles are attained from Liu et al. (2013) and ifeu (2008). These studies focused on the VKT and fuel consumption rate in China, which are the most reliable data that are available for applying to this study. Indeed, energy types such as natural gas and electricity have begun to be used in urban passenger transport, especially urban public buses and taxis in the cities of China. However, due to the data availability, this study does not take them into consideration. Meanwhile, their usage is still very limited compared with gasoline. For instance, gasoline accounted for over 94% of the energy consumption of urban passenger transport in Beijing in 2011 (Yu et al. 2013). Moreover, some factors influencing the CO2 emissions from urban transport cannot be included in the model, such as vehicle design, transport condition, travel behavior of different cities due to data availability (Cheng et al. 2013). In the future, more investigations about different transport situations at provincial or city level are needed to improve the accuracy of the estimation of CO2 emissions from urban passenger transport in China.

4.2.2 Decomposition Analysis There are various decomposition methods to analyze the impact of different factors on the change of CO2 emissions, mainly including the structural decomposition analysis (SDA), the index decomposition analysis (IDA) and the production-theoretical decomposition analysis (PDA) (Zhang and Da 2015). The SDA approach needs to depend on input-output tables, which constrains its application; the PDA approach has a disadvantage that it cannot reflect the effect of structure components as SDA and IDA (Zhou and Ang 2008). Thus, the IDA approach is preferred and used most widely. Logarithmic mean Divisia index (LMDI), refined Lasperes index (RLI), and modified Laspeyres index (MLI) methods are the most popular IDA methods (Mishina and Muromachi 2012). According to Ang (2004), the LMDI analysis has advantages of strong theoretical foundation, adaptability, and ease of use and results interpretation. It also has the advantage that it does not has residual problem and can well solve the “zero-value” problem in the data (Ang and Liu 2007). Compared with LMDI, the other two methods, i.e. RLI and MLI, can be used when the data set contains negative values. However, the RLI method is very complex when the number of factors exceeds three, and the MLI method is even more complex than the RLI (Mishina and Muromachi 2012). Since this study considers six factors and the data set contains no negative value, it adopts LMDI method for the decomposition analysis. Hence,

4.2 Methodology

81

Road vehicle number (10000 Units)

Road vehicle number (10000 Units)

Road vehicle number (10000 Units)

JJJ 1400 1200 1000 800 600 400 200 0 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

YRD

1800 1600 1400 1200 1000 800 600 400 200 0

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

PRD

800 700 600 500 400 300 200 100 0

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

Urban public buses Private LDVs

Taxis Motorcycles

Business LDVs

Fig. 4.2 Road vehicle numbers in the three metropolitan regions (2000–2013)

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4 Railways and Regional Carbon Emissions from Passenger Travel in China

based on the estimation of CO2 emissions from both freight and passenger transport in the three metropolitan regions, this study chooses LMDI decomposition analysis to analyze the main factors influencing the regional carbon emissions from transport in each region. As shown in Eq. (4.3), the Kaya identity includes several factors that influencing the total CO2 emissions (G). The factors considered in this study are population (P), economic welfare (EW ), transport dependence (TD), transport type mix (TM), modal energy intensity (ME), fuel mix (FM), and emission factor (EF) (Loo and Li 2012). G=

 i,k

=

P×



E Fi,k G i,k T Ti Fi × × × × × P E T Ti Fi Fi,k

P × E W × T D × T Mi × M E i × F Mi,k × E Fi,k

(4.3)

i,k

where i represents the transport mode, k represents the fuel type, P represents the population, E represents the GDP, T represents the transport turnover volume, F represents the fuel consumption, and G represents the CO2 emission. The multiplicative decomposition and additive decomposition of the aggregate change is further shown in Eq. (4.4) and (4.5), respectively. Dtot =

Gt = D P D E W DT D DT M D M E D F M D E F G0

(4.4)

 G tot =G t − G 0 = G P +  G E W +  G T D +  G T M +  GME+  GFM+  GEF

(4.5)

where Gt and G0 refers to the CO2 emissions in period 0 and period t, respectively, tot represents the total change of CO2 emission from period 0 to period t, the subscripts P, EW, TD, TM, ME, FM, and EF, denote the effects associated with population, economic welfare, transport dependence, transport type mix, modal energy intensity, fuel mix, and emission factor, respectively. Following Ang (2005), the LMDI formula for decomposing changes of CO2 emissions from transport sector to the effects of each factor are summarized in Eq. (4.6)– (4.12). Then, the contribution of each factor to the total change of CO2 emissions from transport will be revealed for each metropolitan region, respectively.  

     T 0 T 0 G i,k / ln G i,k − G i,k − ln G i,k PT     ln D P = exp i,k P0 G T − G 0 / ln G T − ln G 0  T T 0  G i,k − G i,k P G P = ln i,k ln G T − ln G 0 P0 i,k i,k   T      0 T 0  G i,k − G i,k / ln G i,k − ln G i,k EWT     ln D E W = exp i,k EW0 G T − G 0 / ln G T − ln G 0 

(4.6)

4.2 Methodology

83

 EWT (4.7) G E W = ln i,k ln G T − ln G 0 EW0 i,k i,k   T      0 T 0  G i,k − G i,k / ln G i,k − ln G i,k T DT     ln DT D = exp i,k T D0 G T − G 0 / ln G T − ln G 0   T 0  G i,k − G i,k T DT (4.8) G T D = ln i,k ln G T − ln G 0 T D0 i,k i,k   T      0 T 0  − ln G i,k G i,k − G i,k / ln G i,k T MiT     ln DT M = exp i,k T Mi0 G T − G 0 / ln G T − ln G 0   T 0  G i,k − G i,k T MiT (4.9) G T M = ln i,k ln G T − ln G 0 T Mi0 i,k i,k   T      0 T 0  G i,k − G i,k / ln G i,k − ln G i,k M E iT     ln D M E = exp i,k M E i0 G T − G 0 / ln G T − ln G 0   T 0  G i,k − G i,k M E iT (4.10) G M E = ln i,k ln G T − ln G 0 M E i0 i,k i,k    T     0 T 0  F Mi,T j G i,k − G i,k / ln G i,k − ln G i,k     ln D F M = exp i,k F Mi,0 j G T − G 0 / ln G T − ln G 0   T 0 T  F Mi,k G i,k − G i,k ln G F M = (4.11) 0 i,k ln G T − ln G 0 F Mi,k i,k i,k    T     0 T 0  E Fi,Tj G i,k − G i,k / ln G i,k − ln G i,k     ln D E F = exp i,k E Fi,0 j G T − G 0 / ln G T − ln G 0   T 0 T  E Fi,k G i,k − G i,k ln G E F = (4.12) 0 i,k ln G T − ln G 0 E Fi,k i,k i,k 

T 0 G i,k − G i,k



4.3 CO2 Emissions from Transport Sector in Three Metropolitan Regions 4.3.1 CO2 Emissions from Transport Sector in JJJ Since 2000, the regional CO2 emissions from transport sector in JJJ have grown rapidly, increasing from 27.8 million tons in 2000 to 198.9 million tons in 2013, with an annual average growth rate of 16.3%. The freight transport contributed most to

84

4 Railways and Regional Carbon Emissions from Passenger Travel in China

CO2 emission (Million tons)

200 180

Urban passenger transport

160

Intercity passenger transport

140

Freight transport

120 100 80 60 40 20 0 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

Fig. 4.3 The CO2 emissions from passenger and freight transport in JJJ, 2000–2013

the CO2 emissions in JJJ, the share of which has increased from 54.3% in 2000 to 71.4% in 2013 (Fig. 4.3). In the emissions from passenger transport, urban passenger transport was the major source, accounting for more than 90% of the CO2 emissions from passenger transport during the past decade. In respect of the CO2 emissions from different transport modes, road transport was the leading contributor, followed by rail transport. The CO2 emissions from road transport increased from 24.4 million tons in 2000 to 189.6 million tons in 2013, and the CO2 emissions from rail transport increased from 3.3 million tons in 2000 to 7.1 million tons in 2013. As shown in Fig. 4.4, the proportion of CO2 emissions from road, rail, and water transport was 87.6%, 11.8%, and 0.6% in 2000, 95.4%, 3.6%, and 1.1% in 2013, respectively. Thus, the main pathway of reducing CO2 emissions from transport sector in JJJ should be considered from freight transport and road transport. Table 4.2 further presents the distribution of CO2 emission from transport sector and emission intensities inside the metropolitan region. It shows that most CO2 emission from transport sector came from Hebei, accounting for 61.2% of regional aggregate in 2000 and 79.1% in 2013. The annual average growth rate of CO2 emissions from transport sector during the past decade was also much higher in Hebei (18.7%) than in Beijing (9.6%) and Tianjin (13.1%). In terms of CO2 emission intensity of freight transport, it was much higher in Hebei than in Beijing and Tianjin, mainly because of their different modal structures. In Beijing, the dominant freight transport mode was railways, undertaking 77.2% of its freight ton-km in 2000 and 85.1% in 2013. In Tianjin, the dominant freight transport mode changed from railways to water transport in the past decade, both of which had low carbon emission intensities. In Hebei, however, the dominant freight transport mode changed from railways to road transport, and the freight transport CO2 emission intensity almost

4.3 CO2 Emissions from Transport Sector in Three Metropolitan Regions

85

200

CO2 emission (Million tons)

180

Water

Rail

Road

160 140 120 100 80 60 40 20 0 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

Fig. 4.4 The modal structure of CO2 emissions from transport sector in JJJ, 2000–2013

doubled in the past decade, from 56 g/ton-km in 2000 to 116 g/ton-km in 2013. Moreover, there was no significant difference of intercity passenger transport CO2 emission intensities among Beijing, Tianjin and Hebei. For urban passenger transport, the CO2 emission intensity in Beijing got decreased from 128 g/pkm in 2000 to 116 g/pkm in 2013. It may be explained by the increasing share of metro in passengerkm of Beijing, from 10.3% in 2000 and 18.4% in 2013, despite the increasing share of private LDVs from 23.7% to 53.3%. Both Tianjin and Hebei had an increase of urban passenger transport CO2 emission intensities, mainly because of their growing reliance on private LDVs and less importance of public buses. In the past decade, the proportion of private LDVs in total urban passenger-km increased from 19.7% to 66.8% in Tianjin, from 19.2% to 70.2% in Hebei, while the proportion of public buses in total urban passenger-km decreased from 31.5% to 14.4% in Tianjin and from 17.7% to 9.2% in Hebei.

4.3.2 CO2 Emissions from Transport Sector in YRD As Figs. 4.5 and 4.6 show, the regional CO2 emissions from transport sector in YRD have increased from 29.7 million tons in 2000 to 163.5 million tons in 2013. The CO2 emissions from freight transport in YRD were nearly equal to that from passenger transport, which is different from the situation of JJJ. The urban passenger transport dominated the CO2 emissions from passenger transport in YRD, accounting for 81.5% of the passenger transport CO2 emissions in 2000 and 95.0% in 2013. Road transport was the largest contributor of transport CO2 emissions in YRD, increasing

27.8

Total

PRD

0.7

0.7

0.7

Zhongshan

Huizhou

Zhaoqing

13.1

1.2

Dongguan

Total

1.4

0.6

0.5

Zhuhai

Jiangmen

2.2

Foshan

5.1

Total

Shenzhen

29.7

Zhejiang

Guangzhou

13.4

10.1

Jiangsu

6.2

17.0

Hebei

Shanghai

3.6

Tianjin

YRD

7.2

Beijing

JJJ

74.0

1.8

4.1

4.5

6.3

3.4

8.4

2.1

16.7

26.8

163.5

65.7

76.0

21.8

198.9

157.3

17.7

23.9

14.2%

7.5%

14.0%

15.5%

13.8%

14.3%

14.6%

12.1%

16.7%

13.7%

14.2%

15.5%

14.3%

10.2%

16.3%

18.7%

13.1%

9.6%

2000–2013

2000

2013

Annual growth rate of CO2 emission from transport sector (%)

CO2 emission from transport sector (Million tons)

Province/City

Metropolitan region

48

153

155

151

146

99

115

154

64

66

45

57

59

23

53

56

40

50

2000

66

161

107

170

68

142

171

105

64

50

52

48

75

29

100

116

42

37

2013

Freight transport CO2 emission intensity (g/ton-km)

16

17

17

18

18

17

17

17

16

15

16

16

17

15

15

15

14

15

2000

17

19

18

19

19

19

19

18

17

15

15

15

16

15

13

12

13

15

2013

Intercity passenger transport CO2 emission intensity (g/pkm)

Table 4.2 Distribution of CO2 emissions and emission intensities from transport sector in three metropolitan regions

92

71

88

92

100

81

78

127

96

96

89

94

81

97

118

106

123

128

2000

112

92

118

125

149

85

126

130

108

100

123

131

125

99

128

136

138

116

2013

Urban passenger transport CO2 emission intensity (g/pkm)

86 4 Railways and Regional Carbon Emissions from Passenger Travel in China

4.3 CO2 Emissions from Transport Sector in Three Metropolitan Regions

87

180

CO2 emission (Million tons)

160 140 120

Urban passenger transport Intercity passenger transport Freight transport

100 80 60 40 20 0 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

Fig. 4.5 The CO2 emissions from passenger and freight transport in YRD, 2000–2013

180

CO2 emission (Million tons)

160

Water

Rail

Road

140 120 100 80 60 40 20 0 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

Fig. 4.6 The modal structure of CO2 emissions from transport sector in YRD, 2000–2013

from 24.3 million tons in 2000 to 143.4 million tons in 2013. It was followed by the water transport, which rose from 4.2 million tons to 18.1 million tons. In contrast, the CO2 emissions from rail transport were quite stable, which were 1.2 million tons in 2000 and 2.0 million tons in 2013. Thus, the proportion of road, rail and water

88

4 Railways and Regional Carbon Emissions from Passenger Travel in China

transport in YRD was 81.7%, 4.1% and 14.2% in 2000, and 87.7%, 1.2%, and 11.0% in 2013, respectively. Comparatively speaking, the role of water transport was more important in YRD than that in JJJ. In terms of spatial distribution of CO2 emissions from transport sector within YRD (Table 4.2), they got more and more concentrated in Zhejiang and Jiangsu provinces, instead of Shanghai. The share of CO2 emissions from transport sector in Shanghai in the regional aggregate decreased from 20.8% in 2000 to 13.3% in 2013, and the annual average growth rate of CO2 emissions from transport sector was lower in Shanghai (10.2%) than in Jiangsu (14.3%) and Zhejiang (15.5%). For transport CO2 emission intensity, it was much lower in Shanghai than in Jiangsu and Zhejiang, for both freight transport and urban passenger transport. In 2013, the freight transport CO2 emission intensity was only 29 g/ton-km in Shanghai but 75 g/ton-km in Jiangsu and 48 g/ton-km in Zhejiang, mainly because the higher proportion of water transport in ton-km in Shanghai (91.1%) compared with Jiangsu (58.8%) and Zhejiang (76.8%). The urban passenger transport CO2 emission intensity in 2013 was only 99 g/pkm in Shanghai but 125 g/pkm in Jiangsu and 131 g/pkm in Zhejiang, due to the higher proportion of metro in urban passenger-km in Shanghai (25.8%) than in Jiangsu (1.8%) and Zhejiang (0.4%). However, all of their urban passenger transport relies more and more on private LDVs. In 2012, the proportion of private LDVs in urban passenger-km has reached 37.6% in Shanghai, 58.0% in Jiangsu, and 63.8% in Zhejiang according to the estimation.

4.3.3 CO2 Emissions from Transport Sector in PRD The CO2 emission from transport sector in PRD was much less than JJJ and YRD. It was 13.1 million tons in 2000 and 74.0 million tons in 2013. Similar with YRD, the freight transport emitted nearly equal CO2 with the passenger transport in PRD, and urban passenger transport contributed to nearly 90% of the total passenger transport CO2 emissions (Fig. 4.7). In PRD, road transport was also the largest contributor of transport CO2 emissions, accounting for 86.4% in 2000 (11.3 million tons) and 91.1% in 2013 (67.5 million tons). The CO2 emissions from water transport and rail transport were much lower than that from road transport. From 2000 to 2013, the CO2 emissions from water transport increased from 1.3 million tons to 5.5 million tons, and the CO2 emissions from rail transport increased from 0.4 million tons to 1.1 million tons (Fig. 4.8). In the nine cities of PRD, Guangzhou had the largest CO2 emissions from transport sector, accounting for 38.6% of the regional aggregate in 2000 and 36.1% in 2013 (Table 4.2). It was followed by Shenzhen, which accounted for 17.1% in 2000 and 22.5% in 2013. Thus, the CO2 emissions from transport sector in PRD became more and more concentrated in the largest two cities of this region, i.e. Guangzhou and Shenzhen. However, Guangzhou, Shenzhen had lower freight transport CO2 emission intensities than other cities of PRD. It was because of their relative higher proportion of inland and coastal water transport in total freight transport turnover volume. The

4.3 CO2 Emissions from Transport Sector in Three Metropolitan Regions

89

80 CO2 emission (Million tons)

70

Urban passenger transport Intercity passenger transport

60

Freight transport

50 40 30 20 10 0 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

Fig. 4.7 The CO2 emissions from passenger and freight transport in PRD, 2000–2013

lowest urban passenger transport CO2 emission intensity in 2013 was in Jiangmen and Zhaoqing, followed by Guangzhou and Shenzhen. It may be explained by the lower motorization rate in Jiangmen and Zhaoqing, and the availability of metro in Guangzhou and Shenzhen for urban passenger transport. 80

CO2 emission (Million tons)

70

Water

Rail

Road

60 50 40 30 20 10 0 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

Fig. 4.8 The modal structure of CO2 emissions from transport sector in PRD, 2000–2013

90

4 Railways and Regional Carbon Emissions from Passenger Travel in China

CO2 emission (Million tons)

250 200 150

JJJ 100

YRD PRD

50 0

Fig. 4.9 The growth of CO2 emissions from transport sector in three metropolitan regions

4.3.4 Comparison Between Three Metropolitan Regions The CO2 emissions from transport sector in these three metropolitan regions were juxtaposed in Fig. 4.9. It is shown that the growth rate of CO2 emission from transport sector of JJJ was lower than that of YRD before 2008, while it became the highest since 2008. Accordingly, the CO2 emission from transport sector in JJJ outpaced YRD and became the largest since 2010. Due to the relatively small size of population and economic activities, the PRD has the lowest level of CO2 emission from transport sector. As shown in Table 2, the freight transport CO2 emission intensity was highest in JJJ in 2013, followed by PRD and YRD, mainly due to the higher proportion of road transport in ton-km in JJJ than other two metropolitan regions. The intercity passenger transport CO2 emission intensity in 2013 was highest in PRD, then YRD, and lowest in JJJ. It was mainly because that the role of railways was more important in intercity passenger transport in JJJ than in PRD and YRD. The urban passenger transport CO2 emission intensity in 2013 was highest in JJJ, then YRD, and lowest in PRD, mainly because the proportion of private LDVs in passenger-km was a little lower in PRD (51.7%) than JJJ (63.0%) and YRD (57.1%), and the proportion of public buses was higher in PRD (19.4%) than JJJ (12.0%) and YRD (12.4%). Thus, promoting the modal shift from transport modes with high carbon emission intensity to those with lower carbon emission intensity is important in the future. Table 4.3 further compares the average transport CO2 emissions per capita, per km2 and per unit GDP in these three metropolitan regions in 2000 and 2013, respectively. The regional disparity was noticeable. In terms of per capita CO2 emission from transport sector, it was lower in YRD than PRD and JJJ in both 2000 and 2013. In terms of per hectare CO2 emission from transport sector, it was much higher in

4.3 CO2 Emissions from Transport Sector in Three Metropolitan Regions

91

Table 4.3 Comparison of per capita, per km2 and per unit GDP CO2 emissions from transport sector in three metropolitan regions 2000

2013

JJJ

YRD

PRD

JJJ

YRD

PRD

Population (million persons)

90.4

136.2

42.9

109.2

158.5

57.2

Area (million hectares)

21.7

22.0

5.5

21.7

22.0

5.5

GDP (100 million yuan, 2000 constant price)

9207

19170

8422

36399

69279

30796

CO2 emission from transport sector (million tons)

27.8

29.7

13.1

198.9

163.5

74.0

per capita CO2 emission from transport sector (tons)

0.3

0.2

0.3

1.8

1.0

1.3

per hectare CO2 emission from transport sector (tons)

1.3

1.3

2.4

9.2

7.4

13.5

per million yuan GDP CO2 emission from transport sector (tons)

30.2

15.5

15.6

54.6

23.6

24.0

PRD than in JJJ and YRD in both 2000 and 2013. In terms of per unit GDP CO2 emission from transport sector, it was much higher in JJJ than in YRD and PRD in both 2000 and 2013. Moreover, all the indicators of average CO2 emission from transport sector increased in the last decade for all three metropolitan regions, which highlights the challenges to control the transport CO2 emissions in these regions as the population and economy grows.

4.4 Factors Influencing CO2 Emission from Transport Sector in Three Metropolitan Regions Based on the estimation about CO2 emission from transport sector in three metropolitan regions, LMDI decomposition analysis was conducted to quantify the contribution of different influencing factors in each region. Six factors are analyzed, including population, economic welfare, transport dependence, transport type mix, modal energy intensity, and fuel mix. Since the emission factor was assumed to be constant in this study, it will not be analyzed separately. The trends of all six factors are shown in Figs. 4.10, 4.11, 4.12, 4.13 and 4.14. As Fig. 4.10 shows, the trends of population growth in all three metropolitan regions were quite similar, while the growth rate of economic welfare was slightly higher in PRD than JJJ and YRD. Figure 4.11 suggests that the transport dependence in all three regions was higher in 2013 than in 2000, and it was higher in JJJ than YRD and PRD. In particular, the transport dependence in JJJ got decreased before 2007 and then turned to increase after 2007. For the factor of transport type mix, the general trend in three metropolitan regions was that the rail transport was replaced by road transport, especially in JJJ

4 Railways and Regional Carbon Emissions from Passenger Travel in China 60000

Population (10000 persons)

18000 16000

50000

14000

40000

12000 10000

30000

8000

20000

6000 4000

10000

2000

0

0

P_JJJ

P_YRD

P_PRD

EW_JJJ

EW_YRD

Economic welfare (GDP per capita)

92

EW_PRD

Transport dependence (ton-km/GDP)

Fig. 4.10 Trends in population (P) and economic welfare (EW) in three metropolitan regions

0.50 0.45 0.40 0.35 0.30 0.25 0.20 0.15

JJJ YRD PRD

0.10 0.05 0.00

Fig. 4.11 Trends in transport dependence (TP) in three metropolitan regions

region. In YRD and PRD regions, due to their geographical conditions, the water transport that covers inland river and coastal transport played an important role in transport system (Fig. 4.12). In terms of modal energy intensity, it was much lower for rail and water transport than for road transport. As Fig. 4.13 shows, except for the road transport in YRD and PRD, the modal energy intensities of different transport

4.4 Factors Influencing CO2 Emission from Transport Sector … 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0%

100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0%

100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0%

93

JJJ

Water Rail Road

YRD

Water Rail Road

PRD

Fig. 4.12 Trends of transport type mix (TM) in three metropolitan regions

Water Rail Road

JJJ

12000

600

10000

500

8000

400

6000

300

4000

200

2000

100

0

0

ME_Rail

ME_Water

YRD

14000

700

12000

600

10000

500

8000

400

6000

300

4000

200

2000

100

0

0

ME_Road

ME_Rail

ME_Water

PRD

12500

700

12000

600

11500

500

11000

400

10500

300

10000

200

9500

100

9000

0

ME_Road

ME_Rail

ME of ral or water (ton fuel/ton-km)

ME of road (ton fuel/ton-km)

ME_Road

ME of road (ton fuel/ton-km)

700

ME of rial or water (ton fuel/ton-km)

14000

MEof rail or water (ton fuel/ton-km)

4 Railways and Regional Carbon Emissions from Passenger Travel in China

ME of road (ton fuel/ton-km)

94

ME_Water

Fig. 4.13 Trends in modal energy intensity (ME) of each transport mode in three metropolitan regions

4.4 Factors Influencing CO2 Emission from Transport Sector …

95

100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Road_Gasoline

Road_Diesel

100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Rail_Coal

Rail_Diesel

Rail_Electricity

Fig. 4.14 Trends of fuel mix (FM) of road and rail transport in JJJ region

modes in all three metropolitan regions were lower in 2013 than in 2000. For the factor of fuel mix, since the trends in all three metropolitan regions are quite similar, Fig. 4.14 only shows the trend in JJJ region as the representation. In road transport, the proportion of diesel was getting larger than gasoline when comparing their weight. In rail transport, the steam locomotives were replaced by diesel and electric locomotives, and the proportion of electric trains that replies on thermal power and coal became the largest.

96

4 Railways and Regional Carbon Emissions from Passenger Travel in China

Due to the statistical scale change of road and water transport in China in 2008, the decomposition analysis was conducted by two sub-periods, i.e. 2000–2007 and 2008–2013. Figures 4.15 and 4.16 show the results of multiplicative and additive decomposition analysis, respectively. Most factors had driven the increase of regional CO2 emission from transport sector. In both sub-periods, economic welfare was the most important factor that led to the increase of CO2 emissions in three metropolitan regions. Decoupling elasticity was further calculated to estimate the sensitivity of CO2 emissions from transport sector to economic welfare, where the percentage change of CO2 emissions from transport sector was divided by the percentage change of GDP per capita in the study period (Tapio 2005). From 2000 to 2013, the decoupling elasticity was 2.1, 1.8, and 2.1 in JJJ, YRD, and PRD, respectively, which suggested that the growth of CO2 emission was expansive negative decoupling with increasing economic welfare. Thus, strategies and technologies to improve the efficiency of increasing economic welfare and controlling the growth rate of CO2 emissions from transport sector at the same time are needed in the future. The second most important factor that drives the CO2 emission growth differed by three metropolitan regions in two sub-periods. In JJJ, modal energy intensity and transport type mix was the second most important factor in 2000–2007 and 2008–2013, respectively. In YRD, it was modal energy intensity in both sub-periods. In PRD, it was transport type mix in 2000–2007 and transport dependence in 20008–2013. In addition, the factors that decreased the CO2 emissions from transport sector include transport dependence and transport type mix in 2000–2007 and transport dependence and modal energy intensity in 2008–2013. In other words, transport dependence may decrease the CO2 emission in JJJ and PRD, and transport type mix may decrease the CO2 emission in JJJ and YRD in 2000–2007. However, in 2008–2013, only transport dependence decreased the CO2 emission in YRD, and modal energy intensity decreased the CO2 emission in JJJ. Thus, the trend was that the transport dependence and transport type mix tended to increase the CO2 emissions in the metropolitan regions. Furthermore, there are kinds of policies conducted in the three metropolitan regions to conserve the energy consumption and reduce CO2 emissions from transport sector in the study period. The main policies include reducing transport dependence by urban and regional planning, shifting transport type mix by encouraging public transport and railways, lowering modal energy intensity and improving fuel economy by enacting fuel economy standards, supporting vehicle replacement and introducing alternative fuels. Most policies are national wide, targeting at lowering modal energy intensity and improving fuel economy at the same time (Loo and Li 2012). For instance, in 2007, the State Council of China (2007) proposed several strategies to control the energy consumption and CO2 emissions in transport, including developing urban public transport by bus rapid transit and rail transit systems, increasing the fuel economy standards of vehicles and ships, replacing the vehicles with high emissions, developing alternative energy vehicles, and promoting integration between different transport modes. Public transport, especially urban rail transit systems were developed rapidly in the megacities of the three metropolitan regions, such as Beijing,

4.4 Factors Influencing CO2 Emission from Transport Sector …

97

2000-2007

Population 4.0 3.0 Fuel mix

2.0

Economic welfare

1.0

JJJ

0.0

YRD PRD

Modal energy intensity

Transport dependence

Transport type mix

2008-2013

Population 4.0 3.0 Fuel mix

2.0

Economic welfare

1.0

JJJ

0.0

YRD PRD

Modal energy intensity

Transport dependence

Transport type mix Fig. 4.15 LMDI multiplicative decomposition results in three metropolitan regions

98

4 Railways and Regional Carbon Emissions from Passenger Travel in China

2000-2007

CO2 emission (Million tons)

40 30 20 JJJ 10

YRD PRD

0 -10

Population Economic Transport Transport welfare dependence type mix

Modal energy intensity

Fuel mix

-20

CO2 emission (Million tons)

50

2008-2013

40 30 JJJ 20

YRD PRD

10 0 -10

Population Economic Transport Transport welfare dependence type mix

Modal energy intensity

Fuel mix

Fig. 4.16 LMDI additive decomposition results in three metropolitan regions

Shanghai and Guangzhou (Loo and Li 2006). Without these strategies, the CO2 emissions from transport in these regions would be even higher because of the prevalence of private vehicles. However, from the decomposition analysis, it can be seen that the transport demand grows so rapidly due to drastic increasing population and economic welfare, the effects of these policies until currently still cannot effectively control the energy consumption and CO2 emissions from transport in these regions and stricter policies are needed in the future. In addition, the policy about lowering CO2 emission from transport sector in each metropolitan region should have slightly different emphasis based on the different decomposition results in these regions. For instance, the JJJ region needs to put more emphasis on promoting modal shift from road transport to rail and water transport. The YRD region needs to focus more on

4.4 Factors Influencing CO2 Emission from Transport Sector …

99

lowering the modal energy intensity by developing alternative energy vehicles and supporting vehicle replacement. The PRD region should focus more on controlling the growth of transport dependence by rational land use and regional planning.

4.5 Summary As the most developed areas in China, the three major metropolitan regions (JJJ, YRD and PRD) have experienced quite rapid growth of population, GDP and travel demand. This chapter shows that the CO2 emission from transport sector in these regions also increased quite rapidly. It has increased 6.2, 4.5 and 4.6 times in JJJ, YRD, and PRD, respectively in the study period. The JJJ region has the largest CO2 emission from transport sector in 2013 with the most rapid growth rate. It also has the highest per capita and per unit GDP CO2 emission from transport sector in 2013. Thus, the JJJ region needs particular attention of reducing energy consumption and CO2 emissions in the transport sector. In JJJ, freight transport contributed much more CO2 emissions than passenger transport, while in YRD and PRD, the emissions from freight and passenger transport were nearly equal. In all three metropolitan regions, urban passenger transport was the major source of passenger transport. Moreover, road transport was the leading contributor of CO2 emission from transport sector. Thus, future strategies to promote low carbon emissions in urban passenger transport and on-road vehicles are needed. In terms of transport CO2 emission intensity, JJJ had the highest freight transport CO2 emission intensity, due to its lower proportion of water transport in freight transport than PRD and YRD. It was determined by their different geographical characteristics, with more coastal lines and ports in PRD and YRD than in JJJ. Nevertheless, JJJ may develop railways as alternative to water transport, which was also more environmentally friendly than road transport. PRD had the highest intercity passenger transport CO2 emission intensity, due to its lower proportion of railways in intercity passenger transport. Indeed, the railway infrastructure was less developed in PRD than in JJJ and YRD currently, and an intercity rail transit system was planned and in construction in PRD. It is expected railways will play a more important role in PRD, which may decrease its CO2 emission from intercity passenger transport. Moreover, JJJ had the highest urban passenger transport CO2 emission intensity, due to its high reliance on private LDVs and less reliance on public buses. Actually, all metropolitan regions are experiencing rapid motorization, with public transport replacing by private vehicles. Therefore, developing public transport facilities and restricting motor vehicles use is important for these metropolitan regions to decarbonize their urban transport system. The decomposition analysis in this chapter showed that the factors that drive the CO2 emission from transport sector growth include population, economic welfare, transport dependence, transport type mix, and fuel mix. Economic welfare was the most important factor that drives the CO2 emission growth in all three metropolitan regions. However, since controlling population and economic development may be difficult and not necessary in some cases, the emphasis needs to be focused on

100

4 Railways and Regional Carbon Emissions from Passenger Travel in China

controlling the growth of transport demand, adjusting the transport modal structure and improving the modal energy efficiency and fuel emission efficiency. Although kinds of strategies have been adopted in these metropolitan regions such as investing in urban rail transit construction, regulating strict fuel economy standard, encouraging alternative vehicle fuels, the effects are still limited. Based on the experiences of some developed countries, multiple strategies are needed in the aspects of regulation, investment, and fiscal measures, including supporting lower emitting vehicles, promoting biofuels usage, providing and subsidizing public transport and other low carbon emission transport modes, constructing the regional carbon trading systems, adopting fiscal measures to encourage the choice of lower carbon transport modes, and promoting the integration of different transport modes (Department for Transport of UK 2009; Bowyer et al. 2015). In addition, based on the results of decomposition analysis in different metropolitan regions, the strategies should be slightly different. For instance, the JJJ region needs to put more emphasis on promoting modal shift from road transport to rail and water transport; the YRD region needs to focus more on lowering the modal energy intensity by developing alternative energy vehicles and supporting vehicle replacement; the PRD region should focus more on controlling the growth of transport dependence by rational land use and regional planning. This chapter has estimated the CO2 emission from transport sector in three metropolitan regions of China and revealed some influencing factors. However, there are still some limitations about the estimation. For instance, the VKT level of different vehicles at the city level is not available in China and some factors cannot be included when estimating, such as vehicle design, transport condition, travel behavior in different cities of different regions. Moreover, the limitation of statistical data availability is also one of the significant issues of this chapter, especially the lack of transport volume about urban passenger transport in the metropolitan regions of China and the statistical scale change of road and water transport in 2008. In the future, more investigations about the transport in the developing countries are needed to support the research and policies about low carbon transport. Also, future research is needed to explore the disparity of CO2 emissions from transport sector inside the metropolitan regions and facilitate more specific policies about energy conservation and low carbon emissions.

Part II

Current Situation: Competition and Integration Between Railways and Other Transport Modes

Chapter 5

Competition Between Railways and Other Transport Modes

5.1 Introduction Compared with road and air transport, railways are sustainable transport modes since they can handle large volumes of passengers with less pollution (Givoni et al. 2009; Loo and Cheng 2010). Also, they have the advantages in promoting economic development and social equity (Litman 1997; Monzón et al. 2013). However, the reality is that since the middle of the 20th century railways have been replaced by highways and aviation around the world. This phenomenon is in accordance with Owen’s stage of mobility theory (1987). According to the theory, the evolution of transport modes is interlinked with human’s activities scale. In the early stage of mobility, when activities are confined in regional dimension, railways undertake most traffic volume; however, when the activities scale is widened to the national and international dimensions, alternative modes like motor cars and airplanes become more important than railways. Thus, the shift of passengers from railways to road and air transport is promoted by the increase of mobility demand following economic development (Ausubel et al. 1998; Schafer et al. 2009). In that case, regions in different mobility stages may show different dependence on railways. For instance, railways in China are much more intensively utilized than in USA, and the USA has a greater dependence on automobiles and domestic air flights (Loo and Liu 2005). Nevertheless, following the mobility evolution, future passenger transport is likely to be dominated by road and air transport, which rely more on fossil fuel and contribute more to climate change. Therefore, the development of transport is in a dilemma of high mobility and low sustainability. Fortunately, improvement of railways, especially the introduction of high-speed rail (HSR) since the 1960s, may provide a way to solve the dilemma, because modern railways not only keep the environmental advantages of conventional railways, but also have the ability to increase capacity, reduce travel time and provide high level of service. The 21st century may become a period of renaissance for railways. On

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 L. Li, Railways and Sustainable Low-Carbon Mobility in China, https://doi.org/10.1007/978-981-15-9081-8_5

103

104

5 Competition Between Railways and Other Transport Modes

one hand, modern railways, such as HSR and urban rapid rail transit systems, have developed in many countries since the 1960s, such as Japan, France, Germany, Spain, UK, Italy and China (Eastham 1998). On the other hand, modal shift is happening from road and air transport to passenger railways. Based on the four-step model (i.e., trip generation, trip distribution, modal split and traffic assignment) of transport planning (Dickey 1983), railway improvement could change the modal share by increasing its utility and changing passengers’ travel choice. Many experiences show that HSR can attract passenger travel from air and road transport (Givoni 2006), and railways are expected to promote sustainable transport in the future by changing the modal shift trend back from road and air transport. This chapter takes China as a case study, and discusses the competition between railway and other transport modes. As discussed in Chaps. 3 and 4, railways performed much better than air and road transport in China in terms of carbon dioxide emissions, but the role of railways in low carbon emissions decrease due to the modal shift from railways to road and air transport. Therefore, exploring the competition between railways and other transport modes in China is helpful for making strategies of reversing current modal shift trend. Since the data are limited for road transport in China, this chapter concentrates on the competition between railways and air transport. The framework of this chapter is as follows. First, Sect. 5.2 describes the background of China and provides an overview of railway improvement in China since 1949. Then, the relevance of railway development and both air flight patronage and air transport supply between city pairs in China is discussed in Sect. 5.3. After that, the potential of developing railways to reduce CO2 emissions from air transport in China is discussed and some recommendations for developing low carbon transport system by railway development are provided in Sect. 5.4. Finally, Sect. 5.5 provides the summary of this chapter.

5.2 Background of China Many studies focus on the air flight patronage change due to HSR operation. Table 5.1 shows several cases about the intercity modal share before and after the operation of HSR, especially the air transport shares, around the world. It is found that after the initiation of HSR, the air flight patronage between city pairs have reduced. However, the reduction extent differed from line to line. In some lines such as Tokyo-Nagoya in Japan and Paris-Brussels, Cologne-Frankfurt in Europe (Sinclair Knight Merz 2010), the air flight patronage almost discontinued; whereas in other lines such as Tokyo-Yamagata, the air flight share kept at about 31%. Although the experiences from other countries may give some implications for China, considering the specific background of China, what happens in foreign countries may not happen in China. First, as air transport develops, China’s transport structure also changes. Highway, railway, aviation and waterway are the four transport modes for intercity passenger transport in China. In 2010, their market shares are 53.8%, 31.4%, 14.5% and 0.3%, respectively (National Bureau of Statistics of China 2011). Railway and highway

5.2 Background of China

105

Table 5.1 Modal share contrast before and after HSR HSR

Before/After

Air share (patronage: 10,000 persons)

Rail share (patronage: 10,000 persons)

TGV, Paris-Lyon line (France)

Before (1981)

31% (90)

40% (116)

After (1984)

7% (27)

72% (286)

AVE, Madrid-Seville line Before (1991) (Spain) After (1994)

40%

16%

13%

51%

Shinkansen, Tokyo-Nagoya (Japan)

Before (1963)

4% (22)

96% (508)

After (1964)

3% (29)

97% (612)

After (1965)

1% (9)

99% (696)

After (1966)

0.3% (2)

99.7% (726)

After (1967)

0.1% (1)

99.9% (832)

Shinkansen, Before (1990) Tokyo-Yamagata (Japan) After (1995)

31% (40)

67% (87)

10% (34)

89% (315)

KTX, Gyeongbu direct (South Korea)

Before

11.9% (2)

30.1% (5)

After

6.2% (1)

42.7% (7)

KTX, Honam direct (South Korea)

Before

9.0% (0.3)

22.5% (9)

After

6.3% (0.2)

27.0% (10)

Taiwan high speed rail (Taiwan)

Before (2006)

3%

61%

After (2007)

2%

65%

Source Lee (2007), European Commission & Directorate General Transport (1998), Ou (2007), Lee and Chang (2006)

are dominant transport modes, accounting for over 90% of the passenger-kilometers before 2000. The market share of railway is always decreasing, from 61% in 1980 to 31% in 2010. It may be due to the slow speed and train service shortage of railway system in China. The density of railway network is only 0.06 km/thousand people, much lower than UK 0.33, Japan 0.21 and Germany 0.41 (Yang and Sun 2010). It is always hard for passengers to buy a ticket in transport peak time such as spring festival. Air transport demand rises greatly, with its proportion over 10% since 2004. It can be concluded that people in China incline to travel by road and rail traditionally instead to aviation. Moreover, the average quantity of civil airports in China is only 0.35 per million people, much lower than USA’s 49.6 and Japan’s 1.38 in 2006 (Mao et al. 2009). However, more and more people begin to travel by air in China. Average passenger travel distance in China (Table 5.2) reveals that air transport undertakes long distance travel, whereas rail and road travel is shorter. All are increasing over the years, except water transport. Second, both HSR and air transport in China are new and rapid expanding markets. The first HSR operated in China is an intercity railway between Beijing and Tianjin, opened before 2008 Beijing Olympic Games, while the first HSR in the world was opened in Japan in 1964. Only three years passed, there are more than ten HSR lines opened in China. Never before has one country developed HSR as rapidly as China.

106

5 Competition Between Railways and Other Transport Modes

Table 5.2 Average passenger travel distance and annual increase rate of China (1980–2010) Rail

Road

km

%

1980

150

1990

273

2000 2010

Water km

Air %

km

Total

km

%

%

km

%

–

33

–

49

–

1153

–

69

–

6.2

40

1.9

61

2.2

1388

1.9

73

0.6

431

4.7

49

2.1

52

−1.6

1444

0.4

83

1.3

523

2.0

49

0.0

32

−4.7

1509

0.4

85

0.2

Source National Bureau of Statistics of China (2011)

Hence, the relevance of HSR to air flight in China is definitely abrupt and great. Both passenger and operation’s response to HSR are hard to predict. Air transport has a longer history than HSR in China. However, its rapid increase began from 1980 and it gradually turns into a popular travel mode. In 2005, the proportion of air tickets bought by the travelers at their own costs (also called self-expense air tickets) in China exceeds air tickets bought by the business travelers with grants from government, corporations, or other public organizations (also called publicexpense tickets) (CAAC 2008). This trend will probably further encourage the air industry development in China. In other countries such as the USA, Japan, Germany and France, their air markets are mature with low growth rate (Leahy 2010), which is much different from China. Third, China’s socioeconomic background is complex, including its stable GDP growth, unprecedented urbanization and big regional disparity. The travel demand is growing rapidly due to income increase and economic development. Meanwhile, passengers’ requirement for transport comfort and efficiency is raised. During the period 1978–2010, the annual GDP growth rate is 15% (based on 2005 constant price), and the annual Air RPK growth rate is 25% (Fig. 5.1). So, the average RPK/GDP ratio is 1.72, which is higher than world average RPK/GDP ratio 1.33 (Jin et al. 2004). It means air transport will have great expansion in the future as GDP grows. In addition, motorization also became a trend in China with more and more private cars. Considering the background of China and its short history of HSR, a time-series approach that intends to throw light on the future scenario by analyzing the historical data, is adopted in this thesis. By using this approach, we can follow the development path of rail and air passenger transport in China; at the same time analyze their interaction in the past. Then, the future situation of HSR and air flight may become clear.

5.2.1 Conventional Railway Improvement Since 1949, conventional railway development in China is mainly in two forms, i.e. network expansion and acceleration. The network expansion happens over the years

5.2 Background of China

Air RPK (billion)

400 350 300

Air

GDP

35000 30000 25000

250

20000

200

15000

150 100 50 0

10000

GDP (billion RMB)

450

107

5000 0

Fig. 5.1 Air revenue passenger-kilometers (RPK) and GDP growth in China (1978–2010). Source National Bureau of Statistics of China (2001, 2011)

since 1949, along with railway electrification. Railway acceleration mainly initiates after 1997, with six rounds of acceleration campaigns. Since 2008, China has begun to develop its high speed rail network, which is a great improvement for railway development. Figure 5.2 shows the national railway network length extension in China from 1949 to 2011, which increases from 21.8 thousand km to 93.2 thousand km, with an average annual growth rate of 2.3%. The percentage of railway electrified grows from 0.3% to 36.8%. However, the railway length per capita in China only increases from 0.04 m to 0.07 m. From the network map of Chinese railway (Fig. 5.3), we can see that before 1949, most railways in China concentrated in the northeastern and eastern part of China; from 1950 to 1969, railway network began to expand to the western part of China; from 1970 to 1989, most railways are in the middle part to connect the north and south as well as the east and the west; from 1990 to 2009, the network expansion seems to be equal in space around the country; since 2010, some HSRs began to open. Another important aspect of railway improvement in China is the railway acceleration. Since 1997, there are six rounds of railway acceleration campaigns to increase the speed of train travel in China, which have made the average speed of passenger trains from 43 km/h to 70 km/h (Table 5.3). Table 5.4 and Fig. 5.4 further show the travel time in main railway line in China in 1991 and 2011, respectively, which are before and after the railway acceleration. There is great time saving for travelling between these cities pairs, especially for the city pairs with HSR.

5 Competition Between Railways and Other Transport Modes

10 9 8 7 6 5 4 3 2 1 0 1949 1952 1955 1958 1961 1964 1967 1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000 2003 2006 2009

10000 km

108

Length of railways in operation

National electrified railways

Fig. 5.2 Railway extension and railway electrified in China (1949–2011). Source National Bureau of Statistics of China (2012)

5.2.2 High-Speed Rail Development Although the Tenth Five-Year Transport Plan of China has proposed to build passenger dedicated line and HSR, the first complete plan about the high-speed railway network in China should be the Mid-to-Long Term Railway Network Plan formulated in 2004. The strategy of this plan is to develop intercity express passenger systems in urban areas with prosperous economy and dense population. The highspeed railways can also connect some other remote areas along the railways lines. The scheme includes a national high-speed rail grid composed of 8 corridors, 4 running north-south and 4 running east-west as well as three intercity passenger express systems located in the Bohai Economic Rim, Yangtze River Delta and Pearl River Delta. The total passenger dedicated lines will reach 12,000 km in 2020. A basic framework of high-speed railway network in China has established. However, in 2008, the global financial crisis occurred and Chinese government made macroeconomic adjustments, establishing economic stimulus policy. One important measure is accelerating the railway construction in order to promote the economic growth. Meanwhile, both Eleventh Five-Year Railway Plan and Mid-toLong Term Comprehensive Transport Network proposed to expand the size of highspeed railway. These new contexts made the former Ministry of Railway revise the Mid-to-Long Term Railway Network Plan, extending the passenger dedicated lines to 16,000 km and adding five intercity passenger systems. In 2020, the high-speed railway network will connect all provincial capital and cities with population more

5.2 Background of China

109

Fig. 5.3 Railway network expansion in China over the years

than 500,000 and the total network length will reach more than 50,000 km (Ministry of Railway 2008). Based on the nationwide high-speed railway plan, many metropolitan areas in China made its own intercity high-speed railways plan. These areas include the Bohai Economic Rim, Yangtze River Delta, Pearl River Delta, Central plains urban agglomeration, Wuhan Metropolitan area and Changsha-Zhuzhou-Xiangtan urban agglomeration. All are developed and densely populated areas. However, the plans are adjusted frequently both in terms of size and spatial organization. For instance, the size of the intercity railway plan of the Pearl River Delta was changed from an initial

110

5 Competition Between Railways and Other Transport Modes

Table 5.3 Six rounds of railway acceleration campaign in China Round

Time

Railway lines with acceleration

Average speed (km/h)

1st round

1997-04-01

Beijing-Guangzhou, Beijing-Shanghai, Beijing-Harbin

54.9

2nd round

1998-10-01

Beijing-Guangzhou, Beijing-Shanghai, Beijing-Harbin, Guangzhou-Shenzhen

55.2

3rd round

2000-10-21

Lianyungang-Lanzhou, Lanzhou-Urumqi, Beijing-Hong Kong, Hangzhou-Zhuzhou

60.3

4th round

2001-11-21

Wuchang-Chengdu, Beijing-Guangzhou south section, Beijing-Hong Kong, Hangzhou-Zhuzhou, Shanghai-Hangzhou, Harbin-Dalian

62.6

5th round

2004-04-18

Beijing-Shanghai, Beijing-Harbin

65.7

6th round

2007-04-18

Beijing-Harbin, Beijing-Guangzhou, Beijing-Shanghai, Lianyungang-Lanzhou, Lanzhou-Urumqi, Guanghzou-Shenzhen, Qingdao-Jinan, Wuchang-Jiujiang, Xuancheng-Hangzhou

70.2

Source SwissRail Industry Association (2011), Wikipedia (2013) Table 5.4 Travel time in China in 1991 and 2011

Origin

Destination

1991

2011

Beijing

Shanghai

17:24

4:48

Beijing

Wuhan

16:40

9:40

Beijing

Guangzhou

32:55

20:32

Beijing

Xian

16:58

11:58

Shanghai

Beijing

17:24

4:48

Shanghai

Wuhan

26:39 (no direct train)

5:10

Shanghai

Guangzhou

33:06

15:57

Shanghai

Xian

22:46

14:09

Wuhan

Beijing

16:40

9:40

Wuhan

Shanghai

26:39 (no direct train)

5:10

Wuhan

Guangzhou

16:59

3:33

Wuhan

Xian

18:44

10:51

Guangzhou

Beijing

32:55

20:32

Guangzhou

Shanghai

33:06

15:57

Guangzhou

Wuhan

16:59

3:33

Guangzhou

Xian

36:37

21:18

Xian

Beijing

16:58

11:58

Xian

Shanghai

22:46

14:09

Xian

Wuhan

18:44

10:51

Xian

Guangzhou

36:37

21:18

Source Ministry of Railway (1991, 2012)

5.2 Background of China

111

Fig. 5.4 Travel time in China in 1991 and 2011. Source Cheng et al. (2013)

600 km to 1890 km in 2008. The optimal size and plan should be studied further. And for every station in a specific town or district, there will be station area plans which are important for the land use and economic development around the station. However, some are very simple without consideration of their own characteristics and diverse station development models. Until 2012, there were about 22 lines in operation in China, with a total length of about 7900 km, as Table 5.5 shown. The distribution of HSR lines in China is represented in Fig. 5.5. So, the network of HSR in China is still under formulation.

112 Table 5.5 High speed rail opened in China until 2012

5 Competition Between Railways and Other Transport Modes Line

Open time

Length

Hefei-Nanjing

2008/4/18

166

Beijing-Tianjin

2008/8/1

120

Jinan-Qingdao

2008/12/20

362

Hefei-Wuhan

2009/4/1

356

Shijiazhuang-Taiyuan

2009/4/1

190

Wuhan-Guangzhou

2009/12/26

968

Zhengzhou-Xi’an

2010/2/6

458

Ningbo-Wenzhou-Fuzhou

2010/4/26

562

Fuzhou-Xiamen

2010/4/26

275

Chengdu-Dujiangyan

2010/5/12

72

Shanghai-Nanjing

2010/7/1

300

Nanchang-Jiujiang

2010/9/20

92

Shanghai-Hangzhou

2010/10/26

158

Hainan east circle

2010/12/30

308

Changchun-Jilin

2011/1/11

96

Beijing-Shanghai

2011/6/30

1318

Guangzhou-Zhuhai (include Extend 2011/1/7(2012) Line)

142

Guangzhou-Shenzhen (Hong Kong) 2011

104

Wuhan-Yichang

293

2012

Hefei-Bengbu

2011

131

Mianyang-Chengdu-Leshan

2012

316

Beijing-Wuhan

2012

1122

5.3 The Relevance of Railway Improvement to Air Transport The relevance of railway improvement to air flight patronage can be explored from the perspective of transport node or the perspective of transport corridor. From the passenger amount change of railway station and airport in the same city, the shift of passenger flows between railways and air transport is reflected in Table 5.6. The share of rail and air transport means the percentage of rail and air passenger volume in the total passenger volume of all transport modes, respectively. The total passenger volume includes both international, intercity, and inner city passenger numbers. Due to the large difference of highway transport shares in different cities, the shares of rail and air transport also exhibited some wide variations. It is shown that except for a few cities in the central part of China, such as Nanchang, Wuhan, Guiyang and Nanning, most cities in China had railway passenger percentage decreased and air transport passenger percentage increased from 1990 to 2010. However, between 1995

5.3 The Relevance of Railway Improvement to Air Transport

113

Fig. 5.5 High speed rail opened in China until 2012. Data. Source UIC (2013)

and 2010, about 8 cities were with railway passenger share increased, and between 2000 and 2010, about 12 cities were with railway passenger share increased. Some studies explore the substitution effect of railway acceleration on air patronage change, which show that the speed acceleration of railways in China between 1997 and 2009 had a negative impact on air patronage growth, especially for the air patronage at regional airports (Wang and Yip 2013). To further study the railway improvement to air patronage, the following part will discuss railway and air patronage changes between city pairs, for conventional railway and high speed rail, respectively.

114

5 Competition Between Railways and Other Transport Modes

Table 5.6 Changes in passenger flow shares in several cities Unit: % Hub

1990

1995

Rail

Air

Rail

Beijing

51

2

51

Tianjin

79

0

54

Shijiazhuang

49

0

Taiyuan

58

Hohhot

34

Shenyang

2000 Air

2005

Rail

Air

6

24

1

46

40

0

0

49

1

23

54

0

Changchun

50

Harbin

58

Shanghai

65

2010

Rail

Air

Rail

Air

5

9

2

33

5

6

4

4

11

9

0

2

8

0

9

2

1

47

1

15

1

29

6

46

11

1

12

6

27

31

1

7

45

2

33

4

13

1

0

49

1

61

1

29

1

38

1

19

2

2

64

1

34

2

29

5

56

3

11

43

9

45

22

35

41

Nanjing

26

1

12

1

8

1

7

1

6

3

Hangzhou

18

0

9

1

6

1

8

2

8

3

Hefei

22

0

41

3

9

1

10

1

8

1

Fuzhou

10

1

5

1

5

1

5

3

7

3

Nanchang

16

0

20

1

23

1

26

3

19

2

Jinan

27

0

19

1

27

1

26

2

20

2

Zhengzhou

15

0

13

1

13

1

16

2

10

1

Wuhan

25

0

24

1

22

1

30

3

32

4

Changsha

17

0

9

1

11

1

11

4

5

3

Guangzhou

18

5

16

4

12

3

16

8

15

9

Nanning

7

0

9

2

7

2

6

1

10

3

Haikou

0

0

0

3

0

4

0

5

0

4

Chengdu

13

1

9

1

6

1

21

2

14

1

Chongqing

8

0

5

0

2

0

2

0

2

1

Guiyang

1

0

9

1

4

0

3

1

3

2

Kunming

26

2

20

6

12

4

10

6

12

14

Xi’an

20

1

30

1

20

2

23

3

9

6

Lanzhou

39

1

32

2

24

2

23

2

26

5

Xining

10

0

21

0

7

0

7

1

8

2

8

0

5

0

6

1

6

2

7

32

23

2

98

0

56

4

37

9

19

11

Yinchuan Urumqi

Data Source Department of Urban Socioeconomic Surveys (1991–2011), CAAC (2002)

5.4 The Relevance of Railway Improvement to Air Flight Patronage

115

5.4 The Relevance of Railway Improvement to Air Flight Patronage 5.4.1 Methodology Air travel demand is derived from other activities at the destinations (Grosche, Rothlauf, and Heinzl 2007); thus, this section attempts to identify all the key factors that determine air patronage between city pairs before exploring the association between railway development and air patronage. Theoretically, factors influencing air patronage between city pairs are categorized into two types: geo-economic and service-related (Ghobrial and Kanafani 1995; Jorge-Calderon 1997; Rengaraju and Arasan 1992; Grosche et al. 2007; Loo, Ho and Wong 2005). Geo-economic factors are determined by the economic and geographical characteristics of the origin and destination cities, including income, population, percentage of university degree holders, percentage of full-time employees, employment composition, characteristics of regional productive structure, distance, proximity of competing airport(s), and intermodal competition. Service-related factors refer to the characteristics of air flight services, including the frequency of departures, aircraft size or technology, and airfare price (Grosche et al. 2007; Loo 2009). In particular, the distance between a city pair is an important factor. Hypothetically, social and commercial interactions between a city pair decrease as the distance increases. However, as the distance increases, the relative competitiveness of air transport improves compared with that of other modes. Hence, various distance groups are usually used to analyze air patronage (Jorge-Calderon 1997). This section adopts a time-series approach to analyze the association between railway development and air patronage in China over the years by controlling other major influencing factors of air patronage. The analysis in this chapter focuses on association rather than causality. Two hypotheses are proposed and explained below. Hypothesis 1: Both railway extension and railway acceleration are negatively associated with air patronage. Railway development in China can be generally categorized into two types: railway extension and railway acceleration. Railway extension signifies that the shortest railway travel distance between a city pair is reduced because of new railway construction; railway acceleration refers to the increase of technical railway speed between a city pair. Both railway extension and railway acceleration are expected to increase the competitiveness of railways, whereas air patronage may decrease. Hypothesis 2: The association between railway development and air patronage is more significant for short- and medium-haul city pairs than for long-haul ones. Majority of the existing research on HSR in developed countries suggests that the substitution effect of HSR to air transport is larger for short-haul journeys than for long-haul travels (Watkiss et al. 2001). For instance, HSR may compete with air transport within a distance of 1000 km; when the distance is over 1000 km, air transport tends to dominate in Europe (European Commission, Directorate General

116

5 Competition Between Railways and Other Transport Modes

Transport 1998). Another study indicates that HSR may compete with air transport only when the rail journey time is less than 5 h (Steer Davies Gleave 2006). The findings in Europe are also consistent with the reports in China, which suggest that that HSR is competitive when the travel distance is less than 1100 km (Zhang and Zhao 2012) or 1200 km (Fu et al. 2012). Although the speed and competiveness of conventional railways is much lower than that of HSR, some empirical study in China suggests that the conventional railways with night berths may compete with air transport when the travel distance is between 1100 and 2000 km (Zhang and Zhao 2012). Nevertheless, when the travel distance is longer than 2000 km, air transport dominates the passenger travel market. The present ex-post study includes both conventional railways and HSR. Based on the empirical finding in China, with straight-line distance as an indicator of different hauls, city pairs are divided into less than 1100 km (short haul), between 1100 km and 2000 km (medium haul), and more than 2000 km (long haul). The association between railway development and air patronage is hypothesized to be more significant for short- and medium-haul city pairs than for long-haul ones. Gravity model can examine air patronage between city pairs considering both geoeconomic and service-related factors (Jorge-Calderon 1997). This model exhibits a good fit to the observed data of air patronage, which were statistically tested and validated (Grosche, Rothlauf, and Heinzl 2007). Therefore, the present study adopts the gravity model as a conceptual framework to analyze the association between the two types of railway development, namely, railway extension and railway acceleration, and the air patronage between city pairs. First, air patronage data between 104 city pairs in China from 1993 to 2012 are collected. The air patronage data can be obtained from the Compiled China Aviation Statistics from 1949 to 2000 by the Civil Aviation Administration of China (CAAC) (2002) and the Data on Civil Aviation of China (CAAC 1994–2013). The former covers primarily scheduled flights from 1949 to 2000, whereas the latter covers both chartered and scheduled flights from 1993 to 2012. Data from these two sources cannot be integrated because of the differences in statistical compilations. Air transport was first used as a major transport mode in China in the 1990s. Thus, this research uses only the data on civil aviation of China from 1993 to 2012. Particularly, all the air patronage data of 104 city pairs in 20 years are collected. Second, on the basis of the data availability and an empirical study in China (Zhang and Zhang 2007), several geo-economic and service-related factors are selected as controlled variables to analyze the association between railway development and air patronage: GDP (GDP), population (POP), employment (EMP), economic structure (GST ), employment structure (EST ), distance (DIST ), frequency of departures (AIRFREQUENCY ), and travel cost of highway (ROADCOST ). Both GDP (GDP) and travel cost of highway (ROADCOST ) are deflated to constant prices based on 2000. The economic structure (GST ) and employment structure (EST ) refer to the percentage of the tertiary industry in GDP and employment, respectively. Based on the three-sector theory (Fisher 1939), the economies can be divided into three sectors of activity: extraction of raw materials (primary), manufacturing (secondary), and services (tertiary). The percentage of GDP and employment in the tertiary industry

5.4 The Relevance of Railway Improvement to Air Flight Patronage

117

is generally higher in highly developed regions. All geo-economic variables are in their functional form of the products of the origin and destination. For instance, GDPodt refers to the product of GDP of origin city o and destination city d in the year t (GDPot *GDPdt ). In 2003, Severe Acute Respiratory Syndrome (SARS) was an important factor that critically affected air passenger travel in China. Hence, we model the influence of SARS as a dummy variable. We also tried to control other years such as 1997 1998, and 2008, when there were financial crisis, and the results are not significantly different from the model with only the SARS year controlled. In addition, when removing all year dummies, the results are similar with the results when these year dummies are controlled. Nonetheless, the model fit (as measured by R2 ) is better when the yearly dummy variables are included. Hence, the final model includes the selected yearly dummy variables that are both statistically significant and analytically meaningful. However, airfare is ommited as a level-of-service attribute because no appropriate data can be obtained for multiple city pairs over the decades. Additionally, four city pairs exhibit specific missing geo-economic data; thus, the geo-economic and service-related data of 100 city pairs from 1993 to 2012 are gathered. Third, data on the indicators of railway development are collected, including the technical railway speed (RAILSPEED) and the shortest railway length (RAILSLEN) of 100 city pairs. In this chapter, railways refer to both conventional railways and HSR. Since the samples of HSR are limited, this study doesn’t analyze conventional railways and HSR separately but treats HSR as an increase of railway speed. Before 1997, the railway speed in China was only 80 km/h to 100 km/h (Hua 1996). After six rounds of national railway speed acceleration campaigns between 1997 and 2007, the railway speed has accelerated to 120–300 km/h. According to the Chinese Railway Train Number Rule published in 2014, the railway speed in China, that is, high-speed trains (300 km/h), multiple-unit trains (200 km/h), direct express trains (160 km/h), express trains (140 km/h), and ordinary trains (120 km/h), are denoted by “G,” “D,” “Z,” “T,” and “K,” respectively. Data about the type of trains and the shortest railway distances are calculated from the China railway timetables; the information on the railway speed acceleration campaigns is attained from the Yearbook of China Transportation and Communications (1998–2008). Although most city pairs have direct railway connections, there are a few city pairs without railway connection in some years (107 out of 2000 observations). For these cases, the railway speed of the city pairs is assumed to be the lowest railway speed in that specific year; its railway distance is assumed to be five times the longest railway distance between two railway stations in China. We have also attempted to use the railway distance that is 10 times the longest railway distance for city pairs without a direct railway connection, and the results are highly similar. In addition, the shortest railway length between city pairs is obtained from the Chinese railway network over the years (Ministry of Railways 1993–2012). For each year during the study period, the variable RAILSLEN is based on the railway network in that year. All the variables are explained and described in Table 5.7.

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5 Competition Between Railways and Other Transport Modes

Table 5.7 Summery of Variables in the Gravity Model Variable

Description Mean

St. Dev. Min.

APod

Air patronage

529377

50537 3847136 CAAC (1994–2013)

3879

463

557690

AIRFREQUENCY od The annual 4713 frequency of air flight between the city pair

Max.

26985

GDPod

Product of 1.59e + 3.11e + 4.50e GDP of the 15 15 + 11 origin and destination cities

2.73e + 16

POPod

Product of population of the origin and destination cities

EMPod

605396

594326

3784

4770461

Product of .156 the percentage of full-time employees in population of the origin and destination cities

.155

.008

0.801

GST od

Product of 0.246 the percentage of the tertiary industry in GDP of the origin and destination cities

0.068

0.065

0.525

EST od

Product of the percentage of people engaged in the tertiary industry

0.099

0.046

0.540

0.253

Data source

Department of Urban Socio-economic Surveys (1994–2013)

(continued)

5.4 The Relevance of Railway Improvement to Air Flight Patronage

119

Table 5.7 (continued) Variable

Description Mean

St. Dev. Min.

Max.

Data source

DIST od

Air flight distance

560

353

3324

Airline websites

SARS

1 if the year 0.05 is 2003 with SARS, otherwise 0

0.218

0

1

RAILSLEN od

The 3262 shortest railway length between the city pair

6517

488

30500

RAILSPEEDod

The 122 technical speed of railways between the city pair

29

80

300

ROADCOST od

The 5421 product of annual gasoline price and road distance between the city pair

3841

958

32544

1145

Ministry of Railways (1993–2012)

Regulations about gasoline price adjustment from National Development and Reform Commission; http:// map.baidu.com/

Based on the straight-line distance, the short-, medium, and long-haul city pairs are differentiated. The distance of city pairs in the present study is mostly between 500 km and 2500 km, with 54, 38, and 8 city pairs of short-, medium-, and longhaul railways, respectively (Fig. 5.6). During the study period from 1993 to 2012, for the short-haul city pairs, 27 railway extensions and 128 railway accelerations are observed. For the medium-haul city pairs, 23 railway extensions and 80 railway accelerations are evident. For the long-haul city pairs, 2 railway extensions and 16 railway accelerations exist. A total of 52 observations present railway extension, and 224 observations show railway acceleration. On the basis of the aforementioned database of 100 city pairs from 1993 to 2012, a gravity model is established to model the variability of air patronage between city pairs. All the linear variables are transformed by logarithm to linearize gravity model and achieve variable distributions that are more symmetrical (Hair et al. 1995). The double-log transformation of the model also allows the estimated coefficients to be directly interpreted as elasticities. The corresponding gravity function is calculated as follows (Model 5.1):

120

5 Competition Between Railways and Other Transport Modes

Fig. 5.6 Histogram of Straight-line Distance of City Pairs

ln A Podt =β0 + β1 ln G D Podt + β2 ln P O Podt + β3 ln E M Podt + β4 ln G STodt + β5 ln E STodt + β6 ln D I STod + β7 S A RSt + β8 ln R AI L S L E Nodt + β9 ln R AI L S P E E Dodt +β10 ln R O ADC O STodt + β11 ln AI R F R E QU E N CYodt + αod + νodt

(5.1)

where o and d refer to the origin and destination cities, respectively; t refers to the year; β is a vector of coefficients; and α, ν are the error terms. A panel data regression analysis is conducted uisng the gravity model to test Hypothesis 1. We attempt to analyze the correlation between the shortest railway length (RAILSLEN odt ) and railway technical speed (RAILSPEEDodt ) and the level of air patronage (APodt ) with all the other controlled variables. Model 1 has passed the multicollinearity diagnostics, with a variance inflation factor (VIF) of less than 10 for all the independent variables. The variables of DIST, ROADCOST, EMP, and GDP have high VIFs, which are 8.44, 8.37, 5.82, and 5.09, respectively, while the variables about railway development are relatively low, that is, 2.65 for RAILSPEED and 1.91 for RAILSLEN. To test Hypothesis 2, the city pairs are categorized into three different groups in accordance with their straight-line distance: short-haul (2000 km) groups. The model is tested for each group of sub-samples after testing the multicollinearity among the variables. By

5.4 The Relevance of Railway Improvement to Air Flight Patronage

121

comparing the model results for different sub-samples of city pairs, varying interrelationships between railway development and air patronage at different distances are revealed.

5.4.2 Results a. Regression Analysis of Different Types of Railway Development Table 5.8 summarizes the results of panel data regression analysis of the gravity model. The regression coefficients of all the controlled and railway development factors are presented. Generally, the ordinary least squares (OLS) estimator, leastsquares dummy variables (LSDV) estimator for fixed effects (FE) models and generalized least squares (GLS) estimator for random effects (RE) models are most commonly used panel regression techniques. By applying the F test, FE models are better than pooled OLS models. Using the Breusch-Pagan Lagrange multiplier (LM) test, RE models are also found to be significant for the panel data. When analyzing the autocorrelation by Wooldridge test and heteroskedasticity by LR test of the panel data, first-order autocorrelation within panels and heteroskedasticity across panels are evident. Due to the presence of autocorrelation and heteroskedasticity, the robust variance estimator (robust VCE) is adopted for FE models and feasible generalized least squares (FGLS) estimator with correlated disturbances is adopted for RE models. It is worth noting that the assumptions of RE models are very difficult to be fulfilled, which requires the unobserved effects must not be correlated with the independent variables. However, some omitted variables exist in our model due to data availability, such as the airfare, which is very likely to be correlated with the included independent variables. Considering the high risk of biased estimates of RE model, FE model is more suitable for the regression. The coefficients of FE model indicate that the GDP, percentage of tertiary industry in GDP, air travel distance, travel cost of highway, annual frequency of air flight, and the shortest railway length are positively correlated with the air patronage between city pairs. By contrast, the percentage of tertiary industry in employment, SARS, and railway technical speed are negatively correlated with the air patronage. Thus, both railway extension and railway acceleration are negatively associated with air patronage. The coefficients of the shortest railway length (0.040) and railway technical speed (−0.165) also indicated that the elasticity of railway extension and railway acceleration is about − 0.040 and −0.165, respectively, signifying that air patronage is inelastic with respect to both railway extension and railway acceleration. When the shortest railway length is reduced by 100%, air patronage decreases by 4.0%; however, when the railway technical speed increases by 100%, air patronage may possibly decrease by 16.5%.

122

5 Competition Between Railways and Other Transport Modes

Table 5.8 Results of Panel Data Regression Analysis by Gravity Model Independent variables

FE model (robust VCE)

RE model (FGLS)

lnGDPodt

0.108*** (0.022)

0.042*** (0.006)

lnPOPodt

0.037 (0.032)

−0.002 (0.008)

lnEMPodt

0.005 (0.018)

0.046*** (0.007)

lnGST odt

0.097* (0.037)

0.134*** (0.018)

lnEST odt

−0.183*** (0.049)

−0.018 (0.019)

lnDIST od

106.495*** (19.466)

−0.113*** (0.022)

SARS t

−0.048*** (0.011)

−0.038*** (0.007)

lnRAILSLEN odt

0.040*** (0.010)

0.021** (0.007)

lnRAILSPEEDodt

−0.165** (0.051)

−0.087*** (0.023)

lnROADCOST odt

0.168*** (0.036)

0.159*** (0.019)

lnAIRFREQUENCY odt

0.870*** (0.028)

0.949*** (0.010)

Constant

−736.654*** (134.857)

3.744*** (0.185)

Number of observations

2000

2000

R2

Within: 0.9620 Between: 0.0014 Overall: 0.0002

–

Wald Chi2

–

32602.17***

Note *, **, ***represents a significance level of p < 0.05, p < 0.01, p < 0.001, respectively; the data in brackets denote the standard error

b. Regression Analysis of Different Distances The gravity model is also applied to analyze the association between railway development and air patronage in the three sub-sample groups. The results are presented in Table 5.9. Similar to the model of the overall samples, FE model is the most appropriate for panel data regression analysis and the robust variance estimator (robust VCE) is adopted due to autocorrelation and heteroskedasticity. The railway technical speed is significantly associated with air patronage for short haul (