Lifestyle Change and Transport in China (Population, Regional Development and Transport) 9811943982, 9789811943980

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Population, Regional Development and Transport

Pengjun Zhao Di Lyu

Lifestyle Change and Transport in China

Population, Regional Development and Transport Series Editor Pengjun Zhao, College of Urban and Environmental Sciences, Peking University, Beijing, China

This book series chiefly explores population change, regional development and transport in contemporary China. Its goal is to enhance our current understanding of population, regional development and sustainable transport in a context of rapid urbanization and transition – characterized by the shift from a centrally planned system to a market system, together with growing economic globalization and political decentralization. The series will enrich the existing literature on population studies, regional development studies and transport studies. In particular, it highlights academic research on the interactions between population, regional development and transport. It will also shed new light on government practices with regard to regional development planning and management and transport investment.

Pengjun Zhao · Di Lyu

Lifestyle Change and Transport in China

Pengjun Zhao College of Urban and Environmental Sciences Peking University Beijing, China School of Urban Planning and Design of Peking University Shenzhen Graduate School Shenzhen, China

Di Lyu Peking University Beijing, China School of Urban Planning and Design of Peking University Shenzhen Graduate School Shenzhen, China

This study was financially supported by National Natural Science Foundation of China (41925003; 42130402). ISSN 2662-4613 ISSN 2662-4621 (electronic) Population, Regional Development and Transport ISBN 978-981-19-4398-0 ISBN 978-981-19-4399-7 (eBook) https://doi.org/10.1007/978-981-19-4399-7 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022, corrected publication 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Preface

Although there is no uniform definition of lifestyle, the use of the lifestyle concept in transport has been on the rise in the past 10 years, and the interrelationship between transport and lifestyles still needs further clarification. A large number of studies have proven that lifestyles influence people’s travel behaviour. Transport itself is a kind of lifestyle. Different travel modes, transport facilities and transport technologies have a profound influence on lifestyles. Lifestyles mainly affect people’s long-term travel behaviour. However, most existing research focuses on developed regions in Europe and the United States, with very few studies on developing countries. China ranks among the top in the world for transport infrastructure. Since 2011, China’s lengths of high-speed railways in operation, expressways in use and urban rail transport in service are all ranked first in the world. China is building up its strength in transport. Accordingly, Chinese lifestyles have undergone radical changes. The previously isolated and slow-paced lifestyle under the small-peasant economy has transformed into open, fast-paced and diversified lifestyles under the market economy. The development of new transport technologies such as shared transport and intelligent transport has brought about growing interactions between transport and lifestyles. It is increasingly important to study the changes in transport and lifestyles in China to provide strong evidence for the application of the lifestyle concept in transport in developing countries. The aim of this book is to provide a complete overview of the current situation of lifestyle and transport changes in China, focusing on the ongoing trends in lifestyle and transport technologies, which are shaping a new lifestyle and transport system. An additional focus is to discuss the mechanism behind the influence of transport on lifestyles and to analyse the influence of transport facilities on lifestyles, which will be helpful in developing efficient and effective transport solutions. Chapter 1 explores the importance of this research after bringing forward the research background and topic. Chapter 2 makes clear the connotations of lifestyle when explaining the evolution of the concept of lifestyle in sociology and transport. It summarises current indicators and types of lifestyle segmentation, and it constructs an index system for individual and overall lifestyle measurement in transport on this basis. Chapter 3 discusses links between lifestyles and transport. It explores the v

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theoretical basis for such links. Then it explores the interaction between lifestyles and transport and summarises the methods, models and findings of current empirical research on lifestyle in transport. Chapter 4 analyses the connections between Chinese residents’ lifestyles and transport from the perspectives of consumption, transport means, environmental facilities, activities, time use and value orientations. Chapter 5 mainly focuses on an empirical analysis of the impacts of lifestyles on transport. There are many aspects of the impacts of various lifestyles on transport. Limited by the availability of data, this chapter mainly analyses the relationship between the overall lifestyle and transport volumes through macro-statistical data. Moreover, the impacts of lifestyles on commuting time are verified by China Family Panel Studies data. Chapter 6 takes Beijing as an example to analyse the lifestyle characteristics in China’s megacities. This chapter analyses the connection and interactions between Beijing residents’ lifestyles and travel behaviour, focusing on the dimensions of consumption, time use and activities. In addition, using data from the 2016 Subway Survey and the 2017 Questionnaire Survey on Jobs–Housing Balance in Beijing, it applies structural equation modelling to study the impacts of lifestyles on travel behaviour for work and leisure purposes. Chapter 7 is about rural China. It is based on survey data about town and village residents from a detailed survey about towns across China in 2016. The clustering method is used to analyse different types of rural lifestyles, and the OLS regression approach is utilised to study the impacts of lifestyles on the travel frequency and travel time of rural residents. Chapter 8 first analyses the impacts of transport facilities on lifestyles in 365 prefecture-level cities. Second, in accordance with China’s development status, Chap. 8 analyses the impacts of China’s latest transport development on lifestyles looking at three aspects: high-speed rail, shared transport and new transport technologies. Chapter 9 proposes policy suggestions for future transport development from four perspectives: enhancing quality of life, promoting transport equity, establishing a smart transport system for smarter life and developing new business forms in transport. Chapter 10 offers conclusions and a discussion. It also summarises the research results of this book, presents key conclusions, discusses the limitations and deficiencies of this study and proposes future research directions. Beijing, China

Pengjun Zhao Di Lyu

Acknowledgements We acknowledge the financial support of the National Natural Science Foundation of China (No. 41925003 and No. 42130402). The authors are responsible for all errors and interpretations.

The original version of this book was revised: Funder information has been updated. The correction to this book can be found at https://doi.org/10.1007/978-981-19-4399-7_11

Contents

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Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 Research Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1.1 Current Research on Lifestyle in Transport . . . . . . . . . . . 1.1.2 Changing Chinese Lifestyles in Transport . . . . . . . . . . . . 1.1.3 Transport Development Supporting New Lifestyle Changes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Research Importance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2.1 Theoretical Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2.2 Potential Contributions to Policymaking . . . . . . . . . . . . . 1.3 Organisation of the Book . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lifestyle and Its Measurements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Definitions of Lifestyle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.1 Concepts of Lifestyle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.2 Connotations of Lifestyle . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.3 Definitions of Lifestyle in Transport . . . . . . . . . . . . . . . . . 2.2 Lifestyle Measurement Approaches . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.1 The AIO Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.2 Values, Attitudes and Lifestyles (VALS) Model . . . . . . . 2.2.3 The Model of Consumers’ Psychological Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.4 Current Index System to Measure Lifestyles in China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Measurements of Lifestyles in Transport . . . . . . . . . . . . . . . . . . . . . 2.3.1 Current Measurements and Classification of Lifestyle Dimensions in Transport . . . . . . . . . . . . . . . . 2.3.2 Construction Measurement Index System for Transport-Related Lifestyles . . . . . . . . . . . . . . . . . . . .

1 1 1 5 8 12 12 13 14 16 18 23 23 23 28 31 34 35 36 39 39 40 41 46

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2.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Links Between Lifestyle and Transport . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Theoretical Perspectives on Links Between Lifestyle and Transport . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.1 The Common Theoretical Basis of Lifestyle and Transport . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.2 Main Theoretical Basis of Lifestyle Impact on Transport . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.3 Main Theoretical Basis of Transport Impact on Lifestyle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Characteristics of the Links Between Lifestyles and Transport . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.1 Reciprocal Causation Between Lifestyles and Transport . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.2 Mutual Subordination of Lifestyles and Transport . . . . . 3.2.3 Multilevel Nature of Lifestyles and Transport . . . . . . . . . 3.2.4 Synchronous Development of Lifestyles and Transport . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 The Conceptual Framework of the Links Between Lifestyle and Transport . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.1 Current Theoretical Model . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.2 Complex and Procedural Perspective on Conceptual Framework . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 Research Method for the Links Between Lifestyle and Transport . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.1 Common Data in Research . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.2 Model Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Lifestyle Changes in China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Consumption Dimension: China’s Consumption Patterns and Transport . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1.1 Income Level . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1.2 Consumption Level . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Vehicle Dimension: China’s Motorised Lifestyle . . . . . . . . . . . . . . 4.2.1 Changes in the Characteristics of the Motorisation Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.2 Cars Are Mainly Used for Commuting and Travelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.3 A New Mode of Urban Travel Under the Sharing Economy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

59 60 63 64 66 66 67 67 68 68 68 80 84 84 85 87 90 97 97 97 99 104 107 110 113

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Environmental Dimension: China’s Urban System and Transport . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.1 Regional Transport System . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.2 Urban Transport System . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.3 Changes in the Community Structure . . . . . . . . . . . . . . . . 4.4 Activity Dimension: Work and Life in China . . . . . . . . . . . . . . . . . 4.4.1 Employment Patterns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4.2 Changes in Leisure Activities . . . . . . . . . . . . . . . . . . . . . . . 4.4.3 Increased Employment Hours, Reduced Domestic Labour Hours and Increased Modernisation . . . . . . . . . . . 4.5 Time Dimension: Chinese Residents’ Time Use and Transport . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5.1 General Characteristics of Time Use . . . . . . . . . . . . . . . . . 4.5.2 Significant Changes in the Structure of Unpaid Working Hours, and Increased Time Spent with Family Members . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5.3 More Reasonable Allocation of Disposable Personal Time, and Increased Time Spent on Leisure and Fitness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5.4 Acceleration of Informatisation and Substantial Increase in Time Spent Online . . . . . . . . . . . . . . . . . . . . . . 4.5.5 Participation and Time Characteristics of Transport Activities and Changes in Commuting Time . . . . . . . . . . 4.6 Chinese Residents’ Values and Transport . . . . . . . . . . . . . . . . . . . . 4.6.1 Diversification of Value Orientations and Pursuit of Quality of Life . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.6.2 An Obvious Trend of Fast-Paced and Convenient Life Among Residents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.6.3 A Green and Low-Carbon Lifestyle: The Demand of the Times . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.6.4 More Attention Paid to Inclusive Development and Social Equity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Impacts of Lifestyles on Transport . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 Impacts of Lifestyles on Transport Volumes . . . . . . . . . . . . . . . . . . 5.1.1 Data Source . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1.2 Building Lifestyle Indexes . . . . . . . . . . . . . . . . . . . . . . . . . 5.1.3 Comprehensive Assessment of the Overall Lifestyle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1.4 Impacts of Lifestyles on Transport Volumes . . . . . . . . . . 5.2 Empirical Research: Impacts of Lifestyles on Travel Behaviour . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.1 Data Source . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

114 114 116 119 124 124 129 133 134 135

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135 138 138 141 141 142 142 143 144 146 151 151 152 152 154 159 166 166

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5.2.2

Clustering of Residents’ Lifestyles Based on CFPS Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.3 Impacts of Lifestyles on Commuting . . . . . . . . . . . . . . . . 5.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

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Beijing Case . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1 Urban Development of Beijing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1.1 History of Beijing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1.2 Urbanisation and Population Growth in Beijing . . . . . . . 6.1.3 Urban Expansion in Beijing . . . . . . . . . . . . . . . . . . . . . . . . 6.1.4 Transport System Changes in Beijing . . . . . . . . . . . . . . . . 6.2 Lifestyle Changes in Beijing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.1 Consumption Dimension . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.2 Time Dimension . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.3 Activity Dimension . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3 Surveying the Links Between Lifestyles and Travel Behaviour . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.1 Data Source . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.2 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.3 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

181 181 181 182 184 186 191 191 196 200

Stories About Rural China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1 Data Source . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2 Rural Lifestyles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2.1 Consumption Dimension . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2.2 Time Dimension . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2.3 Activity Dimension . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3 Rural Residents’ Travel Behaviour . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3.1 Town Residents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3.2 Village Residents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.4 Clustering Analysis of Rural Lifestyles . . . . . . . . . . . . . . . . . . . . . . 7.4.1 Data Source . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.4.2 Variable Selection and Description . . . . . . . . . . . . . . . . . . 7.4.3 Statistical Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.4.4 Travel Characteristics of Different Lifestyles . . . . . . . . . . 7.4.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.5 Impacts of Rural Lifestyles on Travel Frequency and Time . . . . . 7.5.1 Variable Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.5.2 Impacts of Rural Lifestyles on Travel Frequency . . . . . . 7.5.3 Impacts of Rural Lifestyles on Travel Time . . . . . . . . . . . 7.5.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

219 220 223 223 227 229 233 234 237 240 240 241 242 242 246 247 248 248 250 263 264 265

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Impacts of Transport Facilities on Lifestyles . . . . . . . . . . . . . . . . . . . . . 8.1 Impacts of Transport Facilities on Lifestyles . . . . . . . . . . . . . . . . . . 8.2 Impacts of High-Speed Rail on Lifestyles . . . . . . . . . . . . . . . . . . . . 8.2.1 Development of High-Speed Rail in China . . . . . . . . . . . 8.2.2 Impacts of High-Speed Rail on Lifestyles . . . . . . . . . . . . 8.3 Impacts of Shared Transport on Lifestyles . . . . . . . . . . . . . . . . . . . 8.3.1 Development of Shared Transport in China . . . . . . . . . . . 8.3.2 Impacts of Shared Transport on Lifestyles . . . . . . . . . . . . 8.4 Impacts of New Transport Technologies on Lifestyles . . . . . . . . . 8.4.1 Influencing Factors of Travel Trends . . . . . . . . . . . . . . . . . 8.4.2 Impacts of the 5G Era on Future Transport . . . . . . . . . . . 8.4.3 Three Future Mobility Models . . . . . . . . . . . . . . . . . . . . . . 8.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

269 269 270 270 272 274 274 275 276 276 281 283 285 286

9

Policy Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.1 Enhancing Quality of Life . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2 Promoting Transport Equity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.3 Smart Transport System for Smarter Life . . . . . . . . . . . . . . . . . . . . 9.3.1 Establishing a Smart Transport System . . . . . . . . . . . . . . . 9.3.2 Door-to-Door Delivery . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.3.3 Integrated Transport System . . . . . . . . . . . . . . . . . . . . . . . . 9.3.4 Intelligent Transport System . . . . . . . . . . . . . . . . . . . . . . . . 9.4 New Business Forms in Transport . . . . . . . . . . . . . . . . . . . . . . . . . . 9.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

291 291 296 302 302 304 305 305 308 311 312

10 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.1 Changing Lifestyles in China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.2 Links Between Transport and Lifestyles . . . . . . . . . . . . . . . . . . . . . 10.3 Future Research Agenda . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.3.1 Human Lifestyle Changes and Future Transport Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.3.2 China’s Social Transformation and Transport Demands . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

315 315 317 319

Correction to: Lifestyle Change and Transport in China . . . . . . . . . . . . . .

C1

319 320 322

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 325

Chapter 1

Introduction

1.1 Research Background 1.1.1 Current Research on Lifestyle in Transport The concept of lifestyle originated in sociology and was then widely used in marketing, psychology, housing, transport and medicine. Many scholars define lifestyle in their own fields of research. Some literature reviews are full of insights into the concept of lifestyle. For example, Veal (1993) reviewed the application of the lifestyle concept in sociology and leisure, Jensen (2007) wrote a review on lifestyle from an environmental protection perspective, van Acker (2015) summarised the definitions of lifestyle in transport, Jansen (2011) discussed conceptual and theoretical aspects of lifestyle and Zarrabi et al. (2021) reviewed the application of the lifestyle concept in housing. Despite the extensive use of the lifestyle concept, there is no universally agreed-upon definition of lifestyle, mainly due to the complexity, breadth and inclusiveness of the lifestyle concept. Theoretically speaking, all manifestations of human life can be defined by lifestyle. The definition of lifestyle depends on the perspectives and dimensions of research. Only discussions about domain-specific lifestyles serve useful purposes (Thøgersen, 2018; van Raaij & Verhallen, 1994). The concept of lifestyle was introduced by Reichman (1977) in transport research in the 1970s. At first, lifestyle was associated with socioeconomic differences, and then it evolved into activity and time use patterns, values and behavioural orientation (Kitamura, 1988), along with mobility styles regarding travel mode preferences (Götz et al., 1997; Krizek & Waddell, 2002; Lanzendorf, 2002; Ohnmacht et al., 2009). Current researchers mostly regard lifestyle expressions as individuals’ behavioural patterns indicating their social status, and lifestyle as behavioural types of activity and time use patterns (Pas, 1988; Salomon & Ben-Akiva, 1983; van Acker, 2015). Kitamura (2009), van Acker (2015, 2017) and van Acker et al. (2014) had a profound influence on the definition of lifestyle in transport. Although the concept of lifestyle is widely used in travel behaviour research, there is no unified definition of lifestyle. The concept of lifestyle extends the scope of travel behaviour analysis, which may © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. Zhao and D. Lyu, Lifestyle Change and Transport in China, Population, Regional Development and Transport, https://doi.org/10.1007/978-981-19-4399-7_1

1

2

1 Introduction

lead to improved predictive performance in forecasting models (Kitamura, 2009), reveal the travel preferences of specific target groups and provide a useful conceptual framework for transport research (van Acker, 2015). The concept of lifestyle is replacing economic and social class as the principal feature that distinguishes population groups (van Acker, 2015). Several studies have proved that lifestyle is a key factor affecting travel behaviour (Kitamura, 2009). There is a complex interrelationship between lifestyle and travel behaviour changes. Lifestyle is related to socioeconomic and demographic attributes (Ganzeboom, 1988); age, family and consumption (Giddens, 1991; Sobel, 1981); work location (Ettema, 1996); residential location choice (Ærø, 2001; Ardeshiri & Vij, 2019; BenAkiva et al., 1985; Bagley & Mokhtarian, 1999; Waddell, 2001; Walker & Li, 2007); car type (Choo & Mokhtarian, 2004; Kitamura et al., 1997; van Acker et al., 2014); employment, leisure activities and hobbies. These choices are often interdependent and together define one’s lifestyle (Krizek, 2006). These factors are important determinants of travel behaviour. In recent research, lifestyle has emerged as an important determinant of travel behaviour. For example, Transportation Research Part A published a special issue entitled “Life-Oriented Travel Behaviour Research: An Overview”, in which van Acker et al. studied the impacts of lifestyles on transport, as well as how COVID-19 has changed people’s lifestyles and affected transport (Awad-Núñez et al., 2021; Bhaduri et al., 2020). Previous research mainly revolves around lifestyles as behavioural types of activity and time use patterns (Etminani-Ghasrodashti & Ardeshiri, 2015; Krizek, 2006; Krizek & Waddell, 2002; Krueger et al., 2016a, 2016b; van Acker et al., 2010), or behavioural orientations—values, attitudes and preferences—and potential factors that stimulate behavioural patterns (Ardeshiri & Vij, 2019; Lanzendorf, 2002; Ohnmacht et al., 2009; Prato et al., 2017; Vij et al., 2013). When studying the relationship between lifestyles and transport through the impacts of lifestyles on travel behaviour, the most important determinant of this connection is how lifestyles are segmented and how lifestyles affect people’s travel preferences and choices, especially non-work travel such as leisure travel (Lanzendorf, 2002). Therefore, it is very important to understand the mechanism behind lifestyle measurement and travel behaviour and to explore the possibility of promoting the coordination of transport and the needs of people with different lifestyles in this context, which will not only help to enhance the current understanding of influencing factors on travel but also provide insightful information for the formulation of transport policies from the perspective of lifestyle segments. Despite an extensive literature that explores the connection between lifestyles and travel behaviour, there are some research voids to be addressed. Most of the current studies on lifestyle segmentation in transport are empirical ones, which are inductive and data-driven rather than theory-driven. They tend to use survey-based methods to identify lifestyles, with each one finding new lifestyles (van Acker, 2015). Empirical lifestyle research has been criticised for its inductive nature and weak theoretical basis (Anderson & Golden, 1984; Kahle & Valette-Florence, 2014; Lastovicka, 1982; Sobel, 1981; Thøgersen, 2018).

1.1 Research Background

3

First of all, the interpretation of the relationship between lifestyles and transport depends largely on how lifestyles are measured, and which variables are used for measurement. Almost all the literature is based on survey data. Different scholars use different indicators to measure lifestyles, and they discover and define lifestyles based on the results of data clustering. There are no generally accepted measurement dimensions and core variables, and it is difficult to compare these different lifestyles due to different measurement indicators and research goals. How one measures lifestyle depends on how one defines lifestyle. The following methods are used for lifestyle measurement in the transport sector: (1)

(2)

There are three main types of indicators: (a) socioeconomic and demographic characteristics of the individual and the household (Hildebrand, 2003; Kitamura, 1988; Reichman, 1977; Salomon & Ben-Akiva, 1983; Sharp, 1987). However, van Acker (2015) has criticised this, as she believes that these studies refer to the stage of life cycle or household composition rather than to lifestyles. (b) Socioeconomic attributes, demographic attributes and daily behaviour, such as activity time, travel distance and number of trips (Bagley & Mokhtarian, 1999, 2002; Krizek, 2006; Krizek & Waddell, 2002; Lin et al., 2009; van Acker et al., 2014). (c) Psychology and culture, such as values and attitudes (Ardeshiri & Vij, 2019; Lanzendorf, 2002; Ohnmacht et al., 2009; Prato et al., 2017; Scheiner, 2010; Vij et al., 2013). There are four main perspectives on the measurement dimensions of lifestyle. Krizek and Waddell (2002) developed a framework to analyse household lifestyles via (a) travel characteristics, (b) activity frequency, (c) car ownership and (d) urban form. Krizek (2006) also used this method to analyse household lifestyles via (a) travel characteristics, (b) activity duration and (c) neighbourhood characteristics. Van Acker (2015) analysed seven lifestyle measurement approaches after an in-depth review: (a) a socioeconomic and demographic lifestyle approach that measures stage of life and household composition, (b) a psychographic lifestyle approach that analyses personality traits and related motives and values, (c) a cultural lifestyle approach that shifts the focus from individuals to communities, (d) a sociographic lifestyle approach that shifts the focus to individual opinions and attitudes, (e) a mechanistic lifestyle approach that regards lifestyle as a way of living, (f) a post-structural lifestyle approach that disconnects lifestyles from social structure and (g) a geographic lifestyle approach that combines individual information with spatial information on residential location. Thøgersen (2018) proposed five dimensions of lifestyle for measuring transport-related lifestyles, namely quality aspects, buying motives, ways of shopping, travel and transport routines and consumption situations. Due to the complexity and diversity of lifestyles, there are no agreed-upon lifestyle variables. Too many variables will also bring about many problems, such as operational difficulties (Kamakura & Wedel, 1995) caused by over 300 questions involved in the activities, interests and opinions (AIO) approach to measure lifestyles in sociology (Plummer, 1974; Wells & Tigert, 1971). However, from a practical perspective, it is feasible to construct unified lifestyle

4

1 Introduction

measurement dimensions in transport, which requires further research. In addition, the existing lifestyle measurement dimensions in transport mostly focus on individual lifestyle segmentation, with little discussion on measurement of the overall lifestyle. Second, there are no well-established theories and mechanisms relating to the relationship between lifestyles and travel behaviour. Due to the lack of a good theoretical basis and reliance on data results, each study has discovered a new lifestyle (van Acker, 2015). Currently, the impacts of lifestyles on travel behaviour are mainly studied by adding lifestyle elements into the theoretical framework of travel behaviour (Anas et al., 2021; Krueger et al., 2016a, 2016b; van Acker et al., 2014; Zhang & van Acker, 2017), without in-depth theoretical discussions about how lifestyles affect travel behaviour. Some empirical studies put forward conceptual models explaining the relationship between lifestyles and travel behaviour without presenting in-depth theoretical analysis (Bhat et al., 2016; Etminani-Ghasrodashti & Ardeshiri, 2015; Krueger et al., 2016a, 2016b; Lee & Circella, 2019; Scheiner & Holz-Rau, 2007; van Acker et al., 2010, 2014). Although some studies pay attention to the theoretical basis of the impacts of lifestyles on travel behaviour (Krueger et al., 2016a, 2016b; Zhang & van Acker, 2017), few people realise the causal mechanism between lifestyles and transport. In general, the mechanism behind the interaction between lifestyles and travel behaviour remains unclear in the existing literature. In addition, the application of the lifestyle concept in transport goes beyond the individual level, but there is no theoretical discussion about the relationship between transport and the overall lifestyle of a country or region (Jensen, 2007). Third, currently, the lifestyle concept in transport is mainly applied to explain the determinants of individuals’ travel choices. However, the connotations of lifestyle are stratified and multidimensional. Jensen (2007) suggested understanding lifestyle from (1) the global level, (2) the structural or national level, (3) the positional or subcultural level and (4) the individual level. Most of the previous literature focuses on the impacts of individual lifestyles on travel behaviour, neglecting the relationship between transport and the overall lifestyle in a country or region. Therefore, it is necessary to explore the connection and interaction between transport and lifestyles at different levels, as well as the relationship between a specific lifestyle and transport. For example, Lee and Circella (2019) probed into the relationship between travel choices and lifestyles oriented toward information and communication technology (ICT), and Macher (2020) studied the relationship between climate-friendly lifestyles and travel behaviour. Moreover, the current research has geographical limitations (Thøgersen, 2018), and most of the research is conducted in particular regions or cities (Bagley & Mokhtarian, 2002; Etminani-Ghasrodashti & Ardeshiri, 2015; Scheiner, 2010). A great number of studies are conducted in Western countries (Most are in Europe and the United States). Their results may not apply to other countries, especially China, as there is a lack of research in this field. Therefore, research on Chinese regions is of great necessity to support the existing findings.

1.1 Research Background

5

This book addresses the above-mentioned research void with Chinese case studies. China is experiencing a fast growth in transport facilities, with its high-speed rail and expressways ranking first in the world. The rapid growth of China’s car parc is particularly prominent, and China’s per-capita GDP has exceeded US$10,000 in a short time. Tremendous lifestyle changes have also taken place in China. The rapid development of transport and lifestyle changes, as well as China’s traditional culture and population policies, have influenced and distinguished Chinese people’s housing, lifestyles and travel from Westerners (Zhao & Zhang, 2018), making China an ideal object for this study.

1.1.2 Changing Chinese Lifestyles in Transport The transformation of the traditional Chinese lifestyle to a modern one raises the requirements for transport modernisation. Since its founding, China has gone through a transformation from traditional lifestyles to modern ones. Once, China was a semicolonial, semi-feudal society,1 and the traditional Chinese lifestyle had an obvious isolated nature (Liu, 1988). The traditional lifestyle refers to the way of life in China’s feudal agricultural society, characterised by isolation, slow pace, frugality and self-sufficiency. Its isolation in terms of territory, interaction, industry and information resulted in narrow-mindedness and hindered the development of the economy, science and technology, which in turn led to the monotony of production and life. In the isolated, slow-paced traditional society, transport relied on natural forces such as human and animal power,2 and there were basically no mechanical vehicles (Zhang, 2013). This determined the traditional social lifestyle with strong characteristics of a natural economic lifestyle, i.e., isolation, slow pace, low consumption and low socialisation of life services. Lifestyle change is a long-term objective process, predicated on the development of productivity causing changes in production and social relations (Shi, 1986). Before the middle of the twentieth century, China experienced a transition from a traditional agricultural, rural, isolated and semi-isolated society to a modern industrial, urban, open society. Since the 3rd Plenary Session of the 11th Central Committee of the Chinese Communist Party,3 the implementation of the reform and opening-up policy and the household contract responsibility system has promoted the rapid development 1

A Semi-colonial, semi-feudal society is a Marxist concept, which means a society that in form retains the state organs and sovereign ownership of a feudal society, while being controlled and oppressed by foreign capitalist countries in economic, political and cultural terms. As these capitalist countries exert more control, some countries will completely lose their state sovereignty and become colonial countries; others will gather strength in the most difficult situation and gain independence. 2 Animal power refers to the power of working animals, like cattle and horses, used to transport people and goods or to pull farm implements. 3 The 3rd Plenary Session of the 11th Central Committee of the Chinese Communist Party was held in Beijing from December 18–22, 1978. It was decided that the emphasis of the Party’s work should shift to socialist modernisation.

6

1 Introduction

of agricultural production and a quick improvement in rural productivity, which has completely solved the problem of food and clothing supply for the Chinese people. The establishment of the socialist market economy system and the abolishment of the planned economy have led to a period of rapid economic development. The reform of the economic system has greatly enhanced economic vitality, boosted productivity and improved the living standards and life quality of the general public. Especially with the development of the reform and opening up and the introduction of Western culture blending with Chinese culture, significant lifestyle changes have occurred. People have more choices and pursuits in material and spiritual life. For instance, with the introduction of new technologies, the development of the film industry and the rise of entertainment venues such as internet bars, tea bars, movie bars, saunas, etc., people’s spiritual life has been enriched, and their lifestyles have become diversified and modernised. New lifestyles that meet the requirements of modernisation have gradually emerged, and people are pursuing a better life on top of food and clothing, with their traditional lifestyles turning into modern ones. The ongoing transformation to well-off lifestyles in the countryside and affluent lifestyles in cities in China requires better transport services. In the twenty-first century, China joined the WTO and integrated into the world economic system. The reform of government functions4 has made steady progress, the agricultural and rural reform has been solidly implemented, and the new system of open economy has been continuously improved, providing strong impetus and institutional support for stable economic operation and quality development. The Belt and Road, Green Development, poverty alleviation and Rural Revitalization5 strategies have been established, and the Two Centenary Goals6 have been set. People’s living standards are constantly improving, and their lifestyles are transforming to a well-off type in the countryside and to an affluent type in cities. Judging from the trend of Engel’s coefficient, from 1993 to 2016, Chinese residents’ consumption structure underwent two major transitions. The consumption structure of urban residents turned into a well-off type in 1996 and an affluent type in 2000. The development pace of rural residents is slower than that of urban residents. Their consumption structure only started to turn into a well-off type in 2000 and an affluent type in 2012 (Zou, 2017). With the improvement in living standards, it has become increasingly difficult for Engel’s coefficient to reduce. It requires more effort to transform from a well-off type into an affluent type than to transform consumption at the subsistence level into a well-off type. In general, starting from 2012, the overall consumer demand of Chinese residents gradually shifted from a well-off type to an affluent type. During the transition from 4

The reform of government functions means streamlining the government, delegating power and improving government services. 5 The overall goal of the Rural Revitalization Strategy is to build rural areas with thriving businesses, pleasant living environments, social etiquette and civility, effective governance and prosperity. 6 The Two Centenary Goals are a set of goals advanced by General Secretary Xi Jinping, including turning China into a moderately well-off society at the centenary of the founding of the Communist Party of China in 2021 and turning it into a strong, democratic, civilised, harmonious and modern socialist country at the centenary of the founding of the People’s Republic of China in 2049.

1.1 Research Background

7

consumption at the subsistence level to a well-off type, the fastest-growing proportions of residents’ consumption were expenditures on household appliances and services, housing expenditures, expenditures on cultural, educational and entertainment products and services, and expenditures on transport and communication. In the process of transition from a well-off type to an affluent type, the fastest-growing proportions were expenditures on transport and communication, medical care expenditures, and cultural, educational and entertainment products and services. As of the end of 2020, expenditures on transport and communication accounted for 13.0% of the total consumption of urban residents (National Bureau of Statistics, 2020). Information consumption—expenditures on hardware devices such as tablet computers and smartphones, as well as software consumption such as online shopping and voice communications—showed a very strong growth momentum. The upgrading of residents’ consumption levels has put forward higher demands on the efficiency, comfort, etc., of transport services. The transition from a collectivist orientation to the integration of collectivism and individualism in lifestyles puts forward new requirements for transport diversification. The lifestyles of Chinese people have changed with the rapid political and economic development since China’s reform and opening up, showing the typical stage characteristics of political, economic and social development. In general, the reform and the opening up have changed the collectivist lifestyle of the Chinese people and significantly improved their quality of life. The unified and undifferentiated collectivist lifestyle of the past has collapsed, replaced by transitional lifestyles and living conditions in which diversity and unity coexist, divergence and convergence are intertwined, and collectivism and individualism merge (Liu, 2003). Before 1978, China was under a planned economy in which production, resource allocation and consumption were all planned by the government in advance, and production and consumption were carried out according to government instructions. As a result, Chinese people’s lifestyle was collectivist in orientation, with a high degree of homogeneity and consistency. Due to the low level of productivity, the collectivist lifestyle was maintained at the subsistence level, and there was no such thing as quality of life. After the 3rd Plenary Session of the 11th Central Committee of the Chinese Communist Party, the historic decision for reform and opening up contributed to the great turnaround since the founding of New China. China has gradually completed the transition from a planned economy to a market economy. The economic development level has improved rapidly. Social consumer goods are abundant and diverse, and the market is playing an increasingly important role in resource allocation and consumption. The government’s role has transformed into a service-oriented type, and the ownership structure of enterprises has been reformed. Citizens have a larger space for unconstrained social activities, and the labouring population is free to flow. Employment patterns are increasingly diversified, and new employment patterns such as online ride-hailing and food delivery drivers are emerging. With the rapid development of urbanisation and the gradual reduction of the urban–rural dichotomy, urban residents’ lifestyles are spreading to the countryside. With China’s accession into the WTO and economic globalisation, the lifestyles of Western developed countries are gradually spreading to China. Chinese residents

8

1 Introduction

are progressively accepting international lifestyles. With the penetration of computers and mobile phones, the advent of the internet and mobile internet era is having an increasingly significant impact on people’s lifestyles. Residents’ leisure time has increased, and leisure activities are becoming increasingly diversified. Meanwhile, consumption continues to increase, and consumption diversification and personalisation are increasingly evident. Residents’ living standards, quality of life and happiness index are constantly improving. Emerging lifestyles create new demand for new business forms of transport. Times change, but the pursuit of a comfortable life remains unchanged. Every aspect of people’s life is changing, with material life becoming increasingly affluent, spiritual pursuit being constantly enriched, technology continuously evolving and consumption ability and patterns progressing. Expenditures on education, entertainment and culture have also grown steadily, supported by income growth and demand upgrading. In 2019, expenditures on education, entertainment and culture accounted for 11.7% of residents’ total annual consumption (National Bureau of Statistics, 2020), exceeding items such as clothing expenditures, expenditures on household appliances and services and housing expenditures. This reflects people’s demand for a higher level and greater weight of spiritual life. Leisure consumption, mainly consisting of tourism, sports and fitness consumption, has taken off and is changing Chinese residents’ mindsets and lifestyles. The Golden Week7 and the paid holiday system make it possible for urban residents to travel and consume. The concept that leisure is as important as work has gradually become a new social fashion. The boundary between work and leisure is blurred. The attitude of life that honours working from home, online shopping, high consumption and high waste, as well as frugal and ecological lifestyles, is being greatly promoted. In terms of living consumption, more attention is being paid to the scientific aspect and the quality of consumption. As modern lifestyles evolve, people’s pursuit of travel has changed from convenient travel to green travel. New requirements for new business forms of transport are more intelligent, safer and greener.

1.1.3 Transport Development Supporting New Lifestyle Changes There are radical changes in the transport system in China. Before the reform and opening up, China’s transport infrastructure was weak. With the deepening of the reform and opening up, the national economy underwent rapid development, and the incompatibility between transport and economic development became increasingly prominent. In particular, passenger transport was in short supply, with the difficulty of buying tickets, difficulty in travelling and difficulty in taking the plane 7

Golden Week is the name given to the national holiday normally running from October 1 to 7 every year in China, although sometimes it may be extended if the holiday coincides with the Mid-Autumn Festival, which is based on the lunar calendar.

1.1 Research Background

9

becoming prominent social issues at that time. The changes in transport mean seriously lagged behind the demand for social lifestyle changes. At present, various means of transport are developing rapidly in China, and the comprehensive transport system is constantly being improved to meet the requirements of economic and social development. Since 2011, in terms of transport infrastructure, China’s lengths of high-speed railways in operation, expressways in use and urban rail transport in service, as well as the number of berths of the 10,000-tonne class and above at coastal ports, are all ranked first in the world. The development of the natural gas pipeline network has accelerated, and the transport infrastructure network has taken its initial shape. The capacity of the comprehensive transport system is constantly rising. From 2010 to 2015, the average annual growth rates of railway passenger volume and civil aviation passenger volume both exceeded 10%. The capacity of multiple-unit trains amounted to 46%. The express delivery business was growing at an average annual rate of over 50%.8 Intercity, urban and rural transport service capacity have been enhanced, and the connections between modern comprehensive transport hubs and stations have been improved. In the history of world transport, China Speed and the China Model have gained worldwide attention. All means of transport have achieved rapid development. Additionally, the number of airports and the pipeline mileage rank among the top in the world, making China a country with a strong transport industry. At the end of 2020, China’s railway operating mileage reached 146,000 km, five times longer than that at the end of 1949, including 38,000 km of highspeed railways, which accounted for more than 60% of the world’s total high-speed railways. Highway mileage was 113,000 km. There are 240 airports and 237 cities with regular flights.9 China’s transport has achieved a major leap from bottleneck constraints to overall relief to basic adaptation. A road of transport development with Chinese characteristics has been developed. The 19th National Congress of the Chinese Communist Party10 set out a grand blueprint for building a great modern socialist country and proposed the ambitious goal of building a country with a strong transport industry. China is building up its strength in transport. Transport facilities are starting to play a leading role, instead of the supportive role they used to play, with increasing interactions with lifestyles. Transport is a fundamental, pioneering and strategic industry, an important support and strong guarantee for economic and social development. Since the founding of New China 70 years ago, Chinese people have witnessed the transition from pulling and shouldering, donkey-riding and cart-pushing to 30,000 km of high-speed rail covering 80% of large- and medium-sized cities and Fuxing trains traveling at 350 km per hour.11 In 70 years, history has been made. The distance in time and space and the whole country has changed. The 19th National Congress of the Chinese Communist Party proposed that the main contradiction facing China has transformed into 8

Source: http://www.gov.cn/zhengce/content/2017-02/28/content_5171345.htm. Source: http://www.gov.cn/xinwen/2021-05/19/content_5608523.htm. 10 The 19th National Congress of the Chinese Communist Party was convened in Beijing from October 18 to 24, 2017. 11 Source: http://chnrailway.com/html/20190902/1882845.shtml. 9

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1 Introduction

the contradiction between unbalanced and inadequate development and the people’s ever-growing needs for a better life. The supply-side structural reform of transport should focus on meeting the people’s growing demand for high-quality transport. It should provide safe, convenient and comfortable transport services, and it should continuously enhance people’s senses of well-being, fulfilment and security. In the future, the focus will gradually change from construction to operations and maintenance, and infrastructure construction will mainly shore up weak spots and optimise networks. It should be highlighted that the basic function of transport is to provide quality and efficient transport services, to adapt to the new requirements put forward by people’s changing lifestyles and to serve the economy and society. This is also the most fundamental requirement for building national strength in transport. The reform and opening up12 in 1978 started a new chapter in the history of China’s transport industry and brought about radical changes in Chinese lifestyles. With the introduction of Western values, the previously isolated and slow-paced lifestyle under the small-peasant economy has transformed into open and fast-paced lifestyles in the market economy (Zhang, 2013). The high mobility of the population has somehow accelerated the changes in the means of transport. With reduced prices for commercial aeroplanes and constant off-season discounts, common people can also travel by plane. High-speed trains have become people’s first choice for domestic travel. Full coverage of rail transport such as subways and light railways in largeand medium-sized cities, which is safe, comfortable and environmental-friendly, has made rail transport the most common choice for residents in large- and mediumsized cities. City buses and taxis have become the first choice of people in smallsized cities for green travel, changing from fuel cars to hybrid cars to electric cars. Online ride-hailing makes up for the shortage of taxis, allowing people to make oneclick appointments anytime and anywhere. Shared and customised transport, such as shared bicycles and customised buses, have made travel much more convenient. In the vast countryside, electric bicycles and tricycles have entered thousands of households. Private cars are very common. On June 30, 2020, the rural passenger transport line from Abuluoha Village to Tuojue Town, which is in Liangshan Yi Autonomous Prefecture, Sichuan Province, was officially opened, making Abuluoha the last administrative village in China to be connected to China’s road network. Due to the improvement of transport conditions, especially the improvement of the high-speed rail network, Chinese people really feel that the space–time distance is getting shorter. This has brought about huge changes to social lifestyles, and the interactions between transport and lifestyles are increasing. New transport technologies have changed people’s sense of time and space and achieved time-space convergence and time-space compression (Falk & Abler, 1980; Harvey, 1990, 1999; Janelle, 1968). The development of transport technologies profoundly affects people’s lifestyles and mindsets. The discovery of petroleum and 12

Reform and opening up is a policy on domestic reforms and opening to the outside world that China began to implement in the 3rd Plenary Session of the 11th Central Committee in December 1978. Opening to the outside world became a basic national policy of China and a powerful driving force for the cause of socialism. The reform and opening up brought about tremendous changes in China.

1.1 Research Background

11

the prevalence of cars, trains and ships have promoted transport development and expanded the scope of people’s activities (Black, 2003). In recent years, the development of high-speed rail and rail transport has not only changed the speed of travel but also changed people’s concept of time and space and their lifestyles. With quick access to affordable transport, it is possible to work in large cities and live in sized cities. An intelligent transport system (ITS) facilitates the expansion of smart lifestyles. An ITS is based on smart transport, and it incorporates high-tech IT technologies such as the internet of things (IoT), cloud computing, big data and the mobile internet. Through a high-tech collection of traffic information, it can provide traffic information services based on real-time traffic data. Smart transport is also one of the frontiers of transport in China. An ITS enables people, vehicles and roads to work closely together, achieving harmony and unity and having a synergistic effect. It ensures traffic safety and greatly improves transport efficiency, the transport environment and energy efficiency. With the strong support of national policies, coupled with social demand and technology development, China’s intelligent transport industry has developed rapidly in recent years. In the process of its technological evolution, it has also brought about a series of changes to lifestyles. Shared transport13 drives the development of green lifestyles. The development of transport has increasingly led to changes in public consumption patterns and lifestyles. Shared transport, which is based on the efficient use of resources rather than consuming more resources, reflects the core principle of harmony between economic development and environmental protection, with green development as its purpose. The Report to the 19th National Congress of the Communist Party of China proposes that China’s economy has shifted from the stage of high-speed growth to the stage of high-quality development. Green development is one of the new development concepts, and it is an inherent requirement for high-quality development. Transport is a major source of energy consumption and pollution emissions, and it is also a key area for promoting green development and forming green lifestyles. Lowcarbon travel is being promoted, and new energy vehicles are becoming popular. In 2020, the production and sales of new energy vehicles were 25.525 million and 25.311 million, respectively, and the new energy vehicle consumption was 76 times that of 2015.14 The new energy vehicle industry will be one of the few fast-growing industries in the coming years. In recent years, against the backdrop of the new technological revolution represented by the mobile internet and big data technology, shared mobility, based on big data technology and characterised by efficient resource allocation, has become the most active and rapidly developing segment in the transport sector in terms of innovation. It is also playing an increasingly important role in spurring green travel and green lifestyles. 13

Shared transport refers to a new type of transport in which people do not require vehicle ownership; instead, people share vehicles with others in various forms, such as carpooling, and pay corresponding user fees according to their own travel requirements. Shared transport includes a large number of innovative models represented by ride-hailing software and shared bicycles. 14 Source: https://baijiahao.baidu.com/s?id=1688822426043441676&wfr=spider&for=pc.

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1.2 Research Importance 1.2.1 Theoretical Contributions This book explores the characteristics and mechanism of the impacts of lifestyle changes on transport in China. Scientific insights concerning lifestyles are shallow and lack a deeper analysis of mechanisms (Hansson, 1999; Jensen, 2009). Social scientists may have tried, but they have mostly focused on the mechanisms that form self-identity (Jensen, 2007). Transport-related lifestyle research has important weaknesses, including a weak theoretical foundation with no or only a vague definition of lifestyle (or other key constructs) (Thøgersen, 2018), too many measurement methods, and ambiguity in the theoretical relationship between lifestyles and transport. The relationship between life choices and daily travel behaviour is still greatly under-researched (Zhang & van Acker, 2017). Significant improvements in the theoretical basis are still necessary. This book is an attempt to sort out the evolution of the concept of lifestyle, measurement methods, the impacts of lifestyles on travel behaviour and existing research models. Its aim is to clarify the current trends in the impacts of lifestyles on travel behaviour to provide a new perspective and lesson for research on travel behaviour in China, improve the accuracy and precision of predictions by travel models and promote studies on urban travel behaviour. This book systematically summarises relevant research results regarding the definition and impacts of current lifestyles on travel behaviour. It provides an in-depth summary of the concept of lifestyle in transport, constructs an index system for individual and overall lifestyle measurement and analyses the theoretical framework of the interaction between lifestyles and transport. From a methodological point of view, this measurement system and framework can provide methodological guidance and theoretical explanations for the interrelationship between transport and lifestyles at different levels. From an empirical point of view, the measurement system can be used to measure lifestyles across different regions, explore the association between lifestyles and transport and further improve the theoretical research on traffic behaviour. Current research mostly focuses on Europe and the United States, with much less attention paid to China. Through in-depth and quantitative exploration, this book explores the interrelationship between lifestyles and transport. It uses macrostatistics from 1978 to 2020 to measure the changes in China’s transport-related lifestyles; it uses data from a household follow-up survey in China to explore the relationship between lifestyles and commuting distance through linear correlation analysis; it uses data from the Beijing Jobs-Housing Balance Survey in 2016 to explore the impacts of lifestyles on work and leisure travel through structural equation modelling (SEM); it uses data from a survey of small towns across China in 2016 to explore the impacts of Chinese rural lifestyles on leisure and commuting through OLS regression analysis; and it uses data about prefecture-level cities in China to explore the relationship between lifestyles (based on the definition of living standards) and passenger and freight volumes. The results of this study may enrich the evidence

1.2 Research Importance

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about the impacts of lifestyles on transport. This book conducts a comparative study of the relationship between Chinese people’s lifestyles and transport from both a theoretical and an empirical perspective, as it explores and examines the impacts of different lifestyles on transport differences and the impacts of transport facilities and technologies on lifestyle changes. It addresses the research void on the relationship between transport and lifestyles in China, and it provides Chinese cases for the application of the lifestyle concept in transport. Future research may build on the findings of the current study.

1.2.2 Potential Contributions to Policymaking The aim of this book is to discover the mechanism of the impacts of lifestyle changes on transport demands to provide a decision-making basis for improving transport management. The 19th CPC National Congress15 made a major judgment on the change in the principal contradiction facing Chinese society. This historical change of overall significance has had a profound impact on the development of transport. From the perspective of the people’s needs for a better life, the insufficient supply of transport in China has undergone a fundamental change. The key to meeting people’s travel needs has changed from whether there are enough transport facilities to whether the transport facilities are good enough (Yang & Li, 2018). People’s lifestyle changes have led to requirements for more individualised, diversified and efficient transport services with higher quality, as well as more refined transport management. Refinement will be a new direction for China’s transport design, management and research: using information technology to improve the refined management system continuously. The study of the interrelationship between lifestyles and transport is crucial in formulating strategies adaptable to population development, creating new concepts of the modern transport system and adapting to the change in the principal contradiction facing Chinese society. It can also urge adherence to the people-centred development idea, fit in with the refined development of transport, and satisfy the objective requirements to meet people’s increasing needs for a better life. In-depth research on new lifestyle changes will help people to grasp changes in transport demands clearly and effectively to implement people-oriented transport planning, construction and governance, adapting to the new needs of future transport development and the new changes in residents’ lifestyles. It is also conducive to accelerating the development of new business forms of transport, meeting people’s demands for quality life arising from lifestyle changes, developing user-centred policies, making transport facilities a driving force for regional development, promoting the improvement of lifestyles and living standards of residents in less developed areas and proposing policy suggestions for transport optimisation adaptable to lifestyle

15

The 19th National Congress of the Communist Party of China was held in Beijing from October 18 to 24, 2017.

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changes, thus playing an important role in building China’s strength in transport and improving the accurate and scientific nature of policies. This book also clarifies the impacts of transport facilities and technologies on lifestyles so that people can better deal with future lifestyle changes. Means of transport influence the way people perceive the world and fundamentally determine people’s social lifestyles. Every revolutionary change in transport has effectively promoted economic and social progress, thus changing people’s lifestyles. At present, China is witnessing great changes in the means of transport, and the number of cars per household has increased sharply. High-speed public transport methods such as subways, light rails, expressways and high-speed railways gradually work their way into people’s daily lives. Smart transport, green transport, shared transport, internet plus transport, smart transport plus and 5G smart road infrastructure construction have made modern transport means increasingly multidimensional, smart and diversified. The transformation of transport means should not be simply regarded as a change in tools. Technology is the way of human existence. People’s concepts of time and space are also changeable and evolving (Falk & Abler, 1980; Harvey, 1990, 1999; Janelle, 1968). After nearly 50 years of development, high-speed rail technology has gradually matured and stabilised. Although high-speed rail has only been open and operating in China since 2010, it has already had a profound impact on people’s values and patterns of life. Now when people travel, they tend to measure distance by time instead of in kilometres. Therefore, systematically discussing the impacts of transport facilities and technologies on social lifestyles plays a very positive and important role in the formation of reasonable social lifestyles.

1.3 Organisation of the Book This book is centred around the core issue of the interrelationship between lifestyles and transport in China. In the next chapter (Chap. 2), we discuss the definition of lifestyle in sociology and transport and construct an index system for lifestyle measurement in transport. Chapter 3 introduces the main theoretical basis for the links between transport and lifestyles, including the value theory, the theory of planned behaviour (TPB), the consumer behaviour theory, the space-time prism theory, transport supply and demand theory, space choice theory and space-time compression theory. On this basis, the relationship between lifestyles and transport is studied. Transport and lifestyles have an effect on each other. Transport is a key factor in shaping lifestyles, and lifestyles affect travel activities. In addition, transport itself is a kind of lifestyle. Only discussions about specific lifestyles are of great significance. Chapter 4 presents lifestyle changes in transport-related fields in China in the past 40 years at the national level. Chapter 5 discusses the impacts of lifestyles on transport volumes at the national level. In addition, the impacts of lifestyles on commuting distance are also studied at the national level using CFPS data. Chapter 6 takes Beijing as an example to analyse the relationship between lifestyles and transport in China’s megacities. Chapter 7 offers an insight into rural lifestyles and transport in

1.3 Organisation of the Book

15

China using survey data about towns and villages across China. Chapter 8 discusses the impacts of transport on lifestyles. Chapter 9 proposes policy suggestions for future transport development in accordance with lifestyle changes. Chapter 10 offers conclusions and discussions. The detailed organisation of this book is as follows. Revolving around the core issue of the interrelationship between lifestyles and transport in China, Chap. 1 brings forward the research background of this book from both theoretical and practical perspectives, as well as its practical significance in the coordinated development of transport and lifestyles in China. This book demonstrates the impacts of Chinese lifestyles on transport, as a case of developing countries. Chapter 2 mainly introduces the evolution of the lifestyle concept and constructs lifestyle measurement indexes in the transport sector. First of all, it sorts out the evolution of the lifestyle concept in sociology, especially the application and development of the lifestyle concept in transport. Although the concept of lifestyle is widely used, currently there is no generally accepted concept of lifestyle in either sociology or travel research. Next, this chapter introduces the three index systems of lifestyle measurement in sociology, as well as the measurement and segmentation of lifestyles in current empirical research on lifestyle in transport. On this basis, an index system for individual and overall lifestyle measurement in transport is constructed. The individual lifestyle indexes mainly involve the dimensions of socioeconomic attributes, space, consumption, activities, time and values, while the overall lifestyle indexes mainly involve the dimensions of consumption, transport facilities, environment, time, activities and value preferences. Chapter 3 is mainly about the relationship between lifestyles and transport. It first introduces the main theoretical basis for the links between transport and lifestyles, including value theory, TPB, consumer behaviour theory, the time–space prism theory, transport supply and demand theory, space choice theory and space–time compression theory. Lifestyles and transport are at the same stage of development. They are interconnected and subordinate to each other while featuring inconsistency in choice. The interaction between lifestyles and transport can be approached from individual (micro) and social (macro) perspectives. Finally, the conceptual models and mathematical methods of research on lifestyle in transport are summarised. The common theoretical basis of lifestyle and transportation includes value theory, TPB, consumer behaviour theory and time–space convergence theory. The theories of how lifestyle affects transportation include transport supply and demand theory. The impact of transport on lifestyle is covered by space choice theory and space–time compression theory. Chapter 4 uses macro-statistical data to analyse lifestyle changes in transportrelated fields in China from the national level. It analyses the connections between Chinese residents’ lifestyles and transport from the perspectives of consumption, transport means, environmental facilities, activities, time use and value orientations. Chapter 5 mainly verifies the impacts of lifestyles on transport through an empirical analysis. Limited by the availability of data, the relationship between lifestyles and transport volumes is analysed primarily through macro statistical data. In addition, data used in this chapter are retrieved from the China Family Panel Studies

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1 Introduction

(CFPS) database released by the Institute of Social Science Survey, Peking University. Using the clustering method, it studies the lifestyles of five groups, i.e., young and middle-aged achievers in cities, middle-aged and older people residing in the countryside, middle-aged people under pressure in cities, retired older people in cities and single, wealthy young people migrating to cities. The analysis suggests that different lifestyles have varying effects on commuting time, including both positive and negative effects. Chapter 6 takes Beijing as an example to analyse the lifestyle characteristics in China’s megacities. It analyses Beijing residents’ lifestyle changes in the past 40 years, as well as the connection and interaction between residents’ lifestyles and travel behaviour, via the dimensions of consumption, time use and activities. In addition, based on the Questionnaire Survey on Jobs-Housing Balance in Beijing, residents’ lifestyles are segmented through clustering analysis, and the SEM method is used to study the impacts of Beijing residents’ lifestyles on their travel behaviour. Chapter 7 is based on the survey data about town and village residents from a detailed survey of towns across China in 2016. It describes the characteristics of daily and non-daily trips of town and village residents from different perspectives. The clustering method is adopted to analyse village residents’ different types of lifestyles, and the OLS regression method is used to study the influence of four types of lifestyles on village residents’ travel frequency and time. Chapter 8 first analyses the impacts of transport facilities on lifestyles, conducting correlation analysis to assess the relationship between the level of subway, highspeed railway and train facilities and lifestyles in 365 prefecture-level cities. Second, in accordance with China’s development status, this chapter analyses the impacts of three aspects of China’s latest transport development on lifestyles: high-speed rail, shared transport and new transport technologies. Chapter 9 proposes policy suggestions for four aspects of future transport development: enhancing quality of life, promoting transport equity, establishing a smart transport system for smarter life and developing new business forms in transport, based on the interrelationship between lifestyles and transport. Chapter 10 provides conclusions and discussion. It provides a review of the main concepts and themes of the book, presents key conclusions, discusses the limitations and deficiencies of this study and proposes future research directions.

1.4 Summary This chapter sets forth the research background and significance as well as the organisation of this book. First, it introduces the current progress of research on lifestyle in transport and clarifies the theoretical background of this book. Second, it presents two aspects of lifestyles and transport in China: new requirements for transport development put forward by lifestyle changes in China and the role transport development

1.4 Summary

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plays in leading and supporting new lifestyle changes. It points out the research significance from both theoretical and practical perspectives. Finally, the organisation of this book is made clear. In summary, scholars have obtained rich research results on the definitions and measurement dimensions of lifestyle. Lifestyles mainly exert influence on transport by affecting travel behaviour. Overall, there are more empirical studies than theoretical studies, but there is no unified conclusion yet. Significant improvements in the theoretical basis are still necessary. Research on the relationship between lifestyles and transport is an important future direction, though with certain limitations, such as the lack of a generally accepted definition and a unified measurement method, lack of a theoretical mechanism behind the relationship between lifestyles and transport, lack of discussion on the overall lifestyle and geographical limitations regarding the research object. There is barely any relevant research in China. In the future, with the increase in the income level of Chinese residents, the improvement of transport infrastructure and built environment, the diversification of lifestyles and transport means and the increasingly prominent impacts of lifestyles on travel behaviour, there will be actual demands for research on the relationship between lifestyles and transport. In light of the deficiencies in the research on lifestyle in the transport field, this book is an attempt to provide a definition of lifestyle in transport. It also includes the construction of an index system for individual and overall lifestyle measurement, proposes a theoretical framework of the relationship between lifestyles and transport and applies it to Chinese cases. This book may further improve the theoretical research on travel behaviour, extend the scope of lifestyle research, become a ground-breaking interdisciplinary study on Chinese lifestyles and transport, establish a decision basis for improving China’s refined transport management and building up its strength in transport16 and provide guidance on how to respond to future lifestyle changes.

16

On September 19, 2019, the Central Committee of the Communist Party of China and the State Council issued the Outline for Building China’s Strength in Transportation. This stated that from 2021 to the middle of this century, China will build up its strength in transport in two stages. By 2035, it will basically realise this goal. Equipped with an advanced express network, a sound trunk network, and an extensive basic network, the system will raise the coordinated transport development in urban and rural areas to new heights. The National 1-2-3 Travel Circle (1 h to commute in cities, 2 h to travel within city clusters and 3 h to travel between major domestic cities) and the Global 1-2-3 Logistics Circle (1 day to deliver within China, 2 days to deliver to neighbouring countries and 3 days to deliver to major global cities) will basically take shape. Source: http://www.gov.cn/ zhengce/2019-09/19/content_5431432.htm.

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Vij, A., Carrel, A., & Walker, J. L. (2013). Incorporating the influence of latent modal preferences on travel mode choice behavior. Transportation Research Part A: Policy and Practice, 54, 164–178. https://doi.org/10.1016/j.tra.2013.07.008 Waddell, P. (2001). Towards a behavioral integration of land use and transportation modeling. In Hani Mahmassani, 9th International Association for Travel Behavior Research Conference. Elsevier. Walker, J. L., & Li, J. (2007). Latent lifestyle preferences and household location decisions. Journal of Geographical Systems, 9(1), 77–101. https://doi.org/10.1007/s10109-006-0030-0 Wells, W. D., & Tigert, D. J. (1971). Activities, interests and opinions. Journal of Advertising Research, 11(4), 27–35. Yang, C. T., & Li, X. P. (2018). To embark on a new journey of building a strong transport system (in Chinese). Finance and Accounting for Communications, 3(368), 6–8, 11. Zarrabi, M., Yazdanfar, S. A., & Hosseini, S. B. (2021). Usage of lifestyle in housing studies: A systematic review paper. Journal of Housing and the Built Environment, 36, 1–20. https://doi. org/10.1007/s10901-021-09883-4 Zhang, Y. (2013). Research on the impacts of the reform of travel modes on social lifestyles (in Chinese). Science and Technology Information, 26(74), 77. https://doi.org/10.3969/j.issn.10019960.2013.26.059 Zhang, J., & van Acker, V. (2017). Life-oriented travel behavior research: An overview. Transportation Research Part A: Policy and Practice, 104, 167–178. https://doi.org/10.1016/j.tra.2017. 06.004 Zhao, P., & Zhang, Y. (2018). Travel behaviour and life course: Examining changes in car use after residential relocation in Beijing. Journal of Transport Geography, 73, 41–53. https://doi.org/10. 1016/j.jtrangeo.2018.10.003 Zou, Y. H. (2017). Trend analysis of China’s resident consumption rate (in Chinese). Macroeconomic Management, 9, 31–36.

Chapter 2

Lifestyle and Its Measurements

2.1 Definitions of Lifestyle 2.1.1 Concepts of Lifestyle The concept of lifestyle originated in sociology and was then widely used in marketing and medicine. The lifestyle concept underwent four periods of evolution. From the 1960s to the 1970s, lifestyle was regarded as an objective symbol of class or social status that could not be changed. After the 1980s, lifestyle was mainly regarded as a symbol of differences in group style or cultural identity and cultural activities from the perspective of subjective personal cognition. (1)

Lifestyle Concepts in the Embryonic Stage Before the 1970s

Lifestyle is an important issue in Western sociological research. Lifestyle has experienced an evolution from a phrase (style of life) to a compound word (lifestyle) to a word (lifestyle) in English. Lifestyle research originated with Karl Marx (1867). The main viewpoint of Marx and Engels on lifestyle was that lifestyle is an index of social class closely related to the mode of production. According to Gao’s (1998) interpretation, Marx and Engels used the concept of mode of life mainly in two senses. First, lifestyle is an important index of social class. Second, lifestyle is closely related to the mode of production in the sense that the mode of production determines lifestyle, and it is an aspect of lifestyle in a broader sense. The Marxist exposition of lifestyle was recognised and inherited by Veblen and Weber. Weber (1922) was one of the first social science scholars to discuss lifestyle. Weber defined lifestyle as a pattern of observable and expressive behaviour. He believed that people can choose the lifestyle they adopt in life opportunities. A person’s behaviour is determined by his or her economic status (i.e., possession of means of production), what a person produces (i.e., the economic dimension) and what he or she consumes (i.e., the cultural/symbolic dimension). Veblen (1899) systematically discussed the correlation between a specific lifestyle and a specific class from the perspective of consumption and used the method of © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. Zhao and D. Lyu, Lifestyle Change and Transport in China, Population, Regional Development and Transport, https://doi.org/10.1007/978-981-19-4399-7_2

23

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2 Lifestyle and Its Measurements

historical sociology. He took lifestyle as the manifestation of consumption, reflected the relationship with class and reflected the cognitive value and explanatory power of class and social status through the concept of lifestyle. Veblen believed that the lifestyle exists as a social symbol of class status and honour. However, in different historical development stages of human society, because people had different judgment standards for honour, lifestyle as a symbol of different class status also differed. Weber and Veblen’s special discussion on the correlation between class status and lifestyle suggests that lifestyle has become an important social phenomenon in social science investigation and research. The impact of their theories and methods on future generations can be roughly divided into two: one is the understanding of social differentiation and social group differences according to lifestyle differences, and the other is the study of the transformation of lifestyle into consumption mode (Gao, 1998). (2)

Lifestyle Became a Technical Term in the 1970s

After that, lifestyle was widely used in the 1970s, marking the development of lifestyle into a special term. At this stage, the definition of lifestyle concept emphasised representativeness, group characteristics and differences between groups. Individuals also begin to receive attention. At the same time, the research object was no longer regarded as the whole of life, but the representative behaviour and behaviour orientation selected by researchers according to the research purpose. Lifestyle had no objective and unified research scope. Sharp (1979) defined lifestyle from the perspective of pragmatism as “a household’s size and age, the social roles contained within it, the resources available to it, and the density and variety of activities open to it” (p. 8). Life cycle stage, income and residential location are used to define lifestyle, but this definition involves a type of family resemblance and lacks consideration of behaviour patterns. Reichman (1977) defined lifestyle as behavioural responses to socioeconomic differences, as well as personal and social behaviour. Bourdieu (1979) regarded lifestyle as a behaviour pattern indicating personal social status, taking preferences and behaviour as the expression of lifestyle, and defined lifestyle through unified sociodemographic variables and specific behaviour patterns. The main determinant of lifestyle is the different types of social, economic and cultural capital owned by a social class, which is mainly reflected in the mode of eating or various rest activities. (3)

Lifestyle Evolved as a Word in the 1980s

In the 1980s, lifestyle was stabilised from a compound word to a single word. In this period, although the basic meaning of the concept of lifestyle was still the relative difference of research objects, it was often discussed from the perspective of individuals. Since the 1980s, modifiers focusing on personal differences such as interest, style consciousness, performance and choice have been widely used in the discussion of the concept of lifestyle (Featherstone, 1987). Sobel (1981) first put forward a formal definition of lifestyle, using this concept at the individual level as a description of expressive and observable behaviour, and he

2.1 Concepts of Lifestyle

25

regarded consumption as an activity that can best capture different lifestyles. Lifestyle is defined as the characteristics of individuals, groups and even a culture. Sociologists usually use this concept at the individual level, which reflects the importance of individuality in the definition of lifestyle. Allaman et al. (1982) defined lifestyle as how individuals and households allocate time to activities such as work, in-home time and recreation. Therefore, lifestyle is a behavioural pattern regarding time use. Featherstone (1987) defined lifestyle as the distinct type of life of specific status groups from the perspective of consumption. Body, clothes, speech, leisure, eating hobbies, housing, car, choice of vacation, etc., are indicators of the interesting personality and style awareness of occupants and consumers. This definition implies individuality, self-expression and a stylistic self-awareness of style. Giddens (1991) defined lifestyle as consumption behaviour. He proposed that individuals embrace different consumption patterns not only because they want to meet different utilitarian needs but also because they attribute specific self-identity narratives to material forms. Therefore, the countless choices that everyone faces every day lead to decisions not just about how to act but also about who they are. Schulze (1992) added the spatial dimension to the discussion of lifestyle. Leisure consumption usually occurs outside the family, such as in cafes, in shopping centres, at football fields and in other specific places, attracting speculative groups sharing similar lifestyles. It comes from a combination of a harmonious lifestyle group and similar leisure consumption behaviour. Veal (1993) thought that lifestyle is the distinctive pattern of personal and social behaviour characteristic of an individual or a group. Behaviour includes activities involved in relationships with partners, family, relatives, friends, neighbours and colleagues, consumption behaviour, leisure, work (paid or unpaid) and civic and religious activity. Patterns of behaviour are linked to values and to sociodemographic characteristics, may involve varying degrees of social interaction, coherence and recognisability, and are formed through a process of wide or limited choice. Munters (1992) believed that lifestyle is observable. The concept of lifestyle includes style elements and activities, and lifestyle is a personal perspective and motivation or orientation. Spellerberg (1996) believed that lifestyle is “a specific form of daily life organisation, which is symbolically expressed in cultural taste and leisure activities” (p. 57). Lifestyle always includes one aspect of freedom of action and voluntarism (style), “social conditions and personal behavior, objective living conditions and cultural life” (p. 58–59). Chaney (1996) defined lifestyle from the perspective of behaviour as “patterns of action that differentiate people” (p. 4), revealing the reasons why people do certain things. Solomon (1999) provided a more specific definition of lifestyle: the way a person spends time and money. People will assign themselves to specific groups based on what they like to do and how they spend their time and disposable income. Veal (1993) defined lifestyle as the distinctive pattern of personal and social behaviour characteristics of an individual or a group. Kaynak and Kara (2001) stated that lifestyle is individuals’ life patterns, which describe how they use their time and

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2 Lifestyle and Its Measurements

money. Blackwell et al. (2001) defined lifestyle as the mode of people’s life and spending time and money, reflecting people’s AIO. (4)

The Concept of Lifestyle Has Been Applied in Multiple Disciplines Since 2010

In medical research, this concept is mainly related to lifestyle factors that present health risks. Many medical studies associate diseases such as diabetes, obesity and cancer with lifestyle factors (such as smoking and alcohol abuse) and regard lifestyle as a person’s unique habitual activities, which bring activities, behaviours, coping styles, motivations and thinking processes into correspondence with each other and determine his or her lifestyle. Lifestyle activities include diet, physical activity level, substance abuse, social interaction and personal interaction (Segen, 2011). Table 2.1 summarises these definitions. Table 2.1 Main definitions of the lifestyle concept Scholar

Year

Definition of lifestyle

Marx and Engels

1867

Lifestyle is an important index of social class closely related to the mode of production

Weber

1921

A social symbol that distinguishes social positions and confirms status groups

Veblen

1970

Consumption patterns of the leisure class

Tallman and Morgner

1970

A number of behavioural activities and orientations

Plummer

1974

Lifestyle is a mode of living to deal with various affairs, closely related to people’s attitudes towards work and life

Zablocki and Kanter

1976

Motivations and consumption frequencies

Sharp

1979

Life cycle stage, income and residential location

Bourdieu

1979

Behavioural patterns that indicate an individual’s social position

Sobel

1981

A property of an individual, a group, or even a culture

Allaman et al.

1982

A behavioural pattern regarding time use

Featherstone

1987

Indicators of the individuality of taste and sense of style of the owner/consumer

Veal

1993

Lifestyle is the distinctive pattern of personal and social behaviour characteristic of an individual or a group

Spellerberg

1996

Group-specific forms of organisation of daily life that are expressed symbolically in cultural taste and leisure activities

Solomon

1999

The way a person spends time and money

Blackwell et al.

2001

Lifestyle refers to people’s behavioural habits and individual characteristics, that is, how they live

Segen

2011

Lifestyle factors that present health risks

2.1 Concepts of Lifestyle

27

Table 2.2 Definitions of lifestyle by Chinese scholars Scholar

Year

Gao

1986 Lifestyle is the basic characteristics of behavioural habits of carrying out practical activities in various fields of life by people of different societies, nationalities, classes and strata, with certain views on life and under certain material conditions, to meet their needs in life

Lu

1993 The sum of forms and characteristics of all the activities of people in social life, including material activities, political activities and spiritual life

Wang

1995 Lifestyle refers to the stable forms and behavioural characteristics of all life activities of individuals, groups or all members of society, guided by certain values and aimed at meeting their own needs for survival and development, under certain objective social conditions

Definition of lifestyle

Ahuvia and Yang 2005 Lifestyle is a comprehensive concept, which can be defined as the types of time and money spent, reflecting a person’s activities, interests and opinions

(5)

Chinese Concepts of Lifestyle

Lifestyle has become an important theoretical category of the humanities (e.g., philosophy) and social sciences (e.g., sociology) in China since the early 1980s. China’s lifestyle research mainly draws on the theoretical methods of the former Soviet Union, and it basically combines the research results of the two stages in the West (Gao, 1998). For a long period, lifestyle research was constrained by the analytical framework of ideology, and it did not receive due development and attention (Ma & Xia, 2004). A view commonly accepted by scholars is that lifestyle is a concept that answers the question of how to live (Table 2.2), which means the activity and configuration patterns of people using various material, spiritual and cultural resources provided by the social environment to meet their own needs in life based on certain cultural patterns (Wang, 2013). Gao (1998) discussed the historical evolution of Western lifestyle research since Weber and Veblen: lifestyle evolved from a variable explaining classes and positions to an object of study, the research object changed from lifestyle to consumption, and the research focus shifted from group styles to personalised ones. The Encyclopedia of China—Sociology Volume made the following definitive statement on lifestyle: a system of all activity forms and behavioural characteristics formed by different individuals, groups or all members of society to meet their own needs in life under the constraints of certain social conditions and the guidance of values (Hu et al., 1992). Mao (1986) put forward that lifestyle is the basic characteristic of behavioural habits of carrying out practical activities in various fields of life by people of different societies, nationalities, classes and strata, with certain views on life and under certain material conditions, to meet their needs in life. Lu et al. (2003) defined lifestyle as the sum of forms and characteristics of all the activities of people in social life, including material activities, political activities and spiritual life. Wang (1995) held that as a science category,

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lifestyle refers to the stable forms and behavioural characteristics of all life activities of individuals, groups or all members of society, guided by certain values and aimed at meeting their own needs for survival and development, under certain objective social conditions. Ahuvia and Yang (2005) believed that lifestyle is a comprehensive concept, which can be defined as the types of time and money spent, reflecting a person’s activities, interests and opinions.

2.1.2 Connotations of Lifestyle A number of concepts and issues arise from the review of the literature on lifestyle, and these are discussed below under the headings activities/behaviour, values and attitudes, groups versus individuals, group interaction, coherence, recognisability and choice (Veal, 1993). In conclusion, social scientists have not reached a consensus on the exact definition of lifestyle. In a narrow sense, lifestyle refers to the daily activity patterns of individuals and their households, including clothing, food, housing, transport and their use of leisure time. In a broad sense, it refers to the sum of the typical ways and characteristics of all people’s life activities, including work life, consumption life, spiritual life (e.g., political life, cultural life and religious life) and other activity patterns (Wang, 1995). The mode of production is not only the production of essential materials for life and the reproduction of the physical existence of individuals but also a definite form of activities of these individuals, a definite form of life expressions and a definite mode of life on their part (Zhu, 2000). Therefore, lifestyle refers to a series of living habits, daily regimes and life consciousness formed by people under the influence of certain social cultures, economies, customs and families over a long time. Lifestyle is the styles and characteristics of individuals, social groups and the entire society determined by their nature and economic conditions, as well as physiographic conditions. It can be understood as behavioural patterns shown by different classes of people in their life circle and cultural circle. People’s behaviour is directly manifested and constitutes the visible part of lifestyle, while the values that govern people’s behaviour are implicit, but still an important component that should never be ignored. A person’s lifestyle always has an objective existence, either traditional or modern. However, lifestyle, of all kinds, is always constrained by many factors, including behavioural habits, time use, pace of life, living space and consumption. The lifestyle concept features the following characteristics (Fig. 2.1): (1) There are individual lifestyles and social lifestyles; (2) lifestyle is jointly determined by objective and subjective factors, and it can thus be divided into objective lifestyles and subjective lifestyles; (3) lifestyle is changeable, so there are changing lifestyles and coherent lifestyles; and (4) lifestyle is multidimensional and can be divided into specialised lifestyles and multidimensional lifestyles. These characteristics make it difficult to give an accurate and generally accepted definition of lifestyle; its definition actually depends on the research subject and purpose.

2.1 Concepts of Lifestyle

29

Individual lifestyles and social lifestyles

Specialized lifestyles and multidimensional lifestyles

Lifestyle

Objective lifestyles and subjective lifestyles

Changing lifestyles and invariable lifestyles Fig. 2.1 Lifestyle Characteristics

(1)

There are individual lifestyles and social lifestyles.

Lifestyle is the way in which one exists. Everyone’s lifestyle is unique, which explains the existence of individual lifestyles. The concept of individual lifestyle is the basis of Adler’s (1929) work. Ansbacher (1967) proposed that the term lifestyle can be used in three ways. First, it can be used in reference to individuals, as in Adler’s work; second, it can be used in reference to groups, where a lifestyle can emerge through the process of small group dynamics within families, couples or small-scale subcultural groups; and third, it can be used as a generic term that refers to class, occupational, status, cultural and other social groups. Groups can be defined at different spatial scales, such as the globe, countries, regions and communities. When individual lifestyles are similar in a certain dimension, a social lifestyle emerges and some people will adopt a certain lifestyle to be a part of a group. The issue of group and individual analysis depends on lifestyle segmentation (Veal, 1993). Jensen (2007) believed that lifestyle can be understood at four levels: (1) the global level, (2) the structural or national level, (3) the positional or subcultural level and (4) the individual level. The first three levels—the global level, the national level and the positional level—can all be classified into the group level. Therefore, there are two levels in general, namely the group level and the individual level. (2)

Lifestyle is jointly determined by objective and subjective factors.

The mode of production determines lifestyle, and it is an aspect of lifestyle in a broader sense (Marx & Engels, 1972). They are constrained but not determined

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2 Lifestyle and Its Measurements

by the environment (e.g., economic conditions, health and family commitments) (Thøgersen, 2018). It is thus clear that lifestyle is influenced by objective factors. Also, lifestyle involves activities/behaviours, including consumption patterns, leisure activities and domestic practices. Another group of variables is values and attitudes (Veal, 1993). Adler (1929) believed that lifestyle is the sum of values, passions, knowledge, meaningful behaviours and quirks, which makes everyone unique. Rooted in beliefs about the world, lifestyle is the embodiment of personal values and preferences. Its constancy over time is led by the intention to achieve goals or subgoals (Jensen, 2009), which shows that lifestyle is influenced by attitudes, preferences and values. In a word, there are many factors influencing lifestyle: objective conditions including gender, age, income and other socioeconomic factors, and subjective factors such as preferences and tastes. (3)

Lifestyle is changeable.

We should not see lifestyle as something fixed, as we believe that habits are. Habits are changeable, and therefore lifestyle must be changeable (Jensen, 2009). When one sets a goal, one’s behaviour and personality will change accordingly, which indicates that lifestyle may change over time (Adler, 1929; Jansen, 2011). One’s lifestyle may change with age, income, work, etc., and in this process, one may be included in another group, such as the transition from the young person’s lifestyle to older people’s lifestyles. Meanwhile, lifestyle also features some kind of continuity and compatibility (Adler, 1929; Earl, 2017). While people seek continuity in their lives, the extent to which they realise it is still unclear (Veal, 1993). Ganzeboom (1988) emphasised the existence of a continuum between lifestyle types, rather than the occurrence of specific lifestyle types. This continuum is determined by three dimensions: (1) an economic dimension, (2) a cultural dimension and (3) a stagein-life dimension. It is increasingly acknowledged that people may not have just one, but perhaps several interconnected lifestyles and that rather than one overall lifestyle, it is more meaningful to speak about domain-specific lifestyles (van Raaij and Verhallen, 1994). Lifestyle may change over time, not in a crazy or random way, but systematically. We can regard it as an attempt to maintain a balance between environmental changes and one’s own value system (Grunert, 2006). Van Acker et al. (2016) argued that lifestyles need to be considered as dynamic rather than as static and given. (4)

Lifestyle is multidimensional.

One’s lifestyle can be divided into different types in different domains. For example, in the domain of age, there are young people’s lifestyles, older people’s lifestyles, etc. In the domain of income, there are low-income lifestyles, high-income lifestyles, etc. In the domain of transport, there are lifestyles with a private car, lifestyles without a private car, etc. People may have a variety of interconnected lifestyles, and it is more meaningful to speak about domain-specific lifestyles (van Raaij and Verhallen, 1994). Therefore, the description and measurement of lifestyle must be domainspecific (Thøgersen, 2018), such as food (Grunert, 1993, 2006; Grunert et al., 2001;

2.1 Concepts of Lifestyle

31

Thøgersen, 2017), transport (Kitamura, 1988; van Acker, 2015; van Acker et al., 2010), and housing (Ærø, 2006; Scheiner, 2010).

2.1.3 Definitions of Lifestyle in Transport Lifestyle research in transport mainly focuses on travel behaviour. The application of lifestyle in transport research originated in the 1970s and received more attention only after 2010. However, compared with sociology, marketing, health sciences and other research fields, currently, there is not much lifestyle research in travel behaviour research. Due to its complexity, there is no unified definition of lifestyle. Similarly, there is no generally accepted definition of lifestyle in transport. The concept of lifestyle in transport research was first introduced by Reichman (1977). Initially, lifestyle was simply regarded as socioeconomic differences (Kitamura, 1988; Reichman, 1977; Sharp, 1987), such as differences in life stage or family composition (Cooper et al., 2001; Hildebrand, 2003; Salomon & Ben-Akiva, 1983). Although to some extent lifestyle is indeed influenced by life stage or family composition, lifestyle is something different (Van Acker, 2010). Reichman (1977) was the first to introduce the concept of lifestyle in transport research. Reichman put forward the following question: “Is transportation only a means to an end, or does it really fulfill some ends in itself?” Households and individuals may have established a certain travel-related lifestyle, where mobility patterns fulfil some ends in themselves. Lifestyle is related to individual and household activities. It is shaped by “recurrent behavioral responses to socioeconomic conditions, as well as to deeper personal or social attitudes, roles, or values” (p. 39). The preference pattern or taste system of individuals or households is called the lifestyle aspect, another dimension that reflects the habitual behaviour of individual decision-makers during travel. Lifestyle is thus considered as the basis of travel behaviour, related to the basic values and needs of human beings (Kitamura, 1988; Reichman, 1977). Havens (1981) defined lifestyle as different time allocations for different activities within and outside the family. Travel is also a kind of time allocation. The two social factors, role and lifestyle, influence the demand for various activities, thus affecting travel behaviour. Salomon and Ben-Akiva (1983) defined lifestyle as a pattern of behaviour under constrained resources that conforms to the orientations an individual has towards three major life decisions he or she must make: (a) formation of a household (of any type), (b) participation in the labour force and (c) orientation towards leisure. Lifestyle is the basis of behaviour, and it is also long-term decisions and observable behavioural patterns. Socioeconomic and demographic variables are mainly used to classify lifestyle groups through the clustering method. Pas (1988) defined travelrelated lifestyle as weekly travel-activity patterns—that is, daily trip frequency. Kitamura (1988) gave two meanings to lifestyle: (1) activity and time-use patterns and (2) values and behavioural orientation. These two are interrelated, but there is a critical difference between them. Lifestyle changes are realised through changes

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in values, attitudes and preferences. Lifestyle as activity patterns may change as individuals adapt to changes in the environment, whereas lifestyle as an orientation is maintained by individuals by modifying behavioural patterns and adapting to changes. In previous empirical analyses, lifestyle was only studied as a typology of behaviour, while lifestyle as values and behavioural orientation provides a theoretical medium linking revealed behaviour and measurable individual characteristics. Jones et al. (1990) believed that lifestyle represents an individual’s preference for his or her daily activities and travel behaviour, which is related to socioeconomic and demographic attributes, such as household structure and housing type, while transport is considered as travel patterns of one or more days, caused by differences in people’s lifestyles and activity participation. Götz et al. (1997) transformed the concept of lifestyle into mobility styles, mainly based on preferences for travel patterns. Ohnmacht et al. (2009) applied this concept to leisure activities, mode shares and leisure travel distance, and they proposed a lifestyle concept oriented towards mobility and leisure activities, which they termed leisure mobility styles. This refers to a specified lifestyle area. In addition to the classic factors that explain individual travel behaviour (i.e., income, age, gender, etc.), various other personal characteristics (i.e., attitudes, opinions, and values) also determine travel behaviour. In actual research, this means that methods of attitude and lifestyle research can be combined with those of travel behaviour research (Götz & Ohnmacht, 2011). Jensen (2007) suggested a plural definition based on a level analysis: lifestyle can be understood from (1) the global level, (2) the structural or national level, (3) the positional or subcultural level and (4) the individual level. He associated mobility-related lifestyle with the positional level and the individual level. Lifestyle at the positional level is mainly related to housing, which is still a lifestyle related to social class. Lifestyle at the individual level is defined as the visual expression that distinguishes one from another. Lifestyle reflexively indicates to the individual that the project of self is ongoing (4a), and it is also a set of habits guided by the same main goal (4b). Transport is mainly related to particular destinations, activity locations and means of transport. According to van Acker et al. (2010), the longest term decision is the choice of a lifestyle. Individuals make short-term activity decisions and medium-term spatial decisions to satisfy their lifestyle decisions. That is why lifestyle also influences daily travel behaviour. One’s lifestyle is under the influence of one’s outlooks on life and motivations, including beliefs, interests and general attitudes. The impacts of lifestyle on travel behaviour have increased, so the consideration of lifestyle will offer interesting insights into travel behaviour. Vij et al. (2013) defined lifestyle as mobility style, pointing out that residents’ preferences for travel mode can reflect their lifestyles, and these preferences can affect all personal travel behaviour and activity behaviour. The application of the lifestyle concept in transport was characterised by socioeconomic features in early stages, and later lifestyle was deemed as behavioural expressions and value orientation. Although the concept of lifestyle is frequently used in

2.1 Concepts of Lifestyle

33

travel behaviour, currently, there is no unified definition of lifestyle (Table 2.3). Kitamura (1988), van Acker (2015, 2017) and van Acker et al. (2010) have had a profound influence on the definition of lifestyle in transport. One of the reasons why people are interested in lifestyle is that it highlights the importance of subjective factors in addition to traditional objective factors (van Acker, 2015). Lifestyle is used both quantitatively and qualitatively to improve the prediction of travel demands and to reveal the travel preferences of certain target groups, which are based on a specific aspect of the lifestyle they share, providing a useful conceptual framework. Travel-related lifestyle is a specific domain of lifestyle. Transport itself is a part of lifestyle, or rather, a perspective to describe lifestyle. The term lifestyle has two meanings in related research: (a) activity and time-use patterns and (b) values and behavioural orientation (Kitamura, 2009). In this book, lifestyle in transport comprises behavioural expressions and value preferences in transport. Travel-related behavioural expressions mainly include consumption behaviours, housing choices, work, leisure activities, etc., as well as money and time spent on these behaviours Table 2.3 Definitions of lifestyle in travel behaviour Scholar

Year

Viewpoint

Reichman

1977

Lifestyle is considered as the basis of travel behaviour. It is related to the basic values and needs of human beings

Havens

1981

The two social factors, role and lifestyle, influence the demand for various activities, thus affecting travel behaviour

Salomon and Ben-Akiva

1983

The choice of a lifestyle is regarded as a long-term decision, which determines short-term decisions such as daily travel choices

Kitamura

1988

Lifestyle as an orientation is more stable, and changes in the orientation will only take place in the long term through changes in values, attitudes and preferences

Pas

1988

Travel-related lifestyle is defined as daily trip frequency

Munters

1992

Lifestyle is a personal perspective and motivation or orientation, and travel behaviour is a behavioural pattern expressing lifestyles

Götz et al

1997

Mobility styles are based on preferences for travel patterns

Jensen

2007

Mobility-related decisions are positioned on the third (subcultural) and fourth (individual) levels, which involve the choice of activity locations, travel frequency and the choice of patterns

Ohnmacht et al.

2009

Mobility styles have significant effects on leisure activities, mode share and leisure travel distance

Van Acker et al.

2010

Lifestyle influences people’s long-term decisions. One’s lifestyle is under the influence of one’s outlooks on life and motivations, including beliefs, interests and general attitudes

Vij et al.

2013

Lifestyle is a mobility style, so residents’ preferences for travel mode can reflect their lifestyles

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2 Lifestyle and Its Measurements

and activities. Value preferences mainly include life philosophies, hobbies and preferences. The application of the lifestyle concept in travel demands is essentially a category analysis of travel demands.

2.2 Lifestyle Measurement Approaches Many scholars believe lifestyle is a multidimensional concept with broad theoretical implications (Koshksaray et al., 2015). While the concept of lifestyle seems appealing, it remains unclear how to measure lifestyle properly (Kitamura, 1988). Plummer (1974) regarded measurement approaches to lifestyles as mostly based on consumers’ perspective, investigating various dimensions of lifestyle at the individual level, including both subjective and objective content. Based on the perspectives of consumers, he divided customers into different lifestyle types based on people rather than products. Lifestyle variables measure people’s activities in terms of (1) how they spend their time; (2) their interests, what they place importance on in their immediate surroundings; (3) their opinions in terms of their view of themselves and the world around them, and some basic characteristics such as their stage in life cycle, income, education and where they live. Ganzeboom (1988) believed that lifestyle is a continuous unity of three dimensions: an economic dimension, a cultural dimension and a life stage dimension. Lifestyle should not be regarded as a specific type, but as a continuous unity determined by the three dimensions. The dimension of life stage is based on traditional socioeconomic variables, such as family and occupation. He believed that some sociodemographic variables are dynamic and need to be treated differently. Lifestyle is related to the socioeconomic characteristics of individuals, but it is also affected by the intermediate variables of opportunities and constraints provided by time budget, income, cognitive skills and status. Müller (1992) believed that lifestyle can be divided into four dimensions: an expression dimension (e.g., leisure preference/behaviour, daily aesthetics, consumption behaviour), an interaction dimension (e.g., social contact, communication), an evaluation dimension (e.g., norms, values, concepts) and a cognitive dimension (e.g., self-identity, subordination). Since 1963, methods of measuring lifestyle patterns and their relationships to consumer behaviour have been developed and refined (Plummer, 1974). Generally, there are three basic approaches to the classification of lifestyle dimensions and lifestyle measurement: the AIO approach, the values, attitudes and lifestyles (VALS) model and the model of consumers’ psychological characteristics. The AIO approach, which classifies lifestyle into the three dimensions of activities, interests and opinions, was first proposed in 1971 by Wells and Tigert and has since been widely applied by scholars. AIO and the VALS model are derived from the idea of consumer segmentation. The model of consumers’ psychological characteristics abstracted from the AIO approach and the VALS model identify consumers’ psychological characteristics and relate them to specific consumer behaviour to establish a

2.2 Lifestyle Measurement Approaches

35

causal relationship model, allowing scholars to analyse the inner forces resulting in the development of such behaviour (Gao, 2017).

2.2.1 The AIO Approach The most widely used approach to lifestyle measurement has been AIO, which provides useful consumer typologies extracted through clustering analysis of opinions, values and interests (Vyncke, 2002). The AIO, first designed and proposed by Wells and Tigert (1971) with 300 questions, has been widely used in the field of consumer research By examining consumers’ activities, interests and opinions, lifestyle segmentation can provide a unique and important view of markets, characterising customers based on their ways of living (Chen et al., 2009). The basic idea of the AIO questionnaire is to reflect lifestyles by examining residents’ activities, interests and opinions (Wells & Tigert, 1971). The scale uses 300 statements to measure the three dimensions of how participants spend their free time, what they are interested in, and their views on various lifestyles. Consumers with similar responses to the AIO statements belong to the same lifestyle segment. In this measurement, different AIO results indicate different lifestyles. Businesses can develop different marketing strategies or product/service solutions to target lifestyle segments. From the perspective of the means-end chain theory, the AIO scale is a very powerful tool to predict behaviour (Akkaya, 2021). Later, scholars further defined and explained the three dimensions of the scale, introducing corresponding demographic characteristics for explanation (Reynolds and Darden, 1974; Plummer, 1974). The widespread application of the lifestyle market segmentation approach has much to do with the multiple dimensions it provides to understand consumers (Ahuvia & Yang, 2005) (Table 2.4). Table 2.4 Lifestyle dimensions (Plummer, 1974)

Activities

Interests

Opinions

Demographics Age

Work

Family

Themselves

Hobbies

Home

Social issues Education

Social events

Job

Politics

Income

Vacation

Community

Business

Occupation

Economics

Family size

Entertainment Recreation Club membership

Fashion

Education

Dwelling

Community

Food

Products

Geography

Shopping

Media

Future

City size

Sports

Achievements Culture

Stage in life cycle

36

2 Lifestyle and Its Measurements

However, it has been criticised for its weak theoretical basis and unstable market segmentation determined by tools. Without considering consumers’ values, which exert a significant influence on the formation of consumers’ lifestyles as an integral part of their worldviews (Vyncke, 2002), the AIO approach is insufficient to explain lifestyles. Kamakura and Wedel (1995) also pointed out that the lengthy questionnaires involved in the AIO approach are increasingly troubling survey researchers and respondents. Therefore, many scholars have tried other methods than AIO to measure lifestyle (Kucukemiroglu, 1999; Moore & Homer, 2000; Orth et al., 2004). Some have attempted to simplify the AIO approach based on their research purposes to reduce the difficulty respondents may have in answering a large number of questions. Using the AIO approach, Plummer (1974) conceptualised the implications of lifestyle. Kucukemiroglu (1999) adopted the AIO approach to analyse the beliefs of Turkish consumers. One representative study in this respect comes from Plummer (1974), who added demographics to the traditional AIOs, giving rise to a revised AIO approach that incorporates four dimensions and 36 subdimensions (Table 2.5).

2.2.2 Values, Attitudes and Lifestyles (VALS) Model While Weber and Adler appear to offer theory without measurement, the market research approach appears to offer the opposite: measurement without theory. Some other scholars have analysed consumers’ lifestyles from the perspective of values (Veal, 1993). One representative achievement in this respect is the VALS model (Fig. 2.2) developed at SRI International by Mitchell (1983). While the system has been used commercially in the market research world, it has its origins in academic enquiry with Weberian and Adlerian antecedents (Veal, 1993). VALS is a long measurement, containing over 30 questions regarding participants’ attitudes, demographics, activities, financial issues, household inventory and product use and specific foods and products (Mitchell, 1983). With the data of a national probability sample of 1,635 Americans and their spouses/mates (1,078) who responded to an SRI International mail survey in 1980, Mitchell classified US consumers into nine types in three main categories by their values, dreams and attitudes. These lifestyle groups were survivors (4%), sustainers (7%), belongers (35%), emulators (9%), achievers (22%), I-am-me (5%), experiential (7%), societally conscious (9%) and integrated (2%). Mitchell’s study also included a number of questions about consumer behaviour. The impact of VALS has been widespread and dramatic (Kahle et al., 1986). In 1986, SRI revised two aspects of the VALS model, resources and selforientation and introduced the VALS2 model, which groups US consumers into eight segments. From these two aspects, US consumers are classified into eight segments, namely innovators, thinkers, believers, achievers, strivers, experiencers, makers and survivors. Compared with the VALS model, the VALS2 model has a more solid sociopsychological foundation. In the VALS2 model, data are usually collected on an individual basis, but consumption decisions are usually made by families. While resources involved in both family and individual consumption can be examined,

2.2 Lifestyle Measurement Approaches

37

Table 2.5 Lifestyle types in travel behaviour studies Author

Year

Lifestyle types

Krizek and Waddell

2002

Analysis of family choices related to three lifestyle dimensions: travel patterns (including vehicle ownership), activity participation and living quarters (neighbourhood types)

Bagley and Mokhtarian

2002

Lifestyle variables can be grouped into 11 factors, such as hobbyist, nest-builder, athletic, child-oriented and couch potato

Collantes and Mokhtarian

2007

The survey contains 18 Likert-type scales related to work, family, money, status and the value of time. These 18 questions generate four lifestyle factors: status seeker, workaholic, family/community-oriented and frustrated

Choo and Mokhtarian

2008

Eighteen statements related to work, family, money, status and the value of time. The respondent agrees or disagrees with the statement using a 5-point Likert-type scale; four factors of factor analysis: status seeker, workaholic, family/community-oriented and frustrated

Scheiner

2010

Leisure preferences, values, life goals, aesthetic tastes (measured using preferences in books and TV programmes) and frequency of social communication

Etminani-Ghasrodashti and Ardeshiri

2015

Leisure and consumption activities are represented by three travel patterns (private cars, public transport and walking/cycling)

Thøgersen

2018

Urban, cost-conscious travellers; enthusiastic travellers; engaged travellers; uninvolved travellers; unenthusiastic car drivers, family-oriented travellers

Note This table is not exhaustive, as types may have been missed while studying the literature or new types might have emerged since. It just provides an indication of existing lifestyle types, especially with regard to transport

self-orientation cannot measure the true inner orientation of consumers. Meanwhile, differences in self-orientation among consumers make it even harder to ensure the results of measurement. Nonetheless, the VALS2 model remains the most complete model, and it has been widely applied to studies on European and US consumers’ lifestyles. However, the VALS2 model, being developed based on US consumers’ lifestyles, lacks interpretability when being applied to the identification of lifestyles of other countries’ consumers. On the basis of the VALS2 model, many scholars developed their own VALS models in light of their actual national conditions. For example, the Japan-VALS model was established in 1991 (Winters, 1992). In 2002, Sinomonitor

38

2 Lifestyle and Its Measurements Survivors Need-directed Sustainers Belongers The VALS model

Outer-directed

Emulators Achievers I-am-me Experiential

Inner-directed Socially conscious Integrated

Fig. 2.2 The Three dimensions and nine types of the VALS model. Source Mitchell (1983), edited by the author

International developed a China-VALS model based on the results of a 1997 market survey among Chinese consumers, classifying Chinese consumers into 14 clusters (Fig. 2.3) involving 7 cultural elements and 3 Chinese sociopsychological structures

The stable and realistic group

Social classes

The pragmatic group

Rational, career-oriented 7.34%

Upper class 7.34% Accomplished 6.7%

Economically minded 6.2%

Stable job holders 6.00%

Stable advancement Seekers 6.45%

Personality demonstrators 6.98%

Stable and moderately prosperous life owners 6.26%

Social followers 13.95%

Trendy 8.54%

Down-to-earth life owners 6.79%

Traditional livers 6.31%

Pragmatic, stable 5.17%

Thrift practitioners 6.85%

Economical 6.46

Upper-middle class 12.9%

Middle class 48.18%

Trendy 18.27%

Lower class 13.31%

The positive group

Lifestyles

Fig. 2.3 The China-VALS model. Source Chen (2011), edited by the author

2.2 Lifestyle Measurement Approaches

39

(Chen, 2011). In addition, the E-ICP Research Centre and the NCCU MBA Program in Taiwan, China also jointly developed an E-CIP targeting Asian consumers.

2.2.3 The Model of Consumers’ Psychological Characteristics The consumer psychological trait model mainly starts from the psychological traits of consumers, pays attention to a specific lifestyle of consumers and its relationship with consumer behaviour, and studies and analyses the impact of different dimensions of this lifestyle on consumer behaviour. Scholars use different dimensions according to the focus of their research. In research on the relationship between lifestyle and consumption behaviour, lifestyle is divided into five dimensions: fashion taste, moderation and introversion, perfectionism, positive enterprising and advocating freedom (Pan et al., 2009) and nine dimensions: fashion trend, freedom and detachment, savings and financial management, rational consumption, money taste, experience novelty, follow the purchase, self-discipline achievement and travel (Xue, 2010); fashion awareness, price awareness, leadership awareness and nostalgic awareness also play a role (Chen, 2011). In the dimension division of specific lifestyles, scholars also use different dimensions according to the research object, due to the lack of standards. When studying green consumption behaviour, He and Yang (2004) divided lifestyle into three dimensions: price sensitivity, impulse purchase and information attention; Koshksaray et al. (2015) also divided lifestyles into three dimensions: demand-driven, internal-driven and social-driven in the study of information lifestyles; Sheng and Gao (2016) divided lifestyle into fashion awareness, leadership awareness, price awareness and development awareness in their research on green lifestyle.

2.2.4 Current Index System to Measure Lifestyles in China Wang (2013) believed that the indexes in the index system to measure lifestyles can be divided into two categories: quantitative indexes, which are the chief components of the index system mainly used to reflect the quantitative changes and levels of the development conditions of lifestyles, and subjective indexes, which reflect the trend of life activities and the degree of satisfaction of the subject’s activities. These subjective indexes only reflect the tendency of change, the exact quantity of which cannot be measured. Meanwhile, Wu (2002) held that the index system to measure lifestyles is mainly composed of two parts (Fig. 2.4): indexes of activities, including those of people’s labour activities, consumption activities, social and political activities, leisure activities, interaction activities, etc., and indexes of life activity

40

2 Lifestyle and Its Measurements Lifestyle index system

Objective indexes

Subjective indexes

Behaviour indexes

Living standard

Degree of satisfaction with life

Appropriateness of life

Economy Social order

Consumption

Living environment

Leisure life

Work

Work life

Family

Political life

Social structure

Population quality

Life guarantee

Social stability

Health

Fig. 2.4 Structure Chart of the lifestyle index system of urban residents. Source Wu (2002), edited by the author

conditions, that is, the quality and quantity indexes of people’s existence and development conditions. This lifestyle index system basically covers the main content of lifestyles and points the way to the future construction of index systems to measure lifestyles. However, this kind of dichotomous approach is still relatively vague, and it does not explicitly embody the structural elements of lifestyle: the details of the three components—conditions for life activities, subjects of life activities and forms of life activities.

2.3 Measurements of Lifestyles in Transport Lifestyle measurement refers to the use of quantitative indicators to identify different lifestyle groups. To date, there are no recognised dimensions and indicators of

2.3 Measurements of Lifestyles in Transport

41

lifestyle measurement. Researchers usually define different lifestyles based on exploratory analysis of data. Due to the differences in measurement indicators and research goals, it is difficult to compare these different lifestyles. However, how one measures lifestyle depends on how one defines lifestyle.

2.3.1 Current Measurements and Classification of Lifestyle Dimensions in Transport Defining the lifestyle concept is one thing; measuring it is another one (van Acker, 2015). Van Acker (2015) illustrates the usefulness of three lifestyle approaches: (a) the socioeconomic and demographic lifestyle approach, based on socioeconomics and demographics; (b) the sociographic approach, based on attitudes towards the family-work balance and leisure time and (c) the mechanistic approach, based on holiday and leisure activities. Ta et al. (2016) divided the indicators for lifestyle segmentation into social statistical indicators (such as age, income and gender), psychological and cultural indicators (such as values and attitudes), geospatial indicators (such as residential location and workplace) and daily behavioural indicators (such as travel distance and number of trips) (Krizek & Waddell, 2002; Lin et al., 2009). Lin et al. (2009) summarised three methods to classify individuals into different travel/activity-related lifestyle clusters. (1) The earliest one identifies, using cluster analysis only, distinct lifestyle groups based on individual and household socioeconomic and demographic characteristics. (2) The second method, which has become more popular recently, uses short-term daily activity-travel characteristics and long-term household socioeconomic and demographic characteristics to classify lifestyles. (3) The third method is based on the additional use of long-term information on individual activity participation. However, as such kinds of data are seldom collected, the third method is not often used. The following three lifestyle measurement methods in transport are available: (1) Using socioeconomic and demographic characteristics to measure lifestyle (Hildebrand, 2003; Kitamura, 1988; Reichman, 1977; Salomon & Ben-Akiva, 1983; Sharp, 1987). (2) Using socioeconomic attributes, demographic attributes and daily behaviour as indicators, such as activity time, travel distance and number of trips (Bagley & Mokhtarian, 1999, 2002; Krizek, 2006; Krizek & Waddell, 2002; Lin et al., 2009; van Acker et al., 2014). (3) Using attitudes and cultural indicators, such as values and preferences (Ardeshiri & Vij, 2019; Lanzendorf, 2002; Ohnmacht et al., 2009; Prato et al., 2017; Scheiner, 2010; Vij et al., 2013).

2.3.1.1 (1)

Lifestyle Measurement Index

Using socioeconomic and demographic characteristics of individuals and households to define lifestyle, such as gender, age, household structure, income

42

(2)

(3)

2 Lifestyle and Its Measurements

and car ownership, was a common practice in the transport field in the early days (e.g., Hildebrand, 2003; Kitamura, 1988; Reichman, 1977; Salomon & BenAkiva, 1983; Sharp, 1987). In Reichman’s (1977) research, lifestyle aspects are supplemented by socioeconomic variables, despite their deficiencies. Social groups can be differentiated even on the basis of insufficient data and research design. Reichman used household income to represent economic affluence and the propensity to consume; age (represented by the conventional classification of young adulthood, middle age and old age) reflects social engagement; travel modes of the head of a household represent role differentiation (the head of a household is the shaper and the mobile element of the household’s mobility mode); and time control is represented by education level and vehicle availability, mainly through cross-sectional data. Instrumental aspects are the total trips, which represent overall mobility, and subsistence trips, including work and business trips. Salomon and Ben-Akiva (1983) defined lifestyle in terms of the types of longer term activity patterns (work, leisure and family structure) available to the household and they operationalise the concept by using several sociodemographic variables, e.g., income, education and age to assign households to lifestyle groups. Wachs (1979) believed that a specific combination of income, family status, education level, residential density and similar variables distinguishes the lifestyles of those sharing them from those represented by other ranges of the same variables. Therefore, these early lifestyle studies on travel behaviour used the geodemographic approach, but they did not contain any information about individuals’ orientations towards family, work, leisure and consumption. Van Acker (2015) believed that these studies refer to the stage of life cycle or household composition rather than to lifestyles. These characteristics do not necessarily reflect how people want to distinguish themselves from others in society. This method has the virtue of easily accessible socioeconomic and demographic data. Socioeconomic attributes, demographic attributes and daily behaviour as indicators, such as activity time, travel distance and number of trips (Bagley & Mokhtarian, 1999, 2002; Krizek, 2006; Krizek & Waddell, 2002; Lin et al., 2009; van Acker et al., 2014). For instance, Krizek and Waddell (2002) employed travel characteristics (measured by the number of driving trips, carpool trips, public transport trips and walking trips; travel distance; and household commuting distance), activity frequency (measured by the number of life activities, maintenance activities and discretionary activities), car ownership (measured by the number of cars: 0, 1, 2 or more) and urban form (measured by residential population density, street patterns, land use combination and regional accessibility). Van Acker et al. (2014) conducted an internet survey in Flanders, Belgium in 2007. The internet survey included a list of more than 100 types of holiday activities, literary interests and leisure activities. Lin et al. (2009) explored lifestyle classifications with and without activity-travel patterns, and found that the two sets of lifestyle clusters were similar. Psychological and cultural indicators, such as values and attitudes (Ardeshiri & Vij, 2019; Lanzendorf, 2002; Ohnmacht et al., 2009; Prato et al., 2017;

2.3 Measurements of Lifestyles in Transport

43

Scheiner, 2010; Vij et al., 2013). Several population groups are identified. These groups differ from each other in terms of activity participation and attitudes towards modes, travel frequency and distance and the habitual choice of travel modes. For example, Lanzendorf (2002) explored the role of lifestyle in leisure travel. He took mobility styles as a key measurement dimension, and the indicators used for lifestyle segmentation mainly involved four aspects: (a) the importance of leisure objectives (11 items: spending time with friends, enjoying the open air, experiencing excitement, travelling, assisting other persons, being independent, spending time with family, having time for personal activities, being creative, having a rest and getting away from home); (b) the frequency of leisure activities (21 items: reading a book, using the computer, working in the garden, playing with children, watching TV or video, meeting people at home, being lazy, going out with friends, doing community work, going to theatre or classical concerts, visiting an exhibition or gallery, going to a film, going to a pub, bar or café, dining in a restaurant, visiting sports events, practicing sports, bicycling, cruising by car or motorcycle, going for a stroll or hiking, shopping and visiting a zoo or a leisure park); (c) the importance of mode attributes for leisure travelling on Sundays (eight items: price, convenience, speed, pleasure of use, recreation by travelling, environmental concern, flexibility and physical exercise) and (d) the attitudes towards specific transport modes for leisure travelling on Sundays (18 items: convenience, speed, pleasure and recreation of car, bus/rail, bicycle and walking, as well as the price for car and for bus/rail) (Scheiner, 2010). Lifestyles are presented in the data using four domains: leisure preferences, values and life aims, aesthetic taste (measured using preferences in reading and television viewing) and frequency of social contacts. These were represented by a total of 34 items measured by 5-point Likert-type answer scales. In terms of lifestyle measurement, lifestyle research in transport relies on data collected from surveys, and with the increase in variables, factor and clustering analyses are often used for lifestyle segmentation. This method was criticised by Prato et al. (2017) for its major deficiencies. For one thing, some indicators that are not related to travel have been selected (e.g., Walker & Li, 2007). For another, this method has certain limitations: (a) whether questionnaire surveys, especially attitude-related questions, are scientific or not can affect the validity of lifestyles and (b) endogenous measurement errors can skew parameter estimates and affect the validity of any conclusions on lifestyle expressions.

2.3.1.2

Lifestyle Measurement Dimension

Currently, there are no recognised dimensions of lifestyle measurement. Krizek and Waddell (2002) developed a framework for analysing household lifestyles from four dimensions of household decision-making, namely: (1) travel behaviour characteristics; (2) activity frequency; (3) car ownership and (4) urban form. Krizek (2006)

44

2 Lifestyle and Its Measurements

also used this method to analyse household lifestyles based on three dimensions: (1) travel characteristics, (2) activity durations and (3) neighbourhood characteristics. As many variables are involved, factor analysis was adopted first to extract several fundamental factors and then cluster analysis was used to classify the population into several homogeneous groups based on the extracted factors. Lanzendorf (2002) developed a framework for leisure travel, to analyse household decisionmaking, which is part of household lifestyles, from the perspectives of (1) travel characteristics, (2) activity frequency, (3) car ownership and (4) urban form. Van Acker (2015) analysed seven lifestyle measurement approaches after an indepth review: (1) a socioeconomic and demographic lifestyle approach that measures stage of life and household composition; (2) a psychographic lifestyle approach that analyses personality traits and related motives and values; (3) a cultural lifestyle approach that shifts the focus from individuals to communities, from individual personalities to underlying common norms and values; (4) a sociographic lifestyle approach that shifts the focus to individual opinions and attitudes. Analyses are not exploratory but based on an a priori-determined model. This approach is a combination of a psychographic lifestyle approach and a cultural and sociographic approach; (5) a mechanistic lifestyle approach that regards lifestyle as a way of living or as “a condition of existence and a manner of being” (p. 76), focusing on behavioural patterns; (6) a post-structural lifestyle approach that disconnects lifestyles from social structure. Relevant studies focus on individual choices, which are highly dependent on the local and temporal context; and (7) a geographic lifestyle approach that combines individual information with the spatial information of any location that is important to an individual. Relevant studies can be considered as analyses of geodemographic differences between neighbourhoods. The end result is not a lifestyle typology, but rather an understanding of geographic submarkets or neighbourhoods. Lin et al. (2009) used socioeconomic, demographic and activity-travel data to study issues related to the development of homogeneous clusters. The first factor, urban form, represents variables related to urban areas, measured by household density, population density and retailer density. The second factor, walk-only, contains information on the frequency and total duration of walk-only trips and the number of trips. The third factor, routine, captures the most regular travel mode on working days, including the frequency and total duration of trips where only one public transport mode is used. Scheiner (2010) presented lifestyles using four domains: leisure preferences, values and life aims, aesthetic taste (measured using preferences in reading and television viewing) and frequency of social contacts. These were represented by a total of 34 items measured by 5-point Likert-type answer scales. Thøgersen (2018) proposed a new instrument for measuring transport-related lifestyles, and its usefulness as a segmentation tool for the population of private transport consumers has been demonstrated. Thøgersen proposed five dimensions of lifestyle to measure transportrelated lifestyles, namely quality aspects, buying motives, ways of shopping, travel and transport routines and consumption situation.

2.3 Measurements of Lifestyles in Transport

45

Overall, four dimensions of lifestyle are generally measured in current research: activities such as travel characteristics and frequency, car ownership, urban environment and consumption.

2.3.1.3

Lifestyle Classification Types

Generally, there are four main types of lifestyle segmentation in transport: segmentation by socioeconomic differences, which was often used in early research (e.g., Kitamura, 1988; Reichman, 1977; Salomon & Ben-Akiva, 1983; Sharp, 1987); segmentation based on the analysis of different population groups, sometimes called the category analysis of travel needs. Different population groups are assumed to have distinct residential choices and travel modes (e.g., Prato et al., 2017; Thøgersen, 2018); market segmentation, focusing on special groups in society such as the poor and older people (e.g., Hildebrand, 2003) or the needs of different family types (e.g., Ardeshiri & Vij, 2019); and segmentation by socioeconomic attributes, activities and preferences (e.g., Lin et al., 2009). (1)

(2)

(3)

Socioeconomic differences. Salomon and Ben-Akiva (1983) identified five lifestyle types through the formation of a household, participation in the labour force and orientation towards leisure (education level and the proportion of white-collar workers in the household), namely family-oriented workers, the upper class, young groups, low-income or less-educated groups and older groups. Based on socioeconomic and demographic lifestyle factors related to the stage of life, van Acker (2015) identified three lifestyle groups: student living at home, older family with employed adults and young family. Classes of travel needs. Prato et al. (2017) analysed short trip chains of a representative sample of the Danish population in the Copenhagen region and estimated a latent class choice model to uncover latent lifestyle groups and choice-specific travel behaviour. Four lifestyle groups were identified: oriented towards the use of the car; oriented towards the use of the bicycle; oriented towards the use of walking and public transport; and no clear orientation towards a specific travel mode. Each lifestyle group has specific perceptions of travel time (with extremely different substitution rates between alternative travel modes), transfer penalties in public transport trip chains, weather influence (especially on active travel modes) and the impact of trip purposes on mode selection. Thøgersen (2018) classified consumers using different transport modes in ten countries into six types: urban, cost-conscious travellers, enthusiastic travellers, engaged travellers, uninvolved travellers, unenthusiastic car drivers and family-oriented travellers. The research showed that lifestyle segments differ in car use. Special groups in society. When Hildebrand (2003) studied the travel behaviour of older people in Amsterdam, the Netherlands, he used data about personal attributes to divide older people into six lifestyle groups, namely workers, widowed women who migrate frequently, older people living alone in cottages,

46

(4)

2 Lifestyle and Its Measurements

people with mobility impairments, wealthy men and disabled drivers. Based on sociographic lifestyle factors related to leisure time, van Acker (2015) identified outdoor leisure activities and home leisure activities. Socioeconomic attributes, activities and preferences. Krizek and Waddell (2002) distinguished nine lifestyle types through empirical research in the United States, namely retirees; single, busy urban residents; older male homebodies; urban residents with high income; public transport users; suburban commuters; family- and activity-oriented people; suburban households with dual income; and exurban family commuters. In a travel study targeting San Francisco residents, Bagley and Mokhtarian (2002) identified seven lifestyle groups, namely adventurers, those who pursue culture and art, those who have a hobby, family caregivers, breadwinners, outdoor activity enthusiasts and those who are inclined to relax. Lin et al. (2009) used activity-travel patterns (e.g., number and duration of private mobile trips) to identify six lifestyle groups: home-centred persons, household makers, discretionary activity participants, urban travellers, regular travellers and high-income residents. Van Acker et al. (2014) conducted an internet survey in Flanders, Belgium in 2007. Their internet survey included a list of more than 100 types of holiday activities, literary interests and leisure activities. They identified five lifestyle factors: culture lovers, friends and trends, low-budget and active/creative, home-oriented but active family and home-oriented traditional family. With 1,878 samples from this online survey, which ran from May to October 2007, van Acker (2015) explored travel differences between three lifestyle groups in terms of car use and active travel. Based on sociographic lifestyle factors related to family-work balance, three groups were identified: friends-oriented, family-job-oriented, and career-oriented.

2.3.2 Construction Measurement Index System for Transport-Related Lifestyles Lifestyles are often defined pragmatically rather than theoretically in behaviour studies (van Acker et al., 2014). Typically, lifestyle approaches to travel include subjective attitudes, values, housing or leisure preferences and wishes, rather than just the mere objective circumstances of daily life, such as employment, age or gender roles (Scheiner, 2010). Time use patterns, activity participation and neighbourhood characteristics are related to lifestyle choices (e.g., Fan & Khattak, 2012; Krizek & Waddell, 2002; Schwanen & Mokhtarian, 2005). Travel modes are also related to factors such as individual and household characteristics, the availability of transport modes, and urban morphological elements (e.g., living quarters, garden ownership and housing type) (Lanzendorf, 2002). The complexity and diversity of lifestyles make it impossible to reach a consensus on specific lifestyle variables. Too many variables will also bring about many problems, such as operational difficulties (Kamakura & Wedel, 1995) caused by over 300 questions involved in

2.3 Measurements of Lifestyles in Transport

47

the AIO approach to measuring lifestyles in sociology (Plummer, 1974; Wells & Tigert, 1971). However, from a practical and convenient perspective, it is feasible to construct unified lifestyle measurement dimensions in transport, which requires further research. In addition, the existing lifestyle measurement dimensions in transport mostly focus on individual lifestyle segmentation, with little discussion on measurement of the overall lifestyle. Individual lifestyles emphasise the classification of varying lifestyles, while the overall lifestyle is regarded as an identification label or as a symbol of group identity. This book constructs an index system for individual and overall lifestyle measurement, with a view to providing methodological guidance for future lifestyle measurement in transport.

2.3.2.1

Measurement Index System of Overall Lifestyle

Measuring overall lifestyle mainly includes the identification of a certain group by a certain geographic unit, a cultural attribute or a certain perspective. Overall lifestyle measurement in transport mainly involves the dimensions of consumption, transport facilities, environment, time, activities and values (Fig. 2.5). There are several complex indicators to measure lifestyles. Overall lifestyle is mostly Income consumption level

Consumption Consumption structure

Vehicle

Public transportaion Private cars

Environmental

Transportation facilities Urban spatial organization

Overall lifestyles

Work

Activities

Shopping Leisure and entertainment Sports, etc. Working time

Times

Leisure time Spare time, etc.

Values

Personal preferences Subjective attitudes

Fig. 2.5 Measurement dimensions of overall lifestyle

48

2 Lifestyle and Its Measurements

measured by macro-statistics. In consideration of data availability and the core elements of lifestyle, consumption is mainly measured by core indicators such as income level, consumption structure and transport expenditure; transport facilities are mainly measured by road facilities and transport means, such as car ownership; the environment is mainly measured by indicators such as urban spatial structure, spatial organisation of communities and jobs-housing relationship; time is mainly measured by core indicators such as commuting time, shopping time and leisure time; activities are mainly measured by core indicators such as employment patterns, leisure patterns and frequency of shopping, leisure and sports; values are mainly measured by survey data about social interaction patterns, cultural value orientation and subjective attitudes and preferences. (1)

Consumption

Lifestyle is the embodiment or expression of self-identity, and consumption is a way to maintain such a lifestyle; lifestyle may be defined as unified behavioural patterns that both determine and are determined by consumption (Anderson & Golden, 1984). According to Sobel (1981), lifestyle comprises expressive and observable behaviours, and consumption is the activity that best captures different lifestyles. Giddens (1991) proposed that individuals embrace different consumption patterns not only because they want to meet different utilitarian needs but also because they attribute specific self-identity narratives to material forms. Therefore, the countless choices that everyone faces every day lead to decisions not just about how to act but also who they are. The basic definition of lifestyle should at least refer to how individuals indicate their social position through specific behavioural patterns, mainly in consumption and leisure behaviours (van Acker, 2015). As a catalyst for travel, economic resources are used to overcome the monetary cost of travel and increase the propensity to consume goods and services at the travel destination. The consumption situation can reflect travel time, transport-related activities and the social aspect of travelling with other family members and friends (Julsrud, 2013; Kroesen, 2015; Thøgersen, 2018). Therefore, consumption is an important indicator for lifestyle measurement in transport. (2)

Transport facilities

Transport is a manifestation of lifestyle, and itself is a kind of lifestyle. From a macro-perspective, the development level of different transport facilities, such as aeroplanes, expressways, railways, high-speed railways and tracks, determines the travel efficiency of society and thus affects social lifestyles. Furthermore, the instrumental aspect of travel should be supplemented by another dimension that reflects the habitual behaviour of individual decision-makers (Reichman, 1977). Lifestyle is related to car ownership, and the latter is considered a crucial factor affecting travel behaviour; growing car ownership leads to increased car use (Dieleman et al., 2002; van Acker et al., 2010, 2014) and promotes long journeys (Bagley & Mokhtarian, 2002; van Acker et al., 2014). Therefore, transport facilities are an important indicator for lifestyle measurement.

2.3 Measurements of Lifestyles in Transport

(3)

49

Environment

Transport is about movement in space, which is closely related to urban forms and functions. A large number of studies have emphasised the correlation between urban forms and travel behaviour (Klinger & Lanzendorf, 2016; for reviews see Crane, 2000; Ewing & Cervero, 2001). Urban spatial structure is very important for travel behaviour. Kitamura et al. (1997) analysed the relationship between mobility-related attitudes and urban morphological elements in a micro-study. Their research also pointed out that urban structure affects travel attitudes both directly and indirectly (Lin et al., 2009), emphasising the importance of urban structure to travel behaviour (Næss, 2009). The indirect influence of urban structure is accompanied by differences in budget allocations in areas such as housing or travel related to different lifestyles (Große et al. 2019). Therefore, the environment is an important indicator of overall lifestyle. (4)

Activities

As a type of activities (van Acker et al., 2014), lifestyle in transport mainly includes employment patterns, leisure patterns, shopping patterns, etc. Prices, time and other travel mode information are taken into consideration when choosing a travel mode for these activities (Garvill et al., 2003; Klöckner, 2014). These activities together constitute lifestyle expressions and directly affect transport, so they are an important indicator for lifestyle measurement in transport. (5)

Time

Time control and allocation of individuals or households affect people’s daily lives and the feasibility of life choices. It is a factor that bears on whether households can plan and arrange their daily routine or pace of life. Some individuals or households may have great control over daily activities, while others may have relatively low control over their daily lives. This is consistent with the identification of lifestyle groups based on the time spent on various activities (Salomon & Ben-Akiva, 1983). Lifestyle trends that have contributed to increased feelings of time pressure among adults in many Western societies include longer working hours, longer commutes and increased time spent outside the home (Tranter, 2010). Time spent on travelling is an important indicator for travel analysis, which also affects people’s living arrangements. Therefore, time is an important indicator for lifestyle measurement in transport; and time spent on commuting, time spent on shopping and time spent on leisure are key indicators in this dimension. (6)

Values

Lifestyles are assumed to be shaped by recurrent behavioural responses to socioeconomic conditions, as well as by deeper personal or social attitudes, roles or values (Reichman, 1977). Since the 1990s, sociologists have argued that lifestyle is based on values, attitudes, perceptions, leisure behaviour and consumption (e.g., Bagley & Mokhtarian, 2002; Kitamura et al., 1997; Müller, 1992; Scheiner & Kasper, 2003; Schulze, 1995). Molin et al. (1996) believed that “choices are assumed to reflect

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preferences” (p. 298). Social stratification is distinguished by a horizontal dimension comprised of different attitudes, opinions, tastes and values (Bergman, 1998; Bourdieu, 1984). Vij et al. (2013) pointed out that residents’ preferences regarding travel modes can reflect their lifestyles, and such preferences can affect all travel behaviours and activities of individuals. Furthermore, the instrumental aspect of travel should be supplemented by another dimension that reflects the habitual behaviour of individual decision-makers (Reichman, 1977). This dimension is sometimes referred to as the preference pattern or taste system of individuals or households. Therefore, people’s attitudes or preferences regarding travel (reflecting people’s value orientations) are also an important indicator for lifestyle measurement.

2.3.2.2

Measurement Index System of Individual Lifestyle

Individual lifestyles in transport refer to the classification of individuals into differentiated groups. We can directly set lifestyle types and classify individuals into these types, or we can conduct an exploratory analysis of relevant indicators for individuals to find the commonalities of different groups and to classify individual lifestyle types. Individual lifestyle indexes mainly involve the dimensions of socioeconomic attributes, space, consumption, activities, time and values (Fig. 2.6). Socioeconomic and demographic attributes are considered the main determinants of lifestyles (Ganzeboom, 1988; Kitamura, 1988). Ganzeboom (1988) believed that lifestyles are related to individual socioeconomic characteristics. However, this relationship is susceptible to intermediate variables. These intermediate variables include the opportunities and constraints brought about by time budget, income, cognitive skills (e.g., knowledge) and status considerations (i.e., the influence of the social context and the purpose of obtaining social appreciation). Socioeconomic characteristics mainly include gender, age, the number of children in the household, the total number of family members, education level, per-capita household income and employment (Limtanakool et al., 2006). Lin et al. (2009) believed that lifestyle represents an individual’s orientations or preferences regarding daily decisions about activities and travel, which are usually related to socioeconomic and demographic variables, such as household structure, job involvement and housing type. Lifestyles seem to change systematically with these variables (Havens, 1981). An individual’s age and household affect his or her life cycle. The number and age of children in a household have different impacts on family travel. For instance, extensive empirical evidence shows that the presence of children in a household and their age have a great impact on family members’ travel behaviour (Kitamura, 2009). Allaman et al. (1982) concluded that “such life-cycle effects as having preschool children present, having the youngest child reach school age, and progressing to other points in the life-cycle do indeed prompt changes in time allocation” (p. 2). Transporting schoolage children to and from school, as well as winter and summer holidays, affects households’ number of trips and travel modes. People who are flexibly employed or unemployed have more discretionary time. Car availability can also be regarded as a common lifestyle expression. Research findings show that a particularly active

2.3 Measurements of Lifestyles in Transport

51 Age, gender and family

Socioeconomic attributes

Car ownership Occupation, etc.

spatial location

residential location employment l location,etc.

Consumption

Consumption structure Transport expenditure

Individual lifestyles

Work

Activities

Shopping Leisure and entertainment Sports, etc. Working time

Time

Leisure time Spare time, etc.

Value

Personal preferences Subjective attitudes

Fig. 2.6 Measurement dimensions of individual lifestyles

and sociable lifestyle that combines household activities with leisure and/or social activities is associated with a higher level of car availability (van Acker et al., 2014) and that people with higher social status own more cars (Scheiner, 2010). In addition, some studies show that households with similar socioeconomic attributes may have different travel modes (Mokhtarian & Cao, 2008; van Wee, 2002), so other dimensions are necessary to distinguish them. The space dimension mainly includes the spatial characteristics of residential locations, workplaces or travel destinations. Travel behaviour may be influenced by location decisions of individuals or households, who decide to choose a specific location type that meets their needs and behaviours (Boarnet & Crane, 2001; Scheiner, 2006). Residential location has been related to the lifestyle of households (e.g., Prato et al., 2017; Smith & Olaru, 2013; van Acker et al., 2014; Walker & Li, 2007). The choice of residential locations is a long-term decision for households and has a significant impact on short-term decisions about transport (Scheiner & Holz-Rau, 2007). For example, different residential locations differ in traffic accessibility, and the surrounding transport and public service facilities affect people’s commuting behaviour, shopping, sports and leisure activities. People who live near public facilities tend to use them more often, and the time and economic costs of going to the

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city centre for consumption and gatherings are higher for those living in the suburbs, which also affects people’s lifestyles. The consumption dimension at the individual level mainly refers to the income, consumption structure and transport expenditure of individuals or households. The space dimension at the individual level mainly refers to environmental indicators of residential and other locations such as building density, transport facilities and public service facilities. The activity dimension at the individual level mainly refers to the frequency and preferences regarding activities such as work, shopping, leisure and entertainment and sports. The time dimension at the individual level mainly includes working time, leisure time and spare time. The value dimension at the individual level mainly includes personal travel preferences, styles and subjective attitudes.

2.4 Summary In summary, lifestyle research revolves around some related theoretical categories in sociology, especially the two fields of classes and consumption. Perhaps the most striking feature of the literature on lifestyle is the lack of consensus on the meaning of this term, with at least 30 definitions provided (Veal, 1993). Although the term lifestyle is frequently used in daily life, there is neither an officially agreed definition nor a theoretical and practical system of lifestyle. It is agreed that lifestyle is a comprehensive concept with social beings as the research object, exploring the overall structure and mode of their survival, development and activities; lifestyle is the sum total of human activity forms under certain social and historical conditions, and the product of certain objective conditions combined with subjective conditions. There are some problems with current research, such as the preponderance of theoretical discussions over specific research, qualitative research over combined qualitative and quantitative research and research on individual lifestyles over research on overall lifestyles. In particular, extremely few dynamic studies involving major events have been conducted, and most of them are critical without offering constructive opinions. Since the 1970s, the lifestyle concept has been used in transport research as a variable explaining people’s travel behaviour. However, there is no formally agreed definition of lifestyle because its elaboration is pragmatical rather than theoretical (van Acker, 2015). It is still an open question how to measure travel/activity-related lifestyle (Lin et al., 2009). Current research mainly revolves around lifestyle as behavioural types of activity and time-use patterns or behavioural orientations— values, attitudes and preferences—and potential factors that stimulate behavioural patterns. There are three main lifestyle measurement methods: (1) Using socioeconomic and demographic characteristics; (2) using socioeconomic attributes, demographic attributes and daily behaviour as indicators and (3) using attitudes and cultural indicators, such as values and preferences. Four dimensions of lifestyle are often measured: travel characteristics and frequency, car ownership, the urban environment and consumption. There are four main types of lifestyle segmentation: (1)

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segmentation by socioeconomic differences; (2) segmentation based on the analysis of different population groups, sometimes called the category analysis of travel needs; (3) market segmentation, focusing on special groups in society such as the poor and older people or the needs of different family types; and (4) segmentation by socioeconomic attributes, activities and preferences. This book interprets lifestyle in transport as people’s behavioural expressions and value preferences in transport. Travel-related behavioural expressions mainly include consumption behaviours, housing choices, work, leisure activities, etc., as well as money and time spent on these behaviours and activities. Value preferences mainly include life philosophies, hobbies and preferences. The application of the lifestyle concept in travel demands is essentially a category analysis of travel demands. An index system for individual and overall lifestyle measurement is also constructed. The overall lifestyle indexes mainly involve the dimensions of consumption, transport facilities, environment, time, activities and value preferences, while individual lifestyle indexes mainly involve the dimensions of socioeconomic attributes, space, consumption, activities, time and values. It is expected that this research can serve as a reference for the subsequent research on and application of the lifestyle concept in transport.

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Thøgersen, J. (2018). Transport-related lifestyle and environmentally-friendly travel mode choices: A multi-level approach. Transportation Research Part A: Policy and Practice, 107, 166–186. Tranter, P. J. (2010). Speed kills: The complex links between transport, lack of time and urban health. Journal of Urban Health, 87(2), 155–166. Van Acker, V. (2010). Spatial and social variations in travel behaviour: Incorporating lifestyles and attitudes into travel behaviour-land use interaction research (Unpublished dissertation). Ghent University. Van Acker, V. (2015). Defining, measuring, and using the lifestyle concept in modal choice research. Transportation Research Record, 2495(1), 74–82. Van Acker, V. (2017). Lifestyles and life choices. In A. N. Editor (Ed.), Life-oriented behavioral research for urban policy (pp. 79–96). PUBLISHER. Van Acker, V., Mokhtarian, P. L., & Witlox, F. (2014). Car availability explained by the structural relationships between lifestyles, residential location, and underlying residential and travel attitudes. Transport Policy, 35, 88–99. https://doi.org/10.1016/j.tranpol.2014.05.006 Van Acker, V., Goodwin, P., & Witlox, F. (2016). Key research themes on travel behavior, lifestyle, and sustainable urban mobility. International Journal of Sustainable Transportation, 10(1), 25– 32. https://doi.org/10.1080/15568318.2013.821003. Van Acker, V., van Wee, B., & Witlox, F. (2010). When transport geography meets social psychology: Toward a conceptual model of travel behaviour. Transport Reviews, 30(2), 219–240. Van Raaij, W. F., & Verhallen, T. M. (1994). Domain-specific market segmentation. European Journal of Marketing, VOLUME, PAGE–PAGE. https://doi.org/10.1108/03090569410075786. Van Wee, B. (2002). Land use and transport: Research and policy challenges. Journal of Transport Geography, 10(4), 259–271. https://doi.org/10.1016/S0966-6923(02)00041-8 Veal, A. J. (1993). The concept of lifestyle: A review. Leisure Studies, 12(4), 233–252. Veblen, T. (1899). The theory of the leisure class, Chinese edn. Macmillan.; Veblen, T. (2004). You xian jie ji lun (S. B. Cai, Trans.). Commercial Press. Vij, A., Carrel, A., & Walker, J. L. (2013). Incorporating the influence of latent modal preferences on travel mode choice behavior. Transportation Research Part A: Policy and Practice, 54, 164–178. https://doi.org/10.1016/j.tra.2013.07.008 Vyncke, P. (2002). Lifestyle segmentation: From attitudes, interests and opinions, to values, aesthetic styles, life visions and media preferences. European Journal of Communication, 17(4), 445–463. https://doi.org/10.1177/02673231020170040301 Wachs, M. (1979). Transportation for the elderly: Changing lifestyles, changing needs. University of California Press. Walker, J. L., & Li, J. (2007). Latent lifestyle preferences and household location decisions. Journal of Geographical Systems, 9, 77–101. https://doi.org/10.1007/s10109-006-0030-0 Wang, Y. L. (2013). Contemporary significance of lifestyle studies: Experiences and implications of lifestyle research in the past 30 years in China (in Chinese). Sociological Review of China, 1(1), 22–35. Wang, Y. L. (1995). Review of lifestyle research (in Chinese). Sociological Research, 4, 41–48. Weber, M. (1922). Wirtschaft und Gesellschaft (in German). Mohr. Wells, W. D., & Tigert, D. J. (1971). Activities, interests and opinions. Journal of Advertising Research, 11(4), 27–35. Winters, L. C. (1992). International psychographics. Marketing Research, 4(3), 48. Wu, H. W. (2002). Preliminary study on life style index system. Journal of Shandong Normal University (humanities and Social Sciences Edition), 5, 80–83. Xue, P. (2010). A comparative analysis of different social economic status group living styles— Beijing, Shanghai, Guangzhou and Chengdu city residents as an example (in Chinese). Journal of Hubei Administration Institute, 3, 68–72. https://doi.org/10.3969/j.issn.1671-7155.2010.03.014 Zablocki, B. D., & Kanter, R. M. (1976). The differentiation of life-styles. Annual Review of Sociology, 2(1), 269–298. https://doi.org/10.1146/annurev.so.02.080176.001413 Zhu, Y. T. (2000). Comment on the secularization trend of lifestyle and values. In Symposium on Social Transformation and Values (pp. 46–49).

Chapter 3

Links Between Lifestyle and Transport

3.1 Theoretical Perspectives on Links Between Lifestyle and Transport Van Acker et al. (2010) developed a conceptual model of travel behaviour using key variables in transportation geography (such as time geography and activity-based methods) and social psychology (such as planned behaviour theory and repetitive behaviour theory). Krueger et al. (2016) integrated the key elements and social psychological methods of travel behaviour, and they proposed a comprehensive conceptual framework to understand the relationships between normative concepts, modal styles and travel behaviour. Zhang and van Acker (2017) proposed a consistently oriented approach, suggesting a life-oriented approach to positioning travel behaviour research. Anas et al. (2021) put forward a perspective of complexity and travel behaviour. Their perspective of complexity provides methods including time and space analysis, psychological mechanisms, lifestyles and mobility biographies (Krueger et al., 2016; Zhang & van Acker, 2017), but few people associate activities with transportation. In general, the existing literature is not clear about the interaction mechanism between lifestyle and travel behaviour. The process and determinants of travel decision-making are relatively complex, involving geography, economics and psychology. Singleton (2013) comprehensively reviewed 16 theories of transportation, and López and Wong (2019) reviewed the research involved in travel behaviour. They examined 29 theories/frameworks and introduced a holistic and dynamic mobile decision process and determinants framework. Jensen (2009) proposed six major mechanisms of lifestyle using the six elements of cognitive science influencing lifestyle: concreteness, closeness, habit, attraction, perceived risk and abstract risk. The current research on lifestyle and transport theory mechanism mainly focuses on the discussion of individual lifestyles and the field of travel, and there is a lack of research on the theoretical relationship between overall lifestyle and transportation. Due to the complex interactive relationship between lifestyle and transport, their common theoretical basis is value theory, TPB, consumer behaviour theory and space-time prism theory. The impact of lifestyle on transportation mainly involves © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. Zhao and D. Lyu, Lifestyle Change and Transport in China, Population, Regional Development and Transport, https://doi.org/10.1007/978-981-19-4399-7_3

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supply and demand theory; the impact of transportation on lifestyle mainly includes space choice theory and space-time compression theory (Fig. 3.1).

3.1.1 The Common Theoretical Basis of Lifestyle and Transport 3.1.1.1

Value Theory

In social psychology, Schwartz (1992) put forward the theory of personal values, which assumes that individuals have a set of values that shape their lives and establish their preference model. Schwartz identified four basic issues: the substantive contents of human values; identification of comprehensive sets of values; extent to which the meaning of particular values was equivalent for different groups of people; and how the relationships among different values were structured. The theory of basic human values (Schwartz, 1992) specifies ten broad values that are ordered in a circular motivational structure (Schwartz, 2012a, 2012b). The value theory is used to explain behaviour—attitudes, beliefs, norms and traits. The actions taken by individuals are based on the concept of value, and personal values also guide people’s behaviour (Bardi & Schwartz, 2003). Value theory involves social psychology, which is the application of theoretical explanations in travel behaviour. These psychological mechanisms provide clues on people’s subjective characteristics, such as attitudes, social norms, perceived behaviour control, expected emotions, desires and goals and personal behaviour (van Acker et al., 2010). They may determine people’s lifestyle choice and affect people’s travel behaviour.

3.1.1.2

Theory of Planned Behaviour (TPB)

The TPB (Ajzen, 1991) is the most influential theoretical model for predicting human social behaviour (Ajzen, 2011). TPB argues that behaviour is the result of performing a particular action with behavioural intent, in which behavioural attitudes, subjective norms and perceived behaviours control function. Adjacent elements of a particular behaviour are the intention to perform or act (Ajzen, 1985, 1991). Ajzen and Fishbein (2005) think that general attitudes towards physical objects; racial, ethnic or other groups; institutions; policies; events; or other general targets and the attitudes towards performing specific behaviours with respect to an object or target influence behaviour. Behavioural beliefs, normative beliefs and perceived behavioural control are three core anchors that influence an individual’s behavioural intentions. Social norms are another possible behavioural determinant (EtminaniGhasrodashti & Ardeshiri, 2015). Lifestyle reflects value orientation and behavioural attitude, which is a behavioural pattern for people to meet their own needs in life under the guidance of social values. It reflects how consumers use their time and money.

Impacts of transport on lifestyles Time-space compression theory

Consumer behaviour theory

Spatial selection theory

Impacts of lifestyles on consumer economics

Impacts of lifestyles on travel behavior

Time-Space Prism theory

Consumer behaviour theory

Fig. 3.1 The main theoretical basis for the links between lifestyle and transport

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Lifestyle is the expression of value orientation and subjective preference, and it is also a behaviour pattern. TPB is widely used by scholars in various fields, and recent research has successfully used TPB to explain how people create behavioural intentions (Ahmad et al., 2020; Han et al., 2017; Ye et al., 2017). In travel behaviour analysis, TPB support has been provided in various empirical applications, such as transport mode selection (Bamberg & Schmidt., 2003; Donald et al., 2014; Fu, 2021; Haustein & Hunecke, 2007; Krueger et al., 2016) and travellers’ behavioural intention of travelling in the period of the coronavirus (Hamid & Bano, 2021).

3.1.1.3

Theory of the Time-Space Prism

The space-time prism is one of the important concepts in the theory of time and geography proposed by Hägerstrand (1970). It focuses on the possibility of human behaviour under the constraints of time and space. The theory assumes that the materialisation of people’s activities and travel is the result of complex interdependence and/or interconnection among needs, constraints and resources. Such a space-time prism can easily construct a person. However, it will become more difficult when several people must analyse the activity patterns. Hägerstrand and most researchers of time geography focus on the constraints of the spatial path and the prism that affect the amount of time available for activity (van Acker, 2015). However, the original time-geographic framework focused mainly on human activities in physical space without accommodating virtual space in the framework (Yu & Shaw, 2007). In addition, the classical space-time prism admits only deterministic travel speeds and ignores the stochastic nature of travel environments (Chen et al., 2013). Lifestyle is a comprehensive representation of people’s various behaviours under the constraints of time and space. Travel is a human behaviour, and various human behaviours restrict each other, so this theory has become one of the theoretical foundations for lifestyle and transport.

3.1.1.4

Consumer Behaviour Theory

According to Taylor (1974), Raymond Bauer first formally proposed that consumer behaviour be viewed as risk-taking in 1960. Consumer behaviour is used to study the characteristics of psychological activities and behavioural laws of consumers in the process of acquiring, using, consuming and disposing of products and services. The central problem of consumer behaviour is choice (Taylor, 1974). Consumer behaviour focuses on exploring the factors that influence consumer decision-making. When discussing the theory of consumer behaviour research, Vakratsas and Ambler (1999) summarised five models used to explain consumer behaviour: the market response model, the cognitive information model, the pure affective model, the persuasive hierarchy and the low-involvement hierarchy. Most models related to consumer behaviour

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include the interaction of emotion and cognition. Another important research direction for consumer behaviour is consumption motivation (Folkes, 1988). Representative models include the hierarchical model of consumer goals (Pieters et al., 1995), the consumption motivation model (Ratneshwar et al., 2003), the means-end chain model (Claeys & Abeele, 2001), etc. Consumption is an important representation of lifestyle, and the theory of consumption behaviour explains people’s consumption behaviour. In theory, travel is considered an intermediate commodity, and demand for it arises from the demand for activities performed at the travel destination. In a broader sense, this function of transportation is called the means of travel. The activities of travel should be related to a series of actual needs or requirements of families who need to move between locations in the real world. The aspect of transportation has been widely used in the methodology of travel research, partly because it has a clear connection with the assumptions of consumer behaviour theory (Reichman, 1977).

3.1.2 Main Theoretical Basis of Lifestyle Impact on Transport According to transportation supply and demand theory, transport needs are largely derived from other activities and goals people strive for, including those related to family, work and leisure (Oakil et al., 2014; Thøgersen, 2018), and people’s other activities which also constitute a way of life. Lifestyle and life course events related to housing, education and employment are highly interdependent, and they trigger changes in commuting (Zhang & van Acker, 2017) and inter-city travel. This has an impact on transport demand at the level of urban and regional traffic. The travel demand generated by people’s different lifestyles is the basis for the supply of transport facilities, and changes in people’s travel mode directly determine the generation, mode and distribution of traffic demand. The spatial distribution of different lifestyles determines the spatial and temporal distribution of population travel and the spatial layout of transport facilities. It also fundamentally determines the generation, mode and temporal and spatial distribution characteristics of traffic travel, which has a critical impact on the transport system. Temporal and spatial changes in population travel will bring changes in the traffic state of the road network. People’s travel preferences affect the mode, quality and conditions of transport facilities (e.g., Wojahn, 2002). The theory of transport supply and demand equilibrium is derived from the relationship between supply and demand, or the theory of the relationship between transport supply and demand. Transport demand refers to the demand for road transport facilities and public transport caused by social and economic activities (Wang et al., 2020). There are basic and derivative transport demands. Depending on the existence and realisation of transport demand at a certain time, both include actual and potential demand. Transport supply is a large and complex system. In a broad

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sense, in addition to transport facilities and networks, it should also include all aspects of transport land, transport facilities, transport management, traffic laws, transport costs, transport operations, transport environment, etc., which will affect transport demand services. For example, when choosing transport, many people think that their transport mode (biking, driving), especially their vehicles (particularly cars) is an important means of expressing their personal identity (Dalpian et al., 2015; Stradling, 2002; Thøgersen, 2018). There is an interaction between transport demand and supply. Transport supply can restrain and stimulate traffic demand. The restriction and stimulation of transport supply on traffic demand are mainly reflected in the quantity and type of transport demand. A shortage of traffic demand can restrain supply growth, and excess traffic demand will stimulate supply. The restriction and stimulation of travel demand on transport supply are mainly reflected in the quantity, quality and structure of transport supply.

3.1.3 Main Theoretical Basis of Transport Impact on Lifestyle Transport involves different aspects of the transportation system, such as means of transportation, transportation facilities, transportation operations and transportation management. Lifestyle also involves people’s consumption, activities, time utilisation, value preferences, etc. Transportation is a way of life. The main theories related to the influence of transport cover the influence of transport on people’s travel behaviour and the choice of residence and employment. The theoretical basis is the theory of space choice; the main influence of travel behaviour on consumption lifestyles is the theory of consumer behaviour; the influence of traffic on the way people spend time is mainly covered by the theory of space-time compression.

3.1.3.1

Spatial Choice Theory

Spatial choice theory is guided by spatial science and location theory. Transport affects the accessibility of different areas, thus affecting the location of individuals and enterprises and affecting people’s spatial choices. Spatial choice behaviour is related to the theories of economic utility, preference and attitude of choice. In the decision-making process, individuals will evaluate environmental characteristics according to particular standards and attach subjective utility to the attributes of environmental characteristics. These effects are combined according to rules to form an overall utility or preference structure. Under the strict utility theory, each function is unique, and the preference structure determines the choice (Luce, 1958). Timmermans and Golledge (1990a, 1990b) praised the theory of spatial preference and choice. The spatial choice model was first proposed by the economist Harold Hotelling (Brown, 1989). The spatial choice discrete choice model (Ben Akiva et al.,

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1985; Fischer & Nijkamp, 1985; Longley, 1984) has been applied to a wide range of spatial phenomena, including residential choice, shopping behaviour and travel analysis. The improvement and promotion of transportation will increase the population along the transportation line, increase the population scale and density, affect population migration and spatial distribution and thus have an impact on the lifestyle of the population. At the same time, with the emergence of modern and convenient transportation facilities, especially rapid transportation such as high-speed rail, and the reduction of transportation costs, the rapid expansion of urban space has accelerated the spatial dispersion and reorganisation of the population. This has an impact on people’s travel, consumption, leisure, value preferences and people’s spatial choice of residence and employment. These help to shape and change people’s lifestyles.

3.1.3.2

Time-Space Convergence Theory

The time-space convergence phenomenon produced by transportation technology has long been the focus of scholars. Geographer Janelle (1966, 1968) coined the term time-space convergence. The innovation in transportation technology has improved the running speed and shortened the travel time between cities, so the original spatial distance seems to be compressed (Janelle, 1968) This change has caused a cultural shift in the way people experience space and time, that is, a new round of spacetime compression has taken place (Harvey, 1999). Time-space compression broadly conceived involves the multitude of ways in which human beings have attempted to conquer space, to cross distances more rapidly and to exchange goods and information more efficiently (Warf, 2008). With the continuous progress of transportation technology and communication technology, space has been compressed, and space is no longer an obstacle to people’s production and life. Due to the compression of space, the production and life cycle are accelerated, and time is accelerating, resulting in space-time compression. With the change in modern transportation technology and the popularisation of information and network technology, the time and relative distance required for interpersonal communication in a certain area will be shortened with the progress of transportation and communication technology, resulting in the phenomenon of timespace convergence. Accordingly, space-time compression theory has been applied in human geography (Dodgshon, 1999; Rey & Janikas, 2005; Warf, 2008, 2018). Economic globalisation (Agnew, 2001) and other aspects have been further developed. A group of scholars has discussed the impact of high-speed rail (Janelle, 2014; Jiao et al., 2014; Spiekermann & Wegener, 1994; Yao et al., 2020) and aircraft (Goetz, 2017; Simonsen, 2005) on space-time compression. With the development of transportation and communication technology, people generally obtain different social space and time experiences. These new experiences and changes have changed and reshaped people’s way of life.

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3.2 Characteristics of the Links Between Lifestyles and Transport The relationship between lifestyles and transport has four major characteristics: reciprocal causation, mutual subordination, multiple levels and synchronous development.

3.2.1 Reciprocal Causation Between Lifestyles and Transport There is a reciprocal causation between lifestyles and transport. Lifestyle is the choice of various behavioural patterns such as housing, employment and leisure in everyday life, and transport supports people’s various behaviours. Lifestyles affect transport, and transport also guides and supports lifestyles. The two are interconnected. Lifestyles affect people’s travel behaviour, which in turn influences urban transport (Anable, 2005). The academic circle has attempted to conduct more targeted lifestyle research on transport planning and travel activities, to define domainspecific and transport-related lifestyles (Thøgersen, 2018), and to identify such lifestyles by survey-based methods (e.g., Krizek & Waddell, 2002; Lanzendorf, 2002; Lee & Sparks, 2007; Lin et al., 2009). People’s residential location choices (Pinjari et al., 2007) and their lifestyles and activities normally generate transport demands (Bin & Dowlatabadi, 2005; Zhao & Zhang, 2018). Many studies have confirmed that lifestyles affect people’s travel behaviour. In addition, socioeconomic characteristics and residential location choices that affect lifestyles have a direct bearing on transport. Lifestyles cannot be directly measured; instead, they can be inferred through these variables. Lifestyles, as a latent variable, can exert an intermediate effect on travel behaviour. Ganzeboom (1988) believed that an individual’s socioeconomic and demographic characteristics will affect his or her lifestyle. For example, a household with grown-up children may be more involved in outdoor activities than a household with underage children. However, it can also be the other way round: lifestyles may affect socioeconomic and demographic characteristics, such as life stage or household composition. For example, a family-oriented lifestyle may lead to a larger household size than a work-oriented lifestyle. A similar duality is observed in the relationship between attitudes and behaviours (van Acker et al., 2014). This contributes to a complex causal relationship between lifestyles and transport.

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3.2.2 Mutual Subordination of Lifestyles and Transport Lifestyles and transport are subordinate to each other, which means transport itself may be a kind of lifestyle. Travel is generally considered a derived demand. Theoretically, travel is considered an intermediate good, for which the demand is derived from the demand for the activity performed at the trip destination (Reichman, 1977). However, the use of transport means must be understood in the context of the choice and the symbols in the social space of lifestyles. Frequent trips are an activity. Reaching the destination is not necessarily the only purpose of travel (Lanzendorf, 2002), and there may be an inherent motivation for travel. Travelling itself can be a pleasure or a source of relaxation, and sometimes people may travel just for entertainment (e.g., Mokhtarian & Salomon, 2001; Mokhtarian et al., 2001). Travelling is a daily activity and a symbol of individual lifestyles. Vij et al. (2013) pointed out that residents’ preferences regarding travel modes can reflect their lifestyles, and these preferences can affect all travel behaviours and activities of individuals. Lifestyles can describe individuals in a more comprehensive context than socioeconomic characteristics such as age, household structure, income and cars. Travel activities should correspond to a series of tangible needs or requirements of individuals or households. Therefore, lifestyles have behavioural expressions, and travel behaviour is also a lifestyle expression (van Acker et al., 2016) and a concrete manifestation of lifestyles. People’s varied activities, such as shopping, sports, attending school and working, travel frequency and duration, activity distribution, selection of travel modes and choice of departure time influence each other and together form different lifestyle clusters. For example, sports activities and active travel behaviour (e.g., walking and cycling) are part of a healthy lifestyle (Kroesen, 2019).

3.2.3 Multilevel Nature of Lifestyles and Transport The relationship between lifestyles and transport can be understood on multiple levels, including the group level and the individual level. Current lifestyle research in transport tends to focus on individual lifestyles while neglecting the overall lifestyle. Also, most studies are about the application of the lifestyle concept in travel, while other aspects of the transport system are rarely discussed. Jensen (2007) suggested understanding lifestyle on four levels: (1) the global level, (2) the structural or national level, (3) the positional or subcultural level and (4) the individual level. The first three levels can all be classified into the group level, that is, the overall lifestyle; and the individual level refers to individual lifestyles. Research on the overall lifestyle takes the whole as the object of study, namely a certain group of people, such as the people in a country, region or community, or groups with specific cultural attributes or preferences (e.g., environmentalists or intellectuals). Research on individual lifestyles takes individuals as the object of study and classifies people into different categories, such as lifestyle segmentation for the population in a certain area. The overall lifestyle

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is based on individual lifestyles. Transport is a systematic concept. In addition to travel, it includes various transport facilities, such as roads, waterways and aviation and passenger and freight volumes. Lifestyles affect different aspects of transport, not only in travel. For instance, the online shopping lifestyle can affect the daily freight volume and the logistics system in a city through consumption and shopping. Lifestyles and transport influence each other in terms of intra-city, inter-city and interregional transport facilities, traffic operations and traffic management, which is often neglected in current research.

3.2.4 Synchronous Development of Lifestyles and Transport Lifestyles and transport feature synchronous development. Lifestyles change with the social economy, systems and technologies (Kitamura, 2009). Increased income, reduced working hours and new technologies all contribute to the continuous evolution of lifestyles. Changes in life may not be independent of each other. Lifestyle changes are realised through changes in values, attitudes and preferences. According to Pineda-Jaramillo (2021), lifestyles may also evolve over time, affecting people’s travel modes within a specific timeframe. The travel mode decision within a specific timeframe includes short-term, medium-term and long-term variables of an individual. In the agricultural society where people travelled either by carriage or on foot, the lifestyle was relatively isolated. In the industrial society, people’s sphere of activities was expanded thanks to trains, cars and aeroplanes and their lifestyles became increasingly diversified. In modern society, with the development of rapid transit such as high-speed rail, the pace of life has accelerated and new lifestyles such as intercity travel have emerged. In the future, with the emergence of new technologies such as autonomous driving and aerial vehicles, lifestyles will undergo new changes.

3.3 The Conceptual Framework of the Links Between Lifestyle and Transport 3.3.1 Current Theoretical Model The theoretical model connecting lifestyle and transport currently focus on four areas: (1) travel modelling of lifestyle as an activity type, (2) lifestyle as a value orientation modelling, (3) life-oriented theoretical models and (4) specific lifestyle theoretical models. Nevertheless, most of the model frameworks only discuss the relationship between lifestyle and travel behaviour, and the frameworks in other fields of lifestyle and transport need to be expanded.

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3.3.1.1

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Conceptual Framework of Lifestyle as Behavioural Activities

Salomon and Ben-Akiva (1983) first applied lifestyle to travel models (Fan & Khattak, 2012). They defined a lifestyle as a pattern of behaviour that conforms to the individual’s orientation towards the three major roles of household member, worker and consumer of leisure, and that conforms to the constrained resources available. Thus, lifestyle can be defined as an extension of the hierarchical choice structure of mobility and travel (Salomon & Ben-Akiva, 1983). This model includes three blocks (Fig. 3.2): lifestyle choice, mobility choices and activity and travel choices (nonwork). The first block is the lifestyle choice block, including family formation, participation in the labour force and orientation

Ⅰ. Life-style choice Family formation Participation in labor force Orientation toward leisure

Ⅱ Mobility choices Employment location Residential location Housing type Automobile ownership Mode to work

Ⅲ Activity and travel choices (nonwork) Activity type Activity duration Destination Route Mode

Fig. 3.2 Extended choice hierarchy. Source Salomon and Ben-Akiva (1983), edited by the author. Note The dotted box corresponds to the social, cultural and political environment (Salomon & Ben-Akiva, 1983)

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towards leisure. The mobility choices block mainly includes long-term decisions, such as residential location, employment location, car ownership and mode of travel to work. The activity and travel choices block include short-term nonwork travel decisions or activity patterns, such as activity duration, destination, route and mode. It is the outcome of this joint choice that is the motivation for mobility and travel. The conceptual framework was validated using the Federal Highway Administration’s travel demand survey data set for 521 families in Baltimore in 1977. Socioeconomic and demographic variables were used to define lifestyle. The author verified the hypothesis of identifying lifestyle groups and tested the hypothesis that different lifestyle groups have different behaviours or preferences. This model treats lifestyle as a set of long-term choices and an explanatory factor in travel behaviour, and it treats daily activities as short-term expressions of lifestyle. Scheiner and Holz-Rau (2007) derived six model structures to describe the interrelations between life situation, lifestyle, location requirements, location choice and travel behaviour (Fig. 3.3). The authors believed that lifestyles include behaviours (e.g., leisure, consumption and social networks). Lifestyles are dependent on social and demographic structures, in other words, on life situations (Schneider & Spellerberg, 1999). Lifestyles influence spatial mobility. Lifestyle-oriented mobility research usually treats lifestyle types as independent variables and therefore as autonomously emerging styles. Scheiner and Holz-Rau (2007) derived the structures shown in Models 1 and 2, and they attempted to reveal the interdependence between residential mobility and travel behaviour through Model 3. Location attitudes are used as further intervening variables between life situation and lifestyle on the one hand and location choice on the other hand (Model 4). Model 4 includes life situation and location choice as objective explanatory variables of travel behaviour, as well as lifestyle and location attitudes as subjective explanatory variables. The actual location decision can be described by location attributes. This means that the impact of these attributes on travel behaviour can be interpreted either as an impact of the urban form or as an impact of certain location behaviour that reflects subjective location attitudes. These two interpretations can be separated in the model. Whenever attitudes are critical, the location choice will definitely reflect them. In addition, travel behaviour may be strongly influenced by location attitudes. When the urban form is of vital importance, travel behaviour will be influenced by (objective) location attributes rather than (subjective) location attitudes. The attribute of space structure (workplace density) is modified by the attribute of individuals’ activity space. The authors further integrated car availability (Model 5), which, like location choice, is an important predetermination for travel behaviour. The last step (Model 6) is to explore the direct impacts of life situation, lifestyle and car availability on location choice since it cannot be assumed that location choice is entirely determined by individuals’ location attitudes. The authors used interview questionnaires for 2,691 inhabitants in the region of Cologne in 2002 and 2003 for verification. Van Acker et al. (2010) considered that travel behaviour originates from shortterm activity decisions, medium-term location decisions and long-term lifestyle decisions. This decision hierarchy is inspired by the activity-based method and the principles of lifestyle theory. By considering the derivative nature of travel behaviour,

3.3 The Conceptual Framework of the Links …

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Model 2 Lifestyle

Lifestyle

Life situation

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Model 3 Life situation

Model 4 Lifestyle

Life situation

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Model 5 Life situation

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Life situation

Lifestyle

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Fig. 3.3 Model structures to describe the interrelations between life situation, lifestyle, location requirements, location choice and travel behaviour. Source Scheiner and Holz-Rau (2007), edited by the author

behaviour insights into travel patterns are obtained, which are lacking in the previously commonly used travel-based models. In addition, behavioural decisions are often the result of the evaluation between rational and irrational effects, and the relationship between lifestyle and irrational effects is also considered. The authors combined and linked theories stemming from transport geography (e.g., time geography, the activity-based approach) and social psychology (e.g., TPB, theory of repeated behaviour). Using key variables from these theories, they developed a conceptual model for travel behaviour (Fig. 3.4). This conceptual model considers travel behaviour as derived from locational behaviour and activity behaviour, and it

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REASONED INFLUENCES

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Individual opportunities and constraints Social opportunities and constraints

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individual social environment spatial environment

Fig. 3.4 A conceptual model of travel behaviour. Source van Acker et al. (2010), edited by the author

adds concepts such as lifestyle, perceptions, attitudes and preferences, which indirectly influence travel behaviour. The conceptual model described above focuses on the travel behaviour of the individual. However, the individual belongs to a social network of family, friends and colleagues and lives within a particular neighbourhood, which can affect the individual’s behaviour. So finally, the model as a whole should be placed (1) on an individual level, (2) in the social environment and (3) in the spatial environment. The dotted arrows in Fig. 3.4 refer to feedback mechanisms: individuals can learn from previous experiences. Consequently, lifestyles, habits, perceptions, attitudes and preferences are not fixed in time. This model is based on behavioural and psychological perspectives. It puts daily travel behaviours in a decision hierarchy consisting of short-term, medium-term and long-term decisions, and it evaluates the reasoned and unreasoned decision outcomes of these behaviours, taking into consideration the restrictions imposed by social and spatial environments on individual decisions and behaviours. However, the authors did not verify this theoretical model framework with living examples. Etminani-Ghasrodashti and Ardeshiri (2015) chose activities including various economic and cultural conditions to measure lifestyles in Iran. They took travel behaviour as non-work travel amounts in three different modes (car, public transport and walking/bicycling) to contribute to lifestyles and the built environment

3.3 The Conceptual Framework of the Links …

73

Socio-economic attributes

Individual attitudes

Built environment

Lifestyle

Travel behavior

Direct effects of key variables on travel behaviour Interrelations between lifestyle and other variables Feedback procedures not modeled

Fig. 3.5 Conceptual model of travel behaviour. Source Etminani-Ghasrodashti and Ardeshiri (2015), edited by the author

while taking into consideration socioeconomic characteristics and individual attitudes towards travel and residential neighbourhoods. Figure 3.5 visualises how key variables and other factors explain travel behaviour. Considering Iran’s special context, socioeconomic attributes are regarded as the factor that influences travel behaviour in the decision hierarchy of travellers. It is assumed that individuals’ socioeconomic attributes affect their attitudes towards travel and residential neighbourhoods and that individual attitudes influence their lifestyles. In their conceptual model, socioeconomic attributes also affect travel behaviour through the built environment directly or indirectly. In addition, the built environment that affects travel behaviour also produces an effect on individual lifestyles. It can also be the other way round, but their study does not model this.

3.3.1.2

Conceptual Framework of Lifestyle as Value Preferences

Relevant models of value preferences include attitudes, preferences, value orientations, model styles and other models related to lifestyle. Vij et al. (2013) defined a lifestyle as a behavioural tendency or morphological style, which refers to a certain

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3 Links Between Lifestyle and Transport

Characteristics of the Individual and Household

Long-term decisions

gender

income

employment

level of car ownership

individual modality style

transit season pass

travel time

household size

utility of travel mode for work tour

marital status

parenthood status

disturbances

utility of travel mode for work tour

disturbances

travel time

disturbances choice of travel mode for work tour

choice of travel mode for work tour

Fig. 3.6 Model framework. Source Vij et al. (2013), edited by the author

travel mode or a group of personal habitual travel modes. Based on this, the developed model framework (Fig. 3.6) captures the impact of morphological style on two dimensions of personal behaviour: travel mode choice for work tours and travel mode choice for non-work tours (p. 164). This model framework gropes for the influence exerted by a single overarching modality style on travel mode choice behaviour over multiple work and non-work tours over an extended period of time. The framework was tested using travel diary data collected over an observation period of 6 weeks from 117 individuals residing in Karlsruhe, Germany (Vij et al., 2013). Krueger et al. (2016) proposed an integrated conceptual framework, combining lifestyle-oriented and socio-psychological approaches in travel behaviour analysis to explain the formation of modality styles (Fig. 3.7). They studied the interrelation of normative beliefs and modality styles. Normative beliefs are an individual’s perception of the beliefs of others regarding a specific behaviour, and the modality styles represent the part of an individual’s lifestyle that is characterised by the use of a certain set of modes. The core element of this framework is individuals’ modality styles, which are identified through latent class segmentation. The engagement in a particular modality style is a function of sociodemographic characteristics, ecological normative beliefs and subjective normative beliefs towards the use of different modes. Each modality style is described by probabilities for different values of mobility attributes. The engagement in a certain modality style is consistent with the prevalence of a specific habitual mindset. Each modality style is also characterised by latent attitudes towards the use of transport modes, including public transport, bicycling and walking. Modeuse frequencies, mobility attributes and mode-specific habit strengths and attitudes

3.3 The Conceptual Framework of the Links …

75

Latent normative belief submodel mode-specific and ecological normative beliefs

indicators of modal and ecological normative beliefs

mode-use frequencies

modal habit strengths (Verplanken et al.1994) socio-demographics

modality styles mobility attributes Latent modality styles submodel

latent variable

mode-specific attitudes (Haustein 2012).

observed variable

Class-specific submodels

Fig. 3.7 Conceptual model framework. Source Krueger et al. (2016), edited by the author

depend on the individual’s modality style. This model framework is applied to a sample of 516 residents in Australia’s major metropolitan areas, and a latent class and latent variable model is used to demonstrate how to understand the relationship between normative beliefs, modality styles and travel behaviour with this framework. Thøgersen (2018) used Grunert’s (2006)food-related lifestyle model for reference and proposed the transport-related lifestyle model from a consumption perspective. Based on Grunert’s (1993) definition of lifestyle as “categories, scripts, and their associations, which relate a set of products to a set of values” (p. 13), Thøgersen constructed a transport-related lifestyle model. This model has three levels. At the top of the model are general values and goals, followed by purchase motives and quality aspects, which play a key role in consumers’ meaning-producing narratives related to transport. At the bottom of the model are three broad cognitive scripts the system of cognitive learned through and guiding social practices related to transport: ways of shopping, travel and transport routines and consumption situations. Some of these narratives and social practices are related to long-term decisions, while others are related to more short-term decisions. Together, these five components are assumed to capture the defining characteristics of an individual’s transportrelated lifestyle, as Fig. 3.8 shows. Thøgersen (2018) presented a first application of the instrument for identifying national and cross-national transport-related lifestyle segments based on a survey (N = 3,216) in ten European countries. This model provides an exploratory analysis of transport-related lifestyles from the perspective of consumption.

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3 Links Between Lifestyle and Transport

General values and goals

Transport-related Lifestyle

Quality aspects

Purchasing motives

Travel and Transport routines

Consumption situations

Ways of shopping

Perceptions

Behavior

Fig. 3.8 The transport-related lifestyle model. Source Thøgersen (2018), edited by the author

3.3.1.3

Conceptual Framework of the Life-Oriented Approach

Zhang (2014) and Zhang and van Acker (2017) formally proposed a life-oriented approach, which focuses on the interdependence between life choices and travel behaviour when analysing travel behaviour. It is believed that travel behaviour results from different life choices and at the same time affects people’s life choices. It is suggested as a paradigm shift. This is illustrated in Fig. 3.9, which has three axes representing theory, practice and policy. The vertical axis represents the theory ranging from the lower very specific travel behaviour theories to the upper more general life choice theories. The horizontal axis represents the practice ranging from Theory Inter-discipline

Public policy

Life choices Life-oriented approach

Hierarchical scheme

Transport policy

Integrated scheme

Cross-sector

Tour-based approach Trip chaining approach Trip-based approach

Single sector

Activity-based approach

Urban policy

Practice

Travel behavior Single discipline

Fig. 3.9 The life-oriented approach and transport/urban/public policies. Source Zhang and van Acker (2017), edited by the author

3.3 The Conceptual Framework of the Links …

77

single sector practices on the left to cross-sector practices on the right. Finally, policy is represented by a diagonal axis ranging from the bottom-left transport policy to the upper-right general and integrated public policy, where urban policy is positioned in the middle. Life choices and travel behaviour influence each other in both the short and the long terms. Findings from such life-oriented travel behaviour research should also be translated into behavioural insights and integrated into travel demand forecasting models (Zhang & van Acker, 2017). Van Acker et al. (2014) proposed a conceptual framework that analyses land use effects on car ownership and accounts for attitudinal influences that are fundamental to the complex relationships between lifestyles, residential location choices and car ownership. Various studies argue that car ownership is significantly influenced by land use patterns (see Arrow 1 in Fig. 3.10). Furthermore, medium-term decisions about car ownership and residential location are in turn influenced by longterm lifestyle decisions (see Arrow 3 in Fig. 3.10). The dashed arrows in Fig. 3.10

Socio-economic and demographic variables

Lifestyle 3

3

3

Land use 1

2

Attitudes

Car ownership

1

direct land use effects on car ownership

2

influence of attitudes fundamental to behaviour

3

influence of long-term lifestyle decisions feedback mechanisms modelled feedback mechanisms not modelled

Fig. 3.10 Conceptual model of car ownership (Van Acker et al., 2014, edited by the author)

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3 Links Between Lifestyle and Transport

represent feedback mechanisms. The socioeconomic and demographic characteristics of an individual and the individual’s lifestyle influence each other. A similar dual relationship exists between attitudes and behaviour. The authors modelled the feedback mechanisms between lifestyles, socioeconomic and demographic characteristics, attitudes and behaviour. Due to the complexity of the recognition model, van Acker et al. did not take into consideration feedback loops between different time ranges. Only cross-sectional data were available. The authors used data from an internet survey completed by 1,800+ respondents in Flanders, Belgium to verify this model.

3.3.1.4

Conceptual Framework of Specific Lifestyles

With data from the 2014 Puget Sound household travel survey, Bhat et al. (2016) studied two basic lifestyle-related factors—green lifestyle propensity (GLP) and luxury lifestyle propensity (LLP)—to explain multiple mixed dependent variables (Fig. 3.11). These two latent and random psychosocial constructs influence dependent variables and produce covariance among them. A joint mixed model was introduced, which includes a minimum detectable change outcome and a nominal discrete outcome, as well as the count, binary/ordinal outcomes and continuous outcomes. By specifying latent unobserved individual lifestyles, personality and attitudinal factors, these outcomes were modelled together. Figure 3.11 shows the impacts of GLP and LLP constructs on residential density, commute distance and auto ownership endogenous outcomes (but not on the activity time-use variable, because this is a multiple discrete variables in which the latent constructs have varying effects on different activities).

+ Green Lifestyle Propensity (GLP)

Luxury Lifestyle Propensity (LLP)

-

Residential Density

Commute Distance

Auto Ownership

Activity Time-Use

+

Fig. 3.11 Effects of latent constructs and endogenous effects. Source Bhat et al. (2016), edited by the author

3.3 The Conceptual Framework of the Links …

79

According to Lee and Circella (2019), ICT lifestyle choices affect domain-specific travel behaviours and mobility choices directly (e.g., the use of online social network sites directly generates/removes physical trips) or indirectly (e.g., ICT-oriented lifestyles spur individuals to try new mobility services). On this basis, they proposed a conceptual framework for analysing the relationship between ICT lifestyles and travel, which specifies the relationships between socioeconomics/demographics, built environment attributes, attitudes, indicators and ICT lifestyles in the study. ICT use patterns are measured by indicators that record the frequency of individuals’ use of various ICT applications. Suppose there is a latent (i.e., unobservable) categorical variable explaining individuals who choose certain ICT use patterns. Socioeconomics and demographics, the built environment, attitudes and other characteristics account for individuals choosing certain ICT-related lifestyles. In Fig. 3.12, travel mode choice refers to the travel frequency for various mode–purpose combinations. The authors investigated adults (18 years or above, N = 3,631) in California in 2018 and examined them by computing their correlations with individual characteristics and their chosen ICT lifestyles. However, attitudes that affect individuals’ choices of ICT and mobility lifestyles were neglected in the model analysis. On the whole, scholars have constructed conceptual modelling of the influence of different lifestyles on travel behaviours according to their research foci, and they have incorporated lifestyles into the interpretation framework of travel behaviours. Although the existing literature contains many effective conceptual frameworks, they are all limited to the interpretation of lifestyles for transport and travel behaviour, and they do not involve fields other than travel. The existing theoretical frameworks

Socioeconomics/ demographics

Built environment attributes

Attitudes

ICT Lifestyles

lndicators

Categorical latent variable

Causality/measurement Correlation Observed variables lndicators

Latent variables

Fig. 3.12 Conceptual framework of factors affecting ICT lifestyles and travel outcomes. Source Lee and Circella (2019), edited by the author

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3 Links Between Lifestyle and Transport

Table 3.1 Conceptual model of the impact of lifestyle on travel behaviour Author

Year

Conceptual model

Krizek and Waddell

2002

Behavioural framework for imitating family travel behaviour

Scheiner and Holz-Rau

2007

The relationship between living conditions, lifestyle, location requirements, location choice and travel behaviour

Van Acker et al.

2010

The model as a whole should be built at the personal, social environment and spatial environment levels to build a conceptual model

Etminani-Ghasrodashti and Ardeshiri

2015

Travel behaviour is regarded as non-work travel volume in three different modes (car, public transport, walking and bicycle travel) to promote a particular lifestyle and a building environment, and a conceptual model is constructed

Krueger et al.

2016

A comprehensive framework to explain the formation of different forms and styles and their subsequent impacts on different dimensions of individual travel behaviour

Bhat et al.

2016

A conceptual model of the impact of the GLP and LLP structures on the endogenous results of residential density, commuting distance and automatic ownership

are verified, but they mostly verify specific (causal) links based on survey data and provide limited empirical evidence. The existing conceptual model contains many relationships, which are used to specify the relationship between several groups of variables (Table 3.1). However, lifestyle has direct and indirect effects on travel behaviour, and there is a causal relationship between the two two-way links. The challenge of verifying the theoretical model requires a complex modelling structure and omitting either data or a factor analysis of the key variables is not possible.

3.3.2 Complex and Procedural Perspective on Conceptual Framework There is a complex relationship between lifestyles and transport, both of which have a hierarchical nature. When analysing the impacts of lifestyles on transport, transport supply and demand, the value theory, TPB, and the time-space prism are employed; when discussing the impacts of transport on lifestyles, the spatial selection theory, the time-space compression theory and the consumer behaviour theory are used. With the help of these theories, we propose a conceptual framework model of lifestyles and transport based on their complex relationship and hierarchical nature. The complexity

3.3 The Conceptual Framework of the Links …

81

of the relationship between lifestyles and transport lies in the following facts: (1) lifestyles and transport influence each other, (2) the factors that constitute lifestyles interact with transport and (3) overall and individual lifestyles also interact with transport. The hierarchical nature of this system is reflected in the relationship between individual lifestyles and travel behaviour, and in the complex relationship between the overall lifestyle and the transport system. Our model framework is in Fig. 3.13, which illustrates the relationship between lifestyles and transport from both overall and individual perspectives. This framework explains and extends the complex interrelationship between lifestyles and transport at different levels. The impacts of lifestyles on travel behaviour have been proven in several studies (e.g., Bagley & Mokhtarian, 2002; Collantes & Mokhtarian, 2007; Etminani-Ghasrodashti & Ardeshiri, 2015; Kitamura et al., 1997; Lanzendorf, 2002; Redmond, 2000; van Acker et al., 2014), and relevant literature reviews are also available (van Acker, 2015; Zhang & van Acker, 2017). Some travel characteristics are actually part of a lifestyle, such as car ownership, travel frequency and travel expenses (including tourism expenditure). Lifestyles and transport are subordinate to each other, and there is a reciprocal causation between them. They influence each other in direct and indirect ways, which leads to a complex interrelationship between lifestyles and transport. From the individual perspective, the factors affecting travel can be categorised into four aspects: (1) socioeconomic attributes such as age, occupation, household and car ownership; (2) spatial features of the built environment such as residential and working locations; (3) shopping, leisure and other activities; and (4) subjective attitudes such as value orientations. These factors themselves influence travel behaviour, and at the same time, they constitute different dimensions of a lifestyle, which means the combined effects of these factors influence travel. Individual lifestyles affect travel in terms of travel modes, travel distance, travel duration, travel frequency and so on. The relationship between each element, lifestyle and travel behaviour is as follows: (1) Socioeconomic characteristics such as age, income and household affect both lifestyles and travel behaviour, as many studies have shown. Socioeconomic attributes are the key variable affecting lifestyles (Hildebrand, 2003; Salomon & Ben-Akiva, 1983), and lifestyles affect travel distance, travel modes and more. (2) There have been many empirical studies attempting to measure the impacts of the built environment on travel activities. The built environment involves residential and working locations, building density, land use diversity, road intersection density and destination accessibility. For literature reviews in this regard, see Ewing and Cervero (2001, 2010) or van Wee (2002). The results are generally controlled for socioeconomic and demographic differences among individuals and households (van Acker et al., 2010). The built environment influencing travel behaviour also affects lifestyles (Etminani-Ghasrodashti & Ardeshiri, 2015). (3) Travel behaviour is directly related to activities (Fried et al., 1977). The distribution of activity opportunities is considered an important influencing factor of activities and travel behaviour (van Acker et al., 2010). The impacts of activities on travel behaviour have been proved in many studies. For relevant literature reviews, see Algers et al. (2005) and Buliung and Kanaroglou (2007). Furthermore, activities are also an important factor affecting

Age

Sociology

Family

Overall Lifestyle

Environment

Individual Lifestyles

Occupation

…

Car…

Fig. 3.13 Complexity and hierarchy of the conceptual framework

Macro perspective

Economics

Micro perspective

Income

Socio‐economic

Transport Facilities

Transport System

Transport operation

shopping..

Residence …

Travel Behaviour

Activities.

Built Environment

Transport Management

Attitudes

Individual Attitudes

…

Preferences…

82 3 Links Between Lifestyle and Transport

3.3 The Conceptual Framework of the Links …

83

lifestyles (Krizek, 2006; Krizek & Waddell, 2002; Lanzendorf, 2002). (4) Preferences, value orientations and other subjective attitudes also have an impact on travel behaviour and lifestyles. The impacts of subjective attitudes on travel have also been shown in many studies (e.g., Mokhtarian & Cao, 2008; van Wee, 2002). Travel lies somewhere between objective transport indicators and subjective modes. For example, when choosing a travel mode, many people have no choice but to stick with a certain travel mode. However, when people have multiple choices, subjective attitudes will impact travel. For instance, people who prefer to travel by car will choose cars over subways, though subways are also a good choice. Subjective attitudes are an important and constitutive dimension of lifestyles. Lifestyles also refer to opinions and motivations, including beliefs, interests and attitudes (Ganzeboom, 1988). Lifestyles are individuals’ opinions and motivations, or orientations (Salomon & Ben-Akiva, 1983). Van Acker (2015) believed that lifestyles refer to an individual’s orientations towards general themes such as family, work and leisure. Personal attitudes, such as preferences and values, feelings and other subjective elements not only constitute lifestyles but also influence travel behaviour. From a macro-perspective, a certain type of individual lifestyle can be directly used as a label to describe a more general lifestyle, thus directly affecting the overall lifestyle. Lifestyles are influenced by economic and social systems (Hildebrand, 2003; Salomon & Ben-Akiva, 1983; van Acker et al., 2010), the environment and more, such as restrictions on travel due to limited income and the impacts of the type of car purchased on lifestyles in the transport field. Meanwhile, lifestyles can also affect the economy, society, the environment and so on. For instance, lifestyle choices, such as consumption, activities and residential and working locations, will in turn have direct or indirect effects on the economy. Bhat et al. (2016) studied the interactions between two types of lifestyles—the green lifestyle and the luxury lifestyle— and economic, social and environmental factors such as residential density, cars and commuting. Hafner and Mayer-Foulkes (2013) discovered the causal relationship between the developed lifestyle and income, economic and population growth. Travel behaviour is the medium for connecting overall lifestyle and the transport system. People’s choice of residential and working locations constitutes an important dimension of lifestyle, and different choices lead to diversified lifestyles and different travel needs. Residents’ travel behaviour involves internal dynamic links of the spatial system, reflecting their spatiotemporal participation in the spatial environment. Individuals’ travel behaviour affects transport facilities, transport operation, transport management and transport means, thus affecting the entire transport system. Transport influences people’s housing and employment choices. The evolution of the spatial distribution of people’s jobs and housing determines the spatial and temporal distribution of trips and fundamentally determines the generation, mode and spatial and temporal distribution characteristics of urban trips. This has a key influence on the transport system and determines the scale and mode of the supply of transport facilities. Different transport costs, time, modes and services in turn directly affect people’s travel behaviours, which form a part of different lifestyles. All of this contributes to the complex interrelationship between individual lifestyles, the overall lifestyle, travel behaviour and the transport system at different levels.

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3 Links Between Lifestyle and Transport

Based on theories of transport geography, social psychology and economics, we propose a more comprehensive conceptual framework for lifestyles and transport (Fig. 3.13). A model framework based on complexity and hierarchy is established between lifestyles, transport and factors that affect travel, including socioeconomic attributes, the built environment, activities and personality characteristics. This comprehensive descriptive framework allows the use of lifestyle itself and other factors to analyse travel behaviour, as well as the evaluation of the direct and indirect effects of the overall lifestyle on the transport system from a macro perspective. This framework may be further verified in future work.

3.4 Research Method for the Links Between Lifestyle and Transport In the empirical research on the impact of lifestyle on travel, quantitative methods dominate the literature. Most research paradigms are based on the questionnaire survey data of a certain region, the lifestyle types are divided by means of factor analysis and cluster analysis and the model results are analysed by the SEM in combination with research on lifestyle and travel.

3.4.1 Common Data in Research There are two main types of current research data sources: one is second-hand data such as travel or population surveys conducted by the government. Generally, the measurement indicators for lifestyle are relatively weak, but the relative sample size is larger. For example, Reichman (1977) used the data that were collected as a follow-up of the 1972 census. The survey was conducted between November 1972 and June 1973 in 70 localities and included 55,000 households. Kitamura (2009) used published personal consumption expenditure statistics to make up the database for his discussion. Bhat et al. (2016) used data from the Puget Sound household travel survey conducted by the Puget Sound Regional Council (PSRC) in the spring (April– June) of 2014 in the four-county PSRC planning region (King, Kitsap, Pierce and Snohomish Counties) in the State of Washington. The survey collected information from a total of 6,036 households. Lin et al. (2009) used primary data from the Hong Kong Travel Characteristics Survey 2002 commissioned by the Hong Kong transportation department in December 2002. A total of 30,005 households and more than 92,500 individuals were interviewed. The other source is the first-hand data from researchers’ surveys. The element indicators for lifestyle measurement and travel are relatively comprehensive, but the relative sample size is small and no more than 10,000. Almost all studies use cross-sectional data collected at a certain point in time, and almost no studies use

3.4 Research Method for the Links Between Lifestyle and Transport

85

Table 3.2 Method of lifestyle segmentation Analytical method

Related literature

Factor analysis

Krizek and Waddell (2002), Lanzendorf (2002), Ohnmacht et al. (2009), Krizek (2006)

Cluster analysis

Hildebrand (2003), Rashidi et al. (2010), Krizek and Waddell (2002)

time-series data or panel longitudinal data (Bhat et al., 2016). For example, Bagley and Mokhtarian (2002) used 515 questionnaires from five residential areas in the San Francisco Bay Area; Scheiner and Holz-Rau (2007) used data from a standardised household survey within the scope of the StadtLeben project.1 The survey took place in ten study areas in the region of Cologne in 2002 and 2003. Some 2,691 inhabitants took part in extensive face-to-face interviews about their travel behaviour, housing mobility, life situation, lifestyle, location attitudes and residential satisfaction. Etminani-Ghasrodashti and Ardeshiri (2015) used data from 900 respondents in 22 urban areas in Shiraz, Iran. Thøgersen (2018) used data from ten European countries (N ≈ 1,000 in each country). The survey data were collected by an international market research agency (YouGov). Generally speaking, most scholars have collected first-hand data through sample surveys. In addition, the current research areas are mostly concentrated in certain areas or cities in Western countries (e.g., Bagley & Mokhtarian, 2002; EtminaniGhasrodashti & Ardeshiri, 2015; Scheiner, 2010). Certain geographical restrictions apply (Scheiner, 2010; Thøgersen, 2017), especially the current lack of research in this field in China.

3.4.2 Model Method Most of the research on the relationship between lifestyle and travel behaviour uses quantitative methods to find regularity through data analysis. Lifestyle is divided mainly through factor analysis and cluster analysis. Due to the two-way causal relationship between lifestyle and travel behaviour, the quantitative relationship between lifestyle and travel behaviour is mainly analysed through structural process models (Table 3.2).

1

“StadtLeben—Integrated approach to lifestyles, residential milieux, space and time for a sustainable concept of mobility and cities” (2001–2005). Transport Planning (see http://www.isb.rwth-aac hen.de/stadtleben/). “Intermobil Region Dresden” (1999–2004). Both projects were funded by the German Federal Ministry of Education and Research (BMBF) (Scheiner and Holz-Rau, 2007).

86

3.4.2.1

3 Links Between Lifestyle and Transport

Classification Method for Lifestyle Types

In the current research, with the increase in lifestyle measurement indicators, lifestyle segmentation was mainly conducted via a two-stage method. Researchers use clustering and factor analysis to simplify the indicators of lifestyle types from survey data through cluster analysis (e.g., Collantes & Mokhtarian, 2007; Krizek & Waddell, 2002; Ohnmacht et al., 2009). In addition, Lin et al. (2009) developed a technique called a support vector machine to create a classification function based only on socioeconomic and demographic characteristics. However, these findings are based on descriptive comparisons rather than multivariate analysis (Scheiner, 2010), so the potential third variable is not controlled. Factor analysis is a multivariate statistical analysis method that extracts common factors from variable groups. It was first applied to psychology and has now been widely used in applied sciences such as marketing, social sciences and behavioural sciences. It is used to find hidden representative factors from a large number of observed interrelated variables, group variables according to the magnitude of correlation and group variables of the same nature into one factor. This can reduce the number of variables and reduce data dimensionality, which can also test whether there are relationships between the variables. One disadvantage of this method is that a large number of samples may not belong to a particular category and may not be classified (Ganzeboom, 1988). The lifestyle clustering method does deal with individual cases but examines the pattern of activity generation in detail. The individual’s socioeconomic and demographic characteristics, as well as his or her pattern of scattered activities, divide the population into homogeneous groups. One can then develop the travel demand model of each cluster separately (Lin et al., 2009). Unlike cluster analysis, the latent category model is centred on the individual. It is consistent with the purpose of cluster analysis, which is to divide individuals into different groups. Because it is a model-based method, the clustering criteria and result tests are more reasonable. In addition, covariates can be included in the latent category model to improve the accuracy of classification. The type of variable is not limited, and it can act on any scale or on a mixture of variables of various scales. Because of factor analysis and cluster analysis, these methods are relatively lacking in theoretical support and have been criticised. Veal (1993) believes that the underlying claim of these methods is that certain indicators are clustered together. However, although factor and cluster analysis will inevitably produce groupings, the validity of such groupings is often questioned and its meaning is often unclear. The division of these lifestyles is based on the indicator system designed by the researcher and the researched case area. Differences in the indicator system or the case area may result in different lifestyle types. Although the study of lifestyle segmentation in transportation has its limitations, it can produce some interesting empirical patterns that need to be explained and may lead to more theoretical information and explanatory research when appropriate.

3.5 Summary

3.4.2.2

87

Analysis Model of the Impact of Lifestyle on Travel

The logistic model was mainly used in the early stage of research on the relationship between lifestyle and transport (e.g., Salomon & Ben-Akiva, 1983). Due to the endogenous relationship between lifestyle and transport, SEM is the most commonly used method in current research (Table 3.3, e.g., Bagley & Mokhtarian, 2002; Etminani-Ghasrodashti & Ardeshiri, 2015; Scheiner & Holz-Rau, 2007). SEM is mostly used in psychology, education and management. The equation model is a method of establishing, estimating and testing causal relationship models. It can process multiple dependent variables at the same time, and it can compare and evaluate different theoretical models. It allows measurement errors in independent variables and dependent variables, and it can show the relationships between variables intuitively via path diagrams. However, SEM also has certain shortcomings. For example, it is limited by the use of cross-sectional data (Kline, 2015). SEM is not suitable where one or more endogenous variables are polynomials (Mokhtarian & Cao, 2008). In addition, Ben-Akiva et al. (2002) added the consideration of subjective psychological factors into travel behaviour modelling by introducing latent variables into the discrete choice model, which they named the hybrid choice model. Walker and BenAkiva (2002) presented a generalised random utility framework, which encompasses all models, describes each enhancement and shows relationships between models, including how they can be integrated. Krueger et al. (2016) proposed a conceptual framework that integrates social psychology and lifestyle-oriented methods to provide a complete model recognition and description of the formal style. The model consists of three elements: (1) a latent normative belief submodel with structural and measurement components, (2) a latent modality styles submodel, i.e., a latent class membership model, which includes both observed and latent variables as predictors of class membership and (3) several class-specific submodels for nominal and ordered outcome variables. They also included latent class and latent variable models, which are nested by the more general HCM framework application, lifestyle and travel-related research.

3.5 Summary Since the current theoretical discussions between lifestyle and transport are mostly concentrated in the field of travel behaviour, this chapter started from the macro- and micro-perspectives of the mutual influence of lifestyle and transport to improve the theoretical foundation and theoretical framework of different fields between lifestyle and transport further. This chapter first explored the theoretical basis of the relationship between lifestyle and transport from a theoretical perspective. It focused on the perspectives of transport geography, social psychology and economics. The common theoretical basis of lifestyle and transportation was mainly value theory, TPB, consumer behaviour theory and space-time prism theory; the way lifestyle

Research area

Baltimore

San Francisco Bay Area

Cologne Region, Germany

Year

1983

2002

2010

Author

Salomon and Ben-Akiva

Bagley and Mokhtarian

Scheiner

Conducted in 10 research areas in the Cologne region in 2002 and 2003. Some 2,691 residents participated in extensive face-to-face interviews about their travel behaviour, housing mobility, living conditions, lifestyle, location preference and housing satisfaction

Five residential areas in the San Francisco Bay Area in 1993 (final N = 515)

A data set from a 1977 Federal Highway Administration survey of travel demand in Baltimore, which covered 521 families

Data

Table 3.3 Mathematical model of the impact of lifestyle on travel behaviour

Leisure preferences, values and life goals, aesthetic tastes (measured using preferences in reading and TV viewing) and frequency of social contact

Lifestyle variables are divided into 11 factors, such as being an amateur, a nest builder, an athlete, a child-oriented person and a couch potato

SEM

SEM

(continued)

Research methods

Middle-aged households Logistic (35–64 years old) and very large households; young family-oriented childbearing households; low-income families with children; low-income middle-aged and older families

Lifestyle dimension

88 3 Links Between Lifestyle and Transport

Puget Sound

2016

2016

Bhat et al.

Krueger et al.

Major metropolitan areas in Australia including Adelaide, Brisbane, Melbourne, Perth and Sydney

Shiraz City, Iran

2015

Etminani-Ghasrodashti and Ardeshiri

Research area

Year

Author

Table 3.3 (continued)

Major metropolitan areas in Australia including Adelaide, Brisbane, Melbourne, Perth and Sydney; 516 questionnaires

PSRC conducted the Puget Sound Family Trip Survey in the PSRC planning area of four counties (King, Kitsap, Pierce and Snohomish) in the spring of 2014 (April–June) in Washington State. Information came from 6,036 households

Data from 900 respondents in 22 urban areas of Shiraz, Iran

Data SEM

Research methods

(1) Public transport-oriented, Latent class and latent (2) car-oriented, and (3) car- variable model and bicycle-oriented

GLP positive Latent variable SEM living/environmental attitude and travel affinity/desire for privacy; LLP

There are three modes of travel for leisure and consumption activities (private car, public transport and walking/cycling)

Lifestyle dimension

3.5 Summary 89

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affects transportation, it mainly included transport supply and demand theory. The impact of transport on lifestyle focused on space choice theory and space-time compression theory. These theories influence each other in both lifestyle and transport behaviour, and they are the theoretical basis for explaining the relationship between them. Second, this chapter has shown that lifestyle and transport have interactive influence, that the two are causal to each other, and that lifestyle as a latent variable affects transport through direct and indirect effects. Lifestyle and transport are mutually representative. Transport itself is a way of life. At the same time, different transport activities can form special lifestyle clusters; lifestyle and transport have hierarchical characteristics, and lifestyle has two levels: individual and overall. On the spatial scale, transport also has intra-city and inter-city features, etc., and the two interact on different levels and scales; lifestyle and transport have evolutionary consistency in the long term. The current model of lifestyle and travel mainly focuses on four areas: (1) travel model of lifestyle as an activity type, (2) the modelling of lifestyle as a value orientation, (3) a life-oriented theoretical model and (4) a theoretical model of specific lifestyles. This book presents topics from complexity and process, and it proposes a conceptual framework model for the relationship between lifestyle and transport, which can further expand the theoretical research on lifestyle and transport. At the end of this chapter, the quantitative methods of current lifestyle in the field of travel were explained. Lifestyle classification mainly uses factor analysis and cluster analysis, which is a two-stage method (Lin et al., 2009); the causal relationship between lifestyle and transport was mainly analysed through SEM.

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Chapter 4

Lifestyle Changes in China

4.1 Consumption Dimension: China’s Consumption Patterns and Transport By comparing the cross-sectional data of income and consumption expenditure in 1981, 1990, 2000, 2010 and 2019, this section discusses consumption patterns in China, analyses the historical changes in consumption and describes the longterm trends in consumption and consumption expenditure in lifestyles. This section focuses on the analysis of consumption and transport consumption that affect travel.

4.1.1 Income Level Individual disposable income plays a decisive role in consumption. The same is true for transport consumption. Consumption expenditure increases with income, as Carruthers et al. (2005) and Olvera et al. (2008) have explained. Since 1990, the disposable income of Chinese residents has continued to grow, and residents have gradually become well-off. In 1990, the national per-capita disposable income was 903.9 CNY. In 2019, the national per-capita disposable income increased to 30,733 CNY, 34 times that of 1990. In 1990, the per-capita disposable income of urban residents was 1,510 CNY, and that figure increased to 42,359 CNY in 2019 (Fig. 4.1). Regardless of price factors, the per-capita disposable income of urban residents in 2019 was 28.1 times larger than that of 1990. Meanwhile, in 1990, the per-capita disposable income of rural residents in China stood at 686 CNY, and that figure increased to 16,021 CNY in 2019. Regardless of price factors, the per-capita disposable income of rural residents in 2019 was 23.4 times the amount of 1990. The increase in consumption is generally smaller than the growth of income, which is known as the diminishing marginal effect. The average propensity to consume (APC) is the ratio of consumption to income, or APC = C/Y. As Fig. 4.2 shows, the © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. Zhao and D. Lyu, Lifestyle Change and Transport in China, Population, Regional Development and Transport, https://doi.org/10.1007/978-981-19-4399-7_4

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Fig. 4.1 Disposable income of Chinese residents from 1990 to 2019. Source National Bureau of Statistics (2020) 73.0%

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Fig. 4.2 Income and expenditure of Chinese residents from 2013 to 2019. Source National Bureau of Statistics (2020)

APC of Chinese residents fluctuated and generally reduced from 2013 to 2019, from 72.2 to 70.3%. The per-capita disposable income of households of different income levels also underwent major changes in these 6 years. In 2013, the per-capita disposable income of 20% of low-income households was 4,402.4 CNY. In 2019, this value increased to 7,380.4 CNY, 1.67 times the original value (Fig. 4.3). In 2013, the per-capita disposable income of 20% of high-income households was 47,456.6 CNY. In 2019, this value increased to 76,400.7 CNY, 1.6 times the original value. It can be seen that the income growth of low-income households is higher than that of high-income households.

4.1 Consumption Dimension: China’s Consumption Patterns and Transport

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Fig. 4.3 Nationwide per-capita disposable income of households by income quintile from 2013 to 2019. Source National Bureau of Statistics (2020)

4.1.2 Consumption Level 4.1.2.1

Longitudinal Changes in Consumption Levels

Overall, the per-capita consumption expenditure of China’s urban and rural residents has been increasing since the reform and opening up. There were differences between urban and rural residents in consumption expenditure, with the latter showing a more significant increase. The per-capita consumption expenditure of China’s urban residents stood at 457 CNY in 1981 and increased to 28,063 CNY in 2019 (Fig. 4.4), 61.4 times as large as that of 1981. In 1981, the per-capita consumption expenditure of China’s rural residents was 324 CNY, and the figure amounted to 13,328 CNY in 2019. Price factors aside, the per-capita consumption expenditure of China’s rural residents in 2019 was 41.2 times larger than that of 1981. The household consumption growth rate has been decreasing and the growth of household consumption has remained slower than economic growth. Since the reform and opening up, China’s economy has been characterised by a large amount of investment, a large volume of exports and a low level of consumption. Though 28063

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Fig. 4.4 Changes in the per-capita consumption expenditure of urban and rural residents from 1981 to 2019. Source National Bureau of Statistics (1981, 2011, 2020)

4 Lifestyle Changes in China

40.0% 35.0% 30.0% 25.0% 20.0% 15.0% 10.0% 5.0% 0.0% -5.0%

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 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

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GDP Growth Rate

Fig. 4.5 Household consumption growth rate and GDP growth rate in China from 1980 to 2020. Source CEIC database

China’s GDP and household consumption level have maintained a positive rate of growth, the relative relation between the two has changed with time. As Fig. 4.5 shows, in the early stage of the reform and opening up, household consumption grew faster than GDP. Since the late 1990s, however, the growth of household consumption has become significantly slower than economic growth and the gap between the two is becoming increasingly large. With the diversification of household consumption patterns, greater attention has been paid to the quality of consumption. China’s consumption has witnessed a shift from rapid growth to quality growth. From the perspective of development, a well-off society represents a transition from a sufficiency of food and clothing to an abundance of wealth. On the one hand, the proportion of material consumption has fallen. The percentage of retail sales of consumer goods that meet basic daily needs in the total retail sales has dropped sharply, while the percentage of the retail sales of durable goods that reflect the upgrading of consumption has increased. Changes in the consumption of daily necessities can be reflected in changes in Engel’s coefficient. As Fig. 4.6 shows, Engel’s coefficient of urban households stood at 29.2% in

1978 1979 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 2010 2011 2012 2013 2015 2016 2017 2018 2019 2020

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

Engel’s coefficients of urban households

Engel’s coefficients of rural households

Fig. 4.6 Trends of Engel’s coefficients of urban and rural households from 1978 to 2020. Source http://d.qianzhan.com

4.1 Consumption Dimension: China’s Consumption Patterns and Transport

101

2020, down by 28.3% points from the 57.5% of 1978, while Engel’s coefficient of rural households was 32.7% in 2020, down by 35.0% points from the 67.7% of 1978. Engel’s coefficient of Chinese residents continues to decline, and the consumption structure continues to be upgraded in an all-round way (Table 4.1). In the new century, China is undergoing an upgrade of the enjoyment consumption structure dominated by cars, housing and communications (Sun & Hu, 2013). Of the percapita national household consumption expenditure, housing consumption, transport and communication consumption, education and entertainment consumption, and medical consumption accounted for 24.6%, 13.0%, 9.6% and 8.7%, respectively, in 2020, representing an increase from 12.2%, 4.3%, 7.7% and 8.5%, respectively, in Table 4.1 Structure of China’s per-capita household consumption expenditure Per-capita disposable income of Chinese residents

1980

1990

2000

2010

2020

246.84

903.9

3,721.34

12,519.51

32,188.80

By permanent residence

Urban residents

477.6

1,510.16

6,255.66

18,779.07

43,833.80

Rural residents

191.3

686.3

2,282.12

6,272.44

17,131.50

By source of income

Income from wages and salaries

None

17,917

Net income from operations

5,307

Income from properties

2,791

Income from transfers

6,173

Per-capita consumption expenditure of Chinese residents

210.7

768

2,914.00

9,378.30

21,210

By permanent residence

Urban residents

412.44

1,278.89

5,026.70

13,820.70

27,007

Rural residents

162.21

584.63

1,714.30

4,944.80

13,713

By category of consumption

Food, tobacco and alcohol

None

1,231.20

3,136.80

6,397

238

759.1

1,238

Clothing Housing

419.2

1,927.90

5,215

Household goods and services

184.3

569

1,260

Transport and communication

210.9

1,129.60

2,762

Education, culture and recreation

365.8

999.9

2,032

Health care and medical services

173.2

625.2

1,843

Other goods and services

91.4

230.8

462

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4 Lifestyle Changes in China

50.0% 45.0% 40.0% 35.0% 30.0% 25.0% 20.0% 15.0% 10.0% 5.0% 0.0%

Proportion of food consumption Proportion of clothing consumption Proportion of housing consumption Proportion of living goods consumption Proportion of transport and communication consumption

Fig. 4.7 Changes in the proportions of eight types of consumption from 1998 to 2020. Source CEIC database

TEN BILLION CNY

1998 (Fig. 4.7). Among them, the increase in the proportion of transport and communication consumption was the most obvious, the proportion of which in 2020 was three times that of 1998. With the continuous growth of the total consumption of residents and the steady transformation and upgrading of the consumption structure, consumption patterns are also changing. Thanks to the rapid development of the Internet, the integration of the Internet and consumption has boosted the development of logistics and the dissemination of information, thus generating new consumption patterns. The new patterns are represented by the online and offline retail model, as well as the rise of the sharing economy. According to the 47th China Statistical Report on Internet Development, released by the China Internet Network Information Centre in February 2021, China now has a total of 989 million Internet users, 854 million users of Internet payment, and 782 million online shoppers. In 2020, national online retail sales amounted to 11.76 trillion CNY (Fig. 4.8). The ratio of online retail sales to total retail sales of 20.0%

500000 400000

15.0%

300000 10.0% 200000 5.0%

100000

0.0%

0 2015

2016

2017

Online retail sales of social consumer goods

2018

2019

2020

Total retail sales of social consumer goods

Proportion of online retail sales

Fig. 4.8 China’s retail and online retail sales of social consumer goods from 2015 to 2020. Source CEIC database

4.1 Consumption Dimension: China’s Consumption Patterns and Transport

103

social consumer goods increased to 17.4% in 2019 from 7.4% in 2015. With the upgrading of consumption, people’s requirements for travel satisfaction and comfort will also increase.

4.1.2.2

Transport Consumption Expenditure

Transport consumption expenditure is a major component of residents’ living consumption, reflecting their consumption intention and capacity when it comes to transport. Therefore, it is an indicator that draws many scholars’ attention and is thus frequently measured. Some scholars start from the differences in transport consumption. Transport expenditure increases with household expenditure (Olvera et al., 2008), and different residential locations also have differences in transport expenditure (Haas et al., 2008; Venter, 2011). Due to the booming car and electronic information industries, means of transport and communication are continually improving in performance with new styles being constantly introduced, which further stimulates Chinese residents’ consumption desires and thus makes transport and communication consumption new hotspots for urban households. In 2020, the per-capita transport and communication expenditure was 2,761.8 CNY, accounting for 13.0% of the per-capita consumption expenditure of Chinese residents (Fig. 4.9). It was three times that of 1998 (108.7 CNY), indicating a growth rate of 201.3%. Transport and communication expenditure has become an important part of household expenditure. There is a big difference between urban and rural residents in transport and communication expenditure. As Fig. 4.9 shows, the per-capita transport and communication expenditure of urban residents has been higher than that of rural residents since 1998. In 2020, the per-capita transport and communication expenditure of urban residents was 3,474.3 CNY, while that of rural residents was only 1,840.6 CNY. The per-capita transport and communication expenditure of urban residents was 1.9 times that of rural residents.

CNY

4000.0 3500.0 3000.0 2500.0 2000.0 1500.0 1000.0 500.0 0.0

National Residents

Urban Residents

Rural Residents

Fig. 4.9 Changes in national, urban and rural residents’ transport and communication expenditure from 1998 to 2020. Source CEIC database

104

4 Lifestyle Changes in China 30000000 25000000

one car

20000000 15000000 10000000 5000000 0 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Fig. 4.10 Total consumption of passenger cars in China from 2005 to 2020. Source http://d.qia nzhan.com

Residents’ car ownership intention has grown over the years (Fig. 4.10). According to the China Economic Life Survey (China Central Television et al., 2021), on the 2019 list of things people were willing to spend their money on, the most notable was cars. People’s car ownership intention increased by 12.5% points from 2018. Once again, cars were ranked among the top three things people would like to consume. As China enters a period when people are tending to replace their cars, the car market is full of potential for growth. The increase in the demand for MPVs and SUVs is closely related to the increase in the number of two-child1 families and the strengthened awareness of being a family. In addition, China’s countryside has also entered a period of consumption upgrading. New urban residents, who have relocated themselves from the countryside to cities, have also expressed a strong desire for car ownership. Transport consumption has grown rapidly, and the total passenger volume has increased significantly. As indicated by China’s passenger volume and average transport distance in recent years (Figs. 4.11 and 4.12), the average mileage of travel of Chinese residents has grown significantly. The average distance of railway travel has also increased year by year, while the average distances of travel by highway, water and air remain almost unchanged. As far as long-distance travel is concerned, residents are more inclined to travel by railway (Fig. 4.13).

4.2 Vehicle Dimension: China’s Motorised Lifestyle Compared with household income, car ownership has a more direct relationship with transport. Generally speaking, higher car ownership will lead to higher travel rates, car usage and travel mileage. The increase in the freight volume, motor vehicle ownership, number of trips and consumption travel times will cause traffic congestion 1

The two-child policy is a family planning policy implemented in China, which stipulates that eligible couples are allowed to have two children. Since October 2015, all couples have been allowed to have two children.

105 250

4000000 3500000 3000000 2500000 2000000 1500000 1000000 500000 0

150 100

kilometres

200

50 0 1985 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019

ten thousand people

4.2 Vehicle Dimension: China’s Motorised Lifestyle

Passenger volume

Average transport distance

ten thousand cars

Fig. 4.11 Passenger volume and average transport distance in China. Source Ministry of Transport of the People’s Republic of China

30000.00

70%

25000.00

60% 50%

20000.00

40%

15000.00

30%

10000.00

20%

5000.00

10%

0.00

0%

Civil vehicle ownership

Growth rate

Fig. 4.12 China’s civilian car ownership from 1978 to 2019. Source National Bureau of Statistics (2020)

to a greater extent (Allaman et al., 1982; Li & Yu, 2021). The heads of households with cars use cars more frequently in their daily commuting, indicating that residents buy cars mainly to meet actual needs rather than to show off (Wang and Wang, 2014; Rearick and Newmark, 2018). Cars and lifestyles are considered to have an interrelationship. One’s lifestyle and travel attitude may affect one’s travel mode choice, as well as the car type if one chooses to travel by car (Choo & Mokhtarian, 2004). Vij et al. (2013) studied the impacts of different lifestyles on how people apply for different travel modes. Many believe that mobility is part of an overall lifestyle. According to research findings, people with a preference for cars tend to drive cars on daily trips, whatever the purpose of the trip. Meanwhile, an individual/family’s car ownership, whether a driving license is held, and whether a parking space has been rented will also affect individuals’ travel choices (Vovsha & Petersen, 2009). As a general rule, cars are the most attractive travel mode due to their strengths in convenience, speed, comfort and individual freedom (Anable, 2005; Hagman, 2003; Jensen, 1999). By modelling the car ownership level and country characteristics, Dargay et al. (2007) predicted that by 2030, 56% of the world’s cars will be owned by people in non-OECD countries, up from 24% in 2002, which means that developing countries

106

4 Lifestyle Changes in China

(a) Traditional farming mode

(c) Traditional man-power transportation means

(e) Traditional entertainment

(b) Modern farming mode

(d) Modern vehicles

(f) Modern entertainment

Fig. 4.13 Images indicating the transition from the traditional lifestyle to modern lifestyles2

will own the largest number of cars in the future. It is also predicted that by 2030, China’s car ownership will increase nearly 20 times to 390 million.

2

Sources (a) https://www.sohu.com/a/434391671_483291; (b) https://www.sohu.com/a/377582727_120062805; (c)https://new.qq.com/omn/20220422/20220422A07O1Q00.html; (d)https://www.thepaper.cn/ newsDetail_forward_2037281; (e) https://baijiahao.baidu.com/s?id=1643837195642044873&wfr=spider&for=pc; (f) https://china.caixin.com/2012-10-04/100444479.html?p5.

4.2 Vehicle Dimension: China’s Motorised Lifestyle

107

4.2.1 Changes in the Characteristics of the Motorisation Process

ten thousand

Since the reform and opening up, the motorisation process in China has been advancing rapidly. From 1980 to 2020, the total number of motor vehicles in China increased from 2.089 million to 372 million, with an average annual growth rate of 13.8% (Fig. 4.14). During the same period, the average annual growth rates of GDP and per-capita GDP were 14.5% and 13.5%, respectively. The overall pace of motorisation was basically equivalent to economic growth. After 40 years of development, China’s motor vehicle composition has undergone significant changes. In 2006, civilian cars accounted for 25.5% of motor vehicles. Due to the accelerated increase in private car ownership and various policy restrictions on motorcycles, the proportion of civilian cars gradually increased to 75.5% in 2020. The proportion of other motor vehicles (with motorcycles as the main component) showed a slow downward trend after 2006, and it peaked at 123.77 million in 2009, as a result of changes in purchasing needs arising from a reduction in the number of potential buyers of motorcycles and residents’ increased purchasing capacity after the urbanisation of the population (Zhang et al., 2017). From 1978 to 2020, China’s civilian car ownership was in a state of continuous growth (Fig. 4.15). In 1978, the number of civilian cars in China was only 1,358,400, and it increased to 288.7 million in 2020. The fluctuation of the growth rate was also obvious. The number of civilian cars in 2005 increased by 58% compared to 2004, the fastest growth rate in history. Since 2010, the growth rate of civilian car ownership has continued to decline, from 19% in 2010 to 7% in 2020. With the further improvement of public transport facilities and the further popularisation of the concept of low-carbon transport, it is believed that the growth rate of China’s civilian car ownership will maintain a downward trend in the future. The elasticity coefficient of motor vehicle ownership per 1,000 people/per-capita GDP was 1.2 in 2001, and it has remained greater than 1 in most years since then (Fig. 4.16). This is because in 1994, China’s car ownership was less than 10 million, 40000 35000 30000 25000 20000 15000 10000 5000 0

80.0% 70.0% 60.0% 50.0% 40.0% 30.0% 20.0% 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 Number of motor vehicles

Number of civilian cars

Number of other vehicles

The porpotion of civilian cars

Fig. 4.14 Changes in motor vehicle ownership and composition from 2006 to 2020. Source National Bureau of Statistics (2020)

108

4 Lifestyle Changes in China

30000

ten thousand

25000 20000 15000 10000 5000 0 1975

1980

1985

1990

1995

2000

2005

2010

2015

2020

Number of Civilian Cars

Fig. 4.15 Changes in the number of civilian cars from 1978 to 2020. Source National Bureau of Statistics (2020) 250.0

2.8

one car

200.0

1.9

150.0 100.0 50.0

1.5 1.2

1.2

1.1 0.7

1.3 1.0

0.8

0.9

1.1

1.1

0.0

3.0 2.5 2.0 1.6 1.5 1.0 1.0 0.5 0.0

8717 9506 10666 12487 14368 16738 20494 24100 26180 30808 36277 53783 65534 70328 72000

Per-capita GDP/CNY Motor vehicle ownership per 1,000 people

Elasticity coefficient

Fig. 4.16 Changes in the elasticity coefficient of motor vehicle ownership per 1,000 people/percapita GDP from 2000 to 2020. Source World Bank, CEIC database

while the annual car production exceeded 1.3 million. The state’s subsequent promulgation of the Auto Industry Development Policy confirmed the status of the car industry as a pillar industry in the national economy, thus promoting the development of the civilian car industry and stimulating the rapid growth of civilian car production and sales volume. The experience of developed countries shows that once per-capita GDP reaches a certain level, it remains higher than the growth rate of motor vehicle ownership per 1,000 people (Fig. 4.17). This is the case with China. In 2004, China’s percapita GDP was 12,487 CNY; the elasticity coefficient of motor vehicle ownership per 1,000 people/per-capita GDP fell to 0.7, which means the growth rate of motor vehicle ownership per 1,000 people was lower than per-capita GDP. In 2004, faced with new developments in China’s car industry after the accession to the WTO, the State Council issued a new edition of the Auto Industry Development Policy, which promoted the development of the car industry and resulted in a modest recovery of the elasticity coefficient in 2005. After the state promulgated the Auto Industry Adjustment and Recovery Plan in 2009, the production and sales volume of cars exceeded 13 million, ranking first in the world. The promulgation of these policies in the car industry boosted the purchase of cars, and the elasticity coefficient of motor vehicle ownership per 1,000 people/per-capita GDP reached a recent peak of 2.8

4.2 Vehicle Dimension: China’s Motorised Lifestyle

109

1978 1979 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 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

900 800 700 600 500 400 300 200 100 0

UK

USA

South Korea

Japan

China

Fig. 4.17 Car ownership in various countries from 1978 to 2020. Source CEIC database

one car

in 2009. Since then, the value of the elasticity coefficient has declined, but it has remained greater than 1, indicating that the growth rate of motor vehicle ownership per 1,000 people in China is still higher than per-capita GDP. The data show that there is a certain difference in the amount of family car ownership between urban and rural residents (Fig. 4.18). On average, urban residents have more family cars per 100 households at the end of the year than rural residents. Although the ownership of family cars by urban and rural residents has been increasing year by year, the gap between urban and rural areas has gradually widened. In 2013, the difference between the two was 12.4 cars per 100 households, and in 2019, the difference between the two increased to 18.5 cars. While motorisation has generally improved mobility, it has also brought about problems such as deterioration of urban traffic conditions, rapid growth in transport energy consumption and increased environmental loads. Cities such as Beijing, Shanghai, Guangzhou and Shenzhen have introduced restrictions on the purchase of private cars to control the number of cars. Meanwhile, with the increase in public transport services and the diversification of transport demand management measures, these cities have already seen a downward trend in car usage. 45 40 35 30 25 20 15 10 5 2013

2014

2015

Cars Owned Per 100 Households Nationwide

2016

2017

2018

2019

Cars Owned Per 100 Urban Households

Cars Owned Per 100 Rural Households

Fig. 4.18 Cars owned per 100 households from 2013 to 2019. Source National Bureau of Statistics (2020)

110

4 Lifestyle Changes in China

Box 4.1 Car Purchase Restrictions in Some Cities In the past 10 years, the number of cities with car purchase restrictions has increased, affecting residents’ car ownership choices. Shanghai is the first and only city in China that has generally imposed restrictive policies on private car license plates. Since 1994, Shanghai has regulated the total number of new private cars in the central city through bid auctions. Shanghai has adopted an auction system for newly added license plates. In each auction, the brand price is determined based on the owner’s bid, and second-hand cars can be transferred with a license. Over time, the price of Shanghai’s license plate has exceeded 80,000 CNY, and it has been recognised as “the most expensive iron sheet”. Beijing has adopted a lottery system for all newly added car license plates each month, and second-hand cars cannot be transferred with licenses. In other words, you can only get a Beijing license plate by luck. Some people get license plates the first time they participate, and some have not received one for several consecutive years. Learning from the one-size-fits-all lesson of Shanghai and Beijing, Guangzhou and Tianjin adopted a half lottery, half auction system for the allocation of new car license plates. This also means that it is possible to get a license plate by spending money or by luck. Since July 2011, to control the growth rate of cars in the old urban area of Guiyang further, the Interim Regulations on the Management of Car License Plates in Guiyang were implemented. Newly registered cars in Guiyang City can only enter the first ring road of the city after obtaining the corresponding license plate by lot. On March 25, 2014, the Hangzhou Municipal Government suddenly held a press conference, announcing that car license plates would be restricted from midnight on March 26. In the future, the quota of new cars in the administrative area of Hangzhou will be managed, as licenses will need to be obtained by lot or by bidding.

4.2.2 Cars Are Mainly Used for Commuting and Travelling The daily travel modes of Chinese residents are more diverse. According to the 2019 National Travel Green Paper jointly produced by Giant Engine and Kantar, among the daily transport modes of Chinese residents in 2019, public transport (including buses, subways and taxis) occupied an unshakable position due to its low prices, safety and eco-friendly features (Fig. 4.19).

4.2 Vehicle Dimension: China’s Motorised Lifestyle Car

111 38%

Bicycle

40%

Online Car Hailing

52%

Taxi

55%

Subway

56%

Bus

66% 0%

10%

20%

30%

40%

50%

60%

70%

Fig. 4.19 National daily transport choices. Source Giant Engine and Kantar (2019)

In addition to public transport, car travel is an important type of transport. As mentioned in Sect. 4.2.1, the vehicle penetration rate in China is getting higher and higher. With economic development and lower car prices, as well as the rise of independent brands, Chinese residents can buy the high-performance vehicles they want to own at a lower price than before. China’s car ownership is also on par with developed countries. Buying a car can satisfy residents’ daily travel needs. For working people, a car can be used for commuting. Compared with those without a car, those with a car have longer daily commuting distances and have a wider range of activities on rest days (Fig. 4.20). Among the people who drive, 83% drive for 15 days or more in a month, and they use the car more frequently (Fig. 4.21). China and Western developed countries differ a lot in car use. For example, the United States is a highly motorised country. Every year automakers produce nearly 200 new car types and sell millions of new cars to meet the huge demand for cars (Choo & Mokhtarian, 2004). Cars also account for a very large proportion of travel modes used in daily trips, and most people are now highly dependent on cars for travel activities (Anable, 2005). The huge demand for cars has a lot to do with the unique urban space character of the United States. Newman and Kenworthy (1989) discussed the relationship between cities characterised by low density in residences and residents’ car consumption in the United States, and their views are widely 50

kilometer / day

41 40 30

34

33

27

20 10 0 People without cars

Car owners Weekdays

Weekends

Fig. 4.20 National daily travel distance (km/day). Source Giant Engine and Kantar (2019)

112 Fig. 4.21 National distribution of the number of driving days in a month. Source Giant Engine and Kantar (2019)

4 Lifestyle Changes in China

Less than 10 days 6%

10-14 days 11%

More than 25 days 19%

20-25 days 38%

15-19 days 26%

accepted. Unlike the United States, China will not construct such cities featuring low density in residences because of its large population and limited space for urban construction. Consequently, residents rely less on cars. China is also very different from developed countries such as the United States and the United Kingdom in commuting mode choices. As early as 2005, 70% of commutes in the UK were undertaken by car (Gardner & Abraham, 2007). In many developed countries, the large car parc and the large number of trips have brought about some problems, the most serious of which are environmental. Traffic emissions have led to severe climate change and air pollution (Oskamp, 2000). Governments and scholars have been working on countermeasures to reduce car trips (Eriksson et al., 2008). Although cars are not the most mainstream and most important commuting mode in China, with the increase in car parc, China’s environment is also threatened by pollution caused by the growth in car trips (Han & Hayashi, 2008). Despite the differences in car parc and commuting modes, developing public transport and reducing vehicle emissions is an issue that China and developed countries need to face and solve together. According to the China Economic Life Survey (China Central Television et al., 2021), tourism topped the list of residents’ consuming intentions in 2019 for the fifth year in a row, reflecting the people’s unceasing demand for tourism in recent years. Tourism has gradually become an important leisure pattern for people to improve their sense of well-being in life. As Fig. 4.22 shows, the proportion of interviewees feeling happy due to tourism is higher than the overall proportion of interviewees who feel happy.

4.2 Vehicle Dimension: China’s Motorised Lifestyle 60.00%

55.24%

113

50.99%

50.00% 35.39%

40.00%

38.46%

30.00% 20.00% 9.42%

10.57%

10.00% 0.00% happy

neutral tourism

unhappy

all options

Fig. 4.22 Sense of well-being of interviewees choosing tourism. Source China Central Television et al. (2021)

4.2.3 A New Mode of Urban Travel Under the Sharing Economy The Internet is reshaping the entire socioeconomic structure with subversive thinking, changing people’s lifestyles and thinking habits while promoting the transformation and upgrading of business models and business thinking. The sharing economy is another economic form that has changed people’s travel habits. With the sharing of use rights as its main feature, the sharing economy drives at the economic activities that make use of modern information technologies (including computer information, big data, etc.), integrate massive and decentralised resources, and satisfy diversified needs. As early as 2012, scholars studied the characteristics of car-sharing and found that access-based sharing behaviour features temporality, anonymity and market mediation (Bardhi & Eckhardt, 2012). Studies have also found that car-sharing increases the likelihood of participants selling or not buying private cars, and also encourages participants to choose walking or to take public transport (Firnkorn & Muller, 2011; Shaheen et al., 2009). Car-sharing is mainly prevalent in developed countries and countries with a large car parc. Although car-sharing appeared in Europe between 1940 and 1980, it was not until the 1990s that the concept gradually became popular. Since 2000, car-sharing has spread more rapidly in Europe and the United States than in Asia (Shaheen & Cohen, 2007). The number of participants in China’s sharing economy in 2019 was around 800 million, and the number of service providers was approximately 78 million, a yearon-year increase of 4%. The number of employees in platform enterprises was 6.23 million, a year-on-year increase of 4.2%.3 Online ride-hailing is an important component of the sharing economy. The online car-hailing market continued to expand. By December 2020, the number of online car-hailing users in China had reached 365 million, an increase of 2.98 million over 3

Source https://www.ndrc.gov.cn/xxgk/jd/wsdwhfz/202003/t20200310_1222769.html?code=and state=123.2020-3-20.

114

4 Lifestyle Changes in China

March 2020, accounting for 36.9%4 of the total number of Internet users. The sharing concept and the experience economy are widely popular among young consumers. In the future, more and more people may choose to use shared car rental instead of buying a car, which will lead to greater growth of shared car and online car rental services.

4.3 Environmental Dimension: China’s Urban System and Transport 4.3.1 Regional Transport System On a national scale, the development of various transport system indicators from 1990 to 2019 shows obvious periodic features. From the perspective of the road transport system, the mileage and density of grade highways show a steady growth trend. They underwent a period of rapid growth around 2005 and have maintained a steady growth trend in recent years. The mileage and density of expressways show an accelerated growth trend. Since 2000, the national planning and construction of expressways have proceeded apace. The national mileage of expressways in 2010 was four times that in 2000 and twice that in 2019. The trend of the road passenger volume was the same as that of the passenger turnover volume, both of which rose first and then fell, indicating that there was little difference in passenger travel distance in the whole study period. The period from 1990 to 2000 was a period of steady growth; the period from 2000 to 2012 was a period of accelerated growth, indicating that during this period, passengers mainly travelled by road; and the period from 2012 to 2019 was a period of rapid decline, partly due to the greater accessibility of rapid transport modes such as civil aviation and high-speed rail, which were favoured by more passengers. The trend for road freight volume was inconsistent with that of the freight turnover volume. Around 2006, the road freight turnover volume increased rapidly, reflecting an increase in long-distance road freight transport demand. On the whole, the road freight volume and the freight turnover volume showed a growth trend, and the growth rate accelerated around 2006, indicating that freight transport was still dominated by roads, and that other transport modes did not have a great impact on it. From the perspective of the railway transport system, the mileage and density of railways in operation show a steady growth trend, and the growth rate has gradually accelerated in the last 10 years, reflecting that China is accelerating the construction of the national railway network (Fig. 4.23). Especially with the construction of the high-speed railway network, the railway passenger volume shows a trend of accelerated growth, which shows that the construction of the national railway network 4

Source 9-29.

http://www.cnnic.cn/hlwfzyj/hlwxzbg/hlwtjbg/202009/t20200929_71257.htm.2020-

115

16 14 12 10 8 6 4 2 0 1978 1980 1985 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019

10000 km

4.3 Environmental Dimension: China’s Urban System and Transport

Length of Railways in Operation

Length of Expressways

Fig. 4.23 Length of railways in operation and length of expressways from 1978 to 2019. Source National Bureau of Statistics (2020)

has not only improved the accessibility of national railway passenger transport, but also caused accelerated nonlinear growth in the attraction of railway travel to passengers. The railway freight volume shows a fluctuating growth trend, and the trend is consistent, which shows that the demand for railway freight transport is relatively stable to some extent due to the increase of railway mileage in recent years, mainly high-speed railway lines serving passenger transport. At the same time, due to the slow change in the railway freight network, the growth trend of the freight turnover volume is consistent with that of the freight volume. From the perspective of the air transport system, the number of airports in China has increased steadily, from 65 in 1990 to 240 in 2019. The accelerated growth of air passenger transport may be due to improvements in residents’ living standards, which increases the economic accessibility of civil aviation travel and makes civil aviation more attractive for long-distance travel. Air freight transport shows a steady rising tendency, but the total amount is very low, which reflects that air freight transport is not the main mode of freight transport. The internal driving force of its growth lies in the growth of residents’ demand for high-end cargo transport and high transport speed. From the perspective of the water transport system, the mileage of inland waterways increased to a certain extent around 2000 and has remained basically unchanged in the last 10 years, which reflects that the water transport system partly depends on physiographic conditions. Both the water passenger volume and the passenger turnover volume fluctuate, and the total amounts are small, indicating that water passenger transport is a secondary travel mode. Compared with passenger transport, the water freight volume and the freight turnover volume show an obvious growth trend, indicating that the water transport system mainly serves cargo transport. From the perspective of the traffic management system, the number of transport workers per 10,000 population shows a downward trend and has only rebounded in recent years. This may be due to the limited growth of the number of transport workers and the rapid growth of the regional population. This phenomenon has been alleviated in recent years. The per-capita transport investment shows an obvious accelerated growth trend, reflecting the development characteristics of infrastructuredriven investment.

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4.3.2 Urban Transport System 4.3.2.1

Urban Road System

500000

20

400000

15

300000 10

㎡

kilometres

The urban road system is a vital part of the transport infrastructure, greatly influencing urban residents’ travel activities. In the face of the rapid growth in the demand for motor vehicles and transport services, China has invested heavily in urban road construction to speed it up. The length of urban roads increased from 26,966 km in 1978 to 459,304 km in 2019, with a total growth rate of 1,600% and an average annual growth rate of 7.2% (Fig. 4.24). The urban road surface area per capita also increased from 2.93 m2 in 1978 to 17.36 m2 in 2019, with a total growth rate of 592% and an average annual growth rate of 4.4%. This huge investment plays an important role in the preliminary construction of China’s urban transport system, which has also mitigated the problem of insufficient transport infrastructure supply to a certain extent (Liu, 2010). As a large number of natural roads within communities and units are not urban roads, China’s urban road network density is generally low. In recent years, increasing the road network density has become an important target of China’s urban planning. In 2017, China’s urban road network density was only 5.89 km/km2 , which increased to 6.32 km/km2 in 2018 and 6.65 km/km2 in 2019 (Fig. 4.25). The growth rates were 7.3% and 5.2%, respectively, demonstrating a preliminary increase in road network density. According to the Annual Report on Road Network Density and Traffic Operation in Major Chinese Cities issued by the Ministry of Housing and Urban-Rural Development (2019a, 2020), the average road network densities of built-up areas in the central urban areas of China’s 36 major cities were 5.89, 5.96, 6.10 and 6.20 km/km2 during the period from 2018 to 2021. The growth rates were 1.2%, 2.3% and 1.6%, respectively, showing an upward trend. Among the six cities in Fig. 4.26, Shenzhen has the highest road network density (9.6 km/km2 in 2021), while Hohhot has the lowest road network density (4.6 km/km2 in 2021). Since the beginning of the twenty-first century, China’s modernisation, urbanisation and motorisation have experienced a period of fast growth. This rapid economic

200000 100000

5

0

0

Length of Urban Roads

Urban Road Surface Area Per Capita

Fig. 4.24 Length of urban roads and urban road surface area per capita from 1978 to 2019. Source Ministry of Housing and Urban-Rural Development of the People’s Republic of China (2019b)

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117

6.8 6.6 6.65 6.4 6.2

6.32

6

km/k㎡

5.8

5.89

5.6 5.4 2017

2018

2019

Fig. 4.25 Road network density in national built-up areas from 2017 to 2019. Source Ministry of Housing and Urban-Rural Development of the People’s Republic of China (2017, 2018, 2019b)

10 9.50

9.60

8.30

8.40

6.36 5.96 5.64

7.20 6.60 6.10 5.70

7.20 6.70 6.20 5.70

4.24

4.42

4.50

4.60

2018

2019

2020

2021

9.50

9.50

8

8.02

8.07

7

7.10

7.15

6

6.22 5.89 5.59

km/k㎡

9

5 4

Average of 36 Cities

Shenzhen

Chengdu

Zhengzhou

Shanghai

Hohhot

Beijing

Fig. 4.26 Road network densities in some cities from 2018 to 2021. Source China Academy of Urban Planning and Design (2019), Wang et al. (2021)

and social development has brought a large number of people to cities, generating a large traffic demand and making urban traffic problems increasingly prominent. Traffic congestion has spread from large-sized cities to small- and medium-sized cities, and urban transport is facing severe challenges. Although China has attempted to increase the supply of transport facilities, the increase in supply is not keeping up with the increase in motor vehicles and traffic demand.

4.3.2.2

Development of the Urban Public Transport System

In recent years, the State Council and relevant ministries and commissions have promulgated a series of policies to encourage the priority development of public transport. Various regions have also implemented different measures to promote the development of public transport in light of their own conditions. The public transport

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priority strategy has won support among the people. Public transport has developed considerably, and green travel is becoming more and more common. The construction of urban rail transport has been expedited. In the 41 years from 1978 to 2019, the number of cities with rail transport increased from 1 to 41, showing an average annual growth rate of 9.5%, and the length of completed rail lines nationwide increased from 23 to 6,058.9 km, showing an average annual growth rate of 14.6% (Fig. 4.27). The establishment and improvement of rail transport has greatly facilitated urban residents’ lives. The rail transport passenger volume increased from 562 million in 1997 to 23.878 billion in 2019, showing an average annual growth rate of 18.6% (Fig. 4.28). Cities such as Beijing, Hangzhou, Changzhou, Xiamen and Jinan have developed the Bus Rapid Transit system and opened it to the public in quick succession. As an efficient public transport mode, this system offers many advantages, such as less required investment than rail transport, a shorter construction period, a relatively larger transport volume, and a faster running speed, the effects of which have begun to emerge. Urban public transport services have further improved. The number of public transport vehicles and routes in various cities keeps growing, accompanied by 45 40 35 30 25 20 15 10 5 0

7000

kilometres

6000 5000 4000 3000 2000 1000 0

The Length of Completed Rail Transport Lines

Number of Cities with Completed Rail Transport Lines

million people

Fig. 4.27 China’s rail transit construction and development from 1978 to 2019. Source Ministry of Housing and Urban-Rural Development of the People’s Republic of China (2019b)

30000

60%

25000

50% 40%

20000

30%

15000

20%

10000

10%

5000

0% -10%

Rail Transit Passenger Volume

2019

2017

2018

2016

2015

2014

2013

2012

2011

2010

2009

2008

2006

2007

2005

2004

2003

2001

2002

1999

2000

1998

1997

0

Growth Rate

Fig. 4.28 China’s rail transit passenger volume from 1997 to 2019. Source CEIC database

119 20%

700 600 500 400 300 200 100 0

15% 10% 5% 0% -5% 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019

thousand untis

4.3 Environmental Dimension: China’s Urban System and Transport

Number of public transport vehicles in operation

Growth Rate

Fig. 4.29 Number of public transport vehicles from 1990 to 2019. Source CEIC database

standard unit

12

25% 20% 15% 10% 5% 0% -5% -10%

10 8 6 4 2 0

Public transport vehicles per 10,000 people

Growth Rate

Fig. 4.30 Public transport vehicles per 10,000 people. Source Ministry of Housing and Urban-Rural Development of the People’s Republic of China (2019b)

constant upgrading of technologies. In 1990, there were only 62,000 public transport vehicles in operation in China (Fig. 4.29). By 2019 the number had increased to 625,000, about 10 times that of 30 years earlier, suggesting an average annual growth rate of 8.3%. From 1986 to 2008, the number of public transport vehicles per 10,000 people in China grew from 2.5 to 11.13, representing an average annual growth rate of 8.1% (Fig. 4.30). The number of public transport vehicles per 10,000 people in various cities more than doubled. For example, during this period, the number of public transport vehicles in Beijing increased from 5,182 to 23,221. Calculated on the basis of the permanent population, the number of public transport vehicles per 10,000 people increased from 4.73 to 13.7.

4.3.3 Changes in the Community Structure 4.3.3.1

From Work-Unit Communities to Urban Communities

China’s urban communities have undergone a transformation from the work-unit system to the community system, which embodies the meaning of modern urban

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life. During the planned economy period, the work unit was the most basic organisational form in China’s socioeconomic life. The social system or institutional structure based on unit organisations was the so-called work-unit system (Wang, 2010). It was the central system regulating the operation of the entire society with respect to economic performance, urban social management and the formation of urban living space. Under the planning system, work-unit compounds and work-unit communities turned into the principal parts of urban structure. As the urban management system at the primary level has changed with times since the founding of the People’s Republic of China, with the two-way development of communities and work units, legal communities (at the municipal level) developed in the 1950s but were marginalised in the 1960s and 1970s, while the work-unit society entered its heyday (Hua, 2000). It was in this process that urban communities and work units became highly overlapping urban management units. Work units incorporated communities and communities were absorbed into work units—the basic characteristics of urban social life and management were thus reflected. This is the type of community we call a work-unit community. In the early 1990s, China’s urban government departments officially proposed the development of communities. In recent years, there has also been an upsurge in community construction in cities across China. Nowadays, urban community construction is not only an important task of government departments, but also a crucial way for common people to participate in and contribute to community affairs.

Box 4.2 The Work-Unit System The work-unit system has political, economic and social functions, characterised by administration, isolation and monotony. The characteristics of the work-unit system were most prominently manifested during the period when China was conducting the redistributive economic system. Back then, work units in China’s cities were the central system regulating the operation of the entire society not only in terms of economic performance, but also in terms of urban social management and the formation of urban living space. Among the series of social systems that constituted the work-unit system, the implementation of the work-unit-based housing distribution system had a direct impact on the living space of cities in China (Wang, 2011). At that time, the government allocated land to work units free of charge, and as the subject of housing construction and operations, the work units allotted the land to their employees in accordance with certain rules. The rent or purchase cost was very low. Therefore, housing provided by work units could be taken as a kind of welfare. Urban housing allotted by work units would be located close to the production space of the relevant work units, based on the principle of production before living. Over time, the work-unit space combining working space and living space was formed in China’s cities. Each work-unit space was not only

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121

a workplace, but also a dwelling place, including housing and various facilities for leisure and entertainment, as well as cultural education. Cafeterias, clinics, kindergartens, bathhouses, cultural palaces and other facilities were all provided by work units. Among them, state-owned enterprise units were a typical type of work-unit space. Compared with the increasing separation of workplaces and residences brought about by the current commodity-based communities, people used to save a lot of traffic time, so that they could learn to improve their individual productivity and quality of life, which in fact contributed to the conservation of social resources (Yu et al., 2007).

4.3.3.2

Work-Unit System Period: Workplace—Residence Integration

In the layout planning of the unit, the production area and the living area were often constructed adjacently to improve the efficiency of production and management, which made the unit both a workplace and a residence. The unit under the planned system was not only a place for people to work, but it also provided housing for employees. Most of the employees’ living areas were near the workplace, forming a space in which the unit’s living area and the workplace were adjacent to each other. Therefore, it is an urban spatial organisation mode that combines workplaces and residences. In the work-unit system period, residents’ travel distance was relatively short, and a resident would not leave the work-unit community at all, but would move within it. In addition, the frequency of residents going outside the community was also low, as there were abundant facilities in the work-unit community, which could meet most of their needs. In this case, residents’ demand for private cars and public transport was not very large. The end of welfare housing allocation in 1998 relieved the unit of the responsibility of supplying housing and service facilities. Urban residents began to accept nonunit housing and service supplies. The unit was no longer the basic living unit, and the work-unit space gradually disappeared. Urban residents started to choose residences in suitable locations within the city according to their own financial status and market rules, with the residential space of residents constantly differentiating between various groups. Economic development, the improvement of urban public service facilities and the improvement of residents’ living standards have greatly expanded the types and scope of residents’ daily activity spaces, which have gradually diversified and tended to differentiate.

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4.3.3.3

4 Lifestyle Changes in China

Marketisation Period: Social Stratification and Residential Differentiation

With the continuous deepening of the reform and opening up, the impact of the transition from a planned economic system to a market economic system on residents’ daily lives has gradually penetrated into all aspects including housing. First of all, since the commercialisation of the welfare housing system in Chinese cities, the differentiation of families and neighbourhoods in social space has begun (Huang, 2007), and it has become a prerequisite for residential differentiation in the transitional period. Since the central government ordered the suspension of public housing distribution in 1998, all units have privatised housing by building private housing and selling public housing. Families living in public housing have been encouraged to purchase the houses at subsidised prices or to buy newly built commercial housing at market prices. In 2000, more than 70% of urban householders became homeowners, which was in sharp contrast to the situation when the proportion of homeowners was less than 20% in the 1980s (National Bureau of Statistics, 2002). Second, since the reform and opening up, with the deepening of the reform of state-owned enterprises and the rapid rise of private and individual enterprises under the market economy, the income and living standards of urban residents have been continuously improving, and the socioeconomic gap between the rich and the lowand middle-income class has rapidly widened. This has provided a foundation for the diversified housing options (i.e., relocation options) for different families. Third, the reform of the housing system gave birth to the emergence and development of commercial housing, and local governments’ land revenue, and dependence on the huge economic effects of the property market promoted the massive development of commercial housing and rapid urban expansion. Under the combined effect of these forces, on the one hand, the unit housing system continued to weaken: the unit was no longer the basic organisational unit of people’s daily life, and the homogeneous housing status in the work-unit community was broken. Families with better economic conditions gradually moved out of the work-unit compound and into the commercial housing community with a relatively better living environment, while renting or selling their former houses; families with average economic conditions remained in the work-unit compound; and other families moved into the workunit compound in consideration of housing prices, work and children’s schooling. Therefore, residents living in the work-unit community no longer had close work connections, and people from the same work unit would also be scattered in different residential communities due to their different financial statuses, resulting in a residualisation phenomenon similar to that in British public housing (Li and Gu, 2011). On the other hand, the migration and mobility of different social classes and the gradual maturity of the commercial housing market promoted the continuous reorganisation and alienation of residential spaces in cities, and residential differentiation gradually intensified. Since 2000, the rapidly rising housing prices and the chaotic market order have continuously increased the housing burden of urban residents and affected the improvement of urban living standards. In the face of this situation, the central

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123

government began to promulgate a series of regulations in 2005. To solve the housing problem of low- and middle-income families, a large number of security housing communities in cities were built. However, they did not alleviate residential differentiation in cities; instead, together with other types of communities, including the remaining work-unit communities and commercial housing communities of various levels, they exacerbated residential differentiation in cities. In the security housing communities where low-income groups gather, residential differentiation further intensified in different communities.

4.3.3.4

Marketisation Period: Workplace—Residence Separation

With the continuous deepening of the reform of the social and economic system, the front factory, back house residential spatial pattern gradually disappeared. On the housing supply side, the end of welfare public housing supply marked the collapse of the work-unit system (Chai et al., 2011). The mobility of urban residents increased, and work-unit communities where workplaces and residences used to be close to each other gradually turned to urban communities with residential functions as the mainstay. At the same time, the continuous development of the commercial housing market made the commercial housing dominated by property developers the main body of urban housing supply (Wu, 1996), and profit-driven property companies in the market economy could build residential areas in any profitable corner of cities. As a result, a large number of commercial houses were built in suburbs with relatively low land prices. In addition, due to the relatively low land prices and the sufficient land supply in the suburbs and urban fringe areas, these commercial houses solved the housing problems of lowand middle-income families. Security housing communities are far away from workplaces, which increases residents’ commuting distance. In fact, the workplace–residence separation is a widespread problem all over the world. The Western research on this issue is mainly based on the suburbanisation that began in the first half of the twentieth century (Jackson, 1987). In addition to the possible increase in residents’ commuting distance and time, the workplace–residence separation may also leave disadvantaged groups in an inferior position in employment (Kain, 1968; Shen & Sanchez, 2005; Stoll, 1999). On the supply side of the land market, first of all, the land market reform allowed urban land use choices to be made according to market prices, resulting in the separation of residential, commercial and industrial land, which directly promoted workplace–residence separation. Under the guidance of the law of urban land prices, industrial enterprises moved to the suburbs of cities to obtain additional development funds through land replacement (Feng & Zhou, 2004), leading to the relocation of jobs. In the end, the incomplete land system reform created the dual-track urban–rural land market and the dual-track urban land market made land revenue a huge driving force and goal for the development of local governments. Old city

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4 Lifestyle Changes in China

renovation and new district development have become the only way for the development of almost all cities in China. Districts, university towns, and new towns have sprung up in major cities, facilitating the reorganisation of residential and employment spaces. The trend of suburbanisation of residences is unacceptable, and urban residents’ workplace–residence separation has further intensified. For example, the commuting time of urban residents in Beijing has shown an increasing trend from the inside to the outside. Residents in many regions spend more than 30 min a day commuting.

4.4 Activity Dimension: Work and Life in China 4.4.1 Employment Patterns 4.4.1.1

More Flexible and Diverse Employment Patterns

The employment patterns of urban residents in China have undergone major changes in the past 3 decades. The work-unit system used to be the basic organisational system of Chinese urban society during the planned economy period, which determined employment patterns and income distribution modes. Since the market-oriented reform, the work-unit system has begun a process of change, and the work-unit system factors and market-oriented factors have jointly affected the employment patterns of residents (Wu, 2007). The employment patterns of residents after the reform are characterised by the continuation of the original employment patterns and the rise of market-oriented employment patterns. Accordingly, employment organisations comprise organisations within the system and organisations outside the system. Organisations within the system are the original administrative organs, public institutions, and state-owned and jointly owned enterprises, collectively referred to as state-owned units. These units still retain certain characteristics of the workunit system to some extent, such as the implementation of a strict personal records system. Most of them use the permanent employment model, and the turnover rate is relatively low. Organisations outside the system mainly include civilian-run, private and joint-stock enterprises. They were first allowed to develop in open coastal areas, and then in inland areas. These organisations have only one fundamental purpose— profits. Their organisational structure is also relatively simple, and the workforce configuration is basically in accordance with the laws of the markets. Since the reform, the proportion of employment in organisations within the system in the whole society has declined, while the proportion of employment in organisations outside the system (i.e., employment in the modern, urban market economy) has increased significantly, demonstrating a shift between the two employment patterns. Table 4.2 shows that from 2014 to 2019, the number of employed workers remained largely unchanged. The number of employees in state-owned units and urban collectively owned units within the system continued to drop significantly, while the number

4.4 Activity Dimension: Work and Life in China

125

Table 4.2 Comparison of the number of employees in different types of units from 2014 to 2019 (Unit: Ten Thousand People) Year

2014

2015

2016

2017

2018

2019

Type of unit State-owned Urban collectively owned

6,312

6,208

6,170

6,064

5,740

5,743

537

481

453

406

347

296

Other urban non-private

11,146

11,089

10,975

10,884

10,873

10,909

Urban private enterprises or individual businesses

16,866

18,980

20,710

22,675

24,393

26,259

Note. Source National Bureau of Statistics (2020)

of employees in other organisations outside the system increased substantially year by year. From 2014 to 2019, the numbers of employees in state-owned units and urban collectively owned units reduced from 63.12 million and 5.37 million to 57.4 million and 2.96 million, respectively. The number of employees in other urban nonprivate units (mainly including limited liability companies, joint-stock companies, etc.) did not change much, while the number of employees in urban private enterprises or individual businesses increased from 168.66 million to 262.59 million, indicating an increasing degree of marketisation of the labour force. The statistical results show that the increased workforce in organisations outside the system probably comprised new labour force entrants and personnel leaving state-owned units and collectively owned units. These changes in the number of employees reflect the continuous growth of economic organisations and employees outside the system and the marketisation of employment units within the system. Chinese people’s employment patterns have changed from the traditional assignment of jobs in a unified way to independent job selection and competition for posts, which arouses people’s self-awareness of career goals and sense of self-responsibility. With the increase in the frequency of career changes and the changes in occupational mobility types, the number of permanent occupations that stick with one organisation for a lifetime is constantly being reduced, while independent self-employed occupations that do not stick with any organisation keep emerging, such as personal trainers, consultants, individual practitioners, tutors, etc. With the evolution of Internet technologies, the threshold of online entrepreneurship has been lowered to a large extent (in terms of infrastructure support, marketing costs, sales channels, financing channels, etc.). More and more people are choosing to start a business instead of seeking a job as their first choice of employment. For instance, in 2019, Taobao created nearly 50 million jobs.5 In 2020, the number of flexible employees in China reached about 200 million.6 In light of career development trends and the COVID-19 pandemic, the figure is expected to increase further. The high demand for side jobs has become the keyword of the workplace of the year. Against the backdrop of the global spread of COVID-19, 5 6

Source http://www.guannews.com/zhichang/155136.html.2020-8-18. Source http://m.cnr.cn/chanjing/edu/20200811/t20200811_525200923.html.2020-8-19.

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4 Lifestyle Changes in China

young people with slash careers promote the formation of new employment patterns and economic forms in enterprises and society (Du Xiaoman Financial, 2020). Young people with slash careers are young part-timers and entrepreneurs who have side jobs in addition to their primary jobs, with multiple identities and occupations (Alboher, 2007). They may be employed or have their own business as a primary job, and at the same time they may have a part-time job or be starting a business as a side job. A 2016 study found that 20–30% of the working-age population in the United States and 14 countries in the European Union engage in freelance work, including casual earners who use independent work for additional income by choice (Manyika et al., 2016). Looking at occupational characteristics, practitioners in industries relevant to the online economy and those in industries severely affected by the pandemic are most eager to have slash careers. The pandemic has had enormous effects on catering, tourism, lodging and other service industries, as well as the retail trade industry, and some employees in these industries are eager to start second careers in their spare time. When it comes to choosing a side job, e-commerce and short video careers have become the first choice. Literary careers, such as part-time writers and self-media professionals, and service careers, such as takeaway delivery persons and private-hire car drivers, are increasingly attractive to young people with slash careers. The sharing economy is another economic form that has created a great number of employment opportunities. The sharing economy is playing a larger role in promoting employment, upgrading the structure of the service industry and transforming consumption patterns. In 2019, new sharing economy business models in taxi, catering and accommodation services accounted for 37.1%, 12.4% and 7.3% of their respective industries, 20.5, 7.8 and 3.8% points higher than in 2016.7 The penetration rates of online ride-hailing, food delivery, accommodation sharing and healthcare sharing among Internet users amounted to 47.4%, 51.58%, 9.7% and 21%, respectively, 15.1, 21.58, 4.7 and 7% points higher than 2016.7 Due to COVID-19, the number of self-employed individuals has increased. Individual economies such as the street-stall economy have existed for a long time, but are now bursting with new life. The street-stall economy played a significant role in solving China’s unemployment issue in the 1980s and 1990s. However, it lacked effective management and was likely to bring forth food hygiene and safety issues, as well as urban traffic problems (Fig. 4.31). Therefore, it was almost banned across China for being filthy and disordered. In May 2020, the street-stall economy rapidly heated up, as Premier Li Keqiang praised some cities for taking the lead in recovering the street-stall economy and emphasised that the street-stall industry and the small shop industry provide many jobs for the society. Subsequently, various local governments of China introduced policies to support the development of the street-stall economy. According to incomplete statistics, as of June 4, 2020, at least 50 regions in China had explicitly encouraged the development of the street-stall economy through the establishment of specific areas for street stalls and other methods. They loosened 7

Source 0-3-4.

https://baijiahao.baidu.com/s?id=1660213476257875613andwfr=spiderandfor=pc.202

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127

Fig. 4.31 Street-stall economy. Source http://www.gov.cn/xinwen/2020-06/03/content_5516959. htm#3

restrictions on the activities of vendors mainly through setting up specific areas for vendors and allowing operations on roads. These regions also promulgated regulations stipulating the time and scope regarding setting up street stalls to encourage the recovery of the street-stall economy in cities. It can be seen from the number of regional supporting measures that these regions are mainly located in central and southwestern China (Forward Industry Research Institute, 2020).

4.4.1.2

The Growing Trend of Telecommuting

There is a remarkable trend of telecommuting, working from home, and mobile working. The progress of mobile and information technologies, flexible working systems, office hoteling (without assigned desks), teleworking and the advancement of communication technology are affecting people’s working and commuting methods (Páez & Scott, 2007; Kwan et al., 2007). In its broad sense, telecommuting refers to establishing a secure temporary connection on the Internet through a virtual private network to help company employees, branches, business partners, suppliers and other entities to establish connections with the company’s intranet. In its narrow sense, telecommuting refers to operating remote computers through remote control software or technology to realise remote office operations such as working from home, working away from offices and mobile working (Wang, 2020). Currently, most companies in China can only achieve telecommuting in a narrow sense. Compared with centralised working, telecommuting has the advantages of overcoming geographical restrictions, reducing employees’ time investment and cutting down enterprises’ operating costs. The disadvantages lie in its reliance on communication software and its potential weakening impact on corporate cohesion. Due to the COVID-19 pandemic in 2020, the market demand for telecommuting has increased, and there is a noticeable trend towards mobile working. In June 2020, there were only 199 million remote workers in China, and this figure rose to 346 million 6 months later, with an average half-year compound growth rate of 74% (Fig. 4.32). In June 2021, the number of remote workers in China reached 381

one hundred million

128

4 Lifestyle Changes in China 4.00 3.50 3.00 2.50 2.00 1.50 1.00 0.50 0.00

3.81

100%

3.46

80%

74%

60%

1.99

40% 20% 10% 0% 2020.6

2020.12 Number of remote workers

2021.6 Growth Rate

Fig. 4.32 Changes in the number of remote workers in China from June 2020 to June 2021. Source China Internet Network Information Centre (2021)

million, and its growth rate slowed down. The main reason is that China’s epidemic was basically brought under control in 2021: office working has gradually resumed in most regions, with the demand for telecommuting reducing.

4.4.1.3

Increase in Long-Distance Commuting

According to the Urban Transport Infrastructure Monitoring and Governance Laboratory of the Ministry of Housing et al. (2020),8 the average commuting time in major cities is 36 min. The average commuting time is 41 min in super large-sized cities, 37 min in megacities, and 34 and 33 min, respectively, in Type I and Type II large-sized cities.9 The commuting distances of Beijing, Shanghai, Chongqing and Chengdu are all over 9 km, requiring 40 min for commuting. These four cities are characterised by the longest commuting distances and times in China. Among them, Beijing ranks first with an average commuting distance of 11.1 km, requiring 47 min for commuting. It is the only city in China where the average one-way commuting time is over 45 min. Zhao et al. (2020) found that 15% of long commuters spend over 45 min on a one-way commute. More than 10 million people in 36 major cities 8

This report explores six aspects of urban commuting characteristics, namely, commuting range, spatial matching, commuting distance, pleasant commuting experiences, public transport services and track coverage in 36 major cities in China, using Baidu Map’s location-based services and data from mobile operators. It is expected to serve as a reference for policy formulation, urban planning, traffic organisation and academic research. 9 According to the State Council’s notice regarding applying new city classification standards, cities are classified into five tiers and seven types based on the permanent population. Cities with a permanent population under 500,000 are called small-sized cities, which can be divided into Type I, with a permanent population between 200,000 and 500,000 and Type II, with a permanent population under 200,000. Cities with a permanent population between 500,000 and 1 million are medium-sized cities. Cities with a permanent population of 1–5 million are defined as large-sized cities: those with a permanent population of 3–5 million are Type I large-sized cities, and those with a permanent population of 1–3 million are Type II large-sized cities. Cities with a permanent population between 5 and 10 million are called megacities, while those with a permanent population of more than 10 million are classified as super large-sized cities.

4.4 Activity Dimension: Work and Life in China

129

of China are suffering from commuting that takes over 60 min, accounting for 13% of the commuting population. Some 18% of the commuting population in central urban areas of super large-sized cities spend over 60 min on a one-way commute, and the figure is as high as 26% in Beijing. Long commuting takes up people’s time for daily exercise, sleep and so on, having an adverse impact on physical and mental health and affecting quality of life (Lyons & Chatterjee, 2008; Olsson et al., 2013). According to Sandow (2019), if one spouse commutes for more than 45 min, the couple is 40% more likely to get divorced.

4.4.2 Changes in Leisure Activities Leisure life is a lifestyle that involves the use or arrangement of one’s own free time, especially activities individuals engage in to meet their own physical, psychological, spiritual and cultural needs (Liu et al., 2018). It refers to the various ways in which people carry out leisure activities in their free time, which can show different people’s hobbies and traits of character. It is closely related to and interacts with the political, economic, cultural and other environmental characteristics of society.

4.4.2.1

A Sharp Reduction in Working Hours and Diversified Leisure Activities

There have been major changes in the allocation of three main aspects of residents’ time: the continuous reduction in working hours, the increasing time necessary for personal life, and the trend of diversification in leisure activities. With the launch and implementation of the paid holiday system, Chinese residents are enjoying more leisure time. A joint research group of the National Academy of Economic Strategy of the Chinese Academy of Social Sciences found that in 2018 the average number of paid holiday days per capita in China reached 7.7 days, an increase of 92% from 2012 when the last survey was conducted (Table 4.3). In addition, the average leisure time per capita was 2.8 h per day (excluding the time spent on schooling, work and sleep), which also marked an increase from 2.3 h in 2017. Regarding leisure activities, people were most willing to surf the Internet (71%), watch TV (55%) and go shopping (46%) during their leisure time. Growing travel opportunities for residents increase the safety requirements for travel (Rong et al., 2018). In the early stage of the reform and opening up, science and technology were poorly developed. Residents only had one travel pattern, and postal delivery was the sole method for correspondence. The consumption frequency was low, and residents did not spend much on transport and communications. Since China entered a period of rapid development, the transport and communications industry has aligned itself to national development, which is reflected in the continuous increase of residents’ expenditures on transport and communications.

Note. Source Song et al. (2017)

5

14

18

Change

6

19

24

2016

1996

Reading books

Reading newspapers

Year

19 7

99

26

Rest

−27

72

Watching TV

−32

42

10

Study and research

−1

11

10

Exercise

Table 4.3 Differences in leisure patterns in Beijing from 1996 to 2016 (Unit: Minutes per Day)

8

19

27

Strolling

−1

17

7

Interpersonal communication

−2

6

4

Educating children

130 4 Lifestyle Changes in China

4.4 Activity Dimension: Work and Life in China

4.4.2.2

131

Tourism: A Lifestyle

million trips

The domestic tourism market has been growing since 1994 (Fig. 4.33). In 1994, 524 million domestic drips were made. Of them, 205 million trips were made by urban residents, while 319 million trips were made by rural residents, accounting for 39.1% and 60.9%, respectively. In 2019, the number of domestic trips increased to 6,006 million, with an average annual growth rate of 10.2%. Among them, 4,471 million trips were made by urban residents, while 1,535 million trips were made by rural residents, accounting for 74.4% and 25.6%, respectively. This shows that urban residents have replaced rural residents as major participants in domestic trips. Tourism is gradually becoming an important part of Chinese residents’ everyday lives. The outbound tourism market has developed rapidly, with its scale gradually expanding and the industry being increasingly open (Fig. 4.34). In 1994, the number of outbound trips from China for private purposes was 16.42 million, which increased to 1,621.14 million in 2019. Compared with domestic tourism, China’s outbound tourism saw a greater increase from 1994 to 2019. The Annual Report of China Outbound Tourism Development released by the China Tourism Academy states 7000 6000 5000 4000 3000 2000 1000 0

Domestic trips

Domestic trips by urban residents

Domestic trips by rural residents

ten thousand trips

Fig. 4.33 Changes in the number of domestic trips from 1994 to 2019. Source Ministry of Culture and Tourism 200000

60% 50%

150000

40%

100000

30% 20%

50000

10%

0

Number of outbound trips for private purposes

2019

2017

2018

2016

2015

2013

2014

2011

2012

2010

2009

2008

2007

2006

2005

2004

2003

2001

2002

2000

1998

1999

1997

1996

1995

1994

0%

Growth Rate

Fig. 4.34 Changes in the number of outbound trips from 1994 to 2019. Source Ministry of Culture and Tourism

132

4 Lifestyle Changes in China

that in 2018 the top 15 outbound tourist destinations with the largest market share of Chinese tourists were: Hong Kong, Macao, Thailand, Japan, Vietnam, South Korea, the United States, Taiwan, Singapore, Malaysia, Cambodia, Russia, Indonesia, Australia and the Philippines. Although the tourism data for China in 2020 have not yet been released, it is already foreseeable that both the number of trips and income will decline from 2019, mainly due to the raging COVID-19 pandemic around the world. COVID-19 has exerted a major influence on the tourism industry: to prevent its spread, the Chinese government requires residents to reduce unnecessary interregional trips, with the domestic tourism industry being the first to be affected. One-hour short-distance leisure tours in peripheral regions of cities are popular. Residents mainly relax on weekends and short holidays. With the changes to the Labour Day Golden Week,10 the Spring Festival and the National Day are the only 7-day holidays left; the rest are all 3-day short holidays. Residents are having fewer long holidays. The travel radius of 1-day tours is about 140 km, and that of 2-day tours is about 300 km. Tours in peripheral regions of large-sized cities can cover these two spheres. Tours in peripheral regions of cities are short, cheap and fast, which can cater to consumers’ new consumption habits and personal traits. It is worth noting that peripheral travel mostly lasts 2–3 days. Short holidays such as the Qingming Festival, Labour Day and Dragon Boat Festival are the best times for residents to travel, and Saturday is the peak time for travel. In addition, peripheral travel only costs a few hundred yuan, which also improves the frequency of residents’ peripheral travel. Peripheral travel products are also getting more and more diverse, including outings in spring and farm stays. As farmers’ income and consumption levels increase, they will gradually become important subjects of tourism consumption. Income growth, scattered holidays and the development of self-driving tours and high-speed rail will spur the constant improvement of the penetration rate of peripheral travel in the next 5 years, with first-tier cities being the key markets. First of all, short holidays are scattered throughout the year. For busy office workers, tours in nearby regions during holidays and weekend tours are particularly suitable. Second, transport networks and facilities are well developed now. On the one hand, private cars are quite popular in China, and most self-driving people will choose excursions in nearby regions. On the other hand, the layout of China’s high-speed rail network has been improving, and high-speed rail transport is a fast and convenient vehicle for peripheral travel. Third, first-tier cities, such as Beijing, Shanghai, Guangzhou

10

In 1999, the State Council amended the Measures for Holidays on National Festivals and Commemoration Days, deciding to form 7-day holidays by adding the weekends before and after to the Spring Festival, Labour Day and National Day holidays. In June 2000, the General Office of the State Council reposted Several Proposals on Further Developing Holiday Tourism by nine ministries and commissions, including the China National Tourism Administration. The Golden Week holiday scheme was thus formally established in China. In December 2007, the Decision of the State Council on Amending the Measures for Holidays on National Festivals and Commemoration Days clarified China’s future holiday scheme from 2008: it replaced the Labour Day Golden Week with a 3-day holiday.

4.4 Activity Dimension: Work and Life in China

133

and Shenzhen, are key areas for the development of peripheral travel. Their advanced economies and high population densities signify a great demand for peripheral travel.

4.4.2.3

Robust Supply and Demand Regarding Online Cultural Leisure Activities, with a Surge in the Number of Live Broadcast Users

Due to the evolving Internet technology and increasingly diverse recreational modes, mobile entertainment that transcends the limitations of time and space is gaining importance in Chinese people’s leisure life. According to (Song et al., 2020), in 2020, the average daily online leisure time of Chinese reached 4.9 h. Men and women spent roughly the same time on online leisure activities. For people of different age groups, the younger they were, the more time they spent online for leisure purposes. For people with different educational backgrounds, the lower the educational attainment, the longer the online leisure time. Online leisure activities have accelerated the transformation of recreational modes. In addition to traditional online leisure activities such as chitchat on social media, watching film and television, listening to music online, listening to Internet radio and playing games, new online leisure activities, such as watching short videos, watching live broadcasts and online travel, have also emerged in the past 2 years. Live broadcasts are the most eye-catching online cultural leisure activities due to their rapid development. Till June 2021, there were 638 million live broadcast users in China, accounting for 63.1% of Internet users worldwide. Live broadcast users increased by 75.39 million year-on-year. Of them, 384 million were users of live e-commerce broadcasts, a year-on-year increase of 75.24 million, accounting for 38.0% of Internet users; 264 million were users of live game broadcasts, a year-onyear decrease of 4.52 million, accounting for 26.2% of Internet users; 177 million were users of live reality shows, a year-on-year decrease of 8.75 million, accounting for 17.6% of Internet users; 130 million were users of live concerts, a year-on-year increase of 8.96 million, accounting for 12.8% of Internet users; and 246 million were users of live sports, a year-on-year increase of 53.05 million, accounting for 24.4% of Internet users (China Internet Network Information Centre, 2021).

4.4.3 Increased Employment Hours, Reduced Domestic Labour Hours and Increased Modernisation Over the past decade, residents’ paid working hours have decreased. In 2018, residents’ paid working hours per day stood at 4 h and 24 min, dropping by 4 min from 2008 (Fig. 4.35). The paid working hours fell by 1.5%, while the per-capita GDP almost doubled and there was an obvious increase in the production per unit time, indicating the continuous improvement of productivity over the past 10 years. Such a reduction in the time spent on paid working hours could be attributed to the sharp

134

4 Lifestyle Changes in China

Family production by females Family production by males Female employment Male employment Family production Employment 0

50

100

150

200

2018

2008

250

300

350

400

450

500

Fig. 4.35 Changes in paid working hours in China in 2008 and 2018. Source Compilation of statistics from the 2008 time use survey and report on the 2018 time use survey in China

reduction in the time spent on domestic labour activities. The time spent on domestic labour activities decreased by 32 min from 2008 to 1 h and 27 min, accounting for 6% of a day, down by 2.2% points. Meanwhile, residents’ input of time in employment increased by 28 min to 2 h and 57 min, from 2 h and 29 min in 2008, accounting for 12.3% of a day, up by 1.9% points. The reduction in the time for domestic labour activities and the increase in the time spent on employment reflect the acceleration of urbanisation and the modernisation of domestic labour. From the perspective of participants, the working hours of both employed workers and those engaged in domestic labour have increased from a decade ago, but the rates of participation in employment and domestic labour have reduced. In addition, males worked longer than females did. The average working hours of male employees per day increased by 1 h and 26 min from 2008 to 7 h and 52 min in 2018, while the average working hours of female employees per day grew by 1 h and 20 min from 2008 to 7 h and 24 min. The working hours of males and females engaged in domestic labour activities were 6 h and 40 min and 5 h and 47 min, respectively, both up by 14 min from 2008. In 2018, the rate of participation in employment stood at 38%, down by 1% point from 2008, while the rate of participation in domestic labour dropped by 10% points to 23% from 33% of 2008.

4.5 Time Dimension: Chinese Residents’ Time Use and Transport Time use refers to the input of time by residents into different types of activities that describe their daily lives. In 2018, the National Bureau of Statistics conducted the second national survey on time use in 11 provinces and cities, sampling a total of 48,580 people from 20,226 households. Compared with the first time-use survey conducted in 2008, the second one revealed a vast change in the allocation of time by residents and reflected the economic growth of society. Transport time is significantly shortened, and commuting is more convenient and efficient.

4.5 Time Dimension: Chinese Residents’ Time Use and Transport

135

4.5.1 General Characteristics of Time Use Chinese residents have gradually changed from pursuing basic food and clothing to pursuing other leisure activities. Generally speaking, with the development of China’s economy, Chinese residents are spending less time on meeting their basic physical needs, and they have gradually shifted their focus from basic needs to leisure activities (Fig. 4.36). The time used for necessary physical activities has increased, indicating that residents are adopting a healthier way of living. In 2018, the time spent on necessary physical activities increased by 19 min from 2008 to 11 h and 53 min, accounting for 49.5% of a day, up by 1.3% points. The time spent on sleep increased by 17 min from 2008 to 9 h and 19 min, accounting for 38.8% of a day, up by 1.2% points. The time used for personal hygiene care reduced by 2 min from 2008 to 50 min. The time used for eating and drinking activities increased by 4 min from 2008 to 1 h and 44 min. The increase in the total time spent on sleep and eating reflects a healthier way of living.

4.5.2 Significant Changes in the Structure of Unpaid Working Hours, and Increased Time Spent with Family Members Compared with 10 years ago, the time residents spent on housework decreased, while the time spent with family members increased. In 2018, the average unpaid working hours of residents per day were 2 h and 42 min, rising by 12 min from 2008 (Fig. 4.37). The time spent on housework decreased by 17 min to 1 h and 26 min, accounting for 6% of a day, down by 1.2% points. The time spent with family members and used to take care of them (including the time spent with children and used to take care of their life, escort and tutor them, and take care of senior family members) increased by 1.3 times or from 30 min from 2008 to 53 min, 85% of which was used to take care of children’s life and study. The time spent with family members and used to care for them accounted for 3.7% of a day, up by 2.1% points. The significant changes in the structure of unpaid working hours indicate that more attention was paid to family members, especially children’s education. In addition, housework became more socialised. The rapid development of social services such as food delivery and hourly work contributed to the reduction of the time spent on housework.

4.5.3 More Reasonable Allocation of Disposable Personal Time, and Increased Time Spent on Leisure and Fitness From 2008 to 2018, the disposable personal time increased by 12 min. Watching TV was still the major leisure pattern, though the proportion of time spent on this activity

136

4 Lifestyle Changes in China Teaching and Traffic, 2.70% training, 1.90%

Free activities, 16.40%

Sleep, 38.80%

Unpaid work, 11.30% Personal hygiene care, 3.40%

Paid work, 18.30% Dining or other eating and drinking activities, 7.20%

(a) 2008 Teaching and Traffic, 5.2% training, 2.0%

Free activities, 15.6%

Sleep, 37.6%

Unpaid work, 10.4% Paid work, 18.6% Personal hygiene care, 3.6% Dining or other eating and drinking activities, 6.9%

(b) 2018 Fig. 4.36 Time use by Chinese residents per day in 2008 and 2018. Source Compilation of statistics from the 2008 time use survey and report on the 2018 time use survey in China

4.5 Time Dimension: Chinese Residents’ Time Use and Transport

137

Time spent with family members and used to care for them Time spent on housework

Unpaid work 0

20

2018

40

60

80

100

120

140

160

180

2008

Fig. 4.37 Changes in unpaid working hours in China in 2008 and 2018. Source Compilation of statistics from the 2008 time use survey and report on the 2018 time use survey in China

dropped by 26 min in 2018 to 1 h and 40 min, accounting for 6.9% of a day, down by 1.8% points (Fig. 4.38). The time spent on leisure and entertainment increased by 25 min from 2008 to 1 h and 5 min, accounting for 4.5% of a day, up by 1.7% points. The time spent on fitness increased by 8 min. Above all, the total time spent on leisure, entertainment and fitness increased. A comparison between the cities and the countryside shows that urban residents had more disposable personal time than rural residents did and that the gap between the two significantly narrowed. In 2018, the disposable personal time of urban residents was 37 min more than that of rural residents, while the difference between the two in 2008 stood at 82 min. The time spent on watching TV and leisure and entertainment changed most significantly. Urban residents spent 35 min less on watching TV than in 2008, while rural residents reduced the time by 13 min. Urban residents spent 16 min more on leisure and entertainment than in 2008, while rural residents increased the time by 33 min. Fitness Leisure and entertainment Watching TV Disposable personal time of rural residents Disposable personal time of urban residents Disposable personal time 0

50 2018

100

150

200

250

300

2008

Fig. 4.38 Changes in the disposable personal time of Chinese residents in 2008 and 2018. Source Compilation of statistics from the 2008 time use survey and report on the 2018 time use survey in China

138

4 Lifestyle Changes in China

4.5.4 Acceleration of Informatisation and Substantial Increase in Time Spent Online Ten years ago, when there was little wireless network coverage, smartphones were not popular and people accessed the Internet mostly through wired computer networks. The average time spent online per day was only 14 min. With the rapid development of the Internet, particularly the popularisation of mobile phones, PDAs and computers, the time spent online by residents per day increased by 2 h and 28 min from 2008 to 2 h and 42 min in 2018. The rapid increase in the time spent online reflects the improvement in people’s living standards and the advancement of science and technology.

4.5.5 Participation and Time Characteristics of Transport Activities and Changes in Commuting Time In 2018, residents spent 38 min a day on transport activities, accounting for 2.7% of a day, which was nearly half of the 75 min in 2008, a drop of 2.6% points (Fig. 4.39, Tables 4.4 and 4.5). Among them, the commuting time for office workers was reduced by 16 min compared with 2008, and the commuting time for students was reduced by 27 min. The shortening of transport time reflects the continuous improvement of transport facilities, transport means and the degree of convenience. The average time spent by residents on transport activities was 38 min: 44 min for urban residents and 30 min for rural residents. The average participation rate of residents in transport activities was 50.8: 56.9% for urban residents and 41.3% for rural residents. The average participation rate of residents in transport activities related to paid labour was 36.4%. The average time spent by residents on transport activities related to paid labour was 61 min: 63 min for men and 58 min for women; 63 min for urban residents and 56 min for rural residents (Fig. 4.40). 50 40

44 38

56.9%

50.8%

30

30

41.3%

20 10 0 residents

urban residents Time spent on transport in a day/min

60.0% 55.0% 50.0% 45.0% 40.0% 35.0% 30.0% 25.0% 20.0%

rural residents Participation Rate

Fig. 4.39 Residents’ daily transport time in 2018. Source Compilation of statistics from the 2008 time use survey and report on the 2018 time use survey in China

4.5 Time Dimension: Chinese Residents’ Time Use and Transport

139

Table 4.4 Average time spent on main activities by residents in 2018 Activity category

Total

Total

1,440 1,440 1,440

Males Females Cities Countryside 1,440 1,440

1. Activities of daily living

713

708

718

713

713

Sleep and rest

559

556

562

556

563

50

48

52

52

47

Eating

104

104

105

105

103

2. Paid labour

264

315

215

239

301

Employment

177

217

139

197

145

Domestic labour

87

98

76

42

156

3. Unpaid labour

162

92

228

165

159

Housework

86

45

126

79

97

Accompanying and caring for family members

53

30

75

58

45

Purchasing goods or services (including seeking medical advice)

21

15

26

25

14

Personal hygiene care

Public service activities 4. Discretionary activities Exercise

3

3

3

3

2

236

253

220

250

213

31

32

30

41

16

6

6

5

6

5

100

104

97

98

104

9

11

8

12

5

Leisure and entertainment

65

73

58

69

58

Social communication

24

27

22

24

25

5. Training and learning

27

28

27

29

24

6. Transport activities

38

44

33

44

30

162

174

150

203

98

Listening to the radio or music Watching TV Reading

Others: web use Note. Source National Bureau of Statistics (2018)

The average participation rate of residents in employment-related transport activities was 30.3%. The average time spent by residents on employment-related transport activities was 66 min: 67 min for men and 63 min for women; 66 min for urban residents and 65 min for rural residents (Fig. 4.41). The average participation rate of residents in transport activities related to domestic labour was 6.4%. The average time spent by residents on transport activities related to domestic labour was 35 min: 37 min for men and 33 min for women; 34 min for urban residents and 36 min for rural residents (Fig. 4.42). The average participation rate of residents in transport activities related to learning and training was 3.6: 4.4% for urban residents and 2.3% for rural residents. The average time spent by residents on transport activities related to learning and training was 53 min: 62 min for residents aged 15 to 19.

140

4 Lifestyle Changes in China

Table 4.5 Average time spent on main activities by residents in 2008 Activity category

Total Males Females Cities Countryside

Sleep and rest

559

Personal hygiene care

556

562

556

563

50

48

52

52

48

Eating

105

104

105

105

104

Employment

461

472

444

457

468

Domestic labour

374

400

347

361

380

Housework

147

110

166

137

163

Time spent with children and used to take care of them

188

137

211

177

210

Time spent on escorting and tutoring children

92

85

96

94

88

Time spent with adult family members and used 163 to take care of them

157

167

162

164

80

79

80

78

84

Seeking medical advice

158

175

145

153

169

Public service activities

62

67

58

61

65

101

103

99

106

86

Purchasing goods or services

Exercise Listening to the radio or music

84

86

83

81

91

151

154

148

153

147

92

95

88

90

97

Leisure and entertainment

159

165

152

158

159

Social communication

136

142

129

135

136

Training and learning

372

394

353

365

384

75

78

72

76

73

Watching TV Reading

Transport activities Note. Source National Bureau of Statistics (2008) Rural participants

56

Urban participants

63

Female participants

58

Male participants

63

All participants

61 52

54

56

58

60

62

64

Fig. 4.40 Average time spent on transport activities related to paid labour in 2018. Source Compilation of statistics from the 2008 time use survey and report on the 2018 time use survey in China

4.6 Chinese Residents’ Values and Transport

141

Rural participants

65

Urban participants

66

Female participants

63

Male participants

67

All participants

66 61

62

63

64

65

66

67

68

Fig. 4.41 Average time spent on employment-related transport activities in 2018. Source Compilation of statistics from the 2008 time use survey and report on the 2018 time use survey in China

Rural participants

36

Urban participants

34

Female participants

33

Male participants

37

All participants

35 31

32

33

34

35

36

37

38

Fig. 4.42 Average time spent on transport activities related to domestic labour in 2018. Source Compilation of statistics from the 2008 time use survey and report on the 2018 time use survey in China

4.6 Chinese Residents’ Values and Transport 4.6.1 Diversification of Value Orientations and Pursuit of Quality of Life Due to economic development, the vast majority of urban households in China have begun to shift from subsistence to affluence; meanwhile, people’s consumption structure has also changed from survival to enjoyment. The consumption focus of residents has shifted, expenditures on housing, transport, communications, education, tourism, and other areas have increased significantly, and the urban lifestyle has gradually become increasingly personalised, modern and diversified in line with international standards.

142

4 Lifestyle Changes in China

4.6.2 An Obvious Trend of Fast-Paced and Convenient Life Among Residents A fast-paced life refers to a lifestyle featuring a rigorous sense of time, high efficiency, strong adaptability and competitiveness, which is also a current social phenomenon. The traditional way of production and life has been changed by a new round of scientific and technological revolution. New and advanced technologies, such as information, the Internet, big data and cloud computing, have begun to influence all aspects of people’s daily life. For one thing, the needs of residents are constantly being met; on the other hand, information technology is creating new demands. It can be predicted that the cross-sector integration and infiltration led by Internet plus will continue to develop, and the internet will further penetrate all fields of production and life, changing the way of life while presenting new products, new business types and new business models. In this case, social development requires more diversified transport means, and transport needs reform to adapt to residents’ lifestyles.

4.6.3 A Green and Low-Carbon Lifestyle: The Demand of the Times China made a solemn promise at the 75th United Nations General Assembly: to strive to reach a peak of carbon dioxide emissions by 2030 and strive to achieve carbon neutrality by 2060. Carbon peaking and carbon neutrality have become a national strategy, which will change the production methods, lifestyles and spatial patterns of Chinese cities. According to the report of the 19th CPC National Congress, “Building an ecological civilisation is vital to sustain the Chinese nation’s development. We must realise that lucid waters and lush mountains are invaluable assets and act on this understanding”. Promoting green development and lifestyles is a profound revolution in development philosophy. China will take the promotion of green consumption to a new height; strengthen the publicity and education regarding ecological civilisation in all respects; raise people’s environmental awareness and facilitate the formation of a moderate, low-carbon, civilised and healthy way of life and consumption mode, in an effort to create a decent fashion for the common participation from all walks of life. A low-carbon life means reducing the energy used in daily life as much as possible to cut the combustion of carbon-containing materials, especially carbon dioxide emissions, thus slashing atmospheric pollution and slowing down ecological deterioration and the greenhouse effect. Transportation is one of the main industries that emit carbon dioxide. In the transportation industry, cars, road transportation and aviation are the three branches that emit the most carbon dioxide (Chapman, 2007). People can take small actions in life such as electricity saving, gas conservation and recycling. On the ecological front, the “implementation of low-carbon life and moderate

4.6 Chinese Residents’ Values and Transport

143

Table 4.6 Completion of the transport goals in the 14th five-year plan for Beijing, Shanghai, Guangzhou and Shenzhen Beijing Goal

Shanghai 2015 data Goal

Proportion 76.5% 70.7% of green travel in central urban areas

Guangzhou

2015 data Goal

≥75% /

Shenzhen

2015 data Goal

≥70% 67.5%

2015 data

≥81%* /

*

Green travel share Source Annual Reports of Urban Traffic Development in these cities

consumption” means that the specific consumption behaviour should adapt to the actual conditions of the natural environment and match the level of personal income (Gao & Zhang, 2008, p. 18). Low-carbon living habits and living standards are being implemented. The Ministry of Housing and Urban-Rural Development among others issued the Circular on Comprehensively Carrying out the Classification of Household Waste in Cities at and above the Prefecture-Level across China. Since 2019, the classification of household waste has been fully implemented in cities at and above the prefecture level. China is steadily promoting the construction of a water-saving society, introducing a string of national policies and concepts to champion a low-carbon lifestyle. Green travel will become the prevailing travel pattern in the future. Bicycle travel is a green mode of travel, so many countries are vigorously promoting bicycle travel, including the Netherlands and Germany (Pucher & Buehler, 2008). Many domestic cities, including such first-tier cities as Beijing, Shanghai, Guangzhou and Shenzhen, have formulated demand management policies for car travel (Table 4.6). However, the sharing proportion of car travel is still too large, and road congestion persists. Meanwhile, the decreasing trend of the proportion of non-motor vehicles is apparent. Hence, it is necessary to ramp up efforts to invest in and reconstruct non-motorised traffic to create a proper road environment for green travel.

4.6.4 More Attention Paid to Inclusive Development and Social Equity In terms of urban transport development, relying solely on the construction of more roads, airports and public transport infrastructure will only aggravate rather than address the environmental and economic problems stemming from urban growth. In addition, more capacity expansion will intensify complexity and induce high costs. At the same time, it will exert some impact on social equity. For example, many cities are building new overpasses and railways; indeed, overpasses are no longer popular

144

4 Lifestyle Changes in China

in the United States, and many residents are concerned about the noise, pollution, visual impact and divided communities such roads cause. Inclusiveness is also worth our attention, like how to determine the level of service in relatively poor areas and the share of costs that should be apportioned to users. For example, Malaysia’s transport system is relatively inclusive, which allows motorcycles (a vehicle adopted by many poor people) to travel free of charge on expressways. To achieve traffic fairness in the future, it will be necessary to address different vulnerable groups from various angles, and each traveller may belong to a vulnerable group under certain circumstances. In the process of realising travel fairness, there are vulnerable groups with the following three hallmarks: first, vulnerable groups regarding transport means, such as those who travel on foot or by bicycle rather than travelling by motor vehicle; second, socially vulnerable groups, such as lower income travellers and those with low social status rather than higher level travellers; third, physiologically vulnerable groups, such as those with weak physical strength and even certain physiological defects rather than the able-bodied (Su, 2014).

4.7 Summary This chapter has analysed the connections between Chinese residents’ lifestyles and transport from the perspectives of consumption, transport means, environmental facilities, activities, time use and value orientations. (1)

(2)

(3)

Consumption: Since the reform and opening up, Chinese residents have experienced an evolution from a single consumption pattern in the planned economy era to diversified, high-quality consumption patterns, and online shopping has grown steadily. The consumption focus has shifted from basic material needs to spiritual needs, and from offline shopping to online shopping. The proportion of transport consumption of Chinese residents keeps increasing. Transport means: China’s motorisation process has been advancing rapidly since the reform and opening up. The pace of motorisation is basically equivalent to economic growth. The motorisation process in China is mainly characterised by an increase in private cars. However, the per-capita motor vehicle ownership in China is still lower than in major developed countries. Though the figure is expected to grow, China has attempted to reduce the level of motorisation in areas with traffic congestion through the development of public transport and the car purchase restriction policy. In China, cars are mainly used for commuting and leisure travel. Environmental facilities: China is equipped with a relatively complete regional transport system of highways, railways, air and water routes, conducive to the convenience and diversification of residents’ everyday trips and lifestyles. The urban road system constantly improves, and the proportion of public transport trips keeps growing. In terms of urban spatial organisation, the residential areas in China’s cities have undergone a transformation from the work-unit

4.7 Summary

(4)

(5)

(6)

145

system to the community system with the influence of the market economy, and workplace–residence separation has intensified. Activities: Employment patterns are getting more flexible and diversified. Due to COVID-19, more people are engaged in self-employment and part-time jobs. There are also a growing number of remote workers, and the commuting distance and time in major cities are increasing. Leisure activities are becoming increasingly diversified and individualised. A new lifestyle—leisure tourism— has come into being. More online leisure activities are available against the backdrop of the COVID-19 pandemic, and more time is spent on them. Time use: Chinese residents have gradually shifted their focus from basic needs to leisure activities. Increased employment hours, reduced time spent on domestic labour and more family time reflect a higher level of modernisation. Chinese residents allocate their disposable personal time in a more reasonable way. They spend more time on leisure and fitness and less time on transport, indicating the greater convenience and higher efficiency of commuting. There is also a dramatic increase in time spent online due to the rapid development of information technology. Value orientations: Chinese residents’ value orientations have become diversified in the pursuit of quality life. The urban lifestyle has become increasingly personalised, modern and diversified in line with international standards. While the pace of life is becoming faster, residents are also enjoying a more convenient life. Inclusive development and social equity are increasingly being valued. A green and low-carbon lifestyle has become the demand of the times. Green travel will be the prevailing travel pattern in the future.

By describing the relationship between the six aspects of lifestyle and transportation in detail, this chapter offers a systematic introduction to understanding the characteristics of Chinese residents’ lifestyle and traffic behaviour. Lanzendorf (2002) proposed that in addition to the variables of the traditional model, travel behaviour analysis and personal transportation choices should also consider lifestyle factors such as personal attitudes, values and orientations. Western academia has made rich research and progress in this area (Anable, 2005; Choo & Mokhtarian, 2004). However, the research in this area in Chinese academic circles is not sufficient. So far, there is a lack of a systematic introduction to the relationship between Chinese residents’ lifestyle and travel mode. This is the purpose of this chapter. This chapter provides a total analysis of the relationship between six aspects of lifestyle and travel behaviour, which may be helpful to enhance the understanding of domestic and foreign scholars on the characteristics of Chinese residents’ life attitudes and traffic behaviours. This chapter may also help policymakers to modify and improve future transportation policies. In each section, we have tried our best to include and collect the policies and measures called for by the Chinese government in related areas, which can help policymakers to review past developments. At the same time, with the knowledge that lifestyles may affect all aspects of traffic behaviour, policymakers

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can also analyse the possible consequences and impact of new policies in a more comprehensive manner.

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Chapter 5

Impacts of Lifestyles on Transport

5.1 Impacts of Lifestyles on Transport Volumes The differences between lifestyles manifest the peculiarities of each individual. A growing number of scholars has discovered that different lifestyles may cause different impacts on society and the environment by different groups. Against the backdrop of sustainable development, what kind of lifestyles can help to build a sustainable society is a concern of academia (Heinonen et al., 2013). For instance, Western families that rely heavily on cars as a travel mode are eight times more harmful to the environment than families who choose green travel (Christensen, 1997). A similar conclusion has been drawn for China. Subjective awareness has a significant impact on household carbon emissions, while lifestyle plays a role in the relationship between subjective awareness and actual emissions (Li et al., 2019). Lifestyle research is a microanalysis. By comparing people with different lifestyles, scholars can study their impacts on the environment and society. Lifestyle is closely related to living standard, which can be used as a manifestation of modern lifestyle. Therefore, this chapter measures the impacts of lifestyles on transport volumes. Richard (2000) summarised the changes in the definition and indicators of living standards since 1800. In the post-World War II period, per-capita GDP was mainly used to represent residents’ living standards, but a sole emphasis on GDP might not generate authentic assessment results. Since then, indicators such as life expectancy (health), educational level (culture) and individuals’ sense of well-being (psychology) have been taken into consideration and become important indicators for measuring living standards. Transport volume, including passenger volume and freight volume, is an important indicator in the macro-field of transport, which can reflect the development level of urban and regional transport and the connectivity of the transport network. Some previous studies have focused on the differences in transport volumes between cities and related objective factors. For example, an analysis of the levels of and changes in passenger volume in major cities in China’s domestic passenger airline network shows that there are great differences in the development of air transport in Chinese © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. Zhao and D. Lyu, Lifestyle Change and Transport in China, Population, Regional Development and Transport, https://doi.org/10.1007/978-981-19-4399-7_5

151

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cities (Song et al., 2008). The spatial connections generated by air passenger transport are mainly found in eastern China, while central China lags behind western China in the importance of the air network. Beijing, Shanghai, Guangzhou and Shenzhen constitute the backbone of the national air network. Zhang and Zhang (2007) made a detailed analysis of the air passenger volume between cities. The results show that the distance between cities, airport passenger throughput, population density, total postal and communications services, number of public transport vehicles per capita, nature of cities and GDP are closely related to the air passenger volume of cities. There are similar studies abroad. Matsumoto (2004) introduced a gravity model composed of GDP, population and distance to define the degree of hubness of major cities in the world from 1982 to 1998. The results show that Tokyo, Hong Kong and Singapore in Asia, London, Paris, Frankfurt and Amsterdam in Europe, and New York and Miami in the United States are strengthening their status as international transport hubs.

5.1.1 Data Source The statistical data for this study came from sources such as the China Statistical Yearbook, the China Urban Construction Statistical Yearbook, the China Health Statistical Yearbook, the China Statistical Yearbook on Environment and the National Bureau of Statistics, with 365 prefecture-level cities in China as research targets.

5.1.2 Building Lifestyle Indexes The living standard is a key index of lifestyles. Due to the difficulty of collecting data related to lifestyles at the overall level, this study uses the living standard as a measure of lifestyles. As for the impacts of lifestyles on transport volumes, indexes are constructed at the overall level for the analysis of the living standards of Chinese urban residents (Table 5.1). However, from a micro-perspective, the living standards of specific individuals or a minority of people cannot represent those of Chinese urban residents as a whole. Meanwhile, considering that China is still at an early stage of socialism, where higher spiritual needs will not be pursued until material needs are sufficiently satisfied, only objective indexes are selected to reflect China’s social development and residents’ living standards accurately. According to all the available statistical data, there is a significant lack of subjective indexes that can be derived from field investigations. Due to limits in time and energy, however, it is impossible to conduct such field investigations using a large number of samples. There is also no fixed standard for reference when it comes to subjective indexes such as personal feelings, interpersonal relations and family relations. In light of these factors, only objective indexes are applied in this study to the analysis of living standards. A review of previous studies at home and abroad, especially those on the

5.1 Impacts of Lifestyles on Transport Volumes

153

Table 5.1 Index system for the overall lifestyle of urban residents in China System

Field

Dimension

Index

Economy

Economic growth

Economic conditions

Per-capita GDP

Material welfare

Income level

Average wage of employed workers

Society

Health

Fiscal conditions

Public fiscal expenditure

Consumption level

Total retail sales of social consumer goods

Health resources

Number of beds in hospitals and healthcare institutions per 1,000 people Number of physicians (physicians + assistant physicians) per 1,000 people

Education

Educational resources

Teacher–student ratio in regular institutions of higher education Number of teachers per 1,000 people

Educational results

Number of students in regular institutions of higher education per 100,000 people

Housing

Housing conditions

Investment in residential development

Living facilities

Transport facilities

Area of urban roads owned per person

Communication facilities

Percentage of mobile phone users Percentage of broadband users

Resources and the environment

Resource and environmental conditions

Infrastructure

Expenditure on urban maintenance and construction

Resources

Popularity rate of natural gas for daily use

Environment

Harmless treatment rate of urban living waste

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5 Impacts of Lifestyles on Transport

construction of indexes of living standards, reveals that a variety of index standards has been developed based on different foci. In this study, an index system is developed from the three dimensions of economy, society and resources and the environment in light of the actual conditions in China, to reflect the living standards of urban residents. The measurement indexes are as follows. The economic system is at the core of residents’ living standards as a material guarantee of their life, which consists of economic growth that reflects the overall economic conditions and material welfare that reflects residents’ income and consumption levels and the government’s public expenditure. Economic growth reflects the overall social production capacity, while material welfare sheds light on the benefits residents gain from the total economic output of society. The social system measures the basic types of resources provided by society for the development of local residents, covering fields such as health, education, housing and living facilities. The resource and environmental system reflects the relationship between human activities and the ecological environment. As an integral part of the living standard, sound resource and environmental conditions guarantee the improvement of the living standard. This system includes the two dimensions of resources and the environment, and representative indexes are selected on the basis of these two dimensions.

5.1.3 Comprehensive Assessment of the Overall Lifestyle Indexes selected in Table 5.1 for measurement of residents’ living standards are adopted to assess residents’ living standards in Chinese prefecture-level cities in 2016 comprehensively. Based on the availability of data, representative indexes that can reflect the living standards of urban residents and compare different regions are selected. Quantitively, factor analysis is adopted as a statistical method for dimension reduction. On the premise of minimum loss of original information, unrelated indexes are extracted from a large set of indexes to obtain the variance contribution rate, determine the weight and calculate the total score for assessment and analysis. Factor analysis can avoid the impact of human factors as much as possible and generate more scientific and objective findings. Due to differences between the selected indexes in dimensions, it is impossible to perform calculation directly. It is therefore necessary to eliminate such differences by standardising the statistical data to ensure their relative stability. The formula is expressed as follows: Xn =

x−x . σ

(5.1)

5.1 Impacts of Lifestyles on Transport Volumes Table 5.2 KMO statistic and the result of Bartlett’s test of sphericity

5.1.3.1

155

KMO Statistic Bartlett’s test of sphericity

0.866 Approx. chi-square

2,770.985

df

120

Sig

0.000

Feasibility of Factor Analysis

Factor analysis constructs a small number of relatively representative factor variables from a large set of original variables. It requires a strong correlation among original variables to help to extract the common factor variables that can reflect the commonality of certain variables. A Bartlett’s test of sphericity and a Kaiser–Meyer–Olkin (KMO) test were used for factor analysis. If a KMO test returns a value larger than 0.6, then it is suitable for factor analysis. If the results of Bartlett’s test of sphericity indicate that different variables are not independent, then factor analysis will be applicable (Table 5.2).

5.1.3.2

Extraction of Common Factors and Variance Contribution Rates

In factor analysis, there are generally two key standards for extracting common factors: a cumulative contribution rate above 70% and an eigenvalue greater than 1. As Table 5.3 shows, there are four factors with eigenvalues greater than 1, with their cumulative variance contribution rate amounting to 68.91%. These factors basically meet the requirements and can reflect most information for all variables. Among them, the first common factor, with an eigenvalue of 6.38, can explain 39.9% of the total variance of the original variables. The second common factor, with an eigenvalue of 2.17, can explain 13.6% of the total variance of the original variables, causing the cumulative variance contribution rate to reach 53.4%. The third common factor, with an eigenvalue of 1.28, can explain 8.0% of the total variance of the original variables, causing the cumulative variance contribution rate to reach 61.4%. The fourth common factor, with an eigenvalue of 1.20, can explain 7.5% of the total variance of the original variables, causing the cumulative variance contribution rate to reach 68.9%. Thus, the four extracted common factors represent residents’ living standards in 365 prefecture-level cities of China well. Figure 5.1 shows that the eigenvalues of the first four common factors are relatively greater than others, and they contribute much to explaining the original variables, while the remaining factors with smaller eigenvalues are not representative enough. This shows that the first four factors include most information and are highly representative.

0.062

16

100.000

99.615

98.886

97.546

95.958

94.022

92.057

89.791

86.827

83.617

79.523

74.690

68.907

61.433

53.447

39.885

Extraction method: principal component analysis (PCA)

0.385

1.340

0.729

0.214

0.117

14

1.588

0.254

13

15

1.964

1.937

0.314

0.310

11

12

2.266

0.363

10

3.211

2.964

0.514

0.474

4.094

8

0.655

7

4.833

5.783

7.474

7.986

13.562

39.885

9

0.925

0.773

5

1.196

4

6

2.170

1.278

2

3

6.382

1.196

1.278

2.170

6.382

7.474

7.986

13.562

39.885

% of variance

68.907

61.433

53.447

39.885

Cumulative variance contribution rate

Extraction sums of squared loadings

Cumulative variance contribution rate

Total

% of variance

Initial eigenvalues

Total

1

Component

Table 5.3 Extraction of common factors and cumulative variance contribution rates

1.803

2.193

3.127

3.902

11.268

13.707

19.547

24.386

% of variance

68.907

57.639

43.933

24.386

Cumulative variance contribution rate

Rotation sums of squared loadings Total

156 5 Impacts of Lifestyles on Transport

157

Eigenvalue

5.1 Impacts of Lifestyles on Transport Volumes

Component Number

Fig. 5.1 Scree plot

5.1.3.3

Factor Loading Matrix

A factor loading matrix can reflect the linear relationship between the extracted common factors and the original variables. Considering that information in the original variables is scattered across the four initial common factors, we decided to rotate these factors using the equamax method. After 12 rotations, the structure of the factors was effectively simplified and the number of loadings on the common factors obviously diverged, making it easier to analyse, as Table 5.4 shows. Factor 1 (F1) has high loadings on X1: Per-capita GDP, X2: Average wage of employed workers, X3: Public fiscal expenditure, X4: Total retail sales of social consumer goods, X10: Investment in residential development and X14: Expenditure on urban maintenance and construction, which reflects the impacts on living standards caused by the economic growth and construction investment of a city, as well as the income and consumption levels of local residents. Factor 2 (F2) has high loadings on X1: Per-capita GDP, X6: Number of physicians per 1,000 people, X11: Area of urban roads owned per person, X12: Percentage of mobile phone users and X13: Percentage of broadband users, which reflects the states of public service facilities such as urban roads and communications. Factor 3 (F3) has high loadings on X8: Number of teachers per 1,000 people and X9: Number of students in regular institutions of higher education per 100,000 people, which reflects the states of healthcare and educational resources in the social system. Factor 4 (F4) has high loadings on X5: Number of beds in hospitals and healthcare institutions per 1,000 people, X7: Teacher–student ratio in regular institutions of higher education and X15: Popularity rate of natural

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Table 5.4 Factor loading matrix after rotations Factors 1

2

X1: Per-capita GDP

0.415

0.745

X2: Average wage of employed workers

0.674

0.483

X3: Public fiscal expenditure

0.928

X4: Total retail sales of social consumer goods

0.922

0.178

X5: Number of beds in hospitals and healthcare institutions per 1,000 people

3

4 0.178

0.165 0.131

0.123

0.391

0.689

X6: Number of physicians per 1,000 people

0.152

0.519

0.345

0.475

X7: Teacher–student ratio in regular institutions of higher education

0.153

−0.157

−0.365

0.690

X9: Number of students in regular institutions of higher education per 100,000 people

0.270

0.240

0.784

X10: Investment in residential development

0.864

0.844

X8: Number of teachers per 1,000 people

X11: Area of urban roads owned per person

0.189

0.258

0.713

0.160

0.270

0.300

X12: Percentage of mobile phone users

0.339

0.619

0.345

0.270

X13: Percentage of broadband users

0.157

0.734

0.241

−0.109

X14: Expenditure on urban maintenance and construction

0.716

X15: Popularity rate of natural gas for daily use

0.204

X16: Harmless treatment rate of urban living waste

0.167 0.506

0.359

0.533

0.458

Extraction method: PCA Rotation method: Equamax with Kaiser normalisation a. Rotation converged in 12 iterations

gas for daily use, which reflects the states of urban resources, the environment and social educational resources.

5.1.3.4

Calculation of Factor Scores and Comprehensive Assessment

Based on the factor loading matrix, the score of each of the four common factors can be calculated by summing the loadings of all the items onto each of these factors, as shown below: F1 = 0.415 ∗ X1 + 0.674 ∗ X2 + 0.928 ∗ X3 + 0.922 ∗ X4 + 0.152 ∗ X6 + 0.153 ∗ X7 + 0.270 ∗ X9 + 0.864 ∗ X10 + 0.339 ∗ X12 + 0.157 ∗ X13 + 0.716 ∗ X14 + 0.204 ∗ X15.

(5.2)

5.1 Impacts of Lifestyles on Transport Volumes

159

The common factors F2, F3 and F4 have similar matrices of factor scores. The data for X1 through X16 are obtained after a standardised treatment of the different indices of living standards. Using the formula above, standard scores can be calculated for the four common factors of F1, F2, F3 and F4 in each prefecture-level city. Based on the variance contribution rates of the four common factors (0.3989, 0.1356, 0.0799 and 0.0747), an overall score can be generated to reflect the living standing of each Chinese prefecture-level city in 2016. As shown in the Annexed Table 5.1, the top 10 cities are Beijing, Shanghai, Chongqing, Guangzhou, Shenzhen, Nanjing, Tianjin, Wuhan, Hangzhou and Suzhou, most of which are economically developed provincial cities, while the last 10 cities are mostly from economically backward regions in central and western China, namely, Luohe, Jixi, Dandong, Qitaihe, Shangluo, Longnan, Tieling, Suizhou, Xianning and Qinzhou (Fig. 5.2).

5.1.4 Impacts of Lifestyles on Transport Volumes Based on the indexes of residents’ overall lifestyle, analyses are made of the impacts of residents’ living standards in prefecture-level cities across China on transport. Transport generally refers to the movement of people and goods. Considering different means of transport, with relevant variables under control, this study analyses the relationships of residents’ living standards to road passenger and freight transport, water passenger and freight transport, and air passenger and freight transport. With the volumes of different transport means as dependent variables, based on relevant studies, indexes that reflect economic growth, population, industrial scales and consumption are selected as control variables, including per-capita GDP, permanent population, total retail sales of social consumer goods and the use of foreign capital. Through correlation testing and regression screening, a general multivariate regression model is constructed to explore the relations between the scores of living standards and passenger and freight volumes. y = β0 + β1 x1 + β2 x2 + β3 x3 + β4 logx4 + β5 x5 .

(5.3)

In Eq. 5.3, y represents the road passenger volume, the water passenger volume, the air passenger volume, the road freight volume, the water freight volume and the air freight volume; x 1 is the total score of the living standard of each prefecture-level city, x 2 is the per-capita GDP, x 3 is the permanent population, x 4 is the total retail sales of social consumer goods, the log of which is applied to narrow the value range of the variable and x 5 represents the use of foreign capital. The coefficients and statistical values of each regression model are in Tables 5.5 and 5.6. The results of tests of relevant coefficients, t-tests, f -tests and multicollinearity tests are consistent with the conclusions drawn from the scatterplots showing residents’ living standards and urban transport volumes (Figs. 5.3, 5.4, 5.5, 5.6 and 5.7) (Fig. 5.8).

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5 Impacts of Lifestyles on Transport

Legend

Legend

Common factor (F1)

Common factor (F2)

No data

No data

boundary Municipal boundaries Provincial boundaries boundary

boundary Municipal boundaries Provincial boundaries boundary

(a) Common Factor ( F1)

Legend

(b) Common Factor ( F2)

Legend

Common factor (F3)

Common factor (F4)

No data

No data

boundary Municipal boundaries Provincial boundaries boundary

boundary Municipal boundaries Provincial boundaries boundary

(c) Common Factor ( F3)

(d) Common Factor ( F4)

Legend Comprehensive Score

No data Municipal boundaries Provincial boundaries

(e) Comprehensive Score Fig. 5.2 Comprehensive assessment

(1)

Relationships Between Living Standards and Transport Volumes

With economic growth, population, industrial scales and consumption as control variables, the living standards of urban residents significantly influence passenger and freight volumes. The living standards of residents in various prefecture-level cities have the strongest impact on air passenger and freight volumes. There is a

5.1 Impacts of Lifestyles on Transport Volumes

161

Table 5.5 Overview of regression models Model

R-squared

Adjusted R-squared

Standard error of the estimate

F-change

Significance

Durbin-Watson statistic

y1

0.169

0.153

11,786.955

10.965

0.000

1.658

y2

0.266

0.153

239.177

4.106

0.001

1.870

y3

0.705

0.699

567.671

128.803

0.000

1.855

y4

0.116

0.100

19,260.206

7.078

0.000

1.972

y5

0.116

0.100

14,061.282

7.088

0.000

2.017

y6

0.672

0.699

101,629.026

110.869

0.000

2.043

remarkable positive correlation between air transport volumes (including passenger and freight volumes) and urban residents’ living standards. The significance level is 0.01, and the correlation coefficients are 1.181 and 1.161, respectively. This means for every 1-unit rise in urban residents’ living standard, there is a 1.181-unit rise in the air passenger volume and a 1.161-unit rise in the air freight volume. According to the previous factor analysis, the higher the living standard of urban residents, the higher the income and the degree of economic development (F1), which drives the demand for air transport. Only a limited number of residents can afford air transport due to its high construction cost. Therefore, only high-income cities with highly developed economies, such as Beijing, Shanghai, Guangzhou and other megacities, can generate great air transport demand. Also, the upper limit of air transport volumes is subject to the development level and carrying capacity of the city’s airport, that is, the level of the supply side. A low-throughput capacity means a small transport volume and a high-throughput capacity means a large transport volume. F2 reflects the states of public service facilities such as urban roads and communications. The higher its value, the more complete the city’s public service facilities such as transport. There is also a positive correlation between road passenger volumes and urban residents’ living standards, with a significance level of 0.01 and a correlation coefficient of 0.364, indicating that for every 1-unit rise in urban residents’ living standard, there is a 0.364-unit rise in the road passenger volume. This proves that the higher the living standard of urban residents, the larger the road passenger volume, though the absolute value of the correlation coefficient is not as high as that of the air transport volume. Cars are most frequently used for road passenger transport, carrying passengers in short- and medium-distance trips on highways. Cars can even carry passengers to remote areas that cannot be reached by any other means of transport. Urban residents with a higher living standard are more likely to own fixed assets such as cars. Car ownership will increase the probability of travelling by car, and the road passenger volume increases with the demand for car travel. Although there is no statistically significant correlation between water passenger volumes and urban residents’ living standards, there is a positive correlation between water freight volumes and urban residents’ living standards, with a significance level of 0.01 and a correlation coefficient of 0.503. This means that for every 1-unit rise in

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5 Impacts of Lifestyles on Transport

Table 5.6 Coefficients of regression models Parameter

y1 Road passenger volume

y2 Water passenger volume

y3 Air passenger volume

Standardised t-Statistic Standardised t-statistic coefficient coefficient (standard (standard error) error)

Standardised t-Statistic coefficient (standard error)

X1: Total score of the living standard

0.364*** (3,933.140)

2.726

0.085 (79.810)

0.602

1.181*** (189.424)

14.826

X2: Per-capita GDP

−0.088* (0.040)

−0.914

0.270*** (0.001)

2.633

−0.276*** (0.002)

−4.778

X3: Permanent population

0.326** (5.069)

2.364

0.140* (0.103)

0.958

−0.095* (0.244)

−1.161

−0.897

−0.121* (29.086)

−1.034

−0.133** (69.034)

−2.027

−2.040

−0.108 * (0.000)

−1.117

−0.032 * (0.000)

−0.581

logX4: Total −0.099* retail sales of (1,433.393) social consumer goods X5: Use of foreign capital

−0.186 ** (0.004)

Parameter

y4 Road freight volume

y5 Water freight volume

y6 Air freight volume

Standardised t-Statistic Standardised t-Statistic Standardised t-statistic coefficient coefficient coefficient (standard (standard (standard error) error) error) X1: Total score of the living standard

0.029 (6,426.859)

X2: Per-capita GDP X3: Permanent population

0.503*** (4,692.051)

3.648

1.161*** 13.836 (33,912.170)

0.049 (0.065) 0.486

−0.227 ** (0.047)

−2.272

−0.185 *** (0.343)

−3.048

0.356 ** (8.282)

2.502

−0.247 ** (6.047)

−1.735

0.049 (43.702)

0.567

−0.116

0.165 * (1,709.970)

1.453

−0.273*** −3.941 (12,358.942)

logX4: Total −0.013 retail sales of (2,342.203) social consumer goods

0.211

(continued)

5.1 Impacts of Lifestyles on Transport Volumes

163

Table 5.6 (continued) Parameter

X:5 Use of foreign capital *

y1 Road passenger volume

y2 Water passenger volume

y3 Air passenger volume

Standardised t-Statistic Standardised t-statistic coefficient coefficient (standard (standard error) error)

Standardised t-Statistic coefficient (standard error)

−0.063* (0.007)

−0.128** (0.038)

−0.673

0.036(0.005) 0.382

−2.229

Significance level α = 0.1, ** Significance level α = 0.05, *** Significance level α = 0.01

Fig. 5.3 Scatterplot showing residents’ living standards and road passenger volumes in different cities

Fig. 5.4 Scatterplot showing residents’ living standards and water passenger volumes in different cities

164 Fig. 5.5 Scatterplot showing residents’ living standards and air passenger volumes in different cities

Fig. 5.6 Scatterplot showing residents’ living standards and road freight volumes in different cities

Fig. 5.7 Scatterplot showing residents’ living standards and water freight volumes in different cities

5 Impacts of Lifestyles on Transport

5.1 Impacts of Lifestyles on Transport Volumes

165

Fig. 5.8 Scatterplot showing residents’ living standards and air freight volumes in different cities

urban residents’ living standard, there is a 0.503-unit rise in the water freight volume. This contrast may have something to do with the characteristics and applicability of water transport. Water transport is the mode of transport with the longest history, featuring a large load capacity and low cost, suitable for long-distance transport of low-value, cumbersome bulk goods—maritime transport is perfect for the import and export of various foreign trade goods. In contrast, in terms of passengers, water transport is no longer the mainstream mode of transport due to its lower efficiency than road and railway transport most of the time. In cities with higher living standards, water transport is not the preferred option, as urban residents have higher incomes and require higher transport efficiency. Air transport is their first choice, followed by road transport. Also, cities with higher degrees of economic development generally have more thriving import and export trade, especially those with ports offering river and maritime transport, such as Shanghai, Guangzhou and Shenzhen. These cities can tap their potential for water passenger transport. (2)

Relationships Between Control Variables and Transport Volumes

The population size of a city is closely related to the transport volume of the entire city. A large population base is always accompanied by a large transport volume. This study uses the permanent population to represent the total population of each prefecture-level city. The table above shows that the road passenger volume, the water passenger volume and the road freight volume are positively related to the permanent population, while the air passenger volume and the water freight volume are negatively related to the permanent population, with a significance level of 0.1. There is a high positive correlation between road transport volumes (including passenger and freight volumes) and the permanent population. The significance level is 0.05, and the correlation coefficients are 0.326 and 0.356, respectively, indicating that there is a strong connection between the urban population size and road transport volumes.

166

5 Impacts of Lifestyles on Transport

5.2 Empirical Research: Impacts of Lifestyles on Travel Behaviour Regarding the influence of lifestyle on individual transportation behaviour, there has been a wealth of research in the academic field. Since the 1980s, scholars have studied the relationship between lifestyle and transportation needs (Salomon, 1980; Salomon & Ben-Akiva, 1983; Wachs, 1979). Subsequent studies have become more specific and targeted. Lanzendorf (2002) and Ohnmacht et al. (2009) focused on the impact of lifestyle on leisure travel, Schwanen and Mokhtarian (2005) studied commuting behaviour, and Ory and Mokhtarian (2009) mainly studied short-distance travel. Etminani-Ghasrodashti and Ardeshiri (2015) found that personal lifestyle has a strong and significant influence on the non-work travel of residents in 22 urban areas of Iran. The existing research on the impact of lifestyle on travel mainly started by grouping people according to different lifestyles to compare the differences in travel behaviour between groups with different lifestyles. For example, Hildebrand (2003) identified six different lifestyle groups according to sociodemographic features when studying older people, namely, workers, mobile windows, those in granny flats, the mobility impaired, affiliated males and disabled drivers. The research indicates that the travel behaviours of the six groups are different from each other. Krizek and Waddell (2002) considered the interaction between lifestyle and travel behaviour, summarised nine lifestyles by analysing the data of the Puget Bay Transportation Group and discussed the application space of lifestyle grouping in land use and transportation planning. Krizek (2006) revealed seven categories of lifestyles through a cluster analysis of data from the Twin Cities metropolitan area in Minnesota. Chen et al. (2009) divided tourists into three categories: (a) family-oriented tourists, (b) social-oriented tourists and (c) fashion tourists, and they analysed their differences in travel characteristics. This section draws lessons from existing research, uses 24,560 samples from the CFPS database, divides lifestyles into five types through cluster analysis and uses regression analysis to analyse the relationships between different lifestyles and commuting time.

5.2.1 Data Source The data came from the CFPS database released by the Institute of Social Science Survey, Peking University. The CFPS is a nationwide, large-scale, multidisciplinary social follow-up survey project. Its samples cover 25 provinces (autonomous regions and municipalities), with a target sample size of 16,000 households (Fig. 5.9). The respondents include all family members in the sample households. The CFPS conducted initial and follow-up surveys in Beijing, Shanghai and Guangdong in 2008 and 2009, and they officially launched interviews in 2010. All baseline family members identified by the 2010 baseline survey and their future blood and adopted

5.2 Empirical Research: Impacts of Lifestyles on Travel Behaviour

167

Fig. 5.9 CFPS data sampling areas. Source CFPS user manual: http://www.isss.pku.edu.cn/cfps/ wdzx/tcwj/index.htm

children will be the gene members and permanent targets of the CFPS. There are four types of questionnaires: community questionnaires, family questionnaires, adult questionnaires and child questionnaires. On this basis, long questionnaires, short questionnaires, substitute questionnaires and telephone interview questionnaires were developed for different types of family members. According to the CFPS, the existing database includes data from 2010, 2012, 2014, 2016 and 2018.

5.2.2 Clustering of Residents’ Lifestyles Based on CFPS Data 5.2.2.1 (1)

Selection of Factors

Research Variables (Data of 2018) a. b. c.

d.

Basic variables: age, gender, schooling (schl), marriage (marr). Family variables: number of family members (fmlno), ratio of income to expenditure of a family (exin). Occupation variables: full-time working background (wb), working status over the last week (wlw), salary per month (sm), job class (jbcls), industry (inds). Income and consumption variables: gross personal income (income), proportion of expenditure on tourism and leisure (eecpart), proportion

168

5 Impacts of Lifestyles on Transport

e.

f.

g. h.

i.

5.2.2.2

of expenditure on food (engl), proportion of expenditure on transport (trpart). Time-use variables: weekly working time (wtm), weekly sleeping time (slptm), weekly housework time (hswktm), weekly TV and movie time (mvtm), weekly exercise time (exctm), weekly web time (webtm). Transport variables: having taken the train or not (train), having taken the plane or not (plane), number of cars owned per family (car), car consumption (carco), family transport consumption (trco). Activity variables: reading or not, KTV frequency, frequency of going to dancing clubs, frequency of going to cybercafes, frequency of dining out. Web variables: frequency of web use for study (webst), frequency of web use for socialising (webscl), frequency of web use for work (webw), frequency of web use for amusement (webamu), frequency of web use for business (webbs). Value variables: satisfaction with income from work (sainc), satisfaction with social status (safame). Description of Variables

The descriptive statistics on the major variables of the model are outlined in Table 5.7. For the basic variables, 1 represents males, while 0 represents females. Educational degrees range from 0 to 8, the highest of which is the doctoral degree. For the job class, 1 represents agricultural workers, 2 represents family agricultural producers, 3 represents individually owned businesses or other self-employment and 4 represents non-agricultural employees. For industry type, industries range from agriculture and manufacturing to services, commerce and politics. For activity variables, since the frequency of going to KTVs, dancing clubs and cybercafes and the frequency of dining out are 0, such activity variables are treated as invalid variables. The missing values of industry and web variables are properly dealt with based on the reasons why they are missing.

5.2.2.3

Principal Component Analysis

PCA is used for dimensionality reduction due to the large number of variables and the high correlation between them. The results of the KMO test and Bartlett’s test of sphericity are in Table 5.8, based on which PCA is conducted with promising effects. The KMO value is 0.828. The approximate chi-square value from Bartlett’s test of sphericity is 248,000, higher than the critical value of the chi-square test, which has a significance level below 0.01. There is a 95% probability that the selected factors are suitable for PCA. According to Table 5.9, there are 10 principal components whose eigenvalues are higher than 1 and whose cumulative variance contribution rates reach 60.4%. In light of the position where a significant change takes place, which the scree plot of

5.2 Empirical Research: Impacts of Lifestyles on Travel Behaviour

169

Table 5.7 Descriptive statistics for major variables Sample Total samples size Average Standard deviation

Urban samples

Rural samples

Average

Average

Standard deviation

Standard deviation

Age

32,376

44.13 18.61

43.97 18.41

44.60

44.60

Gender

32,376

0.50 0.50

0.49 0.50

0.50

0.50

Fmlno

32,251

4.18 2.09

3.92 1.98

4.50

4.50

Exin

31,455

0.84 7.22

0.77 9.82

0.90

0.90

Wb

32,376

0.35 /

0.43 /

0.27

/

Wlw

32,376

Last month’s 32,376 salary

0.61 / 1,364.82 3,062.50

0.57 / 3,100.67 3,429.11

0.65

/

1,639.52

1,018.57

Eecpart

29,092

0.09 0.12

0.09 0.11

0.09

0.09

Engl

29,092

0.29 0.17

0.32 0.17

0.27

0.27

0.08 0.06

0.07 0.05

0.09

0.09

Trpart

29,092

Income/year

32,376

Reading or not

32,376

14,026.12 29,712.68 26,019.83 34,374.34 11,369.58 8,855.52 0.31 /

0.36 /

0.25

/

Wtm/week

32,376

30.09 28.66

48.21 28.80

44.34

30.28

Slptm/day

32,342

7.58 1.54

7.44 1.45

7.72

7.71

Hswktm/day

32,290

2.22 1.95

2.04 1.81

2.41

2.41

Mvtm/week

32,317

10.42 10.35

11.04 10.88

9.85

9.85

Exctm/week

32,338

4.18 8.72

4.12 7.34

4.29

4.29

14.09 12.32

Webtm/week 32,376

7.20 11.53

12.82

5.55

Having taken 32,376 the train or not

0.45 /

0.50 /

0.40

/

Having taken 32,376 the plane or not

0.07 /

0.11 /

0.02

/

32,375

0.34 /

0.40 /

0.28

/

Having a car or not Carco

31,502

5,645.01 29,906.55

6,966.21 32,644.87 4,364.43

4,364.43

Trco

32,248

5,955.21 7,148.17

6,719.63 7,821.54

5,208.70

5,208.71

Webst

32,376

2.37 2.84

4.24 2.82

4,77

2.08

Webscl

32,376

1.10 1.69

2.01 1.72

2.18

0.95

Webw

32,376

1.52 2.62

3.64 2.52

5.08

1.51

Webamu

32,376

2.36 1.80

2.31 1.83

2.47

1.07

Webbs

32,376

4.66 2.78

4.43 2.72

5.03

2.19

Sainc

28,014

2.91 1.07

2.86 1.03

2.97

2.97

Safame

29,799

3.09 1.08

3.00 1.03

3.21

3.21

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5 Impacts of Lifestyles on Transport

Table 5.8 The KMO test and Bartlett’s test of sphericity

KMO test Bartlett’s test of sphericity

0.828 Approx. chi-square

248,000

df

528

Sig

0.000

eigenvalues in Fig. 5.10 shows, only the first eight principal components are extracted for PCA. Due to the large number of original variables, only the first eight principal components are selected, as they are sufficient to explain the information they represent. The PCA loading matrix is shown in Table 5.10. According to the loading matrix, the eight components represent the following characteristics, respectively: F1 F2 F3 F4 F5 F6 F7 F8

(development potential): age, job class, web use for work and study, schooling. (web use): frequency of web use. (transport expenditure): proportion of transport expenditure, private car ownership and consumption. (personal quality): educational degree, current and past states of working, reading. (life satisfaction): satisfaction with salary, satisfaction with social status. (leisure life): gender, expenditure on tourism and transport, housework and TV time. (family pressure): marital status, sleeping time, car consumption, age. (family consumption characteristics): proportion of expenditure on tourism and food, gender, proportion of transport expenditure.

5.2.2.4

Clustering of Lifestyles

Clustering analysis is conducted by k-means to help to find appropriate clusters through minimised data sets and the nearest cluster centre. This method can maximise the similarity between members of the same cluster and minimise the similarity between members of different clusters. Factors obtained by this means group samples into five clusters. The results are in Tables 5.11 and 5.12. Cluster 1 (Young and middle-aged achievers in cities): This cluster of members with an average age of 36 has the highest educational level and gross income from work. The average gross income from work is about 47,000 CNY. Most of them are from the urban population. It is presumed that these people are mostly young and middle-aged urban residents who have relatively high social status and income from work, a stable job and a wealthy life. The ratio of family income to expenditure is the lowest (about 63%), which implies relatively low life pressure. For consumption, tourism and leisure expenditure is ranked in the middle. Food expenditure accounts for the lowest proportion. They come under the most pressure from transport expenditure, accounting for about 10% of total expenditure. The proportion of car ownership

5.2 Empirical Research: Impacts of Lifestyles on Travel Behaviour

171

Table 5.9 Total variance explained Component

Initial eigenvalues

Extraction sums of squared loadings

Total

% of variance

Cumulative variance contribution rate

Total

% of variance

Cumulative variance contribution rate

Comp1

5.7789

0.1751

0.1751

5.7789

0.1751

0.1751

Comp2

2.7784

0.0842

0.2593

2.7784

0.0842

0.2593

Comp3

2.0185

0.0612

0.3205

2.0185

0.0612

0.3205

Comp4

1.9961

0.0605

0.3810

1.9961

0.0605

0.3810

Comp5

1.4760

0.0447

0.4257

1.4760

0.0447

0.4257

Comp6

1.3269

0.0402

0.4659

1.3269

0.0402

0.4659

Comp7

1.2486

0.0378

0.5037

1.2486

0.0378

0.5037

Comp8

1.1960

0.0362

0.5400

1.1960

0.0362

0.5400

Comp9

1.0573

0.0320

0.5720

Comp10

1.0426

0.0316

0.6036

Comp11

0.9995

0.0303

0.6339

Comp12

0.9890

0.0300

0.6639

Comp13

0.9423

0.0286

0.6924

Comp14

0.8857

0.0268

0.7193

Comp15

0.8224

0.0249

0.7442

Comp16

0.7835

0.0237

0.7679

Comp17

0.7280

0.0221

0.7900

Comp18

0.6849

0.0208

0.8107

Comp19

0.6391

0.0194

0.8301

Comp20

0.6204

0.0188

0.8489

Comp21

0.5857

0.0177

0.8667

Comp22

0.5304

0.0161

0.8827

Comp23

0.5151

0.0156

0.8983

Comp24

0.4831

0.0146

0.9130

Comp25

0.4593

0.0139

0.9269

Comp26

0.4154

0.0126

0.9395

Comp27

0.3828

0.0116

0.9511

Comp28

0.3595

0.0109

0.9620

Comp29

0.3163

0.0096

0.9716

Comp30

0.2665

0.0081

0.9796

Comp31

0.2567

0.0078

0.9874

Comp32

0.2253

0.0068

0.9942

Comp33

0.1898

0.0058

1.0000

172

5 Impacts of Lifestyles on Transport

Fig. 5.10 Scree plot of eigenvalues of the correlation matrix

is very high (close to 100%). The average amount of money spent on car purchases is the highest (some 27,000 CNY), about 10 times that of other clusters. The average number of family members is also the largest (about 4.6 members), indicating that families in this cluster are mostly nuclear families with three or more members. Cluster 2 (Middle-aged and older people residing in the countryside): This cluster of members with an average age of 54 has the lowest educational level. Most people are rural residents. The gross income from work averages 6,800 CNY. The proportion of full-time workers accounts for about 28%. The ratio of income to expenditure of a family is about 82%. The car ownership rate is the lowest (19.4%). This cluster has the smallest number of people who have flown. It is presumed that these people are mostly indigenous rural residents who are poorly educated due to a lack of educational resources. Farming is their major source of income, which is stable but small in amount. People in this group are not adaptable to new things. Cluster 3 (Middle-aged people under pressure in cities): This cluster of members with an average age of 42 has a medium educational level. The gross income from work averages 11,700 CNY, also on the medium level. The proportion of full-time workers is 37%. Most of them are in the rural population. The average number of family members is large (about 4.4 members). The ratio of family income to expenditure is about 76%. Tourism and leisure expenditure accounts for a relatively high proportion (9.4%). Food expenditure is on the average level. The car ownership rate stands at about 32%, second only to that of Cluster 1. The average amount of money spent on car purchase is about 3,380 CNY. It is presumed that these people are mostly middle-aged rural people living in cities and working with an average income from work in basic service industries due to poor education. Cluster 4 (Retired older people in cities): This cluster of members has an average age of 62, most of whom are females (the gender ratio is balanced in the other four clusters). The educational level is relatively low. The average number of family members is small (about 3.6 members). Most people are urban residents. The average gross income from work is the lowest, only about 480 CNY. The proportion of fulltime workers is also the lowest, barely 3%. The ratio of family income to expenditure

5.2 Empirical Research: Impacts of Lifestyles on Travel Behaviour

173

Table 5.10 PCA loading matrix PCA loading matrix Variable

Comp1

Age

−0.2919

Comp2

Comp3

0.0158 −0.0371

Comp4

Comp5

Comp6

Comp7

Comp8

0.2712 −0.0504

0.0279

0.1992

0.1817

Gender

0.0769 −0.1127 −0.1236 −0.0045

0.1905

0.471

Schl

0.2561 −0.03

0.0734 −0.3489

0.0496 −0.1293 −0.1002 −0.0165 0.1029

Marr Fmlno Exin

−0.2088 −0.3665

−0.1397

0.0496

0.0182

0.012

0.0129

0.0098

0.2936

0.2329 −0.1911 −0.0729 −0.0895 −0.1132

−0.0129

0.0095 −0.0048 −0.0069

0.4999

0.0327

0.0135 −0.0141

0.0808 −0.0675

0.0586 −0.0454

0.083

Wb

0.2439 −0.1866 −0.0681

0.1141

Wlw

0.1835 −0.2982 −0.1581

0.3353 −0.0257

0.0885

0.0437

0.0394 −0.037

0.2686

0.1435 −0.3229

0.0874

0.0391

0.0868

Sm

0.1646 −0.1392

0.0694 −0.0884

0.1683

0.1256

0.1522 −0.0919

Jbcls

0.2746 −0.2468 −0.1259 −0.0204

0.0002 −0.0038

0.1591

0.163

Inds

0.2354 −0.1479 −0.0081 −0.2346

0.0368 −0.1042

0.1565

0.2045

Employ

Eecpart

−0.1954

0.1247

0.0448

0.0061

0.0295

0.0588 −0.101

0.0625 −0.2524 −0.1517

0.0941

−0.3787 −0.0103 −0.4631

Engl

−0.0458

Trpart

0.044

Income

0.2238 −0.1867 −0.0034 −0.1841

0.1442

Read

0.1583 −0.0166

0.0355 −0.2195 −0.1507 −0.0845

Wtm

0.2082 −0.2575 −0.1733

Slptm

0.013

−0.0291

0.0198

0.1109 −0.0446 −0.0398 −0.3366 −0.0352

Hswktm −0.086

0.0396

0.0548

0.1583 −0.1095 −0.3738

−0.0621

0.3147

0.0516

0.2236 −0.1064

0.0555 −0.2901

0.1948 −0.032

0.1858

0.129

0.2671 −0.2457 0.0138 0.0697

0.399 0.3565

0.1302 −0.0166 0.1194

0.0616

0.2318

0.3234

0.1285

0.2366 −0.1341

0.0037

0.1503

0.0709 −0.008

Mvtm

−0.0553

0.0682 −0.0018 −0.192

Exctm

−0.037

0.0138

0.0107 −0.0088

0.1331

0.0632 −0.1875 −0.0201 −0.0582 −0.2146

−0.1605

Webtm

0.2217

Car

0.1346 −0.0464

0.4626 −0.0214 −0.0044

0.0911

0.1417 −0.0394

Carco

0.0501 −0.0296

0.2742 −0.0464

Trco

0.1259 −0.0615

0.5311

Webst

0.2661

0.3532 −0.027

Webscl

0.2117

0.344

Webw

0.2495

0.2148 −0.11

Webamu

0.2289 0.2696

Webbs

0.0464

0.067

0.2878 −0.2606

0.0269 −0.049

0.2075

0.0037

0.1291

0.1174

0.038

0.0464

0.0344

0.0385

0.094

0.1115

0.0315

0.0661 −0.0262

0.2851

0.0361

0.1083

0.0645 −0.0133

0.3275 −0.0281

0.096

0.0936 −0.0186

0.09

0.3674 −0.0403

0.0277 −0.0049

−0.0316

0.0285

0.0922

0.077

Sainc

−0.0335 −0.0884

0.1152

0.1667

0.5976 −0.2301 −0.1542

0.1006

Safame

−0.089

0.1241

0.1878

0.5847 −0.2072 −0.1099

0.0721

−0.0714

0.007

0.0018

174

5 Impacts of Lifestyles on Transport

Table 5.11 Clustering results k-mean

Freq

1

3,015

2

8,142

3

4,644

4

4,534

5

4,225

Table 5.12 Clustering analysis k-mean

f1

f2

f3

f4

f5

f6

f7

f8

1

2.737

−0.964

1.722

−0.596

0.206

0.253

0.343

0.016

2

−1.569

−1.303

−0.275

0.664

−0.178

−0.012

−0.057

−0.070 −0.092

3

1.754

1.814

−0.430

1.363

0.123

0.190

0.162

4

−2.822

1.407

0.486

−1.245

0.231

0.118

0.128

0.092

5

2.172

−0.305

−0.749

−1.017

−0.188

−0.493

−0.450

0.126

is above 1, suggesting that life consumption is supported not only by income from work, but also by other sources. Tourism and leisure expenditure is the lowest, about 6.0% on average. Food expenditure is the highest, about 35%. Transport expenditure is also the lowest, about 6.2%. It is presumed that these people are retired older urban residents, mostly females, whose daily life consumption is supported not only by retirement pension, but also by subsidies from their children. Food expenditure is the major form of consumption. Cluster 5 (Single, wealthy young people migrating to cities): This cluster of members has an average age of 32, the youngest of the five clusters. The marriage rate and the average number of family members are the lowest (about 3.4 members). Most of them are from the rural population. The educational level, income from work and ratio of full-time workers in this cluster are second to those of Cluster 1. The gross income from work averages 34,000 CNY. The ratio of family income to expenditure is relatively low (about 63%), which implies relatively low pressure. Tourism and leisure expenditure accounts for the highest proportion, about 9.6%. Food expenditure is also high, about 32%. Transport expenditure only accounts for 6.5% of total expenditure. The car ownership rate is low, with only 944 CNY spent on car purchases. It is presumed that these people are the younger generation from the countryside who are now working in cities after receiving higher education. Most of them are single with no plan to purchase cars due to little transport pressure.

5.2 Empirical Research: Impacts of Lifestyles on Travel Behaviour

175

5.2.3 Impacts of Lifestyles on Commuting Based on these clustering results of the household lifestyle follow-up survey in China in 2018, correlation analysis and regression analysis were conducted to assess the relationship between lifestyles and commuting time (Table 5.13). As Table 5.14 shows, different lifestyles have varying effects on commuting time, including both positive and negative effects. The lifestyles of middleaged people under pressure in cities, young and middle-aged achievers in cities, and retired older people in cities have a positive effect on commuting time, while the lifestyles of the middle-aged and older people residing in the countryside and single, wealthy young people migrating to cities have a negative effect on commuting time. The correlation coefficient between commuting Table 5.13 Pairwise correlations between lifestyles and commuting time Variable

(1)

(1) commuting time

(2)

(3)

(4)

(5)

(6)

1.000

(2) s1

0.397***

(3) s2

0.047***

1.000 −0.213***

1.000

(4) s3

−0.164***

−0.187***

−0.181***

(5) s4

−0.162***

−0.184***

−0.177***

−0.156***

(6) s5

0.020***

−0.185***

−0.178***

−0.157***

1.000 1.000 −0.154***

1.000

Note s1 to s5 denote Lifestyles 1 to 5. In this table, boldface represents the correlation coefficient and the p-value, with *** showing significance

Table 5.14 Linear regression of commuting time on lifestyles Commuting time

Coef

Std. Err

t-value

p-value

[95% Conf Interval] Sig

s1

21.650

0.301

72.01

0.000

21.061

22.240

***

s2

7.229

0.306

23.62

0.000

6.629

7.829

***

s3

−2.800

0.327

−8.56

0.000

−3.441

−2.159

***

s4

−2.855

0.330

−8.64

0.000

−3.502

−2.207

***

s5

6.207

0.329

18.85

0.000

5.562

6.853

***

Constant

−1.808

0.196

−9.23

0.000

−2.193

−1.424

***

Mean dependent var

3.407

SD dependent var

19.445

R-squared

0.195

Number of observations

32,376.000

F-test

1,569.624

Prob > F

0.000

Akaike crit. (AIC)

277,018.065

Bayesian crit. (BIC)

277,068.376

***

p < 0.01, ** p < 0.05, * p < 0.1

176

5 Impacts of Lifestyles on Transport

time and the lifestyle of middle-aged people under pressure in cities is 0.397, with a p-value of 0.000, indicating a strong positive correlation. The correlation coefficient between commuting time and the lifestyle of young and middle-aged achievers in cities is 0.047, with a p-value of 0.000, indicating a strong positive correlation. The correlation coefficient between commuting time and the lifestyle of single, wealthy young people migrating to cities is −0.164, with a p-value of 0.000, indicating a strong negative correlation. The correlation coefficient between commuting time and the lifestyle of the middle-aged and older people residing in the countryside is −0.162, with a p-value of 0.000, indicating a strong negative correlation. The correlation coefficient between commuting time and the lifestyle of retired older people in cities is 0.020, with a p-value of 0.000, indicating a strong positive correlation. We performed regression analysis with five lifestyles as independent variables and commuting time as the dependent variable, and we analysed the positive and negative effects according to the regression coefficient and the p-value. Table 5.14 shows that the regression coefficient of commuting time on the lifestyle of young and middle-aged achievers in cities is 21.650, with a p-value of 0.000, indicating a strong positive effect. The regression coefficient of commuting time on the lifestyle of the middle-aged and older residing in the countryside is 7.229, with a p-value of 0.000, indicating a strong positive effect. The regression coefficient of commuting time on the lifestyle of middle-aged people under pressure in cities is − 2.800, with a p-value of 0.000, indicating a strong negative effect. The regression coefficient of commuting time on the lifestyle of retired older people in cities is −2.855, with a p-value of 0.000, indicating a strong negative effect. The regression coefficient of commuting time on the lifestyle of single, wealthy young people migrating to cities is 6.207, with a p-value of 0.000, indicating a strong positive effect.

5.3 Summary This chapter has mainly verified the impacts of lifestyles on transport through an empirical analysis. Limited by the availability of data, the relationship between lifestyles and transport volumes has been analysed primarily through macrostatistical data. The core conclusion we reached in this regard is that the lifestyle of urban residents can explain the size of local traffic volume to a certain extent. In the second part, we conducted a study on the individual scale. We also used CFPS data to verify the impacts of lifestyles on commuting time. The important conclusion we reached is that the differences in individual lifestyle choices affect traffic behaviour characteristics. In this section, we chose commuting time to represent traffic behaviour. In the first part, we created core objective indicators for a comprehensive evaluation of lifestyles in consideration of the availability of data. Then we conducted comprehensive evaluations of lifestyles in 365 prefecture-level cities in China and multiple regression analysis of the relationship between the overall lifestyle and

5.3 Summary

177

transport volumes. We found that with urban social and economic growth, population, industrial scale and consumption as control variables, the living standards of urban residents significantly influence passenger and freight volumes. The living standards of residents in various prefecture-level cities have the strongest impact on air passenger and freight volumes. Our conclusion on the impacts of urban population on air transport volumes is consistent with that of Zhang and Zhang (2007), but we have given more consideration to the impacts of lifestyles and have obtained significant results. Our conclusion is of great statistical significance for interdisciplinary work on lifestyle and transport. The analysis of residents’ lifestyle and traffic volume will help the government to promote transportation policies suitable for local conditions and improve the layout of future facility development. In this part, we have not only explored the research characteristics of transportation geography and discussed the regional law of the distribution of transportation volume, but also analysed the various factors that affect the transportation volume. Among them, residents’ lifestyle is a factor that has rarely been considered before. Through multiple regression analysis, we confirmed that it has a certain impact on the urban traffic volume. For policymakers, it is necessary to know the relationship between differences in residents’ lifestyles and differences in transportation volume. In the future, when beginning a new round of transportation network layout and providing suggestions for the development of various transportation tools and facilities in the region, residents’ lifestyles are important factors that should be considered. In the second part, using the clustering method with the CFPS database, we divided lifestyle into five types, i.e., young and middle-aged achievers in cities, the middleaged and older people residing in the countryside, middle-aged people under pressure in cities, retired older people in cities, and single, wealthy young people migrating to cities. We concluded through analysis that different lifestyles have varying effects on commuting time, including both positive and negative effects. In this part of research, we adopted the research method currently used in most literature on lifestyle and classified Chinese residents’ lifestyles. While foreign research on this topic is abundant (Chen et al., 2009; Hildebrand, 2003; Krizek & Waddell, 2002), there is very little relevant research in China. In addition, when discussing the travel differences of residents with different lifestyles, we have focused on commuting time and conduct linear regression analysis quantitatively to show the impacts of lifestyles on travel behaviour. Research on an individual scale is also beneficial to regional transportation development planning. The results show that with the rapid growth of China’s economy and the continuous development of society, great changes have taken place in residents’ attitudes, values, and life orientations, and residents’ lifestyles have shown a significant grouping phenomenon. Every lifestyle implies a variety of travel needs, and economic and social growth has also created new ways to meet the travel needs of different groups of people. However, we must admit that the current transportation system and facilities cannot meet the needs of all types of people. Therefore, we hope that the results of this study can encourage the government to understand individual needs in depth, so it can carry out more detailed transportation planning to meet

178

5 Impacts of Lifestyles on Transport

the expectations of different people. In addition, for the transportation industry, this research can also help in the planning of system operation strategy and promote a deeper understanding of its users.

References Chen, J. S., Huang, Y.-C., & Cheng, J.-S. (2009). Vacation lifestyle and travel behaviors. Journal of Travel & Tourism Marketing, 26(5–6), 494–506. https://doi.org/10.1080/10548400903163038 Christensen, P. (1997). Different lifestyles and their impact on the environment. Sustainable Development, 5(1), 30–35. https://doi.org/10.1002/(SICI)1099-1719(199703)5:1%3C30::AIDSD59%3E3.0.CO;2-5 Etminani-Ghasrodashti, R., & Ardeshiri, M. (2015). Modeling travel behavior by the structural relationships between lifestyle, built environment and non-working trips. Transportation Research Part A: Policy and Practice, 78, 506–518. https://doi.org/10.1016/j.tra.2015.06.016 Heinonen, J., Jalas, M., Juntunen, J. K., Ala-Mantila, S., & Junnila, S. (2013). Situated lifestyles: II. The impacts of urban density, housing type and motorization on the greenhouse gas emissions of the middle-income consumers in Finland. Environmental Research Letters, 8(3), 035050. https:// doi.org/10.1088/1748-9326/8/3/035050. Hildebrand, E. D. (2003). Dimensions in elderly travel behaviour: A simplified activity-based model using lifestyle clusters. Transportation, 30(3), 285–306. https://doi.org/10.1023/A:102394933 0747 Krizek, K. J. (2006). Lifestyles, residential location decisions, and pedestrian and transit activity. Transportation Research Record, 1981(1), 171–178. https://doi.org/10.1177/036119810619810 0124 Krizek, K. J., & Waddell, P. (2002). Analysis of lifestyle choices: Neighborhood type, travel patterns, and activity participation. Transportation Research Record, 1807(1), 119–128. https://doi.org/ 10.3141/1807-15 Lanzendorf, M. (2002). Mobility styles and travel behavior: Application of a lifestyle approach to leisure travel. Transportation Research Record, 1807(1), 163–173. https://doi.org/10.3141/180 7-20 Li, J., Zhang, D., & Su, B. (2019). The impact of social awareness and lifestyles on household carbon emissions in China. Ecological Economics, 160, 145–155. https://doi.org/10.1016/j.eco lecon.2019.02.020 Matsumoto, H. (2004). International urban systems and air passenger and cargo flows: Some calculations. Journal of Air Transport Management, 10(4), 239–247. https://doi.org/10.1016/j.jairtr aman.2004.02.003 Ohnmacht, T., Götz, K., & Schad, H. (2009). Leisure mobility styles in Swiss conurbations: Construction and empirical analysis. Transportation, 36(2), 243–265. https://doi.org/10.1007/ s11116-009-9198-8 Ory, D. T., & Mokhtarian, P. L. (2009). Modeling the structural relationships among short-distance travel amounts, perceptions, affections, and desires. Transportation Research Part A: Policy and Practice, 43(1), 26–43. https://doi.org/10.1016/j.tra.2008.06.004 Richard, R. A. (2000). The worldwide standard of living since 1800. Journal of Economic Perspectives, 14(1), 7–26. https://doi.org/10.1257/jep.14.1.7 Salomon, I. (1980). Life style as a factor in explaining travel behavior (Unpublished dissertation). Massachusetts Institute of Technology. Salomon, I., & Ben-Akiva, M. (1983). The use of the life-style concept in travel demand models. Environment and Planning A, 15(5), 623–638. https://doi.org/10.1068/a150623.

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Schwanen, T., & Mokhtarian, P. L. (2005). What affects commute mode choice: Neighborhood physical structure or preferences toward neighborhoods? Journal of Transport Geography, 13(1), 83–99. https://doi.org/10.1016/j.jtrangeo.2004.11.001 Song, W., Li, X., & Xiu, C. (2008). Analysis of urban hierarchy in China based on air passenger flow (in Chinese). Geographic Research, 04, 917–926. Wachs, M. (1979). Transportation for the elderly: Changing lifestyles, changing needs. University of California Press. Zhang, Y., & Zhang, X. (2007). Empirical analysis on influencing factors of intercity air passenger volume in China (in Chinese). Economic Geography, 4, 658–660 + 671.

Chapter 6

Beijing Case

6.1 Urban Development of Beijing 6.1.1 History of Beijing Beijing, abbreviated as “Jing” and formerly known as Yanjing and Peiping, is the capital of the People’s Republic of China, a provincial-level administrative region, a municipality directly under the central government, a key city in China, and a super large-sized city (Fig. 6.1). It is also China’s political centre, cultural centre, international exchange centre, and science and technology innovation centre approved by the State Council. As of 2020, Beijing had 16 districts with a total area of 16,410.54 km2 . At end of 2020, there was a permanent population1 of 21.893 million, and the urban population was 19.166 million, with an urbanisation rate of 87.5%. Beijing is on the North China Plain. It is adjacent to Tianjin in the east and to Hebei on the other sides. The latitude and longitude of the city centre are 39°56' N and 116°20' E. Beijing is a world-renowned ancient capital and a modern international city. It is also the office location of the Central Committee of the Communist Party of China, the Central People’s Government of the People’s Republic of China and the Standing Committee of the National People’s Congress. As of 2020, Beijing had jurisdiction over 16 municipal districts, namely, Dongcheng, Xicheng, Chaoyang, Fengtai, Shijingshan, Haidian, Shunyi, Tongzhou, Daxing, Fangshan, Mentougou, Changping, Pinggu, Miyun, Huairou and Yanqing. Beijing has absorbed various design concepts of capital planning from ancient China. According to research, it has been over 3,000 years since the construction of Beijing began when King Wu of Zhou established the ancient state of Ji before the Common Era. The Liao dynasty renamed the City of Ji as Nanjing, making it a secondary capital in 938. After the Jin dynasty destroyed the Liao dynasty, it moved its capital there in 1153 and renamed the city Zhongdu. Later, after development 1

The permanent population refers to the total number of people living in a given area within a period for half a year or longer.

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. Zhao and D. Lyu, Lifestyle Change and Transport in China, Population, Regional Development and Transport, https://doi.org/10.1007/978-981-19-4399-7_6

181

182

6 Beijing Case

Heilongjiang

Jilin Xin Jiang Inner Mongolia

Ningxia Qinghai

Liaoning

Beijing

Tianjin Hebei Shanxi Shandong

Gansu Shaanxi Henan

Tibet

Jiangsu Anhui

Shanghai

Hubei

Sichuan Chongqing

Jiangxi

Zhejiang

Hunan Guizhou

Fujian

Yunnan Guangxi Guangdong

Taiwan

Hainan South China Sea Islands

Fig. 6.1 Beijing’s location in China

and evolution in the Yuan, Ming and Qing dynasties, the Republic of China and the People’s Republic of China, Beijing retains abundant relics and a relatively complete ancient cityscape, making it the epitome of China’s history and current state. The old Beijing city had a complete urban planning system, with the central axis running through the city and an imperial palace, the Forbidden City, built on the central axis. Due to rapid economic development, Beijing has witnessed a surge in its urban population and the acceleration of urbanisation since the 1990s, causing dramatic urban land expansion. The expansion of urban land in Beijing is mainly occurring in the fringe areas of the central city, demonstrating the combination of urban sprawl along concentric circles and extensional growth of the strongholds along the routes (Xiao, 2011).

6.1.2 Urbanisation and Population Growth in Beijing From the reform and opening up to 2020, Beijing’s permanent population grew from 8.715 to 21.893 million, an increase of about 2.51 times (Fig. 6.2). In these 40 years or so, the growth rate of the permanent population kept fluctuating. As Fig. 6.2 shows, from 1978 to 1994, the growth of the permanent population was relatively slow, with an average annual growth rate of about 1.6%. From 1994 to 1999, the growth rate of the permanent population escalated. From 1999 to 2010, the permanent population

ten thousand persons

6.1 Urban Development of Beijing

183

2,500.0 2,000.0 1,500.0 1,000.0 500.0 0.0

Permanent population

Fig. 6.2 Changes in Beijing’s permanent population from 1978 to 2020. Source Beijing Municipal Bureau of Statistics (2021)

was in a period of rapid growth, with an average annual growth rate of approximately 4.1%. Beijing experienced steady population growth from 2010, which slowed down year by year. At the end of 2020, Beijing’s permanent population decreased by 11,000 from 2019, showing a downward trend for 4 consecutive years (Fig. 6.3). From 2010 to 2020, there was a slowdown in the growth of Beijing’s permanent population, with

Fig. 6.3 Permanent population density of Beijing from 2019 to 2020

184

6 Beijing Case

100.0% 80.0% 60.0% 40.0% 20.0% 0.0%

Urbanization rate

Fig. 6.4 Urbanisation rate of Beijing’s permanent population from 1978 to 2020. Source Beijing Municipal Bureau of Statistics

an average annual growth rate of 1.1%, a reduction of 2.7% points from the average annual growth rate in the previous decade. Beijing is at the forefront of urbanisation in China. Beijing’s urbanisation rate reached 55.0% as early as 1978, while China’s overall urbanisation rate at that time was only around 17.9%.2 In 2020, the urbanisation rate of Beijing’s permanent population increased to 87.5%, ranking ninth in China (Fig. 6.4).

6.1.3 Urban Expansion in Beijing Beijing is strategically positioned as the national political centre, cultural centre, international exchange centre, and science and technology innovation centre. Since the 1990s, Beijing has expanded rapidly in its urban scale. The built-up area has expanded from more than 400 km2 to nearly 1,200 km2 , and the population has increased from nearly 8 million to over 20 million, more than twice the original size, with more than half of the population in the main urban area. There are 22,546 people per square kilometre in the core functional area of the capital. The number of motor vehicles has grown from 0.6 million to over 4.1 million, over six times the original number (Yu, 2010). The unexpected constant urban expansion of Beijing has brought tremendous pressure on its resources, environment, transport, public security, daily operations and management. The spatial form of modern Beijing was developed from the outline of Beijing acquired in Ming and Qing dynasties, a shape of the Chinese character “凸”. Beijing’s development was based on a single centre (Fig. 6.5). The urban land area of Beijing in 1932 was 67.91 km2 , and the urban land area in 2007 was 19.29 times that of 1932. Since the 1990s, the evolution of Beijing’s spatial pattern and form has been affected by the natural geographic environment, rapid population and industry aggregation, major social and cultural activities (e.g., the 2008 Summer Olympic Games), and 2

Source http://www.stats.gov.cn/ztjc/ztfx/ggkf40n/201809/t20180910_1621837.html.

6.1 Urban Development of Beijing

185

Fig. 6.5 Spatial distribution of construction in Beijing in different periods. Data Source https://so. csdn.net

changing urban development models. Its urban structural and morphological evolution has demonstrated obvious spatial variation and stage characteristics (Du et al., 2019). Kuang et al. (2009) analysed the changes in land use and building density in Beijing’s main urban areas since 1956. They found that before 1984, urban expansion featured a relatively high building density, and that after 1984 it was characterised by an extremely low building density. Urban sprawl between the fifth and the sixth ring roads of Beijing between 2000 and 2007 was an ongoing problem. Beijing’s transport system, featuring ring and radial roads, and its spatial structure planning exert a great impact on urban sprawl and the finger-like spatial form resembling clusters of grapes.

186

6 Beijing Case

During Beijing’s urban expansion, residential land mainly expanded along traffic arteries. Areas along expressways in Beijing have been inhabited by the rich, such as the fifth and sixth ring roads and several expressways entering and exiting Beijing. Residences along these routes are relatively upscale. Residential areas along subways are generally ordinary. For example, Tiantongyuan, Asia’s largest community near Metro Line 5, has a resident population of about 400,000. It is mainly inhabited by people with lower incomes in Beijing, exacerbating Beijing’s residential differentiation to a certain extent. Also, Beijing underwent multiple planned economy periods. Work-unit compounds arising from housing allocation by work units led to a certain residential isolation and jobs-housing imbalance. With the marketisation of housing, residential differentiation has taken shape around different workplaces. For example, Zhongguancun and Xibeiwang, known for the electronics industry and the Internet industry, respectively, are mainly inhabited by residents with higher incomes such as senior executives in companies. In the Beijing Economic-Technological Development Area, most posts offered by the industrial parks there are ordinary technical ones, attracting many blue-collar workers to move in. The villa areas around the scenic spots and resorts in western Beijing have attracted those with high income who stress life quality. Due to the lack of rail transport, these people mainly travel by car. Beijing’s suburbanisation relies heavily on public transport (e.g., subways and light rail transport), and the surrounding areas boast commercial facilities of communities and small- and medium-sized shopping centres.

6.1.4 Transport System Changes in Beijing The rapid economic and social development and the acceleration of modernisation in Beijing have provided unprecedented opportunities and conditions for Beijing’s transport development. Beijing has achieved leapfrog development in both external transport and urban public transport.

6.1.4.1 (1)

External Transport

Railways

The railway mileage in Beijing grew in leaps and bounds between 1951 and 1970 (Fig. 6.6). In 1960, the total railway mileage in Beijing was only 520 km, and in 2019 the operating mileage of railways in Beijing reached 1,350.7 km, 2.6 times that of 1960, showing an average annual growth rate of about 1.6%. In 2019, the number of railway departure passengers in Beijing amounted to 147.549 million. Railways have become a crucial part of Beijing’s external transport, connecting China’s capital Beijing with other domestic regions. Therefore, Beijing’s railway stations are also important railway hubs in China.

187

0.18 0.16 0.14 0.12 0.1 0.08 0.06 0.04 0.02 0 1951 1953 1955 1957 1959 1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017 2019

Million kilometres

6.1 Urban Development of Beijing

Fig. 6.6 Operating mileage of railways in Beijing from 1951 to 2019. Source http://d.qianzhan. com

(2)

Highways

2.5

6.0%

2

5.0% 4.0%

1.5

3.0%

1

2.0%

0.5

1.0%

0

0.0% 1957 1959 1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017 2019

Million kilometres

Highways are also an indispensable part of urban external transport. Since 1957, Beijing’s total highway and expressway mileage have both expanded considerably (Fig. 6.7). In 1957, Beijing’s total highway mileage was only 1,200 km, and in 2019, the figure reached 22,400 km, 18.7 times that of 1957, showing an average annual growth rate of about 4.8%. Beijing’s highways have developed faster than railways in the past 60 years. This may have something to do with the relatively low cost of investment and the low construction difficulty of highways. In terms of the highway network structure, low-level highways make up the highest proportion by mileage and expressways have the lowest proportion, but the proportion of expressways is rising year by year. By the end of 2020, the total highway mileage in Beijing reached 22,264 km, including 1,173.3 km of expressways, 1,368.7 km of first-level highways, 3,995.8 km of second-level highways, 4,118.7 km of third-level highways and 11,607.5 km of fourth-level highways. The highway network density reached 135.7 km/100 km2 . The proportion of expressways in the total highway mileage increased from 0.9% in 1992 to 5.4% in 2019, 5.78 times that of 1992.

Proportion of the expressway mileage

Total highway mileage

Expressway mileage

Fig. 6.7 Operating mileage of highways and expressways in Beijing from 1957 to 2019. Source http://d.qianzhan.com

188

6 Beijing Case

(3)

Civil Aviation

In 2020, there were three certified general aviation airports in Beijing, namely, Beijing Badaling Airport, Beijing Miyun Mujiayu Airport and Haidian Airport. There were also seven registered general aviation airports, namely, Beijing Dingling Airport, Shifosi Airport, Beijing Pinggu Jinhaihu Airport, Huairou Beizhai Airport, Tongzhou Xiji Airport, Tongzhou Lucheng Airport and Beijing Daxing International Airport. The Beijing Capital International Airport was put into use in 1958, with a passenger throughput of 95,000. In 2011, terminals T1, T2 and T3 were opened, and the passenger throughput reached 78.67 million, ranking second in the world. In 2019, its passenger volume was 108.209 million. In 2019, the Beijing Daxing International Airport was officially opened to air traffic. The airport has a total of four runways and 235 aircraft parking bays, with the terminal covering an area of 700,000 km2 . A total of 7,525 parking spaces are available in parking structures, car parks and other locations. It is designed for a short-term passenger throughput of 72 million passengers/year, a long-term passenger throughput of 100 million passengers/year, a short-term cargo and mail throughput of 2 million tonnes/year, and a long-term cargo and mail throughput of 4 million tonnes/year. There were 161 regular destinations in 2020, including 148 domestic destinations and 13 international destinations.

6.1.4.2 (1)

Urban Transport

Urban Roads

Square metres

Urban roads are very important to urban residents’ travel activities in the city. The urban road area per capita of Beijing residents fluctuated between 1995 and 2019 (Fig. 6.8). In 1995, the urban road area per capita was 5.5 km2 , and this figure increased to 7.68 km2 in 2019, 1.4 times that of 1995, indicating an average annual growth rate of about 1.4%. By the end of 2020, the total length of urban roads in Beijing’s urban areas was 6,147 km, including 390 km of expressways, 1,020 km of main roads, 682 km of secondary main roads, and 4,055 km of minor roads and 12

60.0%

10

40.0%

8

20.0%

6

0.0%

4 2

-20.0%

0

-40.0%

Urban road area per capita

Growth rate

Fig. 6.8 Urban road area per capita in Beijing from 1995 to 2019. Source http://d.qianzhan.com

189

Million people

5,00,000

800 700 600 500 400 300 200 100 0

4,00,000 3,00,000 2,00,000 1,00,000 0

Operating mileage of rail transit

kilometres

6.1 Urban Development of Beijing

Total passenger volume of rail transit

Fig. 6.9 Rail transit development in Beijing from 1997 to 2019. Source http://d.qianzhan.com

others. The total road area was 106.54 mm2 , of which 18.55 mm2 were pavements, accounting for 17% of the total road area. (2)

Rail Transport

Rail transport is a vital component of urban public transport and a prime target for public transport construction and innovation in large-sized cities. In 1987, Beijing had only two rail transport lines with an operating mileage of 40 km, and rail transport was not yet developed. In 2006, the operating mileage of rail transport in Beijing reached 114 km, 2.85 times that of 1987. In 2019, Beijing had 24 subway routes in operation, with an operating mileage of 696 km, 17.4 times that of 1987 (Fig. 6.9). There were 428 subway stations altogether, including 64 interchange stations. The total passenger volume in 2019 reached 3.962 billion. Beijing’s rail transport was well developed in 2019, leading the world in the number of operating lines, the operating mileage and the passenger volume. (3)

Ground Public Transport

Ground public transport is the earliest urban transport facility constructed in the urban public transport system. In 1949, Beijing had 11 ground public transport routes and 164 vehicles, with a daily passenger volume of 79,000. In 2019, the number of public transport routes had increased to 1,158, with an operating route length of 27,632 km and a public transport lane mileage of 952 km. The total passenger volume in 2019 reached 3.564 billion (Fig. 6.10).

6.1.4.3

Motorised Development

Since Beijing’s implementation of the regulation and control policy on the number of small passenger vehicles in 2011, the growth rate of Beijing’s motor vehicle ownership has generally shown a downward trend.3 The growth rates of motor vehicles and 3

Notes: (1) Motor vehicles include passenger vehicles, cargo vehicles, low-speed vehicles, semitrailer towing vehicles, special operation vehicles, electric vehicles, motorcycles, wheeled selfpropelled vehicles, trailers, military vehicles and tractors. (2) Private motor vehicles include private

6 Beijing Case 6,00,000

30,000

5,00,000

25,000

4,00,000

20,000

3,00,000

15,000

2,00,000

10,000

1,00,000

5,000

0

One car

Million people

190

2018

2019

2017

2016

2014

2015

2013

2011

2012

2010

2009

2008

2006

Number of buses and trams operated

2007

2004

2005

2003

2002

2001

1999

2000

1998

1997

0

Total passenger volume of buses and trams

Ten thousand motor vehicles

Fig. 6.10 Bus and tram development in Beijing from 1997 to 2019. Source http://d.qianzhan.com 700 600 500 400 300 200 100 0

Motor vehicle ownership

Fig. 6.11 Growth trend of motor vehicle ownership in Beijing since 2000. Source Beijing Transport Institute (2021)

private motor vehicles were 3.2% and 4.2%, respectively. In 2020, Beijing’s motor vehicle ownership reached 6.57 million, an increase of 205,000 over the previous year, and its private motor vehicle ownership reached 5.343 million, an increase of 213,000 over the previous year (Fig. 6.11).

passenger vehicles, cargo vehicles, low-speed vehicles, semi-trailer towing vehicles, special operation vehicles, electric vehicles, motorcycles, wheeled self-propelled vehicles and trailers. (3) Private small and mini passenger vehicles include private small passenger vehicles and private mini passenger vehicles.

6.2 Lifestyle Changes in Beijing

191 1.00

60,000 50,000 40,000 30,000 20,000 10,000 0

CNY

0.80 0.60 0.40 0.20 0.00

Per capita disposable income

Per capita consumption expenditure

Average propensity to consume

Fig. 6.12 Per-capita disposable income and per-capita consumption expenditure of Beijing residents from 1978 to 2019. Source Beijing Municipal Bureau of Statistics (2020)

6.2 Lifestyle Changes in Beijing 6.2.1 Consumption Dimension 6.2.1.1

Income Level

As the income level rises, the consumption level also increases significantly (Fig. 6.12). As China’s capital and a premier super large-sized city, Beijing has been ranking among the top in China regarding its economic development speed and level, and it has been enjoying relatively high income and consumption levels. The disposable income and consumption expenditure of Beijing residents have increased greatly. Figure 6.12 shows that in 1978, the per-capita disposable income of Beijing residents was only 302 CNY, and the per-capita consumption expenditure was only 281 CNY. In 2019, the per-capita disposable income of Beijing residents increased to 62,361 CNY, about 206 times that of 1978. The per-capita consumption expenditure increased to 39,843 CNY, only 142 times that of 1978. The income gap between urban and rural residents is large (Fig. 6.13). In 1978, the per-capita disposable income of urban residents in Beijing was 365 CNY, and the per-capita disposable income of rural residents was 225 CNY. These figures increased to 67,990 CNY and 26,490 CNY, respectively, in 2019. Figure 6.13 shows that from 1985 to 2004, the actual growth rate of the disposable income of urban residents was higher than that of rural residents, but from 2005 to 2018, the growth rates of urban and rural residents’ disposable income became increasingly close.

6.2.1.2

Consumption Level

The growth rate of residents’ consumption level is on the whole lower than that of Beijing’s GDP, reflecting that the growth of economic output has not fully driven residents’ consumption behaviour and tapped their consumption potential (Fig. 6.14).

192

6 Beijing Case

60,000

30

40,000

20

20,000

10

0

0

197819801982198419861988199019921994199619982000200220042006200820102012201420162018 Per capita disposable income of urban residents Per capita disposable income of rural residents Actual growth rate of the per capita disposable income of urban residents

CNY

Actual growth rate of the per capita disposable income of rural residents

%

Fig. 6.13 Changes in the disposable income of Beijing’s urban and rural residents from 1978 to 2019. Source Beijing Statistical Yearbook (2020) 0.5 0.4 0.3 0.2 0.1 0 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017 2019 Growth rate of residents’ consumption level

GDP growth rate

Fig. 6.14 Growth rates of residents’ consumption level and GDP in Beijing from 1978 to 2018

Since 1996, the growth rate of GDP has been higher than that of the consumption level, which means that since the reform and opening up, the speed of economic development has surpassed the growth rate of residents’ consumption level. This may be related to the traditional Chinese concept of prioritising housing: one may control one’s consumption behaviour to a certain extent due to the importance attached to the purchase of a property. In 1978, the per-capita consumption expenditures of all residents, urban residents and rural residents in Beijing were 281 CNY, 360 CNY and 225 CNY, respectively (Fig. 6.15). In 1995, they were 4,366 CNY, 5,020 CNY and 2,336 CNY, respectively. In 2019, the figures leaped to 39,843 CNY, 42,926 CNY and 20,195 CNY, respectively. In 30 years, the consumption expenditure of Beijing residents has increased by a factor of nearly 140, and residents’ lives have been greatly improved.

6.2.1.3

Consumption Expenditure Structure

Since the reform and opening up, Beijing’s urban and rural residents’ food consumption as a proportion of total consumption has continuously reduced (Fig. 6.16). According to the classification of food consumption stages put forward by the Food

6.2 Lifestyle Changes in Beijing

193

45,000 40,000 35,000

CNY

30,000 25,000 20,000 15,000 10,000 5,000 0

Per capita consumption expenditure of urban residents Per captita consumption expenditure of rural residents Per capita consumption expenditure

Fig. 6.15 Changes in the per-capita consumption expenditure of Beijing Residents from 1978 to 2019. Source Beijing Statistical Yearbook 2020

50 30 10 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017 2019 Engel's coefficient of Beijing households Engel's coefficient of urban households in Beijing Engel's coefficient of rural households in Beijing

Fig. 6.16 Changes in Engel’s coefficient of Beijing households from 1978 to 2018. Source Beijing Municipal Bureau of Statistics (2019)

and Agriculture Organization of the United Nations, Engel’s coefficient of Beijing residents was above 50% from 1978 to 1993, and consumption remained at the subsistence level. Engel’s coefficient was between 40 and 50% from 1993 to 1998, with consumption being at the well-off level. Engel’s coefficient was between 30 and 40% from 1999 to 2003, denoting affluent consumption. Since 2003, the food consumption of Beijing residents has entered the most prosperous stage, and Engel’s coefficient has been maintained below 30%. In addition to food, tobacco and alcohol, household consumption expenditure has another seven categories, including housing, clothing, education, transport, etc. For Beijing residents, food, housing and transport are the major consumption categories, accounting for more than 67.6% of the total (Fig. 6.17). In 2018, housing expenditure constituted the highest proportion of household consumption expenditure in Beijing, which was 35.4%. Expenditure on food, tobacco and alcohol accounted for 20.2%,

194

6 Beijing Case Expenditure on other goods and services, 2.9%

Healthcare expenditure, 9.4%

Expenditure on food, tobacco and alcohol, 21.3%

Expenditure on education, culture and recreation, 10.8%

Clothing expenditure, 5.6%

Expenditure on transport and communication, 12.5%

Housing expenditure, 39.5%

Expenditure on household goods and services, 6.0%

Fig. 6.17 Consumption expenditure structure of Beijing residents in 2019. Source Beijing Statistical Yearbook (2020)

expenditure on transport and communication accounted for 12.0%, and expenditure on education, culture and recreation accounted for 10.0%.

6.2.1.4

Longitudinal Changes in the Consumption Level

The expenditures on housing and healthcare of Beijing residents have shown an obvious upward trend, reflecting a further increase in housing costs in Beijing and residents’ growing demand for healthcare services (Fig. 6.18). Expenditures on food, tobacco, alcohol, clothing, transport and communication have shown a remarkable 35.0% 30.0% 25.0% 20.0% 15.0% 10.0% 5.0% 0.0%

2015

2016

2017

2018

2019

Expenditure on food, tobacco and alcohol

Clothing expenditure

Housing expenditure

Expenditure on household goods and services

Expenditure on transport and communication

Expenditure on education, culture and recreation

Healthcare expenditure

Expenditure on other goods and services

Fig. 6.18 Changes in the consumption expenditure structure of Beijing residents from 2015 to 2019. Source Beijing Statistical Yearbook (2020)

6.2 Lifestyle Changes in Beijing

195

700.0 600.0 500.0 400.0 300.0 200.0 100.0 0.0

30.0% 25.0% 20.0% 15.0% 10.0% 5.0% 0.0% -5.0%

10,000 Number of motor vehicles

Growth rate

Fig. 6.19 Changes in the number of motor vehicles in Beijing from 2013 to 2019. Source Beijing Municipal Bureau of Statistics (2014, 2015, 2016, 2017, 2018, 2019, 2020)

downtrend (Fig. 6.19). The proportions of Beijing residents’ housing expenditure and healthcare expenditure in the per-capita consumption expenditure increased from 30.6% and 6.6% in 2015 to 35.4% and 8.2% in 2018, increase of 4.8% and 1.6%, respectively. The proportions of expenditure on food, tobacco and alcohol; clothing expenditure and expenditure on transport and communication in the percapita consumption expenditure decreased from 22.4%, 7.2% and 13.3% in 2015 to 20.2%, 5.5% and 12.0% in 2018, decrease of 2.2% points, 1.7% points and 1.3% points, respectively. The development of the Internet has facilitated the rapid development of online consumption, a new consumption pattern, and information consumption has become increasingly important in residents’ lives. The online retail sales of Beijing’s retail industry rose from 145.69 billion CNY in 2014 to 263.29 billion CNY in 2018, and its share in the total retail sales of social consumer goods rose from 15.1% in 2014 to 22.4% in 2018 (Fig. 6.20). 30.0%

4,000

25.0%

CNY

3,000

20.0% 15.0%

2,000

10.0%

1,000

5.0% 0.0%

0 2014

2015

2016

2017

2018

2019

Online retail sales of the wholesale and retail industry above designated size Share in the total retail sales of social consumer goods

Fig. 6.20 Changes in the online retail sales of Beijing’s wholesale and retail industry from 2014 to 2019. Source Beijing Municipal Bureau of Statistics (2019)

196

6 Beijing Case

6.2.2 Time Dimension The Beijing Municipal Bureau of Statistics conducted surveys on residents’ time use in 1986, 2008 and 2018. These data can well reflect the characteristics and changes of Beijing residents’ time use over the past 30 years. The time-use survey takes natural persons as the survey object and obtains the time spent by residents on work and study, housework, fitness and exercise, and leisure and entertainment by constantly recording the activities of the respondents 24 h a day. The 2008 Time Use Survey for Beijing Residents4 was conducted among 1,500 urban and rural households in Beijing (1,000 urban households and 500 rural households). There were 3,733 respondents altogether, including 1,843 males and 1,890 females. The 2018 Time Use Survey for Beijing Residents5 was conducted among 1,700 urban and rural households in Beijing (1,280 urban households and 420 rural households). There were 4,238 respondents altogether, including 2,063 males and 2,175 females.

6.2.2.1

From 1986 to 2008

Since the 1986 survey was conducted a long time ago and the result was not completely consistent with that of the 2008 survey, only the samples of urban residents are reliable and can be used for comparison. Therefore, the analysis of changes in this period is only targeted at urban residents. In general, there was a qualitative leap in the socioeconomic level and living standards of residents during this period. First, Beijing’s urban residents paid more attention to enjoyment and physical and mental health, and they spent more time on meals, hygiene activities, leisure and entertainment. Second, due to the implementation of the 5-day workweek system, residents’ working hours decreased, but their commuting time increased significantly due to other factors. Third, the affluent material life, which was easy to obtain, led to a reduction in housework hours. (1)

Extended Time for Meals and Personal Hygiene Activities

In the 1980s, the income level of urban residents in Beijing was relatively low. Dining out was a luxury for residents. With the improvement in living standards, eating was no longer just to meet survival needs; instead, it became a way of enjoying life and an important means of interpersonal communication. In 2008, the average daily dining time for urban residents in Beijing was 1 h and 42 min, 1.4 times that in 1986 (Fig. 6.21). In addition, residents paid more attention to their appearance and physical health, which is reflected in the increase in the average daily time spent on personal hygiene activities. In 2008, the value was 55 min, 1.5 times that in 1986. 4 5

Source http://tjj.beijing.gov.cn/zxfbu/202002/t20200216_1641182.html. Source http://tjj.beijing.gov.cn/zxfbu/202002/t20200216_1633486.html.

6.2 Lifestyle Changes in Beijing

197

55

Time spent on personal hygiene activities

37

102

Average daily dining time

73 0

20

40

60

80

100

120

minutes 2008

1986

Fig. 6.21 Changes in the necessary activity time of Beijing’s urban residents in 1986 and 2008. Source Survey Office of the National Bureau of Statistics in Beijing (2009)

(2)

Reduced Working Hours and Increased Travel Time

Residents’ working hours changed a lot, mainly due to the 5-day workweek system introduced in China on May 1, 1995 (Hu, 2019). Previously, domestic employees generally had to work 6 days a week. Therefore, in 2008, the average daily working time of urban residents in Beijing was 5 h and 55 min, which was 43 min less than in 1986 (Fig. 6.22). The variation pattern of commuting time is opposite to working hours. In 2008, the average daily commuting time of urban residents in Beijing was 70 min, which was 16 min more than in 1986. The mode of working and living in the same unit was no longer common, and workplace-residence separation had begun. It also had something to do with the increasing car ownership year by year. (3)

Reduced Housework Intensity and Hours

From 1986 to 2008, the social economy was booming, and material living standards were qualitatively improved. There was an abundant supply of various daily necessities, and the popularity of household appliances such as washing machines increased. As a result, urban residents gradually got rid of onerous housework, and housework hours were shortened. Figure 6.23 shows that in 2008, the average daily housework time of urban residents was 138 min, which was 10 min less than in 1986.

70 54

Commuting time

355

Average daily working time 0

50

100

150

398 200

250

300

350

400

450

Minutes 2008

1986

Fig. 6.22 Changes in the working hours of Beijing’s urban residents in 1986 and 2008. Source Survey Office of the National Bureau of Statistics in Beijing (2009)

198

6 Beijing Case 300

Minutes

250 200 150 100 50 0 Housework time

Time spent on food preparation

Time spent on washing and tidying up clothes

1986

Discretionary time

TV time

Exercise time

Time spent online

2008

Fig. 6.23 Changes in the housework hours and discretionary time of Beijing’s urban residents in 1986 and 2008. Source Survey Office of the National Bureau of Statistics in Beijing (2009)

(4)

More Discretionary Time and Colourful Leisure Activities

Another manifestation of the improvement in living standards is that residents had more time at their disposal, and that leisure and entertainment activities were more colourful. Figure 6.23 shows that in 2008, the average daily discretionary time of urban residents in Beijing was 4 h and 21 min, an increase of 22 min from 1986. The content and composition of leisure and entertainment activities also underwent major changes (Fig. 6.23). First, residents’ preferences for watching TV further strengthened. Second, urban residents’ health awareness was raised, and the time spent on fitness and exercise increased rapidly. Third, home computers and the Internet became new platforms for residents’ leisure and entertainment activities. In 2008, urban residents spent 32 min online on average every day.

6.2.2.2

From 2008 to 2018

As the economic development slowed down, residents faced more pressure from work and their working hours increased significantly. Meanwhile, residents had higher requirements for life quality, and their leisure and entertainment time also increased to a certain extent. Residents began to use and rely more on machines or third-party services for family labour. The mobile Internet became one of the main means of leisure and entertainment activities for residents. Its popularisation and development had a huge impact on residents’ lives. (1)

Further Increase in Sleeping and Dining Hours

From 2008 to 2018, China’s sustained rapid economic growth tended to slow down, and residents’ requirements for life quality were further improved (Fig. 6.24). The sleeping and dining hours of Beijing residents both increased by nearly 20 min per day.

6.2 Lifestyle Changes in Beijing

199 552 532

Sleeping time

117 97

Average daily dining time 0

100

200

300

400

500

600

Minutes 2018

2008

Fig. 6.24 Changes in the necessary activity time of Beijing residents in 2008 and 2018. Source Survey Office of the National Bureau of Statistics in Beijing (2019)

89 89

Commuting time

514

Average daily working time

458 0

100

200

300

400

500

600

Minutes 2018

2008

Fig. 6.25 Changes in the working hours of Beijing residents in 2008 and 2018. Source Survey Office of the National Bureau of Statistics in Beijing (2019)

(2)

An Increase of Nearly 1 Working Hour for Office Workers with Unchanged Commuting Time

Compared with 2008, the average daily working hours of Beijing residents increased in 2018 (Fig. 6.25). The average working time of office workers in 2008 was 7 h and 38 min, and in 2018 it was 8 h and 34 min, an increase of 56 min. The proportion of Beijing’s office workers was gradually decreasing, but their working hours were increasing (Survey Office of the National Bureau of Statistics in Beijing, 2019). In 2008, the commuting time of Beijing residents was 1 h and 29 min, and in 2018, it almost remained unchanged. Although the permanent population of Beijing rose during this decade (17.71 million in 2008 and 21.7 million in 2017) and so did the traffic pressure, the further increase in commuting time was effectively curbed. That was because the Beijing Government adopted a series of traffic control measures and initiated a number of subway infrastructure projects. (3)

Increased Family Labour Time and Reduced Housework Hours

In recent years, Beijing residents have paid more attention to life quality and comfort, thus spending more time caring for their families. In 2008, the per-capita family labour time of Beijing residents was 152 min, and in 2018, it was 172 min, an increase of 20 min from 2008.

200

6 Beijing Case

300 250 200 150 100 50 0 Family labour time

Minutes

Housework time Discretionary time 2008

Exercise time

Leisure time

Time spent online

2018

Fig. 6.26 Changes in the family labour time and discretionary time of Beijing residents in 2008 and 2018. Source Survey Office of the National Bureau of Statistics in Beijing (2019)

(4)

Significantly Longer Discretionary Time with Mobile Internet Access Taking the Lead

The length of leisure time and the content of leisure activities are important indicators of residents’ life quality. In 2008, the per-capita discretionary time of Beijing residents was 257 min, and in 2018 it was 274 min, an increase of 17 min, indicating that residents’ life quality improved (Fig. 6.26). Among them, the per-capita exercise time in 2018 increased by 14 min compared with 2008, and the per-capita leisure time increased by 25 min. In 2008, the average time spent online by Beijing residents was 25 min. Due to the continuous expansion of network coverage and the gradual popularity of smartphones, the time spent online in 2018 reached 3 h and 6 min, an increase of 2 h and 41 min, accounting for 68% of personal discretionary time. During this period, the Internet developed rapidly, and it has become an important part of residents’ lives today. The mobile Internet occupies 75% of the time spent online.

6.2.3 Activity Dimension 6.2.3.1 (1)

Changes in Employment

Changes in the Employment Structure

The employment choices made by college graduates can to some extent reflect the basic conditions of employment of local residents. According to the 2019 Annual Report on the Quality of Employment of College Graduates in Beijing (Fig. 6.27; Beijing Municipal Education Commission, 2019), the proportion of graduates from different colleges in Beijing employed by enterprises dropped by 4.0% points to 58.6%. The proportion of college graduates employed by institutional units and public institutions increased, accounting for 6.1% and 15.6%, respectively. The proportion of college graduates employed by other enterprises (mainly private enterprises) amounted to 34.91%, higher than that of college graduates employed by state-owned enterprises (20.08%), which indicates that private enterprises remain the major choices of employment.

6.2 Lifestyle Changes in Beijing

201 16.84%

Others Administrative rural villages

0.05%

Urban communities

0.11%

Army forces

0.41%

Scientific research and design institutions

2.68%

Medical and health institutions

2.74%

Other public institutions Foreign-invested enterprises Institutions of primary and secondary education Institutions of higher education

3.15% 3.56% 3.99% 4.96% 6.53%

Institutional units

20.08%

State-owned enterprises

34.91%

Other enterprises

0.00%

5.00%

10.00%

15.00%

20.00%

25.00%

30.00%

35.00%

40.00%

Fig. 6.27 Types of employers of college graduates in Beijing 2019. Note Other enterprises refers to all enterprises except for state-owned enterprises and foreign-invested enterprises, in which private enterprises predominate. Others covers 14,136 graduates who did not specify the type of their employers or those who chose the type of others, most of whom are freelancers

As regards the industries of employment (Fig. 6.28), the four most attractive industries to college graduates are information transmission, software and information technology services (14.3%), education (13.3%), scientific research and technical services (8.2%) and finance (7.7%). The IT industry has been a fast-growing industry in China over the past 2 decades. Its related industry, known as the Internet industry, has become the most attractive industry in recent years due to its most rapid growth, fastest expansion and huge profit. The entry into this industry of 14.26% of college graduates can be attributed to the large number of Internet companies in Beijing, which is ranked among the top industries nationwide. (2)

New Forms of Employment: Megacities as Reservoirs for Employment

Flexible employment refers to a type of employment that is part time, temporary, flexible or self-directed. It is different from traditional employment in terms of working hours, income, workplaces, insurance, welfare and labour relations. With the penetration of the Internet platform economy, flexible employment has developed rapidly into several new forms that are platform based, self-directed and flexible. Through the Internet platform, the supply end can directly be connected to the consumption end. Specifically, such new forms of employment involve, among others, ecommerce merchants, life service delivery persons, shared vehicle drivers, sellers on micronet platforms, knowledge service providers, live streamers and self-media workers (Zhuo, 2020). Among them, the number of life service delivery persons, shared vehicle drivers, self-media workers and knowledge service providers is increasing in megacities, mainly as a result of increasing demands for related services there.

202

6 Beijing Case Others Internatioal organizations

7.26% 0.02%

Army forces Minig Water conservancy, environment and public utility management Accommodation and catering services Agriculture, forestry, husbandry and fihery Real estate

0.41% 0.66% 0.91% 0.98% 1.29% 1.30%

Transport, warehousing and postal services Electricity, heat, gas, and water production and supply services Community services, repair and other services

2.34% 2.57% 2.79%

Wholesale and retail services

2.35%

Leasing and buiness services

3.81%

Construction

3.98%

Health and social work

4.43%

Manufacturing

5.99%

Culture, sports, and entertainment

6.75%

Public administration, social welfare and social organization Finance

7.71% 7.72%

Scientific research and technical services Education

8.16% 13.30%

Informatin transmission, software, and information technology services

0.00%

14.26% 2.00%

4.00%

6.00%

8.00%

10.00% 12.00% 14.00% 16.00%

Fig. 6.28 Types of industries where college graduates in Beijing sought employment 2019. Note Others refers to those who did not specify the type of the industry where they sought employment, including 11,307 freelancers

According to the 2019 Investigation Report on China’s Delivery Service Providers released by the China Post and Express News, the development of online retail markets and the improvement of related infrastructure have boosted the development of delivery services in China. From 2016 to 2018, the number of delivery persons in China increased by 50% to over three million (China Post and Express News, 2020). The business forms of shared transport include peer-to-peer ridesharing, shared parking and bicycle-sharing, of which peer-to-peer ridesharing contributes directly to employment. With a low threshold, ridesharing services are quick to master and offer a certain degree of freedom and a high income, which have become some people’s career choice. According to Ridesharing Baodian (2019), as of December 2019, the number of ridesharing drivers in China reached 38.09 million, the growth rate of which amounted to 36% from 2016 to 2019. The ridesharing industry is experiencing a phase of rapid growth. Despite a lack of official statistics on the

6.2 Lifestyle Changes in Beijing

203

number of ridesharing drivers in Beijing, as of May 31, 2019, 38,000 people in Beijing had received ridesharing driving licenses6 and become legitimate ridesharing drivers. This figure, however, is far smaller than the actual one. The new forms of employment that rely on the Internet economy have created lots of jobs for Beijing and alleviated employment pressure. As emerging industries, they are still in the process of development and evolution.

6.2.3.2 (1)

Changes in Leisure Lifestyles

Urban Recreation

Urban recreation is a form of urban development in the post-industrial era. To meet people’s increasing demands for leisure and entertainment due to improvements in the environment and living standards, government departments and companies are committed to developing a series of recreational facilities, including art theatres and museums, as well as fitness clubs and sports stadiums. The recreational function of a city has been enriched so much that it is expected to improve people’s living standards further (Tang, 2020). According to the 2020 China Urban Recreation Index, jointly published by the Leisure Research Centre of the School of Business Administration of East China Normal University and the Leisure and Tourism Research Centre of Shanghai Normal University, Beijing and Shanghai took the lead in terms of the urban recreation index, followed by Guangzhou, Chongqing and Shenzhen. This result reveals the reciprocal interaction between economic growth and leisure. The economic growth of a city is the basis of urban residents’ leisure. As one of the municipalities and super largesized cities of China, Beijing has a strong economy in which local residents have more leisure to enjoy themselves. (2)

Leisure Tourism

As an important way of enjoying one’s life and feeling happy, leisure tourism has become an integral part of people’s daily life. In 2016, the amount of money spent on tourism by local residents in Beijing amounted to 9,769 CNY, 1.3 times the amount of 2011, with an average annual growth rate of 6.0% (Fig. 6.29). Such an increase indicates that local residents are willing to spend more money on tourism to improve their living standards. A comparison of three types of tourist destinations reveals that the amount of money spent on city tours, domestic travel and outbound travel increased to 1,725, 5,356 and 15,134 CNY, with growth rates of 10.0%, 5.9% and 4.2%, respectively. Among Beijing residents, rural tourism and agritourism are becoming more popular. Beijing is home to characteristic rural areas, some of which have developed rural tourism into a pillar industry based on favourable geographical locations and tourist resources. Now in Beijing, there are three rural tourism cycles, namely, 6

Source http://bj.people.com.cn/n2/2019/0621/c82840-33065047.html.

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6 Beijing Case

Average tourism consumption Outbound travel Domestic travel City tours 0

2000

4000

6000

8000

10000

12000

14000

16000

RMB 2016

2011

Fig. 6.29 Tourism consumption by Beijing residents in 2011 and 2016. Source Leisure Economy Research Centre, Renmin University of China (2011, 2016)

inner suburbs, middle suburbs and outer suburbs, each of which has developed its own rural tourism with its own characteristics (Table 6.1) (Li, 2016). The inner suburbs of Chaoyang, Haidian and Shijingshan are committed to the development of creative agriculture and agricultural parks for tourism. The middle suburbs of Daxing, Tongzhou, Shunyi and Changping, based on their agricultural resources, are developing leisure farms. The outer suburbs of Fangshan and Miyun, where natural landscapes and forest resources abound, are prioritising the development of natural sightseeing.

6.3 Surveying the Links Between Lifestyles and Travel Behaviour 6.3.1 Data Source This part is based on data from the 2016 Subway Survey and the 2017 Questionnaire Survey on Jobs-Housing Balance in Beijing, conducted by the Urban and Traffic Planning Research Centre of Peking University, with a total of 3,783 valid questionnaires collected (Fig. 6.30). The descriptive statistics are in Table 6.2. In terms of gender, female samples totalled 1,868, accounting for 49.4% and male samples totalled 1,915, accounting for 50.6%. The gender ratio is relatively balanced. In terms of age, 142 samples were from people aged 20 and under, accounting for 3.8%; 1,752 samples were from people aged 20 to 30, accounting for 46.3%; 1,298 samples were from people aged 30 to 40, accounting for 34.3%; 409 samples were from people aged 40 to 50, accounting for 10.8%; 131 samples were from people aged 50 to 60, accounting for 3.5% and 51 samples were from people aged above 60, accounting for 1.3%. In terms of education, 52 samples were from people who have received primary school or lower education, accounting for 1.4%; 331 samples were from people who have received junior secondary or vocational secondary school education, accounting for 8.7%; 494 samples were from people who have received senior

2

1

30

Folk villages

Folk households

–

3

2

64

1

8

506

21

6

1,387

30

3

100

3

7

40

4

10

1,563

25

8

793

14

9

1,385

29

7

1,404

27

6

1,339

23

7

1,359

27

7

9,970

207

83

Chaoyang Fengtai Haidian Mentougou Fangshan Tongzhou Shunyi Changping Daxing Huairou Pinggu Miyun Yanqing Total

Number in each district

Agricultural demonstration zones for sightseeing

Programme

Table 6.1 Status of rural tourism development in the districts of Beijing 2013

6.3 Surveying the Links Between Lifestyles and Travel Behaviour 205

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6 Beijing Case

Fig. 6.30 Location of the case neighbourhoods in Beijing

Table 6.2 Descriptive statistics for sample variables (partial) Sample

3,783

Gender

N

%

Education

N

%

Male

1,915

50.6

Primary school or lower education

52

1.4

Female

1,868

49.4

Junior secondary or vocational secondary school education

331

8.7

Senior secondary or vocational high school education

494

13.1

Age ≤20

142

20–30

1,752

3.8 46.3

Bachelor’s or college degree

2,271

60.0

Master’s or higher degree

635

16.8

30–40

1,298

34.3

Employment

40–50

409

10.8

Without a job

475

12.6

50–60

131

3.5

With a job

3,308

87.4

60+

51

1.3

secondary or vocational high school education, accounting for 13.1%; 2,271 samples were from people who have bachelor’s or college degrees, accounting for 60.0% and 635 samples were from people who have master’s or higher degrees, accounting for 16.8%. In terms of employment, 475 samples were from people who do not have

6.3 Surveying the Links Between Lifestyles and Travel Behaviour

207

a job, accounting for 12.6% and 3,308 samples were from people who have a job, accounting for 87.4%. In terms of the built environment and transport facilities, the community area averages 6.52, with a variance of 2.855; population density averages 999.00, with a variance of 370.031; the degree of land use mixture averages 0.65, with a variance of 0.009; the number of intersections averages 14.34, with a variance of 41.960; road density averages 10.18, with a variance of 10.086; the distance to the closest subway station averages 770.93, with a variance of 519,380.382; the distance to the closest bus stop averages 92.68, with a variance of 5,230.281; the number of bus stops averages 5.93, with a variance of 4.273 and the number of subway stations averages 0.50, with a variance of 0.326.

6.3.2 Methodology SEM is a multivariate statistical analysis technique used to verify the interrelations of one or more independent variables with one or more dependent variables. It is a model widely applied in fields such as psychology, sociology and management. Compared with traditional multivariate statistical analysis techniques, SEM has its own strengths. Most traditional multivariate methods are used only to test single relations between independent and dependent variables, for instance, the multivariate analysis of variance though relationships between more than one independent and dependent variables can be handled using this method. In contrast, the methods of SEM, including regression analysis, factor analysis and path analysis, can be applied to deal with multiple interrelations between variables. In addition, SEM is theoretically a priori, and it can be used for both measurement and analysis. In the analysis of relations between different variables, SEM can allow for errors that occur in the process of measurement. It is also capable of integrating the concept of measurement reliability into deduction processes such as path analysis. In view of the above, SEM is applied in this research to the analysis of research questions. In studying the impacts of lifestyles on travel behaviour, it is necessary to take into consideration the socioeconomic attributes of individuals, the built environment of the communities where individuals are located and the transport facilities around communities. Travel behaviour includes work travel behaviour and leisure travel behaviour. The conceptual model constructed for this study is shown in Fig. 6.31. This part explores both the work travel behaviour and the leisure travel behaviour of individuals. There are several main features of travel behaviour, including trip frequency, trip length, mode choice and vehicle miles travelled (Ewing & Cervero, 2001). In our study, both trip frequency and mode choice are regarded as travel outcomes. The primary focus of this study is on the impacts of lifestyles on the time, methods, frequency and costs of working and leisure trips. This study centres around the impacts of lifestyles on travel behaviour. Recent studies mainly use the behavioural patterns of activities to measure different lifestyles (van Acker et al., 2014). The variables relevant to lifestyles in this study include

208

6 Beijing Case Socioeconomic attributes

Work travel behaviour Lifestyle

Transport facilities Leisure travel behaviour

Built environment

Fig. 6.31 Conceptual model

income, consumption, individual travel attitudes, family conditions, time-use patterns and frequency of socioeconomic activities. To reduce the dimensions of variables related to lifestyles, this study uses the varimax rotation method to analyse the factor loading matrix. In factor analysis, the number of factors is determined based on the interpretability of factors, coupled with the scree plot and all the eigenvalues that are larger than 1, from which seven factors are extracted (Fig. 6.32). The KMO value is 0.696, the chisquare value of Bartlett’s test of sphericity is 13,870.018 and the p-value is 0.000, which is significant. The seven factors can explain 67.124% of the variance. The results suggest that factor analysis is a valid way to proceed. The final results of the factor rotation matrix are in Table 6.3. According to the table, the variables of the travel time saving group include “Travel is mostly a waste of time”, “I usually plan for daily travel to reduce the number of trips” and “The only purpose of travel is to reach the destination”; the variables of the green travel group include “I like walking”, “I like riding bicycles”, “I like taking the bus” and “I like taking the subway”; the variables of the group of car lovers include “I like driving”, “Driving is safer than other means” and “Life is in trouble without a private car”; the variables of the high-income group include “family income” and “personal income”; the variables of the group with children to feed include “times of sending children to school” and “number of family members”; the variables of the group of expenditure lovers include “shopping times” and “times of dining out” and the variable of the group of motor owners includes “number of cars owned”. Lifestyles are grouped into seven categories, namely, the travel time saving group (TS), the green travel group (GT), the group of car lovers (CL), the high-income group (HI), the group with children to feed (CF), the group of expenditure lovers (EL) and

6.3 Surveying the Links Between Lifestyles and Travel Behaviour

209

Fig. 6.32 Scree plot

the group of motor owners (MO). The factor loading matrix for each category is outlined in Table 6.3. Utilising data from land use databases in ArcGIS, we obtained several spatial characteristics of the built environment in 800 m buffer zones. The built environment features include diversity measures (degree of land use mixing), design measures (street density and internal connectivity) and population density measure. All these different measures are calculated at the starting point of the trip. Hence, it is possible to evaluate the impacts of the built environment of buffer zones on travel behaviour. The variables of transport facilities include the number of intersections around communities, road density, the distance to the closest subway station, the distance to the closest bus stop, the number of bus stops and the number of subway stations. All these variables can measure the conditions of transport facilities around the communities where individuals are located well. The impacts of key objective variables (e.g., sociodemographic characteristics) are also taken into consideration. Sociodemographic characteristics mainly include the gender, age, education and employment of individuals (Table 6.4). In our research context, the use of structural equation models is closer to reality and more effective than single equation models. SEM can simulate the effects of variables simultaneously (Hoyle, 2012). This study uses the maximum likelihood method to analyse the regression coefficients of the model. The second column of Table 6.5 shows the regression results with work trip frequency as the dependent variable. Gender has a standardised coefficient of 0.037 and a significance level of 0.021, which is significant. Employment has a standardised coefficient of 0.297 and a significance level of 0.000, which is significant. EL has a standardised coefficient of 0.140 and a significance level of 0.000,

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6 Beijing Case

Table 6.3 Pattern matrix for lifestyles Variable

Lifestyle factor TS

GT

CL

Family income

HI

CF

EL

MO

0.908 0.904

Personal income Travel is mostly a waste of time

0.699

I usually plan for daily travel to reduce the number of trips

0.768

The only purpose of travel is to reach the destination

0.757

Number of cars owned

0.990

Times of sending children to school

0.759

Number of family members

0.786 0.743

Shopping times

0.739

Times of dining out I like walking

0.645

I like riding bicycles

0.654

I like taking the bus

0.785

I like taking the subway

0.754

I like driving

0.869

Driving is safer than other means

0.876

Life is in trouble without a private car

0.579

which is significant. CF has a standardised coefficient of 0.029, which is significant. The number of subway stations has a standardised coefficient of −0.058 and a significance level of 0.043, which is significant. Overall, gender has a significant positive impact on work trip frequency, which means males have a higher work trip frequency than females do. Employment has a significant positive impact on work trip frequency. It reveals that those with a job have a higher work trip frequency than those without. EL is significantly positively associated with work trip frequency, indicating that those who love shopping have a higher work trip frequency. CF also has a higher work trip frequency. The number of subway stations exerts a significant negative influence on work trip frequency, which shows that the larger the number of subway stations around communities, the lower the work trip frequency. The third column of Table 6.5 shows the regression results with work trip duration as the dependent variable. Education has a standardised coefficient of 0.076 and a significance level of 0.000, which is significant. Employment has a standardised coefficient of 0.331 and a significance level of 0.000, which is significant. TS has a standardised coefficient of 0.039, which is significant. HI has a standardised coefficient of 0.037, which is significant. CF has a standardised coefficient of −0.029,

6.3 Surveying the Links Between Lifestyles and Travel Behaviour

211

Table 6.4 Checklist of variables showing the impacts of lifestyles on travel Categories of lifestyles

TS

Travel is mostly a waste of time I usually plan for daily travel to reduce the number of trips The only purpose of travel is to reach the destination

GT

I like walking I like riding bicycles I like taking the bus I like taking the subway

CL

I like driving Driving is safer than other means Life is in trouble without a private car

HI

Family income Personal income

CF

Times of sending children to school Number of family members

EL

Shopping times Times of dining out

MO

Number of cars owned

Socioeconomic attributes

Gender, age, education, employment

Transport facilities

The number of intersections, road density, the distance to the closest subway station, the distance to the closest bus stop, the number of bus stops, the number of subway stations

Built environment

Community area, population density, degree of land use mixture

which is significant. EL has a standardised coefficient of −0.046, which is significant. The community area has a standardised coefficient of 0.092, which is significant. The degree of land use mixture has a standardised regression coefficient of 0.042, which is significant. The distance to the closest subway station has a regression coefficient of 0.049, which is significant. Overall, education has a significant positive impact on work trip duration. This means those with higher education tend to spend more time on work trips. Employment has a significant positive impact on work trip duration, which indicates that those with a job spend more time on work trips than those without. TS has a significant positive impact on work trip duration, which reveals that those who lay emphasis on saving travel time tend to spend more time on work trips. HI significantly positively impacts work trip duration, which means the higher the income, the longer the work trip duration. CF spends less time on work trips because caring for children and the family takes time. EL is significantly negatively related to work trip duration, meaning that those who love shopping tend to spend less time on work trips. The community area significantly positively impacts work trip duration. This means the larger the community area, the longer the work trip duration. The greater the degree of land use mixture, the longer the duration of individual work

212

6 Beijing Case

Table 6.5 Model regression table Variable

Work trip frequency

Work trip duration

Work trip cost

Leisure trip frequency

Leisure trip duration

Lifestyles TS

−0.015

GT

0.002

0.039** −0.011

0.004 −0.034**

−0.038*** 0.061***

−0.048*** −0.012

CL

−0.003

0.011

0.077***

−0.034***

−0.017

HI

−0.002

0.037**

0.124***

−0.036***

−0.010

0.051***

0.207***

0.029*

−0.029*

EL

0.140***

−0.046***

MO

0.000

CF

0.009

0.200*** 0.001

0.741*** −0.005

0.040** 0.238*** 0.006

Socioeconomic attributes Gender

0.037**

Age

−0.006

Education

−0.034

0.004

0.026

−0.002

−0.084***

−0.019

0.001

−0.007

−0.013

0.076***

−0.044**

0.297***

0.331***

0.036**

−0.005

0.062***

Community area

0.011

0.092***

0.049*

−0.042**

0.007

Population density

0.019

Employment

0.032***

0.117***

Built environment

Degree of land −0.004 use mixture

−0.005 0.042*

−0.013

−0.014

−0.018

0.036

0.014

−0.027

0.000

0.012

0.008

Transport facilities Number of intersections

0.033

Road density

−0.007

−0.005 0.027

0.012

0.006

−0.015

Distance to the −0.018 closest subway station

0.049*

0.005

0.030

−0.068**

Distance to the −0.002 closest bus stop

0.014

0.019

−0.036**

−0.017

Number of bus −0.005 stops

0.025

0.006

0.002

−0.015

0.052***

−0.032

Number of subway stations

−0.058**

−0.002

−0.055*

Notes * means p < 0.1; ** means p < 0.05; *** means p < 0.01. TS is an abbreviated form of “travel time saving group”, GT is an abbreviated form of “green travel group”, CL is an abbreviated form of “group of car lovers”, HI is an abbreviated form of “high-income group”, CF is an abbreviated form of “group with children to feed”, EL is an abbreviated form of “group of expenditure lovers” and MO is an abbreviated form of “group of motor owners”

6.3 Surveying the Links Between Lifestyles and Travel Behaviour

213

trips. The farther the distance to the closest subway station, the longer the duration of individual work trips. The fourth column of Table 6.5 shows the regression results with work trip costs as the dependent variable. Education has a standardised coefficient of −0.044, which is significant. Employment has a standardised coefficient of 0.036, which is significant. GT has a standardised coefficient of −0.034, which is significant. CL has a standardised coefficient of 0.077, which is significant. HI has a standardised coefficient of 0.124, which is significant. CF has a standardised coefficient of 0.051, which is significant. EL has a standardised coefficient of 0.200, which is significant. The community area has a standardised coefficient of 0.049, which is significant. The number of subway stations has a standardised coefficient of −0.055, which is significant. Overall, education has a negative impact on work trip costs, meaning that the higher the education, the lower the work trip costs. Employment has a significant positive impact on work trip costs, which indicates that those with a job tend to spend more money on work trips than those without. GT significantly negatively impacts work trip costs, meaning that those who love green travel spend less money on work trips. CL significantly positively influences work trip costs, which reveals that those who love travelling by car spend more money on work trips. HI significantly positively impacts work trip costs, which means the higher the income, the higher the work trip costs. CF has a significant positive association with work trip costs, which means those with children to feed spend more money on work trips. EL significantly positively impacts work trip costs, showing that those who love shopping spend more money on work trips. The larger the community area, the higher the costs of individual work trips. The larger the number of subway stations around communities, the lower the costs of individual work trips. The fifth column of Table 6.5 shows the regression results with leisure trip frequency as the dependent variable. Education has a standardised coefficient of 0.032, which is significant. TS has a standardised coefficient of −0.038, which is significant. GT has a standardised coefficient of 0.061, which is significant. CL has a standardised coefficient of −0.034, which is significant. HI has a standardised coefficient of −0.036, which is significant. CF has a standardised coefficient of 0.207, which is significant. EL has a standardised coefficient of 0.741, which is significant. The community area has a standardised coefficient of −0.042, which is significant. The distance to the closest bus stop has a standardised coefficient of − 0.036, which is significant. The number of subway stations has a standardised coefficient of 0.052, which is significant. Overall, education exerts a positive influence on leisure trip frequency, indicating that the higher the education, the higher the leisure trip frequency. TS has a significant negative impact on leisure trip frequency. This means those who lay emphasis on saving travel time demonstrate a lower leisure trip frequency. GT significantly positively impacts leisure trip frequency, which means those who love green travel have a higher leisure trip frequency. CL significantly negatively influences leisure trip frequency, which implies that those who love travelling by car have a lower leisure trip frequency. HI significantly negatively impacts leisure trip frequency, which means those with higher income have a lower leisure trip frequency. CF has a significant positive impact on leisure trip frequency, indicating

214

6 Beijing Case

that those with children to feed have a higher leisure trip frequency. EL significantly positively impacts leisure trip frequency, which means those who love shopping have a higher leisure trip frequency. The community area significantly negatively impacts leisure trip frequency, which means the larger the community area, the lower the leisure trip frequency. The distance to the closest bus stop significantly negatively influences leisure trip frequency, which means the farther the location of bus stops, the lower the leisure trip frequency. The number of subway stations significantly positively influences leisure trip frequency, which means the larger the number of subway stations, the higher the leisure trip frequency. The sixth column of Table 6.5 shows the regression results with leisure trip duration as the dependent variable. Gender has a standardised coefficient of −0.084, which is significant. Education has a standardised coefficient of 0.117, which is significant. Employment has a standardised coefficient of 0.062, which is significant. TS has a standardised coefficient of −0.048, which is significant. CF has a standardised coefficient of 0.040, which is significant. EL has a standardised coefficient of 0.238, which is significant. The distance to the closest subway station has a standardised coefficient of −0.068, which is significant. Overall, males spend less time on leisure trips than females do. The higher the education, the longer the leisure trip duration. Those with a job spend more time on leisure trips than those without. The travel time saving group spends less time on leisure trips. The group with children to feed spends more time on leisure trips. The farther the distance to the closest subway station, the shorter the individual leisure trip duration.

6.3.3 Conclusion In addition to the socioeconomic attributes of individuals, the built environment has an influence on work and leisure travel behaviours. Previous research has proved that the built environment has an impact on travel behaviour. For example, residents living in high-density and easily accessible communities drive less (Bagley & Mokhtarian, 2002; Handy et al., 2005). This book has shown that the larger the community area, the longer the work trip duration, the higher the costs of such trips, and the lower the leisure trip frequency. Transport facilities such as subway stations and bus stops around communities also impact work and leisure travel behaviours. With both the environmental variables (the built environment and transport facilities) and individuals’ socioeconomic attributes taken into consideration, differences in lifestyles can to some extent explain individual differences in work and leisure travel behaviours, which are summarised below. Groups with different lifestyles differ in work and leisure travel behaviours. The travel time saving group spends more time on work trips. This group of people lays more emphasis on saving travel time probably because the group members have to spend more time on work trips. Despite their efforts, the time they spend on commuting is still above average. This group of people who lay more emphasis on saving commuting time and actually spend more time on daily work trips tends to

6.4 Summary

215

hate leisure trips. That is why the leisure trip frequency is relatively low and the duration of leisure trips is relatively short in this group. The green travel group prefers green trip modes such as walking, riding bicycles and taking the subway, and it spends less money on work trips. This conclusion is also confirmed by Kahn and Morris (2009). People who have green travel awareness and favour green travel tend to use public transport or walk when travelling (Chen et al., 2014). Due to low trip costs, this group of people has a higher leisure trip frequency. Car lovers prefer to travel by car in their daily work trips. Therefore, in this group, work trip costs are relatively high and leisure trip frequency is relatively low, which may have something to do with the inconvenience of driving. China and other countries differ a lot in car use. Chinese car lovers tend to use cars for commuting. However, in foreign countries, especially in the United States, cars are also used for a series of activities such as shopping, leisure and entertainment (Wolf, 1996). The high-income group spends more time and money on work trips. This may be because this group of people with a higher income is less sensitive to work trip costs. It may also have something to do with the type of work these people do, such as types of work that require frequent business trips. In addition, in the high-income group, leisure trip frequency is relatively low. Instead of choosing inbound leisure trips, this group of people tends to travel abroad on a regular basis. The group with children to feed loves both working and leisure trips and tends to reduce work trip duration. This may be explained by the needs to care for family members. This group of people usually hopes to devote a lot of time to family. The frequency and costs of leisure family trips in this group are relatively high. Expenditure lovers also like both working and leisure trips. This group of people with a strong desire for shopping spends more money on work trips and demonstrates a relatively high frequency and long duration of leisure trips. Motor owners do not demonstrate any difference from those without a car in work and leisure travel behaviours.

6.4 Summary This chapter has analysed residents’ lifestyle changes and their impacts on transport in Beijing, a representative of China’s super large-sized cities. Section 6.1 has briefly introduced the population, urban expansion and transport system of Beijing. Section 6.2 has introduced Beijing residents’ lifestyle changes in different dimensions in turn, including the consumption dimension, time dimension and activity dimension. The following conclusions are drawn. First, Beijing residents’ lifestyle changes are consistent with the overall trend in China. The per-capita consumption expenditure of urban and rural residents in Beijing is on the rise; the consumption expenditure of urban residents has always been higher than that of rural residents, and there are large differences between urban and rural residents. Second, the growth rate of household consumption is

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6 Beijing Case

lower than that of GDP, reflecting that the growth in economic output has not fully driven consumption. Third, from 1986 to 2008, with the cancellation of the 5-day workweek system, residents’ working hours were reduced, and more attention was paid to life enjoyment and physical and mental health. From 2008 to 2018, residents’ family labour time underwent changes, and they began to turn to third-party services in regard to family labour. Meanwhile, the mobile Internet played an increasingly important part in residents’ entertainment and leisure activities. Fourth, relying on the Internet economy, new employment patterns have created many jobs in Beijing. Fifth, more and more Beijing residents are choosing rural tourism, leisure agriculture and other tourism types. With data from the Questionnaire Survey on Jobs-Housing Balance in Beijing, Sect. 6.3 has applied SEM to study the impacts of Beijing residents’ lifestyles on their travel behaviour. The results show that aside from individuals’ socioeconomic attributes, built environment factors such as the community area, as well as transport facilities such as subways and bus stations around the community, also affect travel behaviour for work and leisure purposes. This part has successfully integrated these three important parts of the study, avoiding the lack of important factors that can lead to unscientific research errors. The conclusions we have obtained are basically consistent with previous studies, that is, residents’ travel behaviour is not solely affected by one type of factor, but is affected by a variety of exogenous and endogenous factors. Considering all these three types of factors, differences in lifestyles can explain the differences in individuals’ travel behaviour for work and leisure purposes to a certain extent. This chapter has divided the purpose of travel into work and leisure, and then discussed the impact of three types of factors on these two types of travel behaviour, which is also an important achievement of this chapter. This chapter has used Beijing as the representative of a more specific analysis because the development of megacities often involves the real lives of tens of millions of residents. Every specific and detailed policy or regulation modification may have an impact on the lives of residents living in this city. The analysis and discussion in this chapter will not only promote the development of the academic field, but will also be helpful to the transportation policy of Beijing, the capital and China’s premier megacity. In the past, transportation policy studies have basically taken into account the influence of traditional variables such as sociodemographics, but few have taken into account the attitudes, values and lifestyles of residents. The conclusions from the SEM in this chapter show that the lifestyles of the residents of megacities have an impact on traffic behaviour and choices, and this is exactly what the city government needs to consider. So far, the development of urban transportation in Beijing has been relatively extensive. Of course, this is one of a series of consequences of not paying much attention to urban planning. The lack of careful consideration of the transportation system and layout has indeed brought some inconvenience to urban residents. We hope that in the future, the design and improvement of traffic in big cities will give more consideration to the travel needs of different groups of people and ensure that they have fast, convenient, stable and diverse transportation options.

References

217

References Bagley, M. N., & Mokhtarian, P. L. (2002). The impact of residential neighborhood type on travel behavior: A structural equations modeling approach. Annals of Regional Science, 36(2), 279–297. https://doi.org/10.1007/s001680200083 Beijing Municipal Bureau of Statistics. (2014). Beijing statistical yearbook, 2015. Beijing Municipal Bureau of Statistics. (2015). Beijing statistical yearbook, 2015. Beijing Municipal Bureau of Statistics. (2016). Beijing statistical yearbook, 2016. Beijing Municipal Bureau of Statistics. (2017). Beijing statistical yearbook, 2017. Beijing Municipal Bureau of Statistics. (2018). Beijing statistical yearbook, 2018. Beijing Municipal Bureau of Statistics. (2019). Beijing statistical yearbook, 2019. Beijing Municipal Bureau of Statistics. (2020). Beijing statistical yearbook, 2020. Beijing Municipal Bureau of Statistics. (2021). Beijing statistical yearbook, 2021. Beijing Municipal Education Commission. (2019). 2019 annual report on employment quality of college graduates in Beijing (in Chinese). http://jw.beijing.gov.cn/xxgk/zxxxgk/201912/t20191 227_1521978.html. Beijing Transport Institute. (2021). 2021 Beijing transport development annual report. Chen, H., Long, R., Niu, W., Feng, Q., & Yang, R. (2014). How does individual low-carbon consumption behavior occur?—An analysis based on attitude process. Applied Energy, 116, 376–386. https://doi.org/10.1016/j.apenergy.2013.11.027 China Post and Express News. (2020). 2019 National express practitioners occupation survey report (in Chinese). http://m.cnr.cn/news/20200102/t20200102_524923032.html. Du, J., Ning, X., Liu, J., Qiu, S., Wang, H., & Wang, C. (2019). Analysis of Beijing urban spatial expansion pattern and morphological characteristics based on remote sensing monitoring (in Chinese). Areal Research and Development, 38(2), 73–78. Ewing, R., & Cervero, R. (2001). Travel and the built environment: A synthesis. Transportation Research Record, 1780(1), 87–114. https://doi.org/10.3141/1780-10 Handy, S., Cao, X., & Mokhtarian, P. (2005). Correlation or causality between the built environment and travel behavior? Evidence from Northern California. Transportation Research Part d: Transport and Environment, 10(6), 427–444. https://doi.org/10.1016/j.trd.2005.05.002 Hoyle, R. (2012). Handbook of structural equation modeling. Guilford Press. Hu, P. (2019). The introduction of the five-day work system in China (in Chinese). Baixing Shenghuo, 9, 2. Kahn, M. E., & Morris, E. A. (2009). Walking the walk: The association between community environmentalism and green travel behavior. Journal of the American Planning Association, 75(4), 389–405. https://doi.org/10.1080/01944360903082290 Kuang, W., Shao, Q., Liu, J., & Sun, C. (2009). Analysis on the characteristics and mechanism of land use spatial expansion in Beijing’s main urban area since 1932 (in Chinese). Journal of Geo-Information Science, 11(4), 428–435. Leisure Economy Research Centre, Renmin University of China. (2011). Survey on Time Allocation by Beijing Residents in 2011. Leisure Economy Research Centre, Renmin University of China. (2016). Survey on Time Allocation by Beijing Residents in 2016. Leisure Research Centre of the School of Business Administration of East China Normal University & Leisure and Tourism Research Centre of Shanghai Normal University. (2020). 2020 China urban recreation index. Li, M. (2016). Research on the construction of new countryside for leisure and tourism from the perspective of landscape architecture (in Chinese) (Unpublished dissertation). Beijing Forestry University. Ridesharing Baodian. (2019). 2019 Market analysis report on peer-to-peer ridesharing. Survey Office of the National Bureau of Statistics in Beijing. (2009). 2008 Beijing residents’ time utilization survey report.

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Survey Office of the National Bureau of Statistics in Beijing. (2019). A Beijinger’s day—2018 Beijing residents’ time utilization survey report (in Chinese). http://tjj.beijing.gov.cn/zxfbu/202 002/t20200216_1633486.html. Tang, F. (2020). Analysis of the 2019 China urban leisure index report (in Chinese). Industrial Innovation, 2, 37–40. Van Acker, V., Mokhtarian, P. L., & Witlox, F. (2014). Car availability explained by the structural relationships between lifestyles, residential location, and underlying residential and travel attitudes. Transport Policy, 35, 88–99. https://doi.org/10.1016/j.tranpol.2014.05.006 Wolf, W. (1996). Car mania: A critical history of transport. Pluto Press. Xiao, Y. (2011). Analysis of the characteristics and dynamics of the evolution of Beijing urban spatial structure (in Chinese). Beijing Planning Review, 6, 107–110. Yu, Z. (2010). Background of building a world city and the relevant challenges confronting Beijing (in Chinese). Studies on Socialism with Chinese Characteristics, 12(2), 11–15. https://doi.org/ 10.3969/j.issn.1008-2271.2010.02.004 Zhuo, X. (2020). New employment forms are the driving force for stable employment. Beijing Daily. http://www.xinhuanet.com/politics/2020-09/28/c_1126552191.htm.

Chapter 7

Stories About Rural China

Two main types of rural residents are introduced in this chapter: one is town residents, and the other is village residents. This part first introduces the difference in administrative meaning and scale between town and village. Towns are townshiplevel administrative divisions in China, under the jurisdiction of municipal districts, county-level cities, counties, autonomous counties, banners, autonomous banners, special administrative regions or forestry districts, or under the direct jurisdiction of prefecture-level cities or autonomous prefectures. Townships and towns are both township-level administrative divisions. The difference between the two is that towns cover a larger area, with a larger population, a more developed economy and certain industrial areas. The majority of the people in towns are non-agricultural. Townships generally consist of several administrative villages, with populations between 5,000 and 20,000. The population size of townships is smaller than that of towns, and the management system is different (Fig. 7.1). Administrative villages are rural primary units of government established by the state in accordance with laws and regulations. Administrative villages are administered by villagers’ committees—rural organisations of self-government. Villagers’ committees may establish a number of villagers groups based on natural villages. A natural village is one or more settlements naturally formed on the basis of blood ties, such as families and clans, or for other reasons: villagers spontaneously form a village where they live together after long-term residence in a natural environment. The natural village is the unit of farmers’ daily life and communication. In normal circumstances, an administrative village manages several natural villages. In special circumstances, to facilitate management, a large natural village can also be an administrative village, or it can be divided into several administrative villages. The characteristics of the respective lifestyles and travel behaviours of town and village residents are described in detail later in this chapter. Here we just provide a brief overview. The characteristics of the village relate mainly to agriculture and animal husbandry, and the occupations of residents also revolve around agriculture and animal husbandry. Most residents are engaged in agricultural activities. Villages © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. Zhao and D. Lyu, Lifestyle Change and Transport in China, Population, Regional Development and Transport, https://doi.org/10.1007/978-981-19-4399-7_7

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7 Stories About Rural China

Natural village boundary Administrative village boundary Town boundary Town construction scope Rural Settlement

Fig. 7.1 Schematic diagram of China’s rural system

generally have extensive land for residents to cultivate. The characteristic of a town is between a city and a village. Although its industry and economic structure are not as diverse and complex as a city, they are far more abundant than in a village. One town can provide more advanced services and goods to multiple villages, which is why village residents often go to town to purchase.

7.1 Data Source The rural residents’ travel data came from a detailed survey of small towns across China conducted by Peking University and other universities in cooperation with the Ministry of Housing and Urban-Rural Development in 2016. The survey lasted 1 year and was targeted at 121 small towns in 31 provinces (autonomous regions and municipalities) in China (Fig. 7.2). The survey includes questionnaires for town residents, villagers and enterprises. The data for this study came from a Questionnaire for Town Residents, a Questionnaire for Urban Space and Construction and a Questionnaire for Villagers. The Questionnaire for Town Residents was completed by permanent town residents. The total number of households investigated in each town was about 120, and the sample totalled 12,524 from 19 towns. After removing missing data, the number of effective samples was 11,295. The Questionnaire for Urban Space and Construction was filled out by investigators after field surveys by town, with 119 samples in total. For the Questionnaire for Villagers, based on the distance between villages and towns, three villages were selected for each town (at short, intermediate and long distances), and about 10 households were randomly

7.1 Data Source

221

Fig. 7.2 Spatial distribution of sample towns

selected in each village to complete the questionnaires. There were 2,693 valid questionnaires. The transport information of village residents in the questionnaire mainly includes the frequency, vehicles, time and purposes of trips to towns and the country. Of the 11,295 valid samples of town residents, male respondents accounted for 62.3%, while female respondents accounted for only 37.7% (Table 7.1). There was a big difference in their respective proportion. The average age of the respondents was 48.8 years, and the overall age was fairly high, which might be related to the relatively high proportion of older people in these towns. In terms of ethnicity, Han people accounted for 87.8% of the respondents, and non-Han peoples accounted for 12.2%. Regarding hukous, 62.4% of the respondents had agricultural hukous, while those with non-agricultural hukous and residential hukous made up 37.6%. Unlike urban residents, most of whom had non-agricultural hukous, a considerable number of town residents were village residents. The education level of town residents was generally low. Just 67.2% of the respondents had only completed the 9-year compulsory education and had not received any further education. Furthermore, only 13.9% of the respondents had obtained higher education diplomas. Of the 2,693 effective samples of villagers, male respondents accounted for 75.7%, while female respondents accounted for only 24.3% (Table 7.2). The difference in their respective proportion was larger than that in the samples of town residents. This phenomenon might be due to the preference of boys over girls and the gender imbalance in young migrant workers. The average age of the respondents was 50.0, older than that of town residents. In terms of ethnicity, 86.0% of the respondents were Han. The education level of villagers was also lower than that of town residents. Some

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Table 7.1 Basic characteristics of samples of town residents Variable

Mean or percentage

Gender Male

62.3%

Female

37.7%

Age

48.8

Ethnicity Han people

87.8%

Non-Han people

12.2%

Hukou Agricultural hukou

62.4%

Non-agricultural hukou

35.6%

Residential hukou

2.0%

Education level Primary education

26.9%

Lower secondary education

40.3%

Upper secondary education (including secondary vocational training)

18.8%

College education (%)

8.4%

Bachelor’s (%)

5.2%

Master’s or above (%)

0.3%

Sample size (persons)

11,295

Table 7.2 Basic characteristics of samples of villagers Variable

Mean or percentage

Gender Male

75.7%

Female

24.3%

Age

50.0

Ethnicity Han people

86.0%

Non-Han people

14.0%

Education level Primary education

29.8%

Lower secondary education

45.5%

Upper secondary education (including secondary vocational training)

19.5%

College education

4.0%

Bachelor’s

1.2%

Master’s or above

0.0%

Sample size (persons)

2,693

7.2 Rural Lifestyles

223

75.3% of the respondents had only completed the 9-year compulsory education and had not received further education. Furthermore, only 5.2% of the respondents had obtained higher education diplomas, and only 0.04% of the respondents had master’s degrees or above.

7.2 Rural Lifestyles 7.2.1 Consumption Dimension 7.2.1.1

Household Income Level

As Fig. 7.3 shows, the monthly disposable income of 68.9% of town households was between 1,000 and 5,000 CNY, and the monthly disposable income of 47.7% of town households was between 2,000 and 5,000 CNY, indicating that this was an average case. In terms of extremely low or high incomes, the proportion of town households with extremely low incomes (below 1,000 CNY) was 13.9%, showing that a considerable number of town households had extremely low incomes and were relatively poor. Only a few town households had extremely high incomes, and town households with disposable incomes above 8,000 CNY only made up 6.1%. Odd jobs and farming were the main sources of income for 40.0% and 35.5% of town households, respectively. A total of 75.5% of town households made a living from these two means (Fig. 7.4). Moreover, according to further statistics, 16.8% of town households derived income from both farming and odd jobs: more than 1/6 of town residents did odd jobs while farming. Running businesses and engaging in office work were the main sources of income for 26.3% and 24.4% of town households, respectively. Old-age pensions, family subsidies and government subsidies are incomes of a strong welfare nature. In terms of household income, as Fig. 7.5 shows, 73.0% of rural households had an annual income below 50,000 CNY. The variation trend of income levels 30.0 24.1

25.0

23.5

21.2

%

20.0 15.0

13.9 11.1

10.0

6.1

5.0 0.0 Below 1,000

1,000—2,000

2,000—3,000

3,000—5,000

5,000—8,000

CNY

Fig. 7.3 Distribution of monthly disposable income of town households

Above 8,000

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7 Stories About Rural China Others

1.9%

Government subsidies Financial income (funds, stocks, etc.)

5.6% 0.7%

Old-age pensions, retirement pensions, etc.

13.6%

Family subsidies Real estate leasing

6.3% 1.4%

Running businesses

26.3%

Office work (education institutions, public institutions, etc)

24.4%

Odd jobs

40.0%

Farming

0.0%

35.5% 5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

40.0%

45.0%

Fig. 7.4 Frequency of occurrence of main income sources of town households 25.0

20.7

%

20.0 15.0

16.5 10.7

13.4

11.6

8.3

10.0

3.9

5.0

3.5

1.6

2.6

0.0

CNY

Fig. 7.5 Distribution of annual income of rural households

was clear: 10.7% of rural households had an annual income below 10,000 CNY, while the proportion of rural households with an annual income between 10,000 and 20,000 CNY quickly rose to 20.7%. Thus, the annual household income of most respondents was between 10,000 and 50,000 CNY, and households with an annual income between 10,000 and 20,000 CNY and those with an annual income between 20,000 and 30,000 CNY both accounted for a considerable portion (over 15%). In addition, the proportions of high-income households were relatively small, with only 18.8% of rural households with annual incomes of over 60,000 CNY. Farming and odd jobs were also the two major income sources for rural households (Fig. 7.6). Compared with town residents, farming and odd jobs were selected more frequently by village residents, but the frequency of running businesses and engaging in office work was significantly lower. This is closely related to rural lifestyles and labour modes. The frequency of occurrences of odd jobs was 58.1%, only slightly lower than that of farming. This is because in the slack season when village residents are idle, part-time and casual jobs have become an almost necessary way to increase income. In addition, village residents differ a lot from town residents in welfare

7.2 Rural Lifestyles

225

Government subsidies

18.1

Family subsidies

6.2

Running businesses

15.7

Office work (education institutions, public institutions, etc.)

13.2

Odd jobs

58.1

Farming

76.8 0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

90.0

%

Fig. 7.6 Frequency of occurrence of main income sources of rural households

income. Government subsidies have become the third main source of income for rural households, demonstrating the government’s support and special care for village residents.

7.2.1.2

Consumption Expenditure Structure

The total monthly household expenditure of 69.6% of all town households was between 1,000 and 4,000 CNY (Fig. 7.7). The monthly expenditure of most households was within this range. Just 27.9% of town households had monthly expenditures between 1,500 and 2,500 CNY. In addition, households with extremely low expenditures (monthly expenditures below 500 CNY) accounted for 5.7%, and households with extremely high expenditures (monthly expenditures above 4,000 CNY) accounted for 11.7%, indicating that the overall consumption level of town residents was lower than that of urban residents. The consumption structure for town residents is in Table 7.3. Since the specific data of monthly household expenditures were not measured, it is impossible to calculate the proportions of various expenditures. The proportions of sample households with certain types of expenditures are in Table 7.3, as well as the relevant mean values and 27.9

30.0 25.0

%

13.0

15.0 10.0

21.9

19.8

20.0

11.7

5.7

5.0 0.0 Below 500

500—1,000

1,000—1,500 CNY

Fig. 7.7 Monthly expenditures of town households

1500—2500

2,500—4,000

Above 4,000

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Table 7.3 Breakdown of monthly expenditures of town households Proportion of households with this kind of Mean (CNY) Standard deviation expenditure (%) Housing

94.4

436.3

798.4

Food

97.3

856.3

726.6

Clothing

80.0

278.9

427.3

Transport

72.0

224.7

355.0

Entertainment

22.0

249.0

340.9

Communication 89.3

143.1

175.6

Child education 46.9

749.6

1,079.5

Medical care

64.7

370.7

1,152.2

Elder care

34.4

364.1

445.7

%

standard deviations. In general, residents who were willing to consume for enjoyment were still in a minority. The categories with the highest mean values were food expenditure, child education expenditure and housing expenditure. First let us look at the proportions of expenditures. The proportion of basic consumption expenditures was obviously higher than that of expenditures for development and enjoyment. Housing and food expenditures accounted for 94.4% and 97.3%, respectively, close to 100%, while entertainment expenditure only made up 22.0%. The proportion of child education expenditure was 46.9%. Regarding the mean values of actual expenditures, food expenditure had the highest mean value (856.3 CNY), which was in line with the general rule. It was followed by child education expenditure (749.6 CNY). This means that the education expenditure of households with children who are at school accounts for a large part of the total household consumption, reflecting the importance town residents attach to child education. Housing expenditure had the third highest mean value (436.3 CNY). There were big differences in annual expenditure of rural households (Fig. 7.8). As Fig. 7.8 shows, the annual household expenditure of 63.6% of all rural households was between 5,000 and 30,000 CNY. More than half of the respondents’ annual household 16.0 14.0 12.0 10.0 8.0 6.0 4.0 2.0 0.0

13.1

15.2

13.2 10.9

11.2 6.5

4.7

CNY

Fig. 7.8 Annual expenditures of rural households

10.5 6.9 4.3

3.6

7.2 Rural Lifestyles

227

expenditures were 30,000 CNY or less. Rural households with annual expenditures between 10,000 and 15,000 CNY occupied the highest proportion (15.2%). Rural households with extremely low expenditures (below 5,000 CNY) accounted for 4.7%, and those with extremely high expenditures (above 50,000 CNY) accounted for 10.5%, indicating that there are dramatic differences in consumption amongst village residents.

7.2.2 Time Dimension This part uses data from the Report on the 2018 Time Use Survey in China to compare the average time village residents spent on various activities and their participation rate with the total residents (Table 7.4). The average time village residents spent on paid labour was 301, 37 min longer than the time spent by all residents. Paid labour includes employment and domestic labour. The average time spent by village residents on employment was limited, while the average time spent on domestic labour was 69 min longer. This had much to do with the rural industrial structure and the occupational composition of village residents. On the whole, the average time spent by village residents on unpaid labour was 159 min, 3 min shorter than the time spent by all residents. To be more specific, village residents spent more time on housework than all residents. Rural residents spent an average of 97 min on housework, while all residents spent an average of 86 min on housework. However, village residents spent less time on accompanying and caring for family members and purchasing goods or services (including seeking medical advice) than all residents: village residents spent 45 min and 14 min on them, respectively, while all residents spent 53 min and 21 min, respectively. The participation rate of village residents in purchasing goods or services (including seeking medical advice) was 8.3% lower than that of all residents, showing that village residents were not interested in enjoyment or developmental activities. In general, the average time spent by village residents on discretionary activities was less than the time spent by all residents, and the participation rates of village residents were also lower. The time spent by village residents on all discretionary activities was 213, 23 min less than the time spent by all residents. Apart from watching TV and social communication, village residents spent less time on all other activities than all residents, and their participation rates were also lower. Compared with all residents, the personal entertainment activities of village residents were relatively monotonous. The average time spent by village residents on training, learning and transport activities were shorter than the time spent by all residents, and the participation rates of village residents were also lower. Rural residents spent less time on training and learning. The reason might be that village residents led comfortable lives with a limited pursuit of self-education, and that their children’s academic pressure was lower than children in large-sized cities. The geographical restrictions and the smaller

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7 Stories About Rural China

Table 7.4 Average time spent by residents on main activities and their participation rate in 2018 Activity category

Average time spent by all residents (min)

Average time spent by village residents (min)

Participation rate of all residents (%)

Participation rate of village residents (%)

Total

1,440

1,440

1. Activities of daily living

713

713

Sleep and rest

559

563

100.0

100.0

Personal hygiene care

50

47

98.0

97.2

99.7

99.8

Eating

104

103

2. Paid labour

264

301

Employment

177

145

38.4

31.0

23.1

41.1

Domestic labour

87

156

3. Unpaid labour

162

159

Housework

86

97

58.5

59.4

Accompanying and caring for family members

53

45

/

/

Purchasing goods 21 or services (including seeking medical advice)

14

23.3

15.0

Public service activities

3

2

4.1

3.5

4. Discretionary activities

236

213

Exercise

31

16

30.9

18.7

Listening to the radio or music

6

5

6.8

5.6

Watching TV

100

104

66.5

70.8

Reading

9

5

10.1

5.0

Leisure and entertainment

65

58

40.7

36.2

Social communication

24

25

17.6

18.1

5. Training and learning

27

24

7.2

6.2

6. Transport activities

38

30

50.8

41.3

Others: web use

162

98

/

Note. Source Report on the 2018 time use survey in China

7.2 Rural Lifestyles

229

administrative area of the countryside led to a significantly smaller traffic scope and lower travel frequency than cities, so village residents tended to spend less time on transport activities.

7.2.3 Activity Dimension 7.2.3.1

Employment Patterns

Figure 7.9 shows the occupational choices of town residents. Farming was the single item with the highest proportion, which means that 1/4 of the respondents engaged in farming. It was followed by running businesses, doing odd jobs, working in government departments and public institutions, and working in enterprises. The frequency of selection was 20.8, 14.2, 13.9 and 6.4%, respectively. Although the size and scale of towns are smaller than those of cities, they generally have complete urban functions and can provide a wide variety of occupational choices, ranging from public institutions to private enterprises. Residents’ working modes are in Fig. 7.10. Flexible working was the single item with the highest proportion (38.5%), which means that working hours are not fixed. It was followed by long continuous working hours from morning to evening, accounting for 20.0%. This type of working mode is likely to belong to manual labour and farming, which requires more physical effort and time. Figure 7.11 shows the occupational choices of village residents. Farming was also the single item with the highest proportion, and 65.1% of the respondents engaged in farming, determined by the development characteristics of rural agriculture. None of the proportions of other occupations exceeded 20%, and the proportions of doing odd Others

2.1

Retired

0.0

Unemployed

11.5

Attending school

1.2

Farming

25.0

Odd jobs

14.2

Running businesses…

20.8

Working in enterprises

6.4

Working in government departments… 0.0

13.9 5.0

10.0

15.0 %

Selected frequency

Fig. 7.9 Occupational choice frequency of town residents

20.0

25.0

30.0

230

7 Stories About Rural China Flexible working

38.5

The nine-to-five office routine

17.7

Long continuous working hours from morning to evening

20.0

Half of the day

1.5

Others

3.3

No working

18.9 0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

40.0

45.0

% Percent

Fig. 7.10 Working modes of town residents

Others

3.1

Retired

2.8

Unemployed

6.4

Attending school

0.3

Farming

65.1

Odd jobs

15.4

Running businesses… Working in enterprises

8.3 2.6

Working in government departments… 0.0

6.0 10.0

20.0

30.0

40.0

50.0

60.0

70.0

% Selected frequency

Fig. 7.11 Occupational choice frequency of village residents

jobs, running businesses and working in government departments and public institutions were 15.4%, 8.3% and 6.0%, respectively. Rural labour life boasts obvious seasonal characteristics, including both busy seasons and slack seasons. During slack seasons, a considerable number of village residents will their spare time and supplement family income by doing odd jobs.

7.2.3.2

Leisure Patterns

Amongst the numerous leisure and entertainment activities for town residents (Fig. 7.12), watching TV occupied a dominant position, and its frequency of selection was 81.6% (the original question was a multiple response question). This means that watching TV was the most popular leisure activity, though not the only one, and that

7.2 Rural Lifestyles

231 None Others Chatting

2.9% 3.4% 2.5%

Participating in religious activities

13.5%

Playing cards and Mahjong

3.6%

Playing chess

3.1%

Square dancing or exercise

17.5%

Surfing the Internet

17.6%

Playing with mobile phones

35.8%

Reading

16.6%

Watching TV 0.0%

81.6% 10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0% 80.0% 90.0%

Selected frequency

Fig. 7.12 Recreational activities of town residents in their leisure time

other activities were much less popular. Only the frequency of selection of playing with mobile phones surpassed 30% and stood at 35.8%. The frequency of selection of other activities, including square dancing or exercise, surfing the Internet, reading and participating in religious activities, was between 10 and 20%—they were niche activities with only a small number of participants. Figure 7.13 shows the frequency of dining out for town residents. Generally speaking, the frequency of dining out was low. Some 43% of respondents never dined out, and only about 15% of respondents dined out frequently (once every week or more frequently). The average cost of each meal was 201.6 CNY, and the standard deviation was 198.2. In terms of travelling, to the question “Have you ever travelled with your family”, only 23.8% of the respondents chose “Yes”. Nearly 70% of them never travelled with their family members, showing that town residents were not very keen on travelling. Next, we analyse the daily leisure patterns of two special groups of town residents: children and older people (Figs. 7.14 and 7.15). Watching TV remained the most Once every one to three days 3% Once every week 12%

Once every half a year or less frequently 19%

Once every month 23% Never 43%

Fig. 7.13 Frequency of dining out of town residents

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7 Stories About Rural China Others

11.1%

Visiting parks

6.6%

Visiting amusement parks

4.9%

Playing in streets

22.7%

Surfing the Internet

10.3%

Playing with mobile phones

18.3%

Reading

20.4%

Watching TV

52.0%

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

Selected frequency

Fig. 7.14 Daily recreational activities of children amongst town residents

None Others

10.6% 3.1%

Chatting Participating in religious activities

27.4% 3.4%

Playing cards and Mahjong Playing chess

10.6% 3.3%

Square dancing or exercise Surfing the Internet Playing with mobile phones Reading

14.1% 1.6% 4.1% 10.2%

Watching TV 0.0%

64.8% 10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

Selected frequency

Fig. 7.15 Daily recreational activities of older people amongst town residents

frequently selected activity for children, accounting for 52.0%, but the frequency was much lower than that of all town residents. Playing in the streets made up 22.7%, which was in line with children’s recreational habits. It was followed by reading and playing with mobile phones, accounting for 20.4% and 18.3%, respectively. Watching TV was also the first choice for older people. However, unlike all residents, older people had chatting as the second most popular hobby, chosen by 27.4% of older respondents. It was followed by square dancing or exercise, accounting for 14.1%.

7.2.3.3

Other Activities

This part mainly focuses on residents’ Internet-related lifestyles. The Internet has changed urban residents’ lifestyles and also brought new changes to rural lifestyles.

7.3 Rural Residents’ Travel Behaviour

4.18%

0.11%

233

0.68%

2.44%

1.38%

91.22%

Letter

Telephone

QQ

WeChat

E-mail

No contact

Fig. 7.16 Means for town residents to contact relatives and friends

Mobile phones were very popular, but Internet access through mobile phones was not. In addition, town residents contacted their relatives and friends mainly by telephone, and the frequency of selection of this means was 91.22% (Fig. 7.16). Amongst the other means, only WeChat accounted for a relatively high portion, reaching 4.18%. The main channel for town residents to obtain daily information was television, and the frequency of selection of this channel was 75.5%, followed by mobile phones and WeChat, making up 13.7% (Fig. 7.17).

7.3 Rural Residents’ Travel Behaviour There is not much existing research on rural residents’ travel activities, with much more waiting to be uncovered. Some previous studies choose specific regions as the object of study, such as Suzhou City, Jiangsu Province (Wu et al., 2013) and Guangdong Province (Yang, 2017). National-level overall research is sparse, and there is the problem of coarse data granularity with the few existing national-scale studies. For instance, Wang et al. (2013) and Yang et al. (2016) both studied regions at and above the county level. In the past 2 years, fine-grained research on a national scale has begun to appear, involving 121 investigated villages and towns as the research targets and paying more attention to residents’ individual travel behaviour (Zhao et al., 2019; Zhao et al., 2020a, 2020b).

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7 Stories About Rural China 0.4%

2.8% 0.5% 13.7%

0.8%

6.3%

75.5%

Television

Internet

Newspapers and magazines

Radio

Mobile phones and WeChat

Other people

Other channels

Fig. 7.17 Channels for town residents to obtain daily information

7.3.1 Town Residents 7.3.1.1

Daily Trips

This part mainly analyses ways to commute to and from work and school. The ways to commute to and from work are in Fig. 7.18. Except for “None”, the proportions of walking, bicycles/electric bicycles, motorcycles and cars were relatively high (17.6, 9.5, 8.8 and 7.0%, respectively). Walking took up the highest proportion, indicating that the workplace–residence distance of town residents was relatively short and that None Others Walking School buses Buses Bicycles/electric bicycles Electric cars Motorcycles Trucks Motorised farm vehicles Vans Cars

49.9 0.5 17.6 0.2 3.0 9.5 1.5 8.8 0.5 0.5 1.0 7.0 0.0

10.0

20.0

30.0

% Percent

Fig. 7.18 Town residents’ ways to commute to and from work

40.0

50.0

60.0

7.3 Rural Residents’ Travel Behaviour None Others Walking School buses Buses Bicycles/electric bicycles Electric cars Motorcycles Trucks Motorised farm vehicles Vans Cars 0.0

235 67.8

1.0 15.2 1.0 2.8 5.5 0.8 3.1 0.3 0.2 0.5 1.9 10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

% Percent

Fig. 7.19 Town residents’ ways to commute to and from school

serious workplace–residence separation in large-sized cities was less common in towns. As they are convenient and inexpensive vehicles in towns, the utilisation rate of bicycles and motorcycles was also high. The ways to commute to and from school are in Fig. 7.19. Except for “None”, walking took up a dominant position, accounting for 15.2%, while the proportions of other ways were small. Bicycles/electric bicycles made up a large part (5.5%).

7.3.1.2

Non-daily Trips

This part mainly analyses the means of transport for fair trips and for entertainment activities. Walking was the major means of transport for fair trips and accounted for a larger proportion (30.4%) than it did in daily trips, probably due to the fact that fairs were scattered in different residential areas and were more accessible (Fig. 7.20). Walking was followed by bicycles/electric bicycles and motorcycles. This was also the major means of transport for entertainment activities, followed by cars (4.8%). It was different from other travel activities (Fig. 7.21). This might be because the destination of entertainment activities was usually far away, making it more convenient to travel by car. Also, families that could engage in entertainment activities were relatively wealthy, so they could afford cars.

7.3.1.3

Public Transport Service Quality

There are two questions in the questionnaire to measure the convenience of public transport. One is “is there any bus stop around your residence?” Some 42.9% of the respondents selected “No”, and 57.1% of them selected “Yes” (Fig. 7.22). this shows that more than half the respondents thought that there were bus stops nearby, and that

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7 Stories About Rural China None

45.5

Others

0.5

Walking

30.4

School buses

0.3

Buses

2.0

Bicycles/electric bicycles

9.6

Electric cars

1.3

Motorcycles

6.5

Trucks

0.5

Motorised farm vehicles

0.5

Vans

0.8

Cars

2.1 0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

40.0

45.0

50.0

% Percent

Fig. 7.20 Town residents’ means of transport for fair trips

None

69.3

Others

0.6

Walking

15.7

School buses

0.3

Buses

1.8

Bicycles/electric bicycles

2.8

Electric cars

0.4

Motorcycles

3.0

Trucks

0.3

Motorised farm vehicles

0.1

Vans

0.8

Cars

4.8 0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

% Percent

Fig. 7.21 Town residents’ means of transport for entertainment activities

public transport was quite popular for these towns. The other question is “is it convenient for town residents to travel to counties or peripheral areas?”, as Fig. 7.22 shows. A total of 72.6% of the respondents selected “relatively convenient” or “convenient”, indicating that most people recognised the traffic accessibility between towns and the country.

7.3 Rural Residents’ Travel Behaviour

237

Not aware 2.4%

Very inconvenient 4.9% Kind of inconvenient 6.9%

Fig. 7.22 Whether it is convenient for town residents to travel to the country or peripheral areas

Neutral 13.1% Relatively convenient 21.4%

Convenient 51.2%

Table 7.5 Vehicle ownership by village residents Unit: %

Cars

Electric bicycles

Motorcycles

Farm vehicles

Bicycles

0

75.0

59.8

44.8

74.0

70.6 24.5

1

22.1

33.1

48.1

23.4

2

2.2

5.5

6.0

2.3

3.9

3 and above

0.7

1.8

1.1

0.3

0.9

7.3.2 Village Residents 7.3.2.1

Vehicles

Table 7.5 shows the ownership of different vehicles by village residents. Amongst the five types of vehicles, the most likely to be owned by village residents was motorcycles, and 55.2% of the respondents owned at least one. Motorcycles were followed by electric bicycles, with 40.2% of the respondents owning at least one. The ownership conditions of the other three vehicles were similar. The probability of respondents owning at least one car, farm vehicle or bicycle was 25.0, 26.0 and 29.4%, respectively. As mentioned earlier, electric bicycles and motorcycles have many advantages as vehicles in the countryside, as they are compatible with the rural living environment and economic conditions, so they were favoured by village residents.

7.3.2.2

Town Trips

This part analyses the frequency and means of transport of village residents’ town trips. Figure 7.23 shows that on the whole, village residents travelled frequently to

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Never 3%

Every half a year or less frequently 11%

Every day 18%

Every three months 7%

Every two to three days 20%

Every half a month 19% Every week 22% Fig. 7.23 Frequency of village residents’ town trips

towns, with a frequency equal to or higher than once a week accounting for about 60%, indicating that 60% of the respondents travelled to towns at least once a week. The proportion of those who never travelled to towns was very small (only 3%). Village residents maintained a high demand for town trips, for towns provided necessary resources for them. Motorcycles and bicycles/electric bicycles remained the major means of transport, accounting for 30.7 and 24.1%, respectively (Fig. 7.24). This implies that towns were not far away from villages, otherwise these vehicles with limited allowable travel distance would not be used. In addition, buses were also a major vehicle, accounting for 12.0%, which indicates that public transport between villages and towns was relatively convenient. None Electric tricycles Hitchhiking Walking School buses/regular buses Buses Bicycles/electric bicycles Electric cars Motorcycles Trucks Motorised farm vehicles Vans Cars

3.3 1.0 0.6 8.5 0.4 12.0 24.1 2.6 30.7 0.2 2.6 3.1 11.0 0.0

5.0

10.0

15.0

20.0

% Percent

Fig. 7.24 Means of transport for village residents’ town trips

25.0

30.0

35.0

7.3 Rural Residents’ Travel Behaviour

7.3.2.3

239

Trips to Nearby Counties

This part analyses the frequency and means of transport of village residents’ countryside trips. Since the distance between nearby counties and villages is much farther than that between towns and villages, there was a significant reduction in travel frequency. Figure 7.25 shows that the overall frequency of trips to nearby counties was relatively low, with a frequency equal to or higher than once a week only accounting for 14%, indicating that only 14% of the respondents travelled to nearby counties at least once a week. The frequency of trips to nearby counties between every half a month and every three months accounted for a relatively high portion (40%). The means of transport also underwent major changes (Fig. 7.26). Buses were the first choice, accounting for 54.5%, which was mainly for two reasons. The long distance made it impractical to use small vehicles, and village residents were unlikely to own cars. These two reasons made buses the best choice. It also reflects Every day 2%

Never 10%

Every half a year or less frequently 36%

Every three months 16%

Every two to three days 3% Every week 9% Every half a month 24%

Fig. 7.25 Frequency of village residents’ trips to nearby counties

None Electric tricycles Hitchhiking Walking School buses/regular buses Buses Bicycles/electric bicycles Electric cars Motorcycles Trucks Motorised farm vehicles Vans Cars 0.0

9.9 0.1 1.1 0.3 3.2 54.5 3.6 0.6 6.4 0.2 1.1 4.6 14.4 10.0

20.0

30.0

40.0

% Percent

Fig. 7.26 Means of transport for village residents’ trips to nearby counties

50.0

60.0

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that the transport convenience and accessibility between villages and nearby counties depended greatly on the operating level of public transport. If there were more buses running between nearby counties and villages with higher efficiency, there would be fewer objective limitations for village residents travelling to nearby counties.

7.4 Clustering Analysis of Rural Lifestyles Research methods in the field of lifestyle have undergone changes. In early days, the inductive method was mainly used (Hawes, 1988). As data analysis methods advance, clustering analysis has occupied an increasingly important position in lifestyle analysis. Data simplification techniques (such as factor analysis and multidimensional scaling) are first used to determine lifestyle groups or segmentation, and then clustering analysis is performed based on the dimensions of the data (Thøgersen, 2018). Both quantitative methods (Axsen et al., 2012) and qualitative methods (Evans & Abrahamse, 2009; Lorenzen, 2012) can be used in lifestyle research in sociology. Clustering analysis can indeed show group differences in lifestyles to a certain extent, and many scholars have revealed a great number of issues and phenomena using this method. Axsen et al. (2012) used clustering analysis to explore the relationship between lifestyles and sustainable consumption. Gehlert et al. (2011) further classified car users into subgroups and found that families of different composition vary in car use. Through clustering analysis on data from the Puget Sound Transportation Panel, Krizek and Waddell (2002) uncovered nine lifestyles and discussed how decisions on residential location affect daily travel decisions. In this section, clustering analysis is employed to study the group differences in Chinese rural residents’ lifestyles. As a matter of fact, while urban residents are often studied (Fleischer, 2007; Li et al., 2012; Tai & Tam, 1997), research on Chinese rural residents’ lifestyles is sparse (Xue & Cheng, 2017). This chapter reveals the life of Chinese rural residents.

7.4.1 Data Source The data in this part come from the aforementioned questionnaire survey data. After removing missing data, the number of effective samples is 11,959. The travel time data processing is as follows. The original options for travel time were A (within 10 min), B (10–20 min), C (20–30 min), D (30–40 min), E (40–50 min), F (50– 60 min), G (1–2 h) and H (2 h or above), which were assigned values of 10, 20, 30, 40, 50, 60, 90and 120 min, respectively. The travel frequency data processing is as follows. The original options for travel frequency were A (never), B (every half a year or less frequently), C (every 3 months), D (every month), E (every week) and F (every 1–3 days), which were renamed using travel times per month: 0, 0.1, 0.3, 1,4 and 10.

7.4 Clustering Analysis of Rural Lifestyles

241

7.4.2 Variable Selection and Description As mentioned earlier, rural lifestyles show strong homogeneity and small individual differences. The traditional multi-index clustering technique cannot distinguish different lifestyles (Table 7.6). Considering the characteristics of the Internet-loving lifestyle (including online shopping, dining out and travelling), the monotonous lifestyle (including traditional farming), the leisure lifestyle (including fitness activities and shopping) and the migratory bird lifestyle, which is typical of village residents (doing odd jobs away from hometowns during slack seasons and returning home for farming during busy seasons) in the countryside, we selected specific indexes to measure lifestyles instead of covering all available dimensions. The selected indexes are in Table 7.7. The descriptive statistics of the selected indexes are in Table 7.7. Table 7.6 Index selection for lifestyle classification of town residents Clustering indexes for lifestyle classification

Consumption

Expenditure on dining out entertainment expenditure

Employment patterns

Occupation types

Social activities and communication

How often do you dine out? How often do you travel? What recreational activities do you pursue in your spare time? Can your mobile phone access the Internet?

Table 7.7 Statistical table of variables Variable

Sample Size

Minimum

Maximum

Mean

Standard deviation

Entertainment expenditure

11,958

0.00

5,500.00

55.5188

189.83085

Whether the mobile phone can access the Internet

11,958

0

1

0.64

0.481

Travel frequency

11,958

0.0

41.0

0.516

2.1774

Frequency of dining out

11,958

0.0

4.0

1.718

1.6293

Expenditure on dining out

11,958

0

8,000

117.17

196.180

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Table 7.8 Clustering analysis results

Clustering

1

1,799

2

5,015

3

2,189

4

2,955 11,958

Sample size

300

264.1

250 183.21

200 150 2.53

89.16 63.68

100

0.109

54.22 50

0.635

18.89

0

0.818 1

0.654

0.008

1.107

2.915 2.86

0.037

70.41

4.96

0 Entertainment expenditure

Whether the mobile phone can access the internet

Monotonous lifestyle

Travel frequency

Migratory bird lifestyle

Frequency of dining out

Internet-loving lifestyle

Expenditure on dining out

Leisure lifestyle

Fig. 7.27 Comparison of four lifestyles

7.4.3 Statistical Description We used the k-means clustering method to perform clustering analysis on the samples. The variables selected in the clustering analysis include whether the mobile phone can access the Internet, travel frequency, frequency of dining out, expenditure on dining out, entertainment methods, entertainment expenditure, whether one engages in fitness activities, and occupation. The clustering analysis results are in Table 7.8. The values of the variables of four selected lifestyles are in Fig. 7.27. Based on the analysis of the characteristics of various samples, we named the first category the monotonous lifestyle, the second category the migratory bird lifestyle, the third category the Internet-loving lifestyle and the fourth category the leisure lifestyle.

7.4.4 Travel Characteristics of Different Lifestyles 7.4.4.1

Comparison of Travel Time of Different Lifestyles

Based on the lifestyle classification, the comparison of travel times for four lifestyles is in Fig. 7.28.

Travel time (minutes)

7.4 Clustering Analysis of Rural Lifestyles 45 40 35 30 25 20 15 10 5 0

Travel time

Commuting time

Time spent on commuting to and from school

Monotonous lifestyle

Shopping time

243

Time spent on dining out

Migratory bird lifestyle

Time spent on fair trips

Farming time

Internet-loving lifestyle

Time spent on social visits

Entertainment time

Leisure lifestyle

Fig. 7.28 Comparison of travel time for different travel purposes of different lifestyles 40.5 40

Travel time (minutes)

39.5 39 38.5 38 37.5 37 36.5 36 Monotonous lifestyle

Migratory bird lifestyle

Internet-loving lifestyle

Leisure lifestyle

Fig. 7.29 Comparison of travel time of different lifestyles

The travel time of the Internet-loving lifestyle was the longest, followed by the leisure lifestyle, the migratory bird lifestyle and the monotonous lifestyle in descending order (Fig. 7.29). The time spent on commuting to and from work of the Internet-loving lifestyle was the longest, followed by the leisure lifestyle, the migratory bird lifestyle and the monotonous lifestyle in descending order (Fig. 7.30). The time spent on commuting to and from school of the leisure lifestyle was the longest, followed by the Internet-loving lifestyle, the monotonous lifestyle and the migratory bird lifestyle in descending order (Fig. 7.31). The shopping time of the leisure lifestyle was the longest, followed by the Internet-loving lifestyle, the monotonous lifestyle and the migratory bird lifestyle in descending order (Fig. 7.32). The time spent on dining out of the Internet-loving lifestyle was the longest, followed by the leisure lifestyle, the migratory bird lifestyle and the monotonous lifestyle in descending order (Fig. 7.33).

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Commuting time (minutes)

14 12 10 8 6 4 2 0 Monotonous lifestyle

Migratory bird lifestyle

Internet-loving lifestyle

Leisure lifestyle

Time spent on commuting to and from school (minutes)

Fig. 7.30 Comparison of commuting time of different lifestyles 9 8 7 6 5 4 3 2 1 0

Monotonous lifestyle

Migratory bird lifestyle

Internet-loving lifestyle

Leisure lifestyle

Fig. 7.31 Comparison of commuting time to and from school of different lifestyles 9

Shopping time (minutes)

8 7 6 5 4 3 2 1 0

Monotonous lifestyle

Migratory bird lifestyle

Internet-loving lifestyle

Leisure lifestyle

Fig. 7.32 Comparison of shopping time of different lifestyles

The time spent on fair trips of the leisure lifestyle was the longest, followed by the monotonous lifestyle, the Internet-loving lifestyle and the migratory bird lifestyle in descending order (Fig. 7.34). The farming time of the monotonous lifestyle was the longest, followed by the migratory bird lifestyle, the leisure lifestyle and the Internet-loving lifestyle in descending order (Fig. 7.35).

Time spent on dining out (minutes)

7.4 Clustering Analysis of Rural Lifestyles

245

18 16 14 12 10 8 6 4 2 0 Monotonous lifestyle

Migratory bird lifestyle

Internet-loving lifestyle

Leisure lifestyle

Fig. 7.33 Comparison of time spent on dining out of different lifestyles

Time spent on fair trips(minutes)

12 11.5 11 10.5 10 9.5 9

Monotonous lifestyle

Migratory bird lifestyle

Internet-loving lifestyle

Leisure lifestyle

Fig. 7.34 Comparison of time spent on fair trips of different lifestyles

Farming time(minutes)

12 10 8 6 4 2 0 Monotonous lifestyle

Migratory bird lifestyle

Internet-loving lifestyle

Leisure lifestyle

Fig. 7.35 Comparison of farming time of different lifestyles

The social visiting time of the leisure lifestyle was the longest, followed by the Internet-loving lifestyle, the monotonous lifestyle and the migratory bird lifestyle in descending order (Fig. 7.36).

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Social visiting time (minutes)

17 16.5 16 15.5 15 14.5

Monotonous lifestyle

Migratory bird lifestyle

Internet-loving lifestyle

Leisure lifestyle

Fig. 7.36 Comparison of social visiting time of different lifestyles

Entertainment time (minutes)

12 10 8 6 4 2 0 Monotonous lifestyle

Migratory bird lifestyle

Internet-loving lifestyle

Leisure lifestyle

Fig. 7.37 Comparison of entertainment time of different lifestyles

The entertainment time of the Internet-loving lifestyle was the longest, followed by the leisure lifestyle, the migratory bird lifestyle and the monotonous lifestyle in descending order (Fig. 7.37). The frequency of trips to nearby counties of the monotonous lifestyle was the highest, followed by the migratory bird lifestyle, the leisure lifestyle and the Internetloving lifestyle in descending order. The frequency of city trips of the leisure lifestyle was the highest, followed by the migratory bird lifestyle, the Internet-loving lifestyle and the monotonous lifestyle in descending order (Fig. 7.38).

7.4.5 Conclusion Due to the strong homogeneity and small individual differences in Chinese rural lifestyles, this section incorporates new rural lifestyle features such as online shopping, dining out, travelling and zeal for the Internet, as well as the indexes of difference of traditional Chinese farmers such as farming, fitness activities, shopping and working outside the hometown. Through clustering analysis, we divide the surveyed

7.5 Impacts of Rural Lifestyles on Travel Frequency and Time

247

Travel frequency (times/month)

6 5 4 3 2 1 0

Monotonous lifestyle

Migratory bird lifestyle

Frequency of trips to nearby counties

Internet-loving lifestyle

Leisure lifestyle

Frequency of city trips

Fig. 7.38 Comparison of travel frequency of different lifestyles

village residents’ lifestyles into four categories: the monotonous lifestyle, the migratory bird lifestyle, the Internet-loving lifestyle and the leisure lifestyle. Through regression analysis, we find that respondents living a monotonous lifestyle were mainly engaged in farming and had little travel time. Respondents living a migratory bird lifestyle were mostly migrant workers whose commuting time was relatively long, while they spent little time on other matters. The respondents living an Internet-loving lifestyle favoured a modernised life and spent less time on traditional farming. Respondents living a leisure lifestyle spent most time on various leisure activities while spending little time on traditional farming, leading a leisurely life. The monotonous lifestyle accounted for about 15%, the migratory bird lifestyle accounted for about 42%, the Internet-loving lifestyle accounted for 18% and the leisure lifestyle accounted for 25%.

7.5 Impacts of Rural Lifestyles on Travel Frequency and Time In the previous section, four lifestyles of rural residents in China were derived through clustering analysis. Next, the differences in travel behaviour such as travel frequency and time amongst groups with different lifestyles are explored. In fact, previous studies in the field of transportation related to lifestyle are not uncommon. Lifestyle affects people’s travel behaviour, which in turn affects the operation of urban traffic (Anable, 2005). Academia has tried to carry out more detailed lifestyle research on transportation planning, define specific areas of transportationrelated lifestyles and use survey-based methods to identify such lifestyles (Krizek & Waddell, 2002; Lanzendorf, 2002; Lee & Sparks, 2007; Lin et al., 2009). The locations where different people live (Pinjari et al., 2007) and their lifestyles (Bin & Dowlatabadi, 2005; Zhao et al., 2011) generate distinct demand for transport and travel behaviour. For example, Bagley and Mokhtarian (2002) found that lifestyle

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Table 7.9 Variables Kernel variable

Clustering results of lifestyles

Socioeconomic attributes Gender, age, monthly income Transport facilities

Share of land used for roads, road area per capita, density of main roads (km/km2 ), density of minor roads, space on main roads (m), width of red lines of main roads, bus frequency to nearby counties, bus frequency from towns to villages

Built environment

Towns: proportion of commercial lands, proportion of public facilities, proportion of green space, proportion of industrial lands, building density, number of stores, number of superstores, fair frequency, number of schools, number of hospitals, number of parks and squares, number of town-level centres for cultural activities, number of stadiums Outbound transport from towns: distance to nearby counties, distance to large-sized cities, travel time to the nearest airport (h), travel time to the entrance/exit of the nearest expressway (h), travel time to the nearest railway station (h), travel time to the nearest national highway (h), bus frequency to nearby counties

attitudes have a greater influence on travel need than sociodemographic variables and community types.

7.5.1 Variable Selection This part analyses the travel frequency of village residents’ county trips and city trips, using the two as dependent variables with socioeconomic attributes, transport facilities and the built environment as control variables. The variable selection and regression analysis results are in Table 7.9.

7.5.2 Impacts of Rural Lifestyles on Travel Frequency 7.5.2.1

Analysis of the Impacts of Different Rural Lifestyles on Trips to Nearby Counties

We used the frequency of trips to nearby counties as the dependent variable, various rural lifestyles as independent variables and the variables in Table 7.9 as control variables for regression analysis. The results are in Table 7.10. The frequency of trips to nearby counties of people living a monotonous lifestyle was significantly higher than the mean values of people living other lifestyles (0.122). The frequency of trips to nearby counties of people living a migratory bird lifestyle was significantly higher than the mean values of people living other lifestyles (0.084).

7.5 Impacts of Rural Lifestyles on Travel Frequency and Time Table 7.10 Regression results of frequency of trips to nearby counties

Independent variable

P-value

Monotonous lifestyle

0.122

0.003

Migratory bird lifestyle

0.084

0.004

Internet-loving lifestyle

−0.479

0.000

0.032

0.304

Leisure lifestyle

Table 7.11 Regression results of frequency of city trips

Regression coefficient

249

Independent variable

Regression coefficient

P-value

Monotonous lifestyle

−0.531

0.000

Migratory bird lifestyle

−0.340

0.000

Internet-loving lifestyle

0.277

0.000

Leisure lifestyle

0.576

0.000

The frequency of trips to nearby counties of people living an Internet-loving lifestyle was significantly lower than the mean values of people living other lifestyles (0.479). There was no significant difference in the frequency of county trips between people living a leisure lifestyle and the mean values of people living other lifestyles.

7.5.2.2

Analysis of the Impacts of Different Rural Lifestyles on City Trips

We used the frequency of city trips as the dependent variable, various rural lifestyles as independent variables and the variables in Table 7.9 as control variables for regression analysis. The results are in Table 7.11. The frequency of city trips of people living a monotonous lifestyle was significantly lower than the mean value of people living other lifestyles (0.531). The frequency of city trips of people living a migratory bird lifestyle was significantly lower than the mean value of people living other lifestyles (0.340). The frequency of city trips of people living an Internet-loving lifestyle was significantly higher than the mean value of people living other lifestyles (0.277). The frequency of city trips of people living a leisure lifestyle was significantly higher than the mean value of people living other lifestyles (0.579). The frequency of trips to nearby counties and city trips of people with different lifestyles suggests that people living a monotonous lifestyle may be more inclined to travel to a nearby county instead of a city. Most of their needs can generally be met by travelling to nearby counties, so the frequency of city trips is relatively low. People living a migratory bird lifestyle travel more frequently to nearby counties, as they work there. Their daily trips mainly consist of commuting to and from work, and they tend to work in counties nearby, so the frequency of city trips is relatively low. In most cases, people living an Internet-loving lifestyle favour a modernised life. Local counties may not meet all their daily needs. Therefore, they like to travel to

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cities. People living a leisure lifestyle enjoy the relaxed pace of life. Their frequency of trips to nearby counties is at a medium level. Their daily trips to nearby counties can meet their basic leisure needs. However, these people often have more leisure needs, which may not be met in local counties. That is why they will also travel to cities to go shopping or enjoy shows, resulting in a relatively high frequency of city trips.

7.5.3 Impacts of Rural Lifestyles on Travel Time To examine the impacts of different lifestyles on travel, we used various rural lifestyles as independent variables and various travel activities as dependent variables, and we selected a number of control variables. We used the OLS method for regression analysis, and the regression results are in Tables 7.12, 7.13, 7.14 and 7.15. The control variables include road area per capita, bus frequency, bus frequency from towns to villages, density of minor roads, density of main roads, per-capita commercial lands, per-capita public facilities, per-capita public expenditure, per-capita green space, per-capita industrial lands, number of stores, fair frequency, number of schools, number of medical facilities, number of nursing homes, number of parks, number of stadiums, travel time to large-sized cities, travel time to counties, travel time to expressway entrances and travel time to national highways. When the independent variable is the monotonous lifestyle, the regression results show that the commuting time, time spent on commuting to and from work, time spent on commuting to and from school, shopping time, time spent on dining out, visiting time and entertainment time of respondents living this type of lifestyle were significantly shorter than those of respondents living other lifestyles. However, the farming time of respondents living this type of lifestyle was significantly longer than that of respondents living other lifestyles, indicating that these respondents were mainly engaged in farming and had little travel time. When the independent variable is the migratory bird lifestyle, the regression results show that the time spent on commuting to and from work of respondents living this type of lifestyle was significantly longer than that of respondents living other lifestyles, but the time spent on commuting to and from school, shopping time, time spent on dining out, time spent on fair trips, farming time, visiting time and entertainment time were significantly shorter than those of respondents living other lifestyles. This indicates that these respondents were mostly migrant workers whose commuting time was relatively long, while they spent little time on other matters. When the independent variable is the Internet-loving lifestyle, the regression results show that the commuting time, time spent on commuting to and from work, shopping time, time spent on dining out and entertainment time of respondents living this type of lifestyle were relatively long, while farming time was relatively short. This shows that the lifestyle of these respondents was more modernised, and they spent less time on traditional farming.

0.000

Per-capita public facilities

Per-capita green space

0.002***

0.001

0.000

−0.004***

−0.001

0.008***

0.000

−0.002***

0.002*

0.001

0.002

−0.003***

−0.003

0.002***

−0.005**

0.002**

0.053***

0.002**

0.104***

−0.012*** −0.007***

0.002***

Per-capita commercial lands

Per-capita public expenditure

0.045***

Density of main roads

−0.029**

−0.039**

0.025**

Density of minor roads

0.053*** −0.005***

0.049***

0.006

0.003*** −0.003***

Bus frequency

Bus frequency from towns to villages

0.002***

−0.003***

Road area per capita

0.004***

−0.004***

−0.002

−0.001**

0.003***

0.104***

−0.085***

−0.003***

0.030***

0.003***

−0.002**

0.004*

−0.004***

0.003***

0.091***

−0.137***

−0.005***

0.067***

0.007***

−0.007***

0.002

0.001

0.004***

0.034**

−0.109***

−0.002**

0.008

0.000

−0.007***

−0.006*

0.002**

(continued)

−0.001

−0.005**

−0.001**

0.002***

0.093**

−0.065*** 0.004***

−0.057***

−0.004***

0.039***

0.002***

0.036*

−0.002**

−0.001

−0.001

Travel time Time spent on Time spent on Shopping time Time spent on Time spent on Farming time visiting time Entertainment commuting to commuting to dining out fair trips time and from work and from school

Control variables

Variable

Table 7.12 Regression results for the monotonous lifestyle

7.5 Impacts of Rural Lifestyles on Travel Frequency and Time 251

Travel time to large-sized cities

Number of stadiums

0.016***

0.030***

0.075***

−0.011*** −0.001

0.010***

−0.095***

Number of −0.026 nursing homes

Number of parks

0.015***

−0.001

Number of medical facilities

0.022*

0.012***

−0.006***

Number of schools

Fair frequency −0.005

0.000***

0.068***

−0.012**

0.007**

−0.078***

0.017***

0.007***

0.038***

0.000***

0.028*

−0.002

0.012***

−0.190***

0.013***

−0.007***

0.061***

0.000

0.077***

−0.010*

0.018***

−0.140***

0.017***

0.012***

0.031**

0.000

0.000

0.044***

−0.036***

0.008**

−0.168***

0.014***

0.012***

0.212***

0.000**

0.000

0.113***

0.114***

−0.012*

−0.015***

−0.017*** −0.022***

0.131***

0.012***

0.003

−0.186***

0.027***

0.007***

0.017

0.000***

−0.001*** 0.058***

−0.001***

−0.003***

(continued)

0.064***

0.005

0.023***

−0.026

0.020***

0.014***

0.003

0.000**

0.000

0.000**

0.002***

Number of stores

−0.001***

−0.001***

Per-capita industrial lands

0.001*

Travel time Time spent on Time spent on Shopping time Time spent on Time spent on Farming time visiting time Entertainment commuting to commuting to dining out fair trips time and from work and from school

Variable

Table 7.12 (continued)

252 7 Stories About Rural China

Monotonous lifestyle

−0.099*** −0.233***

Independent variable −0.047

0.076

−0.060***

−0.003

−0.131***

0.019

−0.067***

−0.501***

−0.071***

−0.006

−0.141***

0.007

−0.069***

−0.043**

0.054***

0.114***

0.052**

−0.073***

−0.002

−0.090*

0.155***

−0.132***

−0.117***

−0.282***

−0.027

−0.058***

−0.103***

Travel time to national highways

−0.099***

−0.040**

0.000

−0.043**

Travel time to expressway entrances

−0.061***

−0.029**

Travel time to nearby counties

−0.056***

Travel time Time spent on Time spent on Shopping time Time spent on Time spent on Farming time visiting time Entertainment commuting to commuting to dining out fair trips time and from work and from school

Variable

Table 7.12 (continued)

7.5 Impacts of Rural Lifestyles on Travel Frequency and Time 253

0.000

Per-capita public facilities

0.000

−0.002***

−0.005**

0.002**

0.052***

0.002**

0.105***

−0.012*** −0.007***

0.002***

Per-capita commercial lands

Per-capita public expenditure

0.046***

Density of main roads

−0.028**

−0.039**

0.025**

0.006

0.003*** −0.003***

Bus frequency

Bus frequency from towns to villages

Density of minor roads

0.053*** −0.005***

0.049***

−0.003*** 0.002***

0.008***

−0.001

0.002*

0.001

0.002

−0.003***

−0.003

0.002***

−0.003

−0.001*

0.003***

0.107***

−0.086***

−0.003***

0.030***

0.003***

0.004

−0.004***

0.003***

0.089***

−0.136***

−0.005***

0.067***

0.007***

0.002

0.001

0.004***

0.033**

−0.109***

−0.002**

0.008

0.000

−0.006**

0.002**

(continued)

−0.005**

−0.001*

0.002***

0.094***

−0.065*** 0.004***

−0.057***

−0.004***

0.039***

0.002***

0.036*

−0.002**

−0.001

−0.001

Time spent on Shopping time Time spent on Time spent on Farming time Visiting time Entertainment commuting to dining out fair trips time and from school

Road area per capita

0.004***

Travel time Time spent on commuting to and from work

Control variables

Variable

Table 7.13 Regression results for the migratory bird lifestyle

254 7 Stories About Rural China

0.000**

Number of stores

0.011***

−0.011***

Number of stadiums

0.000

0.016***

−0.096***

Number of −0.027* nursing homes

Number of parks

0.015***

−0.001

Number of medical facilities

0.024*

0.013***

−0.006***

Number of schools

Fair frequency −0.004

0.001*

−0.001***

Per-capita industrial lands

0.000***

0.001

0.002***

Travel time Time spent on commuting to and from work

Per-capita green space

Variable

Table 7.13 (continued)

−0.012**

0.006**

−0.078***

0.017***

0.007***

0.038***

0.000***

−0.001***

−0.004***

−0.002

0.012***

−0.190***

0.013***

−0.007***

0.061***

0.000

0.002***

0.000

−0.009*

0.018***

−0.144***

0.017***

0.013***

0.033**

0.000*

0.000

−0.004***

−0.036***

0.008**

−0.166***

0.014***

0.011***

0.212***

0.000***

0.000

−0.002**

−0.012*

−0.016***

−0.017*** −0.022***

0.131***

0.012***

0.003

−0.185***

0.027***

0.007**

0.018

0.000***

−0.001*** 0.058***

−0.001**

−0.007***

−0.003***

−0.007***

(continued)

0.005

0.023***

−0.027

0.020***

0.015***

0.005

0.000**

0.000

−0.001

Time spent on Shopping time Time spent on Time spent on Farming time Visiting time Entertainment commuting to dining out fair trips time and from school

7.5 Impacts of Rural Lifestyles on Travel Frequency and Time 255

Migratory bird lifestyle

−0.025

0.194***

−0.058***

−0.004

Travel time to national highways

Independent variable

−0.099***

−0.038**

0.001

Travel time to expressway entrances

−0.091***

0.078***

−0.060***

−0.055***

−0.029**

Travel time to nearby counties

0.067***

0.073***

−0.138***

0.021

−0.066***

−0.043**

0.027

−0.377***

−0.068***

−0.001

−0.140***

0.074***

−0.082***

−0.067***

−0.044***

0.055***

0.044***

−0.070**

0.051**

−0.074***

−0.002

0.114***

−0.106***

0.157***

−0.132***

−0.117***

0.114***

−0.256***

−0.023

−0.056***

−0.102***

0.062***

Time spent on Shopping time Time spent on Time spent on Farming time Visiting time Entertainment commuting to dining out fair trips time and from school

0.029**

Travel time Time spent on commuting to and from work

Travel time to large-sized cities

Variable

Table 7.13 (continued)

256 7 Stories About Rural China

−0.002***

−0.007***

0.000

−0.012***

Per-capita public facilities

Per-capita green space

0.002***

0.000

0.002**

0.002***

Per-capita commercial lands

Per-capita public expenditure

0.002**

0.106***

0.045***

Density of main roads

0.001

0.008***

0.000

−0.004***

−0.001

0.002*

0.002

0.001

−0.003***

−0.003

0.002***

Shopping time

−0.005**

0.054***

−0.029**

−0.040***

0.025**

Density of minor roads

0.053*** −0.005***

0.048***

0.003***

−0.003***

0.005

Bus frequency from towns to villages

0.002***

Time spent on commuting to and from school

Bus frequency

0.004***

Time spent on commuting to and from work

−0.003***

Travel time

Road area per capita

Control variables

Variable

Table 7.14 Regression results for the internet-loving lifestyle

−0.004***

−0.002

−0.001**

0.003***

0.107***

−0.089***

−0.003***

0.027***

0.003***

−0.002**

0.004*

−0.004***

0.003***

0.092***

−0.137***

−0.005***

0.067***

0.007***

Time spent on Time spent dining out on fair trips

−0.007***

0.002

0.001

0.004***

0.035**

−0.108***

−0.002**

0.009

0.000

Farming time

−0.007***

−0.006*

0.002**

(continued)

−0.001

−0.005**

−0.001**

0.002***

0.093***

−0.063*** 0.004***

−0.059***

−0.004***

0.038***

0.002***

Entertainment time

0.036*

−0.002**

−0.001

−0.001

Visiting time

7.5 Impacts of Rural Lifestyles on Travel Frequency and Time 257

0.001*

0.000***

−0.001***

0.000**

Per-capita industrial lands

0.076***

−0.057***

Travel time to −0.030** nearby counties

−0.002

0.031***

Travel time to large-sized cities

−0.011***

Number of stadiums

−0.098***

−0.027*

Number of nursing homes

0.015***

0.015***

−0.001

Number of medical facilities

0.010***

0.013***

−0.006***

Number of schools

Number of parks

0.023*

−0.005

Fair frequency

Number of stores

Time spent on commuting to and from work

Travel time

Variable

Table 7.14 (continued)

−0.061***

0.068***

−0.012**

0.007**

−0.079***

0.017***

0.007***

0.038***

0.000***

−0.001***

Time spent on commuting to and from school

−0.044**

0.029*

−0.003

0.012***

−0.192***

0.013***

−0.007***

0.061***

0.000

0.002***

Shopping time

−0.144***

0.079***

−0.012**

0.017***

−0.148***

0.018***

0.013***

0.031**

0.000**

0.000

0.054***

0.044***

−0.036***

0.008**

−0.168***

0.014***

0.012***

0.212***

0.000**

0.000

Time spent on Time spent dining out on fair trips

−0.001

0.112***

−0.117***

0.114***

−0.012*

−0.015***

−0.016*** −0.021***

0.129***

0.012***

0.003

0.017

0.000***

−0.001**

Visiting time

−0.185***

0.027***

0.007**

0.058***

0.000***

−0.003***

Farming time

(continued)

−0.105***

0.066***

0.003

0.021***

−0.030

0.020***

0.015***

0.003

0.000***

0.000

Entertainment time

258 7 Stories About Rural China

Internet-loving lifestyle

0.156***

Independent variable

0.302***

−0.059***

−0.002

Travel time to national highways 0.029

0.076***

−0.099***

−0.040**

Time spent on commuting to and from school

Time spent on commuting to and from work

0.000

Travel time

Travel time to expressway entrances

Variable

Table 7.14 (continued)

0.175***

0.020

−0.066***

Shopping time

0.602***

−0.069***

−0.004

−0.050

−0.070***

−0.043**

Time spent on Time spent dining out on fair trips

−0.235***

0.050**

−0.073***

Farming time

0.057

0.155

−0.132***

Visiting time

0.452***

−0.024

−0.058***

Entertainment time

7.5 Impacts of Rural Lifestyles on Travel Frequency and Time 259

0.002***

0.000

Per-capita commercial lands

Per-capita public facilities

0.000

−0.002***

−0.005**

0.002**

0.050***

−0.028*

0.002**

0.102***

−0.037**

−0.012*** −0.007***

0.046***

Density of main roads

Per-capita public expenditure

0.025**

0.006

0.003*** −0.003***

Bus frequency

Bus frequency from towns to villages

Density of minor roads

0.054*** −0.005***

0.050***

−0.003*** 0.002***

0.008***

−0.001

−0.003

−0.001*

0.003***

0.100***

−0.001 0.002*

−0.083***

−0.003***

0.031***

0.003***

0.003

−0.003***

−0.002

0.002***

0.004

−0.004***

0.003***

0.088***

−0.136***

−0.005***

0.067***

0.007***

0.002

0.001

0.004***

0.033**

−0.109***

−0.002**

0.008

0.000

−0.006**

0.002**

0.004***

−0.068***

0.037*

−0.002**

0.000

−0.001

(continued)

−0.005**

−0.001*

0.002***

0.091***

−0.056***

−0.004***

0.040***

0.002***

Time spent on Shopping time Time spent on Time spent on Farming time Visiting time Entertainment commuting to dining out fair trips time and from school

Road area per capita

0.004***

Travel time Time spent on commuting to and from work

Control variables

Variable

Table 7.15 Regression results for the leisure lifestyle

260 7 Stories About Rural China

0.000**

Number of stores

0.011**

−0.011***

Number of stadiums

0.001

0.016***

−0.092***

umber of −0.027* nursing homes

Number of parks

0.015***

−0.001

Number of medical facilities

0.023*

0.012***

−0.006***

Number of schools

Fair frequency −0.004

0.001*

−0.001***

er-capita industrial lands

0.000***

0.001

0.002***

Travel time Time spent on commuting to and from work

Per-capita green space

Variable

Table 7.15 (continued)

−0.012**

0.007**

−0.075***

0.017***

0.007***

0.038***

0.000***

−0.001***

−0.004***

−0.001

0.012***

−0.187***

0.013***

−0.007***

0.061***

0.000

0.002***

0.000

−0.008

0.019***

−0.135***

0.017***

0.011***

0.033**

0.000*

0.000

−0.004***

−0.036***

0.008**

−0.164***

0.014***

0.011***

0.212***

0.000***

0.000

−0.002**

−0.011

−0.015***

−0.017*** −0.022***

0.134***

0.012***

0.003

−0.186***

0.027***

0.007**

0.018

0.000***

−0.001*** 0.058***

−0.001***

−0.007***

−0.003***

−0.007***

(continued)

0.006

0.023***

−0.023

0.020***

0.014***

0.004

0.000**

0.000

−0.001

Time spent on Shopping time Time spent on Time spent on Farming time Visiting time Entertainment commuting to dining out fair trips time and from school

7.5 Impacts of Rural Lifestyles on Travel Frequency and Time 261

Leisure lifestyle

0.021

0.227***

−0.057***

−0.004

Travel time to national highways

Independent variable

−0.100***

−0.039**

0.001

Travel time to expressway entrances

0.116***

0.079***

−0.059***

−0.053***

−0.029**

Travel time to nearby counties

0.067***

0.073***

0.157***

0.022

−0.067***

−0.042**

0.026

0.463***

−0.065***

−0.004

−0.137***

0.072***

0.107***

−0.066***

−0.045***

0.056***

0.044***

−0.049*

0.052**

−0.074***

−0.002

0.114***

0.146***

0.158***

−0.133***

−0.115***

0.113***

0.263***

−0.023

−0.057***

−0.100***

0.061***

Time spent on Shopping time Time spent on Time spent on Farming time Visiting time Entertainment commuting to dining out fair trips time and from school

0.029**

Travel time Time spent on commuting to and from work

Travel time to large-sized cities

Variable

Table 7.15 (continued)

262 7 Stories About Rural China

7.5 Impacts of Rural Lifestyles on Travel Frequency and Time

263

When the independent variable is the leisure lifestyle, the regression results show that the time spent on commuting to and from work, time spent on commuting to and from school, shopping time, time spent on dining out, time spent on fair trips, visiting time and entertainment time of respondents living this type of lifestyle were relatively long, while farming time was relatively short. In addition, there was no significant difference in commuting time between respondents living this type of lifestyle and those living other lifestyles. This shows that these respondents spent most time on various leisure activities while spending little time on traditional farming, leading a leisurely life.

7.5.4 Conclusion In this section, we repeatedly used the OLS regression method to model travel frequency and time of rural residents to quantify the impacts of lifestyles. It is clear that people with different lifestyles have different travel frequency and time, which is consistent with previous research findings (Anable, 2005; Bagley & Mokhtarian, 2002). The results show that lifestyles play an important role in determining rural residents’ daily travel behaviour. We have compared the travel behaviour of rural residents with different lifestyles and found that the impacts of lifestyles on travel frequency are as follows: (1)

(2)

(3)

(4)

People living a monotonous lifestyle are more inclined to travel to a nearby county instead of a city. Most of their needs can generally be met by travelling to nearby counties, so their frequency of city trips is relatively low. People living a migratory bird lifestyle travel more frequently to nearby counties, as they work there. Their daily trips mainly consist of commuting to and from work, and they tend to work in counties nearby, so their frequency of city trips is relatively low. In most cases, people living an Internet-loving lifestyle favour a modernised life. Local counties may not meet all their daily needs. Therefore, they like to travel to cities. People living a leisure lifestyle enjoy the relaxed pace of life. Their frequency of county trips is at a medium level. Their daily trips to nearby counties can meet their basic leisure needs. However, these people often have more leisure needs, which may not be met in local counties. That is why they will also travel to cities to go shopping or enjoy shows, resulting in a relatively high frequency of city trips.

The results also prove the impacts of lifestyles on travel time. In terms of the impacts of lifestyles on travel time, people living a monotonous lifestyle spend much more time on farming than those living other lifestyles, indicating that these respondents are mainly engaged in farming, spending little time on travelling. People living a migratory bird lifestyle are mainly migrant workers, and they spend much

264

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time on travelling and little time on other things. The Internet-loving lifestyle is modernised, and people living this kind of lifestyle spend little time on traditional farming. The leisure lifestyle features leisure activities and people living this kind of lifestyle spend more time on various forms of leisure consumption and less time on traditional farming.

7.6 Summary Rural residents have very different lifestyles from urban residents in China, as a result of which the two groups differ from each other in respect of travel behaviour. This chapter has described the characteristics of town and village residents’ lifestyles and travel activities, using data from a detailed survey about small towns across China conducted by Peking University and other universities in collaboration with the Ministry of Housing and Urban-Rural Development in 2016. Section 7.2 mainly studied the characteristics of rural lifestyles and showed that they have three important characteristics. First, compared with town residents, village residents have a small income range. Second, the rural economy is highly selfsufficient. Village residents can rely heavily on local crops, having fewer external consumption needs and weaker consumption power. Third, they differ slightly in occupational choices and hobbies. They usually lead confined lives, and their lifestyles change slowly. Section 7.3 has described the characteristics of town and village residents’ daily and non-daily trips from different perspectives. The public transport system in towns is more convenient and can meet local residents’ needs to travel to and from neighbouring counties. The daily travel distance is relatively short, so walking and nonmotorised vehicles are the main means of transport. Nearly half of village residents own motorcycles, which are the major means of transport they use to travel to towns. When it comes to travelling to nearby counties, buses are the main means of transport. Section 7.4 used the clustering method to analyse four different types of rural lifestyles, namely the monotonous lifestyle, the migratory bird lifestyle, the Internetloving lifestyle and the leisure lifestyle. This section contributes to the existing literature in this area, as previous studies paid much more attention to urban lifestyles than rural lifestyles (Wu et al., 2013; Yang, 2017). Section 7.5 utilised the OLS regression approach to study the impacts of the four types of rural lifestyles on village residents’ travel frequency and time. The results showed that people living a monotonous lifestyle and a migratory bird lifestyle travel more frequently to nearby counties, while people living an Internet-loving lifestyle and a leisure lifestyle travel more frequently to cities. The travel time spent on farming by people living a monotonous lifestyle is the longest, while the travel time spent on farming by people living an Internet-loving lifestyle is the shortest. The commuting time of the Internet-loving lifestyle is the longest. The travel time of people living a leisure lifestyle is mostly spent on leisure activities, with little travel time spent on traditional farming.

References

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This chapter also has its limitations. The OLS regression model shows that there is a linear relationship between lifestyles and travel modes, but in fact direct and indirect effects are mixed in with this process. The linear regression model is applicable to our research. However, more sophisticated statistical approaches are required for future research on changes in rural residents’ lifestyles and travel behaviour, such as SEM. In short, the research in this chapter can be understood as the first step to analyse the complex relationship between rural residents’ lifestyles and travel frequency and time on a national scale in China. For a long time, rural-related research has not been as abundant as that in urban areas. Compared with rural areas with simple structures and relatively single industries, cities with high research complexity and greater impact on society seem to be more worthy of study. In the smaller field of lifestyle analysis, this rule also exists. There are very few domestic academic studies on the lifestyle of rural residents (this was also discussed in the previous chapter). But the indisputable fact is that rural residents account for a very large proportion of the total population, and rural land accounts for a very large proportion of the total land territory. They should not be ignored by academic researchers. In the first few chapters, this book has done a detailed analysis of the lifestyles of its residents and provided policy recommendations for China’s on a national scale, city scale and Beijing scale. The section of this chapter on rural residents’ lifestyle and transportation relations also integrates new information for the field of lifestyle. In terms of policy recommendations, we have also discovered unique characteristics of rural areas that are different from cities. At present, the degree and level of transportation development in rural areas are relatively uneven, the road network in areas with better economies is also more developed, and the degree of transportation facilities is relatively complete, so it can basically meet the needs of various types of residents. However, there are still some rural areas with poorly developed transportation. Residents in these areas may not be able to complete their desired and demanded travel behaviours due to restrictions on transport facilities. Therefore, we believe that policymakers should give priority to improving the transport system and capacity in underdeveloped rural areas, and then focus on making more choices of transport options in developed rural areas, improving their convenience and ensuring that when rural residents want to complete the journey from village to town or town to city, they can pass unimpeded.

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Bagley, M. N., & Mokhtarian, P. L. (2002). The impact of residential neighborhood type on travel behavior: A structural equations modeling approach. Annals of Regional Science, 36(2), 279–297. https://doi.org/10.1007/s001680200083 Bin, S., & Dowlatabadi, H. (2005). Consumer lifestyle approach to US energy use and the related CO2 emissions. Energy Policy, 33(2), 197–208. https://doi.org/10.1016/S0301-4215(03)00210-6 Evans, D., & Abrahamse, W. (2009). Beyond rhetoric: The possibilities of and for ‘sustainable lifestyles.’ Environmental Politics, 18(4), 486–502. https://doi.org/10.1080/09644010903007369 Fleischer, F. (2007). “To choose a house means to choose a lifestyle.” The consumption of housing and class-structuration in urban China. City & Society, 19(2), 287–311. Gehlert, T., Kramer, C., Nielsen, O. A., & Schlag, B. (2011). Socioeconomic differences in public acceptability and car use adaptation towards urban road pricing. Transport Policy, 18(5), 685–694. https://doi.org/10.1016/j.tranpol.2011.01.003 Hawes, D. K. (1988). Travel-related lifestyle profiles of older women. Journal of Travel Research, 27(2), 22–32. https://doi.org/10.1177/004728758802700204 Krizek, K. J., & Waddell, P. (2002). Analysis of lifestyle choices: Neighborhood type, travel patterns, and activity participation. Transportation Research Record, 1807(1), 119–128. https://doi.org/ 10.3141/1807-15 Lanzendorf, M. (2002). Mobility styles and travel behavior: Application of a lifestyle approach to leisure travel. Transportation Research Record, 1807(1), 163–173. https://doi.org/10.3141/180 7-20 Lee, S. H., & Sparks, B. (2007). Cultural influences on travel lifestyle: A comparison of Korean Australians and Koreans in Korea. Tourism Management, 28(2), 505–518. https://doi.org/10. 1016/j.tourman.2006.03.003 Li, G., Li, G., & Kambele, Z. (2012). Luxury fashion brand consumers in China: Perceived value, fashion lifestyle, and willingness to pay. Journal of Business Research, 65(10), 1516–1522. https://doi.org/10.1016/j.jbusres.2011.10.019 Lin, H. Z., Lo, H. P., & Chen, X. J. (2009). Lifestyle classifications with and without activity-travel patterns. Transportation Research Part A: Policy and Practice, 43(6), 626–638. Lorenzen, J. A. (2012, March). Going green: The process of lifestyle change 1. Sociological Forum, 27(1), 94–116. https://doi.org/10.1111/j.1573-7861.2011.01303.x. Pinjari, A. R., Pendyala, R. M., Bhat, C. R., & Waddell, P. A. (2007). Modeling residential sorting effects to understand the impact of the built environment on commute mode choice. Transportation, 34(5), 557–573. https://doi.org/10.1007/s11116-007-9127-7 Tai, S. H., & Tam, J. L. (1997). A lifestyle analysis of female consumers in greater China. Psychology & Marketing, 14(3), 287–307. https://doi.org/10.1002/(SICI)1520-6793(199705)14: 3%3C287::AID-MAR5%3E3.0.CO;2-7 Thøgersen, J. (2018). Transport-related lifestyle and environmentally friendly travel mode choices: A multi-level approach. Transportation Research Part A: Policy and Practice, 107, 166–186. https://doi.org/10.1016/j.tra.2017.11.015 Wang, W., Cao, X., & Huang, X. (2013). Research on rural road development in China and its impact factors from 1980 to 2010 (in Chinese). Economic Geography, 33(3), 22–27, 51. Wu, J., Zhang, X., Ji, Y., & Li, H. (2013). Transport influences on rural settlement landscape pattern at county scale: A case study of Yongqiao of Suzhou (in Chinese). Human Geography, 28(1), 110–115. https://doi.org/10.13959/j.issn.1003-2398.2013.01.004. Xue, X., & Cheng, M. (2017). Social capital and health in China: Exploring the mediating role of lifestyle. BMC Public Health, 17(1), 1–11. https://doi.org/10.1186/s12889-017-4883-6 Yang, R. (2017). An analysis of rural settlement patterns and their effect mechanisms based on road traffic accessibility of Guangdong (in Chinese). Acta Geographica Sinica, 72(10), 1859–1871. Yang, R., Xu, Q., & Yu, C. (2016). Spatial coupling cooperative analysis of transport superiority and rural development in China (in Chinese). Scientia Geographica Sinica, 36(7), 1017–1026. https://doi.org/10.13249/j.cnki.sgs.2016.07.007.

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Zhao, P., Lu, B., & de Roo, G. (2011). The impact of urban growth on commuting patterns in a restructuring city: Evidence from Beijing. Papers in Regional Science, 90(4), 735–754. https:// doi.org/10.1111/j.1435-5957.2010.00343.x Zhao, P., Yu, Z., & Jia, Y. (2019). Shopping travel range and its determinants in rural towns of China (in Chinese). Economic Geography, 39(12), 70–79. https://doi.org/10.15957/j.cnki.jjdl. 2019.12.008. Zhao, P., Hu, H., & Yu, Z. (2020a). Rural travel behavior and areal system in China (in Chinese). Human Geography, 35(04), 1–8+138. https://doi.org/10.13959/j.issn.1003-2398.2020a.04.001. Zhao, P., Yu, Z., & Jia, Y. (2020b). Cross-regional travel and regional system of rural China (in Chinese). Scientia Geographica Sinica, 40(04), 498–508. https://doi.org/10.13249/j.cnki.sgs. 2020b.04.001.

Chapter 8

Impacts of Transport Facilities on Lifestyles

8.1 Impacts of Transport Facilities on Lifestyles Many domestic and foreign research results have shown that the development of transport facilities, especially in China, significantly affects residents’ travel activities and promotes the progress of the economy and even the times (Banerjee et al., 2012; Démurger, 2001; Sahoo et al., 2010; Yu et al., 2013). China is currently undergoing rapid development of high-speed rail, which provides easy access and guarantees intercity transport due to its advantages such as fast speed, large passenger volume, convenience, and temporal and spatial stability. It ensures the steady development of economic activities and also accelerates the cooperation and interaction between cities, which is conducive to regional economic integration and the overall growth of the regional economy (Chen & Haynes, 2017; Diao, 2018; Gao et al., 2019). New transport technologies and the improvement of transport facilities have promoted the rapid improvement of residents’ living standards and have also brought about changes in people’s concept of time and space. The traditional concept of workplaceresidence integration is gradually being discarded, and intercity travel within the day is becoming more and more common. In the communication and integration of different cities characterised by convenient transport and shortening of temporal and spatial distance, city boundaries are becoming blurred, and urban service functions are increasingly being shared by neighbouring cities. Humans, logistics, information and business are flowing and being allocated in wider city clusters, forming a closeknit community. Next, the correlation between the level of the three types of road facilities of subways, highways and railways and the composite scores of residents’ living standards is analysed from the perspective of prefecture-level cities (Table 8.1, Figs. 8.1, 8.2 and 8.3). The total lengths of the three types of subways, highways and railways in each prefecture-level city are used to represent the level of road facilities. The correlation coefficients between them and the composite scores of residents’ living standards obtained before are as follows, all showing positive correlations at the 99% confidence level. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. Zhao and D. Lyu, Lifestyle Change and Transport in China, Population, Regional Development and Transport, https://doi.org/10.1007/978-981-19-4399-7_8

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Table 8.1 Variables Correlation coefficient

Significance

Length of subways and residents’ living standards

0.802

0.000

Length of highways and residents’ living standards

0.418

0.000

Length of railways and residents’ living standards

0.407

0.000

Composite score

Variable

Length of subways (km) Fig. 8.1 Scatterplot of residents’ living standards and subway facilities in various cities

8.2 Impacts of High-Speed Rail on Lifestyles 8.2.1 Development of High-Speed Rail in China The introduction of high-speed rail has once again improved the efficiency of intercity transport in China (Cao et al., 2013; Wang et al., 2012). China boasts a vast territory and complex terrain. Railway vehicles running at an ordinary speed have been well configured, but they cannot meet the increasing demand for intercity communication at speed. High-speed rail makes it possible to travel to and from important cities within only a few hours, which has a huge impact on three aspects of the regional economy: urban integration, resource reallocation, and economic and social transformation (Gu & Guo, 2018). Urban integration refers to significant coupling effects between adjoining areas. Urban integration brought about by high-speed rail has significantly

Composite score

8.2 Impacts of High-Speed Rail on Lifestyles

Length of highways

Composite score

Fig. 8.2 Scatterplot of residents’ living standards and highway facilities in various cities

Length of railways (km) Fig. 8.3 Scatterplot of residents’ living standards and railway facilities in various cities

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changed people’s work and lifestyles and pushed forward the rearrangement of urban functions. Resource reallocation refers to the fact that the construction of highspeed rail can guide the rearrangement and integration of industries, especially the human capital-intensive service industry. Resource reallocation will contribute to the improvement of resource utilisation efficiency. Economic and social transformation is facilitated by the positive impact of time saving trips on the free flow of talent.

8.2.2 Impacts of High-Speed Rail on Lifestyles Studies on high-speed rail in China mostly focus on the impacts of high-speed rail on the urban economy, industries and regional space structure, with only a few studies on the impacts of high-speed rail on lifestyles. High-speed rail is a carrier and booster of lifestyle changes in modern society (He, 2015). At present, researchers are sharing their views on the impacts of high-speed rail on lifestyles: the emergence of high-speed rail accelerates population mobility, expands the space for people’s activities and interaction, contributes to workplace–residence separation and speeds up information dissemination, thus influencing people’s lifestyles. Hayashi and Feng (2017) combed through the history of the Shinkansen of Japan and pointed out that the Shinkansen expanded the commuting range of office workers and made people’s communication more frequent, which affected the city’s industries and society. Taiwan High-Speed Rail, in the medium and long term, resulted in an increase of residential and employed populations in areas with high-speed railway stations more significant than that in areas without high-speed railway stations, as well as an increase of these populations in metropolitan areas more significant than that in non-metropolitan areas, and the influence weakened as the distance from high-speed railway stations increased. Feng (2011) changed the spatial structure of the region from three areas to a huge 1-day living circle. After an analysis of 1,400 empirical questionnaires, Hou et al. (2011) concluded that the Beijing–Tianjin Intercity Railway changed the original intercity traffic distribution, improved the frequency of intercity travel and increased people’s demand for intercity travel; at the same time, it affected people’s spatial awareness of intercity travel, making some people accept workplace–residence separation. As a result, more and more people are willing to accept this new employment pattern. The above-mentioned literature on the impacts of high-speed rail on lifestyles is mostly composed in an argumentative manner, and the impacts are seldom analysed in a quantitative manner. Also, most of the literature only discusses the impacts of high-speed rail on workplace-residence separation. The impacts of high-speed rail on other aspects of life should not be ignored. At the end of 2020, the national railway operating mileage reached 146,000 km, of which high-speed railways accounted for 26.0% (over 38,000 km) (Fig. 8.4). According to the 13th Five-Year Development Plan for Railways issued by the National Development and Reform Commission, the national railway operating

8.2 Impacts of High-Speed Rail on Lifestyles

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20.0

30% 25% 20% 15% 10% 5% 0%

15.0 10.0 5.0 0.0 2009

2010

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High-speed railway operating mileage/10,000 kilometers railway operating mileage/10,000 kilometers Proportions of high-speed railways

Fig. 8.4 China’s railway operating mileage and proportion of high-speed railways 40.0

80%

30.0

60%

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0% 2009

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Passenger volume of railways/one hundred million people Passenger volume of high-speed railways/one hundred million people Proportions of high-speed railways

Fig. 8.5 Passenger volume proportion of high-speed railways from 2009 to 2020

mileage was expected to reach 150,000 km by 2020, including 30,000 km of highspeed railways. In 2019, high-speed rail passenger traffic accounted for 64% of railway traffic, an increase of 53% points from 2009 (Fig. 8.5). Due to the lack of 2020 high-speed rail passenger volume data, and considering that the overall traffic volume in 2020 was seriously affected by COVID-19, we did not compare the proportion of high-speed rail traffic in 2020. Fuxing has transported a total of 193 million passengers in the seven quarters since it was put into operation. There was an increasing number of railway passengers choosing high-speed rail. Travelling by high-speed rail is normal in China nowadays. The opening and optimisation of high-speed rail has changed the travel choices of ordinary people. People choose to travel by high-speed rail, as it has very obvious advantages. Its efficiency of travel within 1,200 km is comparable to that of aeroplanes, and it is much more comfortable and standardised than cars or traditional trains. More and more residents visiting relatives or returning to their hometowns are choosing to travel by high-speed rail, which is highly efficient, and weekend trips have become the new normal led by high-speed rail. High-speed rail has also had an impact on the development direction of many industries, which may have been unexpected to its builders. This also shows that improvements in transport efficiency can provide a great boost to the economy. Tourism is one of the many affected industries. High-speed rail significantly reduces

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the travel duration during trips, which naturally increases the time spent on sightseeing and having fun and is of great help to improve the sense of well-being of tourists. Also, high-speed rail can help to boost the regional economy. Four horizontals and four verticals and eight horizontals and eight verticals connect cities in China, creating countless opportunities for economic exchanges and promoting GDP growth in cities along the routes. Take the Beijing–Guangzhou High-Speed Railway as an example. According to the statistics from China’s High-Speed Rail Development, an annual growth of 7% in the flow of people between Beijing and Guangzhou will double their economic exchanges. It predicted that 5 years after the opening of the Beijing–Guangzhou High-Speed Railway, it could drive an annual GDP growth of 3% to 5% in cities along the route. The dual-city lifestyle means working in one city but living in another (Wu et al., 2015; Zhang et al., 2016). High-speed rail has not only changed travel modes, but also caused qualitative changes to people’s lifestyles. In the Yangtze River Delta Region, the Pearl River Delta Region, the Beijing–Tianjin–Hebei Region and other economically developed regions in China, working and living in two cities has become very common. By working in Beijing or Shanghai to achieve the income level of firsttier cities and returning to Tianjin or Kunshan after work, one can not only enjoy life with family members, but also secure an abundant material life. For instance, the economically developed Yangtze River Delta Region with dense high-speed rail lines has attracted office workers who travel to work by high-speed rail. This is because its facilities have been greatly improved, meeting people’s requirements for travel speed and comfort.

8.3 Impacts of Shared Transport on Lifestyles 8.3.1 Development of Shared Transport in China Shared transport (also called shared mobility) is a manifestation of the sharing economy in the transport field (Machado et al., 2018). It generally refers to a new transport service supply mode that is based on strangers and involves the temporary transfer of the right to use vehicles for the main purpose of obtaining a certain reward through a market mechanism (Feigon & Murphy, 2016). Its essence is to emphasise the right to use vehicles rather than ownership. With the Internet as the medium, shared transport is characterised by the improvement of the utilisation efficiency of vehicles, seeking to integrate and reuse idle vehicles (Yang & Feng, 2017). The rapid growth of car-sharing business worldwide in recent years is believed to be driven by a lifestyle that values the convenience of car-sharing (Lempert et al., 2019). Studies have also found that lifestyle differences affect people’s acceptance level of car-sharing and frequency of utilisation. Young people are used to multiple travel modes such as car-sharing and public transport, while middle-aged and older people are more accustomed to driving (Lee & Circella, 2019).

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275

Shared transport in China, based on different shared vehicles, currently has such different forms as bicycle-sharing, electric bicycle-sharing and car-sharing. The material carrier of bicycle-sharing is bicycles which are Internet-based rental bicycles instead of privately owned ones (Zhang & Mi, 2018). Some first-tier cities in China started to provide public bicycles in 2007. These bicycles could be picked up and dropped off at fixed racks. This operating mode had high requirements for racks, so the popularity and utilisation rate remained low. Since 2016, shared bicycles operated by Internet companies can be located by installing GPS on them, and they generally have fewer restrictions on parking locations (Zhang et al., 2015). These unconstrained and convenient features have attracted more users. According to statistics from the Ministry of Transport, as of October 2020, the nationwide Internet-based bicycle renting companies had put over 19 million vehicles into use.1 The number of users sharing two-wheelers in China increased from 28 million in 2016 to 287 million in 2020, and the transaction scale of the shared two-wheeler market increased from 8.44 billion CNY in 2017 to 29 billion CNY in 2020.2 Electric bicycle-sharing is similar to bicycle-sharing in terms of function, except that the vehicles rented are electric bicycles (Guidon et al., 2019). The cost of electric bicycle-sharing is relatively high, so the relevant companies only offer a limited number of shared electric bicycles, mainly in schools, scenic spots and other fixed areas where charging is convenient. Car-sharing is a kind of miniature car rental in nature (Ferrero et al., 2018). At present, the development patterns of car-sharing in different countries vary (Baptista et al., 2014; Becker et al., 2017; Loose, 2010; Nijland & van Meerkerk, 2017). In China, companies invest in cars and then build platforms to lease them to consumers who have short-term car needs (Yun et al., 2020). In recent years, to comply with the policies for promoting the growth of clean energy cars, local governments have supported the build-up of charging infrastructure. Therefore, a considerable proportion of shared cars is new energy ones (Yang & Feng, 2017). According to statistics from Aurora Mobile, the number of monthly active users in the shared car industry reached a high of 5.54 million in September 2019; then, the COVID-19 pandemic affected the number of active users in February and March 2020. In April 2020, the number of active users of shared cars rebounded to 3.68 million; this means that with the epidemic under control, the demand for cars of stock users was reactivated.3

8.3.2 Impacts of Shared Transport on Lifestyles It is widely believed that shared transport can reduce carbon emissions (Amatuni et al., 2020). Take shared bicycles as an example. Shared bicycles can provide convenience for public transport services, spur increases in the ratios of bicycle trips and

1

Source https://www.sohu.com/a/505975873_114835. Source https://new.qq.com/rain/a/20210302A000OZ00. 3 Source https://www.jiguang.cn/reports/494. 2

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public transport trips and control the use of cars, which is conducive to saving automotive fuels and reducing air pollutants and greenhouse gas emissions (Shaheen et al., 2010; Sun et al., 2020). According to the data released by Hellobike (one of the biggest shared bicycle firms in China), as of the end of October 2020, Hellobike’s users had ridden a total of 24 billion kilometres, reducing carbon emissions by nearly 2.8 million tonnes.4 The traditional travel service industry is moving towards digital upgrades driven by the mobile Internet. Starting from 2017, through vehicle networking technologies, bicycle and car rental gradually realised the digitalisation of vehicle location, vehicle status and user portraits, and also realised convenient picking up and dropping off of vehicles and mobile payment. Since taxis were upgraded and started to provide online car-hailing services, bicycle-sharing and the time-sharing rental industry have realised the digital upgrade of traditional bicycle and car rental industries. In the future, urban transport will achieve a comprehensive digital upgrade (Li et al., 2013; Liu & Peng, 2013). At present, Internet-based travel and other industries have initially realised the digitisation of location information and payment methods. The focus of future development will be on the interconnection and sharing of various travel data, to complete the digital dispatch and operation of urban transport and truly to realise digital and intelligent urban transport (Townsend, 2013). Car-sharing reduces the cost of car use, and it is unrestricted, convenient and highly cost-effective for those without cars. In some special cases, such as road space rationing5 or business trips, shared cars can also replace private cars. In essence, cars have obvious advantages over bicycles. Whether it is windy, rainy or hot, driving a car is more comfortable and reliable than riding a bicycle. Shared bicycles cannot be privately locked at will. In addition, shared cars can excuse users from maintenance, annual inspections, purchase of parking spaces and other issues that need to be considered when owning a car. In other words, car-sharing allows users to use a car without worrying about whether the car is out of order or has any maintenance issues.

8.4 Impacts of New Transport Technologies on Lifestyles 8.4.1 Influencing Factors of Travel Trends Shared mobility. Shared mobility has developed rapidly in recent years, with ridehailing service providers competing with traditional carpooling service providers. Ride-hailing services are among the fastest growing, with a large number of venture capital-funded start-ups emerging around the world. Ride-hailing service companies 4

Source https://www.dsb.cn/136152.html. Road space rationing, also known as driving restrictions, is a travel demand management strategy designed to reduce urban air pollution or urban travel peaks that exceed available supply or road capacity by rationing road capacity and artificially limiting vehicle travel.

5

8.4 Impacts of New Transport Technologies on Lifestyles

277

Fig. 8.6 New energy vehicles and charging stations6

received $11 billion in funding globally in 2015. In a highly competitive market, many ride-hailing service providers are now fighting for market share. China has the largest number of urban commuters in the world, as well as a low vehicle ownership rate, so ride-hailing services are experiencing rapid growth. Vehicle electrification. Global electric vehicle sales have grown rapidly since 2015, largely due to high purchase subsidies, declining battery costs, energy efficiency regulation, efforts by manufacturers and increased consumer interest (Eberle & von Helmolt, 2010) (Fig. 8.6). In 2015, electric vehicle sales witnessed a year-on-year growth of 60% to 450,000 units, compared with 50,000 units in 2010. Many of the early electric vehicle users came from Europe and the United States, but then the number of Chinese electric vehicle users grew exponentially (Li et al., 2016; Zhang et al., 2017; Lin & Wu, 2018). While electric vehicles account for less than 1% of overall vehicle sales in most markets, there are some countries with high electric vehicle penetration, such as Norway, where electric vehicles contribute more than 25% of new vehicle sales. Global and national fuel economy regulations have played an important role in promoting hybrids and electric vehicles: the United States, the EU and China have set strict standards for autonomous vehicle manufacturers. Many of these can still be met by upgrading internal-combustion-engine vehicles, but as standards become more stringent, it will be very difficult to continue to meet them through internal combustion engines in the future. As countries increasingly emphasise fuel economy, all types of electric vehicles will benefit from this. Car manufacturers are also responding to such pressure and have increased the number of electric vehicles to launch in the next few years. Self-driving cars. Self-driving technology has made great progress recently due to concerns about road safety, potential cost savings and a series of technological innovations (Daily et al., 2017; Shalev-Shwartz et al., 2017). The emergence of selfdriving cars marks the transformation from highly automated vehicles to fully automated ones. However, it is also pointed out that self-driving cars have not brought about major changes in mobility (Metz, 2017). Based on the current state of the technology, anticipated technology advances, and a series of plans announced by 6

Sources http://www.gov.cn/xinwen/2016-04/05/content_5061465.htm; https://3g.163.com/dy/art icle/H0G4Q98M0511CTRI.html.

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car manufacturers and other companies, it is likely that self-driving cars will be on the road by the mid-2020s. Car manufacturers and new competitors just entering the market are scrambling to launch self-driving technology.7 Tesla has already said it will enable autonomous pickups across the United States in 2018, meaning you could summon an electric car in Los Angeles from your New York home. Traditional automakers such as BMW, Ford, General Motors and Volkswagen have also designed and developed their autonomous vehicles. Domestic manufacturers, including technology giants such as Ali, Baidu, Didi and Huawei; traditional car companies such as Changan Auto, Geely Auto, SAIC Motor and BAIC Motor; new players such as Nio, Xpeng Motors, Li Auto and Weltmeister and third-party autonomous driving companies such as Pony AI and IDRIVERPLUS have emerged as major players in the autonomous driving track, constantly upgrading and perfecting their autonomous driving hardware and software systems. Having launched their Level 2 mass production models in quick succession, they are planning for Level 3 and Level 4 mass production models and have started Level 4 autonomous driving tests. An example of self-driving cars in China: Baidu’s unmanned vehicles were officially launched on Baidu Maps, a national-level application, and China’s robotaxis entered a new stage of large-scale operation. On April 19, 2020, the third anniversary of the release of Baidu’s Apollo platform, Apollo Robotaxi service was provided on Baidu Maps and Baidu Smart App,8 becoming the first self-driving taxi service open to the public through a national-level application in China. Connectivity and the IoT. The IoT consists of three elements: sensors, the ability to connect to a network and the ability to compute incoming data quickly (Li et al., 2015). Traditionally, vehicles have been passive aids to help humans get to places they cannot go on foot, always following human commands in the process. However, because of the development of communication and embedded systems, it is believed that the future of smart cars is not far away. The ideal smart car is a sensor platform in its own right, and humans can rely on the car to obtain information from their surroundings that will help them to make decisions such as traffic management and pollution treatment (Gerla et al., 2014). Tesla has introduced a smartphonestyle upgrade approach that provides major upgrades to cars over the air, such as automatic emergency braking. In the future, this upgrade approach may be adopted by other car manufacturers. In terms of policy, China’s industrial system has been fully established, and the development of intelligent vehicle connectivity is the focus of future competition. The Intelligent Connected Vehicle Technology Roadmap 2.0 sets out specifications for partially automated driving and conditional automated driving. The penetration rate of intelligent networked vehicles continues to increase, likely reaching 50% in 2025 and over 70% in 2030; in 2025, the new car assembly rate of C-V2X terminals will reach 50% and in 2030, the C-V2X will be basically universal on all the new cars.9

7

Source http://qiche.caigou2003.com/qichecaigouyaowen/2549122.html. Source https://www.sohu.com/a/394886096_267831. 9 Source http://www.gov.cn/xinwen/2020-11/19/content_5562464.htm. 8

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Fig. 8.7 Online bus-hailing services/customised buses in Beijing11

Public transport. Many cities in the world are promoting and improving their public transport networks. Ultimately, autonomous driving functions will reduce operating costs, while network optimisation will lead to better reliability and greater traffic capacity. New shared vehicle network solutions will help cities to reduce the costs associated with poorly planned bus routes (Lazarus et al., 2018). As long as it can meet the needs of public transportation and meet the trade-offs of capacity, safety and sustainability, sharing automated vehicles will make public transportation more convenient. In addition, the opportunities created by on-demand services provided by trucks and buses will improve first-mile and last-mile options, allowing current routes to carry more passengers.10 As an example of public transport in China, from September 22, 2018, Beijing Public Transport opened a web-based bus service at Beijing South Railway Station (Fig. 8.7). It features the ability to meet the personalised needs of passengers and to provide point-to-point services. It is understood that bus pooling is a product of innovation and development based on the existing transport mode of regular buses, a breakthrough from fixed line operation organisation to dynamic schedule operation organisation. This is a new service model with dynamic lines, stations and departure times in a demand-responsive manner. The online Beijing customised bus platform collects similar travel needs in terms of direction and time, and then provides customised, quasi door-to-door bus-hailing services by online reservation. Small- and medium-sized vehicles are the best option to undertake operational tasks to improve the occupancy rate and operational efficiency and to reduce waiting time. Liveability and sustainability. In many metropolitan areas, there are growing concerns about air quality. For example, various microparticle and nitrogen oxide emissions affect people’s health. For this reason, large cities such as London and Amsterdam will continue to promote low-emission vehicles in the future (Holman et al., 2015). To comply with air quality limits and to protect human health, many European cities have introduced low-emission zones. European cities have also introduced many other city-scale traffic measures, such as parking restrictions, road and 10

Source https://www.nengapp.com/news/detail/1203157. Source http://www.gov.cn/xinwen/2020-02/27/content_5483847.htm#1; https://www.thepaper. cn/newsDetail_forward_6746413.

11

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Fig. 8.8 Beijing’s first designated bicycle lane12

bridge tolls, and bus lanes that discriminate against high-emission vehicles. Besides air quality, urban planners are increasingly concerned about liveability and sustainability, reclaiming urban space through the establishment of urban parks (Ma´ckiewicz et al., 2018). Investments in bike lanes and transport infrastructure will discourage new car purchases and make public transport and shared transport an attractive alternative. An example of liveability and sustainability in China: Beijing’s first road built specifically for bicycles starts at the intersection of Tongcheng Street and Wenhua Road in Changping District, runs roughly westward along the green area north of Metro Line 13, crosses the Beijing–Tibet Expressway and the Beijing–Baotou Railway, extends along Longyu Ring Road to Xi’erqi North Road and ends at the intersection of Shangdi West Road and Houchangcun Road in Haidian District, with a total length of 6.5 km. Just like the expressway, this special road uses a combination of an elevated section and a paved area to achieve a fully enclosed design separated from existing roads. The elevated area is located in the most densely populated area of Changping District, Huilongguan, and the G6 auxiliary road is the most complex ground traffic in the entire Huilongguan Area, Longze Subway Station on Line 13 (Fig. 8.8). Huilongguan and Xi’erqi have always been the most ferocious commuting battlefield during the morning and evening rush hours, and this bicycle lane will be dedicated to alleviating this phenomenon—commuters can ride from Huilongguan in Changping District to Shangdi Software Park in Haidian District in 30 min—and it will serve a population of 11,600 along the route. Like urban expressways, there will be no traffic lights throughout the road, and there will be eight pairs of entrances and exits for bicyclists to get in and out. Meanwhile, taking account of the speed, convenience and safety of traffic, the design speed of the special road is 15 km per hour. It is important to note that, unlike ordinary non-motorised roads in the city, electric bicycles and pedestrians are not allowed to enter the road. Urbanisation and other macro-trends. Between 2015 and 2030, urbanisation and population growth will drive an increase in the average urban population density of at least 30%. In response, the demand for mobility in densely populated cities 12

Source https://tech.ifeng.com/c/7n7mRX1FTYO.

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will double (if miles travelled per capita remain stable and the ratio between car ownership and GDP growth remains at historical levels). There is no doubt that this will put additional pressure on an already strained mobility system, making private car purchase less attractive. Local policies based on liveability and sustainability will also accelerate vehicle electrification, and other incentives include national and global regulation of exhaust emissions, as well as support for renewable energy sources. Decentralisation of energy systems. Traditionally, power plants have been large centralised units. Distributed energy generation is developing as a new trend, which means that energy generation units will be closer to the consumers of energy than before, and that large units will be divided into smaller ones (Visions, 2001). In the last case, distributed energy generation allows buildings to become self-sufficient in energy without having to obtain energy from outside (Alanne & Saari, 2006). The cost of generating renewable energy has declined significantly over the past decade (Goldthau, 2014). If that cost continues to decline, then it can be assumed that distributed generation will make a significant contribution to the energy supply over the next 20 years. The development of distributed generation will undoubtedly also contribute to the further spread of electric vehicles, which are still not available on a large scale because the distribution and supply of charging stations is still relatively limited. In a distributed generation scenario, the government can implement a series of initiatives to control and manage the charging behaviour of electric vehicles to prevent oversupply during peak periods. In addition, in areas where light energy is abundant, buildings can be powered by photovoltaic systems so that consumer demand on the grid can be reduced. However, as solar photovoltaic systems become more popular and daytime electricity prices continue to drop, the cost of supplying electricity from distributed generation systems will be much lower than the cost of purchasing electricity in the future.

8.4.2 Impacts of the 5G Era on Future Transport 5G technology will be the high-tech and economic growth driver of the next decade (Ahmad et al., 2019). 5G technology can support a vast array of devices, machines and processes that promise to connect all things in a more efficient way. The following are the most important elements in the description of 5G: high throughput, low latency, high reliability, enhanced scalability and energy-saving mobile communication technology (Mitra & Agrawal, 2015). According to a report by Qualcomm (Huan, 2017), there are three ways in which 5G technology will be applied to create value. Enhanced mobile broadband (eMBB): To meet the needs of eMBB, 5G needs to achieve significant improvements over 4G in terms of spectrum efficiency, signalling efficiency, bandwidth and coverage (Gamage et al., 2017). There are two key aims of eMBB. The first is to extend cellular network coverage to larger buildings, including

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Fig. 8.9 Three application scenarios of 5G technology13

office buildings, industrial parks, shopping centres and large venues. The second is to increase capacity to meet the needs of more terminals that use large amounts of data. Massive Internet of Things (MIoT): The IoT is the interconnection of smart devices and management platforms, requiring almost no human intervention jointly to promote a smart and interconnected world. From health and health monitoring to smart utilities, from integrated logistics to autonomous drones, our world is becoming a hyper-automated world (Mumtaz et al., 2017). 5G can significantly reduce costs in MIoT scenarios by better meeting requirements for low power consumption, enabling work in both licensed and unlicensed spectrum and providing deeper and more flexible coverage. This will also support the scaling up of the MIoT and will facilitate the wider adoption of mobile technology for MIoT applications. Mission-critical services: Mission-critical services represent a new market opportunity for mobile technology, which will be an important growth sector for 5G and will support applications requiring high reliability, ultra-low latency connectivity, high security and high accessibility (Fig. 8.9). 5G is an end-to-end ecosystem. It will create an all-mobile and all-connected society that will further blur the concept of geographic boundaries. In short, 3G and 4G are designed to connect people, while 5G is designed to connect everything. 4G only provides smooth streaming and gaming experience, but 5G is designed for other devices in life, such as better control of smart homes with mobile phones. 5G provides the technological support for the promotion of self-driving cars. On the automotive side, with 5G connection, your self-driving car will take the best route based on the traffic data communicated from other vehicles and roads (Camacho et al., 2018). Although a few technology companies are already researching and producing such self-driving cars, large-scale adoption of self-driving cars will require the intervention and support of smart devices and 5G networks.

13

Source https://www.pianshen.com/article/3292254075/.

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5G facilitates the development of smart transport. With the rapid development of modern urban transport, the rise of the car ownership rate has led to the increasingly prominent conflict between supply and demand of urban transport infrastructure. Therefore, it has become the general trend to accelerate the construction of urban ITSs. At present, the integration of the mobile Internet and the IoT has become increasingly close. At the same time, China’s 5G trial work has been carried out smoothly, reaching a peak rate of 10 GB per second. Against this promising backdrop, transport can apply a variety of high-tech such as big data technology, origin-destination surveys, etc. In the intelligent transport network, cell phones on pedestrians, GPS on vehicles and traffic monitoring cameras at intersections will perform intelligent identification through radio frequency and 5G signal technology, connect to each other according to certain protocols and constantly exchange information.14 The rail transport industry also needs the support of 5G technology to ensure high-quality transmission of passengers and railway operation and control systems (Ai et al., 2020). At the New Species Every Day launch site of the 2019 National Mass Entrepreneurship and Innovation Week, Zhejiang Geely New Energy Commercial Vehicles Group unveiled a new generation of 5G intelligent buses.15 These new buses were jointly developed by Zhejiang Geely New Energy Commercial Vehicles Group, China Mobile, Hangzhou Public Transport Group, Soyea Technology Co., Ltd. and Huawei, invited and led by the Zhejiang 5G Industry Alliance. The 5G intelligent bus has four major features: modular design, high-level safety, dynamic performance and fuel economy, and intelligence. This type of smart bus has a modular design, so it can be quickly adjusted to different models according to different needs. Second, the performance of the 5G smart bus is also very reliable, and it can be used only after it has passed harsh hightemperature, low-temperature and high-altitude tests. Third, because the body of the 5G smart bus is very lightweight (all bodies are made of aluminium), its fuel economy is better. Most importantly, Zhejiang Geely New Energy Commercial Vehicle Group has developed a series of new functions for 5G smart public transportation by virtue of the new generation of 5G communication technology with large bandwidth, low latency and high broadband access.

8.4.3 Three Future Mobility Models In a broad sense, the best way to get around is a combination of shared mobility, autonomous driving, clean energy and public transportation. At the regional level, each region will make different choices based on local conditions, including population density, wealth, road conditions, public transportation infrastructure, pollution, traffic congestion and local regulations. All these factors will determine which 14 15

Source https://kuaibao.qq.com/s/20190614A0KNSP00. Source http://auto.zjol.com.cn/zjcw/xyzx/201906/t20190619_10372073.shtml.

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changes will occur and how quickly they will evolve. In the near future, for different cities around the world, we expect three modes of transportation to emerge, with trends such as sharing, autonomous driving and electrification all likely to emerge and develop. Each mode will be appropriate for a different type of city, such as a densely populated city or an emerging metropolis. Clean energy and sharing. Cities in the first category are rapidly growing cities in developing countries, and typical cities in this category are Delhi and Mumbai. These cities are densely populated and are in the early and middle stages of urbanisation. They also have to deal with the urban diseases that come with them. Widespread adoption of autonomous vehicles in cities like these is not a viable option, at least in the short or medium-to-long term, because of their weak infrastructure, greater intervention from pedestrians and various vehicles, and low compliance with traffic laws. A relatively viable option is to shift to clean energy transport, i.e., electric vehicles, while also limiting the number of private cars, optimising shared mobility and expanding public transport (Dominkovi´c et al., 2018). Combined with some connectivity and autonomy, traffic conditions in these cities will improve and safety will be enhanced. Our study demonstrates that if the corresponding Asian cities adopt the model, car-sharing will account for about 50% of passenger turnover by 2030, mainly thanks to higher usage and passenger capacity (per trip). A survey from Kathmandu showed that clean energy transportation’s intervention in existing public transportation can have a significant positive impact on greenhouse gas emissions and current fuel consumption (Pradhan et al., 2006). Private self-driving cars. The future of self-driving cars has the promise of using clean energy and supporting shared transportation services, in addition to autonomy. By integrating several advanced technologies, motorised transport can become more sustainable, environmentally friendly and safe (Acheampong & Cugurullo, 2019). Many cities around the world are experiencing rapid development and increasing commuting patterns. In these cities, it is crucial to own a car. This pattern will continue, at least for the foreseeable future. There is no doubt that the cost of this travel pattern is relatively high. For example, traffic jams cost Los Angeles $23 billion annually.16 To address this issue, we believe that consumers in these cities will adopt new vehicle technologies, such as self-driving and electric vehicles. For self-driving cars, we can also set aside special lanes. Increasing connectivity (wireless and Internet connections, among other connectivity options) also allows us to impose congestion fees according to demand. At the same time, car-sharing and ride-hailing services can serve as complements, but are unlikely to replace private cars on a large scale. The model also has some drawbacks—with the use of electric or self-driving cars, the demand for mobility may increase as the cost of travel decreases and no human intervention is required, which may cause traffic jams. Consumer travel miles are expected to increase by 25% in 2030, mainly from private self-driving cars. Seamless mobility. This mobility model has the greatest changes compared with the current travel model, and it is most likely to emerge in the short term in densely 16

Source jams.

https://www.economist.com/the-economist-explains/2014/11/03/the-cost-of-traffic-

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populated, high-income cities such as Chicago, Hong Kong, London and Singapore. The transportation networks of these cities are facing the challenge of solving the increasing demand for mobility while improving their economic benefits and environmental sustainability. In this model, mobility is essentially a door-to-door and on-demand model (Costa et al., 2016). Travellers have many clean, affordable and flexible travel options to choose from, and the lines between private cars, shared vehicles and public transport are increasingly blurred. Vehicles in this model include self-driving cars, shared vehicles and quality public transport. Electric vehicles will become increasingly popular, thanks to tough emissions regulations, declining technology costs and growing consumer interest. Under this seamless mobility system, people will travel 20–50% more miles due to the low cost and ease of travel. However, the number of cars owned will remain unchanged, or even decrease, as vehicles are used more frequently and more people use sharing modes. Electric vehicles will then account for two-third of the overall car ownership, and the percentage of self-driving cars will exceed 40%.

8.5 Summary This chapter has analysed the impacts of transport facilities on lifestyles. Many research results at home and abroad show that the development of transport facilities has a noticeable effect on residents’ travel activities and promotes the progress of the economy and even the times. The advancement of transport facilities and technologies has gradually expanded human spheres of activities. From walking, taking carriages and driving cars to taking high-speed rail, human travel efficiency has gradually increased. Section 8.2 has discussed the impacts of high-speed rail on Chinese residents’ lifestyles. China is vast in territory and densely populated. The opening of high-speed rail is of great significance to improve the freedom of intercity and interprovincial migration in China, which is also conducive to the realisation of the dual-city lifestyle. Meanwhile, high-speed rail has obvious advantages. Its efficiency is comparable to that of aeroplanes, and it is more comfortable and standardised than cars and other trains. Section 8.3 has described the impacts of shared transport on Chinese residents’ lifestyles. The concept of sharing originated from the Internet and has been thriving in China with the help of Internet technology. Shared transport in China mainly involves bicycle-sharing and car-sharing. Shared transport facilitates people’s lives, changes their living habits and contributes to emissions reduction and environmental protection. The last section has introduced various new types of transport technologies and their impacts on residents’ lifestyles. New transport technologies include but are not limited to vehicle electrification, shared mobility, autonomous driving, the IoT, sustainability, 5G, etc. In today’s era of rapid development of various technologies, the development of transportation technology is naturally also very rapid. This

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chapter has sequentially described the changes that high-speed rail, shared transportation and new transportation technologies have brought to the lives of Chinese residents. Some of them may seem familiar to foreign readers, and some may feel unfamiliar. This is normal. There are similarities between China and foreign countries, but there are also significant differences. Therefore, this chapter can be used as a window to guide readers from all over the world to understand China’s latest traffic developments and technologies. In China, new technologies may be developed by private companies for R&D and innovation, or they may be led by the government. High-speed rail is the representative of the latter. This chapter may also help readers to understand how China’s new transportation technology has occurred and is developing.

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Chapter 9

Policy Recommendations

9.1 Enhancing Quality of Life Future policies should pay more attention to people’s demands for quality of life with improved living standards, and people-centred policies focusing on the following issues need to be developed: (1)

(2)

(3)

Upgrade all aspects of transport services. It is necessary to switch from emphasising the supply of facilities to laying equal stress on the supply of facilities and quality services and from focusing on mobility to emphasising location accessibility and multimodal integrated configurations, connections and services. There is also a need to increase the transfer efficiency of internal and external transport (Hernandez & Monzon, 2016), improve transport punctuality and travel satisfaction (Lois et al., 2018), provide diversified and inclusive travel options, make transport services more refined and smarter (Melo et al., 2017), strengthen transport demand management (Batur & Koç, 2017) and enhance the professional qualities of transport personnel. Connect internal and external transport to form an integrated transport network. It is essential to improve the transfer efficiency of internal and external transport and shift from only paying attention to configurations and connections to taking the configuration of facilities, planning management and operation organisation into consideration, fully reflecting the basic requirements for continuity, smoothness and comfort in the connection process. Meanwhile, efforts are necessary to fully guarantee the requirement for functional land use of external hubs, properly locate external long-distance passenger terminals, pay attention to the reasonable transport design of the entrance and exit sections of cities and tackle the increasingly slow intra-city transport in contrast to the increasingly fast inter-city transport. Multimodal integrated configurations, connections and services. It is necessary to have integrated planning, configurations and design arrangements for all corridors, networks and nodes; to coordinate the networks of high-speed rail, expressways and airports; and to clarify the objectives of stratification

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. Zhao and D. Lyu, Lifestyle Change and Transport in China, Population, Regional Development and Transport, https://doi.org/10.1007/978-981-19-4399-7_9

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and grading. It is of critical importance to create quality integrated multidimensional transport corridors, strengthen the integration of transport facilities in respective regions and actively develop intra-city and inter-city transport networks. Integrated connections are not limited to physical ones in terms of corridors; instead, the functions, operations and services of effective connections between various means of transport, different hubs and places within and outside China should also be emphasised. Integrated connections also call for the unified design, synchronised construction and coordinated management of various means of transport at hubs to improve their collection and distribution systems. The goal of integrated services is to achieve zero-distance transfer for passengers, as well as intensive and efficient freight transport. The construction of an integrated information service platform (Morfoulaki et al., 2011) is expected to improve the convenience of networking, interlining, off-site and round-trip ticketing services, to enhance the continuity and seamlessness of services and to reinforce adaptability and flexibility.

Box 9.1: Domestic Case 1: Shenzhen—Overall Planning of Comprehensive Transport Hubs In the formulation of a development plan, the visionary city of Shenzhen has always been planning ahead. In the early 1990s, the Communist Party of China Shenzhen Committee and the Shenzhen Government initiated the development strategy of twin ports and kept increasing financial investment and policy support to promote the development of these ports. In 2020, Shenzhen Port achieved a container throughput of 26.55 million TEUs, ranking as the fourth largest container port in the world.1 Shenzhen Bao’an International Airport achieved a passenger throughput of 37.92 million in 2020, ranking as the third largest in China.2 Both have helped Shenzhen to become the most important trade gateway and international hub in the Pearl River Delta Region.

1 2

https://www.sohu.com/a/444489453_120581729. Source http://www.air66.cn/mh/10/23301-1.html.

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Due to its special geographical location and good advance planning, Shenzhen Bao’an International Airport has become a large-scale comprehensive transportation hub that integrates sea, land, and air transportation, and connects internal and external transportation. Advance planning is very important. The planning for Bao’an Airport not only covered the interior of the airport, but also unified the surrounding facilities, including the expressway network, urban rail transport and industrial parks (Fig. 9.1)

Fig. 9.1 Shenzhen Bao’an International Airport3

Domestic Case 2: Shanghai—Hongqiao Comprehensive Transport Hub Shanghai Hongqiao International Airport, a comprehensive transport hub, provides centralised transfer services for civil aviation, high-speed rail, maglev transport, inter-city rail transport, expressway passenger transport, urban rail transport, public transport and other means of transport (Liu & Huang, 2010). The Hongqiao Airport Hub is already an aorta in the Yangtze River Delta region, quickly connecting various regions in the Yangtze River Delta, and also promoting the interconnection and economic cooperation of the region. The Hongqiao Hub operates two major external transportation modes, aviation and train (Fig. 9.2). The east of the hub is Shanghai Hongqiao Airport, and the west is Shanghai Hongqiao Railway Station. Hongqiao Airport is the closest airport to downtown Shanghai, and it mainly operates flights from Shanghai to various parts of China. Hongqiao Railway Station is one of Shanghai’s main railway stations. It operates important high-speed railway lines such as the Beijing–Shanghai high-speed railway. Inside the hub, passengers can easily travel between the airport and the railway station, and they only need to walk about 500 m between the airport and the railway station.

3

Source https://www.szairport.com/.

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Fig. 9.2 An aerial view of the Hongqiao comprehensive transport Hub4

In addition, the Hongqiao Hub is also connected to a number of intra-city transportation lines, including subways and buses, which greatly facilitates the traffic between downtown Shanghai and the hub. Hongqiao Hub operates six bus lines, including Hongqiao Hub Line 4, Hongqiao Hub Line 9, Airport Line 1, Bus 941, Bus 316 and Bus 320. It is commendable that the Shanghai Hongqiao Hub has truly achieved direct bus access to the airport. In some domestic cities, there is still a last mile phenomenon between the bus and the airport, that is, the connection is not completely convenient. However, Hongqiao Hub has achieved high-density, multi-shift bus access.

(4)

(5)

4

Improve transport punctuality. It is necessary to strengthen the monitoring and recording of transport information, enhance the predictive capability of the management system, improve information disclosure and emergency response measures and promote the orderly operation of transport services. It is also necessary to improve the punctuality of civil aviation and rail transport, ensuring 80% of civil flights operate normally and striving to reach 90%. Meanwhile, it is necessary to attempt to reduce the time and economic loss to passengers caused by late and delayed trains and flights (Preston et al., 2009; Vansteenwegen & van Oudheusden, 2007), improve the compensation mechanism, compensate for the corresponding unexpected conditions, and ensure passengers’ travel experience (Corman et al., 2012; Robenek et al., 2016). Improve travel satisfaction. It is of paramount importance to meet the needs of passengers with different travel purposes and under different travel conditions (Atasoy et al., 2015; Davison et al., 2014; Nelson et al., 2010), provide comprehensive, quality and friendly transport services (Brake et al., 2007), increase financial investment in upgrading transport infrastructure, replace damaged and old parts in a timely manner and improve service quality and density. It is necessary to crack down on illegal behaviours that disrupt public travel order, regulate begging and forced consumption in stations and trains and create a comfortable, neat and pleasant travel environment. It is also necessary to promote the

Source https://www.163.com/dy/article/EFMA20AI055040N3.html.

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(6)

(7)

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integration of passenger transport services with catering, leisure and other life services to widen transport functions and enrich passengers’ travel experience while focusing on the economic efficiency, comfort, reliability and accessibility of transport services to enhance overall satisfaction. Provide diversified and inclusive travel options. The aim is to provide targeted, special and diversified transport services based on people’s diversified needs, reduce the restrictions set by defective transport services on disadvantaged groups or non-mainstream travel habits (Delbosc & Currie, 2011; Litman, 2002; Pereira et al., 2017) and enhance the inclusiveness of passenger transport services. For travellers who have difficulty accessing the traditional transport service system, some targeted social support mechanisms should be established, such as providing car driving training for the working population of unemployed families or providing low-cost short-term car rental services to low-income families without cars. There is also a great necessity to provide more comfortable, accessible and caring travel routes and transport services for older people, the infirm, the sick, people with disabilities, pregnant women and different cultural groups, update unreasonable design leveraging ergonomics, realise a user-friendly process of waiting, riding and transferring and increase the number of beneficiaries of upgraded transport services. Create a bus-oriented inter-city rapid passenger transport network and improve the new system of quality transport services for the main bus network (Curtis et al., 2009; Dittmar & Ohland, 2012; Newman & Kenworthy, 2006). A multidimensional transport network that combines rail transport, buses and nonmotorised transport is the objective. In the Circum–Bohai–Sea Region, the Yangtze River Delta Region and the Pearl River Delta Region, a bus-oriented inter-city rapid passenger transport network will be developed: a 1–2 h travel circle between adjacent provincial capitals, as well as a half to 1 h travel circle between provincial capitals and neighbouring cities, will be formed.

Box 9.2: Domestic Case: the Yangtze River Delta Region to Build an Integrated Transport Network with Rail Transport as the Mainstay The Development Plan for Integrated Transport with Higher Quality in Yangtze River Delta Region, announced on April 2, 2020, proposed the establishment of an integrated transport infrastructure network by 2025 (National Development and Reform Commission and Ministry of Transport of the People’s Republic of China, 2020). Main external transport corridors, inter-city transport mainstays, and a commuting network in metropolitan areas will be efficiently connected, basically achieving the transport integration of cities in the Yangtze River Delta Region. Specifically, this is a multilevel comprehensive transport network with rail transport as the mainstay, the highway network as the foundation, water transport and civil aviation as the support, and Shanghai, Nanjing, Hangzhou, Hefei,

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Suzhou, Wuxi, Changzhou and Ningbo as the main nodes, to provide efficient external connections and organic internal connections. First, it is necessary to create multidimensional rapid transport corridors with large capacity and inbound and outbound connections, and to coordinate and optimise the configurations of trunk railroads, expressways, inland waterways such as the Yangtze River Golden Waterway, ports and airports, to achieve efficient connectivity with other international and domestic economic sectors. Second, it is necessary to build a fast and efficient inter-city transport network. Relying on fast transport corridors, inter-city railroads, expressways, and national and provincial highways will be the focus to achieve fast and direct inter-city connections within the region. Third, it is necessary to establish an integrated commuting network in metropolitan areas. Centring around the Shanghai Metropolitan Area and Nanjing, Hangzhou, Hefei, Suzhou, Wuxi, Changzhou and Ningbo metropolitan areas, it is possible to create a 1 h commuting circle in metropolitan areas, with inter-city railroads, urban and suburban railroads, urban rail transport and urban expressways as the backbone.

9.2 Promoting Transport Equity Future policies should advance transport equity by forming a social security system for transport services (Murray & Davis, 2001; Pereira et al., 2017; Ricciardi et al., 2015; Vasconcellos, 2014). It is necessary to guarantee the construction of transport facilities in backward areas, implement refined and differentiated transport measures for poverty alleviation, improve the accessibility and service level of transport facilities in backward areas, strengthen external exchanges, promote the flow of populations and resources, provide transport subsidies and ensure that poor areas can afford to build roads and that people can afford to ride in cars. It is also necessary to pay attention to the adaptability of the construction speed of transport facilities in developed areas to population growth and economic growth and to meet the transport needs of new floating populations in cities. To emphasise transport equity in densely populated areas and cities, the construction of urban public transport facilities should favour the residential areas of low-income groups, and it is essential to improve low-income groups’ ability to pay for public transport in the form of government subsidies, to provide high-level public transport services and to improve the travel environment for low-income groups. It is also necessary to ensure the travel of physiologically disadvantaged groups in cities (Park & Chowdhury, 2018), strengthening the barrier-free design of road transport facilities, transport stations and vehicles to provide more convenient travel conditions for physiologically disadvantaged groups.

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Box 9.3: Domestic Case: Hangzhou Vigorously Promotes the Construction of a Barrier-Free Environment Where “Love Is Not Hindered” In recent years, Hangzhou has vigorously promoted the construction of a barrier-free environment. Barrier-free facilities and signs can be seen everywhere in subway stations, shopping centres, overpasses, hospitals, libraries, etc. In accordance with international standards, Hangzhou will optimise and improve these facilities, and build itself into a city where love is not hindered (Fig. 9.3).

Fig. 9.3 The barrier-free facility in Hangzhou5

The construction of a barrier-free environment in Hangzhou began in 2002. In this year, public buses with barrier-free travel facilities were introduced to Bus 900. In 2015, Bus 16 set up special seats on buses, calling for these seats to be given priority to passengers such as older people, the weak, the sick, the pregnant, etc. In 2018, Hangzhou Public Transport set up special waiting seats on some platforms for older, weak, sick, and pregnant groups. In November 2018, Bus 42 introduced a guidance system for the visually impaired. Subsequently, more and more bus lines introduced this system, which greatly facilitated bus travel for the visually impaired and older people. According to statistics, by the end of March 2020, there were 4,809 buses in the main urban area of Hangzhou, of which 1,841 were equipped with wheelchair ramps.6 Most of the 10 m-long and 12 m-long buses are equipped with barrier-free footboards and wheelchair spaces. Bus drivers offer help to passengers in need. Future policies should focus on the integrated development of urban and rural transport, including strengthening the accessibility of transport services and population aggregation and offering more equitable access to public service facilities. 5 6

Source https://zj.zjol.com.cn/news/1476542.html. Source https://baijiahao.baidu.com/s?id=1670881177919507878&wfr=spider&for=pc.

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It is of great necessity to accelerate the improvement of urban and rural road network construction and the connection between these two networks and to narrow the gap between urban and rural transport infrastructure. It is necessary to raise the construction level of rural roads, build a transport network system that adapts to the reconfiguration of the rural geographical and spatial system and cautiously proceed with village-to-village services in villages without working populations, thus enhancing the efficiency of transport resources. It is advisable to fully achieve the modernisation of transport infrastructure, pay attention to the maintenance of rural transport facilities, improve the quality of rural transport services, optimise transport services, promote the integration of urban and rural passenger transport services and strengthen the connection between public service facilities and transport networks to improve the accessibility of public service facilities. Box 9.4: Domestic Case: Zhejiang Province Advances the Construction of High-Quality Rural Roads to Accelerate the Development of Urban and Rural Delivery Systems Zhejiang Province has always put great emphasis on the construction of both the road network system and the logistics network system. Rural roads have been regarded as an important way to make agriculture stronger, the countryside more beautiful and farmers wealthier. During the 13th Five-Year Plan period, Zhejiang attempted to implement the overhaul and medium repair of 15,000 km of rural roads to ensure that more than 75% of rural roads are in good condition (Fig. 9.4).

Fig. 9.4 Rural e-commerce logistics7

7

Source http://js.zhonghongwang.com/show-227-19086-1.html.

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Zhejiang has formed a new rural logistics model: a multi-point multi-use network. This model has accelerated the construction of the urban and rural distribution system. Last year, the two major enterprises in Lishui City, Zhejiang Province cooperated, and the supply and marketing cooperative and the postal company jointly supported the e-commerce logistics business of agricultural products. Their cooperation can give full play to their respective advantages and help farmers’ agricultural products to enter the market faster and more efficiently, thereby increasing farmers’ income. In addition, Zhejiang has also vigorously integrated the transportation and human resources of a number of express companies, and established express e-stations in each township. Through this, the originally dispersed express delivery resources can be gathered, villagers can enjoy more efficient and convenient express delivery services, and different enterprises can also use each other’s resources to achieve long-term development. Future policies should make transport facilities a driving force for regional development and promote the improvement of living standards of residents in less developed areas. (1)

(2)

(3)

Improve targeted poverty-alleviation-by-transport policies and strengthen the implementation assessment of these policies. With population concentration as the guide, initiatives should take into consideration land policy, industrial development and village relocation and annexation policy, having a fully scientific forecast and prediction. The policies should always align with local economic and social development, and village-to-village access should be selectively implemented to avoid new road construction in villages with declining populations and to get better benefits from transport investment. Governments should continue to relocate and combine administrative villages with large populations connected to asphalt and concrete roads, implement the construction of village roads for people’s livelihoods, fully upgrade and reconstruct county and town roads and build village lanes. For rural highway and other local highway construction, governments ought to promote the agent–construction system, the design-build system, integration of construction, management and maintenance and other modes of construction and management. Strengthen the construction of the express delivery system in impoverished areas and open up logistics channels. It is necessary to cultivate a number of Taobao villages and express villages and actively help agricultural products into the urban market, export and industrial products to the countryside to connect rural and urban logistics, build low-end service outlets in cities and the countryside and serve the economic and social development of impoverished areas. Carry out customised passenger transport services in the countryside (Ji et al., 2021). Governments should engage in regional operations and offer fixed shifts,

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fixed lines, market buses and weekend shuttles based on local conditions, promote innovative development and effectively improve the accessibility of rural passenger transport to administrative villages. It is possible to encourage the extension of urban public transport to the countryside and to promote the transformation of rural passenger transport routes into bus lines. In relatively scattered areas where travel demand is small, it is advisable to encourage the development of telephone reservations, network reservations and other customised passenger transport services. Relying on the internet, it is possible to explore the integration of idle transport resources in the countryside to solve the outstanding problem of insufficient capacity and the difficulty of matching people and vehicles.

Box 9.5: Domestic Case: Jiangsu Province—Town–Village Buses Cracking the Last Mile in Rural Transport The town–village bus is a bus that connects villages to villages and villages to towns. After nearly a decade of efforts, a total of 907 towns and villages in Jiangsu Province have introduced town–village buses, with a coverage rate of 83.7%, benefiting 30 million rural residents within the province (Xinhua Daily, 2019). By 2020, Jiangsu will basically achieve a coverage rate of 100% (Fig. 9.5).

Fig. 9.5 Town–Village buses in Huai’an (Left8 ) and Nanjing (Right9 )

In 2010, Jiangsu Province was the first to propose the development of town– village buses, while establishing the 100% coverage by 2020 goal. After nearly a decade of exploration, the province’s efforts at improving people’s livelihoods through little buses are deeply rooted among the people.

8 9

Source https://baijiahao.baidu.com/s?id=1709514846598204272&wfr=spider&for=pc. Source https://www.sohu.com/a/284591455_188842.

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The beneficiary project of 1RMB bus fare in urban areas and 2RMB bus fare between cities and the countryside has brought the countryside and urban areas closer. After the introduction of full-range buses and town–village buses, people’s travel frequency increased significantly. It has helped to bring farmers’ vegetables and fishermen’s quality fishery products into cities, and also helped to solve recruitment difficulties in town and village enterprises. The reason why it can develop to this level is related to the unique urban and rural passenger transport development model established by Jiangsu. Its urban-rural passenger transportation integration development model connects urban transportation and township passenger transportation through county and township passenger transportation. These three levels of passenger transport services are seamlessly connected and form a honeycomb integrated urbanrural passenger transport network.

(4)

Actively promote poverty alleviation models such as Transport Plus Distinctive Industries, Transport Plus Tourism Plus Leisure and Transport Plus E-commerce Plus Express Delivery. It is necessary to support impoverished areas actively in their construction of roads serving resource, tourism and industry development. It is necessary to support the development of distinctive industries and tourism in poor areas, accelerate poverty alleviation there and build a half-hour travel circle from towns to expressways.

Box 9.6: Domestic Case: The Countryside in Sichuan and Chongqing to Promote the Construction of High-Quality Rural Roads China is promoting the construction and integrated development of high-quality rural roads, rural revitalisation, poverty alleviation and tourism development, striving to build a safe and easily accessible rural transport network that links villages and towns, connects them with other parts of the country, and facilitates the movement of people and goods (Fig. 9.6). The construction of rural transportation infrastructure has a vital impact on local economic development. On the left of the picture is Nanshan Village in Shuangbai Town, Xiaojin County, Aba Tibetan and Qiang Autonomous Prefecture in Sichuan Province. The mountain roads are winding and the height difference between the upper and lower mountain roads is also very large. Without these roads, it would be very difficult to connect inside and outside the mountain village. The right side of the picture is Nanmen Village of Heishan Town in Wansheng Economic Development Zone of Chongqing City. The scenery here is very beautiful, and the combination of farmland and mountains is also beautiful. The presence of asphalt roads makes it easy for tourists to come here and promotes the development of local rural tourism.

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Fig. 9.6 The construction of high-quality rural roads10

9.3 Smart Transport System for Smarter Life 9.3.1 Establishing a Smart Transport System A smart transport system should be established, centring on the constant promotion of transport safety and the use of technological innovation and process innovation to modernise the national transport system (Haque et al., 2013; Meneguette et al., 2018; Su et al., 2011; Zawieska & Pieriegud, 2018). Transport is a basic, leading and strategic industry in the national economy. At present, China’s transport infrastructure network has taken its initial shape, its transport services and technologies have been greatly improved and transport reforms have made positive progress. However, the gap between transport development and the need of economic and social development is still obvious. The configuration of the transport network is incomplete, the construction of comprehensive transport conjunctions lags behind and the institutional mechanism for the modern comprehensive transport system is not sound. Reforms on railway marketisation, airspace management, the operating system of the oil and gas pipeline network and transport investment and financing still need further deepening.

Box 9.7: What Achievements Has China Made in ITS? From the perspective of application services, China’s ITS has mainly developed towards intelligent transport management, transport services and decision support (Fig. 9.7).

10

Source https://baijiahao.baidu.com/s?id=1700758115595161409&wfr=spider&for=pc.

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Fig. 9.7 Typical intelligent transport application systems11

As an important part of the field of smart cities, intelligent transportation technology has been applied in parts of China. Its subdivision areas include intelligent transportation demand management, intelligent public transportation services, etc. The achievements are as follows. Intelligent urban transport management and services: More than 400 cities in China have built intelligent transport coordination and control centres, traffic signal control systems, traffic guidance systems, traffic monitoring systems, and electronic police systems that integrate alarm receiving and dispatching, information collection, and traffic control functions (Guan, 2018). An intelligent transportation demand management platform has also been established in many cities, and its name is the Transportation Operation Coordination Centre. The cities of Beijing, Shanghai, Shenzhen and Guangzhou have all introduced this technology. This centre can comprehensively coordinate various types of transportation inside and outside the city, including city road traffic, city public transport, shipping, land transport, and so on. Intelligent public travel services: China’s car parc exceeds 200 million, and the proportion of motorised trips has risen sharply. There are nearly 500 million bus card users, and the all-in-one bus card enables cross-regional use in multiple cities. Mobile navigation and vehicle navigation systems have been widely used by over 700 million people (Guan, 2018). Various traffic information and ticketing apps have also been widely used. The number of shared bicycles and new energy vehicles in China still leads the world.

11

Source https://news.bjd.com.cn/2021/04/23/72968t100.html.

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9.3.2 Door-to-Door Delivery With the changes in lifestyles and the development of the service-oriented economy, more high-value-added, lightweight products, such as medicines and personal electronic products, will be produced and consumed. As the number of imported overseas products increases, the nature and purpose of freight transport will also gradually change, demanding higher freight efficiency. The popularity of online shopping has greatly increased the proportion of home delivery in freight transport. The first mile and last mile delivery of freight transport has become more complicated, and the transport of goods in densely populated cities is becoming more and more important and challenging. Therefore, it is of great necessity to build strategic distribution centres and intermodal transport centres to promote more efficient entry and exit of goods in and out of cities.

Box 9.8: Smart Delivery The use of smart delivery lockers can improve the efficiency of delivery and save labour costs (Fig. 9.8). With smart delivery lockers, only five people will be enough for the work undertaken by 10 people originally, which can also save on labour costs. The benefits of smart delivery lockers to property management companies are reflected in the fact that it is more convenient for property owners in the community to receive and send packages, and the property management company does not need to arrange a person in the mail room to take care of the owners’ packages. It can not only save labour costs, but also improve owner satisfaction. The property management company can also share a certain percentage of benefits with the company to which these smart delivery lockers belong based on its annual revenue.

Fig. 9.8 Smart delivery lockers12

12

Source http://news.sohu.com/a/523466075_120512342.

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9.3.3 Integrated Transport System To improve the availability, convenience and efficiency of transport options for different groups of people, the comprehensive transport system should aim for integrated and diversified transport (May, 2004; Potter & Skinner, 2000). The key is rationally optimising the transport system, avoiding excessive traffic flow on some roads and nodes and minimising the loss of socioeconomic efficiency caused by congestion. China’s future comprehensive transport system needs close connections and a denser high-speed rail network to make it more convenient for users to travel. Meanwhile, real-time data sharing provides a guarantee for the alleviation of traffic congestion and the optimisation of the comprehensive transport system. The planning and management of the traditional transport system mainly use historical data obtained from scattered travel statistics to predict transport capacity. With the rapid construction of new technologies such as 5G and intelligent transport infrastructure, real-time monitoring of public travel data is possible.

9.3.4 Intelligent Transport System – Formulate a general plan for the construction of transport big data, establish a national big data centre for comprehensive transport and develop transport big data standards. Governments should clarify construction ideas, overall goals and construction tasks to achieve intensive development of transport information construction, operation, maintenance and management. They should also establish a national big data centre for comprehensive transport, offer access to relevant data resources to mobilise market capital fully, and encourage capable companies to invest in big data development and operation as well as value-added services using the PPP mode to accelerate the cultivation and development of the big data industry. – Build a comprehensive management and control platform for smart transport, and establish an information monitoring mechanism for transport service data. Governments need to integrate multiple operation systems such as smart access surveillance, electronic police, high-definition monitoring, traffic guidance, traffic flow collection and integrated management, and to find internal connections through big data mining to manage traffic control. Meanwhile, they should establish an information monitoring mechanism for transport service data and grasp the law of dynamically changing transport demands accompanied by new lifestyles by monitoring and analysing transport big data to offer transport services that meet the needs of users. It is also necessary to facilitate research on cloud computing services for ITS and research on key technologies for big data application and to carry out application demonstrations in cloud migration, data exchange and sharing, road network management, travel services and more.

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– Improve comprehensive ITS and service coordination in all aspects of transport. Governments should promote the smart, integrated and humanised development of the transport system, boost information sharing on various means of transport, provide one-stop services and adapt to residents’ internet plus travel trends. They should also comprehensively advance the transformation from traditional ticket collection and ticket checking to automated face recognition and ID card recognition, and the development of information-based transport services. In addition, they need to spur the application of 5G driverless vehicles, encourage technological innovation in passenger transport services, establish corresponding standards and supervision systems and gradually popularise safe and efficient 5G driverless technology. – Accelerate the construction of new smart transport infrastructure and energy distribution systems. Governments should step up the construction of smart transport infrastructure by combining big data, 5G and other technologies to accelerate the construction of the IoT, internet of vehicles, internet of ships and internet of machines, promoting the systematic coordination of smart transport networks, optimising transport organisation design, facility design and route design, and improving traffic engineering to make it suitable for the smart development environment against the backdrop of the internet plus strategy (Zhang et al., 2011). They should also fully realise smart transport equipment and vehicles by building an unmanned container terminal system, promoting automatic logistics by drones in an orderly manner, steadily advancing autonomous driving on urban rail transport lines, encouraging research on and development of customised smart vehicles and expanding the coverage of ETC in transport fields such as highways, urban buses, taxis, parking, road passenger transport, railway passenger transport, etc. Governments should develop a cutting-edge sensing and monitoring system in transport featuring dynamic perception, complete coverage and ubiquitous interconnection, intensify the deployment of observation points for real-time transport operations, enhance the sensing and monitoring of highway and waterway transport infrastructure and the application of big data, strengthen the supervision and control of infrastructure and ensure the safe and efficient operation of smart transport network infrastructure. – Improve the smart support for the safe operation of the transport system. To improve the smart security supervision system for the transport system, it is important to use transport big data, cloud computing, information sharing and other technologies to establish security monitoring, risk assessment, emergency prediction and other guarantee mechanisms, analyse accident causes, evolution laws, management and control strategies and design active safety technologies and management methods, in an effort to achieve integrated prevention and control of safe transport operations from the perspective of driver–vehicle–road coordination. Governments should also enhance high-level supervision and emergency response capabilities, comprehensively maintain the safety, efficiency and sustainability of transport operations, develop self-inspection and self-maintenance technologies for system operations to realise automatic smart supervision of the transport system, improve the accuracy of safety hazard investigation, strengthen the

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timeliness of emergency measures, minimise travel risks and freight transport risks and provide smart protection.

Box 9.9: Domestic Case: Shenzhen—Building a High-Level Transport Operations Command Centre Shenzhen has built a domestic high-level integrated traffic operation command centre (Fig. 9.9). The transportation operation and management of the centre is based on more than 100 dynamic data of various transportation modes, from shipping, railways, highways, and waterways for external transportation to buses, subways, and taxis for internal transportation. Its command and decision-making follow the results of scientific data analysis, and through the application of advanced algorithms and analysis methods, it mainly solves the problem of urban traffic congestion.

Fig. 9.9 Shenzhen transport operation command centre13

The problem of traffic congestion in big cities is very common. The congestion problem in Shenzhen used to be very serious, and Shenzhen has the highest density of cars in China. After implementing a series of traffic congestion management measures, Shenzhen’s congestion situation became better than Beijing, Shanghai and other cities.

13

Source https://www.163.com/dy/article/GV2B3ITT05340LS4.html.

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9.4 New Business Forms in Transport Future policies should adapt to the new changes in residents’ lifestyles and accelerate the development of new business forms in transport. (1)

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Encourage the development of new business forms in transport services such as internet plus and the sharing economy. Governments should promote the development of new means of transport such as online ride-hailing, shared transport, time-sharing rental and combined transport, break through the traditional mode of transport services and facilitate the efficient use of social resources. They should also encourage the development of complementary transport means, such as community buses, reserved buses, public bicycles and ultra-small vehicles. Explore new business forms in smart transport to realise more diversified and customised transport with higher quality. With the rapid development and application of new technologies such as big data, the IoT, cloud computing, the internet and augmented reality and the gradual maturation of automation and other technologies, it is possible to realise more diversified and customised transport with higher quality through real-time and efficient information services. It is possible to use technologies like GPS, sensors, camera image processing and radar, monitor and collect transport information such as location, speed and route to help with the construction of the internet of vehicles, promote the development of driverless vehicles and automatic parking technology, advance the transformation and upgrading of the traditional vehicle manufacturing industry and build new models and business forms of vehicles and transport services. Governments should encourage innovation in transport services, such as the new business form of DiDi-style car rental, customised transport services provided by the combination of driverless vehicles and the mobile internet and automatic truck platooning. By leveraging the creativity of the transport market, they can promote the intelligence, networking and electrification of road transport. Improve the urban freight transport system under the new logistics demand. It is necessary to facilitate the infrastructure construction of the freight transport system according to residents’ freight transport demand due to online shopping by improving the efficiency of freight transport in densely populated cities, improving the first mile and last mile system of freight transport, contributing to the connection and integration of expressways, high-speed railroads, air freight, water transport and other transport systems and enhancing the smoothness of freight transport. With big data and the cloud computing system to conduct a smart analysis of freight characteristics under the new shopping demand, it is possible to monitor the supply level of logistics and transport services in real time to deploy logistics resources reasonably according to the demands of time and space in certain stages, to improve the efficiency of long-distance transport, as well as medium- and long-haul transport, and to perfect the connection between the domestic freight network and the overseas transport system to

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shorten the time and manpower consumption of freight transport. Governments should also optimise the short-haul freight network, realise door-to-door direct delivery and improve the convenience and flexibility of logistics. Establish a corresponding standardised system for the management of new business forms and improve the development path of new transport means. Governments must establish a scientific management mechanism for new transport means such as online ride-hailing, car-sharing and bicycle-sharing, unify the normative standards in the market and strengthen the bottom-line awareness in transport innovation. They also need to encourage the integration of new forms of transport services with traditional ones and avoid mutual exclusion in limited space to avoid breaking the original balance while also avoiding the waste of resources due to lack of management. Additionally, they should formulate development plans for new business forms to match residents’ increasingly convenient, fast and environmentally friendly life. In this way, disruption or even loss of control of the market due to unrealistic innovations can be avoided.

Box 9.10: Domestic Case: DiDi Will Cooperate with the Chengdu High-Tech Industrial Development Zone to Pilot Web-Based Buses The DiDi Western Innovation Centre is located at the AI International Hub of Singapore-Sichuan High-Tech Innovation Park (SSCIP), covering projects such as the Western Operation Centre of Online Ride-Hailing, Community E-commerce Demonstration Centre, and Dynamic Bus Demonstration Base. Among them, DiDi will rely on the big data advantages of its platform in transport, take the lead in piloting web-based buses in the Chengdu High-Tech Industrial Development Zone (CDHT), SSCIP and other areas, and build a demonstration base for dynamic buses (Red Star News, 2020). The CDHT will work to balance the relationship between employment and housing among residents in the region. This is a good time to cooperate with Didi Company. With the help of Didi’s technology and experience in the transportation field, CDHT will establish a public transport demonstration base. By providing flexible and fast bus services, workers in this area can enjoy convenient commuting. The expected characteristics of public transportation services include zero transfers, less walking, and real-time dynamic generation of routes based on the needs of different passengers. Future policies should accelerate the formation of a transport governance system compatible with a strong transport industry and conducive to its quality development. (1)

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Speed up the innovation of the transport governance system. Governments should rectify the deficiencies in the existing transport governance system, draw on the past experience and establish a large-department system14 to

The large-department system reform was first proposed in 2008 to help to improve the administrative efficiency of the government by readjusting some departments with similar functions.

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implement overall management, transform government functions and highlight public services, improve transport policies and ensure their implementation and promote communication and coordination regarding legal planning, development strategies, plans and schemes, industrial policies, standards, performance evaluation, statistical indexes and other systems. They also need to establish a data management system for future cities, use the internet, the IoT and new types of transport to propel the overall upgrade of decision-making capabilities, management and control capabilities, service capabilities and co-governance capabilities, enhance coordinated development capabilities in terms of space, organisation, interests and time series, focus on the improvement of passengers’ travel quality and seek innovation in system construction, technology application and management models. In addition, they must remain people-oriented in actual management practice, crack down on phenomena that disturb public transport order, collaborative management and technological innovation and build a green and civilised transport governance system. Improve transport demand management (Zhang et al., 2015). First, for existing areas or old cities, whose existing transport facilities and transport systems are complete, transport problems can be tackled from the perspective of planning (Yang, 2018). Second, governments can optimise management by reasonably optimising the structure of transport demand, such as developing efficient public transport, taking rail as the principal transport pattern in large cities and properly controlling the number of private car trips. Third, they can improve the efficiency of the transport system by optimising the organisation and management of the urban road system. In the case of low efficiency of the transport system, the distribution of traffic flow in the entire system is relatively uneven. The traffic flow on main roads or central sections is often large, and the flow on secondary roads is often small, so traffic jams are likely to form at interchanges. The purpose of system optimisation is to solve the traffic congestion caused by this at particular nodes so that the distribution of traffic flow in the system is as balanced as possible, and the capacity of each road is fully utilised. Establish and improve the supervision and evaluation network mechanism of transport services and strengthen public participation. It is necessary to establish a supervision and evaluation system for the quality and functions of service stations along various transport lines, especially along the lines of expressways, give full play to the public’s role in monitoring transport services through the internet, offer access to the evaluation system to monitor transport services in real time, adjust the allocation of transport service resources according to the temporal and seasonal features of travel activities and strengthen the adaptability of the transport service system to people’s changing needs.

Future policies should facilitate international transport and reinforce the construction of cross-border transport infrastructure. ● Upgrade the cross-border administration system targeted at tourists. It is necessary to enhance the convenience and efficiency of cross-border administration, meet the growing demand for international travel in response to new lifestyles, shorten the

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time needed for relevant procedures, establish a smooth entry–exit permit-issuing mechanism, align national policies with those of frequently visited countries and promote channel opening and simplify cross-border administration procedures. It is also necessary to improve the security check efficiency and quality at entry and exit gates through advanced smart technology and to realise fully electronic, automated and smart security checks to ensure national security and efficient and worry-free travel for tourists. ● Improve the supervision efficiency of cross-border logistics. With the development of international online shopping and the globalisation of trade, freight transport requires more efficient public transport services, which means governments should perfect security inspection regulations in freight transport, connect logistics systems of different routes, improve coherence in freight transport and strengthen the efficiency, security and preciseness of cross-border logistics supervision. They should also use smart supervision technologies to promote the smoothness and convenience of the international IoT and provide support for the development of international trade. ● Promote the construction of cross-border transport infrastructure. Governments should facilitate the upgrading and transformation of key cross-border transport lines, expand services for the lines with high transport pressure and traffic congestion and strengthen the transport connection between key transport cities and important passenger and freight destinations to meet cross-border needs of passengers and freight. They should also increase the application of automated security checks, smart monitoring and big data analysis in cross-border transport construction, carry out institutional innovation for important nodes, enhance openness and ensure the security of cross-border passenger and freight transport. ● Propel the internationalisation of plans, standards and systems in the transport sector. This can be achieved through the participation of Chinese companies in foreign transport and urban development projects, thus strengthening the coordination of different countries’ transport policies and aligning China’s transport development with the international community. It is also of great significance to ensure the security of maritime transport, encourage transport companies to expand overseas business, improve existing logistics modes, enhance the international competitiveness of China’s transport and logistics networks, establish a closer connection with international maritime transport networks and improve the advanced and innovative nature of construction and management systems.

9.5 Summary Based on the aforementioned interrelationship between Chinese lifestyles and transport, this chapter has put forward policy suggestions for four aspects of future transport development: enhancing quality of life, promoting transport equity, establishing

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a smart transport system for smarter life and developing new business forms in transport to adapt to new lifestyle changes characterised by diversification, personalisation and convenience. To meet people’s demand for quality life as their living standards improve, it is necessary to formulate user-centred policies by upgrading all aspects of transport services, connecting internal and external transport to form an integrated transport network, realising multimodal integrated configurations and connections, offering multimodal integrated services, improving transport punctuality and travel satisfaction and providing targeted, special and diversified transport services based on people’s diverse needs. It is necessary to form a social security system for transport services to advance transport equity: making transport facilities a driving force for the improvement of residents’ living standards in less developed areas, strengthening the construction of the express delivery system in impoverished areas and opening up logistics channels, and carrying out customised passenger transport services in the countryside. It is necessary to expedite the development of smart transport to fit in with people’s smart lives. For instance, governments can dig into transport big data, build intelligent transport and accelerate the construction of intelligent transport infrastructure by building a comprehensive management and control platform for smart transport, establishing an information monitoring mechanism for transport service data, comprehensively improving integrated intelligent transport and service coordination, accelerating the construction of new smart transport infrastructure and energy distribution systems and improving the smart support for the safe operation of the transport system. It is of great necessity to accelerate the development of new business forms in transport. For instance, it is necessary to encourage the development of new business forms in transport services such as internet plus and the sharing economy, improve the urban freight transport system under the new logistics demand, establish a corresponding standardised system for the management of new business forms and improve the development path of new transport means. Based on a detailed understanding of the various lifestyles of various types of residents in China, this chapter has provided some suggestions for the development and policy orientation of China’s future transportation industry.

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Park, J., & Chowdhury, S. (2018). Investigating the barriers in a typical journey by public transport users with disabilities. Journal of Transport & Health, 10, 361–368. https://doi.org/10.1016/j.jth. 2018.05.008 Pereira, R. H., Schwanen, T., & Banister, D. (2017). Distributive justice and equity in transportation. Transport Reviews, 37(2), 170–191. https://doi.org/10.1080/01441647.2016.1257660 Potter, S., & Skinner, M. J. (2000). On transport integration: A contribution to better understanding. Futures, 32(3–4), 275–287. https://doi.org/10.1016/S0016-3287(99)00097-X Preston, J., Wall, G., Batley, R., Ibáñez, J. N., & Shires, J. (2009). Impact of delays on passenger train services: Evidence from Great Britain. Transportation Research Record, 2117(1), 14–23. https://doi.org/10.3141/2117-03 Red Star News. (2020). Didi Western Innovation Centre project landed in Chengdu High-Tech Zone and will pilot network-hailing public transport (in Chinese). https://baijiahao.baidu.com/s?id= 16c9173595121918106&wfr=spider&for=pc. Ricciardi, A. M., Xia, J. C., & Currie, G. (2015). Exploring public transport equity between separate disadvantaged cohorts: A case study in Perth, Australia. Journal of Transport Geography, 43, 111–122. https://doi.org/10.1016/j.jtrangeo.2015.01.011 Robenek, T., Maknoon, Y., Azadeh, S. S., Chen, J., & Bierlaire, M. (2016). Passenger centric train timetabling problem. Transportation Research Part B: Methodological, 89, 107–126. https://doi. org/10.1016/j.trb.2016.04.003 Su, K., Li, J., & Fu, H. (2011, September). Smart city and the applications. In IEEE Xplore (Ed.), 2011 International Conference on Electronics, Communications and Control (ICECC) (pp. 1028– 1031). IEEE. https://doi.org/10.1109/ICECC.2011.6066743. Vansteenwegen, P., & van Oudheusden, D. (2007). Decreasing the passenger waiting time for an intercity rail network. Transportation Research Part B: Methodological, 41(4), 478–492. https:// doi.org/10.1016/j.trb.2006.06.006 Vasconcellos, E. A. (2014). Urban transport environment and equity: The case for developing countries. Routledge. Xinhua Daily. (2019, April 21). Town and village buses open up the “last mile” of rural travel (in Chinese). http://xhv5.xhby.net/mp3/pc/c/201904/21/c622678.html. Yang, L. (2018). Analysis of traffic demand management measures (in Chinese). Technology and Economic Guide, 26, 1. Zawieska, J., & Pieriegud, J. (2018). Smart city as a tool for sustainable mobility and transport decarbonisation. Transport Policy, 63, 39–50. https://doi.org/10.1016/j.tranpol.2017.11.004 Zhang, M., Yu, T., & Zhai, G. F. (2011). Smart transport system based on “the internet of things”. In Z. X. Hou (Ed.), Applied mechanics and materials (Vol. 48, pp. 1073–1076). Trans Tech. https:// doi.org/10.4028/www.scientific.net/AMM.48-49.1073. Zhang, X., Shao, Y., Song, J., Lv, G., & Chen, W. (2015). Thoughts on the rule of law in Shenzhen’s traffic demand management policy (in Chinese). Urban Transport of China, 13(4), 1–7. https:// doi.org/10.13813/j.cn11-5141/u.2015.0401.

Chapter 10

Concluding Remarks

This study systematically summarises the existing research results on the interrelationship between lifestyles and transport in China. An index system is also constructed to measure lifestyles. Using data from the Chinese Family Panel Studies, the Beijing Jobs-Housing Balance Survey and questionnaire data about town and village residents, we performed a quantitative analysis of China’s lifestyles and the interrelationship between lifestyles and transport at the individual level. We propose policy suggestions for transport optimisation in China that adapt to lifestyle changes and, to a certain extent, extend the scope of research on the interrelationship between lifestyles and transport in China.

10.1 Changing Lifestyles in China Lifestyle is essentially a reflection of production relations and productivity. With 40 years of rapid development since China’s reform and opening up, the social economy has entered a new stage of development. The lifestyles of the Chinese people have undergone tremendous changes, and their quality of life is constantly improving (Hubacek et al., 2007, 2009). Lifestyle changes have the following characteristics: (1)

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The previous unified lifestyle has transformed into diversified and individualised lifestyles (Dai, 2002). With the restructuring of the planned economy, the relaxation of household registration and the influence of the globalisation process as well as market-oriented and diversified employment patterns, people’s lifestyles are becoming increasingly diversified, and inclusive development and social equity are increasingly valued (Guarin & Knorringa, 2014; Zheng, 2009). The consumption pattern has changed from a single one in the planned economy era to being more diversified. Residents’ consumption patterns have become diversified and are of high quality, and online shopping is growing steadily. The focus of consumption has shifted from basic material needs to spiritual needs

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. Zhao and D. Lyu, Lifestyle Change and Transport in China, Population, Regional Development and Transport, https://doi.org/10.1007/978-981-19-4399-7_10

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(Han, 2013), and from offline shopping to online shopping (Martinsons, 2008; Xiang & Jing, 2014). Sustainable consumption is also receiving increasing attention (Liu et al., 2016). With the advancement of production technologies, the modernisation of agriculture and the adoption of paid holidays, residents’ labour time has been significantly shortened and leisure activities have become increasingly diversified. Self-driving travel and one-hour short-distance leisure tours in peripheral regions of cities are becoming more and more popular (Zhou & Huang, 2016), and the rural tourist population is also growing. Outbound travel has become a lifestyle. With the development of the Internet and the impact of COVID-19, the trends of telecommuting, working from home, online learning (Xiong et al., 2021) and mobile officing are remarkable. With the widespread use of the Internet and the support of online work software, telecommuting and mobile officing have become a new lifestyle (Narayanan et al., 2017). With the expansion of cities and the increase in transport mobility, as well as the development of suburban public transport, the separation of workplaces and residences in large-sized cities has intensified, which has resulted in an increase in long-distance commuting and cross-city commuting, and the reconstruction of geographical scales and time-space distance (Chung et al., 2020; Wan et al., 2018; Wang et al., 2021; Zhao et al., 2010). Long-distance commuting and cross-city commuting have become a new lifestyle. Residents’ pace of life is becoming faster and their lives more convenient. Internet plus transport is emerging (Jiao et al., 2020; Zeng & Yang, 2015), and the sharing economy is prevailing (Chi et al., 2020; Ma et al., 2019). This requires transport to be more efficient, smart, green, diversified, comfortable and fair. In a low-desire society in a stay-at-home era, the demand for errand services has grown as a result of the so-called lazy economy.1 It has resulted in the emergence of new career paths such as take-away riders and delivery people, forming a new lifestyle. The Chinese government attaches great importance to environmental protection and is changing lifestyles towards a low-carbon economy (Hubacek et al., 2012). China will enhance its nationally determined contribution and adopt more vigorous policies and measures, striving to hit peak emissions before 2030 and for carbon neutrality by 2060.2 With the switch from coal to gas

The lazy economy is a new type of economic trend characterised by the pursuit of time-saving, labour-saving and convenient products and services. 2 On December 12, 2020, China’s President Xi Jinping delivered a speech entitled “Building on Past Achievements and Launching a New Journey for Global Climate Actions” via video link at the Climate Ambition Summit 2020. He announced that China will scale up its nationally determined contribution and adopt more vigorous policies and measures, striving to peak carbon dioxide emissions before 2030 and achieve carbon neutrality before 2060. President Xi also made some further announcements for 2030: China will lower its carbon dioxide emissions per unit of GDP by over 65% from the 2005 level, increase the share of non-fossil fuels in primary energy consumption to around 25%, increase the forest stock volume by 6 billion cubic metres from the 2005 level, and

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in heating and the adoption of new energy vehicles, environmentally friendly lifestyles are being promoted.

10.2 Links Between Transport and Lifestyles The problem with the concept of lifestyle is that it is defined in many different ways or not defined at all (Jensen, 2007). Lifestyle is everything to everyone, but this very fact that makes this concept so appealing hinders further precision (Anderson & Golden, 1984). The core of lifestyle research is the differences amongst research objects (Gao, 1998). Scholars have tried to work out different lifestyles in society and to clarify the characteristics and differences of these lifestyles. Different social groups show different lifestyles, while people from the same social group show convergence in lifestyles (Lazer, 1963). Unlike the traditional academic approach, which treats lifestyle as a subordinate concept of class or group differences, the introduction of the lifestyle typology and relevant case studies indicates that lifestyle has finally become a specialised, independent research direction (Gao, 1998). At present, the lifestyle concept is mainly applied in research on travel behaviour, and less involved in other fields of transport. There is no generally accepted definition of lifestyle in transport, and lifestyle is mostly defined as behavioural activities or value orientations. There is also a lack of unified methods for lifestyle measurement and segmentation in transport. Lifestyle segmentation is based on findings from survey data results, and almost every study has defined its own lifestyle segments. Meanwhile, there is no in-depth theoretical discussion on the mechanism behind the interrelationship between lifestyles and transport. Moreover, scholars hold different opinions on the impacts of lifestyles on travel behaviour. Some studies have found that compared with socioeconomic attributes, lifestyles have a relatively small impact on travel, while other studies have found that lifestyles are a key factor affecting travel, especially leisure travel. Most of the research has been conducted in specific cities or regions, and there are few Chinese case studies. This book interprets lifestyle in transport as behavioural expressions and value preferences in transport. Travel-related behavioural expressions mainly include consumption behaviours, housing choices, work, leisure activities, etc., as well as money and time spent on these behaviours and activities. Value preferences mainly include life philosophies, hobbies and preferences. The application of the lifestyle concept in travel demands is essentially a category analysis of travel demands. An index system for individual and overall lifestyle measurement is constructed in this book. The individual lifestyle indexes mainly involve the dimensions of socioeconomic attributes, space, consumption, activities, time and values, while the overall lifestyle indexes mainly involve the dimensions of consumption, transport facilities, environment, time, activities and value preferences, aiming to provide methodological guidance and reference for lifestyle measurement. bring its total installed capacity of wind and solar power to over 1.2 billion kilowatts. Source https:// baijiahao.baidu.com/s?id=1686004431180702139&wfr=spider&for=pc.

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The relationship between lifestyles and transport needs to be analysed from the perspectives of transport geography, social psychology and economics. This book employs the value theory, TPB, consumer behaviour theory and time-space convergence theory, which are the common theoretical bases of lifestyle and transportation; it employs the theory of supply and demand to explore the impacts of lifestyles on transport; and it discusses the impacts of transport on lifestyles using spatial selection theory and time-space compression theory. Lifestyles and transport are at the same stage of development. They are interconnected and subordinate to each other while featuring inconsistency in choice. This book constructs a theoretical framework of the relationship between lifestyles and transport. The impacts of lifestyles on transport are both positive and negative. The interrelationship between lifestyles and transport is mainly reflected in two aspects: the regional or subregional scale and the individual scale. (1) Regional or subregional scale: This shows the impacts of the overall level of residents’ lifestyles in certain subregions on the transport facilities in these regions, and the impacts of residents’ lifestyles in certain subregions on the local transport system, such as the total amount of regional traffic flow, congestion status and ratios of various travel patterns. (2) Individual scale: This focuses on the impacts of individual lifestyles on individual travel behaviour, such as travel patterns, travel distance and travel time. Transport facilities and technologies influence people’s living standards, scope of activities and residential choices. Transport facilities and technologies affect people’s lives mainly through travel behaviour. Travel behaviour is the medium through which transport and lifestyles interact. As a dynamic link within the urban activity system, travel behaviour is actually travelled activities derived from people’s daily activities, reflecting residents’ spatial and temporal participation in cities. Different lifestyles, with different travel needs, determine the scale and manner of transport facility supply. Different transport facilities in turn directly affect people’s travel choices. Transport expenditures, travel time, transport means and transport services directly affect people’s travel behaviour, and different travel behaviours form part of different lifestyles. Transport influences people’s residential and employment choices, and the evolution of the spatial distribution of residences and workplaces determines the spatial and temporal distribution of travel. It fundamentally determines the characteristics of urban travel regarding its formation, means and spatiotemporal distribution, and thus it has a key influence on the transport system. Through empirical research in this book, we have found that (1) the total traffic demand in cities witnesses sustained growth with the acceleration of China’s urbanisation process and the diversification of lifestyles. (2) With the increase in residents’ leisure time and the prevalence of freelancing, flexible working hours and web-based mobile officing, the proportion of commuting trips will reduce, while the proportion of leisure trips is expected to grow gradually. (3) With a higher income level, the accelerated motorisation process and the improvement of urban and rural public transport supply, travel patterns are becoming more diversified. (4) Residents have higher requirements for travel efficiency and convenience, while geographical distance is being displaced by travel time in people’s minds. (5) The requirements for transport integration, humanisation and smart services have increased. There is an

10.3 Future Research Agenda

319

increased demand for demand-responsive transport services and door-to-door transport services, which calls for the establishment of an inclusive and diversified transport system and real-time data sharing. (6) With the deep penetration of digital life and the arrival of advanced population aging, urban logistics requires higher efficiency, and the proportion of home delivery demand has increased. (7) Governments should push ahead with international transport facilitation and reinforce the construction of cross-border transport infrastructure. (8) They should accelerate the formation of a transport governance system compatible with a strong transport industry and conducive to its quality development. Data from the household follow-up survey in China show that different lifestyles have both positive and negative effects on commuting time. Data from the Beijing Jobs-Housing Balance Survey suggest that different lifestyles have varying effects on trips for work and leisure purposes. As the results of Questionnaires for Town Residents show, rural lifestyles are relatively more monotonous. People living monotonous lifestyles or migratory bird lifestyles travel more frequently to nearby counties, while people living Internet-loving lifestyles and leisure lifestyles travel more frequently to cities. In the future, further studies will extend the scope of research on lifestyle in transport, such as combining lifestyles and individual characteristics or investigating the impacts of Internet-loving lifestyles or environment-friendly lifestyles on transport. The impacts of individual lifestyles on transport will be the starting point, and then the relationship between transport, the lifestyles of specific groups and the overall lifestyle will be studied. Lifestyle changes that will be brought about by new transport development in the future, as well as the changes in transport that arise therefrom, will be the focus. In the field of transport, most studies focus on the question of whether lifestyle variables affect travel behaviour, but not the extent of the effect (Van Acker, 2015). Most studies use cross-sectional designs that can only examine associations between different variables and do not provide information on causality. The lifestyles of population groups may evolve over time. Current research is mostly based on cross-sectional data. In the future, data from multiple periods will be used to measure whether lifestyle changes influence travel modes, which is also an issue worth exploring.

10.3 Future Research Agenda 10.3.1 Human Lifestyle Changes and Future Transport Development (1)

With the advent of 5G networks, the full coverage of high-speed telecommunication networks will greatly accelerate the development of frontier technologies such as autonomous driving and the IoT. Human lifestyles will become more intelligent (Chen et al., 2016; Takenaka et al., 2016; Tian et al., 2019;

320

(2)

(3)

10 Concluding Remarks

Verbeek, 2009), complex and diversified, highlighting spiritual needs rather than material ones. Artificial intelligence will transform our lifestyles (Li & Du, 2017), and the value dimension will have a more significant impact in the smart era. The consumption, time, activity and organisational dimensions of lifestyle will be increasingly influenced by the value dimension. In the future, not only the impacts of changes in the value dimension on travel choices but also the changes in space usage by human beings due to lifestyle changes should be paid attention to. Work and life will present a highly compounded space, bringing changes in transport demands. With the emergence of new vehicles such as driverless cars (Kaur & Rampersad, 2018) based on clean energy and aerial vehicles, people will have more choices in space usage. Their views of space and time will face new challenges, and lifestyles will be transformed. The redefinition of lifestyle and challenges facing it brought about by new transport technologies are worth attention. There are many other factors that may have a significant impact on urban residents’ lifestyles and travel behaviour in the future. Behind the large number of consumer goods, including cars, are rising levels of income. Increased discretionary spending will generate demand for new types of goods and services, greater awareness of health and physical conditions, and demand for educational and cultural activities. Lifestyles will continue to evolve as car-centric lifestyles and travel habits take shape. Societal attitudes towards gender roles are changing. New values constantly emerge and shape new lifestyles. The discussion of the many changes associated with travel behaviour suggests that future efforts must focus on understanding and anticipating the direction and magnitude of behavioural changes (Hara & Yamaguchi, 2021; McGuckin et al., 2005).

10.3.2 China’s Social Transformation and Transport Demands Rising living standards and diversified lifestyles bring a diverse array of transport demands. The inertia of China’s future total population growth is weakening, and since 2015, the size of the floating population has been slowly declining from the previous steady rise. The population continues to gather in first- and second-tier large-sized cities, metropolitan areas and regional central cities. In 2019 China’s per-capita gross national income reached US$10,276,3 higher than the average level of middle-income countries, and residents’ income will continue to rise in the future. The increase in personal discretionary time, the steady increase in leisure and tourist activities, online shopping, sharing economy, Internet plus and cross-city commuting all contribute to more diversified lifestyles, which will lead to diversified transport demands. 3

Source http://www.stats.gov.cn/tjsj/pcsj/.

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Favourable policies towards Internet plus, big data and artificial intelligencerelated technologies lay the cornerstone of a smart society. China is now actively developing 5G and blockchain technologies. 5G technology will have great potential in the commercial field, and blockchain technology will also bring many possibilities to help the Chinese Central Bank to launch digital currency. China has been working towards the top-level construction of the Internet, creating increasingly accepted concepts such as smart cities, smart transport (Huang et al., 2017) and smart energy. The government, companies and the people are working hard to build a smart society and lay the foundation for a smart life and a smart society. The problem of population aging has become increasingly prominent, with the increasing demand for a transport system suitable for older people (Cheng et al., 2019). The proportion of China’s working-age population is declining, and subreplacement fertility is prominent. China’s population aging has been accelerating, and the trend will persist for a long time. It also shows the characteristics of unbalanced regional development, an obvious urban-rural reversal, gender differences and aging before getting rich. In 2020, China’s population over 60 years old accounted for 18.7% of the total population, and that over 65 years old accounted for 13.5%.4 In the future, China’s aging rate will further increase, and the proportion of productive trips will reduce, putting forward the demand for an age-appropriate transport system. With sustained urbanisation and deep integration between cities and the countryside, the differences between urban and rural lifestyles have narrowed. Urbanisation will keep changing residents’ lifestyles, and residents will pursue a smarter and more environment-friendly living environment of higher quality. China’s urban population is expected to exceed one billion by 2030.5 The gap between urban and rural income levels is tending to narrow, the proportion of agricultural population is low, and the rural population is maintaining its level. Part-time employment is widespread, and the countryside is not merely a place for farming, but part of the urban-rural space. Villages are a form of urban-rural residence in which residents are not necessarily farmers. Cities and the countryside will be further integrated. Lifestyle-related studies currently focus on sociology, consumption and health research in China. Theoretical research on the interrelationship between lifestyles and transport needs to be improved further. Lifestyle is a comprehensive issue, the components of which need further elaboration. For example, a comparative study of groups with different lifestyles by gender, age, occupation, social class, city and region, and the dynamic impacts of lifestyle changes on transport of specific groups are both possible subjects for further investigation. The study of different transport facilities, different travel behaviours and other lifestyles also needs further exploration. These studies will help to promote the sustainable development of transport and strengthen the effectiveness of transport policies of China.

4 5

Source Major Figures on 2020 Population Census of China. http://www.stats.gov.cn/tjsj/pcsj/. Source http://www.scio.gov.cn/m/video/zxtj/Document/1171237/1171237.htm.

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Correction to: Lifestyle Change and Transport in China

Correction to: P. Zhao and D. Lyu, Lifestyle Change and Transport in China, Population, Regional Development and Transport, https://doi.org/10.1007/978-981-19-4399-7 The original version of the book was inadvertently published with incorrect Funder information in the front matter. The corrected book has been updated with the changes.

The updated version of the book can be found at https://doi.org/10.1007/978-981-19-4399-7 © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Zhao and D. Lyu, Lifestyle Change and Transport in China, Population, Regional Development and Transport, https://doi.org/10.1007/978-981-19-4399-7_11

C1

Index

A Activities, Interests, and Opinions (AIO), The, 26, 34–36, 47 Activity-based method, The, 59 Activity-travel patterns, 42, 46 Administrative village, 219 Age-appropriate transport system, 321 Agricultural hukou, 221, 222 Ali, 278 Amsterdam, 152 Animal power, 5 Artificial Intelligence (AI), 309 A Semi-colonial, semi-feudal society, 5 Asia, 152 Aurora Mobile, 275 Average Propensity to Consume (APC), 97, 98

B Baidu, 278 Beijing, 109, 110, 118, 119, 124, 128–130, 132, 143 Beijing–Guangzhou high-speed railway, The, 274 Beijing Jobs-Housing Balance Survey, The, 12 Beijing–Tianjin–Hebei Region, The, 274 Belt and Road, The, 6 BMW, 278

C California, 79 Carbon neutrality, 316 Car-centric lifestyles, 320

Car-sharing, 274, 284 CEIC database, 100, 102, 103, 108, 109, 118, 119 Cellular-vehicle to everything(C-V2X), 278 Chengdu High-Tech Industrial Development Zone (CDHT), The, 309 China, 4–17 China Family Panel Studies (CFPS), 15, 16 China Health Statistical Yearbook, The, 152 China Speed and the China Model, 9 China Statistical Yearbook, 152 China Statistical Yearbook on Environment, The, 152 China Urban Construction Statistical Yearbook, The, 152 China-VALS model, The, 38 Chinese Yuan (CNY), 97–99, 102, 103, 108 Communist Party of China Shenzhen Committee, The, 292 COVID-19, 125–127, 132, 145

D Didi, 278

E Eastern Integrated Consumer Profile( E-ICP), 39 E-ICP Research Centre and the NCCU MBA Program in Taiwan, 39 Encyclopedia of China, The, 27 Engel’s coefficient, 6 Enhanced Mobile Broadband (eMBB), 281 EU, 277

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. Zhao and D. Lyu, Lifestyle Change and Transport in China, Population, Regional Development and Transport, https://doi.org/10.1007/978-981-19-4399-7

325

326 Europe, 152

F Family-oriented lifestyle, 66 13th Five-Year Development Plan for Railways, The, 272 Food-related lifestyle, 75 Ford, 278 Frankfurt, 152

G General Motors, 278 5th Generation Mobile Communication Technology (5G), 14, 281–283, 285 Giant Engine, 110–112 Golden Week, 8 Green lifestyle, 39 Green Lifestyle Propensity (GLP), 78, 80, 89 Gross Domestic Product (GDP), 100, 107, 108 Guangdong Province, 233 Guangzhou, 109, 110, 132, 143

H Han, 221, 222 Hangzhou, 110, 118 Hebei, 181 Hellobike, 276 High-speed rail, 65, 68 Hong Kong, 152 Hongqiao Hub, 293, 294 Huawei, 278, 283 Hukou, 221, 222

I ICT lifestyle, 79 Information and Communication Technology (ICT), 4 Intelligent Transport System (ITS), 11 Internet-loving lifestyle, The, 241–247, 249, 250, 257, 259, 263, 264 Internet of Things (IoT), 11

J Jiangsu Province, 233 Jin dynasty, The, 181

Index K Kaiser–Meyer–Olkin (KMO), 155, 168, 170 Kantar, 110–112 King Wu of Zhou, 181

L Lazy economy, 316 Leisure lifestyle, The, 241–247, 249, 250, 260, 262–264 Liao dynasty, The, 181 Life-Oriented Approach, The, 59, 76 London, 152 Long-term lifestyle, 70, 77 Los Angeles, 278, 284 Low-carbon economy, 316 Low-carbon transport, 107 Low-carbon travel, 11 Luxury lifestyle propensity (LLP), 78, 80, 89

M Massive Internet of Things (MIoT), 282 Master Of Business Administration (MBA), 39 Miami, 152 Migratory bird lifestyle, The, 241–250, 254, 256, 263, 264 Ministry of Housing and Urban-Rural Development, The, 264 Ministry of Transport, The, 275 Monotonous lifestyle, The, 241–251, 253, 263, 264 Multi-Purpose Vehicle (MPV), 104

N National Bureau of Statistics, The, 7, 8, 152 19th National Congress of the Chinese Communist Party, The, 9 Natural village, 219 New York, 152 Non-agricultural hukou, 221, 222 Non-Han, 221, 222 Non-work tours, 74 North Carolina Central University (NCCU), 39 North China Plain, The, 181

O Ordinary Least Squared (OLS), 12, 16

Index Overall goal of the Rural Revitalization Strategy, The, 6 Overall lifestyle, 30, 47, 49, 52, 53

P Paris, 152 Pearl River Delta Region, The, 274 Peiping, 181 Peking University, 166 Per-capita Gross Domestic Product (GDP), 107–109, 133 Permanent population, The, 181–184, 199 3rd Plenary Session of the 11th Central Committee of the Chinese Communist Party, The, 5, 7 Premier Li Keqiang, 126 Principal Component Analysis (PCA), 156, 158, 168, 170, 173

R Reform and opening-up, 5 Residential hukou, 221, 222 Rural E-Commerce Logistics, 298, 299

S Shanghai, 109, 110, 128, 132, 143 Shanghai Hongqiao International Airport, 293 Shenzhen, 109, 116, 133, 143 Shenzhen Government, 292 Shenzhen Port, 292 Short-term nonwork travel, 70 Sichuan, 10 Singapore, 132, 152, 285, 309 Singapore-Sichuan High-Tech Innovation Park (SSCIP), 309 Sport Utility Vehicle (SUV), 104 SRI, 36 Street-stall economy, 126, 127 Structural Equation Modelling (SEM), 12, 16

327 T Theory of Planned Behaviour (TPB), The, 14 Time-space compression, 10 Time-space prism, 62, 80 Time-use patterns, 31, 33 Time Use Survey in China, 134, 136–138, 141, 196, 228 Tokyo, 152 Travel-related lifestyle, 31, 33 Two Centenary Goals, The, 6 Two-child policy, 104

U UK, 112 United States (US), 4, 12, 152

V Values, Attitudes, and Lifestyles (VALS), 34, 36–38 Values, Attitudes, and Lifestyles 2 (VALS2), 36, 37 Value theory, 59, 60, 80, 87 Volkswagen, 278

W Work-oriented lifestyle, 66 Work-unit community, 120–122 Work unit system, 119, 121, 123, 145 WTO, 6, 7

Y Yangtze River Delta Region, The, 274, 293, 295 Yanjing, 181 Yuan, Ming and Qing dynasties, The, 182

Z Zhejiang Province, 298, 299