Global Industry Chains: Creating a Networked City Planet: The Global Urban Competitiveness Report (2018–2019) 9811620571, 9789811620577

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Pengfei Ni Marco Kamiya Jianfa Shen Weijin Gong

Global Industry Chains: Creating a Networked City Planet The Global Urban Competitiveness Report (2018–2019)

Global Industry Chains: Creating a Networked City Planet

Pengfei Ni · Marco Kamiya · Jianfa Shen · Weijin Gong

Global Industry Chains: Creating a Networked City Planet The Global Urban Competitiveness Report (2018–2019)

Pengfei Ni Center for City and Competitiveness Chinese Academy of Social Sciences Beijing, China

Marco Kamiya Urban Economy and Finance Branch UN-HABITAT Nairobi, Kenya

Jianfa Shen Institute of Asia-Pacific Studies Chinese University of Hong Kong Hong Kong, Hong Kong

Weijin Gong National Academy of Economic Strategy Chinese Academy of Social Sciences Beijing, China

ISBN 978-981-16-2057-7 ISBN 978-981-16-2058-4 (eBook) https://doi.org/10.1007/978-981-16-2058-4 Jointly published with China Social Sciences Press The print edition is not for sale in China (Mainland). Customers from China (Mainland) please order the print book from: China Social Sciences Press. ISBN of the China edition: 978-7-5203-6495-9 Translation from the language edition: 全球城市竞争力报告 (2018–2019). 全球产业链: 塑造群网化 城市星球 by Pengfei Ni, et al., © China Social Sciences Press 2020. Published by China Social Sciences Press. All Rights Reserved. © China Social Sciences Press 2021 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publishers, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publishers nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publishers remain neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Senior Consultant

Xie Fuzhan, President, CASS McMurray Sharif, Deputy Secretary-General of the United Nations, Executive Director of UN-HABITAT Wang Weiguang, Member of the Standing Committee of the 13th National Committee of the Chinese People’s Political Consultative Conference, Director of the Ethnic and Religious Committee, former Dean of the CASS Huaan Clos, Former Deputy Secretary-General of the United Nations, former Executive Director of UN-HABITAT Gao Peiyong, Vice President, CASS He Dexu, Director, National Academy of Economic Strategy, CASS Fan Gang, Vice President, China Society of Economic Reform Saskia Sassen, Professor, Columbia University, USA Peter Taylor, Fellow of the Academy of Social Sciences, and GaWC Director Fernan Henderson, Professor of Economic Geography, London School of Economics and Political Science

The Main Authors Ni Pengfei, Director, Center for Urban and Competitive Research, Chinese Academy of Social Sciences Marco Kamiya, Senior Economist, Knowledge & Innovation Branch, UNHABITAT Shen Jianfa, Professor, Hong Kong Institute of Asia-Pacific Studies, The Chinese University of Hong Kong Gong Weijin, Ph.D., National Academy of Economic Strategy, Chinese Academy of Social Sciences

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Senior Consultant

Members of the Editorial Board Peter Karl Kresl, President of the GUCP, Professor at Bucknell University, USA Kathy Pain, Professor, Studies for Real Estate & Planning, University of Reading, UK Yang Rong, Interregional Adviser, UN-HABITAT Zhang Zhenshan, Representative of UN-HABITAT to China Shen Jianfa, Professor, Hong Kong Institute of Asia-Pacific Studies, The Chinese University of Hong Kong Li Bo, male, is an instructor at Tianjin University of Technology and has a doctoral degree in economics. His research interests include urban and regional economics. Li Qihang, male, is an associate professor at Shandong University of Finance and Economics and has a doctoral degree in economics. His research interests include industry economics, regional economics, and microeconometrics Liu Xiaonan, female, is a Ph.D. candidate at the Graduate School of Chinese Academy of Social Sciences and conducts research on urban and real estate finance. Wang Yufei, female, is a lecturer at the School of Economics and Management, Beijing University of Posts and Telecommunications and has a doctoral degree in management. Her research interests include urban and regional economics Gong Weijin, male, is postdoctoral research fellow at NAES, CASS. His research interests include real estate economics, coordinated regional development, and regional distribution of poverty Ma Hongfu, male, is a lecturer at Tianjin University of Finance and Economics and has a doctoral degree in economics. His research interests include regional and urban economics Shen Li, male, is a Ph.D. candidate in Finance at the Graduate School of the Chinese Academy of Social Sciences. His research interests include urban and real estate finance Cao Qingfeng, male, is a lecturer at Tianjin University of Finance and Economics. His research interests include urban and regional economics Wang Haibo, male, is a postdoctoral research fellow at NAES, CASS. His research interests include urban competitiveness and real estate economics Xu Haidong, male, is a Ph.D. candidate in Finance at the Graduate School of the Chinese Academy of Social Sciences. His research interests include urban and real estate finance Ni Pengfei, Director and Research Fellow at the City and Competitiveness Research Center, CASS Diane-Gabrielle Tremblay, Professor of labor economics, innovation, and human resources management at the Télé-université of the University of Québec, Canada Marco Alberio, Professor in social and territorial development at the University of Québec Jaime Sobrino, Professor and director of the Center for Demographic, Urban and Environmental Studies at the College of Mexico Oswaldo Molina, Professor of Universidad del Pacífico, Lima, Peru

Senior Consultant

Thomas Farole, World Bank Leonardo Ortega Moncada, Georgia Tech Carlo Pietrobelli, University Roma Tre and UNU-MERIT Timothy J. Sturgeon, MIT Industrial Performance Center

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Contents

1 Global Urban Competitiveness Ranking 2018–2019 . . . . . . . . . . . . . . . . 2 The Planet of Cities Toward Diverse Agglomeration, Global Connection, and Extensive Sharing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pengfei Ni, Marco Kamiya, Jianfa Shen, Bo Li, Hongfu Ma, Yufei Wang, and Haidong Xu 2.1 Cities Have Become More Global, Networked, and Intelligent Over the Past 40 Years . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.1 The Non-agricultural Aggregation of Factors of Production: Tremendous Changes on the City Meaning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.2 Tremendous Changes on World City Functions Caused by Division of World Cities . . . . . . . . . . . . . . . . . . . . . 2.1.3 Global Space Competition: A Global City Connected by Internet Infrastructure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Humanity Moving Toward a Connected, Gathering and Sharing City Planet Over the 40 Years . . . . . . . . . . . . . . . . . . . . . 2.2.1 The Nature of World Has Changed Because of the Change of City Status: It is a World of Cities . . . . . . . 2.2.2 Functions of the World Have Changed Because of the Change of Urban Functions: The World Becomes a Large Group . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.3 The World Form Has Changed Because of the Change of Urban Form: The World Becomes a City . . . . . . . . . . . . . . 2.2.4 The World Pattern Has Been Reshaped by the Evolution of Urban Pattern: The World Becomes a Multicentric World Under Time–space Compression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Market System and IT Shape the Planet of Cities Over the Past 40 Years . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.1 Market System: Victory and Gradual Deepening of Market Economy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1 33

35

36 72 90 99 99

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2.3.2 Technical Innovation: Promotion and Change by Information Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133 2.3.3 Global Connection: Enhancement and Leap Forward of Soft Connection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145 3 Global Industry and City Evolution Patterns . . . . . . . . . . . . . . . . . . . . . . Pengfei Ni, Marco Kamiya, Jianfa Shen, Qingfeng Cao, and Li Shen 3.1 Problem Statement and Literature Review . . . . . . . . . . . . . . . . . . . . . . 3.1.1 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Theoretical Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.1 Model Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 Evolution of Global Production and Trade Patterns Across Different Stages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.1 Theoretical Reasoning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5 Analytical Framework, Methodology, and Data Description . . . . . . 3.5.1 Analytical Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5.2 Data Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5.3 Construction and Calculation of Industry Relocation Indexes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.6 Empirical Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.6.1 Evolution of GVCs and the Pattern of World Cities . . . . . . . 3.6.2 Global Industry Evolution Patterns . . . . . . . . . . . . . . . . . . . . . 3.7 Empirical Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.7.1 Empirical Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.7.2 Results of the Empirical Research . . . . . . . . . . . . . . . . . . . . . . 3.8 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Analysis on the Economic Competitiveness of Global Cities . . . . . . . . Haibo Wang and Xiaonan Liu 4.1 Global Urban Economic Competitiveness: An Annual Review . . . . 4.1.1 Overall Pattern: The Economic Competitiveness of European and American Cities Takes the Lead, While Urban Performance in China is a Highlight . . . . . . . . 4.1.2 Historical Comparison: Asian Urban Competitiveness Keeps Rising, While Its Internal Differences Drop . . . . . . . . 4.1.3 Individual Indexes: The Indexes of Local Demands, Infrastructure, and Technology Innovation Are Critical Factors Affecting Global Urban Economic Competitiveness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Comparative Analysis of Urban Competitiveness in China and the US . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.1 Current Pattern: The Urban Economic Competitiveness of the US is Higher Than China on the Whole . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

155 155 155 156 158 158 161 163 163 163 164 166 168 168 175 188 188 192 204 205 207 208

208 213

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4.3

4.4

4.5

4.6

xi

4.2.2 Historical Comparison: The Rise of Chinese Cities Is Changing the Pattern that Developed Economies Dominate World Development . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.3 Comparison of Individual Indexes: The Social Environment Index and Business Cost Index in Chinese Cities Are Higher Than Those in the US . . . . . . . Contrastive Analysis of the Competitiveness of North America, West Europe, and East Asia . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.1 Current Pattern: Cities in North America Take the Lead, and the Most Competitive Cities Are Concentrated in the Top Trio Regions . . . . . . . . . . . . . . . . . . . 4.3.2 Historical Comparison: Urban Competitiveness in East Asia is Rapidly Rising, While Its Internal Differences Are Dropping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.3 Individual Indexes: North America and West Europe Each Has Its Advantages, While East Asia Has an Edge in Social Environment and Business Cost . . . . . . . . Comparative Analysis of the Competitiveness of the Four Major Bay Areas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4.1 Current Pattern: The San Francisco Bay Area Takes the Lead, While the Guangzhou-Hong Kong-Macao Greater Bay Area Is at the Bottom . . . . . . . . . . . . . . . . . . . . . . 4.4.2 Historical Comparison: The Tokyo Bay Area and the San Francisco Bay Area Take Turn to Lead, and the Guangzhou-Hong Kong-Macao Greater Bay Area Rises Rapidly . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4.3 Comparison of Individual Indexes: The Guangzhou-Hong Kong-Macao Greater Bay Area Has Advantages in Social Environment and Business Cost . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Comparative Analysis of the Competitiveness of the Ten Major Urban Agglomerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5.1 Current Pattern: The Urban Agglomerations in Developed Countries Take the Lead . . . . . . . . . . . . . . . . . . 4.5.2 Historical Comparison: Urban Agglomerations in Developed Countries Decline While Those in Developing Countries Rise . . . . . . . . . . . . . . . . . . . . . . . . . . Comparative Analysis of the Top 20 Cities in Economic Competitiveness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.6.1 Current Pattern: China and the US Dominate the World’s Top 20 Cities in Economic Competitiveness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.6.2 Historical Comparison: The US Consolidates Its Advantages, While Top Cities in China Keep Increasing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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4.6.3 Comparison of Individual Indexes: Financial Service and Social Environment Are Important Reasons Causing the Intercity Differences Among the World’s 20 Cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.7 Analysis of the Coupling Coordination Degree of the Elements of Economic Competitiveness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.7.1 Kernel Density Distribution and Scatter Diagram of the Coupling Coordination Degree of Cities Worldwide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.7.2 Spatial Distribution Characteristics of the Coupling Coordination Degree of Cities Worldwide . . . . . . . . . . . . . . . 4.7.3 The Regression Analysis of Economic Competitiveness by the Coupling Coordination Degree . . . . 5 Analysis on Sustainable Competitiveness of Global Cities . . . . . . . . . . Weijin Gong and Qihang Li 5.1 Cities with, Respectively, Strong and Weak Sustainable Competitiveness are Clearly Distributed in a Large Portion in the Middle and a Small Portion at Both Ends, and Sustainable Competitiveness of Asian Cities Constantly Enhances . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1.1 The Level of Economic Development is Highly Positively Correlated with the Overall Manifestation of Sustainable Competitiveness of Cities . . . . . . . . . . . . . . . . 5.1.2 High-Level Equilibrium is the Best Goal and Path to Enhance Sustainable Competitiveness of Cities . . . . . . . . . 5.1.3 Technological Innovation and Human Capital Potential Have the Greatest Impacts and Magnify the Positive Effects by Means of Direct Effect, Indirect Effect, and Feedback Effect . . . . . . . . . . . . . . . . . . . . 5.2 Global Ranking of Cities by Sustainable Competitiveness 2018 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.2 Chinese Cities Versus American Cities: There are Notable Disparities Between Chinese and American Cities in Sustainable Competitiveness. The Number of American Cities Performing Well in Sustainable Competitiveness Indicators Is Far Larger than the Number of Such Chinese Cities. However, the Internal Divergence Between American Cities Is Widening, Whereas the Development of Chinese Cities Is Overall More Coordinated . . . . . . . . . . .

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5.2.3 The World’s Three Major Economic Centers: Contrary to Western Europe and North America, East Asia’s Economic Level is Low, the Difference is Large, and the Promotion is Fast . . . . . . . . . . . . . . . . . . . . . . . 5.2.4 Sustainable Competitiveness of the Four Bay Areas: The Tokyo Bay Area Is the Most Competitive. The Guangdong-Hong Kong-Macau Bay Area Scores Lowest in Sustainable Competitiveness but Is Catching Up with the Three Mature Bay Areas . . . . . . . . . . . 5.2.5 Global Urban Clusters: The Northeast Region of the US Has the Best Urban Clusters, While a few Mature Urban Clusters Have Stable Development and the Overall Polarizing Urban Agglomerations Face Long-Term Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.6 Globe Top 20: Leading the World in Sustainable Competitiveness with Technology and Human Capital as the Largest Two Driving Forces . . . . . . . . . . . . . . . . . . . . . . 5.2.7 The Largest Cities in the World’s Major Countries: The Competitiveness of the Largest Cities Is Intensifying, and the Strength and Competitiveness of the Country Determine the Sustainable Competitiveness of the Largest Cities . . . . . . . . . . . . . . . . . . . 5.3 Environmental Quality Index Analysis: Environmental Negative Impacts in Urban Clusters . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.1 Overall Pattern: Environmental Endowment and the Kuznets Curve Together Determine the Quality of Urban Environment . . . . . . . . . . . . . . . . . . . . . . 5.3.2 Global Top 20: Coastal Cities and Cities Exhibiting Moderate Economic Growth . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.3 Comparison of Countries: Wide Disparities Between Chinese and American Cities in Quality of Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.4 Urban Cluster Pattern: Urban Clusters Bring Negative Environmental Impact . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.5 World City Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4 Social Inclusion Index Analysis: Europe and East Asia Lead in Social Inclusion Index, Culture and Tradition Determine the Level of Tolerance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4.1 Overall Pattern: Western Europe and East Asia Perform Best in Inclusiveness . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4.2 Global Top 20: East Asian Cities Lead the World in Inclusiveness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4.3 Comparison Between Countries’ Cultural Difference Leads to Disparities in Inclusiveness in China and Chinese . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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287 291

293 294 295

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5.5

5.6

5.7

5.8

5.4.4 Global Ranking of Countries by Quality of Environment of the City with Best Environment in Each Country . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Analysis of the Science and Technology Innovation Index: The Strength of the Emerging Cities in Developed and Emerging Economies is Dazzling . . . . . . . . . . . . . . . . . . . . . . . . . 5.5.1 Overall Pattern: There is a Divergence in Innovation in Geographical and Political Terms Between Developed and Developing Countries . . . . . . . . . . . . . . . . . . . 5.5.2 Global Top 20: Emerging Cities . . . . . . . . . . . . . . . . . . . . . . . . 5.5.3 Comparison Between Countries: China and US . . . . . . . . . . . 5.5.4 Comparison Between Urban Clusters: Goals and Structures Determine Innovation Capacity of Urban Clusters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5.5 World City Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Global Connectivity: Geographical Location and Economic Position Decide Global Connectivity . . . . . . . . . . . . . . . . . . . . . . . . . . 5.6.1 Overall Pattern: Most Highest Ranking Cities in Global Connectivity Are Located in Developed Countries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.6.2 Global Top 20: Global Centers in Both Geographical and Economic Terms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.6.3 Comparison Between Countries: China and the US Lead the World in Global Connectivity . . . . . . . . . . . . . . . . . . 5.6.4 Comparison Between Urban Clusters . . . . . . . . . . . . . . . . . . . 5.6.5 World City Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Analysis of Human Capital Potential Index: Talent Flow Direction Determines the Potential Pattern of Human Capital in Global Cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.7.1 Overall Pattern: Developed Countries Lead in Human Capital, and Immigration Policy Decides Human Capital Potential . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.7.2 Global Top 20 Cities: Concentrated in the US . . . . . . . . . . . . 5.7.3 Comparison Between Countries: American Cities Dominate the Human Capital Ranking, and Central European Cities Have Seen a Rise in Human Capital Scores . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.7.4 Comparison Between Urban Clusters: Urban Clusters Increase the Advantage of Leaders . . . . . . . . . . . . . . . . . . . . . . 5.7.5 World City Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Infrastructure: GDP and Demand Decide the Development of Infrastructure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.8.1 Overall Pattern: Correlation Between Economic Growth and Infrastructure Development . . . . . . . . . . . . . . . . . 5.8.2 Global Top 20: Shared Use of Infrastructure . . . . . . . . . . . . .

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5.8.3 Comparison Between Countries: China Balances the US In Infrastructure Density . . . . . . . . . . . . . . . . . . . . . . . . 5.8.4 Comparison Between Urban Clusters: Urban Clusters in China Are Catching Up with Developed Countries in Infrastructure Development . . . . . . . . . . . . . . . . . . . . . . . . . 5.9 Econometric Analysis of Global Cities’ Sustainable Competitiveness: Technological Innovation and Human Capital Potential Have the Greatest Impact, and Positive Effects Are Amplified Through Direct, Indirect, and Feedback Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.9.1 Construction of the Empirical Model and Selection of Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.9.2 Direct and Indirect Effects: Feedback Effects on Factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 365 Afterword . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 631

Chapter 1

Global Urban Competitiveness Ranking 2018–2019

City

Rank Country

Economic Ranking Sustainable Ranking competitiveness competitiveness

New York-Newark

A+

US

1

1

1

1

Singapore

A

Singapore

0.9719

2

0.8487

4

London

A+

UK

0.9335

3

0.8858

3

Shenzhen

B

China

0.932

4

0.602

48

San Jose

A

US

0.9312

5

0.6896

19

Munich

B+

Germany

0.9309

6

0.654

29

San Francisco-Oakland

A

US

0.9289

7

0.7315

13

Tokyo

A−

Japan

0.8964

8

0.964

2

Los-Angeles

A

United States of Americ

0.8841

9

0.837

5

Houston

A−

US

0.8836

10

0.7399

9

Hong Kong

A

China

0.8836

11

0.8084

6

Dallas-Fort Worth

A−

US

0.878

12

0.6282

36

Shanghai

A−

China

0.8544

13

0.658

28

Guangzhou

B+

China

0.8501

14

0.5707

59

Seoul

A−

Republic of Korea

0.8082

15

0.7312

14

Dublin

A−

Ireland

0.8003

16

0.6008

50

Miami

B+

US

0.7984

17

0.6201

40 (continued)

© China Social Sciences Press 2021 P. Ni et al., Global Industry Chains: Creating a Networked City Planet, https://doi.org/10.1007/978-981-16-2058-4_1

1

2

1 Global Urban Competitiveness Ranking 2018–2019

(continued) City

Rank Country

Economic Ranking Sustainable Ranking competitiveness competitiveness

Boston

A−

US

0.7968

18

0.774

7

Beijing

A−

China

0.7965

19

0.6644

27

Frankfurt am Main

A−

Germany

0.7965

20

0.5961

52

Chicago

A−

US

0.7963

21

0.7075

16

Stockholm

B+

Sweden

0.7891

22

0.6533

30

Paris

A−

France

0.7726

23

0.7295

15

Seattle

B+

US

0.7637

24

0.7451

8

Tel Aviv-Yafo

B−

Israel

0.7481

25

0.4378

182

Baltimore

B−

US

0.7426

26

0.6298

35

Suzhou

C+

China

0.7398

27

0.4307

185

Philadelphia

B+

US

0.7352

28

0.6812

23

Bridgeport-Stamford

B

US

0.7293

29

0.5358

81

Dusseldorf

B−

Germany

0.7249

30

0.5279

87

Stuttgart

B−

Germany

0.7218

31

0.5571

67

Geneva

B

Switzerland

0.7193

32

0.5678

60

Cleveland

B−

US

0.7161

33

0.5465

74

Osaka

B−

Japan

0.7159

34

0.7371

11

Toronto

B+

Canada

0.7151

35

0.7374

10

San Diego (US)

C+

US

0.7092

36

0.6845

21

Perth

B

Australia

0.7081

37

0.5633

65

Atlanta

B+

US

0.7047

38

0.6862

20

Denver-Aurora

B

US

0.7042

39

0.5421

79

Wuhan

C+

China

0.7036

40

0.4469

172

Detroit

B−

US

0.7018

41

0.5525

70

Tianjin

B−

China

0.6996

42

0.4573

159

Vienna

B−

Austria

0.6981

43

0.6131

42

Istanbul

B

Turkey

0.698

44

0.5241

91

Nanjing

B−

China

0.6969

45

0.4994

110

Taipei

B−

China

0.6948

46

0.634

33

Hamburg

B−

Germany

0.6918

47

0.6203

39

Nashville-Davidson

B−

US

0.688

48

0.3696

243 (continued)

1 Global Urban Competitiveness Ranking 2018–2019

3

(continued) City

Rank Country

Economic Ranking Sustainable Ranking competitiveness competitiveness

Cologne

C+

Germany

0.6845

49

0.5249

90

Doha

B−

Qatar

0.6845

50

0.5092

99 84

Charlotte

B−

US

0.6825

51

0.532

Zurich

A−

Switzerland

0.6803

52

0.6831

22

Berlin

C+

Germany

0.6799

53

0.584

54

Minneapolis-Saint Paul

A−

US

0.6797

54

0.5721

58

Las Vegas

C+

US

0.6774

55

0.4883

126

Austin

B−

US

0.6687

56

0.6747

26

Raleigh

C+

US

0.6682

57

0.6033

46

Moscow

B

Russian Federation

0.6661

58

0.6038

45

Milwaukee

C+

US

0.6579

59

0.4682

146

Chengdu

C+

China

0.6576

60

0.4613

153

Richmond

C+

US

0.6558

61

0.5179

94

Salt Lake City

C+

US

0.6548

62

0.5595

66

Abu Dhabi

B+

United Arab Emirates

0.6523

63

0.5639

64

Orlando

C+

US

0.6501

64

0.5333

83

Sydney

A−

Australia

0.6492

65

0.7325

12

Copenhagen

B

Denmark

0.6482

66

0.6306

34

Birmingham

B−

UK

0.6469

67

0.5721

57

Dubai

B+

United Arab Emirates

0.6442

68

0.5558

68

Brussels

B

Belgium

0.6405

69

0.5482

72

Essen

C

Germany

0.6393

70

0.4948

119

Changsha

C

China

0.6391

71

0.3871

225

Hannover

C

Germany

0.6388

72

0.5278

88

Wuxi

C−

China

0.6385

73

0.3678

247

Hangzhou

C+

China

0.6382

74

0.4978

113

Columbus

B−

US

0.6367

75

0.5431

76

Vancouver

B−

Canada

0.6351

76

0.6985

18 (continued)

4

1 Global Urban Competitiveness Ranking 2018–2019

(continued) City

Rank Country

Economic Ranking Sustainable Ranking competitiveness competitiveness

Barcelona

B−

Spain

0.6338

77

0.6265

37

Louisville

C+

US

0.6298

78

0.4725

142

Baton Rouge

C+

US

0.6295

79

0.4673

148

Nagoya

C+

Japan

0.6239

80

0.644

32

Manchester

C+

UK

0.6226

81

0.5749

55

Chongqing

C+

China

0.6218

82

0.4111

204

Ulsan

C

Republic of Korea

0.6198

83

0.4379

181

Calgary

B−

Canada

0.6178

84

0.61

Qingdao

C+

China

0.616

85

0.4926

120 123

44

Dortmund

C+

Germany

0.6154

86

0.4908

Oslo

A−

Norway

0.6124

87

0.6025

47

Riyadh

B−

Saudi Arabia

0.6118

88

0.4187

197

Amsterdam

B+

Netherlands

0.6116

89

0.7013

17

Sendai

C

Japan

0.61

90

0.5646

63

Antwerp

C+

Belgium

0.6093

91

0.4587

157

Washington, D.C

A−

US

0.6014

92

0.6458

31

Foshan

C

China

0.6003

93

0.3734

242

Oklahoma City

C+

US

0.5991

94

0.4677

147

Hamilton

B−

Canada

0.5989

95

0.5499

71

Kuala Lumpur

B-

Malaysia

0.5984

96

0.5234

92

Virginia Beach

C

US

0.5984

97

0.4474

171

Hiroshima

C-

Japan

0.5971

98

0.4819

131

Zhengzhou

C

China

0.5964

99

0.3737

241

Phoenix-Mesa

C+

US

0.595

100

0.5025

107

Ningbo

C

China

0.5937

101

0.4269

190

Melbourne

B

Australia

0.5936

102

0.6763

25

Tampa-St. Petersburg

C+

US

0.5909

103

0.5427

77

Jedda

C

Saudi Arabia

0.5809

104

0.2445

478

Indianapolis

B−

US

0.5809

105

0.4819

132

Bristol

C+

UK

0.5808

106

0.5557

69

Changzhou

C

China

0.5798

107

0.3451

282 (continued)

1 Global Urban Competitiveness Ranking 2018–2019

5

(continued) City

Rank Country

Economic Ranking Sustainable Ranking competitiveness competitiveness

Macao

B−

China

0.5753

108

0.3836

231

Gold Coast

C

Australia

0.5752

109

0.419

196

Hague, The

C+

Netherlands

0.5751

110

0.4905

125

Cincinnati

B−

US

0.573

111

0.5672

61

Montreal

B−

Canada

0.573

112

0.6802

24

Haifa

C

Israel

0.5728

113

0.4906

124

Jakarta

B−

Indonesia

0.5718

114

0.3981

217

Kansas City

C+

US

0.571

115

0.4608

156

Birmingham (US)

B−

US

0.5682

116

0.498

111

Hartford

C

US

0.5674

117

0.4614

152

Pittsburgh

C+

US

0.5672

118

0.5995

51

Provo-Orem

C

US

0.5665

119

0.3363

295

San Antonio

C+

US

0.5664

120

0.5036

106

Madrid

B−

Spain

0.5661

121

0.6125

43

Rome

C+

Italy

0.566

122

0.5129

96

Dongguan

C

China

0.5644

123

0.401

215

Rotterdam

C+

Netherlands

0.5634

124

0.5273

89

Dalian

C-

China

0.5605

125

0.4361

183

Kaohsiung

C

China

0.5602

126

0.4399

177

Dresden

C

Germany

0.5581

127

0.4777

137

Ottawa-Gatineau

C+

Canada

0.5549

128

0.5289

86

Nantong

C−

China

0.5516

129

0.3868

227

Buenos Aires

C+

Argentina

0.5496

130

0.4742

140

Charleston-North Charleston

C

US

0.5492

131

0.4687

145

Leipzig

C

Germany

0.548

132

0.4663

149

Bangkok

C+

Thailand

0.5475

133

0.5094

98

Hefei

C

China

0.5469

134

0.4302

187 193

Mexico City

B-

Mexico

0.5466

135

0.4204

Brisbane

C

Australia

0.5465

136

0.6195

41

Sapporo

C+

Japan

0.546

137

0.5746

56 (continued)

6

1 Global Urban Competitiveness Ranking 2018–2019

(continued) City

Rank Country

Economic Ranking Sustainable Ranking competitiveness competitiveness

Helsinki

B−

Finland

0.5458

138

0.6009

49

Milan

B−

Italy

0.5449

139

0.5071

100

Incheon

C

Republic of Korea

0.5445

140

0.5052

102

Providence

C+

US

0.5443

141

0.5482

73

West Yorkshire

C

UK

0.5437

142

0.4492

166

Xiamen

C

China

0.5436

143

0.5008

108

Glasgow

C+

UK

0.5434

144

0.5338

82

Lille

C−

France

0.5425

145

0.4491

167

Allentown

C

US

0.5424

146

0.4196

194

Worcester

C+

US

0.5403

147

0.4973

116

Colorado Springs

C

US

0.5383

148

0.4515

164

Riverside-San Bernardino

C

US

0.5349

149

0.3453

281

San Jose

A

Costa Rica

0.5347

150

0.4728

141

Grand Rapids

C

US

0.5345

151

0.4455

173

Gothenburg

C+

Sweden

0.5345

152

0.4692

144

Liverpool

C+

UK

0.5331

153

0.5038

105

New Haven

C

US

0.5323

154

0.5864

53

Edmonton

C

Canada

0.5258

155

0.5463

75

jinan

C

China

0.5237

156

0.3466

279

Changwon

C−

Republic of Korea

0.5226

157

0.4499

165

Dayton

C

US

0.5205

158

0.4192

195

Quanzhou

C−

China

0.5204

159

0.3624

253

Samut Prakan

C−

Thailand

0.5202

160

0.2288

528

Knoxville

C

US

0.518

161

0.4948

118

Honolulu

C+

US

0.5172

162

0.5049

104

Cape Coral

C

US

0.5171

163

0.3778

240

Kitakyushu-Fukuoka

C

Japan

0.5159

164

0.4781

136

Lyon

C+

France

0.5159

165

0.4963

117

Yantai

C−

China

0.5155

166

0.3966

218

Columbia

C

US

0.5155

167

0.5376

80 (continued)

1 Global Urban Competitiveness Ranking 2018–2019

7

(continued) City

Rank Country

Economic Ranking Sustainable Ranking competitiveness competitiveness

Zhenjiang

C−

China

0.5147

168

0.3488

Zhongshan

C−

China

0.5141

169

0.3965

219

Shenyang

C

China

0.5134

170

0.3619

256

Xi’an

C

China

0.5124

171

0.4055

209

Busan

C−

Republic of Korea

0.5118

172

0.4805

134

276

Fuzhou (FJ)

C−

China

0.5102

173

0.4018

211

Mecca

C−

Saudi Arabia

0.5076

174

0.2705

405

Santiago de Chile

C+

Chile

0.5069

175

0.4179

198

Medina

C−

Saudi Arabia

0.5065

176

0.3907

223

Akron

C

US

0.5064

177

0.4387

179

Lima

C+

Peru

0.5058

178

0.3665

248

Yangzhou

C−

China

0.5055

179

0.3324

299

Auckland

C+

New Zealand

0.5036

180

0.6245

38

Adelaide

C

Australia

0.503

181

0.5654

62

Jerusalem

C−

Israel

0.5025

182

0.4855

127

Ogden

C−

US

0.5014

183

0.4549

162

Gebze

C

Turkey

0.5004

184

0.3508

267

Nottingham

C−

UK

0.4986

185

0.4979

112

Bogota

C+

Colombia

0.4982

186

0.4486

168

Zhuhai

C−

China

0.4981

187

0.3869

226

Delhi

C+

India

0.4973

188

0.3506

269

Bucuresti

C

Romania

0.4969

189

0.3632

251

Leicester

C

UK

0.4966

190

0.4753

138

Buffalo

C

US

0.4962

191

0.4566

161

Xuzhou

C−

China

0.4955

192

0.3459

280

Omaha

C+

186

US

0.495

193

0.4305

Marseille-Aix-en-Provence C

France

0.4942

194

0.4

216

Daegu

Republic of Korea

0.4936

195

0.44

176

C

Shaoxing

C−

China

0.4923

196

0.292

359

Belfast

C

UK

0.4905

197

0.4751

139 (continued)

8

1 Global Urban Competitiveness Ranking 2018–2019

(continued) City

Rank Country

Economic Ranking Sustainable Ranking competitiveness competitiveness

Panama City

C

Panama

0.4897

198

0.4109

205

Dongying

C−

China

0.4895

199

0.2326

515

Valencia

C−

Spain

0.4893

200

0.4624

150

Montevideo

C

Uruguay

0.4871

201

0.3627

252

Venice

C

Italy

0.4868

202

0.3696

244

Astana

C−

Kazakhstan

0.4859

203

0.3044

340

Gwangju

C−

Republic of Korea

0.4854

204

0.4011

214

Nanchang

C−

China

0.4854

205

0.3431

288

Kuwait City

C+

Kuwait

0.4852

206

0.2519

450

Memphis

C

US

0.4842

207

0.4389

178

Daejeon

C−

Republic of Korea

0.4837

208

0.4974

115

Sheffield

C

UK

0.481

209

0.516

95 143

Sacramento

B−

US

0.4806

210

0.47

Hsinchu

C−

China

0.4784

211

0.4978

114

Prague

C+

Czech Republic

0.4763

212

0.452

163

Monterrey

C

Mexico

0.4762

213

0.3413

289

Sao Paulo

C−

Brazil

0.4756

214

0.443

175

Toulouse

C

France

0.4749

215

0.4617

151

Rosario

C−

Argentina

0.4733

216

0.2738

399

Zaragoza

C−

Spain

0.4728

217

0.5059

101

Taizhou (js)

C−

China

0.4726

218

0.3353

297

Bursa

C−

Turkey

0.4723

219

0.4071

207

Shizuoka-Hamamatsu M.M.A

C

Japan

0.4721

220

0.5002

109

Manila

C+

Philippines

0.4712

221

0.3683

246

Warsaw

B−

Poland

0.4708

222

0.4925

121

Tulsa

C+

US

0.4701

223

0.4014

212

Nantes

C

France

0.4667

224

0.4586

158

Weihai

C−

China

0.4655

225

0.3356

296

Zibo

C−

China

0.464

226

0.2875

368 (continued)

1 Global Urban Competitiveness Ranking 2018–2019

9

(continued) City

Rank Country

Economic Ranking Sustainable Ranking competitiveness competitiveness

Liege

C−

Belgium

0.4636

227

0.3797

238

Jiaxing

C−

China

0.4633

228

0.3345

298

Weifang

C−

China

0.4625

229

0.3221

313

Naples

C

Italy

0.4611

230

0.4277

189

Guiyang

C−

China

0.4607

231

0.294

353

Changchun

C−

China

0.4573

232

0.3831

233

Tangshan

C−

China

0.455

233

0.2875

367

Bordeaux

C

France

0.455

234

0.4918

122

Nice

C−

France

0.455

235

0.4782

135

Verona

C

Italy

0.4545

236

0.4135

202

Izmir

C−

Turkey

0.4539

237

0.3846

230

Taichung

C−

China

0.4537

238

0.4282

188

Poznan

C−

Poland

0.4521

239

0.3604

259

Toulon

C−

France

0.4519

240

0.3612

258

Sarasota-Bradenton

C−

US

0.4499

241

0.4134

203

Lisbon

C+

Portugal

0.449

242

0.5051

103

Winnipeg

C

Canada

0.4487

243

0.4572

160

Ankara

C

Turkey

0.4475

244

0.4148

200

Bologna

C

Italy

0.4475

245

0.4609

155

Yichang

C−

China

0.4458

246

0.2516

452

Mumbai

B−

India

0.445

247

0.4357

184

Kumamoto

C−

Japan

0.4447

248

0.4382

180

Rochester

C

US

0.4445

249

0.4837

129

Sharjah

C−

United Arab Emirates

0.4439

250

0.3893

224

Tongling

C−

China

0.4433

251

0.2368

502 333

Shijiazhuang

C−

China

0.4417

252

0.3079

Maracaibo

C−

Venezuela

0.4414

253

0.1696

738

Wuhu

C−

China

0.4408

254

0.3219

314

Quebec

C

Canada

0.4392

255

0.5423

78

Malaga

C−

Spain

0.4365

256

0.4069

208 (continued)

10

1 Global Urban Competitiveness Ranking 2018–2019

(continued) City

Rank Country

Economic Ranking Sustainable Ranking competitiveness competitiveness

Tainan

C−

China

0.4354

257

0.4145

201

Budapest

C

Hungary

0.4345

258

0.4444

174

Santa Fe

C−

Argentina

0.4343

259

0.2662

417

Yancheng

C−

China

0.434

260

0.3257

306

Jundiai

C−

Brazil

0.4337

261

0.2385

496

Bremen

C

Germany

0.4327

262

0.5231

93

Saint Petersburg

C−

Russian Federation

0.4321

263

0.3963

220

Muscat

C−

Oman

0.4317

264

0.3488

274

Florence

C

Italy

0.4308

265

0.4254

191

Surabaya

C−

Indonesia

0.4299

266

0.2808

384

Niigata

C

Japan

0.4289

267

0.482

130

Wenzhou

C−

China

0.4287

268

0.3203

317

Baotou

C−

China

0.4282

269

0.2462

471

Maracay

C−

Venezuela

0.428

270

0.1468

818

Harbin

C−

China

0.4273

271

0.3488

275

Ordoss

C−

China

0.4264

272

0.2252

537

Kunming

C

China

0.4258

273

0.3652

249

Albany

C

US

0.4237

274

0.4842

128

Dammam

C−

Saudi Arabia

0.4231

275

0.2341

510

Tripoli

C−

Libya

0.4224

276

0.1905

670

Guadalajara

C−

Mexico

0.4206

277

0.3595

260

Xiangyang

C−

China

0.4195

278

0.2399

492

El Paso

C−

US

0.4191

279

0.4039

210

Genoa

C−

Italy

0.4186

280

0.3847

229

Zhoushan

C−

China

0.4178

281

0.287

369

Newcastle upon Tyne

C

UK

0.4143

282

0.4483

169

Mendoza

C−

Argentina

0.4139

283

0.2293

527

Huizhou

C−

China

0.4137

284

0.3501

272

New Orleans

C

US

0.4136

285

0.448

170

Pretoria

C−

South Africa

0.4132

286

0.3001

347 (continued)

1 Global Urban Competitiveness Ranking 2018–2019

11

(continued) City

Rank Country

Economic Ranking Sustainable Ranking competitiveness competitiveness

Hohhot

C−

China

0.4128

287

0.2827

Valencia (Venezuela)

C−

Venezuela

0.4102

288

0.2311

521

Santo Domingo

C−

Dominican Republic

0.4101

289

0.3128

327

Tyumen

C−

Russian Federation

0.4093

290

0.3153

322

Torino

C−

Italy

0.4082

291

0.4245

192

Ahvaz

C−

Iran (Islamic Republic of)

0.4069

292

0.1679

743

San Juan

C−

Puerto Rico

0.4064

293

0.3408

290

Rio de Janeiro

C

Brazil

0.4046

294

0.3809

236

Baku

C−

Azerbaijan

0.404

295

0.3154

321

Buraydah

C−

Saudi Arabia

0.4035

296

0.2472

467

Urumqi

C

China

0.4025

297

0.2836

372

Johor Bahru

C−

Malaysia

0.402

298

0.3058

338

Nanning

C−

China

0.4011

299

0.3405

291

Jining

C−

China

0.4009

300

0.2451

476

Barcelona-Puerto La Cruz

C−

Venezuela

0.4006

301

0.2198

557

Bakersfield

C

US

0.4004

302

0.3204

316

Taizhou (zj)

C−

China

0.4001

303

0.2693

409

Johannesburg

C+

South Africa

0.3997

304

0.3801

237

Huaian

C−

China

0.3995

305

0.2794

387

Jinhua

C−

China

0.3992

306

0.2641

421

Minsk

C−

Belarus

0.3989

307

0.3583

261

Bangalore

C

India

0.3978

308

0.3835

232

Krakow

C−

Poland

0.397

309

0.362

255

Catania

C−

Italy

0.3967

310

0.3851

228

Leon

C−

Mexico

0.3963

311

0.3503

271

Taian

C−

China

0.3954

312

0.2397

494

Langfang

C−

China

0.3927

313

0.2643

419

Ashgabat

C−

Turkmenistan 0.3915

314

0.1285

878

Huzhou

C−

China

0.3904

315

0.2578

438

Luanda

C−

Angola

0.3903

316

0.1607

775

375

(continued)

12

1 Global Urban Competitiveness Ranking 2018–2019

(continued) City

Rank Country

Economic Ranking Sustainable Ranking competitiveness competitiveness

Almaty

C

Kazakhstan

0.3897

317

0.2781

389

Lodz

C−

Poland

0.3882

318

0.3068

335

Karaj

C−

Iran (Islamic Republic of)

0.3877

319

0.2119

590

Caracas

C−

Venezuela

0.3847

320

0.2362

505

Taiyuan

C−

China

0.3842

321

0.2956

351

McAllen

C

US

0.3821

322

0.3124

329

Xiangtan

C−

China

0.382

323

0.2808

383

Yueyang

C−

China

0.3819

324

0.2475

464

Putian

C−

China

0.3819

325

0.2367

503

Cairo

C

Egypt

0.3803

326

0.3641

250

Sofia

C−

Bulgaria

0.3803

327

0.3692

245

Medellin

C−

Colombia

0.3801

328

0.3794

239

Fresno

C−

US

0.3793

329

0.3478

277

Porto

C

Portugal

0.3789

330

0.4609

154

Adana

C−

Turkey

0.376

331

0.3111

330

Albuquerque

C

US

0.3759

332

0.417

199

Zhuzhou

C−

China

0.375

333

0.2812

382

Mar Del Plata

C−

Argentina

0.3747

334

0.2646

418

Greater Vitória

C−

Brazil

0.3746

335

0.2288

529

Xuchang

D

China

0.3738

336

0.2371

500

Be’er Sheva

C

Israel

0.3737

337

0.3207

315

Oran

C−

Algeria

0.3719

338

0.2205

553

Jiaozuo

C−

China

0.3712

339

0.2057

616

Amman

C−

Jordan

0.3708

340

0.3152

323

Antalya

C−

Turkey

0.3703

341

0.3154

320

Luoyang

C−

China

0.368

342

0.2803

386

Porto Alegre

D

Brazil

0.3669

343

0.3287

303

Portland

C+

US

0.3661

344

0.5125

97

Padova

C−

Italy

0.366

345

0.3811

235

San Luis Potosi

C−

Mexico

0.3655

346

0.2836

371 (continued)

1 Global Urban Competitiveness Ranking 2018–2019

13

(continued) City

Rank Country

Economic Ranking Sustainable Ranking competitiveness competitiveness

Ipoh

C−

Malaysia

0.3654

347

0.2777

390

Dezhou

C−

China

0.3653

348

0.2433

482

Campinas

C−

Brazil

0.3648

349

0.3478

278

Tijuana

C

Mexico

0.3638

350

0.2923

355

Merida

C−

Mexico

0.3638

351

0.3402

292

Torreon

C−

Mexico

0.3636

352

0.2322

517

Suqian

C−

China

0.3634

353

0.227

532

Guatemala City

C−

Guatemala

0.3634

354

0.2664

416

Lianyungang

C−

China

0.3629

355

0.3621

254

Liaocheng

C−

China

0.3626

356

0.232

519

Rizhao

C−

China

0.3625

357

0.2381

497

Haikou

C−

China

0.3624

358

0.3448

283

Cordoba

C−

Argentina

0.3612

359

0.3149

324

Dhaka

C−

Bangladesh

0.3609

360

0.1581

779

Cangzhou

C−

China

0.3608

361

0.1956

652

Zaozhuang

C−

China

0.3603

362

0.2023

630

Cancun

C−

Mexico

0.36

363

0.2459

472

Pekanbaru

C−

Indonesia

0.3599

364

0.2026

628

Nairobi

C−

Kenya

0.359

365

0.305

339

Thessaloniki

D

Greece

0.3582

366

0.3432

287

Lanzhou

C−

China

0.3566

367

0.3073

334

Quito

C

Ecuador

0.3564

368

0.3616

257

Ma’anshan

C−

China

0.3563

369

0.2073

611

Binzhou

C−

China

0.3562

370

0.2374

499

Villahermosa

C−

Mexico

0.356

371

0.2528

448

Panjin

C−

China

0.3543

372

0.1816

701

Yinchuan

C−

China

0.3537

373

0.2731

400

Wuhai

C−

China

0.3532

374

0.1742

724

Linyi

C−

China

0.3531

375

0.2687

410

Ezhou

C−

China

0.3517

376

0.1439

823 (continued)

14

1 Global Urban Competitiveness Ranking 2018–2019

(continued) City

Rank Country

Economic Ranking Sustainable Ranking competitiveness competitiveness

Xinyu

C−

China

0.3517

377

0.2142

Sao Jose dos Campos

C−

Brazil

0.3514

378

0.2191

559

Changde

C−

China

0.3508

379

0.2496

457

Bari

C−

Italy

0.3507

380

0.3527

265

Havana

C

Cuba

0.3506

381

0.2754

395

Xianyang

C−

China

0.3505

382

0.2062

615

Lagos

C

Nigeria

0.3497

383

0.2834

373

San Miguel de Tucuman

D

Argentina

0.3471

384

0.1694

739

Chennai

C

India

0.3465

385

0.3518

266

Shantou

C−

China

0.3465

386

0.3251

309

Asuncion

C−

Paraguay

0.3464

387

0.2467

469

Batam

C−

Indonesia

0.3461

388

0.183

695

Ribeirao Preto

C−

Brazil

0.346

389

0.1942

659

Yingkou

C−

China

0.3433

390

0.2231

543

Cali

C−

Colombia

0.3433

391

0.3294

302

Liuzhou

C−

China

0.3424

392

0.2211

550

578

Samarinda

C−

Indonesia

0.3397

393

0.1884

678

Beihai

C−

China

0.3397

394

0.2475

465

Queretaro

C−

Mexico

0.3388

395

0.2707

404

Zagreb

C

Croatia

0.3386

396

0.3508

268

Belo Horizonte

C−

Brazil

0.3351

397

0.3278

305

Zhaoqing

C−

China

0.3346

398

0.2826

376

Matamoros

C−

Mexico

0.3343

399

0.2052

619

Jiangmen

C−

China

0.3336

400

0.272

402

Jieyang

C−

China

0.3333

401

0.2335

511

Hengyang

C−

China

0.3318

402

0.2425

488

Santiago de Los Caballeros C+

Dominican Republic

0.3293

403

0.3566

262

Huangshi

C−

China

0.3287

404

0.2139

581

Maoming

C−

China

0.3278

405

0.2474

466

Curitiba

C−

Brazil

0.3265

406

0.3124

328 (continued)

1 Global Urban Competitiveness Ranking 2018–2019

15

(continued) City

Rank Country

Economic Ranking Sustainable Ranking competitiveness competitiveness

Tucson

C

US

0.326

407

0.5291

85

Ufa

C−

Russian Federation

0.3251

408

0.2751

396

Kuching

C−

Malaysia

0.3248

409

0.2957

350

Riga

C−

Latvia

0.3246

410

0.3913

222

Valparaiso

C−

Chile

0.3242

411

0.3241

312

Joinville

C−

Brazil

0.3235

412

0.29

361

Toluca

D

Mexico

0.3229

413

0.267

414

Deyang

C−

China

0.3227

414

0.2269

533

Port Harcourt

C−

Nigeria

0.3226

415

0.1484

810

Benin City

C−

Nigeria

0.3224

416

0.1287

876

Cape Town

C

South Africa

0.3207

417

0.394

221

Yulin (sx)

C−

China

0.3204

418

0.2598

432

Hufuf-Mubarraz

C−

Saudi Arabia

0.3202

419

0.1426

827

Heze

C−

China

0.3197

420

0.2111

594

Ningde

D

China

0.3191

421

0.2781

388

Brasilia

C−

Brazil

0.319

422

0.3439

284

Longyan

C−

China

0.3188

423

0.2634

423

Recife

C−

Brazil

0.3182

424

0.2979

348

Sorocaba

C−

Brazil

0.3177

425

0.2443

479

Panzhihua

C−

China

0.3175

426

0.2227

545

Baghdad

C−

Iraq

0.3172

427

0.1293

873

Samsun

C−

Turkey

0.3171

428

0.2607

428

Sanming

C−

China

0.317

429

0.2161

572

Palermo

C−

Italy

0.3166

430

0.3823

234

Shangrao

D

China

0.3142

431

0.2074

609

Durban

C−

South Africa

0.3141

432

0.3501

273

Wroclaw

C−

Poland

0.3117

433

0.3248

311

Xinxiang

C−

China

0.3116

434

0.2489

461

Zunyi

C−

China

0.3105

435

0.2125

588

Beirut

C

Lebanon

0.3094

436

0.3366

294

Handan

C−

China

0.3089

437

0.2458

473 (continued)

16

1 Global Urban Competitiveness Ranking 2018–2019

(continued) City

Rank Country

Economic Ranking Sustainable Ranking competitiveness competitiveness

Zhanjiang

C−

China

0.3086

438

0.3137

Balikpapan

C−

Indonesia

0.3084

439

0.2069

612

Kaifeng

C−

China

0.3076

440

0.2115

593

Samara

C−

Russian Federation

0.3074

441

0.2813

381

Tehran

C−

Iran (Islamic Republic of)

0.3069

442

0.2883

366

Ta’if

C−

Saudi Arabia

0.3066

443

0.2165

570

Perm

C−

Russian Federation

0.3064

444

0.2642

420

Erbil

C−

Iraq

0.306

445

0.1242

888

San Salvador

D

El Salvador

0.3058

446

0.23

526

Yangjiang

C−

China

0.3056

447

0.1957

651

Pingxiang

C−

China

0.3054

448

0.1409

836

Chenzhou

C−

China

0.3052

449

0.2208

551

Seville

C−

Spain

0.3048

450

0.4013

213

325

Guayaquil

C−

Ecuador

0.3041

451

0.3063

337

Zigong

C−

China

0.3039

452

0.2156

575

Saltillo

C−

Mexico

0.3036

453

0.2755

394

Tbilisi

D

Georgia

0.3036

454

0.3034

343

Puyang

C−

China

0.3036

455

0.1688

741

Zhangzhou

C−

China

0.3031

456

0.2713

403

Aguascalientes

C−

Mexico

0.3026

457

0.258

437

Juarez

C−

Mexico

0.3026

458

0.3005

346

Bandung

C−

Indonesia

0.3025

459

0.2507

454

Anshan

D

China

0.3015

460

0.231

522

Bengbu

C−

China

0.3014

461

0.2323

516

Liaoyang

C−

China

0.3009

462

0.1401

842

Jiujiang

C−

China

0.3007

463

0.2178

566

Xining

C−

China

0.3005

464

0.2354

507

Baoji

C−

China

0.3002

465

0.2156

574

Benxi

C−

China

0.2997

466

0.1797

707

Anyang

D

China

0.2996

467

0.1506

804 (continued)

1 Global Urban Competitiveness Ranking 2018–2019

17

(continued) City

Rank Country

Economic Ranking Sustainable Ranking competitiveness competitiveness

Cartagena

D

Colombia

0.299

468

0.3505

270

Belgrade

C−

Serbia

0.299

469

0.3254

307

Jingmen

C−

China

0.298

470

0.1938

660

Sanya

C−

China

0.2975

471

0.2564

440

Colombo

C−

Sri Lanka

0.2973

472

0.3542

263

Villavicencio

C−

Colombia

0.2972

473

0.2524

449

Londrina

C−

Brazil

0.2972

474

0.2529

447

Liaoyuan

C−

China

0.2967

475

0.1199

897

Luohe

D

China

0.2956

476

0.1472

814

Liupanshui

C−

China

0.2949

477

0.1662

754

Quzhou

C−

China

0.2946

478

0.2083

606

Hebi

C−

China

0.2943

479

0.1422

831

Jilin

C−

China

0.294

480

0.292

358

Denizli

C−

Turkey

0.2936

481

0.2922

356

Huainan

C−

China

0.2935

482

0.2044

625

Songyuan

D

China

0.2931

483

0.2202

555

Karachi

C

Pakistan

0.2931

484

0.2088

603

Ziyang

C−

China

0.2926

485

0.2056

617

Yuxi

C−

China

0.292

486

0.2321

518

Phnom Penh

C−

Cambodia

0.2912

487

0.1906

669

Ganzhou

C−

China

0.2911

488

0.2624

425

Sanmenxia

D

China

0.2903

489

0.1338

858

Xiaogan

D

China

0.2898

490

0.2047

623

Daqing

C−

China

0.2897

491

0.2305

524

Loudi

C−

China

0.2893

492

0.1608

774

Chaozhou

D

China

0.2893

493

0.2227

547

Semarang

D

Indonesia

0.2892

494

0.1856

686

Fangchenggang

C−

China

0.2891

495

0.1621

769

Laiwu

C−

China

0.2886

496

0.1688

742

Coimbatore

D

India

0.2886

497

0.2759

393 (continued)

18

1 Global Urban Competitiveness Ranking 2018–2019

(continued) City

Rank Country

Economic Ranking Sustainable Ranking competitiveness competitiveness

Yichun (jx)

D

China

0.2881

498

0.174

Neijiang

C−

China

0.2881

499

0.1733

728

Belem

C−

Brazil

0.2874

500

0.2094

600

Owerri

C−

Nigeria

0.2874

501

0.1026

926

Ho Chi Minh City

C

Viet Nam

0.2872

502

0.3009

345

Yibin

C−

China

0.2866

503

0.2129

586

Baoding

D

China

0.2862

504

0.2468

468

Nanyang

D

China

0.2858

505

0.2571

439

Guilin

C−

China

0.2851

506

0.2597

433

Yaroslavl

D

Russian Federation

0.2848

507

0.2448

477

Kochi

D

India

0.2848

508

0.313

326

Kolkata

C−

India

0.2841

509

0.2613

427

Hanoi

C

Viet Nam

0.2831

510

0.2952

352

Uberlandia

C−

Brazil

0.2823

511

0.224

539

Mianyang

C−

China

0.2823

512

0.2745

397

Reynosa

C−

Mexico

0.2819

513

0.2074

610

Pune

C−

India

0.2819

514

0.3198

318

726

Makassar

C−

Indonesia

0.2817

515

0.2148

577

Manaus

C−

Brazil

0.2816

516

0.2605

429

Zhoukou

D

China

0.2815

517

0.18

706

Jingdezhen

C−

China

0.2814

518

0.2202

554

Karamay

C−

China

0.2814

519

0.1888

675

Huaibei

C−

China

0.2812

520

0.2089

601

Xinyang

C−

China

0.2806

521

0.2357

506

La Plata

C−

Argentina

0.2804

522

0.2921

357

Fushun

C−

China

0.2803

523

0.2117

592

Concepcion

C−

Chile

0.28

524

0.2683

411

Jingzhou

C−

China

0.2793

525

0.1931

662

Ikorodu

D

Nigeria

0.2793

526

0.1406

840

Xianning

D

China

0.2784

527

0.2615

426 (continued)

1 Global Urban Competitiveness Ranking 2018–2019

19

(continued) City

Rank Country

Economic Ranking Sustainable Ranking competitiveness competitiveness

Yiyang

C−

China

0.2783

528

0.2087

604

Uyo

C−

Nigeria

0.2781

529

0.1135

913

Leshan

C−

China

0.278

530

0.164

761

Algiers

C−

Algeria

0.2769

531

0.167

749

Goiania

C−

Brazil

0.2768

532

0.2223

549

Jinzhou

C−

China

0.2765

533

0.2438

480

Hyderabad

C−

India

0.2759

534

0.3435

286

Malappuram

D

India

0.2759

535

0.1543

789

Palembang

D

Indonesia

0.2758

536

0.1913

667

Luzhou

C−

China

0.2753

537

0.2183

565

Zhumadian

D

China

0.2753

538

0.1441

822

Tunis

C−

Tunisia

0.2744

539

0.2899

362

Nanping

D

China

0.2744

540

0.2632

424

Gaza

D

State of Palestine

0.2733

541

0.1927

665

Pingdingshan

D

China

0.2733

542

0.2185

563

Ahmedabad

C−

India

0.2726

543

0.1839

690

Tongliao

C−

China

0.2702

544

0.1827

696

Gaziantep

C−

Turkey

0.2701

545

0.2914

360

Shangqiu

D

China

0.2696

546

0.1667

752

Meishan

C−

China

0.2695

547

0.2163

571

Trujillo

C−

Peru

0.269

548

0.1721

731

Arequipa

D

Peru

0.269

549

0.1745

723

Alexandria

C−

Egypt

0.2682

550

0.3539

264

Tolyatti

C−

Russian Federation

0.2677

551

0.2304

525

Mersin

C−

Turkey

0.2669

552

0.3064

336

Chuzhou

D

China

0.2667

553

0.2199

556

Abuja

C−

Nigeria

0.2664

554

0.1575

783

Aba

D

Nigeria

0.2654

555

0.1635

763 474

Qinhuangdao

C−

China

0.265

556

0.2456

Medan

C−

Indonesia

0.2631

557

0.1996

637 (continued)

20

1 Global Urban Competitiveness Ranking 2018–2019

(continued) City

Rank Country

Economic Ranking Sustainable Ranking competitiveness competitiveness

Shizuishan

C−

China

0.2616

558

0.2131

583

Weinan

D

China

0.2602

559

0.1471

815

Culiacan

C−

Mexico

0.2591

560

0.2257

535

Saratov

D

Russian Federation

0.259

561

0.282

378

Jincheng

D

China

0.258

562

0.2003

636

Guangan

C−

China

0.2578

563

0.158

782

Wuzhou

C−

China

0.257

564

0.1895

673

Veracruz

C−

Mexico

0.2558

565

0.2825

377

Port Elizabeth

D

South Africa

0.2558

566

0.2559

442

Warri

C−

Nigeria

0.2555

567

0.1332

862

Tegucigalpa

C−

Honduras

0.2547

568

0.253

446

Lishui

D

China

0.2547

569

0.2588

434

San Pedro Sula

D

Honduras

0.2546

570

0.1954

655

Grande Sao Luis

C−

Brazil

0.254

571

0.185

687

Fortaleza

D

Brazil

0.2537

572

0.2771

391

Nanchong

C−

China

0.2537

573

0.2316

520

Huanggang

D

China

0.2536

574

0.2428

485

Port Said

D

Egypt

0.253

575

0.1903

671

Cuernavaca

C−

Mexico

0.2528

576

0.2676

413

Chihuahua

C−

Mexico

0.2526

577

0.2667

415

Xuancheng

D

China

0.2521

578

0.2191

561

Hermosillo

C−

Mexico

0.2519

579

0.3253

308

Shiraz

D

Iran (Islamic Republic of)

0.2518

580

0.2017

632

Xalapa

C−

Mexico

0.2517

581

0.2583

435

Lahore

D

Pakistan

0.2517

582

0.1609

771

Barnaul

D

Russian Federation

0.2517

583

0.3109

331

Santa Cruz

C−

Bolivia

0.2513

584

0.3438

285

Kano

D

Nigeria

0.2511

585

0.1652

758

Puebla

C−

Mexico

0.2505

586

0.3014

344

Cochabamba

D

Bolivia

0.2505

587

0.1536

791

Maturín

C−

Venezuela

0.2505

588

0.1049

924

Yangquan

C−

China

0.2505

589

0.153

796

Siping

D

China

0.2502

590

0.2498

455

Padang

D

Indonesia

0.2493

591

0.1747

720

Shaoguan

C−

China

0.2492

592

0.2012

634

Mudanjiang

D

China

0.2484

593

0.1879

681 (continued)

1 Global Urban Competitiveness Ranking 2018–2019

21

(continued) City

Rank Country

Economic Ranking Sustainable Ranking competitiveness competitiveness

Yulin (gx)

D

China

0.2482

594

0.1968

645

Shiyan

C−

China

0.2474

595

0.2639

422

Kampala

D

Uganda

0.2469

596

0.1425

828

Tonghua

C−

China

0.2465

597

0.1618

770

Xingtai

D

China

0.2456

598

0.1926

666

Krasnodar

C−

Russian Federation

0.2454

599

0.2762

392

Juiz De Fora

D

Brazil

0.2453

600

0.2335

514

Zaria

D

Nigeria

0.244

601

0.1658

755

Suzhou (AH)

C−

China

0.2439

602

0.1636

762

Suining

C−

China

0.2436

603

0.1977

642

Celaya

C−

Mexico

0.243

604

0.2168

568

Suizhou

C−

China

0.2424

605

0.1285

879

Hengshui

D

China

0.2424

606

0.1838

691

Qinzhou

C−

China

0.2418

607

0.2169

567

Shuozhou

C−

China

0.2416

608

0.1773

713

Kozhikode

D

India

0.2411

609

0.1945

656

Kiev

C−

Ukraine

0.2411

610

0.2704

407

Anqing

D

China

0.2404

611

0.2416

489

Shymkent

D

Kazakhstan

0.2401

612

0.133

864

Sulaymaniyah

C−

Iraq

0.2389

613

0.1299

871

Tomsk

C−

Russian Federation

0.2382

614

0.2896

364

Chifeng

C−

China

0.2371

615

0.2141

580

Yongzhou

C−

China

0.2365

616

0.1517

800

Jiayuguan

D

China

0.2363

617

0.1677

746

Can Tho

D

Viet Nam

0.2363

618

0.1886

677

Chittagong

C−

Bangladesh

0.2362

619

0.1025

927

Qujing

C−

China

0.2358

620

0.1803

705

Pachuca de Soto

C−

Mexico

0.2355

621

0.2804

385

Malang

D

Indonesia

0.2354

622

0.2335

512

Khartoum

D

Sudan

0.2347

623

0.1881

680

Puducherry

D

India

0.2344

624

0.1785

709

Cebu

D

Philippines

0.2342

625

0.2479

463

Ryazan

D

Russian Federation

0.2336

626

0.248

462

Tabriz

D

Iran (Islamic Republic of)

0.2318

627

0.2006

635

Bucaramanga

C−

Colombia

0.2315

628

0.3039

342 (continued)

22

1 Global Urban Competitiveness Ranking 2018–2019

(continued) City

Rank Country

Economic Ranking Sustainable Ranking competitiveness competitiveness

Kazan

C−

Russian Federation

0.2315

629

0.3249

310

Dehra Dun

D

India

0.2307

630

0.1161

907

Hanzhong

D

China

0.2304

631

0.1934

661

Dazhou

D

China

0.2296

632

0.1879

682

Ibadan

D

Nigeria

0.2294

633

0.2099

599

Anshun

D

China

0.229

634

0.2142

579

Kemerovo

D

Russian Federation

0.2279

635

0.2274

531

Huaihua

D

China

0.2277

636

0.1377

847

Datong

C−

China

0.2275

637

0.2306

523

Shaoyang

D

China

0.2274

638

0.1452

821

Casablanca

C−

Morocco

0.2266

639

0.2958

349

Chengde

D

China

0.2264

640

0.1832

694

Yunfu

D

China

0.2257

641

0.2036

627

Chizhou

C−

China

0.2254

642

0.1581

780

Bhiwandi

D

India

0.2251

643

0.1207

895

Orenburg

D

Russian Federation

0.2248

644

0.2348

508

Dandong

D

China

0.2236

645

0.213

585

Kollam

D

India

0.2232

646

0.2409

491

Shanwei

D

China

0.2231

647

0.2433

483

Yan’an

D

China

0.2227

648

0.2284

530

Barquisimeto

C−

Venezuela

0.2226

649

0.0956

938

Joao Pessoa

D

Brazil

0.2225

650

0.1973

643

Vereeniging

D

South Africa

0.2219

651

0.152

797

Changzhi

D

China

0.2219

652

0.2436

481

Enugu

D

Nigeria

0.2203

653

0.1354

856

Pereira

D

Colombia

0.2203

654

0.3157

319

Cuiaba

D

Brazil

0.2199

655

0.163

765

Florianopolis

C−

Brazil

0.2196

656

0.2335

513

Ulan Bator

C−

Mongolia

0.2196

657

0.1089

920

Teresina

D

Brazil

0.2195

658

0.1782

711

Denpasar

D

Indonesia

0.2195

659

0.2055

618

Fuyang

C−

China

0.2194

660

0.2107

597

Barranquilla

C−

Colombia

0.2192

661

0.3095

332

Mexicali

C−

Mexico

0.2186

662

0.2109

596

Ji’an

D

China

0.2185

663

0.2452

475 (continued)

1 Global Urban Competitiveness Ranking 2018–2019

23

(continued) City

Rank Country

Economic Ranking Sustainable Ranking competitiveness competitiveness

Irkutsk

C−

Russian Federation

0.2183

Davao

D

Philippines

0.218

665

0.2227

546

Kannur

D

India

0.2177

666

0.17

736

Campo Grande

D

Brazil

0.216

667

0.2599

430

Feira De Santana

D

Brazil

0.2159

668

0.1929

664

Tongchuan

C−

China

0.2157

669

0.1133

914

Mashhad

D

Iran (Islamic Republic of)

0.2154

670

0.2168

569

Konya

C−

Turkey

0.2149

671

0.2854

370

Poza Rica

C−

Mexico

0.2139

672

0.1741

725

Huangshan

C−

China

0.2138

673

0.2496

456

664

0.2426

487

Cagayan de Oro

D

Philippines

0.2138

674

0.1677

745

Zhangjiakou

D

China

0.2136

675

0.2237

542

Bozhou

D

China

0.2123

676

0.1198

898

Haiphong

D

Viet Nam

0.212

677

0.1404

841

Acapulco

D

Mexico

0.2114

678

0.1887

676

Managua

C−

Nicaragua

0.2109

679

0.2184

564

Da Nang

D

Viet Nam

0.2104

680

0.208

608

Morelia

C−

Mexico

0.2104

681

0.2581

436

Jinzhong

D

China

0.2103

682

0.2051

621

Jos

D

Nigeria

0.2098

683

0.136

853

Yuncheng

D

China

0.2082

684

0.2375

498

General Santos City

D

Philippines

0.2082

685

0.1969

644

Oshogbo

D

Nigeria

0.2078

686

0.1136

912

Qingyuan

C−

China

0.2077

687

0.2544

444

Thiruvananthapuram

D

India

0.2076

688

0.2465

470

Kayseri

D

Turkey

0.2071

689

0.2892

365

Suihua

D

China

0.2067

690

0.1427

826

Baishan

C−

China

0.206

691

0.1318

867

Ankang

D

China

0.2059

692

0.1478

812

Eskisehir

D

Turkey

0.2048

693

0.3399

293

Libreville

C−

Gabon

0.2046

694

0.1622

768

Chongzuo

D

China

0.2046

695

0.1354

855

Heyuan

C−

China

0.2044

696

0.1911

668

Tampico

C−

Mexico

0.2041

697

0.2492

459

Astrakhan’

D

Russian Federation

0.204

698

0.2051

620

Diyarbakir

C−

Turkey

0.204

699

0.1697

737 (continued)

24

1 Global Urban Competitiveness Ranking 2018–2019

(continued) City

Rank Country

Economic Ranking Sustainable Ranking competitiveness competitiveness

Hulunbuir

D

China

0.2035

700

0.1598

776

Ludhiana

D

India

0.203

701

0.1287

875

Surat

C−

India

0.2027

702

0.2398

493

Thrissur

D

India

0.2025

703

0.1826

697

Rajshahi

D

Bangladesh

0.2021

704

0.1174

906

Kota

D

India

0.2019

705

0.2118

591

Accra

D

Ghana

0.2019

706

0.2226

548

Mombasa

D

Kenya

0.2018

707

0.1991

639

Port-au-Prince

C−

Haiti

0.2018

708

0.1891

674

Khabarovsk

C−

Russian Federation

0.2016

709

0.2257

536

Linfen

D

China

0.2011

710

0.1809

703

Abidjan

C−

The Republic 0.2006 of Cote d’ivoire

711

0.1821

698

Meizhou

D

China

712

0.2387

495

Kingston

D

Jamaica

0.1999

713

0.4103

206

Nagpur

D

India

0.1998

714

0.1729

729 672

0.2005

Visakhapatnam

D

India

0.199

715

0.19

Ibague

C−

Colombia

0.1989

716

0.2409

490

Hamadan

D

Iran (Islamic Republic of)

0.1983

717

0.142

833

Marrakech

C−

Morocco

0.1982

718

0.2722

401

Baise

D

China

0.1979

719

0.1424

829

Zhangjiajie

D

China

0.1965

720

0.1371

848

Huludao

D

China

0.1965

721

0.1161

908

Asansol

D

India

0.1961

722

0.0965

936

Akure

D

Nigeria

0.196

723

0.1394

843

Meknes

C−

Morocco

0.1957

724

0.208

607

Chisinau

D

Republic of Moldova

0.1953

725

0.2101

598

Jiamusi

C−

China

0.1953

726

0.1531

794

Shangluo

D

China

0.195

727

0.1409

837

Cucuta

D

Colombia

0.1947

728

0.1873

683

Ulanqab

D

China

0.1942

729

0.1712

733

Baicheng

D

China

0.1941

730

0.1676

747

Guigang

D

China

0.1939

731

0.129

874

Fuzhou (JX)

D

China

0.1923

732

0.1497

807

Tangier

C−

Morocco

0.1921

733

0.2239

541 (continued)

1 Global Urban Competitiveness Ranking 2018–2019

25

(continued) City

Rank Country

Economic Ranking Sustainable Ranking competitiveness competitiveness

Tiruppur

D

India

0.1903

734

0.1191

901

Fuxin

C−

China

0.1897

735

0.1808

704

Novosibirsk

C−

Russian Federation

0.1889

736

0.3281

304

Tasikmalaya

D

Indonesia

0.1885

737

0.1463

819

Oaxaca

D

Mexico

0.1878

738

0.2929

354

Harare

C−

Zimbabwe

0.1877

739

0.2017

633

Bayannur

C−

China

0.1876

740

0.1503

805

Rostov-on-Don

C−

Russian Federation

0.1872

741

0.2111

595

Rabat

C−

Morocco

0.1871

742

0.3308

300

Ya’an

D

China

0.1869

743

0.2089

602

Tlaxcala

C−

Mexico

0.1864

744

0.2123

589

La Paz

C−

Bolivia

0.1861

745

0.2229

544

Huambo

D

Angola

0.1858

746

0.0989

932

Laibin

D

China

0.1857

747

0.1668

750

Novokuznetsk

C−

Russian Federation

0.1856

748

0.2131

584

Patna

D

India

0.1846

749

0.1285

877

Krasnoyarsk

C−

Russian Federation

0.1846

750

0.2816

379

Durg-Bhilai Nagar

D

India

0.1844

751

0.0792

965

Madurai

D

India

0.1844

752

0.2129

587

Chaoyang

D

China

0.1843

753

0.1757

718

Asmara

D

Eritrea

0.1842

754

0.0836

957

Liuan

C−

China

0.1836

755

0.1379

846

Guangyuan

C−

China

0.1835

756

0.2048

622

Sanliurfa

D

Turkey

0.1824

757

0.1551

786

Tuxtla Gutierrez

C−

Mexico

0.1824

758

0.186

685

Aracaju

D

Brazil

0.1813

759

0.1848

688

Kitwe

D

Zambia

0.181

760

0.1967

647

Izhevsk

D

Russian Federation

0.1797

761

0.224

540

Chiclayo

D

Peru

0.1797

762

0.1183

903

Kayamkulam

D

India

0.1797

763

0.1646

760

Guwahati

D

India

0.1796

764

0.1985

641

Qiqihar

D

China

0.1795

765

0.237

501

Bandar Lampung

D

Indonesia

0.1791

766

0.1456

820

Ciudad Guayana

C−

Venezuela

0.1791

767

0.0754

968 (continued)

26

1 Global Urban Competitiveness Ranking 2018–2019

(continued) City

Rank Country

Economic Ranking Sustainable Ranking competitiveness competitiveness

Nizhny Novgorod

D

Russian Federation

0.1783

768

0.33

301

Maceio

D

Brazil

0.1779

769

0.1705

735

Bacolod

D

Philippines

0.1772

770

0.1761

717

Mangalore

D

India

0.1767

771

0.2207

552

Douala

D

Cameroon

0.176

772

0.0956

940

Rajkot

D

India

0.176

773

0.1967

646

Bogor

D

Indonesia

0.175

774

0.2198

558

Jalandhar

D

India

0.1746

775

0.1112

917

Qingyang

D

China

0.1741

776

0.2698

408

Jodhpur

D

India

0.1738

777

0.1535

792

Namangan

D

Uzbekistan

0.1724

778

0.1336

859

Omsk

D

Russian Federation

0.1723

779

0.2427

486

Kabul

D

Afghanistan

0.172

780

0.0326

1000

Dar es Salaam

D

United Republic of Tanzania

0.1714

781

0.1956

654

Hezhou

D

China

0.1704

782

0.1962

650

Karbala

D

Iraq

0.1696

783

0.0908

949

Jinchang

D

China

0.1696

784

0.1837

693

Amritsar

D

India

0.1695

785

0.1217

893

Pointe-Noire

D

Congo

0.1692

786

0.0803

962

Kirkuk

D

Iraq

0.1689

787

0.1006

929

Volgograd

C−

Russian Federation

0.1685

788

0.2189

562

Zamboanga

D

Philippines

0.1682

789

0.1723

730

Erode

D

India

0.1672

790

0.1658

756

Krivoi Rog

D

Ukraine

0.1671

791

0.1238

889

Vijayawada

C−

India

0.1671

792

0.1931

663

Luliang

D

China

0.1671

793

0.1817

700

Tieling

D

China

0.1668

794

0.1423

830

Chelyabinsk

D

Russian Federation

0.1663

795

0.2346

509

Bahawalpur

D

Pakistan

0.1661

796

0.0979

933

Hyderabad

C−

Pakistan

0.1656

797

0.2531

445

Kathmandu

D

Nepal

0.1653

798

0.1369

850

Xinzhou

D

China

0.1652

799

0.2509

453

Orumiyeh

D

Iran (Islamic Republic of)

0.1645

800

0.1407

838 (continued)

1 Global Urban Competitiveness Ranking 2018–2019

27

(continued) City

Rank Country

Economic Ranking Sustainable Ranking competitiveness competitiveness

Jamnagar

D

India

0.1642

801

0.1769

715

Onitsha

D

Nigeria

0.1641

802

0.0446

995

Baoshan

D

China

0.1637

803

0.2704

406

Vientiane

D

Lao People’s Democratic Republic

0.1632

804

0.186

684

Jaipur

D

India

0.1628

805

0.1598

777

Raurkela

D

India

0.1627

806

0.0969

935

Nouakchott

D

Mauritania

0.1625

807

0.1495

808

Zhaotong

D

China

0.1621

808

0.1609

772

Vladivostok

C−

Russian Federation

0.162

809

0.2517

451

Ilorin

D

Nigeria

0.1613

810

0.13

869

Mosul

C−

Iraq

0.1611

811

0.0951

941

Fes

D

Morocco

0.161

812

0.2133

582

Siliguri

D

India

0.1608

813

0.1326

865

Voronezh

D

Russian Federation

0.1608

814

0.2598

431

Natal

D

Brazil

0.1606

815

0.2815

380

Salvador

D

Brazil

0.16

816

0.2682

412

Basra

D

Iraq

0.1598

817

0.0546

983

Sylhet

D

Bangladesh

0.1592

818

0.0907

950

Salem

D

India

0.1592

819

0.3039

341

Jambi

D

Indonesia

0.1589

820

0.1349

857

Tashkent

D

Uzbekistan

0.1588

821

0.2432

484

Tirupati

D

India

0.1588

822

0.158

781

Tiruchirappalli

D

India

0.158

823

0.1299

870

Lincang

D

China

0.1572

824

0.1713

732

WuZhong

D

China

0.1572

825

0.181

702

Tianshui

C−

China

0.1571

826

0.2191

560

Zhongwei

D

China

0.1569

827

0.1649

759

Kaduna

D

Nigeria

0.1567

828

0.0956

939

Kurnool

D

India

0.1561

829

0.1103

919

Bhubaneswar

D

India

0.156

830

0.2083

605

Safaqis

D

Tunisia

0.1551

831

0.1331

863

Brazzaville

D

Congo

0.1551

832

0.0599

981

Kolhapur

D

India

0.1551

833

0.1737

727

Jixi

D

China

0.1551

834

0.1266

882

Rasht

D

Iran (Islamic Republic of)

0.1545

835

0.2018

631 (continued)

28

1 Global Urban Competitiveness Ranking 2018–2019

(continued) City

Rank Country

Economic Ranking Sustainable Ranking competitiveness competitiveness

Kumasi

D

Ghana

0.1544

836

0.2024

629

Gwalior

D

India

0.1542

837

0.093

943

Santa Marta

D

Colombia

0.1533

838

0.274

398

Nashik

D

India

0.1531

839

0.2063

614

Meerut

D

India

0.153

840

0.0857

956

Dakar

D

Senegal

0.1528

841

0.199

640

Yingtan

D

China

0.1513

842

0.1551

788

Agadir

D

Morocco

0.1511

843

0.2561

441

Jamshedpur

D

India

0.1501

844

0.1553

785

Islamabad

C−

Pakistan

0.1477

845

0.1837

692

Yekaterinburg

C−

Russian Federation

0.1472

846

0.2045

624

Lucknow

D

India

0.1472

847

0.1627

766

Indore

D

India

0.1469

848

0.1195

900

Bazhong

D

China

0.1452

849

0.1436

824

Athens

C-

Greece

0.1448

850

0.4818

133

Nasiriyah

D

Iraq

0.1441

851

0.0878

955

Baiyin

D

China

0.1431

852

0.1608

773

Gujranwala

D

Pakistan

0.1429

853

0.0671

975

Faisalabad

D

Pakistan

0.1428

854

0.1034

925

Rawalpindi

D

Pakistan

0.1423

855

0.0922

945

Khulna

D

Bangladesh

0.1423

856

0.0738

970

Ulyanovsk

D

Russian Federation

0.1421

857

0.2158

573

Latakia

D

Syrian Arab Republic

0.1413

858

0.0469

990

Lijiang

D

China

0.1412

859

0.2559

443

Cherthala

D

India

0.1409

860

0.1517

798

Kigali

D

Rwanda

0.1406

861

0.1672

748

Rangoon

C−

Myanmar

0.1404

862

0.0935

942

Makhachkala

D

Russian Federation

0.1403

863

0.1883

679

Misratah

D

Libya

0.1402

864

0.0667

977

Lome

D

Togo

0.1402

865

0.105

923

Shuangyashan

D

China

0.1399

866

0.1183

902

Zhangye

D

China

0.1399

867

0.1783

710

Bhopal

D

India

0.1398

868

0.1298

872

Mysore

D

India

0.1397

869

0.2252

538

Pu’er

D

China

0.1395

870

0.1517

801 (continued)

1 Global Urban Competitiveness Ranking 2018–2019

29

(continued) City

Rank Country

Economic Ranking Sustainable Ranking competitiveness competitiveness

Vellore

D

India

0.1393

871

0.2068

613

Wuwei

C−

China

0.139

872

0.1844

689

Chandigarh

D

India

0.138

873

0.1942

658

Kinshasa

D

Congo

0.138

874

0.0397

997

Qitaihe

D

China

0.1378

875

0.0617

980

Aurangabad

D

India

0.1375

876

0.2363

504

Najaf

D

Iraq

0.1372

877

0.1023

928

Imphal

D

India

0.1372

878

0.1209

894

Bokaro Steel City

D

India

0.137

879

0.053

985

Sekondi

D

Ghana

0.1368

880

0.147

816

Sangali

D

India

0.1362

881

0.0793

964

Vadodara

D

India

0.136

882

0.2148

576

Tabuk

C−

Saudi Arabia

0.1358

883

0.1656

757

Sokoto

D

Nigeria

0.1357

884

0.1664

753

Maiduguri

D

Nigeria

0.1353

885

0.1418

834

Banjarmasin

D

Indonesia

0.1353

886

0.1406

839

Sana’a’

D

Yemen

0.1348

887

0.092

947

Sialkot

D

Pakistan

0.1341

888

0.0883

954

Pingliang

D

China

0.1331

889

0.1678

744

Esfahan

D

Iran (Islamic Republic of)

0.1327

890

0.1942

657

Qom

D

Iran (Islamic Republic of)

0.1318

891

0.136

854

Guntur

D

India

0.1316

892

0.1626

767

Peshawar

D

Pakistan

0.1313

893

0.111

918

Lusaka

D

Zambia

0.1312

894

0.204

626

Ranchi

D

India

0.1309

895

0.1366

852

Saharanpur

D

India

0.1308

896

0.0748

969

Srinagar

D

India

0.1305

897

0.1596

778

Hegang

D

China

0.1303

898

0.0665

978

Warangal

D

India

0.1298

899

0.1963

649

Bogra

D

Bangladesh

0.1297

900

0.0443

996

Aden

D

Yemen

0.1289

901

0.0914

948

Hubli-Dharwad

D

India

0.1288

902

0.1517

799

Sukkur

D

Pakistan

0.1283

903

0.0514

987

Heihe

D

China

0.1275

904

0.1535

793

Bien Hoa

D

Viet Nam

0.1275

905

0.1469

817

Niamey

D

Niger

0.1257

906

0.1257

885

Yerevan

D

Armenia

0.1255

907

0.2832

374 (continued)

30

1 Global Urban Competitiveness Ranking 2018–2019

(continued) City

Rank Country

Economic Ranking Sustainable Ranking competitiveness competitiveness

Pontianak

D

Indonesia

0.1254

908

0.1472

813

Damascus

D

Syrian Arab Republic

0.1253

909

0.1177

904

Bhavnagar

D

India

0.1246

910

0.1513

802

Kerman

C−

Iran (Islamic Republic of)

0.1237

911

0.2493

458

Varanasi

D

India

0.122

912

0.125

887

Freetown

D

Sierra Leone

0.1219

913

0.1764

716

Durango

C−

Mexico

0.1211

914

0.2897

363

Solapur

D

India

0.1209

915

0.1334

860

Addis Ababa

D

Ethiopia

0.1208

916

0.1956

653

Nyala

D

Sudan

0.1188

917

0.1277

880

Suez

D

Egypt

0.1186

918

0.175

719

Jabalpur

D

India

0.1169

919

0.1261

883

Hechi

D

China

0.1169

920

0.1388

844

Cuttack

D

India

0.1161

921

0.1141

910

Dhanbad

D

India

0.1159

922

0.1266

881

Ardabil

D

Iran (Islamic Republic of)

0.1158

923

0.1421

832

Agra

D

India

0.1153

924

0.1333

861

Amravati

D

India

0.1147

925

0.1774

712

Nellore

D

India

0.1142

926

0.1259

884

Malegaon

D

India

0.1139

927

0.0885

953

Ujjain

D

India

0.1129

928

0.1132

915

Hamah

D

Syrian Arab Republic

0.1124

929

0.079

966

Yazd

D

Iran (Islamic Republic of)

0.111

930

0.1551

787

Zanzibar

D

United Republic of Tanzania

0.1106

931

0.125

886

Aligarh

D

India

0.1103

932

0.1198

899

Bareilly

D

India

0.1101

933

0.0723

971

Lubumbashi

D

Congo

0.1101

934

0.0668

976

Kermanshah

D

Iran (Islamic Republic of)

0.1101

935

0.1411

835

Guyuan

D

China

0.1101

936

0.1965

648

Jiuquan

D

China

0.1098

937

0.1995

638

Multan

D

Pakistan

0.1095

938

0.09

951

Donetsk

D

Ukraine

0.1094

939

0.1488

809 (continued)

1 Global Urban Competitiveness Ranking 2018–2019

31

(continued) City

Rank Country

Economic Ranking Sustainable Ranking competitiveness competitiveness

Moradabad

D

India

0.1086

940

0.0523

986

Kanpur

D

India

0.1081

941

0.1481

811

Al-Raqqa

D

Syrian Arab Republic

0.1046

942

0.0354

999

Salta

C−

Argentina

0.1046

943

0.1432

825

Tirunelveli

D

India

0.1042

944

0.1745

722

Nanded Waghala

D

India

0.1021

945

0.1086

922

Ajmer

D

India

0.1016

946

0.1218

892

Mwanza

D

United Republic of Tanzania

0.1014

947

0.1634

764

911

Allahabad

D

India

0.1008

948

0.1137

Belgaum

D

India

0.1006

949

0.1304

868

Firozabad

D

India

0.0992

950

0.0124

1004

Quetta

D

Pakistan

0.0985

951

0.1087

921

Nnewi

D

Nigeria

0.0976

952

0.0806

961

Dingxi

D

China

0.095

953

0.2492

460

Mogadishu

D

Somalia

0.0945

954

0.0559

982

Mathura

D

India

0.0937

955

0.1667

751

Gulbarga

D

India

0.0934

956

0.1116

916

Longnan

D

China

0.093

957

0.1819

699

Jhansi

D

India

0.093

958

0.0684

974

Cotonou

D

Benin

0.0926

959

0.1225

891

Durgapur

D

India

0.0913

960

0.1324

866

Jammu

D

India

0.0912

961

0.1708

734

Yichun (hlj)

D

China

0.0887

962

0.099

931

Muzaffarnagar

D

India

0.0881

963

0.0211

1003

Lvov

C−

Ukraine

0.0842

964

0.1506

803

Bishkek

D

Kyrgyzstan

0.0842

965

0.1206

896

Zaporizhzhya

D

Ukraine

0.0838

966

0.0831

959

Dnipropetrovs’k

D

Ukraine

0.0835

967

0.0958

937

Yaounde

D

Cameroon

0.0829

968

0.1369

849

Kharkov

D

Ukraine

0.0821

969

0.1771

714

Nay Pyi Taw

D

Myanmar

0.0815

970

0.0491

988

Hargeysa

D

Somalia

0.0789

971

0.0972

934

Ouagadougou

D

Burkina Faso

0.0783

972

0.1563

784

Bouake

D

The Republic 0.0776 of Cote d’ivoire

973

0.1538

790

Odessa

D

Ukraine

974

0.1745

721

0.0774

(continued)

32

1 Global Urban Competitiveness Ranking 2018–2019

(continued) City

Rank Country

Economic Ranking Sustainable Ranking competitiveness competitiveness

Benghazi

D

Libya

0.0774

975

0.0834

Djibouti

D

Djibouti

0.0761

976

0.0899

958 952

Bamako

D

Mali

0.0749

977

0.1235

890

Zahedan

D

Iran (Islamic Republic of)

0.0731

978

0.1384

845

Bikaner

D

India

0.0725

979

0.1148

909

Blantyre-Limbe

C−

Malawi

0.0714

980

0.169

740

Maputo

D

Mozambique

0.067

981

0.153

795

Gorakhpur

D

India

0.0659

982

0.0456

994

Abomey-Calavi

D

Benin

0.0651

983

0.1

930

Sargodha

D

Pakistan

0.0645

984

0.0688

973

Antananarivo

D

Madagascar

0.0559

985

0.1367

851

Raipur

D

India

0.0538

986

0.0928

944 1005

Tshikapa

D

Congo

0.0534

987

0.0088

Matola

D

Mozambique

0.0531

988

0.0825

960

Lilongwe

D

Malawi

0.0522

989

0.1175

905

Mandalay

D

Myanmar

0.0514

990

0.08

963

Bobo Dioulasso

D

Burkina Faso

0.0489

991

0.179

708

Bujumbura

D

Burundi

0.0468

992

0.0463

Mbuji-Mayi

D

Congo

0.0435

993

0

Nampula

D

Mozambique

0.0434

994

0.0921

946

Monrovia

D

Liberia

0.0427

995

0.2266

534

Conakry

D

Guinea

0.0377

996

0.1499

806

992 1007

Dushanbe

C−

Tajikistan

0.0374

997

0.063

979

Kananga

D

Congo

0.0317

998

0.0014

1006

Taiz

D

Yemen

0.0314

999

0.0689

972

Hodeidah

D

Yemen

0.0256

1000

0.0472

989

Bukavu

D

Congo

0.0228

1001

0.0468

991

homs

D

Syrian Arab Republic

0.0199

1002

0.0545

984

Bulawayo

D

Zimbabwe

0.0173

1003

0.0762

967

Aleppo

D

Syrian Arab Republic

0.0127

1004

0.0457

993

Bangui

D

Central African Republic

0.0065

1005

0.0356

998

N’Djamena

D

Chad

0.0048

1006

0.0314

1001

Kisangani

D

Congo

0

1007

0.0254

1002

Chapter 2

The Planet of Cities Toward Diverse Agglomeration, Global Connection, and Extensive Sharing Pengfei Ni, Marco Kamiya, Jianfa Shen, Bo Li, Hongfu Ma, Yufei Wang, and Haidong Xu

Preface Human beings, evolved from nature, have the innate nature of pursuing self-interests and maximum profits to meet unlimited demands. This is an instinct based on the need of survival and the most underlying driving force for human development. Meanwhile, as advanced creature with intelligence, human beings have the gift of proactive creativity. Human being’s demands can only be fulfilled by carrying out human activities, like living and working, through utilizing certain environment and conditions. Due to the law of returns to scale, human being, along with most of living creatures, tends to live in agglomeration, with the extent decided by the conditions of the times. In the nomadic era, human beings can only survive by picking up wild fruits, hunting, or fishing thanks to restrictions of technology and capability. Under the influence of environment, they have to migrate from one place to another and live separately. The gathering region for human beings is scattered and floating. In the agricultural era, human beings survive by planting and raising livestock thanks to restrictions of technology and capability. Under the influence of environment, they have to live in a relatively fixed area in small scale. The gathering region for human beings is scattered but relatively stable. In the industrial era, human beings survive by manufacturing products based on technological advance and the improvement of their capabilities. Under the influence of environment, they live together in large scale. The gathering regions for human beings are concentrated and stable. Such fixed concentrated residential areas are cities. Cities are stable physical and social space that serves human agglomeration activities and takes shape with the force of human being on natural conditions. Cities are made up by urban population (people chose to live together and pursue opportunities for physical contacts out of rational choice), people’s activities, and environmental facilities (space agents and municipal government). Besides the stable location that distinguishes it from non-urban residential areas, cities have three interconnecting characteristics of agglomeration, connection, and sharing, which not only distinguishes cities from non-urban areas, but also determines the meaning, function, scale, and form of the cities themselves. © China Social Sciences Press 2021 P. Ni et al., Global Industry Chains: Creating a Networked City Planet, https://doi.org/10.1007/978-981-16-2058-4_2

33

34

2 The Planet of Cities Toward Diverse Agglomeration, Global …

In the nomadic and agricultural eras, human beings live in self-sufficient. They work to feed themselves. The supporting facilities for life and work are provided by individuals. There are no connection between different individuals. In cities or the industrial era, the subjects of life, work, and supporting facilities are separate. People live in families, work in factories, and their supporting facilities are provided by government or businesses or families funded by the government. The spatial distribution of population, the spatial function of subjects, and the spatial sharing of objects are becoming increasingly closer. The distribution of economic and dispersion. Economic entities’ pursuit of maximum profits and the law of returns to scale determine the tendency of aggregation in terms of population, industrial activities, products, and facilities. However, on the one hand, there is a limit when the payment accrues. On the other, the natural space supporting human activities is heterogeneous. As a result, under certain technological conditions, human beings’ spatial gathering may be on different locations. Aggregation aims for connection and sharing. It includes the aggregation of population, industry, and public facilities and covers both soft and hard aspects. It also varies on scale and density and exerts impacts on the meaning, function, scale, and form of a city. The function of economic entities: connection and division. Economic entities’ pursuit of maximum profits and the law of returns to scale determine the tendency of aggregation in terms of population, industrial activities, products, and facilities. The division of labor is an important source of improving productivity and returns to scale. The division of labor means exchanges and connections. Human beings’ intelligence and wisdom make exchanges and connections an important condition for innovation and realization of returns to scale. But the objective conditions and people’s subjective willingness influence the profundity and breadth of the connection. Division, as the opposite of connection, always exists to some extent. Connection comes from aggregation and aims for sharing. It includes intra-city and intercity contacts as well as soft and hard connections. It varies by range, intensity, and extent. The sharing of urban infrastructure and its environment exerts impacts on the meaning, function, scale, and form of a city. The utilization of facilities and products: sharing and monopoly. Economic entities’ pursuit of maximum profits and the law of returns to scale determine the tendency of sharing and full utilization in terms of human activities, products, and facilities. Sharing of products, services, and facilities is another important source of increasing returns to scale. The sharing includes those in space and time. The degree to which the marginal cost of using products and facilities decreases determines the degree to which the returns to scale and efficacy of utilization increase. But this could only happen under certain technological conditions, and the property rights are explicit and well-protected. The conditions determine the boundary between sharing and monopoly of the products and facilities. One of the purposes for aggregation and connection is to realize the sharing of facilities, services, and products. The sharing is not only about public facilities and services and private facilities and services, but also about the hard products and services and the soft products and services. It

2 The Planet of Cities Toward Diverse Agglomeration, Global …

35

Fig. 2.1 Three inscapes of a city and interconnection among its components. Source compiled by the author

varies by scale and extent. The sharing of urban infrastructure and its environment influences the meaning, function, scale, and form of a city. As the market-based systems are widely adopted around the world and new technologies like the information technology emerge, the aggregation, connection, and sharing of cities underwent tremendous changes in the past 40 years. Correspondingly, the meaning, function, scale, and form of cities also changed a lot, bringing transformation and significant changes to the world at the same time (Fig. 2.1).

2.1 Cities Have Become More Global, Networked, and Intelligent Over the Past 40 Years The tremendous changes over the past 40 years and the non-agricultural aggregation and flow of factors of production brought profound changes to the meaning of global cities. The space human beings live in has undergone profound changes. A simultaneous aggregation and dispersion of population and factors of production have taken place. The profound changes on the contents and methods of human activities lead to changes on the functions of cities. Intangible products and services and virtual contacts become main contents of human activities. The service facilities enjoyed by

36

2 The Planet of Cities Toward Diverse Agglomeration, Global …

human beings also underwent profound changes: Soft facilities become dominant, bringing changes on city form, and eventually resulting in evolution of urban patterns. Non-agricultural aggregation of factors of production, changes on division of labor, and global spatial competition drive the globe to evolve from a closed, dispersed, and exclusive earth of agriculture to an interconnected, aggregated, and sharing planet of cities.

2.1.1 The Non-agricultural Aggregation of Factors of Production: Tremendous Changes on the City Meaning 2.1.1.1

a.

The Non-agricultural Aggregation of Population Evolves from at a Lower Speed to Faster One and from Locally to Globally

The non-agricultural aggregation of global population speeds up

Compared with the past centuries, urbanization is advancing at an even faster pace. First, the urbanization ratio grew over the recent 40 years at the speed 2.33 times faster than the 1950–70 period. As shown in Fig. 2.2, the urbanization ratio increased by seven percentage points from 29.6% in 1950 to 36.6% in 1970. The 60.0 50.0

y = 29.1e0.0438x R² = 0.9954

40.0 30.0 20.0 10.0 0.0 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 Fig. 2.2 Urbanization ratio across the world during 1950–2015 period. Source compiled by the author based on statistics by the United Nations Population Division

2.1 Cities Have Become More Global, Networked, and Intelligent Over …

37

figure is 16.3 percentage points from 37.7% in 1975 to 53.9% in 2015. The growth rate is 2.33 times than the 1950–70 period. Second, the acceleration period of urbanization in emerging economies is far shorter than that in developed economies. As defined by the UN Population Division, developed economies include European countries, North America, Australia, New Zealand, and Japan. There has been no accurate definition on emerging economies yet. This study cites the definition given by Economist. The emerging economies include Brazil, Russia, India, China, South Africa, Mexico, South Korea, Poland, Turkey, and Egypt among other countries. The underdeveloped countries in this study are those bar from advanced economies and emerging ones. Urbanization in developed economies started from the industrial revolution spreading from the UK in the 1760s. The process stepped into the stage of acceleration from the middle eighteenth century to the late nineteenth century. As shown in Fig. 2.3, the urbanization ration in 1950 had reached 54.8% and 70.3% in 1980, since when the phenomenon of deurbanization has begun to appear. The urbanization process in emerging economies started late, with the ration being only 28.3% in 1950 and 45.1% in 1980. But the speed is faster, outstripping developed nations over the same period. The acceleration period came in the middle and late 1970s. In 2015, the urbanization ration reached 62.7% as shown in Fig. 2.4. The acceleration period for urbanization in emerging industrialized countries only lasted for 40 years, far less than the 150 years spent by developed countries. Third, the annual gradient of urbanization ration over the recent four decades presents the shape of a flat S curve. As shown in Fig. 2.5, the population base in urban areas across the world was small after the Second World War. At the beginning, the urbanization evolved fast. In 1970, the ratio dropped sharply, increased

2010 2000 1990 1980 1970 1960 1950 0.0

20.0

40.0

City population(0.1billion)

60.0

80.0

100.0

urbanization ratio (%)

Fig. 2.3 Urbanization process in developed economies. Source compiled by the author based on statistics by the United Nations Population Division

38

2 The Planet of Cities Toward Diverse Agglomeration, Global …

2010 2000 1990 1980 1970 1960 1950 0

10

20

30

City population(0.1billion)

40

50

60

70

urbanization ratio (%)

Fig. 2.4 Urbanization process in emerging economies. Source compiled by the author based on statistics by the United Nations Population Division

3.0 2.5 2.0 1.5 1.0 0.5 0.0

Fig. 2.5 Annual gradient of urbanization ration across the world in 1950–2015. Source compiled by the author based on statistics by the United Nations Population Division

only by one percentage point compared with 1965. From 1975 to 2005, the growth of urbanization ratio fluctuated and presented flat S curve. Entering the twenty-first century, the growth rate slowed down and the urbanization rate maintained at a stable level.

2.1 Cities Have Become More Global, Networked, and Intelligent Over …

b.

39

The non-agricultural aggregation of emerging economies accelerates

First, the proportion of aggregated urban population in emerging economies has significantly risen over the recent four decades. By comparing the proportions of increased urban population in developed economies, emerging economies, and underdeveloped nations in the 1950–2015 period, it can be seen that the proportion of increased urban population in emerging economies is substantial and hit 51.9% in 1975. The proportion has been outnumbering the developed economies and underdeveloped nations throughout to 2015 and totaling 54.5 that year, as shown in Fig. 2.6. Second, the increased urban population in East Asia accounts for the highest proportion over the recent 40 years. By comparing the changes of increased urban population in Europe, North America, Asia, South America, Africa, and Oceania in the last 40 years, it can be seen that the proportion of increased urban population in Asia is the highest. The figure reached 62.8% in 2015 as shown in Fig. 2.7. Further analysis on the changes of increased urban population in Asia shows that the proportion of East Asian cities is the highest, reaching 34.4% of the Asia’s total in 2015 as shown in Fig. 2.8. The non-agricultural aggregation of population in East Asia is the most prominent. With most emerging economies, the economic development

Fig. 2.6 Proportions of increased urban population in developed economies, emerging economies, and underdeveloped nations in 1950–2015. Source compiled by the author based on statistics by the United Nations Population Division

40

2 The Planet of Cities Toward Diverse Agglomeration, Global …

Fig. 2.7 Increased population in cities by continent over the period of 1950–2015. Source compiled by the author based on statistics by the United Nations Population Division

Fig. 2.8 Changes of increased urban population in Asian cities in 1950–2015. Source compiled by the author based on statistics by the United Nations Population Division

is dynamic in East Asia. As a result, cities expand rapidly, and the non-agricultural aggregation of population is prominent. Third, emerging economies have evolved into urbanized society over the last 40 years. From the changing trend of urbanization ratio in all continents in the period of 1950 to 2015 (Fig. 2.9), Asia replaced South America as the continent with the fastest urbanization growth rate in 2015. With the ongoing rising urbanization, the majority of the world population have migrated into cities. By reviewing the changing

2.1 Cities Have Become More Global, Networked, and Intelligent Over …

41

Fig. 2.9 Changing trend of urbanization ratio in all continents in 1950–2015. Source compiled by the author based on statistics by the United Nations Population Division

pattern of urbanization in regions with different development levels, it can be seen that the emerging economies stepped into urbanized society one after another. By comparing the changes of urbanization ratios in developed economies, emerging economies, and underdeveloped nations in 1950–2015, it can be seen that the urbanization growth rate in emerging economies outstripped the developed economies and underdeveloped nations in 1975 and has been leading all the way through 2015 (see Fig. 2.10).

Fig. 2.10 Changes of urbanization ratio in developed economies, emerging economies, and underdeveloped nations in 1950–2015. Source compiled by the author based on statistics by the United Nations Population Division

42

2 The Planet of Cities Toward Diverse Agglomeration, Global …

Closer observation on the changes of urbanization ratios in emerging economies in 1950–2015, it can be seen that the emerging economies entered urbanized society in batches as shown in Table 2.1. Russia, Mexico and Brazil had their urbanization ratio hit 50% as early as in 1960. South Korea reached the level in 1980, South Africa and Turkey in 1990, and China and Indonesia in 2015. As of 2015, the urbanization ratio in Egypt, India, and the Philippines had still not reached 50%. Fourth, central cities in emerging economies and South Asia and East Asia have risen rapidly. Since 1950, with the rapid economic development (see Figs. 2.11 and 2.12), the general characteristic of global urbanization shows a significant decrease in the growth rate of urban population and a slowly expanding population in developed economies, whereas a significant increase is in urban population and a rapidly expanding population in emerging economies. Further comparison on Figs. 2.12 and 2.13 indicates that the increase trend of urban population in East Asia is generally in line with the overall trend of emerging economies, meaning that emerging economies in East Asia are typical representatives of all emerging economies. In specific, by comparing the maps on the urban systems in developed and emerging economies in 1950 and 2015, it can be seen that central cities in developed economies continue to rise, whereas the marginal areas are in constant recession; central cities and some regions in emerging economies and East Asia are rising rapidly. In 1950, the top five cities with the most population in developed economies were New York, Tokyo, London, Osaka, and Paris, which are mainly in North America, East Asia, and Western Europe. In the same period, the top five cities with the most population in emerging economies were Moscow, Calcutta, Shanghai, Mexico City, and Mumbai, which are mainly in Central and Eastern Europe, South Asia, East Asia, and North America. The entire size was relatively small (see Fig. 2.14). The overall growth rate of urban population in developed Table 2.1 Changes of urbanization ratios in emerging economies in 1950–2015 Emerging economies

1950

1960

1970

1980

1990

2000

2010

2015

Egypt

31.9

37.9

41.5

43.9

43.5

42.8

43.0

42.8

South Africa

42.2

46.6

47.8

48.4

52.0

56.9

62.2

64.8

China

11.8

16.2

17.4

19.4

26.4

35.9

49.2

55.5

South Korea

21.4

27.7

40.7

56.7

73.8

79.6

81.9

81.6

India

17.0

17.9

19.8

23.1

25.5

27.7

30.9

32.8

Indonesia

12.4

14.6

17.1

22.1

30.6

42.0

49.9

53.3

The Philippines

27.1

30.3

33.0

37.5

47.0

46.1

45.3

46.3

Turkey

24.8

31.5

38.2

43.8

59.2

64.7

70.8

73.6

Russia

44.1

53.7

62.5

69.8

73.4

73.4

73.7

74.1

Mexico

42.7

50.8

59.0

66.3

71.4

74.7

77.8

79.3

Brazil

36.2

46.1

55.9

65.5

73.9

81.2

84.3

85.8

Source compiled by the author based on statistics by the United Nations Population Division

2.1 Cities Have Become More Global, Networked, and Intelligent Over …

43

1600.00 1400.00 1200.00 1000.00 800.00 600.00 400.00 200.00 2010

2013

2004

2007

2001

1995

1998

1992

1989

1983

1986

1980

1974

1977

1968

1971

1965

1959

1962

1953

1956

1950

0.00

Fig. 2.11 Population changing trend in developed economies in 1950–2015. Source compiled by the author based on statistics by the United Nations Population Division

1400.00 1200.00 1000.00 800.00 600.00 400.00 200.00 2013

2010

2007

2001

2004

1998

1992

1995

1989

1986

1983

1980

1977

1974

1971

1968

1962

1965

1959

1956

1953

1950

0.00

Fig. 2.12 Population changing trend in emerging economies in 1950–2015. Source compiled by the author based on statistics by the United Nations Population Division

economies in 1978–2015 slowed down with the top five cities still being New York, Tokyo, London, Osaka, and Paris in East Asia, North America, and Western Europe. Central cities continue to rise, whereas the marginal areas are still in recession (see Figs. 2.14, 2.15 and 2.16). However, the top five cities with the largest population scale in emerging economies in 1978 were Mexico City, Sao Paulo, Calcutta, Mumbai, and Rio de Janeiro, which are mainly in North America, South America, and South Asia (see Fig. 2.15). In 2008, they changed into Delhi, Mexico City,

44

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1400.00 1200.00 1000.00 800.00 600.00 400.00 200.00 1950 1953 1956 1959 1962 1965 1968 1971 1974 1977 1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010 2013

0.00

Fig. 2.13 Population changing trend in emerging economies in East Asia in 1950–2015. Source compiled by the author based on statistics by the United Nations Population Division 90 70 50 30 10 -180 -160 -140 -120 -100 -80 -60 -40 -20 -10 0

20

40

60

80

100 120 140 160 180

-30 -50 -70 -90

Fig. 2.14 Map on urban systems of developed economies and emerging economies in 1950. Source compiled by the author based on statistics by the United Nations Population Division

Sao Paulo, Shanghai, and Mumbai, gathering mainly in South Asia, North America, South America, and East Asia (see Fig. 2.16). In 2015, the top five cities in terms of population scale were slightly changed to Delhi, Shanghai, Mexico City, Sao Paulo, and Mumbai, gathering mainly in South Asia, North America, and South America (see Fig. 2.17). It is evident that the emerging economies and South Asia and East Asia have risen over the last 40 years.

2.1 Cities Have Become More Global, Networked, and Intelligent Over …

45

90 70 50 30 10 -180 -160 -140 -120 -100 -80 -60 -40 -20 -10 0

20

40

60

80

100 120 140 160 180

-30 -50 -70 -90

Fig. 2.15 Map on urban systems of developed economies and emerging economies in 1978 (in terms of population). Source compiled by the author based on statistics by the United Nations Population Division 90 70 50 30 10 -180 -160 -140 -120 -100 -80 -60 -40 -20 -10 0

20

40

60

80

100 120 140 160 180

-30 -50 -70 -90

Fig. 2.16 Map on urban systems in developed economies and emerging economies in 2008 (in terms of population). Source compiled by the author based on statistics by the United Nations Population Division

46

2 The Planet of Cities Toward Diverse Agglomeration, Global … 90 70 50 30 10

-180 -160 -140 -120 -100 -80 -60 -40 -20 -10 0

20

40

60

80

100 120 140 160 180

-30 -50 -70 -90

Fig. 2.17 Map on urban systems in developed economies and emerging economies in 2015 (in terms of population). Source compiled by the author based on statistics by the United Nations Population Division

c.

The inflow of global high-end population to cities in developed nations

First, the global urban population mainly flows to the US. The World Immigration Report 2018, jointly published by the International Organization for Migration (IOM) and China and the Globalization Think Tank (CCG), states that there are 232 million international immigrants in the world, and more than half of international immigrants live in ten highly urbanized, high-income, countries with strong economic competitiveness (Fig. 2.18), such as Australia, Western Europe, Canada, and the US. From 2000 to 2017, the trend of population migration in various countries and regions around the world can be seen (see Fig. 2.19). Some countries in North America, Oceania, and Central Asia receive the largest number of immigrants, Eastern Europe, North Africa, and East Asia. Some countries are the regions with the largest number of immigrants; by 2017, the US has become the largest immigrant destination country, with India and Mexico becoming the largest immigrant countries (see Fig. 2.20). Second, the high-end population has reshaped the global urban system. Due to the expansion of global liquidity, the flow of high-end population has long been restricted by national borders, such as skilled immigrants, educational immigrants, and investment immigrants. For example, educational immigrants have seen an increase in the number of international students in recent years. According to data from the OECD and the UNESCO Institute for Statistics, in 1975, there were only about 800,000 students studying abroad; by 2010, the number of international students had grown to about 4.1 million. In addition to educating immigrants, skilled immigrants are also the main form of high-end population mobility, as shown in

2.1 Cities Have Become More Global, Networked, and Intelligent Over …

The year of 2000

47

The year of 2017 4%

5%

32%

37% 58%

Low-income countries Middle-income country High-income countries

64%

Low-income countries Middle-income country High-income countries

Fig. 2.18 Proportion of immigrants receiving land according to income in 2000 and 2017. Source of data Authors draw according to UN Population Data, World Immigration Report

Fig. 2.21. In developed regions such as Europe and North America, where skilled migrants are concentrated, such as the US, France, Germany, and South Korea. The top 100 global innovation companies are concentrated; the top 100 global innovation centers in Asia, Oceania, and other high-end populations are less distributed, and China and Japan are on the list, especially Japan. In the least developed regions, such as Africa, there are very few high-end populations, and the number of global innovation centers is almost zero. Therefore, it can be found that the high-end population has broken the boundaries of national borders, realized global mobility, and shaped the global urban system.

2.1.1.2

a.

The Concentration of City Population Shifts from a Single Form to Diverse Forms

The simultaneous concentration and dispersion of city population

First of all, the population of some cities is concentrated, and the population density is getting higher and higher. Although the population density of different global cities varies according to the national conditions of different countries, global cities are often the most concentrated areas of a country. From the 1961–2015 world population density map (see Fig. 2.22), the cities with the highest population density in the world are concentrated in some cities in East Asia, South Asia, Western Europe, the US, and South America. Secondly, the urban population tends to be scattered and concentrated, and the density of population in some cities is getting lower and lower. The figure shows that while urban populations are concentrated, the population density of some cities in Eastern Europe, North Africa, Canada, Oceania, and Central South America is getting lower and lower.

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2 The Planet of Cities Toward Diverse Agglomeration, Global …

Australia United Kingdom Saudi Arabia Canada Ukraine France India Germany Russia United States

4.4 4.7 5.3 5.5 5.5 6.3 6.4 9 11.9 34.8

0 5 10 15 20 25 30 35 40 10 countries with the highest number of internaƟonal immigrants accepted in 2000 Spain Australia Canada France United Arab Emirates United Kingdom Russia Germany Saudi Arabia United States

0

10

20

30

40

50

60

The 10 countries with the highest number of internaƟonal immigrants accepted in 2017 Fig. 2.19 Ten countries with the highest number of international immigrants accepted in 2000 and 2017. Source of data Authors draw according to UN Population Data, World Immigration Report

b.

The non-agricultural aggregation of population is carried out in both real and virtual space

The spatial state of human existence has undergone profound changes. In addition to the accumulation of real space, such as the “Silicon Valley” in the US, the Science City in Southern California, Seattle, and Austin, virtual gathering is becoming a major gathering trend. The way human activities have changed has changed, and the way cities are connected to the world has changed. The network world not only changes the structure and living form of society from the macro level, but also affects people’s living conditions from the microlevel. According to TechCrunch, a new report released today by the International Communications Alliance shows that there will be 4.4 billion mobile broadband users worldwide by the end of 2018, an

2.1 Cities Have Become More Global, Networked, and Intelligent Over …

12 10

49

10.7 9.6 8

8 6 4

5.8

5.6

5.4

4.5

3.9

3.69

3.4

2 0

Major countries that flowed out of the country in 2000 (million people) 18 16 14 12 10 8 6 4 2 0

The main country of immigraƟon in 2017 (million people) Fig. 2.20 Distribution of population outflows in various countries and regions around the world. Source of data Authors draw according to UN Population Data, World Immigration Report

increase of 1.1 billion compared with three years ago. From the 1990–2016 trend graph of the number of Internet users in the global region (see Fig. 2.23), the number of Internet users worldwide has grown rapidly. The regions with the highest Internet user rates are often concentrated in more developed regions such as North America, Europe, and Oceania. From the distribution map of the number of Internet users in various countries in the world in 2005–2018 (see Fig. 2.24), users in Asia are about half of the global Internet users and have huge potential for Internet development.

50

2 The Planet of Cities Toward Diverse Agglomeration, Global …

3%

3%

3%

1%

1% 1%

Japan United States

3%

France 4%

Germany

39%

Korea

7%

Switzerland Netherlands China Finland 36%

Sweden Ireland

Fig. 2.21 Top 100 global innovative enterprises in 2014. Source The author draws according to the Innovation Cities Index, 2014 The populaƟon density (people per sq. km of land area)

1400 1200 1000 800 600 400 200

East Asia and Pacific LaƟn America and the Caribbean North America Sub-Saharan Africa

2015

2012

2009

2006

2003

2000

1997

1994

1991

1988

1985

1982

1979

1976

1973

1970

1967

1964

1961

0

Europe and Central Asia Middle East and North Africa South Asia

Fig. 2.22 Trends in population density around the world from 1961 to 2017. Source the United Nations

2.1 Cities Have Become More Global, Networked, and Intelligent Over …

51

4500 4000 3500 3000 2500 2000 1500 1000

3896 3650 3417 3170 2880 2631 2424 2184 1991 1729 1547 1367 10241147

500 0 2004

2006

2008

2010

2012

2014

2016

2018

2020

Global Internet users (million people) Fig. 2.23 Trends in the number of global Internet users from 2005 to 2018. Source Authors draw according to the International Trade Center (ITC)

0.70%

7.50% 4%

10.10% 11.20%

50.10%

16.40%

Asia Africa North America Oceania

Europe LaƟn America and the Caribbean the Middle East

Fig. 2.24 Distribution of Internet users in various countries around the world. Image source Author drawn according to Internet World Stats

52

2 The Planet of Cities Toward Diverse Agglomeration, Global …

2.1.1.3

a.

The Contents of Population Aggregation Shift from Tangible Ones to Intangible Ones

In terms of consumption, spending on intangible products and services is on the rise while that on tangible goods is on decline

In recent decades, intangible products and service-oriented consumption have become a trend, manifested in two aspects: One is the intangible payment method, and the other is the intangible consumption object. The trend of shifting to a cashless society is becoming a wave of sweeping the globe. In 2011, 66% ($42 trillion) of total global consumer spending was done through cashless payments, and this number is rising rapidly. In 2013, 80% of consumer spending came from cashless payments, and 72% of the population had debit cards. China’s e-commerce grew by 31.4% in 2014, with a total market capitalization of more than $2.1 trillion. From the perspective of the proportion of consumer transactions using cashless transactions in various countries and regions around the world in 2014 (see Fig. 2.25), e-commerce in North America reached $1 trillion for the first time in 2012, and Europe also broke through in 2015 trillions of dollars. The amount of cashless transactions in the US is much higher than in other countries, making it the world’s largest cashless trading place. Followed by the euro zone, China’s cashless business maintained a high growth rate Cashless transacƟon (billion$)

600 500 400

161.1 152.5

300

127.7 83.5

0

143.7 108.5 100.8

200 100

136.6

87.9

93.5

33.5 23.9 31.1 32

37.3 29.2 34.5 35.2

41.3 38.7 38.3 37

2012

2013

2014

45.3 56.4

50 70.6

44.6 38.9

52.2 40.1

2015

2016

LaƟn America

Central Europe, Middle East and Africa

Emerging Asian economies

Mature Asia Pacific

Europe (including the Eurozone)

North America

Fig. 2.25 Cashless transaction volume in various countries and regions in the world in 2012– 2016. Source The author draws on the 2018 Capgemini Financial Services Analysis, the 2016 ECB Statistical Data Warehouse, the 2016 BIS Red Book, and the 2017 National Central Bank Annual Report

2.1 Cities Have Become More Global, Networked, and Intelligent Over …

53

Cashless transacƟon (billion$)

160 148.5 140 140.5 120 100 74.5 80 48 60 69.7 40 38.1 20 0 United Eurozone China States

29.1 28.7 Brazil

24.8 22.7

23.2 21.1

United Korea Kingdom

2015

17.3 12.7 Russia

15.3 13.9 Japan

12.6 12

10.6 9.6

Canada Australia

2016

Fig. 2.26 Trends in the volume of global remote services and other services in 2015–2016 and changes in the proportion of digital services. Source The author draws on the 2018 Capgemini Financial Services Analysis, the 2016 ECB Statistical Data Warehouse, the 2016 BIS Red Book, and the 2017 National Central Bank Annual Report

of 26.2% in 2015. The growth rate of cashless consumer payments is one of the fastest in the world (see Fig. 2.26). The world’s fastest-growing service exports are digital services such as telecommunications, computer and information services, other business services and financial services, as shown in Fig. 2.26, where trade in these services is growing much faster than traditional trading services. Since 2014, digital services have accounted for more than half of total trade in services. Tourism is a typical intangible service. In the regions where global tourism services grew rapidly in 1995–2015, apart from traditional North America and Western Europe, East Asia has become the fastest-growing region of tourism services in emerging economies, as shown in Fig. 2.27. b.

Knowledge and technological innovation contribute more and more in production

In the past 40 years, the industrial economy has evolved into a knowledge economy, and the economic center of gravity has shifted from “production” to “discovery, invention, and innovation.” The ability of knowledge to produce and allocate knowledge in economic activities determines the degree of modernization and operational efficiency of a city’s economic development. From the Global Urban Innovation Index from 2012 to 2018 (see Figs. 2.28, 2.29 and 2.30, the abscissa is longitude, the ordinate is latitude, the bubble size is the innovation index), and the global innovation index is high in 2012–2013. The cities are mainly concentrated in North America and Europe. According to each city, the top five cities in the innovation index are Boston, New York, San Francisco, Seattle, and Toronto. The top five cities in 2015 are still mainly concentrated in North America and Europe. The regional rankings were slightly adjusted to San Francisco, Boston, New York, Toronto, and Seattle.

54

2 The Planet of Cities Toward Diverse Agglomeration, Global …

3E+12 2.5E+12 2E+12 1.5E+12 1E+12 5E+11 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

0

East Asia and Pacific LaƟn America and the Caribbean North America Sub-Saharan Africa

Europe and Central Asia Middle East and North Africa South Asia

Fig. 2.27 Growth of global tourism services from 1995 to 2015. Source Authors draw based on World Bank data

70 50 30 10 -180 -160 -140 -120 -100 -80 -60 -40 -20 -10 0

20 40 60 80 100 120 140 160 180

-30 -50 -70 -90 Fig. 2.28 Regional distribution of the 2012–13 Global Urban Innovation Index. Image source author draws based on 2-think now data

Overall, in 2018, the global innovation index is well-known in the Asian cities of the former city. The top five are Tokyo, Singapore, Sydney, Seoul, and Melbourne. It is worth mentioning that China’s Hong Kong, Shanghai, Beijing, and Shenzhen rankings are also relatively high for the first time, and they have won good rankings of

2.1 Cities Have Become More Global, Networked, and Intelligent Over …

55

70 50 30 10 -180 -160 -140 -120 -100 -80 -60 -40 -20 -10 0

20 40 60 80 100 120 140 160 180

-30 -50 -70 -90 Fig. 2.29 Regional distribution of the 2016 Global Urban Innovation Index. Image source author draws based on 2-think now data

90 70 50 30 10 -180 -160 -140 -120 -100 -80 -60 -40 -20 -10 0

20 40 60 80 100 120 140 160 180

-30 -50 -70 -90 Fig. 2.30 Regional distribution of the 2018 Global Urban Innovation Index. Image source author draws based on 2-think now data

56

2 The Planet of Cities Toward Diverse Agglomeration, Global …

6, 7, 8, and 11, respectively. Emerging economies have entered the stage of modern economic development, and they are facing more intense international competition in the process of catching up with developed countries. The key to winning competition is economic innovation, that is, knowledge production capacity and knowledge allocation capability. Competition in various countries is not limited to the ranking of economic status is a test of a country’s ability to innovate. The economic value created by knowledge innovation and technological innovation is increasingly important in the factors affecting national development. c.

The growth rate of exchange and trade slows down, but exchanges and contacts become more frequent

The exchanges of goods and trade show signs of declining to varying degrees. In September 2016, the Bank for International Settlements released the latest triennial survey report on foreign exchange and OTC derivatives trading, which reviews the global foreign exchange turnover over the past three years. Statistics from the survey showed that the average daily trading volume of foreign exchange decreased from $5.4 trillion in April 2013 to $5.1 trillion in April 2016, down by 5%, which was the first downward trend in 15 years. Spot transactions fell even more sharply, down from an average daily trading volume of $2 trillion in 2013 to $1.7 trillion in 2016. Survey by Global Data showed that global financial trading dropped 26% this year against the backdrop of rising fever on investment in digital currencies. A report by the United Nations Conference on Trade and Development (UNCTAD) said following a shrink in 2016, foreign direct investment (FDI) dropped even more sharply in 2017 as shown in Fig. 2.31. On the other hand, international exchanges and exchanges are constantly rising. In global interactions, a country’s production is no longer an internal problem of a country, and it may be built around the global economy through multinational companies. Countries are also increasingly exhibiting “multidimensional and vertical relationships”, such as BRICS, WTO, APEC, World Bank, Arab Petroleum Exporting Countries, Group of 15. In the core of technology that uses information as a global engagement, information communication itself has a direct infiltration function, which integrates global economic life in the fastest and most convenient way. From

Fig. 2.31 Global FDI inflows in 2005–2017 ($trl). Source UNCTAD

2.1 Cities Have Become More Global, Networked, and Intelligent Over …

7.60% 1.10%

57

1.30%

14.40% 19.30%

6.90%

40.50%

Oceania

Africa

North America

East Asia

Eastern Europe

Other parts of Europe

Other parts of the Americas Fig. 2.32 2016 broadband service world market share by region. Image source Author drawn according to Internet World Stats

the perspective of 2016, global regional broadband service market shares distribution map (see Fig. 2.32). The regions with the highest market share of broadband services are mainly concentrated in countries and regions such as East Asia, North America, Europe, and Oceania. The regions with higher broadband penetration are mainly concentrated in Western European countries and developed countries or regions in Asia (see Fig. 2.33). Monaco, Denmark, South Korea, Hong Kong, and China. Information exchange connects the world commodity market, the world service market, the world investment market, and the internal and external markets of the world. The unified communication network has formed globalization within and between countries communication network.

2.1.1.4 a.

World Cities Witness Ongoing Expanding Sizes

World cities increase in number

At present, the urbanization ratio has exceeded 50% worldwide. The urbanization has been at the middle and late stage. According to statistics by United Nations Population Division, the number of world cities is on the rise since the 1970s as shown in Table 2.2. Generally speaking, besides declining number of some small cities, cities of other sizes witnessed prominent increase in number. First, megacities with population outnumbering 10 million emerged from four in 1975 to 29 in 2015, a significant increase over the past 40 years. Super cities with population between 5 to 10 million grew rapidly, with the number reaching 45 in the past 40 years. Big cities, medium-sized cities, and small ones, which have already existed in large numbers,

58

2 The Planet of Cities Toward Diverse Agglomeration, Global …

Monaco

51.30%

Denmark

43.40%

Switzerland

42.60%

France

42.20%

Netherlands

41.20%

Norway

40.60%

Korea

40.10%

China Hong Kong

39.90%

Malta

39.70%

United Kingdom

39.10%

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

Ten countries with the highest penetraƟon of broadband services Fig. 2.33 Distribution of regional broadband service penetration rates in various countries in the world in 2016. Image source Author drawn according to Internet World Stats

Table 2.2 Changes in number of world cities Year Megacities (10 million or more people)

1975

1985

1995

2005

2015 29

4

9

14

20

14

18

23

36

45

Big cities (1 to 5 million people)

145

202

276

343

439

Medium-sized cities (500,000 to 1 million people)

223

279

342

456

554

Small cities (300,000 to 500,000 people)

258

347

460

591

707

53

51

49

45

42

Super cities (5 to 10 million people)

Other small cities (Fewer than 300,000 people)

Source compiled by the author based on statistics from the United Nations Population Division

had their numbers increased year by year. Comparatively speaking, following the law of regionalization, small cities with close physical distance are becoming less and less under the radiation influence of expanding big, mega, and super cities. The rising emerging economies in Asia reshaped the global urban scale system. With the rapid economic development since the mid-1990s (see Tables 2.3, 2.4 and Fig. 2.35), the global urbanization is characterized by decline of the growth rate of city number and coexistence of shrink and expansion of city size in developed economies; rapid increase in city number and expansion of city size in emerging economies; and slowdown in the growth of city number and size in underdeveloped economies. In specific, according to statistics by the United Nations Population Division, the city number in developed nations rose to 393 from 161 in the 1950–2015 period, an increase of 144.10%. City number in emerging economies rose to 894 from 96 in the same period, an increase of 831.25%, much higher than developed economies. The

2.1 Cities Have Become More Global, Networked, and Intelligent Over …

59

Table 2.3 City number in developed economies Country

1950

1975

1980

2005

2010

2015

2035

Australia

5

5

5

8

9

10

11

Austria

1

1

1

1

1

1

2

Belgium

5

5

5

5

5

5

5

Canada

4

10

10

16

16

16

18

Cyprus

0

0

0

0

0

0

1

Czech Republic

1

2

3

3

2

2

2

Denmark

1

1

1

1

1

1

2

Estonia

0

1

1

1

1

1

1

Finland

1

1

1

1

2

2

2

France

9

16

16

19

20

20

23

German

18

22

22

20

20

22

23

Greece

1

2

2

2

2

2

2

Iceland

0

0

0

0

0

0

0

Ireland

1

1

1

1

1

1

1

Israel

1

3

3

4

4

4

4

Italy

16

22

23

29

31

31

34

Japan

8

12

14

35

34

33

30

Latvia

1

1

1

1

1

1

1

Lithuania

0

2

2

2

2

1

1

Luxembourg

0

0

0

0

0

0

0

Malta

0

0

0

0

0

0

0

The Netherlands

3

4

4

5

5

5

5

New Zealand

1

2

2

3

3

3

3

Norway

1

1

1

1

1

1

2

Portugal

2

2

2

2

2

2

2

Puerto Rico

1

1

1

2

2

2

1

South Korea

3

8

11

24

24

25

30

Singapore

1

1

1

1

1

1

1

Slovakia

0

1

1

1

1

1

1

Slovenia

0

0

0

0

0

0

1

Spain

4

8

9

13

13

14

14

Sweden

2

2

2

2

2

3

3

Switzerland

1

3

3

5

5

5

5

22

22

22

25

26

28

31

The UK The United Nations Total

44

71

78

114

123

140

165

161

240

255

357

370

393

437

Source compiled by the author based on statistics from the United Nations Population Division

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2 The Planet of Cities Toward Diverse Agglomeration, Global …

Table 2.4 City numbers in emerging economies Country

1950

1975

1980

2005

2010

2015

Brazil

6

19

25

49

52

57

64

China

32

77

87

322

355

399

504

Egypt

2

2

5

9

11

13

17

India

18

54

69

130

144

170

255

Indonesia

4

13

17

27

29

31

41

Mexico

3

15

17

46

49

52

63

The Philippines

1

3

4

15

23

30

41 30

South Korea Russia South Africa Turkey Total

2035

3

8

11

24

24

25

19

46

53

62

64

65

66

4

8

8

12

13

16

21

1

5

9

21

24

26

36

96

257

312

727

798

894

1148

Source compiled by the author based on statistics from the United Nations Population Division

difference on growth rate is better shown in Fig. 2.34. The United Nations Population Division estimated that the city number in developed nations will have reached 437 and 1,148 in emerging economies by 2035. On the other hand, from the changing trend in the number of megacities as shown in Table 2.5, the number increased from three to six over the recent years in developed

Fig. 2.34 Comparison of city numbers in developed economies and emerging ones. Source compiled by the author based on statistics from the United Nations Population Division

2.1 Cities Have Become More Global, Networked, and Intelligent Over …

61

90 70 50 30 10 -10 0 -180-160-140-120-100 -80 -60 -40 -20

20 40 60 80 100 120 140 160 180

-30 -50 -70 -90 Fig. 2.35 1950 global city scale system map (population perspective). Source: The author draws data from the UN Population

Table 2.5 Changes in the number of mega cities

Year

World megacity number Developed nation Developing nation

Underdeveloped nation

1975

3

1

0

1985

4

5

0

1995

4

10

0

2005

6

14

1

2015

6

23

2

Source compiled by the author based on statistics from the United Nations Population Division

nations; from one to 23 in developing countries; and from zero to two in underdeveloped nations. It further shows that the rising of cities in emerging economies, especially those from Asia, reshaped the global urban system. b.

The size of world cities continues to expand

By analyzing the population share in cities of different sizes as shown in Table 2.6, it can be seen that there is a coexistence of expanding and shrinking cities. From 1950 to 2015, the population share of megacities increased from 1.83% to 11.72%, up by 540.44%. The share of smaller cities decreased from 62.84% to 43.09%, down by 31.43%. From the number of cities in different sizes and the share of their population, we can see small cities are still the mainstream. As people have been flowing to megacities, super cities, and big cities over the past 40 years, the share of

62

2 The Planet of Cities Toward Diverse Agglomeration, Global …

Table 2.6 Population shares of cities in different sizes (%) Year Megacities (10 million or more) Super cities (5 to 10 million)

1950

1975

1980

1985

2005

2010

2015

1.83

3.03

3.86

8.28

9.44

10.58

11.72

3.29

7.49

8.81

7.86

7.81

7.66

7.44

17.34

19.09

18.66

21.61

20.98

21.08

21.84

Medium-sized cities (500,000 to 1 million)

8.29

10.01

9.74

9.19

9.64

9.69

9.34

Small cities (300,000 to 500,000)

6.42

Big cities (1 to 5 million)

Smaller cities (Fewer than 300,000) 62.84

6.5

6.41

6.57

53.88

52.51

46.48

6.54 45.6

6.57

6.55

44.43

43.09

Source compiled by the author based on statistics from the United Nations Population Division

population in small cities has been declining. The size of world cities continues to expand. A closer observation on the population size of cities with different sizes in developed economies, emerging economies, and underdeveloped nations as shown in Table 2.7 shows that there is a coexistence of expanding and shrinking cities in developed economies, expanding city sizes in emerging economies and rapid expanding city sizes in underdeveloped nations during the period of 1950–2015. In specific, megacities, super cities, big cities, and medium-sized cities mainly concentrated in developed nations before 1975, and there was no megacity in emerging economies and underdeveloped nations. During the period of 1975–1980, cities, especially megaones, in emerging economies expanded rapidly. In the period of 1980–2015, population of megacities flew to emerging economies, whereas the population in big and super cities had exceeded developed economies. The urban population in emerging economies grew rapidly, making their cities concentrating the most world population. From Table 2.7, we can also see that the urban population growth in developed economies went into a stagnation stage after 2005, whereas smaller cities in developed countries witnessed a decline in urban population. Besides, city size of underdeveloped countries showed trends of rapid expansion during the period of 1950–2015 (Table 2.8). c.

The change of rank-size rule for world cities

First, the rank-size rule for world cities has changed over the past 40 years. Cities in East Asia have expanded significantly. By comparing the top ten cities in terms of population in 1970–2015 (see Fig. 2.9), it can be seen that there were three from North America, three from Asia, two from South America, and the other two from Europe in 1970; while in 2015, there were six from Asia, two from North America, one from South America, and one from Africa, and the top three cities in terms of urban population were all from Asia. It shows that cities in Asia especially in East Asia have expanded significantly over the past 40 years and conventional population centers in North America and Europe are shifting to East Asia. According to the UN Population Data, the map of global urban scale system from 1950 to 2016 is compared from the perspective of population size (Figs. 2.35, 2.36,

154,919.4 102,777.8

Emerging economies

Underdeveloped economies

9106.7 194,299.7

Developed economies

Underdeveloped economies

16,222.3

Emerging economies

12,991.3 25,199.1

Underdeveloped economies

Developed economies

19,178.0

Emerging economies

12,331.7 35,174.8

Underdeveloped economies

Developed economies

46,121.1

Emerging economies

5166.1 68,370.9

Underdeveloped economies

Developed economies

237,217.8

320,685.4

258,858.6

25,011.1

43,781.2

29,892.5

46,981.0

56,030.8

54,355.8

67,181.1

97,140.9

126,834.0

9143.2

59,736.7

45,893.7

0.0

10,733.9

58,793.1

1975

275,978.0

369,208.4

267,911.0

28,359.8

52,230.0

34,878.7

46,887.9

67,120.9

56,265.5

90,510.8

106,598.5

135,691.5

20,046.8

79,633.1

48,170.0

0.0

25,117.1

61,177.5

1980

Source compiled by the author based on statistics from the United Nations Population Division

Smaller cities

Small cities

Medium-sized cities

Big cities

5356.4

Emerging economies

0.0

Underdeveloped economies 21,649.1

Emerging economies

Developed economies

Developed economies

Megacities

Super cities

23,613.1 0.0

1950

Year

Table 2.7 Urban population in different economies (thousand people)

494,488.5

655,518.5

302,848.9

61,272.3

110,975.3

52,911.5

80,748.9

166,498.3

73,841.2

195,231.1

302,549.7

181,849.6

70,174.8

119,283.6

61,219.7

36,792.0

176,128.6

94,540.5

2005

546,275.7

721,654.7

316,189.5

75,725.3

118,616.6

53,650.5

97,123.2

187,041.2

76,718.3

216,451.4

358,568.4

188,737.6

85,919.8

112,708.8

73,550.2

52,029.5

237,901.2

97,157.8

2010

607,285.4

776,840.3

313,000.9

87,828.4

124,889.3

59,892.8

105,044.2

204,841.0

79,048.7

254,159.4

425,088.0

189,151.3

88,038.2

131,678.8

93,098.7

80,797.9

283,700.1

98,287.2

2015

2.1 Cities Have Become More Global, Networked, and Intelligent Over … 63

64

2 The Planet of Cities Toward Diverse Agglomeration, Global …

Table 2.8 Top ten cities in terms of population in 1970–2015 Ranking

1970

1980

1990

2000

2015

1

Tokyo

Tokyo

Tokyo

Tokyo

Tokyo

2

New York

Osaka

Osaka

Osaka

Delhi

3

Osaka

New York

New York

Mexico City

Shanghai

4

Mexico City

Mexico City

Mexico City

New York

Mexico City

5

Buenos Aires

Sao Paulo

Sao Paulo

Sao Paulo

Sao Paulo

6

Los Angeles

Buenos Aires

Mumbai

Mumbai

Mumbai

7

Paris

Los Angeles

Buenos Aires

Delhi

Osaka

8

Sao Paulo

Mumbai

Calcutta

Shanghai

Carol

9

London

Calcutta

Los Angeles

Carol

New York

10

Calcutta

Rio de Janeiro

Seoul

Calcutta

Beijing

Source compiled by the author based on statistics from the United Nations Population Division

90 70 50 30 10 -10 0 -180-160-140-120-100 -80 -60 -40 -20

20 40 60 80 100 120 140 160 180

-30 -50 -70 -90 Fig. 2.36 1978 global urban scale system map (population perspective). Source The author draws data from the UN Population

2.37, and 2.38, the abscissa is longitude, the ordinate is latitude, and the bubble size is population size.), it can be found that global cities are experiencing a transition from polarization to equilibrium. Observing the 1950 global city scale system map, as shown in Fig. 2.35, it can be found that there are only a few sporadic world cities in the world after Second World War, mainly distributed in New York, Tokyo, and London, and the global cities are polarized; by 1978, the global urban scale system map showed that the expansion of the world’s cities with a population of over 10

2.1 Cities Have Become More Global, Networked, and Intelligent Over …

65

90 70 50 30 10 -10 0 -180-160-140-120-100 -80 -60 -40 -20

20 40 60 80 100 120 140 160 180

-30 -50 -70 -90 Fig. 2.37 2008 global city scale system map (population perspective). Source The author draws data from the UN Population 90 70 50 30 10 -180 -160 -140 -120 -100 -80

-60

-40

-20-10 0

20

40

60

80

100 120 140 160 180

-30 -50 -70 -90

Fig. 2.38 2015 global urban scale system map (population perspective). Source The author draws data from the UN Population

million is still not obvious, mainly in Western Europe, North America, Asia and China, Japan and other countries, as shown in Fig. 2.35; after the global financial turmoil, the global city scale system map shows that the world cities with a population of over 10 million have expanded significantly. In addition to the US, Western Europe, China, Japan, and other countries, the number of new world cities in South America

66

2 The Planet of Cities Toward Diverse Agglomeration, Global …

1.40 1.20 1.00 0.80 0.60 0.40 0.20 0.00 1945

1955

1965

1975

1985

1995

2005

2015

2025

2035

2045

Fig. 2.39 Trend of Zipf’s indices of major countries in 1950–2015. Source compiled by the author based on statistics from the United Nations Population Division

has increased significantly, as shown in Fig. 2.36. By 2016, the global city scale system map shows that the global urban scale system is gradually balanced, and the world cities are distributed in countries in Asia, North America, Western Europe, South America, and Africa, as shown in Fig. 2.37. Second, by observing world city size systems, it can be seen that the city sizes in different geographical locations (for instance, country, state, and province) follow closer and closer to Zipf’s law. According to Zipf’s index regarding world city sizes as shown in Fig. 2.39, it can be seen that the Zipf’s index was on the rise from 1950 to 1995, signaling world city sizes are becoming balanced. In 1950–1975, the index rose slowly, less than 1, meaning the distribution of city sizes is dispersed. In 1975–1995, the growth of the index was accelerated, approaching 1, indicating that cities with higher rankings in size are expanding faster. In 1995–2015, the Zipf’s index was higher than 1, showing the distribution of city sizes is too concentrated, but the growth is flat. This means that the distribution of city sizes follows closer to the Zipf’s law. Further classification on the distributions of city sizes in developed nations and emerging economies shows that urban population concentrates in the largest city. But the patterns are different (see Figs. 2.40 and 2.41). For developed nations, it was an upward trend before 2006 and a downward one after the year, approaching the standard set by Zipf’s law. For emerging economies, there is always an upward trend. With 2005 as the dividing year, the speed slowed down. d.

Urban agglomeration system is taking shape

By calculating the Zipf’s indices for major urban agglomerations across the world, it can be seen that the indices are approaching to 1. As the urban agglomerations stay on different development stage, the city sizes change in different patterns inside the agglomerations. By observing the Zipf’s indices for major urban agglomerations in 1950–2015, three patterns can be summed up: first, urban population relatively concentrates in the largest city, for instance the Piedmont Atlantic Megaregion (see Fig. 2.42a), the Mexico City Metropolitan Area (see Fig. 2.42b), the Yangtze

2.1 Cities Have Become More Global, Networked, and Intelligent Over …

67

1.40 1.20 1.00 0.80 0.60 0.40 0.20 0.00 1945

1965

1985

2005

2025

2045

Fig. 2.40 Trend of Zipf’s indices of developed nations in 1950–2015. Source compiled by the author based on statistics from the United Nations Population Division

1.40 1.20 1.00 0.80 0.60 0.40 0.20 0.00 1945

1955

1965

1975

1985

1995

2005

2015

2025

2035

2045

Fig. 2.41 Trend of Zipf’s indices of emerging economies in 1950–2015. Source compiled by the author based on statistics from the United Nations Population Division

River urban agglomeration (see Fig. 2.42c), the Pearl River urban agglomeration (see Fig. 2.42d), Beijing–Tianjin–Hebei urban agglomeration (see Fig. 2.42e), and Sao Paulo Metropolitan Circle (see Fig. 2.42f); the second is distribution by order, for instance, the Netherlands–Belgium urban agglomeration (see Fig. 2.43g) and the German Rhine-Ruhr urban agglomeration (see Fig. 2.43h). The degree of distribution by order is being intensified, showing the distribution of city sizes in these regions is relatively even; the third one approaches the standard distribution set by Zipf’s law, for instance, the Bangalore metropolitan circle (see Fig. 2.44i), US eastern urban agglomeration (see Fig. 2.44j), and London–Liverpool city belt (see Fig. 2.44k). All in all, although the distributions of world major urban agglomerations show different trends, they are approaching to an even distribution pattern.

Fig. 2.42 Trend of Zipf’s indices of major urban agglomerations in 1950–2015 (I). Source compiled by the author based on statistics from the United Nations Population Division

68 2 The Planet of Cities Toward Diverse Agglomeration, Global …

2.1 Cities Have Become More Global, Networked, and Intelligent Over …

69

Fig. 2.43 Trend of Zipf’s indices of major urban agglomerations in 1950–2015 (II). Source compiled by the author based on statistics from the United Nations Population Division

2.1.1.5 a.

Challenges Urban Population Faces

The coexistence of declining developed economies and rising emerging economies

World urban population is characterized by simultaneous expansion and shrink. By comparing urban population growth by continent from 1970 to 2015 (see Fig. 2.45), it can be seen that world population is flowing to Asia, leading to a rapid expansion of population in the continent. The population growth in Asia rose from 53.46% in 1970–80 to 62.81% in 2000–2015. Second, European cities lost their attraction on people. The population sizes stopped to grow, leading to declining European cities. The population growth in Europe dropped from the 13.52% in 1970–80 to 2.45% in 2000–2015. Besides, Africa witnessed a rapid expansion in population in the past 40 years, leading to the formation of big cities with large population. Population growth in Oceania has been extremely slow over the past 40 years. The growth is basically stagnant, and cities are declining. On further observation of the change in the birth rate of the countries in the world from 1979 to 2017 (see Fig. 2.46), it can also be found that the global birth rate has further decreased, while the developed economies have always maintained a low birth rate, and the emerging economies have also been born. b.

Racial conflicts and social contradictions

Since the end of the 1980s, after the transition of the world pattern, ethnic and social conflicts in some countries and regions have shown an increasing trend. According to a survey from the Washington Post, more than 40% of respondents believe that India has a tendency to social stratification and social contradictions. Looking at the world history of more than 40 years, ethnic conflicts in many countries and regions have led to ethnic conflicts, and they have not been able to clean up. According to a survey of ethnic reports issued by the US in 2019, as shown in Fig. 2.47, racial discrimination has greatly affected the development of individuals in the US. According to the Pear Research Center’s August 2017 survey, 58% of respondents believe that racism is a big problem in American society, an increase of 8 percentage points from two years ago and about double the number in 2011. And the statistics released by the US

Fig. 2.44 Trend of Zipf’s indices of major urban agglomerations in 1950–2015 (III). Source compiled by the author based on statistics from the United Nations Population Division

70 2 The Planet of Cities Toward Diverse Agglomeration, Global …

2.1 Cities Have Become More Global, Networked, and Intelligent Over …

71

Fig. 2.45 Share of population growth by continent in 1970–2015. Source compiled by the author based on statistics from the United Nations Population Division (per1,000 people)

50 40 30 20 10 0 1979

1991

1992

2008

2009

North America

Europe and Central Asia

East Asia and Pacific

Middle East and North Africa

LaƟn America and the Caribbean

South Asia

2017

Sub-Saharan Africa

Fig. 2.46 Change in the crude birth rate of the countries in the world from 1979 to 2017. Source Authors draw based on World Bank data

Federal Bureau of Investigation on November 13, 2017, show that there were 6,121 hate crimes in the US in 2016, reaching a high point that has not been seen in recent years.

72

2 The Planet of Cities Toward Diverse Agglomeration, Global …

Fig. 2.47 Survey and test on the ability of individuals affected by ethnicity. Image source “Race in American 2019”

2.1.2 Tremendous Changes on World City Functions Caused by Division of World Cities Reviewing on world cities is a new perspective for understanding the global economy, as it reflects new developments on both world economy and world cities. The formation of world cities is the result of “separation” and “aggregation” of economic globalization. Cities serve the economic globalization, and the globalization highlights the function of service provided by world cities. At present, information technologies help the formation of a network made up by world cities. The network brings world cities closer. As a result, with technological innovations and industrial revolution prompted by it, especially the global infiltration of information technology, the global decomposition of industries has led the division of world cities to shift from regional and country scale to global scales. The changes of division have caused tremendous changes on the functions of world cities. a.

Emerging industries in some cities are rising rapidly

At present, human beings’ habitation has evolved from a closed, dispersed, and exclusive agricultural earth to an interconnected, concentrating, and sharing planet of cities. Globalization has become a distinctive feature in city development. At different development stages in human society, industries and city development are closely related, but the two follow respective economic laws. This paper explores the changes of industries in world cities and further analyzes the formation and development of

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the division system for world cities and its implication on the expansion of the cities based on data on Fortune top 500 and world listed companies by Osiris in 1989–2017. The development of global technological industries and the rising of global technological centers From the perspective of historical evolution, global central cities took shape and shift thanks to opportunities brought after major technological revolutions. Those seized the historical opportunities brought by each major technological revolution and corresponding industrial revolution are able to secure rapid rising and dominate global economy. Cities, in particular, capable of attracting factors of innovation gradually emerged as global centers of innovation. At present, global central cities of innovation are mainly in the US, Europe, and East Asia. From the spatial evolution pattern, global technological centers have shifted from London to Paris, then to Berlin, Boston, and Silicon Valley since the late seventeenth century. The paper examines the changes of high-tech industries like electronics manufacturing, aerospace, biotechnology, pharmaceuticals, and communication equipment among others (see Figs. 2.48, 2.49 and 2.50). Before the 1990s, high-tech industries concentrate in cities of developed economies like the US, Europe, and Japan. They include London, Paris, Finland, and Tokyo. In specific, global technological central cities have been shifting to the US since the 1970s. High-tech industries have also shifted to manufacture-related service industries like IT services. Finance, research and development, and innovation are sectors undergoing rapid development like in the Silicon Valley and the San Francisco Bay Area, Greater London, and Erlangen. 90 70 50 30 10 -10 0 -180-160-140-120-100 -80 -60 -40 -20

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-30 -50 -70 -90 Fig. 2.48 Spatial distribution and its changes of high-tech industries in world cities in 1989–1991. Source data on global public companies in 1989–2017 released by Osiris

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-30 -50 -70 -90 Fig. 2.49 Spatial distribution and its changes of high-tech industries in world cities in 1992–2008. Source data on global public companies in 1989–2017 released by Osiris

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-30 -50 -70 -90 Fig. 2.50 Spatial distribution and its changes of high-tech industries in world cities in 2009–2017. Source data on global public companies in 1989–2017 released by Osiris

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Against the backdrop of ongoing globalization and global industrial restructure in the 1980s, the industries in global technological central cities underwent constant exchanges. Cities of developed nations or regions moved up to the industrial chain, leading to the emerge of a raft of renowned global technological centers, like Boston and Seattle in the US, London in the UK, Tel Aviv in Israel, Tsukuba Science Town in Japan, Seoul in South Korea, Singapore, and Taipei in China. Since the 1990s, the rapid industrial development in world cities has fostered the rising of central cities of technological innovation. Especially with the advancement of globalization in 1992–2008, high-tech industries in Western developed economies began to spread and transfer to other cities across the world. The degree of concentration slightly declined. Emerging economies rose via taking over the industries transferred by cities in developed nations, like the coastal regions in east China, India’s technological city Bangalore, Moscow, and Sao Paulo. However, high-tech industrial development slowed down after the global financial turmoil in 2008. The three-center pattern for cities of technological innovation became more prominent. The rising, changing, and polarization of global centers of technological innovation are essentially determined by the historical evolution of technological revolution, institutional innovation, and the waves in economic life. They are also the outcome of interaction of space and time factors. The San Francisco Bay Area, with the Silicon Valley, the “Holy Land” of global innovation, as the hinterland, has established itself as a global innovation center combining technology (radiation), industries (network), and institutions (business environment) by taking advantage of the spillover and radiation of knowledge and capital from Silicon Valley, the high-tech industrial cluster in San Jose, the high-end manufacturing in Auckland, and the professional services (for instance, financial services) and tourism in San Francisco. b.

The development of manufacture-related service industries and the rising of world cities

With the transformation of mode of production and the deepening division of labor in a global scale since the 1970s, the economies in Western developed nations have been restructuring by shifting from manufacturing-driven growth to services-driven expansion (Bell, 1973). Manufacture-related service industries like research and development, finance, consultancy, and data services have been gradually taking over the manufacturing sector as the dominant industry driving global economic growth and restructuring of world city system. Besides, the spatial structure of world production network is built on a system with information, finance, logistics, and talents as the basis. Among them, information and finance are gradually replacing logistics to become the core that controls and influences the entire global production network. Finance sector is representative of manufacture-related services industries and is characterized by spatial concentration and hierarchy. From a historical perspective, the origin of global finance can be traced back to Amsterdam in the seventeenth century and the first half of the eighteenth century. However, thanks to the industrial revolution in the late eighteenth century, London replaced Amsterdam as world’s largest international financial center. During the Second World War, New York replaced London as the global financial center as the

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-30 -50 -70 -90 Fig. 2.51 Spatial distribution of public companies in the banking and financial sectors and its changes in 1989–1991. Source data on global public companies in 1989–2017 released by Osiris

US build up the Bretton Woods system. From Figs. 2.51, 2.52, and 2.53, it can be seen that the spatial distribution of global financial cities is shifting from concentration in developed nations to emerging economies or developing countries. But New York in the US, London and Frankfurt in Europe, and Tokyo and Hong Kong in East Asia are still the major three global financial centers. In specific, financial services in US cities far outperformed other countries in 1989–1991. With the industrial restructure in the 1970s and 1980s, a raft of international financial centers emerged, like Seoul, Singapore, Boston, Copenhagen, the Virgin Islands, Qatar, Guangzhou, and Shenzhen. Since the 1990s, financial sectors in some emerging economies have grown rapidly, resulting in the rising of some regional financial centers like Sao Paulo, Melbourne, Busan, Doha, Monaco, Manila, and St. Petersburg. In the period of 1992–2008, the financial services were greatly improved in US cities, so were those in Europe. In 2009–2018, some indigenous regional financial centers rose in some emerging economies. The spatial distribution of financial cities with London, New York, Tokyo, Hong Kong, Frankfurt, and Beijing as the international financial centers, Seoul, Los Angeles, Sydney, the Virgin Islands, Qingdao, Guangzhou, and Mumbai as second-level international centers, and Budapest, Sao Paulo, Helsinki, and Melbourne as regional or indigenous financial cities has taken shape. c.

Global urban functions have changed significantly

With the development of urban economies, the division of urban functions has become more and more specific. Along with the process is the emerging of world cities gathering labor forces and consumers with diverse skills and demands, factors

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-30 -50 -70 -90 Fig. 2.52 Spatial distribution of public companies in the banking and financial sectors and its changes in 1992–2008. Source data on global public companies in 1989–2017 released by Osiris

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-30 -50 -70 -90 Fig. 2.53 Spatial distribution of public companies in the banking and financial sectors and its changes in 2009–2017. Source data on global public companies in 1989–2017 released by Osiris

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of innovation, and services sectors, which are gradually replacing traditional cities with manufacturing as the dominant industry. They begin to control the global production network. With these cities as the knots in the network, the importance of information, talents, and finance is becoming prominent. The urban functions are upgrading from serving manufacturing, trading, and exchanges to facilitating innovation, communication, and contacts. First, living is always the basic function of all cities. But the fundamental functions of cities may vary in different economies. From the development history of cities across the world, we can see that the fundamental functions of cities in developed economies are about serving a better-off life, whereas in emerging economies, living and production are combined, but began to separate from each other. In underdeveloped economies, cities have the basic functions for living. As an international city, New York has its function of serving a better-off especially prominent. For example, the renovation of Hudson Square in 2001 effectively improved local cityscape and attracted more global leading firms to set businesses there, generating economic and social benefits to Manhattan and the entire New York City. Its competitiveness and influences were also enhanced greatly. London also introduced a raft of measures to expand city space and upgrade urban functions to better serve the residents. The urban functions in emerging economies like China, Brazil, and India combine living and production, but the trend is that the two functions are separating from each other. For example, with China’s economic restructure, Shenzhen has been relocating its manufacturing business to other places while keeping the services businesses. Shanghai is also working to relocate its industries to improve the service function of the city and promote the upgrade of urban structure and expansion of its space. Besides, urban function in underdeveloped countries is still about serving basic life, which is especially true to African cities. Disparity is a prominent issue in some cities, like the large quality of slums surrounding the city such as in the Philippines and Brazil. Living has been the basic function of a city, but due to the differences in speed and quality a city upgrades, the urban functions in different economies are different. Second, the production functions of a city are being replaced by innovation. Knowledge, information, and original ideas become core elements that dominate urban functions. World cities are those have the capability of spearheading development. They could well manage capitals, information, and industries and take the pivotal position in world city system. Against the backdrop of globalization and IT application across the world, the world is at the critical stage of shifting from industrial civilization to information civilization. With the rapid development of information technology and increasing prevalence of digital lifestyle, especially the rapid penetration of Internet, elements represented by finance, information, technology, and talents are becoming more important for economic growth. It has become an inevitable trend that the new round of economic globalization depends on various resource elements, which are the key to consolidate and showcase the core competitiveness of world cities. Knowledge, information, and ideas are becoming core elements dominating urban functions, which is indicated by the aggregation of knowledge-intensive industries.

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From Figs. 2.54, 2.55, and 2.56, it can be seen that knowledge-intensive industries have been growing rapidly in world cities especially those in the US and Europe from 1989 to 2017. New York, Washington D.C., and Silicon Valley in the San Francisco Bay Area in the US, London in the UK, Berlin in Germany, and Frankfurt in France gathered the most knowledge-intensive businesses and become the global centers for the aggregation and dispersion of knowledge and information. At the same time, with the deepening economic globalization in 1992–2008, the knowledge-intensive industries grew rapidly in East Asian cities like Tokyo in Japan, Seoul in South Korea and Hong Kong, Taiwan, Beijing, Shanghai, and Shenzhen in China, but still lag behind Western developed countries. After the 2008 global financial crisis, knowledge-intensive industries boomed across the world. Besides the rising of cities with knowledge-intensive businesses in the US and Western Europe, there are some in emerging economies, like Bangalore, Calcutta and New Delhi in India, Sao Paulo in Brazil, and Moscow in Russia. Second, trade and exchanges among world cities are being replaced by communication and contacts. With information technology widely applied, world cities are closed connected, with more and more medium-sized and small cities joining in the global network. Unlike the previous way of contacts in which mediumsized and small cities got connected via big cities as intermedium, medium-sized and small cities are able to contact directly, prompting the shifting from a relationship driven by trade and exchanges to one boosted by communication and contacts. This further boosted a raft of emerging industries, especially Internet plus traditional industries and then led to the changes of urban functions. While reinforcing the existing dominance as global technological centers of cities in developed nations, 90 70 50 30 10 -10 0 -180-160-140-120-100 -80 -60 -40 -20

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-30 -50 -70 -90 Fig. 2.54 Changes of knowledge-intensive industries in world cities in 1989–1991. Source compiled based on data on global public companies released by Osiris

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-30 -50 -70 -90 Fig. 2.55 Changes of knowledge-intensive industries in world cities in 1992–2008. Source compiled based on data on global public companies released by Osiris

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-30 -50 -70 -90 Fig. 2.56 Changes of knowledge-intensive industries in world cities in 2009–2017. Source compiled based on data on global public companies released by Osiris

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Internet-related industries fostered the rapid rising of cities in emerging economies. For example, Hangzhou in China has risen in the new round of industrial restructure thanks to the booming Internet-based services and retail sectors offered by Alibaba Group. By reviewing the Fortune Global 500 companies, it can be seen that businesses are booming thanks to the emerging high-tech sectors like Internet-based services and retail, IT services, and pharmaceuticals. This study looks at data on the Fortune Global 500 companies and finds that there was no company specializing in IT services among them in 1995–1998. IT services were still provided by traditional manufacturing companies. Firms specializing in software and data services came into being in 1998–2008, like the rising of IT services providers such as IBM, Microsoft, and Accenture. After 2008, the booming IT services represented by Internet-based services and retail and intelligent logistics spurred the emerging of Internet giants such as Amazon, Google, Facebook, International Business Machines Corporation, Alibaba Group, and NEC Corporation. New industries with computers and Internet as the core are growing rapidly in this new round of technological and industrial revolution, new business models also come into being, like modern logistics and intelligent industries. The emerging new industries and business models are changing the meanings of city development, making smart cities and intelligent cities the new direction for world cities to develop. d.

The dominant functions of world cities are deeply globalized and hierarchical

Economic globalization, IT application, and Internet penetration are redefining the concept of city, bringing profound changes to the form, function, and role of world cities. Economic globalization spurs new division of urban functions. IT application and Internet penetration bring cities together, changing the way of contacts among cities. As the global system for division of labor is taking shape, the division of urban functions becomes deeply globalized and hierarchical. The functions of world cities showcase the following characteristics: First, finance and technology services are the core and dominant functions world cities possess to exert global control and influences. The core and dominant functions of world cities are in “dynamic evolution,” which is shown in the fact that the functions of world cities are transforming from previous economic functions to manufacturing industries to functions of providing financial services and asset management and then to functions of serving sustainable development and innovation. The functions of financial and technology services are more important. From Fig. 2.57, it can be seen that cities with outstanding financial services also excel in technological innovation. Finance and technology services have become the core and dominant functions for world cities. The most typical examples include New York and London. These cities had been restructuring the economies since the 1970. From 2008 on, New York has attracted some 7000 IT start-ups, becoming one of the incubators for high-tech firms in the US. Its influences have gradually increased and been approaching the Silicon Valley. By giving strong support to high-tech industries, London and Tokyo have gathered world talents and innovation elements. Finance is

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-30 -50 -70 -90 Fig. 2.57 Distribution of financial and high-tech sectors in world cities. Source compiled based on data on global public companies released by Osiris. Red triangles stand for financial sector, and blue circles stand for high-tech industries

the blood of modern economy. Cities are the product of modern economic development. By this logic, financial services determine the economic development of a city, and they are the blood for the development of a city. Meanwhile, high-tech businesses are the major forces to support the financial functions of world cities. Technological innovation in financial sector with IT as the basis is the major means of market transactions. Financial function of world cities is capable of addressing the financing demands of high-tech firms. The two support world cities to exert control and influences over the global division of labor. Second, the service function of world cities become more prominent. All cities involved in globalization serve for globalization. Global division of labor and industrial relocation prompt the division of urban functions to some extent, which in turn lead to the improving on a distinctive function of a city. Therefore, industrial restructure is the key to promote the upgrading of urban functions. From the distribution of the Fortune Global 500 companies in world cities, it can be seen that services sectors expanded rapidly across the world in 1995–2018, with more and more cities included in global division of labor. Meanwhile, analysis of data on global public companies released by Osiris shows that the distribution of global services sector is highly dispersed after the 1980s, and both the scale and business model of services providers in world cities are being constantly upgraded1 as shown 1 From data on global public companies in 1989–2017 released by Osiris, the study reviews financial

and insurance sectors, professional, scientific and technological services, retail, health care, and social assistance services to reflect the service level of world cities and its global distribution.

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-30 -50 -70 -90 Fig. 2.58 Industrial service level of world cities and its changes in 1989–1991. Source compiled based on data on global public companies in 1989–2017 released by Osiris

in Figs. 2.58, 2.59, and 2.60. From the figure, we can see that the service level of developed countries is generally higher than emerging economies. In particular, the serving functions of cities in the US and Europe are improving and become more specialized. A typical example is New York in the US, which is the financial, service, and management center of the country, and even the entire world. From the three figures, it can be seen that the service level of world cities has been greatly improved in 1980–2017. Services sectors requiring higher professionalisms like finance and insurance and specialized services in science and technology have boomed, promoting the serving functions of world cities. However, the disparity in service levels is huge. From the perspective of time, in 1989–1991, cities in the US and Europe and Tokyo in Japan had high level of services, whereas the level was low in other regions. In 1992–2008, the services levels of world cities improved greatly, but the disparity was still huge. The professionalism in services of cities in the US, Europe, and Japan continued to be improved. Meanwhile, cities in emerging economies excelled in services, which included coastal cities in east China, Hong Kong, Taiwan, Singapore, India, Russia, Brazil, and South Africa. After the 2008 global financial crisis, the services sectors in cities of developed economies like the US, Europe, and Japan slowed down, whereas those in East Europe, Central Asia, South America, and Africa had been promoted. This indicates that with the deepening economic globalization, global services have been relocated and transferred. But global financial, management, and technological centers including New York, Los Angeles, London, Berlin, and Paris are still dominating the sectors. New York and London gather 90% of world service businesses, signaling their high service professionalism.

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-30 -50 -70 -90 Fig. 2.59 Industrial service level of world cities and its changes in 1992–2008. Source compiled based on data on global public companies in 1989–2017 released by Osiris

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-30 -50 -70 -90 Fig. 2.60 Industrial service level of world cities and its changes in 2009–2017. Source compiled based on data on global public companies in 1989–2017 released by Osiris

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Third, world cities have developed leading functions that complement each other at different levels. Since the 1950s, there have been three rounds of industrial relocation. Countries have integrated into global production system by taking over the industries relocated by developed economies based on their distinctive standing in terms of natural resources and factors of production. The industrial relocation was mostly based on the choice of multinational companies on investment. The ideal destinations were those with favorable conditions and low production costs. In general, multinational companies relocated the intermediate links to other cities, while keeping research and development and sales, up along the industrial chain, in their home countries. For example, Apple Inc. has its headquarters for research and development and sales in the US, but manufacturing factories and assembly lines in China and other countries. It is the global division of labor that fosters cooperation among different world cities at each distinctive level. By reviewing the distribution changes of manufacturing and services sectors in world cities in 1989–2017, it can be seen that the urban functions of cities in developed economies are shifting from serving manufacturing to integrating financial services and global technological innovation. Cities in newly developed economies and emerging economies also possess functions of finance and technology services, but the functions are restricted to serve domestically. In other words, world cities have developed leading functions that complement each other at different levels. From the distribution of the Fortune Global 500, we can see that the industrial layouts show remarkable differences, and the division of labor among world cities is hierarchical. Traditional industries like mining, refining, and energy rely on natural resources, and headquarters of firms in these sectors are located in cities rich in natural resources. The global distribution is also shifting to cities in emerging economies including El Dorado, Essen, Rio de Janeiro, Bangkok, Mumbai, Mexico City, Melbourne, and Moscow. Pharmaceuticals mainly concentrate in cities of the US, Germany, the UK, and France, like Basel, Paris, Beijing, Brentford, Darmstadt, Foster City, Mannheim, New York, and Chicago. Finally, as an emerging industry in the IT era, IT services started to boom in cities with technological foundations or favorable geographical locations. IT services were not fully independent from manufacturing before 1995 and concentrated in cities in developed countries including New York, London, Tokyo, Dublin, and Seattle and some cities from emerging economies like Beijing, Hangzhou, Nanjing, and Shenzhen in China and India’s Bangalore. Last, the two-center world city system has evolved into a three-center one with Europe, North America, and East Asia. From the above analysis, we learn that the industrial division of world cities has evolved into a three-center system. One is in the US, covering New York, San Francisco Bay Area, and Huston. The second center is in Europe covering London, Berlin, and Frankfurt. The third center is in East Asia with Tokyo, Seoul, Beijing, and Shanghai. Improving urban functions of world cities brought by global industrial division helps reshape world cities. Through the analysis on global industrial relocation, we learn that emerging industries boost the rising of cities in emerging economies and developing countries. For example, the urban functions of Hangzhou in China are remarkably improved by means of

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emerging Internet-based businesses. Since the 1990s, the global industrial structure has been featuring “Core and Periphery,” in which the US and Europe are the core of world industry. Meanwhile, as the Asian economy rises, East Asia, represented by China and Japan, has emerged as a new core of global industry and becomes a robust competitor with the traditional two core areas of the US and Europe. At the same time, some developing countries have gradually integrated in the global industrial system and participated in the global production. By analyzing the layout of Fortune Global 500 companies in world cities and the spatial layout by industry, we can see that the Fortune Global 500 mainly concentrates in certain areas and becomes less dispersed, with the US plus one country as the core region, three regions of North America, European Union, and East Asia as the three centers, and other regions as the peripheral areas. Therefore, the layout of Fortune Global 500 companies shows, to some extent, the new round of global division of labor. The world core regions are changing and upgrading. In particular, East Asia has become new global core area and is capable of competing with the US and Europe. The change also shows that as the global division of labor deepens, it is spreading to peripheral areas. e.

The economic and industrial development of world cities

Economic divide of world cities The economic scale, growth rate, and efficiency of a city reflect its economic development. This study selects the GDP, GDP growth rate, and GDP per capita to comprehensively reflect the evolution of world urban economies. On the basis of the data on GDP per capita of world cities in 2001–2017 released by Economist Intelligence Unit (EUI) (see Figs. 2.61, 2.62 and 2.63), the study finds that the divide and disparity in 90 70 50 30 10 -180 -160 -140 -120 -100 -80 -60 -40 -20 -10 0

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-30 -50 -70 -90 Fig. 2.61 GDP per capita of world cities in 2001. Source EIU Data Services

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-30 -50 -70 -90 Fig. 2.62 GDP per capita of world cities in 2008. Source EIU Data Services

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-30 -50 -70 -90 Fig. 2.63 GDP per capita of world cities in 2017. Source EIU Data Services

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economic development among world cities are huge. In terms of GDP per capita, the figure in developed nations outstrips the developing nations. For example, GDP per capita of international cities like New York, Tokyo, London, and Paris far outweighs other cities. Both the GDP aggregates and per capital values in emerging economies lag behind the developed nations. By looking at the GDP growth rate in world cities, it can be seen that the GDP growth rate in emerging economies bar from South Africa and Brazil is higher than developed economies after 2001. For example, Beijing has maintained a growth rate over 10% since 2008. Meanwhile, the growth rate of GDP per capita in emerging economies also exceeded developed nations. From the figures, we can see that the growth rate of GDP per capita in East European countries, Central America, and Brazil was slow in 2001–2008. After 2008, the growth rate of GDP per capita in world cities is lower than that before the year. In particular, the growth rate in Brasilia, Moscow, and Cape Town was close to zero. This shows that as economic globalization advances, economic divide is also deepening and development disparity is still a serious issue. Economic bubble of world cities Bubbles in real estate sectors are indicative of economic bubbles of world cities. As one of an important sectors in urban economy, the real estate and its price have significant impact on families, cities, and the world. The complicated relations between the housing price and the competitiveness of cities are mainly reflected in the following aspects. On the one hand, when the housing price varies in a reasonable range, the price and its fluctuations will boost economic growth, technological innovation, and industrial upgrading, in turn improving the competitiveness of the city. On the other hand, when the housing price is too high or low, it will damage the city’s competitiveness. In some cities, the city competitiveness and the housing price are improved in tandem, whereas in others, high or low real estate pricing hinders the city’s competitiveness from improving. For example, after the 1980s, as Silicon Valley, Manhattan, and Munich had the economy expanded quickly, the real estate market in these areas was also booming. The break of real estate bubbles in Tokyo and Osaka in Japan during the 1990s had a significant negative impact on the urban development. In the twenty-first century, cities like Madrid suffered skyrocketing housing price and backlog of unfinished buildings, leading the city on the brink of bankruptcy. The real estate recession during US subprime mortgage crisis caused economic turbulence. East European cities like Warsaw and Budapest fell into the dilemma of low housing price and economic stagnation. Therefore, housing price, as an important force to change cities and the world, has complicated influences on a city’s competitiveness. There is an intensified Matthew Effect in the real estate market, which is directly showed in the positive correlation between the trend of housing price and the price level. As an outcome of long-term Matthew Effect, the real estate market of world cities is characterized by prominent polarization. Under the influence of Matthew Effect, cities with high housing price will stray away from its basic economic and geographical conditions and develop bubbles in real estate sector. With housingprice-to-income ratio as the benchmark to measure real estate bubble, the study

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compares the cities in developing nations to those in developed economies. There is a positive correlation between the housing price and housing-price-to-income ratio no matter in developed economies or developing countries. In other words, the higher the real estate price is, the huger the bubble in the real estate market is. However, the real estate markets in developed economies and developing countries are different. With housing-price-to-income ratio at 3–6 as the reasonable range of a city’s real estate price, a large share of cities in developing economies has huge bubbles in the real estate market, whereas the bubbles in developed economies are relatively small as shown in Fig. 2.64. The huge real estate bubbles in developing economies mean that the development real estate market deviates significantly from the cities’ economic and geographical conditions. But the deviation does not mean cities with disadvantageous economic and geographical conditions could realize a booming real estate market. In opposite, only cities with economic and geographical advantages could sustain higher housing price, for instance Beijing in China, Mumbai in India, and Dubai in the United Arab Emirates. Therefore, the bubble in real estate market could be attributed to the Matthew Effect, which means that the economic and geographical advantages have been amplified in the self-circulation and development of a city’s real estate market. The economic imbalance of world cities One of the important features of today’s world economy is economic imbalance. Globalization is an encompassing complicated historical process, involving revolution in various sectors of economy, politics, technology, culture, and religion. 90 70 50 30 10 -10 0 -180-160-140-120-100 -80 -60 -40 -20

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-30 -50 -70 -90 Fig. 2.64 Housing-price-to-income ratio in global cities. Source the database on city competitiveness index by Chinese Academy of Social Sciences

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Economic globalization is a double-edged sword. While bringing dividends to global economic growth, it causes imbalances, conflicts, and risks to global economy. In the process, global issues like ecological exacerbation, environmental pollution, and resources shortage become rampant. The global economic imbalance, in particular, has further led to changes in existing international political and economic order. Financial turmoil becomes the inevitable outcome of global economic imbalance, which is characterized by structural imbalance and imbalance of relations in globalization. The structural imbalance is shown in the asymmetry of global financial system and production system. Intercity financial and industrial structures are increasingly out of balance. Since the 1950s, cities in developed nations realized industrial restructure by relocating some industries to countries with lower production costs and favorable natural resources standing. On the other hand, developing countries and emerging economies integrated into global value chain by accepting the relocated industries. After relocating the manufacturing industries, developed nations devoted to services sectors like finance and shifted the economies toward deindustrialization. However, as emerging economies export quantity of cheap goods, cities in developed nations experience huge trade deficit. The industrial hollowing-out leads to capitals to be fictitious, then causing global financial crisis. The above analysis on the Fortune Global 500 companies shows that developed countries gather a large number of Fortune Global 500 companies specializing in services. The economy in developed nations is dominated by services sector, whereas manufacturing sectors grow rapidly in emerging economies. Although developed countries envisaged reindustrialization after the 2008 financial turmoil, the policy has not produce expected outcomes. The declining industrial competitiveness of developed economies exacerbated their resistance and fear toward economic globalization. They, with the US as the representative, started trade frictions to maintain its industrial competitiveness. But it turbocharges the insecurity of global production system and risks of economic globalization.

2.1.3 Global Space Competition: A Global City Connected by Internet Infrastructure Due to shortage and difference of resources and their heterogeneous spatial distribution, human beings have to choose space for their activities, which inevitably leads to competition on space. Competition is an eternal theme in the development of human society. As human society progresses, competitions expand from those inside isolated cities or regions to external ones of connected regions, countries, and world system. Over the past 40 years, globalization has upgraded competition among cities from local levels to national and global levels. The vicious competition of repeated construction has also upgraded to healthy competitions by playing out each city’s advantages. Meanwhile, the competition among local government, agents of cities, is becoming fiercer, also being improved from local levels to national and

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international levels. Competition inevitably leads to profound changes in the space, infrastructure, and ecological environment of the city.

2.1.3.1

a.

World Cities Become Larger, Continuous, and Connected in Physical Forms

Isolated single-center medium-sized and small cities are evolving to connected multicenter large cities

In general, 40 years of global cities have moved from the main body of a single center to the main body of a metropolitan area. The law of urban system development is that in an underdeveloped society, all cities are small, and there is little difference between cities; in the development of a society, some cities will grow too fast and too large, while other cities lag behind. In the case of the situation, the difference between cities becomes larger; when a society is highly developed, there is a situation of balanced development between cities, and large, medium, and small cities have their own place, forming an urban system structure with orderly and healthy development. b.

Urban space expands to coastal, tropical, and frigid areas

In recent decades, cities in the coastal, tropical, and frigid regions have expanded, some industrialized cities in developed regions are shrinking, and some cities in Central and Western Europe are reviving. In recent years, the popularity of airconditioning technology has greatly enhanced the livability of tropical and frigid areas, prompting the global population to migrate to the tropics and frigid areas, and bringing urban expansion in these areas (as shown in Figs. 2.65, 2.66, 2.67 and 2.68) horizontally. The coordinates are longitude, the ordinate is latitude, and the bubble size is the size of the built-up area. From the distribution map of the built-up area of the global cities, it can be seen that from 1984 to 1994, the world’s extra large and large cities were scattered only in Japan, the US, and Western Europe; from 1998 to 2003, the world’s extra large and large cities were in the US, and in addition to expansion in Western Europe and other places, a large number of large cities have emerged in Asia and South America. The city has expanded rapidly from 2009 to 2016. It can also be seen that today’s large and megasized cities are mainly concentrated in the US, Western Europe, and Asia. In coastal areas, cities in coastal areas have expanded significantly. While the city is expanding, other cities are shrinking, such as Eastern Europe, East Germany, and the former Soviet Union. The number of cities in some developed countries is relatively sparse, and the cities are shrinking, mainly due to social institutional changes and deindustrialization, such as Detroit in the US, Manchester, Liverpool in the old industrial countries, and the aging of the population, such as Japanese cities. In addition, there are some cities in Central and Western Europe, such as Venice, Milan, and the Thames in London.

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-90 Fig. 2.65 Area distribution of urban built-up areas in the world from 1984 to 1994. Source The author draws data from the World Urban Expansion Atlas

90.000

60.000

30.000

0.000 -180.000-150.000-120.000 -90.000-60.000 -30.000 0.000 30.000 60.000 90.000 120.000 150.000 180.000 -30.000

-60.000

-90.000 Fig. 2.66 Area distribution of urban built-up areas in the world from 1998 to 2003. Source The author draws data from the World Urban Expansion Atlas

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-90 Fig. 2.67 Distribution of the area of the built-up area of the global city from 2009 to 2016. Source The author draws data from the World Urban Expansion Atlas

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-150

-120

-90

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-90 Fig. 2.68 Change rate of annual built-up area of global cities. Source The author draws data from the World Urban Expansion Atlas

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2.1.3.2

a.

Infrastructure of World Cities Becomes Connected and Convenient Thanks to IT Application

Public facilities become connected and convenient thanks to IT application

Public facilities of a city like telecommunication and transportation infrastructure, Internet, wireless connection, and subways are becoming intelligent and more convenient thanks to IT application. First, the rapid development of science and technology has changed the way of communication among people. From the earliest correspondence by letter, to telegram, to modern telephone, and Internet, changes of ways in communication offer people more and more freedom. Their minds get open and broadened. People in different countries and regions are interconnected with each other. Second, economic growth is premised on improvement in transportation infrastructure. China, as a representative of emerging economy, has established a railway network connecting major cities and counties with capital cities as regional centers since the inception of reform and opening-up. Its total length of domestic railway mileage has risen to 124,000 km in 2016 from 53,000 km in 1978. The improvement of transportation infrastructure significantly shortens the physical distance between regions and accelerates the trans-regional flow of passengers, goods, and information, playing a positive role in optimizing resource allocation and promoting free competition. Third, the number of Internet users grows exponentially. In 1997, the number of Internet users across the world was 70 million, which rose to 147 million in 1998. During 2002 to 2009, the global Internet users were at a period of steady growth. In the end of 2008, the world’s Internet users reached 1.574 billion, 19.2% up in 2007. The Internet penetration was 23.5%. Internet devices were distributed across the world. Comparatively, developed economies like Europe and North America have better Internet access. Fourth, smartphones have infiltrated into every aspect of daily life. At present, there are over 4.6 billion active phones across the world, 370 folds of 1990. In 2015, smartphone users increased by 21%. Eighty-seven percent of Internet users had a smartphone in 2016 as shown in Fig. 2.69. Fifth, subway reshapes the urban space and then the lifestyle of urban residents. During the 20 years from 1975 to 1995, great headway has been made on subway construction on previous basis. Over 30 cities across the world built-up or are building subways. They include nine cities in North America like Washington D.C and Vancouver, nine cities in Europe like Brussels, Lyons, and Warsaw, and 16 more in Asia including Kobe, Hong Kong, Calcutta, Tianjin, and Shanghai. Statistics show that there are 127 cities in over 40 countries and regions having built subways, with a total length of 5263.9 km, and transporting 23 billion passengers annually. b.

Infrastructure among cities get more intelligent and convenient thanks to IT application

With the development of global transportation, communications and energy infrastructure, highways, railways, airports, oil and gas pipelines, power grids, and fiber

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Fig. 2.69 Smartphone ownership among global Internet users. Source www.techweb.com.cn

optic cables are reshaping the future: Connectivity determines fate. The development of global infrastructure is moving the world from fragmentation to interconnection, from ethnic isolation to integration. Infrastructure is like a nervous system that connects all the tissues on earth. Capital and code are the blood cells that flow through the nervous system. In view of the completion of the global submarine cable network, the world has been connected as a whole, while infrastructure construction in the Asia-Pacific region, Southeast Asia, the Atlantic region, and Europe is relatively good. These cables connect the world. According to statistics from Washington Telecom, the global submarine cable has reached its peak since 1989. In the past 25 years, the submarine cable has rapidly developed from 155 km to 8.94 million kilometers. Various information is transmitted to all corners of the world through submarine cables. The blood of the information age is constantly flowing,

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and almost 95% of the information on the Internet is transmitted through cables routed between countries.

2.1.3.3 a.

Changes in Spatial Pattern of Urban Ecology and Environment

Rampant expansion and spread of urban space

Urban expansion is another reference on urbanization. It refers to the migration of people from densely populated towns or cities to sparsely populated residential areas. The ultimate outcome will be the spread of city and its suburbs to rural areas. The rampant expansion of cities started a long time ago, resulting in overcrowded cities and suburbs. The main reasons include low land price, improved infrastructure and livelihood, poor urban planning, low taxation on real estate, uncontrolled population expansion, and consumer preferences. The rampant expansion encroaches limited land resources and causes overconsumption on natural resources. b.

Poor and mismatched urban public facilities

Poor infrastructure in medium-sized and small cities Infrastructure construction in small and medium-sized cities is slow and imperfect. Many small towns in Bantustan, South Africa, are thriving centers, but the economic base is limited, so the level of development is much lower than in urban areas, and inadequate infrastructure, such as running water, garbage removal, street lights and electricity. There are significant differences in public green infrastructure services in different areas of the city. In the mountainous provinces of western China, infrastructure construction such as urban water conservancy, energy, transportation, and communications is still not perfect, and the transformation of power grids is lagging behind. The development of tertiary industry such as post and telecommunications, information networks, cultural education, tourism ecology, health sports, and financial economy is also very slow and cannot meet the needs of urbanization. Poor quality of the infrastructure Urban infrastructure and public facilities are integral constituent parts of public goods offered by the city. In the process of urbanization, different governmental departments have different principles on plans on infrastructure building, which is unfavorable for the implementation of construction projects. There are problems of unchecked and repeated construction in many cities. The construction term for some projects is too long. Meanwhile, huge expenses of demolition and relocation stress are funding for urban construction. Nigeria is one of the fast-growing economies, but in Onne, garage is ubiquitous across the city and the quality of public health facilities is worrying.

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Aging infrastructure in established cities As the representative of early industrialized economies, the US has many cities with increasingly aging public facilities. Since the 1970s, construction of railway, power grid, airports, and bridges has been in stagnation. Another example is Osaka in Japan. An earthquake of 6.1 magnitude hit northern region of the city on June 18, 2018, killing four people and injured about 300. Transport in surrounding regions was paralyzed, affecting workers and students. There was no supply of gas or tap water in the affected areas. The fragility of the aging public facility is highlighted in the event of a natural disaster. Poor infrastructure quality in emerging cities There are a lot of issues with the infrastructure in emerging cities as they expand rapidly, including poor urban planning or rampant construction of public facilities. Taking emerging economy India as an example, as the city expands in terms of land and economic size, basic public facilities like housing, water, and power supplies and sanitary conditions are so poor to meet residents’ need for survival. A national census in the country shows that 25% of urban housing are slums, and nearly 26% of families lack basic sanitary facilities. Financing difficulties and debt crisis regarding infrastructure Infrastructure is vital to improving people’s livelihood, attracting industrial cluster, and promoting economic growth of a city. However, financing for infrastructure has been a conundrum both for developing countries and developed nations. Urban infrastructure projects are general long-term ones. The accountability of government in honoring the project agreement weighs heavily for investors. If government fails to observe the terms and conditions of the agreement, the damage will be profound. Before 1970s, infrastructure projects were mainly invested and operated by the government in the UK, which burdens the government debts. There was a huge gap in infrastructure financing. Later, it was eased as Thatcher raised fund from the market. Japan, the US and developing countries also suffered such difficulties. c.

Resource consumption and ecological destruction in cities

About 70% of global carbon dioxide emissions come from urban areas. The spillover effect of climate changes, environmental pollution, and resource consumption in different regions is global despite of different degrees. When human beings overconsume natural resources or overload the ecosystem, the stability and order of the entire natural system are broken. The ecosystem will lost capability to exchanging substance and energy, inevitably leading to imbalance and destruction of the ecosystem. Global substance and energy circulation have great impact on the biosphere. Light pollution, typical in urban life, and the increase of radiant energy on the earth will present huge threats to the biological world, causing global issues like irreparable damage on human beings’ genetic functions or genetic mutation, and breaking the balance in ecosystem. During the smog incident in Los Angeles in the US, a large area of pine forest at an altitude of 2000 m, 100 km away from the incident, withered;

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Fig. 2.70 Contributions to observed surface temperature change over the period of 1951–2010. Source Climate Change 2014: Synthesis Report (https://www.ipcc.ch/report/ar5/syr/)

a total of 400 senior citizens above 65 died, three times more than ordinary period; and about more than 75% residents got pinkeye. In addition, resource consumption leads to global warming. From Fig. 2.70, it can be seen that greenhouse gas emissions contribute the most for temperature rise over the period of 1951 to 2010. From Fig. 2.71, it can be seen that fossil fuels and industrialization are the major sources of greenhouse gases, accounting for the highest share of 59%. In the period from 1970 to 2010, global emission of carbon dioxide continued to climb. In 2000–2010, the emission of carbon dioxide is as high as 2.2%. So far, developed countries have consumed the majority fossil fuels produced by world countries. Their accumulative emissions of carbon dioxide have reached an alarming level. In the early 1990s, the figure in the US was as high as 170 billion tons, 120 billion tons in European Union, and 110 billion tons in former Soviet Union. The exponentially high quantity of carbon dioxide emitted to the atmosphere will inevitably cause irregular climate changes, especially in coastal cities like Shanghai and London. The typical cases caused by global warming include Tuvalu’s request on migrating to New Zealand and Australia and the frequent El Nino phenomena.

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Fig. 2.71 Total annual anthropogenic GHG emissions by gases in 1970–2010. Source Climate Change 2014: Synthesis Report (https://www.ipcc.ch/report/ar5/syr/)

2.2 Humanity Moving Toward a Connected, Gathering and Sharing City Planet Over the 40 Years Global cities have developed from “a single spark” in the early agricultural civilization period into “a prairie fire” in the context of economic globalization now. Urban population and economic contribution rates both have exceeded 50% of the world’s total and highlighted the unique position and role in aspects of S&T innovation, cultural heritage and influence. Cities dominate the development direction of the world economy and have changed the nature, connotation, functions, and pattern of the world profoundly. The world today is of cities, which has turned from a closed, decentralized, and exclusive agricultural earth into a connected, centralized, and shared city planet.

2.2.1 The Nature of World Has Changed Because of the Change of City Status: It is a World of Cities Over the 40 years, cities have increasingly become the principal part, engine, and carrier of economic activities in the world. Before the eighteenth century, humans were totally in an agricultural society. Since their emergence in the era of agricultural civilization, cities have experienced a development course where political influence has been weakened gradually and economic influence strengthened increasingly. Also, the status of cities has changed from being attached to the country, being consubstantial with the country, surpassing the country to dominating the country. In

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the era of agricultural civilization, production technology developed slowly, science was still at the incubation stage, and the promotion role that science played in the development of technology was not shown. Land was the most important means of production in that period. Agricultural production became the mainstay and dominant power in world economy. Cities existed by attaching themselves to agricultural economy. Since the industrial revolution, the booming development of modern mechanical industry and socialized mass production has given rise to the change of industrial structure. Agricultural output efficiency has increased constantly, while its proportion in the economy has continued to decrease. Cities have grown up rapidly in this context. The advent of information civilization has not only accelerated the development and growth of cities, but also strengthened the connection between cities. A network of cities has been spread out all over the globe, which determines the direction of global economic and social development. It is a world of cities. Firstly, cities have become the principal part of world development gradually Firstly, urban population makes up a larger and larger proportion of the world’s total: The globe enters the society of cities. Urban development is a process of constant population aggregation. In the world today, humans mainly work and live in cities. The world’s total population was 4287 million in 1978 and reached 7530 million in 2017, with an annual growth of 1.9%. Wherein, the annual growth was 3.72% for urban population, while it was only 0.7% for rural population. Urbanization rate rose constantly as the population gathering in cities continued to grow. Humans moved to an urban world from an agricultural world. In 2008, the urbanization rate in the world exceeded 50% for the first time, marking the advent of the urban age (as shown in Fig. 2.69). Viewed from the distribution of population density in the world, population is mainly concentrated in cities. However, due to the difference in natural conditions, geographical environment and population base, and unbalanced social and economic development, urbanization level and pace are quite different in each country. On the whole, the urbanization level of developed countries is far higher than that of developing countries. But in developed countries, the population grows very slowly. Some countries even see a negative population growth. In contrast, fast-growing population and rapid rise of urbanization rate in developing countries have driven steady increase of global urbanization rate. In 1980, the average proportion of urban population was 70.9% in developed countries; in 2015, the average urbanization rate of developed countries was 78.1%, wherein the US represented 81.7%, Japan 91.4%, Germany 77.2%, and the UK 82.6%. Compared to developed countries’ high level and slow growth of urbanization rate, developing countries experienced a rapid rise in the urbanization rate from 29.4% in 1980 to 49% in 2015 which promoted humans to enter the urban society (Fig. 2.72). Secondly, urban economy makes up a larger and larger proportion in global economy: Urban GDP plays a dominating role in global economy. Urban economy emerges with separation between urban and rural areas resulting from the separation between agriculture and handicraft industry and development of commodity

2.2 Humanity Moving Toward a Connected, Gathering and Sharing City …

101 60

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30

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rate

Population (100 million)

70

20

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1990

2000

Global population (100 million)

2005

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Urban population (100 million)

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Urbanization rate(%)

Fig. 2.72 Proportion of urban population in global population. Data source WDI Database of the World Bank, World Population Report of the United Nations

exchange. Before the industrial revolution, businessmen and handicraftsmen gathered in cities. As political, military, and religious centers then, cities were not eyecatching in the world economy. With the advent of modern mechanical industry, industries began to be concentrated in cities which made the scale of cities expands constantly. This contributed to the formation of domestic market and world market together with the world trade. Cities became the center of industrial production, commerce, finance, and transportation. Urban economy represented an increasingly higher proportion in the world economy. After the S&T revolution, with high development of the tertiary industry, the change of economic structure made cities the place of economic agglomeration, not only greatly widening the economic gap between urban and rural areas, but also enabling cities to be the command center of global economy and dominate the development of global economy. In 2016, the global top ten cities in terms of GDP (Tokyo, New York, Los Angeles, Paris, London, Chicago, Shanghai, Houston, Osaka, and Milan) contributed 8% to the total GDP in the world (see Table 2.9). Taking Chinese cities as an example, the rapid development of some cities has made them “as rich as a country” (see Table 2.9). Economically developed cities have participated in global economic competition on behalf of the country and played an important role (Table 2.10). From the perspective of industrial development, urban economy makes up a larger and larger proportion in global economy. The value added of non-agricultural industries in the world had accounted for more than 96% of GDP in 2010, and the proportion showed an upward trend year by year (see Fig. 2.73). Non-agricultural industries are mainly concentrated in cities. Urban economy is closely linked to global economy and dominates the development direction of global economy, forming a linkage effect of “rising and falling together.”

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Table 2.9 Proportion of global top ten cities in their respective national GDP (2016) Country City

GDP (100 million USD) Respective National GDP Proportion (%) (100 million USD)

Mexico

Mexico City 3690.5

10,460.02

35.28

France

Paris

7350.6

24,632.22

29.84

Italy

Milan

3782.6

18,507.35

20.44

UK

London

5187.8

26,291.88

19.73

Japan

Tokyo

9472.7

49,386.44

19.18

Osaka

3876.7

49,386.44

7.85

New York

4.85

US China

9006.8

185,691

Los Angeles 7530.9

185,691

4.06

Shanghai

4066.3

112,182.81

3.62

Beijing

3690.8

112,182.81

3.29

Data source Collation of the economic analysis bureau of each country

Table 2.10 Comparison between top ten Chinese cities and corresponding countries in 2017 Rank City

Total Population Per GDP capita GDP

Corresponding Total Population Per Country GDP capital GDP

1

Shanghai

4464 2420

18,446 Thailand

2

Beijing

4148 2173

19,089 Austria

3

Hong Kong 3416

46,350 South Africa

4

Shenzhen

26,345 Ireland

5

Guangzhou 3185 1448

21,981 Malaysia

6

Chongqing

2893 3048

7

Tianjin

2754 1562

8

Suzhou

9

Chengdu

10

Wuhan

6732

6589

4170

887

47,404

3490

5652

6175

3345

479

69,809

3145

3205

9812

2769

1863

14,860

17,631 Bangladesh

2744 16,365

1677

2565 1068

24,017 Finland

2533

551

45,987

2057 1604

12,824 Greece

2008

1074

18,696

1986 1091

18,203 Iraq

1926

3884

4958

737

3301 1253

9491 Chile

4552

Note The unit of total GDP is 100 million USD, and the unit of per capital GDP is USD

Secondly, urban development land makes up a larger and larger proportion in global reclaimed land: The area of built-up regions has shown a steady increase for consecutive years. Urban development and construction are directly reflected in the landscape. Great changes have taken place in urban and rural land over the 40 years. With constant expansion and development of urban areas, urban development land makes up a larger and larger proportion in global reclaimed land. Developed cities in developed and developing countries have even seen the spread of urban development land. From 1960 to 1980, the urban development land increased by 65% in New York, 45% in Chicago, and 33% in Cleveland. The Asian-Pacific

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97.00% 96.00% 95.00% 94.00% 93.00% 92.00% 91.00% 90.00% 1980

1985

1990

1995

2000

2005

2010

2015

2016

Value Added of Non-Agricultural Industries in the World / GDP

Fig. 2.73 Value added of non-agricultural industries in the world/total GDP. Data source database of the World Bank

region is the world’s most economically vibrant region currently. As the proportion of urban economy and urbanization rate continues to grow, the area of urban built-up regions increases accordingly. According to Table 2.11, between 2000 and 2010, the area of main urban built-up regions kept expanding in East Asian and Southeast Asian emerging economies. Rapid urban development has not only promoted the extension of built-up regions in central cities, but also driven rapid expansion of built-up areas in the metropolis circle or urban agglomeration. In contrast, urban expansion in developed countries is not prominent, because of sufficient economic development, small urban and rural gap, and the area of built-up regions keeping unchanged for many years. Overall, with the development of emerging economies, the proportion of urban areas in global reclaimed land has shown a steady increase. According to the survey and study on the number of cities by the project team of the international knowledge center, Chinese Academy of Engineering, there are 937 cities of 100 km2 in the world, 2036 cities of 50 km2 , 3520 cities of 30 km2 , 5404 cities of 20 km2 , 9043 cities of 10 km2 above, and 13,810 cities of 1 km2 above (as shown in Fig. 2.74). Secondly, cities have become the engine of world development gradually Firstly, urbanization is the engine of world economic growth. The growth trend of world economy is consistent with the change of urbanization rate. Essentially, urbanization is an agglomeration process of production factors, mainly reflected in the concentration of geographically dispersed population, capital, land, and other factors in cities (see Fig. 2.75). So far, the world has experienced three waves of urbanization: the first one started in Europe with the UK as a representative, accompanied by the development of industrial revolution. In 1750, the urbanization rate in the UK was 20%, and the urbanization was basically completed by 1950; the second one took

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Table 2.11 Area and proportional change of built-up regions in six cities in the world (Unit km2 ) City

Beijing

Year

2000

Seoul

Area of built-up regions in central cities

289.8

883.3

352.6

362.5

581.3

Area proportion of built-up regions in central cities (%)

21.2

64.6

58.2

59.9

93.6

Area of built-up regions in metropolis circle

1041

3377.2

837.2

1205.1

Area proportion of built-up regions in metropolis circle

6.3%

2012

20.1%

2000

Tokyo 2010

7.1%

2000

10.2%

–

–

Singapore 2010

2000

2010

581.9

361

374.5

93.6

52.9

52.6

3429.5

25.3%

–

–

Data source Seoul Institute

Number of Cities 16000

13810

14000 12000

9043

10000 8000

5404

6000 4000 2000 0

937 Cities of 100 square kilometers

2036 Cities of 50 square kilometers

3520

Cities of 30 square kilometers

Cities of 20 square kilometers

Cities of 10 square kilometers

Cities of 1 square kilometers

Fig. 2.74 Number of cities of different size in the world. Data source collation of the data released by the project team of the international knowledge center, Chinese Academy of Engineering

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60.00

90 80

50.00

70

40.00

60 50

30.00

40

20.00

30 20

10.00

10

0.00

0 1977

1982

1987

1992

Global GDP (1 Trillion USD)

1997

2002

2007

2012

2017

Growth of World Urban Population (100 Million People)

Fig. 2.75 Global Economy and Urban Population Growth. Data source database of the World Bank

place in North America represented by the US. The urbanization rate was 20% in the US in 1860 which reached 71% in 1950; the third one happened in Latin America and other developing countries. The urbanization rate of South American countries was 20% in 1930, and the urbanization was basically completed by 2000. The rapid development of the third wave of urbanization has driven constant agglomeration of such modern service industries as finance, commerce, education, and management in cities, making particularly significant contributions to the world economic growth. According to the prediction in McKinsey’s “Global Top 600 Cities,” viewed from the scale of global cities, medium cities have contributed to more than 50% of the global GDP growth, megacities have contributed to 11%, and other cities and rural areas have contributed to the remaining 30%. Viewed from the development extent, cities in developing regions have contributed to 73% of global growth; therefore, urbanization is the biggest engine for the growth of global economy. Secondly, information economy leads the growth of world economy. Since the beginning of the new century, the expansion of information economy has led the growth of global economy. The advent and high-speed development of information equipment manufacturing and information service industry have brought about substantial changes in traditional industries. The proportion of physical manufacturing in national economy has decreased, and the tertiary industry represented by the information service industry has increased its proportion significantly and gradually played a dominant role. The production of information and communication technology products and services has contributed to 6.5% of global GDP. Cloud computing, big data, artificial intelligence, Internet of Things, and other information products have dominated urban economy increasingly. Between 2010 and 2015, the export volume of information and communication technology services increased by 40%. It is expected that the global Internet traffic in 2019 will be 66 times as much as that of 2005 (2017 Information Economy Report of the United Nations).

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Thirdly, urban innovation drives global sustainable upgrading. Urban innovation has led to significant increase in urban total factor productivity (TFP) and will finally drive global TFP growth. The New Growth Theory believes that TFP is the source of economic growth and driving force of endogenous evolution, while the TFP growth mainly comes from R&D innovation and knowledge spillover. In modern economy, innovation and knowledge spillover can be easily generated in individuals who are close to each other spatially, while cities provide opportunities for individuals to approach each other. TFP growth is most directly reflected in urban industrial upgrading and productivity growth. Cities have significantly promoted the growth of global TFP from three aspects of efficiency increase, technological improvement, and scale effect (see Fig. 2.76). Viewed from the average global TFP of 5 years, the global TFP showed an upward trend from 1990 to 2005. In 2005, the urban TFP reached the peak 1.8% and global TFP increased to 3.6%. Since the financial crisis in 2008, the growth of global TFP remained sluggish, consistent with the development period of world cities. Urban TFP decreased to −2.4%, which resulted in great decline of global TFP. Wherein, the TFP of three economies: the US, EU, and Japan (in these countries, urban TFP was consistent with national TFP) went through a near-zero growth or even showed a negative growth. For nearly a decade, developing countries have continued to increase their TFP through S&T innovation and major reforms and become new growth points for the rise in world TFP. Thirdly, cities have become the carrier of world development gradually. Firstly, cities are the carrier of main human activities. As the main carrier and platform of economic activities in the world, cities are both production bases and livable places for humans, with important functions of politics, economy, culture, education, medical care, transportation, and international exchanges. Before the industrial revolution, cities were business centers and main places of commodity exchange 7 6 5 4 3 2 1 0 -1

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in a country. After the outbreak of industrial revolution, cities became industrial production bases gradually. Urban development has no longer been separated from industrial development since then. Cities have become the carrier of production. With the support of information technology, globalization is not only the background of urban development, but also a huge driving force of it. Production factors such as information, capital, and labor force have flowed rapidly on a global scale. Almost all cities have been included into the global urban system, hence becoming the main carrier and platform of economic activities in the world. What is more, the urban hinterland range has been constantly expanded via radiation and spillover. However, with the acceleration of world urbanization, economic hinterlands have been centered on cities, rather than rural areas in the past. Cities have developed in a centralized manner, forming stronger economic power of hinterlands, and adding radiance to central cities. Secondly, cities are the principal part of human infrastructure. The principal part of global infrastructure takes cities as the carrier. As urban population grows and the scale of urban land expands, urban infrastructure continues to be improved. Infrastructure construction, including the construction of transportation, communication, water conservancy, urban water supply and drainage, and power supply facilities, is an essential condition for urban development. Besides, with the increase of population, economic level, and living standard, people begin to pursue a better quality of life, and have a strong desire to improve urban ecological environment. Thus, the structure of land use in cities has changed greatly, and the function of cities has shifted from production to living, with more input in infrastructure. Increasingly perfect and intelligent infrastructure makes the life in cities more comfortable and efficient, thus attracting more non-agricultural population to congregate in cities. With the continuous expansion of scale, cities have become the principal part of human settlements. The global total railway mileage has showed an upward trend since 1980, which has strengthened the interconnection between cities (see Fig. 2.77). The continuous rise in airline passenger traffic has intuitively reflected that urban infrastructure construction is the principal part of global infrastructure. In 2017, the global airline passenger traffic volume reached 3.5 billion, while the recipients of air service mainly came from cities. Additionally, the global smart city construction market has a huge scale. Booz & Company predicts that the investment in infrastructure construction of global smart cities will increase to 41 trillion USD in 2030. According to the prediction of Nikkei BP CleanTech Institute, the market scale of all kinds of smart services developed by the smart city architecture will reach 1 billion JPY. In addition, the intercontinental distribution of global infrastructure investment is uneven. African infrastructure accounts for more than 20% of total fixed investment, while American infrastructure accounts for 9% only (see Fig. 2.78). Thirdly, cities are global infrastructure network nodes. Urban infrastructure constitutes the principal part and framework of global infrastructure. Especially, the global transportation network consisting of highways, railways, air transport, and waterways has further reduced transportation costs and promoted trading, investment, and production, thus driving global economic growth. The density of road

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network is the main index to measure whether urban infrastructure is perfect or not. Major international cities including London, Paris, Beijing, Seoul, Tokyo, and New York all have formed interregional road network (see Fig. 2.79). From the perspective

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Fig. 2.79 Road network density of main cities in the world. Data source Seoul Institute

of air transport, the above cities have ranked top in the world in terms of destinations and number of flights made and constitute main nodes of global air transport network. The formation of global high-speed communication network in the information age has made raw material acquisition, division of production, merchandise trade and information service more convenient and efficient on a global scale. In this process, cities as the carrier of global production, service, finance, innovation, and circulation inevitably become global infrastructure network nodes. Cities will be interconnected, smart, and instrumented in the future. There will be more and more smart cities. Through the global infrastructure network, these cities will form a smart planet finally. With the development of global urbanization and communication technology, physical infrastructure and communication infrastructure are forming a unified smart planet infrastructure, laying a foundation for the development of smart planet (Fig. 2.80).

2.2.2 Functions of the World Have Changed Because of the Change of Urban Functions: The World Becomes a Large Group From the agricultural age to the industrial stage, then the post-industrial age and the information age finally, urban functions have experienced the shift from business administration to production organization, consumption service, and to financial information, S&T innovation, and spiritual creation. With the change of economic and social forms, drivers of agglomeration in cities have shifted from production scale effect, efficiency improvement, and promotion of social division of labor in

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Fig. 2.80 Number of destinations and number of flights made per year for international airports in main cities of the world. Data source Seoul Institute

the early period to service functions and satisfaction of demands for diversified consumption and information exchange. The functions of cities in the world are changing profoundly from the production, exchange, transportation and consumption of goods and hardware in the early period to the production, exchange, transportation and consumption of knowledge and software; and from local self-supporting and self-living in the early period to global cooperation with a due division of labor. The world is like a group consisting of a number of interconnected companies. Each city is a workshop. World cities are integrated as a whole under the guidance of international division of labor. Therefore, the change of urban functions has changed the connotation of world. Firstly, urban functions have been modernized and are no longer traditional ones: The content of world activities has changed. Urban development has changed human production and exchange methods. Cities have played a very important role in the shift from the trading and production of goods and hardware in the past to the trading and production of knowledge and software and from the production, exchange, transportation, and consumption activities of goods and hardware in the past to the production, exchange, transportation, and consumption activities of knowledge and software. Since the 1980s, the service economy has impelled cities to shift their focus from production function to service function. The service sector is playing a more and more important role in economic growth of many countries. Its growth has changed not only the composition of world economic production and employment, but also global trade patterns. In 2015, the value added of service sector accounted for 74% of the GDP in high-income countries. Compared to other high-income countries, the USA’s value added of service sector made a higher contribution to its GDP. In low- and middle-income countries, the proportion of service sector in the GDP was particularly prominent, increasing from 48% in 1997 to 57% in 2015 (see Fig. 2.81). The higher the contribution of service sector to the output is, the lower the contribution of industry and agriculture to the GDP will be.

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With further S&T development, knowledge economy and S&T innovation are becoming the leading force in economic structure increasingly. The scale and proportion in the whole industrial structure keep increasing. As an important service industry, urban knowledge economic industry has developed accordingly. Much of emphasis for urban functions has been shifted to the satisfaction of diversified needs on the spiritual plane. The financial crisis in 2008 has made the world move to the stage of innovation competition from industry and capital competition. Countries have formed new drivers of growth through S&T innovation with one accord and stepped out of shadows of the crisis completely. This development trend has been first indicated in global cities with S&T, talent, and system advantages. New York, London, Tokyo, and other major cities began to compete for innovation advantages, instead of global economic hubs, so as to achieve the shift from global capital centers to global S&T innovation centers. Human activities developed toward the field of knowledge technology widely. The creative industry of London has become the second-largest pillar industry only next to the financial service industry, which creates the output value of 21 billion pounds every year. Between 2010 and 2015, the growth rate of the industry was 34%, far higher than that of other industries. In Silicon Valley, the software industry contributes 1.14 trillion USD to the USA’s GDP directly or indirectly every year. Against the backdrop of rapid Internet economic growth, the software industry has been expanded throughout the US. The Internet industry in China represented by Hangzhou has profoundly changed the people’s consumption methods, resulting in a rapid decline of urban trading functions. According to the prediction, Alibaba is expected to rank among the “Global Top 20 Economies” through its trading flow and size based on the current growth rate. In the future

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20 years, Alibaba will become the global “Top 5 Economies,” only next to the US, China, EU, and Japan. Secondly, urban functions have been professional, rather than just comprehensive: The professional division of labor in the world has changed. Cities have changed diversified and comprehensive functions before and tend to be professional in dominant functions, changing the regional self-supporting in the world previously, and facilitating the division of labor and market to go global. Before the industrial revolution, social division of labor developed insufficiently, production was not so professional, and urban functions were mixed. Under the influence of industrial revolution, social division of labor has been further refined. Professional division of labor in cities has given rise to the difference in urban development paths. Plus the influence of resources endowment on the positioning of urban development, industries of different categories have been concentrated in specific cities, and even formed the division of urban functions by industry category. For example, there are rich coal and iron ore resources in Ruhr, Saar, Alsace, and Lorraine of Germany. Abundant resources have made Germany an important base for coal, iron, and other heavy chemical industry products. Information revolution has led to all-around development of Internet technology, shift of economic growth to innovation-driven development and accelerated flow of production factors, and contributed to new international division of labor, making urban development more professional in the direction of depth, rather than just pursuing larger economic scale. Hangzhou, China, is also a city that develops for Internet economic specialization. It has ranked top among Chinese cities and even world cities in fields of urban industry and life informatization, digitization, and mobility. Compared with world cities, Hangzhou has a relatively small economic size, but its professional division of labor enables it to mobilize and connect with a huge amount of resources with high S&T innovation capability, promote urban industrial upgrading, and improve its ranking continuously. Thirdly, urban space has seen a local-to-global shift: The territorial division of labor of the world has changed. Radiation is the core of urban functions. The main feature of cities is to provide products and services for economic hinterlands. Under the support of information technology, economic globalization has become a huge driver of urban development. Production factors such as technology, information, and human capital have flowed rapidly on a global scale. The global urban network is forming gradually. Most cities in the world are important nodes of the network and are committed to global flow of all kinds of factors and global interconnection for accelerating the flow. As a result, the scope of urban radiation continues to expand. The grade or importance of cities, as nodes in the global economic network, is determined by their radiation energy. The functional radiation of some world cities in the global urban network has shifted its focus from regional hinterlands to the whole world, changing the regional self-supporting of the world in the past and facilitating the global space to be integrated. World cities play the control and pivotal role in the global urban network and serve as the agglomeration or output center of international capital, technology, personnel,

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information, and culture. They have brought urban agglomerations and metropolis circles constituted by regional hinterland cities together with high potential energy and are often regions with the most developed economy and highest degree of internationalization in a country or even in the world which can provide radiation, guidance, and service with global influence. The Atlantic coastal urban agglomeration, US, Great Lakes urban agglomeration, North America, Pacific coastal urban agglomeration, Japan, London-centered urban agglomeration, UK, and Northwestern European urban agglomeration have developed mainly relying on such global cities as New York, Chicago, Tokyo, London, and Paris and occupy important positions in global economy. As the urban agglomerations with the largest economic size, fastest development, highest degree of openness, and strongest developmental vitality in China, the Yangtze River Delta urban agglomeration centered on Shanghai, Beijing–TianjinHebei urban agglomeration centered on Beijing and Tianjin, and Pearl River Delta urban agglomeration centered on Guangzhou and Shenzhen have begun to stand out conspicuously from the global space. Fourthly, the urbanization of emerging economies has changed the global functional distribution pattern gradually. Firstly, the global functional pattern has evolved into the three-pole pattern of European and American innovation and consumption, manufacturing and processing of emerging economies and raw materials and energy supply of backward economies from the dual pattern of industrial manufacturing in developed economies and agriculture in backward economies. After two industrial revolutions, western developed countries represented by the UK, US, and Japan stepped toward the path of industrialization and began to promote their industrial products all over the world; while in less developed regions, economic development mainly relied on the export of primary products such as agricultural products. Thus, the global functional pattern was mainly divided into two parts: “developed economies—industrial manufacturing and backward economies—agriculture.” Since the 1990s, globalization was accelerated continuously, the division of labor in the world became more prominent, and the layout of global industry chain tended to be stable. Developed economies began to carry out transformation and upgrading, phased manufacturing out and shifted their focus to innovation and consumption; emerging economies took over the industries transformed by developed economies in global production because of such factors as technological progress and rich labor resources and begun to be engaged in processing and manufacturing; while backward economies had no choice but to provide raw materials and energy resources for the world division of labor by relying on their natural resources due to the lack of technology and talents. Figure 2.82 shows the variation in the GDP growth of developed and emerging economies between 2010 and 2014. Emerging economies basically showed a trend of continuous increase, while developed economies showed different levels of sluggish growth. EU and Japan even experienced an economic recession. Secondly, the shift of cities from centers of finance and production service industries to S&T and financial centers determines the pattern of global development. Economic development phase, industrial structure, and social form are main factors affecting urban functions. After the third S&T revolution, knowledge

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economy, S&T innovation, and financial service became the backbone of economic development gradually. The scale and proportion in the whole industrial structure kept increasing. As an important service industry, urban financial industry developed accordingly. Urban functions of information exchange, knowledge production, and innovation carrier became prominent increasingly owing to S&T, education, and talent aggregation. With the rise of emerging economies, urbanization has been accelerated in developing countries, and cities in emerging economies have become new centers of finance and production service industries, changing the urban development layout of developed countries in the past. In developed countries, cities have changed to function as S&T and financial centers. For example, in the US, centers of finance and production service industries developed relying on the New York urban agglomeration are changing gradually. Emerging S&T and financial cities are moving toward the world stage represented by Silicon Valley. As the most successful high-tech park in the world, Silicon Valley boasts a developed financial market and perfect S&T innovation system. It is an important S&T center and financial center in the world which changes the pattern of global development.

2.2.3 The World Form Has Changed Because of the Change of Urban Form: The World Becomes a City The spatial form of global human activities has changed because of the change of urban form in the world. Since the industrial revolution, human settlements have

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experienced a shift from rural agglomeration to urban agglomeration. As the industrialization progresses, more land, capital, and labor force are required to develop industrial economy in cities, thus to attract people to move to cities; with infrastructure improvement and expansion of urban functions, small cities have developed into large cities and even central cities, pooling together high-quality resources of economic industry, education, and medical care; with the formation of the world economic network, the division of labor for cities has been achieved worldwide. In the context of increasingly perfect global infrastructure network, especially information network, cities have developed into metropolitan areas under the action of siphon effect, accompanied by the emergence of “urban diseases.” Along with the people’s pursuit of a better quality of life, more and more satellite cities and small towns spring up, thus forming urban agglomerations (see Fig. 2.83). Urban agglomerations will be the major form of urbanization in the future owing to stronger clustering ability, larger economic size, and higher spatial efficiency. With the shift of global economic growth center to the Asian-Pacific region, China has become a new growth pole of world economic development. It is perfectly possible that China will enjoy the rise of new world-level urban agglomerations, urban belts, and urban network. Secondly, global infrastructure interconnection has resulted in the densification and expansion of global infrastructure network from underground to sky. With the advancement of economic globalization, the world urban network system has taken shape gradually. The connection between cities has become even stronger. Previous isolated mode of development has been discarded, and the focus of urban infrastructure development has shifted to infrastructure network interconnection. From ground pipelines to ground transportation, airline network, and even to space satellites, a three-dimensional, networked, intensive, and planet-oriented expansion of infrastructure network space has been realized from underground to sky. The age of agricultural economy featured simple water conservancy facilities and inhabited villages and relatively sparse road network. In those years, infrastructure was

Fig. 2.83 Population density of urban agglomerations in the world. Data source Reldresal

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isolated, scattered, and less developed. In the age of industrial economy, more raw materials and broader markets were required for the development of massive industrial production. There were huge demands for the transportation of raw materials and products. The invention of trains and automobiles even led to a revolution in the history of infrastructure development. After that, the railway network extending in all directions was established. Also, the invention of very large crude carriers and airplanes made long-distance transportation possible. Various regions of the world began to be connected thanks to the infrastructure framework. Meanwhile, the infrastructure network of post, communication, and high-voltage electricity began to take shape. Internal urban infrastructure continued to be improved. Metropolitan cities became not only transportation hubs, but also regional centers of politics, economy, culture and education, and medical care and entertainment. In the information age, people pursue a higher level of information timeliness. Internet as a wholly new path of infrastructure is playing a huge role. Thirdly, the world is a big city. On the one hand, the rapid development of modern transportation means and upgrading of communication technology and comprehensive application of network technology have made the world a global village; on the other hand, with the comprehensive development of urban infrastructure, an all-around three-dimensional transportation network from underground pipelines to ground transportation and to aerospace has been established, turning the world into a big city. All cities in the world have been connected to different degrees through two ways of transportation and communication. The world becomes a big city or big community. Each city is a settlement. In other words, the world is a public space, and each city is a part of the public space. Fourthly, the world is like a city planet. Every city is unique, just like every star in the sky; mono-centric towns which are close are connected through certain conditions and form a metropolitan area or urban agglomeration, just like stars in the sky which are close are concentrated in a certain area; as a number of urban agglomerations are linked together, an urban belt forms. While when all cities in the world participate in the global connection, the global urban network takes shape, just like the starry sky. Therefore, from this point of view, global cities are like a mapping of the starry sky, and the world is like a city planet (see Fig. 2.84).

2.2.4 The World Pattern Has Been Reshaped by the Evolution of Urban Pattern: The World Becomes a Multicentric World Under Time–space Compression Cities are places where modern industries and population are concentrated, as a sign of human civilization and social progress. Cities, either large or small, have always played the leading role in their respective regional economic and social development. The development of cities in a country determines its national economic level, and the development of cities in the world determines the world economic pattern. After

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Fig. 2.84 City planet. Data source Internet

the financial crisis in 2008, the global economic center of gravity is shifted toward the Asian-Pacific region. Cities in the region have risen quickly, and the world urban pattern has changed significantly. According to the ranking of world cities released by Gawc, among the top 20 cities in the world, there are eight in Asia. Especially, Chinese cities have shown an obvious trend of rise, shaping the multicentric world pattern. In addition, with the establishment of global urban network, the intercity transportation network has had global cities connected. Meanwhile, the construction of aviation network and high-speed railways has shortened the time distance between global cities and formed a new world under time–space compression. Cities have not only changed the regional and national pattern, but also changed the intercontinental and world pattern profoundly. Firstly, urbanization of emerging economies has broken the world urban pattern gradually. Huge changes are taking place in the world today. Since the financial crisis, a number of developed economies have experienced a sluggish economic growth, been immersed in debt, and even seen a negative growth, while emerging economies represented by China have risen rapidly. In 2010, China became the second-largest economy in the world. Countries such as India, Brazil, Russia, and Malaysia have shown a strong momentum of rapid economic growth. The global economic center of gravity has shifted toward emerging economies including Asia-Pacific. The world urban pattern is undergoing profound changes. Since the 1980s, the increase in urbanization rate of emerging economies has changed the world economic pattern greatly. Emerging countries led by China have increased their share in the global output to 50% above from 37% in 2000; increased their share in the global trade to 40% above

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from 20%; and increased their share in the global stock market value to 15% from less than 5%. This trend has driven and contributed to another trend changing the world, that is, urbanization. The enhancement of industrialization will speed up the urbanization process. It is expected that the global urbanization rate will increase to 70% by 2030. In 2012, the average growth of urbanization rate was 5.1% in emerging economies. It was 7.8% in China. And in developed countries, it was only 1.2%. For more than 40 years, emerging economies such as Malaysia, Singapore, South Korea, and Thailand have seen the average annual growth of GDP of not less than 5%. Developed countries which have dominated the world for two centuries are giving way to a new world order. Secondly, the urbanization of emerging economies has changed the global center-periphery pattern gradually. Firstly, the rise of East Asian cities has driven the rise of economy in the East Asian region. After the Second World War, East Asian countries which obtained political independence successively began to be committed to economic recovery and development and created the “East Asian Miracle” of rapid economic growth. The rise of East Asian economy started in Japan. Between 1950 and 1980, Japanese economy continued to grow rapidly, with the actual average annual growth of 10% above, ahead of the US and Germany then. After the 1970s, economic growth slowed down in Japan, while the “Four Asian Tigers” showed a tendency of leap-forward economic development. The average annual growth of GNP was about 10%, and exports expanded rapidly. The gross export value of South Korea in 1980 was 534 times as much as that of 1960. They seized the opportunity of undertaking the industrial transfer of developed countries and achieved the adjustment and upgrading of industrial structure in a short period. National economy had an explosive growth and led the economy in East Asia. Besides, since the reform and opening-up, Chinese economy has obtained remarkable achievements. Economic size and aggregate have kept expanding. After the Asian financial crisis in 1997 and global financial crisis in 2008, Chinese economy still remained a strong momentum of growth and continued to promote economic development in East Asia. Between 2002 and 2012, China’s average annual GDP growth was 10.7%. In 2010, China surpassed Japan in GDP and ranked No. 2 in the world. With the development of major economies in East Asia, East Asian economy has risen gradually and become an important part of world economy. East Asia has developed into one of the most dynamic economic regions in the world from the periphery of global economy before. Secondly, the divergence in urban population, urban areas, and economic growth in developed economies including Europe and America has given rise to economic divergence of these regions. In the age of industrialization, professional division of labor in cities, infrastructure, and more abundant resources made a large number of people move to cities in Europe and America. Global population activities were mainly concentrated in European and American urban areas. In the post-industrial age led by information technology, European and American urban development tended to be supersaturated. A series of problems related to urbanization arose such as environmental pollution and land shortage. More and more European and American people chose to move to rural areas from urban areas because of small

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gap in rural–urban development, increasingly perfect rural infrastructure and better environment in these areas. Thus, the phenomenon of counter-urbanization occurred. Europe and America showed a contraction in urban areas. In addition, places where infrastructure was very weak before have got the investment in capital and technology now. For example, Asian Infrastructure Investment Bank is committed to improving infrastructure construction of Asian and European regions and building perfect transportation network in Asian and European regions finally for the transportation of commodities, raw materials, energy resources and talents and transmission of technology, information, and culture. The infrastructure network space has expanded constantly in Asia. Humans mainly worked on agricultural production in the early period. Cities are mostly distributed along rivers with abundant freshwater resources or valleys–alluvial plains. With the development of marine trade, marine transport forts have fostered a number of economically developed cities in the world, among which coastal emerging cities have developed rapidly in recent years. Especially, coastal cities in East China and Southeast Asia have enjoyed fast development and ranked among the most dynamic economic regions in the world. Figures 2.85 and 2.86 reflect the variation in the distribution of global cities in 2000 and 2016, respectively. Global cities have expanded in Asian coastal regions and contracted in European and American regions. Next, the rise of economy in East Asia has contributed to the situation of tripartite confrontation for West Europe, North America, and East Asia. The economic rise of the East Asian region has made this region an important economic center in the world and resulted in the change of global economic pattern. According to the statistics of the World Bank, the total GDP of West Europe, North America, and East Asia was 5.7 billion USD in 2017, accounting for 70.6% of the world’s total. 90 70 50 30 10 -180

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North America represented 26.09%, West Europe represented about 21.41%, and East Asia represented about 23.1%. The global pattern of “tripartite confrontation” basically took shape. Urban development played a significant role in the economic rise of East Asia. The economic development of cities in the East Asian region has driven the wave of economic growth in East Asia and promoted steady economic development of this region. Viewed from the wealth change of high net worth individuals in six regions of the world, the total wealth of the Asian-Pacific region shows a huge increase from 2011 to 2017 and ranks No. 1 among the six regions. Last, global urban integration has given global financial and S&T cities higher capability of world control. Global divergence has become worse. Global financial center cities and S&T center cities, with higher capability of global economic control, are the highest stage of urban development. The development of economic globalization has made unobstructed cross-border capital flows possible in the world. Mergers and acquisitions of international financial enterprises have occurred on a large scale, resulting in the concentration of increasingly more financial activities. International financial center cities such as London, New York, Hong Kong, and Tokyo control the lifeline of global economy by controlling international finance. Their economic competitiveness index also ranks top among global cities. S&T development requires financial support. Global S&T center cities and global financial center cities overlap largely. They both have strong economic competitiveness. Figure 2.87 shows that there are significant correlations between financial index/technology index and economic competitiveness in global sample cities. Only a few cities have high financial index and technology index. The vast majority of

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cities are at a low level, which indicates that global finance and S&T are controlled by a few international financial and S&T centers. Global divergence turns out to be very serious. According to the calculation, the average financial index of 20 selected financial center cities in the world is 0.57, while the average financial index of global sample cities is 0.08. The gap between the two further confirms the judgment of serious divergence between global financial indexes and between technology indexes. In terms of the increment of global high-income population from 2001 to 2016, financial and S&T center cities including Seoul, Tokyo, Paris, New York, Los Angeles, London, and Sydney are all at the forefront in the world, indicating more serious divergence among global cities. Thirdly, the temporal–spatial distance of global cities has formed multiscale superposition of the world. Innovations in communication and transportation technology are springing up in the world today and are changing society and our understanding and expression of society profoundly. There are a variety of transportation means in the world under the time–space compression. Actually, the global pattern has been reshaped because of the transportation and communication. The contraction of temporal–spatial distance changes society and reshapes the spatial range of human activities, ways, frequency, and quality of interaction and communication and even economic network. With constant S&T progress, humans have invented faster and faster transportation means.

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It is widely felt that the earth seems to be shrinking. Indeed, the spatial compression can be felt in the real world which becomes a “global village.” The development of transportation can be divided into five stages roughly (see Fig. 2.88). Firstly, the age of walking. In the early period, human transportation mainly relied on walking, carriages, and sailboats. Backward transportation means greatly restricted the range of social and economic activities. At that time, it was impossible to have an around-the-world trip because of the infinite distance. Secondly, the age of around-the-world voyage. The steam engine technology was invented in the period of industrial revolution and applied in ships and trains. An around-the-world trip was impossible until then. As the technology progressed constantly, less and less time was required to complete an around-the-world trip. By the late twentieth century, people could circle the earth in one day by taking a jet airplane. Thirdly, the age of high-speed railways. With rapid S&T development, more and more high-speed railways have been put into operation, thus reducing the time cost of movement. Shortened temporal distance between cities has expanded the radiation range of cities quickly. People are provided with more and more options as cross-city living and working can be realized by relying on the urban agglomeration. Highspeed railways have revitalized the economy of the region and urban agglomeration, achieved frequent connection of human resources and capital in a shorter period of time, and shortened the distance between regions. Fourthly, the age of aviation flight. Civil aviation transportation is the main choice of people for long-distance travel now. Especially, transnational aviation network has shortened the temporal distance between countries. More importantly, Internet Beijing London

Airplane High-speed train Automobile Walking

Fig. 2.88 Schematic diagram of transportation means changing temporal–spatial distance. Data source prepared by the author

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as one of the fastest transportation means, civil aviation has been developed very well together with its supporting infrastructure. Airport construction and air route maintenance have become more and more mature through S&T development. Flight frequency has increased in some economically developed cities and those located in transportation hubs. 90% of the busiest air routes in the world are in the AsianPacific region. The development of aviation transportation has made international exchanges increasingly frequent and shortened the temporal–spatial distance in the world. Lastly, the age of Internet. The revolution of information technology has brought about the Internet technology. Technological progress has shortened the distance between the people and between cities all over the world. Internet has covered every corner of the world. Instant transmission of information has gone beyond country boundaries. Online interconnection has made the earth shrink to a point, beyond the spatial distance, making the world a “global village” in the real sense.

2.3 Market System and IT Shape the Planet of Cities Over the Past 40 Years Over the past 40 years, as the IT reform entered the new stage and the marketbased reform was unfolded in many countries, the cities across the globe entered the age of globalization. The globalized hardware infrastructure including informatization and infrastructure and the globalized software infrastructure of market-based system together determined the non-agricultural aggregation of global population, global industrial division, and global city competition and therefore drove the cities to greatly develop cities, industries, and economy. The global industrial chain took shape consequently. Global industrial division determined non-agricultural aggregation of cities and urban competition and thus promoted cities to participate in global competition and attract global talents, elements, and capital, which caused global flow of elements and capital to shape element globalization and eventually formed market globalization. In the process of global flow of the software and hardware infrastructure, industry, population and capital, cities, as the carrier, inevitably underwent profound changes in their features, functions, forms, and patterns.

2.3.1 Market System: Victory and Gradual Deepening of Market Economy 2.3.1.1

Content of Market System

Global institutional competition over the past 40 years indicated that market economy won and planned economy failed. Though planned economy had its edges, as under

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the market economy, rights, responsibilities, and interests of economic entities were defined distinctly, market determined resource allocation, market competition was fair and effective, and market economy considerably outplayed planned economy in the process of economic development. First, property protection was the footstone of market economy. A matured property protection system could ensure operation of market economy by law, safeguard its fairness and effectiveness, and eventually promoted national economy to improve. Second, market allocation played a decisive role in the process of resource allocation. Amid economic operation, various resources directly or indirectly entered market and freely flew among different economic entities as guided by the pricing mechanism, the supply and demand mechanism, and the competition mechanism. As they were driven to flow toward areas, departments, and enterprises with high efficiency, their rational allocation was facilitated. At last, government regulation was the guarantee of effective operation of market economy. Under the conditions of market economy, some problems would break out inevitably, such as inconsistency between effect of market allocation and expected target of the society, ineffectiveness of market regulation, time lag and time cost of market regulation, and inefficiency and unfairness of market competition. Especially in the case of economic slowdown or overheating, government would conduct reverse operation through fiscal, monetary, and other macro regulation policies. For instance, the “liberalist market economy” of the US, the “social market economy” of Germany, the “circle market economy” of Japan, and the “socialist market economy” of China all highlighted the role of government.

2.3.1.2

Process of Market System

From the perspective of the process of market system, it has experienced a selfsufficient natural economy to a capitalist market economy and a socialist planned economy and ultimately to a market economy. During this period, most countries have implemented market economic systems. In 1980, 38 countries implemented the planned economy. By 2008, only North Korea and Cuba implemented the planned economy, and most countries in the world have established market economic systems. With the advancement of time, the market economy system of various countries is gradually strengthening. Around 1980, apart from the US and Canada, the economic freedoms of other countries in the world were at a low level, and the socialist countries such as the former Soviet Union were still in a planned economy. Around 1995, most countries in the world already had certain economic freedoms, and compared with the economic freedom of each country in 1980, there was a significant increase. By 2016, the economic freedom of various countries in the world has been qualitatively improved, and the global economic freedom is basically above 5. From 1980 to 2017, with the deepening of the market economy system, the economic freedom of all countries in the world has increased significantly. The practice of the countries indicates that the large-scale transformation from planned economy to market economy in the world happened in the 1990s, mainly in Russia, Eastern European countries, and other former socialist countries. Besides,

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Fig. 2.89 Actual and fitting economic growth rate of China. Source Compiled by the author. Note Fitting economic growth rate was the result of synthesizing the economic growth of major countries in the world with the synthetic control method

China and Vietnam deepened their market system in 1992 and 2001, respectively. As shown by the effect of the market-based reform in China (Fig. 2.89), the market economic reform achieved a tremendous success. Since 1992 when China’s marketbased reform was deepened, the country’s actual growth rate was considerably higher than the fitting growth rate, with the latter fluctuating around 5%, while the former fluctuating around 10%. This means that as result of the deepening of the market economic system, Chinese economic growth rate was elevated by around 5%. The market economic reform in Eastern European countries (Fig. 2.90) shows that market economy had become the basic status of the economic operation of the countries. Except for 2008 and 2009 when growth slightly decreased in the economic crisis, the countries maintained a relatively high economic growth rate in other years.

2.3.1.3 i.

Influence of Market System

Market system causes population aggregation and affects connotative meaning of cities.

As the market economic system was improved, in the pursuit of maximized interest, population in the countries and regions started to get engaged in non-agricultural industries, aggregated toward cities, and migrated among cities, promoting the economic development of the cities. We measure the level of national market system

2010

Estonia

2020

Belarus

10

5

0

10

5

0

2000

2010

2020

1990

2000

2010

2020

1990

-10

-5

10 5 0

-15

1990

2000

2010

2020

Russia

1990

2000

2010

Fig. 2.90 Economic growth rate of major Eastern European countries. Source Compiled by the author according to statistics of the World Bank

2000

Lithuania -10

-5

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100

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80 60 40 0

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2.3 Market System and IT Shape the Planet of Cities Over the Past 40 Years

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Economic freedom of major countries (2005) Economic freedom of major countries (2015) Urbanization rate Fitted values Fitted values Urbanization rate of major countries of major countries

Fig. 2.91 Scatter diagram of economic freedom and urbanization rate of all the countries in 1995, 2005, and 2015. Source Collected by the author

with economic freedom index of the countries and measure population aggregation with urbanization rate. With respect to market system and population aggregation of cities, from 1995 to 2017, global marketization and urbanization were both steadily enhanced, indicating that under a freer market system, urban population would rise and population would aggregate toward cities. Judging from the scatter fitting diagram of market system and urbanization rate in 1995, 2005, and 2015 (Fig. 2.91), we see that economic freedom and urbanization rate of the countries worldwide were positively correlated noticeably. In other words, in the cities of higher economic freedom, the urbanization rate was higher correspondingly. As revealed by the correlation coefficient between economic freedom and urbanization rate from 1995 to 2017 (Fig. 2.92), the correlation coefficient between the two was steady increased, and the correlation was gradually enhanced. It means that as economic freedom was deepened, i.e., market freedom was deepened, population accelerated to aggregate toward cities. Globalization caused by marketization drove global population to flow in a large scope and global immigrant population grew year by year. The population was 153 million in 1990, rose to 173 million in 2000, further increased to 195 million in 2005, climbed to 222 million in 2010, and reached 244 million by 2015. By 2017, global immigrant population was 258 million, accounting for 3.417% of global population, increased by 68.6% than 1990 with an annual average growth rate of 2.5%. Among them all, more than half migrated to the US, Canada, Australia, France, Germany, Britain, Spain, Russia, Saudi Arabia, and the United Arab Emirates (Fig. 2.93). From 2000 to 2015, immigrants contributed 42% to the population growth in North America. The top 15 immigrant routes across the world in 2000 and 2017 in Fig. 2.102 show that the US remained the most popular destination, followed by Saudi Arabia and Russia. Besides, Fig. 2.102 also tells us that as for the direction of global immigrants in 2015, 64% immigrants, up to 157 million, went to high-income countries, 32% medium-income countries, and only 4% low-income countries. This means that

2 The Planet of Cities Toward Diverse Agglomeration, Global …

.4

.45

.5

.55

128

1995

2000

2005

2010

2015

Correlation between economic freedom and urbanization rate of major countries Fig. 2.92 Correlation coefficient between economic freedom and urbanization rate of major countries worldwide. Source Collected by the author

ProporƟon of immigrants in the economies in 2015 Lowincome countries 4% Midincome countries 32%

High-income countries

Mid-income countries

Highincome countries 64%

Low-income countries

Fig. 2.93 Proportion of global immigrants in the economies and global immigrant routes. Source International Migration Report 2017 by United Nations International Organization for Migration

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129

in the perspective of global states, immigration toward high-income countries was still the general trend. Looking at the population mobility within the countries, we find that emerging economies and developed ones presented entirely different statuses. In emerging economies, population would migrate to big cities in the pursuit of interest. Take China and India for example. After the market system was implemented in China, the urbanization rate grew unprecedentedly, with 260 million people migrating from rural areas to cities to seek better jobs. From 2000 to 2010 alone, a 117 million rural population migrated to big cities, mainly Beijing, Shanghai, Shenzhen, and Guangzhou. Among them, talents took up a large proportion because most Chinese well-known hi-tech businesses were concentrated in downtown Beijing. In India, by 2017, 33.6% population lived in cities, amounting to 449 million, increased by 10% than 1980. As the market system is deepened, the figure is expected to double by 2050. Similarly to China, India saw its domestic population basically migrate from rural areas to cities, mainly the world’s most populous ones such as Mumbai, New Delhi, and Kolkata, to seek higher interests. Its talents and education were also concentrated in these cities. Developed economies displayed a reverse trend, with population gradually flowing out of big cities. Take the US for example. According to the research of American demographer Wendell Cox, among the most appealing US cities to talents, the top ten were mostly in the south or the mid-west, with none in the northeast. Boston and Chicago that were dubbed as “Talent Magnet” only ranked in the middle, while New York and Los Angeles ranked even lower, with the growth of college graduate in-migrants being 1.4% and 0.7%, respectively, only. Besides, most colleges and universities were located in suburbs and among the 20 most-educated counties in the US, only two were located in downtown areas. ii.

Market system results in capitalization and financialization of resource elements.

Under the market system conditions, global resource elements could flow freely and in order to get transformed and transferred under a rational trading mechanism, and they were inevitably capitalized and financialized. The financialization gave all the resource elements a price as basis and thus realized optimized allocation of the resources. At this time, due to capitalization of capital assets and global operation of capital lease, global financial centers started to rise one after another and emerging economies were born. For instance, after 1970s, as many developed countries opened up capital account and financial innovation projects were gradually opened, original international financial centers such as London, New York, Paris, Zurich, and Frankfurt expanded rapidly, further taking global resource elements under control. Meanwhile, a group of new international financial centers such as Singapore, Bahrain, Bahamas, Cayman Islands, Tokyo, and Hong Kong were gradually born, starting to form a diversified and multilayered pattern of international financial centers. According to Global Financial Centers Index, London, New York, Hong Kong, Singapore, and Tokyo had been occupying the top five positions of the index for a long time (Fig. 2.94), but currently, rating of Shanghai financial center substantially increased to the similar level as Singapore and other financial

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New York London Hong Kong Singapore Shanghai

Fig. 2.94 Index change of the top five cities in the Global Financial Centers Index. Source Global Financial Centers Index Report 24

centers also underwent apparent changes. The changes with rankings and ratings of these global major financial centers indicated that internal differentiation among top financial centers was increasingly intensified and the old pattern was being broken. Moreover, deepened market system would cause globalization of assets and finance, and the monetary, financial, trade, and investment systems of the countries across the world were already intertwined in a complicated way. Wherever financial currency flew into, resource elements of the world would flow there accordingly, enabling the countries with the right of coinage to print currencies recklessly and accumulate global resources. Though it might result in global financial crisis and economic imbalance, this enabled the countries with control of finance and currency to dominate and organize all the economic activities globally. For instance, New York firmly occupied the position of the top international financial center since the Second World War, and an important reason was that it seized the global fund flow and had liaison of major cities globally under control (Fig. 2.95). iii.

Market system leads to global division of enterprises and influences functions and patterns of cities.

Under market system conditions, enterprises would realize global industrial division in the pursuit of maximized profit. For example, during the first industrial transfer in the 1950s, the US transferred steel, textile, and other traditional industries to the defeated countries such as Japan and Germany. During the second industrial transfer in the 1970s, Japan and Germany transferred light industry, textile, and other labor-intensive processing industries to “Four Asian Tigers” and some Latin American countries. During the third industrial transfer in the 1980s, developed

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Fig. 2.95 Liaison of London with other cities globally. Source Global Financial Centers Index Report 22

countries such as Europe, US, and Japan and emerging industrial countries such as “Four Asian Tigers” transferred labor-intensive industries and low-technology industries with high consumption to the developing world. During the current fourth industrial transfer, the industries are transferred from China to Vietnam, Malaysia, Thailand, and India. In the process of industrial division amid globalization caused by marketization, substantial impact was exerted over functions and patterns of global cities. Regarding the functions, globalization as a result of institutional marketization and technology informatization first enhanced the decisive role of funds and the cities with control of finance became global decision-making, organizing and commanding centers. Afterwards, advance of science and technology made technology similarly important. To be specific, through global industrial division and transfer, London, New York, Tokyo, Seoul, and Hong Kong were gradually transformed from original manufacturing cities to financial services-based ones, taking the control of global funds. The activities were changed from processing and manufacturing to financial trade, consultation, design, advertising, and other financial industries and high-end services. The cities became regional or global financial centers to provide global cities with financial services. As scientific technology developed, hi-tech industries such as electronics, communications, and information technology became increasingly important. Silicon Valley, Shenzhen, Bangalore, Boston, and San Jose rapidly rose because of technological innovation and became global or regional technology centers. Under such a background, established financial centers also made great effort to develop technology and promote innovation. For instance, from 2010 to 2016, New York greatly developed technology industries with a growth rate of 25.5%, ranking third across the US. With the number of technology companies

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reaching 7600 in 2016, New York became a new-type global city supported by both finance and technology. Besides, the countries or cities that undertook the industries transferred from these financial and technological centers also developed economy, acquired resources, and created jobs in various ways and correspondingly became manufacturing- and resource-based cities. Countries, areas, or cities such as Brazil, India, Vietnam, Taiwan, and Dongguan correspondingly became global manufacturing centers. In terms of the pattern of cities, each round of industrial transfer brought about by globalization of market system would generate changes with the pattern of global cities. From the first industrial transfer to the fourth, the pattern gradually changed from the dual-center city structure predominated by New York and centered on US and Europe to the multicenter structure predominated by New York, London, Tokyo, Hong Kong, and Seoul and Asian emerging cities became increasingly more important. iv.

Cities participate in domestic and international competition for technology and talent, affecting the landscape of cities.

Deepening of the market system also brought about competition among the cities. Local government of domestic cities first started competition and had it gradually intensified. Before the city competition was deepened, the cities promoted investment and attracted enterprises with their own industries, resources, and talents and during this time, and it was enterprises that determined city competition. For instance, technology-based multinationals were mainly distributed in cities with high technology level, while labor-intensive multinationals in cities with strong manpower. However, currently, government gradually took part in city competition and the cities competed intensively to attract better resources, talents, and enterprises and thus to develop local economy. For example, Chinese cities started the “snatch for talents” and local government implemented various policies such as subsidy and household registration to solicit talents. As the market system was deepened, market broke the boundary of countries and engaged all the cities globally in competition. The competition among cities was transformed from within the countries to both within and among the countries. During this process, government took a series of policies for regulation to snatch global capital, enterprises, and talents or promote capital, enterprises, and talents to flow back. For instance, the US substantially reduced business income tax from 35 to 20% and changed the principle of person for taxation to principle of territory, which facilitated global subsidiaries of American enterprises to turn their capital, technology, and talents back to the US and US enterprises for reinvestment and safeguarded the country’s economic interest. As the direct consequence of international competition, urban infrastructure was improved and soft environment was changed, requesting the cities for better infrastructure, quality service, and affordable housing. As revealed by the scatter diagram of infrastructure and competitiveness of global cities, the higher urban infrastructure index, the stronger city competitiveness (Fig. 2.96). For another instance, the world’s largest airports and highest-income population were distributed in Chicago, Atlanta, New York, London, Tokyo, Paris, Los Angeles, Frankfurt, Hong Kong, Amsterdam, Osaka, Seoul, Dallas, and Houston and compared to other cities, and these cities had a proper housing price-to-income

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1

Urban compeƟƟveness

0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 0

0.2

0.4

0.6

0.8

1

Urban infrastructure Fig. 2.96 Scatter diagram of infrastructure and competitiveness of global cities. Source CASS Center for City and Competitiveness

ratio and mostly ranked below 60th, thus quite appealing to global capital and talents (Table 2.12). Besides, domestic and international city competition would also indirectly cause transfer of elements and their industries. Due to different global division, various cities performed different functions. Some became important infrastructure hubs, such as Dubai, London, Hong Kong, Amsterdam, and Paris that became major aviation hubs (Fig. 2.97); some became global information hubs, such as Silicon Valley, New York, Boston, London, Beijing, and Shenzhen that became global AI hubs; and some became entertainment hubs, such as Singapore, Las Vegas, and Macao that developed finance and entertaining business. Such market-driven domestic and international competition strongly boosted urban infrastructure, human capital, and industrial systems and therefore profoundly changed the landscape of the cities.

2.3.2 Technical Innovation: Promotion and Change by Information Technology 2.3.2.1

Content of Technological Innovation

New technologies and new inventions of science tended to cause birth and prosperity of new industries and gradually resulted in economic prosperity and development of cities, countries, regions, and until the whole world. It was always true from the first industrial revolution in the 1750s when mankind entered the age of steam, to the second industrial revolution in the 1850 when mankind entered the age of

New York

London

Tokyo

Paris

Los Angeles

Frankfurt

Hong Kong

Amsterdam

3

4

5

6

7

8

9

10

112

3

166

155

28

63

8

79

242

10

9

8

7

6

5

4

3

2

Houston

Dallas

Seoul

Chicago

London

Paris

Los Angeles

Osaka

New York

Tokyo

250

247

25

220

8

28

155

140

79

63

Ranking for housing price-to-income ratio

Source Numbeo database and Eiu database Note The ranking for housing price-to-income ratio is based on the data of 285 cities at the beginning of 2018 on the Numbeo Web site

87

16

39

3

4

1

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14

1

Atlanta

2

220

Chicago

1

6

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Ranking

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Infrastructure (largest airport)

Table 2.12 Top ten cities in airports and talents globally

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Top 20 global air passenger transport hubs in 2016 9000 8000 7000 6000 5000 4000 3000 2000 1000

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Passenger throughputof international air transport (10,000)

Fig. 2.97 Top 20 cities as international air passenger transport hubs in 2016. Source ACI WORLD

electricity, to the third industrial revolution in the 1950s when mankind entered the early stage of atomic energy and computer, then to the new stage of the third industrial revolution in the 1980s when mankind entered the age of Internet and information technology, and eventually to the fourth industrial revolution featured by artificial intelligence, Internet of Things, quantum information technology, and biotechnology in 2010. Especially, since 1980 when the third technological revolution entered the new stage and various industries from electricity, railway, automobile, and airplane to chemical, pharmaceutical and electronic industry and further to information resource, biotechnology, new materials and new energy, each technological revolution exerted immeasurable influence over the economy, functions, and patterns of the cities. The current AI and quantum information technology reform was another great leap since the technology reforms of steam, electricity, and computer in the history of human civilization. This information technology revolution considerably promoted the development of social productive forces, drove changes of the social economic structure and the social life structure, pushed the adjustment of international economic pattern, tightened the connection among areas, and intensified the competition between state capitalism and technology. Table 2.13 shows the main technological inventions with influence over connotations, functions, and landscape of cities. It can be seen that these inventions cover various aspects of mankind such as daily life, society, health care, and traffic and have major influence over content, ways, cooperation, and connection of human activities. So far, most industries are closely tied with new technologies, which tend to integrate with industries to generate new industries. On the side of high-end manufacturing, new technologies

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Table 2.13 Main technological inventions Time

Electronic information

Biological medicine

New materials and new energy

Aeronautics and astronautics

Before 1991 Semiconductor, video recorder, computer, television, landline telephone, credit card

Antibiotics, penicillin, synthetic insulin, DNA technique, prevention and cure technology of infectious diseases

Refrigerator, washing machine, automobile, high-speed railway, air-conditioner

Space shuttle, civil aircraft, man-made earth satellite, weather satellite

1991–1998

Internet, mobile phone, camera, motion picture technology

Bio-gene, clone technology

Automatic robot

Global positioning, satellite navigation

1998–2008

Matured Internet, Matured clone smart phone, technology, Kindle reader, hepatic support automatic vending machine, Internet of Things

2009–2018

Ipad, 4G/5G network, e-commerce, AI, self-service supermarket

Electric vehicle

Biological nanotechnology, artificial embryo, gene divination

Source Collected by the author

such as semiconductor, Internet, computer, mobile phone, Internet of Things, airconditioning, and artificial intelligence will drive economic development of cities. For example, in Silicon Valley in the US, Shenzhen in China, and Bangalore in India, scientific technology drives economic development and changes connotation of the cities. Regarding information traffic, development of information infrastructure such as subway, high-speed railway, airplane, and satellite greatly expands the space of cities and shortens the distance among cities by time and space. With respect to medical care, maturing and application of antibiotics, penicillin, prevention and cure technology of infectious diseases, and gene technology substantially improve the medical care level and prolong human’s life expectancy. As for digital information, the integration of digital information and cities can change functions of cities. Today, there are many smart cities where cities are integrated with various industries such as digital media and entertainment, education and training, financial service, manufacturing and logistics, smart traffic system, healthcare and biotechnology, artificial intelligence, and virtual reality, such as Singapore, London, New York, San Francisco, Chicago, Seoul, Berlin, Tokyo, Barcelona, Melbourne, Dubai, and Hangzhou.

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2.3.2.2

137

Influence of Technological Innovation

First, technological innovation influenced population aggregation and human activities. On the aggregation side, development of technological innovation accelerated the course of global urbanization. Accelerated global urbanization was closely related to technological innovation including IT development, which greatly improved the industrial structure of cities, triggered the birth of hi-tech industries such as e-commerce, software service, and electronic entertainment, attracted talents, and promoted urbanization and economic development of cities. Looking at the correlation between global technological innovation level and urbanization rate (Fig. 2.98), we see that the two are positively correlated. The higher the technological innovation level, the higher the urbanization rate is. On the side of human life, technological innovation profoundly changed the life of urban residents. As technology advanced, need of urban residents was changed from basic physical need to higher-level spiritual need in knowledge and service. Before the twenty-first century, the content and ways of human activities were confined to reading newspaper and watching TV and everything must be done in person. In the twenty-first century, greater focus of human life was put on virtual and intangible activities, such as chatting and playing games online and even raising intangible pets online and spending money for them. As shown by a survey on US consumer expenditures, since 2007, money spent on tangible goods by the richest group (over USD 300,000 income annually) that accounted for 1% of US population was substantially reduced. In order to avoid excessive focus on materials, the rich heavily invested money in non-material sectors such as education, retirement, and medical care. An iiMedia Research survey indicated Chinese

UrbanizaƟon rate of the world

55 54 53 52 51 50 49 48 47 46 45 0.2

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Level of technological innovaƟon Fig. 2.98 Correlation between technological innovation level and urbanization rate of the world. Source World Bank database

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population that paid for knowledge surged from 48 million in 2015 to 292 million in 2018, increased by 5 times (Figs. 2.99, 2.100, 2.101 and 2.102). Second, industrialization of high technologies caused rise and prosperity of cities concentrated with hi-tech industries. Financial and technological development of cities cannot be separated from technological innovation, which will speed 80 60 40 20 0 -200

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-20 -40 -60 Fig. 2.99 Distribution of top 50 financial enterprises globally. Source City and competitiveness database of Chinese Academy of Social Sciences

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-30 -50 -70 -90 Fig. 2.100 Distribution of top 20 cities by income of global listed financial companies in 1990. Source Database on global listed companies

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-30 -50 -70 -90 Fig. 2.101 Distribution of top 20 cities by income of global listed financial companies in 2004. Source Database on global listed companies

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-20 -40 -60 Fig. 2.102 Distribution of top 20 cities by income of global listed financial companies in 2017. Source Database on global listed companies

up industrial transformation of the cities and promote rise of technological centers and financial centers of developed economies. Regarding global financial centers and technological centers, development of London, New York, and San Francisco Bay Area was all triggered by technological innovation. Among them, since 1984, the financial sector of London started to develop rapidly and distinctly changed the city’s

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industrial structure. From 1981 to 1987, jobs in personal services in London were increased by 20% and jobs in banking and securities increased by 13%. Before 2007, 325,000 people were engaged in financial services, and today, over 85% people are engaged in services. Besides, there are more than 480 foreign banks in London, the most in the world, and 75% of the Fortune 500 has branches in the city. New York occupies an even more prominent position as a financial center. After a short period of pains of industrial transformation, its services accounted for over 80% and 45 Fortune 500 companies are headquartered in New York. Figure 99 shows that global top 50 financial enterprises are mostly distributed in the three areas of New York, London, and Tokyo, which all stand at a high level in finance. Figures 2.100, 2.101, and 2.102 represent the top 20 cities by income of global listed financial companies in 1990, 2004, and 2017, respectively. It can be seen that as technological finance develops, the position of New York as a financial center is apparent, and the financial level of London, Hong Kong, Paris, and Tokyo is high as well. With respect to technological centers, San Francisco Bay Area in the US was still a remote place in the middle of the nineteenth century, but now becomes the hub of the world’s hi-tech industry as the age of information technology comes. Venture capital of Silicon Valley accounts for one-third of the total in the US, and around 1,500 computer companies have settled in Silicon Valley, including Google, Facebook, HP, Intel, Apple, Cisco, NVIDIA, Oracle, Tesla, and Yahoo, etc. Third, traffic technology expanded the spatial size within cities. Improvement of traffic technology broke the original spatial form of cities and gradually expanded their spatial size. At the initial stage when traffic technology was less developed, in cities where walking and bicycle riding were sufficient to meet the need for public transport, spatial size of the cities was generally small. If citizens were confined to the narrow downtown area as limited by such traffic ways as walking and carriage riding, size of the cities would be at a low level. As sailing boat, steamship and other shipping technologies were improved, and spatial size of port cities was enormously expanded, such as Seoul, Tokyo, Osaka, Hong Kong, Macao, New York, Washington D.C., London, Los Angeles, and other big cities located in the coastal area, and London city agglomeration in Britain, northwestern city agglomeration in Europe, city agglomeration along the Pacific Ocean in Japan, Great Lakes city agglomeration in North America and city agglomeration along the Atlantic Ocean in the northeast of the US that are formed in the port or coastal areas. As scientific technology developed, traffic infrastructure such as automobile, subway, and airplane further expanded the spatial size of cities. Besides the above-mentioned coastal cities, inland cities such as Moscow also rapidly expanded in space. Looking at the correlation between GDP and population with infrastructure, we see that in cities with stronger infrastructure, urban population and economy are stronger correspondingly (Figs. 2.103, 2.104 and 2.105). Correlation rate between infrastructure and GDP and between infrastructure and population is 0.55 and 0.39, respectively. Fourth, development of information technology supported global division and dissemination of industries. Improvement of traffic technologies such as shipping and aviation and information technologies such as information and Internet

2.3 Market System and IT Shape the Planet of Cities Over the Past 40 Years

141

80 60 40 20 0 -200

-150

-100

-50

0

50

100

150

200

-20 -40 -60

Fig. 2.103 Infrastructure distribution of main cities globally. Source City and competitiveness database of Chinese Academy of Social Sciences

90 70 50 30 10 -180 -150 -120

-90

-60

-30-10 0

30

60

90

120

150

180

-30 -50 -70 -90 Fig. 2.104 GDP distribution of main cities globally. Source City and competitiveness database of Chinese Academy of Social Sciences

brought competition, cooperation, labor division, and trade of cities to an unprecedented level promoted division of global industries and formed global industrial chains. Lower traffic and communication cost caused by information technology made logistics and global supply chains more effective, reduced trade cost, and shortened the distance among cities in time and space. All of these would help open new

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90 70 50 30 10 -180

-150

-120

-90

-60

-30 -10 0

30

60

90

120

150

180

-30 -50 -70 -90 Fig. 2.105 Population distribution of main cities globally. Source City and competitiveness database of Chinese Academy of Social Sciences

markets. For economic activities, the cities were no longer limited to distance or location, but more relied on their own capability in participating in global industrial chain, and decomposition of manufacturing and services into production, consumption, and transportation in time and space became possible. In the age of road and railway, economic activities among cities were limited to land and cooperation, competition, labor division, and trade among cities could only reach as far as wherever railway reaches, but could not break the restriction of ocean. Birth of shipping, aviation, and information technology changed the situation, and trade and cooperation among cities broke the boundary of space. Especially under the condition of market allocation, industrial division started to be realized and economic globalization started. It was these high technologies that connected global cities and brought the cities new industries and prosperity. Fifth, air-conditioning technology resulted in development of cities in tropical and cold areas. Birth of the air-conditioning technology drove the transfer of global population toward tropical and cold areas, made development of tropical area possible, and promoted development of cities. Popularization of air-conditioners also changed the economic structure of many countries and regions without being noticed. For example, in the US, before air-conditioners were popularized, industrial areas were highly concentrated in the northeastern industrial zone along the northern border and the Great Lakes industrial zone. As air-conditioners were popularized, population in the Sunbelt Area in the south surged. Industrial development in these areas provided the country with new space of economic growth. Especially in California, with cooling by air-conditioners becoming available, a stable working environment was created for computers, and programmers were able to crowd in one room for

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143

programming, providing the condition for the electronic information industry to take off here first and for the wonder of Silicon Valley to be shaped. Similarly in Singapore and Hong Kong, popularization of air-conditioners also enabled local economy to develop rapidly. The correlation between penetration rate of air-conditioners and per capita GDP in the countries (Fig. 2.106) also indicates the same. Sixth, advance of medical technology expanded the population size of cities. Advance of medical technology avoided outbreak of plague and some other diseases in a large scale and promoted the size of urban population to expand constantly. In nature, such population growth was both the direct achievement of educational and economic development and the result of improved medical technology and public health, such as improved hygiene of drinking water, addressed malnutrition, curbed infectious diseases and parasitic diseases, and reduced maternal and infant mortality rate. Since the nineteenth century, especially after the 1950s, as bacteriology and epidemiology developed and the public health system was improved, diseases that were once raging and regarded incurable such as smallpox, pulmonary tuberculosis, and pestilence were eliminated by mankind or basically taken under control. For instance, smallpox was once the most critical infectious disease in the west and around 10% children died of smallpox annually in the eighteenth and nineteenth century. As the medical technology advanced, WHO announced in 1980 that smallpox was eliminated and mortality rate of mankind was substantially lowered (Fig. 2.107). Besides, as estimated by a WHO report, vaccination could avoid death of 2–3 million people from diphtheria, tetanus, tuberculosis, whooping cough, and measles annually. Improvement of the medical technologies caused human life expectancy to be constantly prolonged and population size continuously expanded.

Fig. 2.106 Scatter diagram of air-conditioner penetration rate and per capita GDP of major countries. Note The horizontal coordinate represents per capita GDP and vertical coordinate penetration rate of air-conditioners. Size of circles represents size of the air-conditioner market. Source Internet

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2 The Planet of Cities Toward Diverse Agglomeration, Global …

1956 105

1966

1976

1986

1996

2006

2016 2 1.8

95

1.6 85

1.4

75 65

Life expectancy at birth, general (Year)

1

Measles vaccination rate (percentage in 12-23 months old)

0.6 0.4

45

PTB Treatment Success Rate (in registered cases)

1.2

0.8

55

Mortality rate, children aged below 5 (per 1,000 live-born infants)

Prevalence of TB (per 1,000 people) (negative) HIV infection rate, general (percentage in 15-49 years old)

0.2

35 1960

1970

1980

1990

2000

2010

0

Fig. 2.107 Global medical care status and life expectancy. Source World Bank database

Seventh, technological advance brought about differentiation among cities. Areas and people of technological innovation became richer, while low-end manufacturing and low-end population became jobless and poorer. Judging from the technological innovation level and per capita income of main cities globally (Fig. 2.108), we see that in cities with higher technological innovation level, local per capita income is higher. Correlation coefficient between the two reaches 0.685. As the innovation level is improved, high-income population of the cities is also growing year by year. Take

CorrelaƟon between per capita income and technology in ciƟes 140000 120000 100000 80000 60000 40000 20000 0 0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Fig. 2.108 Correlation between urban per capita income and technological innovation level. Source City and competitiveness database of Chinese Academy of Social Sciences

2.3 Market System and IT Shape the Planet of Cities Over the Past 40 Years 2000 20000000

2005

2010

2015

145

2020 30000 London

18000000 25000

16000000

New York San Francisco

14000000

20000 Los Angeles

12000000 10000000

15000

Beijing Seoul

8000000 10000

6000000 4000000

5000

2000000

Sydney Delhi Mumbai

0 2000

2005

2010

2015

0 2020

Fig. 2.109 Change of high-income population in major technological center cities. Source EIU database

main global technological centers for example (Fig. 2.109). High-income population in London, New York, San Francisco, Los Angeles, Beijing, Seoul, Sydney, Delhi, and Mumbai is rising noticeably. On the side of people of technological innovation, knowledge-driven wealth replaced resource-based wealth. In the age of industrial economy, wealth of the world’s richest was based on numerous material resources, while in the age of information technology in the twenty-first century, whoever seized technology seized wealth. Among the top 50 in the 2007 Forbes World’s Billionaires list, seven were engaged in the technology industry, while among the top 50 in the 2018 list, 11 were engaged in technology, and only one was in manufacturing. Besides, those in the technology industry were richer than others in other industries, and three were in the technology industry among the top five (Table 2.14).

2.3.3 Global Connection: Enhancement and Leap Forward of Soft Connection 2.3.3.1

Intangible Products and Services Developed in an Expedited Way in the Past 40 Years

First, tangible products and intangible products. In the contemporary era of informatization and globalization, connection among cities became the important foundation of promoting open cooperation, fair competition, and win–win mutual

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2 The Planet of Cities Toward Diverse Agglomeration, Global …

Table 2.14 Forbes list of top 50 people in technology business Ranking

Name

Fortune

Industry

Ranking

Name

Fortune

Industry

1

Jeff Bezos

$112 B

Amazon

17

Ma Huateng

$45.3 B

Internet media

2

Bill Gates

$90 B

Microsoft

20

Jack Ma

$39 B

e-commerce

5

Mark Zuckerberg

$71 B

Facebook

22

Steve Ballmer

$38.4 B

Microsoft

10

Larry Ellison

$58.5 B

Software

39

Michael Dell

$22.7 B

Dell computers

12

Larry Page

$48.8 B

Google

44

Paul Allen

$21.7 B

Microsoft, investments

13

Sergey Brin $47.5 B

Google

Source Forbes 500

benefit among regions and driving economic development of the cities. As the technological level was elevated, the content of global connection was profoundly changed as well. Products can be categorized into tangible products and services and intangible products and services. Tangible products refer to tangible resources, properties, and articles, and tangible service refers to manpower and machines. These require actual element input during production and trade, and they can be seen and felt with materiality and definite value. Intangible products and services mean those that are not visible or touchable, such as computer software, entertainment, electronic reading, information service, knowledge, data, and mind. As they are intangible and immaterial, consumers cannot feel their existence by touching or seeing. Compared to tangible products with actual materiality and definite value, intangible products can connect economic affairs via intangible connection. Being indefinite, the value and value in use of intangible products are greater than that of tangible products. Value created by intangible products shows the apparent trend of increased returns to scale and has a noticeable diffusion effect. For instance, an advertising company inputs certain capital to improve intangible value of its brand. The higher input is, the greater value turns out. The brand effect will be diffused continuously. Besides, the information technology revolution increased the proportion of production, transportation, consumption, and storage of intangible products and services and raised the share of intangible assets (intellectual property right) such as patent and trademark in social economy. For instance, the brand value of Coca Cola was USD39 billion, and the market value of Microsoft shares once reached up to over USD100 per share. Table 2.15 shows that digital economy in the US, China, Japan, and Britain rapidly developed from 1996 to 2016. World Intellectual Property Organization studied the global value chain of production of various enterprises and concluded that nearly one-third of the value of finished products sold globally were originated from brand, industrial design, technology, and other “intangible assets and services.” From 2000 to 2014, intangible assets and services accounted for 30.4% of total value of sold finished products on average; the share of intangible assets and

2.3 Market System and IT Shape the Planet of Cities Over the Past 40 Years

147

Table 2.15 Change of digital economy in main countries (USD trillion) Digital economy

US

China

Japan

Britain

0.04

0.10

0.28

1996

2.66

2016

11.00

3.80

2.30

1.43

3.14

87.37

1.31

4.16

Increased by (time) Source Collected by the author

services climbed from 27.8% in 2000 to 31.9% in 2007 and stayed stable ever since. Generally speaking, income of intangible assets actually grew by 75% from 2000 to 2014. With respect to services, traditional services were basically low-efficiency industries, such as delivering newspaper, delivering mails, and running errands that required face-to-face and person-to-person services at the same time and place and watching films and watching games that requested visit to cinemas and gymnasiums. As Internet and information technologies developed over the past 40 years, application of digitalized products such as software, film, music, electronic reading, and information service enormously improved services. Services or product information could be delivered to consumers conveniently at low cost, and people had easy and low-cost access to information, films, games, and products they need and could conveniently handle the issues to be addressed. It is fair to say that intangible products and services realized rapid development in the past 40 years. Second, hard carrier and soft carrier. The experience of hi-tech industrial development in the world indicates that carrier development is an effective measure to cultivate emerging industries and promote economic growth. Cities centered on technology industries such as Silicon Valley, Boston, Seattle, Shenzhen, and Bangalore are all based on soft and hard carriers. To be specific, hard carrier refers to hard materials such as paper books, newspapers, ships, airplanes, subway, and highspeed railway, and these hard carriers are presented as material carriers. Soft carrier refers to soft materials such as media and communication, and these soft carriers are presented as invisible and untouchable virtual carriers. For instance, before 1980s, exchanges and trade between two cities were basically realized via hard products and hard carriers such as establishing multinationals to trade resources, goods, and commodities. At the beginning of the twenty-first century, as aviation technology was deepened, exchanges and trade between cities were gradually changed to talent exchange and asset transfer. Today, in the age of information and digitalization, the exchanges and trade are focused on knowledge, information, data, and other intangible assets. Regarding application of soft and hard carriers in the modern time, hard carriers are mostly taken as foundation, while soft carriers are intended for improvement. For example, modern information products such as computers, telephones, mobile phones, laptops, and robots resort to hard carriers to seek substantial improvement.

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Third, hard connection and soft connection. There exist hard connection and soft connection based on tangible or intangible activity elements among cities such as production, logistics, trade, technology, information, and knowledge. Hard connection refers to tangible connection or various transport connection, connecting economic entities via physical infrastructure such as road, railway, vehicle, highspeed railway, and shipping for the purpose of tangible materials such as goods, commodities, and resources. Given its nature and way of embodiment, hard connection tends to change and eventually disappear as time passes by. Soft connection, i.e., intangible connection and connection of information technologies, refers to connecting economic entities via communication, information technology, and digital network, etc. Due to the development of the five information technologies, namely language, writing, data, telephone and network, soft products, and services tend to be saved and shared, causing soft connection to last longer in time and become wider and more convenient in space. To sum up, because of the development of information technologies such as Internet and Internet of Things, both hard connection and soft connection have undergone tremendous changes, and they are becoming increasingly intertwined.

2.3.3.2

Global Soft and Hard Connection and Their Changes

First, hard connection becomes faster and easier. Along with innovation of scientific technologies, the content and nature of hard connection were substantially improved. Hard connection among the cities experienced the change from walking, carriage, and bicycle in the non-motor age, to road and shipping in the motor age, and eventually to expressway, high-speed railway, and aviation in the rapid motor age. Hard connection within the cities was transformed from walking and carriage, to bicycle and electric vehicle, further to bus and automobile and eventually to subway and intercity railway. Besides, the traffic technology revolution resulted in by technological innovation and improvement drove hard connection such as automobile, subway, railway, shipping, and aviation faster and more convenient (Fig. 2.110). Second, soft connection has broken the restriction of time and space. The content and form of soft connection experienced five changes. The first was the use of language, which became an indispensable tool for mankind to exchange ideas and disseminate information. The second was the appearance and use of words, Walking, carriage, cart-pulling, shipping, railway

Expressway, automobile, bus, bicycle

Early 20th century and before

Fig. 2.110 Change of hard connection

Mid and late 20th century

High-speed railway, subway, aviation Early 21st century

2.3 Market System and IT Shape the Planet of Cities Over the Past 40 Years Books, mails, telegram

Early 20th century and before

Broadcasting, newspaper, TV

Mid-20th century

149

Computer, Internet, communication

Early 21st century

Fig. 2.111 Change of soft connection

which helped mankind make major breakthrough in saving and disseminating information and break the boundary in time and space. The third was the invention and use of printing, which made books and newspapers important media for saving and disseminating information. The fourth was the use of telephone, broadcasting and television, ushering in mankind to the age of information dissemination via electromagnetic wave. The fifth was the use of computer and Internet, fundamentally changing the way and content of human communication and life. Over the past 40 years, soft connection was changed from corresponding to telegram, further to landline telephone and mobile phone, and eventually to popularized computer and Internet (Fig. 2.111). The content was intangible assets such as knowledge, ideas, communications, and information. Meanwhile, soft connection was free from restrictions of space and was displayed in the same form with same content both among and within the cities. As technology developed, its carrier also changed from telegram and mobile phone to computer and Internet, while medium of information knowledge changed from books and newspapers, to broadcasting and television and further to electronics and Internet today. The current soft connection not only carries forward the resources and experiences of previous hard connection, but is timelier, wider, further enriched, and more influential. Third, temporality and spatiality of soft and hard connection. On the temporal side, both hard connection and soft connection are one way and irreversible, and their direction can only go from ancient times to today. For example, people today can analyze what happened in the ancient time through antiques and legacy architectures and analyzed the philosophy and culture of the ancient time through books, knowledge, and other soft connection, but cannot realize exchanges in two ways. On the spatial side, both soft connection and hard connection are in two ways. Though the two are both free from the restriction of space, there are differences in details. Hard connection, such as automobile, aviation, and high-speed railway, is limited in space to some extent due to high time cost and spatial cost, while soft connection, such as knowledge, information, and communication, is entirely free from spatial restriction and can be realized anywhere (Fig. 2.112).

2.3.3.3

Impact of Soft Connection on Global Cities and Their Pattern

Under the condition of hard connection, connotation meaning of the cities was noticeably limited by time and space, and it took tens, hundreds, and even thousands of years to develop, such as the Athens spirit of Athens and the perpetual connotation

150

2 The Planet of Cities Toward Diverse Agglomeration, Global …

SoŌ/hard connecƟon

Space SoŌ/hard connecƟon

Time

SoŌ/hard connecƟon

Fig. 2.112 Temporality and spatiality of soft and hard connection

of Rome. This severely restricted the improvement of city connotation. Regarding functions, the cities were changed merely from resource- and production-based ones to hub cities and financial centers, and as for the pattern, the cities were presented as cities or regional central cities. For example, under the condition of road and carriage, cities were simply single ones with simple hard connection within due to limited external connection. The cities at this time exclusively enjoyed their internal resources and elements and developed based on their own resource gift, such as Pittsburgh, Ruhr, Dortmund, and Birmingham. As the railway technology advanced, hard connection among the cities was enhanced slightly, and the cities gradually turned into metropolitan areas or city agglomerations. Besides, as limited by hard connection, not all the cities took part in the global city system and the pattern of global cities was divided into countries or regional centers. For instance, in the age of hard connection with carriage, capitals of the countries, as political center, connected some cities in the countries, while in the age of hard connection with railway, cities slightly broke the regional boundary and became regional centers. The pattern of the cities at this time was divided in general. The change of global connection from hard to soft one caused thorough changes with connotation, functions, form, and pattern of the cities. First, soft connection shortened the distance in time and space and virtual activities brought about new connotation of the cities. As scientific technologies developed, connection among people, objects and cities were effectively enhanced, and the spatial status of people, objects, and cities was profoundly changed. Global soft connection derived from information technology broke the boundary in time and space, enabled the cities to rapidly exchange and cooperate with other cities in the world, accelerated the shape of city connotation, and made it further diversified and inclusive. Within the cities, because of soft connection, the trend of aggregation and the trend of diffusion of population and productive factors appeared simultaneously, while the aggregation and diffusion under global connection conditions were displayed by physical aggregation of goods and population and virtual aggregation of ideas and knowledge. Over the past 40 years, the change from hard to soft connection also showcased the process from physical to virtual aggregation, and the latter has become the main way of aggregation of the cities. For instance, New York, London,

2.3 Market System and IT Shape the Planet of Cities Over the Past 40 Years

151

Silicon Valley, and Shenzhen all resorted to global soft connection to attract talents, capital, and ideas in a large scale to work, study, or consume. Singapore, Paris, Toronto, and Hangzhou resorted to Internet and information technology to attract global cities for trade, service, and exchanges. Second, soft connection pooled city functions and a smart planet has taken shape. As global soft connection of cities was deepened, functions of cities were changing as well. Along with birth of multinationals, industrial division of cities was increasingly clear, and cities gradually developed into cities based on resource, production, education, entertainment, start-up, politics, transport hubs, and finance. Their functions were determined by their own industries and role. For instance, iPhone was designed at the Apple headquarter in Silicon Valley, California, and raw materials needed for manufacturing were provided by Latin American countries. Various parts were shipped to Zhengzhou, China, for assembling and then transported to the global market for sale. Eventually, Apple went public in New York, realizing the connection among global cities. During this process, main functions of the cities were shaped. For example, Silicon Valley, US, became an IT center, cities in Latin America resource-based cities, Zhengzhou, China, manufacturing cities, and New York center of power. In the current age of information and network, soft connection is gradually deepened and becomes the key element to change functions of cities. Whoever grasps global soft connection has the ability to change the world. Compared with hard connection, soft connection features lower cost, wider coverage, and greater benefit. Take Silicon Valley for example. With its technological edges and connection with and control of other cities, it acts as the brain and key point of global connection and distributes enormous resources to external places, creating 5% GDP of the US with only less than 1% population of the country. Norway, with control of 1% of global stock market value and 3% of European stock market value, has considerably greater say in over 1000 large-sized multinationals. Hangzhou takes a leading position globally in e-commerce, but 90% of the links are outside Hangzhou. As the center of e-commercial connection, the city connects the merchant, logistics, and customers worldwide and constructs a business world regardless of distance. As soft connection facilitates the cities in exchanges with their counterparts for talent, logistics, and information, the cities gradually develop their own functions and become livable and smart metropolitans with multiple functions and multiple centers (Table 2.16). Table 2.16 Top 20 smart cities globally Ranking City

Ranking City

Ranking City

Ranking City

1

New York

6

Singapore

11

Berlin

16

Stockholm

2

London

7

Seoul

12

Melbourne

17

Los Angeles

3

Paris

8

Toronto

13

4

Tokyo

9

Hong Kong 14

Chicago

19

Vienna

5

Reykjavík 10

Amsterdam 15

Sydney

20

Washington

Source Statistics on IESE Cities in Motion Index

Copenhagen 18

Wellington

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2 The Planet of Cities Toward Diverse Agglomeration, Global …

Third, sharing of city products was deepened. As soft connection of cities developed, the cities could connect with all the cities globally, changing from regional central cities to global central ones, and their infrastructure network could be shared instead of being exclusively owned. Under the conditions of market economy, global connection, and developed network, sharing of urban resources became the new trend. Today, when the material life was greatly enriched, resources lying idle became normal, while sharing activated such resources. Only by enhancing the connection among economic entities can sharing under market economy be developed. Under such a mode, economic entities put into play the spirit of sharing and resorted to convenience as a result of network connection to recirculate the resources lying idle and realize true market allocation. Regarding the nature of sharing, it involves both public and private products and services, and sharing of soft products such as knowledge and ideas has broken the restriction in time and space, such as Airbnb, Uber, Esty, and historic antiques and books shared by national museums and libraries (Table 2.17). However, the sharing driven by technological innovation was far greater than these and covered various sectors such as education, health, food, logistics and warehousing, services, traffic, infrastructure, space, urban construction, and finance. It involved various economic entities including individuals, groups, and enterprises and has profoundly changed our way of work, trade, and life. Among them, the most important was, with no doubt, sharing of information knowledge and trading methods. From the perspective of information knowledge, people can get access to all the information they want via Internet and as long as they can get online, knowledge, culture, opinions, and information would be presented in front of them right away. With respect to trading methods, thanks to the development of Internet technology, e-commerce and self-service supermarkets have risen rapidly. Table 2.17 Global unicorn enterprises in sharing economy

Enterprise name

Industry

Enterprise name

Industry

Uber

Carpooling

Olacabs

Carpooling

Airbnb

Shared space Funding circle

Shared finance

Wework

Shared space Zocdoc

Shared medical care

Lendingclub

Shared finance

Coursera

Shared education

Lyft

Carpooling

Taskrabbits

Shared service

Esty

Shared goods Quora

Shared knowledge

Grabtaxi

Carpooling

Shared catering

Source Collected by the author

Eatwith

2.3 Market System and IT Shape the Planet of Cities Over the Past 40 Years

153

Fourth, soft connection caused the pattern of global cities to change from the single-center pyramid structure to the multicenter hierarchical bell-shaped structure. As shipping technology developed, portal cities became the key to global connection thanks to their location advantage. Along with intensified connection with the outside, they gradually became the leading roles in the world, such as New York, Tokyo, Sydney, Singapore, and Hong Kong that rapidly rose into international metropolitans. At this time, as restricted by hard connection among cities, these cities alone were large and strong in the global pattern, predominating the global city system. On the other hand, information technology and digital computing became important drivers of economic development and construction of smart cities, causing central cities in developed economies to rise continuously, marginal areas in developed economies to decline and central cities, and some areas in emerging economies and East Asia to rise swiftly. Cities took part in the global city system through connection and sharing (Fig. 2.113). New York, London, Paris, Silicon Valley, Tokyo, Hong Kong, and Shanghai resorted to key elements such as information technology to continuously rise. Bangalore, Mumbai, Shenzhen, Guiyang, and Bangkok replaced tangible hard connection with intangible soft connection based on global spatial connection and explored new paths and patterns of urban development in the age of information. Meanwhile, the world’s major emerging hub cities have been gradually integrated into the global city network and got engaged in cooperation, competition, exchanges, and trade with cities of higher or same level. These economic activities not only improved their own strength, but empowered the cities that they had connection with. The stronger the connection was, the greater the improvement was. The global city system was transformed from the previous single-center pyramid structure to a multicenter hierarchical bell-shaped structure. As indicated by the GAWC ranking of global cities, from 2000 to 2016, the number of alpha-level global cities 90 70 50 30 10 -180

-150

-120

-90

-60

-30 -10 0

30

60

90

-30 -50 -70 -90 Fig. 2.113 Main competitive cities globally. Source Compiled by the author

120

150

180

154

2 The Planet of Cities Toward Diverse Agglomeration, Global …

Table 2.18 GAWC number of alpha-level cities Level Alpha++

2000

2004

2008

2010

2012

2016

2

2

2

2

2

2

Alpha+

4

4

8

8

8

7

Alpha

11

11

9

18

13

19

Alpha–

16

18

22

19

22

21

Total alpha cities

33

35

41

47

45

49

Source Information from GAWC official Web site

rose from 33 to 49, and the number of beta-level cities increased from 35 to 81 and that of gamma-level cities climbed from 53 to 84. The number of global cities was considerably higher. Within the alpha cities (Table 2.18), the number of alpha+, alpha, and alphacities also grew noticeably. It is worth noticing that from 2000 to 2016, London and New York remained global megacities and led other global cities on the alpha++ level.

Chapter 3

Global Industry and City Evolution Patterns Pengfei Ni, Marco Kamiya, Jianfa Shen, Qingfeng Cao, and Li Shen

3.1 Problem Statement and Literature Review 3.1.1 Problem Statement Since the 1980s, the rapidly evolving globalization has led to the increasing integration of national economies across the world and ushered in the era of global cities. In the meantime, the rapid growth of global trade and foreign direct investment (FDI) driven by multinational corporations has reshaped the spatial structure of the global economy. On the one hand, global value chains (GVCs), which are an important feature of the current global economy, have tightened the ties between countries and cities around the world, creating huge material, information, and money flows, and changed the pattern of the world economy as well as the trade, financial and industrial ties between countries and cities. On the other hand, with global production networks (GPNs) dominated by multinational corporations becoming an important way of organizing production activity, trends such as intra-product specialization, slicing up of global value chains, service outsourcing, and flexible manufacturing systems enable cities across the world to participate in production or supply activity led by multinational companies and increase the complexity of the global production system. Therefore, in the era of global cities, the impact of GVCs and GPNs on urban development is worth special attention. To a large extent, the competitiveness of a city is determined by factors such as its position of degree of integration in GVCs and GPNs. However, the evolution of GVCs and GPNs has also increased the complexity of the global economic structure. As for benefits from globalization, it is not a simple “the winner take it all” situation, and different groups benefit differently from globalization. For example, although upstream sectors in GVCs and GPNs are still dominated by cities in developed countries, the transfer of low value-added manufacturing activity from developed countries to developing countries has led to, one the one hand, the rise of cities in developing countries and, on the other hand, the decline of some cities in developed countries, which in turn causes the widening divergence © China Social Sciences Press 2021 P. Ni et al., Global Industry Chains: Creating a Networked City Planet, https://doi.org/10.1007/978-981-16-2058-4_3

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between cities in different countries as well as between different cities within the same country. Therefore, in the era of global cities, it is necessary to gain insight into the impact of GVCs and GPNs on the competitiveness of cities.

3.2 Literature Review GVCs and GPNs are the important analytical frameworks that are used to explain the participation of countries, regions, and companies in global markets and shed light on the global spatial patterns of value creation, retention, and acquisition activity. First, the theoretical basis of the GVC concept can be traced back to the value chain theory first introduced in the 1980s by Michael Porter (1985) in his competitive advantage. On the basis of Porter’s concept, Kogut (1985) developed the concept of valueadded chains. Gereffi and Korzeniewicz (1994) introduced the global commodity chain concept and discussed the internal structure of global commodity chains which consist of different value-adding stages; on this basis, Gereffi and Kaplinsky (2001) defined GVCs and built a theoretical framework to explain the concept. The United Nations Industrial Development Organization (UNIDO) defined global value chains in Industrial Development Report 2002/2003: Competing Through Innovation and Learning as global networks of enterprises linking the entire sequence of activity, from raw material extraction through intermediate stages of production and marketing to eventual sales and recycling, including all participants, organizing of production, sales, and other activity, and distribution of value and profit. Global production networks are essentially emerging from global spatial division of labor. For example, some producers may outsource some less profitable production activity. The global production network theory was developed in the twentyfirst century by a group of economic geographers, including Peter Dicken, Jeffrey Henderson, Neil Coe, and Henry Yeung of the University of Manchester in the UK, on the basis of the global commodity chain and global value chain frameworks. So far, the theoretical research of GPNs can be divided into two stages. During the first stage (2001–2011), at the core of the GPN theory were value, power, and embedding, and most studies focused on four dimensions of GPNs—enterprises, sectors, networks, and systems—to analyze how the value is created, improved, and captured, how power is created and maintained, and how the actors and structures are embedded. The second stage is from 2012 till now, during which the global production network concept was redefined as the nexus of production and service functions organized and coordinated through leading global companies, participated by economic and noneconomic actors at different spatial scales across the world. Studies of GNPs covered topics such as strategic coupling, economic and social upgrading, path dependence, spatial lock-in, and vulnerability. GVCs and GPNs have important implications for regional economic development. First, plugging into GVCs is conducive to, among others, regional economic growth, industrial upgrading, and innovation (Gereffie et al., 2001; Sturgeon, 2001).

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Bazan and Aleman (2003) find that embedding in GVCs can capture advanced technology, which is a key force driving industrial upgrading. Crestanello and Tattara (2011) believe that plugging into GVCs offers opportunities to developing countries to upgrade technological and innovation capabilities and increasing industrial competitiveness. In the meantime, GVCs can also promote the transformation and upgrading of industries in low-income countries by promoting innovation. UNIDO (2004) divides global value chain innovation into four basic types: process innovation, product innovation, functional innovation, and interchain innovation. It also believes that participants in any stage of GVCs can leverage the four types of innovation to improve their competitiveness and gain higher profits. John and Dong-Sung (2000) analyze the industrial upgrading paths of developing countries after participating in GVCs. They believe that developing countries primarily take two industrial upgrading paths according to market access and technological capabilities: (i) from original equipment manufacture (OEM) through global logistics contracting (GLC) to own-brand manufacture (OBM) and (ii) from OEM through own design and manufacture (ODM) to OBM. Second, global production networks also have an important impact on regional development through interaction with local production networks. Coe et al. (2004) introduce the "strategic coupling" of the global production networks and regional economies and maintain that the strategic coupling of leading companies and their strategic partners and suppliers are central to value creation, enhancement and capture processes in regional economies. Yeung (2009) observes that the interconnections between global production networks of multinational companies, the strategies of local companies, and geographic advantages of companies have an important impact on regional development in the context of globalization. Wang and Lee (2007) study the development of the IT industry in Taiwan and its relocation to Dongguan and Suzhou in the Mainland China and observe that the integration of GPNs and local institutional environments drives regional economic growth. Third, after strategic coupling, decoupling and recoupling may occur by combining new actors or recombining existing actors, which will also have an important impact on regional development (Coe & Yeung, 2015). Horner and Strategic (2013) observe, through the case of the Indian pharmaceutical industry, that the strategic coupling, decoupling and recoupling between 1947 and 2005 in the Indian pharmaceutical industry is why India becomes one of the world’s leading center for pharmaceutical manufacturing. The theoretical frameworks of GVCs and GPNs provide new analytical tools and research perspectives for studying cities and urban systems across the world in the context of globalization. By plugging into GVCs and GPNs, cities across the world are involved in the relocation and upgrading of industries through participation in global allocation of a sequence of activity from production to delivery, sale and aftersale services, which profoundly affects the evolution of the functions and patterns of cities and urban systems around the world. The world city concept can be traced back to Friedmann and Wolff (1982) and Friedmann (1986), and the term “global city” can be traced back to Sassen (1991). Beyond world cities, an interlocking network of world cities represented by the connections between advanced producer service firms has developed. For example, Taylor (2004) identifies a world city network

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by connecting headquarters, regional centers, and offices of producer service firms across the world. Taylor et al. (2012) analyze the networks of headquarters and branches of 175 global producer service firms in the Forbes Global (2000) worldwide and the relevant importance of the producer service firms’ headquarters and branch locations in the network. However, the above scholars only analyze the networks of a few advanced producer services firms to study the limitations of the global city system and fail to shed light on the network of all cities in the world (Robinson, 2002). If we use the theoretic frameworks of GVCs and GPNs to analyze cities and urban systems, it is necessary to gain insight into the dynamics of spatial distribution of economic activity. In his spatial economic study, Krugman (1995) refers to spatial segmentation of economic activity as the “slicing up of value chains”, which is aligned with the GPN concept. Gersbach and Schmutzler (2000) find that decreasing internal communication costs tends to favor agglomeration. Fujita and Gokan (2005) observe that, with the reduction of communication costs between headquarters and plants, companies facing high transportation costs tend to distribute plants in different countries to better meet the needs of different markets. On this basis, Fujita and Thisse (2006) further observe that, provided that the wage of unskilled workers is the same across a region, the declining transportation and communication costs will foster the agglomeration of headquarters in the core region and, by contrast, wide wage gaps will trigger the relocation of plants into the periphery. The model constructed by Duranton and Puga (2005) explains why some cities specialize in hosting headquarters and business services, while others specialize in the production of final products and sector-specific intermediate inputs. As we can see from above, first of all, the majority of existing literature primarily focuses on the impact of GVCs and GPNs on countries, and studies on the impact of GVCs and GPNs cities are limited. Second, most researchers analyze the connections between global cities from the perspective of multinational corporations, and further studies of GVCs and GPNs are necessary. Third, it is necessary to conduct case studies and examine the rise and fall and divergence of countries and cities within the theoretical frameworks of GVCs and GPNs.

3.3 Theoretical Analysis 3.3.1 Model Assumptions The model employed in this study assumes that there are two countries (i.e., C1 and C2) in the global economy. C1 is a developed country, whereas C2 is a developing country, and the workforce of C2 is larger than that of C1, depicted as L 1 = L 2 /a = 1/2(a > 1). But the labor productivity of C2 is lower than C1. The larger the value of a is, the larger the labor-related comparative advantage the developing country has over the developed country.

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The second assumption is that the economy of either country consists of three segments: high-end, low-end, and traditional. The high-end segment provides differentiated technologies and services, while the low-end segment uses the technologies and services provided by the high-end segment to produce homogeneous end products. Therefore, there is an input–output relationship between the high-end and low-end segments. The high-end sector engages in upstream activity of value chains. The terms “low-end” and “high-end” are used here to describe the relative position of a sector in a value chain. It is assumed that sectors manufacturing end products are low-end, low value-added sectors, while sectors that produce intermediate inputs are high-end, higher value-added sectors. This assumption is consistent with the current global division of labor. It does not mean that the end products produced by a lowend sector are low-end consumer goods. For instance, the companies assembling iPhones are at the lower end of the value chain, but iPhones assembled by them are not low-end consumer goods. Space is introduced into the model in the form of iceberg transport costs. The model assumes the transport costs of high-end products (τ I ) and low-end products (τ M ) between the two countries are of the iceberg type andτ M > 1, τ I > 1. Products produced by the high-end segments (hereafter “high-end products”) are primarily intangible products such as technologies and services, and their transport costs are in large affected by communications and information technology. Therefore, the transport cost of high-end products is considered as “soft linkage”. In contrast, products produced by the low-end segment (hereinafter “low-end products”) are primarily physical goods, and their transport costs are mainly affected by the conditions of transport infrastructure. In other words, the transport costs of low-end products are “hard linkage”. The traditional segment mainly produces agricultural products. According to Krugman (1991), transport of agricultural goods is assumed to be costless, and thus, agricultural products can be used as a valuation tool in the model. The model also assumes that high-end sectors are capital-intensive, requiring both labor and capital inputs, while low-end sectors and traditional sectors are laborintensive, requiring only labor input, and that labor cannot flow between countries. It further assumes that existing capital in the economy K w = 1, and that capital can flow between countries. However, capital gains will flow back to the capital owner. In other words, capital gains obtained from international investment will completely return to the country the capital is from. 1.

Consumer goods

Residents maximize their utility by consuming homogeneous final products (products produced by low-end industries, hereinafter referred to as “low-end products”) and agricultural products provided by the traditional segment. Their preferences can be represented by the following C-D function. Ur = Mrμ Ar1−μ s.t.

prM Q r + prA Ar = Yr

(3.1)

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where Mr is the quantity of low-end products consumed by the residents of Country r, prM the price of low-end products, prA the price of agricultural products, and Yr the income of the residents of Country r. According to the conditions for consumer utility maximization, the demand functions of low-end products and agricultural products can thus be derived. 2.

Low-end segment

Assume that the low-end segment has a C-D production function exhibiting constant returns to scale and perfectly competitive markets, and the inputs are labor L r and differentiated high-end products Ir . It should be noted that here we assume highend products are differentiated, because innovative ideas, technologies and services are often heterogeneous. Elasticities of substitution (σ ) between different high-end products are constant. A constant elasticity of substitution (CES) production function is used to aggregate different high-end products as intermediate inputs in the production of low-end products. The inclusion of high-end products in the production function of low-end products means that the marginal cost of production in the low-end segment is primarily determined by two factors: labor cost and the aggregate price index of high-end products. Similarly, changes in output from the low-end segment can also lead to changes in the demand in the high-end segment. In addition, the production of agricultural products also exhibits constant returns to scale, and agricultural product markets are perfectly competitive. Furthermore, agricultural products are transported between countries at no cost. The model assumes that the number of units of labor input required for the production of one unit of agricultural products in C1 is 1, and the number of labor units required for the production of one unit of agricultural products in C2 is a(a > 1). Since the prices of agricultural products in the two countries are the same ( prA = 1), w1 = 1 and w2 = 1/a(a > 1), where w1 is the wage level of workers in C1, and w2 is the wage level in C2. The wage disparity reflects the gap in labor productivity between the two countries. 3.

High-end segment

The high-end segment exhibits increasing returns to scale under monopolistic competition. Assume a company in which the high-end segment needs am units of labor input which is the marginal cost of production and one unit of capital which is the fixed cost. The value of capital in the model is equal  to the number of companies in the high-end segment. By setting am = (σ − 1) σ , with w1 = 1 and w2 = 1/a, we can derive the prices of differentiated high-end products in different countries: p11 = 1, p12 = τ I , p22 = 1/a, p21 = τ I /a, where pi j is the price of high-end products produced in Countryi. On this basis, we can further derive the price index of the high-end products in C1 and C2 and the total demand of a specific high-end company. Since the fixed input of a company is one unit of capital, according to the zero-profit condition in the long-term equilibrium, all operating profit (revenues minus variable costs) of a company in the high-end segment will be converted into capital input.

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In addition, since low-end products are homogeneous, we will prove below that, when there is transportation cost (τ M > 1), bilateral trade in low-end products is non-existent. When there is bilateral trade in low-end products between C1 and C2, we obtain M M M M = τ M p22 , p22 = τ M p11 p11

(3.2)

where M is low-end products. As we can see, the above formulae only hold when τ M = 1, which contradicts the τ M > 1 assumption. Therefore, bilateral trade in low-end products cannot exist. As we have established there is only unilateral trade, we then move on to analyze evolution of global production and trade patterns in the following three stages: Stage 1: The developed country has both high-end and low-end industries, whereas the developing country has only traditional ones. Stage 2: High-end industries are only located in the developed country, while both the developed and developing countries have low-end industries; in the meantime, the developed country imports low-end products from the developing country. Stage 3: Both the developed and developing countries have high-end and low-end industries, and the developed country imports low-end products from the developing country.

3.4 Evolution of Global Production and Trade Patterns Across Different Stages 1.

Preconditions for Stage 1:

First, in Stage 1, we assume that high-end industries are located only in C1, which means only C1 have the inputs required to produce low-end products. Then, we can M < c2M , where c2M is the cost of production per unit of low-end products obtain τ M p11 in C2 (the derivation omitted). As we can see from above, the production cost per unit of low-end products in C2 is much higher than the transport cost of low-end products from C1 to C2. In other words, it is unprofitable for C2 to produce low-end products. Therefore, we derive the following precondition for Stage 1: τI 1/α τM

>a

1−α α

(3.3)

Second, when only C1 produces both high-end and low-end products, we obtainπ1 > π2 , which means tha the return on capital employed (ROCE) in C1 is higher than that of C2. Therefore, we derive the following precondition for Stage 1: τI > a

(3.4)

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Therefore, only when the following preconditions are met will both high-end and low-end industries be located in C1: 1

τ I > τ Mα a τI > a

1−α α

(3.5)

Based on the above analysis, we arrive at the following conclusion: When the global soft linkage is low (i.e., when τ I is high) while hard linkage is high (i.e., when τ M is low) or when the labor-related comparative advantage of the developing country is low (i.e., when ais low), both high-end and low-end industries will be located in the developed country, and the developed country will export low-end products to the developing country. 2.

Preconditions for Stage 2:

First, when only C1 produces high-end products and both C1 and C2 produce lowM end products, which means C1 exports high-end products to C2 (i.e., τ M p22 = M M M c1 , τ M p11 > c2 ), we can obtain τ M > 1, τ I < a

1−α α

(3.6)

Secondly, when both C1 and C 2 produce low-end products, and C1 imports highend products from C2, we assume the same capital endowment for both countries. Given the expenditure of C1 as percentage of total expenditure s E and the expenditure of C2 as percentage of total expenditure 1−s E , we get s E = 0.5. If high-end products are only produced in C1, we get π1 > π2 , which means that the ROCE of C1 is higher than that of C2. It can be proved that if and only if the following preconditions are met will the scenario of Stage 2 occur τ M > 1,

a < τI < a

1−α α

, α
1, we will find that the preconditions for the scenario that both C1 and C2 produce low-end products and the developed country imports low-end products from the developing country are met. Second, in the above-mentioned scenario, based on the analysis of Stage 2, we find that the precondition for Stage 3 is τ I < a.

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Based on the above analysis, we arrive at the following conclusion: On the basis of Stage 2, the further improvement of global soft linkage will enable both developed and developing countries to deploy high-end and low-end industries within their own boundaries at the same time.

3.4.1 Theoretical Reasoning Based on the above theoretical analysis, we observe that, with the tightening of the soft ties (reflected primarily in falling in communication costs) and hard ties (reflected primarily in falling transport costs) between the two countries, the global production system goes through three stages. In the first stage, developed countries have both high-end and low-end industries, whereas developing countries only have traditional industries. The historical period corresponding to the first stage is the Industrial Revolution, during which developed countries are “world factories”, while developing countries only had traditional industries. In the second stage, with the advancement of communication technology and the tightening of soft ties, lowend industries of developed countries began to relocate to developing countries, and the size of traditional industries of developing countries begin to decrease. An instance of the second stage is the relocation of the industries of Japan to the four “Asian Tigers” in the 1970s and 1980s. During the third stage, with the further decline in global communication costs, developing countries begin to host high-end industries. For example, the rise of developing countries such as China in the twentyfirst century in the global production system is consistent with the characteristics of the third stage. Therefore, the tightening of global soft links and hard links has reshaped the global production system. Overall, the position of developing countries in the global production system is rising.

3.5 Analytical Framework, Methodology, and Data Description 3.5.1 Analytical Framework Global industrial evolution is a process of re-allocation of production factors across the world. The spatial re-allocation of production factors prompts changes in regional comparative advantages and ultimately triggers the spatial redistribution of industries. Over the past four decades, the deepening of the international division of labor has led to large-scale spatial reconfiguration of global trade patterns. The slicing up and reconfiguration of global value chains have reshaped the structure of national economies. The rise and fall of cities depend on the value they create. In the context

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3 Global Industry and City Evolution Patterns Global production factors

Global production networks

World city network

Global value chains

Fig. 3.1 Analytical framework. Source: drawn by the authors

of spatial reconfiguration of production factors and relocation of industries on a global scale, the spatial distribution of global value chains has undergone profound changes, which has in turn triggered huge changes in the world city network as well as city networks within countries. The reconfiguration of the world city network and national city networks is still ongoing. On this basis, in order to explore the relationship between the evolution of global value chains and the rise and fall of cities, the analytical framework employed in this study (see Fig. 3.1) can be summarized as follows: The distribution of global production factors determines the distribution and changes of global production networks which in turn determine global value chains and changes thereof, where changes in global value chains determine the fall and rise of cities around the world, i.e., changes in the world city network. Specifically, we mainly examine global value chain, global production networks, and global distribution of production factors. First, we analyze relationship between the evolution of the world city network and the spatial distribution of global value chains. Then, we look at the slicing up and reconfiguration of global industries in an attempt to gain insight into the industrial basis for the world city network. Finally, we explain the reasons for the evolution of global industrial layout from the perspective of spatial reconfiguration of global production factors to shed light on the evolution of the world city network.

3.5.2 Data Description The data we use are mainly from the global listed company database of Osiris. The Osiris database is a large professional financial analysis database covering all publicly listed companies worldwide. Data provided by Osiris include, among others, detailed financial statements, ratios, ownership structures, ratings, and stock prices of listed companies. We use the revenue, profit, market value, and employee data of 97,259 listed companies in the Osiris database for 1989–2017 and organize the data according to the host cities and industry categories of each listed company to aggregate the revenues, profits, market value, and employees of listed companies in 755 major cities from 1989 to 2017. On this basis, we then analyze the evolution of

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industries in major cities around the world. It should be noted that some of the city data come from the EIU City Database and the database of the City and Competitiveness Research Center, Chinese Academy of Social Sciences. In order to analyze the role of different types of countries in global industrial evolution, we divide all countries into three categories: developed countries, emerging market countries, and less developed countries. First, we integrated the latest data released by the World Bank, the International Monetary Fund, the United Nations Development Programme, and other international agencies and identified 24 developed countries, namely Japan, South Korea, Singapore, Israel, France, Germany, the Netherlands, Denmark, Finland, Luxembourg, the UK, Switzerland, Sweden, Austria, Belgium, Norway, Italy, Spain, Ireland, Iceland, the US, Canada, New Zealand, and Australia. Then, according to the 2009 Morgan Stanley Emerging Markets Index, we identified 21 emerging market economies. They are Brazil, Chile, China, Colombia, Czech Republic, Egypt, Hungary, India, Indonesia, Malaysia, Mexico, Morocco, Peru, Philippines, Poland, Russia, South Africa, South Korea, and Taiwan (China, Thailand, and Turkey). There is a plethora of global industry classification standards, but they are not aligned to each other. In order to facilitate analysis, the study employs the North American Industry Classification System or NAICS and focuses on eight key industry categories. They are manufacturing, information, finance and insurance, real estate rental and leasing, professional, scientific, and technical services, and educational services. Among them, manufacturing is divided into three categories: Manufacturing (31), Manufacturing (32), and Manufacturing (33). Manufacturing (31) includes Food Manufacturing, Beverage and Tobacco Product Manufacturing, Textile Mills, Textile Product Mills, Apparel Manufacturing, and Leather and Allied Product Manufacturing; Manufacturing (32) is further divided into Wood Product Manufacturing, Paper Manufacturing, Printing and Related Support Activity, Petroleum and Coal Products Manufacturing, Chemical Manufacturing, Plastics and Rubber Products Manufacturing, and Nonmetallic Mineral Product Manufacturing; Manufacturing (33) includes Primary Metal Manufacturing, Fabricated Metal Product Manufacturing, Machinery Manufacturing, Computer and Electronic Product Manufacturing, Electrical Equipment, Appliance and Component Manufacturing, Transportation Equipment Manufacturing, Furniture and Related Product Manufacturing, and Miscellaneous Manufacturing. As we can see, the majority of Manufacturing (31) industries are labor-intensive, Manufacturing (32) industries are capital-intensive, and Manufacturing (33) industries are primarily technology-intensive. Therefore, for purposes of this study, Manufacturing (31) is considered as labor-intensive manufacturing, Manufacturing (32) capital-intensive manufacturing, and Manufacturing (33) technology-intensive manufacturing. In the meantime, to shed light on the global distribution and changes of sub-sectors, we calculated the industrial evolution index of three sub-sectors: computer and electronic product manufacturing, electrical equipment, appliance, and component manufacturing, and transportation equipment manufacturing.

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3.5.3 Construction and Calculation of Industry Relocation Indexes Industry relocation is spatial redistribution of economic activity. Generally, the direction and magnitude of industry relocation can be measured by comparing the changes of regional industrial and economic indicators. At present, there are a full range of widely different tools used to measure industry relocation. Given the abundant data on the geographical locations of companies in developed countries, it is feasible to study the direction and magnitude of industry relocation in developed countries by examining the changes in geographical locations of companies (Arauzo et al., 2010; Brouwer et al., 2004; Savona & Schiattarella, 2004). However, due to imperfect corporate information systems in most developing countries, it is difficult to directly observe the direction and magnitude of industry relocation in developing countries. On the global scale, widely different industrial classification standards across countries, combined with unavailability of data on companies, especially SMEs, in developing countries, make it very difficult to study the evolution of global industrial patterns using micro-samples. Therefore, we can only seek an alternative method. Our preliminary analysis of global industrial evolution used the data from the Osiris global listed company database. Location quotient (LQ) computed as an industry’s share of a regional total for some economic statistic (Jianyong, 2004), absolute share (Shide et al., 2015; Xin & Yao, 2012), and the Herfindahl index (Gongwei & Qi, 2010) are among the most widely used tools for measuring industry relocation. However, these tools focus on comparing static conditions of industries. To accurately measure the dynamic changes in the spatial distribution of economic activity, Zhao and Yin (2011) construct a new industry relocation index centered on the changes in an industry’s share of a regional total for some economic statistic. The formula of the index is as follows: I Rci,t = Pci,t − Pci,t0 =

qci,t qci,t − n 0 n   qci,t qci,t0 c=1

(3.8)

c=1

where I Rci,t represents the magnitude of the relocation of Industry i in Region c in nthe t-th year, qci,t is the output value of Industry i in Region c in the t-th year, c=1 qci,t is the gross output value of Industry i across all regions in the t-th year, n is the number of regions, and t0 is the baseline period.

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In this regard, Xiaohua et al. (2018) believe that the above formula does not fully take into account the natural growth of an industry brought about by the expansion of the regional economy and construct a new formula by factoring the proportion of regional economy in the global economy into the above-mentioned formula to eliminate the impact of the changes in regional production conditions on an industry’s share in the regional economy: m 

I

 Rci,t

=

 Pci,t

−

 Pci,t 0

m 

qci,t0 qci,t qci,t0 i=1 i=1 = n / m n − n / m n     qci,t qi,t qci,t0 qi,t0 c=1

qci,t

i=1 c=1

c=1

(3.9)

i=1 c=1

where m is the number of industries examined, qci,t the output value of Industry i m qci,t is the gross output value of all industries in in Region c in the t-th year, i=1  Region c in the t-th year. IfI Rci,t > 0, there is an overall inbound transfer trend in Industry i in Region c during the year examined as compared to the baseline year;  ifI Rci,t < 0, there is an overall outbound transfer trend in Industry i in Region c during the year examined as compared to the baseline year. Given the condition of the existing literature and data, we use the formula designed by Zhao and Yin (2011) to calculate the industry relocation index of 755 cities across the world from 1989 to 2017. Since the relocation trend of an industry cannot be accurately measured by the number of companies in the industry, this study employs operating income to measure industry relocation. The selection of the baseline period is also crucial for the study of the evolution of global industrial patterns. We chose 1989 as the baseline year for the following reasons: First, the deepening of the global division of labor in the 1980s triggered a new round of industry transfer on the global scale and growth in vertical specialization accounted for 30% of the growth in exports worldwide by the end of the 1980s. After entering the 1990s, trade in intermediate goods has entered a period of acceleration, and its proportion of international trade has become larger and larger. Only from 1990 to 2000, the average annual growth rate of trade in intermediate goods was much higher than the average annual growth rate of global GDP and global trade. The growth rate shows that after 1990, the industrial transfer based on the division of labor in the global value chain has gradually entered a climax. During 1990–2000, the average annual growth rate (AAGR) of intermediate goods trade was much higher than the AAGR of world GDP and trade, which means that industry relocation activity based on the slicing up of global value chains grew robustly after 1990. Secondly, it was not until the 1990s did China transition to a socialist market economy, making China a top destination for industry relocation.

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3.6 Empirical Analysis 3.6.1 Evolution of GVCs and the Pattern of World Cities 3.6.1.1

On the one Hand, the Rapid Development of Industries Across the World has Led to a Significant Rise of Global Welfare. On the Other Hand, the Evolution of Global Value Chains has Triggered a Three-step Evolution of the World City Network: From Intra-Country Integration of Cities to Global Integration of Countries and Then to Global Integration of Cities. In Particular, the Position of Emerging Market Economies and Their Coastal Central Cities in GVCs has been Rising. However, the Central Cities of Developed Countries still Dominate the Upstream Part of GVCs. The Connections Between Cities Across the World are Tightening

Aggregate market value of listed manufacturing companies (billion U.S. dollars)

With rapid technological advancement, improvement of productivity, and expansion of global markets, industries, especially manufacturing, across the world have entered a golden age. Specialization within the manufacturing sector deepens with the value of manufacturing output growing rapidly. The social and economic value and benefits generated by manufacturing have also been growing. The growth of manufacturing on a global scale is reflected in the growth of the sum of market value of listed manufacturing companies across the world (see Fig. 3.2). In 2001, the aggregate market value of companies in labor-intensive, capital-intensive, and tech-intensive

Fig. 3.2 Manufacturing market size by type of manufacturing industry

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manufacturing was 53.5, 220.3, and 748.8 billion US dollars, respectively. But, by 2017, their total market value skyrocketed to 2632.5, 4260.1, and 5212.2 billion US dollars. Despite some setbacks in the aftermath of the financial crisis, the overall global manufacturing sector has been growing robustly, contributing significantly to the growth of the global economy and generating unprecedented value for the entire human race. The global division of labor and the evolution of GVCs determine the rise and fall of cities and drive the evolution of the global urbanization pattern. In theory, intra-regional division of labor drives intra-country integration of cities, cross-border division of labor facilitates global integration of countries, and global division of labor drives global integration of cities. In the first stage, developed countries have complete value chains consisting of both high-end and low-end parts, while less developed countries have not yet achieved industrialization and only have traditional agricultural industries. For instance, in the early days of World War II, most of the world’s industries were concentrated in the US. In the second stage, advancements in communication technology and other technologies prompt developed countries to transfer downstream activity to less developed countries, while the traditional segment in less developed countries begins to shrink. For instance, in the 1970s and 1980s, industrialized countries such as Japan moved its downstream activity to the four “Asian Tigers”. In the third stage, with the deepening of globalization, high-end industries in less developed countries begin to grow as a result of industrialization. For example, in the 1980s and 1990s, high-tech industries in South Korea and Taiwan grew exponentially. To examine more thoroughly the changes in GVCs and GPNs, we analyzed international distribution of intermediate goods production over the last three decades. First, we identified immediate goods according to the widely adopted Classification by Broad Economic Categories (BEC). Immediate goods we identified include products in the following BEC classification codes: 111, 121, 21, 22, 31, 322, 42, and 53. Since the Osiris database uses the NAICS (2017), we converted NAICS codes into BEC codes and then calculated the total revenue of companies producing intermediate goods by city and by year. We selected four years, i.e., 1989, 1999, 2009, and 2017, to represent the period from 1989 to 2017. Comparison of Figs. 3.3, 3.4, 3.5 and 3.6 shows that the top ten cities in terms of the total revenue of intermediate goods manufacturers were London, Tokyo, Boston, Houston, Paris, Chicago, Rotterdam, Toronto, Cincinnati, and Essen. Many of these cities are located in Japan, Western Europe, and North America’s east coast. It is safe to arrive at the conclusion that the differentiation level of global value chains in 1989 was still quite low. In 1989, most activity in the global manufacturing value chains was located in developed economies such as the US, Western Europe, and Japan and had not yet be transferred to developing countries such as China and India. Ten years later, in 1999, the concentration of intermediate goods manufacturing in North America and Western Europe declined, and by contrast, the intermediate goods manufacturing industry in China and Southeast Asia grew, albeit rather slowly. Take the US and China for example. The US accounted for 35% of the total revenue of global intermediate goods manufacturers in 1989, but the figure dropped to 24.6% in 1999. By contrast, this

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Fig. 3.3 Distribution of intermediate goods by city based on revenue (1989)

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Fig. 3.4 Distribution of intermediate goods by city based on revenue (1999)

figure grew from 0.08% to 3% in China during the same period. It shows that international specialization of value chain activity deepened in the 1990s, and activity was gradually moved to developing countries such as China and India. In 2009, the total revenue of intermediate goods manufacturers based in North America and Western Europe decreased, while that in China and Southeast Asia climbed quickly.

3.6 Empirical Analysis

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Fig. 3.5 Distribution of intermediate goods by city based on revenue (2009)

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Fig. 3.6 Distribution of intermediate goods by city based on revenue (2017)

For example, by 2009, the percentage of revenue of US intermediate goods manufacturers in the world total dropped to 20.4%, while that of China skyrocketed to 14.3%. By 2017, China and Southeast Asia had overtaken the US, Western Europe and other regions and become the new hubs for value chain activity in this segment of manufacturing. In 2017, the US accounted for only 15.6% of the total revenue of global intermediate goods manufacturers, whereas China saw this figure rising to

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27.7%, far higher than that of the US The above trend fully demonstrates that, with China’s accession to the WTO, specialization in global value chains has reached an unprecedented level. The large-scale relocation of intermediate goods manufacturing activity has enabled emerging market economies such as China to replace traditional industrialized countries as global manufacturing hubs.

3.6.1.2

The Intra-Country Divergence Among Cities in Developed Countries is Widening. In Developed Countries, Central Cities and Emerging Tech Hubs are Thriving, Whereas Manufacturing Cities are in Decline. In the Meantime, the Intra-Country Divergence Within Emerging Market Economies is also Widening, with Coastal Cities with Highly Developed Transportation Infrastructure Rapidly Developing and Traditional Manufacturing Cities with Less Developed Transportation Systems Trapped in Recession. As for Less Developed Countries, Urbanization is Progressing Slowly, and the Divergence is Gradually Widening

High value-added manufacturing is dominated by developed countries. Most highend manufacturing activity is concentrated in central cities, while the older manufacturing cities are declining due to the relocation of low-end manufacturing, which triggers polarization. We constructed an index to measure GVCs of different industries in different cities across the world by using market capitalization data of listed companies (see Figs. 3.7, 3.8, and 3.9). During 1989–2017, the top ten cities in terms 90 70 50 30 10 -180

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Fig. 3.7 Evolution of labor-intensive manufacturing GVCs

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Fig. 3.8 Evolution of capital-intensive manufacturing GVCs

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Fig. 3.9 Evolution of tech-intensive manufacturing GVCs

of growth in the aggregate market cap of labor-intensive manufacturers were Paris, London, Atlanta, Tokyo, Richmond, Accra, Rotterdam, and Chicago; the top ten cities in terms of growth in the aggregate market cap of capital-intensive manufacturers were Mumbai, Tokyo, Paris, London, Cincinnati, São Paulo, Taipei, Osaka, Indianapolis, and Riyadh, most of which are located in developed countries; the top

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ten cities in terms of growth in the aggregate market cap of tech-intensive manufacturers were Tokyo, Boston, Mumbai, San Jose, London, Seoul, Stockholm, Taipei, Stuttgart, and Chicago. In the past three decades, most of the fastest-growing cities in terms of the growth of the aggregate market value of manufacturers are central cities in developed countries rather than emerging market economies hosting low-end manufacturing industries, mainly because, despite large-scale relocation of manufacturing value chain activity to developing countries and regions such as East Asia and South Asia, R&D, design, sale, and other high value-added activity is still concentrated in the central cities of developed countries, which has enabled the developed countries to retain most of the profits arising from global manufacturing value chains, leaving only a small share of profits to emerging market economies. In the meantime, traditional manufacturing cities of developing country have become the biggest victims of manufacturing facility relocation, exacerbating the intra-country divergence in developed countries. Low value-added manufacturing is dominated by emerging market economies. Low-end manufacturing activity is concentrated in coastal cities of emerging market countries with mature transportation systems, while low-end manufacturing activity in inland cities in these countries is also moving to coastal areas due to poor transportation infrastructure, leading to widening internal divergence in emerging market economies. Coastal cities in some emerging market countries, especially Southeast Asian countries, are rising fast. For example, during 1989–2017, the top ten Chinese cities in terms of fastest-growing market capitalization of listed capital-intensive manufacturers were Taipei, Beijing, Shanghai, Hong Kong, Shenzhen, Kunming, Chengdu, Lianyungang, Guangzhou, and Hangzhou, all of which, except Chengdu and Kunming, are coastal cities; the top ten Chinese cities in terms of fastest-growing market capitalization of listed labor-intensive manufacturers were Hong Kong, Taipei, Yibin, Beijing, Shanghai, Tianjin, Tainan, Suqian, Hangzhou, and Ningbo, all of which except Yibin are located in China’s coastal areas. By contrast, the majority of inland cities within emerging market countries have not received the same opportunities as coastal cities to host manufacturing activity relocated from other countries. Some older inland industrial cities, including cities in Northeast China, are at risk of outbound relocation of manufacturing facilities. As we can see from above analysis, although emerging market countries such as China are benefiting significantly from GVCs, the distribution of benefits is unbalanced. Coastal cities with mature transportation systems benefit the most by hosting value chain activity relocated from other countries and cities, while inland cities with less developed transportation fail to grasp the opportunities, and some are even at risk of facing outbound manufacturing facility relocation, leading to widening internal divergence within emerging market countries. Less developed countries are unable to obtain desired benefits through participation in GVCs, and urbanization of these countries is rather slow, except for some big cities. The internal divergence in less developed countries is also widening. For example, among the 13 states in the West African country Nigeria, only the Lagos State has seen significant growth in its capital-intensive manufacturing industry over the past three decades, and the remaining 12 states have performed

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poorly. It is precisely because of the uneven benefit distribution that the internal divergence between cities in many less developed countries is widening.

3.6.2 Global Industry Evolution Patterns 3.6.2.1

Specialization Within GVCs can be Divided into Three Types: Regional Division of Labor, International Division of Labor, and Global Production Networks. They Drive Intra-Country Integration of Cities, Integration of Countries, and Global Integration of Cities. The Global Industrial Structure has Experienced a Major Shift in Development Patterns. There has been a Global Shift in Manufacturing from Developed Countries to Emerging Market Countries, Especially to the Coastal Central Cities of Emerging Market Countries. In the Meantime, Within Developed Countries, There has been a Shift in Manufacturing from Traditional Manufacturing Cities to Surrounding Cities and Secondary Cities. After the 2008 Financial Crisis, Some Manufacturing Value Chain Activity have Returned from Emerging Market Countries to Developed Countries such as the US

Thanks to rapid technological advancement and demand expansion, the past three decades are a golden age for industries, especially manufacturing industries, which have generated unprecedented value and benefits for human societies. We examined the changes in the revenue of listed manufacturing companies across the world to shed light on the development of manufacturing. In 1989, the total revenue of listed companies engaging in labor-intensive, capital-intensive, and tech-intensive manufacturing was 368, 473.8, and 775.2 billion US dollars, respectively. In 2017, the total revenue of listed companies in labor-intensive, capital-intensive, and tech-intensive manufacturing reached 1553.7, 3386.1, and 5420.3 billion US dollars. As we can see, the growth pace of the three segments of manufacturing is unprecedented. Despite some setbacks in the aftermath of the financial crisis, the overall trend of the global manufacturing sector is upward, creating unprecedented benefits for human beings (Fig. 3.10). There is a global shift in labor-intensive, capital-intensive, and tech-intensive manufacturing. In order to shed light on the industry development patterns in cities across the world, we calculated the average value of the industry relocation index for 1990–2017 and used ArcGIS to map the industry transfer index of eight categories of industries in 755 cities worldwide (see Figs. 3.11, 3.12, and 3.13). Red means that the city’s share of global manufacturing is growing, blue indicates a decrease in the city’s share, and yellow means that the city’s share of global manufacturing remains unchanged. As can be seen from Figs. 3.11, 3.12 and 3.13, there was a large-scale global shift in labor-intensive, capital-intensive, and technology-intensive

3 Global Industry and City Evolution Patterns

Aggregate revenue of listed companies (billion U.S. dollars)

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Fig. 3.10 Manufacturing market size by type of manufacturing industry

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manufacturing during 1989–2017, primarily from traditional developed industrial countries such as Europe, North America, and Australia to Asia, Latin America, Africa, and Eastern Europe. The coastal cities in East Asia in particular are the top destinations for manufacturing relocation. For example, the coastal cities in China

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have experienced fast industrialization over the past three decades by hosting lowend manufacturing relocated from other cities and countries and created a relatively complete industry-level value chain. Other developing countries have also undertaken some capital-intensive manufacturing activity but at a scale far less than that of China. In the meantime, marketing, R&D, and other high-end manufacturing activity are still concentrated in the US, Europe, and other developed economies. Central cities in emerging market countries, especially coastal cities with mature transportation systems, are the top destinations of manufacturing relocation. Take labor-intensive manufacturing for example. Although Asia, Latin America, and Africa have all tapped the potential of hosting labor-intensive manufacturing, a closer look at Fig. 3.14 reveals that labor-intensive manufacturing activity is concentrated in a handful of coastal central cities in these regions, such as the Yangtze River Delta urban agglomeration, the Pearl River Delta urban agglomeration, the Beijing–Tianjin–Hebei urban agglomeration, and other coast urban agglomerations in China. Some inland central cities such as the Chengdu–Chongqing city cluster and the urban agglomeration in the middle reaches of the Yangtze River have become the destinations of secondary manufacturing transfer from coastal cities. In addition, although Latin America and Africa have also plug into global labor-intensive manufacturing value chains, they are not first choice destinations for manufacturing facilities. And only a handful of central cities in Latin America and Africa hosts labor-intensive manufacturing activity. In fact, most cities in other less developed countries/regions have failed to plug into GVCs. There is also an internal shift of manufacturing within developed countries, mainly from central cities to peripheral and secondary cities. From a global perspective, 90 70 50 30 10 -180

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labor-intensive manufacturing is still shifting from traditional developed industrial economies such as Europe, North America, and Australia to Asia, Latin America, Africa, and Eastern Europe. East Asia, Southeast Asia, and South Asia are top transfer destinations. China, in particular, has undergone fast industrialization over the past three decades by hosting manufacturing value chain activity. In the meantime, on the regional scale, there has also been an internal shift of manufacturing within developed industrial countries over the past three decades. Take labor-intensive manufacturing for example. As shown in Fig. 3.15, there is a shift of labor-intensive manufacturing from the northeast and central parts of the US to the western and southern parts. The share of global labor-intensive manufacturing activity in western and southern cities such as Houston, Miami, New Orleans, Memphis, Portland, and Las Vegas has risen markedly, whereas that in central and northeastern cities such as Chicago, Pittsburgh, Cincinnati, and Philadelphia has declined. In the meantime, there is also a center-periphery shift of labor-intensive manufacturing. For example, among the northeast and central cities in the US, the share of Columbus and Boston in Ohio of the country’s manufacturing sector has increased. In Europe, labor-intensive manufacturing is shifting to Eastern and Southern Europe. For example, labor-intensive manufacturing in traditional industrialized countries such as France, the UK, and Germany has declined significantly, whereas labor-intensive manufacturing activity in Eastern and Southern European countries such as Poland and Italy has grown markedly (see Fig. 3.16). After the 2008 financial crisis, some manufacturing facilities returned from emerging market countries to developed countries such as the US. Although overall manufacturing has been shifting from developed countries to emerging market 90 70 50 30 10 -180

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countries over the past three decades, some manufacturing activity moved back to the US after the 2008 financial crisis due to the decline in US production costs and incentive measures introduced by the US government. As can be seen from Figs. 3.17 and 3.18, although capital-intensive manufacturing in many American cities is still declining, the west coast and the central and northeastern parts of the country have seen a trend of return. Between 2008 and 2017, the top ten American cities in terms of the scale of return of capital-intensive manufacturing were Houston, Atlanta, Omaha, New Haven, San Jose, Charlotte, Salt Lake City, Dayton, Sacramento, and Boston. Take Dayton for example. Dayton, once devastated by large-scale automobile production outflows, has experienced a revival after 2008. The return of manufacturing has reinvigorated the city. In the meantime, there is an outflow of capital-intensive manufacturing from emerging market countries in Southeast Asia. In 2008–2017, nineteen Southeast Asian cities saw a decline in capital-intensive manufacturing. Capital-intensive manufacturing in 21 Indian cities has also been declining since 2008. By comparing the development patterns of computer manufacturing in North America and East Asia, we can also see the trend of manufacturing’s return from emerging market countries to developed countries such as the US, but this trend is still weak. After the 2008 financial crisis, many American cities, including San Jose, Seattle, Salt Lake Cities, Milwaukee, Boston, Oklahoma City, Richmond, Tulsa, Las Vegas, Cincinnati, Orlando, Rochester, Columbia, and Tucson, have experienced an uptick in local computer manufacturing industries (see Fig. 3.19). In the meantime, some cities in East Asia, Southeast Asia, and South Asia, which are the backbone of the global manufacturing sector, have seen a trend of production outflows

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Fig. 3.18 Development of capital-intensive manufacturing in Southeast Asia (2008–2017). Source drawn by the authors using ArcGIS

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(see Fig. 3.20). Since 2008, the share of global computer manufacturing activity in Chinese cities such as Taipei, Tainan, Xianyang, Wuxi, Beihai, Jiangmen, Dalian, and Daqing has been declining. Seven cities in India, including Chennai, Mumbai, 90 70 50 30 10 -180

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and Kolkata, have suffered the same fate. In Southeast Asia, Singapore, Bangkok, and Ho Chi Minh City have also seen a decline in computer manufacturing activity.

3.6.2.2

The Disparities Between Developed Economies, Emerging Market Economies, and Less Developed Economies in Absolute Terms are Widening, but the Relative gaps are Narrowing. Overall, Regional Disparities Across the World are Narrowing. In the Meantime, the Intra-Country or Intra-Region Divergence Between Cities is Widening. The Internal Imbalance in Developed Countries has Deepened, with Central Cities and Emerging Tech Hubs Growing Rapidly while Manufacturing Cities Trapped in a Recession. Emerging Market Economies have also Suffered the Same Fate, with Easily Accessible Coastal Cities in Terms of Transportation Rising Rapidly While less Accessible Traditional Manufacturing Cities Trapped in a Recession. Widening Intra-Country Divergence also Occurs in Less Developed Countries

The disparities between developed economies, emerging market economies, and less developed economies in absolute terms are widening, but the relative gaps are narrowing. Overall, regional disparities across the world are narrowing. As we can see from Figs. 3.21, 3.22, and 3.23, the disparities between developed economies, emerging market economies, and less developed economies in terms of absolute value of labor-intensive, capital-intensive, and technology-intensive manufacturing industries are widening. However, from the perspective of growth rate, the relative disparities are shrinking. The growth rates of emerging market countries and less developed countries are obviously higher than developed countries, which is also the reason for the continuous decline of the overall coefficient of variation of the three manufacturing sub-sectors (see Fig. 3.24). During 1992–2017, the average annual revenue of listed labor-intensive manufacturing companies in developed, emerging market, and underdeveloped countries increased by 1.5 times, 53 times, and 35 times, respectively, that of capital-intensive manufacturing companies increased by 2.5 times, 58 times, and 67 times, respectively; that of tech-intensive manufacturing companies increased by 1.8 times, 173 times and 12 times, respectively. Data show that there is a shift of manufacturing activity from developed countries to emerging market countries and less developed countries, but the scale of production outflows to emerging market countries is much larger than the outflows to less developed countries. The slicing up of global manufacturing value chains has caused the internal divergence between cities in both developed and emerging market countries to widen. Divergence within emerging market countries is widening, with easily accessible coastal cities in terms of transportation growing rapidly, whereas traditional manufacturing cities with less developed transportation system

3 Global Industry and City Evolution Patterns

Revenue of listed companies in the sub-sector (thousand U.S. dollars)

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Revenue of listed companies in the sub-sector (thousand U.S. dollars)

Fig. 3.21 Divergence between developed, emerging market, and less developed economies in labor-intensive manufacturing. Source drawn by the authors based on revenue data from the Osiris database

Fig. 3.22 Divergence between developed, emerging market, and less developed economies in capital-intensive manufacturing. Source drawn by the authors based on revenue data from the Osiris database

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Revenue of listed companies in the sub-sector (thousand U.S. dollars)

3.6 Empirical Analysis

Fig. 3.23 Divergence between developed, emerging market, and less developed economies in tech-intensive manufacturing. Source drawn by the authors based on revenue data from the Osiris database

Fig. 3.24 Overall coefficient of variation of labor-, capital-, and tech-intensive manufacturing. Source drawn by the authors based on revenue data from the Osiris database

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trapped in a recession. Developed countries have also suffered the same fate, with central cities and emerging tech hubs rising rapidly while manufacturing cities trapped in recession. Divergence within less developed countries is also widening. First, some coastal cities in emerging market countries such as Southeast Asian countries have grown rapidly by plugging into global manufacturing value chains, but other cities are unable to do so, leading to internal divergence between cities. Taking China for example, Chinese coastal cities such as Shanghai, Beijing, and Shenzhen are top destinations for relocated manufacturing activity, but many small- and medium-sized inland cities in the country have been left behind and are becoming increasingly marginalized in the process of globalization. According to the Global Urban Competitiveness Report 2018–2019 jointly released by the Chinese Academy of Social Sciences and UN-HABITAT, Shanghai, Beijing, and Shenzhen are ranked among the global top 30 cities, while the worst-ranked Chinese cities get a rank below 800th. This huge gap indicates that globalization has led to widening internal divergence between cities within emerging market countries. Second, in developed countries, on the one hand, a few central cities have benefitted greatly from global manufacturing value chains, but on the other hand, some cities that rely on labor-intensive and capital-intensive manufacturing industries have fallen into decline. For example, Dayton is a typical industrial city in the central US. It used to be a hub of automotive manufacturing, but with the shift of the automobile industry to cities with low labor costs, the city fell into decline and turned into a ghost town. By contrast, on the west coast of the US, high-tech companies cluster in Seattle and San Francisco, creating enormous value and attracting a large influx of people. This shows how wide the internal disparities between cities within developed countries have become. Globalization has also driven apart cities within in less developed countries. In Nigeria, for example, only the Lagos State benefitted significantly from plugging into global value chains of capital-intensive manufacturing over the past three decades. The rest of the country tells a different story. In general, cities in less developed countries are increasingly polarized.

3.6.2.3

As for Spatial Distribution of Industries, There is Spatial Segmentation of Economic Activity Between Countries. The Spatial Segmentation of Labor-Intensive and Capital-Intensive Manufacturing Industries has Weakened, While that of Technology-Intensive Manufacturing and the Financial and Insurance Sector is Increasing

Using the geographic distance-based weights matrix to analyze the spatial correlation between listed companies in 744 cities from 1989 to 2017, there is a relatively low level of agglomeration of labor-intensive manufacturing activity, but the Moran’s I index value of this sub-sector of manufacturing rose from −0.007 in 1989 to −0.003 in 2017, indicating that the degree of agglomeration was declining. A similar situation exists in capital-intensive manufacturing. The Moran’s I index of capital-intensive manufacturing was −0.009 in 1989. But after 2007, this index was

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no longer statistically significant, indicating that the degree of agglomeration was declining. The reason for the declining agglomeration of the above sectors is that globalization is breaking down barriers created by national borders and promoting cross-border movement of economic activity. However, on the other hand, the spatial segmentation of tech-intensive manufacturing and the financial and insurance sector has increased. Before 2013, the Moran’s I index value of tech-intensive manufacturing was not significant, but its Moran’s I index value became significant, which means that there is a low degree of agglomeration in tech-intensive manufacturing and the financial and insurance sector. Although globalization is increasing, there has not been a large-scale shift of high-tech industries over the past few decades due to restrictions imposed by governments. Instead, the degree of spatial segmentation of high-tech industries is rising. This situation also exists in the finance and insurance sector, and the degree of agglomeration in the finance and insurance sector is increasing. At present, developed countries in Europe and America still host most of the global financial hubs. Although the financial and insurance sectors in some Asian and African cities are growing, they have little impact on the overall pattern. As can be seen from Fig. 3.25, developed countries in North America and Western Europe still dominate the global finance and insurance sector. In the meantime, on the one hand, the concentration of the finance industry has increased. For example, there is a shift of financial activity from inland cities to coastal central cities in the US. On the other hand, although the finance and insurance sectors in some cities in Asia, Africa, and Latin America are growing rapidly, their size is still small. For example, the finance and insurance sectors in emerging market countries such as China and India are still underdeveloped (Tables 3.1, 3.2, 3.3, and 3.4). 90 70 50 30 10 -180

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Table 3.1 Moran’s I index value of labor-intensive manufacturing Year

Moran’s I

Year

Moran’s I

Year

Moran’s I

1989

−0.007***

1999

−0.004***

2009

−0.003*

1990

−0.007***

2000

−0.005***

2010

−0.003*

1991

−0.007***

2001

−0.004***

2011

−0.003*

1992

−0.008***

2002

−0.001

2012

−0.003*

1993

−0.008***

2003

−0.001

2013

−0.003**

1994

−0.005***

2004

−0.004***

2014

−0.003**

1995

−0.004***

2005

−0.004***

2015

−0.003*

1996

−0.005***

2006

−0.004***

2016

−0.003*

1997

−0.006***

2007

−0.003**

2017

−0.002

1998

−0.004***

2008

−0.003*

Note ***, **, and * mean statistical significance at 1%, 5%, and 10%, respectively

Table 3.2 Moran’s I index value of capital-intensive manufacturing Year

Moran’s I

Year

Moran’s I

Year

Moran’s I

1989

−0.009***

1999

−0.005***

2009

−0.002

1990

−0.008***

2000

−0.005***

2010

−0.002

1991

−0.007***

2001

−0.003***

2011

−0.002

1992

−0.008***

2002

−0.003**

2012

−0.002

1993

−0.008***

2003

−0.003**

2013

−0.002

1994

−0.005***

2004

−0.003**

2014

−0.002

1995

−0.007***

2005

−0.003**

2015

−0.002

1996

−0.006***

2006

−0.003*

2016

−0.002

1997

−0.006***

2007

−0.002

2017

−0.002

1998

−0.005***

2008

−0.003*

Note ***, **, and * mean statistical significance at 1%, 5%, and 10%, respectively

3.7 Empirical Analysis 3.7.1 Empirical Design 3.7.1.1

Methods of Empirical Research

In order to further explore the evolution of the global industry and urbanization patterns over the past three decades and the reasons behind the evolution, we constructed the following models:

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Table 3.3 Moran’s I index value of tech-intensive manufacturing Year

Moran’s I

Year

Moran’s I

Year

Moran’s I

1989

−0.003**

1999

−0.002

2009

−0.002

1990

−0.002

2000

−0.002*

2010

−0.002

1991

−0.002

2001

−0.002*

2011

−0.002

1992

−0.002

2002

−0.002

2012

−0.002

1993

−0.002

2003

−0.002

2013

−0.002*

1994

−0.001

2004

−0.002

2014

−0.003**

1995

−0.002

2005

−0.002

2015

−0.003*

1996

−0.002

2006

−0.002

2016

−0.003**

1997

−0.002

2007

−0.002

2017

−0.003**

1998

−0.002

2008

−0.002

Note ***, **, and * mean statistical significance at 1%, 5%, and 10%, respectively

Table 3.4 Moran’s I index value of the finance and insurance sector Year

Moran’s I

Year

Moran’s I

Year

Moran’s I

1989

−0.007***

1999

−0.005***

2009

−0.012***

1990

−0.006***

2000

−0.006***

2010

−0.011***

1991

−0.007***

2001

−0.008***

2011

−0.011***

1992

−0.004***

2002

−0.009***

2012

−0.008***

1993

−0.004***

2003

−0.011***

2013

−0.008***

1994

−0.004***

2004

−0.011***

2014

−0.012***

1995

−0.009***

2005

−0.012***

2015

−0.011***

1996

−0.008***

2006

−0.013***

2016

−0.010***

1997

−0.008***

2007

−0.011***

2017

−0.010***

1998

−0.005***

2008

−0.010***

Note ***, **, and * mean statistical significance at 1%, 5%, and 10%, respectively

(1)

The static relationship between industrial agglomeration and the development of a city ecoi = α0 + α1 indi + αi X i + εi

(3.10)

where ecoi is the economic competitiveness of City i, measured by indicators such as GDP, disposable income, and urban economic competitiveness; indi is an industry, using the NAICS numeric code; X i is a control variable, i.e., factors that affect economic competitiveness other than industries, including the cost of doing business, government, infrastructure, labor, global connections, social environment, and ecological environment.

190

3 Global Industry and City Evolution Patterns

(2)

Dynamic relationship between industrial agglomeration and development of cities gravgi = α0 + α1 iravg31i + α2 iravg32i + α3 iravg33i + α4 iravg51i + α5 iravg52i + α6 iravg53i + α7 iravg54i + α8 iravg61i + εi (3.11)

where gravgi is the coefficient of variation of City i’s GDP for 1989–2017, obtained using formula (3.1); iravg31i , iravg32i , iravg33i , iravg51i , iravg52i , iravg53i , iravg54i , and iravg61i are the industry relocation index of Industries 31, 32, 33, 51, 52, 53, 54, and 61. (3)

Relationship between the clustering of production factors and industry relocation indit = α0 + α1laborit + α2 hrit + α3 f rit + εit

(3.12)

where indit is the industrial indicator of a city, such as the total profit, revenue, market value, and total number of employees of companies based in the city; laborit is labor resources of the city; hrit is human capital of the city; f rit is financial resources of the city.

3.7.1.2 (1)

Variable Selection and Data Description

Economic competitiveness of cities

In order to study the evolution of the world city network and the reasons behind it, we used indicators such as GDP, disposable income, and economic competitiveness to measure a city’s position in global production networks. The GDP data we used are from the EIU City Data, which covers GDP data of 1,128 major cities around the world from 2001 to 2016 (including GDP in both current and constant US dollars). By examining the evolution of GDP in cities around the world, we can gain insight into the development patterns of global economic centers over the past decade. Our disposable income data also come from the EIU City Data, which also covers disposable income data of 128 global cities between 2001 and 2016 (including disposable income in both current and constant US dollars). Compared to GDP, disposable income can more accurately measure the position of a city in global production networks and the benefits it obtains by plugging into global value chains. Our economic competitiveness data come from the database of the Center for City and Competitiveness, Chinese Academy of Social Sciences. From the perspective of the output of economic competitiveness, economic competitiveness of a city can be expressed as a function of economic density and economic growth:  GU C Ii = f

GDP , G D P ar ea i

 (3.13)

3.7 Empirical Analysis

191

where G D P/ar ea is the economic density of a city, reflecting economic rent and benefits per unit of urban space and the efficiency in land use; G D P is the growth of GDP of the city, reflecting the growth of the income level of the city. (2)

Selection of industries

In order to accurately assess the development of industries in major cities worldwide, we employed NAICS 2017 to classify industries. We selected a number of industries assigned with two-digit codes. They are Manufacturing (31), Manufacturing (32), Manufacturing (33), Information (51), Finance and Insurance (52), Real Estate Rental and Leasing (53), Professional, Scientific, and Technical Services (54), and Educational Services (61). As mentioned above, for purposes of this study, Manufacturing (31), Manufacturing (32), and Manufacturing (33) are deemed as labor-intensive manufacturing, capital-intensive manufacturing, and tech-intensive manufacturing, respectively. (3)

Labor resources

Changes in global value chains often entail changes in labor resources. Laborintensive manufacturing industries in cities boasting abundant labor resources often grow rapidly, whereas cities with scarce labor resources are often unable to retain labor-intensive manufacturing activity. Therefore, the evolution paths of industries can often be tracked based on the evolution patterns of labor resources. In order to measure the spatial distribution of global labor resources, the population between the ages of 15 and 59 was selected to represent the labor resources of each city. We also selected the population between the ages of 20 and 29 to represent the youth workforce of a city. These data are also from the EIU City Data. (4)

Skilled human resources

The evolution of GVCs has a significant impact on human resources, especially skilled human resources, which are closely related to industries. To gain insight into the changes in the spatial distribution of industries across the world, it is necessary to study global industrial changes from the perspective of human resources. Since there is no existing tool to accurately measure the human capital of a city, we selected high-income population to represent skilled workforce in a city. It is a reasonable although not accurate proxy for human capital because income and human capital are positively correlated (i.e., the higher the human capital, the higher the income an economy will generate). Therefore, it is reasonable to select high-income population to represent the human capital of a city. Taking to current economic conditions, we defined the population with an annual income above 30,000 US dollars as skilled workers and used data from the EIU City Data. (5)

Financial resources

Financial resources are the lifeblood of industries. Without sufficient financial resources, it is difficult for a company or even an industry to go far. Therefore,

192

3 Global Industry and City Evolution Patterns

global industry relocation is often accompanied by global reconfiguration of financial resources. By tracking the distribution of global financial resources, we can better understand the evolution of industries worldwide in recent years and its causes. Since there is no complete and accurate data describing the financial resources of cities, we used the revenue, profit, market value, and other data of listed financial companies in a city to measure the financial resources of the city. These data of listed companies are mainly from the Osiris database and are classified by city and by industry.

3.7.2 Results of the Empirical Research 3.7.2.1

Static Analysis of the Industry-Driven Evolution of the World City Network: Manufacturing is Central to the Economic Competitiveness of a City. If we Rank Tech-Intensive Manufacturing, Capital-Intensive Manufacturing, and Labor-Intensive Manufacturing Based on Their Influence on the Economic Competitiveness of a City, the Ranking will be Tech-Intensive Manufacturing, Capital-Intensive Manufacturing, and Labor-Intensive Manufacturing with Tech-Intensive Manufacturing Having the Largest Influence. Among Services Sectors, Professional, Scientific, and Technical Services has an Extremely Significant Influence on the Position of a City in Global Production Network

To test the influence of industries on the economic competitiveness of a city, we selected six broad sectors (eight industry groups), including Manufacturing (31–33), Information (51), Finance and Insurance (52), Real Estate Rental and Leasing (53), Professional, Scientific, and Technical Services (54), and Education Services (61). Among them, manufacturing is further divided into three sub-sectors: labor-intensive manufacturing (31), capital-intensive manufacturing (32), and tech-intensive manufacturing (33). According to the results of regression analysis (see columns (1)–(3) in Table 3.5), provided controlled variables are kept the same, the correlation coefficients or all three manufacturing sub-sectors are positive and statistically significant at 1%. The correlation coefficient of tech-intensive manufacturing is 0.214, which is significantly higher than that of capital-intensive manufacturing (0.188), which is higher than the coefficient of labor-intensive manufacturing (0.152). It can clearly be seen that manufacturing is central to the economic competitiveness of a city and can determine the fate of cities. Among the three sub-sectors of manufacturing, the influence of tech-intensive manufacturing on economic competitiveness of cities is the largest, followed by capital-intensive manufacturing and labor-intensive manufacturing. Columns (4)–(8) in Table 3.5 show that the coefficients of information and education services are 0.091 and 0.193, respectively, and both correlation coefficients are significant at 5%; the coefficients of real estate rental and leasing and professional, scientific, and technical services are 0.171 and 0.210, respectively, and

Gov

Buz

lntprofit61

lntprofit54

lntprofit53

lntprofit52

lntprofit51

lntprofit33

lntprofit32

lntprofit31

(6.38)

(5.32)

(5.14)

0.226***

(5.04)

0.189***

0.180***

(6.66)

0.184***

(5.47)

0.188***

Eco

Eco

0.152***

(2)

(1)

Table 3.5 Regression analysis

(5.70)

0.216***

(5.90)

0.209***

(6.60)

0.214***

Eco

(3)

(4.16)

0.204***

(4.76)

0.206***

(2.25)

0.091**

Eco

(4)

(5)

(4.72)

0.256***

(4.45)

0.247***

(1.96)

0.092*

Eco

(6)

(3.67)

0.190***

(4.77)

0.236***

(3.00)

0.171***

Eco

(7)

(4.54)

0.252***

(3.92)

0.197***

(4.17)

0.210***

Eco

(8)

(continued)

(2.19)

0.264**

(0.56)

0.067

(2.26)

0.193**

Eco

3.7 Empirical Analysis 193

R2

(−2.47)

−0.042

(−1.65)

(−3.35)

−0.058**

(−1.98)

0.779

0.763

373

(4.26)

−0.069**

(3.14)

0.790

324

(−2.55)

−0.063**

(−1.69)

−0.048*

(5.06)

(12.89) 0.143***

−0.118***

281

(3) 0.474***

0.130***

(12.79)

0.106***

(14.56)

(2)

0.512***

(1)

0.578***

(4)

0.756

178

(−3.01)

−0.113***

(−1.97)

−0.068*

(3.61)

0.145***

(10.27)

0.554***

(5)

0.816

144

(−1.39)

−0.045

(−2.73)

−0.105***

(2.47)

0.101**

(11.93)

0.537***

(6)

0.716

183

(−0.61)

−0.024

(−0.68)

−0.026

(3.06)

0.147***

(8.42)

0.471***

(7)

0.724

166

(−2.77)

−0.118***

(−1.28)

−0.054

(1.85)

0.085*

(8.80)

0.516***

Note Data are shown with standard error in parentheses, and ***, **, and * mean statistical significance at 1%, 5%, and 10%, respectively

Adj.

N

Env

Sch

Hmc

Infr

Table 3.5 (continued) (8)

0.823

32

(−0.26)

−0.018

(−0.48)

−0.056

(−0.08)

−0.006

(7.91)

0.735***

194 3 Global Industry and City Evolution Patterns

3.7 Empirical Analysis

195

both coefficients are significant at 1%; we also observe a positive and statistically significant correlation in the case of finance and insurance (at the 10% level of significance), with a coefficient of 0.092. As we see above, five out of the selected six broad sectors, except Manufacturing, are significantly correlated to the economic competitiveness of a city, but the influence varies across the different services sectors. Among services sectors, the influence of professional, scientific, and technical services on the economic competitiveness of cities is the strongest, followed by education services, real estate rental and leasing, finance and insurance, and information. In other words, legal, accounting, design, consulting, management, and other professional and technical services industries have a very important impact on the position of a city in global value chains. Thriving education and real estate sectors can also increase the economic competitiveness of a city. Relatively speaking, the role of information and finance and insurance in increasing the economic competitiveness of a city is smaller. However, with advancement of the information technology revolution, the impact of the information sector on the economic competitiveness of a city will increase, and the same goes for finance and insurance. In order to further examine the impact of industries on the economic competitiveness of cities, we use per capita GDP to replace economic competitiveness indicators and conduct a robustness test on this basis. According to the results of regression analysis shown in Columns (1)–(3) in Table 3.6, among the three manufacturing subsectors, labor-intensive manufacturing is not significantly correlated to the economic competitiveness of a city, while the coefficients of capital-intensive manufacturing and tech-intensive manufacturing are still positive and statistically significant at 1%. And the coefficient of tech-intensive manufacturing (0.136) is much larger than that of capital-intensive manufacturing, further validating the above conclusions. According to the results of regression analysis shown in Columns (4)–(8) in Table 3.6, the coefficient of real estate rental and leasing (0.113) is positive and statistically significant at 5%, and that of professional, scientific, and technical services (0.113) is positive and statistically significant at 1%, which shows that the influence of real estate rental and leasing and professional, scientific, and technical services on the economic competitiveness of a city is robust. Information, finance and insurance and education services also have influence on the economic competitiveness of a city, but the influence is much smaller compared to the other sectors.

Gov

Buz

lntprofit61

lntprofit54

lntprofit53

lntprofit52

lntprofit51

lntprofit33

lntprofit32

lntprofit31

(9.61)

(8.09)

(6.88)

0.515***

(6.26)

0.482***

0.295***

(3.91)

0.292***

(0.91)

0.114***

lnrgdp

0.028

(2)

lnrgdp

(1)

Table 3.6 Robustness test (3)

(8.87)

0.549***

(5.92)

0.263***

(3.98)

0.136***

lnrgdp

(4)

(6.10)

0.477***

(6.93)

0.360***

(0.60)

0.022

lnrgdp

(5)

(11.38)

0.604***

(4.59)

0.249***

(0.99)

0.051

lnrgdp

(6)

(6.01)

0.501***

(4.84)

0.323***

(2.28)

0.113**

lnrgdp

(7)

(12.56)

0.624***

(5.25)

0.264***

(2.61)

0.113***

lnrgdp

(8)

(continued)

(7.42)

0.748***

(1.18)

0.170

(0.47)

0.056

lnrgdp

196 3 Global Industry and City Evolution Patterns

R2

(−0.26)

(−0.73)

0.728

0.754

373

−0.010

−0.033

281

(−3.06)

(−4.05)

0.771

324

(−0.26)

−0.010

(−2.73)

−0.106***

(−0.39)

(−0.04)

−0.115***

(−0.91)

−0.182***

(4.89) −0.010

(6.18)

−0.001

(7.32)

−0.032

(3) 0.223***

(2)

0.251***

(1)

0.372***

(4)

0.780

178

(−3.18)

−0.110***

(−2.55)

−0.120**

(−0.66)

−0.024

(6.25)

0.329***

(5)

0.830

144

(−0.85)

−0.044

(−2.39)

−0.107**

(−0.09)

−0.003

(5.36)

0.233***

(6)

0.733

183

(−0.21)

−0.012

(−0.94)

−0.048

(−0.42)

−0.017

(3.93)

0.214***

(7)

0.801

166

(−0.36)

−0.018

(−2.47)

−0.129**

(−2.29)

−0.095**

(5.82)

0.254***

Note Data are shown with standard error in parentheses, and ***, **, and * mean statistical significance at 1%, 5%, and 10%, respectively

Adj.

N

Env

Sch

Hmc

Infr

Table 3.6 (continued) (8)

0.788

32

(−2.02)

−0.175*

(−1.20)

−0.205

(−0.76)

−0.063

(2.62)

0.294**

3.7 Empirical Analysis 197

198

3.7.2.2

3 Global Industry and City Evolution Patterns

Dynamic Analysis of the Industry-Driven Evolution of the World City Network: Manufacturing is Central to the Economic Competitiveness of a City. Among the Three Manufacturing Sub-Sectors, Tech-Intensive Manufacturing has the Largest Influence on the Economic Competitiveness of a City. Among Services Sectors, Real Estate Rental and Leasing has the Largest Influence on the Position of a City in Global Production Network

In order to study how industry relocation affects the world city network, we constructed an industry relocation index and a GDP growth index for eight sectors and conducted regression analysis. As we can see from Table 3.7, first of all, the adjusted coefficient of determination rises when explanatory variables are added. In Column (8), the adjusted coefficient of determination stands at 0.583, indicating that these explanatory variables can explain variations in the response variable. Second, after explanatory variables are added, the coefficients of labor-intensive manufacturing and tech-intensive manufacturing remain positive and statistically significant Table 3.7 Regression analysis (1)

(2)

(3)

Gravg

Gravg

Gravg

Iravg31 0.485** 0.455** 0.267** (2.07) Iravg32

(1.99)

(4)

(5)

Gravg

Gravg

0.248*** 0.271***

(6)

(7)

(8)

Gravg

Gravg

Gravg

0.167*** 0.167*** 0.159***

(2.58)

(2.58)

(2.69)

(3.01)

(3.12)

(3.08)

0.182** 0.090

0.070

0.072

0.081

0.082

0.077

(2.40)

(1.26)

(1.30)

(1.50)

(1.45)

(1.42)

Iravg33

(1.50)

0.497*** 0.419*** 0.416***

0.357*** 0.357*** 0.377***

(4.58) Iravg51

(3.49)

(3.50)

(3.20)

(3.20)

(3.04)

0.144

0.140

0.057

0.059

0.056

(1.51)

(1.48)

(0.56)

(0.60)

Iravg52

(0.55)

−0.088** −0.055* −0.057

−0.057

(−2.18) Iravg53

(−1.70)

(−1.63)

(−1.55)

0.369*

0.370*

0.358*

(1.74)

(1.66)

(1.67)

−0.007

0.001

(−0.08)

(0.01)

Iravg54

−0.068

Iravg61

(−0.90) N

744

Adj. R2 0.234

744

744

744

744

744

744

744

0.266

0.463

0.474

0.481

0.580

0.579

0.583

Note Data are shown with standard error in parentheses, and ***, **, and * mean statistical significance at 1%, 5%, and 10%, respectively

3.7 Empirical Analysis

199

at 1%, indicating that the relocation of labor-intensive and tech-intensive activity is correlated to the changes of the economic competitiveness of a city and that techintensive manufacturing has larger influence on the economic competitiveness of a city than labor-intensive manufacturing. In addition, the coefficient of real estate rental and leasing is also positive and statistically significant at 10%, indicating that the changes in real estate rental and leasing have a significant impact on the economic competitiveness of a city, possibly because the prosperity of other industries will drive the development of the real estate sector which in turn contributes to the economic growth of the city. Except the above sectors, the impact of the other sectors is not very significant. To further verify the above conclusions, we replaced the GDP growth index with the personal disposable income (PDI) growth index and constructed a regression model. As can be seen from Table 3.8, with the continuous addition of explanatory variables, first of all, the adjusted coefficient of determination rises when explanatory variables are added. In Column (8), the adjusted coefficient of determination stands at 0.526, indicating that these explanatory variables can explain variations in the response variable. Second, after explanatory variables are added, the coefficients of labor-intensive manufacturing and tech-intensive manufacturing remain positive and statistically significant. This result is consistent with Table 3.3, indicating that the relocation of labor-intensive and tech-intensive activity is correlated to the changes of the economic competitiveness of a city and that tech-intensive manufacturing has larger influence on the economic competitiveness of a city than labor-intensive manufacturing. Unlike the results in Table 3.3, the coefficient of real estate rental and leasing is no longer positive and statistically significant. This may be because real estate, as fixed assets, has significant impact on personal wealth but has a relatively small impact on PDI. In addition, the coefficient of finance and insurance is negative and statistically significant, indicating that finance and insurance has a negative effect on personal disposable income of residents in a city.

3.7.2.3

Impact of the Clustering of Production Factors on Industry Relocation: Labor Resources are Crucial to the Manufacturing Sector, Especially Labor-Intensive Manufacturing; Financial Resources play a Particularly Important Role in Industrial Development, Especially in the Development of Services Sectors; Third, Human Capital is Especially Crucial for Tech-Intensive Manufacturing, Information, and Professional, Scientific, and Technical Services

In order to shed light on the relationship between factor agglomeration and industry relocation, we selected revenue of key industries based in a city as the explanatory variable, constructed a regression model to analyze human resources, labor resources, and financial resources in city (see Table 3.9), and arrived at the following conclusions: Firstly, based on the results of regression analysis (1), the correlation coefficients of human resources (0.076), labor resources (0.261), and financial resources

0.253

Adj. R2

0.275

744 0.417

744 0.432

744

0.443

744

0.521

0.523

744

(−0.68)

(−0.74)

744

−0.068

−0.075

0.526

744

(−0.74)

−0.062

(1.47)

0.336

(−2.81) (1.48)

(−2.93)

−0.102***

(0.77)

0.108

(1.96)

0.296*

(0.92)

0.073

(3.29)

0.231***

(1.47)

(−2.87)

(−3.11)

−0.101***

(0.81)

0.112

(2.03)

0.277**

(0.95)

0.077

(3.33)

0.239***

(8) Pravg

0.347

−0.083***

−0.112***

(7) Pravg

0.328

(0.68)

0.087

(1.34)

(1.99)

0.161

(2.28)

0.275**

(0.82)

0.064

(3.03)

0.233***

(1.37)

(2.28)

(3.26)

0.328**

(0.71)

0.056

(2.95)

0.326***

(6) Pravg

0.167

0.332**

(0.67)

0.053

(2.71)

0.296***

(5) Pravg

Note Data are shown with standard error in parentheses, and ***, **, and * mean statistical significance at 1%, 5%, and 10%, respectively

744

(4) Pravg

0.422***

(0.89)

(1.63)

(2.70)

0.318*** 0.076

(2.14)

(2.21)

(3) Pravg

0.155

0.478**

0.504**

Pravg

N

Iravg61

Iravg54

Iravg53

Iravg52

Iravg51

Iravg33

Iravg32

Iravg31

(2)

Pravg

(1)

Table 3.8 Robustness test

200 3 Global Industry and City Evolution Patterns

0.301

0.221

12,068

(25.06)

0.228***

(33.21)

0.242

12,068

(22.71)

0.206***

(33.62)

0.304***

(11.86)

0.100***

lnincome33

(4)

0.438

12,068

(37.46)

0.385***

(31.37)

0.282***

(22.80)

0.165***

lnincome51

(5)

0.473

12,068

(40.52)

0.423***

(30.29)

0.292***

(19.85)

0.137***

lnincome53

(6)

0.407

12,068

(36.90)

0.406***

(22.27)

0.223***

(23.89)

0.170***

lnincome54

(7)

Note Data are shown with standard error in parentheses, and ***, **, and * mean statistical significance at 1%, 5%, and 10%, respectively

0.180

R2

12,068

(32.71)

(22.88)

12,068

0.309***

0.186***

(37.26)

(30.14)

0.296***

(4.15)

(−1.03)

(8.11)

0.329***

0.035***

−0.008

0.261***

lnincome32

lnincome31

(3)

0.076***

lnmanufactincm

N

Fr

Labor

Hr

(2)

(1)

Table 3.9 Regression analysis

0.186

12,068

(18.49)

0.280***

(15.58)

0.204***

(3.84)

0.026***

lnincome61

(8)

3.7 Empirical Analysis 201

202

3 Global Industry and City Evolution Patterns

(0.186) are significant at 1%. The coefficient of labor resources is significantly larger than that of human resources and financial resources. Comparison of the results shown in Columns (2)–(4) shows that, among all factors of production, the impact of labor resources on all of the three manufacturing sub-sectors is the largest, but in varying degree. The impact of labor resources on labor-intensive manufacturing is greater than that on capital-intensive manufacturing and tech-intensive manufacturing. Human capital has a significant impact on labor-intensive manufacturing, but its impact on capital-intensive and tech-intensive manufacturing is not so significant. In addition, its influence on tech-intensive manufacturing is larger than on capitalintensive manufacturing, which is in line with our common sense. This shows that labor resources are crucial for the manufacturing sector, especially labor-intensive manufacturing, and manufacturing activity tends to flow to cities with abundant labor resources, which can explain the global shift of manufacturing in the past decades. In the past few decades, the deepening specialization within GVCs has forced laborintensive manufacturing and production capacity to move to areas with lower labor costs. Secondly, compared to other production factors, the influence of financial resources on information, real estate rental and leasing, and professional, scientific, and technical services is obviously the strongest, which also shows that financial resources are crucial for industrial development, especially services sectors. Thirdly, the impact of labor resources on information, real estate rental and leasing, professional, scientific, and technical services and education services is relatively small but cannot be ignored, which of course is also in line with common sense. Last but not least, comparison of the influence of human capital on four services sectors shows that human capital has the greatest impact on information and professional, scientific, and technical services. It means that human capital is central to modern services sectors and explains the flow of information and professional, scientific, and technical services activity to cities with abundant human capital. To further examine the impact of production factors on industries, we replaced the total revenue of listed companies in a city with the total market value of listed companies which can better reflect the development status and potential of an industry. As can be seen from Table 3.10, first of all, among all factors, labor resources have the largest influence on manufacturing industries. If we rank the three manufacturing sub-sectors based on the influence of labor resources on them, the ranking will be labor-intensive manufacturing, capital-intensive manufacturing, and tech-intensive manufacturing with labor-intensive manufacturing most susceptible to the changes in labor resources, which is consistent with the above conclusions. Secondly, compared to other production factors, the influence of financial resources on information, real estate rental and leasing, and professional, scientific, and technical services is obviously the strongest, which also shows the robustness of the above conclusions. Last but not least, comparison of the influence of human capital on different sectors shows that human capital has the greatest impact on tech-intensive manufacturing, information, and professional, scientific, and technical services. The correlation coefficient between human capital and tech-intensive manufacturing reaches as high as 0.148. In

0.292

0.238

12,068

(22.36)

0.224***

(28.59)

0.273***

(14.67)

0.115***

lnmakvalue32

(3)

0.255

12,068

(22.67)

0.228***

(28.10)

0.266***

(18.93)

0.148***

lnmakvalue33

(4)

0.411

12,068

(38.11)

0.438***

(21.71)

0.210***

(20.15)

0.143***

lnmakvalue51

(5)

0.415

12,068

(33.49)

0.402***

(25.56)

0.269***

(18.32)

0.127***

lnmakvalue53

(6)

0.384

12,068

(37.23)

0.455***

(15.29)

0.159***

(19.43)

0.141***

lnmakvalue54

(7)

Note Data are shown with standard error in parentheses, and ***, **, and * mean statistical significance at 1%, 5%, and 10%, respectively

0.212

R2

12,068

(28.32)

(20.85)

12,068

0.302***

0.187***

(31.56)

(26.36)

(4.51)

0.306***

(19.36)

0.240***

0.033***

lnmakvalue31

0.165***

lnmanufactmv

N

Fr

Labor

Hr

(2)

(1)

Table 3.10 Robustness test

0.123

12,068

(15.07)

0.235***

(11.28)

0.154***

(4.07)

0.027***

lnmakvalue61

(8)

3.7 Empirical Analysis 203

204

3 Global Industry and City Evolution Patterns

other worlds, tech-intensive manufacturing, information, and professional, scientific, and technical services activity tend to flow to cities with abundant human capital, which is consistent with the above conclusions.

3.8 Conclusions We analyzed the evolution of GVCs, GPNs, and distribution of factors of production over the past three decades and their relationships with the development of cities across the world and arrived the following conclusions: First, specialization within GVCs can be divided into three types: regional division of labor, international division of labor, and global production networks. They drive intra-country integration of cities, integration of countries, and global integration of cities. The global industrial structure has experienced a major shift in development patterns. There has been a global shift in manufacturing from developed countries to emerging market countries, especially to the coastal central cities of emerging market countries. In the meantime, within developed countries, there has been a shift in manufacturing from traditional manufacturing cities to surrounding cities and secondary cities. After the 2008 financial crisis, some manufacturing value chain activity has returned from emerging market countries to developed countries such as the US. Second, the disparities between developed economies, emerging market economies, and less developed economies in absolute terms are widening, but the relative gaps are narrowing. Overall, regional disparities across the world are narrowing. In the meantime, the intra-country or intra-region divergence between cities is widening. The internal imbalance in developed countries has deepened, with central cities and emerging tech hubs growing rapidly, while manufacturing cities trapped in a recession. Emerging market economies have also suffered the same fate, with easily accessible coastal cities in terms of transportation rising rapidly, while less accessible traditional manufacturing cities trapped in a recession. Widening intra-country divergence also occurs in less developed countries. Third, as for spatial distribution of industries, there is spatial segmentation of economic activity between countries. The spatial segmentation of labor-intensive and capital-intensive manufacturing industries has weakened, while that of technologyintensive manufacturing and the financial and insurance sector is increasing. Fourth, the development of industries drives the evolution of the world city network. From a static perspective, manufacturing is central to the economic competitiveness of a city. If we rank the three manufacturing sub-sectors based on their influence on the economic competitiveness of a city, the ranking will be tech-intensive manufacturing, capital-intensive manufacturing, and labor-intensive manufacturing with tech-intensive manufacturing having the largest influence. Among services sectors, professional, scientific, and technical services has an extremely significant influence on the position of a city in global production network. From a dynamic perspective, manufacturing is central to the economic competitiveness of a city.

3.8 Conclusions

205

Among the three manufacturing sub-sectors, tech-intensive manufacturing has the largest influence on the economic competitiveness of a city. Among services sectors, real estate rental and leasing has the largest influence on the position of a city in global production network. Fifth, the clustering of production factors has a significant impact on industry relocation. First of all, labor resources are crucial to the manufacturing sector, especially labor-intensive manufacturing; second, financial resources play a particularly important role in industrial development, especially in the development of services sectors; third, human capital is especially crucial for tech-intensive manufacturing, information, and professional, scientific, and technical services and whether a city has abundant human capital will decide whether the city can attract high-tech companies.

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Horner, R. (2013). Strategic decoupling, recoupling and global production networks: India’s pharmaceutical industry. Journal of Economic Geography, 14(6), 1117–1140 Jianyong, F. (2004). Integration of the Yangtze river delta, regional specialization and manufacturing relocation [J]. Management World, 11, 77–84 John, A. M., & Dong-Sung, C. (2000). Tiger technology: The creation of a semiconductor industry in East Asia. Asian Pacific Economic Literature, 15(2), 36–36 Kogut, B. (1985). Designing global strategies: Comparative and competitive value-added chains. Sloan Management Review (pre-1986), 26(4), 15. Krugman, P. (1991). Increasing returns and economic geography. Journal of Political Economy, 99(3), 483–499 Krugman, P. (1995). Innovation and agglomeration: Two parables suggested by city-size distributions. Japan and the World Economy, 7(4), 371–390 Porter ME (1985) Technology and competitive advantage. J Bus Strat Robinson, J. (2002). Global and world cities: A view from off the map. International Journal of Urban and Regional Research, 26(3), 531–554 Sassen, S. (1991). The Global City: New York, London and Tokyo, Princeton, NJ: Princeton University Press Sassen, S. (2001). Global city. (2nd ed.). Princeton University Press Princeton. Savona, M., & Schiattarella, R. (2004). International relocation of production and the growth of services: The case of the “Made in Italy” industries [J]. Transnational Corporations, 13(2), 57–76 Shide, F., Kunrong, K., & Kepeng, Z. (2015). Rigidity of labor transfer in China’s manufacturing sector and inter-regional industry relocation: Numerical simulation and empirical research based on core-periphery model [J]. China Industrial Economics, 11, 94–108 Sturgeon, T. J. (2001). How do we define value chains and production networks? IDS Bulletin, 32(3), 9–18 Taylor, P. J. (2004). The new geography of global civil society: NGOs in the world city network. Globalizations, 1(2): 265–277. Taylor, P. J., & Derudder, B. (2015). World city network: A global urban analysis. Routledge. Taylor, P. J., Ni, P., Derudder, B., Hoyler, M., Huang, J., & Witlox, F. (2012). Global urban analysis: A survey of cities in globalization. Routledge. Wang, J.-H., & Lee, C.-K. (2007). Global production networks and local institution building: The development of the information-technology industry in Suzhou, China. Environment and Planning A, 39(8), 1873–1888 Xiaohua, S., Xu, G., & Wei, W. (2018). Industry relocation, agglomeration of production factors and regional economic development [J]. Management World, 5, 47–62 Xin, Z., & Yao, C. (2012). Transportation cost, demand distribution and industry relocation—A model based on the location theory [J]. China Industrial Economics, 2, 57–67 Yeung, H.W.-C. (2009). Regional development and the competitive dynamics of global production networks: An East Asian perspective. Regional Studies, 43(3), 325–351 Zhao, X., & Yin, H. (2011). Industrial relocation and energy consumption: Evidence from China [J]. Energy Policy, 39(5), 2944–2956

Chapter 4

Analysis on the Economic Competitiveness of Global Cities Haibo Wang and Xiaonan Liu

With deepening globalization, the status and role of cities worldwide are becoming increasingly prominent. In recent years, driven by countries in the emerging market, the global economic and trade situation has been improving, so has been the global economic development environment. However, problems such as trade protectionism, de-globalization, and geopolitics have been besetting the stability in the global economic environment. Against the increasingly complex backdrops of global development, to ensure the stability and sustainability of economic development has become the top priority for development in all countries. For this reason, the economic development and improvement of economic competitiveness of cities as the main carriers of global economies have become focuses of competition among countries in the world. In this chapter, 1007 cities are selected worldwide as samples, basically covering all cities with a population above 500,000 in the world. The economic competitiveness index of the sample cities from 2008 to 2018 is measured to analyze the urban pattern, trend, and changes in today’s world. Since the financial crisis in 2008, economic competitiveness of worldwide cities has been improved significantly on the whole: The overall level has been continuously improved, and the overall gap has been gradually narrowed. From 2008 to 2018, GDP of 1007 sample cities increases from USD28.65 trillion to USD44.2 trillion US dollars. The average of competitiveness index increases from 0.307 to 0.325, while coefficient of variation decreases from 0.641 to 0.572. From the perspective of factors contributing to economic competitiveness, local demand, infrastructure and technological innovation are key factors in the economic competitiveness of worldwide cities. Specifically, three key points are explained as follows: Firstly, cities in the northern hemisphere continuously take the lead in terms of competitiveness, and the competitiveness of Asian cities has been improved significantly. According to intercontinental distribution of the top 100 competitive cities in the world, the best performers are North America, Asia, and Europe, where 39, 33, and 26 cities respectively rank among top 100, including most of the top 100 cities in the world. Economic competitiveness of Asian cities is significantly © China Social Sciences Press 2021 P. Ni et al., Global Industry Chains: Creating a Networked City Planet, https://doi.org/10.1007/978-981-16-2058-4_4

207

208

4 Analysis on the Economic Competitiveness of Global Cities

improved, while their internal differences narrow. From 2008 to 2018, the average of competitiveness index rises from 0.252 to 0.291, with an increase of 0.039. The coefficient of variation falls from 0.628 to 0.557, with a decrease of 0.071. Secondly, the four major Bay Areas turns into important engines for city development all over the world. The top ten urban agglomerations provide important support for the global city system. Among the four major Bay Areas in the world, San Francisco Bay Area has the highest level of competitiveness, and the Guangdong-Hong Kong-Macao Bay Area starts late and is developing rapidly. The average of economic competitiveness index of these four major Bay Areas is significantly higher than that of worldwide sample cities. This indicates that these Bay Areas have a high level of competitiveness and act as important engines for global city development. Among the top ten urban agglomerations, except for Mumbai urban agglomeration, the average of competitiveness index of other urban agglomerations is higher than that of worldwide sample cities, which highlights the clustering development advantages of urban agglomerations. Thirdly, coupling coordination degree is a key contributing factor for competitiveness of cities. Coupling coordination degree refers to the degree of integrity, comprehensiveness, and internal developmental coordination strength of economic competitiveness components of cities. As revealed by benchmark regression analysis of economic competitiveness and coupling coordination degree, it is found that with the gradual increase of explanatory variables, significance level of coupling coordination degree correlated to economic competitiveness is as same as that of other explanatory variables. Economic competitiveness index and coupling coordination degree is correlated under significance level of 1%. There is a significant positive correlation between coupling coordination degree and economic competitiveness. Promoting the balanced development of various factors in worldwide sample cities is an important solution to improving the competitiveness of cities.

4.1 Global Urban Economic Competitiveness: An Annual Review 4.1.1 Overall Pattern: The Economic Competitiveness of European and American Cities Takes the Lead, While Urban Performance in China is a Highlight The overall score for the level of global urban economic competitiveness is relatively low. The global urban economic competitiveness index is weighted by two indexes, the increment of global urban GDP for 5 consecutive years and the urban GDP per square kilometer of land. The larger the index, the stronger the urban economic competitiveness is. From statistical data, the gross GDP of 1007 cities in the world in 2018 is about USD 44.2 trillion, accounting for 58.5% of the gross GDP in the world, which is USD 75.6 trillion. The average value of the economic competitiveness index

4.1 Global Urban Economic Competitiveness: An Annual Review

209

of 1007 cities in 2018 is 0.325, the score of the median city is 0.286, and the level of economic competitiveness of 599 cities is lower than the average value, with weaker economic competitiveness on the whole. The number of cities with higher economic competitiveness is small, but their index takes the lead with a great advantage, so that the scores of other cities become relatively low and the median value is lower than the average value. It can be observed from the following chart that cities with a higher level of economic competitiveness are mostly concentrated on the northern hemisphere, especially three regions, North America, West Europe, and East Asia (Fig. 4.1; Table 4.1). From the kernel density distribution graph, it is found that the distribution of global urban economic competitiveness deviates to the right to some extent, and its mode of distribution appears at around 0.2, which means that most cities are in areas with a lower level of economic competitiveness, not obeying normal distribution on the whole, further illustrating the low overall level of urban economic competitiveness and big intercity differences, with a few cities holding a safe lead, while most cities having poor economic competitiveness (Fig. 4.2). 90 70 50 30 10 -180

-140

-100

-60

-10 -20

20

60

100

140

180

-30 -50 -70 -90 Fig. 4.1 Distribution of global urban economic competitiveness in 2018. Source Global urban competitiveness database of CASS

Table 4.1 World’s top 20 cities in terms of economic competitiveness in 2018 Year

Number of samples

Average

Median

Standard deviation

Variable coefficient

Gini coefficient

Theil index

2018

1007

0.325

0.286

0.186

0.572

0.315

0.158

Source Global urban competitiveness database of CASS

210

4 Analysis on the Economic Competitiveness of Global Cities

0

.5

Density 1.5 1

2

2.5

Kernel density estimate

0

.2

.4

eco

.6

.8

1

Kernel density estimate Normal density kernel = epanechnikov ,bandwidth = 0.0420

Fig. 4.2 Kernel density distribution of global urban economic competitiveness in 2018. Source Global urban competitiveness database of CASS

Among the top ten cities, the cities in the US dominate, while Chinese cities are rising steadily. It is estimated that New York, Singapore, London, Shenzhen, and San Jose rank among the top five in global urban economic competitiveness index ranking, of which New York, Singapore, and London continue to hold the top 3 places, while Shenzhen ranks, for the first time, the top 5 in the world. Among the top ten cities, five cities are from North America, accounting for a half, three are from Asia, and two from Europe, while no other continent has any city ranked among the top ten (see Table 4.2). At the continental level, North America, Asia, and Europe form the top trio. In the ranking of urban economic competitiveness of all continents, Oceania, North America, and Europe take the lead, with the average and median of their economic competitiveness both higher than the world’s average level. The average of economic competitiveness in South America is slightly lower than the world’s average level, but the median of the urban economic competitiveness in South America is slightly higher than the world’s average level. In Asia and Africa, however, the average and median of their urban economic competitiveness are both lower than the world’s average level. In terms of the continental distribution of the world’s top 100 cities in the economic competitiveness index, North America, Asia, and Europe have the best performance, respectively having 39, 33, and 26 cities in the world’s top 100; however, Asia has the largest number of sample cities, accounting for over one half

4.1 Global Urban Economic Competitiveness: An Annual Review

211

Table 4.2 World’s top ten cities in economic competitiveness ranking 2018 Ranking

City

Country

Continent

Score

1

New York

US

North America

1

2

Singapore

Singapore

Asia

0.972

3

London

UK

Europe

0.933

4

Shenzhen

China

Asia

0.932

5

San Jose

US

North America

0.931

6

Munich

Germany

North America

0.931

7

San Francisco

US

Europe

0.929

8

Tokyo

Japan

Asia

0.896

9

Los Angele

US

North America

0.8441

10

Houston

US

North America

0.884

Source Global urban competitiveness database of CASS

of all sample cities, but only 32 Asian cities rank among the world’s top 100, so the proportion is still relatively low. Therefore, as far as the number of top 100 cities is concerned, the pioneers and centers of the world’s economic competitiveness are concentrated in the northern hemisphere; while the southern hemisphere is much more backward, particularly with regard to South America and Africa, which have no city ranking among the world’s top 100 in terms of economic competitiveness. Though the average sample value in Oceania is very high, it has only 2 cities ranking among the world’s top 100 because of the small number of sample cities. The top city in Asia is Singapore, which ranks the third in the world with its economic competitiveness index being 0.972. The top city in North America is New York, which ranks the first in the world with its economic competitiveness index being 1. The top city in Europe is London, which ranks the fourth in the world with its economic competitiveness index being 0.933. The top cities in the other three continents all have relatively low scores. At the national level, the US dominates global urban competitiveness, while Chinese cities emerge as new forces. It can be found from the data in Table 4.3 that the US, with 35 cities ranking among the world’s top 100 in urban competitiveness, comes in first in the world. China has 18 cities ranking among the world’s top 100. In the Group of Seven, besides the US, Germany has 10 cities ranking among the world’s top 100, Japan has 5, Canada has 4, the UK has 3, and France has 1; among the BRICS, besides China, only Moscow of Russia ranks among the world’s top 100. In light of average value, the average value of urban competitiveness in Germany is 0.666, ranking No. 1 in the world; the average value of the competitiveness of the US is 0.605, ranking No. 2 in the world. The average value of competitiveness of the Group of Seven is all higher than that of BRICS. The urban competitiveness of BRICS calls for further improvement. Meanwhile, the coefficient of variation of urban competitiveness in the Group of Seven is all less than that of BRICS, which means that the urban development inside each country of the Group of Seven is more

102

Africa

0.173

0.422

0.583

0.307

0.494

0.291

Average value

6

3

1

4

2

5

Average value ranking

0.162

0.442

0.575

0.297

0.515

0.262

Median

Source Global urban competitiveness database of CASS

7

75

South America

126

131

North America

Europe

566

Asia

Oceania

Number of samples

Region

0.587

0.468

0.13

0.324

0.387

0.557

6

4

1

2

3

5

26

2

39

33

Tripoli

London

Perth

Buenos Aires

New York

Singapore

Coefficient of Coefficient of Number of Top city variation variation the world’s ranking top 100 cities

Table 4.3 Statistical characteristics of urban economic competitiveness in different regions

0.422

0.933

0.708

0.55

1

0.972

Index of the top city

276

4

37

130

1

3

Ranking of the top city

212 4 Analysis on the Economic Competitiveness of Global Cities

4.1 Global Urban Economic Competitiveness: An Annual Review

213

balanced. In light of the level of development and balanced development, BRICS countries have a long way to go in urban development. The top city in China is Shenzhen, which ranks the fifth in the world, with its economic competitiveness index being 0.932. The top city in Germany is Munich, which ranks the seventh in the world, with its economic competitiveness index being 0.931. The top city in Japan is Tokyo, which ranks the ninth in the world, with its economic competitiveness index being 0.896. London and Tokyo have both been mentioned above (Table 4.4).

4.1.2 Historical Comparison: Asian Urban Competitiveness Keeps Rising, While Its Internal Differences Drop The level of global urban economic competitiveness keeps improving, and the overall differences drop year by year. In 2008, the gross GDP of 1007 sample cities was USD 28.65 trillion, the gross GDP of all the sample cities in 2013 amounted to USD 40.42 trillion, and the amount rose to USD 44.2 trillion in 2018, increasing by 54.8% from 2008. The average value of economic competitiveness of 1007 sample cities rose from 0.307 in 2008 to 0.314 in 2013 and to 0.325 in 2018. The median in 2008 was 0.242, which rose year by year to 0.271 in 2014 and to 0.286 in 2018. The coefficient of variation in 2008 was 0.641, the largest value in the period of sample statistics, which gradually dropped to 0.572 in 2018 (Table 4.5). The level of urban economic competitiveness keeps rising in Asia. From the average value of urban competitiveness in different continents, it can be discovered that the trend of changes in the level of economic competitiveness has varied in different cities since 2008. In Asia, the average value of the economic competitiveness index has kept improving from 0.252 (2008) to 0.291 (2018), increasing by 0.039; in Europe, affected by the “subprime crisis” and the “European debt crisis,” the level of urban economic competitiveness dropped and then rose again, dropping from 0.452 in 2008 to 0.405 in 2014, and then rising back to 0.422 in 2018, with a total drop of 0.03; in North America, affected by the “subprime crisis,” the level of economic competitiveness dropped and then rose again, dropping from 0.502 in 2008 to 0.467 in 2014, and then rising back to 0.494 in 2018, with a total drop of 0.008; in Oceania, South America, and Africa, economic competitiveness rose on the whole, but the extent of rise was far less than that of Asian cities. The urban economic competitiveness in Oceania stayed even on the whole (see the following table). On the whole, the ranking of cities of all continents has no changes on the whole, but there are ups and downs in the gaps. The gaps among cities of Asia from those in Europe and North America in economic competitiveness have been narrowed (Table 4.6). The decrease of internal differences in Asia and Africa is the most significant. In light of the coefficient of variation of the economic competitiveness index, the following is the order of the continents in terms of their internal sample differences of from 2008 to 2018, starting from the one with the biggest differences: Africa,

33

32

6

Russia

Brazil

South Africa

12

UK

3

35

1

5

0

10

4

0

0

0

1

18

Average value

6

1

7

4

9

3

5

9

9

9

7

2

0.565

0.605

0.514

0.585

0.434

0.666

0.568

0.167

0.321

0.288

0.239

0.327

Average value Median ranking

Source Global urban competitiveness database of CASS

9

10

Japan

75

13

Italy

US

13

Germany

France

9

Canada

100

292

China

India

Sample size

Country

5

2

6

3

7

1

4

12

9

10

11

8

Coefficient of variation ranking

0.234

0.244

0.198

0.241

0.165

0.183

0.156

0.446

0.237

0.274

0.434

0.45

Coefficient of variation

Table 4.4 Comparison of BRICS and the Group of Seven in terms of their financial service index

5

8

4

7

2

3

1

11

6

9

10

12

London

New York

Paris

Tokyo

Rome

Munich

Sao Paulo

Delhi

Pretoria

Toronto

Moscow

Shenzhen

Number of the Top city world’s top 100 cities

0.933

1

0.773

0.896

0.566

0.931

0.476

0.497

0.413

0.715

0.666

0.932

Index of the top city

4

1

23

9

122

7

214

188

286

35

58

5

Ranking of the top city

214 4 Analysis on the Economic Competitiveness of Global Cities

4.1 Global Urban Economic Competitiveness: An Annual Review

215

Table 4.5 Statistical characteristics of global urban economic competitiveness in past years Year

Average value

Median

Standard deviation

Coefficient of variation

2008

0.307

0.242

0.197

0.641

2009

0.297

0.238

0.192

0.648

2010

0.306

0.251

0.194

0.635

2011

0.308

0.251

0.191

0.619

2012

0.311

0.267

0.185

0.596

2013

0.314

0.271

0.186

0.591

2014

0.312

0.271

0.184

0.591

2015

0.323

0.282

0.187

0.581

2016

0.321

0.279

0.187

0.585

2017

0.338

0.294

0.193

0.571

2018

0.325

0.286

0.186

0.572

Source Global urban competitiveness database of CASS

Table 4.6 Average values of regional samples of urban economic competitiveness in different years Year

Asia

Europe

North America

South America

Africa

Oceania

2008

0.252

0.452

0.502

0.294

0.167

0.586

2009

0.249

0.426

0.483

0.278

0.158

0.565

2010

0.259

0.437

0.482

0.292

0.165

0.574

2011

0.261

0.433

0.497

0.288

0.167

0.562

2012

0.273

0.417

0.477

0.303

0.169

0.578

2013

0.279

0.416

0.471

0.305

0.168

0.588

2014

0.280

0.405

0.467

0.300

0.166

0.583

2015

0.287

0.426

0.483

0.313

0.174

0.595

2016

0.286

0.420

0.481

0.308

0.172

0.584

2017

0.302

0.439

0.511

0.320

0.180

0.606

2018

0.291

0.422

0.494

0.307

0.173

0.583

Source Global urban competitiveness database of CASS

Asia, Europe, North America, South America, and Oceania. In light of the direction and extent of changes of the coefficient of variation, the coefficient of variation in Asia and Africa dropped by 0.071 and 0.046 from 2008 to 2018, marking the biggest extents of decrease, that in Europe dropped by 0.026 from 2008 to 2018, that in North America dropped by 0.005 from 2008 to 2018, that in South America rose by 0.005, and that in Oceania rose by 0.026 (see the following Table 4.7). The number of most competitive cities has increased substantially in Asia. The top 100 cities in the 1007 sample cities rated for their urban competitiveness are considered the most competitive cities. In terms of their regional distribution, the number of Asian cities keeps increasing, and the number of European cities keeps

216

4 Analysis on the Economic Competitiveness of Global Cities

Table 4.7 Coefficient of variation of regional samples in global urban economic competitiveness Year

Asia

Europe

North America

South America

Africa

Oceania

2008

0.628

0.488

0.388

0.320

0.633

0.121

2009

0.636

0.507

0.410

0.328

0.640

0.134

2010

0.631

0.490

0.415

0.325

0.626

0.141

2011

0.603

0.479

0.405

0.305

0.577

0.134

2012

0.595

0.468

0.401

0.313

0.612

0.154

2013

0.593

0.462

0.407

0.309

0.622

0.161

2014

0.596

0.459

0.413

0.310

0.624

0.162

2015

0.584

0.447

0.398

0.308

0.605

0.151

2016

0.584

0.468

0.401

0.307

0.606

0.147

2017

0.562

0.462

0.383

0.325

0.590

0.127

2018

0.557

0.468

0.387

0.324

0.587

0.130

Source Global urban competitiveness database of CASS

decreasing, while no city from South America or Africa was in the world’s top 100 from 2008 to 2018. In 2008, the number of top 100 cities in North America, Europe, Asia, and Oceania was respectively 41, 35, 22, and 2; in 2012, the number of most competitive cities in Asia rose to 35, exceeding Europe; in 2014, the number of most competitive cities in Asia was 38, exceeding North America; however, the number of most competitive cities in North America picked up, and the ranking of Asia was maintained at No. 2. The “Subprime Crisis” exerted a major impact on the country pattern of global urban competitiveness. Except for China, the urban competitiveness of BRICS has dropped. In the Group of Seven, the urban competitiveness of France and Italy has dropped on the whole, while that of other countries has been steady (see Figs. 4.3 and 4.4). The trend of changes in the national distribution of the world’s top 100 cities in urban economic competitiveness is consistent with this situation (Fig. 4.5). The number of top cities has kept increasing in China. In 2008, 6 Chinese cities were ranked among the world’s top 100; after the “Subprime Crisis”, 18 Chinese cities became the world’s top 100. In Italy, two cities were among the world’s top 100 in 2008, but their ranking kept dropping in the “Subprime Crisis”, and after 2010 no city was among the world’s top 100. The number of top 100 cities in the UK also dropped from 6 in 2008 to 3 in 2009 in the economic crisis and has been maintained at 3 by now (see the following Table 4.8).

4.1 Global Urban Economic Competitiveness: An Annual Review

217

45 40 35 30 25 20 15 10 5 0 2008

2009

2010

Asia

2011

2012

2013

Europe

2014

2015

North America

2016

2017

2018

Oceania

Fig. 4.3 Trend of historical changes in the regional numbers of the world’s top 100 cities in terms of urban economic competitiveness. Source Global urban competitiveness database of CASS

0

2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

2018

0.1 0.2 0.3 0.4 0.5 0.6 China

Russia

India

Brazil

South Africa

Fig. 4.4 Historical trend of the global urban economic competitiveness ranking of BRICS. Source Global urban competitiveness database of CASS

218

4 Analysis on the Economic Competitiveness of Global Cities

2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

2018

0 0.05 0.1 0.15 0.2 0.25 0.3 UK

France

USA

Italy

Japan

Canada

Germany

Fig. 4.5 Historical trend of the global urban economic competitiveness ranking of the Group of Seven. Source Global urban competitiveness database of CASS Table 4.8 Historical changes in the number of cities in different countries rated by urban economic competitiveness Year

China

Russia

The UK

France

The US

Germany

Italy

Japan

Canada

2008

9

1

6

1

37

10

2

5

4

2009

12

1

5

1

35

10

1

5

4

2010

12

1

4

1

35

10

1

5

4

2011

12

1

3

1

39

10

1

6

3

2012

18

1

3

1

34

9

6

3

2013

19

1

3

1

34

9

5

3

2014

21

1

3

1

34

8

5

3

2015

19

1

3

1

34

10

5

3

2016

20

1

3

1

34

10

4

3

2017

18

1

3

1

36

10

4

3

2018

18

1

3

1

35

10

5

4

Source Global urban competitiveness database of CASS

4.1 Global Urban Economic Competitiveness: An Annual Review

219

4.1.3 Individual Indexes: The Indexes of Local Demands, Infrastructure, and Technology Innovation Are Critical Factors Affecting Global Urban Economic Competitiveness The performance of the individual indexes of economic competitiveness varies (see the following Fig. 4.6). The individual indexes refer to the explanatory indexes of economic competitiveness. See the attachment for details. The individual indexes for economic competitiveness in 2018 are arranged in order of their average value starting from the one with the highest value as follows: institutional cost, global connection, business cost, infrastructure, technology innovation, industrial system, social environment, local demands, human capital, and financial service. The average value of institutional cost is the highest (0.647), while its coefficient of variation is the smallest (0.233). Institutional cost includes two indexes, business convenience and economic freedom. The differences of the institutional cost of 1007 sample cities are relatively small. The average value of financial service is the lowest (0.082), while its coefficient of variation is the largest (1.838), which is caused by the concentration characteristic of financial service. In the field of financial industry, since the 1970s, more and more financial institutions have started to adopt the method of interenterprise coordination to organize transaction and production activities. From the concentration of several banks in the beginning to the rise of financial holdings companies, to the spatial agglomeration of different types of financial institutions now, clustering has become the basic form of modern financial industrial organizations, so that top cities of financial services have most financial resources, while other cities have far fewer resources than top cities.

Social Environment

Infrastructure

Financial Services 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0

Global Connections

Technology Innovation

Industrial Systems

Human Capital

System Costs

Local Needs Business Costs

Fig. 4.6 Radar chart of the individual indexes of global urban economic competitiveness. Source Global urban competitiveness database of CASS

220 Table 4.9 Results of regression analysis of global economic competitiveness and explanatory indexes

4 Analysis on the Economic Competitiveness of Global Cities Explanatory index

Coefficient

t value

Financial service index

0.030

1.41

Technology innovation index

0.168***

9.79

Industrial system index

0.042

0.82

Human capital index

0.017

Local demand index

0.850***

20.65

Business cost index

0.078***

6.02

Institutional cost index

0.052**

2.53

Global connection index

0.050**

2.19

Infrastructure index

0.248***

11.00

Social environment index

0.026

Constant term

−0.148***

−9.78

Sample size

1007

–

0.46

1.60

*p

< 0.1, ** p < 0.05, *** p < 0.01 Source Global urban competitiveness database of CASS

Local demands, infrastructure, and technology innovation indexes are critical factors affecting global urban economic competitiveness. To observe the factors influencing global urban economic competitiveness and their relative effects, the regression method is adopted here to analyze the economic competitiveness of 1007 sample cities in the world and the main explanatory factor indexes including the financial service index, the technology innovation index, the industrial system index, the human resource index, the local demand index, the business cost index, the institutional environment index, the global connection index, the infrastructure index, and the social environment index. From the results of regression, it is discovered that all individual indexes have positive effect on economic competitiveness. The order of individual indexes in terms of their correlation of explanatory variable and explained variable starting from the highest correlation is as follows: local demand index, infrastructure index, technology innovation index, business cost index, institutional cost index, global connection index, industrial system index, financial service index, social environment index, and human capital index. Thus, it can be concluded that the local demand, infrastructure, and technology innovation indexes are more critical factors affecting urban economic competitiveness (Table 4.9).

4.2 Comparative Analysis of Urban Competitiveness in China and the US

221

4.2 Comparative Analysis of Urban Competitiveness in China and the US According to World Urbanization Prospects issued by the United Nations Department of Economic and Social Affairs in 2015, sample cities with a population above 500,000 have been selected in this report. According to the conditions of the cities in China and the US, 467 sample cities were selected in the two countries, accounting for 36.44% of the global sample cities, including 292 sample cities from China and 75 sample cities from the US. The cities from these two countries cover three famous Bay Areas in the world: the Guangzhou-Hong Kong-Macao Greater Bay Area, the New York Bay Area, and the San Francisco Bay Area, as well as world famous metropolitan areas such as the northeastern urban agglomeration, the Midwest urban agglomeration, and the northern Californian urban agglomeration in the US, the Yangtze River Delta urban agglomeration and the Pearl River Delta urban agglomeration in China. As cities in China and the US are at different stages of development, there are inevitable gaps between cities of these two countries. Chinese cities started to develop late at a more rapid speed, with a few cities outdoing those in the US, and their development is unique, with wide differences among first-tier, second-tier, and third-tier cities and more obvious multi-polarization in urban development; cities in the US started to develop early and are generally well developed, with a high level of urbanization and industrialization, featuring balanced and stable development. As China and the US are the largest emerging economy and the largest developed economy respectively in today’s world, the dynamic changes in economic competitiveness of the two countries greatly affect the pattern and trend of global urban economic competitiveness. The analysis of the urban competitiveness of China and the US as well as the current situation and historical changes in urban competitiveness in the whole world is of great significance to predicting the developmental trends of urban competitiveness of the two countries and to promoting the intercity cooperation and win-win development in the world.

4.2.1 Current Pattern: The Urban Economic Competitiveness of the US is Higher Than China on the Whole The urban economic competitiveness of the US is better on the whole. From the following table, it can be discovered that the average value of the urban economic competitiveness index in China is 0.33, and the median is 0.29, both less than that in the US (average value 0.6 and median 0.57), while the coefficient of variation, Theil index, skewness, and kurtosis of the economic competitiveness index in Chinese cities are all higher than the respective value in the US. Besides, of the 292 sample cities in China, the economic competitiveness of 180 cities is lower than the average value, accounting for about 61.64% of the total amount of sample cities in China. Of the 75 sample cities in the US, the economic competitiveness of 44 cities is lower

222

4 Analysis on the Economic Competitiveness of Global Cities

than the average value, accounting for about 58.67% of the total amount of sample cities in the US (Table 4.10). From the kernel density distribution of urban economic competitiveness in China and the US, it can be discovered that the crest value of the Kernel density distribution of Chinese economic competitiveness seriously deviates toward the left from normal distribution, which shows that the economic competitiveness of Chinese cities is concentrated in areas with a lower economic competitiveness index; the long tail stretches gently toward the right, indicating big intercity differentiation. In the US, however, the kernel density distribution of urban economic competitiveness slightly deviates toward the left from normal distribution, and the right tail is steep, indicating the high concentration of urban competitiveness and excellent overall quality in the US (Fig. 4.7). The number of the world’s top 100 cities in China is about half that in the US. However, the overall strength of China’s top 100 cities is catching up with those in the US, as some cities in China have started to rise, and some cities in the US have started to decline. From Table 4.11, it can be discovered that 18 cities in China are rated among the world’s top 100 cities, the same as the number last year, but 35 cities in the US are rated among the world’s top 100 cities, one city less than last year. The average value of the economic competitiveness of the world’s top 100 cities in China is 0.71, lower than that in the US, which is 0.73. However, the median, variance, coefficient of variation, Theil index, and Gini coefficient of the economic competitiveness of the world’s top 100 cities in China are basically the same as the values in the US. Of the top ten cities in China, one city is ranked among the world’s top ten, five cities among the world’s top twenty, and ten cities among the world’s top fifty. In the US, five cities are ranked among the world’s top ten, and ten among the world’s top thirty. The overall strength of the top ten cities in the US outshines that of the top ten cities in China. From the comparison of the economic competitiveness of the top ten cities in China and the US in Table 4.12, it can be discovered that the average value and median of the economic competitiveness of the top ten cities in China are 0.79 and 0.77, respectively, lower than that of the top ten cities in the US, which is 0.88 and 0.88, respectively. The variance of the economic competitiveness of the top ten cities in China is all 0.01, and the coefficient of variation is slightly (0.01) higher than that in the US.

4.2.2 Historical Comparison: The Rise of Chinese Cities Is Changing the Pattern that Developed Economies Dominate World Development The average value of the urban economic competitiveness index in China is rising on the whole, while that in the US is undergoing a curly rise and fall trend (Fig. 4.8).

292

75

China

The US

0.6

0.33

Average value

0.57

0.29

Median

Source Global urban competitiveness database of CASS

Number of samples

Scope

0.15

0.15

Standard deviation 0.02

0.02

Variance

0.24

0.45

Coefficient of variation

Table 4.10 Analysis of the indexes of economic competitiveness of cities in China and the US

0.03

0.09

Theil index

0.13

0.24

Gini coefficient

0.64

1.3

Skewness

3.27

5.12

Kurtosis

4.2 Comparative Analysis of Urban Competitiveness in China and the US 223

224

4 Analysis on the Economic Competitiveness of Global Cities

Kernel density estimate

0

0

1

1

Density

Density 2

2

3

3

4

Kernel density estimate

0

.2 .4 .6 .8 1 China's urban economic competitiveness Kernel density estimate Normal density

kernel = epanechnikov, bandwidth = 0.0357

.2 .4 .6 .8 1 American urban economic competitiveness Kernel density estimate Normal density kernel = epanechnikov, bandwidth = 0.0485

Fig. 4.7 Kernel density estimate of economic competitiveness in cities of China and the US. Source Global urban competitiveness database of CASS

The rise of Chinese cities is changing the pattern that developed economies dominate world development. The overall ranking of China’s top ten cities in global urban competitiveness is improving year by year, with Shenzhen’s competitiveness rising substantially to edge into the top five cities in the world, and Macao’s competitiveness slightly dropping; the overall ranking of the US’ top cities in global urban competitiveness has not significant changes, but the internal ranking of urban competitiveness in the country changes slightly, with New York proudly heading the world, while Philadelphia gradually losing its competitiveness (Table 4.13).

4.2.3 Comparison of Individual Indexes: The Social Environment Index and Business Cost Index in Chinese Cities Are Higher Than Those in the US From the explanatory indexes of the economic competitiveness of all the sample cities in China and the US in Table 4.14 and Fig. 4.9, it can be concluded that the average value of each individual index of economic competitiveness of cities in the US is generally higher than that in China. But the social environment index and business cost index in Chinese cities surpass those in the US. This shows that the

35

100

US

Global

0.3

0.73

0.71

Average value

Source Global urban competitiveness database of CASS

18

China

Economic competitiveness

Sample size

Country

Index

0.21

0.7

0.7

Median

0.19

0.11

0.11

Standard deviation

0.04

0.01

0.01

Variance

0.63

0.15

0.15

Coefficient of variation

Table 4.11 Contrastive analysis of the economic competitiveness of the world’s top 100 cities in China and the US

0.18

0.01

0.01

Theil index

0.34

0.08

0.08

Gini coefficient

4.2 Comparative Analysis of Urban Competitiveness in China and the US 225

226

4 Analysis on the Economic Competitiveness of Global Cities

Table 4.12 Comparison of the economic competitiveness of the top ten cities in China and the US Top ten cities in General index China

General ranking

Top ten cities in General index The US

General ranking

Shenzhen

0.93

5

New York

1

1

Hong Kong

0.88

11

Los Angeles

1

2

Shanghai

0.85

13

San Jose

0.93

6

Guangzhou

0.85

14

San Francisco

0.93

8

Beijing

0.8

19

Houston

0.88

10

Suzhou

0.74

27

Dallas

0.88

12

Wuhan

0.7

40

Miami

0.8

17

Tianjin

0.7

42

Boston

0.8

18

Nanjing

0.7

45

Chicago

0.8

21

Taipei

0.7

46

Seattle

0.76

24

Average value

0.79

–

Average value

0.88

–

Median

0.77

–

Median

0.88

–

Standard deviation

0.09

–

Standard deviation

0.09

–

Variance

0.01

–

Variance

0.01

–

Coefficient of variation

0.11

–

Coefficient of variation

0.1

–

Source Global urban competitiveness database of CASS

Fig. 4.8 Trend of changes of the average value of the economic competitiveness index in China and the US over the past years. Source Global urban competitiveness database of CASS

1

Index

1

Index

0.992

0.869

20

0.795

Shenzhen

Source Global urban competitiveness database of CASS

Ranking 9

Index

Hong Kong

3

0.972

0.978

Index

11

0.876

Hong Kong

3

Los Angeles New York

Ranking 2

13

0.854

Shanghai

6

0.931

14

0.85

Guangzhou

8

0.929

15

0.836

Guangzhou

8

0.906

22

0.787

Macao

7

0.914

33

0.744

Shanghai

8

0.877

San Francisco Dallas

14

0.845

Shanghai

7

0.912

27

0.74

Suzhou

12

0.878

26

0.771

Beijing

13

0.855

40

0.717

Beijing

10

0.866

43

0.715

42

0.7

Tianjin

18

0.797

Boston

16

0.814

Miami

34

0.707

Suzhou

19

0.796

49

0.706

60

0.684

Suzhou

19

0.801

24

0.764

27

0.751

Seattle

46

0.695

41

0.69

72

0.656

Tianjin

21

0.792

107

0.595

Nanjing

26

0.765

Philadelphia

44

0.688

Nanjing Wuhan

24

0.776

Boston

45

0.697

Nanjing Taipei

21

0.796

Chicago Seattle

Chicago Boston

38

0.693

Taipei

20

0.782

Chicago Miami

40

0.704

Wuhan

17

0.798

Miami

Guangzhou Taipei

14

0.829

Houston San Jose

18

0.818

Macao

9

0.885

Houston San Jose

19

0.797

Beijing

10

0.884

San Francisco Houston Dallas

Los Angeles San Francisco Dallas

11

0.884

Hong Kong

2

0.997

Los Angeles San Jose

City

China City

2008 US

0.912

Ranking 6

Index

Shenzhen

Ranking 1

New York

City

China City

2013 US

0.932

Ranking 5

Index

Shenzhen

Ranking 1

New York

City

China City

2018 US

Table 4.13 Economic competitiveness rankings of the top ten cities of China and the US in the past years

4.2 Comparative Analysis of Urban Competitiveness in China and the US 227

Institutional cost index

292

1007

US

Global

75

1007

China

Global

75

292

1007

Global

Business cost index China

US

292

75

1007

Global

China

292

US

75

1007

Global

China

292

US

75

1007

Global

China

292

US

75

1007

China

Global

US

Local demand index

Human capital index

Industrial system index

Technology innovation

75

292

China

Financial service index

Sample size

US

Country

Index

0.65

0.89

0.63

0.51

0.63

0.65

0.22

0.38

0.20

0.13

0.31

0.08

0.35

0.48

0.30

0.37

0.66

0.36

0.08

0.11

0.04

Average value

0.63

0.89

0.63

0.62

0.63

0.66

0.21

0.35

0.19

0.08

0.24

0.04

0.30

0.46

0.27

0.33

0.69

0.35

0.03

0.05

0.03

Median

0.15

0.00

0.02

0.20

0.00

0.03

0.10

0.11

0.09

0.14

0.21

0.11

0.12

0.12

0.08

0.23

0.16

0.20

0.15

0.17

0.08

Standard deviation

0.02

0.00

0.00

0.04

0.00

0.00

0.01

0.01

0.01

0.02

0.05

0.01

0.02

0.01

0.01

0.05

0.03

0.04

0.02

0.03

0.01

Variance

0.23

0.01

0.03

0.39

0.00

0.04

0.46

0.28

0.45

1.08

0.70

1.30

0.36

0.24

0.28

0.63

0.25

0.54

1.84

1.58

2.04

Coefficient of variation

Table 4.14 Contrastive analysis of each individual index of economic competitiveness in all cities of China and the US

0.03

0.00

0.00

0.06

0.00

0.00

0.10

0.03

0.11

0.42

0.22

0.45

0.06

0.03

0.03

0.20

0.03

0.15

0.91

0.69

0.55

Theil index

0.12

0.00

0.00

0.18

0.00

0.01

0.25

0.12

0.26

0.49

0.37

0.45

0.18

0.12

0.08

0.35

0.13

0.31

0.68

0.58

0.37

(continued)

Gini coefficient

228 4 Analysis on the Economic Competitiveness of Global Cities

0.53

0.43 0.25

75

292

1007

Global

Source Global urban competitiveness database of CASS

0.33

0.39

1007

Global

Social environment China index US

0.52

0.41

292

75

0.65

0.48

Average value

US

1007

Global

Infrastructure index China

75

292

China

Global connection index

Sample size

US

Country

Index

Table 4.14 (continued)

0.11 0.15

0.33

0.09

0.17

0.13

0.12

0.16

0.10

0.09

Standard deviation

0.24

0.43

0.37

0.49

0.38

0.54

0.64

0.44

Median

0.02

0.01

0.01

0.03

0.02

0.02

0.03

0.01

0.01

Variance

0.47

0.46

0.22

0.43

0.24

0.30

0.30

0.16

0.20

Coefficient of variation

0.12

0.10

0.02

0.09

0.03

0.04

0.03

0.01

0.02

Theil index

0.26

0.25

0.12

0.23

0.12

0.15

0.14

0.09

0.08

Gini coefficient

4.2 Comparative Analysis of Urban Competitiveness in China and the US 229

230

4 Analysis on the Economic Competitiveness of Global Cities

Financial Services 1 Social Environment

0.8

Technological InnovaƟon

0.6 Infrastructure

0.4 0.2

Industrial System China

0 Global connecƟons

USA Human Capital

System Cost

Local Demand Business Cost

Fig. 4.9 Radar chart of individual indexes of urban economic competitiveness in China and the US. Source Global urban competitiveness database of CASS

social environment in China excels that in the US, its business cost is lower than that in the US. The reform and opening-up in China have tremendously improved the Chinese social environment and reduced cost for business operation (Tables 4.15 and 4.16). Based on the regressed Shapley value, r 2 of the urban economic competitiveness of China and the US is calculated, which is the explanatory power for measuring the urban economic competitiveness of China and the US; the Shapley2 value decomposition method based on regression is then applied for quantitative analysis of the contribution of factors such as financial service, technology innovation, industrial systems, business environment, institutional environment, infrastructure, and human resources to the differences of urban economic competitiveness. The absolute value of the influencing factors on the urban economic competitiveness of China and the US amounts to 0.72 and 0.87, respectively, which reflects that the influencing factors selected in this report have strong explanatory power. Meanwhile, the contribution rate of different influencing factors on the difference in economic competitiveness between China and the US varies. First, the differences in technology innovation, local demand, infrastructure, and global connections are important reasons causing the differences in economic competitiveness of Chinese cities; while the differences in local demand, infrastructure, industrial systems, and human capital are important reasons causing the differences in economic competitiveness of US cities. Second, in the US, except for local demand, which exerts a significant impact on the differences of economic competitiveness, the influence of other factors for explaining the differences in economic competitiveness is more balanced. The greater differences of various factors in China are the most important reason leading to the differences in economic competitiveness of Chinese cities (Table 4.17).

Local demand index

Human capital index

Industrial system index

35

The US

100

Global

18

35

The US

China

18

100

Global

China

35

The US

100

Global

18

35

The US

China

18

100

Global

China

35

The US

Technology innovation

18

China

Financial service index

Sample size

Country

Index

0.44

0.37

0.41

0.41

0.37

0.56

0.53

0.53

0.74

0.74

0.77

0.3

0.17

0.24

Average value

0.4

0.37

0.38

0.34

0.3

0.52

0.48

0.46

0.75

0.77

0.77

0.16

0.06

0.1

Median

0.13

0.04

0.21

0.24

0.23

0.15

0.14

0.19

0.12

0.13

0.08

0.26

0.22

0.27

Standard deviation

0.02

0

0.04

0.06

0.05

0.02

0.02

0.04

0.01

0.02

0.01

0.07

0.05

0.07

Variance

0.3

0.09

0.52

0.58

0.62

0.27

0.26

0.37

0.16

0.18

0.11

0.89

1.31

1.14

Coefficient of variation

0.04

0

0.13

0.16

0.18

0.03

0.03

0.06

0.01

0.02

0.01

0.4

0.59

0.5

Theil index

Table 4.15 Contrastive analysis of each individual index of economic competitiveness in the top 100 cities of China and the US

0.13

0.05

0.29

0.32

0.33

0.15

0.13

0.19

0.08

0.09

0.06

0.49

0.56

0.53

(continued)

Gini coefficient

4.2 Comparative Analysis of Urban Competitiveness in China and the US 231

Global

0.4 0.24

18

35

100

Social environment China index The US

Global

0.35

0.65

0.59

35

100

0.67

Global

18

Infrastructure index China

0.74

0.7

0.73

0.82

0.89

0.65

0.57

0.63

0.65

0.4

Average value

The US

35

100

Global

18

35

100

The US

China

18

China

The US

Global connection index

Institutional cost index

35

100

18

Global

100

Global

Business cost index China

The US

Sample size

Country

Index

Table 4.15 (continued)

0.33

0.23

0.39

0.59

0.55

0.65

0.73

0.68

0.72

0.87

0.89

0.63

0.63

0.63

0.66

0.37

Median

0.17

0.12

0.1

0.17

0.15

0.19

0.11

0.11

0.12

0.1

0.01

0.09

0.2

0

0.05

0.1

Standard deviation

0.03

0.01

0.01

0.03

0.02

0.03

0.01

0.01

0.01

0.01

0

0.01

0.04

0

0

0.01

Variance

0.48

0.49

0.26

0.26

0.25

0.28

0.15

0.16

0.16

0.12

0.01

0.13

0.35

0

0.08

0.26

Coefficient of variation

0.11

0.11

0.03

0.03

0.03

0.04

0.01

0.01

0.01

0.01

0

0.01

0.07

0

0

0.03

Theil index

0.26

0.27

0.14

0.15

0.12

0.15

0.08

0.09

0.09

0.06

0

0.03

0.18

0

0.02

0.12

Gini coefficient

232 4 Analysis on the Economic Competitiveness of Global Cities

Top ten cities in the US

Top ten cities in China

0.50

0.53

0.09

Tianjin

Nanjing

Taipei

0.05

0.19

0.16

San Jose

San Francisco

Houston

0.21

0.52

Wuhan

Los Angeles

0.13

Suzhou

1.00

0.91

0.16

Beijing

New York

0.81

Guangzhou 0.19

0.91

0.81

0.85

0.79

0.91

0.80

0.82

0.85

0.71

0.77

0.79

0.85

0.05

Shanghai

0.91

1.00

0.59

0.74

0.44

0.77

1.00

0.51

0.78

0.67

0.67

0.48

0.59

0.74

0.44

0.77

1.00

0.49

0.71

0.61

0.81

1.00

0.65

0.76

0.78

0.41

0.26

0.49

0.71

0.61

0.81

1.00

0.52

0.53

0.44

0.70

1.00

0.45

0.58

0.50

0.46

0.51

0.52

0.53

0.44

0.70

1.00

0.63

0.63

0.63

0.63

0.63

0.63

0.63

0.63

0.63

0.63

0.63

0.63

0.63

0.63

0.63

0.89

0.89

0.85

0.89

0.89

0.89

0.89

0.89

0.89

0.89

0.89

0.89

0.85

0.89

0.89

0.76

0.86

0.63

0.87

1.00

0.70

0.88

0.83

0.82

0.64

0.76

0.85

0.63

0.86

1.00

0.94

0.61

0.48

0.94

0.97

0.91

0.53

0.61

0.59

0.66

0.94

0.61

0.48

0.94

0.97

0.17

0.23

0.26

0.24

0.29

0.28

0.12

0.39

0.21

0.28

0.17

0.23

0.26

0.24

0.29

(continued)

0.74

0.76

0.67

0.80

0.92

0.73

0.78

0.81

0.75

0.69

0.74

0.76

0.67

0.80

0.91

Financial Technology Industrial Human Local Business Institutional Global Infrastructure Social Coupling service innovation systems capital demand cost cost connections environment coordination degree

Hong Kong 0.21

Shenzhen

City

Table 4.16 Contrastive analysis of each individual index of economic competitiveness in the top ten cities of China and the US

4.2 Comparative Analysis of Urban Competitiveness in China and the US 233

0.80

0.51

0.65

0.76 0.45

0.58 0.63

0.63

0.63

0.63

0.89

0.89

0.89

0.89

0.70

0.88

0.83

0.82

0.91

0.53

0.61

0.59

0.28

0.12

0.39

0.21

0.73

0.78

0.81

0.75

0.69

0.09

0.78

0.50

0.46

0.28

Seattle

0.82

0.78

0.41

0.66

0.53

0.67

0.67

0.64

Chicago

0.85

0.72

0.89

0.50

0.63

0.52

0.51

Boston

0.26

Miami

0.48

0.13

Dallas

0.77

Financial Technology Industrial Human Local Business Institutional Global Infrastructure Social Coupling service innovation systems capital demand cost cost connections environment coordination degree

City

Table 4.16 (continued)

234 4 Analysis on the Economic Competitiveness of Global Cities

4.3 Contrastive Analysis of the Competitiveness of North America, West …

235

Table 4.17 Decomposition of factors leading to differences in economic competitiveness of cities in China and the US Factor

China Shapley’s absolute difference

US Percentage (%)

Ranking

Shapley’s absolute difference

Percentage (%)

Ranking

Financial service

0.05

6.08

7

0.06

7.69

6

Technology innovation

0.16

18.11

2

0.05

7.52

7

Industrial systems

0.09

10.04

6

0.08

10.54

4

Human capital

0.09

10.91

5

0.09

12.57

3

Local demand

0.19

21.74

1

0.22

30.19

Business cost

0.01

0.67

10

Institutional cost

0.01

1.03

Global connections

0.13

Infrastructure

1

0

0

8

0.04

5.99

8

14.53

4

0.07

9.94

5

0.14

15.97

3

0.11

15.23

2

Social environment

0.01

0.92

9

0

0.32

9

r 2 -based total difference

0.87

100.00

0.72

10

100.00

4.3 Contrastive Analysis of the Competitiveness of North America, West Europe, and East Asia 4.3.1 Current Pattern: Cities in North America Take the Lead, and the Most Competitive Cities Are Concentrated in the Top Trio Regions Since the Second World War, world economy has kept developing, seeing waves of rapid economic development with technology revolution and market reform. By far, the global economic aggregates have basically formed a top trio including West Europe, North America, and East Asia. The major representative countries in West Europe include the UK, France, Germany, and Italy. The territorial area of West Europe is 5 million km2 , its regional GDP in 2018 amounts to about USD 12 trillion, and its population is about 450 million. In this report, 71 cities are sampled from West Europe, of which top-ranking cities in economic competitiveness are London, Munich, Dublin, and Frankfurt. North America refers to countries and regions north of Mexico, mainly including the US and Canada. In 2018, the GDP of this region is

236

4 Analysis on the Economic Competitiveness of Global Cities

about USD 19 trillion, and its population is about 350 million. In this report, 75 cities are sampled from North America, of which top-ranking cities in economic competitiveness are New York, Los Angeles, San Jose, San Francisco, and Houston. East Asia includes five countries, such as China, Japan, South Korea, North Korea, and Mongolia. In the recent decades, this region has seen rapid economic development. The regional GDP amounts to USD 16 trillion, and its population is 1.6 billion. A total of 311 cities are sampled from East Asia, of which top-ranking cities in economic competitiveness are Singapore, Shenzhen, Tokyo, Hong Kong, and Shanghai. Cities in North America have the highest level of economic competitiveness, and the most competitive cities are concentrated in the top trio regions. The ranking of the three major regions in terms of the average value of their economic competitiveness from top is as follows: North America, West Europe, and East Asia, with scores being respectively: 0.604, 0.544, and 0.341. On the whole, North America and West Europe are far ahead of East Asia. There is no big gap among the peak values of the three regions, which means that the gap between their top cities is small; but the differences among the lowest values are big, with the lowest value in the cities of North America being 0.326, higher than the median of cities in East Asia. On the whole, cities in North American are ahead of cities in East Asia by a wide margin. In light of regional internal differences, the coefficient of variation in North America is 0.244, which marks the smallest internal difference; the coefficient of variation in West Europe is 0.256, which means that its internal difference is slightly higher than that in North America; the coefficient of variation in East Asia is 0.460, which means its internal difference is far greater than that in the other two regions. With regard to the world’s top 100 cities, 35, 25, and 24 cities are from North America, West Europe, and East Asia, respectively. The kernel density distribution of the three regions is consistent with their statistical descriptions, with left deviation to different extents, which means that their median is smaller than there average value and that there is a big gap between cities with a higher level of economic competitiveness and those with a lower level. It can be observed from the following chart (lower right) that the sample distribution of the three regions shows double peaks, which means that the level of urban competitiveness in East Asia is lower than that in West Europe and North America on the whole (Table 4.18; Fig. 4.10).

4.3.2 Historical Comparison: Urban Competitiveness in East Asia is Rapidly Rising, While Its Internal Differences Are Dropping The urban competitiveness of North America takes the lead, while that in East Asia is rapidly rising. First, in light of the average value of competitive indexes, the average value of urban competitiveness ranking in North America average value is the highest, followed by West Europe, while East Asia does the worst. The average value of economic competitiveness of the sample cities in North America was 0.616

4.3 Contrastive Analysis of the Competitiveness of North America, West …

237

Table 4.18 Economic competitiveness of North America, West Europe, and East Asia in 2018 Sample region

Sample number

Average value

Median

Standard deviation

Peak value

Least value

Coefficient Number of variation of the world’s top 100 cities

North America

75

0.604

0.573

0.147

1

0.326

0.244

35

West Europe

71

0.544

0.543

0.139

0.933

0.145

0.256

25

East Asia 311

0.341

0.300

0.157

0.932

0.089

0.460

24

Source Global urban competitiveness database of CASS

Kernel density estimate

0

0

1

1

Density

Density 2

2

3

3

Kernel density estimate

0

.2

.4

ecco

.6

.8

.2

1

.4

.6 ecco

.8

1

Kernel density estimate Normal density

Kernel density estimate Normal density

kernel = epanechnikov, bandwidth = 0.0338

kernel = epanechnikov, bandwidth = 0.0316

Kernel density estimate

0

0

.5

1

Density 2

Density 1 1.5

3

2

4

2.5

Kernel density estimate

0

.2

.4

ecco

.6

Kernel density estimate Normal density kernel = epanechnikov, bandwidth = 0.0246

.8

1

0

.2

.4

ecco

.6

.8

1

Kernel density estimate Normal density kernel = epanechnikov, bandwidth = 0.0324

Fig. 4.10 Kernel density distribution of economic competitiveness of West Europe (upper left), North America (upper right), East Asia (lower left), and all regions (lower right). Source Global urban competitiveness database of CASS

in 2008; the value slightly dropped to 0.575 in 2014 and rose again to 0.604 in 2018. The average value of economic competitiveness of the sample cities in West Europe was 0.599 in 2008, slightly dropped to 0.532 in 2014, and slightly rose to 0.544 in 2018. The average value of economic competitiveness of the sample cities in East Asia was 0.282 in 2008, rose to 0.327 in 2014, and further rose to 0.341 in 2018. In

4 Analysis on the Economic Competitiveness of Global Cities

West Europe

North America

East Asia

.3

.4

mean .5

.6

.7

238

2008

2010

2012

year

2014

2016

2018

Fig. 4.11 Trend of changes in the average value of economic competitiveness index in West Europe, North America, and East Asia over the past years. Source Global urban competitiveness database of CASS

light of the trend of changes, North America is slightly ahead of West Europe, but the margin of the average value of economic competitiveness index of the sample cities in North America over that of West Europe has been widening year by year from 0.017 in 2008 to 0.072 in 2018; while the gap between East Asia and North America and West Europe keeps narrowing (Fig. 4.11). In light of the difference degree of regional urban competitiveness, the inequality of urban economic competitiveness in East Asia keeps dropping, while that in West Europe and North America rises on the whole. The coefficient of variation in cities of West Europe and North America was 0.224 and 0.229, respectively in 2008; the internal gap kept rising afterward, rising to 0.277 in 2014, and to 0.256 and 0.244, respectively, in 2018. The coefficient of variation of cities in East Asia dropped from 0.574 in 2008 to 0.460 in 2018. The internal differences in economic competitiveness have significantly dropped in Asian cities (Table 4.19).

4.3 Contrastive Analysis of the Competitiveness of North America, West … Table 4.19 Trend of changes in the coefficient of variation of economic competitiveness index in West Europe, North America, and East Asia over the past years (backward)

239

Year

West Europe

North America

East Asia

2008

0.224

0.229

0.574

2009

0.245

0.250

0.568

2010

0.248

0.257

0.546

2011

0.234

0.237

0.534

2012

0.255

0.262

0.512

2013

0.265

0.273

0.499

2014

0.277

0.277

0.491

2015

0.266

0.264

0.475

2016

0.269

0.265

0.473

2017

0.253

0.236

0.455

2018

0.256

0.244

0.460

Source Global urban competitiveness database of CASS

4.3.3 Individual Indexes: North America and West Europe Each Has Its Advantages, While East Asia Has an Edge in Social Environment and Business Cost North America and West Europe each has its advantages in individual indexes of economic competitiveness, while East Asia has an edge in social environment and business cost. In terms of technology innovation, industrial systems, global connections, human capital, North America, and West Europe are basically on the same level, while East Asia is backward by a big margin. In terms of technology innovation, the average value of the sample cities from North America and West Europe is 0.662 and 0.664, respectively, while the average value of sample cities in East Asia is only 0.383. In terms of industrial systems, the average value of sample cities in North America and West Europe is 0.501 and 0.481, respectively, while the average value of sample cities in East Asia is only 0.310. In terms of global connections, the average value of the sample cities from North America and West Europe is 0.668 and 0.650, respectively, while the average value of sample cities in East Asia is only 0.491. In terms of human resources, the average value of the sample cities from North America and West Europe is 0.310 and 0.306, respectively, while the average value of sample cities in East Asia is only 0.092. In local demand and institutional cost, the ranking of the average value of the three regions from the highest score is North America, West Europe, and East Asia; in terms of infrastructure and financial service, the ranking of the average value of the three regions from the highest score is West Europe, North America, and East Asia. East Asia is ahead of West Europe and North America in terms of social environment and business cost. In light of the internal differences of sample cities in terms of individual indexes, the internal differences of major individual indexes in East Asia are all bigger than those in West

240

4 Analysis on the Economic Competitiveness of Global Cities

Financial Services 1.000 Technology Social 0.800 Innovation Environment 0.600 0.400 Industrial Systems Infrastructure 0.200 0.000 Global Human Capital Connections System Costs

Local Needs Business Costs

North America East Asia

East Asia Fig. 4.12 Radar chart of the average value of individual indexes of economic competitiveness in North America, West Europe, and East Asia. Source Global urban competitiveness database of CASS

Europe and North America, which means that cities in East Asia have bigger internal differences and uneven urban competitiveness (Fig. 4.12; Table 4.20).

4.4 Comparative Analysis of the Competitiveness of the Four Major Bay Areas 4.4.1 Current Pattern: The San Francisco Bay Area Takes the Lead, While the Guangzhou-Hong Kong-Macao Greater Bay Area Is at the Bottom The four major Bay Areas in the world include the New York Bay Area, the San Francisco Bay Area, the Tokyo Bay Area, and the Guangzhou-Hong KongMacao Greater Bay Area, of which the New York Bay Area, the San Francisco Bay Area, and the Tokyo Bay Area are recognized as the three major Bay Areas in the world with a high level of economic development, where industries are high-end, the service industry accounts for over 80%, and there are many global 500 companies. The Guangzhou-Hong Kong-Macao Greater Bay Area has a lower starting point and a high speed of development. The leading cities in the four major Bay Areas, New York, Shenzhen, San Jose, and Tokyo, take the first, fifth, sixth, and ninth places, respectively, in economic competitiveness ranking. The analysis

4.4 Comparative Analysis of the Competitiveness of the Four Major Bay Areas

241

Table 4.20 Statistical characteristics of the individual index of economic competitiveness in North America, West Europe, and East Asia Region

East Asia

North America

West Europe

Primary index

Average value

Coefficient of variation

Average value

Coefficient of variation

Average value

Coefficient of variation

Financial service

0.052

2.109

0.107

1.579

0.189

1.244

Technology innovation

0.383

0.557

0.664

0.245

0.662

0.174

Industrial systems

0.310

0.313

0.481

0.243

0.501

0.249

Human capital

0.092

1.288

0.306

0.699

0.310

0.495

Local demand

0.209

0.479

0.377

0.279

0.322

0.177

Business cost

0.658

0.114

0.629

0.000

0.385

0.520

Institutional cost

0.643

0.081

0.894

0.005

0.825

0.069

Global connections

0.491

0.212

0.650

0.161

0.668

0.214

Infrastructure

0.424

0.328

0.523

0.242

0.590

0.217

Social environment

0.434

0.257

0.248

0.458

0.353

0.368

Source Global urban competitiveness database of CASS

of the changes in urban economic competitiveness in global Bay Areas is of important reference significance to the Guangzhou-Hong Kong-Macao Greater Bay Area formed by 9 cities including Hong Kong, Macao, and the Pearl River Delta, which is the emerging fourth major Bay Area rapidly growing and going global. Figure 4.13 and Table 4.21 show the urban economic competitiveness of the four major Bay Areas, including the statistical description of the New York Bay Area (2 cities, New York and New Haven) the San Francisco Bay Area (2 cities, San Francisco and San Jose), the Tokyo Bay Area (1 city, Tokyo), and the Guangzhou-Hong Kong-Macao Greater Bay Area (11 cities, including Hong Kong, Macao, and Shenzhen) The San Francisco Bay Area has the highest level of economic competitiveness, while the level of competitiveness of the Guangzhou-Hong Kong-Macao Greater Bay Area is yet to be improved. The ranking of the four major Bay Areas in terms of their average value of economic competitiveness is: the San Francisco Bay Area, the Tokyo Bay Area, the New York Bay Area, and the Guangzhou-Hong Kong-Macao Greater Bay Area, with their score being respectively: 0.924, 0.896, 0.754, and 0.591. On the whole, the San Francisco Bay Area and the Tokyo Bay Area are ahead of the Guangzhou-Hong Kong-Macao Greater Bay Area by a wide margin. The gap between the peak values of the four major Bay Areas is not wide, which shows that there is a narrow gap between their top cities. In terms of regional internal differences, the coefficient of variation of the Guangzhou-Hong Kong-Macao Greater

242

4 Analysis on the Economic Competitiveness of Global Cities

90 70 50 30 10 -180

-140

-100

-60

-10 -20

20

60

100

140

180

-30 -50 -70 -90 Fig. 4.13 Distribution map of economic competitiveness index of the four major Bay Areas. Source Global urban competitiveness database of CASS

Table 4.21 Statistical description of the four major Bay Areas in the world Sample

Number of samples

Average value

Median

Standard deviation

Peak value

Least value

Coefficient of variation

Tokyo Bay Area

1

0.896

0.896

–

0.896

0.896

–

San Francisco Bay Area

2

0.924

0.924

0.007

0.929

0.919

0.008

The Guangzhou-Hong Kong-Macao Greater Bay Area

11

0.591

0.564

0.212

0.932

0.334

0.358

3

0.754

0.729

0.235

1.000

0.532

0.311

New York Bay Area

Source Global urban competitiveness database of CASS

Bay Area is 0.358, which means that it has the largest internal differences; the coefficient of variation of the New York Bay Area is 0.311, and its internal differences are slightly smaller than those in the Guangzhou-Hong Kong-Macao Greater Bay Area, the Tokyo Bay Area, and the San Francisco Bay Area have too few samples.

4.4 Comparative Analysis of the Competitiveness of the Four Major Bay Areas

243

4.4.2 Historical Comparison: The Tokyo Bay Area and the San Francisco Bay Area Take Turn to Lead, and the Guangzhou-Hong Kong-Macao Greater Bay Area Rises Rapidly The Tokyo Bay Area and the San Francisco Bay Area take turn to lead, and the Guangzhou-Hong Kong-Macao Greater Bay Area rises rapidly. In light of the average value of the samples form the four major Bay Areas, the Tokyo Bay Area had the highest value in 2008–2018, ahead of the other three Bay Areas with a big margin. In 2015, the San Francisco Bay Area surpassed the Tokyo Bay Area, and its leading margin widened in 2018. The average value of economic competitiveness of the New York Bay Area is stable on the whole. The Guangzhou-Hong Kong-Macao Greater Bay Area rose against the trend in the “Subprime Crisis,” with the average value rising from 0.541 in 2008 to 0.608 in 2018 (Fig. 4.14). The internal differences in the San Francisco Bay Area and the Guangzhou-Hong Kong-Macao Greater Bay Area have been decreasing year by year. As the Tokyo Bay Area has only one sample, its coefficient of variation cannot be studied. The internal differences of the cities in the San Francisco Bay Area have been decreasing year by year. The coefficient of variation of economic competitiveness dropped from 0.069 in 2008 to 0.008 in 2018. The internal differences of the cities in the New York Bay Area increased first and then decreased. The coefficient of variation was 0.272 in 2008, New York Bay Greater Bay Area

.5

.6

mean .8 .7

.9

1

Tokyo Bay San Francisco Bay

2008

2010

2012

year

2014

2016

2018

Fig. 4.14 Historical trend of the average value of economic competitiveness of sample cities from the world’s famous Bay Areas. Source Global urban competitiveness database of CASS

244

4 Analysis on the Economic Competitiveness of Global Cities

Table 4.22 Historical trend of the coefficient of variation of economic competitiveness in the world’s famous Bay Areas Year

New York Bay Area

San Francisco Bay Area

The Guangzhou-Hong Kong-Macao Greater Bay Area

2008

0.272

0.069

0.403

2009

0.313

0.075

0.402

2010

0.322

0.081

0.390

2011

0.299

0.072

0.390

2012

0.351

0.075

0.378

2013

0.368

0.045

0.378

2014

0.373

0.037

0.378

2015

0.346

0.032

0.360

2016

0.347

0.029

0.355

2017

0.295

0.019

0.335

2018

0.311

0.008

0.358

Source Global urban competitiveness database of CASS

increased to 0.373 in 2014, and then decreased somehow later to 0.311 in 2018. On the whole, the internal differences in this area increased significantly. The internal differences in the Guangzhou-Hong Kong-Macao Greater Bay Area have been big over a long period of time, but have been decreasing year by year. Its coefficient of variation was 0.403 in 2008 and dropped to 0.358 in 2018 (Table 4.22).

4.4.3 Comparison of Individual Indexes: The Guangzhou-Hong Kong-Macao Greater Bay Area Has Advantages in Social Environment and Business Cost The San Francisco Bay Area and the New York Bay Area have their respective advantages in the individual indexes of economic competitiveness, and the GuangzhouHong Kong-Macao Greater Bay Area has advantages in social environment and business cost. However, as the Tokyo Bay Area has only one sample with too large a size, only the San Francisco Bay Area, the New York Bay Area, and the GuangzhouHong Kong-Macao Greater Bay Area are compared. In terms of technology innovation and human capital, the San Francisco Bay Area surpasses the New York Bay Area and the Guangzhou-Hong Kong-Macao Greater Bay Area. In terms of financial service, industrial systems, local demand, and infrastructure, the New York Bay Area surpasses the San Francisco Bay Area and the Guangzhou-Hong Kong-Macao Greater Bay Area. In terms of global connections and institutional cost, the San Francisco Bay Area and the New York Bay Area are about the same, while the Guangzhou-Hong Kong-Macao Greater Bay Area lags behind by a wide margin.

4.4 Comparative Analysis of the Competitiveness of the Four Major Bay Areas

245

The Guangzhou-Hong Kong-Macao Greater Bay Area surpasses the San Francisco Bay Area and the New York Bay Area in social environment and business cost. In light of the internal differences among sample cities in terms of individual indexes, the internal differences of the Guangzhou-Hong Kong-Macao Greater Bay Area are bigger than those in the San Francisco Bay Area and the New York Bay Area in terms of main individual indexes. This is mainly because the Guangzhou-Hong Kong-Macao Greater Bay Area has more samples (Table 4.23). Table 4.23 Statistical characteristics of the individual indexes of economic competitiveness of sample cities in the world’s four major Bay Areas Bay Area

Tokyo Bay Area

Primary index

Average Average Coefficient Average Coefficient Average Coefficient value value of variation value of variation value of variation

Financial service

0.805

0.121

0.791

0.180

1.592

0.363

1.521

Technology innovation

1.000

0.829

0.031

0.656

0.246

0.773

0.156

Industrial systems

0.852

0.589

0.364

0.450

0.458

0.621

0.532

Human capital

0.750

0.658

0.111

0.224

1.023

0.559

0.810

Local demand

0.794

0.484

0.134

0.345

0.193

0.573

0.647

Business cost 1.000

0.629

0.000

0.644

0.139

0.629

0.000

Institutional cost

0.803

0.874

0.033

0.663

0.168

0.894

0.000

Global connections

0.906

0.744

0.211

0.643

0.257

0.754

0.288

Infrastructure 0.990

0.547

0.171

0.638

0.284

0.670

0.388

Social environment

0.243

0.076

0.362

0.323

0.355

0.473

0.675

San Francisco Bay Area

Guangzhou, Hong Kong, and Macao Bay Area

Source Global urban competitiveness database of CASS

New York Bay Area

246

4 Analysis on the Economic Competitiveness of Global Cities

4.5 Comparative Analysis of the Competitiveness of the Ten Major Urban Agglomerations 4.5.1 Current Pattern: The Urban Agglomerations in Developed Countries Take the Lead The urban agglomerations in developed countries have a higher level of economic competitiveness, while the urban agglomerations in developing countries have greater internal differences. Successful urban agglomerations in the world, such as the northeastern urban agglomeration of the US, the London–Liverpool metropolitan regions and the Seoul metropolitan area, play an important role in promoting the economic and social development not only in the cities themselves but also the whole country. Based on data availability and length of the paper, Table 4.24 presents the statistical description of the sustainable competitiveness in cities of ten urban agglomerations in the world. The Seoul metropolitan area includes 2 cities, Inchon and Seoul, the northeastern urban agglomeration of the US includes 11 cities such as New York, the western urban agglomeration of the US includes 13 cities such as Chicago, and the northern Californian urban agglomeration includes 3 cities such as San Francisco, the Bombay metropolis includes 4 cities such as Bombay, the London–Liverpool metropolitan regions include 8 cities such as London, the Yangtze River Delta urban agglomeration includes 26 cities such as Shanghai, the Pearl River Delta urban agglomeration includes 13 cities such as Guangzhou, the Netherlands–Belgium urban agglomeration includes 6 cities such as Amsterdam, and the Rhine-Ruhr urban agglomeration includes 4 cities such as Hamburg. The ranking of the average value of economic competitiveness of the ten major urban agglomerations from the top is: Northern California urban agglomeration, the northeastern urban agglomeration of the US, the Rhine-Ruhr urban agglomeration, the Seoul national urban agglomeration, the Midwest urban agglomeration of the US, the London–Liverpool urban agglomeration, the Netherlands–Belgium urban agglomeration, the Yangtze River Delta urban agglomeration, the Pearl River Delta urban agglomeration, and the Bombay urban agglomeration. On the whole, the level of competitiveness of the urban agglomerations in developed countries is higher than that of the urban agglomerations in developing countries. The gap between the peak values of the economic competitiveness indexes of the ten major urban agglomerations is big, which shows the big gap between top cities. In terms of regional internal differences, the coefficient of variation of the Bombay urban agglomeration is 0.529, with the biggest internal differences among the urban agglomerations. The coefficient of variation of the Pearl River Delta urban agglomeration is 0.526, and its internal differences are only next to those of the Bombay urban agglomeration. The coefficient of variation of the Yangtze River Delta urban agglomeration is 0.330, ranking the third. Therefore, it can be concluded that internal urban development is very unbalanced in the urban agglomerations of developing countries (Fig. 4.15).

6

4

Netherlands–Belgium

Rhine-Ruhr

0.676

0.577

0.454

0.483

0.599

0.26

0.776

0.623

0.682

0.676

Average value

Source Global urban competitiveness database of CASS

13

the Pearl River Delta

Bombay metropolis

8

4

Northern California

26

3

Midwest US

the Yangtze River Delta

13

Northeastern US

London–Liverpool

2

11

Seoul national

Sample size

Sample city

3

7

9

8

6

10

1

5

2

3

Average value ranking

0.688

0.592

0.414

0.468

0.557

0.22

0.919

0.63

0.656

0.676

Median

0.042

0.062

0.239

0.155

0.148

0.137

0.256

0.087

0.138

0.186

Standard deviation

0.711

0.641

0.932

0.854

0.933

0.445

0.929

0.799

1

0.808

Peak value

0.615

0.464

0.204

0.225

0.481

0.153

0.481

0.506

0.532

0.544

Least value

0.062

0.108

0.526

0.322

0.247

0.529

0.33

0.139

0.203

0.276

Coefficient of variation

Table 4.24 Statistical characteristics of economic competitiveness in the world’s ten major urban agglomerations in 2018

1

2

9

7

5

10

8

3

4

6

Coefficient of variation ranking

4.5 Comparative Analysis of the Competitiveness of the Ten Major Urban … 247

248

4 Analysis on the Economic Competitiveness of Global Cities

90 70 50 30 10 -180

-140

-100

-60

-10 -20

20

60

100

140

180

-30 -50 -70 Economic competitiveness index

-90

Fig. 4.15 Distribution map of the economic competitiveness index of the world’s ten major urban agglomerations

4.5.2 Historical Comparison: Urban Agglomerations in Developed Countries Decline While Those in Developing Countries Rise The level of competitiveness of the Yangtze River Delta urban agglomeration and the Pearl River Delta urban agglomeration is rapidly rising. With a comparison of the average values of the competitiveness index of the ten major urban agglomerations in 2008–2018, it can be discovered that the average ranking of sample cities from the Seoul national urban agglomeration and the Bombay urban agglomeration keeps dropping, while the average ranking of sample cities from the northeastern urban agglomeration, the Midwest urban agglomeration of the US, the Northern California urban agglomeration, the London–Liverpool urban agglomeration, the Netherlands– Belgium urban agglomeration, and the Rhine-Ruhr urban agglomeration dropped and then rose again. The average ranking of sample cities from the Yangtze River Delta urban agglomeration and the Pearl River Delta urban agglomeration keeps rising. The score of the economic competitiveness of the Yangtze River Delta urban agglomeration was 0.398 in 2008, and the average ranking was 321; in 2013, its score was 0.463, and average ranking 255; in 2018 its score of economic competitiveness was 0.483, and average ranking 247. The score of economic competitiveness of the Pearl River Delta urban agglomeration was 0.379, and its average ranking 396 in 2008; in 2013, its score was 0.432, and average ranking 358; in 2018, its score of economic competitiveness was 0.454, and average ranking 334. Meanwhile, we

4.5 Comparative Analysis of the Competitiveness of the Ten Major Urban …

249

Table 4.25 Economic competitiveness of the ten major urban agglomerations over the past years Year

2008

Index

Average Coefficient Average Coefficient Average Coefficient value of variation value of variation value of variation

2013

2018

Seoul national

0.706

0.260

0.689

0.343

0.676

0.276

Northeastern US

0.699

0.177

0.652

0.235

0.682

0.203

Midwest US

0.637

0.133

0.595

0.162

0.623

0.139

Northern California

0.747

0.294

0.743

0.328

0.776

0.330

Bombay metropolis

0.248

0.580

0.257

0.612

0.260

0.529

London–Liverpool

0.663

0.228

0.573

0.300

0.599

0.247

The Yangtze River Delta

0.398

0.372

0.463

0.344

0.483

0.322

The Pearl River Delta

0.379

0.567

0.432

0.564

0.454

0.526

Netherlands–Belgium 0.635

0.098

0.563

0.109

0.577

0.108

Rhine-Ruhr

0.076

0.662

0.087

0.676

0.062

0.726

Source Global urban competitiveness database of CASS

should also see that despite the significant improvement in the competitiveness of the Yangtze River Delta urban agglomeration and the Pearl River Delta urban agglomeration, their competitiveness stays below the urban agglomerations in developed countries (Table 4.25).

4.6 Comparative Analysis of the Top 20 Cities in Economic Competitiveness 4.6.1 Current Pattern: China and the US Dominate the World’s Top 20 Cities in Economic Competitiveness The world’s top 20 cities in economic competitiveness create more wealth with a smaller population. With the index system being optimized and individual indexes becoming more precise, according to the latest ranking of urban sustainable competitiveness, the top ten cities are respectively New York, Singapore, London, Shenzhen, San Jose, Munich, San Francisco, Tokyo, Los Angeles and Houston, and the cities from the 11th to the 20th places are, respectively, Hong Kong, Dallas, Shanghai, Guangzhou, Seoul, Dublin, Miami, Boston, Beijing, and Frankfurt. The gross GDP of the world’s top 20 cities in economic competitiveness amounts to about USD 10.98 trillion, accounting for 25% of the total GDP of the 1007 cities, and for about 15% of the global GDP. This shows that the top 20 cities play a pivotal role in global economic activities. In terms of population, the total population of the world’s top

250

4 Analysis on the Economic Competitiveness of Global Cities

20 cities is about 230 million, accounting for 10% of the total population of the 1007 sample cities, and about 3% of the world’s total population. The world’s top 20 cities in economic competitiveness are not evenly distributed. Of the top 20 cities in economic competitiveness ranking, 8 cities are in North America (8 in North America 8, 8 cities are in Asia (7 in East Asia), 4 cities are in Europe (3 in West Europe), while no city in Oceania, Africa, or South America is rated among the top 20. These 20 cities are mainly in developed countries, with a few in developing ones. Most cities are in the US (8), China (5), and Germany (2); Singapore, Japan, Ireland, the UK and South Korea each has one city listed. In terms of per capita income, the per capita GDP of the top 20 cities is all higher than USD 10,000, with one city at USD 10,000-20,000, 5 cities at USD 20,000-50,000, 9 cities at USD 50,000–80,000, 3 cities at USD 80,000–100,000, only one city above USD 100,000 (Fig. 4.16). At the national level, the US is the country with the largest number of cities listed among the world’s top 20, amounting to 9. This shows that despite problems and challenges posed by the “Subprime Crisis,” industrial hollowing out, and trade deficit, the US is still powerful as a traditional economic power. China has 5 cities listed, just next to the US in terms of the number of top 20 cities. This shows that China is emerging as a powerful country by the economic restructuring and upgrading, macroeconomic policy coordination and integrated development of new technologies such as the Internet with economy (See Table 4.26). 90 70 50 30 10 -180

-140

-100

-60

-10 -20

20

60

100

140

-30 -50 -70 -90 Fig. 4.16 Distribution map of the world’s top 20 cities in urban economic competitiveness

180

4.6 Comparative Analysis of the Top 20 Cities in Economic Competitiveness

251

Table 4.26 World’s top 20 cities in economic competitiveness in 2018 Ranking

City

Economic competitiveness

1

New York

1

2

Singapore

0.972

3

London

0.933

4

Shenzhen

5

San Jose

6

Ranking

City

Economic competitiveness

11

Hong Kong

0.884

12

Dallas

0.878

13

Shanghai

0.854

0.932

14

Guangzhou

0.850

0.931

15

Seoul

0.808

Munich

0.931

16

Dublin

0.800

7

San Francisco

0.929

17

Miami

0.798

8

Tokyo

0.896

18

Boston

0.797

9

Los Angles

0.8771

19

Beijing

0.797

10

Houston

0.884

20

Frankfurt

0.796

Source Global urban competitiveness database of CASS

4.6.2 Historical Comparison: The US Consolidates Its Advantages, While Top Cities in China Keep Increasing From the following Table 4.27, it can be discovered that the economic competitiveness ranking of Shenzhen has kept rising from out of top 20 in the world in 2008 to No. 8 in 2015, and then to No. 4 in 2018. The number of Chinese cities in the world’s top 20 keeps increasing from 1 in 2008 to 4 in 2015. Meanwhile, the cities rated among the top 20 in Europe dropped from 6 in 2008 to 3 in 2018. The number of Japanese cities thus rated also dropped from 2 in 2008 to 1 in 2018. The US safety holds the first place, with 9 cities ranked Top 20 in 2008 and 2018.

4.6.3 Comparison of Individual Indexes: Financial Service and Social Environment Are Important Reasons Causing the Intercity Differences Among the World’s 20 Cities The advantages of the world’s 20 cities are mainly concentrated on financial service, technology innovation, industrial systems, human capital, and local demand, especially financial service and human capital. In terms of financial service, the average value of the top 20 cities is 0.516, and their median is 0.526, respectively being 6.32 and 18.28 times of the average value and median of the 1007 cities worldwide; in terms of human capital, the average value of the top 20 cities is 0.630, and their median is 0.677, respectively being 4.73 and 8.58 times of the average value and median of the 1007 cities worldwide; in terms of technology innovation, the average

252

4 Analysis on the Economic Competitiveness of Global Cities

Table 4.27 World’s top 20 cities in urban economic competitiveness Ranking

2018

1

New York

US

2013 New York

The US

London

2008 UK

2

Singapore

Singapore

Singapore

Singapore

Tokyo

Japan

3

London

UK

London

UK

New York

US

4

Shenzhen

China

Tokyo

Japan

Munich

Germany

5

San Francisco

US

San Francisco

US

San Francisco

US

6

San Jose

US

Munich

Germany

Singapore

Singapore

7

Munich

Germany

Dallas

US

Hong Kong China Hong Kong

8

Tokyo

Japan

Shenzhen

China

Dallas

US

9

Los Angles

US

San Jose

The US

Frankfurt

Germany

10

Houston

The US

Hong Kong

China Hong Kong

Houston

US

11

Hong Kong

China Hong Kong

Houston

US

Osaka

Japan

12

Dallas

US

Los Angles

US

San Jose

US

13

Shanghai

China

Seoul

South Korea Los Angles US

14

Guangzhou

China

Osaka

Japan

Miami

US

15

Seoul

South Korea Paris

France

Paris

France

16

Dublin

Ireland

Shanghai

China

Seoul

South Korea

17

Chicago

US

Guangzhou

China

Stockholm

Sweden

18

Miami

US

Frankfurt

Germany

Dublin

Ireland

19

Boston

US

Stockholm

Sweden

Boston

US

20

Beijing

China

Chicago

US

Chicago

US

Source Global urban competitiveness database of CASS

value of the top 20 cities is 0.845, and their median is 0.841, respectively being 2.30 and 2.48 times of the average value and median of the 1007 cities worldwide; in terms of industrial systems, the average value of the top 20 cities is 0.733, and their median is 0.750, respectively being 2.11 and 2.48 times of the average value and median of the 1007 cities worldwide; in terms of coefficient of variation, of the individual indexes of the world’s top 20 cities, the coefficient of variation of financial service is the highest, reaching 0.569, followed by social environment, being 0.449. Therefore, financial service and social environment are important factors causing the gaps of competitiveness among the top 20 cities (Table 4.28).

4.7 Analysis of the Coupling Coordination Degree of the Elements …

253

Table 4.28 Statistical characteristics of the individual indexes of the world’s top 20 cities in urban economic competitiveness Primary index

Sample size

Average value

Median

Variance

Coefficient of variation

Financial service

20

0.516

0.526

0.293

0.569

Technology innovation

20

0.845

0.841

0.077

0.091

Industrial systems

20

0.733

0.750

0.152

0.208

Human capital

20

0.630

0.677

0.203

0.323

Local demand

20

0.504

0.456

0.161

0.320

Business cost

20

0.603

0.629

0.164

0.272

Institutional cost

20

0.839

0.894

0.116

0.138

Global connections

20

0.843

0.855

0.097

0.115

Infrastructure

20

0.794

0.833

0.168

0.212

Social environment

20

0.337

0.283

0.151

0.449

Source Global urban competitiveness database of CASS

4.7 Analysis of the Coupling Coordination Degree of the Elements of Economic Competitiveness The theory of coupling coordination, first proposed by a German physicist Haken in the 1970s, was first applied in the field of laser physics, then applied in the field of motor systems, and later extended to the field of social sciences, especially urban economics. The coupling coordination degree is an index evaluating the integrality, comprehensiveness and internal coordination between the explanatory indexes of urban economic competitiveness. To pursue a higher coupling coordination degree between indexes for measuring urban competitiveness aims to have the indexes reach the ideal state of advancing side by side, improving on the whole, holistic optimization and coordinated development. The sustainable and healthy development of cities cannot be separated from the coupling coordinated development of all elements. The coupling coordination degree is very important to urban development. Constructing a model of the coupling coordination degree of all individual index in economic competitiveness and studying the coupling coordination degree of global urban economic competitiveness.

254

4 Analysis on the Economic Competitiveness of Global Cities

Table 4.29 Classification of the types of the coupling coordination degree

Coupling coordination degree

Coordination type

0.8–1

Excellent coordination

0.6–0.8

Fairly coordinated

0.5–0.6

Barely coordinated

0–0.5

Close to imbalance

This table is made by the authors

The coupling coordination degree is used in this report to judge the overall coupling and coordination of the individual indexes of global urban economic competitiveness. The calculation formula of the coupling coordination degree is:

Cv =

⎧ ⎪ ⎨

n i=1

 ⎪ ⎩ n



i=1,i< j

T =

n 

f i (x)

f i (x)+ f j (x) 2

2/n

⎫1/n ⎪ ⎬ ⎪ ⎭

αi f i (x)

i=1

D=

√ Cv ∗ T

In this formula, f i (x) indicates the value of No. i variable of a city, Cv indicates the coupling degree of n variables of a city, T indicates the comprehensive evaluation index of all variables, reflecting the overall level of development of all explanatory indexes of economic competitiveness,αi indicates the weight given to No. i variable in the evaluation system. It is deemed in this report that all explanatory variables of economic competitiveness are equally important, with αi valued at 1/n, and D indicating the coupling coordination degree between n variables. A high degree of coupling among variables does not mean a high degree of coupling coordination degree; but a high coupling coordination degree of variables shows that the coupling degree must be high, because the weight coefficient of variables is considered. For example, if A and B cities only have two variables, capital and labor, the comprehensive score of capital and labor in city A is respectively 0.7 and 0.83, and the comprehensive score of capital and labor in city B is respectively 0.45 and 0.36, the coupling degree of city A and city B is both 0.98, but the coupling coordination degree of city A is 0.3 higher than city B. The scores and types of the coupling coordination degree are classified as follows (Table 4.29).

4.7 Analysis of the Coupling Coordination Degree of the Elements …

255

4.7.1 Kernel Density Distribution and Scatter Diagram of the Coupling Coordination Degree of Cities Worldwide

3 2 0

1

2

Frequency

4

Kernel density

4

5

6

Figure 4.17 is the comparison chart of kernel density distribution the coupling coordination degree of cities worldwide, from which the distribution characteristics of the coupling coordination degree of cities worldwide can be observed. The coupling coordination degree of cities worldwide deviates to the left to some extent, with two crests, not objecting standard normal distribution on the whole. This shows that the coupling coordination degree is polarized in cities worldwide, with more cities whose coupling coordination degree is at a medium or low level and few cities with a high coupling coordination degree, and the coupling coordination degree of most cities should be optimized. From Table 4.30, it can be found that the average value of the coupling coordination degree of cities worldwide is 0.46, their median is 0.51, variance 0.05, and coefficient of variation 0.47. The value of the coupling coordination degree of 720 cities in the world is above the average level, accounting for 71.5% of the total sample cities. The average value of the coupling coordination degree of these cities is 0.57, and coefficient of variation 0.15; 208 cities is lower

0

0

.2

.4

.6

.8

Coupling coordination degree

1

Kernel density estimate

0

.2

.4

.6

.8

Coupling coordination degree

1

Normal distribution

Fig. 4.17 Kernel density comparison chart of the coupling coordination degree of cities worldwide. Source Global urban competitiveness database of CASS

256

4 Analysis on the Economic Competitiveness of Global Cities

Table 4.30 Coupling coordination degree of cities worldwide Variable

Scope

Sample Median Average Variance Coefficient Theil Gini size value of index coefficient variation

The Cities 1007 coupling worldwide coordination degree

0.51

0.46

0.05

0.47

0.01

0.09

Source Global urban competitiveness database of CASS

1

than the average value of the coupling coordination degree, accounting for 28.5% of the total sample cities. The average value of the coupling coordination degree of these cities is 0.18, and coefficient of variation 1.13. This indicates unbalanced development of global cities. Based on the regression analysis of economic competitiveness and coupling coordination degree in Fig. 4.18, the functional relations of global urban competitiveness and the coupling coordination degree is Y = 2.25X 2 − 1.02X + 0.21, the coupling coordination degree of cities in the middle ranking of economic competitiveness is between 0.4 and 0.6, and the correlation coefficient of the coupling coordination

Fitting line

0

.2

.4

.6

.8

Economic competitiveness

.4

.6

.8

1

Coupling coordination degree

Fig. 4.18 Scatter diagram analysis of global urban economic competitiveness and the coupling coordination degree. Source Global urban competitiveness database of CASS

4.7 Analysis of the Coupling Coordination Degree of the Elements …

257

degree between the global urban economic competitiveness ranking and explanatory index is 0.57, indicating strong correlation between the two. There is a very strong correspondence between economic competitiveness and urban coupling coordination degree; that is, for cities with higher economic competitiveness ranking, the coupling coordination degree of explanatory indexes is higher, while for cities with lower economic competitiveness ranking, the coupling coordination degree of explanatory indexes is lower.

4.7.2 Spatial Distribution Characteristics of the Coupling Coordination Degree of Cities Worldwide Of the world’s top ten cities in terms of the coupling coordination degree, Asia accounts for a half, and both China and Japan have two cities in the list. From Table 4.31, it can be seen that the value of the coupling degree of Tokyo is 1, which makes it No. 1 in the world. The average value of the coupling coordination degree of Hong Kong and Shanghai in China is respectively 0.893 and 0.824, making them No. 3 and No. 6 in the world, respectively. The average value of the coupling coordination degree of all of the world’s top ten cities in the coupling coordination degree exceeds 0.81, being in an excellent state of coordination. The gaps of the coupling coordination degrees of cities in different continents are large. From Table 4.32, it can be seen that the average value of the coupling coordination degree in cities of North America and Europe is respectively 0.59 and 0.54, about 0.15 less than the average value of the coupling coordination degree of cities in Oceania, and these cities are barely coordinated on the whole. The average value of the coupling coordination degree of cities in Asia, Africa, and South America is all lower than 0.45, and these cities are close to imbalance on the whole. The coefficient of variation of the coupling coordination degree in Asian cities is the highest, while there is a big gap between different cities in terms of the coupling coordination degree. To sum up, developed cities in Europe, North America, Oceania, and Asia have a higher coupling coordination degree, and the coordination of their different elements is generally well developed. The coupling coordination degree of under-developed cities in Asia and cities in Africa is generally low. The coupling coordination degree inside the cities of the Group of Seven is generally better developed than that in the cities of BRICS, and the intercity gaps in the Group of Seven are also smaller. From Table 4.33, it can be seen that except for France, where the average value of the coupling coordination degree is 0.53 on the whole, which indicates that the cities are barely coordinated, the

Asia

0.924

1

Country

Continent

Index

Ranking

New York

2

0.915

North America

The US

3

0.893

Asia

China

Hong Kong

Source Global urban competitiveness database of CASS

Tokyo

Japan

City

4

0.872

Asia

Singapore

Singapore

Table 4.31 World’s top ten cities in coupling coordination degree

5

0.871

Europe

The UK

London

6

0.824

Asia

China

Shanghai

7

0.823

North America

Canada

Toronto

8

0.821

Asia

South Korea

Seoul

9

0.818

Asia

Japan

Osaka

10

0.817

Oceania

Australia

Sidney

258 4 Analysis on the Economic Competitiveness of Global Cities

102

Africa

Close to imbalance

Barely coordinated

Fairly coordinated

Close to imbalance

Barely coordinated

Close to imbalance

Coordination status

0.33

0.54

0.69

0.24

0.59

0.46

Average value

Source Global urban competitiveness database of CASS

126

75

South America

Europe

131

North America

7

566

Asia

Oceania

Sample

Region

0.69

0.37

0.1

1.1

0.23

0.42

Coefficient of variation

Table 4.32 Continental distribution of the coupling coordination degree of cities

31

48

4

40

59

144

The number of cities lower than average value

Cairo

London

Sidney

San Diego

New York

Tokyo

City

Peak value

0.68

0.87

0.82

0.71

0.91

0.92

Coupling coordination degree

Fairly coordinated

Excellent coordination

Excellent coordination

Fairly coordinated

Excellent coordination

Excellent coordination

Coordination type

4.7 Analysis of the Coupling Coordination Degree of the Elements … 259

260

4 Analysis on the Economic Competitiveness of Global Cities

Table 4.33 Comparison of BRICS and the Group of Seven in terms of the coupling coordination degree Coupling coordination degree

BRICS

Country

Number of cities

Type of coordination

Average value

Standard deviation

Coefficient of variation

China

292

Close to imbalance

0.48

0.19

0.39

Russia

33

Close to imbalance

0.41

0.2

0.5

India

100

Close to imbalance

0.42

0.16

0.39

Brazil

32

Close to imbalance

0

0

0

South Africa

6

Barely coordinated

0.52

0.07

0.14

12

Fairly coordinated

0.66

0.08

0.12

France

9

Barely coordinated

0.53

0.21

0.4

The US

75

Fairly coordinated

0.64

0.07

0.11

Germany

13

Fairly coordinated

0.66

0.07

0.1

Italy

13

Fairly coordinated

0.6

0.05

0.08

Japan

10

Fairly coordinated

0.66

0.25

0.38

9

Fairly coordinated

0.71

0.07

0.1

The Group The UK of Seven

Canada

Source Global urban competitiveness database of CASS

coupling coordination degree of the cities of the other countries in the Group of Seven is between 0.6 and 0.8, which means that they are fairly coordinated. The average value of the coupling coordination degree in BRICS countries like China, India, Russia, and Brazil is all lower than 0.5; correspondingly, the elements of the cities are close to imbalance in their development, and the coordinated development of city elements should be improved. Urban agglomeration strengthens the importance of coupling and coordinated development of cities. From Table 4.34, it can be seen that the Yangtze River Delta and the Pearl River Delta urban agglomeration represent the most developed urban agglomerations in China, the average value of their coupling coordination

4.7 Analysis of the Coupling Coordination Degree of the Elements …

261

Table 4.34 Analysis of the coupling coordination degree of the world’s ten major urban agglomerations Urban agglomeration

Type of coordination

Average value

Standard deviation

Coefficient of variation

2

Fairly coordinated

0.72

0.14

0.19

Northeastern US

11

Fairly coordinated

0.7

0.1

0.14

Midwest US

13

Fairly coordinated

0.64

0.06

0.09

Northern California

3

Fairly coordinated

0.7

0.05

0.07

Bombay metropolis

4

Close to imbalance

0.33

0.38

1.17

London–Liverpool

8

Fairly coordinated

0.68

0.09

0.13

Yangtze River Delta

26

Barely coordinated

0.58

0.07

0.13

Pearl River Delta

13

Barely coordinated

0.54

0.18

0.34

Netherlands–Belgium

6

Fairly coordinated

0.66

0.08

0.12

Rhine-Ruhr

4

Fairly coordinated

0.68

0.05

0.07

Seoul national

Sample size

Source Global urban competitiveness database of CASS

degree is 0.58 and 0.54, respectively, indicating that the coupling and coordinated development of cities is barely coordinated. This value of the urban agglomerations in South Korea, the US and the UK is all between 0.6 and 0.8, which means that their development is fairly coordinated. In the urban agglomeration of the Bombay Metropolis of India, the average value of the coupling coordination degree is 0.33, the coefficient of variation is 1.17; the coupling coordination degree inside the urban agglomeration is close to imbalance and the problem of coordinated development should be addressed timely.

262

4 Analysis on the Economic Competitiveness of Global Cities

4.7.3 The Regression Analysis of Economic Competitiveness by the Coupling Coordination Degree To verify that the coupling coordination degree of cities is a critical element for urban competitiveness, Table 4.35 presents the benchmark regression analysis of economic competitiveness and the coupling coordination degree. Regression (1) indicates the regression results of the coupling coordination degree with the economic competitiveness index alone; regressions (2)–(5) indicate the regression results of the coupling coordination degree with the economic competitiveness index with the addition of other control variables. From the results of benchmark regression analysis, it can be concluded that with the gradual increase of explanatory variables, the coupling coorTable 4.35 Benchmark regression analysis of economic competitiveness and the coupling coordination degree (1) Coupling coordination degree

(2)

(3)

(4)

(5)

Eco2

Eco2

Eco2

Eco2

Eco2

0.487***

0.370***

0.138***

0.045***

0.042***

(22.02)

(15.79)

(7.53)

(3.12)

(2.93)

0.377***

0.165***

0.045**

0.041*

(11.09)

(6.50)

(1.99)

(1.83)

0.536***

0.206***

0.198***

(30.87)

(12.06)

(11.56)

Industrial systems

0.151***

0.163***

(4.13)

(4.45)

Local demand

0.990***

1.005***

(25.04)

(25.35)

0.096***

0.085***

(7.21)

(6.14)

0.055**

0.051**

Financial service Technology innovation

Business cost Institutional cost

(2.57) Social environment

(2.35) 0.052*** (3.15)

0.101***

0.125***

0.052***

−0.131***

−0.142***

(9.02)

(11.52)

(6.37)

(−10.58)

(−11.08)

N

1007

1007

1007

1007

1007

adj. R2

0.325

0.398

0.691

0.839

0.840

_cons

The values in parenthesis are t values; *, **, and *** respectively show the confidence level of 0.1, 0.05, and 0.01

4.7 Analysis of the Coupling Coordination Degree of the Elements …

263

dination degree and other explanatory variables are consistent with the significance level of economic competitiveness, which indicates that the regression results are viable. In the (1)–(5) regression analysis, the economic competitiveness index and the coupling coordination degree are both correlated at a significance level of 1%, and there is significant positive correlation between the coupling coordination degree and economic competitiveness. From this, we can see that the coupling coordination degree is a critical element of urban competitiveness.

Chapter 5

Analysis on Sustainable Competitiveness of Global Cities Weijin Gong and Qihang Li

How to improve the competitiveness of cities with the minimum resources while minimizing their environmental impact and how to transform environmental and social constraints of cities into a new engine for economic growth, i.e., how to improve the sustainable competitiveness of cities, are questions that have been troubling scholars at home and abroad. According to Yang Xiaolan and Ni Pengfei (2017), sustainable competitiveness is the ability of a city to consistently deliver economic, social, environmental and technological benefits to its citizens in an effective and sustainable manner. From economic competitiveness to sustainable competitiveness, the focus of city development shifts from wealth generation to creation of benefits on all fronts. On the basis of the analysis of economic competitiveness in Chapter Four, this chapter will examine factors affecting sustainable competitiveness of cities and their mechanisms of action, with emphasis on the following six dimensions: the world city network, major countries, regions, Bay Areas, city clusters, and key cities.

5.1 Cities with, Respectively, Strong and Weak Sustainable Competitiveness are Clearly Distributed in a Large Portion in the Middle and a Small Portion at Both Ends, and Sustainable Competitiveness of Asian Cities Constantly Enhances The cities with, respectively, strong and weak sustainable competitiveness are clearly distributed in a large portion in the middle and a small portion at both ends, and sustainable competitiveness of Asian cities constantly enhances. To be specific, there are few top global cities in Western Europe and North America, and the level of sustainable competitiveness of these cities declines rapidly. There are also few cities

© China Social Sciences Press 2021 P. Ni et al., Global Industry Chains: Creating a Networked City Planet, https://doi.org/10.1007/978-981-16-2058-4_5

265

266

5 Analysis on Sustainable Competitiveness of Global Cities

with particularly low level of sustainable competitiveness in South Africa and other countries, and the level of sustainable competitiveness of these cities also declines rapidly.

5.1.1 The Level of Economic Development is Highly Positively Correlated with the Overall Manifestation of Sustainable Competitiveness of Cities Through the analysis of various indicators of sustainable competitiveness, the countries, the urban agglomerations, and the top 20 cities, it is found that the level and growth rate of economic development always dominate in cities. The level and development direction of various types of sustainable competitiveness depend on economic development. In the foreseeable future, the impacts of economic development on sustainable competitiveness of cities will be further enhanced. Approaches to improving sustainable competitiveness through development and making competitiveness grow in conjunction with the economy are main tasks for the future city system.

5.1.2 High-Level Equilibrium is the Best Goal and Path to Enhance Sustainable Competitiveness of Cities According to the empirical data, it can be found that the development of cities with the highest sustainable competitiveness is under more equilibrium state, but the polarization between cities with relatively great sustainability is severe, especially the gaps are obviously wide in terms of multiple indicators. The ways to achieve improvement of sustainable competitiveness of cities and equilibrium with surrounding cities and the possibilities to accelerate the improvement of overall competitiveness of city system and achieve synergetic evolution are important topics for future research in the field of sustainable competitiveness of cities.

5.1.3 Technological Innovation and Human Capital Potential Have the Greatest Impacts and Magnify the Positive Effects by Means of Direct Effect, Indirect Effect, and Feedback Effect Magnifying effect of cities on the factor inputs through spillover effect and feedback effect for the second-order and higher-order neighboring cities cannot be ignored,

5.1 Cities with, Respectively, Strong and Weak Sustainable …

267

which is also one of the important sources to improve the level of sustainable competitiveness of cities. According to the estimation results of GNS model in Sect. 5.9, without regard to spillover effect and feedback effect between cities, elasticity values of “economic vitality” and “technological innovation” for the improvement of sustainable competitiveness of cities are 9.5% and 13.8%, respectively, which are 16.9% and 19%, respectively, with an increase of 77.89% and 37.68% with consideration to spillover effect and feedback effect between cities. Similarly, factors such as “social inclusion” and “global connections” also have positive spillover effect and feedback effect. Therefore, approaches to transforming the spatial spillover effect and feedback effect of factor inputs into the driving force for the improvement of sustainable competitiveness of cities are important paths for cities to enhance their sustainable competitiveness.

5.2 Global Ranking of Cities by Sustainable Competitiveness 2018 5.2.1 Overview 5.2.1.1

Global Ranking 2018: The Number of Cities Performing Extremely Well or Poorly in Sustainable Competitiveness Indicators is Small, and the Majority of Cities Are Performing Just So-So

A study of 1007 sample cities shows that the overall sustainable competitiveness of global cities in 2018 exhibits a trend of polarization. A handful of cities such as New York and Tokyo performs extremely good, while cities such as Kananga and MbujiMayi perform extremely poorly. There is an obvious phenomenon of the strong getting stronger and the weak getting weaker, i.e., the Matthew effect, in sustainable competitiveness among cities across the world, including cities in developed countries such as the US and European countries, emerging East Asian countries, and less developed countries in Central Asia, West Asia, and Africa. Figure 5.1 shows the sustainable competitiveness scores of 1007 cities worldwide in 2018. It can be clearly seen from Fig. 5.1 that the difference between the disparities between cities worldwide, especially between the top-ranking cities, in sustainable competitiveness is widening. In terms of the standardized sustainable competitiveness score, New York ranks first. Toronto ranks the 10th place with a score of 0.737, and Milan 100th place with a score as low as 0.501, 26.3%, and 49.9%, respectively, lower than the score of New York. In order to make the sustainable competitiveness of the 1007 cities comparable, we standardized their sustainable competitiveness scores. Table 5.1 provides a statistical description of the standardized sustainable competitiveness scores of these cities.

5 Analysis on Sustainable Competitiveness of Global Cities

0

.2

norm_2018 .4 .6

.8

1

268

0

200

400

id

600

800

1000

Fig. 5.1 Sustainable competitiveness scores of 1007 cities (2008)

Table 5.1 Statistical description of sustainable competitiveness of 1007 cities Variable

Obs

Mean

Std. dev

Min

Max

norm_2018

1007

0.279

0.159

0

1

norm_2018

1007

0.318

0.127

0

1

Source Database of the City and Competitiveness Research Center, Chinese Academy of Social Sciences

As can be seen from Table 5.1, the average score of the 1007 cities in 2018 is 0.279, lower than the average score in 2017, and the standard deviation is 0.159. However, the variance of the scores in 2018 is larger than that in 2017. Therefore, we believe that there is a phenomenon of the strong getting stronger and the weak getting weaker, i.e., the Matthew effect, in sustainable competitiveness among cities across the world. The kernel density diagram illustrates that the disparities between cities in sustainable competitiveness are widening. Figure 5.2 shows a kernel density diagram, illustrating the continuous empirical distribution of the 1007 cities and the overall trend in their sustainable competitiveness in 2018 and 2017. As can clearly be seen from Fig. 5.2, the peak of the kernel density curve for 2018 is in the left of and lower than the peak of the 2017 curve, indicating the existence of the Matthew effect. It is worth noting that, as indicated by the right half of Fig. 5.1, the sustainable competitiveness

5.2 Global Ranking of Cities by Sustainable Competitiveness 2018

269

0

2

Density

4

6

Kernel density estimate

0

.2 .4 .6 .8 Standardization of sustainable competitiveness

1

kdensity norm_2018 kdensity norm_2017 kernel = epanechnikov, bandwidth = 0.0329

Fig. 5.2 Kernel density estimation of sustainable competitiveness scores

scores of most cities in the 1007 cities are slightly below the mean. In other words, there is plenty of room for improvement for most cities when it comes to sustainable competitiveness.

5.2.1.2

Spatial Patterns of Cities in Terms of Sustainable Competitiveness: Emerging Cities in East Asia Are Gaining Traction Despite the Slow Recovery of the Global Economy

Overall, there are dynamic changes in sustainable competitiveness of cities worldwide. Global central cities such as New York, Los Angeles, Houston, London, France, Paris, and Amsterdam still occupy the top 20 spots in the global sustainable competitiveness ranking. However, emerging cities in East Asia are gradually catching up. Countries with strong sustainable competitiveness are still concentrated in North America and Western Europe, but emerging cities in Asia, especially in East Asia, are catching up. China’s traditional first-tier cities such as Hong Kong, Beijing, Shanghai, Guangzhou, and Shenzhen, and new first-tier cities such as Nanjing, Hangzhou, Ningbo, and Qingdao are rising fast in terms of sustainable competitiveness. Some cities have seen double-digit growth in their sustainable competitiveness score and are advancing toward top 100. However, the overall competitiveness of Asian cities, including Chinese cities, is still low. There is plenty of room for improvement for Asian cities when it comes to sustainable competitiveness.

270

5 Analysis on Sustainable Competitiveness of Global Cities

5.2.2 Chinese Cities Versus American Cities: There are Notable Disparities Between Chinese and American Cities in Sustainable Competitiveness. The Number of American Cities Performing Well in Sustainable Competitiveness Indicators Is Far Larger than the Number of Such Chinese Cities. However, the Internal Divergence Between American Cities Is Widening, Whereas the Development of Chinese Cities Is Overall More Coordinated The US has the largest economy in the world, and China takes up the second spot. Comparing the trends in sustainable competitiveness of cities in the world’s largest and second largest economies has important implications for the development of Chinese cities and cities in other countries. Overall, the gap between Chinese and American cities in sustainable competitiveness is still large, but it is closing. In 2018, the average sustainable competitiveness score of American cities is 2.112 times that of China. The difference is significantly smaller than the difference in 2017, which stood at 2.411 times.

5.2.2.1

Chinese Cities Versus American Cities: Overall, American Cities Perform Better Than Chinese Cities in Sustainable Competitiveness, but the Gap is Closing

In general, American cities score quite high in sustainable competitiveness. To make sustainable competitiveness between cities comparable, we also standardized the sustainable competitiveness scores of Chinese and American cities. Table 5.2 provides a statistical description of the standardized sustainable competitiveness scores of Chinese and American cities. As can be seen from Table 5.2, sustainable competitiveness scores of cities in China and the US show an upward trend. The average score of US cities is 0.526, much Table 5.2 Statistical description of sustainable competitiveness of Chinese and American cities Country

Year

Obs

Mean

Std. dev

Min

Max

US

2018

75

0.526

0.124

0.312

1

2017

75

0.518

0.109

0.367

1

2018

292

0.249

0.138

0.032

1

2017

292

0.243

0.144

0.038

1

China

Source Database of the City and Competitiveness Research Center, Chinese Academy of Social Sciences

5.2 Global Ranking of Cities by Sustainable Competitiveness 2018 Kernel density estimate

0

0

1

1

2

3

Density 2 3

4

4

5

5

Kernel density estimate

Density

271

.2

.4 .6 .8 Standardization of sustainable competitiveness kdensity america_2018 kdensity america_2017

kernel = epanechnikov, bandwidth = 0.0389

1

0

.2 .4 .6 .8 Standardization of sustainable competitiveness

1

kdensity china_2018 kdensity china_2017 kernel = epanechnikov, bandwidth = 0.0284

Fig. 5.3 KDE curves of sustainable competitiveness scores of Chinese and American cities

higher than that of Chinese cities. One of the reasons is that almost all prefecturelevel Chinese cities are included and some cities in Central and West China have dragged the average score down. There is a wide divergence between American cities in sustainable competitiveness, whereas the scores of Chinese cities indicate balanced sustainable competitiveness. It is worth noting that the variance of Chinese cities’ scores drops from 0.144 in 2017 to 0.138 in 2018, while the variance of American cities’ scores rises from 0.109 in 2017 to 0.124 in 2018, indicating the internal gap between Chinese cities in sustainable competitiveness is closing, while the gap between American cities is widening. Figure 5.3 shows the kernel density estimation (KDE) curves of sustainable competitiveness scores of Chinese and American cities. As can be seen from Fig. 5.3, both the peaks of the 2018 KED curves of American and Chinese cities are lower than the peaks of their 2017 curves, and both the 2018 curves are closer to normal distribution than their 2017 curves, indicating that, compared to the year of 2017, American and Chinese cities perform better in sustainable competitiveness in 2018. But, comparison of the right half of the two countries’ curves shows that there are more cities in China scoring lower than the average score.

5.2.2.2

Dynamics in Sustainable Competitiveness of American Cities: The Divergence Between American Cities in Sustainable Competitiveness Is Widening, and the Overall Competitiveness of American Cities Is Declining

American cities are leading the world in sustainable competitiveness, so examining the dynamics in sustainable competitiveness of American cities could have important implications for cities across the world. In 2018, the overall sustainable competitiveness of American cities is still high, but the disparities between American cities are widening. Compared to Chinese cities, the overall competitiveness of American cities is declining.

272

5 Analysis on Sustainable Competitiveness of Global Cities

The scores of American cities are all greater than 0.4, indicating the overall level of sustainable competitiveness of American cities is quite high. Five American cities, namely New York, Los Angeles, Boston, Seattle, and Houston, are among the global top 10 cities in terms of sustainable competitiveness. Northeastern cities, including New York, continue to lead the country in sustainable competitiveness, whereas southern cities, including Dallas, Austin, and Houston, and eastern cities, including San Francisco, Los Angeles, and San Diego, have seen a decline in the growth of sustainable competitiveness. The scores of American cities such as Riverside, Washington, Charlotte, McAllen, and Salt Lake City have declined, with Nashville– Davidson having the largest drop which reached 0.098.

5.2.2.3

Dynamics in Sustainable Competitiveness of Chinese Cities: The Divergence Between Chinese Cities in Sustainable Competitiveness is Closing, and Their Overall Competitiveness is Rising

The continual economic growth for four decades straight has offered cities in China, the world’s second largest economy, opportunities to cultivate sustainable competitive advantages. Most Chinese cities with higher scores, including Hong Kong, Beijing, Shanghai, Shenzhen, and Guangzhou, are located in the eastern coastal areas. The sustainable competitiveness scores of cities in Central China have rose. Among them, Hong Kong ranks among the global top 10, while Beijing and Shanghai rank among the global top 30. The Yangtze River Delta (Shanghai, Nanjing, and Hangzhou), the Pearl River Delta (Hong Kong, Shenzhen, and Guangzhou), the Beijing–Tianjin– Hebei region, and Taiwan (Taipei and Hsinchu) perform especially well. Among the first-tier cities, Nanjing, Xiamen, Hangzhou, and Chengdu have seen an increase in their scores, and new first-tier cities are advancing from the global top 100 to the global top 200. Wuhan, Changsha, Chongqing, and Hefei also perform quite well in sustainable competitiveness. Overall, sustainable competitiveness of Chinese cities from the eastern coastal areas to the central regions is rising.

5.2.3 The World’s Three Major Economic Centers: Contrary to Western Europe and North America, East Asia’s Economic Level is Low, the Difference is Large, and the Promotion is Fast The three major economic centers in the world are Western Europe, North America, and East Asia. Among them, East Asia is an emerging global economic center. By comparing the performance of the world’s three major economic centers in 2018, we found not only the scores of cities in Western Europe and North America are high

5.2 Global Ranking of Cities by Sustainable Competitiveness 2018

273

Table 5.3 Statistical description of sustainable competitiveness of the world’s three major economic centers Variable

Obs

Mean

Mean rank

Std. dev

Std. dev rank

Min

Max

38

0.531

1

0.095

1

0.361

0.885

north_america2018

130

0.443

2

0.162

3

0.174

1

east_asia2018

431

0.238

3

0.122

2

0.012

0.964

38

0.531

1

0.091

2

0.382

0.879

north_america2017

130

0.438

2

0.144

3

0.117

1

east_asia2017

431

0.304

3

0.081

1

0.168

0.739

west_europe2018

west_europe2017

Source Database of the City and Competitiveness Research Center, Chinese Academy of Social Sciences

and balanced while East Asian cities score relatively low and there is an obvious divergence between East Asian cities. There is an accelerated shift of global economic competitiveness from developed countries to Asia, especially East Asia. Cities such as Tokyo, Hong Kong, and Seoul in East Asia are strongly economic competitive, laying the foundation for East Asia to become a new economic center of the global economy. The world’s three major economic centers are North America represented by the US, Western Europe represented by the UK, France, and the Netherlands, and East Asia represented by China, Japan, and South Korea. Although India is located South Asia, given its large population and important position the Asian economy, the sample cities we selected to represent East Asia include 100 Indian cities. Strong sustainable competitiveness is also a necessary condition for East Asia to become a global economic center. Table 5.3 provides a statistical description of sustainable competitiveness of cities in the three major economic centers, including 38 cities in Western Europe, 130 cities in North America, and 431 cities in East Asia. It can be seen from Table 5.3 that, among the three major economic centers, cities in the Central and Western Europe have the highest sustainable competitiveness scores, followed by North America, while East Asian cities score the lowest. Six North American cities, namely New York, Los Angeles, Boston, Seattle, Houston, and Toronto, rank among the global top 10 cities. Among Western Europe, only London enters the top 10 and ranks third. Among Asian cities, Tokyo, Singapore, and Hong Kong rank second, fourth, and sixth, respectively. The average sustainable competitiveness score of Western European cities is 20% higher than that of North American cities and 2.23 times that of East Asian cities. The average score of North American cities is about 1.86 times that of East Asian cities. It should be noted that the average score of East Asian cities is dragged down by Indian cities. However, even if Indian cities were removed, East Asian cities would be still far behind North American and European cities. From 2017 to 2018, the standard deviation of Western European, North American, and East Asian cities increased from 0.091, 0.144, and 0.081 to 0.095, 0.162, and 0.122, respectively, indicating there are widening disparities among North American and East Asian cities in sustainable competitiveness.

274

5 Analysis on Sustainable Competitiveness of Global Cities

KDE curves show that the scores of West European and North American cities are closer to a uniform distribution, whereas the divergence among East Asian cities is severe. The KDE curves of sustainable competitiveness scores of Western European and North American cities are provided here to shed light on the trends of sustainable competitiveness in the three major economic centers (Figs. 5.4 and 5.5). As can be seen from Fig. 5.4, the peak of the 2018 KDE curve of Western European cities is basically at the same height as the peak of its 2017 curve. The peak of the

Kernel density estimate

0

0

1

2

Density

Density

2

4

3

6

Kernel density estimate

.2

.4 .6 .8 west europe Standardization of sustainable competitiveness

1

kdensity west_europe_2018 kdensity west_europe_2017

.2 .4 .6 .8 1 north america Standardization of sustainable competitiveness kdensity north_america_2018 kdensity north_america_2017

kernel = epanechnikov, bandwidth = 0.0312

kernel = epanechnikov, bandwidth = 0.0553

Fig. 5.4 KDE curves of Western European and North American cities

0

2

Density 4 6

8

Kernel density estimate

0

.2 .4 .6 .8 east asia Standardization of sustainable competitiveness 274 east_asia_2018 kdensity kdensity east_asia_2017

kernel = epanechnikov, bandwidth = 0.0219

Fig. 5.5 KDE curve of East Asian cities

1

5.2 Global Ranking of Cities by Sustainable Competitiveness 2018

275

2018 KDE curve of North American cities is slightly lower than and to the right of the peak of the 2017 curve. It means sustainable competitiveness scores of cities in Western Europe and North America in 2018 are basically equal to their 2017 scores. By contrast, sustainable competitiveness scores of cities in East Asia have slightly increased. As can be seen from Fig. 5.5, the peak of the KDE curve of East Asian cities drops by nearly half from the peak of the 2017 curve. It is also obviously to the left of the peak of the 2017 curve. Among the three global economic centers, East Asia has the most severe internal divergence. The possible reason is that it is natural for as an emerging global economic center to undergo a polarization process before its growth can be more inclusive. As can be seen from Fig. 5.5, sustainable competitiveness scores of North American cities have grown in 2018. Among the global top 100 cities, the US claims 35 seats, and North America 42 seats. Western Europe and Asia account for 21 and 19 seats, respectively. The overall level of sustainable competitiveness of Western European cities is significantly higher than the other two economic centers, with most of Western European cities ranking among the first and second echelons, followed by North America. The majority of North American cities rank among first, second, and third echelons with cities in the third echelon account for more than half. East Asian cities have the lowest overall level of sustainable competitiveness and, apart from cities such as Tokyo, Hong Kong, Seoul, Shanghai, Beijing, Guangzhou, and Shenzhen, rank in the third echelon. South Asian cities have the lowest average sustainable competitiveness score and rank primarily in the fifth echelon, except for a small number of fourth-echelon cities. Compared with Western Europe and North America, the internal disparities between Asian cities are relatively severe.

5.2.4 Sustainable Competitiveness of the Four Bay Areas: The Tokyo Bay Area Is the Most Competitive. The Guangdong-Hong Kong-Macau Bay Area Scores Lowest in Sustainable Competitiveness but Is Catching Up with the Three Mature Bay Areas The Guangdong-Hong Kong-Macau Bay Area is the youngest Bay Area and performs worst in sustainable competitiveness among the four Bay Areas. The development paths of the New York Bay Area, the San Francisco Bay Area, and the Tokyo Bay Area show that Bay Area economies, characterized by coordinated development of land, sea, and air transportation, efficient resource allocation, strong spillovers, and high specialization, are major contributors to regional and national economic growth. New York, Tokyo, Hong Kong, and San Francisco lead the four Bay Areas and rank No. 1, 2, 6, and 13, respectively. Studying the sustainable competitiveness of cities in the Bay Area economies across the world has important implications for the development of the Guangdong-Hong Kong-Macau Bay Area which consists

276

5 Analysis on Sustainable Competitiveness of Global Cities

Table 5.4 Statistical description of sustainable competitiveness of four Bay Areas Variable

Obs

Mean

Mean rank

Std. dev

Std. dev rank

Min

Max

3

0.701

2

0.026

1

0.681

0.732

New York Bay 2018 11

0.631

3

0.149

2

0.461

1

Tokyo Bay 2018

1

0.964

1

–

–

0.964

0.964

GHM Bay 2018

11

0.439

4

0.159

3

0.272

0.808

3

0.679

2

0.018

1

0.661

0.698

New York Bay 2017 11

0.615

3

0.151

3

0.468

1

Tokyo Bay 2017

1

0.739

1

–

–

0.739

0.739

GHM Bay 2017

11

0.439

4

0.113

2

0.316

0.677

San Francisco Bay 2018

San Francisco Bay 2017

Source Database of the City and Competitiveness Research Center, Chinese Academy of Social Sciences

of Hong Kong, Macau, and nine cities in the Pearl River Delta and is the world’s youngest and fourth largest Bay Area. Table 5.4 provides a statistical description of the sustainable competitiveness of cities in the four major Bay Areas. The New York Bay Area consists of eleven cities, including New York and Boston; the San Francisco Bay Area consists of San Francisco, San Jose, and Philadelphia; the Tokyo Bay Area only consists of one city, Tokyo; the Guangdong-Hong Kong-Macau Bay Area consists of eleven cities, including Hong Kong, Macau, and Shenzhen. San Francisco bay, New York bay, Tokyo bay, and GHM bay in Table 5.4 denote the San Francisco Bay Area, the New York Bay Area, the Tokyo Bay Area, and the Guangdong-Hong Kong-Macau Bay Area, respectively. As can be seen from Table 5.4, among the world’s four largest Bay Areas, the Tokyo Bay Area performs best in sustainable competitiveness, followed by the San Francisco Bay Area and the New York Bay Area, the average score of which is 0.701 and 0.631, respectively. The average score of cities in the youngest Guangdong-Hong Kong-Macau Bay Area stands at 0.439. It should be noted that the internal disparities between cities in the New York Bay Area are closing, but the opposite is true for the San Francisco Bay Area and the Guangdong-Hong Kong-Macau Bay Area. Among the four Bay Areas, the divergence between the cities within the Guangdong-Hong Kong-Macau Bay Area but its growth in the average sustainable competitiveness score is also the largest. The KDE curves of the Bay Areas show that the internal divergence between the cities within the Guangdong-Hong Kong-Macau Bay Area is greater than that of the New York Bay Area. As both the New York Bay Area and the Guangdong-Hong Kong-Macau Bay Area consist of eleven cities, based on the availability of data, we only compared the KDE curves of the two Bay Areas (Fig. 5.6). As can be seen from Fig. 5.6, the peak of the KDE curve of the Guangdong-Hong Kong-Macau Bay Area in 2018 is lower than that of the New York Bay Area and the x-axis value of the KDE peak of the Guangdong-Hong Kong-Macau Bay Area

5.2 Global Ranking of Cities by Sustainable Competitiveness 2018

277

Kernel density estimate

0

0

1

1

Density 2

Density 2

3

3

4

4

Kernel density estimate

.4

.6

.8 New York bay area economy

1

kdensity Newyork bay 2018 kdensity Newyork bay 2017 kernel = epanechnikov, bandwidth = 0.0550

1.2

.2

.4

.6 GHM-bay area economy

.8

1

kdensity GHM bay 2018 kdensity GHM bay 2017 kernel = epanechnikov, bandwidth = 0.0888

Fig. 5.6 KDE curves of the New York Bay Area and the Guangdong-Hong Kong-Macau Bay Area

is smaller than that of the New York Bay Area. The KDE peak of the GuangdongHong Kong-Macau Bay Area in 2018 is lower than that in 2017 and slightly skewed left, indicating there is a wide internal divergence between the cities within the Guangdong-Hong Kong-Macau Bay Area. In a word, the Guangdong-Hong KongMacau Bay Area is behind the New York Bay Area in both overall sustainable competitiveness and coordinated internal development. The world’s largest four Bay Areas are located in North America and East Asia, with the New York Bay Area and the San Francisco Bay Area in North America, and the Tokyo Bay Area and the Guangdong-Hong Kong-Macau Bay Area in East Asia. The overall sustainable competitiveness of three out of the four Bay Areas has increased except for the Guangdong-Hong Kong-Macau Bay Area. It can clearly be seen from Fig. 5.10 that the New York Bay Area, the Tokyo Bay Area, and the Guangdong-Hong Kong-Macau Bay Area all have seats in the global top 10 cities, whereas San Francisco and San Jose in the San Francisco Bay Area rank 13th and 19th, respectively. However, in terms of overall sustainable competitiveness, the Guangdong-Hong Kong-Macau Bay Area remains behind the Tokyo Bay Area, the New York Bay Area, and the San Francisco Bay Area but the gap with the other three Bay Areas is narrowing.

5.2.5 Global Urban Clusters: The Northeast Region of the US Has the Best Urban Clusters, While a few Mature Urban Clusters Have Stable Development and the Overall Polarizing Urban Agglomerations Face Long-Term Challenges Among the world’s largest ten urban clusters, the Northeast Megalopolis of the US scores the highest in sustainable competitiveness; mature urban clusters also perform well and exhibit steady growth; polarization hinders the sustainable development of some urban clusters. A precondition for the creation of a Bay Area economy is high

278

5 Analysis on Sustainable Competitiveness of Global Cities

accessibility in terms of transportation, e.g., mature land, air, and sea transportation networks. Similar to Bay Area economies, urban clusters can also generate positive spillovers but the preconditions for the creation of an urban cluster are not as stringent as Bay Area economies. Thriving urban clusters, such as the Northeast Megalopolis of the US, the London–Liverpool Corridor and the Seoul Capital Area not only can drive the economic and social development of cities with the clusters but also are major contributors to region and national economies. Table 5.5 provides a statistical description of sustainable competitiveness of the world’s largest ten urban clusters with data limitations fully recognized. The Seoul Capital Area consists of two cities, namely Incheon and Seoul; the Northeast Megalopolis of the US eleven cities, including New York; the Midwest Megalopolis of the US thirteen cities, including Chicago; Northern California Megaregion three cities, including San Francisco; the Mumbai Metropolitan Region four cities, including Mumbai; the London–Liverpool Corridor eight cities, including London; the Yangtze River Delta nineteen cities, including Shanghai; the Pearl River Delta eleven cities, including Guangzhou; the Netherlands–Belgian city group six cities, including Amsterdam; and the Rhine-Ruhr metropolitan region four cities, including Hamburg. Based on changes in sustainable competitiveness scores in Table 5.5, we divided the ten urban clusters into two categories: (i) clusters the average score of which is rising, including the Seoul Capital Area, Northeast Megalopolis of the US, Midwest Megalopolis of the US, Northern California Megaregion, London– Liverpool Corridor, Yangtze River Delta, Pearl River Delta, and Dutch–Belgian Metropolitan Region; and (ii) clusters the average score of which is declining, including the Mumbai Metropolitan Area and the Rhine-Ruhr Metropolitan Region. The ten urban clusters can also be divided into two categories by internal divergence: (i) polarized clusters, including the Seoul Capital Area, Midwest Megalopolis of the US, Northern California Megaregion, Mumbai Metropolitan Area, Yangtze River Delta, Pearl River Delta, Dutch–Belgian Metropolitan Region, and Rhine-Ruhr Metropolitan Region; and (ii) balanced clusters, including the Northeast Megalopolis of the US and the London–Liverpool Corridor. Among the ten clusters, the Seoul Capital Area and the Mumbai Metropolitan Area are the most polarized. Figures 5.7 and 5.8 compare the KDE curves of the Northeast Megalopolis of the US and the London–Liverpool Corridor, and the Midwest Megalopolis of the US and the Pearl River Delta, respectively, to shed light on the year-on-year changes in sustainable competitiveness of the clusters. As can be seen from Fig. 5.7, the KDE peak of the Yangtze River Delta is higher than that of the Northeast Megalopolis of the US and is severely left skewed compared to both the KDE peak of the Northeast Megalopolis of the US and its own KDE peak in 2017. The Pearl River Delta also exhibits a polarization trend in sustainable competitiveness. It can be seen from Fig. 5.8 that the KDE peak of the Pearl River Delta is lower than that of the Midwest Megalopolis of the US and is severely left skewed compared to that of the Midwest Megalopolis of the US. Figure 5.8 also shows that,

5.2 Global Ranking of Cities by Sustainable Competitiveness 2018

279

Table 5.5 Statistical description of sustainable competitiveness of the world’s largest ten urban clusters Urban cluster

Year

Obs

Mean

Seoul capital area

2018

2

0.618

Northeast Megalopolis, US

2018

11

Midwest Megalopolis, US

2018

Northern California megaregion

Mean rank

Std. dev

Std. dev rank

Min

Max

3

0.16

10

0.505

0.731

0.621

2

0.159

9

0.447

1

13

0.524

6

0.08

2

0.419

0.707

2018

3

0.63

1

0.14

8

0.47

0.732

Mumbai metropolitan region

2018

4

0.273

10

0.134

7

0.13

0.436

London–Liverpool corridor

2018

8

0.573

4

0.132

6

0.475

0.886

Yangtze river delta

2018

19

0.383

8

0.1

3

0.258

0.658

Pearl river delta

2018

11

0.378

9

0.119

5

0.243

0.602

Netherlands–Belgian metropolitan region

2018

6

0.518

7

0.108

4

0.38

0.701

Rhine-Ruhr metropolitan region

2018

4

0.541

5

0.056

1

0.491

0.62

Seoul capital area

2017

2

0.61

3

0.132

9

0.516

0.703

Northeast Megalopolis, US

2017

11

0.615

2

0.159

10

0.445

1

Midwest Megalopolis, US

2017

13

0.514

6

0.076

2

0.435

0.703

Northern California megaregion

2017

3

0.622

1

0.115

7

0.489

0.698

Mumbai metropolitan region

2017

4

0.308

10

0.092

6

0.231

0.434

London–Liverpool corridor

2017

8

0.569

4

0.132

9

0.477

0.88

Yangtze river delta

2017

19

0.381

8

0.077

3

0.299

0.608

Pearl river delta

2017

11

0.377

9

0.092

6

0.307

0.568

Netherlands–Belgian metropolitan region

2017

6

0.511

7

0.086

4

0.414

0.656

Rhine-Ruhr metropolitan region

2017

4

0.544

5

0.035

1

0.508

0.586

Source Database of the City and Competitiveness Research Center, Chinese Academy of Social Sciences

280

5 Analysis on Sustainable Competitiveness of Global Cities Kernel density estimate

0

0

2

1

Density

Density

4

2

3

6

Kernel density estimate

.4

.6 .8 1 USA northeast urban agglomeration

1.2

.2

.3

.4 .5 .6 Yangtze River Delta urban agglomeration

kdensity USA northeast 2018 kdensity USA northeast 2017

.7

kdensity Yangtze 2018 kdensity Yangtze 2017

kernel = epanechnikov, bandwidth = 0.0675

kernel = epanechnikov, bandwidth = 0.0500

Fig. 5.7 KDE curves of the Northeast Megalopolis of the US and the Yangtze River Delta

Kernel density estimate

0

1

2

2

Density

Density 3

4

4

6

5

Kernel density estimate

.4

.5 .6 .7 USA middle west urban agglomeration kdensity USA MW 2018 kdensity USA MW 2017

kernel = epanechnikov, bandwidth = 0.0396

.8

.2

.3

.4 .5 Pearl river delta city cluster

.6

.7

kdensity pearl 2018 kdensity pearl 2017 kernel = epanechnikov, bandwidth = 0.0533

Fig. 5.8 KDE curves of the Midwest Megalopolis of the US and the Pearl River Delta

compared with 2017, the overall sustainable competitiveness of 80% of the ten clusters is growing, but 80% of the clusters are also polarized. China’s Yangtze River Delta and Pearl River Delta have a low global ranking by sustainable competitiveness. The year-on-year comparison of the sustainable competitiveness scores of the clusters shows the improvement of the overall sustainable competitiveness of the clusters and the widening divergence between cities. Most of the world’s largest ten urban clusters are located in North America, Western Europe, East Asia, and South Asia. The comparative analysis of the 2017 and 2018 results shows that, although clusters in Western Europe and North America score relatively high, the growth of their scores has declined, with some cities in the Midwest Megalopolis of the US having the largest decline. The average scores of the clusters in East Asia and South Asia are relatively low but are growing, but the polarization within the clusters is significant.

5.2 Global Ranking of Cities by Sustainable Competitiveness 2018

281

5.2.6 Globe Top 20: Leading the World in Sustainable Competitiveness with Technology and Human Capital as the Largest Two Driving Forces According to the latest ranking, the global top 20 cities by sustainable competitiveness are New York, Tokyo, London, Singapore, Los Angeles, Hong Kong, Boston, Seattle, Houston, Toronto, Osaka, Sydney, San Francisco, Seoul, Paris, Chicago, Amsterdam, Vancouver, San Jose and Atlanta. Figure 5.9 compares the annual sustainable competitiveness scores of the global top 20 cities for 2017 and 2018. It can be seen from Fig. 5.9 that the disparities between the top 20 cities in the 2018 sustainable competitiveness ranking are smaller as compared to the 2017 ranking. There are only four new entrants in the 2018 global top 20 cities, namely Amsterdam, Sydney, Osaka, and Vancouver, and the other 16 cities in the 2018 global top 20 cities also ranked among the global top 20 cities in 2017. Only one city, namely New York, has the same ranking (No. 1) in 2017 and 2018. Twelve cities, namely Tokyo, Singapore, Hong Kong, San Francisco, Seattle, Los Angeles, Toronto, Amsterdam, San Jose, Sydney, Osaka, and Vancouver, climbed up. The ranking of seven cities,

0

0

.2

200

.4

400

.6

600

.8

800

1

1000

Fig. 5.9 Comparison of sustainable competitiveness of the global top 20 cities in 2017 and 2018

0

50

per_gdp_rank

sus_compete_rank

100 Fitted values

150

0

20000 norm_compete

40000 per_gdp

60000

80000

Fitted values

Fig. 5.10 Relationship between sustainable competitiveness of highest ranking cities of each of the 135 sample countries and national average income

282

5 Analysis on Sustainable Competitiveness of Global Cities

namely London, Boston, Seoul, Houston, Paris, Chicago, and Atlanta, dropped, with Seoul having the largest decline. The well-developed infrastructure and global connectivity of the Netherlands contributed to the climb of Amsterdam in the sustainable competitiveness ranking. Strong infrastructure and innovation capabilities are important drivers for the growth of Osaka’s sustainable competitiveness score. Sydney’s ranking improved due to its good environment and well-developed infrastructure and global connectivity. Good environment, well-developed infrastructure, and huge human capital potential contributed to the rise of Vancouver in the global ranking.

5.2.7 The Largest Cities in the World’s Major Countries: The Competitiveness of the Largest Cities Is Intensifying, and the Strength and Competitiveness of the Country Determine the Sustainable Competitiveness of the Largest Cities The disparities between the highest ranking cities of each country are severe, and the overall competency of population instead of the size of population is the decisive factor in sustainable competitiveness. At the core of urban development is people, but the size of population is not the only factor determining sustainable competitiveness of cities. In fact, it is human capital, i.e., the stock of knowledge and skills, that has a more important role in improving sustainable competitiveness. Therefore, the most populous city of a country is not necessarily the most competitive city by sustainable competitiveness. But cities with high sustainable competitiveness always boast large human capital. Table 5.6 shows the sustainable competitiveness scores and ranking of the most populous cities of each of the 135 sample countries in Asia, Europe, America, and Oceania. It can be seen from Table 5.6 that the most populous cities in the world’s 135 countries can be divided into two categories: (i) capitals with competitive advantage on all fronts, usually boasting the strongest sustainable competitive advantage in the country; such cities include Tokyo, London, and Sydney; and (ii) economic centers of a country, such as New York, Shanghai, and Mumbai. However, regardless of whether the most populous city of a country is the country’s capital or economic center, it always boasts high human capital. Table 5.6 also shows that sustainable competitiveness of a country’s highest ranking city is significantly positively correlated with the economy of the country in which they are located. Figure 5.16 illustrates the relationship between sustainable competitiveness of a country’s highest ranking city and national income level. The economy of a country is the key to sustainable competitiveness of cities in the country. Figure 5.10 shows that there is a significant positive correlation between sustainable competitiveness of a country’s highest ranking city and economy measured by per capita GDP. The left scatter plot depicts the relationship between

0.612

Madrid

Spain

0.631

0.613

Copenhagen

Denmark

0.658

0.653

0.624

Stockholm

Sweden

Vienna

Shanghai

China

0.683

Austria

Zürich

Switzerland

0.685

0.69

0.701

0.73

0.731

0.733

0.737

0.849

0.886

0.964

1

Sustainable competitiveness

New Zealand Auckland

San Diego

Seoul

South Korea

San Jose

Sydney

Australia

Chile

Toronto

Canada

Costa Rica

Singapore

Singapore

Paris

London

UK

Amsterdam

Tokyo

Japan

Netherlands

New York

US

France

Most populous city

Country

43

42

38

34

30

28

22

21

19

17

15

14

12

10

4

3

2

1

Rank

26,617

44,731

40,332

53,579

51,845

8117

79,866

13,961

11,733

45,638

36,870

27,608

49,897

42,349

55,243

40,412

38,972

57,589

Per capita GDP (US dollars)

26

11

17

7

8

49

1

35

41

10

21

24

9

13

6

16

18

5

Rank

Moldova

Nicaragua

Liberia

El Salvador

Venezuela

Uzbekistan

Paraguay

Kuwait

Honduras

Guatemala

Ukraine

Cuba

Kazakhstan

Armenia

Nigeria

Iran

Tunisia

Morocco

Country

Chi¸sin˘au

Managua

Monrovia

San Salvador

Caracas

Tashkent

Asunción

Kuwait City

Tegucigalpa

Guatemala City

Kiev

Havana

Almaty

Yerevan

Lagos

Tehran

Tunis

Casablanca

Most populous city

0.21

0.218

0.227

0.23

0.236

0.243

0.247

0.252

0.253

0.266

0.27

0.275

0.278

0.283

0.283

0.288

0.29

0.296

Sustainable competitiveness

Table 5.6 Sustainable competitiveness scores and ranking of most populous cities of each of the 135 sample countries

598

564

534

526

505

484

469

450

446

416

407

395

389

374

373

366

362

349

Rank

1913

2144

455

3769

92

90

128

74

40

91

69

25

85

67

87

52

50

77

88

62

76

83

Rank

(continued)

12,237

2106

4078

27,368

2375

4141

2186

7092

7715

3606

2176

5219

3689

2893

Per capita GDP (US dollars)

5.2 Global Ranking of Cities by Sustainable Competitiveness 2018 283

Athens

Buenos Aires

Prague

Bogota

Budapest

São Paulo

Tel Aviv-Jaffa

Argentina

Czech Republic

Colombia

Hungary

Brazil

Israel

Roman

Italy

Warsaw

Kuala Lumpur

Malaysia

Greece

Istanbul

Turkey

Poland

Brussels

Belgium

Lisbon

Abu Dhabi

United Arab Emirates

Portugal

Berlin

Germany

Doha

Helsinki

Finland

Bangkok

Dublin

Ireland

Thailand

Oslo

Norway

Qatar

Moscow

Russia

Table 5.6 (continued)

0.438

0.443

0.444

0.449

0.452

0.474

0.482

0.492

0.505

0.509

0.509

0.513

0.523

0.524

0.548

0.564

0.584

0.601

0.601

0.603

0.604

182

175

174

168

163

140

133

121

103

98

99

96

92

91

72

64

54

49

50

47

45

37,181

8639

12,820

5757

18,484

12,654

17,882

12,415

19,872

5979

59,324

30,669

9508

10,863

41,261

38,518

42,233

43,433

64,100

70,890

8748

20

46

36

59

29

37

30

38

28

58

4

23

44

42

15

19

14

12

3

2

45

Mozambique

Burkina Faso

Bangladesh

Angola

Gabon

Rwanda

Algeria

Sierra Leone

Cote d’Ivoire

Laos

Sudan

Haiti

Libya

Cambodia

Palestine

Tanzania

Ethiopia

Senegal

Ghana

Zambia

Pakistan

Maputo

Ouagadougou

Dhaka

Luanda

Libreville

Kigali

Algiers

Freetown

Abidjan

Vientiane

Khartoum

Port-au-Prince

Tripoli

Phnom Penh

Gaza

Dar es Salaam

Addis Ababa

Dakar

Kumasi

Lusaka

Karachi

0.153

0.156

0.158

0.161

0.162

0.167

0.167

0.176

0.182

0.186

0.188

0.189

0.19

0.191

0.193

0.196

0.196

0.199

0.202

0.204

0.209

795

784

779

775

768

748

749

716

698

684

680

674

670

669

665

654

653

640

629

626

603

382

614

1359

3309

7079

711

3917

481

1535

2339

2415

735

5126

1270

1851

878

713

953

1517

1263

1442

(continued)

131

122

102

80

53

118

70

126

96

86

84

115

63

103

94

110

117

109

98

104

100

284 5 Analysis on Sustainable Competitiveness of Global Cities

0.436

0.411

Kingston

Jakarta

Riga

Jamaica

Indonesia

Latvia

Cairo

Bucharest

Montevideo

Minsk

Colombo

Zagreb

Muscat

Santa Cruz

San Juan

Beirut

Belgrade

Romania

Uruguay

Belarus

Sri Lanka

Croatia

Oman

Bolivia

Puerto Rico

Lebanon

Serbia

Peru

Egypt

Lima

Philippines

0.325

0.337

0.341

0.344

0.349

0.351

0.354

0.358

0.363

0.363

0.364

0.367

0.368

0.369

Sofia

Manila

Bulgaria

0.38

South Africa Johannesburg

0.391

0.398

0.41

0.419

Panama City

0.42

Panama

Mexico

Saudi Arabia Riyadh

Mumbai

Mexico City

India

Table 5.6 (continued) 184

307

294

290

285

274

268

263

261

252

251

250

248

246

245

237

222

217

206

205

197

193

1717

5426

8257

30,833

3117

15,102

12,299

3857

5023

15,298

9532

3479

6031

2951

7469

5280

14,070

3570

4879

14,333

19,982

8444

95

60

48

22

81

32

39

73

64

31

43

79

56

82

51

61

34

78

65

33

27

47

Guinea

Somalia

Tajikistan

Zimbabwe

Eritrea

Djibouti

Yemen

Myanmar

Togo

Mongolia

Malawi

Kyrgyzstan

Benin

Mali

Niger

Iraq

Turkmenistan

Nepal

Madagascar

Cameroon

Uganda

Mauritania

Conakry

Mogadishu

Dushanbe

Bulawayo

Asmara

Djibouti

Sana’a

Yangon

Lomé

Ulaanbaatar

Lilongwe

Bishkek

Cotonou

Bamako

Niamey

Baghdad

Ashgabat

Kathmandu

Antananarivo

Yaoundé

Kampala

Nouakchott

0.15

0.056

0.063

0.076

0.084

0.09

0.092

0.094

0.105

0.109

0.117

0.121

0.123

0.124

0.126

0.129

0.129

0.137

0.137

0.137

0.142

0.149

806

982

979

967

957

952

947

942

923

920

905

896

891

890

885

873

878

850

851

849

828

808

748

472

796

1029

689

1872

660

1196

577

3694

300

1121

789

780

368

4610

6389

729

402

1375

580

1102

114

(continued)

127

111

108

119

93

120

105

124

75

134

106

112

113

133

66

55

116

130

101

123

107

5.2 Global Ranking of Cities by Sustainable Competitiveness 2018 285

Santo Domingo 0.313

Guayaquil

Nairobi

Tbilisi

Ho Chi Minh City

Dominican Republic

Ecuador

Kenya

Georgia

Vietnam

345

343

339

337

327

323

321

2171

3866

1463

6019

6794

4088

3881

89

72

99

57

54

68

71

–

Chad

Afghanistan

Central African Republic

Congo

Syria

Burundi

–

Ndjamena

Kabul

Bangui

Kinshasa

Aleppo

Bujumbura

Source Database of the City and Competitiveness Research Center, Chinese Academy of Social Sciences

0.301

0.303

0.305

0.306

0.315

Amman

Jordan

0.315

Baku

Azerbaijan

Table 5.6 (continued)

–

0.031

0.033

0.036

0.04

0.046

0.046

–

1001

1000

998

997

993

992

–

651

562

382

444

1535

286

–

121

125

131

129

96

135

286 5 Analysis on Sustainable Competitiveness of Global Cities

5.2 Global Ranking of Cities by Sustainable Competitiveness 2018

287

Table 5.7 Statistical description of sustainable competitiveness of highest ranking cities of each of the 135 sample countries Variable

Obs

Mean

Std. dev

Min

Max

sus_compete

135

0.338

0.214

0.031

1

Source Database of the City and Competitiveness Research Center, Chinese Academy of Social Sciences

sustainable competitiveness and per capita GDP rankings, and the right scatter plot shows the relationship between sustainable competitiveness and per capita GDP. Both diagrams indicate the level of sustainable competitiveness of a country’s highest ranking city is closely related to the overall economic strength of the country. There is a widening divergence in sustainable competitiveness between highest ranking cities. The average sustainable competitiveness score of the highest ranking cities of each country is higher than the global average, but the disparities between the highest ranking cities are also greater than the global average. In other words, the highest ranking cities of each country exhibit an obvious trend of polarization. Table 5.7 provides a statistical description of sustainable competitiveness of highest ranking cities of each of the 135 sample countries. As can be seen from Table 5.7, the average sustainable competitiveness score of 135 countries is 0.338, higher than the global average (0.279) and the average score in 2017 (0.318). The variance (0.214) is also much higher than the global average (0.159), indicating widening divergence between major cities. The KDE curve of sustainable competitiveness of the highest ranking cities in 135 countries also validates that the divergence between the highest ranking cities is widening. Figure 5.11 shows the KDE curves of sustainable competitiveness of the highest ranking cities in each of the 135 sample countries (Fig. 5.11).

5.3 Environmental Quality Index Analysis: Environmental Negative Impacts in Urban Clusters 5.3.1 Overall Pattern: Environmental Endowment and the Kuznets Curve Together Determine the Quality of Urban Environment There exists a positive correlation between region and geographical factors and the performance of cities in the quality of environment index The correlation between region and the quality of environment of cities is reflected in the fact that cities in Europe and America perform significant better than Asian and African cities in quality of environment. The correlation between geographical factors and the quality of environment of cities is mainly reflected in the impact of ocean and forest on urban environment.

288

5 Analysis on Sustainable Competitiveness of Global Cities

0

.5

Density 1

1.5

2

Kernel density estimate

0

.2

.4

.6 sus_compete

.8

1

Kernel density estimate Normal density kernel = epanechnikov, bandwidth = 0.0722

Fig. 5.11 KDE of sustainable competitiveness of highest ranking cities of each of the 135 sample countries

Among the European countries, the Nordic countries are the earliest to enter the post-industrial stage. They have reasonable economic structures and superior environmental endowment, which is why they are the world’s most ideal places to live. Cities in America and Oceania perform well in environmental quality with similar scores. Although South America performs poorly in economic competitiveness, it has rich natural endowment. The percentage of South American cities in the global top 100 cities by quality of environment is higher than that of Europe. By contrast, most cities in developing countries in Asia and Africa have the worst environment and are subject to the dual pressures of economic development and relatively harsh environment (Table 5.8). In 2017, the average environmental quality index of the 1,035 sample cities was 0.497, the coefficient of variation 0.528, and the standard deviation 0.263. The x-axis value of the KDE peak of Europe and North America is approximately 0.8, whereas the KDE curve of other continents is relatively fat and has no obvious peak (Figs. 5.12 and 5.13). If we analyze the cities of Europe, America and Asia and Africa with richer natural resources per capita, it is not difficult to see that with the improvement of economic vitality index, the contour line of Asian and African countries shows an obvious environmental Kuznets curve, while Europe and America. The country is basically rising all the way; that is, when the environmental resources are adequately endowed, economic development solves environmental problems at the same time. This obvious difference also provides a new perspective for the urban environmental problems currently facing Asian and African countries (Fig. 5.14).

5.3 Environmental Quality Index Analysis: Environmental Negative …

289

Table 5.8 Quality of environment and percentage of cities entering global top 100 cities with best environment by continent Region

Sample

Number and Mean percentage of cities entering the global top 100

Coefficient of variation

Highest ranking city

Index

World ranking

Asia

563

0(0.00%)

0.330

0. 574

Kerman

0. 834

114

Europe

127

11(8.6%)

0.697

0.156

Stockholm

0.933

25

North America

132

60(45.5%)

0.817

0.132

Honolulu

1.000

1

South America

74

19(25.7%)

0.752

0.186

Feira de Santana

0.972

8

Oceania

7

7(100.00%)

0.977

0.013

Auckland

0.997

2

3(2.88%)

0.541

0.355

Monrovia

0.908

46

100

0.497

0.528

Honolulu

1.000

1

Africa

104

World average

1007

Source Database of the City and Competitiveness Research Center, Chinese Academy of Social Sciences

80 60 40 20

-180

-140

-100

-60

0 -20

20

60

-20 -40 -60 Fig. 5.12 Global distribution of cities with good environment

100

140

180

290

5 Analysis on Sustainable Competitiveness of Global Cities

Environmental Quality Index

Europe and America Others

Quality of Environment

Environmental quality

Fig. 5.13 KDE of environmental quality

Economic vitality CiƟes Europe and America

Asia and Africa World

Fig. 5.14 Environmental quality and economic vitality (comparison between Eurasia and America)

5.3 Environmental Quality Index Analysis: Environmental Negative …

291

5.3.2 Global Top 20: Coastal Cities and Cities Exhibiting Moderate Economic Growth

Environmental quality

Among the global top 20 cities by environmental quality, coastal cities perform exceptionally well. Honolulu, Auckland, and Gold Coast ranks in the top three and six cities in Oceania are among the top ten. Most of the global top 20 cities are located in Oceania and the Americas. Seven out of the global top 20 cities are Australian cities. As can be seen from Fig. 5.13, coastal cities in all continents and regions, including the Asian-African region, have better urban environment than their landlocked counterparts, which indicates the natural endowment has a significant impact on the quality of urban environment. In the meantime, most of the global top 20 cities by environmental quality maintain moderate economic growth. The number of cities among the global top environmentally competitive 20 cities performing extremely well or poorly in economic indicators is small (Fig. 5.15), which is related to the economic structure, environmental resources, and tourism development of the city. It shows for cities with high-quality environment, the impact of economic development is in fact smaller than the impact of natural resource endowment (Table 5.9).

Economic vitality CiƟes Europe and America

Asia and Africa World

Fig. 5.15 Environmental quality and economic vitality (>0.95)

Brazil

Australia

Australia

South America

Oceania

Oceania

City

Melbourne

Perth

Feira de Santana

Adelaide

Cape Coral

Brisbane

Miami

Gold Coast

Auckland

Honolulu

Index

0.965

0.966

0.972

0.981

0.985

0.985

0.990

0.990

0.997

1.000

10

9

8

7

6

5

4

3

2

1

Rank

Region

North America

South America

North America

North America

North America

North America

North America

Oceania

North America

North America

Country

US

Uruguay

US

Cuba

US

US

Canada

Australia

Canada

US

Source Database of the City and Competitiveness Research Center, Chinese Academy of Social Sciences

Australia

Oceania

North America

Australia

US

Oceania

US

Australia

Oceania

North America

New Zealand

North America

Oceania

Country

US

Region

Table 5.9 Global top 20 cities by quality of environment City

McAllen

Montevideo

Orlando

Havana

Albuquerque

Portland

Winnipeg

Sydney

Vancouver

New Orleans

Index

0.945

0.949

0.953

0.953

0.954

0.955

0.957

0.959

0.960

0.963

Rank

20

19

18

17

16

15

14

13

12

11

292 5 Analysis on Sustainable Competitiveness of Global Cities

5.3 Environmental Quality Index Analysis: Environmental Negative …

293

80 60 40 20

-180

-140

-100

-60

0 -20

20

60

100

140

180

-20 -40 -60 Fig. 5.16 Global distribution of inclusive cities

5.3.3 Comparison of Countries: Wide Disparities Between Chinese and American Cities in Quality of Environment Overall, the US performs much better than China in quality of environment. The average ecological environment score of the US cities is three times that of the Chinese cities. Honolulu is ranked No. 1 by quality of environment. The highest ranking Chinese city is Lijiang at the 127th place. The coefficient of variation of Chinese cities is 0.550, about five times that of the US. At the same time, the two factors have caused such a comparison. In terms of per capita natural resources and environmental carrying capacity, the US has a greater natural advantage. At the same time, due to its earlier development, it has already passed the stage of bringing environmental problems at the beginning of industrial development. In the past 40 years of reform and opening-up, China has undertaken the transfer of productive forces in the world. A large number of highly polluting industries and low-end industries in the industrial chain have become the pillars of development. In addition, China has a large population, a small natural environment carrying capacity, and a great presence in the environment. Differences, subjectively, China has neglected the sustainable relationship between environmental and economic development for a period of time, seriously undermining the ecological environment and causing the current environmental quality to be at a lower level (Table 5.10).

294

5 Analysis on Sustainable Competitiveness of Global Cities

Table 5.10 Comparison of quality of environment scores of Chinese and American cities Country

Mean

Variance

Coefficient of variation

US

0.867

0.066

0.076

China

0.261

0.143

0.55

Lowest ranking 1

Highest ranking city Honolulu

172

Lijiang

Source Database of the City and Competitiveness Research Center, Chinese Academy of Social Sciences

5.3.4 Urban Cluster Pattern: Urban Clusters Bring Negative Environmental Impact The pattern of environmental quality scores of urban clusters is basically consistent with that of countries. The majority of urban clusters in developed countries in Europe and America score high, whereas urban clusters in China and India perform poorly in quality of environment. Furthermore, the core–periphery disparities in urban clusters in Europe and America are small. By contrast, the overall environmental quality of Chinese and Indian urban clusters is low, and none of the cities in Chinese and Indian urban clusters have entered the global top 100. In addition, there is a wide internal divergence between cities in Chinese and Indian urban clusters (Table 5.11). Table 5.11 Average environment scores of major urban clusters worldwide Urban cluster

Economic vitality

Standard deviation

Coefficient of variation

Ranking

Highest ranking city

Yangtze river delta

0.174

0.080

0.458

548

Zhoushan

Midwestern Megalopolis, US

0.790

0.031

0.040

83

Pearl river delta

0.327

0.056

0.171

607

Shenzhen

London–Liverpool corridor

0.812

0.031

0.038

106

Liverpool

Northeast Megalopolis, US

0.850

0.039

0.046

36

Providence

Rhine-Ruhr metropolitan region

0.732

0.018

0.024

205

Hamburg

Netherlands–Belgian metropolitan region

0.755

0.031

0.041

127

Amsterdam

Mumbai metropolitan region

0.297

0.084

0.282

578

Tiruchirappalli

Seoul capital area

0.486

0.008

0.016

511

Seoul

Northern California megaregion

0.867

0.026

0.030

55

Minneapolis

San Jose

Source Database of the City and Competitiveness Research Center, Chinese Academy of Social Sciences

5.3 Environmental Quality Index Analysis: Environmental Negative …

295

The average environmental quality scores of cities in developed countries and urban clusters are close and relatively high, but most urban clusters (with the exception of Belgium) in developed countries have underperformed as compared to the average score of the entire country, largely caused by natural resource depletion and insufficient environmental carrying capacity due to overconcentration of population. It shows that even in developed countries in Europe and America, the environmental problems brought about by population growth still exist. But the urban clusters in developed countries perform better in environmental governance. By contrast, the Seoul Capital Area, the Mumbai Metropolitan Region, the Yangtze River Delta, and the Pearl River Delta score far below the average score of developed countries and have a large coefficient of variation. The only exception is China’s Pearl River Delta which consists of four neighboring central cities, namely Hong Kong, Macao, Shenzhen, and Guangzhou. The environmental quality in Pearl River Delta is well controlled, and its environmental quality score is significantly higher than the national average level. It provides proof for the relationship between the layout and the environmental quality of a city cluster and also validates the environment-friendliness of Bay Area economies (Table 5.12).

5.3.5 World City Network Table 5.13 shows the global ranking of cities by quality of environment. Each country is represented by the city performing best in quality of environment in the country.

5.4 Social Inclusion Index Analysis: Europe and East Asia Lead in Social Inclusion Index, Culture and Tradition Determine the Level of Tolerance 5.4.1 Overall Pattern: Western Europe and East Asia Perform Best in Inclusiveness Europe and East Asia perform best in social inclusiveness, followed by Oceania. The divergence between European and East Asian cities in inclusiveness is small. Most Eurasian cities, especially China, Japan, and Western European countries, perform well in inclusiveness. The crime rates in these countries are low, and the rich–poor divide as measured by the Gini coefficient is small, which leads to social stability. However, Central Asian cities perform poorly in inclusiveness. Most North American cities record high crime rates and wide rich–poor divide. Unreasonable social structure is one of the root causes for low inclusiveness in developed countries such as the US. The development of cities in Africa and South America is hindered by

296

5 Analysis on Sustainable Competitiveness of Global Cities

Table 5.12 Performance of countries home to the world’s largest ten urban clusters in quality of environment Country and urban cluster(s)

Quality of environment index

Standard deviation

Coefficient of variation

China

0.261

0.143

0.550

Yangtze river delta

0.174

0.080

0.458

Pearl river delta

0.327

0.056

0.171

US

0.867

0.066

0.076

Midwestern Megalopolis, US

0.790

0.031

0.040

Northeast Megalopolis, US

0.850

0.039

0.046

Northern California megaregion

0.867

0.026

0.030

Germany

0.718

0.031

0.043

Rhine-Ruhr Metropolitan region

0.732

0.018

0.024

Netherlands

0.776

0.032

0.041

Netherlands–Belgian metropolitan region

0.755

0.031

0.041

Belgium

0.735

0.006

0.008

Netherlands–Belgian metropolitan region

0.755

0.031

0.041

South Korea

0.511

0.019

0.037

Seoul capital area

0.486

0.008

0.016

India

0.264

0.176

0.668

Mumbai metropolitan 0.297 region

0.084

0.282

UK

0.822

0.038

0.047

London–Liverpool corridor

0.812

0.031

0.038

Source Database of the City and Competitiveness Research Center, Chinese Academy of Social Sciences

frequent social unrest and local conflicts, and these cities perform poorly in social inclusiveness (Table 5.14). The average inclusiveness score of the 1007 sample cities for 2017 is 0.327, the median is 0.326, and the mean is lower than the median. There are 503 cities below the mean, accounting for only about half of the sample cities, indicating the overall inclusiveness of cities worldwide was low in 2017. The coefficient of variation for the indicator is 0.471 and the standard deviation 0.154. Comparative analysis of the performance of cities in Europe and America and other cities shows that the x-axis value of the KDE peak of other continents is 0.327, whereas the x-axis value of the

Nairobi

San Jose

0.803 156

0.806 151

0.809 142

0.816 137

0.821 127

Costa Rica

0.838 109

97

Kenya

New York

US

0.857

80

82

Niamey

Oslo

Norway

0.871

0.869

Niger

Mogadishu

Somalia

66

Copenhagen

Lisbon

Portugal

0.888

64

64

Amsterdam

Toronto

Canada

0.890

0.890

Denmark

Helsinki

Finland

62

Netherlands

Asmara

Eritrea

0.892

46

43

0.824 121

Dublin

Ireland

0.908

0.912

Madrid

Monrovia

Liberia

25

Spain

Panama City

Panama

0.933

17

19

0.828 119

Stockholm

Sweden

0.953

0.949

13

Guatemala

Zambia

El Salvador

Philippines

Oman

Tunis

0.680 297

0.682 294

0.684 292

0.685 288

0.686 286

0.689 285

0.699 272

Luanda

Guayaquil

Amman

Bogota

Kumasi

Bulawayo

Guatemala City

Lusaka

San Salvador

Manila

Muscat

Tunis

0.631 363

0.639 355

0.639 352

0.648 338

0.651 335

0.656 332

0.659 330

0.662 327

0.665 323

0.667 319

0.669 313

0.671 310

0.673 307

Kyrgyzstan

Egypt

Uzbekistan

Djibouti

Iraq

Chile

Bolivia

Thailand

Kuwait

Armenia

South Korea

Burkina Faso

Georgia

Libya

Malaysia

Burundi

Azerbaijan

Cambodia

Mali

Chad

Yemen

Index Ranking Country

Antananarivo 0.674 306

Colombo

São Paulo

Beirut

Minsk

Abidjan

Conakry

Lilongwe

City

Turkmenistan Ashgabat

Angola

Ecuador

Jordan

Colombia

Ghana

Zimbabwe

Madagascar

Sri Lanka

Brazil

Lebanon

Belarus

Cote d’Ivoire

Guinea

Malawi

Index Ranking Country

0.959

Mozambique Maputo

Havana

Montevideo

Uruguay

Sydney

Australia

Cuba

City

Country

Table 5.13 Global ranking of cities performing best in quality of environment in each country

0.505 493

0.505 493

0.508 491

0.511 488

0.514 484

0.515 482

Index Ranking

Bishkek

Cairo

Tashkent

Djibouti

Baghdad

San Diego

Santa Cruz

Bangkok

Kuwait City

Yerevan

Seoul

Ouagadougou

Tbilisi

Tripoli

(continued)

0.423 588

0.425 587

0.443 564

0.448 561

0.453 556

0.454 555

0.461 549

0.484 529

0.485 525

0.486 520

0.494 511

0.498 505

0.500 502

0.503 496

Kuala Lumpur 0.503 495

Bujumbura

Baku

Phnom Penh

Bamako

Ndjamena

Sana’a

City

5.4 Social Inclusion Index Analysis: Europe and East Asia Lead in Social … 297

Roman

Nouakchott

Brussels

Dakar

Italy

Mauritania

Belgium

Senegal

Port-au-Prince

Haiti

Tel Aviv-Jaffa

Riga

Latvia

Israel

Dar es Salaam

Tanzania

Tokyo

Athens

Greece

Japan

Santo Domingo 0.780 184

Dominican Republic

London

Buenos Aires

Argentina

Gaza

Kingston

Jamaica

Palestine

San Juan

Puerto Rico

UK

Freetown

Sierra Leone

0.803 156

Managua

Nicaragua

0.722 251

0.726 247

0.731 244

0.732 243

0.742 229

0.750 221

0.754 218

0.755 214

0.755 214

0.762 207

0.771 198

0.777 188

0.792 170

0.793 169

0.798 165

0.799 163

0.802 159

Caracas

Venezuela

Table 5.13 (continued)

Algeria

Iran

Nigeria

Syria

Vietnam

Gabon

Serbia

Ukraine

Croatia

Romania

Mexico

Bulgaria

Russia

Moldova

Singapore

Morocco

Austria

Czech Republic

Ethiopia

Algiers

Tehran

Lagos

Aleppo

Ho Chi Minh City

Libreville

Belgrade

Kiev

Zagreb

Bucharest

Mexico City

Sofia

Moscow

Chi¸sin˘au

Singapore

Casablanca

Vienna

Prague

Addis Ababa

0.552 442

0.555 441

0.557 439

0.560 438

0.574 434

0.575 433

0.578 429

0.582 425

0.583 422

0.587 419

0.590 415

0.596 409

0.605 402

0.605 400

0.609 393

0.610 392

0.624 376

0.629 369

0.629 367

Vientiane

Abu Dhabi

Almaty

Yaoundé

Nepal

India

Rwanda

Myanmar

Mongolia

Uganda

Qatar

Togo

South Africa

Pakistan

Afghanistan

Peru

Saudi Arabia

Benin

Kathmandu

Mumbai

Kigali

Yangon

Ulaanbaatar

Kampala

Doha

Lomé

Johannesburg

Karachi

Kabul

Lima

Riyadh

Cotonou

Central African Bangui Republic

Laos

United Arab Emirates

Kazakhstan

Cameroon

(continued)

0.212 818

0.216 816

0.226 808

0.249 788

0.252 785

0.262 776

0.268 768

0.310 708

0.312 703

0.319 698

0.325 691

0.345 672

0.354 661

0.358 657

0.370 649

0.391 625

0.392 623

0.396 616

0.422 589

298 5 Analysis on Sustainable Competitiveness of Global Cities

Paris

Tegucigalpa

Berlin

Sydney

France

Honduras

Germany

Australia

0.959

13

0.702 268

0.703 267

0.710 261

0.711 259

Malawi

Poland

Turkey

Indonesia

Hungary

Lilongwe

Warsaw

Istanbul

Jakarta

Budapest

0.699 272

0.522 474

0.533 462

0.535 460

0.547 446

Source Database of the City and Competitiveness Research Center, Chinese Academy of Social Sciences

Zürich

Switzerland

Table 5.13 (continued)

Yemen

Bangladesh

Congo

Tajikistan

China

Sana’a

Dhaka

Kinshasa

Dushanbe

Shanghai

0.515 482

0.042 984

0.131 922

0.185 845

0.201 831

5.4 Social Inclusion Index Analysis: Europe and East Asia Lead in Social … 299

300

5 Analysis on Sustainable Competitiveness of Global Cities

Table 5.14 Inclusiveness scores and percentage of cities entering global top 100 cities by continent Region

Sample

Number and percentage of cities entering the global top 100

Mean

Coefficient of variation

Highest ranking city

Index

World ranking

Asia

563

78(13.85%)

0.377

0.377

Sapporo

1. 00

1

Europe

127

8(6.30%)

0.331

0.366

Zaragoza

0.859

2

North America

132

7(5.30%)

0.250

0.510

Quebec

0.708

18

South America

74

0(0.00%)

0.173

0.797

Santa Marta

0.496

124

Oceania

7

0(0.00%)

0.316

0.165

Adelaide

0.393

344

Africa

104

7(6.73%)

0.263

0. 619

Bobo-Dioulasso

0.852

3

World average

1007

100

0.327

0.471

Sapporo

1. 00

1

Source Database of the City and Competitiveness Research Center, Chinese Academy of Social Sciences

KDE peak of Europe and America is larger than 0.327. The KDE curve of the Europe and America has a long tail extending to the left, indicating that there are many risks that can cause social unrest in European and American countries. Among European countries, Western European countries perform better in inclusiveness. The internal divergence between European countries is large. Compared to European countries, Asian countries perform better in inclusiveness (Figs. 5.16 and 5.17). As can be seen from Fig. 5.18, whether it is Eurasian and America, the relationship between inclusiveness and economic growth can be expressed as an inverted-U function. However, the average score of cities in America is lower than the average score of cities in Eurasia, which is caused by cultural and institutional factors rather than economic factors.

5.4.2 Global Top 20: East Asian Cities Lead the World in Inclusiveness Asia accounts for 16 seats in the global top 20 cities by inclusiveness. Most of the global top 20 cities by inclusiveness are located in China and Japan. The social inclusion index consists of two secondary indicators: the Gini coefficient and the crime rate. The social inclusion index of China and Japan is leading the world mainly because the crime rate is relatively low, the Gini coefficient is also low globally, and the social development is relatively balanced. Small differences, in terms of numerical

5.4 Social Inclusion Index Analysis: Europe and East Asia Lead in Social …

301

Social inclusiveness index

Europe and America Others

Inclusiveness

Inclusiveness

Fig. 5.17 KDE curves of social inclusiveness

Economic vitality City

Eurasia

America

World

Fig. 5.18 Social inclusiveness and economic vitality (comparison between Eurasia and America)

302

5 Analysis on Sustainable Competitiveness of Global Cities

values, the social inclusion index gap between the top 20 cities is not large, and the index is around 0.7–0.8. Considering that there are huge differences in the level of development, cities are not far behind in terms of the inclusion index, and East Asian culture has a great effect on the impact of social inclusion. On the one hand, equality, righteousness, and people-oriented in Confucian culture have made East Asian society pay more attention to income inequality in the process of development, especially in urban areas (after removing the influence of Chinese dual social conditions). On the other hand, East Asian countries attach great importance to social security governance and have a low crime rate. Especially when China’s economy develops, it has higher requirements for overall social security and faster development of governance mechanisms, further reducing the crime rate of cities (Table 5.15).

5.4.3 Comparison Between Countries’ Cultural Difference Leads to Disparities in Inclusiveness in China and Chinese Under the values of the East and the West, there is a big gap between Chinese and American social inclusions. China is located in East Asia and is deeply influenced by Confucian culture. Confucian culture advocates “harmony and wealth” and pays more attention to social inclusion. The mainstream social values in the US are mostly biased toward individualism. Economic development relies on market competition and appears to be incompatible with economic level and social management in terms of social inclusion. The global distribution of the social inclusion index shows that the Eurasian continent has a pattern of bulging at both ends and sinking in the central part. The social inclusion index of China and Japan in Western Europe and East Asia is generally high, and Central Asia is tempered by social turmoil. The index is lower. The northeastern and western coastal cities of the US have higher social inclusion indices, and the central regions are relatively weak, such as Birmingham and Baton Rouge. There are many cities in India, but the average social inclusion index is 0.369. The degree of social development between cities is balanced, with little difference. The average urban social inclusion index in China is 0.390. The overall performance of the economically developed urban social inclusion index is relatively low. The main reason is that although the crime rate index of Chinese cities is low, the social security is good, but the more economically developed urban residents’ income level. The difference is large, resulting in higher Gini coefficient, such as Guangzhou, Shenzhen, Dongguan, Taiyuan, and other cities. The social inclusion index of Brazil and South Africa is relatively low. The social turmoil and the gap between the rich and the poor are the main bottlenecks of urban development.

Japan

Spain

Burkina Faso

United Arab Emirates

Japan

Qatar

Saudi Arabia

China

Japan

China

Asia

Europe

Africa

Asia

Asia

Asia

Asia

Asia

Asia

Asia

Zhanjiang

Sendai

Yancheng

Buraydah

Doha

Nagoya

Abu Dhabi

Bobo-Dioulasso

Zaragoza

Sapporo

City

0.749

0.768

0.770

0.785

0.785

0.791

0.800

0.852

0.859

1.000

Index

10

9

8

7

6

5

4

3

2

1

Ranking

Europe

Asia

North America

Asia

Asia

Asia

Asia

Asia

Asia

Asia

Region

Germany

Singapore

Canada

Turkey

China

Saudi Arabia

China

Japan

Japan

Japan

Country

Source Database of the City and Competitiveness Research Center, Chinese Academy of Social Sciences

Country

Region

Table 5.15 Global top 20 cities by inclusiveness

Bremen

Singapore

Quebec

Denizli

Taizhou

Medina

Lianyungang

Osaka

Kumamoto

Niigata

City

0.705

0.707

0.708

0.721

0.722

0.725

0.727

0.730

0.730

0.730

Index

20

19

18

17

16

15

14

13

11

11

Ranking

5.4 Social Inclusion Index Analysis: Europe and East Asia Lead in Social … 303

304

5.4.3.1

5 Analysis on Sustainable Competitiveness of Global Cities

Comparison of Urban Clusters: Agglomeration Leads to a Decrease in Inclusiveness

Take typical urban agglomerations for example. The social inclusion index of urban agglomerations is basically consistent with the pattern at the national level. In addition, the cities with the highest social inclusion index within the urban agglomeration are not central cities, such as China’s three major urban agglomerations, the Midwest and Northeastern urban agglomerations, the London–Liverpool urban agglomeration in the UK, and the Bangalore urban agglomeration in India (Tables 5.16 and 5.17). The urban agglomeration index of Asian countries and the national social inclusion index are higher. The social inclusion level within the urban agglomeration is higher than the national average. Except for the higher social inclusion index within the US urban agglomeration, the social inclusion index of the other developed urban agglomerations is slightly lower. The national average proves that the development of the urban agglomeration is less inclusive. Table 5.16 Inclusiveness scores of major urban clusters Urban cluster

Social inclusiveness

Standard deviation

Coefficient of variation

Highest ranking

Highest ranking city

Yangtze River Delta

0.432

0.120

0.279

8

Yancheng

Midwestern Megalopolis, US

0.210

0.079

0.378

421

Pittsburgh

Pearl River Delta

0.330

0.092

0.279

53

Zhaoqing

London–Liverpool Corridor

0.247

0.054

0.219

483

Sheffield

Northeast Megalopolis, US

0.223

0.087

0.392

352

Boston

Rhine-Ruhr Metropolitan Region

0.355

0.086

0.243

285

Dusseldorf

Netherlands–Belgian Metropolitan Region

0.333

0.079

0.238

259

Hague

Mumbai Metropolitan 0.352 Region

0.204

0.578

23

Nashik

Seoul Capital Area

0.354

0.027

0.077

378

Seoul

Northern California Megaregion

0.241

0.011

0.045

683

San Jose

Source Database of the City and Competitiveness Research Center, Chinese Academy of Social Sciences

5.4 Social Inclusion Index Analysis: Europe and East Asia Lead in Social …

305

Table 5.17 Performance of countries home to the world’s largest ten urban clusters in inclusiveness Country and urban cluster(s)

Average inclusiveness score

Standard deviation

Coefficient of variation

China

0.427

0.094

0.221

Yangtze River Delta

0.432

0.120

0.279

Pearl River Delta

0.330

0.092

0.279

US

0.248

0.113

0.455

Midwestern Megalopolis, US

0.210

0.079

0.378

Northeast Megalopolis, US

0.223

0.087

0.392

Northern California Megaregion

0.241

0.011

0.045

Germany

0.402

0.106

0.263

Rhine-Ruhr Metropolitan Region

0.355

0.086

0.243

Netherlands

0.394

0.029

0.074

Netherlands–Belgian Metropolitan Region

0.333

0.079

0.238

Belgium

0.272

0.066

0.242

Netherlands–Belgian Metropolitan Region

0.333

0.079

0.238

South Korea

0.349

0.115

0.330

Seoul Capital Area

0.354

0.027

0.077

India

0.317

0.129

0.406

Mumbai Metropolitan 0.352 Region

0.204

0.578

UK

0.276

0.045

0.163

London–Liverpool Corridor

0.247

0.054

0.219

Source Database of the City and Competitiveness Research Center, Chinese Academy of Social Sciences

5.4.4 Global Ranking of Countries by Quality of Environment of the City with Best Environment in Each Country In the performance of the most inclusive cities in the world’s major countries, the Asian population has a higher social inclusion index. On the contrary, European and American countries have a large urban population, but they do not have an advantage in social inclusion. The main reason is the geographical and cultural influence (Table 5.18).

Tokyo

Conakry

Zürich

Kigali

Vienna

Havana

Yerevan

Cairo

Minsk

Muscat

Ouagadougou

Tegucigalpa

Bucharest

Kumasi

Guayaquil

Baku

Japan

Guinea

Switzerland

Rwanda

Austria

Cuba

Armenia

Egypt

Belarus

Oman

Burkina Faso

Honduras

Romania

Ghana

Ecuador

Azerbaijan

Doha

Singapore

Singapore

Abu Dhabi

United Arab Emirates

Qatar

City

Country

94

91

79

67

40

36

30

24

19

6

4

0.451 206

0.453 202

0.457 193

0.463 178

0.466 174

0.476 161

0.490 134

0.508 103

0.512

0.516

0.529

0.542

0.603

0.614

0.640

0.675

0.707

0.785

0.800

Spain

0.357 425

Freetown

Cotonou

Oslo

Amman

Seoul

Colombo

Dakar

Abidjan

Riga

Tunis

Sydney

Vientiane

Belgrade

Tel Aviv-Jaffa

Kuwait City

Madrid

0.312 539

0.316 525

0.316 525

0.316 525

0.316 523

0.318 520

0.381 511

0.321 509

0.327 500

0.329 489

0.334 477

0.335 475

0.337 474

0.344 462

0.349 456

0.350 454

0.353 439 Roman

Tehran

Jakarta

Brussels

Algiers

City

Tajikistan

Philippines

Niger

Pakistan

Malawi

Iraq

Vietnam

Uganda

Zimbabwe

Uruguay

Morocco

Bolivia

Cambodia

Dushanbe

Manila

Niamey

Karachi

Lilongwe

Baghdad

Ho Chi Minh City

Kampala

Bulawayo

Montevideo

Casablanca

Santa Cruz

Phnom Penh

Mozambique Maputo

Italy

Iran

Indonesia

Belgium

Algeria

Index Ranking Country

Guatemala City 0.357 432

Ashgabat

City

Central African Bangui Republic

Sierra Leone

Benin

Norway

Jordan

South Korea

Sri Lanka

Senegal

Cote d’Ivoire

Latvia

Tunis

Australia

Laos

Serbia

Israel

Kuwait

Guatemala

Turkmenistan

Index Ranking Country

Table 5.18 Ranking of each country’s highest ranking cities in inclusiveness Index Ranking

(continued)

0.162 851

0.165 844

0.166 842

0.168 839

0.177 829

0.182 812

0.182 809

0.186 805

0.186 804

0.193 787

0.196 780

0.198 774

0.199 773

0.199 772

0.199 771

0.200 769

0.201 766

0.213 757

0.217 748

306 5 Analysis on Sustainable Competitiveness of Global Cities

San Jose

Tbilisi

Lomé

Lisbon

Bamako

Port-au-Prince 0.403 319

Budapest

Amsterdam

Djibouti

Copenhagen

Riyadh

Prague

Libreville

Kathmandu

Costa Rica

Georgia

Togo

Portugal

Mali

Haiti

Hungary

Netherlands

Djibouti

Denmark

Saudi Arabia

Czech Republic

Gabon

Nepal

0.371 394

0.372 391

0.377 385

0.383 375

0.385 368

0.386 364

0.388 360

0.394 341

0.407 316

0.409 308

0.411 305

0.423 283

0.426 277

0.429 269

0.460 261

0.435 245

0.442 230

Paris

Poland

Bogota

Warsaw

Sudan

0.447 220

0.451 208

Colombia

Khartoum

Uzbekistan

France

Addis Ababa

Tashkent

Ethiopia

Table 5.18 (continued)

Ukraine

Sweden

New Zealand

Thailand

Panama

Lebanon

Cameroon

Nicaragua

Bulgaria

Paraguay

Russia

India

US

Myanmar

Finland

Moldova

Palestine

Zambia

Dominican Republic

China

0.307 549

Kiev

Stockholm

Auckland

Bangkok

Panama City

Beirut

Yaoundé

Managua

Sofia

Asunción

Moscow

Mumbai

New York

Yangon

Helsinki

Chi¸sin˘au

Gaza

Lusaka

0.242 707

0.250 691

0.257 681

0.260 672

0.260 671

0.265 664

0.266 661

0.271 643

0.280 618

0.282 607

0.284 603

0.285 601

0.287 599

0.288 596

0.288 593

0.290 588

0.301 567

0.303 561

Santo Domingo 0.305 554

Shanghai

Puerto Rico

Eritrea

Peru

Jamaica

Mexico

Bangladesh

Syria

Malaysia

Somalia

Angola

Kenya

Liberia

Nigeria

Tanzania

Kyrgyzstan

Argentina

Libya

Madagascar

Mongolia

El Salvador

0.129 891

0.138 879

0.147 872

0.151 866

0.158 857

0.159 856

San Juan

Asmara

Lima

Kingston

Mexico City

Dhaka

Aleppo

Kuala Lumpur

Mogadishu

Luanda

Nairobi

Monrovia

Lagos

(continued)

0.097 948

0.101 942

0.102 938

0.103 937

0.109 929

0.109 928

0.110 925

0.112 918

0.115 915

0.116 913

0.125 899

0.126 896

0.128 893

Dar es Salaam 0.128 892

Bishkek

Buenos Aires

Tripoli

Antananarivo

Ulaanbaatar

San Salvador

5.4 Social Inclusion Index Analysis: Europe and East Asia Lead in Social … 307

0.361 415

0.357 425

Nouakchott

Berlin

Kinshasa

Mauritania

Germany

Congo

Turkmenistan Ashgabat

UK

Greece

Chile

Ireland

Kazakhstan

Turkey

Istanbul

London

Athens

San Diego

Dublin

Almaty

0.236 717

0.225 740

0.225 739

0.228 734

0.228 732

0.236 718

Source Database of the City and Competitiveness Research Center, Chinese Academy of Social Sciences

0.362 412

0.365 404

0.366 402

Toronto

Canada

0.368 398

Zagreb

Croatia

Table 5.18 (continued) Chad

Ndjamena

0.092 957

Johannesburg 0.042 992

South Africa

0.058 978

0.064 975

0.066 973

0.083 961

Bujumbura

Kabul

Sana’a

São Paulo

Burundi

Afghanistan

Yemen

Brazil

308 5 Analysis on Sustainable Competitiveness of Global Cities

5.5 Analysis of the Science and Technology Innovation Index: The Strength …

309

5.5 Analysis of the Science and Technology Innovation Index: The Strength of the Emerging Cities in Developed and Emerging Economies is Dazzling 5.5.1 Overall Pattern: There is a Divergence in Innovation in Geographical and Political Terms Between Developed and Developing Countries From the intercontinental distribution of the top 100 global cities in the S&T index, the best performers are North America, Asia, and Europe 37, 30, and 29 cities, respectively, entered the top 100 globally, accounting for 28.03% of their respective cities 5.33% and 22.83%. Geographically, the number of cities entering the top 100 cities is concentrated in the northern hemisphere. In contrast, the southern hemisphere is far behind, especially in Africa and South America. Although Oceania has the highest proportion of the world’s top 100 cities, the sample cities are few, and four cities have entered the top 100, ranking relatively low. In summary, in terms of science and technology, the geographical gap between the North and the South is wide. Among them, Asian cities have entered the top 100 cities, but they are far behind Europe and North America in proportion. Most of the high-ranking and innovative cities in Asia come from Japan and China. Although China has more innovative cities, it cannot change the situation in which developed countries have an absolute advantage. The difference between the North and the South in the political sense is actually more obvious (Fig. 5.19 and Table 5.19). 80 60 40 20

-180

-140

-100

-60

0 -20

20

-20 -40 -60 Fig. 5.19 Global distribution of technological innovation

60

100

140

180

310

5 Analysis on Sustainable Competitiveness of Global Cities

Table 5.19 Technological innovation scores and percentage of top 100 cities by continent Region

Sample

Number and Mean percentage of cities entering the top 100

Coefficient of variation

Highest ranking city

Index

World ranking

Asia

563

30(5.33%)

0.321

0.647

Tokyo

1.000

1

Europe

127

29(22.83%)

0.538

0.360

Paris

0.921

4

North America

132

37(28.03%)

0.539

0.431

New York

0.912

6

South America

74

0(0.00%)

0.330

0.491

Rio de Janeiro 0.660

159

7

4(57.14%)

0.668

0.190

Melbourne

0.768

52

0(0.00%)

0.195

0.732

Johannesburg

0.632

179

100

0.367

0.625

Tokyo

1.000

1

Oceania Africa

104

World average

1007

Source Database of the City and Competitiveness Research Center, Chinese Academy of Social Sciences

5.5.2 Global Top 20: Emerging Cities Global technological innovation activity is highly concentrated in a few cities. The technology innovation index consists of two components: patent filing and scientific publication shares. According to estimates, in the 2017 global science and technology innovation index rankings, Tokyo, Beijing, and Seoul ranked in the top three. Among the top 20 cities, North American cities occupy 9 seats, Asia has 7 seats, Europe has 4 seats, and South America, Oceania, and Africa have no cities to enter the world’s top 20; from the national level, the US has the top 20 cities. The top 20 cities in North America that have entered the top of technological innovation are all in the US and are ranked high. The focus of global technological innovation is still in North America, and American cities such as New York, Washington, and Boston are the centers of global technological innovation. With the rise of China, Beijing, Shenzhen, and Shanghai have become the center of science and technology innovation in East Asia with Tokyo, Osaka, and Seoul, Korea, and one of the regions with the most active economic growth in the world. Europe still maintains a world leading position in technological innovation, and the research strength in London and Paris remains strong (Table 5.20). The global urban science and technology innovation index averaged 0.367, with a median of 0.326, and the number of cities below the mean reached 503, exceeding the sample city’s 49.9%. The coefficient of variation is a statistic that measures the degree of variation of each observation in the sample data. The coefficient of variation of the global science and technology innovation index is 0.625, and the standard deviation is 0.229. The global urban science and technology innovation index has little difference, and the degree of dispersion is low.

China

US

US

UK

US

Japan

Asia

North America

North America

Europe

North America

Asia

Osaka

Washington, D.C

London

Houston

New York

Shenzhen

Paris

Seoul

Beijing

Tokyo

City

0.880

0.898

0.904

0.909

0.912

0.920

0.920

0.935

0.940

1.000

Index

10

9

8

7

6

5

4

3

2

1

Ranking

North America

North America

Asia

North America

North America

North America

North America

Asia

Europe

North America

Region

US

US

Singapore

US

US

US

US

China

Switzerland

US

Country

Source Database of the City and Competitiveness Research Center, Chinese Academy of Social Sciences

South Korea

France

China

Asia

Europe

Japan

Asia

Asia

Country

Region

Table 5.20 Global top 20 cities by technological innovation score

0.821 0.815

Chicago

0.834

0.834

0.843

0.847

0.854

0.872

0.873

0.880

Index

Minneapolis

Singapore

Stuttgart

Columbia, South Carolina

San Jose

Boston

Shanghai

Stockholm

San Diego

City

20

19

18

17

16

15

14

13

12

11

Ranking

5.5 Analysis of the Science and Technology Innovation Index: The Strength … 311

312

5 Analysis on Sustainable Competitiveness of Global Cities

Technological innovaƟon index

Europe and America Others

Technological innovaƟon Fig. 5.20 KDE of technological innovation

From the density distribution map, we can observe the distribution law of global science and technology innovation index: The distribution peak of other countries in the world shifts to the left, and the distribution peaks of Europe and the US and other countries in the world, with peaks of 0.2 and 0.7, respectively. The distribution difference is large, indicating that the level of technological innovation in the old developed cities is much higher than other cities (Fig. 5.20).

5.5.3 Comparison Between Countries: China and US Comparing the data of China-US Science and Technology Innovation Index, it is not difficult to find that the US is about twice as large as China in terms of the number of the top 100 and the overall average. For example, the average value of China’s science and technology innovation index is 0.361, while the US science and technology innovation index is 0.664. In contrast, China and the US have large differences, and China is in a weak position in terms of technological innovation. Corresponding coefficient of variation, China’s coefficient of variation is 0.542, the coefficient of variation in the US is 0.243, and the coefficient of variation is twice as large as that of the US. It reflects China’s highly concentrated development model in science and technology innovation, and its ability to innovate in science and technology. The difference between cities is large.

5.5 Analysis of the Science and Technology Innovation Index: The Strength …

313

By analyzing the BRICS and G7 represented by China and the US, we analyze the national pattern of the STI index. Through the mean and coefficient of variation of Table 5.21, it can be seen that the overall innovation strength of emerging countries is obviously weaker than that of the G7. On the whole, the average value of the BRICS technology innovation index is relatively low, and the differences between cities are also relatively large. The coefficient is high; the overall average value of the G7 is around 0.6, the coefficient of variation is very low, the technological innovation capability among cities is relatively balanced, and the state is very strong. Combined with the urban distribution map of the scientific and technological innovation index (Fig. 5.19), it can be seen that most European cities are in Western Europe, and the geographical distribution of science and technology innovation cities is relatively concentrated; the US science and technology innovation cities are mainly concentrated in the northeastern region and the western coast, the central. The region is relatively weak; the distribution of China’s science and technology innovation cities reflects the accumulation of eastern coastal areas, among which the top 100 cities have the highest number and proportion in the BRICS countries, but the central and western regions are relatively weak; Russia is mainly concentrated in Europe and Asia. The vast area is almost blank; India, Brazil, and South Africa are all weak. Table 5.21 Technological scores of BRICS and G7

BRICS

Country

Number of cities

Number and percentage of cities entering the global top 100

Mean

Coefficient of variation

China

292

15(5.14%)

0.361

0.542

Russia

33

1(3.03%)

0.344

0.361

India

100

3(3.00%)

0.248

1.680

Brazil

32

0(0.00%)

0.353

0.434

6

0(0.00%)

0.488

0.337

12

4(33.33%)

0.689

0.110

9

3(33.33%)

0.641

0.213

US

75

31(41.33%)

0.664

0.243

Germany

13

7(53.85%)

0.703

0.142

Italy

13

1(7.69%)

0.601

0.154

Japan

10

6(60.00%)

0.755

0.144

9

5(55.56%)

0.709

0.107

South Africa G7

UK France

Canada

Source Database of the City and Competitiveness Research Center, Chinese Academy of Social Sciences

314

5 Analysis on Sustainable Competitiveness of Global Cities

5.5.4 Comparison Between Urban Clusters: Goals and Structures Determine Innovation Capacity of Urban Clusters From the perspective of important urban agglomerations, the strength of the US and British urban agglomerations is prominent. The average value of the urban science and technology innovation index is basically above 0.5, the level is uniform, and the strength is still strong. Although the urban agglomerations of developing countries such as China and India are large in scale, technological innovation is concentrated only in central cities. Most cities have delayed technological innovation and have large coefficient of variation. The cities within the two major urban agglomerations of the US have balanced development and high average value. The central cities of Chicago and New York are leading the world in technological innovation, and other cities’ technological innovation scores are not low either. The China and India urban agglomeration science and technology innovation index has a clear single-core model. The China and India urban agglomeration science and technology innovation index has a clear single-core model, the central city of the urban agglomeration is prominent, ranking no less than the urban agglomeration cities of developed countries, but the other cities within the group have a very large gap and the overall innovation capability is insufficient (Table 5.22). Developing countries have shown that the average value of technological innovation within urban agglomerations is higher than the national average. Among them, China is particularly prominent. In recent years, China’s technological innovation capability has been continuously improved, especially in the Yangtze River Delta and the Pearl River Delta. Its scientific and technological development of economic development is better, and the level of technological innovation within the urban agglomeration is far superior to the domestic average, which is close to the average level of some developed countries. However, the influence of urban agglomerations in developed countries on urban R&D capabilities is uncertain. Generally speaking, urban agglomerations including central cities, such as urban agglomerations in the US and London urban agglomerations in the UK, are higher than average in urban clusters. The urban agglomerations that are the core of the industry are developed from the perspective of division of labor, lower than the domestic average, such as the Ruhr urban agglomeration in Germany (Table 5.23).

5.5.5 World City Network In the word city network, developed countries still have unique advantages. Most of their cities have strong technological innovation capabilities. Although emerging countries are constantly improving, they still have a long way to go (Table 5.24).

5.6 Global Connectivity: Geographical Location and Economic Position …

315

Table 5.22 Technology innovation scores of major urban clusters worldwide Urban cluster

Technology innovation score

Standard deviation

Coefficient of variation

Ranking

Highest ranking city

Yangtze River Delta

0.550

0.174

0.317

13

Shanghai

Midwestern Megalopolis, US

0.761

0.071

0.094

19

Minneapolis

Pearl River Delta

0.663

0.145

0.219

5

Shenzhen

London–Liverpool Corridor

0.734

0.092

0.126

8

London

Northeast Megalopolis, US

0.799

0.081

0.101

6

New York

Rhine-Ruhr Metropolitan Region

0.622

0.113

0.182

43

Hamburg

Netherlands–Belgian Metropolitan Region

0.660

0.127

0.193

51

Amsterdam

Mumbai Metropolitan Region

0.438

0.255

0.583

71

Mumbai

Seoul Capital Area

0.833

0.102

0.123

3

Northern California Megaregion

0.764

0.093

0.122

15

Seoul San Jose

Source Database of the City and Competitiveness Research Center, Chinese Academy of Social Sciences

5.6 Global Connectivity: Geographical Location and Economic Position Decide Global Connectivity 5.6.1 Overall Pattern: Most Highest Ranking Cities in Global Connectivity Are Located in Developed Countries In terms of global contacts, cities in developed countries still dominate global contacts and exchanges, but cities in emerging market countries represented by China have grown rapidly and have begun to lead the world and become an important part of global communication (Fig. 5.21). The gap in global linkages between cities, with the decline in the global linkage rankings, shows a trend of decelerating first and then slowing down. Specifically, the city’s global linkages fell from 1 to 100, and the city’s competitiveness index fell by 0.816. From 100 to 200, the index fell by 0.066. From 200 to 300, the index fell by 0.026. In the range of 0.026, from 300 to 400, the index fell by 0.012. From the range of 700–800, the index fell by 0. From 800 to 900, the index fell by 0.007, from 900 to 1000. Within the interval, the index fell by 0.024. The gap between the global potentials of cities with better global connections and between cities with poor global

316

5 Analysis on Sustainable Competitiveness of Global Cities

Table 5.23 Technological innovation scores of urban clusters and countries home to the urban clusters Country and urban cluster(s)

Average technological innovation score

Standard deviation

Coefficient of variation

China

0.361

0.196

0.542

Yangtze River Delta

0.550

0.174

0.317

Pearl River Delta

0.663

0.145

0.219

US

0.664

0.162

0.243

Midwestern Megalopolis, US

0.761

0.071

0.094

Northeast Megalopolis, US

0.799

0.081

0.101

Northern California Megaregion

0.764

0.093

0.122

Germany

0.703

0.100

0.142

Rhine-Ruhr Metropolitan Region

0.622

0.113

0.182

Netherlands

0.759

0.009

0.011

Netherlands–Belgian Metropolitan Region

0.660

0.127

0.193

Belgium

0.561

0.113

0.201

Netherlands–Belgian Metropolitan Region

0.660

0.127

0.193

South Korea

0.739

0.088

0.119

Seoul Capital Area

0.833

0.102

0.123

India

0.248

0.169

0.680

Mumbai Metropolitan 0.438 Region

0.255

0.583

UK

0.689

0.076

0.110

London–Liverpool Corridor

0.734

0.092

0.126

Source Database of the City and Competitiveness Research Center, Chinese Academy of Social Sciences

connections is relatively large, while the gap between global potentials of cities with moderate global connections is relatively small (Figs. 5.22 and 5.23). From the perspective of the global regional pattern, among the top 100 cities in the world, Asian cities and European cities occupy 32 seats and 31 seats, respectively. Compared with other continents, they occupy an absolute advantage in quantity, except for Europe and Asia. The Americas have also entered the top 100 cities in the world, with 23 seats. The highest regional average of urban connections is North America, which is 0.265. The average global contact level of cities in each state is between 0.21 and 0.26, with little overall difference. The highest coefficient of variation in global linkages in cities is North America, reaching 0.611, while the

City

Tokyo

New York

London

Stockholm

Shanghai

Singapore

Berlin

Madrid

Moscow

Istanbul

Toronto

Amsterdam

Helsinki

Dublin

Sydney

Vienna

Mumbai

San Jose

Copenhagen

Zürich

Country

Japan

US

UK

Sweden

China

Singapore

Germany

Spain

Russia

Turkey

Canada

Netherlands

Finland

Ireland

Australia

Austria

India

Costa Rica

Denmark

Switzerland

0.737

0.738

0.742

0.746

0.749

0.761

0.762

0.766

0.769

0.782

0.787

0.790

0.799

0.814

0.834

0.872

0.873

0.904

0.912

1.000

Index

78

76

75

71

65

59

57

53

51

46

39

37

30

21

18

13

12

8

6

1

Ranking

Armenia

Venezuela

Vietnam

Indonesia

0.463

0.484

0.494

0.517

0.517

0.518

0.518

0.524

0.527

Index

Montevideo

Beirut

Panama City

Casablanca

Belgrade

Yerevan

Caracas

Ho Chi Minh City

Jakarta

0.379

0.381

0.383

0.415

0.381

0.438

0.446

0.452

0.455

0.460

Tel Aviv-Jaffa 0.461

Tehran

Minsk

Monrovia

Riga

Prague

Bogota

Kiev

Zagreb

Cairo

City

South Korea Seoul

Uruguay

Lebanon

Panama

Morocco

Serbia

Israel

Iran

Belarus

Liberia

Latvia

Czech Republic

Colombia

Ukraine

Croatia

Egypt

Country

427

423

422

383

378

355

343

338

333

330

329

326

306

295

279

277

275

274

269

266

Ranking

Table 5.24 Global ranking of cities performing best in technological innovation in each country

Mozambique

Cote d’Ivoire

Gabon

Nicaragua

Ghana

Angola

Kyrgyzstan

Iraq

Paraguay

Nepal

Palestine

Algeria

Uganda

Honduras

Zambia

Pakistan

Guatemala

Cambodia

Cameroon

Burkina Faso

Country

Maputo

Abidjan

Libreville

Managua

Kumasi

Luanda

Bishkek

Baghdad

Asunción

Kathmandu

Gaza

Algiers

Kampala

Tegucigalpa

Lusaka

Karachi

Guatemala City

Phnom Penh

Yaoundé

Ouagadougou

City

Index

0.156

0.156

0.156

0.158

0.164

0.171

0.172

0.174

0.175

0.179

0.183

0.185

0.192

0.195

0.201

0.203

0.204

0.214

0.215

0.215

(continued)

791

791

789

784

774

763

760

754

752

741

733

727

712

706

695

692

687

664

661

660

Ranking

5.6 Global Connectivity: Geographical Location and Economic Position … 317

Warsaw

Kuala Lumpur

Athens

Budapest

Roman

Kingston

Santa Cruz

Asmara

Poland

Malaysia

Greece

Hungary

Italy

Jamaica

Bolivia

Eritrea

0.629

São Paulo

Paris

Bangkok

Buenos Aires

Brazil

France

Thailand

Argentina

0.602

San Diego

Lisbon

San Juan

Sofia

Chile

Portugal

Puerto Rico

Bulgaria

0.578

0.580

0.597

0.609

Saudi Arabia Riyadh

0.617

0.628

0.460

0.632

South Africa Johannesburg

0.634

0.636

0.639

0.673

0.676

0.686

0.690

0.711

0.721

Brussels

Belgium

0.730

0.727

Oslo

New Zealand Auckland

Norway

Table 5.24 (continued)

84

227

226

219

213

206

198

185

183

183

179

177

175

172

144

142

133

121

103

90

85

Nigeria

Madagascar

Ethiopia

Laos

Tanzania

Senegal

Sudan

Cuba

Bangladesh

Uzbekistan

Philippines

Romania

Dominican Republic

Jordan

Kazakhstan

Kenya

Oman

Georgia

Azerbaijan

Mexico

Tunis

Lagos

0.377

0.292

0.296

0.316

0.319

0.319

0.323

0.325

0.326

0.329

0.355

0.356

0.360

0.361

0.362

0.373

0.376

Antananarivo

Addis Ababa

Vientiane

0.230

0.241

0.248

Dar es Salaam 0.272

Dakar

Khartoum

Havana

Dhaka

Tashkent

Manila

Bucharest

Santo Domingo

Amman

Almaty

Nairobi

Muscat

Tbilisi

Baku

Mexico City

Tunis

428

634

620

609

577

551

546

523

515

513

510

506

505

499

460

457

453

452

451

433

429

Malawi

Djibouti

Mauritania

Central African Republic

Burundi

Zimbabwe

Syria

Yemen

Tajikistan

Haiti

Guinea

Sierra Leone

Niger

Togo

Benin

Myanmar

Rwanda

Afghanistan

Congo

Kuwait

Mali

Lilongwe

Djibouti

Nouakchott

Bangui

Bujumbura

Bulawayo

Aleppo

Sana’a

Dushanbe

Port-au-Prince

Conakry

Freetown

Niamey

Lomé

Cotonou

Yangon

Kigali

Kabul

Kinshasa

Kuwait City

Bamako

0.154

0.074

0.098

0.100

0.104

0.106

0.109

0.119

0.119

0.126

0.126

0.128

0.129

0.130

0.139

0.149

0.151

0.152

0.152

0.152

0.154

793

(continued)

949

914

909

899

892

887

869

868

855

854

850

848

843

826

805

800

799

797

796

794

318 5 Analysis on Sustainable Competitiveness of Global Cities

Abu Dhabi

Colombo

Doha

United Arab Emirates

Sri Lanka

Qatar

0.533

0.560

0.567

0.574

230

263

245

238

Moldova

El Salvador

Ecuador

Libya

San Salvador

Guayaquil

Tripoli

Chi¸sin˘au

0.215

0.217

0.221

0.228

658

654

647

638

Turkmenistan

Mongolia

Somalia

Chad

Source Database of the City and Competitiveness Research Center, Chinese Academy of Social Sciences

Lima

Peru

Table 5.24 (continued)

Ashgabat

Ulaanbaatar

Mogadishu

Ndjamena

0.027

0.035

0.055

0.060

994

989

971

965

5.6 Global Connectivity: Geographical Location and Economic Position … 319

320

5 Analysis on Sustainable Competitiveness of Global Cities

80 60 40 20

-180

-140

-100

-60

0 -20

20

60

100

140

180

901

1001

-20 -40 -60 Fig. 5.21 Global connectivity scores of cities worldwide

1.2 1 0.8 0.6 0.4 0.2 0 1

101

201

301

401

501

601

701

801

Fig. 5.22 Interval estimate of global connectivity of cities

coefficient of global linkage coefficient of cities in other regions is less than 0.25. It can be seen that in all regions of the world, the global linkages between cities in North America are the largest, because in other regions, North America has both high-connected cities and low-connected cities, which are not characteristic of other continents. Observing Table 5.25, it can be found that the global linkage mean of the continent cities is greater than the median, showing that the number of cities with global

5.6 Global Connectivity: Geographical Location and Economic Position …

321

0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0

Fig. 5.23 Disparities of global connectivity scores between cities

Table 5.25 Global connectivity of cities by continent Region

Sample Number Mean Standard Coefficient Highest and deviation of ranking city percentage variation of cities entering the global top 100

Index World ranking

Asia

563

32(5.68%)

0.066 0.082

1.238

Hong Kong

0.707

3

Europe

127

33(25.99%) 0.137 0.124

0.905

London

0.846

2

North America

132

22(16.67%) 0.134 0.119

0.888

New York

1.000

1

South America

74

5(6.76%)

0.725

Buenos Aires 0.326 35

Oceania

7

Africa

104

World average

1007

0.088 0.064

4(57.14%)

0.247 0.109

0.442

Sydney

5(4.81%)

0.078 0.124

0.718

Johannesburg 0.336 29

0.473 11

101

0.088 0.096

1.095

New York

1.000

1

Source Database of the City and Competitiveness Research Center, Chinese Academy of Social Sciences

connections above the regional average is less than the average. Observing Table 5.25, we can find that the average global connections of cities on all continents are greater than the median. It shows that, worldwide, the number of cities with global connections above the regional average is less than the number of cities below the average. North America has the highest level of global connections among cities in the world, and Asia, Europe, and Oceania have similar levels of global connections, while

322

5 Analysis on Sustainable Competitiveness of Global Cities

Global ConnecƟvity Index

Europe and America Others

Global ConnecƟvity Fig. 5.24 Global connectivity scores of cities worldwide

South America has the highest level of global connections with Africa, and South America has the highest global connections. The city is Buenos Aires, which ranks 25th in the world, and the world’s most connected city in Africa is Johannesburg, which ranks only 31 in the world. It can be seen that there are advanced cities in these two continents and advanced cities in other regions. gigantic difference. In the comparison of the global contact index between European and American cities and other countries in the world, the peak of the global contact index of European and American cities is located on the left side of other cities in the world, indicating that it is superior to other countries in global relations, but overall, the gap between European and American cities and other parts of the world is small, and the advantages of Europe and the US are not obvious. This is mainly because no matter which continent, there are not many core contact cities, and each continent must have a certain number of core contact cities. This is due to the inevitable result of the geographical distribution and administrative division of the global cities (Fig. 5.24).

5.6.2 Global Top 20: Global Centers in Both Geographical and Economic Terms As shown in Table 5.26, the world’s top ten cities in terms of global connectivity are: New York, London, Hong Kong, Beijing, Singapore, Shanghai, Tokyo, Paris,

UK

China

China

Singapore

China

Japan

France

Russia

US

North America

Europe

Asia

Asia

Asia

Asia

Asia

Europe

Europe

North America

City

Chicago

Moscow

Paris

Tokyo

Shanghai

Singapore

Beijing

Hong Kong

London

New York

Index

0.482

0.489

0.506

0.565

0.603

0.610

0.624

0.707

0.846

1.000

10

9

8

7

6

5

4

3

2

1

Ranking

Europe

Europe

Asia

North America

Europe

Asia

North America

Asia

Oceania

North America

Region

Country

Netherlands

Spain

South Korea

US

Germany

India

US

United Arab Emirates

Australia

Canada

Source Database of the City and Competitiveness Research Center, Chinese Academy of Social Sciences

Country

US

Region

Table 5.26 Global top 20 cities by global connectivity City

Amsterdam

Madrid

Seoul

San Francisco

Frankfurt

Mumbai

Los Angeles

Dubai

Sydney

Toronto

Index

0.395

0.411

0.414

0.414

0.426

0.435

0.440

0.447

0.473

0.473

Ranking

20

19

17

17

16

15

14

13

11

11

5.6 Global Connectivity: Geographical Location and Economic Position … 323

324

5 Analysis on Sustainable Competitiveness of Global Cities

Moscow, and Chicago. The top 20 cities are located in East Asia, the Middle East, Western Europe, North America, and Oceania. They are the economic and cultural centers of different continents and evenly distribution in the global political and economic system. Eight of these cities are Asian cities. Although there are only five cities in North America, New York is ranked first in global contacts, indicating that the US still has an important position in the world economy. Asian countries represented by China have continuously strengthened their global connections, showing a trend of catching up with developed countries in Europe and the US. In the meantime, there is still no African or South American city entering the top 20. Only five African and South American cities rank among the global top 100.

5.6.3 Comparison Between Countries: China and the US Lead the World in Global Connectivity The US and China account for the largest share of the global top 100 cities by global connectivity. Four US and three Chinese cities rank in the top 20. Data show that, except for China and the US, the number of cities in other members of BRICS and G7 that enter the global top 100 is small. Overall, the G7’s global contact index is significantly higher than the BRICS countries. The differences between the G7 countries in Germany, Italy, Japan, and Canada are small, and the global links between China and the BRICS cities are quite different. However, from the perspective of the national level of the whole country, China is the lowest-connected country even within the BRICS countries. This has a great relationship with China’s urban system and population. The division of labor between cities leads to the need for most cities. Proactively establish links with the global scale, and objectively create a “small city” with a high population number lacking the necessary links (Table 5.27).

5.6.4 Comparison Between Urban Clusters The global links between the US, China, and UK urban agglomerations are strong. From the perspective of global linkages, the top three urban agglomerations are from the US, the UK, and Germany, respectively, the northeastern US urban agglomeration, the London–Liverpool urban agglomeration, and the Northern California urban agglomeration. It is from the Pearl River Delta urban agglomerations of China and Brazil, the Yangtze River Delta urban agglomerations, and the Seoul national urban agglomerations. From the perspective of the number of cities in the world that are connected to the world, the number of cities in the US, China, and the UK is more than 100. The number of cities in which the national urban agglomeration enters the world is small. From the perspective of the coefficient of variation of

5.6 Global Connectivity: Geographical Location and Economic Position …

325

Table 5.27 Global connectivity scores of BRICS and G7 Country

Sample Number and percentage of cities entering the global top 100

Mean

Coefficient of variation

BRICS China

292

12(4.11%)

0.055

1.390

Russia

33

2(6.06%)

0.070

1.231

India

100

3(3.00%)

0.058

1.052

Brazil

32

1(3.13%)

0.083

0.572

6

2(33.33%)

0.141

0.700

12

3(25.00%)

0.190

1.089

9

1(11.11%)

0.136

1.016

US

75

14(18.67%)

0.150

0.931

Germany

13

5(38.46%)

0.163

0.644

Italy

13

5(38.46%)

0.160

0.566

Japan

10

3(30.00%)

0.192

0.692

9

5(55.56%)

0.206

0.511

South Africa G7

UK France

Canada

Source Database of the City and Competitiveness Research Center, Chinese Academy of Social Sciences

global linkages, the global linkages between cities within the urban agglomerations of developing countries such as China vary widely, with China’s Yangtze River Delta urban agglomeration having the largest coefficient of variation. The global linkages within the urban agglomerations of Germany, Europe, America, South Korea, and other developed countries are relatively small, among which the Dutch–Belgian urban agglomeration and the Rhine-Ruhr urban agglomeration have the smallest coefficient of variation (Table 5.28). The global linkage index within the urban agglomerations of developing countries is relatively high, but the national average is low, in stark contrast. The global linkage index of developed urban agglomerations is also significantly higher than the domestic average, but the gap is smaller compared to developing countries, indicating that the urban agglomeration plays a positive role in strengthening the global connection of the cities within the group and has played a clustering advantage (Table 5.29).

5.6.5 World City Network In terms of global connectivity, intra-city population movements and population movements across the world provide a better basis for strengthening global ties. Not only that, but sufficient labor can promote urban development and improve the level of urban access to international information (Table 5.30).

326

5 Analysis on Sustainable Competitiveness of Global Cities

Table 5.28 Global connectivity scores of major urban clusters worldwide Urban cluster

Global connectivity score

Standard deviation

Coefficient of variation

Ranking

Highest ranking city

Yangtze River Delta

0.079

0.112

1.423

6

Shanghai

Midwestern Megalopolis, US

0.158

0.118

0.748

10

Chicago

Pearl River Delta

0.122

0.111

0.904

29

Guangzhou

London–Liverpool Corridor

0.325

0.264

0.813

2

London

Northeast Megalopolis, US

0.302

0.286

0.948

1

New York

Rhine-Ruhr Metropolitan Region

0.190

0.049

0.256

67

Hamburg

Netherlands–Belgian Metropolitan Region

0.220

0.109

0.494

20

Amsterdam

Mumbai Metropolitan Region

0.134

0.179

1.338

15

Mumbai

Seoul Capital Area

0.248

0.165

0.667

17 (joint)

Northern California Megaregion

0.256

0.126

0.490

17

Seoul San Francisco

Source Database of the City and Competitiveness Research Center, Chinese Academy of Social Sciences

5.7 Analysis of Human Capital Potential Index: Talent Flow Direction Determines the Potential Pattern of Human Capital in Global Cities 5.7.1 Overall Pattern: Developed Countries Lead in Human Capital, and Immigration Policy Decides Human Capital Potential From a global perspective, the global human capital potential index presents two levels of geographical distribution and quantity differentiation. The cities with high human capital potential are mainly located in Europe and the US. Although Asian cities are trending later, due to the overall quality of urban development, and there are very few cities with good human capital potential, the overall ranking is lower than North America (Table 5.31). According to the top 20 cities in the world rankings, the ratio of human capital potential index to continents is ranked. Among the top 20, South America, Africa, and Oceania account for 0%, North America accounts for 56%, and Asia ranks second. At 25%, it is not difficult to find through the characteristics of the country and the city that the top 20 cities are generally in countries and cities where immigrants

5.7 Analysis of Human Capital Potential Index: Talent Flow Direction …

327

Table 5.29 Technological innovation scores of urban clusters and countries home to the urban clusters Country and urban cluster(s)

Global connectivity score

Standard deviation

Coefficient of variation

China

0.055

0.076

1.390

Yangtze River Delta

0.079

0.112

1.423

Pearl River Delta

0.122

0.111

0.904

US

0.150

0.140

0.931

Midwestern Megalopolis, US

0.158

0.118

0.748

Northeast Megalopolis, US

0.302

0.286

0.948

Northern California Megaregion

0.256

0.126

0.490

Germany

0.163

0.105

0.644

Rhine-Ruhr Metropolitan Region

0.190

0.049

0.256

Netherlands

0.224

0.121

0.542

Netherlands–Belgian Metropolitan Region

0.220

0.109

0.494

Belgium

0.217

0.095

0.437

Netherlands–Belgian Metropolitan Region

0.220

0.109

0.494

South Korea

0.117

0.114

0.973

Seoul Capital Area

0.248

0.165

0.667

India

0.058

0.061

1.052

Mumbai Metropolitan Region

0.134

0.179

1.338

UK

0.190

0.207

1.089

London–Liverpool Corridor

0.325

0.264

0.813

Source Database of the City and Competitiveness Research Center, Chinese Academy of Social Sciences

(mobile populations) flow in. The potential of urban human capital depends largely on the ability to attract external human resources. Overall, Europe and the US have a greater advantage in capturing top-level human capital resources, and Asian cities are struggling to catch up (Fig. 5.25). Comparing the distribution of human capital index density between cities in Europe and the US and other countries in the world, we can see from the figure that the urban human capital peaks of European and American cities and the rest of the world are located on the left side of 0.198, and the peak-to-peak difference is

Index

Shanghai

Tokyo

Paris

Moscow

Sydney

Toronto

Mumbai

Seoul

Madrid

Amsterdam

Kuala Lumpur 0.357

Brussels

Jakarta

Bangkok

China

Japan

France

Russia

Australia

Canada

India

South Korea

Spain

Netherlands

Malaysia

Belgium

Indonesia

Thailand

0.846

0.329

Mexico City

Buenos Aires

Mexico

Argentina

0.326

0.336

South Africa Johannesburg

0.340

0.345

0.350

0.395

0.411

0.914

0.435

0.473

0.473

0.489

0.900

0.565

0.603

0.610

London

Singapore

1.000

Singapore

New York

US

UK

City

Country

35

32

29

28

27

25

24

20

19

17

15

11

11

9

8

7

6

5

2

1

Ranking

Croatia

Nigeria

Belarus

Brazil

Paraguay

Panama

Bulgaria

Latvia

Cameroon

Kenya

Jordan

Morocco

Saudi Arabia

Norway

Pakistan

Finland

Ukraine

Peru

Romania

Israel

Country

Zagreb

Lagos

Minsk

São Paulo

Asunción

Panama City

Sofia

Riga

Yaoundé

Nairobi

Amman

Casablanca

Riyadh

Oslo

Karachi

Helsinki

Kiev

Lima

Bucharest

Tel Aviv-Jaffa

City

0.128

0.130

0.137

0.137

0.137

0.142

0.144

0.147

0.147

0.154

0.158

0.165

0.168

0.170

0.170

0.184

0.189

0.199

0.199

0.206

Index

Table 5.30 Global ranking of cities performing best in global connectivity in each country

172

169

161

161

161

156

154

148

148

137

128

118

116

110

110

98

95

89

89

86

Ranking

Honduras

Nepal

Mongolia

Cambodia

Sierra Leone

Georgia

Sudan

Mozambique

Eritrea

Tanzania

Somalia

Sri Lanka

Chad

Madagascar

Ghana

Bangladesh

Serbia

Nicaragua

Burkina Faso

Central African Republic

Country

Tegucigalpa

Kathmandu

Ulaanbaatar

Phnom Penh

Freetown

Tbilisi

Khartoum

Maputo

Asmara

Dar es Salaam

Mogadishu

Colombo

Ndjamena

Antananarivo

Kumasi

Dhaka

Belgrade

Managua

Ouagadougou

Bangui

City

0.057

0.059

0.059

0.059

0.061

0.061

0.064

0.064

0.064

0.073

0.073

0.073

0.076

0.076

0.078

0.080

0.083

0.083

0.083

0.085

Index

(continued)

529

511

511

511

504

504

486

486

486

434

434

434

420

420

407

384

366

366

366

350

Ranking

328 5 Analysis on Sustainable Competitiveness of Global Cities

Abu Dhabi

United Arab Emirates

0.255

Cairo

Roman

Tripoli

Lisbon

Athens

Ho Chi Minh City

Copenhagen

Prague

Egypt

Italy

Libya

Portugal

Greece

Vietnam

Denmark

Czech Republic

0.243

0.243

0.246

0.246

0.246

0.248

0.253

0.279

Chile

0.279

0.272

Philippines

0.281

0.291

San Diego

Manila

Ireland

0.291

0.298

0.310

0.312

0.314

0.317

0.322

New Zealand Auckland

Havana

Dublin

Cuba

Vienna

Stockholm

Sweden

Bogota

Warsaw

Poland

Colombia

Istanbul

Turkey

Austria

Zürich

Switzerland

Table 5.30 (continued)

67

67

63

63

63

60

58

57

53

50

50

49

45

45

44

42

40

39

37

36

Addis Ababa Santa Cruz

Bamako

Port-au-Prince

Monrovia

Niamey

Guayaquil

Baku

Caracas

Dushanbe

Beirut

Guatemala City

Yangon

Kuwait City

Santo Domingo

Tunis

Mauritania

Costa Rica Nouakchott

San Jose

Puerto Rico San Juan

Mali

Haiti

Liberia

Niger

Ecuador

Azerbaijan

Venezuela

Tajikistan

Lebanon

Guatemala

Myanmar

Kuwait

Dominican Republic

Tunis

Bolivia

El Salvador San Salvador

Ethiopia

0.087

0.087

0.087

0.090

0.090

0.092

0.095

0.097

0.097

0.102

0.102

0.102

0.104

0.106

0.106

0.113

0.116

0.116

0.118

0.123

184

328

328

328

314

314

296

287

271

271

253

253

253

247

239

239

213

203

203

196

Togo

Rwanda

Guinea

Oman

Palestine

Yemen

Burundi

Zambia

Gabon

Iraq

Djibouti

Afghanistan

Moldova

Jamaica

Kyrgyzstan

Malawi

Uganda

Angola

Iran

Laos

Lomé

Kigali

Conakry

Muscat

Gaza

Sana’a

Bujumbura

Lusaka

Libreville

Baghdad

Djibouti

Kabul

Chi¸sin˘au

Kingston

Bishkek

Lilongwe

Kampala

Luanda

Tehran

Vientiane

0.005

0.009

0.012

0.012

0.017

0.019

0.021

0.026

0.026

0.031

0.033

0.035

0.043

0.045

0.045

0.047

0.050

0.050

0.054

0.057

(continued)

970

956

951

951

931

922

914

876

876

695

680

628

593

581

581

575

564

564

541

529

5.7 Analysis of Human Capital Potential Index: Talent Flow Direction … 329

Budapest

Doha

Almaty

Algiers

Hungary

Qatar

Kazakhstan

Algeria

0.239

0.210

0.213

0.215

0.217

71

82

80

78

77

Uzbekistan

Uruguay

Cote d’Ivoire

Zimbabwe

Armenia

Yerevan

Tashkent

Montevideo

Abidjan

Bulawayo

0.087

0.085

0.085

0.085

0.085

328

350

350

350

350

Benin

Syria

Turkmenistan

Senegal

Congo

Source Database of the City and Competitiveness Research Center, Chinese Academy of Social Sciences

Berlin

Germany

Table 5.30 (continued) Cotonou

Aleppo

Ashgabat

Dakar

Kinshasa

0.000

0.000

0.000

0.000

0.000

979

979

979

979

979

330 5 Analysis on Sustainable Competitiveness of Global Cities

5.7 Analysis of Human Capital Potential Index: Talent Flow Direction …

331

Table 5.31 Global human capital distribution by continent Region

Sample

Number and Mean percentage of cities entering the global top 100

Coefficient of variation

Highest ranking city

Index

World ranking

Asia

563

24(4.26%)

0.178

0.399

Tokyo

0.925

2

Europe

127

27(21.26%)

0.224

0.481

London

0.736

4

North America

132

38(28.79%)

0.280

0.609

New York

1

1

South America

74

4(5.41%)

0.196

0.316

São Paulo

0.516

7

6(85.71%)

0.377

0.327

Sydney

0.526

23

1(0.96%)

0.156

0.227

Cape Town

0.303

96

100

0.198

0.511

New York

1

Oceania Africa

104

World average

1007

25

1

Source Database of the City and Competitiveness Research Center, Chinese Academy of Social Sciences

80 60 40 20

-180

-140

-100

-60

0 -20

20

60

100

140

180

-20 -40 -60 Fig. 5.25 Global distribution of human capital

small. It can be seen that in the global human capital market, the gap between European and American countries and Asian, African, and Latin American countries is shrinking (Fig. 5.26).

332

5 Analysis on Sustainable Competitiveness of Global Cities

Human Capital Index

Europe and America Others

Human Capital Fig. 5.26 KDE of human capital

As the ranking of urban human capital potential declines, the index has been declining. At the same time, the gap in human capital potential between cities has shown a decline and a rising trend with the decline in the ranking of human capital potential. Specifically, the city’s human capital potential ranking dropped from 1 to 100, the city competitiveness index dropped by 0.704, from 100 to 200, the index dropped by 0.070, and from 200 to 300, the index dropped by 0.704. Decrease by 0.028, from 700 to 800, the index fell by 0.008, from 800 to 900, the index fell by 0.013, from 900 to 1000, the index fell by 0.004, which shows that human capital The gap in human capital potential index between cities with good potential and cities with poor human capital potential is relatively large. Among them, the top 100 cities have a more obvious human capital potential index. The human capital potential index of cities with medium human capital potential is more obvious. The gap is relatively small (Figs. 5.27 and 5.28).

5.7.2 Global Top 20 Cities: Concentrated in the US The top ten cities in terms of global urban human capital potential are: New York, Tokyo, Los Angeles, London, Boston, Chicago, Philadelphia, Seattle, San Jose, and Toronto. Among them, the top 20 cities are all located in 14 in North America, and all 13 cities are from the US. This shows that in the performance of human capital in

5.7 Analysis of Human Capital Potential Index: Talent Flow Direction …

333

1.2 1 0.8 0.6 0.4 0.2 0 1

101

201

301

401

501

601

701

801

901

1001

Fig. 5.27 Global distribution of human capital 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0

Fig. 5.28 Global distribution of human capital

global cities, the competitiveness of American cities is obvious, and the performance of urban human capital in other regions is better. There was some improvement in the previous year. With the rapid development of Asian cities, this leading advantage of American cities in the future may be further weakened; especially with the continuous development of Chinese cities, their human capital potential will be further enhanced. However, the human capital potential is a comprehensive index composed of the university index and the proportion of the young people aged 20–29. Due to the long-term existence of the aging population, it is very difficult to challenge the leading position of the US cities in the top cities (Table 5.32).

Philadelphia

Seattle

San Jose

Toronto

North America

North America

North America

North America

Country

Canada

US

US

US

US

US

UK

US

Japan

US

Index

0.639

0.650

0.673

0.698

0.700

0.717

0.736

0.831

0.925

1.000

10

9

8

7

6

5

4

3

2

1

Ranking

Asia

North America

Asia

Asia

North America

North America

Asia

North America

North America

North America

Continent

City

Hong Kong

Austin

Seoul

Singapore

New Haven

Atlanta

Beijing

San Francisco

Baltimore

San Diego

Source Database of the City and Competitiveness Research Center, Chinese Academy of Social Sciences

Boston

Chicago

North America

London

Europe

Europe

Tokyo

Los Angeles

North America

North America

Asia

City

New York

Continent

Table 5.32 Global top 20 cities by human capital Country

China

US

South Korea

Singapore

US

US

China

US

US

US

Index

0.561

0.568

0.572

0.573

0.577

0.580

0.587

0.590

0.592

0.609

Ranking

20

19

18

17

16

15

14

13

12

11

334 5 Analysis on Sustainable Competitiveness of Global Cities

5.7 Analysis of Human Capital Potential Index: Talent Flow Direction …

335

5.7.3 Comparison Between Countries: American Cities Dominate the Human Capital Ranking, and Central European Cities Have Seen a Rise in Human Capital Scores From the perspective of the global pattern, in the top 100 cities with global human capital potential, the US cities occupy 29 seats alone. Compared with other continents, they have an absolute advantage in terms of quantity. In addition to the US, China has entered the human capital potential of global cities. There are also more than one hundred cities, occupying 16 seats. However, from the perspective of data observations, the differences between China and the US are large. First of all, the difference between China and the US in the overall human capital potential index is very obvious, the US average is nearly twice as large as China, and the difference in the maximum is more significant. The number one city in the US is the world’s first. And China’s maximum is only ranked 14th in the world. Secondly, in terms of the coefficient of variation, China’s coefficient of variation is relatively high, close to 1. The US is small compared to China, but the overall value is slightly higher. China’s urban human capital development is not stable enough, and there are large differences between cities. The overall quality needs to be improved (Table 5.33). It can be seen from the average of Table 5.34 that except China and the US, the UK and Canada constitute the second group, and the BRICS and other countries of the G7 have entered the global list of the top 100 human capital, except for China. In addition, the human capital potential of other countries benefits from the entry of immigrants, especially in the US. Overall, the average value of the BRICS Human Capital Index differs greatly from that of the G7, and the difference between cities is smaller than that of the G7. Combined with the urban distribution map of human capital index in Fig. 5.25, it can be seen that the cities with better human capital in the US are mainly concentrated in the northeastern region and the western coast, and the central region is relatively weak: the distribution of cities with better human capital in China. In the eastern coastal areas, the central and western regions are relatively weak; Russia, France, South Africa, and other countries are relatively weak overall, and fewer cities have entered the global economic vitality. Table 5.33 Global distribution of human capital by continent Country

Mean

Coefficient of variation

Highest ranking city

Ranking

Index

China

0.173

0.958

Beijing

14

0.587

US

0.328

0.769

New York

1

1.000

Source Database of the City and Competitiveness Research Center, Chinese Academy of Social Sciences

336

5 Analysis on Sustainable Competitiveness of Global Cities

Table 5.34 Human capital scores of BRICS and G7 Country

Sample Number and percentage of cities entering the global top 100

Mean

Coefficient of variation

BRICS China

292

16(5.47%)

0.173

0.408

Russia

33

1(3.03%)

0.151

0.357

India

100

0(0.00%)

0.170

0.141

Brazil

32

2(6.25%)

0.203

0.360

6

1(16.67%)

0.243

0.200

12

7(58.33%)

0.334

0.443

9

1(11.11%)

0.223

0.388

US

74

29(39.19%)

0.319

0.583

Germany

13

3(23.07%)

0.257

0.326

Italy

13

3(23.07%)

0.230

0.315

Japan

10

2(20.00%)

0.289

0.831

9

7(77.78%)

0.412

0.315

South Africa G7

UK France

Canada

Source Database of the City and Competitiveness Research Center, Chinese Academy of Social Sciences

5.7.4 Comparison Between Urban Clusters: Urban Clusters Increase the Advantage of Leaders From the perspective of the average human capital, the top three urban agglomerations are from the US and the UK, namely the northeastern US urban agglomeration, the London–Liverpool urban cluster, and the midwestern urban agglomeration. The three lower-ranked urban agglomerations are the Yangtze River Delta urban agglomeration, the Mumbai urban agglomeration, and the Seoul national urban agglomeration. From the perspective of the number of human capital entering the world’s 100 cities, the number of cities in the US and China entering the world is more than 100. The urban agglomerations of other countries have entered the world’s 100 cities. The number is small. Except for the Rhine-Ruhr urban cluster, the coefficient of variation of human capital between cities within the urban clusters of developing countries is small, whereas but the coefficient of variation of Chinese urban clusters is larger (Table 5.35). In the comparison of the human capital index of the major urban agglomerations in the world and the national human capital index, the sample changes in China are more obvious. The overall trend of the world is that the average human capital index within the urban agglomeration is higher than the national average, while the average value of the human capital index of the Chinese urban agglomeration is nearly four times the national average. The difference is obvious, which also reflects the uneven distribution of human resources in China (Table 5.36).

5.7 Analysis of Human Capital Potential Index: Talent Flow Direction …

337

Table 5.35 Human capital scores of major urban clusters worldwide Urban cluster

Human capital score

Standard deviation

Coefficient of variation

Ranking

Highest ranking city

Yangtze River Delta

0.212

0.099

0.468

32

Shanghai

Midwestern Megalopolis, US

0.357

0.159

0.447

6

Chicago

Pearl River Delta

0.232

0.063

0.270

66

London–Liverpool Corridor

0.421

0.170

0.403

4

London

Northeast Megalopolis, 0.580 US

0.232

0.400

1

New York

Rhine-Ruhr Metropolitan Region

0.263

0.062

0.235

72

Hamburg

Netherlands–Belgian Metropolitan Region

0.259

0.117

0.452

29

Amsterdam

Mumbai metropolitan Region

0.201

0.037

0.186

146

Seoul Capital Area

0.381

0.191

0.500

18

Northern California Megaregion

0.484

0.194

0.401

9

Guangzhou

Mumbai Seoul San Jose

Source Database of the City and Competitiveness Research Center, Chinese Academy of Social Sciences

According to the city ranking, the sample cities are divided into four echelons: A, B, C, and D. As can be seen from Fig. 5.28, the mean and coefficient of variance of human capital scores decrease with the echelon, indicating the higher the echelon, the wider the divergence between cities in the echelon will be (Fig. 5.29).

5.7.5 World City Network On the whole, it is not that the larger the urban population, the better the human capital conditions, and the development of human capital is restricted by the development conditions of the country. Developed countries have a high level of education and good overall quality, so they have an advantage in human capital; developing countries have a large population and uneven distribution of educational resources. Most of their manpower stays in simple labor jobs, and they are at a disadvantage in human capital. (Table 5.37).

338

5 Analysis on Sustainable Competitiveness of Global Cities

Table 5.36 Technological innovation scores of urban clusters and countries home to the urban clusters Country and urban cluster(s)

Average human capital score

Standard deviation

Coefficient of variation

China

0.173

0.071

0.408

Yangtze River Delta

0.212

0.099

0.468

Pearl River Delta

0.232

0.063

0.270

US

0.328

0.191

0.583

Midwestern Megalopolis, US

0.357

0.159

0.447

Northeast Megalopolis, US

0.580

0.232

0.400

Northern California Megaregion

0.484

0.194

0.401

Germany

0.257

0.084

0.326

Rhine-Ruhr Metropolitan Region

0.263

0.062

0.235

Netherlands

0.297

0.152

0.510

Netherlands–Belgian Metropolitan Region

0.259

0.117

0.452

Belgium

0.220

0.036

0.163

Netherlands–Belgian Metropolitan Region

0.259

0.117

0.452

South Korea

0.248

0.129

0.521

Seoul Capital Area

0.381

0.191

0.500

India

0.170

0.024

0.141

Mumbai Metropolitan 0.201 Region

0.037

0.186

UK

0.334

0.148

0.443

London–Liverpool Corridor

0.421

0.170

0.403

Source Database of the City and Competitiveness Research Center, Chinese Academy of Social Sciences

5.8 Infrastructure: GDP and Demand Decide the Development of Infrastructure 5.8.1 Overall Pattern: Correlation Between Economic Growth and Infrastructure Development In the world’s infrastructure rankings, Europe, America, and Asia have an absolute advantage, with high infrastructure potential index, small coefficient of variation, and

5.8 Infrastructure: GDP and Demand Decide the Development of Infrastructure

339

0.6 0.5 0.4 0.3 0.2 0.1 0 A

B Mean

C

D

Coefficient of Variance

Fig. 5.29 Mean and coefficient of variance of human capital scores in different echelons

high ranking. The rest of the developing countries have large urban infrastructure factors, and the overall level of the region is poor. From the 2017 infrastructure map, it is obvious that the cities with better infrastructure construction are mostly located on the east coast of Asia, the east and west coasts of North America, and most of the European continent, while Africa has few distributions (Fig. 5.30 and Table 5.38).

5.8.2 Global Top 20: Shared Use of Infrastructure Among the top 20 countries in the global infrastructure, Asian countries account for nearly half of the country. European and American countries showed a downward trend with only 9 seats. It can be seen that in recent years Asian countries have made great efforts in the construction of infrastructure. The level of infrastructure construction in Asian countries has been continuously improved, providing facilities for its economic development and mitigating the morbid problems of urban development. In the top ten cities, Japan and China each have two seats. From this point of view, Japan and China attach importance to the construction of urban infrastructure in the development, and the government attaches great importance to this sector. Through the infrastructure to promote the development of urban economy, it can become a learning trend for the future development of Asian cities (Table 5.39). Cities in Europe and America score higher than global average in infrastructure, but the gap between European and American cities and other countries in the world is small. The peak of urban infrastructure index in Europe and the US is between

Sydney

São Paulo

Zürich

Amsterdam

Shanghai

Mexico City

Oslo

Copenhagen

Moscow

Helsinki

Santa Cruz

Kuala Lumpur 0.384

Athens

Australia

Brazil

Switzerland

Netherlands

China

Mexico

Norway

Denmark

Russia

Finland

Bolivia

Malaysia

Greece

0.382

0.400

0.414

0.423

0.436

0.454

0.455

0.462

0.492

0.508

0.516

0.526

0.573

Singapore

Singapore

0.639

0.414

Toronto

Canada

0.506

0.736

0.925

1.000

53

51

50

47

46

45

40

39

32

29

27

25

23

17

17

10

8

4

2

1

Kuwait

Palestine

Paraguay

Bangladesh

Hungary

Serbia

Uganda

Jordan

Pakistan

Belarus

Egypt

Puerto Rico

Indonesia

Kenya

Philippines

Peru

Venezuela

Poland

Vietnam

Sierra Leone

Index Ranking Country

South Korea Seoul

London

Paris

Tokyo

Japan

France

New York

US

UK

City

Country

Kuwait City

Gaza

Asunción

Dhaka

Budapest

Belgrade

Kampala

Amman

Karachi

Minsk

Cairo

San Juan

Jakarta

Nairobi

Manila

Lima

Caracas

Warsaw

Ho Chi Minh City

Freetown

City

0.191

0.192

0.193

0.194

0.195

0.196

0.200

0.203

0.205

0.206

0.208

0.216

0.219

0.221

0.226

0.229

0.230

0.234

0.235

0.235

329

322

320

310

306

303

283

267

258

256

249

230

217

214

198

193

188

183

181

180

Malawi

Afghanistan

Togo

Bulgaria

Uruguay

Moldova

Madagascar

Israel

Burkina Faso

Kazakhstan

Tunis

Benin

Mongolia

Iraq

Libya

Georgia

Cambodia

Turkmenistan

Latvia

Dominican Republic

Index Ranking Country

Table 5.37 Global ranking of cities performing best in global connectivity in each country

Lilongwe

Kabul

Lomé

Sofia

Montevideo

Chi¸sin˘au

Antananarivo

Tel Aviv-Jaffa

Ouagadougou

Almaty

Tunis

Cotonou

Ulaanbaatar

Baghdad

Tripoli

Tbilisi

Phnom Penh

Ashgabat

Riga

0.152

0.152

0.154

0.155

0.156

0.156

0.156

0.157

0.158

0.160

0.162

0.162

0.163

0.163

0.163

0.164

0.164

0.165

0.165

(continued)

683

681

666

643

634

632

631

620

612

581

561

556

551

550

543

538

536

525

520

515

Index Ranking

Santo Domingo 0.165

City

340 5 Analysis on Sustainable Competitiveness of Global Cities

Istanbul

Asmara

Turkey

Eritrea

0.276

San Diego

Dublin

Prague

Brussels

Tehran

Chile

Ireland

Czech Republic

Belgium

Iran

0.265

0.265

0.268

0.276

0.283

South Africa Johannesburg

0.284

0.284

0.289

0.314

Bangkok

Buenos Aires

Argentina

0.326

Thailand

Auckland

New Zealand

0.331

0.305

Kingston

Jamaica

0.333

0.336

0.336

0.344

0.364

0.367

Saudi Arabia Riyadh

Madrid

Berlin

Qatar

Germany

Doha

Sweden

Spain

Roman

Stockholm

Italy

Vienna

Austria

Table 5.37 (continued)

131

130

125

121

119

115

114

112

106

95

91

86

80

78

77

76

74

65

61

Yaoundé

San Salvador

Managua

Yerevan

Bucharest

Dar es Salaam

Lusaka

Djibouti

Bishkek

Baku

Lagos

Guayaquil

Sana’a

Tegucigalpa

Vientiane

Tashkent

Addis Ababa

Guatemala City

Mozambique Maputo

Cameroon

El Salvador

Nicaragua

Armenia

Romania

Tanzania

Zambia

Djibouti

Kyrgyzstan

Azerbaijan

Nigeria

Ecuador

Yemen

Honduras

Laos

Uzbekistan

Ethiopia

Guatemala

0.169

0.169

0.169

0.169

0.170

0.172

0.172

0.173

0.173

0.177

0.178

0.180

0.182

0.183

0.183

0.184

0.186

0.190

0.191

483

482

481

476

469

459

457

455

454

419

418

398

386

382

379

369

357

339

330

Luanda

Conakry

Yangon

Havana

Khartoum

Nouakchott

Kumasi

Kigali

Panama City

Libreville

Algiers

Bujumbura

San Jose

Abidjan

Somalia

Liberia

Congo

Chad

Mogadishu

Monrovia

Kinshasa

Ndjamena

Central African Bangui Republic

Angola

Guinea

Myanmar

Cuba

Sudan

Mauritania

Ghana

Rwanda

Panama

Gabon

Algeria

Burundi

Costa Rica

Cote d’Ivoire

0.131

0.133

0.135

0.136

0.136

0.138

0.139

0.141

0.143

0.146

0.147

0.148

0.148

0.149

0.150

0.150

0.151

0.151

0.151

(continued)

886

866

854

845

842

826

816

802

791

767

756

734

730

723

702

696

694

693

688

5.8 Infrastructure: GDP and Demand Decide the Development of Infrastructure 341

0.263

Muscat

Mumbai

Beirut

Bogota

Lisbon

Oman

India

Lebanon

Colombia

Portugal

171

160

157

146

136

135

Croatia

Morocco

Tajikistan

Haiti

Nepal

Senegal 0.168

0.168

Zagreb

Casablanca

Dushanbe 0.165

0.166

0.166

Port-au-Prince 0.167

Kathmandu

Dakar

513

510

508

501

490

487

Zimbabwe

Niger

Sri Lanka

Ukraine

Mali

Syria

Source Database of the City and Competitiveness Research Center, Chinese Academy of Social Sciences

0.239

0.246

0.248

0.255

0.263

United Arab Abu Dhabi Emirates

Table 5.37 (continued)

Bulawayo

Niamey

Colombo

Kiev

Bamako

Aleppo

0.000

0.115

0.121

0.124

0.126

0.128

1007

969

941

930

918

905

342 5 Analysis on Sustainable Competitiveness of Global Cities

5.8 Infrastructure: GDP and Demand Decide the Development of Infrastructure

343

80 60 40 20

-180

-140

-100

-60

0 -20

20

60

100

140

180

-20 -40 -60 Fig. 5.30 Infrastructure scores of cities worldwide Table 5.38 Infrastructure scores of cities by continents Region

Sample Number Mean Coefficient Median and of percentage variation of cities entering the global top 100

Asia

563

42(7.46%)

Europe

127

28(22.05%) 0.462 0.401

In the world’s London 1 infrastructure rankings, Europe, America, and Asia have an absolute advantage.

North America

132

19(14.39%) 0.438 0.334

0.400

New York

South America

74

4(5.41%)

0.315

Buenos 0.439 34 Aires

Oceania

7

Africa

104

World average

1007

0.343 0.437

0.341 0.302

0.314

Highest Index World ranking ranking city

Tokyo

0.993 2 1

0.960 6

4(57.14%)

0.681 0.270

0.604

Sydney 0.914 16

3(2.88%)

0.209 0.706

0.318

Durban 0.474 33

1007

0.359 0.469

0.332

1

1

London

United Arab Emirates

Asia

Dubai

Barcelona

Osaka

Singapore

New York

Shanghai

Hong Kong

Amsterdam

Tokyo

London

City

0.943

0.943

0.950

0.958

0.960

0.963

0.965

0.985

0.993

1.000

Index

10

9

8

7

6

5

4

3

2

1

Ranking

North America

Asia

Asia

Europe

Oceania

North America

North America

Europe

North America

Asia

Region

US

Japan

Thailand

Germany

Australia

Canada

US

Germany

US

South Korea

Country

Source Database of the City and Competitiveness Research Center, Chinese Academy of Social Sciences

Japan

Spain

Europe

Singapore

Asia

Asia

China

US

North America

China

Asia

Asia

Japan

Netherlands

Europe

UK

Europe

Asia

Country

Region

Table 5.39 Global top 20 cities by infrastructure

Seattle

Nagoya

Bangkok

Cologne

Sydney

Vancouver

Los Angeles

Hamburg

Houston

Incheon

City

0.890

0.892

0.896

0.913

0.915

0.916

0.916

0.920

0.935

0.936

Index

20

19

18

17

16

15

14

13

12

11

Ranking

344 5 Analysis on Sustainable Competitiveness of Global Cities

5.8 Infrastructure: GDP and Demand Decide the Development of Infrastructure

345

Infrastructure Index

Europe and America Others

Infrastructure Fig. 5.31 KDE of infrastructure density

0.359 and 0.500, and the peak of urban infrastructure index in other countries in the world is located on the left side of 0.359, but the overall gap is small (Fig. 5.31).

5.8.3 Comparison Between Countries: China Balances the US In Infrastructure Density China and the United Stated have almost the same infrastructure density. Although the US has a slightly higher overall urban infrastructure index than China, China’s best infrastructure cities are ranked higher in the world than the US, and there are more cities in the world’s top 100 cities, reflecting the importance China attaches to infrastructure in recent years. This reflects the development trend that China has attached great importance to infrastructure in recent years and has gradually moved closer to international high-level countries (Table 5.40). In addition to China, the BRICS infrastructure construction level is relatively poor, few countries have entered the top 100 cities, and China has a stark contrast with it, occupying an important proportion of 20 seats in the top 100 countries. Although China has seized one-fifth of the seats, the coefficient of variation is relatively high, and the variability of urban infrastructure construction is large, which is not conducive to urban development. On the whole, the overall level of urban construction in most developing countries is relatively poor, internal differences are large, and the gap

346

5 Analysis on Sustainable Competitiveness of Global Cities

Table 5.40 Infrastructure scores of China and the US Country

Mean

Coefficient of variation

Highest ranking city

Ranking

US

0.488

0.284

New York

6

Index 0.960

China

0.370

0.330

Hong Kong

4

0.965

Source Database of the City and Competitiveness Research Center, Chinese Academy of Social Sciences

with developed countries is large. The overall infrastructure index of the G7, led by developed countries, is relatively high, ranging from 0.45 to 0.65. The quality of urban infrastructure construction is good, the coefficient of variation is around 0.1–0.2, and infrastructure construction is relatively stable. However, the number of its entry into the world’s top 100 cities is too small, and its side shows that its urban construction is relatively balanced, and the overall level of urban construction is generally high (Table 5.41). Table 5.41 Infrastructure scores of BRICS and G7 Country

Sample Number and percentage of cities entering the global top 100

Mean

Coefficient of variation

BRICS China

292

20(6.85%)

0.370

0.331

Russia

33

2(6.06%)

0.326

0.311

India

100

0(0.00%)

0.235

0.333

Brazil

32

0(0.00%)

0.293

0.164

6

1(16.67%)

0.454

0.362

12

2(16.67%)

0.540

0.274 0.194

South Africa G7

UK France

9

2(22.22%)

0.529

US

75

14(18.67%)

0.488

0.284

Germany

13

6(46.15%)

0.639

0.258 0.130

Italy

13

2(15.38%)

0.495

Japan

10

3(30.00%)

0.572

0.429

9

3(33.33%)

0.547

0.372

Canada

Source Database of the City and Competitiveness Research Center, Chinese Academy of Social Sciences

5.8 Infrastructure: GDP and Demand Decide the Development of Infrastructure

347

Table 5.42 Infrastructure scores of urban clusters Urban cluster

Infrastructure score

Standard deviation

Coefficient of variation

Ranking

Highest ranking city

Yangtze river delta

0.468

0.132

0.282

5

Shanghai

Midwestern megalopolis, US

0.458

0.076

0.166

99

Cincinnati

Pearl river delta

0.554

0.173

0.312

23

Guangzhou

London–Liverpool Corridor

0.628

0.195

0.311

1

London

Northeast Megalopolis, US

0.569

0.162

0.284

6

New York

Rhine-Ruhr Metropolitan Region

0.775

0.172

0.222

13

Hamburg

Netherlands–Belgian Metropolitan Region

0.643

0.178

0.276

3

Mumbai Metropolitan Region

0.384

0.113

0.295

114

Mumbai

Seoul Capital Area

0.869

0.067

0.078

11

Incheon

Northern California Megaregion

0.503

0.101

0.202

70

San Francisco

Amsterdam

Source: Database of the City and Competitiveness Research Center, Chinese Academy of Social Sciences

5.8.4 Comparison Between Urban Clusters: Urban Clusters in China Are Catching Up with Developed Countries in Infrastructure Development China’s major urban agglomerations, the Yangtze River Delta urban agglomeration, the Pearl River Delta urban agglomeration, and the Beijing–Tianjin–Hebei urban agglomerations are no different from those in Europe and the US. The average value of infrastructure is similar to that of European and American countries, but the coefficient of variation is still higher than that of European and American countries. Except for China, the infrastructure construction of urban agglomerations in developing countries is relatively poor, and there are no cities that have entered the top 100. Overall, although China’s urban agglomerations have better infrastructure conditions and provide better infrastructure support for their development, China’s internal urban construction has a serious two-level differentiation. The infrastructure construction conditions of major cities are better, and the construction level of other cities is better. It is difficult to achieve a satisfactory standard, resulting in a higher coefficient of variation. However, the overall level of urban agglomeration construction in Europe and the US is relatively high, and the internal differences are small, which is conducive to giving play to the overall effect of the urban agglomeration and better promoting the sustainable development of the city (Table 5.42).

348

5 Analysis on Sustainable Competitiveness of Global Cities

Table 5.43 Infrastructure scores of urban clusters and countries home to the urban clusters Country and urban cluster(s)

Infrastructure

Standard deviation

Coefficient of variation

China

0.370

0.122

0.331

Yangtze River Delta

0.468

0.132

0.282

Pearl River Delta

0.554

0.173

0.312

US

0.488

0.139

0.284

Midwestern Megalopolis, US

0.458

0.076

0.166

Northeast Megalopolis, US

0.569

0.162

0.284

Northern California Megaregion

0.503

0.101

0.202

Germany

0.639

0.165

0.258

Rhine-Ruhr Metropolitan Region

0.775

0.172

0.222

Netherlands

0.665

0.226

0.339

Netherlands–Belgian Metropolitan Region

0.643

0.178

0.276

Belgium

0.621

0.106

0.171

Netherlands–Belgian Metropolitan Region

0.643

0.178

0.276

South Korea

0.606

0.176

0.290

Seoul Capital Area

0.869

0.067

0.078

India

0.235

0.078

0.333

Mumbai Metropolitan Region

0.384

0.113

0.295

UK

0.540

0.148

0.274

London–Liverpool Corridor

0.628

0.195

0.311

Source Database of the City and Competitiveness Research Center, Chinese Academy of Social Sciences

The major urban agglomerations of the world have presented two levels of differentiation in infrastructure construction. Except for a few urban agglomerations, the infrastructure construction is equivalent to the domestic average. The infrastructure construction level of the other urban agglomerations is higher than the domestic average. The coefficient of variation of urban clusters in China, the Netherlands, and India is higher (Table 5.43).

Abu Dhabi

United Arab Emirates

0.877

0.896

0.915

Paris

Seoul

Buenos Aires

Guayaquil

Brussels

Athens

Kuala Lumpur

Manila

Zürich

Copenhagen

France

South Korea

Argentina

Ecuador

Belgium

Greece

Malaysia

Philippines

Switzerland

Denmark

0.692

0.693

0.693

0.700

0.726

0.727

0.731

0.739

1.733

1.609

Bangkok

Thailand

0.865

Sydney

Australia

0.958

0.960

0.963

0.985

0.993

1.000

Index

New Zealand Auckland

New York

Singapore

Singapore

Shanghai

China

US

Tokyo

Amsterdam

Netherlands

London

UK

Japan

City

Country

52

51

50

48

40

39

36

34

29

28

25

22

18

16

7

6

5

3

2

1

Ranking

Costa Rica

Puerto Rico

Chile

Oman

Iran

Bulgaria

Uruguay

Morocco

Kuwait

Brazil

Latvia

Azerbaijan

Lebanon

Venezuela

Romania

Egypt

Saudi Arabia

Mexico

Israel

Peru

Country 0.516

Index

San Jose

San Juan

San Diego

Muscat

Tehran

Sofia

Montevideo

Casablanca

Kuwait City

São Paulo

Riga

Baku

Beirut

Caracas

Bucharest

Cairo

Riyadh

Mexico City

0.391

0.392

0.393

0.403

0.405

0.414

0.420

0.422

0.427

0.432

0.433

0.448

0.449

0.450

0.458

0.466

0.477

0.479

Tel Aviv-Jaffa 0.510

Lima

City

Table 5.44 Global ranking of cities performing best in infrastructure in each country

336

333

332

305

298

285

276

270

260

243

239

221

218

217

202

194

176

175

142

139

Ranking

Djibouti

Togo

Nepal

Haiti

Benin

Turkmenistan

Ghana

Cote d’Ivoire

Zimbabwe

Myanmar

Ukraine

Yemen

Sudan

Nicaragua

Algeria

Laos

Mongolia

Moldova

Cambodia

Gabon

Country

Djibouti

Lomé

Kathmandu

Port-au-Prince

Cotonou

Ashgabat

Kumasi

Abidjan

Bulawayo

Yangon

Kiev

Sana’a

Khartoum

Managua

Algiers

Vientiane

Ulaanbaatar

Chi¸sin˘au

Phnom Penh

Libreville

City

0.174

0.176

0.198

0.199

0.213

0.218

0.221

0.225

0.230

0.233

0.234

0.235

0.240

0.246

0.247

0.250

0.251

0.255

0.259

0.263

Index

(continued)

919

914

893

892

875

866

859

855

847

837

836

830

816

801

798

790

780

768

756

742

Ranking

5.8 Infrastructure: GDP and Demand Decide the Development of Infrastructure 349

Moscow

Dublin

Oslo

Panama City

Stockholm

Lisbon

Istanbul

Colombo

Lagos

Kingston

Russia

Ireland

Norway

Panama

Sweden

Portugal

Turkey

Sri Lanka

Nigeria

Jamaica

Prague

Vienna

Austria

Czech Republic

Jakarta

Indonesia

Karachi

Helsinki

Finland

Bogota

Toronto

Canada

Colombia

Madrid

Spain

Pakistan

Doha

Qatar

Table 5.44 (continued)

0.574

0.578

0.585

0.586

0.588

0.594

0.597

0.613

0.616

0.616

0.618

0.619

0.631

0.652

0.653

0.666

0.669

0.685

0.689

103

100

96

94

93

91

89

83

80

79

78

77

74

68

67

63

62

57

55

Tunis

Iraq

Paraguay

Armenia

Cuba

Zambia

El Salvador

Guatemala

Georgia

Kazakhstan

Libya

Belarus

Dominican Republic

Jordan

Vietnam

Eritrea

Serbia

Croatia

Bangladesh

Tunis

Baghdad

Asunción

Yerevan

Havana

Lusaka

San Salvador

Guatemala City

Tbilisi

Almaty

Tripoli

Minsk

Santo Domingo

Amman

Ho Chi Minh City

Asmara

Belgrade

Zagreb

Dhaka

0.387

0.285

0.291

0.296

0.302

0.303

0.304

0.305

0.319

0.326

0.342

0.354

0.357

0.366

0.373

0.378

0.383

0.384

0.385

344

662

644

622

604

600

593

590

535

507

454

415

410

389

377

363

355

348

347

Uganda

Madagascar

Mali

Congo

Afghanistan

Tajikistan

Niger

Burkina Faso

Chad

Syria

Tanzania

Mauritania

Kyrgyzstan

Sierra Leone

Senegal

Ethiopia

Mozambique

Rwanda

Cameroon

Kampala

Antananarivo

Bamako

Kinshasa

Kabul

Dushanbe

Niamey

Ouagadougou

Ndjamena

Aleppo

Dar es Salaam

Nouakchott

Bishkek

Freetown

Dakar

Addis Ababa

Maputo

Kigali

Yaoundé

0.160

0.057

0.060

0.061

0.068

0.076

0.081

0.082

0.091

0.103

0.108

0.108

0.118

0.119

0.122

0.130

0.137

0.154

0.155

929

(continued)

989

988

986

985

983

981

980

978

970

965

964

961

959

957

954

949

937

936

350 5 Analysis on Sustainable Competitiveness of Global Cities

Roman

Budapest

Poland

Italy

Hungary

0.518

0.542

0.546

0.547

138

119

116

115

114

107

Honduras

Bolivia

Angola

Kenya

Palestine

Uzbekistan

Tegucigalpa

Santa Cruz

Luanda

Nairobi

Gaza

Tashkent

0.267

0.269

0.272

0.275

0.276

0.280

731

727

715

707

698

684

Burundi

Guinea

Somalia

Malawi

Liberia

Central African Republic

Source Database of the City and Competitiveness Research Center, Chinese Academy of Social Sciences

Warsaw

Germany

0.547

Mumbai

Berlin

India

0.566

South Africa Johannesburg

Table 5.44 (continued)

Bujumbura

Conakry

Mogadishu

Lilongwe

Monrovia

Bangui

0.002

0.026

0.031

0.032

0.032

0.035

1006

1001

1000

999

998

996

5.8 Infrastructure: GDP and Demand Decide the Development of Infrastructure 351

352

5.8.4.1

5 Analysis on Sustainable Competitiveness of Global Cities

World City Network

According to the list of the best cities in the world’s major infrastructures, it is found that some developing countries may still have the situation that infrastructure development cannot keep up with population growth, but in recent years this phenomenon is getting better (Table 5.44).

5.9 Econometric Analysis of Global Cities’ Sustainable Competitiveness: Technological Innovation and Human Capital Potential Have the Greatest Impact, and Positive Effects Are Amplified Through Direct, Indirect, and Feedback Effects Technological innovation and human capital are important factors influencing urban economic development. For the sustainable development of cities, technological innovation and human capital potential are important factors influencing the improvement of sustainable competitiveness of cities. Moreover, if the spatial interaction between cities and their spatial spillover effects are included in the scope of investigation, the potential of scientific and technological innovation and human capital will not only affect the sustainable competitiveness of cities through direct effects and indirect effects, but further amplify them through feedback effects. Promote the promotion of sustainable competitiveness in cities. In order to analyze the impact of global 1007 urban economic vitality, environmental quality, social inclusion, technological innovation, global linkages, government management, human capital potential, and infrastructure on the level of sustainable competitiveness of cities, this section will address the above eight factors. Perform quantitative analysis. From the existing literature, the existing research on sustainable competitiveness basically ignores the impact of the improvement of the level of sustainable competitiveness outside the city on the level of sustainable competitiveness of the city. It only considers the internal economic vitality, technological innovation, and human capital. Factors such as potential, environmental quality, and government management are the driving forces behind the city’s sustainable competitiveness. In this regard, the new economic geography and spatial economics believe that the economic growth of a city depends not only on the input of the regional self-factors, but also on the overall economic growth of the surrounding neighbors, that is, the spatial externalities existing between the regions. The impact on a regional economic growth has been paid more and more attention by scholars. Drawing on the ideas of new economic geography and space economics, it is not difficult to guess that a city’s sustainable competitiveness depends not only on the city’s own factor input, but also on the comprehensive impact of the surrounding cities’ sustainable competitiveness, that

5.9 Econometric Analysis of Global Cities’ Sustainable Competitiveness: …

353

is, the spatial externality between cities. The specific performance is the comprehensive impact of spatial spillover on the sustainable competitiveness of cities. In view of this, this section will combine the latest developments in spatial econometrics to quantify the sustainable competitiveness of cities.

5.9.1 Construction of the Empirical Model and Selection of Variables Based on Elhorst’s (2014), this section begins with the general nesting spatial model (GNS) and analyzes whether GNS should be simplified and improved for use in the quantitative analysis of the sustainable competitiveness of cities. GNS can be expressed as the following function: Y = δW Y + αιn + Xβ + W X θ + u u = λW u + ε where Y is the explained variation, X the explanatory variable, W the spatial weight matrix, ιn the constant term, u the error term, and both a δ, α, β, θ and λ are the parameters to be estimated. W Y . The endogenous interaction between explained variables, W X , is the exogenous interaction between explanatory variables, and W u is the interaction between different units of interference between explanatory variables, which is the interaction between different units of disturbance terms. When θ = 0, λ = 0, and δ = 0, the above formula will be simplified to SAC, SDM, and SDEM models. In a SAC model, when λ = 0, the model will become a SAR model; when δ = 0, it will become a SEM model. In a SDM model, when θ = 0, δ = 0, and θ = −δβ, the model will become SAR, SLX, and SEM models. In a SDEM mode, when λ = 0 and θ = 0, the model will be simplified into SLX and SEM models. When δ = 0 in SER, θ = 0 in SLX, and λ = 0 in SEM, the above formulas will all be turned into an ordinary least square (OLS) model. The empirical model of sustainable competitiveness of cities is expressed as sus_compete = δW × sus_compete + αιn + Xβ + W X θ + u u = λW u + ε where sus_compete denotes the level of sustainable competitiveness of a city; X is an explanatory variable matrix that affects the sustainable competitiveness of the city, including economic vitality (economic), environmental quality (environ), social inclusiveness (society), technological innovation (tech), global connectivity (connect), government management (govern), human capital (psacp), and infrastructure (infrastru):

354

5 Analysis on Sustainable Competitiveness of Global Cities 

X = (economic, envir on, societ y, tech, connect, gover n, psacp, inf r astr u) . Data used in this study come from the database of the City and Competitiveness Research Center, Chinese Academy of Social Sciences.

5.9.1.1

Optimal Model: General Netting Spatial (GNS) Model

In order to analyze the impact of different factors on the level of sustainable competitiveness of cities, the author first uses the stepwise regression method of gradually increasing variables to analyze the impact of different factors on the sustainable competitiveness of cities. The analysis results of the stepwise regression method are shown in Table 5.45. It can be seen from Table 5.45 that the city’s sustainable competitiveness sus_compete is the explanatory variable, the number of explanatory variables is gradually reduced, and the goodness of fit of the estimated results is also reduced. Specifically, when the city’s level of sustainable competitiveness is the explanatory variable and returns with eight explanatory variables of economic vitality, environmental quality, social inclusion, technological innovation, global linkages, government management, human capital potential, and infrastructure, the goodness of fit and maximum were 0.934 and 0.933, respectively. The gradual reduction of infrastructure, human capital potential, government management, global linkages, technological innovation, social inclusion, and environmental quality in explanatory variables fell from 0.933 to 0.921, 0.901, 0.894, 0.877, 0.736, 0.713, and 0.660. According to the estimation results given in Table 5.45, among the eight influencing factors affecting the level of sustainable competitiveness of cities. The three factors of economic vitality, technological innovation and human capital have a greater impact on the level of sustainable competitiveness of cities, and the elasticity value for the improvement of the level of sustainable competitiveness of cities is all more than 10%; that is, the competition of urban sustainable competitiveness is urban manpower. Of course, the city’s environmental quality, social inclusion, global linkages, government management, and infrastructure improvements have an important role in promoting the sustainable competitiveness of cities, and the role of the elastic value of the city’s sustainable competitiveness is enhanced. More than 3% is also an important factor that cannot be ignored. As mentioned above, the above stepwise regression method has shown that the city’s economic vitality, environmental quality, social inclusion, technological innovation, global linkages, government management, human capital potential, and infrastructure are important factors influencing the level of sustainable competitiveness of cities, but the impact of spatial externalities between cities on the level of sustainable competitiveness of cities is not considered. Therefore, the next analysis will introduce the objective spatial externality between cities into the regression model of spatial econometrics and analyze the comprehensive impact of different influencing factors and their spatial spillover effects on the level of sustainable competitiveness of cities. Table 5.46 gives the estimated results of eight spatial econometric models including OLS.

−0.138*** (−9.492)

0.129*** (17.584)

0.090*** (27.198)

0.069*** (14.982)

0.147*** (32.923)

0.032*** (10.825)

0.085*** (10.402)

0.169*** (19.619)

0.056*** (7.486)

0.934

0.933

Economic

Environ

Society

Tech

Connect

Govern

Psacp

Infrastru

R2

ad j − R 2 0.901

0.904

–

–

0.097*** (9.999)

0.046*** (13.203)

0.172*** (33.396)

0.063*** (11.412)

0.092*** (23.385)

0.191*** (28.967)

−0.329*** (−26.765)

Sus_compete

0.894

0.895

–

–

–

0.047*** (12.745)

0.181*** (34.165)

0.076*** (13.545)

0.095*** (22.837)

0.020*** (29.418)

−0.331*** (−25.653)

Sus_compete

0.877

0.878

–

–

–

–

0.192*** (34.124)

0.067*** (11.197)

0.101*** (22.845)

0.225*** (31.525)

−0.426*** (−37.523)

Sus_compete

0.736

0.737

–

–

–

–

–

0.082*** (9.285)

0.105*** (16.157)

0.373*** (44.961)

−0.453*** (−27.274)

Sus_compete

0.713

0.714

–

–

–

–

–

–

0.091*** (13.786)

0.384*** (44.947)

−0.555*** (−42.589)

Sus_compete

0.660

0.661

–

–

–

–

–

–

–

0.405*** (44.208)

−0.612*** (−45.507)

Sus_compete

Note: ***, **, and * denote statistical significance at 1%, 5%, and 10%, respectively, and the t-statistics for parameter estimation are enclosed in parentheses

0.921

0.930

–

0.167*** (18.965)

0.092*** (10.995)

0.033*** (10.768)

0.152*** (33.315)

0.070*** (14.838)

0.097*** (26.695)

0.164*** (27.953)

Sus_compete

Sus_compete

−0.129*** (−9.102)

Constant

Table 5.45 Stepwise regression results of sustainable competitiveness of cities worldwide

5.9 Econometric Analysis of Global Cities’ Sustainable Competitiveness: … 355

0.028* (1.675)

0.129*** (17.584)

0.090*** (27.198)

0.069*** (14.982)

0.147*** (32.923)

0.032*** (10.825)

0.085*** (10.402)

0.169*** (19.619)

0.056*** (7.486)

–

Economic

Environ

Society

Tech

Connect

Govern

Psacp

Infrastru

W × economic

–

0.045*** (6.527)

0.166*** (21.430)

0.064*** (8.524)

0.031*** (11.436)

0.137*** (33.833)

0.065*** (15.581)

0.067*** (20.302)

0.099*** (14.341)

SAR

OLS

−0.129*** (−9.102)

Constant

–

0.094*** (11.403)

0.159*** (21.898)

0.063*** (7.620)

0.025*** (10.190)

0.134*** (35.071)

0.076*** (16.332)

0.061*** (12.865)

0.096*** (14.056)

−0.202*** (−12.15)

SEM

0.067*** (4.329)

0.089*** (9.467)

0.156*** (19.564)

0.056*** (6.160)

0.029*** (10.415)

0.138*** (33.145)

0.078*** (15.465)

0.042*** (7.301)

0.946*** (12.753)

−0.057** (−2.065)

SLX

Table 5.46 Estimates of a different sustainable competitiveness by using different models

−0.012 (−0.798)

0.096*** (11.148)

0.155*** (21.353)

0.056*** (6.816)

0.027*** (10.728)

0.135*** (35.787)

0.078*** (16.938)

0.045*** (8.542)

0.091*** (13.513)

−0.012 (−0.492)

SDM

0.071*** (4.093)

0.087*** (10.557)

0.156*** (20.531)

0.061*** (7.559)

0.029*** (10.825)

0.138*** (35.266)

0.078*** (17.390)

0.047*** (8.945)

0.097*** (14.411)

−0.076** (−2.228)

SDEM

–

0.073*** (9.268)

0.167*** (22.275)

0.062*** (7.777)

0.028*** (11.007)

0.138*** (35.267)

0.075*** (16.294)

0.065*** (15.466)

0.097*** (13.961)

−0.030** (−1.252)

SAC

(continued)

−0.024 (−1.415)

0.096*** (11.152)

0.155*** (21.328)

0.056*** (6.665)

0.026*** (10.573)

0.135*** (35.545)

0.078*** (16.693)

0.045*** (8.632)

0.091*** (13.428)

−0.007 (−0.277)

GNS

356 5 Analysis on Sustainable Competitiveness of Global Cities

–

–

–

–

–

–

0.934

W × connect

W × govern

W × psacp

W × infrastru

ρ

λ

R2

0.008

2.132

532.103

σ2

Durbin–Watson

Log-likelihood

0.933

–

W × tech

ad j −

–

W × society

R2

–

W × environ

Table 5.46 (continued)

1134.448

–

0.006

0.945

0.946

–

0.236*** (14.659)

–

–

–

–

–

–

–

1150.305

–

0.005

0.952

0.953

0.762*** (27.858)

–

–

–

–

–

–

–

–

639.432

2.165

0.006

0.945

0.946

–

–

−0.094*** (−5.930)

0.005 (0.254)

0.050*** (2.858)

0.021*** (3.063)

0.035*** (3.562)

−0.004 (−0.509)

0.060*** (7.429)

1207.382

–

0.005

0.954

0.955

–

0.539*** (13.567)

−0.114*** (−0.785)

−0.091*** (−5.097)

−0.010 (−0.642)

0.003 (0.467)

−0.048*** (−4.430)

−0.035*** (−4.331)

0.006 (0.735)

1198.229

–

0.005

0.953

0.954

0.525*** (12.409)

–

−0.097*** (−5.288)

−0.002 (−0.112)

0.025 (1.317)

0.016** (2.348)

0.028*** (2.769)

0.003 (0.387)

0.057*** (6.352)

1164.782

–

0.006

0.950

0.951

0.522*** (11.627)

0.152*** (7.559)

–

–

–

–

–

–

–

(continued)

1207.958

–

0.005

0.955

0.956

−0.124 (−1.110)

0.613*** (9.032)

−0.115*** (−8.166)

−0.103*** (−5.267)

−0.015 (−0.943)

0.001 (0.048)

−0.059*** (−4.264)

−0.042*** (−4.645)

−0.001 (−0.147)

5.9 Econometric Analysis of Global Cities’ Sustainable Competitiveness: … 357

110.628*** [0.000]

170.236*** [0.000]

81.084*** [0.000]

Robust LM-SAR

LM-SEM

Robust LM-SEM

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

Note ***, **, and * denote the 1%, 5%, and 10% significance levels, respectively, and the t-statistics for parameter estimation are enclosed in parentheses

199.777*** [0.000]

LM-SAR

Table 5.46 (continued)

358 5 Analysis on Sustainable Competitiveness of Global Cities

5.9 Econometric Analysis of Global Cities’ Sustainable Competitiveness: …

359

According to the estimation results in Table 5.46, the goodness of fit of the OLS model estimation results is 0.934 and 0.933, respectively, which are the smallest of the estimation results of all models, and the sum of squared residuals is 0.008, which is the largest of all models. The log-likelihood value 532.103 is also the smallest of all models. At the same time, the result of the Durbin–Watson test is 2.132 close to 2, so it can be considered that the autocorrelation between the variables has been eliminated. It can be seen that not considering the spatial spillover effect between cities is not the best estimation result. Among the seven models considering spatial externalities, the goodness of fit in the estimation results of GNS, i.e., the generalized nested model, is 0.956 and 0.955, respectively, which is the largest of all models, and the sum of squared residuals is 0.055, which is the smallest of all models. The log-likelihood value is 1207.958, which is the largest of all models. At the same time, both the LM-SAR and LM-SEM tests and the robust LM-SAR and robust LM-SEM tests rejected the SAR or SEM as the optimal spatial econometric model at the 1% significance level. It can be seen that the GNS model is the optimal estimation result of the city’s sustainable competitiveness level. According to the estimation results of the GNS model, the city’s technological innovation tech and human capital potential psacp are still the most important factors affecting the level of sustainable competitiveness of cities. Specifically, the elasticity value of urban science and technology innovation to promote the level of sustainable competitiveness of cities is 13.5%, the elasticity value of human capital potential to promote the level of sustainable competitiveness of cities is 15.5%, and both are at 1%. The level of significance is significant. Of course, the improvement of urban science and technology innovation level is based on the potential of human capital. Therefore, the competition level of sustainable competitiveness of cities will be determined to a certain extent by the human capital potential of the city and the level of technological innovation. Cities with strong human capital potential and strong technological innovation will have higher total factor productivity. Secondly, the elasticity of the city’s economic vitality, social inclusion, government management, and infrastructure to promote the level of sustainable competitiveness of cities is 9.1%, 7.8%, 5.6%, and 9.6%, respectively, both exceeding 5%, and both are at 1. The significance level of % is significant. Therefore, the improvement of urban infrastructure conditions is second only to the city’s human capital potential and technological innovation level, and the economic vitality of the city promotes the level of sustainable competitiveness of cities. Economic vitality, social inclusion, government management, and infrastructure improvements will benefit talent and innovation. Compared with the above six influencing factors, the environmental quality and global linkage of the city have a relatively small effect on the promotion of sustainable competitiveness of the city. The elasticity values are 4.5% and 2.6%, respectively, and both are significant at 1%. Compared with the above six influencing factors, the environmental quality and global connection of the city have relatively little promotion effect on the improvement of the sustainable competitiveness of the city, and the elasticity values are 4.5% and 2.6% respectively. The reason may be that the first pollution control is a realistic problem that must be faced in the sustainable development of urban economy.

360

5 Analysis on Sustainable Competitiveness of Global Cities

It is worth noting that among all the influencing factors, the improvement of the sustainable competitiveness of neighboring cities has the greatest impact on the sustainable competitiveness of the city. Its elasticity value is as high as 61.3%, and it is significant at the 1% significance level. In terms of the size of its promotion, it is 3.954 and 4.541 times of the human capital potential, respectively, and even slightly less than the elastic value of the eight influencing factors such as economic vitality, which is 68.2%. This is enough to show that for a city’s sustainable competitiveness level, the improvement of the sustainable competitiveness of neighboring cities is one of the important sources to enhance their own sustainable competitiveness. This explains that cities with higher levels of global sustainable competitiveness are always in urban agglomerations or metropolitan areas, and there is always a concentrated distribution in geospatial space. The improvement of the conditions of neighboring urban elements has different levels of spatial spillover effects on the level of sustainable competitiveness of the city. According to the estimation results of the GNS model, the social inclusion, technological innovation, human capital potential, and infrastructure level of neighboring cities will hinder the improvement of the city’s sustainable competitiveness. The elasticity values are 4.2%, 5.9%, 10.3%, and 11.5%, and both were significant at the 1% significance level. At the same time, the economic vitality, environmental quality, and improvement of government management in neighboring cities will also hinder the improvement of the city’s sustainable competitiveness, but it is not significant. Different from the above seven factors, the enhancement of global linkages in neighboring cities will promote the improvement of the city’s sustainable competitiveness. Unfortunately, this promotion is not significant.

5.9.2 Direct and Indirect Effects: Feedback Effects on Factors Halleck Vega and Elhorst (2012) made it clear that the indirect effect should not be limited to the influence of only the first-order neighbors, but also the second-order or even higher-order effects of the explanatory variables, including the effects of the feedback effects. Therefore, it is necessary to examine the direct and indirect effects of explanatory variables. Table 5.47 shows the direct effects of eight factors affecting the level of sustainable competitiveness of cities, such as economic vitality, environmental quality, social inclusion, technological innovation, global linkages, government management, human capital potential, and infrastructure, indirect effects, and total effects. From the estimation results of different models of urban sustainable competitiveness level, we can see that the optimal estimation model in this section is GNS, which is a generalized nested model. Due to space limitation, this section will mainly discuss the direct effects, indirect effects, and totals of different factors in the GNS

5.9 Econometric Analysis of Global Cities’ Sustainable Competitiveness: …

361

Table 5.47 Direct, indirect, and overall effects of cities on sustainable competitiveness SAR

SEM

SLX

SDM

SDEM

SAC

GNS

Economic 0.099*** 0.096*** -0.057** (14.937) (14.056) (-2.065)

0.095*** (13.562)

-0.076** 0.097*** 0.095*** (-2.228) (14.617) (13.710)

Environ

0.068*** 0.061*** 0.946*** (21.702) (12.865) (12.753)

0.048*** (9.265)

0.097*** 0.065*** 0.048*** (14.411) (15.832) (9.905)

Society

0.066*** 0.076*** 0.042*** (15.941) (16.332) (7.301)

0.076*** (17.180)

0.047*** 0.075*** 0.079*** (8.945) (16.509) (16.973)

Tech

0.139*** 0.134*** 0.078*** (33.187) (35.071) (15.465)

0.138*** (35.966)

0.078*** 0.138*** 0.138*** (17.390) (35.423) (32.974)

Connect

0.031*** 0.025*** 0.138*** (11.388) (10.190) (33.145)

0.028*** (11.038)

0.138*** 0.028*** 0.029*** (35.266) (11.233) (10.566)

Govern

0.065*** 0.063*** 0.029*** (8.653) (7.620) (10.415)

0.058*** (7.388)

0.029*** 0.063*** 0.057*** (10.825) (7.721) (7.120)

Psacp

0.167*** 0.159*** 0.056*** (22.510) (21.898) (6.160)

0.154*** (20.426)

0.061*** 0.168*** 0.154*** (7.559) (22.427) (20.163)

Infrastru

0.045*** 0.094*** 0.156*** (7.051) (11.403) (19.564)

0.090*** (10.861)

0.156*** 0.073*** 0.089*** (20.531) (9.461) (10.888)

Direct effect

Indirect effect Economic 0.029*** – (10.835)

0.067*** (4.329)

0.075*** (2.723)

–

0.017*** 0.076** (6.583) (2.429)

Environ

0.020*** – (12.278)

0.060*** (7.429)

0.064*** (5.955)

–

0.012*** 0.065*** (6.818) (5.405)

Society

0.019*** – (9.774)

−0.004 (−0.509)

0.014 (0.958)

–

0.013*** 0.017 (6.075) (0.953)

Tech

0.041*** – (11.271)

0.035*** (3.562)

0.052*** (2.869)

–

0.024*** 0.058** (6.43) (2.550)

Connect

0.009*** – (8.301)

0.021*** (3.063)

0.036** (2.685)

–

0.005*** 0.042** (5.425) (2.486)

Govern

0.019*** – (7.484)

0.050*** (2.858)

0.041 (1.407)

–

0.011*** 0.046 (5.164) (1.336)

Psacp

0.049*** – (10.007)

0.005 (0.254)

−0.016 (−0.466)

–

0.029*** −0.022 (6.139) (−0.513)

Infrastru

0.013*** – (6.262)

−0.094*** −0.127*** – (−5.930) (−5.025)

0.012*** −0.009*** (5.789) (−4.669)

Economic 0.129*** – (15.725)

–

0.169*** (5.852)

–

0.114*** 0.171*** (14.911) (5.199)

Environ

0.088*** – (23.668)

–

0.112*** (12.467)

–

0.077*** 0.114*** (16.535) (10.812)

Society

0.085*** – (15.709)

–

0.092*** (12.467)

–

0.088*** 0.096*** (15.364) (5.286)

Total effect

(continued)

362

5 Analysis on Sustainable Competitiveness of Global Cities

Table 5.47 (continued) Tech

0.179*** – (29.759)

–

0.190*** (9.839)

–

0.163*** 0.196*** (27.055) (8.133)

Connect

0.040*** – (11.249)

–

0.065*** (4.446)

–

0.033*** 0.070*** (10.595) (3.924)

Govern

0.084*** – (8.763)

–

0.099*** (3.364)

–

0.074*** 0.103*** (7.695) (3.024)

Psacp

0.217*** – (20.271)

–

0.138*** (3.749)

–

0.197*** 0.132*** (19.141) (2.967)

Infrastru

0.058*** – (7.085)

–

−0037 (−1.510)

–

0.086*** 0.080* (9.532) (1.721)

Note ***, **, and * denote statistical significance at 1%, 5%, and 10%, respectively, and the t-statistics for parameter estimation are enclosed in parentheses

model. The effect, while giving the direct, indirect, and total effects of other spatial measurement models as a reference, will not be repeated here. The larger the level of power once again illustrates that the city’s human capital potential and technological innovation capability are important factors influencing the level of sustainable competitiveness of cities. Urban economic vitality, social inclusion, government management, and infrastructure elasticity values are 9.5%, 7.9%, 5.7%, and 8.9%, respectively, both greater than 5%, and both are significant at the 1% significance level, so economic vitality, social inclusion, government management, and infrastructure are important factors in promoting the level of sustainable urban competitiveness. Relative to the above-mentioned influencing factors, the impact of urban environmental quality and global linkage on urban sustainable competitiveness is relatively small, and its elasticity values are 4.8% and 2.9%, respectively, but its impact on urban sustainable competitiveness is also negligible. According to the estimation results of indirect effects in the GNS model, after considering the feedback effect, the elasticity values of urban economic vitality, environmental quality, technological innovation, and global linkage are 7.6%, 6.5%, 5.8%, and 4.2%, respectively, and at least 5. The significance level of % is significant. Therefore, the city’s economic vitality, environmental quality, technological innovation, and global connectivity will not only enhance the city’s sustainable competitiveness through direct effects, but also enhance the sustainable competitiveness of its neighboring cities through indirect effects. Although the indirect effects of urban social inclusion and government management are positive but not significant, the social inclusion of cities and the improvement of government management conditions have not significantly improved the level of sustainable competitiveness of their neighboring cities through indirect effects. In contrast, urban human capital potential and infrastructure improvements will hinder the improvement of the level of sustainable competitiveness of neighboring cities through indirect effects. Specifically, the role of human capital potential in hindering the level of sustainable competitiveness of neighboring cities through indirect effects is not significant, but the elasticity of

5.9 Econometric Analysis of Global Cities’ Sustainable Competitiveness: …

363

infrastructure that hinders the sustainable competitiveness of neighboring cities is 0.9% and at a significant level of 1%. The above is remarkable. In terms of the total effect of different factors on the sustainable competitiveness of cities, urban economic vitality, environmental quality, social inclusion, technological innovation, global linkages, government management, human capital potential, and improvement of basic society will significantly promote urban sustainability. The level of competitiveness has increased and is significant at least at the 10% level of significance. It is worth noting that after considering the feedback effect of spatial spillover between cities, the city’s technological innovation promotes the city’s sustainable competitiveness with a maximum elasticity value of 19.6%, followed by the city’s human capital potential of 13.2%, both are significant at a significance level of 1%, which is consistent with the above conclusion that technological innovation and human capital potential are important factors affecting the level of sustainable competitiveness of cities. The economic vitality of the city, the quality of the environment, and the elasticity of government management to promote the level of sustainable competitiveness of the city were 17.1%, 11.4%, and 10.3%, respectively, both exceeding 10%, and both were significant at the 1% significance level. The social inclusion and infrastructure of cities have elasticity values of 9.6% and 8% for sustainable urban competitiveness, respectively, and their role cannot be ignored. It is worth noting that although the potential of urban human capital and infrastructure will hinder the improvement of the level of sustainable competitiveness of neighboring cities through indirect effects, its effect is less than the direct effect, and after amplification by feedback effect, it will also improve itself. Sustained level of competitiveness and at least at the level of significance of 10% are significant. In summary, the spatial external factors existing between cities are introduced into the generalized nested space model, namely the GNS model. The results show that human capital potential and technological innovation are the most important to enhance the sustainable competitiveness of cities without considering the spatial spillover effect. The two important factors are the elastic values of the level of sustainable competitiveness of cities, which are 15.5% and 13.5%, respectively. After considering the spatial spillover effect, the elastic value of technological innovation and human capital potential through the feedback effect on the level of sustainable competitiveness of cities is 19.6% and 13.2%, respectively, while the contribution of urban economic vitality to the level of sustainable competitiveness rises to 17.1 %. Therefore, the city’s technological innovation, human capital potential, and economic vitality are the most important factors determining sustainable competitiveness.

Appendix

Pengfei Ni, and Weijin Gong

Theoretical Framework Theoretical Framework for Urban Economic Competitiveness and Sustainable Competitiveness In the process of development, the city, depending on its internal organizational efficiency and external economic advantages formed based on its own factor endowment and space environment, needs to create more values and acquire various resource rent in a rapid and extensive way by means of attracting, controlling, and transforming resources and dominating the market, so as to continuously provide and maximize the benefits for its residents. That is the level of the city’s competitiveness. Therefore, the essence of city competitiveness is the ability of the city to create values and continue to create values in the future. In the short run, it is expressed as the output of city competitiveness, including the scale, speed, and efficiency of the value currently created, also known as the city’s economic competitiveness. The city constitutes its own comparative advantage, absolute advantage and competitive advantage through the concentration of subjects of economic activities such as enterprises and qualified personnel. When competing with industries and enterprises in other cities, higher economic rents are obtained on this basis, which constitute an explanatory variable of city competitiveness. In the long run, the urban factor endowment conditions and the spatial environment determine the sustainability of the city’s competitiveness, that is, the input of city competitiveness, which is called the sustainable competitiveness of the city. On this basis, this report intends to construct the following city competitiveness model: urban sustainable competitiveness determines the urban economic competitiveness through the explanatory variables of economic competitiveness,

© China Social Sciences Press 2021 P. Ni et al., Global Industry Chains: Creating a Networked City Planet, https://doi.org/10.1007/978-981-16-2058-4

365

366

Appendix

and the urban economic competitiveness further influences the urban sustainable competitiveness through the explanatory variables of economic competitiveness.

Urban value: economic competitiveness

Urban industry quality: explanatory variable for economic competitiveness

Urban factors and environment: sustainable competitiveness

Economic Competitiveness and Its Explanatory Variables As mentioned above, the essence of city competitiveness is the current ability of the city to create values and acquire economic rent. The size of this capability is reflected by the competition results in the current specific period, which is the city’s current and short-term city competitiveness output. Therefore, the main manifestation of economic competitiveness is the comprehensive growth of urban economic level and comprehensive economic efficiency. This report uses the five-year GDP growth and GDP per square kilometer to reflect the city’s comprehensive long-term growth and economic efficiency and builds the following model of urban economic competitiveness: EC = F(LI, EE) Among them, EC, LI, and EE, respectively, represent the city’s economic competitiveness, comprehensive long-term growth, and comprehensive economic efficiency. The comprehensive long-term growth specifically includes the city’s ability to attract, compete for, occupy and control resources and the market’s ability to create values, the potential to create value in the future and the long-term growth that continuously determines GDP. Therefore, the average annual growth of GDP for five consecutive years is used to measure the comprehensive long-term growth of the city. Different from the indicator of GDP per capita reflecting the level of urban economic development, the comprehensive economic efficiency is expressed by the urban GDP per square kilometer. Specifically, the urban GDP divided by the urban area (in standards of metropolis) is GDP per square kilometer, which reflects the ability of the city to

Appendix

367

create wealth in unit space and also reflects the city’s access to economic rent and benefits and the efficiency of the use of land resources. The constitution of explanatory variables of economic competitiveness is an important way to reflect the development process of city competitiveness. The urban industrial system can be transformed into urban economic competitiveness, and the urban economic competitiveness will adversely affect the sustainable competitiveness of the city through its explanatory variables. The rational men and enterprises in the city and their activities constitute the main body of the urban industrial system. The labor and creation of people constitute the economic activities of enterprises. The sum of different types of enterprises constitutes the industry of the city, and the sum of different industries ultimately constitutes the industrial system of the city. Based on the above analysis, this report builds the following explanatory model of economic competitiveness: EEC = F(FE, TI, IS, HR, LD, CC, SE, IN, LE, GC) Among them, EEC, FE, TI, IS, HR, LD, CC, SE, IN, LE, and GC represent, respectively, the explanatory results of the city’s economic competitiveness, financial service, technological innovation, industrial systems, human resources, local demands, operation costs, institutional environment, infrastructure, living environment, and global connections. Specifically, urban financial service reflects the ability and efficiency of a city to mobilize residents and businesses to save, absorb, and allocate capital and is one of the important determinants of a new-type global city. Technological innovation is the ultimate and inexhaustible driving force for sustainable development of cities and constitutes the basic determining variable of new-type global cities. The industrial system reflects the quality of urban industry and the level of modernization. Human resources are the main body in cities to create wealth and values. The local demands reflect the local market demand and consumption power. The operation costs, from the perspective of enterprises, reflect the time cost and economic cost of the enterprises in compliance with policies and regulations in terms of establishment, operations, trading, tax paying, termination, and contracts execution in the cities. The institutional environment shows the institutional rules and environment for interactions of economic activity subjects. The infrastructure reflects the convenience of urban infrastructure. The living environment reflects the urban living environment and security status. The global connection shows the status and reputation of economic activity subjects in the global industrial system.

368

Appendix Value of the city: economic competitiveness ------

Comprehensive growth

Competitive efficiency

Urban industrial system : explanatory variables of economic Financial service

Technological innovation

Industrial system

Human resources

Local demands

Operation costs

Institutional envirot

Infrastructure

Living environment

Sustainable Competitiveness The essence of sustainable city competitiveness is the urban factor endowment conditions and the spatial environment which constitute the decisive factors in the process of urban development, not only affecting the city’s current development, but also determining its future development. Therefore, the urban sustainable competitiveness can be decomposed into three dimensions: city competitiveness input, sustainability, and long term. According to the definition of sustainability, economic competitiveness emphasizes urban output, and sustainable competitiveness emphasizes input. Therefore, human capital, as the most direct variable of production input, is the most basic indicator for measuring sustainable competitiveness of cities. Therefore, the density and increment of human resources should be used to measure the urban sustainable competitiveness. However, limited by the availability of international urban human capital data and the timeliness of the project, measure of sustainable competitiveness this year does not use human capital but carries out analysis using the explanatory variables that constitute sustainable competitiveness. From the perspective of the scale and increment of urban high-income population, we will further analyze the level of urban sustainable competitiveness. Based on existing studies, the project team has constructed a model of sustainable city competitiveness including eight explanatory variables: SC = F(HRP, EV, TI , SI , EQ, SM , IN , GC) Among them, SC, HRP, EV, TI , SI , EQ, SM , IN , and GC represent, respectively, the level of urban sustainable competitiveness, human capital potential, economic vitality, technological innovation, social inclusion, environmental quality, institutional environment, infrastructure, and global connections. Specifically, unlike the human resources in economic competitiveness, the human capital potential in sustainable competitiveness reflects the state of human capital of the city in the

Appendix

369

future. The economic vitality of the city shows the level of economic development and the speed of development of the city, which is an important manifestation of the city’s sustainable development capability. Technological innovation is the ultimate power source for urban development. Social inclusion reflects the ability of urban social mobilization and social integration. Environmental quality is the result of interaction of natural environment and social environment, reflecting the ability and level of urban sustainable development. Institutional management is the impact of various urban institutional regulations and policies on city competitiveness and the supply of production factors. Infrastructure is the material basis for cities to achieve sustainable development. Global connections demonstrate the status and reputation of economic activity subjects in the global industrial system. The following figure shows the theoretical framework of sustainable competitiveness.

Urban input: sustainable competitiveness ---------

Increment of human capital

Efficiency of human capital

Urban factors and environment: explanatory variables of sustainable competitiveness Human capital potential

Economic vitality

Technological

innovation

Social inclusiveness

Environment quality

Institutional

In short, economic competitiveness and sustainable competitiveness take the quality of economic activity themes as the center, the internal and external links of the theme of economic activities as the main line, the institutional environment of economic activity subject interaction as the basis, and the supply and demand relationship of economic activity subjects as contents. Economic competitiveness and sustainable competitiveness integrate multi-dimensional influential factors such as the supply and demand, stock and increment, short-term and long-term, static and dynamic, software and hardware, internal and external factors of economic activity subjects.

370

Appendix

Index System The Revealed Index System of Global Urban Comprehensive Economic Competitiveness

Items

Name of index

Data source

Revealed economic competitiveness

1.1 Increment of GDP in five consecutive years

EIU database of the economist, base year 2017

1.2 GDP per square kilometer

The urban area data are collected by the research project group. The source of urban GDP data is the Economist EIU database, corrected by GDP per capita

The Revealed Index System of Global Urban Economic Competitiveness

Items

Name of index

Data source and calculation methods

1. Financial service

1.1 Bank index

Data source: Forbes 2000, on a weighted basis

1.2 Exchange index

Data from the World Federation of Exchanges and the World Bank, measured by transaction volume of each exchange

2.1 Patent index

Data from the World Intellectual Property Organization (WIPO), fused by the total number of the city’s historical patents and the number of patents in the year

2.2 Thesis index

Data source: The website of “Web of Science”

3.1 Index of productive service enterprises

Data source: Forbes 2000, on a weighted basis

3.2 Index of technological enterprises

Data source: Forbes 2000, on a weighted basis

2. Technological innovation

3. Industrial system

(continued)

Appendix

371

(continued) Items

4. Human capital

5. Local demands

Name of index

Data source and calculation methods

3.3 Industry development

The national industry is weighted and synthesized by the non-agricultural industry, the distance from the optimal secondary industry and the distance from the optimal tertiary industry, wherein the non-agricultural industry is 100 minus the value of the primary industry; the distance from the optimal secondary industry is 100 minus the absolute value of the secondary industry minus 30; the distance from the optimal tertiary industry is 100 minus the absolute value of the tertiary industry minus 75

4.1 Population of labor force (15–59)

The Economist EIU database

4.2 Percentage of youth population

The percentage of young people (20–29) to the total population, data source: The Economist EIU database

44.3 University index

Data source of world universities ranking: The website of Ranking Web of Universities

5.1 Total disposable income

The Economist EIU database

5.2 Income per capita

The Economist EIU database

6.1 Interest rate of loans

Data source: WDI database of the World Bank

6.2 Ratio of taxation to GDP

Data source: WDI database of the World Bank

7.1 Business convenience

Data source: Business Environment Annual Report of the World Bank

7.2 Economic freedom

Economic freedom index published by the Wallstreet Journal and the Heritage Foundation

8. Global connections

8.1 Degree of multinational company connections

Data source: Website of Forbes 2000. The calculation method is seen in World City Network

9. Infrastructure

9.1 Shipping convenience

The shortest spherical distance between the city and the world’s 100 largest ports

6. Operation costs

7. Institutional costs

(continued)

372

Appendix

(continued) Items

10. Living environment

Name of index

Data source and calculation methods

9.2 Quantity of broadband users

Data source: WDI database of the World Bank. Converted proportionally based on the urban population

9.3 Number of air routes

Data source: Websites of airports of various cities, Wikipedia, and relevant data from IATA website (2016)

10.1 PM 2.5

Data source: World Health Organization and World Bank

10.2 Crime rate

Data source: NUMBEO website. The crime rate data in some Chinese cities are regression calculated based on the general crime rate data of China

The Index System of Global Sustainable Competitiveness

Item

Name of index

Data source and calculation method

1. Human capital potential

1.1 University index

Data source: The website of Ranking Web of Universities

1.2 Percentage of youth population (20–29)

The Economist EIU database

2.1 GDP per capita (USD per person)

The Economist EIU database

2. Economic vitality

2.2 Increment of average annual The Economist EIU database GDP in five consecutive years 3. Technological innovation

3.1 Patent index

Data from the World Intellectual Property Organization (WIPO), fused by the total number of the city’s historical patents and the number of patents in the year

3.2 Number of published theses Data source: The website of Web of Science 4. Social inclusiveness

4.1 Crime rate

Data source: Website of NUMBEO. Some data of Chinese cities are converted proportionally (continued)

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(continued) Item

5. Environment quality

6. Institutional management

7. Infrastructure

Name of index

Data source and calculation method

4.2 Gini coefficient

Data source: The Economist EIU database. Calculated results

5.1 CO2 emission per capita

Data source: WDI database of the World Bank. Converted proportionally based on the urban population

11.2 PM 2.5

Data source: World Health Organization and World Bank

6.1 Operation environment index

Data source: Business Environment Annual Report of the World Bank

6.2 Economic freedom

Economic freedom index published by the Wallstreet Journal and the Heritage Foundation

7.1 Shipping convenience

The shortest spherical distance between the city and the world’s 100 largest ports

7.2 Quantity of broadband users Data source: WDI database of the World Bank. Converted proportionally based on the urban population

8. Global connections

7.3 Number of air routes of airports

Data source: Websites of airports of various cities, Wikipedia, and relevant data from IATA website (2016)

8.1 Degree of multinational company connections

Data source: Website of Forbes 2000. The calculation method is seen in World City Network

Sample Selection and Stratification Definition of City A city in economics refers to a contiguous geographical area with considerable area, economic activity, and household concentration that produces economies of scale in private enterprises and the public sector. A modern city usually refers to a cluster of urban residents with high degree of urbanization. Certainly, different countries and regions have different definitions of the city according to various needs. According to the research needs of this report, the project team defines the city as a combined

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area including a central city as the core and the surrounding regions. Therefore, the definition of the project team emphasizes the city in the sense of a metropolitan areas, not in the sense of an administrative region. It should be noted that, based on the availability of data, there are only statistical data on the level of administrative regions for some sample cities, such as some sample cities in China. Unless otherwise stated, the sample cities in this paper are all cities in terms of metropolitan area.

Sample Cities Determining sample cities is the basis for conducting research on global urban economic competitiveness and sustainable competitiveness. In order to guarantee the typicality and catholicity of samples, the sample cities for the project are based on the World Urbanization Outlook released by the United Nations Department of Economics and Affairs in 2015. Excluding samples with an urban population less than 500,000 and considering the specific conditions in China and other countries, the project finally selected 1007 cities as research subjects. In terms of geographical distribution, the samples of this project involve a total of 1007 cities in 135 countries and regions on 6 continents, including 131 cities in 11 countries in North America, 7 cities in 2 countries in Oceania, 102 cities in 39 countries in Africa, 75 cities in 11 countries in South America, and 127 in 29 countries in Europe, and 565 cities in 43 countries in Asia. These 1007 cities basically cover the cities of various economic sectors and economic development levels in the world today. The detailed information on sample cities and countries can be seen in the part of economic competitiveness and sustainable competitiveness ranking in Chap. 1.

Stratification of Samples The report adopts the method of sample stratification in 2017. A global city, also known as a world city, refers to a city that directly affects global affairs at the social, economic, cultural, or political levels. It is the central node of the global economic system or the organizational node in the world city network system. These nodes assemble into a multi-polar and multi-level world city network system based on the levels, capacities, and connections. From the existing literature, current research tends to use the single index to classify global cities from the perspective of urban functions and value system. The shortcoming is that it cannot fully reflect the status of the city in the world, so global cities shall be accurately positioned multiple dimensions such as population, structure, space, and network. In view of this, the project team, starting from the competitiveness research, believes that the competitiveness of the city is mainly composed of four aspects: factors, industries, functions, and values, of which value is the most fundamental standard. Therefore, oriented on the value

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perspective, this report is based on the revealed comprehensive economic competitiveness index of urban value, including urban agglomeration degree and connection degree, divide global cities into four levels A, B, C, and D by using cluster analysis method. Thereinto, A, B, and C are divided into three ranks, respectively, so the cities include 4 levels and 10 ranks. The results of the specific division can be found in the global city competitiveness index Table in Chap. 1.

Data Source The global city competitiveness study is a research project that is highly demanding for data quality and quantity. The data collection team in the research group started work in last November and organized data translation and collection teams including English, French, German, Spanish, Portuguese, Italian, Arabic, Russian, Japanese, Korean, and other languages. Data have been collected from a variety of sources, including official statistical publications, official networks, and academic research results. In the process, the project was greatly supported by many foreign research scholars and research institutions, as well as international students. After nearly half a year of repeated search and collation, the ideal index coverage was obtained. In view of the differences in the standards of data in different countries, we first studied the statistical items and standards of international organizations such as the United Nations Statistical Distribution (UNSD), the World Bank World Development Indicators), and the OECD Database, then considered the actual situation of each country, and finally determined the statistically appropriate, most comparable, and the most extensive data statistics standards. We applied the standards into data collection and data processing and finally formed a unified standard database covering 1035 global cities. The index data used in the global city competitiveness index system mainly come from four sources, including national government statistical agencies; international statistical agencies; international research institutions or companies’ subject reports and survey data; and big data collection via web crawler. The specific source of data materials and index explanations can be seen in the GUCP database. Despite this, due to limitations of subjective and objective conditions, some cities with special characteristics have to be abandoned, and some important indexes have to be adjusted and deleted, which has left regrets for this study. We hope to make some breakthroughs in our future work.

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Method of Calculation Method of Index Data Standardization The dimensions of each index data of city competitiveness are different. First, all index data have to be non-dimensionalized. Objective indexes are divided into single objective indexes and comprehensive objective indexes. For the nondimensionalization of original data of the single objective indexes, this paper mainly adopts four methods: standardization, indexation, threshold value method, and percentage-level method. −

The standardized calculation formula is Xi = (xiQ−2x) : Xi is the converted value of xi , xi is the original data, x is the average value, Q2 is the variance, and Xi is the standardized data. The calculation formula of the exponential method is Xi = xx0ii , Xi is the converted value of xi , xi is the original value, x0i is the maximum value, and Xi is the exponent. i −xMin ) , Xi is The calculation formula of the threshold value method is: Xi = (x(xMax −xMin ) the converted value of xi , xi is the original value, xMax is the largest sample value, and xMin is the minimum sample value. ni , Xi is the The calculation formula of the percentage level method is: Xi = (ni +N i) converted value of xi , xi is the original value, ni is the number of sample values less than xi , and Ni is the number of sample values greater than or equal to xi except xi . The non-dimensionalization of synthesizing the raw data of objective indexes is: first, to quantify the individual indexes in the composition, and then weight the comprehensive index value by means of the equal weight method.

Methods of Measuring City Competitiveness Economic Density Economic density (GDP per square kilometer weighted by GDP per capita) is an important indicator for measuring comprehensive economic efficiency. It is calculated with nonlinear weighted synthesis. The so-called nonlinear weighted comprehensive method (or “multiplication” synthesis method) refers to the application of a  w nonlinear model g = xj j for comprehensive evaluation. Therein, wi is the weight coefficient, and xi ≥ 1. For a nonlinear model, as long as one of the index values is very small in the calculation, the final value will quickly approach zero. In other words, this evaluation model is sensitive to indexes with smaller values and slow to indicators with greater values. The use of nonlinear weighted comprehensive method for city competitiveness measurement can reflect the comprehensive index values in a more comprehensive and scientific way.

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Economic Competitiveness, Explanatory Variables of Economic Competitiveness, and Calculation Methods of Sustainable Competitiveness Although the explanatory indexes of city competitiveness designed in the report are second-level indexes. Actually, including the original indexes, the explanatory indexes of city competitiveness consist three levels. The third-level indexes are synthesized into second-level indexes and the second-level indexes into the first-level indexes by mean of standardization and equally weighted addition. The standardization method is as described above. Its formula is: Thereinto, zil represents each of the second-degree indexes, and zilj represents each of the third-degree indexes. Zi =



zil

l

Thereinto, Zi represents each of the first-degree indexes, and zil represents each of the second-degree indexes.

Calculation Method of Coupling Coordination Degree ⎧ ⎫1/n ⎪ ⎪ n ⎪ ⎪  ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ fi (x) ⎨ ⎬ i=1 Cv =   ⎪

2/n ⎪ n ⎪ ⎪  ⎪ ⎪ fi (x)+fj (x) ⎪ ⎪ ⎪ ⎪ ⎩ ⎭ 2 i=1,i