The World: 300 Years of Urbanization Expansion: Global Urban Competitiveness Report (2019–2020) 9789819935536, 9819935539

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Pengfei Ni · Marco Kamiya · Jing Guo · Haidong Xu

The World: 300 Years of Urbanization Expansion Global Urban Competitiveness Report (2019–2020)

The World: 300 Years of Urbanization Expansion

National Academy of Economic Strategy, CASS (NAES, CASS) United Nations Human Settlements Programme, UN-HABITAT

Pengfei Ni · Marco Kamiya · Jing Guo · Haidong Xu

The World: 300 Years of Urbanization Expansion Global Urban Competitiveness Report (2019–2020)

Pengfei Ni Center for City and Competitiveness Chinese Academy of Social Sciences Beijing, China Jing Guo School of Government Management Shenzhen University Shenzhen, China

Marco Kamiya Division of Digital Transformation and Artificial Intelligence Strategy United Nations Industrial Development Organization Vienna, Austria Haidong Xu National Academy of Economic Strategy Beijing, China

ISBN 978-981-99-3552-9 ISBN 978-981-99-3553-6 (eBook) https://doi.org/10.1007/978-981-99-3553-6 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. © China Social Sciences Press 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The 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, expressed 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

Contributors

Authors Marco Kamiya, Director of UN-HABITAT Urban Economics & Finance Bureau Ni Pengfei, Director of Center for City & Competitiveness, CASS Guo Jing, Assistant Professor, School of Government Management, Shenzhen University Li Bo, Ph.D., Associate professor at Tianjin University of Technology Ma Hongfu, Ph.D., Lecturer at Tianjin University of Finance and Economics Xu Haidong, Assistant Professor, National Academy of Economic Strategy, CASS Liz Paterson Gauntner, Consultant of the UN-HABITAT Serge Allou, UCLG World Secretariat Luc Aldon, UCLG World Secretariat Huáscar Eguino, Inter-American Development Bank Axel Radics, Inter-American Development Bank Mosha.A.C, University of Botswana Martim O. Smolka, Senior fellow of Lincoln Institute of Land Policy Gong Weijin, Ph.D., National Academy of Economic Strategy, CASS Li Qihang, Ph.D., Associate professor at Shandong University of Finance and Economics Cao Qingfeng, Ph.D., Lecturer at Tianjin University of Finance and Economics Guo Jinhong, Ph.D., Candidate at Nankai University Peng Xuhui, Ph.D., National Academy of Economic Strategy, CASS

Statistical and Big Data group Group Leaders: Wang Yu, Center for City & Competitiveness, CASS

v

vi

Contributors

Li Jianquan, Center for City & Competitiveness, CASS, Beijing Ultrawpower Software, Co., Ltd. Xu Shuai, Beijing Wenge Group Co., Ltd. Wang Xiaodong, Beijing Wenge Group Co., Ltd. Group Members: Liu Xiaokang, Center for City & Competitiveness, CASS Beijing Ultrawpower Software, Co., Ltd. Xing Wentao, Center for City & Competitiveness, CASS Bin Youcai, Center for City & Competitiveness, CASS Hu Min, Center for City & Competitiveness, CASS Hu Xufeng, Center for City & Competitiveness, CASS Luo Zikang, Beijing Wenge Group Co., Ltd. Liu Xingchen, Beijing Wenge Group Co., Ltd. Liu Jing, Beijing Wenge Group Co., Ltd. Chen Jie, Jiangxi University of science and technology Li Moxuan, Nankai University Xu Zhen, School of foreign languages, Peking University Chen Haichao, School of Economics and Management, Dalian University of Technology Zheng Yuhan, School of Art, Peking University Qin Yige, Honorary college of Tianjin Foreign Languages University Fan Uunying, School of International Relations, CASS Tang Keyu, School of English, Tianjin Foreign Languages University

Report Coordinators Huang Jin, Center for City & Competitiveness, CASS Liu Shangchao, Center for City & Competitiveness, CASS Zhang Yi, Consultant of UN-Habitat Guo Jing, Ph.D., Candidate at Graduate School of CASS

Contents

1 Ranking of Global Urban Competitiveness 2019 . . . . . . . . . . . . . . . . . . .

1

2 The World: 300 Years of Urbanization Expansion . . . . . . . . . . . . . . . . . 31 2.1 From the Micro Level, the Change of Leading Cities Causes the Basic “Cell” Change of the World . . . . . . . . . . . . . . . . . . . . . . . . . 31 2.2 From the Perspective of Macro Structure, the Evolution of Global Urban System Determines the Change of the world’s Pattern and System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 2.3 From the Perspective of Macro Gross, Global Urban Development Has Completed the Epoch-Making Transformation of Human Civilization . . . . . . . . . . . . . . . . . . . . . . . . . 73 2.4 From the Perspective of Space, the Changes of Global Cities’ Characteristics Determine the Evolution of world’s Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 2.5 From the Dynamic Mechanism, the Human Development Momentum Bred by Cities Determines the Appearance and Change of the Urban World . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111 3 Experience & Methods of Global Municipal Finance . . . . . . . . . . . . . . 3.1 Global Trends in Municipal Finance . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Municipal Finance, Localization of the SDGs and the Role of UCLG . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Subnational Finance in Latin American and the Caribbean: Recent Trends and Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 Municipal Finance in Africa with Special Focus on Botswana . . . . . 3.5 Charges to Building Rights: A Notable Value Capture Experience from Brazil . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

133 133 144 157 168 179 186 187

vii

viii

Contents

4 Global Urban Economic Competitiveness Performance . . . . . . . . . . . . 4.1 Top 20 Cities: Fierce Competition Makes the Position Fluctuate Greatly, Global Comprehensive Centers and Technology Centers have Generally Improved, while Specialized Cities and Manufacturing Centers Declined Overall . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Top 200 Cities: Europe Has More Cities Declined in the Ranking While Asia Has More Cities Improved . . . . . . . . . . . 4.3 Top 10 Urban Agglomerations: Northern California Has the Highest Average and Rhein-Ruhr Has the Smallest Internal Difference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4 Three Main Economies: China Has More Cities Declined in the Ranking, While Some European Cities Have Declined Steeply . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5 Global Pattern: The Overall Level Has Declined, but the Divergence Has Narrowed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.6 Global Sub-Regional Pattern: Northern China and Eastern Europe Declined While Southern China and India Rose in Ranking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Explanatory Indicators of Global Urban Economic Competitiveness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 Local Factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Living Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3 Soft Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4 Hard Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5 Global Contacts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.6 Industrial Quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.7 Ranking of Explanatory Indicators of Global Urban Economic Competitiveness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Global Urban Sustainable Competitiveness Performance . . . . . . . . . . . 6.1 Top 20 Cities: Europe Holds the Most Seats, While Asia Has the Highest Mean Value . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2 Top 200 Cities: Asia Holds the Most Seats and Europe Has the Highest Mean Value . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3 Top 10 Urban Agglomerations: Seoul Has the Highest Mean Value, and Rhein-Ruhr is Best Balanced . . . . . . . . . . . . . . . . . . . . . . . 6.4 Three Main Economics: The United States and the European Union Far Surpass China, and the Development of US Cities is of Potential . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.5 Global Pattern: North American and Western European Cities Perform Well and with Small Divergence, While Asian Cities Stay in Low Level and with Significant Internal Difference . . . . . . .

193

193 195

198

201 203

206 209 209 216 224 232 242 248 257 307 307 308 311

313

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Contents

ix

6.6 Global Sub-regional Pattern: Coastal Cities and Cities Located in Temperate Zone Are Leading . . . . . . . . . . . . . . . . . . . . . . . 321 7 Explanatory Indicators of Global Urban Sustainable Competitiveness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1 Economic Vitality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2 Environmental Thoroughness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3 Social Inclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.4 Scientific and Technological Innovation . . . . . . . . . . . . . . . . . . . . . . . . 7.5 External Contacts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.6 Ranking of Explanatory Indicators of Global Urban Sustainable Competitiveness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 A New Set of Standards for Global City Classification . . . . . . . . . . . . . 8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2 Theory and Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.3 Empirical Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

325 325 336 344 351 359 367 419 419 423 427 471

Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 473 Afterwords . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 489

Chapter 1

Ranking of Global Urban Competitiveness 2019

City

Country

Level Economic Rank Sustainable Rank competitiveness competitiveness

New York-Newark

United States

A+

1

1

0.8638

3

London

United Kingdom

A+

0.8762

2

0.8246

4

Singapore

Singapore

B

0.8614

3

0.9026

1

Shenzhen

China

C+

0.8403

4

0.7136

19

San Jose

United States

C

0.8359

5

0.6491

33

Tokyo

Japan

A+

0.834

6

0.9012

2

San Francisco-Oakland

United States

B

0.8284

7

0.8083

5

Munich

Germany

B

0.8009

8

0.7238

15

Los Angeles-Long Beach-Santa Ana

United States

B

0.7986

9

0.757

9

Shanghai

China

B+

0.795

10

0.6614

29

Dallas-Fort Worth

United States

B

0.7911

11

0.639

41

Houston

United States

B

0.7892

12

0.7034

24

Hong Kong

China

B

0.7876

13

0.8028

7

Dublin

Ireland

B

0.7857

14

0.5049

134

Seoul

Republic of Korea

B+

0.7752

15

0.7263

14

Boston

United States

B

0.7737

16

0.7161

17

Beijing

China

A

0.7602

17

0.6412

38

Guangzhou

China

C+

0.7509

18

0.6011

67

Miami

United States

C+

0.7257

19

0.6981

25

Chicago

United States

B+

0.7205

20

0.7478

10

Paris

France

A

0.7199

21

0.806

6

Frankfurt am Main

Germany

C+

0.7088

22

0.712

20

Tel Aviv-Yafo

Israel

D+

0.7065

23

0.6338

44 (continued)

© China Social Sciences Press 2023 P. Ni et al., The World: 300 Years of Urbanization Expansion, https://doi.org/10.1007/978-981-99-3553-6_1

1

2

1 Ranking of Global Urban Competitiveness 2019

(continued) City

Country

Level Economic Rank Sustainable Rank competitiveness competitiveness

Seattle

United States

C+

0.704

24

0.6543

30

Suzhou

China

C

0.6879

25

0.611

58

Stockholm

Sweden

B

0.6826

26

0.7289

13

Philadelphia

United States

C+

0.6789

27

0.7092

21

Stuttgart

Germany

C

0.6744

28

0.7182

16

Osaka

Japan

B

0.6645

29

0.7701

8

Toronto

Canada

B

0.6575

30

0.7076

22

Baltimore

United States

C

0.6555

31

0.6177

50

Bridgeport-Stamford

United States

D

0.6549

32

0.5676

81

Dusseldorf

Germany

C

0.6521

33

0.5111

125

San Diego(US)

United States

C

0.6501

34

0.6141

54

Geneva

Switzerland

C

0.6489

35

0.6042

64

Atlanta

United States

B

0.6487

36

0.6514

32

Cleveland

United States

C

0.6486

37

0.6463

34

Perth

Australia

C

0.6422

38

0.6125

55

Denver-Aurora

United States

C+

0.6415

39

0.6321

45

Detroit

United States

C

0.6395

40

0.6021

66

Istanbul

Turkey

B

0.6381

41

0.5814

74

Nanjing

China

C+

0.6331

42

0.565

83

Wuhan

China

C

0.6305

43

0.5128

122

Taipei

China

B

0.6293

44

0.7051

23

Charlotte

United States

C

0.6277

45

0.5023

138

Nashville-Davidson

United States

C

0.6192

46

0.4935

145

Minneapolis-Saint Paul

United States

C+

0.6151

47

0.4856

150

Berlin

Germany

B

0.6148

48

0.689

26

Austin

United States

C

0.6127

49

0.5971

68

Hamburg

Germany

C+

0.6102

50

0.6318

46

Vienna

Austria

B

0.6019

51

0.6372

43

Abu Dhabi

United Arab Emirates

C

0.6

52

0.4269

224

Raleigh

United States

C

0.5999

53

0.6095

59

Chengdu

China

C+

0.5996

54

0.4943

143

Cologne

Germany

D+

0.5985

55

0.5916

71

Las Vegas

United States

C

0.5973

56

0.5197

117

Zurich

Switzerland

C+

0.5966

57

0.6268

47

Salt Lake City

United States

C

0.5848

58

0.6151

53

Richmond

United States

C

0.583

59

0.579

76

Copenhagen

Denmark

C+

0.5817

60

0.6174

51

Orlando

United States

C

0.5795

61

0.5961

69 (continued)

1 Ranking of Global Urban Competitiveness 2019

3

(continued) City

Country

Level Economic Rank Sustainable Rank competitiveness competitiveness

Moscow

Russian Federation

B

0.5784

62

0.7343

12

Sydney

Australia

B

0.5783

63

0.6435

35

Hangzhou

China

C

0.5765

64

0.5085

130

Wuxi

China

D+

0.5763

65

0.5361

103

Barcelona

Spain

B

0.5757

66

0.7366

11

Birmingham

United Kingdom

C

0.5736

67

0.6423

37

Changsha

China

C

0.5727

68

0.4745

165

Milwaukee

United States

D+

0.5692

69

0.5456

95

Vancouver

Canada

C

0.5682

70

0.6075

61

Brussels

Belgium

B

0.5656

71

0.6032

65

Dubai

United Arab Emirates

C+

0.5653

72

0.5408

98

Calgary

Canada

C

0.5652

73

0.5209

116

Doha

Qatar

C

0.5622

74

0.5641

84

Hannover

Germany

C

0.5599

75

0.6158

52

Qingdao

China

C

0.5595

76

0.4941

144

Columbus

United States

C

0.5585

77

0.5727

79

Sendai

Japan

D+

0.5579

78

0.4856

151

Louisville

United States

D+

0.5559

79

0.5323

105

Essen

Germany

D+

0.5548

80

0.5271

107

Chongqing

China

C

0.5545

81

0.3911

284

Tianjin

China

C

0.5543

82

0.527

108

Kuala Lumpur

Malaysia

C+

0.5535

83

0.6093

60

Foshan

China

D+

0.5522

84

0.5264

109

Washington, DC

United States

C+

0.548

85

0.612

56

Ulsan

Republic of Korea

D

0.5456

86

0.5795

75

Oklahoma City

United States

D+

0.5448

87

0.4527

192

Manchester

United Kingdom

C

0.5445

88

0.6517

31

Riyadh

Saudi Arabia

C

0.5434

89

0.5592

89

Ningbo

China

C

0.5429

90

0.4838

154

Phoenix-Mesa

United States

C

0.5427

91

0.5667

82

Antwerp

Belgium

D+

0.5424

92

0.5606

87

Amsterdam

Netherlands

B

0.5416

93

0.607

62

Zhengzhou

China

C

0.5412

94

0.4782

159

Tampa-St. Petersburg

United States

C

0.5396

95

0.5107

126

Baton Rouge

United States

D+

0.5329

96

0.4674

173 (continued)

4

1 Ranking of Global Urban Competitiveness 2019

(continued) City

Country

Level Economic Rank Sustainable Rank competitiveness competitiveness

Cincinnati

United States

C

0.5307

97

0.4216

234

Dortmund

Germany

D+

0.5296

98

0.5334

104

Changzhou

China

D+

0.5291

99

0.4802

158

Haifa

Israel

D+

0.5276

100

0.5618

85

Montreal

Canada

C+

0.5261

101

0.6408

40

Jakarta

Indonesia

C

0.5248

102

0.4229

231

Nagoya

Japan

C

0.5223

103

0.6253

48

Dongguan

China

D+

0.5223

104

0.5166

121

San Antonio

United States

C

0.5215

105

0.491

148

Hiroshima

Japan

D+

0.52

106

0.6429

36

Oslo

Norway

C+

0.5199

107

0.4838

153

Dresden

Germany

D+

0.5192

108

0.5217

113

Hague

Netherlands

D+

0.5163

109

0.5048

136

Indianapolis

United States

C

0.5155

110

0.4631

181

Provo-Orem

United States

E+

0.5147

111

0.4823

157

Hamilton

Canada

D+

0.5136

112

0.5048

135

Macao

China

D+

0.5134

113

0.4929

146

Gold Coast

Australia

D

0.5116

114

0.467

176

Kansas City

United States

D+

0.5109

115

0.4578

186

Leipzig

Germany

D+

0.5105

116

0.5053

133

Virginia Beach

United States

D

0.509

117

0.5209

115

Jedda

Saudi Arabia

D

0.5086

118

0.5089

128

Bangkok

Thailand

C+

0.508

119

0.4673

174

Brisbane

Australia

C

0.508

120

0.5251

111

Nantong

China

D+

0.5076

121

0.4217

233

Pittsburgh

United States

C

0.5073

122

0.4613

183

Melbourne

Australia

C+

0.5064

123

0.6806

27

Helsinki

Finland

C+

0.5042

124

0.5868

72

Madrid

Spain

B

0.5026

125

0.7147

18

Kaohsiung

China

D+

0.4989

126

0.5114

124

Charleston-North Charleston

United States

D+

0.4982

127

0.4407

205

Mexico City

Mexico

C

0.4981

128

0.5608

86

Hartford

United States

D+

0.4977

129

0.5605

88

Ottawa-Gatineau

Canada

C

0.4965

130

0.4652

177

Incheon

Republic of Korea

C

0.4962

131

0.6113

57

Sapporo

Japan

D+

0.4948

132

0.5582

90

Riverside-San Bernardino

United States

D

0.4939

133

0.4368

215 (continued)

1 Ranking of Global Urban Competitiveness 2019

5

(continued) City

Country

Level Economic Rank Sustainable Rank competitiveness competitiveness

Bristol

United Kingdom

C

0.4935

134

0.432

219

Gothenburg

Sweden

D+

0.4934

135

0.4182

241

Allentown

United States

D

0.4912

136

0.3836

298

Rome

Italy

B

0.4864

137

0.6652

28

Colorado Springs

United States

D

0.4863

138

0.4963

142

Grand Rapids

United States

D

0.4861

139

0.4093

257

Lille

France

D+

0.4858

140

0.4829

155

jinan

China

C

0.4848

141

0.4407

206

Kitakyushu-Fukuoka

Japan

D

0.4846

142

0.6231

49

Milan

Italy

B

0.4839

143

0.641

39

Providence

United States

D+

0.4834

144

0.4708

169

Hefei

China

C

0.4831

145

0.4462

199

Lyon

France

C

0.4829

146

0.5072

131

Samut Prakan

Thailand

E

0.4828

147

0.4436

201

Quanzhou

China

D

0.4821

148

0.4021

268

Xiamen

China

C

0.4818

149

0.5486

94

Xi’an

China

C

0.4813

150

0.4477

197

Edmonton

Canada

C

0.48

151

0.4029

265

Rotterdam

Netherlands

C

0.4781

152

0.4754

163

Fuzhou(FJ)

China

C

0.4769

153

0.4163

246

Birmingham(US)

United States

D

0.4766

154

0.406

261

Honolulu

United States

D+

0.4765

155

0.4539

190

Santiago de Chile

Chile

C

0.4758

156

0.5378

100

Columbia

United States

C

0.4756

157

0.437

214

West Yorkshire

United Kingdom

D

0.475

158

0.5917

70

Worcester

United States

D

0.4745

159

0.5086

129

Dayton

United States

D

0.4745

160

0.4248

229

Delhi

India

C

0.4743

161

0.4044

262

San Jose

Costa Rica

D+

0.4713

162

0.5186

119

Yangzhou

China

D

0.4711

163

0.4038

263

Auckland

New Zealand

C+

0.47

164

0.5249

112

Cape Coral

United States

E+

0.4679

165

0.4406

207

Valencia

Spain

C

0.4678

166

0.5764

77

Lima

Peru

C

0.4661

167

0.5128

123

Akron

United States

D

0.4652

168

0.4102

255

Bogota

Colombia

C+

0.465

169

0.5187

118

Liverpool

United Kingdom

C

0.4647

170

0.4824

156 (continued)

6

1 Ranking of Global Urban Competitiveness 2019

(continued) City

Country

Level Economic Rank Sustainable Rank competitiveness competitiveness

Medina

Saudi Arabia

D

0.4646

171

0.5172

120

Knoxville

United States

D+

0.4644

172

0.4068

258

Zhuhai

China

D+

0.4644

173

0.4554

189

Zhenjiang

China

D

0.464

174

0.4363

217

Yantai

China

D+

0.4632

175

0.4116

253

Marseille-Aix-en-Provence France

C

0.463

176

0.4438

200

Sheffield

United Kingdom

D+

0.4625

177

0.4107

254

Jerusalem

Israel

D+

0.4624

178

0.5734

78

Belfast

United Kingdom

D+

0.4602

179

0.5093

127

Taizhou(js)

China

D

0.4593

180

0.3909

285

Panama City

Panama

D+

0.4585

181

0.4173

243

Bucuresti

Romania

C

0.4585

182

0.4371

212

Venice

Italy

D+

0.458

183

0.4771

161

Sacramento

United States

D+

0.4574

184

0.3792

305

Dalian

China

C

0.4573

185

0.4527

193

Glasgow

United Kingdom

C

0.4571

186

0.5425

96

Buffalo

United States

D+

0.4555

187

0.5034

137

Manila

Philippines

D+

0.4554

188

0.3361

380

Mecca

Saudi Arabia

E+

0.4544

189

0.45

196

New Haven

United States

D+

0.4543

190

0.5069

132

Xuzhou

China

D+

0.4533

191

0.3973

278

Busan

Republic of Korea

D+

0.4526

192

0.5496

92

Warsaw

Poland

C+

0.451

193

0.3835

299

Ogden

United States

D

0.4507

194

0.461

184

Changwon

Republic of Korea

E+

0.4501

195

0.4029

264

Buenos Aires

Argentina

C+

0.4484

196

0.638

42

Nanchang

China

D+

0.4451

197

0.4199

238

Gwangju

Republic of Korea

D

0.4435

198

0.5565

91

Daejeon

Republic of Korea

D+

0.4433

199

0.583

73

Shenyang

China

C

0.4429

200

0.4614

182

Zaragoza

Spain

D

0.4426

201

0.4998

140

Adelaide

Australia

C

0.4407

202

0.5417

97

Dongying

China

D

0.4404

203

0.3777

308

Monterrey

Mexico

D+

0.4399

204

0.4021

269 (continued)

1 Ranking of Global Urban Competitiveness 2019

7

(continued) City

Country

Level Economic Rank Sustainable Rank competitiveness competitiveness

Gebze

Turkey

D

0.4387

205

0.3863

290

Zhongshan

China

D+

0.4368

206

0.4903

149

Prague

Czech Republic

C+

0.4368

207

0.4412

203

Montevideo

Uruguay

D+

0.4365

208

0.4558

188

Astana

Kazakhstan

D

0.4359

209

0.6054

63

Shaoxing

China

D

0.4355

210

0.4027

266

Toulouse

France

C

0.4332

211

0.4409

204

Lisbon

Portugal

C

0.433

212

0.4845

152

Taichung

China

D

0.4327

213

0.5262

110

Omaha

United States

D+

0.4327

214

0.3614

330

Jiaxing

China

D

0.432

215

0.4275

222

Bologna

Italy

C

0.4285

216

0.4642

178

Memphis

United States

D+

0.4278

217

0.4362

218

Nantes

France

D+

0.4273

218

0.3897

288

Daegu

Republic of Korea

D+

0.4266

219

0.538

99

Mumbai

India

C+

0.4262

220

0.3443

357

Ankara

Turkey

C

0.4261

221

0.4269

225

Naples

Italy

C

0.4253

222

0.5487

93

Nice

France

D+

0.4242

223

0.3842

296

Liege

Belgium

D

0.4232

224

0.5021

139

Verona

Italy

D+

0.4229

225

0.4694

171

Leicester

United Kingdom

D+

0.4225

226

0.474

166

Poznan

Poland

D

0.4165

227

0.4179

242

Sarasota-Bradenton

United States

D

0.4161

228

0.3423

365

Nottingham

United Kingdom

D+

0.4159

229

0.4154

247

Izmir

Turkey

D+

0.4154

230

0.4191

239

Bordeaux

France

C

0.4149

231

0.3554

342

Changchun

China

D+

0.4149

232

0.4012

270

Budapest

Hungary

C+

0.4146

233

0.4563

187

Toulon

France

D

0.4144

234

0.3507

347

Weihai

China

D

0.4122

235

0.3887

289

Bremen

Germany

D+

0.4118

236

0.3243

405

Shizuoka-Hamamatsu M.M.A.

Japan

D

0.4116

237

0.4704

170

Rosario

Argentina

D

0.411

238

0.4262

227

Wuhu

China

D

0.4105

239

0.3394

373 (continued)

8

1 Ranking of Global Urban Competitiveness 2019

(continued) City

Country

Level Economic Rank Sustainable Rank competitiveness competitiveness

Zibo

China

D

0.4098

240

0.4122

251

Rochester

United States

C

0.4093

241

0.4272

223

Hsinchu

China

D

0.4091

242

0.3771

309

Malaga

Spain

D+

0.4047

243

0.5376

101

Florence

Italy

D+

0.4045

244

0.4599

185

Guiyang

China

D+

0.4033

245

0.373

313

Quebec

Canada

D+

0.4027

246

0.4098

256

New Orleans

United States

D+

0.4006

247

0.4671

175

Genoa

Italy

D+

0.3988

248

0.4213

235

Tainan

China

D

0.3961

249

0.4748

164

Tulsa

United States

D

0.3953

250

0.3404

371

Weifang

China

D

0.3947

251

0.3674

322

Bursa

Turkey

D

0.3944

252

0.4199

237

Albany

United States

D+

0.3938

253

0.2568

576

Kumamoto

Japan

E+

0.3938

254

0.413

250

Yancheng

China

D

0.3923

255

0.3438

358

Winnipeg

Canada

D+

0.3918

256

0.3978

277

Tangshan

China

D

0.3893

257

0.3916

283

Sao Paulo

Brazil

B

0.3874

258

0.5679

80

Dammam

Saudi Arabia

D

0.3863

259

0.4767

162

Shijiazhuang

China

D+

0.385

260

0.3814

301

Santa Fe

Argentina

D

0.3818

261

0.3513

346

Wenzhou

China

D+

0.3814

262

0.3853

294

Yichang

China

D

0.3814

263

0.3281

398

Taizhou(zj)

China

D

0.381

264

0.3701

317

Torino

Italy

D+

0.3809

265

0.4965

141

Pretoria

South Africa

D

0.3805

266

0.5313

106

Kunming

China

C

0.3794

267

0.3648

324

Niigata

Japan

E+

0.3791

268

0.4403

208

Maracaibo

Venezuela

E+

0.3766

269

0.4061

260

Rio de Janeiro

Brazil

C

0.3756

270

0.4917

147

Huizhou

China

D

0.3742

271

0.3901

287

Guadalajara

Mexico

D+

0.373

272

0.3854

292

Surabaya

Indonesia

D

0.3727

273

0.3987

274

Sharjah

United Arab Emirates

D

0.3724

274

0.4201

236

Maracay

Venezuela

E+

0.3716

275

0.4025

267

Bakersfield

United States

D

0.3687

276

0.3685

320

Krakow

Poland

C

0.3682

277

0.3862

291 (continued)

1 Ranking of Global Urban Competitiveness 2019

9

(continued) City

Country

Level Economic Rank Sustainable Rank competitiveness competitiveness

Jundiai

Brazil

E+

0.3673

278

0.3303

391

Baotou

China

D

0.3668

279

0.3301

392

Santo Domingo

Dominican Republic

D

0.3658

280

0.4368

216

Tyumen

Russian Federation

E

0.3645

281

0.3767

310

Tongling

China

D

0.3641

282

0.2571

574

Xiangyang

China

D

0.3641

283

0.3185

417

Kuwait City

Kuwait

D

0.3636

284

0.4224

232

Johannesburg

South Africa

C

0.3624

285

0.4693

172

Saint Petersburg

Russian Federation

D+

0.3621

286

0.5215

114

Bangalore

India

C

0.361

287

0.3688

319

Taiyuan

China

D+

0.3603

288

0.3995

272

Karaj

Islamic Republic of Iran

E+

0.3601

289

0.4276

221

Porto

Portugal

C

0.3597

290

0.4512

195

Huaian

China

D

0.3596

291

0.3309

390

Lodz

Poland

D

0.3595

292

0.3597

333

Nanning

China

D+

0.359

293

0.3608

331

Hohhot

China

D

0.359

294

0.3524

345

Barcelona-Puerto La Cruz

Venezuela

D+

0.3587

295

0.4535

191

Fresno

United States

D

0.3578

296

0.3364

379

Valencia(Venezuela)

Venezuela

D

0.3564

297

0.4427

202

Jining

China

D

0.356

298

0.3386

374

Ordoss

China

E+

0.3559

299

0.3259

401

Ashgabat

Turkmenistan E+

0.3556

300

0.2926

473

San Juan

Puerto Rico

D+

0.3554

301

0.4729

168

Harbin

China

C

0.3553

302

0.3561

339

Ahvaz

Islamic Republic of Iran

E+

0.3533

303

0.4471

198

Cairo

Egypt

C

0.3531

304

0.2925

474

Catania

Italy

D+

0.3508

305

0.4385

210

Almaty

Kazakhstan

D+

0.3501

306

0.3692

318

Zhoushan

China

D

0.3495

307

0.3497

349

Dhaka

Bangladesh

D+

0.3493

308

0.3049

443

Sofia

Bulgaria

C

0.3493

309

0.4634

180

El Paso

United States

D

0.3486

310

0.3728

314

Portland

United States

C

0.3483

311

0.3944

281 (continued)

10

1 Ranking of Global Urban Competitiveness 2019

(continued) City

Country

Level Economic Rank Sustainable Rank competitiveness competitiveness

Oran

Algeria

D

0.3483

312

0.365

323

Johor Bahru

Malaysia

D

0.347

313

0.4396

209

Padova

Italy

D+

0.3468

314

0.3839

297

Jinhua

China

D

0.3458

315

0.3607

332

Caracas

Venezuela

D+

0.3441

316

0.4384

211

Newcastle upon Tyne

United Kingdom

E+

0.344

317

0.414

249

Luoyang

China

D

0.3432

318

0.3451

356

Adana

Turkey

D

0.342

319

0.3803

302

Huzhou

China

D

0.3419

320

0.3476

353

Porto Alegre

Brazil

D+

0.3418

321

0.4182

240

Taian

China

E+

0.3412

322

0.3479

352

Langfang

China

D

0.3411

323

0.3462

354

Antalya

Turkey

D

0.3407

324

0.3193

414

Urumqi

China

D+

0.3401

325

0.3951

280

Baku

Azerbaijan

D+

0.3392

326

0.3747

312

Zhuzhou

China

D

0.3391

327

0.3406

370

Guatemala City

Guatemala

D

0.3389

328

0.3492

350

Putian

China

E+

0.3379

329

0.3226

409

Leon

Mexico

D+

0.3375

330

0.464

179

Albuquerque

United States

D+

0.3354

331

0.3091

435

Xiangtan

China

D

0.334

332

0.3429

362

Bari

Italy

D+

0.3335

333

0.4173

244

Xuchang

China

D

0.3328

334

0.3342

385

Buraydah

Saudi Arabia

E

0.3328

335

0.3056

440

Tripoli

Libya

D

0.3318

336

0.33

395

Muscat

Oman

D

0.3318

337

0.4165

245

Tijuana

Mexico

D

0.3306

338

0.4153

248

Zagreb

Croatia

C

0.3301

339

0.3538

343

Mendoza

Argentina

D

0.3292

340

0.4263

226

Be’er Sheva

Israel

E

0.3289

341

0.3795

304

Nairobi

Kenya

C

0.3281

342

0.324

407

McAllen

United States

E+

0.3276

343

0.3414

369

Minsk

Belarus

D+

0.3274

344

0.4513

194

Amman

Jordan

D+

0.3269

345

0.4772

160

Shantou

China

D

0.3268

346

0.3556

341

Mar Del Plata

Argentina

E+

0.3263

347

0.3159

422

Jiaozuo

China

D

0.3261

348

0.353

344

Havana

Cuba

D

0.3253

349

0.4065

259 (continued)

1 Ranking of Global Urban Competitiveness 2019

11

(continued) City

Country

Level Economic Rank Sustainable Rank competitiveness competitiveness

Ipoh

Malaysia

E+

0.3252

350

0.3678

321

Lianyungang

China

D

0.3251

351

0.3276

400

Dezhou

China

D

0.3238

352

0.3227

408

Samarinda

Indonesia

E+

0.3238

353

0.3427

364

Greater Vitória

Brazil

E

0.3227

354

0.426

228

San Luis Potosi

Mexico

D

0.3222

355

0.3783

307

Yueyang

China

D

0.3218

356

0.3185

418

Suqian

China

E+

0.3184

357

0.299

460

Cordoba

Argentina

D

0.3179

358

0.396

279

Liaocheng

China

D

0.3172

359

0.3277

399

Medellin

Colombia

D+

0.3167

360

0.401

271

Ezhou

China

E+

0.3162

361

0.3174

419

Thessaloniki

Greece

D+

0.3135

362

0.3789

306

Belo Horizonte

Brazil

D+

0.3124

363

0.3589

335

Rizhao

China

E+

0.3122

364

0.3191

416

Chennai

India

C

0.3121

365

0.3015

453

Linyi

China

D

0.3109

366

0.3125

426

Baghdad

Iraq

D

0.3107

367

0.3908

286

Binzhou

China

D

0.3101

368

0.3144

424

Haikou

China

D+

0.3098

369

0.3624

328

Curitiba

Brazil

D

0.3097

370

0.3351

382

Lanzhou

China

D+

0.3085

371

0.3435

360

Cangzhou

China

D

0.3079

372

0.3203

413

Ma’anshan

China

D

0.3073

373

0.2974

465

Hufuf-Mubarraz

Saudi Arabia

E

0.3072

374

0.2244

688

Tehran

Islamic Republic of Iran

D+

0.3072

375

0.474

167

Zaozhuang

China

E+

0.3061

376

0.3416

367

Cali

Colombia

D

0.3049

377

0.3572

337

Luanda

Angola

D

0.3035

378

0.4239

230

Riga

Latvia

C

0.303

379

0.3009

455

Jiangmen

China

D

0.3021

380

0.3241

406

Sao Jose dos Campos

Brazil

E+

0.3019

381

0.3594

334

Beirut

Lebanon

D+

0.3017

382

0.3459

355

Palermo

Italy

D+

0.3016

383

0.3917

282

Pekanbaru

Indonesia

E+

0.3016

384

0.3333

387

Yinchuan

China

D

0.3015

385

0.3381

376

Liuzhou

China

D

0.3013

386

0.2989

462

Xinyu

China

E+

0.3006

387

0.2968

466 (continued)

12

1 Ranking of Global Urban Competitiveness 2019

(continued) City

Country

Level Economic Rank Sustainable Rank competitiveness competitiveness

Merida

Mexico

D

0.2999

388

0.3618

329

Villahermosa

Mexico

E+

0.2997

389

0.3356

381

Cancun

Mexico

D

0.2987

390

0.2907

477

Changde

China

D

0.2985

391

0.3006

456

San Miguel de Tucuman

Argentina

E+

0.2977

392

0.2521

599

Asuncion

Paraguay

D

0.2975

393

0.3847

295

Ribeirao Preto

Brazil

D

0.2974

394

0.3634

326

Lagos

Nigeria

D+

0.2972

395

0.3798

303

Xianyang

China

E+

0.2968

396

0.3212

412

Maoming

China

E+

0.2965

397

0.3065

439

Balikpapan

Indonesia

E+

0.2954

398

0.3053

441

Tucson

United States

C

0.2951

399

0.3416

368

Deyang

China

E+

0.2945

400

0.3034

447

Longyan

China

E+

0.2936

401

0.2696

530

Campinas

Brazil

D+

0.2934

402

0.3853

293

Santiago de Los Caballeros Dominican Republic

D

0.2934

403

0.398

276

Quito

Ecuador

D+

0.2931

404

0.3821

300

Valparaiso

Chile

D

0.293

405

0.3749

311

Queretaro

Mexico

D

0.2922

406

0.3558

340

Joinville

Brazil

E+

0.292

407

0.2975

464

Huangshi

China

E+

0.2907

408

0.302

452

Seville

Spain

D+

0.2906

409

0.3984

275

Zhangzhou

China

D

0.2898

410

0.3285

397

Wroclaw

Poland

D+

0.2893

411

0.2876

486

Zunyi

China

D

0.2893

412

0.2663

537

Benin City

Nigeria

E+

0.2892

413

0.2263

684

Torreon

Mexico

E+

0.2886

414

0.3431

361

Batam

Indonesia

E+

0.2886

415

0.3436

359

Hengyang

China

D

0.2867

416

0.3

458

Sanming

China

E+

0.2863

417

0.2606

559

Kolkata

India

D+

0.2862

418

0.2563

578

Wuhai

China

E+

0.286

419

0.1736

831

Beihai

China

D

0.2858

420

0.3032

449

Panjin

China

E+

0.2855

421

0.316

421

Ho Chi Minh City

Viet Nam

D+

0.2851

422

0.2044

743

Jieyang

China

E+

0.2844

423

0.3011

454

Denizli

Turkey

E+

0.2832

424

0.1933

787

Zhaoqing

China

D

0.2828

425

0.2957

467 (continued)

1 Ranking of Global Urban Competitiveness 2019

13

(continued) City

Country

Level Economic Rank Sustainable Rank competitiveness competitiveness

Ufa

Russian Federation

D

0.2815

426

0.3639

325

Yulin(sx)

China

E+

0.2814

427

0.2814

498

Cape Town

South Africa

C

0.2813

428

0.4304

220

Port Harcourt

Nigeria

E+

0.2812

429

0.2851

491

Matamoros

Mexico

E+

0.2808

430

0.3348

384

Panzhihua

China

E+

0.2799

431

0.2745

517

Jiujiang

China

D

0.2799

432

0.2817

497

Durban

South Africa

D

0.2794

433

0.3725

315

Brasilia

Brazil

D+

0.2793

434

0.437

213

Karachi

Pakistan

D+

0.278

435

0.2661

539

Juarez

Mexico

D

0.278

436

0.4119

252

Kuching

Malaysia

E+

0.2778

437

0.3001

457

Heze

China

D

0.2764

438

0.2806

503

Anyang

China

D

0.2755

439

0.3077

436

Athens

Greece

C+

0.2753

440

0.5363

102

Zhanjiang

China

D

0.2753

441

0.3046

444

Ningde

China

D

0.2749

442

0.2699

528

Baoji

China

E+

0.2747

443

0.2812

500

Puyang

China

E+

0.2746

444

0.2945

471

Chenzhou

China

E+

0.273

445

0.2777

509

Hanoi

Viet Nam

D+

0.2725

446

0.2023

749

Bengbu

China

D

0.2722

447

0.284

494

Kochi

India

D

0.2719

448

0.2903

478

Xining

China

D

0.2713

449

0.3027

450

Xinxiang

China

D

0.2712

450

0.3101

432

Kaifeng

China

D

0.2711

451

0.305

442

Sorocaba

Brazil

E+

0.2711

452

0.3156

423

Toluca

Mexico

E+

0.2707

453

0.3252

404

Coimbatore

India

D

0.2695

454

0.2813

499

Yingtan

China

E+

0.2682

455

0.2981

463

Handan

China

D

0.2677

456

0.322

411

Owerri

Nigeria

E+

0.2674

457

0.1977

770

Yangjiang

China

E+

0.2671

458

0.2761

511

Zigong

China

D

0.2668

459

0.276

512

Aguascalientes

Mexico

E+

0.2662

460

0.3498

348

Saltillo

Mexico

E+

0.2658

461

0.3193

415

Samara

Russian Federation

D

0.2651

462

0.357

338

Malappuram

India

E+

0.2649

463

0.2339

656 (continued)

14

1 Ranking of Global Urban Competitiveness 2019

(continued) City

Country

Level Economic Rank Sustainable Rank competitiveness competitiveness

Bandung

Indonesia

D

0.2647

464

0.3122

427

Sanya

China

D

0.2644

465

0.2912

476

Cartagena

Colombia

D

0.264

466

0.3377

377

Liupanshui

China

E+

0.264

467

0.2633

553

Yingkou

China

E+

0.2631

468

0.2417

621

Recife

Brazil

D

0.2628

469

0.3375

378

Phnom Penh

Cambodia

D

0.2627

470

0.2542

590

Erbil

Iraq

E+

0.2617

471

0.3995

273

Villavicencio

Colombia

E+

0.2613

472

0.1889

796

Pingxiang

China

E+

0.2611

473

0.2803

504

Shangrao

China

E+

0.2609

474

0.2746

516

Luohe

China

E+

0.2605

475

0.2918

475

Hebi

China

E+

0.26

476

0.281

501

Jingmen

China

E+

0.2593

477

0.2648

547

Uyo

Nigeria

E+

0.2586

478

0.1957

778

Aba

Nigeria

E+

0.2576

479

0.2106

729

Yuxi

China

E+

0.2571

480

0.2686

532

San Salvador

El Salvador

D

0.2567

481

0.3349

383

Mersin

Turkey

D

0.2566

482

0.2324

664

La Plata

Argentina

D

0.2565

483

0.2701

527

Nanyang

China

D+

0.2563

484

0.3103

431

Concepcion

Chile

D

0.2562

485

0.2019

751

Samsun

Turkey

E+

0.2551

486

0.2696

529

Guilin

China

D

0.255

487

0.2877

485

Colombo

Sri Lanka

D+

0.2545

488

0.2293

671

Chaozhou

China

E+

0.2543

489

0.2884

483

Baoding

China

D

0.2542

490

0.2885

482

Perm

Russian Federation

D

0.2538

491

0.3293

396

Semarang

Indonesia

D

0.253

492

0.2474

609

Yichun(jx)

China

E+

0.2517

493

0.2659

542

Ganzhou

China

D

0.2513

494

0.272

526

Karamay

China

E+

0.2505

495

0.2537

593

Ikorodu

Nigeria

E

0.2504

496

0.348

351

Guayaquil

Ecuador

D

0.2504

497

0.3161

420

Tbilisi

Georgia

D+

0.2503

498

0.29

479

Jilin

China

D

0.2496

499

0.3221

410

Zhoukou

China

E+

0.2496

500

0.2636

552

Makassar

Indonesia

D

0.2493

501

0.278

508 (continued)

1 Ranking of Global Urban Competitiveness 2019

15

(continued) City

Country

Level Economic Rank Sustainable Rank competitiveness competitiveness

Lahore

Pakistan

D

0.2488

502

0.2395

632

Pingdingshan

China

D

0.2488

503

0.2883

484

Quzhou

China

D

0.2488

504

0.2648

548

Yaroslavl

Russian Federation

E

0.2485

505

0.2859

490

Neijiang

China

E+

0.2484

506

0.2284

674

Liaoyuan

China

E

0.2484

507

0.2364

646

Shangqiu

China

E+

0.2476

508

0.2655

544

Hyderabad

India

C

0.2471

509

0.2446

615

Ziyang

China

E+

0.2467

510

0.2771

510

Pune

India

C

0.2465

511

0.2847

492

Sanmenxia

China

E+

0.2463

512

0.2638

550

Loudi

China

E+

0.2462

513

0.2661

540

Mianyang

China

D

0.2451

514

0.269

531

Belgrade

Serbia

C

0.2447

515

0.358

336

Xiaogan

China

E+

0.2447

516

0.2649

545

Abuja

Nigeria

D

0.2443

517

0.3254

403

Ahmedabad

India

D

0.2441

518

0.2316

666

Huaibei

China

E+

0.2434

519

0.2731

521

Londrina

Brazil

D

0.2422

520

0.2754

514

Jingdezhen

China

E+

0.2421

521

0.2649

546

Qinhuangdao

China

D

0.2418

522

0.2953

468

Benxi

China

E+

0.2414

523

0.2722

524

Yibin

China

D

0.2414

524

0.257

575

Nanping

China

D

0.2413

525

0.2417

622

Port Elizabeth

South Africa

D

0.2412

526

0.3066

438

Culiacan

Mexico

E+

0.2412

527

0.3043

445

Luzhou

China

D

0.2411

528

0.2727

523

Zhumadian

China

E+

0.2407

529

0.2587

568

Ta’if

Saudi Arabia

E

0.2407

530

0.2442

617

Fangchenggang

China

E+

0.2406

531

0.2462

611

Hermosillo

Mexico

D

0.2403

532

0.3098

433

Yiyang

China

E+

0.2402

533

0.2588

567

Liaoyang

China

E+

0.2401

534

0.2754

515

Alexandria

Egypt

D+

0.2399

535

0.2735

519

Trujillo

Peru

E+

0.2398

536

0.2844

493

Anshan

China

D

0.2395

537

0.3256

402

Xinyang

China

D

0.2395

538

0.255

586

Arequipa

Peru

E+

0.2394

539

0.3036

446 (continued)

16

1 Ranking of Global Urban Competitiveness 2019

(continued) City

Country

Level Economic Rank Sustainable Rank competitiveness competitiveness

Huainan

China

D

0.2393

540

0.2553

584

Jingzhou

China

E+

0.2392

541

0.2589

566

Palembang

Indonesia

E+

0.2374

542

0.2894

480

Tolyatti

Russian Federation

E

0.2371

543

0.2658

543

Chihuahua

Mexico

D

0.237

544

0.2757

513

Xianning

China

D

0.237

545

0.2556

583

Chuzhou

China

E+

0.2369

546

0.2517

600

Port Said

Egypt

E+

0.2366

547

0.2151

718

Songyuan

China

E+

0.2363

548

0.2663

538

Reynosa

Mexico

E+

0.2358

549

0.295

469

Shymkent

Kazakhstan

E+

0.2356

550

0.299

461

Shiyan

China

D

0.2353

551

0.2579

572

Gaza

State of Palestine

E+

0.2347

552

0.2383

637

Khartoum

Sudan

D

0.2343

553

0.2397

630

Fortaleza

Brazil

D

0.2339

554

0.3301

394

Uberlandia

Brazil

E+

0.2336

555

0.2867

488

Maturín

Venezuela

E

0.2334

556

0.2497

605

Leshan

China

E+

0.2332

557

0.2524

596

Jinzhou

China

D

0.2331

558

0.2938

472

Barnaul

Russian Federation

E

0.233

559

0.2806

502

Chittagong

Bangladesh

E+

0.2324

560

0.2545

589

Kano

Nigeria

E+

0.2323

561

0.2596

562

Wuzhou

China

E+

0.2322

562

0.2581

571

Goiania

Brazil

D

0.232

563

0.3097

434

Suining

China

E+

0.232

564

0.2523

597

Kozhikode

India

E+

0.2319

565

0.2595

563

Grande Sao Luis

Brazil

E

0.2308

566

0.2868

487

Saratov

Russian Federation

E+

0.2305

567

0.3105

429

Daqing

China

D

0.2304

568

0.3335

386

Fushun

China

D

0.2299

569

0.272

525

Belem

Brazil

D

0.2298

570

0.3105

428

Tunis

Tunisia

D+

0.2294

571

0.3397

372

Meishan

China

E+

0.2288

572

0.2541

591

Jincheng

China

E+

0.2288

573

0.2618

556

Hengshui

China

E+

0.2287

574

0.2591

564

Medan

Indonesia

D

0.2281

575

0.2669

535 (continued)

1 Ranking of Global Urban Competitiveness 2019

17

(continued) City

Country

Level Economic Rank Sustainable Rank competitiveness competitiveness

Weinan

China

E+

0.2276

576

0.2563

579

Guangan

China

E+

0.2269

577

0.2443

616

Huanggang

China

E+

0.2265

578

0.2363

647

Ibadan

Nigeria

E+

0.2263

579

0.2056

738

San Pedro Sula

Honduras

E+

0.226

580

0.2438

618

Tegucigalpa

Honduras

D

0.226

581

0.2781

507

Puebla

Mexico

D

0.2251

582

0.3416

366

Nanchong

China

D

0.2238

583

0.2516

602

Cochabamba

Bolivia

E+

0.2237

584

0.3034

448

Dehra Dun

India

E

0.2236

585

0.2523

598

Shiraz

Islamic Republic of Iran

D

0.2227

586

0.3301

393

Xuancheng

China

E+

0.2225

587

0.2349

653

Shaoguan

China

E+

0.222

588

0.2395

633

Santa Cruz

Bolivia

D+

0.2212

589

0.3628

327

Padang

Indonesia

E+

0.221

590

0.274

518

Lishui

China

D

0.2208

591

0.2436

619

Shizuishan

China

E+

0.2208

592

0.2214

696

Xingtai

China

D

0.2201

593

0.2624

554

Krasnodar

Russian Federation

E+

0.2196

594

0.2599

561

Gaziantep

Turkey

D

0.2192

595

0.3326

388

Tongliao

China

E+

0.2191

596

0.2355

652

Manaus

Brazil

D

0.2178

597

0.3385

375

Veracruz

Mexico

D

0.2177

598

0.2222

693

Konya

Turkey

D

0.2172

599

0.1567

876

Tomsk

Russian Federation

E+

0.2171

600

0.283

495

Enugu

Nigeria

E+

0.2166

601

0.2223

691

Can Tho

Viet Nam

E+

0.2157

602

0.2145

721

Ryazan

Russian Federation

E

0.2156

603

0.2546

588

Yangquan

China

E+

0.2147

604

0.2421

620

Kollam

India

E+

0.2147

605

0.225

686

Yulin(gx)

China

E+

0.2142

606

0.2584

569

Kayseri

Turkey

D

0.2139

607

0.2365

644

Barquisimeto

Venezuela

E+

0.2133

608

0.2384

636

Mudanjiang

China

E+

0.2132

609

0.2567

577

Diyarbakir

Turkey

E+

0.2129

610

0.1988

767 (continued)

18

1 Ranking of Global Urban Competitiveness 2019

(continued) City

Country

Level Economic Rank Sustainable Rank competitiveness competitiveness

Cuernavaca

Mexico

E+

0.2128

611

0.2327

663

Tonghua

China

E+

0.2128

612

0.2399

626

Eskisehir

Turkey

D

0.2118

613

0.2296

670

Suizhou

China

E+

0.2117

614

0.2187

705

Vereeniging

South Africa

E

0.2116

615

0.2343

654

Anqing

China

D

0.2112

616

0.2682

533

Celaya

Mexico

E+

0.2111

617

0.237

642

Kampala

Uganda

D

0.2108

618

0.2332

660

Suzhou (AH)

China

E+

0.2108

619

0.2361

649

Joao Pessoa

Brazil

E+

0.2107

620

0.2664

536

Kemerovo

Russian Federation

E

0.2106

621

0.2515

603

Kannur

India

E+

0.2099

622

0.2549

587

Shuozhou

China

E+

0.2099

623

0.2532

594

Ji’an

China

E+

0.2092

624

0.2369

643

Siping

China

D

0.2092

625

0.2671

534

Malang

Indonesia

E+

0.2092

626

0.2948

470

Chifeng

China

E+

0.2088

627

0.2223

690

Algiers

Algeria

D

0.2083

628

0.3704

316

Pachuca de Soto

Mexico

E

0.2079

629

0.2864

489

Pereira

Colombia

E+

0.2078

630

0.28

505

Jos

Nigeria

D

0.2076

631

0.2116

727

Tabriz

Islamic Republic of Iran

E+

0.2075

632

0.3021

451

Xalapa

Mexico

E+

0.2075

633

0.1838

805

Teresina

Brazil

E+

0.207

634

0.2641

549

Juiz De Fora

Brazil

D

0.2068

635

0.262

555

Yunfu

China

E+

0.2067

636

0.2329

662

Chizhou

China

E+

0.2065

637

0.2186

707

Oshogbo

Nigeria

E

0.2061

638

0.1795

813

Hanzhong

China

E+

0.2061

639

0.2276

679

Dazhou

China

E+

0.206

640

0.2331

661

Qingyuan

China

E+

0.206

641

0.2507

604

Yongzhou

China

E+

0.2053

642

0.2222

692

Qujing

China

E+

0.2044

643

0.2396

631

Qinzhou

China

E+

0.2042

644

0.2609

558

Jiayuguan

China

E+

0.204

645

0.1998

761

Bhiwandi

India

E

0.2039

646

0.2611

557

Anshun

China

E+

0.2037

647

0.2193

703 (continued)

1 Ranking of Global Urban Competitiveness 2019

19

(continued) City

Country

Level Economic Rank Sustainable Rank competitiveness competitiveness

Sulaymaniyah

Iraq

E+

0.2035

648

0.299

459

Huaihua

China

E+

0.2023

649

0.2275

680

Mexicali

Mexico

E+

0.202

650

0.266

541

Orenburg

Russian Federation

E+

0.2013

651

0.2387

635

Thiruvananthapuram

India

D

0.201

652

0.2168

712

Zhangjiakou

China

D

0.201

653

0.2398

628

Chengde

China

E+

0.2006

654

0.2333

659

Cebu

Philippines

D

0.2002

655

0.245

613

Patna

India

E+

0.2001

656

0.1911

792

Warri

Nigeria

E

0.2

657

0.1774

822

Bucaramanga

Colombia

D

0.1999

658

0.1968

773

Kiev

Ukraine

C

0.1994

659

0.3428

363

Feira De Santana

Brazil

E+

0.1994

660

0.2491

606

Yan’an

China

D

0.1993

661

0.2374

639

Irkutsk

Russian Federation

E+

0.1991

662

0.2582

570

Puducherry

India

E+

0.1991

663

0.2266

682

Barranquilla

Colombia

D

0.1991

664

0.2792

506

Baise

China

E+

0.1991

665

0.215

719

Casablanca

Morocco

C

0.1987

666

0.2731

520

Datong

China

D

0.1987

667

0.248

607

Fuyang

China

D

0.1979

668

0.2255

685

Dandong

China

E+

0.1977

669

0.2397

629

Changzhi

China

E+

0.1976

670

0.2552

585

Thrissur

India

E+

0.1967

671

0.199

765

Denpasar

Indonesia

E+

0.1965

672

0.2044

742

Kazan

Russian Federation

D

0.1961

673

0.3322

389

Cuiaba

Brazil

E+

0.1956

674

0.2409

624

Florianopolis

Brazil

D

0.1955

675

0.1972

772

Jinzhong

China

E+

0.1955

676

0.2398

627

Shanwei

China

E+

0.1949

677

0.2447

614

Fuzhou(JX)

China

E+

0.1936

678

0.2266

683

Haiphong

Viet Nam

E+

0.1934

679

0.2209

699

Yuncheng

China

D

0.1929

680

0.2373

640

Campo Grande

Brazil

D

0.1924

681

0.2558

582

Da Nang

Viet Nam

E+

0.1922

682

0.2114

728

Davao

Philippines

E+

0.1919

683

0.2157

716

Shaoyang

China

E+

0.1917

684

0.2211

698 (continued)

20

1 Ranking of Global Urban Competitiveness 2019

(continued) City

Country

Level Economic Rank Sustainable Rank competitiveness competitiveness

Morelia

Mexico

D

0.1912

685

0.2459

612

Cagayan de Oro

Philippines

E+

0.1911

686

0.1966

776

Abidjan

The Republic D of Cote d’ivoire

0.1907

687

0.2524

595

Tongchuan

China

E+

0.1905

688

0.1697

844

Ankang

China

E+

0.1904

689

0.2061

736

Zaria

Nigeria

E+

0.1897

690

0.2476

608

Kayamkulam

India

E

0.1894

691

0.109

965

Pointe-Noire

Congo

E+

0.1892

692

0.199

764

Bozhou

China

E+

0.1891

693

0.2185

708

Suihua

China

E+

0.189

694

0.2278

678

Ibague

Colombia

E+

0.1885

695

0.2219

695

Rajshahi

Bangladesh

E+

0.1885

696

0.2467

610

Astrakhan’

Russian Federation

E

0.1884

697

0.2591

565

Chongzuo

China

E+

0.1882

698

0.2186

706

Baishan

China

E+

0.1882

699

0.2025

748

Managua

Nicaragua

D

0.1879

700

0.2337

658

Mombasa

Kenya

E+

0.1878

701

0.2018

752

Huangshan

China

D

0.1874

702

0.2127

725

Mashhad

Islamic Republic of Iran

D

0.1873

703

0.3072

437

Port-au-Prince

Haiti

E+

0.1868

704

0.2362

648

Guigang

China

E+

0.1863

705

0.2213

697

Surat

India

D

0.1855

706

0.2005

757

General Santos City

Philippines

E

0.1853

707

0.1862

801

Ludhiana

India

E+

0.1846

708

0.2133

722

Kota

India

D

0.1844

709

0.2311

667

Namangan

Uzbekistan

E

0.1839

710

0.1329

927

Bahawalpur

Pakistan

E+

0.1833

711

0.2193

702

Ulan Bator

Mongolia

E+

0.1827

712

0.2517

601

Mangalore

India

E+

0.1815

713

0.2097

732

Tiruppur

India

E

0.1812

714

0.1685

847

Nagpur

India

D

0.1808

715

0.2015

753

Hyderabad

Pakistan

D

0.1803

716

0.1783

816

Accra

Ghana

D

0.1801

717

0.2894

481

Marrakech

Morocco

D

0.18

718

0.2341

655

Heyuan

China

E+

0.1796

719

0.2289

673 (continued)

1 Ranking of Global Urban Competitiveness 2019

21

(continued) City

Country

Level Economic Rank Sustainable Rank competitiveness competitiveness

Ilorin

Nigeria

E+

0.1793

720

0.165

854

Acapulco

Mexico

E+

0.1792

721

0.2181

710

Visakhapatnam

India

E+

0.1786

722

0.1945

783

Meizhou

China

E+

0.1785

723

0.222

694

Kingston

Jamaica

D+

0.1781

724

0.2579

573

Onitsha

Nigeria

E

0.1781

725

0.1664

853

Asmara

Eritrea

E+

0.178

726

0.1755

827

Linfen

China

E+

0.178

727

0.239

634

Ciudad Guayana

Venezuela

E

0.1779

728

0.1883

798

Huludao

China

E+

0.1775

729

0.228

676

Durg-Bhilai Nagar

India

E+

0.1767

730

0.2377

638

Huambo

Angola

E

0.1766

731

0.2636

551

Nouakchott

Mauritania

E+

0.1765

732

0.1726

833

Akure

Nigeria

E+

0.1759

733

0.2161

713

Jiamusi

China

E+

0.1757

734

0.2156

717

Krivoi Rog

Ukraine

E

0.1756

735

0.2198

701

Baicheng

China

E+

0.1754

736

0.2051

739

Hamadan

Islamic Republic of Iran

E+

0.1754

737

0.237

641

Madurai

India

E+

0.1747

738

0.1626

858

Poza Rica

Mexico

E+

0.1744

739

0.2011

755

Asansol

India

E+

0.1739

740

0.1948

781

Tangier

Morocco

E+

0.1739

741

0.1938

785

Cucuta

Colombia

E+

0.1733

742

0.2098

731

Brazzaville

Congo

E+

0.1731

743

0.1899

795

Zhangjiajie

China

E+

0.1731

744

0.1931

788

Novokuznetsk

Russian Federation

E

0.1724

745

0.2188

704

Tashkent

Uzbekistan

D

0.172

746

0.1529

885

Hulunbuir

China

E+

0.1719

747

0.1876

799

Kitwe

Zambia

E+

0.1711

748

0.1967

775

Khabarovsk

Russian Federation

E

0.1707

749

0.1951

779

Meknes

Morocco

E+

0.1701

750

0.2131

723

Kumasi

Ghana

E+

0.1701

751

0.1974

771

Tampico

Mexico

E+

0.1699

752

0.2412

623

Shangluo

China

E+

0.1698

753

0.1951

780

Laibin

China

E+

0.1697

754

0.2036

745

Douala

Cameroon

D

0.1697

755

0.256

580 (continued)

22

1 Ranking of Global Urban Competitiveness 2019

(continued) City

Country

Level Economic Rank Sustainable Rank competitiveness competitiveness

Novosibirsk

Russian Federation

D+

0.1696

756

0.2818

496

Ya’an

China

E+

0.1695

757

0.1944

784

Kaduna

Nigeria

E+

0.1681

758

0.1468

896

Rabat

Morocco

D

0.1679

759

0.1994

763

Rostov-on-Don

Russian Federation

E+

0.1673

760

0.2365

645

Vientiane

Lao People’s Democratic Republic

D

0.1673

761

0.1464

898

Indore

India

D

0.166

762

0.1579

871

Liuan

China

E+

0.1657

763

0.2178

711

La Paz

Bolivia

D

0.1653

764

0.254

592

Guwahati

India

D

0.1653

765

0.2302

668

Rawalpindi

Pakistan

E+

0.1648

766

0.1709

842

Tasikmalaya

Indonesia

E+

0.1639

767

0.2297

669

Chiclayo

Peru

E+

0.1636

768

0.2127

726

Libreville

Gabon

E+

0.1626

769

0.2359

650

Rangoon

Myanmar

E+

0.1624

770

0.1329

926

Bandar Lampung

Indonesia

E+

0.1623

771

0.1774

821

Krasnoyarsk

Russian Federation

E+

0.1621

772

0.2247

687

Izhevsk

Russian Federation

E

0.1613

773

0.2003

759

Tlaxcala

Mexico

E+

0.1611

774

0.1967

774

Harare

Zimbabwe

D

0.161

775

0.2094

733

Ulanqab

China

E+

0.1605

776

0.193

789

Chisinau

Republic of Moldova

D

0.1602

777

0.1853

804

Fuxin

China

D

0.1599

778

0.2103

730

Guangyuan

China

E+

0.1599

779

0.1795

811

Jalandhar

India

E+

0.1597

780

0.1889

797

Oaxaca

Mexico

E+

0.1595

781

0.2069

735

Maceio

Brazil

E+

0.1594

782

0.2185

709

Jodhpur

India

E+

0.1589

783

0.2019

750

Erode

India

E+

0.1587

784

0.1782

817

Qiqihar

China

D

0.1578

785

0.2128

724

Kathmandu

Nepal

D

0.1576

786

0.1987

768

Bayannur

China

E+

0.1575

787

0.1723

834

Tirupati

India

E+

0.1574

788

0.1779

818 (continued)

1 Ranking of Global Urban Competitiveness 2019

23

(continued) City

Country

Level Economic Rank Sustainable Rank competitiveness competitiveness

Chelyabinsk

Russian Federation

E+

0.1574

789

0.2403

625

Gujranwala

Pakistan

E+

0.1574

790

0.136

922

Aracaju

Brazil

E+

0.1572

791

0.2035

747

Luliang

China

E+

0.1568

792

0.2279

677

Omsk

Russian Federation

E+

0.1568

793

0.2268

681

Rajkot

India

D

0.1567

794

0.1625

859

Dar es Salaam

United Republic of Tanzania

D

0.1565

795

0.1936

786

Bogor

Indonesia

D

0.1564

796

0.2728

522

Chaoyang

China

E+

0.156

797

0.2035

746

Hezhou

China

E+

0.1559

798

0.1904

793

Amritsar

India

E+

0.1553

799

0.1918

791

Bacolod

Philippines

E+

0.1552

800

0.1696

845

Tabuk

Saudi Arabia

E+

0.1542

801

0.1722

837

Baoshan

China

D

0.1535

802

0.2003

760

Sokoto

Nigeria

E+

0.1533

803

0.1535

881

Karbala

Iraq

E+

0.153

804

0.2157

715

Xinzhou

China

D

0.1528

805

0.2014

754

Qingyang

China

D

0.1527

806

0.1853

803

Salem

India

D+

0.1516

807

0.2056

737

Sylhet

Bangladesh

E+

0.1512

808

0.1856

802

Salvador

Brazil

D

0.151

809

0.3129

425

Volgograd

Russian Federation

E+

0.151

810

0.1785

815

Tuxtla Gutierrez

Mexico

E+

0.1509

811

0.2042

744

Vijayawada

India

E+

0.1509

812

0.1515

890

Sanliurfa

Turkey

E

0.1508

813

0.2559

581

Tiruchirappalli

India

E+

0.1507

814

0.1728

832

Kirkuk

Iraq

E+

0.1506

815

0.3105

430

Nizhny Novgorod

Russian Federation

D

0.1505

816

0.2073

734

Maiduguri

Nigeria

E+

0.1504

817

0.0935

978

Kabul

Afghanistan

E+

0.1504

818

0.1689

846

Sekondi

Ghana

E

0.1499

819

0.1313

930

WuZhong

China

E+

0.1498

820

0.1776

820

Jaipur

India

D

0.1495

821

0.1816

808

Voronezh

Russian Federation

E

0.1481

822

0.2283

675 (continued)

24

1 Ranking of Global Urban Competitiveness 2019

(continued) City

Country

Level Economic Rank Sustainable Rank competitiveness competitiveness

Jinchang

China

E+

0.1481

823

0.1503

891

Tianshui

China

D

0.148

824

0.1903

794

Jamnagar

India

E+

0.1479

825

0.1582

870

Fes

Morocco

E+

0.1478

826

0.1748

829

Mysore

India

D

0.1476

827

0.1754

828

Orumiyeh

Islamic Republic of Iran

E

0.1475

828

0.2292

672

Dakar

Senegal

D

0.1469

829

0.1946

782

Jambi

Indonesia

E+

0.146

830

0.1676

851

Raurkela

India

E

0.1454

831

0.156

878

Kurnool

India

E+

0.1446

832

0.1628

857

Jamshedpur

India

E+

0.144

833

0.1772

823

Siliguri

India

E+

0.1439

834

0.1642

855

Zamboanga

Philippines

E

0.1436

835

0.1489

893

Kolhapur

India

E+

0.1428

836

0.1723

836

Natal

Brazil

D

0.1421

837

0.2157

714

Meerut

India

E+

0.1417

838

0.1684

849

Basra

Iraq

E+

0.141

839

0.2356

651

Nashik

India

E+

0.1407

840

0.1599

864

Bhubaneswar

India

D

0.1402

841

0.1995

762

Mosul

Iraq

E+

0.1397

842

0.1184

950

Santa Marta

Colombia

E+

0.1395

843

0.1875

800

Yekaterinburg

Russian Federation

E+

0.1393

844

0.2146

720

Rasht

Islamic Republic of Iran

E+

0.1391

845

0.2049

740

Vladivostok

Russian Federation

D

0.1387

846

0.1597

865

Lincang

China

E+

0.1383

847

0.1712

840

Zhaotong

China

E+

0.1379

848

0.1827

807

Hubli-Dharwad

India

E+

0.1371

849

0.1436

905

Tieling

China

E+

0.1365

850

0.2225

689

Lucknow

India

D

0.1364

851

0.1831

806

Bazhong

China

E+

0.1359

852

0.1747

830

Zhongwei

China

E+

0.1358

853

0.1594

866

Khulna

Bangladesh

E+

0.135

854

0.1787

814

Jixi

China

E+

0.1347

855

0.1807

810

Lome

Togo

D

0.1346

856

0.1958

777

Nyala

Sudan

E+

0.1339

857

0.1162

954 (continued)

1 Ranking of Global Urban Competitiveness 2019

25

(continued) City

Country

Level Economic Rank Sustainable Rank competitiveness competitiveness

Baiyin

China

E+

0.1323

858

0.1684

848

Faisalabad

Pakistan

E+

0.132

859

0.1771

824

Agadir

Morocco

E+

0.1319

860

0.0917

980

Warangal

India

E+

0.1316

861

0.1023

971

Bokaro Steel City

India

E

0.1315

862

0.1606

860

Ulyanovsk

Russian Federation

E

0.1313

863

0.1485

894

Gwalior

India

E+

0.1309

864

0.1682

850

Latakia

Syrian Arab Republic

E

0.1306

865

0.101

973

Lijiang

China

E+

0.1302

866

0.157

874

Misratah

Libya

E

0.13

867

0.26

560

Kinshasa

Congo

D

0.13

868

0.1778

819

Nasiriyah

Iraq

E

0.1298

869

0.2006

756

Safaqis

Tunisia

E

0.1292

870

0.2047

741

Chandigarh

India

D+

0.1285

871

0.1426

906

Hechi

China

E+

0.1279

872

0.1638

856

Zhangye

China

E+

0.1278

873

0.1467

897

Srinagar

India

E+

0.1275

874

0.1452

901

Makhachkala

Russian Federation

E

0.1273

875

0.1674

852

Aurangabad

India

D

0.1272

876

0.1924

790

Qitaihe

China

E+

0.1271

877

0.1606

861

Ranchi

India

E+

0.1271

878

0.1706

843

Lusaka

Zambia

D

0.1269

879

0.1989

766

Sangali

India

E

0.1265

880

0.144

903

Shuangyashan

China

E+

0.1254

881

0.1758

826

Pu’er

China

E+

0.1245

882

0.1576

872

Esfahan

Islamic Republic of Iran

E+

0.1244

883

0.2338

657

Sana’a’

Yemen

E+

0.1241

884

0.1402

913

Islamabad

Pakistan

D+

0.1235

885

0.2207

700

Bogra

Bangladesh

E

0.1232

886

0.1271

938

Najaf

Iraq

E+

0.1231

887

0.1603

862

Wuwei

China

E+

0.1225

888

0.155

879

Kigali

Rwanda

D

0.1225

889

0.1369

920

Saharanpur

India

E+

0.1224

890

0.1485

895

Vadodara

India

D

0.1218

891

0.1564

877 (continued)

26

1 Ranking of Global Urban Competitiveness 2019

(continued) City

Country

Level Economic Rank Sustainable Rank competitiveness competitiveness

Qom

Islamic Republic of Iran

E+

0.1211

892

0.2003

758

Donetsk

Ukraine

E+

0.1208

893

0.1168

953

Banjarmasin

Indonesia

E+

0.1205

894

0.1599

863

Guntur

India

E+

0.1203

895

0.1523

887

Bien Hoa

Viet Nam

E+

0.1202

896

0.1334

925

Imphal

India

E+

0.1184

897

0.1366

921

Bhopal

India

D

0.1184

898

0.1575

873

Hamah

Syrian Arab Republic

E

0.1178

899

0.1061

968

Damascus

Syrian Arab Republic

E+

0.1174

900

0.1405

911

Yerevan

Armenia

D

0.1171

901

0.1712

839

Mogadishu

Somalia

E+

0.1165

902

0.0821

987

Guyuan

China

E+

0.1165

903

0.1383

917

Pontianak

Indonesia

E+

0.1158

904

0.157

875

Muzaffarnagar

India

E+

0.1149

905

0.1462

900

Varanasi

India

E+

0.1138

906

0.1711

841

Bhavnagar

India

E+

0.1137

907

0.1276

936

Tirunelveli

India

E+

0.1135

908

0.1124

961

Solapur

India

E+

0.1129

909

0.1263

940

Dhanbad

India

E+

0.1127

910

0.1523

886

Kerman

Islamic Republic of Iran

E+

0.1126

911

0.1795

812

Cherthala

India

E

0.1116

912

0.1531

883

Sialkot

Pakistan

E+

0.1115

913

0.1723

835

Al-Raqqa

Syrian Arab Republic

E

0.111

914

0.0826

986

Belgaum

India

E+

0.1103

915

0.1277

934

Vellore

India

D

0.11

916

0.1719

838

Sukkur

Pakistan

E+

0.1094

917

0.1374

919

Lubumbashi

Congo

E+

0.1091

918

0.1405

910

Peshawar

Pakistan

E+

0.109

919

0.153

884

Agra

India

E+

0.1084

920

0.1381

918

Hegang

China

E

0.1082

921

0.1449

902

Malegaon

India

E

0.1078

922

0.1068

967

Amravati

India

E+

0.1076

923

0.126

941

Niamey

Niger

E+

0.107

924

0.1182

951

Pingliang

China

E+

0.1066

925

0.1534

882 (continued)

1 Ranking of Global Urban Competitiveness 2019

27

(continued) City

Country

Level Economic Rank Sustainable Rank competitiveness competitiveness

Aden

Yemen

E+

0.1064

926

0.0598

997

Nellore

India

E+

0.106

927

0.1103

963

Cuttack

India

E+

0.1055

928

0.1593

867

Ardabil

Islamic Republic of Iran

E+

0.1052

929

0.1592

868

Heihe

China

E+

0.1049

930

0.1398

914

Aligarh

India

E+

0.1047

931

0.1522

888

Zanzibar

United Republic of Tanzania

E+

0.1041

932

0.1412

909

Addis Ababa

Ethiopia

D+

0.1039

933

0.1277

935

Bareilly

India

E+

0.1038

934

0.1421

907

Freetown

Sierra Leone

E+

0.1032

935

0.1184

949

Moradabad

India

E+

0.1031

936

0.134

924

Durango

Mexico

D

0.1028

937

0.1587

869

Gulbarga

India

E+

0.1028

938

0.1274

937

Kermanshah

Islamic Republic of Iran

E+

0.102

939

0.1767

825

Kanpur

India

D

0.1018

940

0.1496

892

Yazd

Islamic Republic of Iran

E+

0.1011

941

0.1394

915

Jabalpur

India

E+

0.0999

942

0.1354

923

Ujjain

India

E+

0.0988

943

0.1214

944

Mwanza

United Republic of Tanzania

E+

0.0969

944

0.123

943

Lvov

Ukraine

E

0.0968

945

0.1414

908

Ajmer

India

E+

0.0967

946

0.127

939

Suez

Egypt

E+

0.0965

947

0.1128

959

Nanded Waghala

India

E

0.0963

948

0.1187

948

Allahabad

India

E+

0.0961

949

0.1515

889

Dnipropetrovs’k

Ukraine

E

0.0958

950

0.1463

899

Zaporizhzhya

Ukraine

E

0.0956

951

0.1403

912

Bulawayo

Zimbabwe

E+

0.0956

952

0.1126

960

Kharkov

Ukraine

E+

0.0947

953

0.1814

809

Firozabad

India

E

0.0941

954

0.1174

952

Jammu

India

D

0.092

955

0.1549

880

Odessa

Ukraine

D

0.0897

956

0.132

928

Jhansi

India

E+

0.0894

957

0.1153

956 (continued)

28

1 Ranking of Global Urban Competitiveness 2019

(continued) City

Country

Level Economic Rank Sustainable Rank competitiveness competitiveness

Multan

Pakistan

E+

0.089

958

0.1389

916

Jiuquan

China

E+

0.0876

959

0.1203

945

Cotonou

Benin

D

0.0875

960

0.1089

966

Salta

Argentina

E+

0.0867

961

0.1315

929

Longnan

China

E+

0.0859

962

0.1251

942

Yaounde

Cameroon

D

0.0856

963

0.1979

769

Dingxi

China

E+

0.0855

964

0.1279

933

Yichun(hlj)

China

E+

0.0848

965

0.116

955

Durgapur

India

E+

0.0846

966

0.1302

931

Quetta

Pakistan

E+

0.0829

967

0.1439

904

Mathura

India

E+

0.0827

968

0.1283

932

Bishkek

Kyrgyzstan

D

0.0814

969

0.0998

975

Ouagadougou

Burkina Faso

E+

0.0769

970

0.0984

976

Nnewi

Nigeria

E

0.076

971

0.0893

982

Bouake

The Republic E+ of Cote d’ivoire

0.0743

972

0.0875

984

Blantyre-Limbe

Malawi

D

0.0721

973

0.1025

970

Bikaner

India

E+

0.0712

974

0.1055

969

Nay Pyi Taw

Myanmar

E

0.0708

975

0.0881

983

Zahedan

Islamic Republic of Iran

E

0.0698

976

0.1191

947

Bamako

Mali

E+

0.069

977

0.1002

974

Monrovia

Liberia

D

0.0666

978

0.1202

946

Djibouti

Djibouti

E+

0.0658

979

0.1016

972

Mandalay

Myanmar

E+

0.0655

980

0.0811

988

Gorakhpur

India

E+

0.0651

981

0.1144

957

Bujumbura

Burundi

E+

0.0651

982

0.0744

991

Conakry

Guinea

E+

0.064

983

0.0903

981

Abomey-Calavi

Benin

E+

0.0631

984

0.1132

958

Hargeysa

Somalia

E

0.0612

985

0.076

989

Matola

Mozambique

E

0.0603

986

0.1105

962

Raipur

India

E+

0.0601

987

0.0958

977

Tshikapa

Congo

E

0.0577

988

0.0725

992

Antananarivo

Madagascar

D

0.0571

989

0.085

985

Sargodha

Pakistan

E+

0.0559

990

0.1094

964

Lilongwe

Malawi

E+

0.0541

991

0.0754

990

Maputo

Mozambique

D

0.0539

992

0.0701

994

Bobo Dioulasso

Burkina Faso

E+

0.0518

993

0.072

993 (continued)

1 Ranking of Global Urban Competitiveness 2019

29

(continued) City

Country

Level Economic Rank Sustainable Rank competitiveness competitiveness

Mbuji-Mayi

Congo

E

0.0483

994

0.064

996

Nampula

Mozambique

E+

0.0401

995

0.0532

999

Dushanbe

Tajikistan

E+

0.0386

996

0.0576

998

Kananga

Congo

E

0.038

997

0.0532

1000

Bukavu

Congo

E+

0.03

998

0.0527

1001

Taiz

Yemen

E+

0.0269

999

0.0325

1002

Hodeidah

Yemen

E

0.0235

1000

0.0315

1003

Bangui

Central African Republic

E+

0.019

1001

0.0164

1005

Benghazi

Libya

E+

0.0177

1002

0.232

665

homs

Syrian Arab Republic

E

0.0172

1003

0.0672

995

N’Djamena

Chad

E+

0.0121

1004

0.0214

1004

Kisangani

Congo

E

0.0085

1005

0

1006

Aleppo

Syrian Arab Republic

E+

0

1006

0.093

979

Chapter 2

The World: 300 Years of Urbanization Expansion

1750–2050 is the 300 years of great changes in the world, in which the status of the city is extraordinary. City is not only the main content of world change, but also the core force. In the 300 years, cities have changed from the world being guided by cities, to being dominated by cities, to city being the main body.

2.1 From the Micro Level, the Change of Leading Cities Causes the Basic “Cell” Change of the World The city is an important symbol of human civilization, and also a beacon to light human progress. In the 300 years from 1750 to 2050, the development of cities is the locomotive that drives the development of the world, while the advanced cities are the engine of the development of cities. The development of global advanced cities leads the world’s development direction and changes the world’s development pattern.

2.1.1 The Evolution of Global Urban Economic System: From Global Duality to Global Integration, from Commodity Trade System to Factor Trade System, and Then from Industrial Chain System to Innovation Chain System Human activities are the foundation of cities. In the 300 years from 1750 to 2050, the content, scale and structure of the activities of cities, the important gathering areas of human beings, have undergone and will undergo profound qualitative changes: from

© China Social Sciences Press 2023 P. Ni et al., The World: 300 Years of Urbanization Expansion, https://doi.org/10.1007/978-981-99-3553-6_2

31

32

2 The World: 300 Years of Urbanization Expansion

the production, exchange and consumption of physical objects to the production, exchange and consumption of labor services, then to the production, exchange and consumption of knowledge, and even to the production, exchange and consumption of wisdom. 1. The leading cities in 1750–1850s: Development was dominated by resource processing, with knowledge innovation being carried out together. In 1750, the city began a new human activity triggered by the industrial revolution. In the 100 years from 1750 to 1850, the cities of the first industrialized countries mainly engaged in the production, exchange and consumption of resources processing products, although most of the world’s cities are still dominated by simple manual production and market trading in the traditional agricultural era. The first industrial revolution took place in British cities. Textile industry cities, metal processing industry cities, coal industry cities, transportation hub cities and trade cities rose in turn. Table 2.1 shows the rapid development of cities in the UK that rely on the advantages of resources and port location. With the export of British Industrial Revolution achievements, Germany, France, the United States and other countries have also carried out industrial revolution. Ruhr district of Germany, New York City of the United States and other cities have accepted the baptism of industrialization at this stage, and the urban industry is still dominated by industry, mining and light industry. The world’s leading industrialized cities mainly produce, exchange and consume primary commodities, metal equipment manufacturing and other industrial products (as shown in Fig. 2.1): in addition to non savings and credit intermediaries, global listed companies are mainly distributed in beverage manufacturing, textile, general merchandise, paper making, glass, industrial machinery manufacturing and other industries, mainly producing primary products and simple industrial products. Table 2.1 Industrial cities and transport hub cities in Britain after the industrial revolution Type

City

Comprehensive metropolis

London

Industry, mining

Textile industry

Manchester, Salford, Stockport, Bolton, Preston, Bollie, Blackburn, Nottingham, Liz, etc.

Metal processing industry

Birmingham, Wolverhampton, Walsall, Sheffield, etc.

Coal industry Cardiff, Swansea, Newport, Mason, winsbury, Dudley, and ironmaking Wassel, willenhower, etc. Center Transportation hub

Railway hub

Bud, Middleborough, Sheldon, swighton, Wolverton, Crewe, etc.

Port

London, Bristol, Liverpool, Newcastle; Sunderland, Portsmouth, Plymouth, etc.

Source Organize according to the data

2.1 From the Micro Level, the Change of Leading Cities Causes the Basic …

33

Fig. 2.1 The top 30 industries listed in 1750–1850. Source Sorting according to the data of Osiris’s global listed companies

In this period, although the technology of resource processing industry in advanced cities is relatively low, and innovation is not universal. However, information, knowledge, technology, culture and other non-material products had become an indispensable part of the production, exchange and consumption of advanced cities. During the first industrial revolution from 1750 to 1850, the most basic problem of human famine was solved. At the same time, the increase of production level and income stimulated more non-material demand, such as newspapers and magazines. Industry makes the line between work and leisure clear. It stimulates the demand for knowledge labor, thus stimulating the development of newspapers and magazines. The leading cities of major industrial countries in the world have established and published a large number of newspapers and magazines, such as 267 newspapers in Britain in 1821, 18 newspapers in London in 1880, more than 300 newspapers in Paris in 1892, and more than 9 million newspapers in Berlin in Germany from less than 100,000 in 1847 to 1870; there were about 400 daily newspapers and more than 3000 weekly newspapers in the United States in the mid-nineteenth century. 2. The leading cities in 1850–1950s: resources, chemical industry dominated, and information activities began to sprout. After the 1850s, the industrial structure of advanced cities began to change from the light textile industry of resource processing to the heavy industry of resource combination. In the next 100 years, advanced cities were mainly engaged in the manufacture, exchange and consumption of composite materials, and heavy chemical industry became the leading industry of cities. To some extent, the adjustment of industrial structure of leading cities had changed the type of global commodity

34 Table 2.2 Types and functions of some American cities in the twentieth century

2 The World: 300 Years of Urbanization Expansion

Types and functions

City

National comprehensive city

New York, Chicago

Specialized local central city

Baltimore, Philadelphia, Cincinnati, St. Louis, etc.

Specialized small cities

Elizabeth, Toledo, great falls, etc.

Satellite city

Pittsburgh, Forrest, etc.

Source Sorting according to western urban history

circulation, and some cities that start industrialization began to engage in resource processing activities. After the 1850s, the second industrial revolution was carried out in the cities of Britain, Germany, France, the Netherlands, Belgium, Luxembourg, Denmark and the United States. Through new construction and transformation, new industrial clusters represented by the electric power industry, chemical industry, petroleum industry and automobile industry appeared in these cities. For example, a group of emerging cities in the United States, such as Detroit, Birmingham, and Pittsburgh, the world’s steel capital, the Great Lakes city group with Chicago as the radiation center (Table 2.2). The second industrial revolution brought important changes to the industrial structure of global cities. The industrial structure of advanced cities began to shift from the light textile industry of resource processing to the heavy industry of resource integration, as shown in Fig. 2.2. From 1850 to 1950, heavy chemical industries such as power equipment, industrial machinery manufacturing, pharmaceutical manufacturing, auto parts, metal mining, semiconductors and other electronic components, and general equipment manufacturing became the leading industries, while traditional resource processing industries in advanced cities declined. At the same time, industrial machinery manufacturing, general equipment manufacturing, electrical equipment and other enterprises continue to increase, improving the level of automation of urban production, automation makes human physical strength further liberated. The invention and application of Telegraph and telephone, as well as the invention of software such as semiconductor and its electronic components, make the production, exchange and consumption of information increase constantly. From the perspective of global cities, the world’s information production, exchange and consumption are mainly concentrated in London, Europe, New York, United States, Japan, Asia and other advanced cities. The gap between global cities is relatively obvious, as shown in Fig. 2.3. In 1989, New York’s information related production, exchange and consumption exceeded ten million US dollars, followed by London and Tokyo, which reached more than 9 million US dollars and 7 million US dollars respectively.

2.1 From the Micro Level, the Change of Leading Cities Causes the Basic …

35

Fig. 2.2 The top 30 industries listed in 1850–1950. Source Sorting according to the data of Osiris’s global listed companies

Fig. 2.3 The distribution of listed enterprises engaged in global information software and hardware production and service during1850–1950. Source Sorting according to the data of Osiris’s global listed companies

36

2 The World: 300 Years of Urbanization Expansion

Fig. 2.4 The global distribution of listed companies of services producer in 2017. Source Sorting according to the data of Osiris’s global listed companies

1. The leading cities in 1950-2050s: from service led development and information led development to intelligent led development. From 1950 to 2000, the activities of leading city changed from material production, exchange and consumption to labor supply, exchange and consumption. World cities formed a pattern of leading cities engaged in services, intermediate cities engaged in manufacturing and bottom cities engaged in processing. The mode of global inter-city division of labor changes from department specialization to function specialization, which is embodied in the concentration of high value-added links such as headquarters, R & D, marketing and investment in the value chain in the central cities, and the concentration of manufacturing links with low value-added in the small and medium-sized cities. The advanced cities’ functions have been transformed into high-end services (see Fig. 2.4). The development of services producer has changed the economic structure and functional divisions of cities. The formation of global financial cities is an important manifestation of global urban functional changes, such as London, New York, Paris, Frankfurt, Tokyo, Hong Kong, Beijing, Shanghai, etc. (Fig. 2.5). From 1950 to 2000, manufacturing of computing software and its related services, special and commercial equipment, real estate and securities related industries became the leading industries of urban development, as shown in Fig. 2.6. At this stage, computer hardware and software and its service industries had developed rapidly. In addition to Tokyo, London, New York, Paris and other developed cities, which gathered a large number of related enterprises, Asian cities such as Beijing, Taipei, Mumbai, Seoul, Bangalore, Shenzhen and other cities have gained rapid rise at this stage. In 2000–2050, the activities of the leading cities will change from the provision, exchange and consumption of labor services to the production, exchange and consumption of information, knowledge and ideas. Instead of physical strength and

2.1 From the Micro Level, the Change of Leading Cities Causes the Basic …

90 Berlin

Toronto Seattle

San Francisco

Boston 60 Atlanta 30 New York

Los Angeles

Beijing

Stockholm

Seoul

Amsterdam London Paris Zurich Tel Aviv-Yafo Dublin Hong Kong Mumbai

Chicago

0 -180

-150

-120

-90

-60

-30

0 -30

30

60

Bangalore

37

90 Singapore

120

Shanghai Tokyo Hangzhou Nanjing Shenzhen Guangzhou 150

180 Sydney

Sao Paulo -60

-90 Fig. 2.5 The global distribution of global financial technology centers. Source 2018 global financial technology center index

Fig. 2.6 The top 30 industries listed in 1950–2000. Source Sorting according to the data of Osiris’s global listed companies

38

2 The World: 300 Years of Urbanization Expansion

Fig. 2.7 The distribution of knowledge intensive industries in global cities in 2017. Source Sorting according to the data of Osiris’s global listed companies

intelligence, intelligent machines provide products and services. Not only the production, exchange and consumption of software products and services become the main content of urban activities, but also the main human activities turn to the innovation of knowledge and technology. With the development of new generation information technology such as artificial intelligence and big data, the level of global urban intelligence will be improved. By 2050, intellectualization will deepen the global urban industrial division pattern, and promote the formation of a new global industrial pattern with high-end cities intelligent creation, middle-end cities intelligent manufacturing and service, low-end cities intelligent consumption and raw materials, other cities knowledge, information and service related industries. At present, the advanced industries of advanced cities, i.e. high-end manufacturing and high-end services, indicate the transformation direction of global advanced cities (Fig. 2.7). It is the common mission of countries and cities all over the world to use science and technology to serve human beings. By 2050, the trend of global urban development must be intelligent. ICT (information, communication and Technology) will make global countries and cities into an intelligent and global city. At present, intelligent manufacturing is essentially the intelligent process of manufacturing industry. It is led by the old world-class manufacturing center cities such as New York, London and Tokyo, as well as the emerging intelligent manufacturing center cities such as Suzhou, Tianjin and Foshan, as shown in Fig. 2.8. In addition, with the rapid development of automation, by 2030, the jobs of 800 million people in the world will be replaced by machines, as shown in Fig. 2.9.

2.1 From the Micro Level, the Change of Leading Cities Causes the Basic …

39

Fig. 2.8 The proportion of existing jobs likely to be replaced in global countries in 2016–2030. Data source Jobs lost, jobs gained: Workforce transitions in a time of automation by McKinsey

2019 World Intelligent Manufacturing City Potential Index

0.7 0.6 0.5 0.4

Foshan

Sydney

Osaka

Melbourne

Chongqing

Amsterdam

Houston

Birmingham

Pittsburgh

Beijing

Singapore

Seattle

Boston

Paris

Berlin

Tianjin

Suzhou

Chicago

Tokyo

Los Angeles

London

Shenzhen

Shanghai

San Francisco

New York

0.3

Fig. 2.9 The spatial distribution of some intelligent manufacturing cities in the world. Source The development trend report of world intelligent manufacturing center (2019)

2.1.2 The Size of the Leading Urban Population Ranges from Tens of Thousands to Hundreds of Thousands, Millions and Tens of Millions The population is the center of the city. Since the twentieth century, with the launch of industrialization in various countries around the world, led by leading cities, the population size of the city has exponentially increased. It is expected that the

40

2 The World: 300 Years of Urbanization Expansion

expansion of the city will usher in a new round of acceleration. From the perspective of historical evolution, In the 300 years from 1750 to 2050, led by leading cities, the population of the city has continuously exceeded the limit, the per capita income of the city has increased significantly, and the structure of urban society has become more complex and diverse. 1. 1750–1850: The typical urban population grows from tens of thousands to hundreds of thousands. In the 100 years from 1750 to 1850, on the one hand, the population scale of advanced cities has expanded several times, up to more than 100,000 people. On the other hand, the social structure of the urban population is more diverse. Although the vast majority of the world’s population is under 100,000 people, the city is mainly business practitioner and commercial and government officials. In 1760, Leading industrialized UK leading city, with a population of more than 100,000 in London, and 12 provincial cities were below 50,000 except Brisbane. By 1801, the population of provincial cities was below 100,000, there were 5 in 5–10 million, and 8 in 20–50 thousand. By 1851, there were more than 100,000 in 7 cities, 13 in more than 50,000, and more than 300,000 in advanced cities such as Manchester, Liverpool and Glasgow. It is estimated that between 1801–1851, the UK textile industry’s population growth rate ranked first with 229%, other port cities increased by 214%, and manufacturing cities increased by 186%; individual cities are growing at even more alarming speeds, such as From 1811–1861, the population of Liverpool and Preston increased fivefold, Brighton grew sevenfold, and the textile textile center Bradford grew eightfold. As the world trade and financial center, the population of London reached 700,000 in 1801, and the population of London soared to 2.36 million in 1850. The population increased by 2.4 times between 1801 and 1850. After the First industrial revolution, the income and utility scale of ordinary people has increased compared with the Pre-industrial revolution. According to Ashton, the British national income increased nearly eight times between 1740 and 1840, and in 1867 it was about 81.11 million pounds. With the start of the industrial revolution, increased productivity has reduced the daily cost of ordinary people, such as clothing and other necessities, which enables workers to have private wealth in their spare time. According to demand—the supply of internal connections, some products such as bread during this period have a backward tilt demand curve—increased incomes that reduce people’s consumption of bread and turn to other necessities such as meat. The development of industry has stimulated the society to generate new demands on the labor force, which will inevitably improve the economic and social infrastructure, such as transportation, commercial services, and power supply. On the supply side, industrial development reduces production costs and increases productivity. This not only helps to increase output, but also improves the quality of the entire process. The demographic structure is characterized by a rapid expansion of the number of workers. The industrial revolution is often associated with a “shift in work”. Before 1700, the proportion of the employed population in the UK, which relied mainly on agriculture, declined. By the time of the first census in 1801, this was

2.1 From the Micro Level, the Change of Leading Cities Causes the Basic …

41

the first official measure, and the agricultural population was less than one-third. By 1851, this figure was less than one-fifth. After the industrial revolution began, due to the use of factory-made production, the contrast between social relations and various class forces has changed dramatically. Many people in the past invested in the construction of factories and gradually transformed into a new industrial bourgeoisie, and industrial cities developed rapidly. According to statistics quoted by British bourgeois historian Toynbee in the Lectures of the British Industrial Revolution in the Eighteenth Century, the phenomenal speed of industrial cities can be seen. Between 1760 and 1781, the famous British city of Liverpool increased from 30,000 to 552,425, Manchester from 30,000 to 393,676; Birmingham increased from 30,000 to 400,757. Although merchants and government public sector personnel are still a minority bourgeois ruler at this time, the United Kingdom has established an unparalleled industrial and commercial status. In 1840, the proportion of trade in the UK accounted for 21% of total world trade. In the same year, France accounted for 11% and the United States accounted for 10%. The working class has grown rapidly, when merchants and the public sector accounted for a small proportion of the entire social class. 2. 1850–1950: The typical urban population grows from hundreds of thousands to millions. The population of major countries in Europe and North America has grown on a large scale. During the period of 1850–1950, the industrialization wave spread to continental Europe and North America, during which many countries in Europe and North America completed the transition from the steam to the electrical age. Especially in the United States, the population increased from 5.3 million in 1800 to 76.20 million in 1900. During the same period, the land area of the United States increased from 891,000 square miles (2,307,690 square kilometers) in 1791 to 30,219,295 square feet. The double growth of population and land area provides the United States with a larger product internal market and a raw material supply market. This expansion has encouraged immigration and migration to the west, and the surge in demand for products has increased, accelerating the development of the industrial revolution in the United States. Between 1870 and 1930, the population of many important industrial cities in the United States increased by several times. Individual cities such as Los Angeles expanded by more than 100 times. In 1990, the population of Los Angeles reached 1.778 million. Chicago was still desolate in the early nineteenth century. It officially formed a city in 1837. In 1890, it crossed the population of 1 million. In 1910, it became the second largest city after New York. It can be seen from the following important industrial revolution cities that the population of major cities in Western countries has doubled (Table 2.3). At the same time, the income and scale utility of the population has changed (Table 2.4). Drawing on the population growth of other countries, during the second industrial revolution, the population of many countries achieved steady growth and national income also increased. In the nineteenth century, the industrialized countries witnessed the improvement of people’s living conditions and the sharp fall in commodity prices. The development of gas and electricity in the nineteenth century,

42

2 The World: 300 Years of Urbanization Expansion

Table 2.3 Changes in population of important cities during the second industrial revolution (Unit: thousand people)

1850

1880

1900

New York

696

1912

3437

London

2681

4767

6581

Tokyo

–

1050

1600

Moscow

365

612

1000

Excerpted from: L.S. Stavrianos: The Book of Global History

as well as the development of durable goods such as bicycles, provided effective demand. Similarly, supply responds to changing demand conditions through technological development. In the nineteenth century, urban development created demand for environmental protection, and new demand spurred the development of cheap, non-permeate pipelines (which were not yet common in the UK before 1846), draining pipes, gutters, and hydraulic components. In the late nineteenth century, the expansion of the suburbs increased the demand for supplies and services. At the time of the first US census in 1790, there were only six cities with a population of over 8,000 in the United States. From 1870 to 1920, the urban population increased from 9.9 million to 54.3 million. In 1920, the urban population was 51.4% of the national population, and in 1930 it increased to 56%, basically achieving urbanization. The contradiction between labor and capital has stimulated the expansion of the public sector. At the turn of the nineteenth and twentieth centuries, the rapid expansion of industrialization expanded the working class. In 1914, there were about 40 million workers worldwide, most of them concentrated in Western Europe and North America. The number of workers in Britain, the United States, Germany and France accounted for about three-quarters of the world’s total workers. During this period, the bourgeoisie of each country basically still treated workers in the first or even the Table 2.4 Percentage of population and income growth in major countries during the second industrial revolution Country

Period

Annual growth percentage Population

National income

Per capita income

France

1845–1950

0.1

1.5

1.4

Germany

1865–1952

1

2.7

1.5

Italy

1865–1952

0.7

1.8

1

United Kingdom

1865–1950

0.8

2.2

1.3

Russia

1870–1954

1.3

3.1

1.5

Switzerland

1865–1952

0.7

3.6

2.8

United States

1975–1952

1.7

4.1

2

Canada

1875–1952

1.8

4.1

1.9

Japan

1885–1952

1.3

4.2

2.6

2.1 From the Micro Level, the Change of Leading Cities Causes the Basic …

43

most primitive way of industrialization. Until the end of the nineteenth century, the working hours of workers in each country were not less than 12 h per working day. The actual wages of workers have risen slightly in some countries, but the growth rate is far below the increase in productivity. When the economy is sluggish, the actual wages of workers will stagnate or even fall, and the harsh labor environment will cause frequent work-related accidents. In addition, workers are also facing the threat of unemployment and semi-unemployment. Under this circumstance, some workers have waged struggles to safeguard their rights and interests, and the contradiction between labor and capital has expanded. A striking feature of the twentieth-century economy is the expansion of the government and its institutions. The expansion of such government agencies can be summarized by the “welfare state”. This term was originally created to alleviate the contradiction between labor and capital. For example, the American reform movement of the early twentieth century required the government to regulate the working and living conditions of child labor, food processing and packaging, and the working class. In the 12 years of Roosevelt’s administration, the total number of federal employees increased from more than 500,000 in 1933 to an all-time high of more than 3.5 million in 1945. It is estimated that in 1911, “every 10,000 inhabitants, there are 200 government officials in Belgium, 176 in France, 126 in Germany, 113 in the United States, and 73 in the United Kingdom.” The size of national bureaucracies and the subsequent economic growth rates have led to government debate in developed countries. Machines have increased social productivity, and labor-management conflicts have caused some social stability problems, causing the public sector to grow rapidly. 3. 1950–2050: The urban population grows from millions to ten millions. The population size of the world’s cities has exploded. When new communication technologies are integrated with new energy systems, a huge economic revolution in history will emerge. After 1950, it can be called worldwide industrialization. The world’s economy, employment, and industrial structure have undergone tremendous changes. The wide application of science and technology has increased the supply of social products. The most political and economic changes reflect the growth of the population and the expansion of the city. In 1900, the world’s total population was about 1.6 billion. In 2018, the world’s total population reached 7.631 billion. The world population has increased by nearly 3.8 times in the past 118 years. The changes in the industrial structure have rapidly transferred the population of the primary industry to the secondary and tertiary industries. As a result, the growth rate of the urban population far exceeds the growth rate of the total population, which has increased by nearly 17.8 times in the past 118 years. The proportion of urban population has also increased from 13.6% in 1900 to 55.3% in 2018. It is estimated that by 2050, the proportion of urban population will reach 68.4% (Table 2.5). During this period, the typical urban population of emerging economies soared. For example, Shanghai in China had a population of 4.288 million in 1950 and rose to 23.382 million in 2015. Shenzhen is China’s first batch of reform and opening-up areas, benefiting from policy and geographical advantages, and

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2 The World: 300 Years of Urbanization Expansion

population growth has doubled 3580.8 between 1950 and 2015. It is estimated that in 2050, the population of Shanghai and Shenzhen in China will exceed 35.841 million and 18.827 million respectively. The economic revolution has led to an increase in the world’s population and urbanization levels, while the per capita income and GDP of the world have also increased. The world’s per capita GDP was $451.06 in 1960 and $2530.23 in 1980. In the past 20 years, the world economy has grown by 4.6 times. By region, the per capita output value of North America during this period was higher than that of other regions. The per capita GDP of East Asia and the Pacific increased rapidly, reaching the world average in 2018 (Table 2.6). The population size utility has also changed to some extent compared to the previous two industrial revolutions. Different from the past, the first industrial revolution generally improved the quality of life, the second industrial revolution established the factory system, and truly realized the urbanization of developed countries. These two industrial revolutions greatly stimulated people’s demand for various products of life. the quality of life has been met, but the form of these two industrial revolutions is still a vertical structure, the management system is top-down, and even monopoly organizations. The flat structure adopted by the third industrial revolution, the emergence of the Internet has connected various organizations in countries, continents and even the world into a network. The most intuitive way for people to live is to browse information across the country through digital devices, and products from around the world are readily available through e-shopping platforms. New technology energy is more dependent on the development of technology, and the combination of information technology and 3D printing technology will set off a new round of technological revolution. In today’s world, there is a need for people with ideas and ideas. In short, the third revolution promoted the utility of population size. Table 2.5 World population growth list (Unit: 100 million, %) Year

Total population

1900

16.08

1950 1960

Urban population

Urban population as a percentage of the total population

2.24

13.6

25.36

7.51

28.1

30.33

10.24

32.9

1970

37.01

13.54

37.3

1980

44.58

17.54

42.2

1990

53.31

22.90

43.0

2000

61.45

28.68

46.7

2010

69.58

35.95

51.7

2020

77.95

43.79

56.2

2030

85.51

51.67

60.4

2040

92.10

59.38

64.5

2050

97.72

66.80

68.4

2.1 From the Micro Level, the Change of Leading Cities Causes the Basic …

45

Table 2.6 World per capita gross income per region by region from 1960 to 2018 (in US dollars) Year World

1960

1980

2000

2010

2018

451.06

2530.23

5491.57

9538.84

11,296.78

147.23

1162.52

4044.32

7673.74

11,132.23

82.28

262.14

447.45

1257.67

1905.77

By region East Asia and Pacific South Asia North America

2942.63

12,437.78

35,147.88

48,373.70

60,968.0

Europe and Central Asia

651.64

5780.59

11,627.68

23,572.56

25,069.77

Latin America and the Caribbean

370.91

2161.97

4389.80

9058.33

9023.50

Middle East and North Africa South Africa

-

2173.95

3066.47

7172.83

8056.95

443.00

2905.93

3032.44

7328.62

6339.57

By income High income

1390.80

9545.32

25,593.38

39,169.91

44,705.87

Middle and upper income

192.86

970.34

1967.25

6344.15

9200.45

medium income

149.93

714.32

1272.37

3916.59

5483.97

Low and medium income

–

412.57

566.62

1659.71

2218.90

Low income

–

–

314.44

638.23

811.18

The number of service workers has increased significantly, and the business group has become more extensive. At the end of the twentieth century, the change in the number of workers showed a difference from the previous two industrial revolutions. First, the number of industrial workers has decreased, and the number of workers in the service industry has increased significantly. In the United Kingdom, for example, in the employment structure of the United Kingdom from 1971 to 1996, the proportion of manufacturing workers fell from 30.6% to 18.2%, while the proportion of employees in the minerals, energy and water supply industries fell from 9.5%. To 1.1%. The manufacturing sector in Manchester, the industrial center of the UK, fell from 70% of total sales in the early 1960s to 20% in the late twentieth century. At the same time, the service industry developed rapidly. From 1971 to 1996, the proportion of UK service workers in the total number of employed people rose from 52.6% to 75.8%. To a certain extent, the proportion of service industry to GDP can also reflect the proportion of employees in the service industry. As can be seen, the trend of the UK service industry and industry as a share of GDP is in the opposite direction, and industrial workers are gradually turning to the service industry (Fig. 2.10). After the mid-twentieth century, the government’s development was expanded into macroeconomic arrangements for greater efficiency and faster growth. After the industrial revolution, the traditional duties of the government have also expanded. Therefore, according to the laws of industrialization in all countries of the world, the industrialization and urbanization of other countries in the world after the midtwentieth century will gradually stabilize the growth of the public sector employment in all countries of the world. At present, as shown in Fig. 2.11, the proportion of the

46

2 The World: 300 Years of Urbanization Expansion

80 70 60 50 40 30 20 10 0

Service industry as a share of GDP

Industry as a share of GDP

Fig. 2.10 Trends in the ratio of service industry to industry in GDP in the United Kingdom from 1801 to 2011

United Kingdom Turkey Thailand Switzerland Sweden Spain Mexico Korea Japan Ireland Hungary Greece Czech Republic 0.00%

5.00%

10.00%

15.00%

20.00%

25.00%

30.00%

35.00%

Fig. 2.11 Proportion of public sector in total employment in some countries in 2016

public sector in the majority of European countries is still higher than the proportion of the public sector in other countries. After the twenty-first century, although the merchants accounted for only a small part relative to the entire social group, the scope of the merchant group dominated by a few bourgeoisie during the previous two industrial revolutions expanded. On the one

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47

hand, in contrast to the changes in the entire business management system, in addition to self-employment, businessmen in the twenty-first century have a form of corporate management. On the other hand, the entrepreneurial identity that emerged through its own efforts has expanded from a middle-aged male with university education to a new era group composed of immigrants and women. The group of merchants is covered more and more widely. According to a survey by the Kauffman Foundation, the highest level of entrepreneurial activity in the United States is between 55 and 64 years old, and the proportion of businesses starting is 28%. More and more employees are looking for a way to start a business. According to the Census Bureau, among the 27 million companies in the United States, unpaid small business enterprises account for more than 70%, and annual sales are $887 billion. Small business enterprises are also flexible, can operate in low-cost locations (such as home offices), and are more flexible than large commercial companies.

2.1.3 The Space of Leading Cities Spread from Single Central Cities to Multi-Center Metropolitan Areas, Megalopolis and Metropolitan Coordination Regions Space is the carrier of the city. Historically, guided by leading cities, urban space has experienced a single-center to multi-center evolution. Today, as the process of urbanization is deepening, countries in the world have replaced the single-center and multi-center spatial structure with urban integration to promote regional division of labor and cooperation to strengthen the overall strength of the region. It has become the active choice of political power institutions in various countries. This is an inevitable outcome of the economic evolution of cities and regions. In densely populated industrialized cities, higher productivity developments tend to make cities more advanced public facilities and transportation networks than rural areas, and road density is far superior to rural areas. During the first industrial revolution, these resources and facilities were mainly located in urban centers. During the second industrial revolution, with the continuous improvement of space resources and facilities in small and medium-sized cities in developed countries, the population was concentrated in small and medium-sized cities. Since the twentieth century, globalization has deepened and developing countries around the world have successively undergo industrial revolutions. At present, resources and facilities based on developing countries are still concentrated in large and medium-sized cities, but with the spread of the Internet, the world The links between cities are becoming more and more flat, and technology resources can be shared between cities through networks. 1. 1750–1850 period: The leading city is a single center with tens of square kilometers (miles) of space. The industrial revolution has accelerated the concentration of population to cities, and the state of the city has changed. Between 1750 and 1850, the steam revolution

48

2 The World: 300 Years of Urbanization Expansion

led to a rapid increase in productivity, and a large concentration of labor to the city, so the scale of urban space continued to expand like a snowball. Take the major European cities of London and Paris as examples, forming a single-center regional city with a space of tens of square kilometers (miles). Take the city of London as an example. Its city from west to east is only 8 km (5 miles) in 1750. By 1850 it was expanded to 24 km (15 miles), and the city area reached 62 square kilometers (24 square miles) in 1841, and further expanded to 316 square kilometers (122 square miles) in 1851, while the urban area of Paris was also From 13.4 square kilometers (5.2 square miles) in 1700 to 34.5 square kilometers (13.3 square miles) in 1850, with the expansion of urban areas, urban public resource facilities are also standing in the city center, it can be said that this time Europe mainly forms urban spatial structure features dominated by a single center. Urban public resource facilities are also becoming more and more abundant. At the same time as the rapid development of the city, some of the most necessary social public facilities have also been added in the industrial city of the United Kingdom. The first is urban architecture, mainly factory buildings and residential buildings. In order to adapt to the development of the industry, the workers’ houses and streets are also covered around the factory. The second is a variety of living andKnowledge density. In the first half of the nineteenth century, several industrial cities in England, such as Pennillas, Rosendale and Nottingham’s Trent, established water plants. Liverpool also built public baths and laundry facilities in the south of the city. The 79 staff schools in the Yorkshire Federation have affiliated libraries. The third is urban transportation. Before the industrial revolution, the traffic between British cities was still in a state of primitive backwardness. After the industrial revolution, the development of highways was far from being able to adapt to the needs of industrial raw materials transportation, and the upsurge of the canal was raised. The invention of the Stephenson locomotive in 1814 brought land traffic into the railway era. The railway between Stockton and Darlington was completed in 1825 and the railway between Manchester and Liverpool was opened in 1830. Then, in the centre of London, some trunk lines and feeder lines were built. By the time the industrial revolution was completed, most of the major railway lines had been completed. At this time, a high-density, centralized, single-center urban structure is formed. The theory of industrial cities that emerged at this time also reflects the characteristics of urban functional elements concentrated in urban centers. For example, at the end of the nineteenth century, young French architects made a clear distinction between the functional elements of industrial cities. The central part is the city center, with assembly halls, museums, exhibition halls, libraries, theaters, etc. The urban living quarters are long strips, the health and medical center is located on the north side of the uphill slope, and the industrial area is located in the southeast of the residential area. Each zone has a green belt isolation. The railway station is located near the industrial zone, and the main railway runs through the underground railway to the interior of the city. Urban traffic is advanced, with fast-tracks and test sites for aircraft launches. The

2.1 From the Micro Level, the Change of Leading Cities Causes the Basic …

49

residential neighborhood is 20 m wide and 150 m long, and each is equipped with corresponding greening to form various neighborhood units with primary schools and service facilities. 2. 1850–1950: Leading cities are multi-centers with hundreds of square kilometers (miles) of space. During the period of 1850–1950, the western countries were in the period of industrialization and post-industrial era. The development of urbanization no longer blindly pursued the growth of urban scale, but focused on the improvement of urban quality and the improvement of residential conditions and public facilities. In addition, convenient and fast transportation makes the city center more closely connected with the surrounding suburbs, and urban public facilities are distributed in a wider space, and the city is shifted from high-density concentration to low-density and contagion. Leading cities such as London and New York are gradually forming multi-centers of 100 square kilometers (miles). Take the city of London as an example. The city from west to east has further expanded from 24 km (15 miles) in 1850 to 50 in 1950. Kilometers (30 miles), the city area reached 1,186 square kilometers (458 square miles) in 1951, while the urbanization of New York City jumped from 23.6 square kilometers (9.1 square miles) in 1850 to 3,245 square meters in 1950. Kilometers (1253 square miles). Under this development trend, the public facilities of the city are distributed to a wider extent, and education and transportation have also developed profoundly during this period. The uneconomical nature of large cities is still taking place during this period, and the multi-center urban structure plan for urban diseases was raised during this period. Urban public facilities are distributed to a wider range. During this period there was the famous Crystal Palace building, the luxurious Paris Opera, the Eiffel Tower and the magnificent Trinity Church in Boston. There is also the Leeds Currency Exchange in London, the Old Railway Station in London, and the Eman Art Museum in Milan. Under the conditions of new structural technology, the building has made a huge breakthrough in the number of layers and heights. The first high-rise building was the Chicago Family Insurance Company Building, which has ten floors. During this period, the improvement of large-scale indoor open spaces such as factories, museums and railway stations was made possible through the improvement of the ironmaking process. Central and local municipal services are also expanding in terms of life and culture. Such as sewage and garbage treatment facilities, arrange urban transportation, build public toilets, houses, markets, and cultural sites such as libraries, expo, art galleries. Founded in 1851, the Crystal Palace in Hyde Park, London is mainly used for the huge international exhibition of industrial products. Wholesale markets and retail stores have also been firmly established. For example, in Prussia, the number of retailers per thousand people is twice as many as 1900 in 1850. In addition, international organizations are also established in Europe (Table 2.7). Education and transportation also experienced profound development during this period. First, there were some developments in the UK, followed by France, followed

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2 The World: 300 Years of Urbanization Expansion

Table 2.7 Number of international organizations from the 1950s to 1910 Before 1857

1870

1880

1890

1900

1910

7

17

20

31

61

108

Source Frederick L. Nasbaum: The History of Modern European Economic Systems

by Germany and the United States. After the American Civil War, only 100 universities and secondary technical schools were established in 1865, including MIT, universities and specialized technical schools to 450. In terms of transportation, trains and airplanes have been widely used during this period. By the end of the nineteenth century, the United States had built five railway lines across the East and West. In 1913, it increased to more than 300,000 miles, equivalent to half of the total length of the world railway at that time. At the beginning of the twentieth century, in 1903, the Wright brothers invented an aircraft powered by an internal combustion engine. The emergence of aircraft, the application of automobiles and the rapid development of railways mark the arrival of a new world of transportation. The railway-based transportation industry has linked all parts of the country into a huge unified market, greatly enhancing the flow rate and process of industrial and agricultural products and other commodities, and further strengthening material exchange and personnel exchanges between cities, between urban and rural areas and between regions, while consolidating the urban system. The urban planning of London, the leading city of this period, also reflects the multi-center spatial structure of the city. At the end of the nineteenth century, the British government authorized British social activist Howard to conduct urban surveys and propose remediation plans. Howard believes that building an ideal city should make urban life and rural life attract and combine with each other like magnets. This urban–rural integration is called a rural city. Howard advocates that when any city reaches a certain size, it should stop growing, and its excess should be accepted by another neighboring city. Thus, residential areas, like cell proliferation, present a multi-center complex urban agglomeration area. The planning of the urban area of London in the 1930s absorbed the essence of the idyllic city and proposed the concept of a combined city. In 1939, Abercrombie presided over the preparation of the Greater London Plan. The plan was divided into four geographical zones from the inside to the outside within a radius of about 48 km from the center of London, namely the inner ring, the suburb, the green belt and the outer ring.. The outer ring outside the green belt is mainly used to evacuate the surplus population and industrial enterprises in Londonshire. The concept of a combined city proposed by the Greater London Plan played a role in controlling the spontaneous spread of the city of London at the time and improving the already chaotic urban environment. 3. The leading cities in the 1950s and 1950s are thousands of kilometers of urban agglomerations. In densely populated industrialized countries, urban areas with metropolitan areas as competition units have also been formed. At the same time, the information revolution broke out during this period. The developing countries have grown rapidly. A large

2.1 From the Micro Level, the Change of Leading Cities Causes the Basic …

51

number of people have flooded into cities. The secondary and tertiary industries have developed rapidly. The land for mining, commerce, housing, transportation, municipal administration and greening has increased. Its cultivated land occupying the edge of the city spreads and expands outward. The world’s leading urban agglomerations form thousands of kilometers of urban agglomerations, such as the total area of the Pacific Rim in Japan, which is about 35,000 square kilometers. The total area of the Atlantic coast of the northeastern United States, centered on New York, is 138,000 square kilometers. During this period, the cities that grew rapidly in developing countries stimulated the rapid expansion of urban space in the world. The public facilities in the city were further enriched, and the urban space was greatly expanded. Urban development in this period generally shows the structural characteristics of the metropolitan area and urban agglomeration. The public facilities in the city are further enriched. In terms of urban construction, the developed countries in the 1950s adopted new city construction and urban sub-centers for the problems of large cities, such as excessive complex pressures, traffic congestion and environmental pollution in the old city, such as Cumbernauld in the United Kingdom. Welwyn in Sweden, the new city in Japan, and Zelenograd in the Soviet Union. Since the 1960s, the Tsukuba Science City, the European and American Science Park, and the Kansai Science City have been established in the developed regions. In addition, this period has a new exploration of the construction of the city center, commercial blocks and residential areas, such as the ancient city of Rome in Italy. The commercial shopping environment has evolved from a single plane to a three-dimensional giant commercial complex that utilizes the comprehensive utilization of above-ground and underground spaces, from the ground-based pedestrian zone to the pedestrian bridge commercial area and underground commercial street of the second-floor system. In terms of living facilities, people gradually replaced manual labor with advanced equipment such as robots, electronic computers, microprocessors, CNC automatic machine tools, telex machines, and data communications. In terms of transportation methods, science and technology developed rapidly after the 1980s. Technological advancement greatly promoted the upgrading of transportation structure from two aspects: transportation demand and transportation supply, which made passenger transportation faster and more comfortable, and cargo transportation became more specialized. Overloaded. The internal structure of various modes of transportation such as expressways, luxury buses, highspeed railways, heavy-duty trains, large-scale ship-specific terminals and wide-body passenger aircraft has been significantly improved and matured. At the same time, at a higher level, the rational allocation of transportation resources and the improvement of comprehensive transportation capacity have also accelerated the adjustment of the overall structure of the comprehensive transportation system. At this time, the overall characteristics of the urban space present the structure of the metropolitan area and the urban agglomeration. In densely populated industrial countries, the dot-like aggregation pattern of large urban populations has gradually been replaced by scattered urban areas. This is a particularly important phenomenon in the development of urban population to today, and it is also a worldwide trend. An example of a major leading urban group cited by French geographer Gottman

52

2 The World: 300 Years of Urbanization Expansion

is the urban area on the northeast coast of the United States, extending from New Hampshire in the south to Virginia, including New York, Baltimore, Philadelphia, Boston, Washington, and others. The city, the entire region is 600 miles long, 30–100 miles wide, and covers an area of 53 500 square miles, with a population of 90%. For example, in the Rhine-Ruhr area of the Federal Republic of Germany, there are 19 large and medium-sized cities in the area of 4,953 square kilometers, which is not only the heart of the Federal German economy, but also the world’s famous coal and steel base. The port city group in the Netherlands, centered on Rotterdam, Amsterdam, The Hague and Utrecht, the four largest port cities, has a city circle of about 70 km from east to west, 60 km from north to south, and an area of 3,800 square kilometers. It accounts for 35% of the total population of the Netherlands and has an average population density that is three times the population density of the rest of the Netherlands. Such giant urban belts (groups, circles) are also in the Los Angeles-San Francisco metropolitan area; in Japan, there are three major metropolitan areas of Tokyo, Osaka, and Nagoya. Since then, the global information revolution has further expanded the scope of the city’s functions. Economic globalization and informatization will realize the cooperation of communication networks and high-speed transportation in the world’s multiple independent but complementary cities, and strive for more economic and social cooperation. The cyberspace of a city changes the concept of time and space in urban physical space. The flow of capital and information flows from one city to another at an invisible speed of light. Compared with traditional urban structure, virtual urban space has greater diversity and creativity, as well as a better urban environment and more freedom of location. It is expected that by 2050, the size and structure of urban space will further present the characteristics of smart cities. The future smart city system will be able to prevent problems before and prevent and solve problems with active and targeted work methods. Government work will use data-supported performance appraisal as a driving force to continuously improve work results. Building a data-driven smart city to create the core competitiveness of urban rapid response, data sharing, and efficient service. The city is adaptable, responsive, and always relevant to all people who live, work, and visit the city. Smart cities integrate technology to accelerate, promote and change this ecosystem.

2.2 From the Perspective of Macro Structure, the Evolution of Global Urban System Determines the Change of the world’s Pattern and System In the development of human history, city is the skeleton and blood of the world, and the urban system and pattern determine the system and pattern of the world. In the 300 years from 1750 to 2050, the formation and evolution of global urban system determines the evolution of global system and pattern. The world takes the lead in the

2.2 From the Perspective of Macro Structure, the Evolution of Global Urban …

53

rise of the country and region driven by the first rising city, and the general prosperity of global cities drives the arrival of the global urban era.

2.2.1 Global Urban Economic System: From Global Duality to Global Integration, from Commodity Trading System to Factor Trading System, and Then from Industrial Chain System to Innovation Chain System Since 1750, the global economy has changed from a global urban system of primary commodities to a global system dominated by capital intensive commodities and transnational corporations as the body, and then to a global system dominated by capital flows. A global urban innovation system dominated by the exchange of knowledge, information and ideas of global cities, with technical products and paper cooperation as carriers, is emerging. Using OECD Global trade data, this report finds that the proportion of manufactured goods in Global trade products increased from 48% in 1962 to 63% in 2016, while the proportion of primary commodities decreased from 44 to 18%, which fully shows the substitution of industrial products for primary commodities. At the same time, the internal structure of industrial manufactured products has also changed significantly. The proportion of capital intensive products is relatively stable, accounting for about half of industrial manufactured products. However, the proportion of technology intensive products has gradually increased, from 6% in 1962 to 23% in 2016 (Fig. 2.12). The production of human beings has changed from supply shortage to overproduction, and people’s consumption has gradually changed from commodity oriented to service-oriented, but there is a large gap between global cities, as shown in Fig. 2.13. Figure 2.13 shows that the developed countries have changed into service stations in the global trade, and the proportion of service industry in the United States has reached 41%; while the less developed countries are still in the commodity oriented stage. 1. The global urban system was mainly centered on Western European cities in 1750–1850s, such as London and Paris, with the rise of North American cities and the relative backwardness of Asian, African and Latin American cities, forming a global trading system dominated by the cities of Western European industrial countries. The industrial revolution promoted the initial formation of the global market and the expansion of the global urban connection. The completion of the industrial revolution made the goods produced by the advanced capitalist countries flow to all parts of the world, and the funds and raw materials from all parts of the world gathered in the industrial developed countries, thus forming the global market dominated by the western developed industrial countries.

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2 The World: 300 Years of Urbanization Expansion

1962

2010

1990

2016

Fig. 2.12 Changes in Global trade goods since the 1960s. Note PP, IP and OP refer to primary commodities, industrial manufactured products and other commodities respectively; LP, CP, TP and OT refer to labor-intensive products, capital-intensive products, technology-intensive products and other unclassified products in industrial manufactured products respectively. In this paper, the classification is mainly based on SITC classification and Yang Rudai et al. (2007, 2008) classification standard. Source Based on OECD data)

In most parts of the world, administrative services, trade, raw material exports and other factors are important factors for global urban growth. After Britain lifted the ban on machine output in 1825, a large number of machines were exported, which had a significant impact on the industrialization of France, Germany, the United States and other countries. It took France, the United States, Germany and Japan only about 60 years, 50 years, 40 years and 30 years to complete the industrial revolution, which established the world pattern of developed industrial countries in Europe and the United States and other regions in the world with backward industries. With the export of machine equipment and products from developed industrial countries, the global urban connection has been expanded, and the pattern of East–West poles of global cities has been formed. As can be seen from Fig. 2.11, until the 1960s, Global trade was still dominated by primary commodities, while service trade was

2.2 From the Perspective of Macro Structure, the Evolution of Global Urban …

1960

2010

55

1990

2017

Fig. 2.13 Proportion of global service trade since 1960s. Source https://ourworldindata.org

in a low position. And the global trade level is at a low level. In 1850, the global trade volume was 14.5 billion mark, with a slow growth. At the same time, the global urban trade pattern has also been solidified due to the industrial revolution, and the global industrial transfer between cities is less, especially in developed countries and backward Asian, African and Latin American countries. In order to export commodities, seize raw materials and overseas markets, developed western industrial countries such as Britain and France have embarked on the road of overseas expansion, respectively plundering backward regions such as India, Southeast Asia, ancient China and Africa. These backward countries or regions are still relying on traditional manual production, so they are forced to open up to the outside world and embed into global urban commodity market. This is mainly because in most parts of Asia and Africa, the low labor cost and the lack of a large consumption base hinder the use of machines, so these regions tend to develop the original rural industry. In the face of the impact of industrial output from developed countries, the industrial system of backward countries such as Asia, Africa and Latin America has also been challenged. In a word, the connections of the global urban economic are mainly based on the export of primary commodities and the transaction

56

2 The World: 300 Years of Urbanization Expansion

of raw materials. Capital flows between cities in some developed industrial countries. The disperse of the global urban industrial and agricultural production pattern is more obvious, and a global urban system has not yet been formed. 2. The global urban system turned to a new stage, which was dominated by European and American cities, the rapid development of North American cities, and the rise of Asian, African and Latin American cities in the 1850–1950s. The cities of European and American still occupy the advantage of manufacturing industry in Global trade, and the cities of Asian, African and Latin American are in the low-end primary product output position. The second industrial revolution, marked by the wide application of internal combustion engine and electric power, not only promoted the unprecedented development of industrial production, but also promoted more products to be sold all over the world and expanded the scope and scale of world trade rapidly, and further developed the world market. By 1950, the global trade volume had increased to about 60 billion US dollars. At the same time, the international division of labor is becoming increasingly obvious, and the international flow of population and capital and trade are gradually expanding. The invention of communication technology, tools and transportation equipment, such as telegraph, telephone, airplane and ocean going ship, further reduced the space cost between global cities and promoted the formation of global market. At this stage, some cities with advantages such as superior location and natural seaport began to rise, such as the rise of the Great Lakes city belt in the United States, Los Angeles and so on. Industrial cities in developed countries were updated through industrial structure transformation, such as the urban renewal in London and the transformation from industrial structure to service industry. On the one hand, the second industrial revolution promoted the competition and connection between the cities. The application of new technologies and the rise of new industries have promoted the significant changes in the economic development of advanced industrial countries. The trend of the socialization of capitalist production has been strengthened, the competition among enterprises has been intensified, and the concentration of production and capital has been promoted. A few enterprises adopting new technologies have crushed a large number of enterprises with backward technology, and the inflow and outflow of enterprises between cities have been enhanced. At the same time, the improvement of transportation network and communication technology has been made. It shortens the distance between cities and strengthens the economic connection between them. In addition, the second industrial revolution expanded the scale and scope of Global trade, finally established the capitalist world system, and gradually made the world as a whole. It also aggravates the imbalance of global economic development. The gap between the western developed industrial countries and the eastern backward countries is further widened. The industrial development level of the Asian, African and Latin American backward countries and regions is locked in the primary commodity stage by the developed industrial countries, mainly focusing on agriculture and mining industry. The global cities are still dominated by commodity export,

2.2 From the Perspective of Macro Structure, the Evolution of Global Urban …

57

at the same time, the internationalization of capital appears in this stage, multinational companies begin to seek to maximize profits through overseas investment, and the global cities initially form a world market dominated by commodity trade and supplemented by capital. 3. The global urban system changed from East–West segmentation to integration from 1950 to 2050. The global urban system was a segmentation system dominated by western developed countries and led by the Soviet Union from 1950 to 1990. In 1990, the global urban system gradually became integration. The global industrial division and global value chain construction of the global industrial system will be dominated by the global urban innovation system by 2050. Since the 1950s, due to international political reasons, The two camps of the East and the West centered on the United States and the Soviet Union have been formed in the world, and the pattern of two poles of global cities has been formed. However, with the disintegration of the Soviet Union in 1991, the process of global urban integration gradually accelerated, and the development of global urban multipolarity has become a new trend. The formation of global urban division network, the formation of global urban division value chain with developed countries’ cities as the leading role, the enhancement of “deindustrialization” and service-oriented trend of developed countries’ cities. The later-development countries’ cities accepting global urban industrial transfer get rapid development, especially the rapid rise of developing countries’ cities represented by China, Southeast Asia, etc., and forming a global urban division network system. Cities in developed countries have realized industrial transformation and upgrading through global industrial transfer, such as London, Paris, New York, Los Angeles and other international metropolises have successfully realized timely industrial transformation and transformation of comparative advantage with the pace of technological progress, and become the center of global finance, science and technology, and economy. The new generation of information and communication technology, represented by computer information technology, is profoundly changing the way of production and life of human beings, the development of high-tech and service economy, and the content and contact way of global urban transactions. As shown in Fig. 2.10, the proportion of primary commodities in Global trade decreased from 44 to 18%, while the proportion of technology intensive products increased from 48 to 63%, indicating the upgrading of global urban trading varieties and the upgrading of global urban industrial structure. In addition, with the increase of the proportion of services in Global trade, the proportion of trade in cities of some countries has exceeded 30% or even more than 50%. In particular, in the current information economy era, the role of knowledge and information in the global urban network is more important. Cities such as Hangzhou, Shanghai, Shenzhen along the east coast of China and Guiyang seize the opportunity of the new generation of information technology and quickly become the leading cities of the new generation of Intelligent Technology. There have been three large-scale industrial transfers in the world. The global manufacturing center has gradually shifted from the developed economies such as

58

2 The World: 300 Years of Urbanization Expansion

Fig. 2.14 The connectivity of global transnational corporations. Data source Cass global urban competitiveness database

the United States to Japan and Germany, then to the “four dragons” region in Asia, and then to the eastern coastal region of China, so as to promote China to become a “world factory”. Among them, transnational corporations are the main body to promote the strengthening of global urban linkages, as shown in Fig. 2.14. As can be seen from Fig. 2.14, the global city transnational corporations’ connection degree shows certain spatial differentiation. Cities in Europe, America, Japan and other countries are densely distributed with transnational corporations’ and have a high degree of connection with global cities. The transnational connection of cities in some Asian countries is gradually improving, such as Beijing, Shanghai, Shenzhen, Hong Kong, Mumbai in India, Bangalore in China, and Brazil in Brazil. Global urban links such as Johannesburg in Asia and South Africa have been pushed into the advanced ranks in the world, and the internal level of global urban network system has been further strengthened with the development of the new generation of information technology revolution. From 2000 to 2050, the global urban innovation system represented by big data and intelligent technology will replace the traditional global urban economic system dominated by commodity and capital flow. Technology, especially intelligent technology services and leading the global urban development are new trends, and promote the global cities to form a new intelligent division of labor pattern. By 2050, global cities will form a global innovation system dominated by major global technological cities, such as London, New York, Beijing, Tokyo, etc. (Fig. 2.15). Intelligence is an important development direction of the innovation system. The development of information and communication technology, represented by big data and artificial intelligence, has broken the traditional “limitation of space boundary”. With the formation of global urban network, human beings have entered a new era in which the intelligent city of interconnection, intercommunication and intelligence is the carrier of production and living space.

2.2 From the Perspective of Macro Structure, the Evolution of Global Urban …

59

Unit: 10000 people 180

170

160 140 120 100

120

115 104

102.4

95.7

94

89.4

85

83.4

80 60 40 20 0

Fig. 2.15 The distribution of intelligent manufacturing technology talents in global cities, top 10. Source The development trend report of world intelligent manufacturing center [2019]

In a certain process of human history development, the city is the skeleton and blood of the world, and the urban system and pattern determine the system and pattern of the world. In the 300 years from 1750 to 2050, the formation and evolution of the global urban system determined the evolution of the global system and pattern. The world’s first rise in the city to lead the country and region to rise first. The universal prosperity of global cities has brought the era of global cities.

2.2.2 Urban Scale System: From the System Dominated by Small Cities in Europe and America to the System Dominated by Big Cities Around the World The flow of economic resources has gradually broken the boundaries between countries, making the role of cities in promoting the development of the global economy more and more prominent, and then emerging in the space power to transcend the national scope and play an important role in the development of the world economy. Big city in the world. Especially since the twentieth century, the acceleration of the flow of various factors between cities has made the world’s cities more closely connected. The multi-level world city scale system led by the world’s big cities has replaced the cities dominated by small cities in Europe and America. The size structure of urban population is an important manifestation of urban development level and welfare level. The scale of cities is directly related to the level of human development and welfare. The larger scale of cities means higher levels

60

2 The World: 300 Years of Urbanization Expansion

of human development and human welfare, because of the increasing returns and diversity. The large-scale system of the world’s cities has determined that human activities are more concentrated, and that human development levels and welfare levels continue to increase. The industrial revolution mainly involved the transfer of an unprecedented number of laborers from rural to urban areas, forming a large and large number of cities. Yale University’s 6,000-year study of urban development and urban population size shows the changing patterns of the number and size of global cities from 3700 BC to AD 2000. The number of important cities in the world is shown in Fig. 2.16. In different periods, in the 3700 BC, the number of cities in the world was very small, mainly concentrated in parts of Europe, and the population growth was slow. From the first year of AD to 1750, as the pre-modern period, the city scale slowly expanded and the city began to Europe has spread to Asia, but large-scale cities still mainly gather in Europe. Since entering the modern era in 1750, the urban population has grown rapidly. From 1750 to 1850, the first industrial revolution led to the rapid expansion of the city of the rising British nation; the second industrial revolution between 1850 and 1950 was mainly carried out in North America and continental Europe, bringing the size of the US and European cities. With greater growth, the United States has been side by side with the United Kingdom; since 1950, emerging economies have emerged, with populations mainly concentrated in large and medium-sized cities, and the population of developed economies has mainly concentrated in small and medium-sized cities, and the world urban scale system has been renewed. According to Yale University’s research, the number of cities in the world in 6,000 years has been shown in Table 2.8. 1. 1750–1850: Small cities with a focus on European cities such as the UK dominate the world city scale system. The size of the UK city is rapidly expanding and its development is unique. In the early eighteenth century, the emerging industrial cities were mainly distributed in Western Europe, where natural resources were abundant, transportation was convenient, and modern industries were suitable for development. The rise of these new cities and industrial clusters is generally a semi-urban area or a small town that does not occupy an important position in the traditional dominant urban system. They attract a large number of people due to industrial development and eventually develop into industries that transcend traditional cities. A town or a regional economic center city. At the beginning of the eighteenth century, the population of the seven counties in the south of England accounted for one-third of the population of the whole of England. After the industrial revolution began, the total population of the United Kingdom increased by 1.54 times from 1801 to 1870, including industrial clusters in the northwest and London in the south. And the suburban population increased by 2.58 times and 2.1 times respectively. It is estimated that between 1801–1851, the UK textile industry population growth rate ranked first with 229%, other port cities increased by 214%, and manufacturing cities increased by 186. %; the speed of individual cities is even more alarming. From 1811–1861, the population of Liverpool and Preston has

2.2 From the Perspective of Macro Structure, the Evolution of Global Urban …

61

Fig. 2.16 Changes in the number and scale of global cities. Source Reba, Meredith, F. Reitsma, and K. C. Seto. “Spatializing 6,000 years of global urbanization from 3700 BC to AD 2000.” Scientific Data 3(2016): 160,034

62

2 The World: 300 Years of Urbanization Expansion

Table 2.8 Statistics of the years with the highest number of global cities from 3700 BC to AD 2000 Year

1900 1800 1850 1950 1975 1750 2000 1700 1600 1925 1500 1400 1300

Number 1094 511 of city Year

484

430

393

294

293

272

238

214

1200 1851 1000 1801 1550 1650 1825 1450 1100 800

Number 147 of city

116

107

91

89

89

84

83

81

75

203

173

172

1250 1875 74

74

Source Reba, Meredith, F. Reitsma, and K. C. Seto. “Spatializing 6,000 years of global urbanization from 3700 BC to AD 2000.” Scientific Data 3(2016): 160,034

increased fivefold, Brighton has increased sevenfold, and the textile textile center Bradford has grown eightfold. The industrialization process of other developed capitalist countries started relatively late, so the scale of urban development was slow during this period. For example, between 1810 and 1840, French agricultural output accounted for about 66.5% of the country’s total output value, still playing a major role in the French economy. In 1806, the rural population of France was 23.69 million, and it increased to 26.75 million in 1846, accounting for 75.6% of the total population of France. During this period, France’s economic thinking of small farmers was deeply rooted and restricted the flow of rural population to cities. 2. 1850–1950: The scale of the world city dominated by small and medium-sized cities with European and American cities as the center of gravity. The scale of cities in Europe and North America has expanded rapidly. During the second industrial revolution, it was mainly based on the population size system of countries such as Europe and America. We mainly take Germany and the United States as examples. The German industrial revolution started during this period. In the short 29 years from 1871 to 1910, Germany adopted the advanced science and technology of Britain, France and other countries to catch up with the United Kingdom, surpassed France and realized the backward agricultural country. The transition to an advanced industrial country. During this period, the number of cities and urban population in Germany increased rapidly, and the size of the city continued to expand. Since the 1840s, there have been a number of emerging cities in Germany, mainly industrial and mining cities, such as Seldorf and Essen in the Ruhr area. These cities rely on local resource advantages and the convenience of land and water transportation to attract large amounts of investment and labor. These cities are expanding along the route and the population is increasing. As shown in Table 2.9, from 1871 to 1910, the population of cities with more than 100,000 people grew the fastest, and its share of the total population of the country increased from 4.8% to 21.3%. The cities with smaller populations have a larger decline, from 63.9 in 1871. % to 39.6% in 1910. Driven by the industrialization process, the US population scale system was also formed. The United States has contributed to the rapid expansion of a series of large

2.2 From the Perspective of Macro Structure, the Evolution of Global Urban … Table 2.9 Germany’s urban population as a percentage of the country’s population from 1871 to 1910 (%)

City size

63

1871

1910

More than 100,000

4.8

21.3

10,000–100,000 people

7.7

13.4

2,000–10,000 people

23.6

25.4

Less than 2,000 people

63.9

39.6

and medium-sized cities mainly due to the progress of the industrial revolution and the formation of monopoly organizations. Chicago was still desolate in the early nineteenth century. In 1837, the city was formally established. In 1880, the city had a population of 500,000. In 1910, it crossed the 2 million mark and became the second largest city in the United States.. Detroit is a car city, and the development of the automotive industry led to the development of oil fields in Southern California, making the western city of Los Angeles the largest refining center in the United States. The development of cities such as Birmingham and Houston is related to the development of steel and oil and automobiles. The development of the service industry and retail industry has led to the rapid expansion of cities such as Liszt and Seattle in Atlanta and the Minnie River. On the basis of the rapid increase in the number of cities, scale expansion, functional development and close interconnection, a nationwide modern urban system has been formed, mainly in the following: the density of the national urban network has increased several times; the distribution of urban areas has been improving day by day, and the cities in the past have been scarce. The western and southern regions already have a certain number of cities at all levels, gradually forming a combined city, a combination of urban and professional cities, a modern urban system combining large, medium and small cities; the function of the city is also constantly expanding (Table 2.10). Table 2.10 The number of American cities and the urban population of the United States from 1790–1950 (one, 100,000 people, %) Year

Total National population

Number of city

Population of city

Urban population as a percentage of total population

1790

3.9

24

0.2

5.1

1810

7.2

46

0.5

7.3

1830

12.9

–

1.1

8.8

1850

23.3

–

3.5

15.3

1870

39.8

663

9.9

25

1900

76.1

1737

30.2

39.6

1910

92.4

–

42

45.7

1930

123.1

–

69

56.2

1950

151.7

4741

96.5

63.6

64

2 The World: 300 Years of Urbanization Expansion

3. 1950–2050: A large-scale, large, medium, and small-scale urban scale system that is heading for the all-round development of the world. The global population is expanding. In 1950, 70.4% of the total population lived in rural areas, 17.8% lived in urban residential areas with a population of less than 300,000, and the remaining 11.8% lived in other larger cities. Over the next three decades, the urban population increased by nearly 920 million people, from 750 million in 1950 to 1.75 billion in 1980. By 2018, the global urban population will exceed the rural population. The global urban population has grown from 1.75 billion in 1980 to 4.22 billion in 2018, and the urban population has increased from 39.3% in 1980 to 55.3% in 2018. The population of all types of cities continues to grow. Among them, the medium-sized cities have a faster population growth rate. Although the total size of megacities has been relatively small, it has increased from nearly 86 million in 1980 to 529 million today, an increase of nearly five times. However, with the acceleration of urbanization, it is expected that the proportion of rural population will fall by 13.1% by 2050 (Table 2.11). The scale of urban systems in large and medium-sized cities in emerging economies continues to expand. From the perspective of the world urban population size system, the cities of major developed countries have achieved high urbanization after the first industrial revolution and the second industrial revolution, and the population size has not increased significantly. As shown in Table 2.12, we can see that In the past 65 years, Tokyo, Osaka, New York, Los Angeles, and Paris have seen an increase in urban population between 0.5 and 2.5 times. Most of the other cities are urban populations in developing countries, and the population size has increased significantly during this period. New Delhi, Beijing, Karachi, Istanbul, Lahore and Bangalore have all experienced population growth of more than 10 times. The population growth of Dhaka and Lagos has exceeded 30 times. This reflects the trend that the scale of emerging economies is concentrated in large and medium-sized cities. It is expected that after 2035, New Delhi will surpass Tokyo to become the largest city in the world. A few cities in developed countries such as Paris and Seoul in 2050 will withdraw from the top 30 cities in the world. After the twenty-first century, the emerging urban agglomerations of East Asia have become the core strength of the world. The East Asia region of the world’s top six cities is about two seats. It mainly includes the Pacific Coast urban agglomerations of Japan’s Tokaido and China’s Yangtze River Delta urban agglomerations. “Tokaido Pacific Coast City Group” consists of three metropolitan areas: Tokyo, Nagoya, and Osaka. The total number of large, medium, and small cities is 310, including Tokyo, Yokohama Kawasaki Nagoya, Osaka, Kobe, Kyoto, and other major cities. Ten of the large cities with a population of over 1 million are located in the urban agglomeration. The three major urban agglomerations cover an area of about 100,000 square kilometers, accounting for 31.7% of the country’s total area; the population is nearly 70 million, accounting for 63.3% of the country’s total population. It brings together two-thirds of Japanese industrial and industrial employment, three-quarters of industrial output and two-thirds of national income. The Yangtze River Delta urban agglomeration centered on Shanghai is mainly composed of Shanghai, Jiangsu

751

24

32

127

67

50

450

1785

Total population of the city

Megacity (over 10 million people)

Large city (500100 million people)

Medium city (100500 million people)

Small city (500,000–1 million people)

Smaller cities (300,000-500,000 people)

Less than 300,000 people

Rural population

2346

730

87

131

244

107

55

1354

3701

1970

2704

908

114

170

336

140

86

1754

4458

1980

3277

1315

200

269

626

214

245

2868

6145

2000

2010

3363

1578

246

355

760

269

387

3595

6958

2018

3413

1750

275

415

926

325

529

4220

7633

2050

3 092

2357

384

578

1370

504

825

6680

9772

70.4

17.8

2.0

2.7

5.0

1.3

0.9

29.6

100

63.4

19.7

2.3

3.5

6.6

2.9

1.5

36.6

100

1970

1950

2536

1950

Total population

Percentage

Population

Table 2.11 Population size and percentage of cities of all sizes in the world

60.7

20.4

2.6

3.8

7.5

3.1

1.9

39.3

100

1980

53.3

21.4

3.2

4.4

10.2

3.5

4.0

46.7

100

2000

48.3

22.7

3.5

5.1

10.9

3.9

5.6

51.7

100

2010

44.7

22.9

3.6

5.4

12.1

4.3

6.9

55.3

100

2018

31.6

28.6

5.1

6.1

14.3

5.7

8.5

68.4

100

2050

2.2 From the Perspective of Macro Structure, the Evolution of Global Urban … 65

66

2 The World: 300 Years of Urbanization Expansion

Table 2.12 The growth trend of the population size of major cities in the world from 1950 to 2020 (10,000 people, 100%) 1950 Tokyo

1970

1990

2010

2015

2035

2050 (predict)

1127.5 2329.8 3253.0 3686.0 3725.6 3601.4 3954.6

Annual growth rate(1950–2015) 2.3

New Delhi

136.9

353.1

938.4 2198.8 2586.6 4334.5 4944.5

17.9

Shanghai

428.8

605.2

860.6 2031.4 2348.2 3434.1 3584.1

4.5

Mexico City

336.5

883.1 1564.2 2013.7 2134.0 2541.5 3020.7

5.3

Sao paulo

233.4

762.0 1477.6 1966.0 2088.3 2449.0 3017.6

7.9

Mumbai

308.9

641.3 1235.5 1825.7 1931.6 2734.3 2962.0

5.3

Osaka

700.5 1527.2 1838.9 1931.3 1930.5 1834.6 2331.8

1.8

Cairo

249.4

989.2 1689.9 1882.0 2850.4 2962.8

6.5

1233.8 1619.1 1608.6 1836.5 1864.8 2081.7 2163.9

0.5

New York

558.5

Beijing

167.1

442.6

678.8 1644.1 1842.1 2536.6 3070.0

10.0

Dhaka

33.6

137.4

662.1 1473.1 1759.7 3123.4 3840.5

51.4

Buenos Aires

516.6

841.6 1114.8 1424.6 1470.6 1712.7 1942.6

1.8

Kolkata

460.4

732.9 1097.4 1400.3 1442.3 1956.4 2059.4

2.1

Karachi

105.5

311.9

714.7 1261.2 1428.9 2312.8 2590.0

12.5

Istanbul

96.7

277.2

655.2 1258.5 1412.7 1798.6 2263.8

13.6

Chongqing

156.7

223.7

401.1 1124.4 1337.2 2053.1 2745.5

7.5

Rio de Janeiro

302.6

679.1

969.7 1237.4 1294.1 1481.0 1767.3

3.3

Manila

154.4

353.4

797.3 1188.7 1286.0 1864.9 2082.5

7.3

Tianjin

246.7

331.8

455.8 1015.0 1251.6 1644.6 2104.6

4.1

Los Angeles

404.6

837.8 1088.3 1216.0 1234.5 1377.8 1606.6

2.1

Lagos

32.5

141.4

476.4 1044.1 1223.9 2441.9 2725.0

36.6

Moscow

535.6

710.6

898.7 1146.1 1204.9 1376.8 1527.5

1.2

Guangzhou

10.1

104.9

154.2

324.6 1027.8 1169.5 1674.1 2108.0

Kinshasa

20.2

107.0

368.3

Shenzhen

0.3

2.2

Paris

938.2 1159.8 2668.2 3594.0

56.4

87.5 1022.3 1127.5 1518.5 1882.7

3580.8

628.3

820.8

933.0 1046.0 1073.4 1199.2

12.935

0.7

Lahore

83.6

196.4

397.0

843.2 1036.9 1911.6

25.535

11.4

Jakarta

145.2

391.5

817.5

962.6 1017.3 1368.8 1557.5

6.0

74.6

161.5

404.3

829.6 1014.1 1806.6 2414.0

12.6

Bangalore Seoul

102.1

531.2 1051.8

979.6

989.7 1363.6 1532.4

8.7

2.2 From the Perspective of Macro Structure, the Evolution of Global Urban …

67

Province, Nanjing, Suzhou, Hangzhou, Ningbo, Zhejiang Province, and Hefei, Anhui Province. It has a land area of 217,700 square kilometers and a total population. 150 million people, accounting for 2.2% and 18.5% of the country respectively. Nowadays, it is among the sixth largest urban agglomerations in the world. It is the most dynamic urban agglomeration in China.

2.2.3 Urban Space System: From Isolated Cities to Urban Agglomerations and Then to the World of Metropolitan Coordination Regions The changing response of the world urban space system is an important manifestation of the spatial scale of human activities and the level of human development. The expansion of urban spatial units from small to large, from isolation to linkage, reflects the expansion of human activity space, the expansion of human interaction, and the increase of human sharing opportunities, which determines the world’s space-divided world becomes space gathering. And the continuous world. With the acceleration of globalization, the obstacles to the capital flow space are gradually eliminated, and the city is also separated from the local space relative to the world. The original world urban space system has gradually been broken, and it is being transformed from a two-dimensional system of cities-states to a more complex three-dimensional space system between “global-state-urban agglomerations”. Therefore, from isolated cities to cities The transformation of the world system from the group to the urban continuous zone is the result of the city’s development to a certain stage. With the further expansion of the population, the population of large cities is gradually replaced by the umbrella-shaped regional cities. The regional cities include urban belts in small and medium-sized cities, and the various economic functions complement each other, and the economic development of the entire country is even The economic status that affects the whole world has a non-negligible effect, which is mainly reflected in the late stage of urban development. During the first industrial revolution, the city group with London as a single center made the UK establish a world position with strong industrial power. After the 1950s and 1960s, a number of central urban agglomerations established within the United States made the United States the first to surpass the United Kingdom to establish the most powerful economic position in the world. After 1950, the developed countries such as Britain and the United States are still the world’s industrial and commercial powers„ However, during this period, a number of urban economic development zones have emerged on a global scale, especially since the twenty-first century,

68

2 The World: 300 Years of Urbanization Expansion

emerging economic belts established in Asia and South America, such as the Japanese Pacific Coast urban agglomeration and the Yangtze River Delta urban agglomeration. 1. 1750–1850: World Urban Space System in the UK’s Leading Isolated City. During the first industrial revolution, the UK took the lead in forming a singlecenter core area centered on Greater London. In the course of its development, a series of small towns in the suburbs that gathered the agglomeration area were included. In 1801, there were approximately 1.2 million inhabitants in the Greater London area and 15% in the outlying areas of London (populations living outside the city boundaries of London). In 1901, the population of the city of London grew to 6.2 million. At the same time, it is not only the core center of Europe, but also the core area of the world city. The pound has become an international currency. London is not only the capital, political and financial center of the United Kingdom, but also the center of world finance. It is called the “world capital”. Mainly in the following aspects: First, priority to the development of water and land transportation in the world. At the end of the eighteenth century, the United Kingdom set off a boom in the construction of the canal, and soon formed a waterway transportation network connecting the mainland and the coast. After that, the UK shifted its focus to railway construction. In 1850, it increased to more than 10,000 km. At this time, the main railway line in the UK has been completed. Second, it takes precedence over coal and iron production in countries around the world. In 1850, the output of pig iron increased to 2.29 million tons, more than double the sum of France, Germany and the United States. In 1850, British coal production increased to 50 million tons, far exceeding the total coal production of France, Germany and the United States (19 million tons). The third is its unrivalled industrial and commercial development. British industry’s share of the world’s industry reached 47% in 1840. In the same year, France accounted for 12% and the United States accounted for 9%. In 1840, it accounted for 21% of total world trade. In the same year, France accounted for 11% and the United States accounted for 10%. The UK has pushed more than half of its industrial products to the world market and has become a veritable “world workshop”. The UK is also the richest country in the world. In 1850, it had assets of 225 million pounds overseas. Its gold reserves amount to £16.6 million. As shown in Table 2.13, in 1850, the number of London residents reached 2.36 million, much larger than the population of Paris during the same period. The industrial revolution prompted the developed countries represented by the United Kingdom to export goods and compete for raw materials overseas. Countries Table 2.13 Regional central city formed during the industrial period City age

City

Year 1800

1850

1900

London

86.5

236.3

453.6

Paris

54.7

105.3

271.4

Berlin

17.2

41.9

188.9

7.9

69.6

343.7

New York

2.2 From the Perspective of Macro Structure, the Evolution of Global Urban …

69

such as Asia, Africa and Latin America became the export destinations and raw materials sources of their overseas markets, and industrial development was locked in the stage of primary commodity production. However, countries such as Asia, Africa and Latin America are still in backward agricultural production. The country’s own economy has not yet undergone an industrial revolution. The industry mainly focuses on the processing of primary commodities such as agriculture and mining, and the production is relatively scattered in rural areas where labor is cheaper. 2. 1850–1950: The world urban space system of the metropolitan urban agglomeration led by the United States After the 1950s and 1960s, the second industrial revolution broke out. The development of Europe and North America showed a leap-forward development. The industrial structure was continuously optimized, the urbanization of the region was faster, and the city’s appearance was completely new. The most striking thing is the achievements of the United States. During this period, the United States first established its own urban space system over the United Kingdom, and at the same time became the most powerful industrial status in the world. First, the urban system was established within the American city. After the 1940s, the United States began its first industrial revolution. The wide application of steam engines has made a new leap in transportation technology, which is beneficial to the development of urban and industrial cities as well as to urbanization. The economic role of the city has been strengthened, and its social nature has changed. It has changed from the original commercial and cultural center to a modern industrial base, and at the same time, it has continuously produced new industrial cities. In the first 20 years of the twentieth century, trams and elevated railways have expanded the city’s radius to more than 10 miles. Central business districts, wage-earning areas, slums, and prosperous suburbs have emerged. The US urbanization process has advanced by leaps and bounds. The number of cities has increased dramatically, the scale has expanded, and the function has developed, basically forming a nationwide modern urbanization system. At the top of the pyramid of urbanization systems are comprehensive national central cities such as New York and Chicago. They play a leading role in the national urbanization system and are the political, economic and cultural centers of the country. Located in Taki, there are thousands of specialized local central cities based on certain industries, such as Baltimore, Philadelphia, Cincinnati, etc., which are the local economic and political centers that connect the integrated city with its surrounding small towns and The intermediary role of the rural hinterland. The large-scale emergence of cities has created a large number of employment opportunities, attracting a large number of immigrants and rural populations to move to cities. From 1851 to 1919, an average of 390,000 foreign immigrants flooded into the United States each year; in 1910, about 11 million of the country’s 42 million urban population flowed from rural to urban areas. 1920 is an epoch-making year in American history. In this year, the total population of the United States reached 106.7 million, and the urban population reached 54.16 million, more than half of the country’s total population. The United States has basically achieved urbanization.

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As shown in Table 2.14, these cities, as the backbone of the US local economy, play an important role in the organization of local economic operations. The world’s industrial powers that the United States has also established during this period are embodied in three aspects: First, the advanced development of heavy industry. In 1870–1913, US industrial growth reached 8.1 times, with an average annual growth rate of 4.3%. 1877–1892 was the fastest industrial development in this period, and industrial production quadrupled, with an average annual growth rate of 7.1%. Second, the advance development of the power industry. In 1882, New York established the world’s first thermal power station. In 1914, the United States had reached 9 million horsepower. At that time, electricity accounted for 30% of the total industrial power, making it the world’s first power. Third, the United States is also the world’s largest automobile kingdom. In 1913, there were 1 million cars, accounting for 1/2 of the world’s total number of cars. In 1914, the annual output of cars reached 573,000. The United States has a rising status in the world of industry. In 1870, it accounted for 23.3% of the world’s industry, 29% from 1881 to 1885, 31% in 1890, more than Britain (22%), and 35.8% in 1913, close to Britain and Germany. The sum of the three countries of France and France. In the 30–40 years after the Civil War, the United States successfully completed the historical mission of industrialization, while at the same time catching up with and surpassing the United Kingdom to become the most powerful industrial country in the world. The achievements of the United States are enormous, and these decades have been called the industrial century and the gilded age of the United States. 3. 1950–2050: Towards a global metropolitan area and a networked development space system. After 1950, although the western developed countries such as the United Kingdom and the United States were still the world’s industrial and commercial powers, there Table 2.14 Functional positioning of major cities in the United States City

Main function positioning

New York

World-class super economic city, the center of the US economy

Chicago

National economic city

Los Angeles

National economic city

San Francisco

The economic strength and radiation range of the western Pacific coast of the United States is second only to important cities in Los Angeles

Boston

New England Regional Economic Center City

Philadelphia

The economic center of the Mid-Atlantic region

Houston

The economic center of Texas and the Gulf of Mexico

New Orleans

The largest commercial and financial center in the southern United States

Seattle

The largest industrial, commercial, and transportation center in the northwestern United States

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71

were already many urban economic development belts in the world during this period. Especially since the twenty-first century, the economic strengths established in the Asian and South American regions have developed at the same speed and scale as the metropolitan belts previously established in the West, such as the Japanese Pacific Coast urban agglomeration and the Yangtze River Delta urban agglomeration. We first understand the hierarchical layout of cities in this period from the perspective of the global system, and then analyze the urban system within the country during this period by taking the Tokyo metropolitan area as an example.

2.2.4 Firstly, the Global Urban Scale System is Gradually Forming In the late twentieth century, Friedman believed that the world’s cities were distributed in a strip of things, from Tokyo to New York to London, forming a linear global world city circle. This global world city circle can be divided into three subsystems: one is the Asian subsystem based on Tokyo-Singapore, in which Tokyo is the world city, Singapore is the regional international city in Southeast Asia, and the second is the American subsystem. New York, Chicago and Los Angeles are major central cities, with Toronto to the north, Caracas and Mexico City to the south, and many small countries in Canada, Central America and the Caribbean to the American metropolitan area. Third, the Western European subsystem to London and Paris. The core of the Rhine Valley is the core. The southern hemisphere constitutes a subsystem centered on Johannesburg and São Paulo. Currently, internationally recognized global cities are New York, Tokyo and London. These three cities are concentrated in far more than the proportion of banks and financial institutions, especially foreign banks, and other companies engaged in financial transactions; they are also the largest concentration of the world’s largest corporate headquarters. In addition, there are about 20 global or regional central cities, including Paris, San Francisco, Los Angeles, and Mexico City (Hall, 1997). In 2016, the world urban system was renewed. There are 2 super-first-tier cities in the world, 7 strong cities in the first-line, 19 in the first-tier cities, and 21 in the first-tier weak cities. According to this list, Guangzhou has become the fifth city in China to be promoted to the Alpha level, ranking second only to Hong Kong, Beijing, Shanghai and Taipei in China (GaWC Report, 2016). Secondly, National Urban Belt System: Taking the Tokyo City Belt in Japan as an Example. Tokyo Metropolitan (City Center) refers to the central business district of Chiyoda, Chuo-ku, and Minato-ku in the center of Tokyo. National ministries. The headquarters of many embassies and major corporations. In the 1950s, with the rapid growth of the Japanese economy, the business function of the city was rapidly developed, and a highly centralized central business district was soon formed. at the same time.

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The price of the land is high. The residence began to shift to the suburbs. Large-scale urban agglomerations such as single-center gathering and long-term commuting have emerged. In the early 1960s, there was a shortage of business office space in the heart of the city. The government began to realize that it was necessary to suppress the convergence of business functions to the heart of the city, and to spread the urban structure that balances work and residence. Therefore, Tokyo has proposed the idea and plan to construct the deputy center of the city and guide the transfer of the city from a single-center structure to a multi-center structure. At present, Tokyo has formed a multi-hearted urban structure including seven sub-centric cities and five nuclear cities in the Ta ma area. As shown Table 2.15, they are basically located at the intersection of the Yamanote Line (loop) and the various railway radiation lines, making full use of the traffic hub’s aggregation effect on business and people flow. The most effective of these are the two sub-centers of Shinjuku and Lin hai, which have the functions of a strong business center. They are located in the western part of the transportation hub of Tokyo. The sub-center of Tokyo is a multi-functional, highly complex area. While meeting business activities. It also has other functions such as business, culture, entertainment, and residence. For example, Shinjuku City has formed a complete layout integrating business, shopping, culture and entertainment with the Shinjuku Station Building and the east of the station as the commercial and entertainment center and the west of the station as the administrative office and business office center. On the other hand, in the twenty-first century, it emphasizes the informationization and intelligence of the new city, and the construction of the Tokyo Communication Port is an important goal for the development of a new metropolis. It is estimated that by 2050, the scale and structure of urban space will be based on the online global world city circle + national internal urban belt system, and the previous planning and design around the core cities of major metropolitan areas with the goal of regional balanced development will be converted into The superlarge metropolitan area gathered in the urban area will alleviate the imbalance of regional development in the unipolar concentration of megacities, the reliance of small and medium-sized cities on the central metropolis, and form a greater regional Table 2.15 List of world first-tier cities City level

City name

(Alpha++) Super London, New York Line Alpha+) First strong line

Singapore, Hong Kong, Paris, Beijing, Tokyo, Dubai, Shanghai

First middle line (Alpha)

Sydney, Sao Paulo, Milan, Chicago, Mexico City, Mumbai, Moscow, Frankfurt, Madrid, Warsaw, Johannesburg, Toronto, Seoul, Istanbul, Kuala Lumpur, Jakarta, Amsterdam, Brussels, Los Angeles

(Alpha-) First weak line

Dublin, Melbourne, Washington, New Delhi, Bangkok, Zurich, Vienna, Taipei, Buenos Aires, Stockholm, San Francisco, Guangzhou, Manila, Santa Fe Bogota, Miami, Luxembourg City, Riyadh, San Diego, Barcelona, Tel Aviv, Lisbon

2.3 From the Perspective of Macro Gross, Global Urban Development Has …

73

international competitiveness. At the same time, on the basis of regional consensus, promote the development of metropolises, propose planning plans to face common problems, and propose different development plans according to the different situations of metropolises and local cities, and connect them according to the actual development status of each region.

2.3 From the Perspective of Macro Gross, Global Urban Development Has Completed the Epoch-Making Transformation of Human Civilization The overall scale of global cities determines the degree of urbanization of human society, In the 300 years from 1750 to 2050, human beings will change from the rural society of urban–rural integration to the urban society of urban–rural integration.

2.3.1 Impact of Urban Industrial Development on the World Economy and Pattern 1. from 1750 to 1850, the city guided the world and the UK became the world industrial country. Before the industrial revolution, most people lived in the countryside and lived by farming and animal husbandry. The production tools were only simple handicrafts, regardless of the production power of the country or the city, such as manpower, animal power, wind power and water power. Lord, so production is limited, production efficiency is low, and people have to do a lot of agricultural production and labor for their lives. It can be seen from the Figs. 2.17 and Figs. 2.18 that before the industrial revolution, the major countries in the world, such as the United Kingdom, the United States, France, Mexico, China, India, Japan and other countries, all occupied a higher level of agricultural employment, The people of most countries are engaged in agricultural activities. However, after the industrial revolution, the factory system replaced the manual workshop, replaced the manual labor with machines, and the productivity has made a huge leap. A large number of laborers gathered in factories and cities, and urbanization and industrial production gradually occupied a dominant position. The proportion of agricultural output has gradually declined (see Figs. 2.17 and 2.18). The industrial revolution has changed the industrial structure of countries. After the industrial revolution, the proportion of agricultural employment in the UK has gradually decreased from 30% to around 20%. The proportion of the national economy has fallen to 21%, becoming the “world industrial country”, and the British cities gradually become the mainstay of the world and guide the world.

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2 The World: 300 Years of Urbanization Expansion

Share of agriculture in employment (%) United Kingdom Netherlands Belgium

United States Sweden Spain

France Mexico

80 70 60 50 40 30 20 10 0 1800

1850

1900

1950

2000

Fig. 2.17 Share of agricultural employment in European and American countries. Data source The author sorts according to our world in data database data

Fig. 2.18 Agricultural output value of major countries as a share of GDP. Data source The author sorts according to our world in data database data

2.3 From the Perspective of Macro Gross, Global Urban Development Has …

75

2. from 1850 to 1950, the city become the backbone of the world, and advanced countries completed industrialization. During the Second Industrial Revolution, the global urban industry showed that developed countries were mainly light industries, and backward countries such as Asia, Africa and Latin America mainly focused on agricultural and mining. Specifically, the second industrial revolution was qualitatively improved compared to the first industrial revolution. The most direct result was the decline in agricultural output and the rapid rise in industrial output (see Figs. 2.18 and 2.19). Industrial cities in advanced countries have begun to support the world. It can be seen from Fig. 2.18 that from 1800 to 1950, the proportion of agricultural output value in the United States, France, New Zealand, and Sweden gradually declined, especially during the second industrial revolution (1850–1950), the proportion of agricultural output value decreased rapidly. Figure 2.19 shows the proportion of industrial output in major countries to GDP. Figure 2.19 shows that developed countries were in the industrial stage from 1800 to 1950, especially during the second industrial revolution, industrial output increased rapidly, from 20 to 50%. At this time, most countries have completed industrialization, and the city as the main body of the country supports the development of the world. 3. from 1950 to 2050, the city become the world, and the service and intelligence led the world. With the advent of the third industrial revolution, the global urban landscape was directly changed. At the beginning of the third industrial revolution, United Kingdom, United States, France, New Zealand, Sweden, Mexico, Belgium, Spain and other

Fig. 2.19 Proportion of industrial output value in major countries. Data source The author sorts according to our world in data database data

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2 The World: 300 Years of Urbanization Expansion

countries took the lead in the industrial revolution, the proportion of its agricultural industry began to decline rapidly from 1800 to 1950, Until 2010, it was close to zero. In Asia, Africa, China, India, Egypt, South Africa, Nigeria and other countries, the industrial revolution began in 1950, and the agricultural employment population began to gradually decrease from this time. The proportion of agricultural employment in Asian and African countries is basically above 50%. Even at 80% or more, the proportion of agricultural employment in major European and American countries has dropped below 30%, even below 20%, and global employment has led Europe and the United States to lead the Asian-African pattern (see Fig. 2.20). In addition, as can be seen from Fig. 2.19, due to the third industrial revolution, informationization and networking dominated the world. At this time, the development of industrial development began, and the output value of service industry increased. Therefore, from 1950 to 2016, industrial output accounted for Compared with the gradual reduction, the global urban pattern dominates the industrialization of Asia and Africa by the high-end service in Europe and America. In addition, the industrial revolution has also brought about a substantial increase in income. Figure 2.21 shows the change in GDP per capita from 1870 to 2016 on all continents. As can be seen from Fig. 2.21, from 1870 to 1950, the per capita GDP of all continents increased slowly, and the economic development increased rapidly from 1950 to 2016. Judging from the differences in various continents, the Share of agriculture in employment (%) 90 80 70 60 50 40 30 20 10 0 1950

1960

1970

1980

1990

2000

Japan

China

India

Egypt

South Africa

Nigeria

Indonesia

Malaysia

Philippines

2010

Fig. 2.20 Share of agricultural employment in major Asian and African countries. Data source The author sorts according to our world in data database data

2.3 From the Perspective of Macro Gross, Global Urban Development Has …

77

Fig. 2.21 Changes in real per capita GDP of the world. Data source The author sorts according to our world in data database data

economic development of developed countries such as Europe and the United States is significantly ahead of other continents and is in a leading position. At this time, the city is the world, and the economic and social development of the city is the economic and social development of the world. Figure 2.22 and Fig. 2.23 show the distribution of urbanization rate and GDP in the world’s major countries in 1950 and 2016. It can be seen from Fig. 2.22 and Fig. 2.23 that there is a significant positive correlation between urbanization rate and GDP, that is, the higher the urbanization rate, the higher the level of economic development. In the future, the city’s smart manufacturing will dominate the world. In the rapid development of automation, many repetitive labor jobs in the city will be replaced by new technologies such as machines, data and automation. McKinsey Global Institute In the report “Unemployment and Employment: Labor Transformation in the Age of Automation”, it is predicted that by 2030, jobs for 800 million people worldwide will be replaced by machines. By 2030, about 70% of companies will adopt at least one form of artificial intelligence. And a large number of large companies will use a full range of technologies, artificial intelligence will bring an additional 13 trillion US dollars in growth for global economic activities, its contribution rate can be comparable to the introduction of revolutionary technologies such as the history of steam engines, including artificial intelligence and Automation technology, including robotics, will bring significant benefits to users, businesses and the economy, increase productivity and boost economic growth. In the future, the industry will develop through traditional industries such as “Internet +”, “Big Data +” and “Artificial Intelligence”, such as intelligent home appliances, automobile networking, manufacturing data, etc., and a new industry through technological breakthroughs. The development of such industries, such as cloud computing, Internet of Things, new

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2 The World: 300 Years of Urbanization Expansion

Fig. 2.22 Urbanization rate and GDP distribution of countries in the world in 1950. Data source The author sorts according to our world in data database data

Fig. 2.23 Urbanization rate and GDP distribution of countries in the world in 2016. Data source The author sorts according to our world in data database data

2.3 From the Perspective of Macro Gross, Global Urban Development Has …

79

Table 2.16 Main function positioning of Tokyo Center and Sub-center Name

Main function positioning

Tokyo

Central political and economic center, international financial center

Shinjuku

The first major sub-center, the business office and entertainment center that drives the development of Jing dong

Ikebukuro

Second largest sub-center, commercial shopping, entertainment center

Ikebukuro

Transportation Hub, Information Center, Business Office, Cultural and Entertainment Center

Ueno-Asakusa

Traditional Culture Tourism Center

Osaki

High-tech Research and Development Center

Kamishi-Kurto

Business, Cultural and Entertainment Center

Binhai Sub-Center

Sub-Center International Cultural, Technology and Information Exchange Center for the Future

energy, etc., often develops or depends on specific integrated application scenarios, such as networked roads, intelligent buildings, smart home systems, smart factories, and so on. In the future, with the highly developed information technology, cities will only be linked together, and smart cities that respond quickly, share data, and efficiently serve will dominate the world. From the ranking of global urban potential in 2019 (see Table 2.16), smart cities and technology cities lead global urban development. In addition, according to the McKinsey Global Institute forecast. According to PwC, an international accounting and consulting firm, the global gross domestic product (GDP) will increase by 14% in 2030, driven by artificial intelligence, which means that by 2030, artificial intelligence will contribute 15.7 trillion to the world economy. The dollar exceeds the sum of the current economic aggregates of China and India.

2.3.2 Impact of Urban Population Development on World Urbanization In the 300 years since the development of the world from the past to the future, the global urbanization process has been slow to fast. During the two hundred years from 1750 to 1950, the global urbanization rate rose from 5.5% to 30% or so, only increased by about 25% in two hundred years, but from 100 years between 1950 and 2050, the global urbanization rate will rise from around 30% in 1950 to around 70%. The rate of growth has increased by 40% and the urbanization process has accelerated significantly (see Fig. 2.24). In addition, the process of global urbanization is still a process from local to diffusion. First, the urbanization of the UK leads to the development of European urbanization, followed by the urbanization of the United States to lead the urbanization of Europe and the United States, followed by the

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Urban (%) 70 60 50 40 30 20 10 0 1500

1550

1600

1650

1700

1750

1800

1850

1900

1950

2000

2050

Fig. 2.24 Changes in global urbanization rate from 1500 to 2050. Data source The author sorts according to our world in data database data

urbanization process of China leading the global cities. In the end, the urbanization of India led the process of global urbanization. 1. from 1750 to 1850, UK became an urban society. The first industrial revolution brought about rapid population growth, because the productivity increase brought about by the industrial revolution would significantly improve people’s living standards, thereby bringing about an increase in population and thus providing a basis for urban development. It can be clearly seen from the figure that before the first industrial revolution, the global population showed a rise and then decline, and the global population was in a state of ups and downs. After the first industrial revolution, the global population rose from 428 million in 1750 to 650 million in 1850, the advancement of the industrialization process and the substantial increase in population have provided a solid foundation for urbanization, and the urbanization process has begun. Before the first industrial revolution, the world’s cities were very limited, and the process of urbanization was stagnant. At this time, tribes and rural areas were the foundation of a country and the center of property. Agricultural production determines the future of the country or the world. The pattern of rural dominated the world was not broken until the industrial revolution of the 1860s. The industrial revolution led to the shift of focus to the city, and the city gradually dominated the world. The industrialization and mechanization brought about by the industrial revolution have caused the rural population to flood into cities and transform into industrial labor, which has led to a rapid increase in the population and urban population. The rotational effect brought about by the industrial revolution has greatly increased agricultural productivity and completely liberated. The agricultural labor force, from 1760 to 1840, the agricultural employment rate in Europe decreased by 12%, while the industrial employment rate increased by 23%; under the motive of human self-interested behavior, the resting productivity flows

2.3 From the Perspective of Macro Gross, Global Urban Development Has …

81

back into the cities will further drive the urbanization process. Therefore, the urban population brought about by industrialization has gradually increased the importance of the city and dominated the world. As can be seen from Fig. 2.24, before the first industrial revolution, from 1500 to 1750, the global urbanization rate rose from 4% to 5.5%, and the global urbanization process basically did not advance; After the sub-industrial revolution, between 1750 and 1850, the global urbanization rate rose from 5.5% to 11%. The urbanization rate process accelerated rapidly compared to before, and the global urbanization process began. The urbanization rate in Asia and Africa has not changed during the first industrial revolution, and it has remained at around 10% and 4%. The urbanization process has not yet begun and is still in the farming society. Europe is the birthplace of the industrial revolution. During the first industrial revolution, the urbanization process began rapidly, and the urban rate rose from 10% in 1800 to 16.7% in 1850. More importantly, between 1750 and 1850, half of the UK’s population lived in cities, and UK took the lead in urbanization and became a city-state. In the 1950s and 1960s, the widespread use of steam engines made the machine industry gradually replace the factory manual, and centralized mass production gradually became the mainstream of production. The concentration of production has brought about population gathering and the birth of industrial towns. The urbanization process has followed. Taking the United Kingdom as an example (see Table 2.17), the UK was the first Western country to move toward urbanization. The level of urbanization in the UK was 17% in 1750 and 33.8% in 1801. It entered the stage of rapid urbanization and reached in 1851. 54.0%, becoming the first country in the world to achieve urbanization. In 1772, Manchester, England, had a population of only 25,000. By 1851, the city had grown to 455,000. The population of Birmingham has increased from 86,000 in 1801 to 233,000 in 1851. With the spread of the industrial revolution, urbanization has gradually emerged throughout Europe and the American continent. For example, the analysis of French professor Philippe Panshmer, French urbanization began in the 1830, and the urbanization level of the United States reached 10.8% in 1840, other countries such as Germany and Canada started the urbanization process at a later stage. 2. from 1850 to 1950, Western Europe and North America became urban areas, and cities became the main body of the world. The improvement of science and technology brought about by the second industrial revolution has led to another qualitative improvement of the global population (see Fig. 2.25). The global population has increased from 655 million to 1.59 billion in 1920, providing the basis for the acceleration of the global urbanization process. The population of the United States, Germany and Britain all rose rapidly. Especially in the second industrial revolution, the population of the United States rose rapidly from 24.13 million in 1850 to 120 million in 1920. At this time, China and India in Asia were still in an agricultural society, and the population was still slowly fluctuating. Driven by the second industrial revolution, the large-scale machinery industry in the world has developed vigorously, which has greatly promoted the specialization of labor and the formation of industrial chain, attracted more and

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Table 2.17 Top 30 long-term developments in global cities Rank

City

Rank

City

Rank

City

1

London

11

Toronto

21

Houston

2

Singapore

12

Geneva

22

Moscow

3

San Francisco

13

Sydney

23

Montreal

4

Amsterdam

14

Melbourne

24

New York

5

Paris

15

Zurich

25

Taipei

6

Tokyo

16

Berlin

26

Dusseldorf

7

Boston

17

Copenhagen

27

Brussels

8

Munich

18

Vienna

28

Prague

9

Dublin

19

Vancouver

29

Washington DC

10

Stockholm

20

Abu Dhabi

30

Frankfurt

Source The author is based on the 2019 Global Urban Potential Index report

20000000

15000000

10000000

5000000 1850

1860

1870

1880

1890

1900

1910

1920

Fig. 2.25 Annual increment of global population in 1850–1920. Data source The author sorts according to our world in data database data

more farmers who originally lived in rural areas and depended on agriculture for their livelihood, promoted the aggregation of industry and population to the city, LED to the continuous expansion of the city scale and increase of the number, and also made the number and place of farmers in the total social population The proportion of urban population is decreasing, and the number and proportion of urban population are increasing, and finally the urban–rural divided world is formed. In addition, the world’s urbanization rate has reached 30%, and Western Europe and North America have become urban areas. The rapid increase of global population

2.3 From the Perspective of Macro Gross, Global Urban Development Has …

83

Urban (%) 80

20 Asia

15

Africa

Europe

60

10

40

5

20

0 1850

1860

1870

1880

1890

1900

1910

1920

1930

1940

0 1950

Fig. 2.26 Urbanization rate change in Asia, Africa and Europe from 1850 to 1950. Data source The author sorts according to our world in data database data

and the acceleration of industrialization accelerated the process of global urbanization. The global urbanization rate increased from 11% in 1850 to 29.6% in 1950 (see Fig. 2.24). The process of global urbanization accelerated. From a regional perspective, Western Europe and North America have become urban areas, and the global urbanization process is mainly led by Europe and the United States (see Fig. 2.26). The urbanization process in Europe rose rapidly from 16.7% in 1850 to 51.7% in 1950, with the urban population exceeding the rural population. At this time, Asia and Africa are just beginning to carry out industrial revolution, and the process of urbanization is slowly starting. The urbanization rate in Asia increased from 10% in 1850 to 17.5% in 1950, and the urbanization rate in Africa increased from 4% in 1850 to 14.3% in 1950, The urbanization process is just starting. Finally, the second industrial revolution brought advanced countries into urbanization one after another. The urbanization level of advanced countries increased from 11.4% in 1850 to 52.1% in 1950. The urban population has exceeded the rural population. At this time, the major western advanced countries have realized urbanization (see Table 2.18). In 1950, the United Kingdom remained in the leading position, with its urbanization level of 79%, achieving a high degree of urbanization. The urbanization level of other developed countries, such as Germany, Austria, the United States, Canada, France, Italy, and Spain, was 64.7%, 64.6%, 64.2%, 60.9%, 55.2%, 54.1% and 51.9%, respectively. During this period, modern cities in advanced countries improved significantly, the main sign of which was the large-scale construction of urban infrastructure. 3. From 1950 to 2050, urbanization from acceleration to completion, from city-led world to city is the transformation of the world. During the third industrial revolution, the high-tech industry with information technology as the core gradually replaced heavy industry as the leading industry, and then moved from the industrial economy era to the knowledge economy era or the information economy era, which had a great impact on the world cities, and

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Table 2.18 Distribution of population cities in the UK (10,000 people, %)

Year

Total population Urban population Proportion of urban population

1750 766.5

130.3

17.00%

1801 1050.1

354.9

33.80%

1811 1197

438.1

36.60%

1831 1626.1

720.3

44.30%

1851 2081.7

1124.1

54.00%

Data source The author collects and organizes

100000000 90000000 80000000 70000000 60000000 50000000 40000000 1950

1960

1970

1980

1990

2000

2010

2020

Fig. 2.27 Annual global population change from 1950 to 2016. Data source The author sorts according to our world in data database data

global urbanization. The rate will increase from around 30% in 1950 to around 70% in 2050. Global urbanization will accelerate from completion to completion. The world-dominant world-to-city is the world. Thus, from 1950 to 2050, global urban development was mainly embodied in two phases. The first phase was the midurbanization of 1950–2008, the city dominated by the world; the second phase was the mid-to-late urbanization of 2008–2050, the city is the world. From a population perspective, the global population has grown from 4.77 billion in 1950 to 8.275 billion in 2008, and the global population has increased by 3.505 billion (see Fig. 2.27). The figures and figures show that in the first industrial revolution and the second industrial revolution, although the population has improved, the increase is not large. From 1750 to 1900, the global population is in a slow growth stage, and after 1900, the population experienced explosive growth; especially during the outbreak of the third industrial revolution, the global population rose rapidly. From a national perspective, the population of Germany and the United Kingdom is slowly increasing, and the number of people in the United States, the birthplace of the third industrial revolution, has been qualitatively improved. The population of the United States has risen from 160 million in 1950 to 2009. With a population of 309 million, the population has basically doubled. At this time, China and India, due to their late-comer advantage, have rapidly increased their population.

2.3 From the Perspective of Macro Gross, Global Urban Development Has …

85

Urban (%) 85 43

Asia

Africa

Europe

75

33

65

23

13 1950

55

1960

1970

1980

1990

2000

2010

45 2020

Fig. 2.28 Changes in urbanization rates in Asia, Africa and Europe. Data source The author sorts according to our world in data database data

From the perspective of urbanization process, during the third industrial revolution, global urbanization rose from 29.6% in 1950 to 50.6% in 2008. For the first time, the global urban population surpassed the rural population, which means entering the urban era. From a regional perspective (see Fig. 2.28), after the urbanization rate in Europe exceeds 50%, the urbanization process tends to be slow, and the urbanization rate in Europe rose from 51.7% in 1950 to 72.5% in 2008, especially After the urbanization process reached 70% in 1990, the urbanization process was basically stagnant. However, Asia and Africa are still in the process of accelerating urbanization. The urbanization rate in Asia rose from 17.5% in 1950 to 43.3% in 2008. The urbanization rate in Africa rose from 14.3% in 1950 to 38% in 2008. The urbanization process is still accelerating. At this time, the global urbanization process is driven by Asia and Africa. The information revolution has made Asia and Africa the engine of promoting urbanization in the world. Overall, from the first industrial revolution to the third industrial revolution (see Table 2.19), before the industrialization from 1500 to 1750, the urbanization of the United States remained basically unchanged, and the urbanization rate increased from 0.2% to 3.5%. Around the world, and after the industrial revolution, especially after the first industrial revolution, the rate of urbanization in the United States has increased rapidly, from 6% in 1800 to 64% in 1950, up to 82% in 2018, urban population. Leading the development of the United States. In contrast, China and India did not carry out the industrial revolution until 1950. They are still in the agricultural society, and their production efficiency is extremely low. Their urbanization rate changes slowly between 1500 and 1950, in the third industry. After the revolution, the urbanization rate was greatly improved. Until now, it is still in the process of urbanization. The developed countries such as Europe and the United States have already completed urbanization in the 2050s, and the developed countries have generally led the developing countries. From a regional perspective, this phenomenon is also very obvious. Europe began to urbanize in 1750. From 1850 to 1950, the urbanization process accelerated, and the overall urbanization rate exceeded 50%. By 2016, the

86

2 The World: 300 Years of Urbanization Expansion

Table 2.19 Comparison of urbanization level between developed and underdeveloped countries during the second industrial revolution (%) Year

Developed country

Underdeveloped countries

Total population

Urban population

Proportion of urban population

1850

3.52

0.4

11.4

1875

4.35

0.75

17.2

1900

5.75

1.5

1925

7.15

2.85

1950

8.13

4.23

Total population

Urban population

Proportion of urban population

9.1

0.4

4.4

9.85

0.5

5

26.1

10.75

0.7

6.5

39.9

12.35

1.15

9.3

52.1

17.07

3.09

18.1

Data source The author collects and organizes

urbanization rate has reached 76%, during the period from 1750 to 1930, the urbanization process in Asia and Africa remained basically unchanged until 1950, but the urbanization rate was far lower than that in Europe and America, so the overall pattern was from Europe and America to Asia and Africa. In the next 30 years, countries and regions with low world rates such as Asia and Africa will enter an accelerated period and complete urbanization (see Fig. 2.29, Table 2.20). Specifically, it is estimated that the level of urbanization in Africa will rise from 38% in 2008 to 59% in 2050, and the overall urbanization rate will increase by 21%; the level of urbanization in Asia will rise from 43% in 2008 to 66% in 2050. The overall urbanization rate will rise by 23%, while Europe, Oceania and the Americas are already in the late stage of urbanization, so the future will not increase much. Therefore, the acceleration and completion of urbanization in Asia and Africa will lead to the global entry into mature urban society. It is expected that the global urbanization rate will rise from 51% in 2008 to 70% in 2050 and enter urban society. In the future, developed countries will achieve a high degree of urbanization, and at the same time, population clusters will be dispersed. On the whole, since the 1950s, Western developed countries have continued to develop at a higher level on the basis of basic urbanization, and have achieved high urbanization. According to UN statistics, by 2015, the overall urbanization level of developed countries reached 78.1%, the urbanization level of Japan, Sweden, New Zealand and Australia exceeded 85%, and France, Germany and Spain exceeded 75%. The level of urbanization in countries such as The United Kingdom, the United States, Canada has reached more than 80%. It is predicted that the level of urbanization in the world will continue to increase to about 70% in 2050, and the level of urbanization in developed regions will reach 86.6%.

2.3 From the Perspective of Macro Gross, Global Urban Development Has … 95

Europe

Oceania

Northern America

South America

Africa

Asia

87 70

85

60

75

50

65

40

55 2005

2010

2015

2020

2025

2030

2035

2040

2045

30 2050

Fig. 2.29 Estimation of urbanization rate levels by region by 2050. Data source The author sorts according to our world in data database data Table 2.20 Urbanization rate of major countries Entity

1750

1800

1850

1900

1950

2000

2016

Brazil

9.200

22.900

36.160

81.192

86.042

Canada

7.900

37.500

60.946

79.478

81.300

China

6.000

6.600

11.803

35.877

56.736

France

9.100

8.800

14.500

55.232

75.871

79.917

Germany

5.600

5.500

10.800

67.944

74.965

77.224

10.000

17.042

27.667

33.182

7.600

India

6.400

Indonesia

2.900

12.400

42.002

53.989

Ireland

7.000

10.200

40.088

59.155

62.737

Italy

14.600

20.300

54.104

67.222

69.855

Japan

5.000

53.402

78.649

91.457

Mexico

5.800

42.655

74.722

79.577

38.338

61.716

60.178

Poland

21.900

1.000

2.500

44.087

73.350

74.164

Spain

8.600

11.100

17.300

51.920

76.262

79.840

Switzerland

4.600

3.700

7.700

67.381

73.383

73.739

15.700

24.774

64.741

74.134

39.977

64.153

79.057

81.862

Russia

9.300

1.900

Turkey

6.300

United States

6.073

14.400

15.413

Data source The author sorts according to our world in data database data

88

2 The World: 300 Years of Urbanization Expansion

2.3.3 Impact of Urban Space Expansion on the World Environment and Economy 1. from 1750 and 1950, the developed urban space of a few developed countries accelerated and the global urban space began to accelerate. Urban space, especially a few developed urban spaces, led and controlled global development. After the industrial revolution began, it not only improved production efficiency, but also expanded people’s activity space. With the improvement of production efficiency, people’s activity space gradually expanded, and a few developed urban spaces led and controlled global development. Before the industrial revolution, although there was a certain degree of urban growth, at that time, social productivity was extremely low, social division of labor was extremely underdeveloped, commodities were extremely poor, and urban development was extremely slow. At that time, the main focus was on the agricultural population. The city’s attractiveness to the rural population was not obvious. Therefore, large-scale urban space has not yet been formed, and the urbanization rate is slow. The table indicates that the global urban area was only 14,104 square kilometers in 1700 years before 1700. The city’s expansion area is only 9867 square kilometers. After the first industrial revolution, the global urban space accelerated (see Table 2.21). It can be seen from the table that during the 200 years from 1700 to 1900, the urban area expanded by 32,593 square kilometers, and the total urban area expanded to 46,697 square kilometers. However, the city at this time is only a small city, and its transportation and service environment are still in the initial stage. Only the transportation infrastructure is only around the city and the factory. From a regional point of view, before the industrial revolution, some areas had a certain degree of urban growth, mainly in Europe. The Middle East and North Africa had a certain urban development foundation earlier. However, during this period, the urban area showed a retrogression. There is no huge development in the area of cities in various regions. This can also be seen from the perspective of urban land use change in various regions (see Fig. 2.30). Figure 2.30 shows the changes in urban land use in various regions from 1600 to 1850, before the first industrial revolution, from 1600 to 1750, urban land use in various regions remained basically unchanged. However, during the first industrial revolution, the amount of urban expansion in North America was the largest. In 200 years, the city expanded a total of 10,943 square kilometers, followed by Western Europe, and the city expanded to 8,574 square kilometers. Other regions, such as Central Europe, During the period of Oceania, the urban area also expanded to a certain extent, with expansion rates of 13.815 square kilometers per year and 9.93 square kilometers per year. From the perspective of urban land use, the continents during the first industrial revolution (see Table 2.22), between the first industrial revolution, urban land use in various regions showed a significant upward trend. From a national perspective (see Fig. 2.31), urban land in the world’s major countries has remained largely unchanged from 1500 to 1750, and during the first industrial revolution, urban land in major countries has rapidly increased, from The

2.3 From the Perspective of Macro Gross, Global Urban Development Has …

89

Table 2.21 Urbanization level of developed countries in major years from 1950 to 2050 (%) Country or region

2010

2015

2020

2050

World

51.6

53.9

56.2

68.4

Developed regions

77.2

78.1

79.1

86.6

Undeveloped area

46.1

49.0

51.7

65.6

United Kingdom

81.3

82.6

83.9

90.2

France

78.4

79.7

81.0

88.3

Germany

77.0

77.2

77.5

84.3

Italy

68.3

69.6

71.0

81.1

Sweden

85.1

86.6

88.0

93.2

Spain

78.4

79.6

80.8

88.0

Austria

57.4

57.7

58.7

70.9

Canada

80.9

81.3

81.6

87.3

United States

80.8

81.7

82.7

89.2

Australia

85.2

85.7

86.2

91.0

New Zealand

86.2

86.3

86.7

91.1

Japan

90.8

91.4

91.8

94.7

Data source The author collected and compiled according to the World Bank database

Total urban area per world region

2500

Asia-Stan 2000

7000

Central Europe

Eastern Africa

Northern Africa

Rest Central America

Rest South America

Southeastern Asia

Southern Africa

Western Africa

Western Europe

6000 5000

1500

4000 3000

1000

2000 500

1000 0 1600

1650

1700

1750

1800

0 1850

Fig. 2.30 Changes in urban land use by region. Data source The author sorts according to our world in data database data

90

2 The World: 300 Years of Urbanization Expansion

Table 2.22 Characteristics of urban area expansion (Unit: square kilometers, square kilometers/ year) Year

Total urban area

Urban expansion area at each stage

Urban expansion rate

Urban land expansion Population elasticity index

0–1700

14,104

9867.00

5.80

0.98

1700–1900

46,697

32,593.00

162.97

1.36

Note The urban land expansion population elasticity index refers to the ratio of the average annual growth rate of the built-up area to the annual growth rate of the non-agricultural population, and is an indicator of the rationality of urban land expansion Data source The author collects and organizes

expansion of urban area and the per capita urban land use are more obvious (see Figs. 2.32, 2.33 and 2.34). As can be seen from Fig. 2.34, except for China, cities in major countries are expanding. It can be seen from Fig. 2.34 before the first industrial revolution, the per capita urban land use of some countries was still declining. During the first industrial revolution, the per capita urban area was basically rising, and the urban activity space was significantly expanded. 2. from 1850 to 1950, urban space expanded rapidly, Europe and North America accelerated, and global urban and rural divisions intensified (Europe and the United States are urban areas, Asian and other rural areas), and European and American cities support world development. During the second industrial revolution from 1850 to 1950, urbanization was continued in major developed countries in Europe and North America. Industrialization entered a subsequent stage, and widespread suburbanization occurred. Urban population gradually concentrated in small and medium-sized cities, and urban space 12000 10000 8000

Canada

United States

Japan

Korea

Russia

China

Brazil

India

Indonesia

Turkey

Ukraine

Mexico

6000 4000 2000 0 -2000

Expansion of urban area from 1700 to 1900

Fig. 2.31 Expansion of urban area from 1700 to 1900. Data source The author sorts according to our world in data database data

2.3 From the Perspective of Macro Gross, Global Urban Development Has …

91

Total urban area 4000

2500

2000

1500

Brazil

Canada

China

India

Indonesia

Japan

3500 3000 2500

Russia

Turkey

Ukraine

United States

2000

1000

1500 1000

500 500 0 1500

1550

1600

1650

1700

1750

1800

0 1850

Fig. 2.32 Global per capita urban area (km2 /person) Data source The author sorts according to our world in data database data 0.0001 0.00008 0.00006 0.00004 0.00002 0 1500

1550

1600

1650

1700

1750

1800

1850

1900

1950

2000

Fig. 2.33 Changes in urban land use in major countries. Data source The author sorts according to our world in data database data

expanded rapidly. At the beginning of the twentieth century, the industrial revolution was carried out in other parts of the world, and various regions of the world had different degrees of urban expansion. However, the scale of expansion was still far lower than that of Western Europe and North America, which were dominated by developed countries (see Table 2.23). At this time, urban space has expanded rapidly, urban agglomerations have become the mainstay of the world, and transportation infrastructure, medical services, and catering services around the city have become more sophisticated. From the perspective of urban land use in various regions (Fig. 2.35), European and American cities support the development of the world. During the second industrial revolution, urban land use in all regions was on the rise, but there were some differences in different regions. Specifically, the overall urban land use of cities in

92

2 The World: 300 Years of Urbanization Expansion

urban area per person 0.00015

0.0001

8E-05

Brazil

Canada

China

India

Russia

World

6E-05

4E-05

United States

2E-05 0.00005 1E-19

0 1500

1550

1600

1650

1700

1750

1800

-2E-05 1850

Fig. 2.34 Changes in per capita urban land use in major countries. Data source The author sorts according to our world in data database data

Table 2.23 Urban expansion area (Unit: Square kilometer)

Area

Urban area expansion in 0–1700

Urban area expansion in 1700–1900

Central Europe

1419

2763

North America

16

10,943

Southeast Asia

290

587

Asia–Pacific

29

344

Middle East

−560

348

Other Central America

129

467

Other parts of South America

367

662

East Africa

69

112

North Africa

−125

390

Southern Africa

79

178

Western Europe

2522

8574

Oceania

49

1986

Data source The author collects and organizes

Western Europe, Central Europe and South America is rising rapidly. At this time, people’s activity space has been mainly in the city, And Africa, Asia still is still in agricultural society. In general, urban land has remained basically unchanged during the second industrial revolution. Only in the second industrial revolution and the third industrial revolution, urban land began to rise rapidly. From the national point of view (see Fig. 2.36, Table 2.24), the overall trend shows the same trend with the

2.3 From the Perspective of Macro Gross, Global Urban Development Has …

93

region. Urban land in developed countries in Europe and America has risen rapidly during the second industrial revolution, and various countries in the world have different levels of urban area. The expansion and expansion scale is still dominated by developed countries (see Table 2.24), which is more obvious from the perspective of per capita land use (see Fig. 2.37). This shows that during the second industrial revolution, the space for people in developed countries has been transferred to cities, while the space for activities in Asian and African countries is still in rural areas. At this time, cities in developed countries directly dominate the world. 3. from 1950 to 2050, the first stage, the city dominated the world, the urban space accelerated and the space efficiency accelerated. In the second stage, the city is Total urban area per world region 35000 6000

Asia-Stan Northern Africa Southeastern Asia Western Europe

4000

Central Europe Rest Central America Southern Africa

Eastern Africa Rest South America Western Africa

25000

15000

2000

5000

0 1850

1860

1870

1880

1890

1900

1910

1920

1930

1940

-5000 1950

Fig. 2.35 Changes in urban land use by region. Data source The author sorts according to our world in data database data

Total urban area per world region 10000 8000 6000

Brazil

Canada

China

India

Indonesia

Japan

Russia

Turkey

Ukraine

United States

40000

4000

20000

2000 0 1850

1860

1870

1880

1890

1900

1910

1920

1930

1940

0 1950

Fig. 2.36 Changes in urban land use in major countries. Data source The author sorts according to our world in data database data

94

2 The World: 300 Years of Urbanization Expansion

Table 2.24 Urban expansion area of each region from 1900 to 1950 (Unit: square kilometers) Area

Urban area expansion from 1900 to 1950

Area

Urban area expansion from 1900 to 1950

Central Europe

2313

Other parts of South America

2550

North America

36,503

East Africa

214

Southeast Asia

1432

North Africa

823

Asia–Pacific

1126

Southern Africa

1031

middle East

742

Western Europe

18,869

Other Central America

1331

Oceania

2398

Data source The author collects and organizes

urban area per person 0.0003

0.0001

Brazil China Russia United States

0.00008 0.00006

Canada India World

0.0002

0.00004 0.0001 0.00002 0 1850

1860

1870

1880

1890

1900

1910

1920

1930

1940

0 1950

Fig. 2.37 Changes in per capita urban land use in major countries. Data source The author sorts according to our world in data database data

the world, the urban and rural space is divided into integration, the rural space is urbanized, and the countryside becomes the city. Mean area Since the third industrial revolution, developing countries have grown rapidly, and a large number of people have flooded into cities. The expansion of urban land is unstoppable. lobal cities are connected through networks. Cities are characterized by urban areas, and the city has a basis to meet all needs of people. The facilities, transportation, medical care, living services, and social services are all networked, intelligent, and integrated, and the urban space is accelerating. By the year 2000, the world urban area has expanded to 538,395 square kilometers, and the urban expansion rate has also increased in space. During the period from 1950 to 2000, the urban expansion rate reached 796.08 square kilometers per year. Moreover, since 1950, the total agricultural land in Oceania and Europe has begun to decrease, and agricultural land in Latin America and Asia has remained basically unchanged. From the point of view of per capita agricultural land, this turning point is more obvious

2.3 From the Perspective of Macro Gross, Global Urban Development Has …

95

(see Fig. 2.38). Figure 2.38 shows that per capita agricultural land has a significant turning effect around 1950. Before 1950, per capita agricultural land use increased, but after 1950, the amount of agricultural land per capita declined rapidly, indicating that the activity space for per capita agriculture has gradually decreased, and people have begun to turn to urban activities. From the perspective of changes in urban area, the urban land area of all continents has shown a rapid increase. Especially after 1950, the urban area of all continents has increased significantly (Table 2.25, see Fig. 2.39), and the same cities in Europe and America. The area is significantly larger than the urban area of Asia and Africa. The cities in all regions have begun to expand on a large scale, and their growth rate has exceeded 3 times. By 2000, the urban area expansion in Europe, America and Western Europe was still the fastest in the world (see Table 2.26). From the perspective of urban area change in major countries (see Fig. 2.40), it can be seen from Fig. 2.40 that the urban area of the United States is significantly higher than that of other countries, while other countries in the same period are basically slowly rising, especially China and India. Since there was no industrial revolution, it was still in an agricultural society at this time, and the urban area did not change significantly (see Table 2.27). Judging from the per capita urban area, the industrial revolution has caused a significant increase in the per capita urban area of each country, and people’s urban activity space has increased significantly. In general, the scope of human activities has shifted from agricultural areas to urban areas, from developed areas in Europe and America to Asian and African developing areas, and the world has gradually become a world of urban leadership. In the future, urban and rural spatial division will be integrated, rural space will be urbanized, and rural areas will become urbanized areas. In 2015–2050, according to the current per capita urban land growth rate, the scale of urban expansion in developed countries will increase by 1.9 times; according to the current per capita urban land growth rate, the scale of urban expansion in developed countries will Africa Europe (excl. Russia) Latin America and the Caribbean (excl. Brazil)

Rest of Asia (excl. India & China) Oceania 40

6

4 20 2

0 1750

0 1800

1850

1900

1950

2000

Fig. 2.38 Per capita agricultural land area of each continent. Data source The author sorts according to our world in data database data

96

2 The World: 300 Years of Urbanization Expansion

Table 2.25 Urban expansion areas of different countries from 1900 to 1950 (Unit: Square kilometer) Country Urban area expansion from 1900 to Country 1950

Urban area expansion from 1900 to 1950

Canada

933

Brazil

2302

United States

35,570

India

2338

Japan

3162

Indonesia 702

Korea

276

Turkey

371

Russia

6761

Ukraine

2405

China

4159

Mexico

1215

Data source The author sorts according to our world in data database data

Total urban area per world region 20000

120000 Asia-Stan Northern Africa Southeastern Asia Western Europe

Central Europe Rest Central America Southern Africa

Eastern Africa Rest South America Western Africa

10000

0 1950

70000

1955

1960

1965

1970

1975

1980

1985

1990

1995

20000 2000

Fig. 2.39 Urban land use change by region. Data source The author sorts according to our world in data database data

increase by 1.5 times; The land area will remain unchanged, and the scale of urban expansion in developed countries will increase by 1.1 times. In 2015–2050, according to the current growth rate of urban land, half of the existing urban land growth rate, and the existing per capita urban land remain unchanged, the scale of urban expansion in developing countries will increase by 3.7 times and 2.5 times respectively. 1.8 times. In the future, with the gradual expansion of the scale of cities and towns in developing countries, along with AI intelligence, new energy, Internet of Things, and cloud computing, urban planning is more perfect, and cities are the world.

2.4 From the Perspective of Space, the Changes of Global Cities’ …

97

Table 2.26 Changes in per capita urban area by region Year

1700

1800

1900

1950

2000

2000/1700

Asia-Stan

29

40

373

1499

5791

199.6897

Central Europe

1419

1403

4182

6495

17,088

12.04228

Eastern Africa

69

73

181

395

5853

84.82609

Northern Africa

170

158

560

1383

13,038

76.69412

Rest Central America

129

200

596

1927

8383

64.9845

Rest South America

367

311

1029

3579

16,232

44.22888

Southeastern Asia

290

398

877

2309

13,989

48.23793

Southern Africa

79

66

257

1288

9108

Western Africa

1096

978

565

1235

13,680

12.48175

Western Europe

2864

3905

11,438

30,307

83,617

29.19588

115.2911

Data source The author sorts according to our world in data database data

90 60 30 0 -180

-120

-60

0

60

120

180

-30 -60 -90

Fig. 2.40 Changes in per capita urban land use in major countries. Data source The author sorts according to our world in data database data

2.4 From the Perspective of Space, the Changes of Global Cities’ Characteristics Determine the Evolution of world’s Characteristics The three important characteristics of human development: aggregation, connection and sharing, have significant differences in different stages. In the 300 years from 1750 to 2050, the characteristics and evolution of global cities determine the characteristics and evolution of the world.

98

2 The World: 300 Years of Urbanization Expansion

Table 2.27 Urban expansion areas of major countries in different periods from 1950 to 2000 (Unit: Square kilometer) Area

Urban area expansion from 1950 to 2000

Area

Urban area expansion from 1950 to 2000

Central Europe

10,593

Other parts of South America

12,653

North America

115,511

East Africa

5458

Southeast Asia

11,680

North Africa

11,655

Asia–Pacific

4292

Southern Africa

7820

Middle East

13,990

Western Europe

53,310

Other Central America

6456

Oceania

9438

Data source The author collects and organizes

2.4.1 The Scale and Density of Global Urban Agglomeration: From Dispersed-Concentration to Concentrated-Concentration and Then to Concentrated-Dispersion In the past 300 years, the global urban agglomeration has three stages: from decentralized agglomeration to centralized agglomeration to centralized decentralization. Each stage presents a different form, which determines the evolution of the world spatial pattern.1 The three stages of global urban agglomeration can be roughly divided into: from 1750 to 1850, due to the heterogeneity of spatial distribution of factors or population distribution, the city as a whole presents decentralized agglomeration; from 1850 to 1950, with the invention of power, telephone and telegraph, the city gradually moves towards centralized agglomeration, sharing aggregation effect; from 1950 to 2050, the city transformed the real space landscape into virtual world by means of science and technology, to some extent, and the global network division of labor promoted the city to transform to the new decentralized aggregation. 1. The leading national cities were decentralized aggregation in 1750–1850s, while the global cities were decentralized aggregation, mainly in Britain, Germany, France and other countries. The global cities form a polarization pattern of highly concentrated cities in the developed countries and scattered cities in the underdeveloped countries. Japanese cities in Asia began to rise, but their degree of aggregation is significantly lower than that in European and American countries, as shown in Fig. 2.41. By 1850, about 1 / 3 of the world’s listed companies had gathered in the cities of Britain, Germany and France, while that of Germany and the United States was 1

This report uses the data of 97,259 listed companies in the Osiris global listed company database from 1989 to 2017 to see the changes in the form, scale and density of global urban agglomeration from the perspective of industrial change and agglomeration.

2.4 From the Perspective of Space, the Changes of Global Cities’ …

99

90 60 30 0 -180

-120

-60

0

60

120

180

-30 -60 -90

Fig. 2.41 The distribution of global listed companies in global cities before 1850. Source Sorting according to the data of Osiris’s global listed companies

relatively low. For example, the German listed companies were mainly scattered in Frankfurt, Berlin, Essen, Hanover and other cities, which also showed that there were differences in aggregation within the developed industrial countries, showing the space of “large aggregation and small dispersion” distribution pattern. At the same time, Japan’s cities such as Osaka, Tokyo, Nagoya, Singapore, Brazil’s Brasilia and other Asian, African and Latin American cities have also achieved corresponding development, but there is a large gap with European and American countries. Therefore, before the 1950s, the global urban agglomeration presented a global spatial distribution pattern of polarization, “large agglomeration, small dispersion”. 2. From 1850 to 1950s, the cities in typical countries were centralized agglomeration and the global cities were decentralized agglomeration. The global urban industrial agglomeration shows a pattern of multipolar agglomeration from European countries during the first industrial revolution to European, North America, Asia and other global cities, as shown in Fig. 2.42. As far as global cities are concerned, there are two forms of industrial agglomeration among countries in the world: one is the highly concentrated form represented by Britain and Japan, with listed companies mainly concentrated in London, Tokyo, Paris and other big cities; the other is the coexistence of urban agglomeration and decentralization in the United States. The industrial agglomeration degree of American cities has been greatly improved. In 1950, American cities gathered more than 570 enterprises around the world, accounting for 9% of the world. However, the industrial agglomeration density of American cities is relatively low compared with that of European countries. The industrial agglomeration mainly centers on New York, Houston, Chicago and other core cities, and the rest cities are relatively evenly distributed, as shown in Fig. 2.43. During this period, the Great Lakes city economic belt with Chicago as the core has

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2 The World: 300 Years of Urbanization Expansion 90 60 30 0

-180

-120

-60

0

60

120

180

-30 -60 -90

Fig. 2.42 The distribution of global listed companies in global cities in 1850–1950. Source Sorting according to the data of Osiris’s global listed companies

been formed in the United States, and a large number of enterprises have gathered in the cities within the economic belt. At the same time, it can also be seen that cities in India, Africa, South Africa and other countries have also achieved some development, but the degree of aggregation is still at a low level compared with developed countries and regions. 3. The leading countries are centralized agglomeration in the 1950s-2050s, and cities around the world changed from centralized agglomeration to centralized decentralization. Since the 1950s, the global cities have formed a tripartite pattern of Europe, North America and Asia, as shown in Fig. 2.44. Cities in developed countries such as 40 35 30 25 20 15 10 5 0

Fig. 2.43 The distribution of listed Companies in American cities 1850–1950. Source Sorting according to the data of Osiris’s global listed companies

2.4 From the Perspective of Space, the Changes of Global Cities’ …

101

90 60 30 0 -180

-120

-60

0

60

120

180

-30 -60 -90

Fig. 2.44 The distribution of global listed companies in global cities after 1950. Source Sorting according to the data of Osiris’s global listed companies

European traditional industrial powers, the United States and Japan are gathering more and more, and the gathering density is also increasing. At the same time, under the trend of global urban agglomeration, the city develops from the center periphery to the city cluster level with the center city as the core, that is to say, the city changes to centralized decentralization. At this stage, the degree of urban agglomeration in Asia is increasing, especially in the eastern coastal cities of China. In terms of global city, there are obvious differences in the degree of industrial agglomeration between different regions. As the first place of industrial revolution, European cities are mainly concentrated in London, France, Paris and other cities, while German cities are relatively scattered or balanced; while American cities develop into three Bay Economic Belts with New York, Chicago and Los Angeles as the core, and the industrial agglomeration degree in the Bay economic belt is constantly improving. At present, urban agglomeration is gradually breaking the regional restrictions, and the most competitive economic core areas in the world, such as New York, London, Paris, Tokyo, Singapore, Beijing, Hong Kong, Shanghai and other major urban agglomerations. Since the 1950s, a prominent feature of world economic development is that the development of urban agglomerations centered on big cities is gradually becoming the dominant trend of world economic development, as shown in Table 2.28. With the development of urbanization in China, the urban agglomerations represented by Beijing-Tianjin-Hebei Urban Agglomerations, Yangtze River Delta urban agglomerations, Guangdong-Hong Kong-Macao Bay area are rising, and their advantages of internal gathering, connection and sharing are gradually released.

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Table 2.28 Total urban area per world region Year

1500

1600

1700

1800

1900

1950

2000

Brazil

9

22

72

163

843

3145

22,168

Canada

0

0

1

7

193

1126

6030

China

4013

5762

3916

3923

2846

7005

43,199

India

942

1154

1419

1729

2615

4953

23,854

Indonesia

11

34

101

134

479

1181

8772

Japan

468

747

1104

504

1887

5049

13,450

United States

4

4

15

139

10,766

46,336

156,919

Russia

19

64

189

203

2416

9177

21,064

Turkey

120

140

134

165

292

663

5203

Ukraine

2

6

22

33

691

3096

8126

Data source The author sorts according to our world in data database data

2.4.2 Urban Connection: From Regional Connections to Global Connections, from “Hard Connections” of Commodity Elements to “Soft Connections” of Information and Service Elements, from Individual Connections to the Internet of Everything Since the industrial revolution, the global urban connection has experienced the transformation from the regional connection to the global interconnection (as shown in Fig. 2.45), from the commodity or raw material based goods connection to the element connection represented by capital to the soft connection such as knowledge (information) and service. The transformation of the global urban connection scope, content and way has profoundly changed the world connection. 1. The local connections of the leading cities were dominant, and the global connections were mainly goods connections of goods and raw materials in 1750–1850s. During the first industrial revolution, the biggest change in transportation field was the introduction and use of steam ships and steam locomotives, which prompted the UK, Germany and other countries to lay a dense transportation network. The development of textile industry, mining industry and metallurgical industry needs to improve the traditional means of transport in order to transport a large number of coal and ore, which set off a wave of canal excavation. By 1830, Britain had 2500 miles of canals. By 1800, Germany had about 490 km of canals. From 1836 to 1849, Bavaria opened the Ludwig canal between the Danube and the Main river. In 1850, the Oberland canal between Ostrod in East Prussia opened. The total length of artificial waterways in Germany has reached 3528 km. Therefore, the invention of the steam wheel and the digging of the canal strengthened the connection between

2.4 From the Perspective of Space, the Changes of Global Cities’ …

103

Fig. 2.45 Global commodity output by purpose, 1827–2014. Source https://ourworldindata.org

cities. At the same time, steam locomotive is applied to railway, which can transport passengers and goods faster and cheaper than highway or canal and dominate longdistance transportation. By 1838, Britain had 500 miles of railway, which increased to 6600 miles in 1850. German railway construction had made rapid progress. In 1835, the operating mileage of German railway was 6 km, in 1845, 2300 km, in 1855, 8290 km, and in 1865, 14,690 km.2 In a word, the invention and application of steam engine promoted the rise of new transportation modes by canal and railway network, and promoted the connection between domestic cities. The invention of steam steamer promoted the urban trade between countries and the expansion of overseas market, as shown in Fig. 2.46. It can be seen that before 1850, the global urban connection was mainly based on trade, and the global trade was mainly based on regional trade, with relatively less trade between regions.3 The invention of steamship broke the limitation of water space for human beings. In 1833, the steamship Royal William sailed from Nova Scotia to England, and the sea connection between cities around the world was expanded. This is conducive to the export of products from advanced industrial countries and the plunder of raw materials such as rubber, jute, oil and various metals from overseas colonies. Some Asian, African and Latin American regions have been opened to a certain extent. 2. In 1850–1950s, the national contact of typical cities was the main body, the global contact was mainly dominated by goods contact (goods contact of finished 2

The data were gathered from General history of the world. The Western European countries represented by Britain, Germany and France are the global manufacturing centers and trade centers in this period. Choosing Western European countries as reference can represent the global trade relations to some extent.

3

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Fig. 2.46 Trade between Western Europe and the rest of the world, 1827–1850. Source https://our worldindata.org

products and raw materials), and the global soft contact began to rise (telegraph and telephone communication). From 1850 to 1950, the global economic relations were mainly trade between North America represented by the United States and Western European developed countries represented by the United Kingdom and Germany. The relations with other mainland countries were relatively small, as shown in Fig. 2.47 and Fig. 2.48. However, with the deepening of global economic connection, Asian and South American countries have gradually developed, especially after the Second World War, the Bretton Woods system dominated by the United States has been built, the global economic integration has accelerated, and the global economic system has formed. The invention of the internal combustion engine makes the automobile enter the popularization stage, and the regional scope of the city can be continuously expanded. The popularity of cars has prompted some urban residents to choose to move to the suburbs, which has led to the emergence of urban suburbanization and changed the urban spatial structure. In the 1840s, after the American successfully used the telegraph technology in practice, it quickly spread all over the world. In the 1850s, western countries were able to lay two to three thousand fathoms of submarine power lines. In 1866, a transatlantic submarine power line was set up between Britain and the United States. In the late 1860s, tsarist Russia crossed Siberia to Vladivostok. The improvement of means of transportation and information and communication technology has reduced the cost of transportation and communication (as shown in Fig. 2.49). 3. From 1950 to 2050, global connection are dominated, and goods connection continued, but service connection and information (knowledge) connection have become more and more dominant, and individual connection became all things connected. The rapid development of transportation and communication technology, especially the rapid development of information technology since the 1990s, has brought countries and regions all over the world closer together and accelerated the international

2.4 From the Perspective of Space, the Changes of Global Cities’ …

105

Fig. 2.47 U.S. Global trade links by destination. Source https://ourworldindata.org

Fig. 2.48 Trade between rich and backward countries in the world, 1827–1950. Source https://our worldindata.org

flow of capital and raw materials. Transnational corporations and various international organizations have become powerful promoters of economic globalization. The development of new generation communication technology breaks the traditional limitation of space–time distance, and global cities enter the cyberspace, forming a global urban cyberspace connection. Since the 1950s, global cities have entered the era of information and knowledge economy. On the basis of increasing hard links, the soft links between cities have been increasing, as shown in Fig. 2.50. As can be seen from Fig. 2.23, the range of global city connection is relatively wide, presenting the global city connection pattern with cities in Europe, North

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Fig. 2.49 Changes in transport and communications costs, 1930–1950. Source OECD (2007), OECD Economic Outlook, Vol. 2007/1, OECD Publishing

Fig. 2.50 Publication of global inter-city cooperation papers. Source Cass urban competitiveness database

America, Asia, China, Japan and other countries as the center. Among them, London, Boston, Beijing, Paris, New York and Sao Paulo have become the cities with high global urban connection, which indicates that these cities are the regions with high degree of global urban information aggregation. By comparing the status of these cities as global economic, financial and technological centers, we can also prove that they are at the core of global urban linkages. At the same time, it can also be seen that there are obvious regional characteristics in the global urban linkages. The global urban linkages show a significant North–South gap. Cities with high urban

2.4 From the Perspective of Space, the Changes of Global Cities’ …

107

linkages are mainly distributed in the northern hemisphere, while cities in Africa and South America have relatively lower urban linkages. In addition, there are obvious spatial distribution differences within a country’s cities. For example, the global urban connectivity of Chinese cities is mainly concentrated in Beijing, Shanghai, Shenzhen and other eastern coastal global cities, while the global connectivity of other cities is relatively lower, with obvious differences.

2.4.3 Urban Sharing: From Basic Infrastructure to Public Services, from Hardware Products to Software Products, from Public Goods to Private Goods Since the industrial revolution, urban sharing has changed from basic infrastructure to public services, from hardware facilities to information knowledge sharing. By 2050, global urban sharing is characterized by diversified, standardized and largescale shared economic system, shared service facilities system dominated by shared urban public space, and high-level social sharing mechanism. 1. The basic infrastructure was shared by leading cities in 1750–1850, and the level of public service sharing was gradually improved. The industrial revolution has promoted the improvement of means of transportation. The railway transportation mileage in Britain, Germany, France and other cities has been greatly increased. At the same time, the urban road infrastructure has also been greatly improved. However, at this time, the global cities are mainly steam ships, and mainly domestic transportation, and auxiliary products export and raw materials import from foreign markets. For example, in the early days of Germany, steam ships were mainly used for transportation between domestic cities. In 1824, a Dutch steam ship went all the way back to Baharah. Three years later, regular steamship flights between Cologne and Mainz opened. In 1830, there were already 12 steamboats on the Rhine. In the 1940s, the number of steamboats dragging barges across the Dusseldorf bridge on the Caine River doubled every 2–3 years: 339 in 1843; 1073 in 1845; 2438 in 1848; 3989 in 1850. At the same time, Hamburg America post shipping company of Hamburg and North Germany Lloyd shipping company of Bremen have successively used steam ships to transport goods by sea, which to a certain extent extends the scope of global urban sharing and expands the scale of global urban sharing. During this period, the scope of global urban sharing is mainly concentrated in Britain, France, Germany and other countries with more developed industrial revolution, and the sharing of light industrial products is the main part, and the sharing scale is relatively small. During 1750–1850, the level of public service in cities around the world gradually increased, mainly in Western Europe, such as Germany, Britain, France and other cities with public service related listed companies, as shown in Fig. 2.51. These listed companies are also mainly concentrated in London, Paris, Berlin and other cities, in

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10 8 6 4 2 0

Fig. 2.51 The distribution of global public service listed companies in 1750–1850. Source Sorting according to the data of Osiris’s global listed companies

addition to Toronto, Canada, Rio de Janeiro, Brazil and other cities in North America The level of public service is also constantly improving. 2. The level of urban infrastructure sharing increased, and information sharing began to improve in 1850–1950s. With the improvement of urbanization level, the scale and level of national or urban infrastructure in the world are constantly improving, as shown in Table 2.29. It can be seen from the figure that since the 1850s, the global railway infrastructure has achieved rapid development, especially in Britain, the United States, Germany and other countries with developed industrial revolution, which shows that the rapid development of industrialization and urbanization is also constantly extending the scope of urban sharing and expanding the scale of urban sharing. The urbanization rate in the Great Lakes region of the United States has increased from 14.1% in 1860 to 60.8% in 1920, which has more than tripled. This is mainly due to the completion of the railway and water transportation network connecting the northeast and the Middle Atlantic region and the Great Lakes in the 1950s, which caused the migration of immigrants to the West and capital to the West. In addition, the Great Lakes region is rich in heavy industry and agricultural resources, and immigrants and capital gather in the Great Lakes region, forming an economic circle centered on Chicago, the so-called American manufacturing belt. The invention of telephone and telegraph has promoted the information connection between cities and enterprises, the improvement of communication technology has reduced the cost of information communication between enterprises and residents, and urban information sharing has gradually increased. Taking the distribution of Listed Companies in global cities as an example, the cities with high level of global urban information service are still some developed cities in Europe and America, as shown in the figure. It can be seen from Fig. 2.52 that London, New York and Tokyo are at a high level in the global urban information service level, and the information service capacity of some cities in Africa and South America is also increasing.

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109

Table 2.29 Five major foreign urban agglomerations Country

Overview of Urban Agglomerations

U.S.A

A urban agglomerations centered on New York along the Atlantic Ocean in the northeast of the United State, including Boston, New York, Philadelphia, Baltimore, Washington, etc.

USA, Canada

The Great Lakes urban agglomerations in North America centered on Chicago, including Chicago, Detroit, Cleveland, Toronto, Ottawa, Montreal, Quebec and other cities

Japan

Japan’s Pacific coast urban agglomerations centered on Tokyo, Nagoya and Osaka, including Tokyo, Yokohama, Shizuoka, Nagoya, Kyoto, Osaka, Kobe and other cities

Britain

London urban agglomerations includes cities like London, Liverpool, Manchester, Liz, Birmingham and Sheffield

France, Belgium, Netherlands, Germany

The urban agglomerations of northwest Europe centered on Paris, including Paris, Brussels, Antwerp, Amsterdam, Rotterdam, the Hague, Essen, Cologne, Dortmund, Bonn, Frankfurt, Stuttgart, etc.

Source Sorting according to the online material

Table 2.30 Comparison of European and American Railway laying mileage in the nineteenth century Unit (miles)

1840

1850

1860

1870

Britain

838

6,620

10,430

15,540

1880 17,930

1890 19,870

France

360

1,890

5,880

9,770

14,500

10,900

Germany

341

3,640

6,980

11,730

20,690

24,270

Russia

16

310

990

7,100

14,020

17,700

Other European countries

324

2,005

7,605

19,160

34,580

47,320

Total of European countries

1,879

14,465

31,885

63,300

101,720

120,060

U.S.A

2,820

9,020

30,630

53,400

93,670

156,080

Source Walter w. Rostow, World economic history: history and prospects

3. Urban basic public service sharing, knowledge, information and other soft sharing become the main trend from 1950 to 2050, and diversified sharing economic system and urban space sharing become the main trend. With the formation and development of economic globalization and global networking, the development of new generation information and communication technology has broken the limitation of traditional geographical space, and the scope of urban sharing has realized globalization. With the deepening of global urban connections, the scale of global urban sharing has been increasing. Urban sharing has changed from the traditional infrastructure such as railway, highway, shopping mall, etc. to the sharing of transportation tools, services and information based on Networking (as shown in Table 2.11). The networked sharing of urban space has become the trend of urban sharing.

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10 8 6 4 2 0

Fig. 2.52 Distribution of information service listed companies in major cities from 1850 to 1950. Source Sorting according to the data of Osiris’s global listed companies

Behind the new sharing field and mode is that the development of the new generation of information technology represented by the Internet in the 1950s enhanced the supply capacity of information, data, knowledge and other elements among global cities. In the information age, the restructuring of social and economic spatial structure mainly embodies the following main carriers and regional units: global production network and global economic system, big urban aggregation and their core cities, industrial clusters are sometimes regional clusters, industrial complexes, industrial zones, new industrial zones and regional innovation systems, smart cities, etc., as shown in Fig. 2.53. Smart city has become a new carrier of urban sharing, and smart infrastructure and services have become the key to realize the sustainable development of the city. With the construction of smart city, the interconnection and intelligent sharing of everything are the new characteristics of smart city. With the development of artificial intelligence, big data and Internet of things, ubiquitous connection and real-time perception can improve the level of urban intelligent service. According to the prediction of Pricewaterhouse Coopers and other institutions, by 2025, the market size of global sharing economy is expected to increase from 15 billion US dollars in 2015 to 335 billion US dollars. At the same time, because of the huge number of Internet users, the service-oriented transformation of consumption characteristics and the gradual improvement of infrastructure built by internet giants, Chinese cities have become the leading force in the global sharing economy. By the end of 2017, there were 60 Chinese enterprises among 224 Unicorn enterprises in the world, including 31 Chinese enterprises with typical sharing economic attributes, accounting for 51.7% of the total.

2.5 From the Dynamic Mechanism, the Human Development Momentum …

111

Fig. 2.53 Comparison of evaluation indexes between smart city and other cities. Source IESE Cities in Motion Index 2018

2.5 From the Dynamic Mechanism, the Human Development Momentum Bred by Cities Determines the Appearance and Change of the Urban World The development process of the city for 300 years has many factors that determine urban development, such as demand, technology, system, elements, knowledge, resources, production interaction and self-interest motivation, but the core is demand, technology and system. Demand is the self-interested survival instinct of human beings. Its goal is to pursue the maximization of utility. It is the most important driving force for human development and the most important driving force for the origin, development and even extinction of the city. Technology is the sum of the methods, skills and means that human beings use to transform nature in nature in order to meet their own needs and desires, to follow the laws of nature, and to accumulate knowledge, experience, skills and means in the process of long-term use and transformation of nature. But also need to practice in order to find their own technical advantages, which is the necessary condition to determine human development, but also the city’s origin, development and extinction of the necessary conditions. An institution is an interactive rule, which is a kind of special knowledge and technology created by human beings. It regulates human thoughts and behaviors, solves incentives and constraints, and matches and USES resources. It has the characteristics of increasing returns to scale and loss, including property right system, interactive system and distribution system. During the three hundred years of 1750–2050, the rotation of the three core elements of demand, technology and system changed the content of urban economic activities, the size of the city’s space and the size of the city’s population. First, the

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Fig. 2.54 Analysis framework of urban development factors and mechanisms. Source Author

system

demand

technology

interaction

human interaction will produce a system in which human intelligence determines the scale of interaction and institutional characteristics, and interacts with spatial states by affecting the use of space resources and the impact of flow costs. Second, the human system affects the scale and depth of human intelligence and interaction, affecting the state of interaction by affecting the use of space resources and the cost of interaction. Third, the scale and depth of human interaction affect human intelligence development and institutional evolution. Finally, after the three roles meet human needs, they will generate higher new demands, which will lead to a new round of three evolution (see Fig. 2.54). On the one hand, these elements and powers are cultivated in the cities, the cities foster the developmental kinetic energy of human beings, and the cities are the containers for gathering and cultivating these forces; on the other hand, the factors and powers cultivated by these cities drive the growth of the cities and Changes determine the appearance and changes of the city, and then determine the appearance and changes of the urban world through the city. 1. The pursuit of good life is the original driving force of the urban world in 300 years. The demand of 300 years in the city dominates the demand of the world for 300 years and determines the development of the world. Human needs are not only born with the pursuit of maximum utility, but also have an internal mechanism that is constantly improving. The unsatisfied demand of mankind is the inexhaustible source of power for human development. The pursuit of a better life by mankind is the source of the origin, development and even demise of the city. Everyone’s behavior is rational and self-interested. Pursuing self-interest is the fundamental driving force for all personal economic activities. Not only natural people, but also various organizations composed of natural persons pursue their own interests. Abandoning ownership, whether private, self-owned or public, as long as it recognizes and defines the relatively independent interests of economic entities, economic entities will have a strong incentive to pursue self-interest, and thus may penetrate all social fields and destroy all high-wall barriers. Break all kinds of inherent order and change the pattern of all interests. Second, human desires promote product

2.5 From the Dynamic Mechanism, the Human Development Momentum …

113

and urban functional diversity and change urban functions. Because of the desire for a better living environment, people are forced to move from rural to urban areas; because they want better living space, the city scale is gradually expanded; world cities such as New York, Chicago, London, Tokyo, Hong Kong control the global financial lifeline. Silicon Valley has a global lifeline. Human demand determines the content of production, consumption, exchange and activities of cities for 300 years. Demand generates in cities and influences the world through cities. Urban demand determines the development of the world. 2. The technological revolution is the core driving force for the formation of the urban world in 300 years. Urban demand brings about an increase in the level of technology, and each technological innovation will bring about a substantial increase in productivity, which will often lead to the birth and prosperity of new industries, which will determine the production content, consumption content, and exchange content of the city. Urban space size and population size. People also pursue profits for self-interested motives, gather in cities and conduct activities in cities, and gradually form small towns, cities, metropolitan areas, and urban agglomerations. The urban system has gradually become a single center, multi-center, and network. From the first industrial revolution from 1750 to 1850, the second industrial revolution from 1850 to 1950, the third industrial revolution from 1950 to 2008, and the fourth industrialization after 2008. The revolution (see Table 2.31), every technological innovation has a significant change in urban economic development, activity content, space scale, population size and urban pattern (Table 2.32). 3. Market system is the key driving force for the formation of the urban world Demand and technological development drive the birth and development of the system. As a rule of human interaction, the system affects economic development performance by influencing resource allocation and subjective dynamics. No matter under any system, cities determined by the system and superior to other settlements are constantly developing, but institutional differences determine the differences in urban development. In addition, the system has only quality and quantity. The quality of the system determines the increase or decrease of the scale return. The property rights system and resource allocation effect, which determines the human behavior, the rise and fall of the city and the development of the world, since 1750, the market economy as a more advanced economic system, its formation, development and maturity affect the global urbanization from rapid to accelerated transformation, where to implement the market economic system, and where the city rises.

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Table 2.31 Sharing fields and companies Sharing fields

Sharing network company (domestic)

Shared network company (foreign)

Traffic sharing

Didi express: didi hitchhiker, express and Uber: provide private car service “special vehicle” Easy to use car: high end special food service

LYFT: provide private car sharing service

Daily use: one-to-one, point-to-point commuting experience

Sidecar: more pure carpooling

Ha ha carpooling: carpooling service in the same area

Flightear: Airport idle car sharing

PP car rental: online car sharing platform Zipcar: membership system sharing idle cars EHI car rental: rent out the car to the tenants in need

Wheelz: P2P car rental business for College Students Getaround: P2P car rental flat Netjets: Rental of idle private aircraft Prop: idle yacht rental

Housing sharing

Piggy rent: Chinese version of airbnb

Airbnb: short stay reservation

Ant short rent: home apartment booking website

Dogvacay: the dog version of airbnb

Tujia.com: domestic travel and holiday apartment booking website

Easynest: sharing the empty bed in the hotel Divvy: looking for roommates and sharing rooms

Diet sharing

Clothing sharing

Other sharing

Love Chef: Chinese version

Eatwise: food sharing of ancestors

Love feast and meet: domestic eat and meet friends

Plenry: eat with friends

Good chef: provide private kitchen service

Feastly: home made meal sharing

Private chef: docking with private chef

Spoonrct: the most convenient ordering service

Magic wardrobe: Sharing in the field of clothing

Rentthe runway: selected brands and fashionable dresses

Mercer: luxury package rental service (closed in 2013)

Poshmark: second hand clothing trading platform

Lazy housekeeping: high-end domestic service talents

Taskrabbit: labor employment platform (continued)

2.5 From the Dynamic Mechanism, the Human Development Momentum …

115

Table 2.31 (continued) Sharing fields

Sharing network company (domestic)

Shared network company (foreign)

Youth dish King: sell and change semi-finished clean dishes

Skillshare: sharing skills

Auntie help: quickly find a satisfied hourly worker

Handybook: integrating Home Economics

Beauty Taoist: door-to-door beauty service

Classpass: integrated gym

Carefree parking network: help find parking space Table 2.32 Comparison of previous technological revolutions Content

First industrial revolution

Second technological revolution

The third technological revolution

Time

1850s–1950s

1950s–early twentieth century

1950s–early twenty-first century

Major achievements

Extensive use of Jenny machines and steam engines

Electric power, internal combustion engine, chemical technology development, steel industry progress

Atomic energy, electronic computer, aerospace engineering, biotechnology

Country of occurrence

Britain expands to Europe and the United States

The United States, United States France, Germany and many countries simultaneously

New transportation Train, steamboat

Aircraft, car

High-speed rail, subway

New energy

Coal

Electricity, oil

Solar energy

New industrial sector

Cotton textile, machine building, transportation

Power industry, New service industry, electrical product intelligent manufacturing manufacturing, petroleum industry, automotive industry

Production organization

Factory system

Monopoly organization

Global industry chain (continued)

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Table 2.32 (continued) Content

First industrial revolution

Second technological revolution

The third technological revolution

Relationship between scientific research and technological revolution

Unbound

True combination

Fully integrated

Era

Steampunk era

Electrical age

Information intelligence era

Traffic

Carriage era

Steam iron era

High speed era

Communication

Letter mailing era

Telegraph era

Information interconnection era

Product

Article processing era

Age of itemization Service era, knowledge age

Main invention mark

Steam engine, shuttle, Jane spinning machine, modified steam engine, steam ship, boiler, railway, miner’s lamp, steam train, telegraph, lithography, vaccinia, urea, morphine

Telephone, car, generator, electric light, aircraft, electric power, petroleum, steel making, steam sterilization, disinfection, penicillin, antibiotics, amino acids, synthetic sulfonamides

Computers, televisions, automobiles, high-speed rail, subway, air-conditioning technology, space shuttles, civil aircraft, satellite, Internet, mobile phones, Internet of Things, artificial intelligence (AI), biological genes, cloning technology, robots

Data source The author sorted out

2.5.1 1750–1850, Demand, Technology and Institutional Rotation Effects Since 1750, on the one hand, the ever-evolving demand of mankind needs to be provided by the city. On the other hand, the population pursuing a better life is increasingly rushing to the city. It is the continuous expansion and upgrading of human needs that has driven the expansion of the city scale. The evolution of the structure and the evolution of its connotations. 1. Urban demand is reflected in food and processing consumer goods. From 1750 to 1850, in order to meet the most basic production and living needs, the urban demand is mainly reflected in food and consumer goods. Under the selfinterested demand, in order to obtain more income and production materials, the invention and manufacture of production tools began to be carried out, which led to the improvement of labor production efficiency, greatly improved the income level of the industrial part of labor, and led to the birth of new technologies. In addition, the self-interested demand has prompted a large amount of labor to be transferred

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from the agricultural production sector to the industrial production sector, and the labor force flows from the rural areas to the cities, driving the urbanization process. 2. The steam engine technology revolution, machine production replaced manual manufacturing for the first time, and the city’s content, scale and shape changed. Due to changes in people’s needs, technological innovations have been promoted, and the way in which urban economic development has changed, and machine production has replaced manual manufacturing. After 1750, the European industrial countries represented by the United Kingdom realized the transformation from manual production to industrial production. The machine substitution manual first liberated human physical strength. The factory production of alternative workshops or manual workshops promoted the accumulation of production activities, the size of the city, Significant changes have taken place in function and morphology. The invention and improvement of mechanical production such as steam engines and textile machines, manual labor was completely replaced by mechanical production, and the production efficiency was greatly improved. Subsequently, many industrial sectors such as coal mining and metallurgy produced more and more machines, and the factory began to form. significantly promoted urban economic growth. Driven by self-interest, people gather in places with high labor productivity such as factories, and the population scale begins to expand, and the city scale begins to expand around it. Secondly, the content of the first technological revolution to promote urban activities has changed, and the endowments of energy and natural resources have determined the city, and the functional central city has been formed. The machine production brought about by the first industrial revolution made energy development possible. In order to pursue greater profits, the capitalists will set the factory in the place of origin of the means of production, and the resource-based city will be formed, so that the endowment of energy and natural resources will be decided. The content of city activities. For example, in the UK, before the Industrial Revolution, most of the industry was concentrated in the southeastern region centered on London, with East England, the southeastern part of England and West Yorkshire being the gathering place for the wool textile industry; the metallurgical industry and the metalworking industry were mainly concentrated in Birmingham., South Wales, Sheffield and Northeast Wales. In the process of industrial revolution, with the rise of the cotton textile industry, a series of small and medium-sized towns dominated by the industry have also risen. This has caused the British industrial center to move westward and northward. The economy is backward and sparsely populated. The northwest region has become the center of cotton textile industry and coal iron industry. Cities such as Manchester, salford, bolton, berry, Preston and oldham all rose with the development of cotton textile industry. The areas of slope, Worcester, south wales and Monmouth with relatively rich coal and iron resources also rose rapidly. Thirdly, the first technological revolution has led to changes in the spatial scale and population size of urban activities. The improvement of transportation technology has determined the changes in the spatial and scale of urban activities. Large cities have begun to take shape, and the improvement of medical technology has provided

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guarantees for the expansion of population. In addition, in order to maximize profits, it is faster and more convenient to transport goods and raw materials, and people want to design vehicles to be modified. For example, cities such as steamboats, steam locomotives, trains and other transportation vehicles have caused the human transportation industry to enter a steam-driven era, and the space and scale of urban activities have changed. Because in the early stage of underdeveloped transportation technology, cities with basic public transportation such as walking and horse-drawn carriages can meet the corresponding scale of urban space. For example, urban residents are affected by transportation methods such as walking and carriages, resulting in residents living only. In the narrow urban center, the city size is at a low level. However, the birth of steam trains and the rapid development of the transportation industry will further strengthen the economic ties between cities and cities, cities and urban and rural areas, so that the urban economy will develop rapidly and accelerate the process of urbanization. The convenience of waterway and land transportation has reduced freight times and costs several times, strengthened economic ties between cities and between urban and rural areas, and enabled cities and towns in the status of transportation hubs to grow rapidly, which has greatly accelerated the city. Process. While the development of urban public transportation has brought convenience to people, the improvement of public transportation has facilitated the travel of residents, and the urban population and scale have been continuously expanded, which has also accelerated the process of urbanization. The exchanges between cities began to increase, which led to the city’s activity space not being confined to the interior of the city. The urban activity space spread from within the city to the city, the city scale expanded, and the big city began to form. In addition, after the industrial revolution began, with the rapid development of medicine and medical and health care, the improvement of disease prevention level brought about the expansion of the city scale. During the first industrial revolution, the invention and application of vaccinia, morphine, ipecaine, strychnine, quinine, caffeine, and chloroform also ensured a steady increase in population size and promoted the expansion of urban scale. Finally, the links between cities have strengthened and the urban system has slowly formed. The technological innovation initiated by the Atlantic Ocean in the Atlantic broke through various obstacles, broke national borders and continents, and expanded across the Atlantic to Europe, the United States and Asia. It strengthened the international division of labor, promoted international population and capital flows, and promoted the United States. The revolution, reforms of Russia, Germany and Italy, the burgeoning urbanization process in Europe and the United States began, which promoted the rise of global cities and accelerated the process of global urbanization. By the middle of the nineteenth century, the UK had become the “world factory”

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with its advanced production technology, strong economic strength, developed transportation and possessing a vast colony. This means that the world market with the UK as the center is formed and around the world. Further strengthening of economic ties. 3. The market economy system has been preliminary established in a few countries and tried in some fields. Driven by demand and technology, the market economy system began to be established in a few countries and made initial attempts in some areas. Specifically, before the industrial revolution, the global economic structure was mainly based on agriculture, which provided limited production and exchange, which limited the spread of the market economy. Under the social structure at that time, people were seriously lacking freedom. Most people are still slaves. Religious thoughts bind people’s freedom of thought and freedom of movement and limit the development of the market economy. In addition, before the industrial revolution, the world was still a feudal society. The mutual interests of various countries and even the colonies seriously hindered the division of labor and the expansion of the market. Even in the early days of the industrial revolution, the market economy was not complete. Both the United Kingdom and the United States have slaves. With the advancement of the first industrial revolution, a large number of legal systems conducive to the protection of property rights and resource allocation were gradually established, and the system that was not conducive to market economy and trade development was abolished. For example, during the Industrial Revolution, the British abolished the apprenticeship regulations, the residence law, the highest wage law, etc., and in the latter part of the industrial revolution, abolished all restrictions on exports, and later abolished the grain regulations and navigation regulations that impeded trade, and abolished them. The bubble regulations that hinder the company’s development have promoted the development of trade. In addition, the government also handed over economic activities to the market, abolished the monopoly power of the concessionaire, canceled or lowered restrictions on import and export products, removed restrictions on prices and interest rates, and even carried out banking system reforms to ensure that the market was in a stable monetary environment. run. More importantly, the industrial revolution has promoted the formation of a modern corporate system. After the merger of the modern corporate system and the factory system, it is more convenient and easy to popularize, which is conducive to the comprehensive development of competition in the market economy. Because it disperses risks, it encourages more people. Investing, making full use of all available economic resources, the market economy has found the most effective form of production and operation organization. A series of laws and regulations have been promulgated to protect the development of modern corporate systems, such as contract law, exchange law, securities law, company law and factory law, and prohibition of stock speculation. These property rights systems and resource allocation systems have laid the foundation for the stable development of cities. In general, during the first industrial revolution, the government adopted a laissezfaire policy and implemented a freely competitive market economy. Market entry

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80

Estevadeordal, Frantz, and Taylor (2003) (upper bound) (%) Estevadeordal, Frantz, and Taylor (2003) (lower bound) (%) 60

Klasing and Milionis (2014) (based on Maddison) (%) Klasing and Milionis (2014) (%) Penn World Tables (9.0) (%)

40

World Development Indicators (Rate)

20

0 1700

1750

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1850

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Fig. 2.55 Globalization process. Notes 1500–1820 come from Estevadeordal, Frantz, and Taylor (2003) (upper bound) (%); 1870–1940 come from Klasing and Milionis (2014) (%); 1950–2014 come from Penn World Tables (9.0) (%). Data source The author sorts according to our world in data database data

restrictions were gradually eliminated, and market relations developed. In the legal system, private law was recognized for private property recognition. Protection emphasizes equality before the law and adjusts the property relationship between equal subjects through human rights, private law, and civil and commercial law. Through the abolition of slavery, human rights have been guaranteed and the development of urban population has been promoted. Through the protection of modern corporate systems, property rights systems and market economic systems, the efficiency of economic operations has been improved, and the expansion of urban industries and space has been promoted. As a result, all regions where market economic systems and property rights systems are implemented, such as the United Kingdom and France, have relatively rapid economic, social, and demographic development. Countries where market economic systems and property rights systems are not operating, such as Asia, which is still in agricultural society, In Africa and other countries, their urban development is relatively slow. Figure 2.55 shows the globalization process from 1500 to 1850. It can be seen from Fig. 2.55 that during the first industrial revolution, the globalization process has accelerated significantly.

2.5.2 1850–1950, Demand, Technology and Institutional Rotation Effects 1. Urban demand is transformed into Heavy chemical products. From 1850 to 1950, after people met the basic needs of production and life, people’s demand structure gradually turned to developmental and enjoyable upgrades, prompting residents to have huge demand for heavy chemical products, leading to major changes in urban demand. The demand for the city has turned to the large-scale

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consumption of durable consumer goods of 10,000 yuan and 100,000 yuan, such as automobiles and housing, which has driven the changes in the world’s demand. 2. The power technology revolution, the city has become the pillar of the world. Driven by urban demand, in order to meet the needs of the city and the world, the level of technology has once again been improved, and the city’s activities, urban space, urban population size and urban system have undergone new changes. First of all, the content of urban activities breaks through the time limit. The application of electricity enables the city to carry out economic activities at any time, and the heavy industry has become the dominant city. With the application of electric energy, people’s lives are no longer limited by time. People’s production efficiency and activity space have been significantly improved. With the wide application of electricity, people’s exchanges have entered the telegraph era. In the era of the first industrial revolution, this greatly reduced the limitations of time and space, and closely linked individuals. More importantly, power technology has brought about the improvement of steelmaking and ironmaking technologies, which has prompted the city to enter industrialization and enter the era of product integration, which has led to a qualitative improvement in industrial production efficiency, significantly promoted urban economic growth, and the city has become the pillar of the world. Secondly, the city’s spatial scale and population size have been further expanded, and urban agglomerations and urban belts have been formed. The invention of sterilization, disinfection, penicillin, antibiotics, synthetic sulfonamides and other drugs brought about by the second industrial revolution provided a solid foundation for the expansion of the urban population. Power brought smelting technology upgrades, steelmaking technology upgrades, and improved steel quality and output. The city’s architecture began to be cast from steel, directly changing the face of the city. The smelting industry and the power industry have been upgraded, which has promoted the innovation of the transportation industry. The railroad tracks are completely made of steel, which leads to the increase of the power and speed of the locomotives. Compared with the era of horse-drawn carriages, the automobile-iron era has further expanded the scale of the city, especially the upgrading of sailing, ships, automobiles, railways and airplanes. The space of port cities has been greatly enhanced, such as Seoul, Tokyo, Osaka, Hong Kong and Macau. New cities such as New York, Washington, London, and Los Angeles are all located in coastal areas, and the London city clusters formed in ports and coastal areas, the northwestern European urban agglomerations, the Japanese Pacific coast urban agglomerations, the Great Walls of North America, and the United States. In the northeastern Atlantic coast, urban agglomerations, urban agglomerations and urban belts are formed. With the use of aircraft, this offshore urban agglomeration has broken the pattern of global cities. Inland cities have also begun to participate in the global urban system, and global urban networks have begun to take shape. The figure shows the flight distance of non-commercial aircraft. It can be seen from Fig. 2.56 that the total flight distance has increased rapidly since the invention of the aircraft. The invention of vehicles, ships, airplanes and other means of transportation completely broke the inherent spatial form of the city and gradually expanded the size of the city.

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non-commercial-flight-distance 20000 18000 16000 14000 12000 10000 8000 6000 4000 2000 0 1850

1870

1890

1910

1930

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Fig. 2.56 Non-commercial aircraft flight distance. Data source The author sorts according to our world in data database data

Finally, the global single-center city system is determined. The second industrial revolution prompted developed countries to take the lead in industrialized society. The production, consumption, division of labor and cooperation of global factors have led to the formation of a global urban pattern, and the single-center urban system has been determined. In order to promote domestic commodity exchanges, European and American countries have engaged in transportation construction on a large scale. In order to expand overseas markets and commit themselves to the development of ocean transportation networks, a global transportation network has gradually formed, and a global urban system has begun to take shape. Especially in the latter part of the Second Industrial Revolution, the First World War and the Second World War accelerated the process of the weak countries becoming colonies and affiliated countries. The European and American powers brought the advanced industrial technology of Europe and America to these areas when they colonized Asia, Africa and Latin America. These countries have slowly embarked on the road of industrialization, but the cities of Asia, Africa and Latin America have become the annexes of European and American cities. The eastern cities are completely subordinate to Western cities, and the pattern of single-center cities in global cities is determined. In addition, in the second industrial revolution, emerging capital and energy-intensive industries adopted a giant factory and a large vertically integrated monopoly organization as their main industrial organization form, which also contributed to the development of the city into the metropolitan era. 3. The market economy system has developed initially in Western countries and deepened in some areas. The deepening of demand and technological revolution has led to the initial development of the market economy system, the development in the Western countries, and the deepening in some areas. This is the mature stage of the capitalist market economy.

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In economic terms, the government intervenes more in the economy, implements social welfare policies, gradually establishes a social security system, emphasizes the concept of “socialization”, safeguards public welfare, and implements “economic democracy”, which stipulates that citizens enjoy the right to education and work. Rights and safeguard the legitimate rights and interests of workers. In the legal system, in addition to traditional public and private law, economic legislation and social law have been produced and strengthened, and the state’s intervention in the economy has been strengthened through economic laws to compensate for the shortcomings of the market mechanism. For example, capitalist countries have separately promulgated anti-monopoly laws, which promote the vitality of the market economy and the creativity of enterprises. In particular, the Industrial Revitalization Law, the Agricultural Adjustment Law, the Emergency Banking Law, and the Emergency Relief Law have implemented comprehensive intervention in economic life. In terms of the protection of private property rights, Western countries have also tightened restrictions. The state may levy or expropriate private property for the needs of the public interest, and even promote “nationalization”, which stipulates that the state can directly own enterprises and undertakings, thus making private property Absolute protection is transformed into relative protection, using state power to develop emerging industries, achieving production and capital concentration, such as: setting up state-owned enterprises, state-owned holdings, using economic instruments and economic policies, and protecting and supporting the development of emerging industries and special industries. These laws and behaviors are a key system for the dynamics of the Western market economy, self-selection and self-elimination, and incentives for corporate innovation. It enhances the vitality and internal ability of the sustainable development of the capitalist market economy and deepens the market economy. Cities in Western countries have thus developed rapidly and dominated the global urban landscape. Figures 2.57 and 2.58 show the urbanization rate of the major countries in the world in 1850 and 1900 respectively. It can be seen from the high and low urbanization rate of each country that the urbanization process of the country implementing the market economy is significantly higher than that of the eastern agricultural society.

2.5.3 1950–2050, Demand, Technology and Institutional Rotation Effects 1. Urban demand is reflected in service products, knowledge products and spiritual life products. As the demand for heavy-duty products such as development and enjoyment is met, science and technology and institutions will bring new demands and promote the development of demand, technology and systems. Therefore, between 1950 and 2050, people want to have a better culture, entertainment, and service environment, diversify the urban industry, diversify the city’s functions, and further expand the

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Fig. 2.57 Urbanization rate of major countries in 1850. Data source The author sorts according to our world in data database data

Fig. 2.58 Urbanization rate of major countries in 1990. Data source The author sorts according to our world in data database data

knowledge products after human beings have basically met the material and labor services in the future. Demand, leading to a new round of technological and institutional changes, will certainly have a new round of impact on the size, structure and content of the city. 2. Information and intelligent technology revolution, the city is the world. First of all, the content of urban activities has changed again because of the information revolution. The urban economic mode has changed from unipolar to multipolar,

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and multi-polar industries such as service industry and smart manufacturing have led the development of cities. From the content of human activities, the content of previous human activities is mainly work, and there are few other economic activities. The development of the information technology and intelligent technology revolution has promoted the content of human activities, including telephone, text messaging, travel, video, film, games, and finance. The urban economic growth mode also has unipolar heavy chemical industry manufacturing, and turns to multi-level high-end manufacturing, service industry, artificial intelligence manufacturing, etc., such as semiconductor, Internet, computer, mobile phone, Internet of things, air conditioning, artificial intelligence and other new technology industries. It will drive urban economic development, such as Silicon Valley in the United States, Shenzhen in China, and Bangalore in India. Science and technology are driving economic development and changing the city’s connotation. Second, the city’s function has also changed, and the smart city was born. Digital information and urban integration change the city’s functions. The current city integrates digital media and entertainment, education and training, financial services, manufacturing and logistics, intelligent transportation systems, health and biological sciences, artificial intelligence and virtual reality. For example, Singapore, London, New York, San Francisco, Chicago, Seoul, Berlin, Tokyo, Barcelona, Melbourne, Dubai, Putlan, Hangzhou and other cities have the world’s leading social, public services and urban management. Intelligent manufacturing leads to the intelligent production of human beings, and the easier work of human beings will increase the employment pressure of cities, but it also creates conditions for new employment space. In addition, the production, exchange and consumption of goods shift to the production, consumption and exchange of knowledge information, and the development of information technology supports the global division of labor and proliferation of industries. Finally, the technological innovation brought about by the third information revolution has intensified population concentration and human activities, changed the scale of urban space and population size, formed a multi-center networked urban system, and the city is the world. The acceleration of global urbanization and the development of information technology and other technological innovations have an important relationship. The development of information technology has significantly improved the industrial structure of the city, and has spawned high-tech industries such as e-commerce, software services, and electronic entertainment, which has greatly attracted the gathering of talents and promoted the city. Process and urban economic development. Since the third information revolution, the world’s major scientific and technological inventions have covered human life, society, medical care, transportation and other aspects, and have had a greater impact on urban functions, urban space, and urban patterns. From the perspective of information traffic, the development of information infrastructure such as subways, high-speed rails, airplanes, and satellites has greatly expanded the city’s spatial content and narrowed the time and space of the city, resulting in the reshaping of urban space, and the metropolitan area has become the direction of urban development.. In addition, biomedicine has improved the health and longevity of urbanization, created many

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employment opportunities, and promoted the development of health industry. The maturity and application of vitamins, penicillin, infectious disease prevention technology, and genetic technology have greatly improved the medical level and human life. Urban space form. 3. The market economy system is expanding globally and innovating in more fields. After the third industrial revolution, the economic systems of major countries in the world mainly included the planned economy and the market economy. However, due to the planned economy, the problem of incentives and rational allocation was not well solved, the competition of most products was weakened, and the economic entities could not be rationally regulated. The economic interests between them, and more importantly, these problems lead to a non-sustainable planned economy, which is not conducive to the long-term development of the country. Under these conditions, most countries in the world have gradually turned to the market economy system. However, some countries have imperfect market economic systems, such as property rights system and resource allocation, which will also lead to the slow development of some market economy systems. And unbalanced. When entering the late stage of the third industrial revolution, after 1990, the market economy entered the stage of globalization. In economic terms, the world’s major powers have allocated economic resources globally, and there have been phenomena such as market integration, financial internationalization, production transnationalization, and economic networking. In law, the global rules dominated by Western capitalist countries, their capital and technology. The global expansion has spread to all parts of the world, and as the status of developing countries rises, its global rules are challenged. The influence of the globalization of market economic system on the market economy of developing countries mainly involves the financial law, securities law, bill law and guarantee law for adjusting financial activities; investment law, tax law, company law and bankruptcy law that regulate the activities of market entities; Antiunfair competition law, anti-monopoly law, consumer rights protection law, product liability law; intellectual property law, technology transfer law, computer software registration law, patent law and copyright law for protecting intangible assets. In particular, WTO rules, the World Intellectual Property Rules, the rules of the World Labor Organization, and the rules of the United Nations environmental organizations have had an inestimable impact on the legal system of developing countries participating in the process of economic globalization. In this process, Western capitalist developed countries make full use of their legal rules and procedures and talent advantages to protect their domestic market interests. With the proliferation of market economic systems, which have affected every city, globalization has become an important feature of modern urban systems. Driven by the new technological revolution, especially the computer network revolution, the international division of labor has become increasingly specialized within the industry. The tremendous development of multinational corporations and multinational banks has further promoted the internationalization of capital. The internationalization of capital has directly led to the integration of the global economy. And ultimately, the city as the main body of various factors determines the future of the

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Fig. 2.59 Relationship between economic freedom and urbanization rate in 1995. Data source The author sorts according to our world in data database data

world. It can be seen from Fig. 2.55 that the level of globalization has increased from 20 in 1950 to 62 in 2008. The globalization process has increased rapidly and cities have participated in the global urban system. With the promotion of transportation technologies such as shipping and aviation, information technology, and other information technologies, the competition, cooperation, division of labor, and trade between cities have reached an unprecedented height. The economic activities between cities are no longer limited to distances. The advantages and disadvantages of the location and the multi-center networked urban system were formed. Figure 2.59 and Fig. 2.60 show the scatter plot between economic freedom and urbanization rate in 1995 and 2017, respectively. Overall, the higher the market economy, the higher the level of urbanization.

2.5.4 The Combination of Urban Development and Rotation 1. Technological innovation promotes institutional change and urban development. The industrial revolution has brought tremendous technological progress, promoted a tremendous leap in the development of productive forces, and caused profound changes in the social system. Before the 1950s, the European industrial countries represented by the United Kingdom and France realized the transformation from manual production to industrial production. The machine substitution manual first

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Fig. 2.60 Relationship between economic freedom and urbanization rate in 2017. Data source The author sorts according to our world in data database data

liberated human physical strength. The industrial revolution was a large-scale production technology revolution that replaced large-scale machine production with manual labor and promoted a huge leap in productivity. The invention and use of steam engines has caused a series of dramatic changes in industrial production. At the same time, the leap in industrial production has led to the rise of agricultural technology and the rise and development of capitalist agriculture. The industrial revolution has greatly promoted the development of science, technology and culture. The industrial revolution has also caused profound changes in the entire society, creating two opposing classes, the industrial bourgeoisie and the proletariat. The birth of these two classes is directly worldwide. The social and institutional systems have promoted the birth of capitalism and socialism. Second, technological innovation has also changed the urban landscape and influenced the formation of modern urban systems. With the deepening and expansion of the industrial revolution, capitalist production methods have been established in advanced countries in Europe and America. Most countries in Asia, Africa and Latin America lost their resilience under the gunships of the great powers, became the colonial and semi-colonial capital of the European and American capitalist powers, became the international market of capitalism, the supply of raw materials and labor, and became the place for their investment. The paradise has become a subsidiary of the capitalist economy. The expansion of the capitalist powers has had a dual impact on the Asian, African, and Latin American countries. On the one hand, the bloody aggression and cruel colonial plunder of the great powers have caused local people to suffer serious disasters and cause long-term poverty in these areas; In the economic aggression, the powers inevitably bring advanced industrial production technology, scientific knowledge and advanced ideas into these countries

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and regions. Thus, from the 1750s to the present, the pattern dominated by cities in western developed countries and dominated cities in western developing countries. 2. System reform promotes technological innovation and urban development. Institutional reform determines the basic principles of property rights and marketization, and provides the basis for urban development and technological innovation. Among them, the establishment of the factory system, the factory production of alternative workshops or manual workshops promoted the accumulation of production activities, expanded the size of the city, and changed the function and form of the city. Market-oriented institutional reform is the basic driving force and condition for urban development. The reform of the system is the foundation of urban development. Whether urban development or rural development, institutional reform has a decisive influence. The market-oriented reform mainly includes two aspects. First, the recognition and clarification of the relatively independent responsibility of the actors is the prerequisite for self-survival and development. Regardless of ownership, as long as they recognize and define their relatively independent and symmetrical rights and interests, economic agents can generate strong motivation to pursue selfinterest. Recognize and clarify the relatively independent responsibility of economic entities, stimulate the enthusiasm of all entities in the whole society to pursue their own interests, and make all undertakings full of vitality and vitality. At the same time, the lack of self-interest in restraint also brings problems from micro to macro, from economy to society to the environment. Secondly, it determines the decisive role of the market in resource allocation, so that all resources are effectively allocated by the invisible hand of the market, and the role of the market is maximized. Further promote technological innovation and urban development under institutional conditions and self-interested behavior. Figure 2.61 shows the number of patent applications in the United Kingdom, Ireland and New Zealand during the industrial revolution, and Fig. 2.62 shows the number of patents granted in the United States. It can be seen from Figs. 2.61 and 2.62 that the number of patent applications in the United Kingdom and the United States rose rapidly during the industrial revolution, far ahead of the rest. 3. Urban development promotes technological innovation and institutional change. In order to develop, cities must attract talents and industrial division of labor, which leads to industrial globalization and resource globalization. The division of labor can improve efficiency and bring more benefits to the city. Therefore, urban entities continue to expand the market and the scope of division of labor through technological innovation and institutional reforms, leading to a new wave of integration and division of labor around the world. The information technology revolution and the rising labor costs in developed countries have promoted the global industrial division of labor to upgrade the global industrial chain. Multinational companies are considering the maximization of profits based on the development of high-tech industries with technological innovation, and based on the value chain on a global scale. Re-layout the industrial chain to transfer technology and low value-added processing and production links to low-cost countries and regions within the country.

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Number of patents awarded (patents awarded per year) 700 600 500

England

Ireland

Scotland

400 300 200 100 0 1700

1720

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Fig. 2.61 The amount of patents granted through the industrial revolution. Data source The author sorts according to our world in data database data

Fig. 2.62 US patent grants. Data source The author sorts according to our world in data database data

Economic globalization and informatization have made the world cities increasingly connected, becoming interdependent and chained, and expanding the division of labor and cooperation between production, exchange, circulation, consumption, services, technology and product development in countries around the world. The global economic system and economic activities bring global cities closer together. As a result, the city has become a world city, a regional central city, a national central city, a specialized production and service center city, and the city has to carry out technological innovation and revolution in order to more effectively control other cities to

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connect with other cities, such as the third. The development of information brought about by the sub-industrial revolution. From the globalization of the great voyage era 1.0 to the UK-led globalization 2.0, to the US-led globalization 3.0, the leaders of globalization rules can get more global dividends, but maintaining these rules also requires corresponding strength. The rules of globalization itself are mirror images of the relative strengths of countries in the world, and the most important of them is economic strength, followed by military strength. Therefore, this requires cities to carry out institutional reforms and adapt to new international forms. In addition, globalization, as an objective requirement of the development of human beings and social productive forces, reflects the inevitable law of the historical development of human society. It is closely related to the emergence and development of capitalism. Its basic driving force comes from the development and expansion of capital. Drive economic change and then impact the entire society. Just as the double shock wave caused by globalization to the world, the impact of globalization on the economic and social development of cities must also be twofold. The various social behaviors, including politics, culture, economy and even government behavior, will be deeply subject to capital. With the vitality of the market and the deepening of competition in the world market, the reform of production and social mechanisms will be carried out under the market regulations. The trend of world development may be tortuous but will not change, because the above mechanism is promoted by the world. Even if there is a hot war and a cold war, it will not be a long-term trend, because war cannot change these mechanisms.

Chapter 3

Experience & Methods of Global Municipal Finance

3.1 Global Trends in Municipal Finance 3.1.1 Introduction Cities are the engine of the global economy. Both the total urban population and GDP arising from cities is rapidly increasing. Municipal finance is a key condition determining the ability of municipalities to successfully manage growth, and will thus be central to the competitiveness of cities. However, there are major differences between cities around the world in terms of their budgets and the challenges and opportunities they face. This chapter seeks to identify some of the major trends by region in terms of expenditures, revenues, challenges and positive trends. It uses data on expenditures and revenues from UN-Habitat’s Global Municipal Database (GMD). To provide and overview of challenges and positive trends, it draws upon inputs from a set of municipal finance experts for each of four regions: Africa, Asia, Europe and Latin America.

3.1.2 Why Municipal Finance? Between 2014 and 2016, the world’s 300 largest metro areas accounted for 36% of global employment but 67% of global GDP growth (Bouchet et al. 2018). More than 80% of global GDP is generated in cities (World Bank, 2019b). In developing regions, cities are the primary location for growth in the manufacturing and services sectors which drive economic structural transformation (UNECA 2017). The economic competitiveness and attractiveness of cities is shaping patterns of global private investment, growth and innovation (Peterson et al. 2018). Already more than

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half the global population lives in cities, and by 2050, the world’s urban populations are projected to increase by 2.5 billion people, with nearly 90% of that growth occurring in Asia and Africa (UNDESA 2018). With their increasing share of population and economic activity, cities globally have increasing financial needs. The global cost to implement the Sustainable Development Goals (SDGs) has been estimated at US$ 5-7 trillion, with a gap of US$ 2.5 trillion in developing countries alone (UNCTAD 2014). Cities will be instrumental in meeting goals on poverty, employment, inequality, health, education, gender, infrastructure and public services, consumption and production, climate change and the environment. Cities are also the focus of Goal 11, to “Make cities and human settlements inclusive, safe, resilient and sustainable” (United Nations 2015a). According to a survey of 101 local governments in 61 countries about localizing the SDGs, finance was the most commonly cited top priority (UCLG 2018). The cost of infrastructure to maintain GDP growth globally is estimated at over 3 trillion a year to 2030 (Dobbs et al. 2013). A commonly cited figure that 1/3 of total investments should be in cities (UCLG 2007) implies that US$ 1 trillion is needed annually for global urban infrastructure investments. Municipalities are the government entities that most closely manage cities, and are well situated to respond to the specific needs of their resident populations and businesses in terms of public services, education, an enabling business environment and governance impacting the local quality of life. Municipal finance, defined as the “revenue and expenditure of local government in urban areas” (Cheeseman and Burbidge 2016, p. 5), is central to the ability of municipalities to meet those needs. However, a lack of resources, capacity and authority often constrains the ability of municipalities to meet the needs of their cities. This is especially the case in lower income countries which often have the fastest urbanizing populations and the highest urban investment needs. Furthermore, only 4% of the 500 largest cities in developing countries have been able to access international financial markets, and only 20% can access national markets, significantly constraining their ability to make growth-driving investments (UCLG 2016). Therefore, improving the state of municipal finance will be critical for development, and is a global priority according to the Addis Abba Action Agenda (United Nations 2015b).

3.1.3 Municipal Expenditures by Region UN-Habitat’s Global Municipal Database (GMD), which includes a sample of 94 cities of over 100,000 population in 49 countries, indicates average per capita expenditures at the municipal level to be US$ 1,610 globally, with municipal investment averaging US$ 277 per capita. There are differences by region. North American municipalities have the highest expenditures per capita at US$ 3,382, with an average US$ 527 per capita in investment expenditures. This is followed by municipalities in East Asia and the Pacific, with US$ 2,521 in total expenditures per capita and US$ 457 per capita in investments. Municipalities in Sub-Saharan Africa and South Asia

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3,500 3,000

US$

2,500 2,000 1,500 1,000 500 East Asia and Pacific

Europe and Central Asia

Latin America and the Caribbean

Total expenditures per capita

Middle East North America and North Africa

South Asia

Sub-Saharan Africa

Investment expenditures per capita

Fig. 3.1 Municipal expenditures per capita (total and investment), regional averages ca. 2014. Data GMD, calculations by authors

have the lowest total per capita expenditures at US$ 138 and US$ 80, respectively (Fig. 3.1).

3.1.4 Municipal Revenues by Region

3,000

1

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0.8

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0.6

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0 Middle East East Asia and Europe and Latin Pacific Central Asia America and and North Africa the Caribbean Own source revenues per capita

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Percent own source revenues

Own source revenues (US$)

According to data on revenues of municipalities from the GMD, per capita revenues are highest in North America and East Asia and the Pacific, at US$ 2,755 and US$ 1,649, respectively. They are the lowest in Sub-Saharan Africa and South Asia, at US$ 77 and US$ 48, respectively. As a percentage of total municipal budgets, own source revenues follow a similar pattern, with the highest percentages in North America and East Asia and the Pacific (86% and 72%, respectively), and the lowest in Sub-Saharan Africa and South Asia (39% and 49%, respectively; Fig. 3.2).

Percent own source revenues (% of municipal budget)

Fig. 3.2 Municipal revenues per capita and as a percent of municipal budgets, regional averages ca. 2014. Data GMD, calculations by authors

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3.1.5 Regional Challenges and Opportunities in Municipal Finance Trends, challenges and opportunities in municipal finance differ by country and by region. Based on the data above, municipal governments in North America and East Asia have the greatest access to resources and capacity for revenue generation, while municipal governments in Africa, the Middle East and Western Asia, and South Asia have fewer resources. Municipal governments also have differing mandates and sets of responsibilities. In the Middle East, municipal budgets tend to be low, but local investments and services are more often funded from the national level. For example, in Saudi Arabia, national ministries provide education, social services and housing, leaving municipalities with more limited roles, including issuing building permits, street lighting, solid waste management and park maintenance (UN-Habitat 2012). In Jordan, the 2007 Municipal Act shifted responsibilities toward municipal councils, but in practice, the central government has privatized many of the competences that would otherwise be responsibilities of the municipalities, and actual responsibilities are limited to solid waste management, street lighting, storm water drainage, public markets and the like (OECD and UCLG 2016). Data from UN-Habitat’s Global Municipal Database confirms that the mandated budget responsibilities of municipalities in the Middle East is much lower than other regions. Municipalities in Africa, on the other hand, have low revenues and expenditures in combination with higher than average mandated responsibilities across major expenditure categories, according to GMD data. Administrative decentralization has outpaced fiscal decentralization (Ndegwa 2002), and with low capacity for revenue generation and a frequent lack of access to external debt markets, African cities are left with unfunded mandates and the inability to close gaps in service provision (UNECA 2019). Data showing the quality of services in urban areas is one indicator of the performance of municipal finance and whether funds are sufficient to meet municipal needs. Figure 3.3 shows that Sub-Saharan Africa faces the greatest urban deficits, followed by South Asia. Cities in the Middle East and North Africa, although having smaller budgets than cities in East Asia and Latin America, do not necessarily have larger service gaps due to their smaller mandates for service provision. Access to debt markets also differs between regions. Data on outstanding bonds illustrates this point (Fig. 3.4). Within Africa, only Nigerian and South African municipalities have issued bonds. In other regions, municipal bonds are more common. Three countries stand out as having between 500 and 1000 outstanding municipal bonds: Canada, Germany and the United States. Only South Korea and China have more than 1000 outstanding municipal bonds, 1331 and 3789, respectively. The vast majority of municipal bonds are issued in domestic currency, but international municipal bods also exist in 18 mostly high-income countries, with Canada and Sweden having the most outstanding international municipal bonds.

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20

137 40

60

80

100

120

East Asia and Pacific Europe and Central Asia Latin America and the Caribbean Middle East and North Africa North America South Asia Sub-Saharan Africa Access to electricity, urban (% of urban population) People using at least basic sanitation services, urban (% of urban population) People using at least basic drinking water services, urban (% of urban population)

Fig. 3.3 Urban electricity, water and sanitation by region, 2015. Data World Development Indicators

Fig. 3.4 Total number of outstanding municipal bonds (domestic and international) by country as of August 2019. Data from cbonds.com; Map by authors

To better understand major challenges and positive trends in municipal finance globally, we requested input from a set of experts in four major regions: Africa, Asia, Europe and Latin America.1 Here we synthesize their ideas on key challenges and positive trends by region.

1

The names and institutions of the independent experts who provided input can be found in the Annex to this paper.

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Africa2

Challenges Decentralization in Africa is still ongoing, and in many countries has faced hurdles; decentralization of responsibilities has not been fully accompanied by fiscal decentralization. Most fundamentally, the “problem of municipal finance in Africa is the gap between financial resources and municipal expenditure needs coupled by inadequate financial systems,” (A.C. Mosha, personal communication). Associated challenges fall into four main categories: low autonomy, limited access to borrowing, low capacity, and low transparency. Low autonomy constrains the sources of revenue municipalities can access and their design. Often, these sources are low yield, inelastic, or difficult to administer. While nearly all African municipalities have the legal authority to levy taxes, many lack the ability to collect as budgeted or needed. The result is heavy reliance on central transfers, which can be unpredictable and are often inadequate to meet mandated responsibilities. Africa’s municipalities also often have limited control over their expenditures. The regional or provincial level often has more authority and implements central decisions on local issues. Limited access to borrowing constrains the ability of African municipalities ability to invest in infrastructure. Few municipalities have access to capital markets and private finance, and budgets are concentrated in expenditures on staffing. Even when borrowing is available, the persistent challenge of project preparation to create bankable investment opportunities is a barrier. The low capacity of municipal governments worsens budgetary woes, resulting in poor operational and financial management. This tends to be worse in smaller and more rural municipalities. There is a lack of understanding of available revenue tools, and a lack of clarity about often overlapping subnational government responsibilities. “Competences allocated to each level of government are well defined in the Constitutions at the regional/provincial level but are not always well defined at the municipal level,” (J. Van Geesbergen, personal communication). Understaffing and too few capacity building opportunities contribute to poor financial management. Low transparency poses a problem for municipal finance in Africa because it limits the ability of citizens to hold local governments accountable. The theoretical benefits of decentralized governance hinge on a social contract between taxpayers and those elected to respond to their needs. Currently, data on municipal finance in African cities is largely unavailable to the public. “Access to reliable data not only supports good governance and improves public trust, but it also enables for better public service delivery by improving citizen engagement and providing a data-driven basis for stronger government accountability and efficiency,” (J. Van Geesbergen, personal communication).

2

Trends related to Africa were synthesized from personal communication in July of 2019 with three municipal finance experts: Liza Rose Cirolia, Aloyisus C. Mosha, & Jennifer Van Geesbergen.

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Positive Trends In spite of the setbacks, fiscal decentralization is slowly moving forward, with rising acknowledgement of the importance of municipal governance in Africa. Often decentralization is being rolled out first in an experimental phase to identify and correct problems. Against the backdrop of other positive trends in urban governance, including the increasing number of National Urban Policies and widespread democratic local elections, fiscal decentralization holds promise. The sources of revenues and finance available to African municipalities are being strengthened and broadened. There have been improvements to the design of central transfers to make them more direct, increased sharing of national taxes, and establishment of subnational development funds and funds supporting infrastructure for low income communities. Land value capture is receiving increasing attention, and there have been some successful cases of its application, including improvements to property tax administration. While creditworthiness is still far off for the majority of African municipalities, many are beginning to improve financial management and transparency in an effort to eventually gain access to borrowing. Capital market are rapidly developing, and social impact investing is newly on the rise. Some municipalities have already issued bonds backed by the central government (Cape Town, Douala), and large municipalities in some countries can borrow from banks to finance infrastructure (including municipalities in Egypt, Rwanda, South Africa and Uganda). Other municipalities have been creative in accessing finance, including through private investments and PPP, and a few are making efforts to attract FDI into major transport projects. Ongoing reforms to improve financial management have included digitalization of records and processes, which hold benefits for both efficiency and transparency. For example, in Nigeria, BudgIT, a civic startup, is increasing budget transparency by making budget information accessible to the public and designing visual representations of the data for citizens with low data literacy. There are increasing legal provisions for community participation. Citizen participation is on the rise, as is women’s representation in government. Ongoing capacity building efforts, for example, South Africa’s Cities Support Programme which aims to improve the fiscal functioning of metropolitan areas through incentives, are having positive impacts.

3.1.5.2

Asia3

Challenges Asia, like Africa, is facing rapid urbanization which requires major investments in cities. Ability to mobilize the requisite funds and fiscal gaps are therefore primary concerns for Asian municipalities. In spite of decentralization, which has been 3

Trends related to Asia were synthesized from personal communication in August of 2019 with four municipal finance experts: Niño B. Alvina, KK Pandey, Ni Pengfei and Omar Siddique.

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proceeding since the 1990s, many municipalities still rely heavily on transfers, which are often insufficient for needs, resulting in unfunded mandates. This issue has arisen from unbalanced decentralization processes where responsibilities have been transferred to local governments faster than powers of revenue generation. Besides basic budgetary concerns, creditworthiness and risk are also impacted by issues of unbalanced decentralization. Among municipal governments, there is still room to improve government capacity. Moreover, improvements to the local tax system, while needed, have often not been prioritized politically. Pressing issues vary. One common issue on the revenue side is adapting to the changing process of industrialization which has been impacted by trends in global technology and trade. Municipalities must generate revenue in the context of new internet-based economic activities and an increasingly digital economy. A common issue on the expenditures side is the need to give new priority to climate change adaptation, mitigation and resilience.

Positive Trends In spite of its challenges, decentralization has had a positive impact on cities, and there have been recent improvements in the rationalization of transfers from available public funds to city governments. Decentralization has been accompanied by a number of reforms and innovations. “These reforms typically consist of policies that empower local governments through rationalizing intergovernmental flows (Philippines, etc.), strengthening own revenues (Indonesia, Sri Lanka) and using specialized financial intermediaries for small and medium city financing (Philippines, Thailand, etc.). Further, recognizing that smaller and medium cities needs are perceived as too small (high transaction costs) for direct market access, many of these emerging economies have invested in structures to pool these demands and lower risks through efficient intermediation (the Indian states of Tamilnadu, Karnataka),” (O. Siddique, personal communication). Other positive trends include increasing use of PPP and credit financing for local development, as well as innovations in land-based finance. One example is Hangzhou municipality, which transformed an existing marsh with low original value into an ecological green space and was able to capture the resulting premium on the transfer of surrounding land. Digital and ICT-based systems have also had a positive impact on municipal finance in many Asian cities, with a movement toward electronic system payments and online processes for permitting, licensing and other government functions. In addition to streamlining business processes and improving the business environment, the availability of information online has been used to improve transparency of municipal finances. For example, in the Philippines, “initiatives on public expenditure tracking are gaining ground in fiscal management reporting, thereby tracing the funds from the original source to their destination or beneficiaries, and ensuring more transparency and accountability” (N. Alvina, personal communication).

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3.1.5.3

141

Europe4

Challenges Europe’s situation is vastly different than the developing world, but municipalities are still facing challenges and innovating. Municipalities in all regions differ depending on the economy, tax system and assignment of responsibilities, but unlike most African and Asian cities, European cities are not necessarily growing rapidly and some are even shrinking. Still, urban revitalization efforts and the changing needs of infrastructure that come with digitalization require investment. This poses challenges for municipalities facing financial bottlenecks caused by local credit limitations (for example, in Germany). Even when financing is available, the capacity to produce a pipeline of bankable projects is often lacking. Even in Europe, many municipalities face funding shortfalls with increasing expenditure needs associated with social and welfare expenses and the investments needed to compete. European cities are also challenged in their ability to align their allocation of expenditures and investments to global sustainability objectives, such as implementing the SDGs and complying with climate change mitigation policies.

Positive Trends Positive trends for municipal finance in Europe include an increased awareness of the important and massive role that cities play in national infrastructure, as well as successes in controlling expenditures, and changes to tax systems. For example, Germany has generated low property tax revenues in comparison with other OECD countries due to valuations that failed to keep up with market prices, but the country is now introducing a new property tax valuation system to correct this. European municipalities are also drawing on the private sector, including through PPP and participation of volunteers in municipal projects. There are also major positive trends in terms of green finance and environmentally beneficial investments. Municipalities and institutional investors “are starting to see climate change mitigation and adaptation as part of their fiduciary duty, and some trends such as green bonds and improved reporting are demonstrating this slow trend towards better ESG [environmental, social and governance criteria] awareness” (L. Downing, personal communication). Nature-based solutions, which can bring environmental and social co-benefits are gaining traction, and tools like the SuRe® standard are being used by municipalities to improve infrastructure investments in terms of their impact on environmental sustainability and resilience. City alliances are also helping move municipal investments toward sustainability. For example, the Cities Climate Finance Leadership Alliance (CCFLA) is a platform to facilitate knowledge sharing and partnerships which impacts cities within and outside of Europe. 4

Trends related to Europe were synthesized from personal communication in July of 2019 with three municipal finance experts: Louis Downing, Pablo Nunez & Chang Woon Nam.

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Latin America Challenges Latin America and the Caribbean is not different to other regions in terms in terms of municipal finance, though municipalities and cities have more capacity than in Africa, the potential for land value finance and Public private Partnership to finance local infrastructure is still limited by lack of technical expertise and absence of appropriate legal framework.

Positive Trends Decentralization has been implemented in the region and though, as indicated by expenditures and revenues and by subnational indicators (Martinez-Vasquez 2011), though those indicators shows variations among cities and countries (according to the GMD). The strengthening of regional or national developing banks supporting local infrastructure is also remarkable, these are the experience of Findeter in Colombia, Banobras in Mexico, and Caixa in Brazil., but more efforts are needed to increase the supply of resources available for local infrastructure.

3.1.6 Conclusions The overview of municipal finance synthesized from experts in the field reveals some common challenges and shared positive trends across regions. Municipal governments in all regions are challenged to find the resources and sources of finance for needed investments. Developing bankable projects is also a common challenge for municipal leaders. Climate change adaptation, mitigation and resilience are both a challenge and an emerging area of opportunity. Climate finance and green infrastructure innovations are positive developments across regions. Social impact investors and volunteers are another emerging trend that promises to leverage socially motivated actors to support the important efforts of municipal governments. In terms of financial management and transparency, digitalization is becoming more widespread, and improving access to information as well as the business environment. Importantly, there is growing global awareness of the role cities play in national economies and the investment landscape, which is contributing to increased support for improving their financial situation. There are also major differences between regions when it comes to municipal finance trends. Municipalities in Africa are the most stretched, having low per capita budgets and accounting for low shares of public revenues and expenditures while at the same time being assigned a rising share of responsibilities often without the needed increase in transfers to tackle them. African municipalities are the most heavily reliant on national transfers, and have low financial autonomy and capacity

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to change their situation. However, decentralization is proceeding with the acknowledgement of the important role of cities, and there are efforts across the continent to improve creditworthiness, land value capture, budget transparency and basic financial management. Decentralization is also proceeding in Asia, with some challenges related to the transfer of responsibilities without adequate powers to fund them, but with many positive developments in terms of revenue generation, improved transfers systems, and pooled finance for smaller municipalities. Nimble adaptation to new economic and environmental trends is a challenge for Asian municipalities, but also a source of potential innovation. In Latin America and the Caribbean, progress in decentralization is improving and uneven, more efforts are needed to expand own source revenues based on land assets and land value capture that can propel revenue by property taxes and better management of public assets. European municipalities are not without challenges, but their challenges are different. They face often lower rates of population growth or even shrinkage, and must strategically orient their public expenditures to respond to changing technology to remain competitive. European municipalities are finding innovative ways to tackle dual environmental and social aims through green standards and collaborative mechanisms that facilitate smart investments. In an urbanizing world, the pressing issues of municipal finance are growing in importance. Strategically addressing the challenges and building upon the positive trends will be central to the competitiveness of cities globally.

3.1.7 Annex: Municipal Finance Experts The following people provided input for their specified global region: Africa: Liza Rose Cirolia, African Centre for Cities, University of Cape Town Aloyisus C. Mosha, BaIsago University, Gaborone, Botswana Jennifer Van Geesbergen, Independent Consultant at the Centre for African Cities Asia: Niño B. Alvina, Bureau of Local Government Finance (BLGF), Department of Finance, The Philippines Professor KK Pandey Ni Pengfei, Director of Center for City and Competitiveness, Chinese Academy of Social Sciences (CASS) Omar Siddique, Economic Affairs Officer, Sustainable Urban Development, United Nations Economic and Social Commission for Asia and the Pacific (UNESCAP)

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Europe: Louis Downing, Global Infrastructure Basel Foundation Pablo Nunez, Global Infrastructure Basel Foundation Chang Woon Nam, ifo Institute Munich and University of Applied Management Ismaning Latin America: Martim O. Smolka Lincoln Institute of Land Policy

3.2 Municipal Finance, Localization of the SDGs and the Role of UCLG Figures are well-known. According to United Nations estimates, over the next 30 years, the world’s urban population is expected to increase by more than 2.5 billion reaching almost 6.5 billion people by 2050. More than 90% of this growth will be in African and Asian countries - China, India and Nigeria accounting for more than a third of the expected urban growth.5 In Africa, where urban growth is 1.5 times faster than in the rest of the world, the urban population is expected to triple to more than 1.2 billion by 2050. 75% of this growth will be concentrated in intermediary cities.6 In 2014, the United Nations Conference on Trade and Development estimated that achieving the Sustainable Development Goals would require an investment of USD 5 to 7 trillion per year by 2030, including about USD 2.5 trillion for the developing countries alone. In cities, the Cities Climate Finance Leadership Alliance estimates that global demand for low-emission, climate resilient urban infrastructure would average $5 trillion per year.7 The numbers may seem considerable (2.5 trillion USD represents roughly the total GDP of Africa today), but 5-7 trillion USD represents only about 5% of today’s world GDP in current PPP dollars and about 20% of the global savings available.8 And let us not forget either that if the necessary investments are not made to accommodate the expected urban growth, retrofitting, i.e. ex-post action to provide cities with the necessary infrastructure and facilities in response to laissez-faire, would be 3 to 9 times more expensive in Africa, according to the research conducted by Cities Alliance under the Future Cities Africa Program. Finally, let us recall that a study conducted by Cities Alliance in 2016 showed that 65% of the targets defined in the SDGs have a primarily local dimension. Specifically, “21% of the 169 targets can only be implemented with local stakeholders, 24% should

5

UNDESA, Revision of World Urbanisation Prospects, 2018 UN Habitat, State of African Cities, 2014. 7 Cities Climate Finance Leadership Alliance, The State of City Climate Finance 2015. See also UCLG (2016) GOLD IV: Co-Creating the Urban Future. 8 According to World Bank estimates, World GDP, in current PPP dollars, amounted to $136.4 trillion in 2018. 6

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be implemented with local actors and a further 20% should have a much clearer orientation towards local urban actors”.9 These few figures, briefly summarized here, show that the Sustainable Development Goals set by the international community in 2015 cannot be achieved without determined and far-reaching financial efforts in terms of capital investment. They also stress that cities must concentrate a significant part of these efforts, as illustrated by the establishment of a specific objective—SDG 11 for sustainable cities and communities—among the 17 SDGs and the adoption in Quito in 2016 of the New Urban Agenda. By stressing the importance of action at the local level, these figures finally show how much the role of local governments in development processes must be affirmed and strengthened. This last point poses many challenges. It refers to the decentralization processes implemented in countries and the sharing of powers and competences between national and local governments in public action. While decentralization processes have made significant progress in most countries of the world over the past three or four decades, situations remain extremely contrasting with regard to the political and institutional leeway given to local governments to truly assume ownership of development projects in their territories, particularly in emerging economies and the least developed countries of Asia and Africa where the needs are most acute. In these countries in particular, enhancing the role of local governments in public action also requires considerable efforts in structuring their institutional organisation and strengthening their planning and management capacities so that they can fully fulfil the missions they are expected to perform. Finally, it is crucial that they have the financial means and power to act. The issue of municipal finances is clearly a key element and that is the aspect we will develop here. Measured in terms of local finances, what can we say today about the role played by local governments in public action? What are the main challenges to be met in this regard and how is UCLG, the global platform representing local governments, working to meet them?

3.2.1 Municipal Finance: Contrasting Situations Rround the World The World Observatory on Subnational Government Finance and Investment,10 jointly set up by the OECD and UCLG, recently published its 2019 report. The report provides detailed information on the structure and organization of local governments, their main responsibilities, the nature and weight of their expenditures, revenues and

9

Misselwitz, P., & Salcedo Villanueva, J. (2015). The urban dimension of the SDGs: Implications for the New Urban Agenda. In Sustainable Development Goals and Habitat III: Opportunities for a successful New Urban Agenda (pp. 13–22). Brussels. Cities Alliance. 10 www.sng-wofi.org

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debt in more than 120 countries, including 23 of the 47 least developed countries. The figures presented below are taken from the Observatory’s report and database.

3.2.2 Local Government Expenditure: Significant Differences Between High-Income and Low Income Countries In 2016, local government expenditure represented 24.1% of total public expenditure and 8.6% of GDP on an unweighted average, or US$2,505 PPP/capita for the 106 countries for which data could be collected by the Observatory. But these percentages show wide disparities between countries and regions of the world. For example, local governments’ expenditure in OECD countries averages US$5,890 PPP / capita, or 31.8% of total public expenditure and 13.4% of GDP, while it only reaches US$285 PPP / capita, or 15.7% of total public expenditure and 4% of GDP in Africa. The situation is more diversified in the Asia-Pacific region: countries such as China, India, Indonesia or Vietnam have percentages of local government expenditure as a proportion of total public expenditure equal to or greater than 50% and weights in GDP comparable to the OECD average, while the region’s least developed countries have percentages that are broadly equal to African averages. These disparities between countries and regions are often also compounded by significant fiscal imbalances within countries between metropolitan areas and intermediary cities, and between regions (Fig. 3.5). Staff costs are the most significant expense item of local governments worldwide. They represent on average 36.1% of their total expenditure, this percentage being more or less the same in all regions of the world. But very clear differences appear again when we consider the weight of this expenditure in total public staff costs: local governments’ staff costs account on average for 50.2% of total public staff costs in OECD countries, but this percentage is only 18.7% in Africa (Fig. 3.6). 40 35 30 25 20 15 10 5 0

33.8

31.8 26.2 13.4

18.3

15.7

12

8.5

7.9

4 OECD

EU 28

24.1

22.7

Africa

% of GDP

Asia Pacific (excl. OECD)

6

8.6

Euro Asia, Latin America Total West Asia & (106 countries) Middle East

% of public expenditure

Fig. 3.5 Local Government expenditure as a share of GDP and total public expenditure 2016. Source World Observatory on Subnational Government Finance and Investment, Database, 2019

3.2 Municipal Finance, Localization of the SDGs and the Role of UCLG 60

147

50.2

50

42.3 36.6

40

34.7

27.5

30

23.7

18.7

20 10 0 OECD

EU 28

Africa

Asia Pacific (excl. OECD)

Euro Asia, West Asia & Middle East

Latin America

Total

Fig. 3.6 Share of Local Government staff costs in total public staff costs 2016. Source World Observatory on Subnational Government Finance and Investment, Database, 2019

The institutional nature of the country (federal/unitary) does not have a significant impact on this average. However, it is important to note that in federal countries, staff expenditure is higher at the state level (62% on world average for federations) than at the municipal level where human resources remain, in general, lower. Similarly, while local governments play a major role in public investment in most middle- and high-income countries - on average they account for almost 50% of public capital investment in OECD countries, almost 40% in Latin American countries - this role is much more limited in African countries or Asian least developed countries where this percentage is below 20%. That being said, in general, public investment by local governments represents only a modest share of GDP: 1.3% on average in the world, less than 1% in Africa (Fig. 3.7). Overall, as we can see, local governments around the world play a relatively significant role in public spending. Nevertheless, large gaps remain in this regard between high-income and least developed countries. Measured as a percentage, these gaps are on average in the order of 1 to 2, even 1 to 3; measured in absolute figures (in PPP US$), they reach 1 to 20 and show, were it necessary, how extremely modest the role of local governments in public action in the latter countries remains, especially in Africa and Asia, where the pace of urbanization would require a much more proactive presence. Total

1.3

Latin America

1.5

Euro Asia, West Asia & Middle East

1.2

Asia Pacific (excl. OECD)

36.6 39.3 37.3 32.8

1.6

Africa

0.9

EU 28

1.2

OECD

1.4 0

19.4 39.7 46.6 5

10

15

% of public investment

20

25

30

35

40

45

50

% of GDP

Fig. 3.7 Local Government investment as a share of GDP and total public investment 2016. Source World Observatory on Subnational Government Finance and Investment, Database, 2019

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3.2.3 Local Government Revenues: Very Limited Autonomy in Most Developing Countries Local government revenues represent 25.7% of total public revenues and 8.6% of GDP on an unweighted average in the 104 countries where data could be collected by the Observatory. The differences between the various countries and regions of the world reflect the observed differences in spending: the weight of income is very modest in lower middle-income countries and the least developed countries in Africa and Asia compared to OECD or EU 28 countries. Grants and subsidies from national governments make up a little more than 50% of these revenues - they vary on average from 48.9% in OECD countries to 57.6% in African countries; the rest of local governments’ revenues, representing own resources, being composed of taxes - 33% on average - and user charges and fees—about 9% (Fig. 3.8). The analysis of the nature of income shows extremely diverse situations between countries in the different regions of the world, which refer to the histories and current contexts of the decentralization processes specific to each country (Figs. 3.9 and 3.10). For instance, in Asia Pacific countries (excluding OECD countries), national government grants and subsidies represent 70% or more of local government resources in Indonesia, Sri Lanka or the Philippines and only around 20% in India, Malaysia or Cambodia. Similarly, in Africa, national government grants and subsidies represent 90% of local government revenues in Kenya, Tanzania and Rwanda, up to 96% in Uganda, around 25% in Senegal, Namibia and Eswatini, and barely 4% in Zimbabwe. Similar situations can be found in Latin America, where the income of local governments in Peru or Mexico depends on more than 90% of central government grants and subsidies, compared to 15% in Costa Rica or 3% in Argentina. The extreme dependence of local governments on state allocations is a sign of the very limited power they are given to collect their own revenues and, often combined Total (104 countries)

50.3

Latin America

49.7

53.7

Euro Asia, West Asia & Middle East

46.3

40.6

Asia Pacific (excl. OECD)

59.4 49.6

Africa

50.4 57.6

EU 28

42.4

54.4

OECD

45.6

48.9 0%

10%

20%

30%

Grants and subsidies

40%

51.1 50%

60%

70%

80%

90% 100%

Own Revenues

Fig. 3.8 Local Government Revenues 2016. Source World Observatory on Subnational Government Finance and Investment, Database 2019

3.2 Municipal Finance, Localization of the SDGs and the Role of UCLG 100.0 90.0 80.0 70.0 60.0 50.0 40.0 30.0 20.0 10.0 0.0

7.6

92.4

7.5

92.5

3.7

5.6 28.8

23.4

71.2

76.6

Azerbaijan

96.3

94.4

Mexico

Malta

Uganda

Sri Lanka

OECD

EU 28

Africa

Asia Pacific (excl. OECD)

Grants & subsidies

149

Peru

Euro Asia, Latin America West Asia & Middle East

Own-revenue

Fig. 3.9 Top countries most dependent on grants and subsidies across regions 2016. Source World Observatory on Subnational Government Finance and Investment, Database, 2019

100.0 80.0 60.0

89.0

73.6 96.3

80.8 98.7

97.3

1.3 Jordan

2.7 Argentina

40.0 20.0 0.0

11.0

26.4

Iceland

Germany

3.7 Zimbabwe

OECD

EU 28

Africa Grants & subsidies

19.2 Malaysia

Asia Pacific (excl. Euro Asia, West OECD) Asia & Middle East Own-revenue

Latin America

Fig. 3.10 Top countries with the highest rate of own revenues across regions 2016. Source World Observatory on Subnational Government Finance and Investment, Database, 2019

with the irregularity and poor transparency of transfers, is a considerable obstacle to the exercise of their responsibilities and their ability to act. At the other extreme, the low weight of transfers and the importance of taxes (which is only in rare cases accompanied by the power to set their base and rates) can be interpreted as a lack of concern to redistribute public income to local governments and therefore as a limited consideration that national governments give to the role of local governments in public action. Certainly, situations must be analysed on a case-by-case basis and hasty interpretations must be avoided. In many countries, in Africa and Asia, decentralization has made significant progress in recent years.11 Nevertheless, it must be 11

For Africa, see UCLGA & Cities Alliance, Assessing the Institutional Environment of Local Governments in Africa, 2018, 3rd Edition. For Asia, see UCLG ASPAC, Cities Alliance and UNDP, City Enabling Environment Rating: Assessment of the countries in Asia and the Pacific, 2018.

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recognized that the increase, but also the issue of stability and predictability of local government resources, remains one of the major challenges facing the developing world. And that a significant part of the solution certainly lies in strengthening the autonomy of local governments in terms of access to income.

3.2.4 Local Government Debt: Almost no Access to Debt in Low-Income Countries The debt of local governments represents 7.5% of GDP and 11.5% of total public debt in the 76 countries where data could be collected by the Observatory (Fig. 3.11). Again on this point, there are considerable differences between regions and countries. The debt of local governments represents 13% of GDP and almost 17% of total public debt on average in OECD countries (8.4% and 11.7% respectively in EU 28): it reaches up to 67% of GDP and 59% of total debt in Canada and represents more than 20% of GDP in countries such as Belgium, Germany, Spain, Switzerland, the USA or Japan and more than 20% of total public debt in Germany, Spain, Switzerland, Australia, the USA, Estonia and northern European countries such as Finland, Norway and Sweden. Conversely, it is almost nil in most African countries except South Africa and Nigeria (where it represents 4.8% and 2.9% of GDP and 9.4% and 21.2% of total public debt respectively) and in non-OECD Asia-Pacific countries where, except China and India where it reaches significant percentages (21% of GDP and 47% and 30% respectively of total public debt), it represents on average only 0.7% of GDP and 1.4% of total public debt. The inability of local governments to access financial markets in the vast majority of low-income countries has multiple causes, ranging from drastic constraints or even the prohibition of local governments from taking out loans under national legislation, and the precarious and unstable financial situation of local governments offering insufficient repayment guarantees to credit institutions, to the poor technical and Total (76 countries)

11.8

7.6

Latin America

10.4

3.5

Africa (excl. South Africa and Nigeria) Africa

0.4 0.8 1.3

Asia Pacific (excl OECD, China & India) Asia Pacific (excl. OECD)

0.7 1.4

4.4 10.6

5.7

Euro Asia, West Asia & Middle East

6.6

1.7

EU 28

11.7

8.4

OECD

16.9

13

0

2

4

% of public debt

6

8

10

12

14

16

18

% of GDP

Fig. 3.11 Local Government debt as a share of GDP and total public debt 2016. Source World Observatory on Subnational Government Finance and Investment, Database, 2019

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financial quality of projects that could be submitted for financing. Combined with their very limited income, these structural limitations in access to private financing constitute a major obstacle to the investment capacity of local governments. By limiting the ability of local governments to provide infrastructure, equipment and services, these constraints in turn limit their ability to generate their own income (via taxes and duties) and, in a sort of vicious circle, perpetuate their relegation.

3.2.5 UCLG and the Issue of Local Finances: A Renewed Strategy Drawing on a Longstanding Engagement From the very first years of its creation in 2004, UCLG, the global network of cities and local, metropolitan and regional governments, has made the issue of municipal finance a key dimension of its action agenda. A Committee on Local Finance and Development bringing together more than 40 mayors and representatives of national associations of local governments was set up in 2005. In a UCLG Policy Paper on Local Finance12 published in 2007, the Committee identified the financing needs of urbanization, proposed a set of recommendations to strengthen the financial power of local governments to meet the related challenges and outlined a roadmap for UCLG’s support action. In 2010, UCLG’s second Global Report of the Observatory on Decentralisation and Local Democracy—Gold II, was devoted to Local Government Finance: The Challenges of the 21st Century. The report provides an in-depth analysis of the fiscal decentralisation architecture in more than 110 countries.13 In 2016, a first pilot report of the Global Observatory on Local Finances, Subnational Governments around the world - Structure and Finance, prepared in partnership with the OECD, was a major step forward in engaging local governments and their association in the production of reliable, harmonised and comparable data on subnational government finance.14 All this effort, combined with advocacy activities carried out within the framework of the Global Task Force of Local and Regional Governments facilitated by UCLG, has contributed significantly to the international community’s thinking and positions on the role of cities and local governments in global development agendas. They have made it possible to inform and monitor the evolution of the institutional and financial environment of the Addis Ababa Action Agenda on Financing for Development Article 34 which stresses the importance of financing at sub-national level,15 or the specific financial aspects of the implementation of the New Urban Agenda.16

12

UCLG, Policy Paper on Local Finance, 2007. UCLG, Local Government Finance : The Challenges of the 21st Century, Gold II, 2010. 14 UCLG, OECD, AFD, Subnational Governments around the World—Structure and Finance. A first contribution to the Global Observatory on Local Finances, 2016. 15 United Nations, Addis Ababa Action Agenda—Financing for Development, 2015. 16 See Habitat III Policy Paper, 5—Municipal Finance and Local Finance Systems, 2016. 13

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Moreover, in 2016, at its World Congress in Bogota, UCLG adopted a mediumterm strategy for 2016-202217 that places the “localisation” of the global development agendas at the heart of its action. The strategy calls for renewed public action, whose “model must be built from the territories and within the framework of a renewed dialogue across the different spheres of government”. The renewal of the “Cities and Territories Financing Model” is a cornerstone of the Bogota Commitment and has led to the development of a specific strategy on “Localising Financing in Support of Sustainable Urbanisation” which was adopted by the organisation’s Executive Bureau at its meeting in Strasbourg in May 2018. In close collaboration with the Global Fund for Cities Development (FMDV), which is now an integral part of UCLG, this “localisation of financing” strategy is gradually being implemented. Today, it structures UCLG’s action on the issue. The strategy for localising financing in support of sustainable urbanization has four main dimensions that relate to the four strategic modalities of action developed by UCLG to place local governments at the heart of the implementation of global agendas: information, monitoring and reporting; advocacy and awareness-raising; learning; and implementation.

3.2.6 Information, Monitoring and Reporting: The World Observatory on Local Government Finance and Investment The production and availability of information and data on local finances are essential prerequisites for action. In many countries, particularly the least developed countries, despite the existence of legal frameworks requiring financial documentation to be made public, problems related to data availability are prevalent. In particular, subnational budget data are often not publicly available, incomplete, unconsolidated or outdated. Accurate situational understanding is a key element in helping to make the case for proposing solutions for change on an informed basis. Moreover, the dissemination and public availability of this information is a necessary condition for public debate between all relevant stakeholders. Without clear, reliable and transparent information, it is unlikely that any recommendations for action can be produced that are both collectively designed and evidence-based. Box 1 Open-Government and Budget Transparency In recent years, an increasing number of local and regional governments around the world have been engaged in advancing reforms in line with the principles of open government by incorporating this new governance model into their administrations’

17

UCLG, Strategic priorities 2016-2022.

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and political agendas. They are developing new tools and solutions to facilitate access to local information and engage citizens, civic organizations and the private sector as strategic partners with whom to work towards achieving more effective and accountable local institutions. In Aragon (Spain), innovative solutions have been introduced to reduce administrative costs in the short term and gain trust and democratic legitimacy in the longer term. In Tshwane (South Africa), the city government promotes the use of open data and new technologies to improve the quality of and access to public services. Lastly, Montevideo (Uruguay) was one of the pioneers in Latin America in developing an open data strategy. Its local government later collaborated with Uruguay’s Electronic Government and Information Society Agency to define the 2018-2020 Open Government National Action Plan.

This is the objective of the World Observatory on Subnational Government Finance and Investment. A joint initiative of UCLG and the OECD, with the support of a range of partners including UNCDF, the French Development Agency, the Council of Europe Development Bank and the DeLog network, this observatory offers a unique database providing standardised and comparable information on multi-level governance and municipal finance in over 120 countries. Some of the data from this Observatory has been presented in the first part of this article and we see how useful they can be in assessing the diversity of situations in the world and in informing thinking on the evolution of national policies in this field. Each of the 120 + country fact sheets that the Observatory has collected also provides detailed qualitative information and comments on country-specific situations that constitute a major source of knowledge and possible cross learning opportunities.

3.2.7 Advocacy and Awareness-Raising: The Malaga Global Coalition for Municipal Finance Advocacy and awareness-raising on the international scene have been a structuring dimension of UCLG’s strategy on local finance since the beginning. Following the adoption of the Addis Ababa Action Agenda on Financing for Development, UCLG has been consistently involved in ensuring that the voice of local governments is heard at the conferences convened annually by the United Nations to monitor the implementation of the Agenda. Progress was achieved in 2018, in connection with the review of SDG 11 by the High Level Political Forum. Nonetheless, the 2019 report of the Inter Agency Task Force on Financing for Development, focusing on national financing strategies for the different development agendas, did not fully take into account the ‘missing link’: local public finance. The adoption of the UCLG strategy on local finance led to a new initiative in this regard: the establishment of the Malaga Global Coalition for Municipal Finance.

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Punctuated by an annual event, first held in April 201818 and then in October 2019 in Malaga (Spain), at the invitation of the Mayor of the city, the Malaga Global Coalition for Municipal Finance aims to advocate for structural changes in the global financial ecosystem to make it more favorable to cities and local governments. Promoted by UNCDF and UCLG with the support of FMDV, in close collaboration with Cities Alliance and UN Habitat, the coalition is designed as a platform for dialogue between mayors and national associations of local governments and their political and institutional partners: national governments, the private sector, bilateral and multilateral cooperation organizations, development banks and international financial institutions. Its objective is to promote public policy objectives that facilitate and strengthen access to finance for local governments. It is intended to consolidate advocacy work by developing a shared rationale for the changes needed in the institutional environment for local governments to take action in a more effective way. A rationale in which fiscal decentralization is no longer seen as a zero-sum game in which states lose what local governments win, but rather as a win-win process for all, in the interest of achieving sustainable development.

3.2.8 Mutual Learning: The Establishment of a Community of Practice Established and convened for the first time in Madrid, at the UCLG Executive Bureau in November 2018, the Community of Practice is a follow-up to the city exchange and networking initiatives that were initially initiated under the Committee on Local Finance and Development. Complementary to the professional networks and facilitation work carried out by UCLG’s regional sections—the network of African City Chief Financial Officers led by UCLG Africa, the network of experts on local finance led by CEMR or the dialogue initiative on metropolitan finance led by Metropolis for example—the main objective of the Community of Practice is to provide a forum for sharing experiences and mutual learning on innovative practices developed by cities on local finance. On this basis, it is also intended to contribute to advocacy work by helping to structure positions and recommendations on the localisation of financing to be carried out on the global stage, and more generally to guide the implementation and monitoring of UCLG’s strategy on the localisation of financing as a whole. The Community of Practice is conceived as a space for dialogue, intended to meet in a flexible way, on the initiative of cities or partners wishing to exchange on their practices to inform and enrich future action. At the time of writing, two meetings of the Community of Practice are scheduled, one on the initiative of the City of Paris in October 2019 to discuss the fiscal and regulatory tools that cities have put in place to fight against the financialisation of housing, the other, led by the South 18

See https://www.uclg.org/en/media/news/uclg-co-organized-high-level-policy-dialogue-transf orm-municipal-finance-malaga.

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African Association of Local Governments (SALGA), with the support of the African Development Bank, at the UCLG World Congress in Durban in November 2019, to discuss the role of national and international development finance institutions in supporting cities’ access to financial markets. Box 2 Generating municipal revenues for capital investment: The potential of land value capture In and around growing cities, development rights can be powerful levers for financing infrastructure. As is the case in Chinese cities, land-based financing—particularly land leasing to property developers—can be a major source of revenue for city governments. It includes selling development rights and permissions to land-owners or the increase in densities or change in land uses. Capturing a share of the increasing value of urban land is a political rather than technical process. This appears clearly from an analysis of experiences in Africa (Addis Ababa, Harare and Nairobi) or Latin America (Bogota, Medellin, São Paulo and Rosario) where a wide range of urban development instruments have been developed to generate revenues to help finance infrastructure and social housing in areas with scarce public services, as well as for public works in general.

3.2.9 Implementation: The launch of the Africa Territorial Agency and the Creation of the International Municipal Investment Fund The investments required to provide cities with the infrastructure and equipment necessary for their development are, as we have seen, considerable. According to the IMF, additional annual capital spending to achieve the 2030 Agenda in 121 emerging market economies and low-income developing countries would amount to 2.6 trillion US dollars (2.5 percent of the 2030 world GDP). Looking at the region-by-region investment gap, the Asia-Pacific region has the largest global additional spending requirement, estimated at 1.5 percent of 2030 world GDP. Sub-Saharan Africa has the second largest additional spending requirement, estimated at 0.4 percent of 2030 world GDP.19 The resources cities can now mobilize for these purposes on the basis of their resources are very limited. Official development assistance (around USD 147 billion

19

IMF, Fiscal Policy and Development: Human, Social, and Physical Investment for the SDGs, 2019.

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in 201720 ) or migrant remittances (USD 529 billion in 201821 ) contribute to investment efforts but are far from sufficient. One of the major challenges facing cities in many rapidly urbanising countries today, particularly in Africa and Asia, is their access to capital markets to finance such infrastructure and facilities. It is on this theme that UCLG has chosen to take concrete action by launching two initiatives, one in Africa and the other at the global level. In Africa, the Africa Territorial Agency, inspired by the experience of Agence France Locale, aims to set up a cooperative fund financed by an initial investment from the continent’s main cities (initially some twenty cities) to raise additional resources from financial institutions and capital markets to finance their investments. A call for expressions of interest was launched by UCLG Africa in January 2019 to mobilise a first group of cities and local governments to be its founding members. At the global level, UCLG, in collaboration with the FMDV and in partnership with UNCDF, is currently setting up an International Municipal Investment Fund. In a first pilot phase, the Fund aims to support investment projects in around ten cities. It will offer technical assistance to finalize the project preparation, provide the necessary support for the city to meet the conditions for access to financial markets, support the financing of the project with a loan in local currency up to 30% of its cost and help the city to mobilize the remaining necessary financing through development financial institutions and on private capital markets. In a context of expanding and increasingly mature financial markets, these two initiatives aim to help bridge the gap between cities’ investment needs and their access to these markets. They want to show that it is possible for cities to mobilize private reimbursable resources to finance their development, set examples and learn from them to trigger a virtuous dynamic of transformational change. At the heart of the global development agendas, the urban issue is a key challenge. Financing cities, especially the upcoming urban explosion in African and Asian countries, requires bold action by all actors at all levels. Meeting the considerable challenges of providing the necessary infrastructure, equipment and services requires giving local governments real power to act. The inequalities in access to resources that we observe today from one country to another, and from one city to another, must be brought to light on the international scene and at national levels to be discussed, fought and remedied. The experience of local governments must be disseminated and shared. Increased funding and innovation in resource access mechanisms are essential. This is what UCLG, the global voice of local governments, intends to contribute to. This is what the international community and all spheres of government must address in a determined manner if we are to achieve the compelling objectives of the New Urban Agenda and the 2030 Agenda for Sustainable Development over the next ten years.

20

See https://www.oecd.org/newsroom/development-aid-stable-in-2017-with-more-sent-to-poo rest-countries.htm. 21 See https://www.worldbank.org/en/news/press-release/2019/04/08/record-high-remittancessent-globally-in-2018.

3.3 Subnational Finance in Latin American and the Caribbean: Recent …

157

Fig. 3.12 Public expenditure: subnational government as a percentage of total government. Source Eguino H and Radics A [2018]

3.3 Subnational Finance in Latin American and the Caribbean: Recent Trends and Challenges 3.3.1 Evolution of the Decentralization and Subnational Finance The decentralization process in the Latin American and Caribbean (LAC) countries has gathered momentum since the 1980s. As a result, subnational governments22 have become increasingly important in the provision of public goods and services that are critical for economic and social development. In fact, the subnational governments’ share of consolidated public expenditures almost doubled between 1985 and 2010, from 13 to 25%, and it has remained at this level up to the present time (2015) (see Fig. 3.12). Political decentralization has followed a similar path: since 1997, municipal mayors in all LAC countries have been elected by popular vote, whereas in 1980 this was the case in only six countries.23 Practically all LAC countries have made efforts over the past three decades to bring government closer to the citizens through greater decentralization to subnational governments and/or territorial deconcentration of the central government. The motives behind fiscal decentralization have been diverse and, in certain cases, concurrent. They include democratization, which created demands for greater political and fiscal autonomy at the subnational level (Arzaghi and Henderson 2005); the economic crisis of the 1980s, which led some countries to transfer spending functions to subnational governments within a context of structural adjustment (Rezende and Veloso 2012); and second-generation institutional reforms, particularly since the mid-1990s, which transferred responsibilities to subnational governments in an effort to make the

22

The paper uses the term “Subnational Governments” to refer both to the intermediate level of government and the municipal level. The intermediate level includes: States in México and Argentina, Provinces in Argentina and Departments in the rest of the region. 23 There are no municipal mayors in Barbados or Suriname as this level of government does not exist.

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public sector more efficient (Lora 2007)24 (IDB, 2018). An important contributing factor in the growth of subnational expenditures during the first decade of this century was the boom in the extractive industries, primarily mining and hydrocarbons, which boosted transfers of tax revenue from these industries to subnational governments (ECLAC 2012). These transfers are characterized by high levels of volatility, which are directly associated with changes in international prices for natural resources. Average spending by subnational governments in LAC conceals pronounced differences among countries based on historical patterns and economic, population, and geographical size.25 While more than 40% of consolidated expenditures in Brazil and Argentina were executed at the subnational level in 2015, the equivalent figure in Costa Rica, Honduras or Panamá was below 5% (see Fig. 3.13).26 In general, the countries of the region can be classified into three main groups: (i) federal, such as Argentina, Brazil, and Mexico; (ii) unitary with a high level of decentralization (above 30%), such as Bolivia, Colombia, Ecuador, and Peru; and (iii) unitary with a low levels of decentralization, such as Guatemala, Chile, Paraguay, Uruguay, and the Central American and Caribbean nations.27 LAC has roughly 17,500 subnational governments offering a variety of public management capacities. Of this total, 2% falls into the intermediate level of government, the rest are municipal governments (see Table 3.1).28 The intermediate level has greater relevance with respect to the delivery of services in the region’s largest countries, particularly in Argentina and Brazil, which have significant own-source revenues.29 In the rest of the countries of the region, the delivery of subnational services is primarily the responsibility of municipal governments. The subnational level of government is highly diverse, with differences not only among but also within countries. This is particularly pronounced at the municipal level. LAC is an increasingly urban region, with eight out of every ten inhabitants residing in cities. However, just 11% of municipalities in the region—all of them urban—account for more than two thirds of the entire population, while roughly 24

For the more structural determinants of fiscal decentralization (e.g. geography), see Canavire et al. (2016). 25 The size of the subnational public sector tends to be greater in larger countries, yet subnational governments in small countries perform essential government functions for the well-being of the population. 26 For a comparative examination of the size, structure, and financing of subnational governments worldwide, see OECD/United Cities and Municipal government (2016). 27 Under a federal system, the constitution guarantees the permanence and independence of subnational governments and grants them their own legislative, executive, and judicial powers. Under a unitary system, subnational governments usually lack constitutional sovereignty, and the central government determines which decision-making powers are devolved to them. Both concepts are ideals in a continuum within which countries are classified on the basis of the constitutional sovereignty granted to subnational governments (Britannica 2014). 28 In six of the region’s countries there are two levels of municipal government, and in Haiti there are three. 29 Bolivia, Chile, Colombia, Mexico, Peru, and Venezuela are examples of other countries where intermediate governments are responsible for important subnational services, despite being highly dependent on central government transfers.

3.3 Subnational Finance in Latin American and the Caribbean: Recent …

159

Fig. 3.13 Subnational government as % of total government (circa 2015). Source Sector Framework Document of Fiscal Decentralization and Subnational Governments [IDB 2018]

50% of municipal governments have less than 10,000 inhabitants each (Eguino et al. 2010). This reality poses challenges not only for urban municipalities, which manage services that must be coordinated with other subnational entities, but also for rural areas, where most municipalities are concentrated, and which have small populations and limited administrative capacity (Martínez-Vázquez 2010).30 Most municipalities are responsible for services that include garbage collection and street sweeping and cleaning; local transportation; maintenance of parks and gardens; public lighting; and the issuance of construction and operating licenses (Eguino et al. 2010). In addition, both municipalities and intermediate-level governments (if any) carry out public investment projects in sectors such as roads and transportation, water and sanitation, productive infrastructure, tourism, health, and education, and are subsequently responsible for the operation and maintenance of these projects.31 In fact, the share of subnational governments in the execution of public investment has grown over the past decade in several countries in the region, and now exceeds 50% of consolidated public investment in some countries (see Table 3.2).

30

The number of municipalities has proliferated in some countries, partly due to the incentive of a guaranteed minimum level of transfers. This has been the case in the Dominican Republic in particular, where the total number of municipal governments has increased by more than 150% over the last 20 years. This creates difficulties in leveraging economies of scale and delivering services efficiently (Martínez-Vázquez et al. 2017). 31 In several of the region´s largest countries, subnational governments also have responsibilities in the area of citizen security. See Citizen Security and Justice SFD (IDB 2017).

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Table 3.1 Number of subnational governments in LAC by level of government and country Country

Number of intermediate governments

Number of municipal governments

Number of subnational governments

Argentina

24

2,218

2,242

Brazil

27

5,570

5,597

Bahamas

0

32

32

Belize

0

203

203

Costa Rica

7

81

88

Dominican Republic

32

392

422

Ecuador*

22

221

243

El Salvador

14

262

276

Guatemala

22

340

362

Guyana

10

146

156

Haiti*

10

182

192

Honduras

18

298

316

0

14

14

Nicaragua

17

153

170

Panama

14

78

92

Paraguay

18

254

271

Suriname

0

10

10

Trinidad and Tobago

0

15

15

Uruguay

19

112

131

Mexico

32

2,464

2,496

Bolivia

9

339

348

Chile

15

345

360

Colombia

33

1,101

1,134

Peru

26

1,871

1,897

Venezuela

23

337

360

392

17,038

17,427

Jamaica

Total Source IDB (2018)

Health and education services significantly affect the size of the subnational public sector, accounting on average for more than 40% of total expenditures (ECLAC 2011b). The largest countries in the region are the ones that have assigned these functions to subnational governments. In Argentina and Peru, these services are for the most part provided by intermediate levels of government; in Colombia, primary responsibility falls to the municipal governments; in Brazil, Mexico, and Venezuela, different levels of government have concurrent responsibilities (Martínez-Vázquez 2010). Subnational government autonomy in managing these services tends to be related to the governments, capacity to generate their own revenue. However, this is

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Table 3.2 Subnational Public Investment (% of all public investment) % Public Investment (2014)

Argentina*

Brasil

Colombia

México** (2013)

Perú**

Central Government

36.4

24.8

67.9

23.9

35.4

Subnational Governments

Intermediate Governments

45.5

43.0

6.5

50.2

19.8

Municipal Governments

18.2

32.2

25.6

23.4

44.0

Number of Subnational Governments Intermediate Governments

Argentina

Brazill

Colombia

México

Perú

24

27

33

32

26

Municipal Governments

2,171

5,570

1,122

2,438

1,838

Total

2,195

5,597

1,155

2,470

1,864

Source Based on OECD & BID (2016) and IERAL (2017)

not the case in all countries: the provinces of Argentina, which have significant own revenues, have greater control over management than Peru’s regional governments, which are funded almost entirely through transfers. In Mexico, however, states have traditionally been largely autonomous in the management of education services, despite being highly dependent on transfers.32

3.3.2 Recent advances and challenges Over the last four years (2015-2018), several LAC countries have made headway in the processes of decentralization. In Chile, for instance, the constitution was reformed in late 2016 to allow the democratic election of regional governors from 2020 onwards (intermediate level of government), thus replacing the institutional figure of the provincial governor appointed by the central government. That country also passed two laws in late 2017 governing the popular election of regional government authorities, accompanied by the transfer of powers to those entities.33 In October 2015, Panama enacted a law that decentralizes public administration, with the following key features: the transfer of real property tax revenue to the municipalities based on redistributive criteria; the strengthening of capacities in the area of public investment management; the gradual transfer of functions to the municipalities through the 32

By implementing the Fondo de Aportaciones para la Nómina Educativa y Gasto Operativo [Conditional Transfer Fund for Education Payroll and Operational Expenses] (FONE), the central government has asserted greater control over the teacher payroll it finances (see IDB 2014b). 33 New powers have been transferred in the areas of productive and industrial development, social and human development, and transportation and infrastructure; a procedure has been established allowing the regions to require new powers in future from the central government; and metropolitan areas can now be created in the country’s main conurbations.

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accreditation of capabilities; and institutional piloting of the process by the National Decentralization Department. A second characteristic of the period has been the end of the boom in transfers to subnational governments linked to extractive industries, particularly in Andean countries. This phenomenon helps to explain the recent stagnation in growth in the size of the subnational public sector (see Fig. 3.5). Thirdly, in terms of the fiscal sustainability of subnational governments, Mexico has made progress by approving, a legal fiscal responsibility framework for federal and municipal entities, which is helping to boost transparency in the recording of subnational debt and, mitigate the incipient fiscal risks that certain subnational governments have exhibited (Rasteletti and Acosta 2016). In Argentina, the Fiscal Responsibility Law was reformed at the end of 2017 with the imposition of a ceiling on growth in current expenditure; this complements the signing of a fiscal pact between the three levels of government, aimed at ensuring fiscal sustainability and reducing distortionary taxes.34 Expanding the analysis to encompass the last 15 years, advances have been made but the traditional challenges that have characterized the decentralization process in the region have also intensified. On one hand, there has been significant improvement in the fiscal sustainability of subnational governments, particularly in Argentina, Brazil, and Colombia, where subnational debt contributed to bouts of macrofiscal instability in the late 1990s and early 2000s. In particular, the implementation of fiscal rules for subnational governments in Brazil and Colombia served as a model for the development of subnational fiscal responsibility frameworks in other countries of the region. On the other hand, and despite these advances, there is still a need to continue the timely monitoring of subnational fiscal risks-for example, in countries experiencing higher volatility in their transfers (particularly Andean region) or in which subnational debt is a concern (Argentina and Brazil). In addition, one of the traditional challenges in the sector persists: the subnational governments’ high dependence on transfers within the financing structure (ECLAC 2011c). Between 2000 and 2015, average expenditures by subnational governments rose by more than two percentage points of GDP.35 However, this rise was primarily due to higher transfers and, to a lesser extent, to the subnational governments own resources. From a comparative viewpoint, the financing structure of subnational governments in the LAC region stands in contrast with its counterparts not only in OECD countries but also in other regions with a relatively similar development level, such as Asia (see Fig. 3.14).

34

Argentina is aiming at reducing the importance of distortionary taxes on economic activity. As a consequence, the country is giving a renewed importance to underused sources of revenues such as the property taxes. 35 The information in Table 2 is a specific contribution of this SFD in view of the limited transparency of subnational finances in the region. It draws on the subnational fiscal database that IFD/FMM has been developing in recent years, as well as a questionnaire completed by specialists in that division who live and/or work in the vast majority of countries in the region. This has made it possible to describe and analyze recent decentralization trends in those countries.

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Fig. 3.14 Subnational government transfers as % of financing for subnational expenditure in the regions of the world (Source IMF Government Finance Statistics, 2016)

This high dependence on transfers reduces subnational governments incentives for accountability, efficient management, and fiscal responsibility, and it reflects weaknesses in the relationship between the different levels of government. At the same time, the performance of subnational governments is also affected by the context of low institutional capacity, limited transparency, and weak capacities for monitoring subnational management. Accordingly, the main challenge for the decentralization and subnational governments is to foster the development of institutional capacities and a suitable incentive structure for more efficient and effective subnational management in LAC, thus helping to improve the quality of life for all citizens. This requires building institutional capacities to tackle four major problems: (i) weak intergovernmental arrangements; (ii) subnational governments with deficient expenditure management and service delivery; (iii) subnational governments with low generation of own revenue and poor access to financing; and (iv) subnational governments with limited management transparency and accountability.

3.3.2.1

Weak intergovernmental arrangements

The problem of weak intergovernmental arrangement is threefold. First, the mismatch between responsibility (expenditures) and abilities (revenues). A poor combination of these two elements causes coordination problems among the different levels of government. For example, an ambiguous spending responsibility causes inefficient expenditures in key sectors, such as education, health, etc. Second, the process of fiscal decentralization (taxing powers) to subnational governments is slow, particularly with respect to intermediate levels of government. This exacerbates the dependence on transfers, except for Argentina and Brazil (where the intermediate levels have broad tax bases). The reason behind the slowness is that tax powers assigned to subnational governments are usually enshrined in national law and, in certain

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cases, the constitution (for example, in Brazil) (Martínez-Vázquez 2010). Third, the design of intergovernmental transfer systems is out of date, especially in terms of addressing the horizontal imbalances that are characteristic of the region. LAC’s high territorial and economics inequality translate into major differences in the ability to generate own-source revenues. However, transfer only provide a limited offset to the inequalities among subnational governments (Beramendi 2012). While many transfer systems incorporate redistributive criteria related to population, rurality, and/or poverty, these criteria are insufficient to close regional gaps (Muñoz et al. 2017; Beramendi et al. 2017). Faced with the political difficulty of modifying their transfer regimes, such as by updating distribution criteria, countries often create new mechanisms and thereby add to the systems’ complexity. In order to mitigate information asymmetry, collaboration between different levels of governments aimed at capitalizing on shared technological solutions can help to reduce asymmetries in institutional capacity at the subnational level. Besides, the creation of spaces for discussion and periodic updating of the distribution criteria for transfers, based on simple systems with clear operating rules, would be an important step toward improving intergovernmental coordination institutions. Although the deconcentrating process is complex and require heavy cost-benefit analysis, the process itself can be an opportunity to evaluate reform options, particularly for those intermediate-level governments with greater ability to raise own-source revenues (IDB 2013a; Fretes Cibils and Ter-Minassian 2015). To optimize the transfer system, the introduction of equalization transfer systems would not only address regional gaps but would also create a platform for determining and periodically updating resource allocation formulas in the territory. It is critical that rules of the platform be simple, transparent, and consistent with the compliance capacities of subnational governments. Exhaustive and continuous monitoring by the central government, with credible and timely corrective action are also recommended (Urrea 2010)

3.3.2.2

Subnational governments with deficient expenditure management and service delivery

Many subnational governments in LAC are deficient in managing expenditures and providing services. This is partly the result of very disparate initial conditions (DNP 2017). For example, municipalities that have larger populations and more average years of schooling (and therefore a trained workforce) typically show higher levels of public investment execution (Loayza et al. 2014) and better indicators of public expenditure efficiency (Herrera and Francke 2007). Similarly, in municipalities with lower literacy levels and higher levels of poverty and inequality, the productivity of public spending on education is relatively lower (Tavares and de Cavalcanti 2014, and Machado 2013). Nonetheless, large variations in management capacity and development results - even between municipalities with similar initial conditions - reveals the usefulness of differentiated policies and the identification of good practices for subnational governments that lag behind (DNP 2017). In the Brazilian states there is a weak correlation between the relative efficiency of spending on education and

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both state spending per capita on education and state GDP per capita. This suggests that simply having more funds does not guarantee better outcomes, unless they are accompanied by improvements in the management of service quality (Boueri et al. 2014). Talking about the management of the subnational public investment cycle (planning, formulation, execution and ex post evaluation), we also find that LAC subnational governments are in a poor situation. This category of spending is of increasing importance, with considerable potential to help reduce territorial gaps in socioeconomic development. Intermediate level governments have an important role to play in this area by adopting a territorial approach to coordinate their own investments with those of the municipalities. Additionally, the ability of subnational governments to attract investment is affected by constraints that limit opportunities for economic development, including export promotion. The poor quality of services helps to create transaction costs in the relationship between subnational governments and citizens. Except for Brazil and Mexico, the development of public-private partnerships and the private sector’s role in providing services to the subnational government level is incipient (Infrascope 2013), limiting its potential to promote capacities, contribute to poverty reduction, and generate equity. In all these respects, central government support is critical, particularly for subnational governments with limited capacity and high poverty and inequality rates. Significant constraints on improving the quality of subnational expenditures include the weak development of the Public Financial Management (PFM) and limited implementation of management models to strengthen centers of government, which foster coordination of government priorities by directly supporting subnational government authorities. In many subnational entities there are also weaknesses in administrative processes, which usually lack the support of basic management tools. Despite advances in the region in modernizing national PFM systems over the past two decades, development of these systems at the subnational level lags. In terms of coverage, worth noting are the cases of Peru, where the PFM-System has been implemented by all subnational governments (IDB 2010a); Guatemala, where all municipalities are connected to a national system (including an own-source revenue management module); and Brazil, where a PFM-System is in operation in all states and has recently been modernized in half of them (Pimenta 2015). This limited development extends to other PFM systems, such as electronic public procurement (aimed at simplifying procedures and improving subnational capacities), payroll, and asset management (including real estate). There is also the challenge of ensuring that the PFM systems are used by the subnational authorities as a management tool rather than primarily as a central government control mechanism. There is limited development of a professionalized civil service, including competitive staff compensation policies that can better attract, retain, and motivate skilled employees in the context of a fiscally sustainable payroll. Development of the civil service should be accompanied by a sustainable improvement in the technical capacities of public officials, which in many LAC countries are limited as regards both subnational government officials and the government teams that manage the decentralization process at the central level. In this respect, the resident technical assistance

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approach used in Peru’s subnational governments—including capacity evaluations and the transfer of skills and knowledge through daily work with subnational officials—was ultimately more effective in the area of public investment management than short-term informational training. This effort has been accompanied by a deconcentration of central government technical officials to the regions, helping to make the improvement in subnational capacities more sustainable (IDB 2012a).

3.3.2.3

Subnational Governments with Low Generation of Own Revenue and Poor Access to Financing

If subnational governments are to reduce their dependence on transfers, they must improve their capacity to generate own-source revenues. This hinges on the allocation of tax bases and tax rates at the subnational level (part of intergovernmental arrangements) and on the subnational governments’ efforts to maximize their tax bases, service charges, and other sources of funds. In this regard, own-source revenue collection by subnational governments in LAC falls short of its potential. This is evidenced by the limited development of instruments to capture increased property values stemming from public investments, as well as low recovery of service costs and weak management of subnational government assets and real property for sale and lease. Also worth noting is the under collection of the real estate property tax, for which average revenue barely amounted to 0.4% of GDP in 2015 - just over one third of the OECD figure (see Fig. 3.15). This problem has been brought about by factors specific to subnational management, as well as by central government restrictions. The former includes outdated land registries and taxpayer records; limited automation of revenue collection functions; limited capabilities for calculating tax and municipal levy amounts; and insufficient oversight efforts. Regarding the last of these factors, the political cycle influences

Fig. 3.15 Property tax collection, 2000 and 2015 LAC vs. OECD (as a percentage of GDP) (Source Bonet, Muñoz, and Pineda [2014])

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local fiscal efforts in several countries, particularly in election years. Consequently, there is a need to reinforce the autonomy of tax administration at the subnational level and to promote its digitization. In addition, there is scant central government support for subnational governments in their management of own-source revenue, partly because in many countries this function is not sufficiently prioritized. As a result, and about property taxes, the periodic appraisals designed to bring real estate values closer to market value are outdated. Similarly, public records are not integrated with municipal property records (IDB 2013a). Furthermore, subnational governments receive limited technical assistance, which is focused on urban entities with greater revenue collection potential and low management capacity, and possibly also rural subnational governments with high per capita income (Sepúlveda and MartínezVázquez 2012). In view of the gap in revenue collection between LAC and OECD countries, especially with regard to property taxes, there is a need to strengthen the payment culture, improve land registry and tax administration systems, and undertake the investments required to bring subnational revenue collection closer to its potential. An additional challenge facing subnational governments is their limited access to financing, which restricts their ability to quickly roll out the social benefits of public investment and expand their opportunities for development. Indeed, there are subnational governments that, while having access to debt financing in a context of fiscal responsibility, are constrained by a lack of capacity, institutional limitations, and an absence of institutional mechanisms. Weaknesses in PFM - including planning, budget management, public investment, and debt management capacities -have an impact on this challenge (as does the limited generation of own-source revenues). In some cases, subnational governments accumulate old debts to other public entities (for example, social security) that prevent them from accessing the market (Llempén et al. 2010). In most cases, weak generation of own-source revenue limits the amount that can be borrowed. In addition, many subnational governments are too small to be considered eligible for credit, despite the existence of instruments that make it possible to pool the resources of multiple entities into a single transaction Moreover, central government support is insufficient in some countries to provide subnational governments with the technical assistance they require to be restored to financial health and subsequently access the market. This is largely due to a lack of information and monitoring capacity in the Ministries of Finance, which limits the ability to classify subnational governments according to their creditworthiness. In this regard, one of the benefits of the effective implementation of fiscal responsibility frameworks in Brazil and Colombia is the central government’s increased ability to understand the state of subnational public finances - particularly in the case of the larger subnational governments. This makes it possible to approve debt transactions with a low risk of default. Subnational governments with limited management transparency and accountability. Finally, there is limited management transparency in subnational governments. In most countries the availability of information is either incipient or restricted.

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Only two countries, Brazil and Peru, make subnational financial information available that is considered sound. Second, the problem of opacity at the subnational level is well recognized by citizens and carries a considerable cost. A survey reveals that along with lack of citizen security, lack of municipal government transparency has become one of their most serious problems (IDB 2014e). The opacity means a significant transfer loss, for example irregularities in the use of local funds affected between 2 and 8% of all transfers audited over the last decade (Ferraz and Finan 2011; Brollo et al. 2013). In municipalities where education transfers were improperly used, basic school supplies are scarce, the quality of education (as measured by standardized tests) is lower, and students are more likely to drop out (Ferraz et al. 2012). Additionally, improper government actions are also accountable for these problems. Programs to promote accountability are still either uncommon or their scope extends only to the municipal administration or executive branch. Such programs are particularly necessary in view of the increase in transfers linked to nonrenewable natural resources, where the evidence points to greater problems with irregularities in the municipalities that benefit most from these resources (Caselli and Michaels 2013; Ferraz and Monteiro 2010; Maldonado 2011). Lastly, monitoring and evaluation of subnational management is insufficient. In Peru, financial audits by the Comptroller General’s Office barely cover 2.4% of the municipalities (IDB 2013d) and in El Salvador, despite the fact that all municipalities are required to undergo external financial audits, only one out of a representative sample of 10 municipalities fulfilled this requirement (World Bank 2010b). It is worth noting the Colombia experience in enhance transparency. Colombia’s royalties mapping system has helped to improve the transparency of subnational governments in executing royalty funds linked to the extractive industries. This allows monitoring of the public investment plans of subnational governments financed with royalty funds. Meanwhile, Brazilian’s experience can be valuable as well. Federal and state auditing offices supported implementation of the Fiscal Responsibility Law, created communities of practice, and expanded their audit activities to include operational audits and audits to evaluate efficiency in the use of funds.

3.4 Municipal Finance in Africa with Special Focus on Botswana 3.4.1 Introduction Cities are the defining form of human organization in the twenty-first century. In 1980, just 1.7 billion people lived in cities. By 2050, the world’s urban population will swell to 6.4 billion. By the end of this century, 80–90% of humanity is expected to be city dwellers. The vast majority of tomorrow’s urban growth will occur in the medium-sized and large sprawling cities of Africa and Asia. And since many cities are urbanizing before they industrialize, the slum population is set to double in the

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next decade. While everyone agrees that cities are growing in size and scale, there’s a worrying silence about what they will look like, much less how they will be financed (Robert Mugger et al. 2019). Municipal finance is about the revenue and expenditure decisions of municipal governments. It covers the sources of revenue that are used by municipal governments—taxes (property, income, sales, excise taxes), user fees, and intergovernmental transfers. According to Mugger, there is a tremendous supply of capital potentially available for financing cities. Much of it is tied to pension, sovereign and insurance funds and endowments. There are also growing numbers of impact investors who are eager to invest in socially and environmentally responsible projects while generating a decent return. Likewise, multinational development banks are also getting into the game, many of them seeking to de-risk investments for private investors, foster public–private partnerships and provide concessional finance, particularly for lower-income and emerging market cities. Cities themselves are actively exploring domestic sources of financing, including municipal bonds, privatizing under-used assets, securitization and eve crowd sourcing. The bottom line is that cities in developed and underdeveloped settings alike underestimate the opportunities to access finance. The truth is that they can deploy a host of regulatory instruments to shape market characteristics to their advantage. It is against this background that this chapter outlines and discusses the experience of municipal financing in Botswana. The paper starts off by first by providing the background on the state of local government administration in Botswana highlighting on the responsibilities of the municipalities in urban development and service delivery. The next section looks at financing capital expenditure from different sources, like own revenue resources, government transfers, the private sector, borrowing and also capital markets. After this the paper moves on to look at infrastructure financing, industrial development and the role of FDIs, and public, private partnerships in municipal development. Finally, the paper provides a brief summary of the challenges and opportunities of municipal finance and lessons to be learnt from Botswana’s experience.

3.4.2 Municipal Finance: Botswana: Overview Botswana, is located at the center of Southern Africa, positioned between South Africa, Namibia, Zambia, and Zimbabwe. One of the world’s poorest countries at independence in 1966, it rapidly became one of the world’s development success stories. Significant mineral (diamond) wealth, good governance, prudent economic management and a relatively small population of slightly more than two million, have made it an upper middle-income country (WB: https://www.worldbank.org/en/ country/botswana/overview). Since gaining independence from the United Kingdom, Botswana has been one of the world’s fastest growing economies, averaging 5% per annum over the past decade. Real economic growth accelerated to 4.4% in 2018 (the fastest growth rate

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in the last five years) and is projected to remain at around 4% up to 2021. The population in 2019 is estimated at 2.30 million, up slightly from 2014’s 2.04 million, which ranks 145th in the world. The country is also one of the world’s most sparsely populated countries with a population density of just 3 people per square kilometer. (World Bank 2019a). Gaborone is the largest city in Botswana, and also the capital. The population in Gaborone is approximately 232,000. More than 10% of the population lives in the capital. The majority of the population in Botswana lives within 100 km of Gaborone. Other major towns include Francistown, Lobatse, Selebi-Phikwe, Jwaneng and Sowa township, In Botswana, 61% of the population resides in urban areas, and the country is currently urbanizing at an annual rate of 2.3%.

3.4.2.1

Administrative and Constitutional and Legal Frameworks

There are sixteen administrative districts and associated councils in Botswana. The general duties of Councils can be summarized as “to exercise its powers so as to secure and promote the health, order and good government of the area for which it has been established”. (Cap 40:01/02; 29). The main duties of the councils are to provide the following services: - Primary education, public health and sanitation, roads, social welfare and community development and primary health care. The urban centres have experienced unprecedented growth over the years causing a strain in financial resources (Eustice, J.B 2001) (GOB (1987) (Mosha A.C. (2001). There are two main categories of current revenue for local government authorities in Botswana. (i) “Own Revenue”, which includes taxes, user fees and various licences, and transfers from the central government in the form of grants and revenues sharing. Unlike other countries, the municipalities are yet given the right to borrow to finance investments in local capital infrastructure.

3.4.3 Financing Urban Development 3.4.3.1

Planning and Budgeting

In general, municipal local authorities are funded through the Revenue Support Grant, known as Deficit Grants from the central government, Own Revenue Sources and Development Grants for major government projects in the municipalities. Currently the Deficit Grant accounts for 64% of urban council’s revenue. In addition, Councils receive 100% of their development funds in form of grants from the Central Government. Councils have legal powers to collect certain taxes, levies and fees in order to defray their operating expenses. With a few exceptions, they are discouraged from trying to raise revenue by embarking upon potentially profit making businesses, for example, provision of rental housing or public transport due to potential problems.

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3.4.3.2

171

Main Sources of Municipal Revenue

Botswana’s Local Government Act (Cap 40.01.) defines the duties, functions and responsibilities of the local authorities. It also stipulates the sources of their finances by making approvals for each authority, depending on sources identified (from the wide range of sources they have as options). Revenue finance is raised to meet the local authorities’ recurrent expenditures while capital finances are raised to carry out capital works, such as roads, water and sewerage expansion, housing, street lighting etc. These municipal authorities are funded through four mechanisms, namely Own Revenue Sources that include land-based revenues (property tax and rates) and on non-land based revenues such as, tax on provision of goods and services etc.; Revenue Support Grants (RSG); loans from the Public Debt Service Fund; and Capital Development Grants from the centre (Derek J. Hudson (1993). The main concern is the low levels of municipal “own revenues”. In the table below, it is shown that between 2009–2014/15(latest available complete figures), annual urban own sources ranged from a low of 15.8% to 36.5% as against RSG that ranged between 64–84.2%, hence a dependency syndrome. Further, more often than not, the RSG is apportioned more funds than the development budget. In 2012/13, for example, 78% the Ministry of Local Government and Rural Development was allocated to recurrent expenditure and only 22% to development expenditure to be shared among all the councils. (MLGRD 2016). This does not seem to adhere to the universal norm of 70:30 apportionment rules between the recurrent and development budgets indicating that the big portion of the budget goes towards administration, and salaries at the expense of capital projects (Table 3.3). Table 3.3 Urban Councils Recurrent Budgets (US$): Revenue Support Grant (RSG) and Own Source 2009/10 & 2013/2014 Urban Councils

2009/10

2013/14

Own Source

%

RSG

%

Francistown

3,794,988.2

14.2

22,972,133.8

85.8

Gaborone

9,643,210.2

22.6

32,890,542.6

77.4

Jwaneng

1,850,398.5

15.3

10,179,355.8

84.7

Lobatse

2,484,202.9

2.4

16,827,927.9

97.6

Selebi Phikwe

1,618,930.6

10.4

13,999,388.2

89.6

Sowa Town

291,468.2

5.6

4,929,486.3

Total

19,242,017.3

15.8

101,798,834.8

Own Source

%

RSG

%

4,456,912

24.5

13,728,191

75.5

11,453,996

40.5

16,760,343

59.5

1,640,792

20.2

6,491,222

79.8

1,497,953

13.2

9,766,547

86.8

1.628,584

15.7

8,709,692

84.3

94.4

437,527

11.8

3,277,960

88.2

84.2

21,115,748

20.9

58,727,956

79.1

Source MLGRD (2016): Department of Local Government Finance and Procurement

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External Revenue Sources: Revenue Support Grant from Central Government

The Revenue Support Grants or deficit grants is the difference between the local authority’s agreed estimates of expenditure and anticipated revenue from local sources and government subsidies. This grant is subject to a high degree of control from the centre. The grant has been fluctuating between a high of 84.2% in 2009/10 to 2013/14. The grant has been rising over the years due to increased demand to operate and maintain the huge infrastructure that had been put up under this fund from all the municipalities and towns. This fact has caused the resources of the municipalities to be spread more thinly in both human and financial terms. This situation is not healthy for political accountability to the electorate. The role of central government finance is also significant in that it finances the bulk of the Councils expenditure on capital account. This is partly due to the fact that the Councils are not permitted to borrow on the open market.

3.4.3.4

Own Revenue Sources

While the revenue from the RSG is high, that from own sources was quite modest. The total revenue of the six municipalities in 2014 was US$10,955,089 out which property rates was US$7,392,837 and the rest was revenue from other sources that totaled US$. 3,562,250. Thus, rates alone accounted for 67.4% of the total own revenue budget, whilst other sources accounted for 32.6%. (MLGRD 2016). This obviously means that the bulk of the revenue for the municipalities came from rates. Rates are property tax levied on land and buildings. However, the Councils do send the money to central government for central allocation on a formula basis. This means that unlike other countries, municipalities do not benefit from their rates. The other revenue incomes include among others, such as plan perusal fees, abattoir fees, insurance commission, lease rentals, sanitation fees and so forth.

3.4.3.5

Property Rates and Service Levies

The table below shows city revenue from property rates and service levies and other taxes. Rates are composed of land rents on property owned by individuals, companies or the government, including parastatals and rates on buildings (i.e. developed property). Other property taxes levied by municipalities also include a levy on transfer of property and some betterment taxes. Rates are a property tax and all those who own property or plots in the city area, whether developed or undeveloped, have to pay. Rates on undeveloped plots are levied at four times of the rates for developed plots. Higher rates are charged due to the government policy of discouraging speculation in land and encouraging plot owners to develop them as quickly as possible. However, this is ineffective in deterring speculation as those with high disposable income have been snapping up such land for development of flats or town houses.

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Table 3.4 Actual “own revenue” from various sources–2014 Local municipality

Property rates

%

Revenue from other sources

%

Total

Francistown

1,041,549.7

59.6

704,448.9

40.4

1,745,998.7

Gaborone

4,516,738.6

91.6

410,147.9

8.4

4,926,886.5

Jwaneng

613,740.7

61.4

639,562.8

38.6

1,270,565.6

Lobatse

458,172.1

32.1

970,471.9

68.8

1,428,644.0

Selibe Phikwe

647,218.1

50.3

639,562.8

49.7

1,286,781.0

72,334.3

24.4

223,878.5

75.4

296,212.8

Sowa Source MLGRD, 2016

As shown in Table 2, in 2014, for the six municipalities total “Own Revenue” was US1,270,565 of which $613,747 (49.7%) was from rates and US$ 639,562 (50.3%) was from other sources like service levies, tax on the provision of goods and services (Business licences and fees), plan perusal fees. However this figure could rise further if municipalities could make more efforts in collecting funds. Unfortunately, a source like rates is inelastic as sometimes the valuation rolls are incomplete, hence depriving the municipalities a major source of income. Further, central government and parastatal organisations are supposed to pay rates to the municipalities, but they do not do so hence, denying the municipalities substantial sums of money. There has also been a problem of ratepayers not receiving their bills, as the Department of Lands had at times not updated their records. Later, however, the department did engage consultants to update property records. It is worth mentioning that city residents also pay income tax to the government revenue offices. This is taxed at progressive rates from 5 to 25%. Gained from the disposal of immovable property (Table 3.4).

3.4.3.6

Rates and Service Levy Debtors/ Defaulters

Although the municipalities have managed to raise quite substantial revenue funds from rates and service levies, it is common practice to find many city dwellers owing the various municipalities vast sums of money. The sums involved have been rising year by year. For example, in the year 2014, rates debts for the six cities and towns were US$10,777,426, whereas service levies debts stood at US$2,044,970.9 (MLGRD 2016). There are many factors that contributed to this state of affairs. To start with, the procedure for collection and recovery of late arrears is generally poor. Enforcement is both time consuming and cumbersome, as follow up measures are not clearly defined. However, efforts are being made time and again to recover rates arrears. Written notices are normally sent to all defaulting ratepayers. Reminders are issued to all plot owners who have not paid their full rates within the allowed period of four months from the date of the invoice, reminding them that they will be liable to pay interest. After issue of another reminder, a Demand Notice is then sent. If rates still

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Table 3.5 Rates & service levy debts: Urban councils -2014: Us Dollars Municipality

Rates

Service levies

Amount

Percentage

Amount

Percentage

Francis town

6,186,087.0

87.2

905,935.40

12.8

Gaborone

3,840,905.8

88.3

508,456.4

11.7

Jwaneng

199,360.9

79.9

50,066.4

20.1

Lobatse

328,115.3

57.8

239,888.6

42.2

Selibe Phikwe

251,914

42.6

338,643.9

57.4

64.5

15,968.9

35.5

Sowa

28,957.2

Source MLGRD, Department of Local Government Finance and Procurement

remain unpaid, then the case is referred to debt collector/attorney for further legal action of collection of rates. In such an event the defaulter has to pay legal costs in addition to the rates. For long outstanding debts, provision is normally made to attach property to recover the debt. Recently, the Council adopted yet another innovative idea of collecting debts by publishing the names of defaulters in the national press. The collection of rates is not administratively difficult and it merely requires a highly determined administration to achieve low default rates (Table 3.5).

3.4.3.7

Overall Assessment.

The main conclusion from the above clearly shows that there is heavy reliance on transfers from central government and on rates, which by the way are less economically efficient than on land tax, and difficulty in collection enforcement. This does not augur well for the municipalities as they have no enough resources of their own. Year after year, mayors and town directors have been recommending that they should be raising more own funds, but these have been mere words and no action. The only step that has been taken is the computerization of their accounting processes which include accounting, human resources and land management. This has been extended to include the rates and payroll accounts. To-date it can safely be said that computerization has brought positive impacts as it has led to increased efficiency and transparency in the various councils.

3.4.3.8

Management of Financial Resources

All municipalities do prepare, on a yearly basis, budget estimates containing the required expenditure, the expected income from internal sources, and the required deficit grants to cover the shortfall. Various reviews of the municipalities’ accounts show that the councils follow procedures of accountability and tendering quite well. This is in contrast to some municipalities in Africa where corruption and mismanagement of funds is endemic. Further, financial controls official discussions revealed

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that the City Treasurer’s Departments always use normal accounting and financial control, plus restrictions of votes. All councils do internal auditing of their accounts. All local authorities in Botswana are required to report upon the progress of projects implementation each quarter to Ministries. The reports cover both financial and physical status. It is encouraging to observe that this procedure has been regularly followed by all the councils. Finally the councils have all computerized all their core processes which include accounting, human resources and land management. This has been extended to include the rates and payroll accounts. To-date it can safely be said that computerization has brought positive impacts as it has led to increased efficiency and transparency in the various councils.

3.4.3.9

Infrastructure Financing

The rapid urbanization Botswana has experienced has been straining the already overstretched infrastructure and created complex problems for local governments. All along much of the municipal infrastructure has been in the hands of government or through its parastatals for water supply, road transport, telecommunications etc. The government has invested heavily in infrastructure in almost all areas of the country with tarred roads reaching most areas. Since the 1980s, the gap between demand and supply for basic infrastructure sectors (including power generation and distribution, water and sanitation, telecommunications and transport) has been rapidly growing in country. While demand has massively increased due to strong population growth as well as high rates of urbanization, supply has remained constant or has even decreased. For some time now, Botswana has been warming up to the idea of using private funds to fund infrastructure, for example, using pension funds for investing in infrastructure and commerce. A good example is a huge regional shopping mall (Airport Junction Mall) to the north of Gaborone city that has been financed using workers’ pension funds. Currently, this mall is the biggest in the whole country. Again, much of the new central business district in Gaborone and also that of Francistown city has been built by using private funds and FDI. The main players are big property developers like Turnstar Holdings Jamal Builders and others. TIME Projects Botswana has also been involved in urban land servicing in partnership with the Gaborone City Council for housing construction. Such initiatives should be replicated in other towns in Botswana. Foreign Direct Investment (FDI) for infrastructure development. Due to its status as a middle income country, Botswana has struggled to attract FDI in the last ten years due to stringent conditions of doing business. However, with the coming of the new president, he has eased this by making it easier for businesses to set up shop in the country. According to UNCTAD, FDI inflows in Botswana rose from USD 177 million in 2017 to USD 230 million in 2018. This is mostly due to an increase in automotive FDI. The total stock of FDI in Botswana reached to USD 4.82 billion in 2018, estimated at 25.4% of the country’s GDP (World Investment Report 2019, UNCTAD) (https://unctad.org/en/PublicationsLib rary/wir2019_en.pdf). The mining sector attracts most of the FDI. However, investments in the services industry (insurance and banking) have been growing in recent years. FDI primarily comes from the Southern African Customs Union (SACU), the

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European Free Trade Association (EFTA), Canada and Zimbabwe. Other inflows come from China, India and Pakistan. Most municipalities benefit from these FDI as investors spend substantial funds in the cities (https://www.ceicdata.com/en/indica tor/botswana/foreign-direct-investment--of-nominal-gdp).

3.4.3.10

Industrial Financing

Industrial development in Botswana is rather on the low side. Much of the industry that is seen in urban areas is mainly packaging and fabrication, beer and soft drinks brewing and little else. Much of this is run by the private sector. In order to boost industrial production the government has introduced many reforms to enable investors to do business in Botswana. One initiative has been the establishment of Special Economic Zones in four of the main towns of Gaborone, Francistown, Selebi-Phikwe and Lobatse. It is anticipated that this initiative will see these centres as hubs for industrial development. Both investors and the municipalities will pump money into these centres to boost exports as well as to provide employment which the country needs very badly.

3.4.3.11

Public/Private Partnerships

All municipalities have in a number of instances embarked on implementation of the government’s policy on privatization (approved by Parliament in April 2000) by way of outsourcing certain activities to the private sector in order to provide essential services in their municipalities. Among the services the Councils have privatised in the past include is solid waste removal, collection of debts, employment of private security guards in a number of schools and leasing out of public transport routes to private operators. They are also working mechanisms of forging partnerships with the private sectors in the areas of parking arrangements, development and running of markets and waste collection and management. Secondly, the private sector plays a very important role in land delivery in the city. In 1994, government declared its intention to involve the private sector in property development especially in the provision of serviced residential land through the Presidential Directive Cab 9/94. The private sector’s participation in urban land servicing is anchored around providing secondary and tertiary services to peripherally serviced blocks of plots at their own cost. The services are later handed over to the responsible authorities free of charge to facilitate future maintenance. Blocks of plots are allocated to developers through a competitive tender system. Successful developers are then required to pay for all infrastructure costs needed to develop land to the specified standard. Blocks of plots have been allocated to private developers in several parts of the municipalities. For example in Gaborone land was allocated in Block 5, block 6, Maruapula and Tsholofelo. (GCC-Draft UDP2, pg.40).

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3.4.4 Municipal Finance Challenges and Lessons Learnt The major financial challenges facing Botswana’s local and urban councils stem largely from (a) the lack of financial autonomy and power; (b) low levels of “Own Resources” and (c) low cost recovery. The lack of autonomy and power means that councils have to take whatever central government gives them as revenue both for development as well as for recurrent expenditure in the form of support grants for which they have little say in the final figures. Further, there is always a mismatch between the recurrent budget and the development budget. As for own revenues, it is evident that the Councils do not have significant independent sources of revenue and even these resources are inadequate and inelastic. For example, the city of Gaborone in 2011/12 had estimated to make P6mil from interest arrears, but fell short by P3.3mil; had hoped to make P4.7mil interest in its investments but managed only P42,000; made only P228,000 from sanitation fees as opposed to a projection of P3.7mil and had a shortfall of P944 for SHHA service charges and P2.7mil for housing rent and P1.3mil for trade licences.(Sunday Standard, 23rd June 2012'' ). Cost recovery is poor because many Councils have not developed implementation guidelines and monitoring mechanisms. To compound this problem, many urban Councils are facing escalating debts particularly those of rates, building material loans and service arrears. For example, Francistown City Council managed to recover only P 13 988 652.46 (US$1,398,865.2) as compared to the P 29 583 474.11(US$2,958,347.4) owed to it for rates during the financial year 2006/2007. In the same year, the Council was also owed P 747 768 (US$74,776.8) on account of building material loans and P 2 194 642 (US$219,464.2) on service levy. Much of this can be attributed to low incomes and unaffordability on the part of the beneficiaries.

3.4.5 Conclusion: Most Pressing Priorities in Financing Botswana’s Towns and Cities in the Future Financing city development in Botswana, just like the rest of the world will take skill and acumen. The municipalities will have to use both traditional and innovative methods of city financing if they are to succeed like in developed countries. As noted above, municipalities in Botswana are likely to continue to suffer in the long run on the two fronts viz: dependency on central government and low own financial resources for budget sustainability. Since the government budget is increasingly becoming constrained new strategies have to be adopted in the new Urban Agenda to improve the situation. The following are key recommendations:Firstly, Botswana cities and towns must strive to be more independent financially. They need to seek local financing capacity while capitalising on all resources available from multilaterals, bilaterals, central and local governments. The municipalities should seek new and innovative sources of finance including, municipal bonds, bank

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loans and the like which are hardly being utilized at the moment. In the continent, very few cities are using bonds or taking massive bank loans. Secondly, the municipal leaders need to increase their capacity. In the case of Botswana, the money is there, but packaging bankable projects is difficult. It takes sophistication and financial knowledge to reach well-balanced financing structures, which requires better education, networking and capacity building that is not available in most municipalities today. By all means, greater efforts should be put in collecting revenues owed to the councils by defaulters. It should be realized that increased cost recovery enhances the financial autonomy of the Councils and reduces dependency on Central Government, overtime. Therefore, if done rigorously this will support the Councils by making them sustainable in the long run. As the sustainability of service provision in the long term should depend on the extent of cost sharing between government and the beneficiaries of such services, implementation and monitoring of cost sharing measures need beefing up. Cost recovery thus provides the fiscal basis for further expansion. Councils should step up public education on cost recovery. Thirdly, as far as infrastructure financing is concerned, Botswana should look into taping money from land value capture as an instrument of harnessing development funds. As has already been seen, infrastructure demand in Botswana has been growing rapidly, notably in the urban transport sector where new roads are constantly been built or old ones being widened as experienced in the major cities of Gaborone and Francistown. The unfortunate thing is that land values along these new roads spike but the Councils do not reap betterment tax from them. Hence, accessing sufficient funding in a timely manner is crucial for the development of public transport. One innovative and increasingly accepted way to fund public transport in many countries and cities like Shenzhen in China today is through Land Value Capture finance (LVC). Urban infrastructure investment induces increases in land value, thus it is possible to recover the capital costs of urban investment by capturing some or all of the increments in land value resultant from the investment; this may be accomplished through a fiscal mechanism such as land value finance (tax, incentives, development agreements). This model leverages partnerships between the public sector, road transport companies and developers to coordinate planning and financing of new roads and adjacent real estate developments. Fiscal decentralisation reform, based on the subsidiarity principle, may be the pivotal point in the implementation of land value capture in the country because the revenue generated by land value capture can be earmarked by local authorities to fund urban expenditure. Fourthly, the towns and cities of this country need to be balanced in their development approach, especially the capital city Gaborone and Francistown facing rapid growth and globalisation. These cities must balance the desire to grow economically with the need to address critical environmental and social equity concerns, whilst being sensitive to local needs and promoting inclusivity. For the small to mediumsized towns like Selebi Phikwe, Jwaneng, Sowa and Lobatse, it is important to tap into all external resources – including grants, subsidies, credit guarantees and more.

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3.5 Charges to Building Rights: A Notable Value Capture Experience from Brazil36 Land value capture (LVC) provides a mechanism for communities to recover financial windfalls accruing to landowners from public investments in infrastructure and services or from beneficial changes in land use norms and regulations. This progressive public financing option is gaining noteworthy traction in nations worldwide. This chapter argues that a major new value capture tool—charges for building rights— can be a significant source of public revenue. This type of fee has the advantage not only of generating much-needed financing for urban infrastructure and services, but also of addressing a fundamental social equity issue in land policy. After outlining the arguments for value capture and providing a brief history of its application, the chapter describes the highly successful implementation of building rights charges in São Paulo, Brazil. This case study also illustrates how this tool can be used to increase local administrative capacity to invest in urban infrastructure and services as well as in social housing.

3.5.1 The Case for Value Capture Rapid urbanization, especially in developing countries, creates mounting demand for costly infrastructure and services to support the mix of gated communities and high-end developments competing for space with informal settlements at the urban fringe. The stakes are high when it comes to land price increases resulting from public infrastructure investments, including roads, water, sewerage, and electricity. According to a report from the Asia Development Bank Developing Asia will need to invest $26 trillion from 2016 to 2030, or $1.7 trillion per year, if the region is to maintain its growth momentum, eradicate poverty, and respond to climate change (climate-adjusted estimate).37 This amounts to about to 5% of projected GDP of Asian countries (excluding China). McKinsey in turn, estimates the urban infrastructure investment need from 2013 to 2030 in cities at over $45 trillion!.38 In the meantime, the Trump administration unveiled a $1.5 trillion infrastructure 10 years plan in 2018 designed to repair and upgrade the country’s aging infrastructure.39 Similar numbers can be quoted to most countries, in special the less developed ones. For the latter an additional stress is 36

The present article is a slightly modified version of an earlier version published as “Charges to Building Rights: Brazil’s Successful Experiment with Value Capture” (WP/18/05 – ATI) Working Paper South Africa, 2018. 37 https://www.adb.org/publications/asia-infrastructure-needs. 38 Cited in Financing the Growth of Cities by By Clare Romanik, Urban Resilience Adviser, USAID Urban Links Media Scan 6/1/2017. 39 https://www.fool.com/investing/how-to-invest-in-infrastructure-stocks.aspx.

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noted as the cost of a materials (cement, steel etc.) and machinery (metro digging machines and the like) tends to be the same if not higher in less developer than in developed countries whereas their per capita income is a fraction of that in the more developed countries. Thus, the ‘fiscal effort’ in less developed countries is much higher! All in all, a $1 trillion a year is needed just to properly support the influx of 200,000 people moving to cities every day with proper infrastructure and services for water, sanitation, transport, and green. (Triveno, L. and Smolka M.O 017) With the general rejection to any additional tax burden one is urged to think more creatively about alternative funds to meet the above challenges. The encouraging news is that land value increment resulting from investments in areas lagging in the supply of urban infrastructure and services are often much higher than the cost of such interventions. In other words, management capacity under the proper legal and institutional framework may account for a substantive share of feasible solution for the above conundrum. In effect the ADB estimates that the land value increment ($3.4 Billion) resulting from Manila’s new metro line combined with the density rezoning of the affected areas to be 5 times as much as the cost of the investment.40 Similarly, investments the revitalization, green areas, and urban infrastructure and services provisions in cities as Quetzaltenango in Guatemala and Xalapa, México have been shown to generate land valorizations (Blanco et al. 2017). Planners who share responsibility for some of this haphazard growth are learning the potential land value increases associated to land use regulations and, more importantly, the significant redistribution of wealth that accompanies public interventions. The so-called urban multiplier effect—the increased value of land when converted from rural to urban use—is typically more than 4:1. Data collected globally on urbanization by Angel and Mayo (1996), and more recently in countries across Latin America (Bouillon 2012), confirm this order-of-magnitude increase in parcel prices. In São Paulo’s high-end areas, the value that developers are willing to pay for the right to build at a floor area ratio (FAR) of two or three (rather than the basic FAR of one) may fetch well over US$1,500.—per square meter (Sandroni 2011). In another example, conversion of land from residential to commercial use - in areas like El Chico in Bogota, Colombia - raised prices by an average of 50% (from $2,143 to $3,214 per sqm) in high strata areas and about 30% in median median-income neighborhoods. In an area in the north of Bogota planned for 140,000 housings through 32 ‘partial plans’ comprehending 1,800 has plots designated for residential use at a FAR of 2.2 are valued at $1285 per sqm whereas a similar one for commercial and offices at and FAR of 2.7 the price is well over $2,500. (Borrero 2018). Value capture policies provide a way to recover these large land value increments and to use this new revenue not only to pay for public investments in infrastructure and social housing, but also improve local land use management practices. The social justice argument for value capture is that all taxpayers bear the costs of providing 40

https://businessmirror.com.ph/2019/06/02/a-genie-called-lvc/.

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adequate transportation, water and sewage systems, and open space to meet more intensive land use, but only certain landowners reap the benefits. Under the equity principle that the public sector cannot favor one citizen over another, public investments and regulations affecting land values must be uniform across a city. If they are not, the government must take measures to redistribute the benefits and burdens of those investments and regulations. Capturing incremental land value for the benefit of society can take a variety of forms, including taxes, contributions, fees, exactions, and regulatory charges (Smolka and Amborski 2007; Smolka 2013). These tools vary from explicit (such as betterment contributions) to implicit (such as land leasing, land banking, and expropriation). Other options include land readjustment, which involves negotiations with landowners that would potentially benefit from an urbanization project, and exactions, which are fees paid for specific land use concessions or flexibility. The revenues collected by value capture tools are included in the municipal budget and are typically managed by the local treasury secretary, although some proceeds may take the form of in-kind compensation.

3.5.2 Growing Popularity of Land Value Capture The notion of paying for the benefits of public investments was well established as early as the Roman Empire, and countries from Europe to the Far East have used a variety of value capture tools ever since that time. Recent applications include a newly granted right in England to tax the increase in value from the rezoning of land, and charges in France for building rights over and above a certain baseline density. In Spain, municipalities capture part of the value increase of urban expansion by requiring landowners to cede some serviced building plots to the municipality, provide the land needed for infrastructure, and pay the costs of service provision, overhead, and a profit margin (Muñoz Gielen 2010). In Latin America, Colombia’s Law 388 of 1997 and the Brazil’s Statute of the Cities of 2001 set the parameters for much of the legislation passed elsewhere in the region. Colombia’s Law 388 introduced the notion that public actions that improve urban land uses, including air space, give the public the right to participate in the resulting land value increments. The law also specifies the sources of these benefits— conversion of rural land to urban uses, changes to zoning and density regulations, and higher allowable rates of land occupation—and the share that local or district councils may take from those sources. National and local legislation throughout Latin America now includes provisions for some form of value capture. Several factors account for this growing popularity. The global trend toward fiscal decentralization—giving municipalities greater fiscal autonomy but also greater responsibility for service provision—has pushed local governments to find new revenue sources. Meanwhile, cities around the world face the challenge of funding a growing backlog of infrastructure improvements. Indeed, in the United States alone, the gap in infrastructure investment has been estimated at over $3.6 trillion. With

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governments also under pressure to address social inequalities, value capture tools provide a way to mobilize new and more flexible funds to finance special public programs.

3.5.3 Relationship of Value Capture to Property Taxation Property taxes may be a form of value capture in that, aside from taxing building values, they apply to accumulated increments in land value. This fact has led to the misconception that implementing value capture tools is “on top of” property taxes and thus represents double taxation. But as the Brazilian Supreme Court has ruled, value capture is not a tax but rather a charge imposed for the use of “additional building rights that are not part of the owner’s assets but a public good that belongs to the city as a whole” (Rabello de Castro 2012, 18). In essence, one cannot be taxed for something one does not own. Similarly, to a land value tax, charges for additional building rights fall entirely on landowners, do not distort the economics of land use decisions, and do not generate the excess burden (deadweight loss) common to most taxes (Oates and Schwab 2009). Another advantage of value capture tools is that they have the potential to fully capture the value of public expenditures for infrastructure and service improvements, changes in norms and regulations affecting land uses, and other locational attributes that are capitalized in land prices, while property taxes do not. Lastly, from the strict point of raising overall local public revenues the property tax is likely to be more effective than value capture. In effect, the former may be accomplished with better fiscal practices as for example updating existing cadasters and value maps, review of exemptions, improving taxpayers’ compliance incentives and the like. The weak technical expertise to promote such improvements reinforced by the unpopularity of the property tax and mounting resistance to any increase in the overall tax burden creates though a barrier to count much on property tax reforms to raise local revenues. The non-tributary nature of most value capture tools - in especial those associated to charges to building rights - added to its designation for capital expenditures is attractive to local authorities eager to promote large scale urban redevelopments projects especially in degraded areas and/or investments in urban infrastructure or in social housing in general.

3.5.4 Evolution of Charges for Building Rights in São Paulo As noted earlier, exactions compel landowners to make cash or in-kind contributions in exchange for special approvals to develop or build on their land. These contributions may be stipulated through subdivision or development agreements or negotiated on an individual basis. While exactions are the most common land-based financing tool used around the world, officials are often unaware that they are a form of value

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Fig. 3.16 Owners of the two buildings shown here, located in Curitiba, Brazil, both paid for the right to build at densities above the basic FAR of about six stories. The building design on the left highlights the area for which building rights were charged © Gislene Pereira

capture. For example, linkage operations are a type of exaction, allowing developers to build at a higher density or floor area ratio (FAR) in exchange for contributions toward affordable housing or other community benefits. In Brazil, building charges have evolved over time from ad hoc exactions into set fees calculated according to predefined criteria and applied to all properties in a specific area. In 2014, the City of São Paulo instituted a basic FAR of 1 that gives all landowners the same building rights, and a maximum FAR that ranges from 1 to 4 depending on city zoning. By setting a universal basic FAR and charging for any additions to that FAR, the city managed to split the interests of developers and landowners, practically eliminating major legal appeals to charges for additional building rights (Outorga Onerosa do Direito de Construir, OODC). These charges are based on the notion that the right to develop land at densities above the basic FAR must be bought from the public as the legitimate owner of those rights. OODC also applies to other types of administrative changes that yield more profitable land uses, such as conversions from rural to urban uses or the rezoning of areas for renovation or commercial purposes.41 The value that developers are willing to pay for these additional building rights is substantial. In São Paulo’s high-end neighborhoods, for example, the charges for building structures that are two or three times higher than the basic FAR are often well over US$500 per additional square meter (Sandroni 2011 op. cit.) (Fig. 3.16). To get a sense of the revenues to be generated from building rights charges, take the following concrete case (simplified for exposition purposes). A developer is interested in a plot of land with 3,200 square meters (at US$6.4 million), and the 41

A variant of charges for such changes in building rights is given by the 1997 Law 388 in Colombia through the Participación en Plusvalías instrument, whereby 30 to 50 percent of the assessed increased land values resulting from administrative actions may also be subject to partial recovery by the public. (Maldonado Copello M M, Smolka M O 2003).

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goal is to build a structure of 8,000 square meters based on the maximum FAR of 2.5. This implies an additional area of 4,800 square meters (1.5 additional FAR × 3,200 sqm). The right to build at this higher density to be acquired for US$9.6 million, or at a cost of US$2,000 per additional buildable square meter. This is the amount the developer would have to pay for another neighboring plot of land with 4,800 square meters and a FAR of 1 to be able to build at the same density. In practice, the formula for calculating building charges is of course more complicated. The developer built a 27-story structure of 16,500 square meters, since about 8,500 square meters of the area used for garage, terraces, and other uses are considered as “non-computable.” That is, the additional 4,800 sqms (additional FAR of 1.5) is net of all these uses. Moreover, the assessed value of land, that goes into the formula to calculate the compensation, is often short from the full market value. That is, aside from a neighboring plot may not be available the additional building rights (virtual plots) are usually acquired at lower than the imputed value for such benchmark plot. In addition, from the developer’s perspective, building at higher density on the original 3,200 square-meter plot may be more profitable depending on economies of scale and other factors related to high-rise versus low-rise designs. To get a sense of the revenues to be generated from building rights charges, take the simplified case of a developer buying a parcel with 3,200 square meters priced at $6.4 million. The goal is to build a structure of 8,000 square meters based on the maximum FAR for the area of 2.5. To do so, the developer has to buy the rights to build at that higher density by adding the cost of 4,800 square meters (1.5 additional FAR × 3,200 sqm) to the purchase price.??? The US$2,000 charge per additional buildable square meter brings the total cost of the parcel to US$9.6 million. This is the amount it would cost the developer to buy a neighboring plot of land with 4,800 square meters and a FAR of 1 to build at the desired density. In practice, the formula for calculating building charges is of course more complicated. Say the developer in this case builds a 27-story structure of 16,500 square meters, but about 8,500 square meters of the area are used for a garage, terraces, and other “non-computable” uses, i.e., the additional 4,800 square meters of buildable space is net of these uses. From the developer’s perspective, building at higher density on the original 3,200 square-meter plot may be more profitable than buying a larger plot depending on economies of scale and other factors related to high-rise versus low-rise designs. The redistributive power of OODCs is considerable. The $9.6 million collected for this single project could subsidize some 320 new units of social housing. In other words, the cost of each of the 32 additional luxury apartments would pay for 10 low-income housing units. The City of São Paulo collected more than US$1 billion in payments from building rights from 2006 to 2017. But even accounting for the slowdown in collections during Brazil’s recent recession, revenues from OODCs have fallen far short of their potential for several reasons. In particular, the city did not set a universal basic FAR until recently, and the cadaster values used to bench building charges are known to be vary in accuracy according to zones not rarely falling below 30 percent of the full market value. Moreover, discounting factors applied for certain structures (e.g.,

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environmentally sustainable buildings), exemptions for social housing and other noncomputable areas of a high-rise (often above 50% of the total area as in lobby hall, playgrounds, balconies etc.), further reduce the potential net collection on public sales of building rights. At their full potential, revenues from additional building charges could account for well over 50 percent of local investment capacity adding at least 40 percent to the amount currently allocated to social housing and urban infrastructure. When sales of Certificates of Additional Potential Construction Bonds (CEPACs) are included, the pool of funds available for public investments would be even higher.

3.5.5 São Paulo’s Success with CEPACs Certificates of Additional Potential Construction Bonds (CEPACs) are a special form of building rights charges that are used to finance urban operations (UOs), i.e., largescale projects in delimited areas and supported by improved infrastructure. These redevelopment projects typically have building rights over and above those imposed by existing zoning ordinances. Unlike OODC payments that are managed from the general fund, CEPAC revenues must be invested in infrastructure and social housing within the UO area. CEPACs are an ingenious answer to the difficult task of valuing building rights because the cost of the bonds is based on how much developers are willing to pay for those additional rights in a competitive market. The bonds are issued by the municipality and regulated by the Comissão de Valores Mobiliários (CVM, the Brazilian equivalent of the US Securities and Exchange Commission) and then sold by electronic auction in the São Paulo Stock Exchange. CEPACS were sanctioned by the Brazilian Land Development Act of 2001 and implemented in 2004. CEPACs offer some noteworthy advantages. First of all, they address the difficult issue of assessing the market value of the increment resulting from public investments in UOs, and they reduce the transaction costs involved in negotiating the impacts of the project on individual properties. In addition, CEPAC auctions help local administrations anticipate the funds they need to invest in urban infrastructure and services in the redevelopment project. Moreover, selling CEPACs in tranches makes it possible to monitor and finely calibrate the market. The fact that the funds are earmarked also reinforces developers’ confidence in the system and prevents legal appeals. Auctions of CEPACs may be public (to acquire development rights) or private (as a currency with which to pay contractors). The face value of a new offering of CEPACs starts with the value from the previous auction. In the seven auctions for the Faria Lima UO, for example, the offered value started at US$550 in 2004 and ended at US$2,100 in 2010. Values for the Agua Espraiada UO increased from US$172 in 2004 to US$636 in 2012. All CEPACs offered were sold in 8 of the 15 auctions, and bidders paid large premiums in 9 of those auctions. Counting the revenues from just these two UOs, the city raised more than US$2.7 billion.

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CEPACss have been used to finance a variety of infrastructure investments and social programs. For example, revenues helped to put to defray US$150 million of the costs for the São Paulo’s new Ouro metro line and supported construction of an iconic bridge that cost over US$100 million. Some $57 million of the CEPAC funds were also used to develop new homes for 552 families from Jardim Edith, a slum located in one of the city’s most expensive areas. to new social housing in a neighboring area. Although São Paulo has been the most successful, other Brazilian cities have also issued CEPACs. For instance, all of the building rights issued for the Porto Maravilha revitalization project in Rio de Janeiro’s old port area were sold to a single buyer, the Real Estate Development Fund created by Caixa Econômica Federal (CEF), the Brazilian social and housing bank. Law 101 of November 2009 had authorized issuing 6,436,722 CEPACs for a total of 4,089,502 square meters of additional building rights at a cost US$1.75 billion. The municipality of Rio de Janeiro thus obtained substantial upfront funds to cover the costs of redevelopment. In addition, responsibility for auctioning CEPACS for this project in the future falls to CEF rather than the city. CEPACs are also partially funding the ‘Linha Verde’ UO in Curitiba (Soffiatti 2012). This project involved converting a congested national highway into an urban avenue, including extension of a bus rapid transit line, addition of new green areas, and increases to land use density. For this US$600 million investment, a municipal decree in 2012 authorized the release of 4,830,000 CEPACs and a minimum initial price of US$100 per CEPAC. The first auction attracted 18 bidders for the 141,588 bonds offered, and a group of three bidders associated with a shopping center development acquired 70 percent of the CEPACs.

3.6 Conclusions Value capture policies and tools are undeniably gaining new acceptability around the world. Initiatives to experiment with the basic economic principles behind value capture have grown in both number and innovation, and value capture tools are often being used in combination with traditional revenue-generation efforts. Public authorities are beginning to realize that they can raise substantial revenues to support the public good from the beneficiaries of their administrative decisions. They can negotiate and charge for changes in land use rights to generate those revenues, while also promoting a fairer distribution of the social costs and private benefits of urbanization. It is however important to notice that successful application of the charge to building rights bears heavily on the local administration capacity of the public to enforce zoning and other land use regulation codes. A stagnant property market would obviously offer no opportunity to apply such tools. Similarly a mot much may be expected from sprawling low density cities that may at best recover some land value increments from the conversion of rural to urban or the changes of residential to commercial uses. In such cases local authorities would better rely

References

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on other value capture tools such as land readjustment, betterment contributions or direct exactions applied to specially favored land developers. Although the actual dollar value of revenues generated from value capture is still relatively small, the examples of São Paulo’s application of OODC to high-end individual projects and the auctioning of building rights through CEPACs show that the potential payoffs are significant. At the same time, the impact of value capture policies on real estate development has been minimal and developers’ willingness to pay is directly linked to perceived benefits. In sum, tools like the charges to building rights are meant to cities experimenting vigorous vertical growth promoted by independent (from landowners’) developers seeking profits from innovative building processes and products rather than speculative operations with land. Note that charges to building rights fall in essence on the landowner having no major bearing on developers. The only difference being that rather than paying the land value increment resulting from a higher FAR to the landowner the developer now pays for the ‘city’ the legitimate owner of the ‘created land’ supported by the investments in urban infrastructure and services paid by the collectivity! Changing from a regime where landowners capitalize unearned income from public investments to one where private benefits are balanced with social costs requires a major cultural shift—one that is likely to meet significant resistance. But the future of cities now depends on developing effective land-based tools to finance urban development, and planners and local treasuries alike should consider adding value capture instruments to their toolboxes. Indeed, improved understanding of the links between public investments and increases in land value is an essential part of building new fiscal and planning cultures that will strengthen collection of property taxes and other local revenues while improving urban management overall.

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Bouchet, M, Liu, S, Parilla, J & Kabbani, N. 2018. Global Metro Monitor, 2018. Washington, DC: Brookings Institution. Bouillon, Cesar Patricio. 2012. Room for development: Housing markets in Latin America and the Caribbean. Hampshire, UK: Palgrave Macmillan Ltd. for Inter-American Development Bank. Boueri, R., C. Mac Dowell, E. Pineda, and F. Bastos. 2014. Analysis of Public Spending. An Evaluation Methodology for Measuring the Efficiency of Brazilian State Spending on Education. Discussion paper IDB-DP-361. IDB. Borrero Ochoa, Oscar. 2018. Economia Urbana Y Plusvalia Del Suelo, Editorial Bhandar, Bogota Colombia, primera edición. “Botswana: Investing in Botswana”. Foreign direct investment Investment framework and opportunities Lloyds Bank. Britannica. 2014. Constitutional Law. Unitary and federal systems. Encyclopedia Britannica, online edition. Brollo, F., T. Nannicini, R. Perotti, and G. Tabellini. 2013. The Political Resource Curse. American Economic Review, 103(5): 1759–1796, August. Canavire, G., J. Martínez Vázquez, and B. Yedgenov. 2016. Re-examining the Determinants of Fiscal Decentralization: What is the Role of Geography? Journal of Economic Geography. Caselli, F. and Micheals, G. 2013. Do Oil Windfalls Improve Living Standards? Evidence from Brazil. American Economic Journal: Applied Economics, 5, pages 208-238. Cheeseman, N. and Burbidge, D. 2016. “Principles of municipal finance.” in Kamiya, M & Zhang, LY (eds.), Finance for City Leaders Handbook: Improving municipal finances to deliver better services [2nd Ed.], pp. 4–15. Nairobi: UN-Habitat. Derek J. Hudson. 1993. Increasing the Own Source Revenues and Fiscal Authority/Accountability of Local Authorities: A Preliminary Review of some Alternatives. Derek J. Hudson. 1994. Financial Arrangements for local Government- Some Comments on the Botswana Situation. A Paper presented at the sub-regional workshop sponsored by Frederick Ebert Stiftung in co-operation with SADC 1 - 3 Dec. 1994. DNP. 2017. Nueva medición del desempeño municipal. Primer informe de resultados 2016. DNP, Colombia. Dobbs, R., Pohl, H., Lin, D. Y., Mischke, J., Garemo, N., Hexter, J., Matzinger, S., Palter, R. and Nanavatty, R. 2013. Infrastructure Productivity: How to save $1 trillion a year. New York: McKinsey Global Institute. ECLAC. 2011b. Macroeconomic Challenges of Fiscal Decentralization in Latin America in the Aftermath of the Global Financial Crisis. J. P. Jiménez and T. Ter-Minassian. ECLAC. ECLAC. 2011c. El financiamiento de los gobiernos subnacionales en América Latina: Un análisis de casos. J. C. Gómez Sabaini and J. P. Jiménez. ECLAC. 2012. The Inter-governmental Allocation of Revenue from Natural Resources: Finding a Balance between Centripetal and Centrifugal Pressure. Chapter 10 in “Decentralization and Reform in Latin America,” edited by Brosio and Jiménez, ECLAC. Egner, B. 1987. The District Councils and decentralization, 1978–1986. A consultancy report to SIDA. Mimeo. Eguino, H. A., Porto, C. Pineda, M. Garriga, and W. Rosales. 2010. Estudio de las características estructurales del sector municipal en América Latina. Discussion paper IDB-DP-145. IDB. Eguino, H.,and Radics, A. 2018. Next Steps for Decentralization and Subnational Governments In Latin America and the Caribbean. IDB. Eustice, J. B. Financial Management and revenues of Local Authorities: A Report of the Local Government Structure Commission. Ferraz, C. and Finan, F. 2011. Electoral Accountability and Corruption: Evidence from the Audits of Local Governments. American Economic Review 101: 1274–311. Ferraz, C. and Monteiro, J. 2010. Does oil make leaders unaccountable? Evidence from Brazil’s offshore oil boom, mimeo PUC-Rio. Ferraz, C. F., Finan, and D. Moreira. 2012. Corrupting Learning. Journal of Public Economics 96(9-10): 712–726.

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Chapter 4

Global Urban Economic Competitiveness Performance

Leading cities are particularly important for the competitiveness of global cities. The report shows that influenced by the decline in the average urban competitiveness of China, the United States and Europe, the average global urban competitiveness slightly declined. This also indicates that if the trade war between major countries continues, it will not only weaken the urban competitiveness of each country, but also weaken the global urban competitiveness and welfare.

4.1 Top 20 Cities: Fierce Competition Makes the Position Fluctuate Greatly, Global Comprehensive Centers and Technology Centers have Generally Improved, while Specialized Cities and Manufacturing Centers Declined Overall Overall, the top 20 cities in the world are highly competitive, with significant changes in rankings (16 cities have changed, with a maximum change of 4 ranking). The global integrated center and technology center have generally improved, and the professional and manufacturing cities have declined overall. According to the Global Urban Competitiveness Report 2019, New York, London, Singapore, Shenzhen, San Jose, Tokyo, San Francisco, Munich, Los Angeles, Shanghai, Dallas, Houston, Hong Kong, Dublin, Seoul, Boston, Beijing, Guangzhou, Miami, and Chicago rank globally Top 20. Table 4.1 shows the spatial distribution of the top 20 cities in the world. At the same time, the top 20 cities in the global economic competitiveness ranked relatively stable, and the rankings of 14 cities remained unchanged or rising, with only 6 cities ranking down. Table 4.2 shows the ranking changes in the city’s economic competitiveness.

© China Social Sciences Press 2023 P. Ni et al., The World: 300 Years of Urbanization Expansion, https://doi.org/10.1007/978-981-99-3553-6_4

193

194

4 Global Urban Economic Competitiveness Performance

Table 4.1 Top 20 cities in the global economic competitiveness Rank

City

Country

1

New York

America

2

London

United states

3

Singapore

Singapore

4

Shenzhen

5

San Jose

6 7

Poor ranking with 2018

Rank

City

Country

Poor ranking with 2018

0

11

Dallas-Fort Worth

America

1

1

12

Houston

America

−2

−1

13

Hong Kong

China

−2

China

0

14

Dublin

Ireland

2

America

0

15

Seoul

South Korea

0

Tokyo

Japan

2

16

Boston

America

2

San Francisco-Oakland

America

0

17

Beijing

China

2

8

Munich

Germany

−2

18

Guangzhou

China

−4

9

Los Angeles

America

0

19

Miami

America

−2

10

Shanghai

China

3

20

Chicago

America

1

Source CCC of CASS

Table 4.2 Top 20 city ranking changes No change in rank

Rise 1 bit

Rise 2 bit

Rise 3 bit

New York, Shenzhen, San Jose, San Francisco, Los Angeles, Seoul

London, Dallas, Chicago

Tokyo, Dublin, Boston, Beijing

Shanghai

Decrease 1 bit Decrease 2 bit

Decrease 4 bit

Singapore

Guangzhou

Munich, Houston, Hong Kong

Source CCC of CASS

In terms of sub-index rankings, the economic growth in San Francisco, Shanghai, Dallas, Houston, Guangzhou, and Chicago has declined slightly, and the rankings in New York, Singapore, and Miami have not changed; The economic density of Shenzhen, San Jose, San Francisco, Los Angeles, Shanghai, Houston, Hong Kong, Guangzhou and Miami has declined slightly, and the rankings of Singapore and Munich have not changed. Table 4.3 gives a statistical description of the economic competitiveness of the top 20 cities in the world. The performance of the top 20 cities in the world has intensified. Compared with 2018, the standardization index of economic increment and economic competitiveness of the top 20 cities in the world declined slightly in 2019, indicating that the overall lead has slowed down. The standard deviation and coefficient of variation of economic increments were expanded from 0.091 and 0.103 in 2018 to 0.116 and

4.2 Top 200 Cities: Europe Has More Cities Declined in the Ranking While …

195

Table 4.3 Descriptive statistics on the economic competitiveness of the top 20 cities in the world Economic Economic Economic Economic Economic Economic increment increment density density competitiveness competitiveness 2019 2018 2019 2018 2019 2018 mean

0.757

0.888

0.775

0.739

0.807

0.878

median

0.78

0.92

0.78

0.738

0.793

0.884

variance

0.116

0.091

0.115

0.115

0.061

0.064

Coefficient 0.153 of variation

0.103

0.148

0.156

0.076

0.072

minimum

0.523

0.697

0.557

0.542

0.721

0.796

maximum

1

1

1

1

1

1

Source CCC of CASS

0.153 in 2019, respectively. The coefficient of variation of economic competitiveness output increased from 0.072 in 2018 to 0.076, indicating an increase in internal differentiation.

4.2 Top 200 Cities: Europe Has More Cities Declined in the Ranking While Asia Has More Cities Improved Compared with 2018, 200 cities were led by North America in 2019, and some cities in Europe and Asia declined. Table 4.4 shows the results of the top 200 cities in the world. The results in Table 4.4 show that the decline in the economic competitiveness of European cities accounts for a maximum of 54.2%, the proportion of economic competitiveness in South American cities decreases by a minimum of 25%, and the decline of Asian cities is 31% less than that of North America is 35.2%, Less than the average of the top 200 cities fell by 38.5%. Figure 4.1 shows the spatial distribution of the competitiveness rankings of the top 200 cities in 2018 and 2019. Figure 4.1 shows the spatial distribution of economic growth and economic density rankings in the top 200 cities in 2018 and 2019.1 Figure 4.1 shows the spatial distribution of economic growth and economic density rankings for the top 200 cities in 2018 and 2019. Figure 4.1 shows that compared with 2018, the number of European cities in the world’s top 200 cities has declined more than the rising number, while the level of economic competitiveness in Asian cities has increased more than the number of declines. The level rises and falls by the same amount in North America cities.

1

The circle indicates a drop, the solid point indicates a rise, the large circle or large point indicates a large change, and the small circle or small dot indicates a small change. The same below.

71

4

48

6

North America

South America

Europe

Oceania

Source CCC of CASS

200

71

Asia

total

total

change

Number

0

8

122

1

0

4

3

constant

3

29

3

47

40

Rise

70

3

18

1

20

28

Decline

35%

50%

37.5%

25%

28.2%

39.4%

Decrease ratio

Economic growth (compared to 2018)

116

3

24

3

45

41

Rise

12

0

6

0

3

3

constant

72

3

18

1

26

27

Decline

36%

0.50%

37.5%

25%

32.4%

38%

Decrease ratio

Economic density (compared to 2018)

Table 4.4 Comparison of the top 200 cities’ economic competitiveness upgrade

112

3

21

3

39

46

Rise

11

0

1

0

7

3

constant

77

3

26

1

25

22

Decline

38.5%

50%

54.2%

25%

35.2%

31%

Decrease ratio

Economic competitiveness (compared to 2018)

196 4 Global Urban Economic Competitiveness Performance

4.2 Top 200 Cities: Europe Has More Cities Declined in the Ranking While …

197

Fig. 4.1 Global top 200 cities competitiveness performance

Table 4.5 Descriptive statistics on the economic competitiveness of the top 200 cities in the world Economic increment 2019

Economic increment 2018

Economic increment 2019

Economic increment 2018

0.56

0.62

0.527

0.594

0.593

0.657

0.56

0.636

variance

0.107

Coefficient of variation

0.19

0.115

0.104

0.11

0.186

0.176

0.167

minimum maximum

0.443

0.471

0.481

0.512

1

1

1

1

0.639

0.707

0.715

0.782

0.606

0.684

0.685

0.745

variance

0.099

0.101

0.088

0.092

Coefficient of variation

0.156

0.143

0.123

0.117

minimum

0.528

0.573

0.61

0.669

maximum

1

1

1

1

mean median

mean median

Top200 cities

Top100 cities

Top150 cities

Top500 cities

Source CCC of CASS

Table 4.5 gives descriptive statistics on the economic competitiveness of the top 200 cities in the world. It is not difficult to find that the average competitiveness of the top 200, 150, 100, and 50 global cities has dropped from 0.62, 0.657, 0.707, and 0.782 to 0.56, 0.593, 0.639, and 0.715, respectively, and the coefficient of variation is from 0.186, 0.167, 0.143 and 0.117 expanded to 0.19, 0.176, 0.156 and 0.123, respectively. And the competition and differentiation between cities increased.

198

4 Global Urban Economic Competitiveness Performance

4.3 Top 10 Urban Agglomerations: Northern California Has the Highest Average and Rhein-Ruhr Has the Smallest Internal Difference Figure 4.2 shows the top ten urban clusters including Seoul Capital Area, Northeast Megalopolis, United States, Western Metropolitan Region United States, Northern California Megaregion, Mumbai Metropolitan Region, London-Liverpool Corridor, Yangtze River Delta, Pearl River Delta, Netherlands-Belgium Metropolitan Region and Rhine-Ru Metropolitan Region An annual comparison of the performance of the city’s economic competitiveness. From Fig. 4.2, we can see that the economic competitiveness of the world’s top ten urban clusters is intensifying, and the urban clusters in East Asia, Western Europe and North America show significant differentiation. Table 4.6 shows the statistical description of 10 urban clusters in Seoul Capital Area, Northeast Megalopolis, United States, Western Metropolitan Region United States, Northern California Megaregion, Mumbai Metropolitan Region, LondonLiverpool Corridor, Yangtze River Delta, Pearl River Delta, Netherlands-Belgium Metropolitan Region and Rhine-Ruhr Metropolitan Region. It can be seen from Table 4.6 that the average economic competitiveness of the northern California Megaregion is 0.707, the lowest value of the urban competitiveness of the Mumbai Megaregion region is 0.241, and the variance of the Rhein-Ruhr urban cluster variance and the coefficient of variation were 0.051 and 0.085, the variance and coefficient of variation of the Seoul metropolitan area were 0.197 and 0.31, respectively. At the same time, the ranks of the Northern California Megaregion have risen and range increased. The Seoul Capital area, the Yangtze River Delta and the Pearl River Delta ranked higher but range smaller, while the northeastern US Megaregion, the Midwestern Megaregion, and the London-Liverpool Corridor, Netherlands-Belgian

Fig. 4.2 Spatial distribution of the world’s top 10 urban cluster

0.471

0.121

variance

0.495

mean

Yangtze River Delta

0.549

Coefficient of variation

median

0.132

variance

0.199

0.241

mean

Mumbai Metropolitan Region

0.137

Coefficient of variation

median

0.077

variance

0.556

0.561

mean

Midwest Megalopolis, U.S

0.31

Coefficient of variation

median

0.197

variance

0.636

0.636

Seoul Capital Area

median

mean

Economic increment 2019

0.127

0.515

0.544

0.529

0.137

0.22

0.26

0.138

0.086

0.63

0.623

0.276

0.186

0.676

0.676

Economic increment 2018

Pearl River Delta

London-Liverpool Corridor

Northern California Megaregion

Northeast Megalopolis, U.S

Table 4.6 Statistical description of the economic competitiveness of 10 typical urban clusters in the world

0.121

0.471

0.495

0.281

0.15

0.479

0.532

0.306

0.216

0.828

0.707

0.256

0.159

0.583

0.622

Economic increment 2019

0.127

0.515

0.544

0.247

0.148

0.557

0.599

0.333

0.259

0.929

0.78

0.203

0.138

0.656

0.682

(continued)

Economic increment 2018

4.3 Top 10 Urban Agglomerations: Northern California Has the Highest … 199

0.245

Coefficient of variation

Source CCC of CASS

0.121

variance

0.471

0.495

Netherlands-Belgian Metropolitan Region

mean

median

0.245

Economic increment 2019

Coefficient of variation

Table 4.6 (continued)

0.233

0.127

0.515

0.544

0.233

Economic increment 2018

Rhine-Ruhr Metropolitan Region

0.085

0.051

0.604

0.598

0.245

Economic increment 2019

0.068

0.046

0.688

0.679

0.233

Economic increment 2018

200 4 Global Urban Economic Competitiveness Performance

4.4 Three Main Economies: China Has More Cities Declined in the Ranking …

201

Metropolitan Region and the Rhine-Ruhr urban clusters have declined but are range smaller. The overall level of the Mumbai Metropolitan Region is poor but the rankings is stable.

4.4 Three Main Economies: China Has More Cities Declined in the Ranking, While Some European Cities Have Declined Steeply China, the United States and the European Union are the three engines of world economic development, and the changes in the level of urban economic power have attracted worldwide attention. Table 4.7 shows the results of the upgrade and competition of the three major economies in China, the United States and the European Union. Judging from the changes in urban economic competitiveness, the overall level of competitiveness of the three major economies in China, the United States and Europe has declined. The number of cities in the United States has less declined, and the number of cities in China has large declined. Compared with 2018, China’s urban economic increment, economic density, and economic competitiveness fell by 82.8%, 27.1%, and 62.5%, respectively. The decline in urban economic increment, economic density, and economic competitiveness in the United States was 29.3%, 37.3% and 36%, The EU’s urban economic increment, economic density and economic competitiveness fell by 37.5%, 37.5% and 47.5%, respectively. As a result, the number of cities in China’s urban economic increment and economic competitiveness has declined more than in the United States and the European Union. In order to more intuitive to see the economic increment and economic density of the three major economies. Figure 4.3 shows the comparison of the economic increment and economic density rankings of China, the United States and the European Union. It can be seen from Fig. 4.3 that the number of cities in the United States is decreasing, and the number of cities in China is decreasing. The level of urban competitiveness of the three major economic centers in China, the United States and the European Union varies widely. Table 4.8 gives a comparison of the statistical characteristics of urban economic competitiveness levels in China, the United States, and the European Union. The overall level of economic competitiveness of China and the United States has declined to a small extent, and the EU cities have experienced a large decline, and the Sino-US divides the EU to converge. Table 4.8 shows that the average economic competitiveness of China, the United States, and the European Union in 2019 decreased from 0.382, 0.603, and 0.536 in 2018 to 0.291, 0.545, and 0.476, respectively, and the EU declined significantly. At the same time, the competitiveness of cities in China and the United States has intensified, and the variation and coefficient of variation has slightly increased from 0.451 and 0.239 to 0.46 and 0.248, respectively. However, the overall differentiation of EU urban competitiveness has slowed down, and the coefficient of variation has dropped from 0.271 to 0.252.

6

25

122

European Union

Total

Source CCC of CASS

0

49

America

4

2

48

China

constant

Rise

Change

278

15

22

241

Decline

0.68.5%

0.37.5%

0.29.3%

0.82.8%

Decrease ratio

Economic growth (compared to 2018)

122

21

44

206

Rise

6

4

3

6

constant

278

15

28

79

Decline

0.30%

0.37.5%

0.37.3%

0.27.1%

Decrease ratio

Economic density (compared to 2018)

Table 4.7 Comparison of the changes in the economic competitiveness of the three major countries (regions)

164

20

41

103

Rise

14

1

7

6

constant

228

19

27

182

Decline

0.56.2%

0.47.5%

0.36%

0.62.5%

Decrease ratio

Economic competitiveness (compared to 2018)

202 4 Global Urban Economic Competitiveness Performance

4.5 Global Pattern: The Overall Level Has Declined, but the Divergence …

203

Fig. 4.3 China, the U.S and Europe economic growth (left) and economic density (right)

Table 4.8 Comparison of economic competitiveness of cities in China, the United States and the European Union Economic increment 2019

Economic increment 2018

Economic increment 2019

Economic increment 2018

0.291

0.328

0.545

0.603

median

0.254

variance

0.134

0.294

0.515

0.573

0.148

0.135

0.144

Coefficient of variation

0.46

0.451

0.248

0.239

minimum

0.085

0.089

0.295

0.326

0.84

0.932

1

1

0.479

0.526

–

–

0.46

0.497

–

–

variance

0.121

0.142

–

–

Coefficient of variation

0.252

0.271

–

–

minimum

0.275

0.145

–

–

maximum

0.876

0.933

–

–

mean

China

maximum mean median

Europe Union

America

–

Source CCC of CASS

4.5 Global Pattern: The Overall Level Has Declined, but the Divergence Has Narrowed. Table 4.9 gives a comparison table of the economic competitiveness levels of 1006 cities around the world. Compared with 2018, the average value of global urban economic competitiveness in 2019 decreased from 0.325 to 0.239, and the variance and coefficient of variation decreased from 0.186 and 0.571 to 0.166 and 0.568,

204

4 Global Urban Economic Competitiveness Performance

respectively. At the same time, the overall level of economic growth between cities has declined, but the economic density has risen slightly, and the economic increment and economic density between cities have intensified. Figure 4.4 shows the spatial distribution of the economic competitiveness of 1006 cities around the world. Figure 4.4 shows that cities with large global economic competitiveness and output are still mainly concentrated in Western Europe and North America. The number and size of cities with stronger economic competitiveness in East Asia is smaller than that in Western Europe and North America. From the perspective of upgrading the competitiveness of global cities, Europe and African cities have risen more and fallen less, while cities in Asia and North America have fallen more and risen less. Table 4.10 shows the results of the upgrade of the global economic competitiveness level. Table 4.9 Comparison table of economic competitiveness levels of 1006 cities in the world Economic Economic Economic Economic Economic Economic increment increment density density competitiveness competitiveness 2019 2018 2019 2018 2019 2018 mean

0.337

0.537

0.38

0.363

0.293

0.325

median

0.304

0.504

0.33

0.318

0.249

0.286

variance

0.103

0.092

0.201

0.191

0.166

0.186

Coefficient 0.307 of variation

0.172

0.529

0.527

0.568

0.571

minimum

0

0

0

0

0

0

maximum

1

1

1

1

1

1

Source CCC of CASS

Fig. 4.4 Spatial distribution of economic competitiveness output of 1006 cities worldwide

448

213

78

26

66

62

3

global

Asia

North America

South America

Africa

Europe

Oceania

Source CCC of CASS

Number of cities rising in ranking

region

0

2

0

1

4

2

10

Number of cities with the same ranking

4

62

36

48

49

349

548

Number of cities falling in ranking

57.1%

49.2%

35.3%

64%

37.4%

61.9%

54.5%

Rate of cities in the ranking declined

Economic growth (compared to 2018)

3

90

42

31

63

330

559

Number of cities rising in ranking

0

8

4

1

5

13

31

Number of cities with the same ranking

4

28

56

43

63

222

416

Number of cities falling in ranking

57.1%

22.2%

54.9%

57.3%

48.1%

39.3%

41.4%

Rate of cities in the ranking declined

Economic density (compared to 2018)

Table 4.10 Global urban economic competitiveness level upgrade comparison results

3

73

56

31

62

230

455

Number of cities rising in ranking

0

2

2

0

8

14

26

Number of cities with the same ranking

4

51

44

44

61

321

525

Number of cities falling in ranking

57.1%

22.2%

54.9%

57.3%

48.1%

39.3%

41.4%

Rate of cities in the ranking declined

Economic competitiveness (compared with 2018)

4.5 Global Pattern: The Overall Level Has Declined, but the Divergence … 205

206

4 Global Urban Economic Competitiveness Performance

Table 4.10 shows that the decline in urban economic increment in South America, Asia and Oceania accounted for 64%, 61.9% and 57.1% respectively higher than the global average of 54.5%, and the economic density of cities in North America, Africa and Oceania decreased 48.1%, 54.9%, and 57.1%, respectively, were higher than the global average of 41.4%, while the competitiveness of cities in North America, South America, and Oceania fell by 48.1%, 57.3%, and 57.1%, respectively, which were higher than the global average of 41.1%.

4.6 Global Sub-Regional Pattern: Northern China and Eastern Europe Declined While Southern China and India Rose in Ranking From the perspective of space, 100 degrees west longitude, 20 degrees east longitude and 110 degrees east longitude has become the watershed of urban economic competitiveness convergence area, and the level of economic competitiveness of high cities has risen to rise between 25–55 degrees north latitude, and other regions have differentiated significantly. Figure 4.5 shows the spatial distribution of global urban economic competitiveness by three meridians at the sub-regional level. It can be seen from Fig. 4.5 that the eastern city of 110 degrees west and 110 degrees east of 110 degrees east longitude is obviously superior to the western city, while the western city of 20 degrees east longitude is obviously superior to the eastern city, and the rectangular area between 25–55 degrees north latitude forms a city cluster. And the rectangular area between 25–55 degrees north latitude forms a watershed of the advantages and disadvantages of urban economic competitiveness. Not only that, the cities on both sides of the west longitude 100 degrees, the east longitude 20 degrees and the east longitude 110 degrees are obviously changing, and

Fig. 4.5 Spatial distribution of global urban competitiveness upgrade comparison

4.6 Global Sub-Regional Pattern: Northern China and Eastern Europe …

207

the competitiveness rankings of the cities of northern South America, Eastern Europe and West Asia converge. Table 4.11 shows the comparison of urban competitiveness between high-competitive regions and low-competitive regions in the world. From Fig. 4.5 and Table 4.11, the cities with increasing global competitiveness are mainly distributed in the west coast cities of the United States on the west side of 100 degrees west longitude, the western European cities at 20 degrees east longitude, and the 110–140 degree cities of China, Japan and south Korea in east longitude. And the latitude is concentrated between 25–55 degrees. In fact, the top 200 cities and the top 500 cities in the global urban economic competitiveness are all distributed in the rectangular box of Fig. 4.5. In order to more intuitively see the upgrade of the competitiveness of 1006 cities around the world, Fig. 4.6 shows the spatial distribution of the competitiveness of global cities. Affected by various factors such as abundant water resources, stable economic environment and political environment, and a livable climate, the western coastal cities of the United States, the western European cities near the prime meridian, and the coastal cities of East Asian, Chinese, Japan and South Korea have become economic increment. And the main gathering region for economic density upgrades. The difference is that Fig. 4–6 shows a comparison of annual changes in economic increment and economic density in 1006 cities around the world. Table 4.11 and Fig. 4.6 show that the coastal areas between 25–55 degrees north latitude, 100 degrees west of west longitude, 20 degrees east longitude, and east longitude 110 degrees are high-city competitiveness level gathering areas, and high. The overall mean of economic increment, high economic density and high economic competitiveness is 1.423, 1.559 and 1.626 times of the overall mean of low economic increment, low economic density and low economic competitiveness, respectively. The coefficient of variation is also small overall., leading the global urban competitiveness spatial pattern.

Table 4.11 Global sub-regional city competitiveness upgrade comparison results High economic growth region

low economic growth region

High economic density area

Low High economic low economic economic competitiveness competitiveness density area area area

mean

0.437

0.307

0.502

0.322

0.369

0.257

median

0.404

0.225

0.495

0.288

0.348

0.227

variance

0.168

0.101

0.218

0.164

0.179

0.147

Coefficient 0.32 of variation

0.301

0.434

0.508

0.486

0.571

minimum

0.185

0

0.067

0

0.052

0

maximum

1

0.801

1

0.86

1

0.861

Source CCC of CASS

208

4 Global Urban Economic Competitiveness Performance

Fig. 4.6 Comparison of annual changes in global economic growth (left) and economic density (right)

Chapter 5

Explanatory Indicators of Global Urban Economic Competitiveness

5.1 Local Factors 5.1.1 Overall Pattern of Local Factors 5.1.1.1

Overview of Leading Cites

The United States, China, and Japan guide the pattern of local factor competitiveness. The top three local factor competitiveness companies in the world are Shenzhen, New York and Mumbai, and the index is also in the first echelon, all above 0.9, leading the local factor competitiveness. Among the top 20, the United States occupies 5 cities, China occupies 2 cities, Japan occupies 2 cities, and others such as India, Malaysia, the United Kingdom, Singapore, Mexico, South Africa, Turkey, Russia, Australia and Canada each occupy 1 city; Asia occupies 10 cities, North America occupies 7 cities, Europe occupies 2 cities, and Oceania occupies 1 city. The overall performance is dominated by Asia and North America (Table 5.1). From the perspective of the top 100 local factors, Asia has the largest number of top 100, occupying 45 cities, followed by North America and Europe, and again South America, Africa, Oceania; from the index, the top 100 within North America Has the strongest competitiveness index, followed by Asia, while Africa’s top 100 local factors have the weakest competitiveness (Table 5.2).

5.1.1.2

Overall Spatial Pattern

Europe and North America dominate the top 100 pattern. From a regional perspective, 3 of the 7 sample cities in Oceania are among the top 100, followed by Europe, 15.87% of the 126 sample cities are among the top 100, and North America again, with 15.27% of the sample The cities are among the top 100, again South America and Asia. 9.33 and 7.96% of the sample cities are among the top 100, © China Social Sciences Press 2023 P. Ni et al., The World: 300 Years of Urbanization Expansion, https://doi.org/10.1007/978-981-99-3553-6_5

209

210

5 Explanatory Indicators of Global Urban Economic Competitiveness

Table 5.1 Local factors indicator top 20 cities in the world Area

Country

City

Index

Global rank

Asia

China

Shenzhen

1.000

1

North America

United States

New York

0.971

2

Asia

India

Mumbai

0.913

3

North America

United States

Chicago

0.894

4

Asia

China

Shanghai

0.883

5

Asia

Malaysia

Kuala Lumpur

0.857

6

Europe

United Kingdom

London

0.851

7

Asia

Singapore

Singapore

0.842

8

Asia

Korea

Seoul

0.828

9

North America

United States

Washington, DC

0.825

10

North America

United States

Philadelphia

0.821

11

Asia

Japan

Kitakyushu–Fukuoka Metropolitan Area

0.818

12

North America

Mexico

Mexico City

0.815

13

Africa

South Africa

Johannesburg

0.813

14

North America

United States

Boston

0.810

15

Asia

Turkey

Istanbul

0.804

16

Asia

Japan

Tokyo

0.802

17

Europe

Russia

Moscow

0.795

18

Oceania

Australia

Sydney

0.791

19

North America

Canada

Toronto

0.790

20

Source CCC of CASS Table 5.2 Distribution of local factors indicator top 100 cities by area in the world Area

N

Mean

Coefficient of variation

Best city

Index

Global rank

Asia

45

0.724

0.120

Shenzhen

1.000

1

North America

20

0.733

0.137

New York

0.971

2

South America

7

0.714

0.075

Sao Paulo

0.776

25

Oceania

3

0.690

0.135

Sydney

0.791

19

Europe

20

0.697

0.099

London

0.851

7

Africa

5

0.686

0.109

Johannesburg

0.813

14

Source CCC of CASS

5.1 Local Factors

211

the lowest is Africa, and only 4.9% of the sample cities are among the top 100. From the perspective of the index, Oceania is the highest, followed by North America, Europe and South America, which are higher than the global average, and finally Asia and Africa, which are lower than the global average. From the perspective of differentiation, Oceania and North America are the least differentiated, and Asia and Africa are the most differentiated. Judging from the distribution type of each region, the overall distribution in Africa, South America, Asia, and Europe is relatively close, and the overall distribution in North America is relatively superior (see Figs. 5.1, 5.2 and 5.3; Table 5.3). The exchange index is generally low and highly differentiated. From the perspective of sub-indicators, the overall level of financing convenience index and thesis index are relatively high, at 0.618 and 0.547, respectively, followed by the labor force index, youth population index, and patent index. From the perspective of differentiation, the exchange index has the most serious differentiation, with a coefficient of variation of 2.783, followed by the patent index and the youth population index. The dissertation index has the lowest differentiation, with a coefficient of variation of 0.318. In addition, the optimal cities for each indicator are also significantly different, namely San Jose, New York, Beijing, Tokyo, Bukavu, Jakarta (Table 5.4). 90

60

30

0 -180

-120

-60

0

60

120

-30

-60

-90

Fig. 5.1 Spatial distribution of local factors indicators worldwide. Source CCC of CASS

180

212

5 Explanatory Indicators of Global Urban Economic Competitiveness 90

60

30

0 -180

-120

-60

0

60

120

180

-30

-60

-90

4

Fig. 5.2 Spatial distribution of local factors indicators in the world’s top 100 cities. Source CCC of CASS

Global 3

Asia Europe

2

SAmerica Africa

0

1

Density

NAmerica

0

.2

.4

.6

.8

1

Local Elements

Fig. 5.3 The nuclear density map of the local element index of global cities. Source CCC of CASS

5.1 Local Factors

213

Table 5.3 Distribution of local factors indicator by area in the world Area

N

Mean

Coefficient of variation

Asia

565

Proportion of top 100 cities (%) 7.96

0.402

0.345

North America

131

15.27

0.519

0.237

South America

75

9.33

0.418

0.307

Oceania

7

42.86

0.584

0.209

Europe

126

15.87

0.429

0.347

Africa

102

4.90

0.347

0.398

Global

1006

–

0.417

0.346

Source CCC of CASS

Table 5.4 Statistical description of sub-indicators of local factors in global cities

Local factors

Mean

Coefficient of variation

Best city

Financing convenience

0.618

0.319

San Jose

Exchange index 0.075

2.783

New York

Thesis index

0.547

0.318

Beijing

Patent index

0.276

0.829

Tokyo

Youth 0.349 population ratio

0.407

Bukavu

Labor force index

0.331

Jakarta

0.451

Source CCC of CASS

5.1.2 Local Element Country Pattern 5.1.2.1

The Overview G20 Countries

China and the United States have relatively high proportions at all levels, and dominate the local factor structure. In terms of the distribution of the top 50 countries in the G20 countries, China has 9 cities in the top 50, and the United States and the EU have 6 cities in the top 50, followed by Canada and India. From the top 100, China has the most, China has the most 19 cities, followed by the United States with 15 cities, and again with the EU with 12 cities; from the top 101–200, the pattern is consistent with the top 100; and from the top 201–500, China is the most, followed by India, again the United States and Mexico; from the top 500, China has the largest share, accounting for 27.8%, followed by the United States and India. In general, China and the United States have a relatively high proportion at all levels and dominate the local factor structure. Before the EU is mainly distributed in the top 200, India is mainly distributed after 200, and other cities are distributed at all levels (Table 5.5).

214

5 Explanatory Indicators of Global Urban Economic Competitiveness

Table 5.5 Distribution of local element index ranking in national sample cities Country

Proportion of top 50 cities (%)

Proportion of top 100 cities (%)

Proportion of 101–200 (%)

Proportion of 201–500 (%)

Proportion of top 500 cities (%)

France

2.0

1.0

0.0

0.3

0.4

United States

12.0

15.0

28.0

9.3

14.2

2.0

1.0

0.0

3.7

2.4

China

18.0

19.0

31.0

29.7

27.8

Turkey

2.0

1.0

2.0

1.7

1.6

Italy

0.0

0.0

1.0

0.0

0.2

Russia

2.0

1.0

1.0

1.0

1.0

Japan

4.0

3.0

2.0

0.0

1.0

Canada

6.0

3.0

2.0

1.3

1.8

Australia

2.0

2.0

2.0

0.7

1.2

Germany

0.0

5.0

2.0

0.3

1.6

Korea

4.0

2.0

0.0

2.0

1.6

India

6.0

7.0

5.0

15.7

11.8

Indonesia

2.0

1.0

0.0

1.0

0.8

Argentina

0.0

1.0

0.0

0.3

0.4

Mexico

2.0

1.0

4.0

8.3

6.0

Brazil

4.0

3.0

0.0

3.0

2.4

Saudi Arabia

2.0

1.0

0.0

0.3

0.4

South Africa

2.0

1.0

3.0

0.3

1.0

EU

12.0

12.0

6.0

2.3

5.0

G20

80.0

78.0

89.0

80.3

81.6

Non-G20

20.0

22.0

11.0

19.7

18.4

United Kingdom

The local factor competitiveness of G20 countries is significantly better than that of non-G20 countries. According to the analysis of the local factor index and coefficient of variation of G20 countries, Canada, Australia, the United States, South Africa and other countries have the strongest local factor competitiveness, Indonesia, Russia, Saudi Arabia, Italy and other countries have the weakest local factor competitiveness, while G20 countries The local factor competitiveness of China is basically higher than that of non-G20 countries. Only Russia, Saudi Arabia and Italy have lower local factor competitiveness than non-G20 countries. From the perspective of coefficient of variation, Mexico, the United States, Canada, Australia and other countries have the lowest degree of differentiation, and the overall local factor competitiveness is relatively uniform. China, France, Japan, Saudi Arabia and

5.1 Local Factors

215

Table 5.6 Statistical description of local element indicators in national sample cities of G20 Country

N

Index

Coefficient of variation

Best city

Global rank

France

9

0.391

0.372

Paris

31

United States

75

0.553

0.212

New York

2

United Kingdom

12

0.489

0.240

London

7

China

291

0.398

0.337

Shenzhen

1

Turkey

16

0.428

0.294

Istanbul

16

Italy

13

0.304

0.319

Milan

131

Russia

33

0.368

0.269

Moscow

18

Japan

10

0.501

0.417

Kitakyushu–Fukuoka Metropolitan Area

12

Canada

9

0.589

0.216

Toronto

20

Australia

6

0.569

0.223

Sydney

19

Germany

13

0.517

0.254

Berlin

57

Korea

8

0.546

0.276

Seoul

9

India

100

0.435

0.240

Mumbai

3

Indonesia

20

0.378

0.281

Jakarta

36

Argentina

9

0.384

0.327

Buenos Aires

61

Mexico

35

0.467

0.189

Mexico City

13

Brazil

32

0.405

0.306

Sao Paulo

25

Saudi Arabia

9

0.341

0.446

Riyadh

44

South Africa

6

0.552

0.296

Johannesburg

14

EU

40

0.499

0.322

Barcelona

22

G20

739

0.434

0.322

Shenzhen

1

Non-G20

267

0.371

0.399

Kuala Lumpur

6

Source CCC of CASS

other countries have the highest degree of differentiation, and the overall gap is large. The differentiation of local factor competitiveness in G20 countries is also more serious, only lower than that of France, Japan and Saudi Arabia. Overall, it shows that G20 countries are significantly better than non-G20 countries (Table 5.6).

5.1.2.2

The Overview Representative Country

According to the intercontinental division, the focus here is to select Asia, China, Japan, Europe, the United Kingdom, North America, the United States, South America, Brazil, Africa, South Africa, and Oceania, Australia for comparative

216

5 Explanatory Indicators of Global Urban Economic Competitiveness

research. Generally speaking, the financing convenience index, youth population index and labor force index are relatively close. The paper index and the patent index show a higher index and a more balanced distribution. From the perspective of the financing convenience index, the United States and Australia have the highest indexes, respectively 0.929 and 0.888, and Japan and Brazil have the lowest indexes, only 0.544 and 0.442; from the perspective of the coefficient of variation, the degree of differentiation of financing convenience in each country Both are relatively low, below 0.1, which is relatively balanced. From the perspective of the exchange index, Japan has the highest average population of 0.393, followed by Australia. In addition, the overall average of other countries is low; from the perspective of differentiation, China has the most serious differentiation, with a coefficient of variation of 8.501, followed by India, up to 7.114, and finally the United Kingdom, the United States, Brazil and other countries. From the perspective of the paper index, Australia, the United Kingdom and Japan have the highest indexes, and China and India have the lowest indexes; China and India are also more severely divided, and the distribution of cities is uneven. The United Kingdom and Japan have the lowest coefficients of variation and the overall distribution is also More balanced. This shows that the distribution of countries with higher mean is also more balanced. From the perspective of the patent index, Japan has the highest level, followed by the United States, the United Kingdom, and Australia, and the lowest is India and Brazil. The performance of the patent index is generally consistent with the paper index. The higher the index, the more balanced the distribution. From the perspective of the youth population index and the labor force index, except for Japan’s youth population index is relatively low and the division is more serious, the indexes of other countries are relatively close, and the relative gap is not large (Table 5.7).

5.2 Living Environment 5.2.1 Overall Pattern of Living Environment Index 5.2.1.1

Overview of Leading Cites

Asia cities account for nearly half of the top 20 cities. In terms of the distribution of the top 20 cities in the global living environment on all continents, Asia has 9 seats, Europe has 5 seats, North America has 4 seats, Oceania and South America have 1 seat each. In terms of the national dimension, Japan alone has 6 seats, followed by the US with 4 seats. Table 5.8 Top 20 cities of living environment index. More than 90% of the top 100 cities of living environment index are concentrated in North America, Europe and Asia, with absolute level close to each other and small fluctuation range. Comparing the mean value and coefficient of

Source CCC of CASS

Local element

Total labor force

Youth population ratio

Patent index

Thesis index

Exchange index

Financing convenience

0.398

0.337

CoV

0.344

CoV

Mean

0.446

0.409

Mean

0.362

CoV

0.598

CoV

Mean

0.296

0.318

CoV

Mean

0.509

8.501

Mean

0.013

CoV

0.064

CoV

Mean

0.589

Mean

China

0.212

0.553

0.293

0.484

0.018

0.255

0.264

0.589

0.206

0.685

3.196

0.077

0.029

0.929

United States

0.240

0.435

0.330

0.439

0.015

0.452

1.203

0.143

0.313

0.509

7.114

0.015

0.046

0.733

India

0.417

0.501

0.443

0.556

2.935

0.051

0.218

0.702

0.103

0.742

1.070

0.393

0.017

0.544

Japan

Table 5.7 Statistical analysis of sub-indicators of local element in representative countries

0.240

0.489

0.283

0.474

0.001

0.184

0.156

0.575

0.089

0.754

3.464

0.074

0.021

0.741

United Kingdom

0.296

0.552

0.261

0.559

0.068

0.499

0.314

0.404

0.360

0.588

2.449

0.129

0.040

0.592

South Africa

0.306

0.405

0.336

0.490

0.000

0.387

0.737

0.220

0.166

0.611

3.164

0.071

0.040

0.442

Brazil

0.223

0.569

0.246

0.511

0.000

0.246

0.225

0.546

0.112

0.771

2.449

0.137

0.016

0.888

Australia

5.2 Living Environment 217

218

5 Explanatory Indicators of Global Urban Economic Competitiveness

Table 5.8 Living environment top 20 cities Content

Country

City

Index

Ranking

Asia

Japan

Tokyo

1.000

1

Asia

Japan

Hiroshima

0.976

2

Asia

Japan

Osaka

0.965

3

Asia

Singapore

Singapore

0.930

4

Asia

Japan

Kitakyushu-Fukuoka

0.920

5

South America

Argentina

Buenos Aires

0.917

6

Europe

Italy

Rome

0.905

7

Europe

Germany

Munich

0.904

8

North America

U.S.A.

Chicago

0.888

9

North America

U.S.A.

Houston

0.883

10

Europe

Germany

Berlin

0.876

11

Asia

Japan

Sapporo

0.874

12

Asia

Japan

Shizuoka–Hamamatsu M.M.A.

0.860

13

North America

U.S.A.

Philadelphia

0.857

14

Oceania

New Zealand

Auckland

0.857

15

Asia

India

Bangalore

0.853

16

Asia

China

Taipei

0.851

17

Europe

Ireland

Dublin

0.851

18

Europe

U.K.

London

0.850

19

North America

U.S.A.

New York–Newark

0.850

20

Source CCC of CASS

variation of the top 100 cities and all 1006 samples, we can find that the mean level of the top 100 cities is significantly higher than the global average and the coefficient of variation is significantly lower than the global average. In terms of the continental distribution of the top 100 cities, North America accounts for nearly 40%, while North America, Europe and Asia together account for more than 90%, with obvious concentration. From the perspective of the leading cities in all continents, Europe, North America, Asia, Oceania and South America all rank in the top 20 globally, while Cape Town, the best city in Africa, ranks only 61st in the world (Table 5.9).

5.2.1.2

Overall Spatial Pattern

Oceania, Europe and North America lead the world. From the average of global urban living environment indicators, the urban living environment in Oceania, Europe and North America is relatively developed, the living environment in Africa is relatively weak, and the degree of living environment in Asia and South America is in the middle. In terms of the coefficient of variation, the fluctuation range of the living

5.2 Living Environment

219

Table 5.9 The continent distribution in the top 100 cities of living environment index Content

Sample

Mean

Coefficient of variation

Leading cities

Index

Ranking

North America

39

0.808

0.040

Chicago

0.888

9

Europe

30

0.805

0.049

Rome

0.905

7

Asia

24

0.836

0.088

Tokyo

1.000

1

South America

4

0.833

0.076

Buenos Aires

0.917

6

Oceania

2

0.825

0.054

Auckland

0.857

15

Africa

1

0.792

–

Cape Town

0.792

61

Global

1006

0.569

0.259

Tokyo

1.000

1

Source CCC of CASS

environment index of cities in Oceania, Europe and North America is small, while that of African cities is more outstanding. In terms of the proportion of global top 100 cities in all continents, North America and Oceania account for nearly 30%, leading the world, with only 4–5% in South America and Asia, and less than 1% in Africa (Figs. 5.4 and 5.5; Table 5.10). According to the sub indicators of living environment, 1006 sample cities are mainly concentrated in the climate livable areas, which do relatively well in the protection of natural and cultural heritage, and the cost of living in most cities is 90

60

30

0 -180

-120

-60

0

60

120

180

-30

-60

-90

Fig. 5.4 Spatial distribution of living environment indexes of 1006 cities in the world. Source CCC of CASS

220

5 Explanatory Indicators of Global Urban Economic Competitiveness 90

60

30

0 -180

-120

-60

0

60

120

180

-30

-60

-90

Fig. 5.5 Spatial distribution of living environment indexes in the top 100 cities in the world. Source CCC of CASS.

Table 5.10 Continental distribution of global urban living environment indicators Content

Sample

Proportion of top 100 cities (%)

Mean

Coefficient of variation

North America

131

29.77

0.689

0.151

Oceania

7

28.57

0.712

0.135

Africa

102

0.98

0.401

0.355

South America

75

5.33

0.564

0.222

Europe

126

23.81

0.679

0.140

Asia

565

4.25

0.546

0.235

Global

1006

9.94

0.569

0.259

Source CCC of CASS

in a reasonable range. However, environmental and ecological problems are more prominent, and the resource capacity of medical and health-care needs to be strengthened. Golf, as a sport that needs a good natural environment and a certain economic foundation, helps to measure the balance of economic level, natural environment and healthy life. However, we can find that the overall performance of global cities in this indicator is relatively weak, and the differentiation is great. It shows that high-quality development still has a long way to go (Fig. 5.6 and Table 5.11).

5.2 Living Environment

221

Fig. 5.6 Density map of global urban living environment index. Source CCC of CASS

Table 5.11 Statistical description of sub indicators of living environment index

Living environment

Mean

Coefficient of variation

Leading cites

Heritage protection

0.622

0.295

Tokyo

Medical and health-care

0.530

0.286

Hyderabad

Climate comfort

0.654

0.271

Pereira

Environmental pollution

0.317

0.265

Singapore

Ecological excellence

0.481

0.412

Chicago

Cost of living

0.910

0.194

(Non-unique)

Golf course

0.202

1.075

Tokyo

Source CCC of CASS

5.2.2 National Pattern of Living Environment Index 5.2.2.1

Overview of G20 Countries

Cities in Japan, the United States, and Europe lead the world, and Chinese cities are poised to take off. Among the top 20 cities of living environment index, Japan has led the way with 30% of the global top 20, followed by the United States with 20% and the EU with 20%. Among the top 100, the United States has the highest number of cities, up to 36%, the EU as a whole has 22%, and Japan has 10%. Among the top 101–200, the United States, the European Union and China account for 21, 20 and 19%, respectively. Among the top 201–500, the number of Chinese cities is the highest, reaching 38%, followed by the European Union, which accounts for 8%, and Mexico which accounts for 7%. Among the top 500, Chinese cities hold the highest proportion, reaching 28%, followed by the United States and the European Union, which account for 14 and 13%, respectively. Saudi Arabia has no cities in

222

5 Explanatory Indicators of Global Urban Economic Competitiveness

the top 500 (Table 5.12). From the distribution of rankings, we can see that cities in developed economies such as Japan, the United States and the European Union are still in the leading position, but China’s leading cities have already ranked among the forefronts in the world, and there are a lot of reserves in the middle and upper level stage. Table 5.12 Ranking and distribution of living environment index of G20 sample cities Country

Proportion of top 20 cities (%)

Proportion of top 100 cities (%)

Proportion of top 101–200 cities (%)

Proportion of top 201–500 cities (%)

Proportion of top 500 cities (%)

China

5.0

6.0

19.0

38.0

28.0

U.S.A.

20.0

36.0

21.0

5.0

14.0

European Union

20.0

22.0

20.0

8.0

13.0

Mexico

0.0

1.0

3.0

7.0

5.0

India

5.0

1.0

5.0

6.0

5.0

Russian

0.0

1.0

1.0

6.0

4.0

Brazil

0.0

2.0

1.0

4.0

3.0

10.0

8.0

3.0

1.0

3.0

Italy

5.0

7.0

4.0

1.0

3.0

Indonesia

0.0

0.0

4.0

3.0

2.0

Turkey

0.0

2.0

0.0

3.0

2.0

U.K.

5.0

5.0

6.0

0.0

2.0

Japan

Germany

30.0

10.0

0.0

0.0

2.0

Canada

0.0

2.0

4.0

1.0

2.0

Republic of Korea

0.0

3.0

1.0

1.0

2.0

France

0.0

1.0

2.0

2.0

2.0

Argentina

5.0

2.0

1.0

1.0

1.0

Australia

0.0

1.0

2.0

1.0

1.0

South Africa

0.0

1.0

0.0

1.0

1.0

Saudi Arabia

0.0

0.0

0.0

0.0

0.0

G20 countries

90.0

95.0

88.0

85.0

88.0

Non-G20 countries

10.0

5.0

12.0

15.0

12.0

100.0

100.0

100.0

100.0

100.0

Global

Source CCC of CASS

5.2 Living Environment

223

In general, the average living environment of non G20 cities is still lower than that of G20 cities, and the degree of internal differentiation is higher. Comparing the living environment of G20 countries, it is found that in terms of the mean value, cities in Japan, Germany, Italy, the United Kingdom and the United States have obvious advantages, while cities in India, Brazil, China, Saudi Arabia and Russia have relatively poor performance. According to the coefficient of variation of living environment, the fluctuation range of cities in Britain, Russia, Japan and Italy is relatively small, while that in Saudi Arabia, Argentina and France is relatively large. From the perspective of the best cities, 8 cities in G20 countries are listed in the top 20 in the world, and 18 cities are within the top 100 (Table 5.13). Table 5.13 Statistical description of living environment index of G20 sample cities Country

Mean

Coefficient of variation

Leading cities

Ranking

Japan

0.8877

0.0825

Tokyo

1

Germany

0.7751

0.0951

Munich

8

Italy

0.7563

0.0840

Rome

7

U.K.

0.7538

0.0736

London

19

U.S.A.

0.7507

0.0954

Chicago

9

European Union

0.7266

0.0974

Dublin

18

Argentina

0.7266

0.1467

Buenos Aires

6

Republic of Korea

0.7020

0.1089

Seoul

49

Canada

0.6966

0.0963

Vancouver

53

Australia

0.6884

0.1142

Melbourne

60

South Africa

0.6691

0.1101

Cape Town

61

Indonesia

0.6600

0.0866

Bandung

106

France

0.6529

0.1220

Paris

55

Turkey

0.6488

0.1166

Antalya

69

India

0.6464

0.0990

Bangalore

16

Mexico

0.6413

0.0976

Mexico City

29

Brazil

0.6404

0.1150

Sao Jose dos Campos

28

China

0.6370

0.0874

Guangzhou

87

Russian

0.6293

0.0779

Moscow

88

Saudi Arabia

0.4156

0.1658

Jeddah

636

G20 countries

0.6098

0.1947

Tokyo

1

Non-G20 countries

0.4559

0.3503

Singapore

4

Global

0.5690

0.2591

Tokyo

1

Source CCC of CASS

224

5.2.2.2

5 Explanatory Indicators of Global Urban Economic Competitiveness

Overview of Representative Countries

We choose China, Japan and India in Asia, Britain in Europe, the United States in North America, Brazil in South America, South Africa in Africa and Australia in Oceania for comparative study. In general, the performance of Japanese cities in the living environment is relatively good. The UK and the US have long and short boards respectively. However, the overall level of emerging economies still needs to be improved, and each sub indicators has a large internal differentiation. From the perspective of the heritage protection, the level of representative countries is good. Japan’s performance is particularly prominent and the fluctuation is very small, while the fluctuation of the United States is slightly larger. From the perspective of medical and health-care, Japan leads, Australia is slightly weaker. China is at a moderately low level and there is still much room for improvement. In terms of climate comfort, cities in Japan and Australia have better performance, while India and the United Kingdom have lower comfort levels. From the perspective of coefficient of variation, cities in Japan, the United Kingdom, and Australia have relatively small fluctuations. From the perspective of environmental pollution, the advantages of cities in Japan and the U.K. are obvious, but the difference between cities in Japan is greater than that in the U.K. India and China have more pressure in environmental pollution and there are large fluctuations between cities. In terms of ecological excellence, cities in the United States, the United Kingdom, and Japan is better, and cities of Brazil, South Africa, and India needs to be improved. From the perspective of cost of living, the problems in Brazil are more obvious, and the division between India and China is relatively large. From the perspective of golf courses, Japan and the United Kingdom are leading, Brazil, India and China are relatively backward, and the gap between cities is relatively large (Table 5.14).

5.3 Soft Environment 5.3.1 Overall Pattern of Software Environment 5.3.1.1

Head City Overview

The head cities of global software environment are unevenly distributed across continents. According to the distribution of the top 20 cities in the global software environment on all continents, North America has 10 seats, Asia has 7 seats, Europe has 2 seats and Oceania has 1 seat. Among them, U.S.A. occupies 8 seats, followed by Canada, Japan and China, each occupying 2 seats (Table 5.15).

5.3 Soft Environment

225

Table 5.14 Statistical analysis of sub indicators of living environment in representative countries Australia

Brazil

U.S.A.

South Africa

Japan

India

U.K.

China

Heritage protection

Mean

0.638

0.745

0.681

0.669

0.825

0.703

0.683

0.729

CV

0.137

0.126

0.165

0.151

0.102

0.135

0.149

0.135

Medical and health-care

Mean

0.489

0.656

0.553

0.530

0.761

0.828

0.542

0.522

CV

0.322

0.160

0.168

0.216

0.076

0.127

0.167

0.129

Climate comfort

Mean

0.792

0.754

0.755

0.743

0.799

0.651

0.690

0.756

CV

0.073

0.139

0.112

0.068

0.048

0.173

0.032

0.112

Environmental pollution

Mean

0.382

0.360

0.373

0.333

0.447

0.277

0.416

0.279

CV

0.061

0.087

0.102

0.104

0.216

0.216

0.153

0.257

Ecological excellence

Mean

0.598

0.488

0.714

0.443

0.652

0.422

0.647

0.572

CV

0.404

0.254

0.260

0.115

0.265

0.235

0.186

0.135

Cost of living

Mean

0.977

0.894

0.979

0.979

0.964

0.928

0.962

0.926

CV

0.022

0.044

0.010

0.006

0.026

0.102

0.053

0.157

Mean

0.391

0.148

0.499

0.481

0.736

0.265

0.626

0.246

CV

0.313

1.284

0.294

0.273

0.178

0.681

0.140

0.747

Mean

0.688

0.640

0.751

0.669

0.888

0.646

0.754

0.637

CV

0.114

0.115

0.095

0.110

0.083

0.099

0.074

0.087

Golf course Living environment index

Source CCC of CASS

Among the top 100 cities in the global software environment, more than 90% of the top 100 cities in the global software environment are concentrated in North America, Europe and Asia, with the mean value close and a small fluctuation range. Comparing the mean value and coefficient of variation of the top 100 cities and the whole sample, we can find that the mean level of the top 100 cities is significantly higher than the global average, while the coefficient of variation is significantly lower than the global average. In terms of the intercontinental distribution of the former 100 cities, North America accounts for nearly 40%, while North America, Europe and Asia together account for more than 95%, with obvious concentration. The leading cities in North America, Europe, Asia and Oceania are New York-Newark, London, Tokyo and Auckland, ranking 4, 5, 1 and 11 in the world in terms of the best software environment cities in all continents (Table 5.16).

5.3.1.2

Overall Spatial Pattern

The cities with superior global software environment are mainly concentrated in North America, Oceania and Europe. According to the mean value of intercontinental distribution of global urban software environment, urban software environment is relatively strong in Oceania, North America and Europe, relatively weak

226

5 Explanatory Indicators of Global Urban Economic Competitiveness

Table 5.15 Software environment index top 20 cities in the world Continent

Country

City

Index

Ranking

Asia

Japan

Tokyo

1.000

1

Asia

Singapore

Singapore

0.998

2

Asia

China

Hong Kong

0.991

3

North America

U.S.A.

New York–Newark

0.981

4

Europe

U.K.

London

0.954

5

Asia

China

Taipei

0.939

6

North America

U.S.A.

Los Angeles–Long Beach–Santa Ana

0.932

7

Asia

Republic of Korea

Seoul

0.919

8

Asia

Japan

Osaka

0.910

9

North America

Canada

Toronto

0.901

10

Oceania

New Zealand

Auckland

0.898

11

North America

U.S.A.

Boston

0.890

12

North America

U.S.A.

Seattle

0.889

13

North America

U.S.A.

San Diego (US)

0.875

14

North America

Canada

Calgary

0.868

15

North America

U.S.A.

Chicago

0.866

16

Asia

United Arab Emirates

Dubai

0.865

17

Europe

Germany

Munich

0.864

18

North America

U.S.A.

Portland

0.861

19

North America

U.S.A.

San Francisco–Oakland

0.861

20

Source CCC of CASS

Table 5.16 The intercontinental distribution of the top 100 cities in the world Continent

Sample

Mean

Coefficient of variation

Best city

Index

Ranking

North America

39

0.827

0.058

New York–Newark

0.981

4

Europe

26

0.805

0.049

London

0.954

5

Asia

33

0.841

0.076

Tokyo

1.000

1

Oceania

2

0.845

0.090

Auckland

0.898

11

Global

1006

0.529

0.318

Tokyo

1.000

1

Source CCC of CASS

in Africa, and moderate in Asia. In terms of coefficient of variation, the fluctuation range of software environment in Oceania and European cities is relatively small, while that in African cities is relatively large. In terms of the proportion of the top 100 global cities in all continents, North America and Oceania account for nearly

5.3 Soft Environment

227 90

60

30

0 -180

-120

-60

0

60

120

180

-30

-60

-90

Fig. 5.7 Spatial distribution of software environment indicators in 1006 cities around the world. Source CCC of CASS

30%, leading the world, followed by 20.63% in Europe, only about 6% in Asia, and 0% in Africa and South America (Figs. 5.7 and 5.8; Table 5.17). The mean value and fluctuation range of sub indicators of global urban software environment are quite different. In terms of sub indicators of software environment, 1006 sample cities do relatively well in terms of business convenience, followed by social security, economic freedom and property protection. The problem of cultural inclusion and Knowledge density is more prominent. Cultural inclusion reflects the ability of a city to carry new things. Excellent cultural inclusion brings new opportunities to the development of a city. Knowledge density contain the city’s accumulation and historical charm. However, we can find that the overall performance of global cities in these two indicators is relatively weak, and the differentiation is great (Fig. 5.9 and Table 5.18).

228

5 Explanatory Indicators of Global Urban Economic Competitiveness 90

60

30

0 -180

-120

-60

0

60

120

180

-30

-60

-90

Fig. 5.8 Spatial distribution of software environment indicators in the top 100 cities in the world. Source CCC of CASS

Table 5.17 Intercontinental distribution of global urban software environment indicators Continent

Sample

Proportion of top 100 cities (%)

Mean

Coefficient of variation

North America

131

29.77

0.677

0.217

Oceania

7

28.57

0.762

0.098

Africa

102

0.00

0.320

0.390

South America

75

0.00

0.408

0.380

Europe

126

20.63

0.655

0.172

Asia

565

5.84

0.517

0.253

Sum

1006

9.94

0.529

0.318

Source CCC of CASS

5.3.2 National Structure of Software Environment 5.3.2.1

Overview of G20 Countries

Cities in U.S.A., China, Japan and Canada lead the world, U.S.A., China and the European Union have an absolute advantage in the number of top cities in the

5.3 Soft Environment

229

Fig. 5.9 Kernel density of global urban software environment indicators. Source CCC of CASS

Table 5.18 Statistical description of sub indicators of global urban software environment Software environment

Mean

Coefficient of variation

Best city

Social security

0.554

0.298

Shizuoka–Hamamatsu M.M.A.

Economic freedom

0.551

0.251

Hong Kong

Cultural inclusion

0.301

0.817

Manila

Property protection

0.519

0.396

San Jose

Knowledge density

0.347

0.637

Moscow

Business convenience

0.631

0.235

Singapore

Source CCC of CASS

software environment ranking of G20 countries. Comparing the ranking of urban software environment of G20 countries, it is found that in the top 20 cities in the world, U.S.A. leads by 40%, followed by China, Japan and Canada by 10% and the European Union by 5%. Among the top 100 cities in the global software environment, U.S.A. accounts for the highest number of cities, up to 32%, China accounts for 14%, and the third is the European Union, accounting for 13%. U.S.A., European Union and China account for 37, 23 and 15% of the top 101–200 cities in the global software environment. Among the top 201–500 cities in the global urban software environment, China has the highest number of cities, accounting for 37%, followed by the European Union, accounting for 11%, followed by Russia, accounting for 10%. Among the top 500 cities in the global urban software environment, the proportion of Chinese cities is the highest, reaching 28%, followed by U.S.A. and the European

230

5 Explanatory Indicators of Global Urban Economic Competitiveness

Union, accounting for 15 and 14%, respectively. Argentina has the least number of cities in the top 500, only 2. From the ranking distribution, we can see that U.S.A., China and the European Union are in absolute advantage (Table 5.19). Generally speaking, the average value of software environment in Non-G20 cities is still lower than that in G20 cities, and the degree of internal differentiation is serious. Comparing the urban software environment of G20 countries, it is found that in terms of the average value of software environment, the cities of Japan, Germany, U.K., U.S.A., Republic of Korea, Canada and Australia have obvious advantages, while the cities of Argentina, India and Brazil have relatively Table 5.19 Ranking and distribution of software environment indicators of sample cities in G20 countries Country

Top 20 proportion (%)

Top 100 proportion (%)

China

10

14

15.0

37.0

28.0

U.S.A.

40

32.0

37.0

2.0

15.0

European Union

5.0

13.0

23.0

11.0

14.0

Mexico

0.0

0.0

1.0

7.0

4.0

India

0.0

0.0

0.0

5.0

3.0

Russian

0.0

1.0

1.0

10.0

6.0

Brazil

0.0

0.0

0.0

1.0

1.0

Germany

5.0

3.0

10.0

0.0

3.0

Italy

0.0

0.0

3.0

3.0

3.0

Indonesia

0.0

0.0

1.0

5.0

3.0

Turkey

0.0

0.0

3.0

4.0

3.0

U.K.

5.0

8.0

3.0

0.0

2.0

Japan

10.0

10.0

0.0

0.0

2.0

Canada

10.0

7.0

2.0

0.0

2.0

Republic of Korea

5.0

6.0

2.0

0.0

2.0

France

0.0

1.0

0.0

3.0

2.0

Argentina

0.0

0.0

0.0

1.0

0.0

Australia

0.0

1.0

4.0

0.0

1.0

South Africa

0.0

1.0

0.0

1.0

1.0

Saudi Arabia

0.0

0.0

2.0

2.0

2.0

G20

85.0

93.0

94.0

86.0

89.0

Non-G20

15.0

7.0

Global

100.0

100.0

Source CCC of CASS

Proportion of 101–200 (%)

Proportion of 201–500 (%)

Top 500 proportion (%)

6.0

14.0

11.0

100.0

100.0

100.0

5.3 Soft Environment

231

Table 5.20 Statistical description of software environment indicators of G20 sample cities Country

Mean

Coefficient of variation

Best city

Ranking

Japan

0.8523

0.0708

Tokyo

1

Germany

0.7532

0.0667

Munich

18

Italy

0.6561

0.0814

Milan

133

U.K.

0.7999

0.0681

London

5

U.S.A.

0.7645

0.0905

New York–Newark

4

European Union

0.6906

0.1168

Munich

18

Argentina

0.4476

0.1691

Buenos Aires

286

Republic of Korea

0.7968

0.0774

Seoul

8

Canada

0.8160

0.0631

Toronto

10

Australia

0.7387

0.0653

Sydney

73

South Africa

0.5359

0.1647

Cape Town

251

Indonesia

0.5670

0.1089

Jakarta

186

France

0.6344

0.1229

Paris

72

Turkey

0.6078

0.1198

Istanbul

130

India

0.4565

0.1496

Delhi

236

Mexico

0.5347

0.1158

Mexico City

188

Brazil

0.4254

0.1149

Sao Jose dos Campos

248

China

0.5380

0.1798

Hong Kong

3

Russian

0.5760

0.1074

Moscow

62

Saudi Arabia

0.6312

0.1098

Riyadh

109

G20

0.5793

0.2341

Tokyo

1

Non-G20

0.3886

0.4353

Singapore

2

Global

0.5287

0.3175

Tokyo

1

Source CCC of CASS

poor performance. In terms of the coefficient of variation of software environment, the fluctuation range of cities in Japan, Germany, Italy, U.K., U.S.A., Republic of Korea, Canada and Australia is relatively small, while that in Argentina, South Africa and China is relatively large. From the perspective of the best cities, 17 cities in G20 countries have entered the top 20 in the world, and 93 cities have entered the top 100 in the world software environment (Table 5.20).

5.3.2.2

Overview of Representative Countries

According to the intercontinental division, China, Japan and India in Asia, the U.K. in Europe, U.S.A. in North America, Brazil in South America, South Africa in Africa and Australia in Oceania are mainly selected for comparative study. In general, Japanese cities have advantages in all sub indicators of software environment,

232

5 Explanatory Indicators of Global Urban Economic Competitiveness

while the overall level of emerging economies is still low, and there is a large internal differentiation in all sub indicators. From the mean of social security, Japan has a high degree of social security and little fluctuation„ and Brazil’s social security problems are obvious. From the mean of economic freedom, Australia and U.K. are far ahead, while the U.S.A. is slightly weaker. China is at a medium level and still has a lot of room for progress. From the mean of cultural inclusion, Japan has a high degree of cultural inclusion, while India and China have a low degree of cultural inclusion. In terms of coefficient of variation, the fluctuation range of Japanese cities is relatively small. From the mean of property protection, Australia, Japan and U.K. have obvious urban advantages. India is in the middle low level and the fluctuation range among cities is large. From the perspective of Knowledge density, U.K. cities are better, and Knowledge density in India and China need to be improved. From the mean of business convenience, the U.S.A. is the leading city, while Brazil, South Africa and India are relatively backward (Table 5.21).

5.4 Hard Environment 5.4.1 Overall Pattern of Hardware Environment 5.4.1.1

Head City Overview

Top 20 global cities hardware environment: Western Europe and North America occupy an overwhelming advantage, and the gap between cities is small. According to the 2019 Global City Economic Competitiveness Report, the top 10 seats in the global cities hardware environment are Amsterdam, Lisbon, Dusseldorf, Vancouver, Paris, Vienna, Brussels, Frankfurt, Sydney and Kansas City, followed by 10–20 for Philadelphia, Dallas, Melbourne, Atlanta, London, Singapore, Baltimore, Hamburg and Cincinnati. Table 5.22 shows the hardware environment index and ranking of the top 20 cities in the global hardware environment. As can be seen from Table 5.22, from a spatial perspective, Western Europe and North America occupy 9 seats and 8 seats respectively, accounting for up to 85%, so Western Europe and North America occupy an overwhelming advantage in the competitiveness of the hardware environment. At the same time, the standardization index of the top 20 global hardware environment in cities decreased from 1 to 0.920, only a decrease of nearly 8%. The decrease was relatively small, and the difference in hardware environment between cities was relatively weak.

Source CCC of CASS

Whole software environment

Business convenience

Cultural facilities

Property protection

Cultural inclusion

Economic freedom

Social security

0.739 0.065

Coefficient of variation

0.041

Coefficient of variation

Mean

0.781

0.423

Mean

0.301

Coefficient of variation

0.023

Coefficient of variation

Mean

0.943

0.274

Coefficient of variation

Mean

0.437

0.022

Mean

0.864

Coefficient of variation

0.992

Coefficient of variation

Mean

0.607

Mean

Australia

0.115

0.425

0.034

0.481

0.511

0.347

0.076

0.505

0.394

0.422

0.076

0.412

0.338

0.308

Brazil

0.091

0.765

0.047

0.804

0.329

0.515

0.043

0.897

0.264

0.557

0.042

0.775

0.262

0.506

U.S.A.

Table 5.21 Statistical analysis of sub indicators of representative national software environment

0.165

0.536

0.017

0.573

0.514

0.496

0.069

0.610

0.314

0.527

0.064

0.591

0.205

0.206

South Africa

0.071

0.852

0.054

0.758

0.263

0.642

0.026

0.929

0.186

0.672

0.026

0.735

0.116

0.856

Japan

0.150

0.457

0.009

0.614

0.733

0.250

0.090

0.482

1.225

0.137

0.083

0.455

0.214

0.578

India

0.068

0.800

0.050

0.799

0.200

0.629

0.031

0.914

0.219

0.554

0.030

0.822

0.116

0.539

U.K.

0.180

0.538

0.028

0.708

0.768

0.224

0.125

0.538

0.907

0.248

0.133

0.517

0.148

0.644

China

5.4 Hard Environment 233

234

5 Explanatory Indicators of Global Urban Economic Competitiveness

Table 5.22 Top 20 cities in the global urban hardware environment City

Country

Continent

Index

Ranking

Amsterdam

Netherlands

Europe

1.000

1

Lisbon

Portugal

Europe

0.985

2

Dusseldorf

Germany

Europe

0.974

3

Vancouver

Canada

North America

0.969

4

Paris

France

Europe

0.968

5

Vienna

Austria

Europe

0.961

6

Brussels

Belgium

Europe

0.960

7

Frankfurt am Main

Germany

Europe

0.959

8

Sydney

Australia

Oceania

0.956

9

Kansas City

U.S.A.

North America

0.951

10

Philadelphia

U.S.A.

North America

0.948

11

Dallas–Fort Worth

U.S.A.

North America

0.945

12

Melbourne

Australia

Oceania

0.941

13

Atlanta

U.S.A.

North America

0.940

14

London

U.K.

Europe

0.936

15

Singapore

Singapore

Asia

0.931

16

Toronto

Canada

North America

0.926

17

Baltimore

U.S.A.

North America

0.925

18

Hamburg

Germany

Europe

0.921

19

Cincinnati

U.S.A.

North America

0.920

20

Source CCC of CASS

5.4.1.2

Overall Spatial Pattern

The overall pattern of the top 100 global cities hardware environment: Asia is struggling to catch up, South America and Africa lag behind. Among the top 100 cities in the global cities hardware environment, the number of European cities occupies up to 45 seats, North America and Asia occupy 37 seats and 15 seats respectively, accounting for about half of the top 100 cities. Oceania has 3 seats and is mainly concentrated in Australia. Africa and South America both have 0 seats, which is quite different from other regions. Table 5.23 gives a statistical description of the top 100 cities in the global hardware environment. It can be seen from Table 5.23 that Amsterdam has the strongest competitiveness in the hardware environment in Europe, and the standardization index is 1, Vancouver in North America, Sydney in Oceania, and Singapore in Asia are 0.969, 0.956, and 0.931, respectively. The competitiveness is relatively strong. Table 5.24 shows the intercontinental distribution of the global urban hardware environment. In the global urban hardware environment, the pattern of duopoly in Europe and North America continues. Among the top 100 cities, Europe and North America

5.4 Hard Environment

235

Table 5.23 Top 100 cities in the global cities hardware environment Region

Sample

Mean

CV

Best city

Index

Global rank

North America

37

0.869

0.061

Vancouver

0.969

4

Oceania

3

0.901

0.091

Sydney

0.956

9

Europe

45

0.881

0.061

Amsterdam

1

1

Asia

15

0.833

0.041

Singapore

0.931

16

Africa

0

0

0

–

–

–

South America

0

0

0

–

–

–

Total

1006

0.871

0.062

Amsterdam

1

1

Source CCC of CASS

Table 5.24 Intercontinental distribution of global city hardware environmental indicators Region

Sample

Proportion of top 100 cities (%)

Mean

Coefficient of variation

North America

131

28.24

0.869

0.061

Oceania

7

42.86

0.901

0.091

Europe

126

35.71

0.881

0.061

Asia

565

2.65

0.84

0.049

Africa

102

0

0

0

South America

75

0

0

0

Total

1006

9.94

0.540

0.334

Source CCC of CASS

occupy 45 seats and 37 seats respectively, accounting for 45 and 35% of the total of the top 100 cities. Within the region, the top 100 cities in Europe and North America accounted for 35.71 and 28.42% of the sample cities respectively, and the average of the top 100 cities was 0.881 and 0.869, both exceeding 0.85, while the coefficient of variation was only 0.061. The difference is that neither South American nor African cities are among the top 100 in the world. Figure 5.10 shows the spatial distribution of the hardware environment index of 1006 cities in the world. Figure 5.10 shows that the cities with the most competitive cities hardware environment in the world are mainly concentrated in Western Europe and North America, while the eastern coastal regions of Asia are relatively competitive, but the city hardware environment in South America and Africa is significantly weaker. Figure 5.11 shows the spatial distribution of the hardware competitiveness of the world’s top 100 cities. It can be more clearly seen from Figure 5.11 that the top 100 cities in the global urban hardware environment are mainly concentrated in North America and Western Europe, and the distribution density of cities in the Western European region with a higher level of urban hardware environment competitiveness is significantly greater

236

5 Explanatory Indicators of Global Urban Economic Competitiveness

Fig. 5.10 Spatial distribution map of global urban hardware environment index. Source CCC of CASS

Fig. 5.11 Spatial distribution map of hardware environment index of the world’s top 100. Source CCC of CASS

5.4 Hard Environment Table 5.25 Descriptive statistics of hardware indicators of 1006 cities worldwide

237

Sub-index

Mean

Coefficient of variation

Best city

Traffic congestion

0.584

0.22

Muscat

Power adequacy

0.64

0.374

London and so on

Information accessibility

0.408

0.543

Bologna

Shipping convenience

0.838

0.186

Sydney

Airport index

0.45

0.44

Atlanta

0.211

Beijing et al

Natural disaster 0.793 index Source CCC of CASS

than that in North America. There are only sporadic cities in East Asia and Oceania, while South Africa and South America are completely blank. Ease of access to information determines the future. The global cities hardware environment competitiveness level is composed of 6 sub-indices including traffic congestion, power adequacy, information accessibility, shipping convenience, airport index and natural disaster index. Table 5.25 gives the descriptive statistics of the hardware environment sub-indicators of 1006 cities worldwide. From Table 5.25, it can be seen that the average difference between the hardware environment sub-indices of 1006 cities in the world is relatively small, and the average value of the convenience of shipping is 0.838 at the maximum, indicating that shipping is still the main transportation route between cities around the world. At the same time, the maximum coefficient of variation of information accessibility between cities is 0.543, that is, the ability to obtain information between cities is an important factor that affects the competitiveness of the city’s hardware environment, and also an important factor that determines the future competitiveness of cities. The difference is that Figure 5.12 shows the kernel density estimation map of the hardware environment of global cities on different continents. From Figure 5.12, it can be seen that the kernel density map of the hardware environment of African cities in the global cities is closest to the normal distribution, the peak of the South American cities is relatively large, and the difference in the competitiveness level of the hardware environment is relatively small. The difference is that the kernel density maps of North America and Europe are both negatively skewed, and there is a large difference between the urban hardware environment, and although the overall competitiveness of the hardware environment is high in Europe and North America, there is a certain number of hardware environments. Poor cities make the gap between cities larger.

5 Explanatory Indicators of Global Urban Economic Competitiveness

0

1

Density

2

3

238

0

.2

.4 .6 Hardware environment kdensity Global kdensity Europe kdensity NAmerica

.8

1

kdensity Asia kdensity africa kdensity SAmerica

Fig. 5.12 Kernel density map of global urban hardware environment sub-index

5.4.2 National Pattern of Hardware Environment 5.4.2.1

G20 Country Profile

The distribution of the hardware environment in different countries: the United States, Germany, France, Britain and Canada have great advantages. At the national level, the spatial distribution of cities with strong competitiveness in the global hardware environment is remarkable. In terms of the top 20 cities with the strongest hardware environment in the world, the United States, Germany, France, the United Kingdom and Canada account for 30, 15, 5, 5 and 5% of the total respectively. Table 5.26 shows the level distribution of the competitiveness level of hardware environment in different countries. It can be seen from Table 5.26 that among the top 100 cities in the world, the United States accounts for 31.25%, the United Kingdom, Germany, and Italy account for more than 5%, while China, South Korea, Japan, India, Russia, Australia, and Canada account for more than 1%. Of the top 101–200 cities, the United States accounts for 18%, and China, South Korea, France, and Mexico account for more than 5%. Among the top 201–500 cities, China accounts for 26%, India accounts for 13%, and the United States, Mexico, and Brazil account for more than 5%. Among the top 500 cities, China has the highest share of 17.2%, followed by the United States with 14.2%, and Indian cities with more than 5%.

5.4 Hard Environment

239

Table 5.26 Rank distribution of city hardware environment competitiveness in different countries Country

Top 20 (%)

Top 20–80 (%)

Top 101–200 (%)

Top 201–500 (%)

Top 500 (%)

China

0.00

2.50

6.00

26.00

17.20

Republic of Korea

0.00

1.25

5.00

0.67

1.60

Japan

0.00

3.75

0.00

1.67

1.60

India

0.00

3.75

1.00

13.00

8.60

Indonesia

0.00

0.00

3.00

2.67

2.20

Saudi Arabia

0.00

0.00

0.00

1.67

1.00

Turkey

0.00

0.00

1.00

0.67

0.60

Russian

0.00

1.25

1.00

3.00

2.20

U.K.

5.00

8.75

2.00

0.33

2.20

France

5.00

0.00

6.00

0.00

1.40

15.00

6.25

3.00

0.00

2.20

0.00

5.00

4.00

1.33

2.40

10.00

30.00

22.00

6.00

13.20

Germany Italy Europe Union South Africa

0.00

0.00

1.00

1.00

0.80

Australia

10.00

1.25

3.00

0.00

1.20

U.S.A.

30.00

31.25

18.00

7.33

14.20

Canada

5.00

3.75

3.00

0.00

1.40

Mexico

0.00

0.00

5.00

5.00

4.00

Brazil

0.00

0.00

1.00

5.33

3.40

Argentina

0.00

0.00

0.00

1.33

0.80

95.00

82.50

74.00

76.67

77.80

G20 Non G20 Total

5.00

17.50

26.00

23.33

22.20

100.00

100.00

100.00

100.00

100.00

Source CCC of CASS

In terms of G20 countries and non-G20 countries, the top 20 cities account for 95% of G20 countries, top 21–100, 101–200, and 201–500 cities account for 82.5, 74 and 76.67% of G20 countries, respectively, much higher than the proportion of non-G20 cities.

5.4.2.2

Representative Country Profile

Germany is the most balanced and Russia is the most diverse. There are also large differences in the competitiveness levels of urban hardware environments among major countries around the world. Table 5.27 gives a statistical description of the

240

5 Explanatory Indicators of Global Urban Economic Competitiveness

hardware competitiveness of cities in major countries and the ranking of the best cities. Among the major countries in the world, the average level of the German city hardware environment represented by Dusseldorf is 0.859, and the minimum coefficient of variation is 0.095, which is the smallest difference between cities in all countries, followed by Canada, Australia, and the United Kingdom. Compared with France and other countries, the hardware environment difference between cities within the country is relatively small. The difference is that the average value of the hardware environmental competitiveness of Russian cities represented by Moscow is 0.460. Not only is it ranked lower in the main countries, but the coefficient of variation of 0.359 is the largest among the main countries, so the overall hardware environment of the Russian cities is poor. , And the hardware environment gap between cities is large. Table 5.27 Descriptive statistics of city hardware environmental competitiveness in major countries Country

Mean

Coefficient of variation

Best city

Global rank

China

0.455

0.265

Taipei

81

Republic of Korea

0.748

0.1

Seoul

37

Japan

0.671

0.194

Tokyo

55

India

0.512

0.213

Chennai

67

Indonesia

0.568

0.248

Jakarta

126

Saudi Arabia

0.542

0.082

Riyadh

354

Turkey

0.486

0.187

Istanbul

183

Russian

0.46

0.359

Moscow

48

U.K.

0.78

0.217

London

15

France

0.777

0.117

Paris

5

Germany

0.859

0.095

Dusseldorf

3

Italy

0.742

0.118

Milan

28

Europe Union

0.706

0.255

Amsterdam

1

South Africa

0.6

0.192

Johannesburg

105

Australia

0.803

0.164

Sydney

9

U.S.A.

0.763

0.143

Kansas City

10

Canada

0.817

0.104

Toronto

17

Mexico

0.581

0.214

Monterrey

101

Brazil

0.515

0.174

Curitiba

200

Argentina

0.505

0.301

La Plata

252

G20

0.546

0.32

Amsterdam

1

Not G20

0.521

0.378

Singapore

16

Total

0.539

0.336

Amsterdam

1

Source CCC of CASS

5.4 Hard Environment

241

Convergence in developed countries, dispersion in developing countries. The degree of traffic congestion, power adequacy, access to information, shipping convenience, airport index and natural disaster index all determine the competitiveness level of the urban hardware environment. The advantages of the sub-indicators of the hardware environment in developed countries such as the United States and the United Kingdom are converging, and the sub-indicators of the hardware environment in developing countries are still dividing. Table 5.28 gives descriptive statistics of 6 sub-indicators of city hardware environment in major countries. Traffic congestion in cities in Australia, the United States, Japan and the United Kingdom is relatively serious. The average value of U.K. city traffic congestion is 0.641 at the maximum, and the coefficient of variation of 0.221 is only lower than that of South Africa. The most serious difference with the U.K. traffic congestion index is that the average power adequacy of U.K. cities is at most 0.973, and the minimum coefficient of variation is 0.052. At the same time, the highest average value of U.K. shipping convenience is 0.937, and the smallest coefficient of variation is 0.042. Therefore, the relative advantages of electricity adequacy and shipping convenience in U.K. cities are obvious, which helps the overall competitiveness of the U.K. cities hardware environment. U.S. cities have better access to information, with the mean being the highest in major countries of 0.77, but the coefficient of variation of 0.099 is slightly higher than 0.088 in the U.K. The highest airport index in Australia is 0.757 among the major countries, and the minimum coefficient of variation is 0.314. The highest average natural disaster index in Brazilian cities is 0.897, but the coefficient of variation is slightly less than 0.092 in the United Kingdom among the major countries. The sub-indicators of developing countries such as South Africa and India are more severely differentiated and have obvious disadvantages. Table 5.28 Descriptive statistics of sub-indicators of city hardware environment in major countries Sub-indicators Statistics Australia Brazil U.S.A. South Japan India Africa

China U.K.

Traffic congestion

Mean

0.623

0.52

0.628

0.481

0.606 0.555 0.538

0.641

CV

0.165

0.171

0.181

0.221

0.027 0.191 0.167

0.198

Power adequacy

Mean

0.947

0.778

0.795

0.826

0.825 0.629 0.52

0.973

CV

0.095

0.234

0.216

0.109

0.165 0.232 0.368

0.052

Information accessibility

Mean

0.622

0.385

0.77

0.343

0.543 0.33

0.324

0.702

CV

0.264

0.197

0.099

0.254

0.214 0.499 0.602

0.088

Shipping convenience

Mean

0.806

0.473

0.865

0.86

0.946 0.858 0.882

0.937

CV

0.302

0.358

0.157

0.116

0.064 0.095 0.116

0.042

Airport index

Mean

0.757

0.364

0.619

0.568

0.609 0.384 0.385

0.573

CV

0.314

0.396

0.454

0.408

0.394 0.392 0.479

0.399

Mean

0.79

0.897

0.843

0.744

0.581 0.838 0.808

0.821

CV

0.092

0.094

0.14

0.206

0.376 0.157 0.132

0.068

Natural disaster index

Source CCC of CASS

242

5 Explanatory Indicators of Global Urban Economic Competitiveness

5.5 Global Contacts 5.5.1 Overall Pattern of Global Contacts 5.5.1.1

Overview of the Head City

North America, Europe and Asia stand at the top of the global connected cities. Analyzing the distribution of the top 20 cities in the world, they are all located in North America, Europe and Asia, 7 cities in North America, 6 in Europe and 7 in Asia (Table 5.29). Among the top 100 cities in the global connection, there is a small gap between continents in terms of mean value and difference. Among them, North America and Europe have the highest mean value of global urban linkages, and North America Table 5.29 Global contact indicators top 20 cities in the world Region

Country

City

Index

Rank

North America

U.S.A.

New York–Newark

1.000

1

Asia

China

Beijing

0.986

2

Europe

France

Paris

0.976

3

Europe

U.K.

London

0.974

4

Asia

China

Shanghai

0.961

5

North America

U.S.A.

Chicago

0.952

6

Asia

Turkey

Istanbul

0.948

7

Europe

Spain

Madrid

0.936

8

Europe

Netherlands

Amsterdam

0.923

9

Asia

Singapore

Singapore

0.907

10

Asia

Hong Kong, China

Hong Kong

0.901

11

Europe

Italy

Milan

0.897

12

North America

Canada

Toronto

0.894

13

North America

U.S.A.

Dallas–Fort Worth

0.891

14

Europe

Russia

Moscow

0.888

15

North America

U.S.A.

Atlanta

0.886

16

North America

U.S.A.

Washington, DC

0.885

17

Asia

The United Arab Emirates

Dubai

0.877

18

North America

U.S.A.

Los Angeles–Long Beach–Santa Ana

0.864

19

Asia

Japan

Tokyo

0.859

20

Source CCC of CASS

5.5 Global Contacts

243

Table 5.30 Intercontinental distribution of the top 100 cities in the world Region

Sample

Mean

CV

Best city

Index

Rank

Asia

37

0.787

0.099

Beijing

0.986

2

Europe

30

0.806

0.103

Paris

0.976

3

North America

24

0.811

0.097

New York

1.000

1

South America

4

0.812

–

Sao Paulo

0.778

88

Oceania

4

0.777

–

Sydney

0.846

21

Global

1006

0.348

0.644

New York

1.000

1

Source CCC of CASS

has the lowest coefficient of urban variation. From the perspective of the best cities in global linkages of all continents, in Europe, North America, Asia, Oceania and South America, the best cities are Paris, New York, Beijing, Sydney and Sao Paulo respectively, with corresponding rankings of 3, 1, 2, 21 and 88 (Table 5.30).

5.5.1.2

Overall Spatial Pattern

European, North American and Asian Cities lead in global connectivity. In terms of the mean value characteristics of the intercontinental distribution of global urban linkages, the global linkages of cities in Oceania, Europe and North America are relatively strong, while the global linkages of cities in Africa and Asia are relatively weak. In terms of the coefficient of variation of the intercontinental distribution of global urban linkages, the fluctuation range of urban global linkages in Oceania is relatively small, and that in Asia is relatively large. In terms of the proportion of global top 100 cities in all continents, Oceania and Europe have a higher proportion of global top 100 cities, while South America and Asia have a lower proportion of global top 100 cities (Figs. 5.13 and 5.14; Table 5.31). In terms of the mean value of the sub indicators of global connection, the financial enterprise connection and scientific research connection of global cities are relatively high, and the number of air routes of global cities is relatively low. In terms of the coefficient of variation of the sub indicators of global connection, the fluctuation range of the global city financial enterprise connection and technology enterprise connection is relatively large, and the fluctuation range of the Information contact degree and Scientific research connection is relatively small. Among the five sub indicators, Paris, New York, London, Beijing and Singapore each have one of the top indicators in the world (Fig. 5.15 and Table 5.32).

244

5 Explanatory Indicators of Global Urban Economic Competitiveness

90

60

30

0 -180

-120

-60

0

60

120

180

-30

-60

-90 Fig. 5.13 Spatial distribution of global connection indicators of 1006 cities in the world. Source CCC of CASS

5.5.2 Global Connection Country Pattern 5.5.2.1

Overview of G20 Countries

There is a large gap between the number of cities in emerging and developed economies. Among them, China, the United States and the European Union occupy an absolute advantage in the number of top cities in the global contact ranking. In terms of the proportion of the top 20 and top 100 global cities, China and the United States and EU are relatively large. From the perspective of the former 20 cities, Italy, U.K., Turkey, Canada and Russia each have one city ranks in the list, while other countries have no cities in the top 20 in the world. Among them, China accounts for the highest proportion of 201–500 cities, far higher than other countries. The proportion of G20 cities in the top 20, top 100, 101–200 and 201–500 is far higher than that of non G20 cities (Table 5.33). Further analysis on the mean value and volatility of global urban linkages in G20 countries. Cities in France, Germany, Italy and the United Kingdom have obvious advantages in terms of the average of global connections, while those in India, Brazil, Saudi Arabia and Russia have relatively poor performance. According to the coefficient of variation of global connection, the fluctuation range of cities in Germany, France and Canada is relatively small, while that in Turkey, Saudi Arabia and India is relatively large (Table 5.34).

5.5 Global Contacts

245

90

60

30

0 -180

-120

-60

0

60

120

180

-30

-60

-90 Fig. 5.14 Spatial distribution of global connectivity indicators of the top 100 cities in the world. Source CCC of CASS

Table 5.31 Intercontinental distribution of global urban connectivity indicators Region

Sample

>100%

Mean

CV

Asia Europe

565

6.55%

0.317

0.614

126

23.81%

0.461

0.573

North America

131

18.32%

0.571

0.492

South America

75

5.33%

0.406

0.695

Oceania

7

57.14%

0.680

0.314

Africa

102

0.00%

0.331

0.241

Global

1006

9.94%

0.348

0.644

Source CCC of CASS

5.5.2.2

Overview of representative countries

This paper focuses on the comparative study of China, Japan and India in Asia, the United Kingdom in Europe, the United States in North America, Brazil in South America, South Africa in Africa and Australia in Oceania. In general, the United States, the United Kingdom, India and Australia are relatively leading in the sub

246

5 Explanatory Indicators of Global Urban Economic Competitiveness

Fig. 5.15 Nuclear density map of global urban connectivity indicators Source CCC of CASS Table 5.32 Statistical description of sub indicators of global urban linkages Global connectivity

Mean

CV

Best city

Number of air lines

0.130

1.248

Paris

Information contact degree

0.347

0.635

New York

Scientific research connection

0.428

0.626

London

Contact degree of financial enterprises

0.429

1.610

Beijing

Contact degree of science and technology enterprises

0.293

1.331

Singapore

Source CCC of CASS Table 5.33 Ranking distribution of global contact indicators of sample cities in G20 countries Country

>20 (%)

>100 (%)

101–200 (%)

201–500 (%)

>500 (%)

China

30.0

23.0

21.0

45.6

35.8

U.S.A

30.0

19.0

25.0

7.3

13.0

European Union

20.0

24.0

13.0

6.0

12.6

Mexico

0.0

1.0

0.0

2.3

1.4

India

0.0

4.0

1.0

3.7

3.4

Germany

0.0

4.0

3.0

2.0

2.4

Italy

5.0

2.0

1.0

2.7

2.2

UK

5.0

3.0

2.0

2.0

2.2

Brazil

0.0

1.0

1.0

2.3

2.0

Japan

5.0

1.0

1.0

1.7

1.4

France

5.0

2.0

4.0

1.0

1.8

Canada

5.0

4.0

1.0

1.3

1.8

Russia

5.0

1.0

0.0

2.0

1.0 (continued)

5.5 Global Contacts

247

Table 5.33 (continued) Country

>20 (%)

>100 (%)

The Republic of Korea

0.0

1.0

0.0

1.3

1.0

Indonesia

0.0

1.0

0.0

2.3

1.8

Turkey

5.0

2.0

0.0

0.7

0.6

Australia

0.0

3.0

2.0

0.7

1.0

Argentina

0.0

1.0

0.0

0.0

0.2

South Africa

0.0

0.0

2.0

0.3

0.6

Saudi Arabia

0.0

1.0

1.0

1.0

0.2

G20 countries

85.0

86.0

69.0

84.0

81.4

Non G20 countries

15.0

14.0

31.0

16.0

18.6

100.0

100.0

100.0

100.0

100.0

Global

101–200 (%)

201–500 (%)

>500 (%)

Source CCC of CASS Table 5.34 Statistical description of global contact indicators of G20 sample cities Country

Mean

CV

Best city

Rank

France

0.6069

0.3020

Paris

3

U.S.A

0.5755

0.3526

New York

1

European Union

0.5881

0.3341

Paris

3

U.K.

0.5643

0.3656

London

4

China

0.2739

0.4019

Beijing

2

Turkey

0.4495

0.8952

Istanbul

7

Italy

0.4810

0.4285

Milan

12

Russia

0.2105

0.8630

Moscow

15

Japan

0.4031

0.5735

Tokyo

20

Canada

0.6299

0.2829

Toronto

13

Australia

0.6492

0.3458

Sydney

24

Germany

0.5846

0.0996

Berlin

27

Korea

0.3699

0.5844

Seoul

38

India

0.1988

0.9166

Bombay

40

Indonesia

0.3848

0.2964

Jakarta

59

Argentina

0.4522

0.3294

Buenos Aires

91

Mexico

0.2373

0.6119

Mexico city

87

Brazil

0.2586

0.6788

Sao Paulo

33

Saudi Arabia

0.1980

0.9383

Riyadh

121

South Africa

0.4008

0.5429

Johannesburg

107

G20 countries

0.3748

0.5874

New York–Newark

1

Non G20 countries

0.2741

0.7963

Singapore

10

Global

0.3481

0.6436

New York–Newark

1

Source CCC of CASS

248

5 Explanatory Indicators of Global Urban Economic Competitiveness

indicators of global links, while the level of new sub indicators of developing countries such as Brazil and South Africa is relatively low, and the degree of differentiation is relatively high. In terms of the average number of air routes, the United Kingdom is dominant, while Brazil’s cities are relatively poor. From the coefficient of variation of the number of air routes, the fluctuation range of cities in U.K. and Australia is relatively small. From the average of information contact degree, the city information contact degree of India and Japan is relatively low, and that of Australia, the United States and the United Kingdom is relatively high. According to the coefficient of variation of information connection, the fluctuation range of cities in Japan and Australia is relatively small, while that in India and South Africa is relatively large. From the mean value of scientific research connection degree, U.K. and Australia are relatively dominant; from the coefficient of variation of scientific research connection degree, the fluctuation range of cities in Japan and Australia is relatively small. In terms of the mean value of financial enterprise connection degree, China and Australia are relatively dominant; in terms of the coefficient of variation of financial enterprise connection degree, the fluctuation range of cities in China and the United States is relatively small. From the mean value of the degree of connection of technology enterprises, the United States and India are relatively dominant; from the coefficient of variation of the degree of connection of technology enterprises, the fluctuation range of cities in the United States and U.K. is relatively small. From the mean value of the overall situation of the global connection, the United States and the United Kingdom are relatively dominant; from the coefficient of variation of the overall situation of the global connection, the urban volatility of the United States and the United Kingdom is relatively small (Table 5.35).

5.6 Industrial Quality 5.6.1 Overall Pattern of Industrial Quality 5.6.1.1

Head City Profile

The global industrial quality of the head city intercontinental distribution is uneven. From the distribution of the top 20 cities in all continents, all of them are concentrated in North America, Europe and Asia. Among them, the number of top 20 cities in North America is 6, the number of cities in Europe is 7, and the number of cities in Asia is 7 (Table 5.36). In the world’s top 100 industrial quality cities, the average of cities across the continent is relatively close. Among them, the average value of urban industrial

Source CCC of CASS

Global connectivity

Contact degree of science and technology enterprises

Contact degree of financial enterprises

Scientific research connection

Information contact degree

Number of air lines

0.379 0.408

CV

2.065

CV Mean

0.152

0.007

CV Mean

0.760

0.771

Mean

0.272

CV

0.313

CV Mean

0.448

1.070

CV Mean

0.140

Mean

China

0.353

0.576

0.575

0.612

0.328

0.633

0.300

0.680

0.356

0.554

1.059

0.206

U.S.A.

0.916

0.199

0.704

0.338

1.514

0.578

0.720

0.319

0.743

0.249

1.423

0.053

India

Table 5.35 Statistical analysis of sub indicators of global links of representative countries

0.574

0.403

0.555

0.594

1.115

0.352

0.202

0.650

1.427

0.152

1.197

0.135

Japan

0.366

0.564

0.748

0.531

0.671

0.484

0.337

0.752

0.471

0.547

0.496

0.318

U.K.

0.543

0.401

1.549

0.280

0.808

0.493

0.254

0.629

0.523

0.315

0.760

0.154

South Africa

0.679

0.259

1.806

0.178

2.469

0.102

0.444

0.502

0.476

0.356

1.054

0.073

Brazil

0.722

0.320

0.513

0.671

0.508

0.692

0.166

0.815

0.230

0.661

0.537

0.188

Australia

5.6 Industrial Quality 249

250

5 Explanatory Indicators of Global Urban Economic Competitiveness

Table 5.36 Industry quality indicators top 20 cities Continent

Country

City

Index

Ranking

Asia

Japan

Tokyo

1.000

1

North America

USA

New York–Newark

0.889

2

Europe

UK

London

0.829

3

Asia

China

Beijing

0.814

4

Europe

France

Paris

0.775

5

North America

USA

San Francisco–Oakland

0.772

6

Asia

China

Taipei

0.760

7

North America

USA

Boston

0.730

8

Europe

Switzerland

Zurich

0.714

9

Asia

South Korea

Seoul

0.711

10

North America

USA

Chicago

0.695

11

Asia

China

Hong Kong

0.681

12

North America

USA

Los Angeles–Long Beach–Santa Ana

0.680

13

Europe

Netherlands

Amsterdam

0.676

14

Europe

Russia

Moscow

0.673

15

Europe

Sweden

Stockholm

0.673

16

Europe

Germany

Frankfurt am Main

0.673

17

Asia

Singapore

Singapore

0.666

18

North America

Canada

Toronto

0.665

19

Asia

China

Shanghai

0.657

20

Source CCC of CASS

quality in Europe and Asia is the highest, the average value of urban industrial quality in South America is the lowest, and the average value of urban industrial quality in North America and Oceania is the middle. Europe, North America, Asia, Oceania and South America are London, New York, Tokyo, Sydney and São Paulo, respectively. The global ranking is 3rd, 2nd, 1st, 25th and 38th (Table 5.37).

5.6.1.2

Overall Spatial Pattern

Cities with high industrial quality are concentrated in Europe, North America and Asia. In terms of intercontinental distribution, the industrial quality of cities in North America, Oceania and Europe is relatively high, the proportion of top 100 cities is also high, the industrial quality of African cities is generally weak, no city has entered the top 100 list, the industrial quality of Asian and South American cities is in the middle, and only a small number of cities have entered the top 100 list (Figs. 5.16, 5.17 and 5.18; Table 5.38).

5.6 Industrial Quality

251

Table 5.37 Intercontinental distribution of top 100 cities in the world Region

Sample

Mean

Coefficient of variation

Optimal cities

Index

Ranking

Asia

29

0.605

0.183

Tokyo

1.000

1

Europe

28

0.618

0.130

London

0.829

3

North America

34

0.573

0.164

New York

0.889

2

South America

5

0.568

0.062

São Paulo

0.609

38

Oceania

4

0.578

0.124

Sydney

0.648

25

Global

100

0.595

0.158

Tokyo

1.000

1

Source CCC of CASS 90

60

30

0 -180

-120

-60

0

60

120

180

-30

-60

-90

Fig. 5.16 Spatial distribution of industrial quality indicators in 1006 cities. Source CCC of CASS

The average value and fluctuation range of the sub-index of global urban industsrial quality are quite different. From the average value of industrial quality sub-index, the average labor productivity of global cities is relatively high, and the average value of transnational scientific and technological enterprises is relatively low. From the coefficient of variation of industrial quality sub-index, the fluctuation range of transnational science and technology enterprises is relatively large, and the fluctuation range of labor productivity is relatively small. Among the five sub-indicators, the best cities of transnational banks, transnational technology enterprises, transnational corporations, labor productivity, and university index are Taipei, Tokyo, New York, San Jose and New York (Table 5.39).

252

5 Explanatory Indicators of Global Urban Economic Competitiveness 90

60

30

0 -180

-120

-60

0

60

120

180

-30

-60

-90

0

1

2

3

4

5

Fig. 5.17 Spatial distribution of industrial quality indicators in top 100 cities. Source CCC of CASS

0

.2

.4 .6 industrial quality Global Europe SAmerica

.8

1

Asia NAmerica Africa

Fig. 5.18 Nuclear density map of global urban industrial quality indicators. Source CCC of CASS

5.6 Industrial Quality

253

Table 5.38 Intercontinental distribution of industrial quality indicators in global cities Region

Sample

Mean

Coefficient of variation

Asia

565

Top 100 cities (%) 5.13

0.224

0.576

Europe

126

22.22

0.378

0.430

North America

131

25.95

0.383

0.400

South America

75

6.67

0.262

0.452

Oceania

7

57.14

0.505

0.220

Africa

102

0.00

0.174

0.578

Global

1006

9.94

0.264

0.580

Source CCC of CASS

Table 5.39 Statistical description of sub-index of global urban industrial quality Industrial quality

Mean

Coefficient of variation

Optimal cities

Transnational banks

0.080

2.070

Taipei

Multinational science and technology enterprises

0.008

6.186

Tokyo

Transnational corporations

0.331

0.763

New York

Labour productivity

0.545

0.339

San José

University index

0.190

1.155

New York

Source CCC of CASS

5.6.2 National Pattern of Industrial Quality 5.6.2.1

G20 Country Profiles

Emerging economies are still lower than advanced economies in the number of leading cities in global industrial quality, and China and the United States occupy an absolute advantage in the number of top cities in G20 countries. Among the top 20 cities in the world, China and the United States accounted for 20 and 25%, respectively. Germany, Italy, the United Kingdom, Japan, France, Canada, Russia and South Korea each had one city in the list. Among the top 100 cities in the world, the proportion of American cities is as high as 27%, followed by China, accounting for 12%. Among the world’s top 101–200 cities, the number of cities in the United States is the highest, up to 22%, followed by China, accounting for 14%. before the global urban industrial quality. Among the top 500 cities in the world, the proportion of Chinese cities is the highest, reaching 24.4%, followed by the United States, accounting for 14.8% (Table 5.40). Overall, the average value of urban industrial quality in emerging economies is still lower than that in developed economies, and the degree of internal differentiation is higher than that in developed economies. In terms of the mean value of industrial quality, the cities of Australia, Canada and Japan have obvious advantages, and the

254

5 Explanatory Indicators of Global Urban Economic Competitiveness

Table 5.40 Distribution of industrial quality indicators G20 national sample cities Country

Top 20 (%)

Top 100 (%)

101–200 (%)

201–500 (%)

Top 500 (%)

China

20.0

12.0

14.0

32.0

24.4

United States of America

25.0

27.0

22.0

8.3

14.8

Mexico

0.0

1.0

0.0

6.0

3.8

India

0.0

1.0

2.0

2.3

2.0

Germany

5.0

5.0

4.0

1.3

2.6

Italy

0.0

2.0

6.0

1.7

2.6

UK

5.0

2.0

5.0

1.7

2.4

Brazil

0.0

1.0

3.0

5.0

3.8

Japan

5.0

3.0

4.0

1.0

2.0

France

5.0

1.0

0.0

2.7

1.8

Canada

5.0

4.0

3.0

0.7

1.8

Russia

5.0

1.0

1.0

4.3

3.0

South Korea

5.0

1.0

0.0

2.3

1.6

Indonesia

0.0

1.0

0.0

1.0

0.8

Turkey

0.0

1.0

0.0

1.3

1.0

Australia

0.0

3.0

2.0

0.3

1.2

Argentina

0.0

1.0

0.0

1.0

0.8

South Africa

0.0

0.0

1.0

1.3

1.0

Saudi Arabia Total

0.0

1.0

1.0

0.7

0.8

80.0

68.0

68.0

75.0

72.2

Source CCC of CASS

cities of India and Indonesia have relatively poor performance. From the coefficient of variation of industrial quality, cities in Germany, Australia, Italy and Canada fluctuate less, while cities in Saudi Arabia, Russia, Indonesia, Argentina and India fluctuate more. According to the best cities of industrial quality in G20 countries, 5 cities have entered the top 20 industrial links in the world, 18 cities have entered the top 100 industrial quality in the world, and 19 cities have entered the top 200 industrial quality in the world (Table 5.41).

5.6.2.2

Representative Country Profiles

According to the intercontinental division, here focus on Asia’s China-Japan India, Europe’s Britain, North America’s United States, South America’s Brazil, Africa’s South Africa, Oceania’s Australia for comparative research. Generally speaking, cities in advanced economies have more obvious advantages in the sub-index of industrial quality, and there is a large internal differentiation in each sub-index of

5.6 Industrial Quality

255

Table 5.41 G20 statistical description of national sample city industrial quality index Country

Mean

Coefficient of variation

Optimal cities

Ranking

France

0.4050

0.3488

Paris

5

United States of America

0.4500

0.2839

New York

2

UK

0.4334

0.3190

London

3

China

0.2409

0.4388

Beijing

4

EU

0.4567

0.2580

London

3

Turkey

0.2258

0.4807

Istanbul

55

Italy

0.4112

0.2136

Milan

26

Russia

0.2283

0.4514

Moscow

15

Japan

0.4918

0.4039

Tokyo

1

Canada

0.4855

0.2020

Toronto

19

Australia

0.5084

0.2392

Sydney

25

Germany

0.4757

0.2338

Frankfurt

17

South Korea

0.3650

0.3976

Seoul

10

India

0.1413

0.5982

Mumbai

48

Indonesia

0.1959

0.4655

Jakarta

66

Argentina

0.2462

0.5222

Buenos Aires

56

Mexico

0.2266

0.3950

Mexico City

54

Brazil

0.2501

0.4165

São Paulo

38

Saudi Arabia

0.2662

0.5706

Riyadh

34

South Africa

0.3028

0.3887

Johannesburg

117

G20

0.2706

0.5449

Tokyo

1

Non G20

0.2191

0.6636

Zurich

9

Global

0.2640

0.5800

Tokyo

1

Source CCC of CASS

cities in emerging economies. From the cross-border banking index, the average value of urban industrial quality in Japan and Australia is higher, and the coefficient of variation of urban industrial quality in China, India and Brazil is relatively high. From the cross-border technology enterprise index, the average value of Japanese cities is higher, and the coefficient of variation of China, India and Brazil is higher. According to the MNC index, the urban mean of the United States and Britain is higher, and the coefficient of variation of Brazil, China and South Africa is higher. From the labor productivity index, the average value of cities in the United States, Japan and Australia is higher, and the coefficient of variation is higher in China and India. From the university index, the average of British and Australian cities is higher, and the coefficient of variation of India and Brazil is higher (Table 5.42).

Source CCC of CASS

Industrial quality

University index

Labour productivity

Transnational corporations

Multinational science and technology enterprises

Transnational banks

0.241 0.439

Coefficient of variation

1.340

Coefficient of variation

Mean

0.121

0.172

Coefficient of variation

Mean

0.542

0.535

Mean

0.298

Coefficient of variation

5.487

Coefficient of variation

Mean

0.005

2.188

Coefficient of variation

Mean

0.062

Mean

China

0.284

0.450

0.663

0.455

0.056

0.861

0.354

0.544

2.711

0.032

1.147

0.140

USA

0.598

0.141

0.904

0.089

0.179

0.360

1.534

0.128

6.701

0.001

3.282

0.036

India

Table 5.42 Statistical analysis of sub-indicators of industrial quality in representative countries

0.404

0.492

0.523

0.416

0.047

0.804

0.405

0.487

2.257

0.139

0.637

0.334

Japan

0.319

0.433

0.236

0.638

0.047

0.788

0.447

0.502

3.144

0.027

2.090

0.102

UK

0.389

0.303

0.642

0.303

0.107

0.519

0.587

0.490

–

0.000

2.449

0.055

South Africa

0.417

0.250

0.799

0.232

0.118

0.533

0.748

0.305

3.935

0.001

2.549

0.050

Brazil

0.239

0.508

0.158

0.680

0.035

0.846

0.350

0.651

1.595

0.008

0.107

0.194

Australia

256 5 Explanatory Indicators of Global Urban Economic Competitiveness

5.7 Ranking of Explanatory Indicators of Global Urban Economic …

5.7 Ranking of Explanatory Indicators of Global Urban Economic Competitiveness

257

Country

U.S.A.

U.K.

Singapore

China

U.S.A.

Japan

U.S.A.

Germany

U.S.A.

China

U.S.A.

U.S.A.

China

Ireland

Republic of Korea

U.S.A.

China

China

U.S.A.

U.S.A.

City

New York–Newark

London

Singapore

Shenzhen

San Jose

Tokyo

San Francisco–Oakland

Munich

Los Angeles–Long Beach–Santa Ana

Shanghai

Dallas–Fort Worth

Houston

Hong Kong

Dublin

Seoul

Boston

Beijing

Guangzhou

Miami

Chicago

20

19

18

17

16

15

14

13

12

11

10

9

8

7

6

5

4

3

2

1

Economic competitiveness

4

32

45

39

15

9

27

28

56

83

5

58

76

70

17

114

1

8

7

2

Local factors

9

47

87

192

23

49

18

108

10

57

197

36

8

130

1

73

162

4

19

20

Living environment

16

49

88

38

12

8

44

3

34

50

47

7

18

20

1

41

59

2

5

4

Soft environment

228

134

209

138

46

37

21

300

131

12

182

132

56

59

55

173

86

16

15

27

Hard environment

6

32

29

2

26

38

22

11

23

14

5

19

34

39

20

122

37

10

4

1

Global contacts

(continued)

11

111

60

4

8

10

24

12

44

31

20

13

21

6

1

69

76

18

3

2

Industrial quality

258 5 Explanatory Indicators of Global Urban Economic Competitiveness

Country

France

Germany

Israel

U.S.A.

China

Sweden

U.S.A.

Germany

Japan

Canada

U.S.A.

U.S.A.

Germany

U.S.A.

Switzerland

U.S.A.

U.S.A.

Australia

U.S.A.

U.S.A.

Turkey

City

Paris

Frankfurt am Main

Tel Aviv–Yafo

Seattle

Suzhou

Stockholm

Philadelphia

Stuttgart

Osaka

Toronto

Baltimore

Bridgeport–Stamford

Dusseldorf

San Diego

Geneva

Atlanta

Cleveland

Perth

Denver–Aurora

Detroit

Istanbul

(continued)

41

40

39

38

37

36

35

34

33

32

31

30

29

28

27

26

25

24

23

22

21

Economic competitiveness

16

122

106

175

115

80

435

161

179

243

97

20

59

71

11

49

63

86

277

90

31

Local factors

75

211

199

473

84

26

194

50

138

144

40

161

3

41

14

63

201

46

440

93

55

Living environment

130

150

24

172

121

75

99

14

141

159

127

10

9

119

39

94

92

13

185

100

72

Soft environment

183

25

152

168

99

14

44

258

3

116

18

17

79

41

11

66

482

144

236

8

5

Hard environment

7

77

43

118

124

16

103

150

141

303

63

13

120

96

62

35

100

44

99

168

3

Global contacts

(continued)

55

73

177

114

82

37

32

35

57

127

96

19

42

63

79

16

250

40

39

17

5

Industrial quality

5.7 Ranking of Explanatory Indicators of Global Urban Economic … 259

Country

China

China

China

U.S.A.

U.S.A.

U.S.A.

Germany

U.S.A.

Germany

Austria

United Arab Emirates

U.S.A.

China

Germany

U.S.A.

Switzerland

U.S.A.

U.S.A.

Denmark

U.S.A.

City

Nanjing

Wuhan

Taipei

Charlotte

Nashville–Davidson

Minneapolis–Saint Paul

Berlin

Austin

Hamburg

Vienna

Abu Dhabi

Raleigh

Chengdu

Cologne

Las Vegas

Zurich

Salt Lake City

Richmond

Copenhagen

Orlando

(continued)

61

60

59

58

57

56

55

54

53

52

51

50

49

48

47

46

45

44

43

42

Economic competitiveness

158

101

155

154

84

221

581

52

178

102

99

72

110

57

82

149

150

48

120

54

Local factors

42

124

99

149

80

157

44

119

34

773

27

21

32

11

71

24

59

17

419

257

Living environment

113

51

129

70

54

125

105

165

63

27

35

101

37

43

69

180

71

6

86

65

Soft environment

34

32

142

26

39

90

108

391

305

275

6

19

198

23

214

249

157

81

443

279

Hard environment

79

65

176

159

48

140

291

28

193

113

42

52

76

27

104

115

49

64

51

41

Global contacts

(continued)

151

29

93

74

9

165

149

101

80

45

27

70

88

102

103

133

94

7

64

77

Industrial quality

260 5 Explanatory Indicators of Global Urban Economic Competitiveness

Country

Russian

Australia

China

China

Spain

U.K.

China

U.S.A.

Canada

Belgium

United Arab Emirates

Canada

Qatar

Germany

China

U.S.A.

Japan

U.S.A.

Germany

China

City

Moscow

Sydney

Hangzhou

Wuxi

Barcelona

Birmingham

Changsha

Milwaukee

Vancouver

Brussels

Dubai

Calgary

Doha

Hannover

Qingdao

Columbus

Sendai

Louisville

Essen

Chongqing

(continued)

81

80

79

78

77

76

75

74

73

72

71

70

69

68

67

66

65

64

63

62

Economic competitiveness

73

543

199

626

130

143

113

509

43

46

137

157

174

128

287

22

182

95

19

18

Local factors

94

179

102

38

39

191

66

804

367

570

203

53

202

112

143

79

251

160

101

88

Living environment

179

190

115

40

67

151

154

168

15

17

231

25

131

157

96

143

139

90

73

62

Soft environment

855

118

117

322

250

423

35

156

33

197

7

4

68

637

36

40

286

472

9

48

Hard environment

46

334

184

557

129

81

256

93

91

18

31

61

272

72

80

21

170

47

24

15

Global contacts

(continued)

119

292

121

113

72

95

132

28

104

59

23

67

156

164

161

78

302

87

25

15

Industrial quality

5.7 Ranking of Explanatory Indicators of Global Urban Economic … 261

Country

China

Malaysia

China

U.S.A.

Republic of Korea

U.S.A.

U.K.

Saudi Arabia

China

U.S.A.

Belgium

Netherlands

China

U.S.A.

U.S.A.

U.S.A.

Germany

China

Israel

Canada

City

Tianjin

Kuala Lumpur

Foshan

Washington, DC

Ulsan

Oklahoma City

Manchester

Riyadh

Ningbo

Phoenix-Mesa

Antwerp

Amsterdam

Zhengzhou

Tampa–St. Petersburg

Baton Rouge

Cincinnati

Dortmund

Changzhou

Haifa

Montreal

(continued)

101

100

99

98

97

96

95

94

93

92

91

90

89

88

87

86

85

84

83

82

Economic competitiveness

30

341

306

606

105

248

140

177

109

786

119

171

44

209

256

340

10

98

6

29

Local factors

163

404

326

171

54

65

187

289

172

151

353

245

759

98

68

222

129

189

175

270

Living environment

31

216

259

200

60

170

196

181

95

225

64

187

109

66

120

91

22

229

138

134

Soft environment

154

104

383

94

20

88

113

368

1

65

297

410

354

24

72

164

193

364

22

261

Hard environment

50

318

313

264

155

217

131

95

9

342

83

146

121

58

236

608

17

364

70

60

Global contacts

(continued)

47

187

347

238

68

220

214

146

14

120

209

128

34

71

116

303

41

342

30

83

Industrial quality

262 5 Explanatory Indicators of Global Urban Economic Competitiveness

Country

Indonesia

Japan

China

U.S.A.

Japan

Norway

Germany

Netherlands

U.S.A.

U.S.A.

Canada

China

Australia

U.S.A.

Germany

U.S.A.

Saudi Arabia

Thailand

Australia

China

U.S.A.

City

Jakarta

Nagoya

Dongguan

San Antonio

Hiroshima

Oslo

Dresden

Hague

Indianapolis

Provo–Orem

Hamilton

Macao

Gold Coast

Kansas City

Leipzig

Virginia Beach

Jedda

Bangkok

Brisbane

Nantong

Pittsburgh

(continued)

122

121

120

119

118

117

116

115

114

113

112

111

110

109

108

107

106

105

104

103

102

Economic competitiveness

112

133

153

21

841

387

588

53

476

576

232

625

132

586

450

41

648

147

33

142

36

Local factors

105

338

294

48

636

141

81

25

181

531

78

693

58

312

206

70

2

35

264

67

210

Living environment

53

280

111

128

197

146

175

85

230

314

87

274

98

116

135

81

46

82

204

21

186

Soft environment

31

390

93

122

384

84

165

10

336

75

135

284

45

85

97

29

398

64

233

206

126

Hard environment

112

349

85

30

528

538

198

163

695

185

439

466

156

404

299

73

399

136

130

229

59

Global contacts

(continued)

75

343

58

36

166

291

197

85

275

256

203

326

109

152

201

51

229

175

245

65

66

Industrial quality

5.7 Ranking of Explanatory Indicators of Global Urban Economic … 263

Country

Australia

Finland

Spain

China

U.S.A.

Mexico

U.S.A.

Canada

Republic of Korea

Japan

U.S.A.

U.K.

Sweden

U.S.A.

Italy

U.S.A.

U.S.A.

France

China

Japan

City

Melbourne

Helsinki

Madrid

Kaohsiung

Charleston–North Charleston

Mexico City

Hartford

Ottawa–Gatineau

Incheon

Sapporo

Riverside–San Bernardino

Bristol

Gothenburg

Allentown

Rome

Colorado Springs

Grand Rapids

Lille

Jinan

Kitakyushu–Fukuoka

(continued)

142

141

140

139

138

137

136

135

134

133

132

131

130

129

128

127

126

125

124

123

Economic competitiveness

12

272

682

285

358

600

372

726

328

391

197

251

196

207

13

245

413

81

100

96

Local factors

5

395

428

136

111

7

153

120

183

514

12

253

254

122

29

293

82

56

176

60

Living environment

68

213

284

118

110

142

279

167

61

164

29

76

117

160

188

195

30

78

55

122

Soft environment

82

471

110

174

269

71

87

51

52

696

217

221

54

129

171

295

438

58

43

13

Hard environment

301

117

183

247

254

25

646

251

101

586

435

333

138

246

87

202

223

8

75

36

Global contacts

(continued)

148

106

252

230

239

92

380

145

150

397

179

366

147

221

54

208

269

22

43

33

Industrial quality

264 5 Explanatory Indicators of Global Urban Economic Competitiveness

Country

Italy

U.S.A.

China

France

Thailand

China

China

China

Canada

Netherlands

China

U.S.A.

U.S.A.

Chile

U.S.A.

U.K.

U.S.A.

U.S.A.

India

Costa Rica

China

City

Milan

Providence

Hefei

Lyon

Samut Prakan

Quanzhou

Xiamen

Xi’an

Edmonton

Rotterdam

Fuzhou (FJ)

Birmingham

Honolulu

Santiago de Chile

Columbia

West Yorkshire

Worcester

Dayton

Delhi

San Jose

Yangzhou

(continued)

163

162

161

160

159

158

157

156

155

154

153

152

151

150

149

148

147

146

145

144

143

Economic competitiveness

324

506

40

263

217

299

126

79

260

430

172

507

238

94

91

180

871

488

50

176

131

Local factors

306

127

145

142

337

31

30

243

121

64

276

298

190

208

560

168

213

195

413

125

52

Living environment

250

263

236

210

189

26

124

166

83

147

214

148

42

126

137

318

291

209

183

145

133

Soft environment

342

461

420

92

223

141

278

853

285

196

688

42

61

405

186

339

63

106

427

190

28

Hard environment

312

169

54

277

704

615

111

160

224

252

149

279

237

55

84

344

711

94

89

204

12

Global contacts

(continued)

323

125

290

260

318

365

108

49

122

86

274

160

91

110

99

261

709

202

142

105

26

Industrial quality

5.7 Ranking of Explanatory Indicators of Global Urban Economic … 265

Country

New Zealand

U.S.A.

Spain

Peru

U.S.A.

Colombia

U.K.

Saudi Arabia

U.S.A.

China

China

China

France

U.K.

Israel

U.K.

China

Panama

Romania

Italy

U.S.A.

City

Auckland

Cape Coral

Valencia

Lima

Akron

Bogota

Liverpool

Medina

Knoxville

Zhuhai

Zhenjiang

Yantai

Marseille–Aix-en-Provence

Sheffield

Jerusalem

Belfast

Taizhou (JS)

Panama City

Bucuresti

Venice

Sacramento

(continued)

184

183

182

181

180

179

178

177

176

175

174

173

172

171

170

169

168

167

166

165

164

Economic competitiveness

191

952

144

266

164

442

375

364

528

189

195

127

258

461

360

26

253

38

383

777

62

Local factors

96

133

209

164

451

123

423

169

427

150

384

350

90

897

188

348

261

184

249

342

15

Living environment

79

207

182

317

300

123

239

93

473

292

307

272

112

222

84

226

178

294

212

256

11

Soft environment

62

109

130

338

428

70

147

133

195

392

309

304

80

477

89

394

73

737

203

119

188

Hard environment

180

620

106

157

350

276

295

335

109

206

331

327

225

796

240

45

562

68

119

947

74

Global contacts

(continued)

204

315

178

90

450

195

191

253

212

248

340

241

180

478

225

81

259

124

224

574

97

Industrial quality

266 5 Explanatory Indicators of Global Urban Economic Competitiveness

Country

China

U.K.

U.S.A.

Philippines

Saudi Arabia

U.S.A.

China

Republic of Korea

Poland

U.S.A.

Republic of Korea

Argentina

China

Republic of Korea

Republic of Korea

China

Spain

Australia

City

Dalian

Glasgow

Buffalo

Manila

Mecca

New Haven

Xuzhou

Busan

Warsaw

Ogden

Changwon

Buenos Aires

Nanchang

Gwangju

Daejeon

Shenyang

Zaragoza

Adelaide

(continued)

202

201

200

199

198

197

196

195

194

193

192

191

190

189

188

187

186

185

Economic competitiveness

210

814

293

225

355

104

61

471

458

66

37

169

185

608

64

203

283

311

Local factors

265

230

115

292

156

235

6

495

272

140

85

357

214

879

117

320

45

95

Living environment

103

220

191

58

136

260

286

162

199

77

33

321

215

227

217

114

52

153

Soft environment

114

292

421

264

115

588

734

180

212

50

107

478

100

599

176

401

53

370

Hard environment

147

678

97

464

644

137

66

924

692

71

245

296

362

829

110

266

108

88

Global contacts

(continued)

153

186

182

282

336

198

56

379

350

53

206

367

184

607

89

205

155

112

Industrial quality

5.7 Ranking of Explanatory Indicators of Global Urban Economic … 267

Mexico

Turkey

China

Czech Republic 207

Uruguay

Kazakhstan

China

France

Portugal

China

U.S.A.

China

Italy

U.S.A.

France

Republic of Korea

India

Turkey

Italy

Monterrey

Gebze

Zhongshan

Prague

Montevideo

Astana

Shaoxing

Toulouse

Lisbon

Taichung

Omaha

Jiaxing

Bologna

Memphis

Nantes

Daegu

Mumbai

Ankara

Naples

222

221

220

219

218

217

216

215

214

213

212

211

210

209

208

206

205

204

203

China

Dongying

Economic competitiveness

Country

City

(continued)

744

135

3

282

732

186

805

363

262

425

51

560

428

598

525

193

75

492

103

720

Local factors

227

260

438

86

341

77

62

224

104

103

137

147

170

361

167

72

232

782

378

493

Living environment

266

177

247

74

246

163

232

206

107

23

155

245

328

359

412

48

198

520

238

367

Soft environment

96

399

83

170

159

57

95

189

112

457

2

161

530

661

143

128

317

571

101

728

Hard environment

244

215

40

387

171

192

222

338

187

443

90

153

294

521

189

57

191

767

323

614

Global contacts

(continued)

240

215

48

262

235

139

118

296

129

219

50

257

255

349

123

61

431

601

228

273

Industrial quality

268 5 Explanatory Indicators of Global Urban Economic Competitiveness

Country

France

Belgium

Italy

U.K.

Poland

U.S.A.

U.K.

Turkey

France

China

Hungary

France

China

Germany

Japan

Argentina

China

China

U.S.A.

China

City

Nice

Liege

Verona

Leicester

Poznan

Sarasota-Bradenton

Nottingham

Izmir

Bordeaux

Changchun

Budapest

Toulon

Weihai

Bremen

Shizuoka–Hamamatsu M.M.A.

Rosario

Wuhu

Zibo

Rochester

Hsinchu

(continued)

242

241

240

239

238

237

236

235

234

233

232

231

230

229

228

227

226

225

224

223

Economic competitiveness

345

138

200

159

309

694

526

384

935

24

376

596

145

373

374

519

447

911

838

791

Local factors

196

113

305

469

217

13

226

155

674

132

502

237

215

76

43

242

177

89

280

248

Living environment

56

57

277

306

744

36

184

346

396

158

173

235

176

80

132

192

102

201

313

254

Soft environment

289

98

226

466

313

486

49

356

259

47

548

145

591

78

202

191

111

103

201

194

Hard environment

197

282

253

339

591

371

310

420

487

56

133

265

517

233

472

346

269

305

721

243

Global contacts

(continued)

211

268

288

433

389

154

210

330

327

100

168

243

338

207

414

265

246

157

263

251

Industrial quality

5.7 Ranking of Explanatory Indicators of Global Urban Economic … 269

Country

Spain

Italy

China

Canada

U.S.A.

Italy

China

U.S.A.

China

Turkey

U.S.A.

Japan

China

Canada

China

Brazil

Saudi Arabia

China

Argentina

China

China

City

Malaga

Florence

Guiyang

Quebec

New Orleans

Genoa

Tainan

Tulsa

Weifang

Bursa

Albany

Kumamoto

Yancheng

Winnipeg

Tangshan

Sao Paulo

Dammam

Shijiazhuang

Santa Fe

Wenzhou

Yichang

(continued)

263

262

261

260

259

258

257

256

255

254

253

252

251

250

249

248

247

246

245

244

243

Economic competitiveness

334

643

610

136

913

25

117

349

290

872

211

350

371

304

535

942

229

226

139

846

874

Local factors

302

228

37

347

922

776

394

166

425

33

333

297

332

131

83

91

100

414

304

74

174

Living environment

303

324

486

249

271

724

275

89

339

45

152

221

336

140

28

281

161

106

342

218

242

Soft environment

743

467

605

260

589

414

604

121

413

550

439

538

495

69

473

247

239

192

519

172

185

Hard environment

357

175

669

142

840

33

211

208

379

844

199

710

410

227

283

598

293

267

134

320

258

Global contacts

(continued)

373

247

508

249

443

38

393

192

496

333

236

461

454

144

188

163

196

216

258

231

217

Industrial quality

270 5 Explanatory Indicators of Global Urban Economic Competitiveness

Country

China

Italy

South Africa

China

Japan

Venezuela

Brazil

China

Mexico

Indonesia

United Arab Emirates

Venezuela

U.S.A.

Poland

Brazil

China

Dominican Republic

Russian

China

China

City

Taizhou (ZJ)

Torino

Pretoria

Kunming

Niigata

Maracaibo

Rio de Janeiro

Huizhou

Guadalajara

Surabaya

Sharjah

Maracay

Bakersfield

Krakow

Jundiai

Baotou

Santo Domingo

Tyumen

Tongling

Xiangyang

(continued)

283

282

281

280

279

278

277

276

275

274

273

272

271

270

269

268

267

266

265

264

Economic competitiveness

230

190

573

782

449

803

416

522

843

239

218

118

116

35

667

845

173

123

745

423

Local factors

500

526

263

200

717

422

231

374

937

853

266

319

134

92

958

22

118

236

51

256

Living environment

363

419

289

762

362

738

171

149

1000

156

223

400

287

364

1001

32

202

285

194

312

Soft environment

904

424

905

330

835

446

277

323

487

210

178

139

796

453

184

293

590

524

343

481

Hard environment

416

498

716

273

268

675

145

679

838

834

284

216

377

102

968

841

78

611

231

289

Global contacts

(continued)

453

482

416

143

293

670

264

346

493

189

392

316

401

190

328

222

223

317

199

390

Industrial quality

5.7 Ranking of Explanatory Indicators of Global Urban Economic … 271

Country

Kuwait

South Africa

Russian

India

China

Iran

Portugal

China

Poland

China

China

Venezuela

U.S.A.

Venezuela

China

China

Turkmenistan

Puerto Rico

China

Iran

Egypt

City

Kuwait City

Johannesburg

Saint Petersburg

Bangalore

Taiyuan

Karaj

Porto

Huaian

Lodz

Nanning

Hohhot

Barcelona–Puerto La Cruz

Fresno

Valencia

Jining

Ordoss

Ashgabat

San Juan

Harbin

Ahvaz

Cairo

(continued)

304

303

302

301

300

299

298

297

296

295

294

293

292

291

290

289

288

287

286

285

284

Economic competitiveness

55

630

181

148

958

884

649

237

405

213

327

357

599

331

749

393

69

60

124

14

534

Local factors

490

929

518

299

930

628

354

942

246

986

328

274

239

406

275

822

352

16

139

285

970

Living environment

478

802

234

704

902

424

366

1004

144

1006

332

219

203

351

330

839

282

241

174

357

374

Soft environment

213

791

760

38

315

670

425

479

266

595

607

778

388

357

102

838

429

91

617

105

74

Hard environment

98

860

86

165

630

328

250

783

691

589

181

116

270

356

82

751

148

69

205

107

132

Global contacts

(continued)

136

686

173

84

372

403

301

281

299

360

308

300

314

616

138

759

297

183

137

117

244

Industrial quality

272 5 Explanatory Indicators of Global Urban Economic Competitiveness

Country

Italy

Kazakhstan

China

Bangladesh

Bulgaria

U.S.A.

U.S.A.

Algeria

Malaysia

Italy

China

Venezuela

U.K.

China

Turkey

China

Brazil

China

China

Turkey

China

City

Catania

Almaty

Zhoushan

Dhaka

Sofia

El Paso

Portland

Oran

Johor Bahru

Padova

Jinhua

Caracas

Newcastle upon Tyne

Luoyang

Adana

Huzhou

Porto Alegre

Taian

Langfang

Antalya

Urumqi

(continued)

325

324

323

322

321

320

319

318

317

316

315

314

313

312

311

310

309

308

307

306

305

Economic competitiveness

567

569

233

468

265

216

440

524

397

268

284

905

635

891

219

444

337

85

386

183

943

Local factors

439

69

458

533

351

258

408

268

571

829

148

109

273

839

97

449

216

205

499

705

193

Living environment

464

244

402

446

674

401

310

331

104

993

373

257

228

943

19

97

295

884

460

467

377

Soft environment

815

582

125

646

419

418

520

516

120

207

534

166

77

329

30

241

549

745

299

984

244

Hard environment

162

581

281

500

571

427

596

186

454

196

401

154

663

360

105

393

67

161

409

257

462

Global contacts

(continued)

321

533

447

545

193

457

732

305

171

52

442

170

294

423

98

329

141

266

437

115

287

Industrial quality

5.7 Ranking of Explanatory Indicators of Global Urban Economic … 273

Country

Azerbaijan

China

Guatemala

China

Mexico

U.S.A.

China

Italy

China

Saudi Arabia

Libya

Oman

Mexico

Croatia

Argentina

Israel

Kenya

U.S.A.

Belarus

Jordan

China

City

Baku

Zhuzhou

Guatemala City

Putian

Leon

Albuquerque

Xiangtan

Bari

Xuchang

Buraydah

Tripoli

Muscat

Tijuana

Zagreb

Mendoza

Be’er Sheva

Nairobi

McAllen

Minsk

Amman

Shantou

(continued)

346

345

344

343

342

341

340

339

338

337

336

335

334

333

332

331

330

329

328

327

326

Economic competitiveness

107

776

286

758

87

515

692

170

273

609

894

867

339

705

584

223

108

446

779

517

336

Local factors

381

673

314

740

496

882

365

135

477

712

359

906

627

165

534

240

554

523

331

362

609

Living environment

456

205

255

268

517

311

711

265

372

273

946

305

589

340

355

169

358

514

509

378

262

Soft environment

341

523

542

149

660

585

914

137

240

187

246

451

298

175

320

227

328

575

692

782

306

Hard environment

358

194

280

771

143

724

697

125

577

372

489

933

503

418

314

311

304

421

255

214

200

Global contacts

(continued)

514

134

169

492

130

337

474

174

364

135

377

695

512

176

374

289

272

452

167

395

280

Industrial quality

274 5 Explanatory Indicators of Global Urban Economic Competitiveness

Country

Argentina

China

Cuba

Malaysia

China

China

Indonesia

Brazil

Mexico

China

China

Argentina

China

Colombia

China

Greece

Brazil

China

India

China

Iraq

City

Mar Del Plata

Jiaozuo

Havana

Ipoh

Lianyungang

Dezhou

Samarinda

Greater Vitória

San Luis Potosi

Yueyang

Suqian

Cordoba

Liaocheng

Medellin

Ezhou

Thessaloniki

Belo Horizonte

Rizhao

Chennai

Linyi

Baghdad

(continued)

367

366

365

364

363

362

361

360

359

358

357

356

355

354

353

352

351

350

349

348

347

Economic competitiveness

390

583

74

559

68

921

382

129

496

510

321

674

214

669

743

774

134

332

558

467

784

Local factors

914

392

182

479

471

510

703

262

685

212

626

456

300

601

442

561

528

234

657

512

269

Living environment

876

457

276

565

617

499

531

267

602

618

472

399

389

903

380

436

441

333

755

476

836

Soft environment

215

433

67

652

347

60

435

245

503

272

369

767

314

326

387

656

554

282

335

573

358

Hard environment

230

367

92

457

391

374

508

212

388

554

456

473

352

846

788

483

413

900

660

460

755

Global contacts

(continued)

309

344

162

376

304

242

521

232

548

345

665

541

335

768

565

582

613

412

735

528

736

Industrial quality

5.7 Ranking of Explanatory Indicators of Global Urban Economic … 275

Country

China

China

Brazil

China

China

China

Saudi Arabia

Iran

China

Colombia

Angola

Latvia

China

Brazil

Lebanon

Italy

Indonesia

China

China

China

Mexico

City

Binzhou

Haikou

Curitiba

Lanzhou

Cangzhou

Ma’anshan

Hufuf-Mubarraz

Tehran

Zaozhuang

Cali

Luanda

Riga

Jiangmen

Sao Jose dos Campos

Beirut

Palermo

Pekanbaru

Yinchuan

Liuzhou

Xinyu

Merida

(continued)

388

387

386

385

384

383

382

381

380

379

378

377

376

375

374

373

372

371

370

369

368

Economic competitiveness

322

612

438

396

686

893

254

767

369

249

904

187

725

42

875

487

563

151

278

351

787

Local factors

400

639

225

511

798

233

802

28

291

152

863

313

567

356

940

731

563

503

373

221

516

Living environment

264

513

413

482

414

323

746

248

371

233

975

296

494

561

361

404

452

334

553

405

511

Soft environment

211

581

843

540

267

265

124

611

291

352

426

287

274

770

409

235

626

800

200

506

465

Hard environment

361

580

484

203

491

481

164

787

492

195

319

359

592

178

795

378

468

123

228

190

499

Global contacts

(continued)

418

572

334

331

517

276

62

385

369

200

194

313

662

227

641

404

310

213

363

352

547

Industrial quality

276 5 Explanatory Indicators of Global Urban Economic Competitiveness

Country

Mexico

Mexico

China

Argentina

Paraguay

Brazil

Nigeria

China

China

Indonesia

U.S.A.

China

China

Brazil

Dominican Republic

Ecuador

Chile

Mexico

Brazil

China

City

Villahermosa

Cancun

Changde

San Miguel de Tucuman

Asuncion

Ribeirao Preto

Lagos

Xianyang

Maoming

Balikpapan

Tucson

Deyang

Longyan

Campinas

Santiago de Los Caballeros

Quito

Valparaiso

Queretaro

Joinville

Huangshi

(continued)

408

407

406

405

404

403

402

401

400

399

398

397

396

395

394

393

392

391

390

389

Economic competitiveness

165

520

270

192

326

621

241

849

495

194

715

319

406

77

729

571

869

702

592

462

Local factors

589

488

252

727

295

527

255

296

481

154

786

321

459

316

525

303

664

322

399

409

Living environment

670

785

290

390

799

797

668

552

537

108

376

411

409

845

899

695

881

417

415

408

Soft environment

655

400

155

492

879

593

334

930

818

416

373

733

663

431

476

469

253

783

349

430

Hard environment

429

870

343

337

179

572

302

649

560

158

386

510

306

151

715

287

808

568

681

879

Global contacts

(continued)

456

356

370

455

172

394

283

413

438

181

486

650

536

126

405

306

811

519

426

520

Industrial quality

5.7 Ranking of Explanatory Indicators of Global Urban Economic … 277

Country

Spain

China

Poland

China

Nigeria

Mexico

Indonesia

China

China

India

China

China

China

Viet Nam

China

Turkey

China

Russian

China

South Africa

Nigeria

City

Seville

Zhangzhou

Wroclaw

Zunyi

Benin City

Torreon

Batam

Hengyang

Sanming

Kolkata

Wuhai

Beihai

Panjin

Ho Chi Minh City

Jieyang

Denizli

Zhaoqing

Ufa

Yulin (SX)

Cape Town

Port Harcourt

(continued)

429

428

427

426

425

424

423

422

421

420

419

418

417

416

415

414

413

412

411

410

409

Economic competitiveness

470

125

400

537

298

691

201

34

908

759

873

23

898

469

728

480

589

464

530

448

854

Local factors

719

61

398

336

259

623

375

126

501

431

907

180

318

433

370

452

650

330

207

283

282

Living environment

922

251

492

388

384

261

598

448

481

672

661

326

590

426

459

422

944

504

193

391

240

Soft environment

386

307

717

332

346

747

600

163

296

441

470

302

915

893

327

361

934

754

158

584

366

Hard environment

903

126

430

758

585

891

394

166

556

417

685

144

512

382

777

954

894

490

219

396

336

Global contacts

(continued)

424

233

480

422

578

739

690

140

587

507

543

226

469

470

829

510

818

458

267

440

254

Industrial quality

278 5 Explanatory Indicators of Global Urban Economic Competitiveness

Country

Mexico

China

China

South Africa

Brazil

Pakistan

Mexico

Malaysia

China

China

Greece

China

China

China

China

China

Viet Nam

China

India

China

China

City

Matamoros

Panzhihua

Jiujiang

Durban

Brasilia

Karachi

Juarez

Kuching

Heze

Anyang

Athens

Zhanjiang

Ningde

Baoji

Puyang

Chenzhou

Hanoi

Bengbu

Kochi

Xining

Xinxiang

(continued)

450

449

448

447

446

445

444

443

442

441

440

439

438

437

436

435

434

433

432

431

430

Economic competitiveness

257

404

162

531

47

418

820

308

645

280

67

224

607

367

227

89

313

163

721

856

565

Local factors

715

492

271

659

114

587

749

315

388

401

325

743

676

247

556

390

159

301

307

475

646

Living environment

407

567

299

526

410

485

506

505

469

352

429

568

634

211

502

573

463

512

387

527

700

Soft environment

281

876

218

792

251

809

615

941

579

475

76

459

738

224

564

365

645

216

674

895

541

Hard environment

307

220

463

398

167

434

624

509

447

369

53

432

525

640

570

127

241

368

526

637

883

Global contacts

(continued)

487

286

319

506

107

483

648

473

500

368

46

550

703

419

362

185

158

307

312

531

788

Industrial quality

5.7 Ranking of Explanatory Indicators of Global Urban Economic … 279

Country

China

Brazil

Mexico

India

China

China

Nigeria

China

China

Mexico

Mexico

Russian

India

Indonesia

China

Colombia

China

China

Brazil

Cambodia

Iraq

City

Kaifeng

Sorocaba

Toluca

Coimbatore

Yingtan

Handan

Owerri

Yangjiang

Zigong

Aguascalientes

Saltillo

Samara

Malappuram

Bandung

Sanya

Cartagena

Liupanshui

Yingkou

Recife

Phnom Penh

Erbil

(continued)

471

470

469

468

467

466

465

464

463

462

461

460

459

458

457

456

455

454

453

452

451

Economic competitiveness

956

231

365

890

796

269

457

281

453

561

300

378

538

637

650

359

859

156

240

660

401

Local factors

861

836

286

349

803

640

281

106

823

466

564

173

694

355

903

520

630

410

486

603

682

Living environment

901

732

759

493

685

421

491

258

852

243

392

298

591

534

918

427

475

471

468

745

455

Soft environment

787

243

766

150

846

255

609

669

586

533

633

324

823

375

689

771

602

319

169

372

700

Hard environment

419

249

389

330

535

633

363

238

845

587

702

794

524

461

927

370

597

408

622

821

326

Global contacts

(continued)

381

270

353

375

619

658

532

358

888

515

476

408

513

657

742

499

552

599

530

503

586

Industrial quality

280 5 Explanatory Indicators of Global Urban Economic Competitiveness

Country

Colombia

China

China

China

China

China

Nigeria

Nigeria

China

El Salvador

Turkey

Argentina

China

Chile

Turkey

China

Sri Lanka

China

China

Russian

Indonesia

City

Villavicencio

Pingxiang

Shangrao

Luohe

Hebi

Jingmen

Uyo

Aba

Yuxi

San Salvador

Mersin

La Plata

Nanyang

Concepcion

Samsun

Guilin

Colombo

Chaozhou

Baoding

Perm

Semarang

(continued)

492

491

490

489

488

487

486

485

484

483

482

481

480

479

478

477

476

475

474

473

472

Economic competitiveness

518

549

330

379

708

329

690

619

215

627

539

320

620

601

634

762

835

739

795

481

684

Local factors

178

339

417

445

447

223

267

524

453

538

482

450

323

921

913

386

763

681

311

565

548

Living environment

283

385

369

620

516

375

316

315

462

821

344

720

440

931

926

490

675

727

639

563

863

Soft environment

625

708

857

355

242

878

521

436

650

252

706

643

804

464

583

817

397

331

922

911

511

Hard environment

422

545

365

519

177

286

743

740

285

593

735

271

325

804

912

634

467

446

480

655

970

Global contacts

(continued)

680

449

465

679

131

341

655

322

542

712

715

295

505

827

760

575

646

664

678

556

676

Industrial quality

5.7 Ranking of Explanatory Indicators of Global Urban Economic … 281

Country

China

China

China

Nigeria

Ecuador

Georgia

China

China

Indonesia

Pakistan

China

China

Russian

China

China

China

India

China

India

China

China

City

Yichun (JX)

Ganzhou

Karamay

Ikorodu

Guayaquil

Tbilisi

Jilin

Zhoukou

Makassar

Lahore

Pingdingshan

Quzhou

Yaroslavl

Neijiang

Liaoyuan

Shangqiu

Hyderabad

Ziyang

Pune

Sanmenxia

Loudi

(continued)

513

512

511

510

509

508

507

506

505

504

503

502

501

500

499

498

497

496

495

494

493

Economic competitiveness

653

924

93

918

65

831

969

499

753

663

623

88

622

798

160

264

482

863

689

459

741

Local factors

649

530

128

736

219

671

658

696

416

484

654

420

317

656

436

710

380

894

890

309

448

Living environment

529

657

345

665

270

616

659

593

350

447

397

489

301

544

224

208

867

935

536

562

549

Soft environment

710

627

345

722

140

807

751

719

148

546

407

536

208

449

902

263

450

374

746

885

498

Hard environment

542

600

114

395

135

575

375

574

737

594

565

182

324

553

347

292

274

762

661

405

561

Global contacts

(continued)

561

624

285

659

279

787

569

704

551

537

563

284

526

713

388

298

234

906

383

635

790

Industrial quality

282 5 Explanatory Indicators of Global Urban Economic Competitiveness

Country

China

Serbia

China

Nigeria

India

China

Brazil

China

China

China

China

China

South Africa

Mexico

China

China

Saudi Arabia

China

Mexico

China

China

City

Mianyang

Belgrade

Xiaogan

Abuja

Ahmedabad

Huaibei

Londrina

Jingdezhen

Qinhuangdao

Benxi

Yibin

Nanping

Port Elizabeth

Culiacan

Luzhou

Zhumadian

Ta’if

Fangchenggang

Hermosillo

Yiyang

Liaoyang

(continued)

534

533

532

531

530

529

528

527

526

525

524

523

522

521

520

519

518

517

516

515

514

Economic competitiveness

888

381

274

931

949

548

456

434

312

936

491

940

184

780

646

547

242

236

751

111

385

Local factors

569

418

432

529

805

644

637

288

415

284

576

602

329

489

557

746

198

539

837

241

480

Living environment

566

610

302

627

309

626

496

525

539

640

677

423

356

577

749

501

319

914

691

341

383

Soft environment

395

897

712

824

508

603

958

509

532

761

950

613

772

622

474

570

681

707

667

222

757

Hard environment

477

610

617

384

803

566

414

807

641

616

609

506

348

425

785

479

297

376

431

152

366

Global contacts

(continued)

466

549

382

555

637

725

638

462

402

501

524

640

475

573

459

602

398

428

733

218

484

Industrial quality

5.7 Ranking of Explanatory Indicators of Global Urban Economic … 283

Country

Egypt

Peru

China

China

Peru

China

China

Indonesia

Russian

Mexico

China

China

Egypt

China

Mexico

Kazakhstan

China

State of Palestine

Sudan

Brazil

City

Alexandria

Trujillo

Anshan

Xinyang

Arequipa

Huainan

Jingzhou

Palembang

Tolyatti

Chihuahua

Xianning

Chuzhou

Port Said

Songyuan

Reynosa

Shymkent

Shiyan

Gaza

Khartoum

Fortaleza

(continued)

554

553

552

551

550

549

548

547

546

545

544

543

542

541

540

539

538

537

536

535

Economic competitiveness

366

927

837

314

806

484

865

832

735

347

344

808

574

572

275

121

424

651

541

78

Local factors

599

789

933

455

917

728

672

948

651

808

204

454

568

594

733

819

600

324

783

692

Living environment

741

750

819

570

854

768

559

868

543

622

395

360

647

474

532

638

453

445

603

528

Soft environment

528

841

412

896

504

537

868

483

596

801

821

308

199

721

273

992

765

732

535

123

Hard environment

668

403

982

436

862

756

552

887

424

451

810

907

690

452

450

753

507

444

738

232

Global contacts

(continued)

325

609

562

497

916

471

654

869

594

651

396

595

682

511

702

647

663

320

660

420

Industrial quality

284 5 Explanatory Indicators of Global Urban Economic Competitiveness

Country

Brazil

Venezuela

China

China

Russian

Bangladesh

Nigeria

China

Brazil

China

India

Brazil

Russian

China

China

Brazil

Tunisia

China

China

China

Indonesia

City

Uberlandia

Maturín

Leshan

Jinzhou

Barnaul

Chittagong

Kano

Wuzhou

Goiania

Suining

Kozhikode

Grande Sao Luis

Saratov

Daqing

Fushun

Belem

Tunis

Meishan

Jincheng

Hengshui

Medan

(continued)

575

574

573

572

571

570

569

568

567

566

565

564

563

562

561

560

559

558

557

556

555

Economic competitiveness

338

862

785

810

202

685

408

166

640

824

514

836

557

881

318

317

800

746

656

966

740

Local factors

366

730

616

595

238

371

521

784

385

541

434

579

340

491

832

497

508

605

470

953

472

Living environment

386

678

600

676

394

842

416

322

381

832

480

541

766

515

936

948

477

500

601

1003

826

Soft environment

219

385

679

830

238

577

864

742

762

555

484

744

403

929

949

686

497

444

858

502

867

Hard environment

248

548

573

354

174

754

397

465

882

815

729

629

699

623

781

892

809

412

564

827

790

Global contacts

(continued)

697

553

529

671

311

415

400

444

747

837

700

734

435

566

808

652

628

491

468

754

460

Industrial quality

5.7 Ranking of Explanatory Indicators of Global Urban Economic … 285

Country

China

China

China

Nigeria

Honduras

Honduras

Mexico

China

Bolivia

India

Iran

China

China

Bolivia

Indonesia

China

China

China

Russian

Turkey

China

City

Weinan

Guangan

Huanggang

Ibadan

San Pedro Sula

Tegucigalpa

Puebla

Nanchong

Cochabamba

Dehra Dun

Shiraz

Xuancheng

Shaoguan

Santa Cruz

Padang

Lishui

Shizuishan

Xingtai

Krasnodar

Gaziantep

Tongliao

(continued)

596

595

594

593

592

591

590

589

588

587

586

585

584

583

582

581

580

579

578

577

576

Economic competitiveness

638

498

658

761

593

521

585

267

748

770

443

454

853

362

152

346

552

394

678

821

880

Local factors

549

426

290

662

895

412

327

684

397

396

725

308

429

464

110

343

468

647

474

620

588

Living environment

608

354

253

479

628

487

337

843

465

625

811

582

951

439

348

873

917

913

535

624

632

Soft environment

683

597

808

614

730

630

507

919

720

698

892

616

989

779

448

768

460

321

822

716

608

Hard environment

618

718

531

546

493

511

725

173

423

440

761

964

671

476

298

321

426

757

433

555

341

Global contacts

(continued)

661

617

411

634

603

540

729

278

516

744

810

683

589

627

361

324

407

546

558

722

620

Industrial quality

286 5 Explanatory Indicators of Global Urban Economic Competitiveness

Country

Brazil

Mexico

Turkey

Russian

Nigeria

Viet Nam

Russian

China

India

China

Turkey

Venezuela

China

Turkey

Mexico

China

Turkey

China

South Africa

China

Mexico

City

Manaus

Veracruz

Konya

Tomsk

Enugu

Can Tho

Ryazan

Yangquan

Kollam

Yulin (GX)

Kayseri

Barquisimeto

Mudanjiang

Diyarbakir

Cuernavaca

Tonghua

Eskisehir

Suizhou

Vereeniging

Anqing

Celaya

(continued)

617

616

615

614

613

612

611

610

609

608

607

606

605

604

603

602

601

600

599

598

597

Economic competitiveness

432

953

773

855

590

950

279

755

879

823

580

719

411

417

775

516

677

504

472

276

474

Local factors

721

487

825

619

476

562

391

457

435

968

606

611

779

814

461

698

905

613

542

604

666

Living environment

716

458

835

613

347

692

650

488

522

1005

278

619

571

498

393

829

929

442

293

533

786

Soft environment

205

691

380

695

910

903

237

774

724

553

517

777

514

621

648

406

709

977

671

500

908

Hard environment

747

534

750

544

822

635

595

985

639

958

782

530

956

407

1002

858

909

812

406

776

453

Global contacts

(continued)

821

621

890

633

708

692

789

701

728

472

581

681

876

527

805

868

924

583

481

509

432

Industrial quality

5.7 Ranking of Explanatory Indicators of Global Urban Economic … 287

Country

Uganda

China

Brazil

Russian

India

China

China

China

Indonesia

China

Algeria

Mexico

Colombia

Nigeria

Iran

Mexico

Brazil

Brazil

China

China

Nigeria

City

Kampala

Suzhou (AH)

Joao Pessoa

Kemerovo

Kannur

Shuozhou

Ji’an

Siping

Malang

Chifeng

Algiers

Pachuca de Soto

Pereira

Jos

Tabriz

Xalapa

Teresina

Juiz De Fora

Yunfu

Chizhou

Oshogbo

(continued)

638

637

636

635

634

633

632

631

630

629

628

627

626

625

624

623

622

621

620

619

618

Economic competitiveness

858

783

826

764

737

348

412

555

335

388

707

714

659

848

673

252

542

768

734

703

234

Local factors

865

632

519

555

621

277

716

795

368

498

780

597

186

622

424

675

634

760

737

753

799

Living environment

941

579

497

848

896

484

773

942

540

503

953

555

252

715

578

644

801

418

871

693

780

Soft environment

606

942

847

649

704

377

788

750

494

276

181

756

845

714

971

526

789

790

496

452

411

Hard environment

889

442

674

385

904

943

819

688

801

739

210

625

709

448

602

543

684

886

855

290

201

Global contacts

(continued)

898

577

761

597

885

801

828

889

850

782

357

523

688

791

710

612

737

778

858

568

387

Industrial quality

288 5 Explanatory Indicators of Global Urban Economic Competitiveness

Country

China

China

China

China

China

China

China

India

China

Iraq

China

Mexico

Russian

India

China

China

Philippines

India

Nigeria

Colombia

Ukraine

City

Hanzhong

Dazhou

Qingyuan

Yongzhou

Qujing

Qinzhou

Jiayuguan

Bhiwandi

Anshun

Sulaymaniyah

Huaihua

Mexicali

Orenburg

Thiruvananthapuram

Zhangjiakou

Chengde

Cebu

Patna

Warri

Bucaramanga

Kiev

(continued)

659

658

657

656

655

654

653

652

651

650

649

648

647

646

645

644

643

642

641

640

639

Economic competitiveness

244

255

760

398

591

485

445

288

817

478

939

978

724

829

980

370

441

727

466

701

716

Local factors

116

517

874

522

185

575

465

250

402

875

546

624

652

884

904

551

446

678

278

699

478

Living environment

444

507

961

524

406

449

425

432

349

545

664

880

742

889

636

435

521

633

370

530

609

Soft environment

376

256

560

515

713

887

906

254

678

776

997

882

798

417

505

658

854

739

440

850

840

Hard environment

139

734

949

537

309

262

471

828

979

726

522

478

676

752

567

659

402

664

329

515

536

Global contacts

(continued)

237

467

813

809

448

386

391

848

757

436

689

554

693

926

588

707

495

711

666

785

672

Industrial quality

5.7 Ranking of Explanatory Indicators of Global Urban Economic … 289

Country

Brazil

China

Russian

India

Colombia

China

Morocco

China

China

China

China

India

Indonesia

Russian

Brazil

Brazil

China

China

China

Viet Nam

China

City

Feira De Santana

Yan’an

Irkutsk

Puducherry

Barranquilla

Baise

Casablanca

Datong

Fuyang

Dandong

Changzhi

Thrissur

Denpasar

Kazan

Cuiaba

Florianopolis

Jinzhong

Shanwei

Fuzhou (JX)

Haiphong

Yuncheng

(continued)

680

679

678

677

676

675

674

673

672

671

670

669

668

667

666

665

664

663

662

661

660

Economic competitiveness

463

582

899

420

323

605

822

439

731

426

294

778

665

368

146

882

247

429

655

352

861

Local factors

661

377

537

545

403

334

513

668

364

633

515

344

704

663

653

421

631

430

591

504

617

Living environment

615

824

576

721

630

743

864

269

368

451

508

510

629

645

320

751

569

683

547

596

897

Soft environment

711

288

899

362

638

303

861

447

257

393

598

780

718

653

167

945

480

220

946

703

759

Hard environment

547

723

583

648

605

706

937

631

843

714

626

437

582

518

128

603

590

775

708

470

816

Global contacts

(continued)

629

749

706

716

623

339

502

271

908

771

534

494

764

488

159

614

479

796

525

504

835

Industrial quality

290 5 Explanatory Indicators of Global Urban Economic Competitiveness

Country

Brazil

Viet Nam

Philippines

China

Mexico

Philippines

Cote d’ivoire

China

China

Nigeria

India

Congo

China

China

Colombia

Bangladesh

Russian

China

China

Nicaragua

Kenya

City

Campo Grande

Da Nang

Davao

Shaoyang

Morelia

Cagayan de Oro

Abidjan

Tongchuan

Ankang

Zaria

Kayamkulam

Pointe-Noire

Bozhou

Suihua

Ibague

Rajshahi

Astrakhan

Chongzuo

Baishan

Managua

Mombasa

(continued)

701

700

699

698

697

696

695

694

693

692

691

690

689

688

687

686

685

684

683

682

681

Economic competitiveness

460

827

959

919

917

813

628

974

811

968

794

664

937

718

316

901

307

711

756

513

629

Local factors

697

701

754

593

580

788

584

881

775

960

911

872

544

695

806

578

229

724

411

287

687

Living environment

813

823

518

764

353

932

599

731

643

977

803

937

612

841

877

649

420

554

466

655

833

Soft environment

666

832

944

820

947

828

632

802

612

901

651

795

829

731

434

592

312

957

884

344

693

Hard environment

670

485

373

505

850

917

973

551

619

766

1000

513

638

308

235

1001

730

656

955

496

764

Global contacts

(continued)

564

351

639

596

774

911

871

784

786

631

823

645

752

724

399

694

498

604

649

610

592

Industrial quality

5.7 Ranking of Explanatory Indicators of Global Urban Economic … 291

Country

China

Iran

Haiti

China

India

Philippines

India

India

Uzbekistan

Pakistan

Mongolia

India

India

India

Pakistan

Ghana

Morocco

China

Nigeria

Mexico

India

City

Huangshan

Mashhad

Port-au-Prince

Guigang

Surat

General Santos City

Ludhiana

Kota

Namangan

Bahawalpur

Ulan Bator

Mangalore

Tiruppur

Nagpur

Hyderabad

Accra

Marrakech

Heyuan

Ilorin

Acapulco

Visakhapatnam

(continued)

722

721

720

719

718

717

716

715

714

713

712

711

710

709

708

707

706

705

704

703

702

Economic competitiveness

302

562

671

704

604

220

479

228

668

377

644

657

977

271

305

930

167

915

995

380

699

Local factors

363

389

828

610

494

850

923

742

793

372

669

967

956

880

566

794

638

769

797

774

310

Living environment

557

592

933

719

431

763

870

523

810

546

778

795

879

614

772

666

398

726

978

816

594

Soft environment

699

578

825

913

389

146

631

513

445

268

562

701

965

580

437

415

363

856

490

886

769

Hard environment

876

719

888

650

607

234

278

707

824

820

658

962

853

651

773

997

459

654

455

749

316

Global contacts

(continued)

891

861

945

605

741

332

844

874

910

518

429

806

982

943

887

800

726

783

840

838

622

Industrial quality

292 5 Explanatory Indicators of Global Urban Economic Competitiveness

Country

China

Jamaica

Nigeria

Eritrea

China

Venezuela

China

India

Angola

Mauritania

Nigeria

China

Ukraine

China

Iran

India

Mexico

India

Morocco

Colombia

Congo

City

Meizhou

Kingston

Onitsha

Asmara

Linfen

Ciudad Guayana

Huludao

Durg-Bhilai Nagar

Huambo

Nouakchott

Akure

Jiamusi

Krivoi Rog

Baicheng

Hamadan

Madurai

Poza Rica

Asansol

Tangier

Cucuta

Brazzaville

(continued)

743

742

741

740

739

738

737

736

735

734

733

732

731

730

729

728

727

726

725

724

723

Economic competitiveness

883

632

799

602

742

292

654

700

957

923

687

907

997

414

922

964

876

1006

866

92

527

Local factors

952

820

750

848

908

467

867

844

547

854

870

954

871

928

689

982

581

971

941

505

369

Living environment

969

775

438

723

808

790

860

635

831

588

927

924

991

849

642

1002

621

996

967

560

581

Soft environment

952

933

340

379

318

456

883

972

764

851

677

959

1006

566

642

925

918

725

569

127

563

Hard environment

823

806

899

941

769

848

861

604

875

520

885

779

793

802

540

851

541

910

893

172

621

Global contacts

(continued)

667

941

905

953

882

929

904

750

779

632

862

836

748

966

570

600

606

972

976

277

677

Industrial quality

5.7 Ranking of Explanatory Indicators of Global Urban Economic … 293

Country

China

Russian

Uzbekistan

China

Zambia

Russian

Morocco

Ghana

Mexico

China

China

Cameroon

Russian

China

Nigeria

Morocco

Russian

Lao

India

China

Bolivia

City

Zhangjiajie

Novokuznetsk

Tashkent

Hulunbuir

Kitwe

Khabarovsk

Meknes

Kumasi

Tampico

Shangluo

Laibin

Douala

Novosibirsk

Ya’an

Kaduna

Rabat

Rostov-on-Don

Vientiane

Indore

Liuan

La Paz

(continued)

764

763

762

761

760

759

758

757

756

755

754

753

752

751

750

749

748

747

746

745

744

Economic competitiveness

212

972

303

614

763

353

675

723

343

536

815

886

422

788

733

830

419

801

486

825

896

Local factors

437

582

720

509

220

596

766

643

577

801

706

796

535

855

756

707

997

862

558

752

441

Living environment

886

550

667

892

327

403

945

712

297

982

687

662

605

800

709

450

820

597

783

483

651

Soft environment

998

644

350

991

310

136

969

870

983

587

888

806

248

231

556

980

793

927

890

294

920

Hard environment

693

475

411

549

486

340

871

381

263

242

677

488

789

800

921

902

854

428

300

934

380

Global contacts

(continued)

410

762

851

406

421

409

922

625

384

425

765

776

816

933

897

522

535

538

354

489

814

Industrial quality

294 5 Explanatory Indicators of Global Urban Economic Competitiveness

Country

India

Pakistan

Indonesia

Peru

Gabon

Myanmar

Indonesia

Russian

Russian

Mexico

Zimbabwe

China

Republic of Moldova

China

China

India

Mexico

Brazil

India

India

City

Guwahati

Rawalpindi

Tasikmalaya

Chiclayo

Libreville

Rangoon

Bandar Lampung

Krasnoyarsk

Izhevsk

Tlaxcala

Harare

Ulanqab

Chisinau

Fuxin

Guangyuan

Jalandhar

Oaxaca

Maceio

Jodhpur

Erode

(continued)

784

783

782

781

780

779

778

777

776

775

774

773

772

771

770

769

768

767

766

765

Economic competitiveness

554

421

802

455

399

594

932

465

985

208

494

766

501

781

954

998

765

864

523

296

Local factors

800

840

550

146

690

618

691

443

821

540

218

382

387

641

483

648

830

635

834

586

Living environment

718

595

894

288

777

694

656

542

548

919

461

443

325

329

855

956

789

604

857

753

Soft environment

629

664

849

917

639

810

862

179

894

572

567

665

987

283

729

875

635

812

690

797

Hard environment

981

864

830

703

923

643

686

288

613

578

963

987

728

736

221

627

992

532

712

523

Global contacts

(continued)

893

845

591

812

920

717

803

417

630

434

865

758

430

745

464

355

723

842

969

719

Industrial quality

5.7 Ranking of Explanatory Indicators of Global Urban Economic … 295

Country

China

Nepal

China

India

Russian

Pakistan

Brazil

China

Russian

India

Tanzania

Indonesia

China

China

India

Philippines

Saudi Arabia

China

Nigeria

Iraq

China

City

Qiqihar

Kathmandu

Bayannur

Tirupati

Chelyabinsk

Gujranwala

Aracaju

Luliang

Omsk

Rajkot

Dar es Salaam

Bogor

Chaoyang

Hezhou

Amritsar

Bacolod

Tabuk

Baoshan

Sokoto

Karbala

Xinzhou

(continued)

805

804

803

802

801

800

799

798

797

796

795

794

793

792

791

790

789

788

787

786

785

Economic competitiveness

356

987

747

553

868

934

402

877

976

545

512

310

613

698

790

676

564

540

944

897

661

Local factors

645

972

973

444

887

585

670

765

607

107

583

573

598

660

758

985

506

625

847

552

835

Living environment

733

905

959

722

437

699

701

734

735

308

890

673

379

586

834

846

382

669

702

564

580

Soft environment

881

735

976

924

672

936

510

939

682

907

740

512

928

673

485

837

799

527

940

619

758

Hard environment

642

837

931

494

890

940

877

665

652

400

188

383

951

606

720

826

898

733

559

727

569

Global contacts

(continued)

718

878

947

804

684

656

925

770

767

615

451

675

775

674

884

983

584

772

626

714

642

Industrial quality

296 5 Explanatory Indicators of Global Urban Economic Competitiveness

Country

China

India

Bangladesh

Brazil

Russian

Mexico

India

Turkey

India

Iraq

Russian

Nigeria

Afghanistan

Ghana

China

India

Russian

China

China

India

Morocco

City

Qingyang

Salem

Sylhet

Salvador

Volgograd

Tuxtla Gutierrez

Vijayawada

Sanliurfa

Tiruchirappalli

Kirkuk

Nizhny Novgorod

Maiduguri

Kabul

Sekondi

WuZhong

Jaipur

Voronezh

Jinchang

Tianshui

Jamnagar

Fes

(continued)

826

825

824

823

822

821

820

819

818

817

816

815

814

813

812

811

810

809

808

807

806

Economic competitiveness

670

695

395

982

618

250

941

887

261

752

490

988

431

839

289

579

683

333

860

141

315

Local factors

767

686

667

866

393

346

924

886

955

927

279

969

614

807

680

553

383

463

677

748

777

Living environment

556

703

679

725

338

343

698

887

976

939

237

895

654

575

660

623

304

728

930

752

681

Soft environment

408

493

943

898

628

351

736

311

848

675

153

668

488

576

301

280

811

316

852

230

926

Hard environment

748

911

645

558

315

213

458

857

501

920

797

759

814

1005

935

852

884

495

977

218

579

Global contacts

(continued)

919

914

763

644

427

576

721

902

636

915

446

849

880

781

727

863

571

359

777

539

668

Industrial quality

5.7 Ranking of Explanatory Indicators of Global Urban Economic … 297

Country

India

Iran

Senegal

Indonesia

India

India

India

India

Philippines

India

Brazil

India

Iraq

India

India

Iraq

Colombia

Russian

Iran

Russian

China

City

Mysore

Orumiyeh

Dakar

Jambi

Raurkela

Kurnool

Jamshedpur

Siliguri

Zamboanga

Kolhapur

Natal

Meerut

Basra

Nashik

Bhubaneswar

Mosul

Santa Marta

Yekaterinburg

Rasht

Vladivostok

Lincang

(continued)

847

846

845

844

843

842

841

840

839

838

837

836

835

834

833

832

831

830

829

828

827

Economic competitiveness

789

647

616

679

497

945

301

354

975

508

452

570

920

577

295

681

857

840

575

633

297

Local factors

747

335

711

708

732

944

360

772

976

888

590

507

536

787

778

912

910

574

961

761

244

Living environment

765

430

865

335

572

949

558

574

921

806

847

641

688

747

866

714

862

519

878

875

584

Soft environment

986

270

866

803

702

544

684

333

623

773

657

499

785

547

727

865

813

860

723

559

501

Hard environment

516

673

976

441

839

975

666

700

988

527

563

680

984

993

969

938

999

584

239

916

732

Global contacts

(continued)

766

371

839

445

921

847

923

795

590

841

753

687

960

959

798

949

957

740

490

870

901

Industrial quality

298 5 Explanatory Indicators of Global Urban Economic Competitiveness

Country

China

India

China

India

China

China

Bangladesh

China

Togo

Sudan

China

Pakistan

Morocco

India

India

Russian

India

Syrian

China

Libya

Congo

City

Zhaotong

Hubli-Dharwad

Tieling

Lucknow

Bazhong

Zhongwei

Khulna

Jixi

Lome

Nyala

Baiyin

Faisalabad

Agadir

Warangal

Bokaro Steel City

Ulyanovsk

Gwalior

Latakia

Lijiang

Misratah

Kinshasa

(continued)

868

867

866

865

864

863

862

861

860

859

858

857

856

855

854

853

852

851

850

849

848

Economic competitiveness

713

1002

709

989

493

809

842

436

550

433

902

910

914

928

816

769

909

205

955

409

834

Local factors

957

925

407

947

726

608

902

785

762

833

812

918

891

679

729

915

755

745

723

532

770

Living environment

984

970

671

923

807

495

817

585

433

779

646

971

859

717

954

680

710

648

663

652

706

Soft environment

974

755

834

568

748

232

565

694

177

574

938

1001

271

844

962

827

966

404

522

359

953

Hard environment

275

881

345

895

952

897

998

942

873

632

415

744

390

657

948

317

438

322

502

930

482

Global contacts

(continued)

544

944

669

989

977

822

955

879

867

794

685

899

463

834

930

567

855

817

769

951

773

Industrial quality

5.7 Ranking of Explanatory Indicators of Global Urban Economic … 299

Country

Iraq

Tunisia

India

China

China

India

Russian

India

China

India

Zambia

India

China

China

Iran

Yemen

Pakistan

Bangladesh

Iraq

China

Rwanda

City

Nasiriyah

Safaqis

Chandigarh

Hechi

Zhangye

Srinagar

Makhachkala

Aurangabad

Qitaihe

Ranchi

Lusaka

Sangali

Shuangyashan

Pu’er

Esfahan

Sana’a

Islamabad

Bogra

Najaf

Wuwei

Kigali

(continued)

889

888

887

886

885

884

883

882

881

880

879

878

877

876

875

874

873

872

871

870

869

Economic competitiveness

603

642

971

951

188

819

415

926

996

852

246

477

999

235

878

325

828

990

198

578

992

Local factors

946

858

983

843

718

892

791

629

885

768

984

665

792

846

771

900

810

592

379

856

979

Living environment

365

696

947

968

607

986

637

739

708

788

814

754

770

653

583

774

730

705

587

793

958

Soft environment

685

981

985

964

337

948

975

967

786

325

982

676

396

654

371

463

988

990

262

378

680

Hard environment

636

662

922

980

209

653

792

449

689

995

261

717

696

878

957

778

332

504

207

818

1005

Global contacts

(continued)

598

792

872

964

439

698

864

691

815

918

348

824

738

940

903

559

730

720

441

931

866

Industrial quality

300 5 Explanatory Indicators of Global Urban Economic Competitiveness

Country

India

India

Iran

Ukraine

Indonesia

India

Viet Nam

India

India

Syrian

Syrian

Armenia

Somalia

China

Indonesia

India

India

India

India

India

India

City

Saharanpur

Vadodara

Qom

Donetsk

Banjarmasin

Guntur

Bien Hoa

Imphal

Bhopal

Hamah

Damascus

Yerevan

Mogadishu

Guyuan

Pontianak

Muzaffarnagar

Varanasi

Bhavnagar

Tirunelveli

Solapur

Dhanbad

(continued)

910

909

908

907

906

905

904

903

902

901

900

899

898

897

896

895

894

893

892

891

890

Economic competitiveness

475

502

546

568

389

771

844

947

983

427

722

1001

342

688

511

489

812

697

615

222

551

Local factors

813

816

713

926

642

934

572

818

1002

876

992

993

358

709

615

811

781

460

859

345

864

Living environment

798

812

796

434

729

748

631

758

980

454

888

950

684

874

840

682

470

781

776

551

883

Soft environment

558

640

634

539

594

814

726

916

970

151

367

961

422

601

290

518

162

204

931

402

921

Hard environment

745

928

917

936

780

906

701

694

849

260

713

996

705

774

811

926

731

989

842

722

741

Global contacts

(continued)

859

927

917

932

973

965

743

780

952

378

793

991

860

877

825

938

833

854

934

643

853

Industrial quality

5.7 Ranking of Explanatory Indicators of Global Urban Economic … 301

Country

Iran

India

Pakistan

Syrian

India

India

Pakistan

Congo

Pakistan

India

China

India

India

Niger

China

Yemen

India

India

Iran

China

India

City

Kerman

Cherthala

Sialkot

Al-Raqqa

Belgaum

Vellore

Sukkur

Lubumbashi

Peshawar

Agra

Hegang

Malegaon

Amravati

Niamey

Pingliang

Aden

Nellore

Cuttack

Ardabil

Heihe

Aligarh

(continued)

931

930

929

928

927

926

925

924

923

922

921

920

919

918

917

916

915

914

913

912

911

Economic competitiveness

503

1004

717

631

617

961

696

797

532

772

925

505

483

906

818

361

544

1003

757

712

556

Local factors

815

841

702

714

849

987

751

980

873

869

893

722

790

963

974

612

485

1006

945

919

809

Living environment

822

606

804

828

782

990

757

972

707

827

686

794

893

988

910

767

792

965

885

805

736

Soft environment

968

836

955

715

455

381

877

618

794

432

697

624

636

880

753

529

468

960

763

360

889

Hard environment

791

683

946

667

915

836

474

514

929

983

550

612

647

880

994

742

990

944

533

932

768

Global contacts

(continued)

942

746

881

856

950

1001

807

799

912

939

820

873

819

756

958

802

928

997

846

948

831

Industrial quality

302 5 Explanatory Indicators of Global Urban Economic Competitiveness

Country

Tanzania

Ethiopia

India

Sierra Leone

India

Mexico

India

Iran

India

Iran

India

India

Tanzania

Ukraine

India

Egypt

India

India

Ukraine

Ukraine

Zimbabwe

City

Zanzibar

Addis Ababa

Bareilly

Freetown

Moradabad

Durango

Gulbarga

Kermanshah

Kanpur

Yazd

Jabalpur

Ujjain

Mwanza

Lvov

Ajmer

Suez

Nanded Waghala

Allahabad

Dnipropetrovs’k

Zaporizhzhya

Bulawayo

(continued)

952

951

950

949

948

947

946

945

944

943

942

941

940

939

938

937

936

935

934

933

932

Economic competitiveness

900

938

870

410

903

736

639

793

792

706

533

672

204

595

666

291

624

804

566

750

851

Local factors

916

543

405

739

824

935

700

462

817

757

889

827

883

738

734

376

964

826

860

845

978

Living environment

940

756

784

690

761

851

818

689

858

838

769

771

740

815

809

428

844

955

856

872

938

Soft environment

705

687

561

647

752

225

900

662

1004

775

620

973

491

963

552

874

869

749

909

525

935

Hard environment

1003

925

874

835

991

770

805

866

869

863

765

859

799

817

908

798

825

865

901

259

961

Global contacts

(continued)

696

886

611

970

962

907

956

608

961

967

984

875

946

896

935

579

978

618

975

593

963

Industrial quality

5.7 Ranking of Explanatory Indicators of Global Urban Economic … 303

Country

Ukraine

India

India

Ukraine

India

Pakistan

China

Benin

Argentina

China

Cameroon

China

China

India

Pakistan

India

Kyrgyzstan

Burkina Faso

Nigeria

Cote d’ivoire

Malawi

City

Kharkov

Firozabad

Jammu

Odessa

Jhansi

Multan

Jiuquan

Cotonou

Salta

Longnan

Yaounde

Dingxi

Yichun (HLJ)

Durgapur

Quetta

Mathura

Bishkek

Ouagadougou

Nnewi

Bouake

Blantyre-Limbe

(continued)

973

972

971

970

969

968

967

966

965

964

963

962

961

960

959

958

957

956

955

954

953

Economic competitiveness

168

929

807

754

963

407

693

500

994

392

437

597

892

885

912

587

680

652

403

850

529

Local factors

896

966

995

990

899

852

868

842

920

838

764

831

158

898

857

962

950

655

735

965

559

Living environment

928

904

973

912

837

891

861

737

787

697

957

791

853

898

538

869

850

713

611

825

760

Soft environment

641

954

741

610

462

842

951

442

826

805

353

994

937

160

956

545

871

557

543

489

659

Hard environment

497

966

905

833

355

698

986

945

672

601

353

539

599

392

469

682

856

831

445

939

760

Global contacts

(continued)

653

980

987

883

485

892

909

913

826

852

832

843

557

560

731

857

974

894

751

985

937

Industrial quality

304 5 Explanatory Indicators of Global Urban Economic Competitiveness

Country

India

Myanmar

Iran

Mali

Liberia

Djibouti

Myanmar

India

Burundi

Guinea

Benin

Somalia

Mozambique

India

Congo

Madagascar

Pakistan

Malawi

Mozambique

Burkina Faso

Congo

City

Bikaner

Nay Pyi Taw

Zahedan

Bamako

Monrovia

Djibouti

Mandalay

Gorakhpur

Bujumbura

Conakry

Abomey-Calavi

Hargeysa

Matola

Raipur

Tshikapa

Antananarivo

Sargodha

Lilongwe

Maputo

Bobo Dioulasso

Mbuji-Mayi

(continued)

994

993

992

991

990

989

988

987

986

985

984

983

982

981

980

979

978

977

976

975

974

Economic competitiveness

970

916

847

451

730

636

1000

473

993

1005

889

833

967

662

895

946

259

738

710

962

641

Local factors

1004

991

744

931

959

683

1000

741

939

996

932

878

1005

975

688

994

949

989

938

901

851

Living environment

995

920

952

916

908

962

994

658

963

983

911

966

979

906

907

964

981

900

882

934

830

Soft environment

872

932

891

996

873

999

993

382

454

1003

348

833

1005

784

859

781

234

458

831

229

912

Hard environment

971

965

226

784

913

576

972

746

813

896

914

529

867

847

967

868

772

588

786

953

973

Global contacts

(continued)

1003

988

477

895

986

705

999

755

979

1004

797

673

995

992

994

981

900

830

936

993

968

Industrial quality

5.7 Ranking of Explanatory Indicators of Global Urban Economic … 305

Country

Mozambique

Tajikistan

Congo

Congo

Yemen

Yemen

Central African

Libya

Syrian

Chad

Congo

Syrian

City

Nampula

Dushanbe

Kananga

Bukavu

Taiz

Hodeidah

Bangui

Benghazi

Homs

N’Djamena

Kisangani

Aleppo

(continued)

1006

1005

1004

1003

1002

1001

1000

999

998

997

996

995

Economic competitiveness

984

973

960

979

986

948

965

933

206

991

611

981

Local factors

998

1001

977

1003

877

951

988

936

981

999

909

943

Living environment

925

998

987

915

960

985

999

997

989

992

909

974

Soft environment

863

995

978

979

551

816

819

923

839

1000

531

1002

Hard environment

763

960

351

919

687

832

1004

872

978

950

628

959

Global contacts

996

1006

580

990

585

954

1002

998

1005

1000

699

971

Industrial quality

306 5 Explanatory Indicators of Global Urban Economic Competitiveness

Chapter 6

Global Urban Sustainable Competitiveness Performance

6.1 Top 20 Cities: Europe Holds the Most Seats, While Asia Has the Highest Mean Value The top 20 cities in the world for sustainable competitiveness basically include the central cities of the world’s major cities and developed countries. There are five cities in the United States, nine in Western Europe, including three in Germany and two in Spain. The rest are cities in East Asia, China, Japan, Korea and Singapore share six positions. It is not difficult to find that all the top 20 cities represent the characteristics and development of the country. These top world cities can be regarded as a symbol of the development and achievements of the whole country (Fig. 6.1 and Table 6.1). The spatial distribution of the top 20 cities is all in the northern hemisphere, near the 120 degrees and the prime meridian in the east and west hemispheres. The geographical distance distribution is divided into two parts: East Asia, Western Europe and North America. The East Asian cities are characterized by coastal exportoriented economic centers. The cities of Western Europe are relatively concentrated, and they are the capitals or economic centers of various countries. The North American cities are divided into two parts: the east coast the west coast, which constitutes the center of economic activity of the United States (Table 6.2). Among the top 20 cities, Asian cities outperform Europe and the United States in both the average and the median, and they are balanced in terms of both high-income population growth and density. Relatively speaking, the high-income population density of North American cities is slightly behind, while Europe Cities are underperforming in terms of high-income population growth, but overall East Asia, North America and Western Europe represent the culmination and peak of sustainable urban competitiveness.

© China Social Sciences Press 2023 P. Ni et al., The World: 300 Years of Urbanization Expansion, https://doi.org/10.1007/978-981-99-3553-6_6

307

308

6 Global Urban Sustainable Competitiveness Performance

60

30

0 -180

-120

-60

0

60

120

180

-30

-60

Fig. 6.1 Spatial distribution of the top 20 cities in the Global Sustainable Competitiveness Index. Source CCC of CASS

6.2 Top 200 Cities: Asia Holds the Most Seats and Europe Has the Highest Mean Value Among the top 200 cities in the 2019 Global Sustainable Competitiveness Index, Asian cities have the largest number of 65 cities, with a mean value of 0.618 and a coefficient of variation of 0.183, indicating that Asia is the fastest growing region in the world, with a strong upward trend, but at the same time It should also be noted that the average value of cities entering Asia in the top 200 is relatively low, and only exceeds the number of non-comparable South America and Africa, indicating that sustainable competitiveness needs to be further improved. North America and Europe are close behind, with 60 cities and 58 cities entering the top 200. The average and coefficient of variation of global sustainable competitiveness in Europe and North America are basically the same. Europe performs slightly better than the United States According to the distribution of the top 200 cities in the global sustainable competitiveness index given in Table 6.3, the intercontinental differences in the development of global core cities are small (Fig. 6.2). Regardless of the continent and country, these cities constitute the core cities and development centers of the region. The overall distribution of the top 200 cities, Asia-Europe and North America are three-legged, the average value is basically the same, and the coefficient of variation is not much different, indicating that between continents, Within the continent, the convergence of the competitiveness of key cities around the world is a major trend, which is in contrast to the large differences between the average city and different regions (Table 6.4). Among the top 200 cities, the gap in sustainable competitiveness of Asian cities has emerged, but the high-income population growth rate is relatively low. North

Chicago

United States

England

America

France

China Hong Kong

Japan

America

America

North America

Europe

North America

Europe

Asia

Asia

North America

North America

Source CCC of CASS

Los Angeles

Japan

Asia

Osaka

Hong Kong

Paris

San Francisco

London

New York

Tokyo

Singapore

Singapore

Asia

City

Nation

Intercontinental

0.8285

0.8386

0.8532

0.8894

0.8930

0.8955

0.9135

0.9570

0.9984

1

Index

10

9

8

7

6

5

4

3

2

1

Ranking

Table 6.1 Top 20 cities in the Global Sustainable Competitiveness Index

Europe

Asia

Europe

North America

Europe

Europe

Asia

Europe

Europe

Europe

Intercontinental

Germany

China

Spain

United States

Germany

Germany

Korea

Sweden

Russia

Spain

Nation

Frankfurt

ShenZhen

Madrid

Boston

Stuttgart

Munich

Seoul

Stockholm

Moscow

Barcelona

City

0.7887

0.7905

0.7918

0.7934

0.7957

0.8019

0.8047

0.8075

0.8135

0.8160

Index

20

19

18

17

16

15

14

13

12

11

Ranking

6.2 Top 200 Cities: Asia Holds the Most Seats and Europe Has the Highest … 309

310

6 Global Urban Sustainable Competitiveness Performance

Table 6.2 Regional statistics of Top 20 cities in the Global Sustainable Competitiveness Index Continent

Index

No

Mean

Std. dev

Min

median

Max

Asia

Sustainable competitiveness

6

0.889

0.008

0.791

0.871

1

High-income population growth

6

0.786

0.020

0.584

0.786

0.988

High-income population density

6

0.820

0.017

0.661

0.829

1

Sustainable competitiveness

5

0.863

0.004

0.793

0.839

0.957

High-income population growth

5

0.886

0.012

0.734

0.872

1

High-income population density

5

0.672

0.007

0.532

0.698

0.745

Sustainable competitiveness

9

0.825

0.002

0.789

0.808

0.914

High-income population growth

9

0.671

0.017

0.522

0.660

0.879

High-income population density

9

0.818

0.005

0.73

0.797

0.926

Sustainable competitiveness

20

0.854

0.005

0.789

0.822

1

High-income population growth

20

0.759

0.023

0.522

0.751

1

High-income population density

20

0.782

0.012

0.532

0.786

1

North America

Europe

Total

Source CCC of CASS Table 6.3 Distribution of the top 200 cities in the Global Sustainable Competitiveness Index region

Sample

Mean

Coefficient of variation

Optimal city

Index

World Ranking

Asia

65

0.618

0.183

Singapore

1

1

Europe

58

0.634

0.170

London

0.914

4

North America

60

0.633

0.171

New York

0.957

3

South America

8

0.578

0.116

Buenos Aires

0.707

42

Oceania

7

0.632

0.133

Melbourne

0.754

27

Africa

2

0.554

0.088

Pretoria

0.589

106

Global

200

0.626

0.171

Source CCC of CASS

6.3 Top 10 Urban Agglomerations: Seoul Has the Highest Mean Value …

311

70

40

10

-180

-120

-60

0

60

120

180

-20

-50

Fig. 6.2 Spatial distribution of the top 200 cities in the Global Sustainable Competitiveness Index. Source CCC of CASS

American cities and European cities are better at high-income population density, while the advantages in high-income population growth are not obvious. It is not difficult to find that the core cities in Europe and the United States have been developing for a long time, and their status is relatively stable, while Asian cities are showing a situation of rapid convergence and catch-up.

6.3 Top 10 Urban Agglomerations: Seoul Has the Highest Mean Value, and Rhein-Ruhr is Best Balanced From the perspective of important urban agglomerations, the strength of the US and British urban agglomerations is prominent. The average value is above the average of all urban agglomerations and the level is uniform. The strength is still strong. Although the urban agglomerations of developing countries such as China and India are large in scale, the sustainable development indexes of central cities and surrounding cities are too large and the standard deviation is large. The cities within the three major urban agglomerations of the United States have balanced urban development and high average value. The sustainable competitiveness of China and India’s urban agglomerations has a clear single-core model. The urban centers of the urban agglomerations are prominent, ranking no less than the urban agglomeration cities of developed countries. Due to the small number of cities in Seoul city cluster, the average sustainable competitiveness index is in a leading position. And urban agglomerations in China, India and other developing countries are large in size, the

312

6 Global Urban Sustainable Competitiveness Performance

Table 6.4 Regional statistics of Top 200 cities in the Global Sustainable Competitiveness Index Continent

Index

No

Mean

Std. dev

Min

median

Max

Asia

Sustainable competitiveness

65

0.618

0.113

0.494

0.594

1

High-income population growth

65

0.487

0.137

0.211

0.449

0.988

High-income population density

65

0.629

0.111

0.405

0.61

1

Sustainable competitiveness

60

0.633

0.108

0.502

0.621

0.957

High-income population growth

60

0.519

0.177

0.251

0.47

1

High-income population density

60

0.625

0.092

0.45

0.614

0.832

Sustainable competitiveness

8

0.578

0.067

0.502

0.571

0.707

High-income population growth

8

0.48

0.11

0.309

0.484

0.635

High-income population density

8

0.564

0.039

0.517

0.553

0.641

Sustainable competitiveness

7

0.632

0.084

0.517

0.6

0.754

High-income population growth

7

0.533

0.143

0.287

0.552

0.706

High-income population density

7

0.608

0.055

0.498

0.63

0.655

Sustainable competitiveness

58

0.634

0.108

0.492

0.598

0.914

High-income population growth

58

0.435

0.139

0.221

0.417

0.879

High-income population density

58

0.71

0.102

0.501

0.716

0.926

Sustainable competitiveness

2

0.554

0.049

0.52

0.554

0.589

High-income population growth

2

0.425

0.022

0.409

0.425

0.441

High-income population density

2

0.575

0.065

0.529

0.575

0.622

Sustainable competitiveness

200

0.626

0.107

0.492

0.596

1

High-income population growth

200

0.482

0.152

0.211

0.451

1

High-income population density

200

0.647

0.107

0.405

0.631

1

North America

South America

Oceania

Europe

Africa

Total

Source CCC of CASS

6.4 Three Main Economics: The United States and the European Union Far …

313

70

40

10

-180

-120

-60

0

60

120

180

-20

-50

Fig. 6.3 Spatial distribution of the top ten urban agglomerations in the world. Source CCC of CASS

gap between central cities and surrounding cities is obvious and the development is unbalanced. And among the urban agglomeration in Europe, the rhine-ruhr urban agglomeration has the lowest standard deviation, which shows the equilibrium of development in the western European countries (Fig. 6.3). From the geographical location, the distribution of urban agglomerations is basically consistent with the distribution characteristics of the 20 major cities, indicating that the formation of urban agglomerations has higher requirements for the leadership of single-nuclear cities. In addition, it is difficult for smaller countries to form urban agglomerations in the true sense, such as South Korea. Singapore, therefore, the urban agglomeration also shows the dual characteristics of urban agglomeration and priority development of core cities (Table 6.5).

6.4 Three Main Economics: The United States and the European Union Far Surpass China, and the Development of US Cities is of Potential In the urban competitiveness system, large cities have a high proportion and have the greatest impact on global cities. From the analysis of economic influence, we regard the EU as a whole, so that 439 cities in China, the EU and the United States have entered the urban competitiveness index, which is close to half of the total number of 1006 cities. The overall performance of the EU is not inconsistent with that of the United States. It is reflected in the higher mean value of the sustainable

314

6 Global Urban Sustainable Competitiveness Performance

Table 6.5 Sustainable Competitiveness Index of the top ten urban agglomerations in the world Urban Agglomeration index

Mean

Std. dev

Mi

Median

Max

Seoul Metropolitan Group

Sustainable competitiveness

0.741

0.09

0.677

0.741

0.805

High-income population growth

0.632

0.191

0.498

0.632

0.767

High-income population density

0.705

0.028

0.685

0.705

0.725

Sustainable competitiveness

0.7

0.141

0.522

0.681

0.957

High-income population growth

0.598

0.242

0.336

0.601

1

High-income population density

0.666

0.101

0.463

0.692

0.766

Sustainable competitiveness

0.585

0.122

0.467

0.538

0.828

High-income population growth

0.471

0.164

0.248

0.448

0.841

High-income population density

0.586

0.127

0.463

0.595

0.832

Sustainable competitiveness

0.678

0.24

0.42

0.719

0.895

High-income population growth

0.646

0.21

0.456

0.611

0.872

High-income population density

0.578

0.241

0.302

0.688

0.745

Sustainable competitiveness

0.266

0.099

0.177

0.253

0.381

High-income population growth

0.135

0.053

0.08

0.133

0.195

High-income population density

0.346

0.127

0.218

0.335

0.494

Sustainable competitiveness

0.667

0.179

0.455

0.712

0.914

High-income population growth

0.487

0.244

0.248

0.513

0.859

High-income population density

0.717

0.092

0.574

0.772

0.79

Sustainable competitiveness

0.446

0.13

0.242

0.446

0.733

High-income population growth

0.342

0.127

0.134

0.326

0.622

Northeastern urban agglomeration

Midwestern United States

Northern California City Group

Mumbai City Group

London–Liverpool City Group

Yangtze River Delta City Group

(continued)

6.4 Three Main Economics: The United States and the European Union Far …

315

Table 6.5 (continued) Urban Agglomeration index

Mean

Std. dev

Mi

Median

Max

0.463

0.114

0.276

0.475

0.701

0.531

0.146

0.328

0.543

0.791

High-income population growth

0.403

0.112

0.268

0.374

0.584

High-income population density

0.555

0.162

0.323

0.595

0.843

Sustainable competitiveness

0.614

0.061

0.56

0.591

0.7

High-income population growth

0.335

0.1

0.239

0.29

0.474

High-income population density

0.774

0.04

0.72

0.78

0.828

Sustainable competitiveness

0.601

0.062

0.527

0.59

0.673

High-income population growth

0.385

0.1

0.234

0.381

0.496

High-income population density

0.699

0.031

0.644

0.717

0.719

Sustainable competitiveness

0.54

0.175

0.177

0.535

0.957

High-income population growth

0.415

0.193

0.08

0.377

1

High-income population density

0.559

0.163

0.218

0.572

0.843

High-income population density Pearl River Delta City Sustainable Group competitiveness

Rhine-Ruhr urban agglomeration

Netherlands-Belgian city group

Total

Source CCC of CASS

competitiveness index. There is still a large gap between China and European and American cities. The urban development of China and the United States and Europe can be regarded as the growth stage and mature stage of the urban development life cycle. From the perspective of the average sustainable competitiveness index, the United States is the highest. From the average point of view, the United States is the best, the coefficient of variation is the lowest, showing strong sustainable competitiveness, while the average of China is relatively low, but the gap is not obvious. In addition, China has a large population and enters the sustainable competitiveness index. There are many cities, and the disadvantages of small cities in terms of competitiveness are obvious. However, the level of urbanization and central cities in the United States is higher, and fewer cities are entering the city. The EU is comparable to the United States in terms of mean and internal differences, and the history of EU cities is much

316

6 Global Urban Sustainable Competitiveness Performance

longer. The EU as a whole shows a strong sustainable competitive advantage. In the US-Europe comparison, the US has a higher cumulative average, indicating that the development potential of American cities is greater than that of Europe. In general, the sustainable competitiveness of Chinese cities has not yet reached the optimal level, and the US and EU cities are still at the peak of sustainable competitiveness of global cities (Figs. 6.4, 6.5, 6.6 and Table 6.6). We further analyzed the two main factors that constitute sustainable competitiveness, namely the mean value and the standard deviation of the high-income population growth and high-income population density. From the perspective of the mean value of sustainable competitiveness index, Europe and the United States are in the same level (the ratio of the US and the European average is 1:0.994), but there are differences in the high-income population growth and the density. The EU cities perform better at the high-income population density, while the advantage of high-income population growth of US is dominant. As mentioned above, as the urbanization process has not yet been fully realized, compared with the US and Europe, Chinese cities are relatively low in terms of the overall sustainable competitiveness and the two indicators (three indicators of China are only 57%, 54%, 59% of that of the US). However, in terms of standard deviation, although the number of Chinese cities far exceeds that of Europe and the United States, the internal standard deviation of Chinese cities is not much different. To a certain extent, it shows that China’s urban development is relatively balanced overall, especially for the comprehensive increase, the difference between American cities. Great, indicating that there is a large difference in the speed of development in American cities. Although the overall development potential is huge, too much concentration in a few core cities may lead to the variability of the future development of the US urban system (Table 6.7). 70

40

10 -180

-120

-60

0

60

120

180

-20

-50

Fig. 6.4 Spatial distribution of sustainable competitiveness in China, US and EU. Source CCC of CASS

6.4 Three Main Economics: The United States and the European Union Far …

317

0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 CN

EU

sustainable competitiveness index

US

TOTAL

Comprehensive increment

Comprehensive density Fig. 6.5 Mean values of sustainable competitiveness in China, US, and EU. Source CCC of CASS

0.2 0.18 0.16 0.14 0.12 0.1 0.08 CN

EU

US

TOTAL

standard deviation of sustainable competitiveness index standard deviation of Comprehensive increment standard deviation of Comprehensive density Fig. 6.6 Comparison of sustainable competitiveness in China, US and EU. Source CCC of CASS

318

6 Global Urban Sustainable Competitiveness Performance

Table 6.6 Comparison of the US and Europe’s Global Sustainable Competitiveness Index Nation

Mean

Standard deviation

Coefficient of variation

Highest ranking

Optimal city

China

0.326

0.11

0.337

7

Hong Kong

America

0.568

0.139

0.245

3

New York

EU

0.565

0.132

0.234

4

London

Source CCC of CASS

Table 6.7 China-US European Cities Sustainable Competitiveness Index Nation

Index

Number Mean Std. dev Min

China

Sustainable competitiveness

284

EU

0.326 0.11

0.128 0.301

0.791

High-income population growth 284

0.25

0

0.226

0.651

High-income population density 284

0.338 0.111

0.078 0.319

0.843

Sustainable competitiveness

0.096

80

0.565 0.132

0.319 0.543

0.914

High-income population growth 80

0.378 0.141

0.154 0.349

0.879

High-income population density 80

0.641 0.131

0.373 0.629

0.926

America Sustainable competitiveness

Total

Median Max

75

0.568 0.139

0.285 0.547

0.957

High-income population growth 75

0.457 0.186

0.198 0.398

1

High-income population density 75

0.569 0.125

0.302 0.571

0.832

Sustainable competitiveness

439

0.411 0.166

0.128 0.375

0.957

High-income population growth 439

0.309 0.149

0

1

High-income population density 439

0.433 0.175

0.078 0.388

0.269

0.926

Source CCC of CASS

6.5 Global Pattern: North American and Western European Cities Perform Well and with Small Divergence, While Asian Cities Stay in Low Level and with Significant Internal Difference In terms of the distribution of global sustainable competitive cities in the world, the number of Asian cities is far ahead of the rest of the continents, but the average is slightly behind the world average, and the average values of North America and Europe are much higher than the world average. The mean for North America is 0.501 and that for Europe is 0.526, which is at the top of global sustainable competitiveness. And the coefficient of variation is relatively lower, indicating that the differences between European and American cities are small. The average value of Asia is only 0.34, and the coefficient of variation is 0.429, which indicates that the level of urban development in the continent is quite different, but it also shows that some central cities in Asian cities are rising rapidly, and vigorous development promotes sustainable competitiveness to a higher level (Table 6.8).

6.5 Global Pattern: North American and Western European Cities Perform …

319

Table 6.8 Urban distribution of Global Sustainable Competitiveness Index Region

Sample

Mean

Var

Optimal city

Index

Ranking

Asia

565

0.34

0.429

Singapore

1

1

Europe

126

0.526

0.405

London

0.9135

4

North America

131

0.501

0.307

New York

0.9570

3

South America

75

0.394

0.299

Buenos Aires

0.7068

42

Oceania

7

0.608

0.090

Melbourne

0.754

27

Africa

102

0.253

0.482

Pretoria

0.589

106

World average

1006

0.381

0.459

Source CCC of CASS

Figure 6.7 shows the spatial distribution of global sustainable competitiveness in 1006 cities around the world. North America’s economic power in Western Europe is still strong, and it is still the gathering place of the strongest global sustainable competitive cities. It can be clearly seen that global sustainable competitive cities are concentrated in North America and Western Europe (Table 6.9). 70

40

10 -180

-120

-60

0

60

120

180

-20

-50

Fig. 6.7 Spatial distribution of global sustainable competitiveness in 1006 cities around the world. Source CCC of CASS

320

6 Global Urban Sustainable Competitiveness Performance

Table 6.9 Overall distribution of Global Sustainable Competitiveness Index Continent

Index

Number

Mean

Std. dev

Min

Median

Max

Asia

Sustainable competitiveness

565

0.34

0.146

0.041

0.312

1

High-income population growth

565

0.219

0.138

0

0.203

0.988

High-income population density

565

0.31

0.148

0.035

0.282

1

Sustainable competitiveness

131

0.501

0.154

0.077

0.508

0.832

High-income population growth

131

0.387

0.18

0.129

0.35

1

High-income population density

131

0.492

0.162

0.176

0.483

0.957

Sustainable competitiveness

75

0.394

0.118

0.09

0.389

0.641

High-income population growth

75

0.261

0.109

0.091

0.232

0.635

High-income population density

75

0.363

0.112

0.146

0.35

0.707

Sustainable competitiveness

7

0.608

0.055

0.498

0.63

0.655

High-income population growth

7

0.533

0.143

0.287

0.552

0.706

High-income population density

7

0.632

0.084

0.517

0.6

0.754

Sustainable competitiveness

126

0.526

0.213

0.134

0.563

0.926

High-income population growth

126

0.324

0.149

0.02

0.298

0.879

High-income population density

126

0.471

0.185

0.129

0.473

0.914

Sustainable competitiveness

102

0.253

0.122

0

0.25

0.622

High-income population growth

102

0.132

0.096

0

0.108

0.441

High-income population density

102

0.213

0.113

0

0.216

0.589

Sustainable competitiveness

1006

0.381

0.175

0

0.347

1

High-income population growth

1006

0.251

0.159

0

0.222

1

High-income population density

1006

0.35

0.173

0

0.311

1

North America

South America

Oceania

Europe

Africa

Total

Source CCC of CASS

6.6 Global Sub-regional Pattern: Coastal Cities and Cities Located …

321

6.6 Global Sub-regional Pattern: Coastal Cities and Cities Located in Temperate Zone Are Leading From the spatial distribution of global city competitiveness (Figs. 6.8, 6.9, 6.10), 120 degrees west longitude, 70 degrees west longitude (American east and west coast), 10 degrees east longitude to 10 degree west longitude (Western European countries) and the east longitude 110–140 degrees (China, Japan and South Korea) become the key areas of urban economic competitiveness distribution. At the same time, the top cities in the above regions are mostly at 25–55 degrees north latitude. We have drawn Fig. 6.8 as the standard. It is not difficult to find that most of the world’s top 200 cities and the top 500 cities are located in the above-mentioned fields. These areas generally have the following characteristics: First, they are the intersection of the mainland and the ocean, which can be observed in either the US or the Europe, indicating that the integration of mainland resources and marine resources will bring sustainable growth to urban development. Second, there are strong and stable countries or inter-state organizations to provide guarantee for the development of the city. After that, we draw the nuclear density estimation map of the full sample, the top 200 cities and 500 cities with the latitude and longitude as the distribution axis, and it is easy to find the characteristics of the above distribution. At the same time, we can notice that with the increase of the city ranking, the western hemisphere countries. The proportion of cities is rising, and in terms of latitude, the advantages of the northern hemisphere countries are always obvious and gradually strengthened (Figs. 6.9, 6.10 and Table 6.10).

70

40

10

-180

-120

-60

0

60

120

-20

-50

Fig. 6.8 Sub-regional distribution of sustainable competitiveness. Source CCC of CASS

180

322

6 Global Urban Sustainable Competitiveness Performance

Fig. 6.9 Distribution of sustainable competitiveness—Longitude. Source CCC of CASS

Fig. 6.10 Distribution of sustainable competitiveness—Latitude. Source CCC of CASS

6.6 Global Sub-regional Pattern: Coastal Cities and Cities Located …

323

Table 6.10 Global urban sub-regional Sustainable Competitiveness Index Nation

Index

Number Mean Std. dev

High sustainable Sustainable 342 competitiveness competitiveness area High-income 342 population growth

0.439 0.169 0.102

0.408

0.998

0.331 0.16

0.014

0.288

1

0.462 0.175 0.132

0.433

0.908

Sustainable 664 competitiveness

0.304 0.156 0

0.267

1

High-income population growth

664

0.209 0.142 0

0.18

0.872

High-income population density

664

0.34

0.16

0

0.312

1

Sustainable 1006 competitiveness

0.35

0.173 0

0.311

1

High-income population growth

1006

0.251 0.159 0

0.222

1

High-income population density

1006

0.381 0.175 0

0.347

1

High-income population density Other areas

Total

Minimum Median Maximum

342

Source CCC of CASS

As can be seen from Table 6.10, in the above-mentioned region, the sustainable competitiveness is 0.439, which is much ahead of 0.304 in other regions, and the average number has increased by nearly 50%, especially the Comprehensive increment, exceeding 50%, which means that cities in this region are not only more sustainable, but also have more impact on Comprehensive increment. It should be noted that in the above sub-regions, the respective coefficients of variation are relatively small, indicating that the differences in cities in their respective regions are not large and belong to a stable state.

Chapter 7

Explanatory Indicators of Global Urban Sustainable Competitiveness

7.1 Economic Vitality 7.1.1 Overall Pattern of Global Urban Economic Vitality 7.1.1.1

Introduction of Global Urban Economic Vitality Distribution

The degree of intercontinental distribution equilibrium of the leading cities of global urban economic vitality needs to be improved. From the perspective of intercontinental distribution of Top 20 cities in the global urban economic vitality, all cities are concentrated on North America, Europe and Asia. Among them, there exist the largest number of cities of global urban economic vitality in North America among the Top 20 around the world, with a total of 12, the followed is European cities with a total of 6, and the remaining 2 cities are all located in Asia. Singapore ranks first all over the world (Table 7.1). In general, the intercontinental distribution of leading cities of global urban economic vitality is more concentrated on continents with stronger economic basement and greater economic development potential. Among the Top 100 of global urban economic vitality, the means of the economic vitality of cities in all continents except South America and Africa are close to each other, but there exist significant differences of the economic vitality among Top 100 cities within continents. As can be seen from Table 7.2, 57 cities in North America rank in Top 100, with which is the largest number. No cities in South America and Africa rank in Top 100, which indicates that there exist great differences of the economic vitality among cities in these continents and developed regions. Specifically, it can be seen that the mean of the economic vitality in the world are close to each other. Among them, the means of Asian cities are relatively higher, while those of Oceania cities are relatively lower. By observing the coefficient of variation, it can be seen that there exist significant differences of the economic vitality among cities in each continent. Among them, the coefficient of variation of cities in Oceania, North America and Europe are lower than Top 100 cities in the world. There are © China Social Sciences Press 2023 P. Ni et al., The World: 300 Years of Urbanization Expansion, https://doi.org/10.1007/978-981-99-3553-6_7

325

326

7 Explanatory Indicators of Global Urban Sustainable Competitiveness

Table 7.1 Top 20 cities of global urban economic vitality in the world Region

Country

City

Standardized index World ranking

Asia

Singapore

Singapore

1.0000

1

North America United States

Bridgeport-Stanford 0.9441

2

North America United States

San Jose

0.9432

3

Europe

Norway

Oslo

0.9291

4

Europe

Switzerland

Geneva

0.9045

5

Europe

Ireland

Dublin

0.8937

6

North America United States

Cleveland

0.8891

7

North America United States

San Francisco

0.8888

8

North America United States

Hartford

0.8873

9

North America United States

Salt Lake City

0.8857

10

Asia

Macao

0.8821

11

North America United States

Macao, China

Richmond

0.8816

12

North America United States

Baltimore

0.8787

13

North America United States

Baton Rouge

0.8771

14

North America United States

Milwaukee

0.8746

15

North America United States

罗利

0.8743

16

Europe

Stockholm

0.8742

17

North America United States

Sweden

Boston

0.8736

18

Europe

Germany

Munich

0.8695

19

Europe

United Kingdom London

0.8678

20

Source CCC of CASS

large differences of Asian cities, which indicate the degree of the economic vitality equilibrium of Top 100 cities in Asian needs to be improved. From the better cities of the economic vitality in each continent, Singapore in Asia, Bridgeport-Stamford in North America, Oslo in Europe and Gold Coast in Oceania rank first, second, fourth and twenty-fifth in the global ranking respectively.

7.1.1.2

Urban Spatial Distribution of Global Urban Economic Vitality

The distribution of global urban economic vitality is mainly concentrated on Asia, North America and Europe. From the perspective of the proportion of Top 100 cities all the world in various continents, there exist no cities in Africa and South America in the Top 100 around the world. The proportions of Top 100 cities of global urban economic vitality in Asia and Europe are relatively low, but are relatively higher in Oceania and North America. From the perspective of means of global urban economic vitality, the values of global urban economic vitality of cities in Oceania, North America and Europe are higher than the average level of global cities, and the

7.1 Economic Vitality

327

Table 7.2 Intercontinental distribution of Top 100 cities in global urban economic vitality Region

Sample

Mean

Coefficient of variation

Better city

Standardized index

World ranking

Asia

12

0.8431

0.0676

Singapore

1.0000

1

Europe

26

0.8327

0.0437

Oslo

0.9291

4

North America

57

0.8389

0.0398

Bridgeport-Stanford

0.9441

2

South America

0

0

0

–

0

0

Africa

0

0

0

–

0

0

Oceania

5

0.8301

0.0348

Gold Coast

0.8587

25

Global

100

0.8374

0.0443

Singapore

1.0000

1

Source CCC of CASS

Table 7.3 Intercontinental distribution of global urban economic vitality Region

Sample

Proportion of top 100 cities

Mean

Coefficient of variation

Asia

565

0.0212

0.5326

0.2483

Europe

126

0.2063

0.6132

0.2712

North America

131

0.4351

0.7119

0.2096

South America

75

0.0000

0.4783

0.2360

Africa

102

0.0000

0.3649

0.3799

Oceania

7

0.7143

0.8155

0.0422

Global

1006

0.0994

0.5470

0.3028

Source CCC of CASS

values of global urban economic vitality of cities in Africa and South America are significantly lower than the average level of global cities. Asian cities are relatively close to the average level of global cities in the indicator. From the perspective of coefficients of variation of global urban economic vitality, the differences in global urban economic vitality between Oceanian and North American cities are small, which is significantly lower than the differences of global urban economic vitality among all cities. The differences in global urban economic vitality among African cities are relatively large, but differences in European, Asian, and South American cities are at a moderate level (Table 7.3). Observing Figs. 7.1 and 7.2, it can be seen that the cities with strong global urban economic vitality in the world are mainly concentrated on North America, Western Europe and Eastern Asia. The cities in these regions mostly have strong economic development strength, sufficient vitality and great development momentum. The intercontinental distributions of most cities of Top 100 in the world are mainly concentrated on developed regions and countries with good economic development foundations such as North America and Western Europe which indicates cities in

328

7 Explanatory Indicators of Global Urban Sustainable Competitiveness 90

60

30

0 -180

-120

-60

0

60

120

180

-30

-60

-90

Fig. 7.1 Spatial distribution of global urban economic vitality of 1006 cities worldwide. Source CCC of CASS

these regions with great economic development foundation and relatively stable development are also show the great vitality in the future. The number of cities distributed in other regions is relatively small. There exist great differences in the means of sub-indicators of global urban economic vitality and the degree of difference among global cities. From the mean of the sub- indicators of global urban economic vitality, the mean of global urban business convenient degree is relatively high, the mean of proportion of global urban young people is relatively low, the means of global urban property right protection, the density of economic growth and labor productivity are at a moderate level, which reflect the levels of global urban business convenient degree are normal higher, but the gap of global urban young people are relatively larger, which needs to be paid attention. From the coefficients of variation of sub-indicators of global urban economic vitality, the difference in the proportion of global urban young people is relatively large, the differences in global urban business convenient degree and the density of economic growth among cities are relatively small, and the differences in global urban property right and labor productivity are at the moderate level. Among the five sub-indicators, there exist two indicators of San Jose that are in the top of the world, while Ulsan, Bukavu and Macau are also in the top of the world in perspective of some indicators (Table 7.4 and Fig. 7.3).

7.1 Economic Vitality

329 90

60

30

0 -180

-120

-60

0

60

120

180

-30

-60

-90

Fig. 7.2 Spatial distribution of global urban economic vitality of Top 100 cities. Source CCC of CASS

Table 7.4 Statistical descriptions of the sub-indicators of global urban economic vitality

Economic vitality

Mean

Coefficient of variation

Better city

Business convenient degree

0.7174

0.2348

Ulsan

Property rights protection

0.5189

0.3963

San Jose

Proportion of young people

0.3488

0.4075

Bukavu

Density of economic growth

0.4985

0.2528

Macao

Labor productivity

0.5454

0.3390

San Jose

Source CCC of CASS

7.1.2 Pattern of Global Urban Economic Vitality 7.1.2.1

Introduction of G20

The number of cities in emerging economies at the top of global urban economic vitality is significantly lower than cities in developed economies, and there exist absolute advantages of cities in traditionally developed countries in the proportion of cities in top rankings of global urban economic vitality among G20. The comparison of rankings of global urban economic vitality of cities in G20 shows that, the number of cities in America, The U.K. and Germany ranks in the Top three, the number of

7 Explanatory Indicators of Global Urban Sustainable Competitiveness NAmerica

SAmerica

Africa

Europe

Asia

2

3

Global

0

1

Density

4

5

330

0

.2

.4

.6

.8

1

Economic Vitality Fig. 7.3 Nuclear density map of global urban economic vitality. Source CCC of CASS

cities in EU ranks fourth. The number of leading cities in China is relatively small and there is no city of other country in Top 20 in G20. In the proportion of Top 100 in global urban economic vitality rankings, the proportion of Top 100 cities in the U.S. is relatively high, up to 70.67%, and urban economic vitalities occupy the leading position. The proportion of Australia is followed with 66.7%, cities in emerging economies like Mexico, India, Brazil, Russia are all not in Top 100 in the world. In the proportion of Top 101–200 of global urban economic vitality, the proportion of Japanese cities is relatively high, accounting for 80%. The proportion of Korean cities is followed with 62.5%, but there are not cities in Mexico, India, Italy, Brazil, Russia and Argentina are the Top 101–200. Therefore, the cities with higher economic vitality are dominated in developed economies. In the proportion of Top 201–500 cities of global urban economic vitality, the proportion of French cities is highest, which is 88.9%, the proportion of South African cities rank the second, which is 83.3%, the economic vitality rankings of most cities in emerging economies are concentrated on this range. In the proportion of Top 500 of global urban economic vitality, the proportion of Top 500 cities in most developed economies such as the U.S.A, Germany, the U.K, and Japan is 100%, which show obvious advantages of global urban economic vitality. On the whole, the economic vitality rankings of cities in G20 are more forward compared with cities in non-G20, and the urban economic vitality of these cities is more vigorous (Table 7.5). On the whole, the means of global urban economic vitality of cities in emerging economies are generally lower than that in developed economies, but the degree of internal differentiation are higher than cities in developed economies. By comparing global urban economic vitality of cities in G20, it is found that in terms of the means, cities in the U.S. rank first, and there exist apparent advantages of cities

7.1 Economic Vitality

331

Table 7.5 Distribution of global urban economic vitality in sample cities of G20 Country

Proportion of top 20 (%)

Proportion of top 100 (%)

Proportion of 101–200 (%)

Proportion of 201–500 (%)

Proportion of top 500 (%)

China

0.34

2.06

11.00

46.05

59.11

United States

16.00

70.67

28.00

1.33

100.00

EU

4.41

23.53

16.18

48.53

88.24

Mexico

0.00

0.00

0.00

54.29

54.29

India

0.00

Germany

7.69%

Italy

0.00

0.00

28.00

28.00

53.85

38.46

7.69

100.00

0.00

0.00

0.00

61.54

61.54

United Kingdom

8.33

58.33

41.67

0.00

100.00

Brazil

0.00

0.00

0.00

28.13

28.13

Japan

0.00

10.00

80.00

10.00

100.00

France

0.00

0.00

11.11

88.89

100.00

Canada

0.00

44.44

55.56

0.00

100.00

Russia

0.00

0.00

0.00

9.09

9.09

Korea

0.00

12.50

62.50

25.00

100.00

Indonesia

0.00

0.00

5.00

40.00

45.00

Turkey

0.00

0.00

6.25%

50.00

56.25

Australia

0.00

66.67

33.33

0.00

100.00

Argentina

0.00

0.00

0.00

22.22

22.22

South Africa

0.00

0.00

16.67

83.33

100.00

Saudi Arabia

0.00

0.00

11.11

77.78

88.89

G20

2.30

12.45

12.58

35.18

60.22

Non-G20

1.12

3.00

2.62

15.73

21.35

Global countries

1.99

9.94

9.94

29.82

49.70

42.19

367.43

441.98

762.60

1472.01

Total

Source CCC of CASS

in developed economies or regions like Australia and the U.K. However, global urban economic vitality of cities in emerging economies such as Brazil, Russia and Argentina shows relatively poor performance. From the coefficient of variation of cities in G20, there exist smaller differences among cities in developed economies such as France, U.K, and Australia with the higher mean in the economic vitality. However, there exist larger differences among cities in emerging economies, such as China and Argentina in economic activity. It is noteworthy that there exists a large

332

7 Explanatory Indicators of Global Urban Sustainable Competitiveness

difference in economic vitality among EU cities, which indicates that the differences in economic development among EU cities are obvious and cannot be ignored. From the economic vitality of better cities in G20, the better cities in most of the developed economies of G20 rank Top 60 in the world except France and Italy. In emerging economies, better cities of China perform well in terms of global urban economic vitality, Macao ranks 11th in the world, the rankings of better cities in other countries are behind 100, and the gap with developed economies is obvious, which needs to be strengthened. On the whole, the mean of global urban economic vitality of cities in G20 is significantly higher than that of non-G20, and the difference of global urban economic vitality between cities is relatively small, so the economic development is progressing steadily (Table 7.6). Table 7.6 Statistical description of global urban economic vitality of sample cities in G20 Country

Mean

Coefficient of variation

Better city

World ranking

U.S.A

0.8153

0.0617

Bridgeport-Stamford

2

Australia

0.8125

0.0452

Gold Coast

25

U.K

0.8060

0.0416

London

20

Canada

0.7986

0.0442

Hamilton

21

Germany

0.7926

0.0612

Munich

19

Japan

0.7300

0.0600

Kitakyushu-Fukuoka

52

Korea

0.7225

0.0641

Ulsan

62

France

0.6786

0.0182

Paris

192

EU

0.6767

0.1685

Dublin

6

South Africa

0.6360

0.0864

Pretoria

159

Saudi Arabia

0.6204

0.1172

Medina

195

China

0.5648

0.1915

Macao

11

Turkey

0.5481

0.1411

Gebze

180

Italy

0.5444

0.0754

Venice

353

Mexico

0.5426

0.0977

Villahermosa

282

Indonesia

0.5268

0.1442

Balikpapan

199

India

0.5065

0.1274

Delhi

255

Brazil

0.4965

0.1275

Jundiai

242

Russia

0.4614

0.1497

Tyumen

257

Argentina

0.4708

0.1755

Rosario

467

G20

0.5954

0.2274

Bridgeport-Stamford

2

Non-G20

0.4128

0.4067

Singapore

1

Global countries

0.5470

0.3028

Singapore

1

Source CCC of CASS

7.1 Economic Vitality

7.1.2.2

333

Introduction of Representative Countries

According to the continental division, this paper choose China, Japan and India in Asia, the U.K in Europe, the U.S. in North America, Brazil in South America, South Africa and Australia in Oceania for comparative study. In general, cities in developed countries among representative countries show more obvious advantages in the subindicators of global urban economic vitality, while the sub-indicators of global urban economic vitality of cities in emerging economies show great internal differentiation. From the mean of business convenient degree, there exist higher means and obvious advantages of cities in developed countries such as the U.K, the U.S.A, Australia and Japan, while the means of cities in Brazil and South Africa are lower relatively. From the coefficient of variation of business convenient degree, there exist small differences among cities in developed countries such as Australia, Japan, the U.K and the U.S in business convenient degree, while the differences are large among cities in China, India, Brazil and South Africa. Among them, the difference in business convenient degree between Chinese cities is the largest, and the business environment of each city needs to be further improved in China. From the mean of property rights protection, the means of global urban property rights protection in Australia, Japan, the U.K and the U.S. are at the leading positions, which indicate that cities in developed countries also have a strong sense of property rights protection, while the means of urban property rights protection in cities in India, Brazil and China are relatively low. From the coefficient of variation of property rights protection, there exist apparent differences among cities in developed countries such as Japan and Australia in property rights protection, which indicates the degrees of property rights protection are generally high, while there exist large differences among cities in China and India in property rights protection, and each city has its own development degree in property rights protection. From the mean of the proportion of young people, the means of proportion of young people of South African, Indian, Brazilian and Chinese cities are relatively high, and the advantages of young labor force in the cities are prominent, but the mean of proportion of young people of cities in Japan is the lowest, and the gap of young labor force is relatively large. From the coefficient of variation of the proportion of young people, it is worthy to note that the difference of the proportion of young people in Japanese cities is the largest, and the degree of distribution equilibrium of young people in Japanese cities needs to be paid attention to. From the mean of density of economic growth, the mean of the density of economic growth of cities in UK are highest, which shows obvious advantages, while mean of cities in China are lowest. From the coefficient of variation of global urban density of economic growth, there are great differences among Chinese cities, which show obvious differences in economic development levels among the cities. The difference in density of economic growth among cities in South Africa is smallest, and the overall economic development foundations of South African cities are relatively great. From the mean of labor productivity, the means of labor productivity of cities in developed countries, such as the U.S., the U.K., Australia and Japan, are higher while those in emerging economies, such as Brazilian and India, are lower. From

334

7 Explanatory Indicators of Global Urban Sustainable Competitiveness

the coefficient of variation of labor productivity, the differences of labor productivity among cities in emerging economies such as China and India are larger, while those of developed countries such as Australia, The U.K., Japan and the U.S. are smaller, with generally high urban productivity. On the whole, the means of global urban economic vitality in traditional developed countries are at the leading position, and the differences among cities are smaller, while the cities in emerging economies are the opposite (Table 7.7). From the perspective of overall pattern of global urban economic vitality indicators, the degree of intercontinental distribution equilibrium of the leading cities of global urban economic vitality needs to be improved, which mainly concentrated on North America and Europe with developed economic, and Asia with great economic development momentum. There exist better economic foundation and development trend in these cities, so they are steady development and less affected by the external economic environment. However, it is worth noting that economic development vitality on all continents is relatively close, but it is significant different of development level among the Top 100 in global urban economic vitality index. From the perspective of each continent, there exist higher levels of urban economic vitality in traditionally developed regions such as Oceania, North America, and Europe, and the development levels among the cities are relatively close and more balanced, which occupy the forefront of global urban economic vitality. However, the urban economic vitality in African or South American countries is relatively low and it is obviously different among the cities in global urban economic vitality, so the balances of economic vitality among the cities need to be improved. Especially, global urban economic vitality in Asian countries is at the intermediate level, but due to the different levels of urban economic development among countries on the continents, it is obvious different in urban economic development levels. In the future, it is should be paid attention to the balance of urban economic development and enhance the overall level of urban economic competitiveness. With the gradual progress of economic integration, cities in the member countries of G20 rank higher in global urban economic vitality than those in non-member countries, the economic development vitality of the cities is relatively sufficient, and the equilibrium degree of the economic vitality among cities in member countries is higher than those of non-member countries. Among G20, the economic vitality of cities in emerging economies is significantly lower than that in developed economies, and the degree of differentiation between the cities is greater than that of developed economies. Therefore, it is of great importance to realize coordinated and common development of urban economy. From the sub-indicators of global urban economic vitality of the cities in representative countries, in the cities of most developed countries, except for the lower mean of the proportion of young people, the means of other indicators are relatively higher, and the differences among cities are significantly lower than that of cities in emerging economies. However, in the cities of emerging economies, the proportion of young people is relatively higher, which indicates the urban labor forces are relatively abundant, but the means of other sub-indicators are lower. In global urban economic vitality, cities in the U.S. show obvious leading advantages and good urban

Source CCC of CASS

Economic vitality

Labor productivity

Density of economic growth

Proportion of young people

Property rights protection

Business convenient degree

0.5648 0.1915

Coefficient of variation

0.1720

Coefficient of variation

Mean

0.5419

0.2208

Coefficient of variation

Mean

0.4264

0.4089

Mean

0.3616

Coefficient of variation

0.1255

Coefficient of variation

Mean

0.5381

0.0775

Coefficient of variation

Mean

0.8124

Mean

China

0.0617

0.8153

0.0557

0.8610

0.2154

0.5006

0.0178

0.2552

0.0431

0.8969

0.0373

0.8901

U.S.A

0.1274

0.5065

0.1794

0.3601

0.2132

0.5088

0.0151

0.4517

0.0900

0.4822

0.0640

0.7094

India

0.0600

0.7300

0.0467

0.8037

0.1510

0.5545

2.9346

0.0512

0.0260

0.9294

0.0233

0.8184

Japan

Table 7.7 Statistical analysis of sub-indicators of global economic vitality of representative countries

0.0416

0.8060

0.0468

0.7876

0.1220

0.5965

0.0010

0.1837

0.0311

0.9138

0.0271

0.8951

U.K

0.0864

0.6360

0.1073

0.5193

0.0612

0.5959

0.0682

0.4991

0.0693

0.6096

0.0560

0.6621

South Africa

0.1275

0.4965

0.1179

0.5327

0.1935

0.5105

0.0000

0.3868

0.0764

0.5048

0.0602

0.5485

Brazil

0.0452

0.8125

0.0354

0.8456

0.2076

0.4990

0.0000

0.2464

0.0232

0.9433

0.0206

0.8612

Australia

7.1 Economic Vitality 335

336

7 Explanatory Indicators of Global Urban Sustainable Competitiveness

development situation. After years of reform in Japan, the foundation and vitality of urban economic growth have been steadily consolidated and strengthened, but the proportion of young people is relatively small, so the large gap of urban young labor force needs to be paid attention to. As an emerging economy, Chinese cities have a good momentum of economic vitality and development. However, there are great differences among cities in the economic vitality. In the future, Chinese cities should make efforts to make up for their shortcomings in achieving balanced development and achieve high-quality economic development.

7.2 Environmental Thoroughness 7.2.1 Overall Pattern of Environmental Toughness 7.2.1.1

Overview of the Head City

Europe leads the world. From the distribution of the top 20 cities of global environmental toughness in all continents, Europe occupies 12 seats, North America 6 seats and Asia 2 seats. In terms of national dimension, Germany has 6 seats, followed by the United States (Table 7.8). More than 90% of the top 100 cities of global environmental toughness are concentrated in North America, Europe and Asia. Comparing the mean value and coefficient of variation of the top 100 cities and all the samples, we can find that the mean level and coefficient of variation of the top 100 cities are significantly higher than the global level. In terms of the intercontinental distribution of the former 100 cities, Europe accounts for 42%, while North America, Europe and Asia together account for more than 90%, with obvious concentration. From the perspective of the best cities with environmental toughness in all continents, the leading cities in Europe, North America and Asia are all in the top 20 in the world, while the best cities in Oceania, Africa and South America are all over 30 in the world (Table 7.9).

7.2.1.2

Overall Spatial Pattern

Oceania, North America and Europe lead the world. According to the mean distribution of global urban environmental toughness, the urban environmental toughness of Oceania, North America and Europe is relatively good, that of Africa and Asia is relatively weak, and that of South America is in the middle. In terms of coefficient of variation, the fluctuation range of environmental toughness in Africa, Europe and Asia is relatively small, while that in Oceania cities is relatively large. In terms of the proportion of the world’s top 100 cities in all continents, Europe and North America account for more than 40%, leading the world, with only 13% in Asia, and less than 3% in Africa, Oceania and South America (Table 7.10, Figs. 7.4 and 7.5).

7.2 Environmental Thoroughness

337

Table 7.8 Environmental toughness index top 20 cities in the world Region

Country

City

Index

World ranking

Europe

Germany

Stuttgart

1.000

1

Europe

Austria

Vienna

0.965

2

Europe

Germany

Hannover

0.959

3

Europe

Germany

Munich

0.958

4

Asia

Singapore

Singapore

0.939

5

North America

Puerto Rico

San Juan

0.924

6

Europe

Switzerland

Geneva

0.920

7

Europe

Germany

Dusseldorf

0.916

8

Asia

Hong Kong, China

Hong Kong

0.910

9

Europe

Sweden

Gothenburg

0.905

10

Europe

Germany

Frankfurt am Main

0.894

11

North America

U.S.A

Kansas City

0.892

12

Europe

Portugal

Lisbon

0.888

13

Europe

Switzerland

Zurich

0.881

14

Europe

Greece

Thessaloniki

0.878

15

North America

U.S.A

Baton Rouge

0.875

16

Europe

Germany

Berlin

0.865

17

North America

U.S.A

Allentown

0.862

18

North America

U.S.A

Baltimore

0.861

19

North America

U.S.A

Knoxville

0.860

20

Source CCC of CASS Table 7.9 Intercontinental distribution of the top 100 cities in the world in terms of environmental toughness index Region

Sample Mean value Coefficient of Optimal city Index World ranking variation

Europe

42

0.846

15.452

Stuttgart

1.000

1

North America 40

0.819

25.285

San Juan

0.924

6

Asia

13

0.826

16.412

Singapore

0.939

5

Oceania

2

0.811

20.481

Melbourne

0.839

34

Africa

2

0.806

29.209

Algiers

0.825

44

South America 1

0.781

–

Brasilia

0.781

98

Global

0.544

3.337

1006

Source CCC of CASS

338

7 Explanatory Indicators of Global Urban Sustainable Competitiveness

Table 7.10 Intercontinental distribution of global urban environmental toughness index Region

Sample

Proportion of top 100 cities (%)

Mean value

Coefficient of variation

Asia

565

13

0.484

3.837

North America

131

40

0.683

5.332

South America

75

1

0.583

4.129

Oceania

7

2

0.763

16.573

Europe

126

42

0.680

3.799

Africa

102

2

0.490

3.140

total

1006

9.94

0.544

3.337

Source CCC of CASS 90

60

30

0 -180

-120

-60

0

60

120

180

-30

-60

-90

Fig. 7.4 Spatial distribution of environmental toughness index of 1006 cities in the world. Source CCC of CASS

The mean value and fluctuation range of the sub indicators of global urban environmental toughness are quite different. According to the sub indicators of environmental toughness, 1006 sample cities are mainly concentrated in areas with less natural disasters, and the climate comfort and power abundance of all cities are at a high level. At present, global cities have low scores in environmental pollution degree and ecological diversity, which indicates that global cities still face great challenges in environmental pollution control and ecological diversity maintenance (Table 7.11 and Fig. 7.6).

7.2 Environmental Thoroughness

339 90

60

30

0 -180

-120

-60

0

60

120

180

-30

-60

-90

Fig. 7.5 Spatial distribution of environmental toughness index of the top 100 cities in the world. Source CCC of CASS

Table 7.11 Statistical description of sub-index of global urban environmental toughness

Environmental toughness

Mean value

Coefficient of variation

Optimal city

Traffic congestion 0.584

4.539

Muscat

Power abundance

0.640

2.676

(Not unique)

Ecological diversity

0.482

2.429

Chicago

Climate comfort

0.654

3.685

Pereira

Environmental pollution degree

0.317

3.768

Singapore

Natural disaster

0.793

4.731

(Not unique)

Source CCC of CASS

7.2.2 National Pattern of Environmental Toughness 7.2.2.1

Overview of G20 Countries

European Union and American cities lead the world, and Chinese cities are emerging. Comparing the ranking of urban environmental toughness of G20 countries, it is found that among the top 20 cities in the world, the European Union leads

340

7 Explanatory Indicators of Global Urban Sustainable Competitiveness

Fig. 7.6 Kernel density map of global urban environmental toughness index. Source CCC of CASS

by 50%, followed by Germany by 30% and the United States by 25%. Among the top 100 cities in global urban environmental toughness, EU has the highest proportion, accounting for 35% and the United States 33%. The EU and the US account for 23% and 17% of the top 101–200 global cities in terms of environmental toughness. Among the top 201–500 cities in global urban environmental toughness, China has the highest proportion, reaching 29%. Among the top 500 cities in global environmental toughness, China has the highest proportion, accounting for 18.2%, followed by the United States and the European Union, accounting for 14.4% and 13.8% respectively. From the ranking distribution, we can see that cities in developed economies such as the European Union and the United States are still in the leading position, but a few of China’s top cities have ranked among the world’s top (Table 7.12). In general, the average environmental toughness of non G20 cities is still lower than that of G20 cities, but the degree of internal differentiation is lower. Comparing the urban environmental toughness of G20 countries, it is found that Germany, EU, Australia, UK and France have obvious advantages in the mean value of environmental toughness, Cities of China, Turkey Russia, India and Saudi Arabia performed relatively poorly. According to the coefficient of variation of environmental toughness, cities in Mexico, Argentina, India, Russia and Saudi Arabia fluctuated less, while those in Australia, Italy, Germany, Britain, Canada and France fluctuated more. From the perspective of the best cities, three cities in G20 countries are in the top 20 in the world, and 11 cities are in the top 100 in the world in terms of environmental toughness (Table 7.13).

7.2 Environmental Thoroughness

341

Table 7.12 Ranking and distribution of environmental toughness index of G20 sample cities Country

Top 20 (%)

Top 100 proportion (%)

Proportion of 101–200 (%)

Proportion of 201–500 (%)

Top 500 proportion (%)

China

5

4

0

29

18.2

U.S.A

25

33

17

7.3

14.4

EU

50

35

23

3.7

13.8

Mexico

0

0

6

5.3

4.4

India

0

0

1

7.7

4.8

Russia

0

0

1

3

2

Brazil

0

1

6

7

5.6

Germany

30

13

0

0

2.6

Italy

0

3

8

0.7

2.6

Indonesia

0

0

0

3

1.8

Turkey

0

0

0

1.7

1

Britain

0

5

6

0.3

2.4

Japan

0

2

5

1

2

Canada

0

6

2

0.3

1.8

South Korea

0

3

1

1.3

1.6

France

0

3

4

0.7

1.8

Argentina

0

0

4

1

1.4

Australia

0

2

4

0

1.2

South Africa

0

0

1

1.3

1

Saudi Arabia

0

0

0

0.3

0.2

G20 countries

80

91

77

73.3

77.6

Non G20 countries

20

9

23

26.7

22.4

Global whole

100

100

100

100

100

Source CCC of CASS

7.2.2.2

Overview of Representative Countries

According to the intercontinental division, China, Japan and India in Asia, the United Kingdom in Europe, the United States in North America, Brazil in South America, South Africa in Africa and Australia in Oceania are mainly selected for comparative study. In general, Australian and British cities have advantages in various sub indicators of environmental toughness, while the overall level of emerging economies is still low.

342

7 Explanatory Indicators of Global Urban Sustainable Competitiveness

Table 7.13 Statistical description of environmental toughness index of G20 cities Country

Mean value

Optimal city

World ranking

China

0.497

Coefficient of variation 5.436

Hong Kong

9

U.S.A

0.732

6.878

Kansas City

12

EU

0.781

8.938

Stuttgart

1

Mexico

0.576

4.840

Tutela Gutierrez

163

India

0.442

3.654

Mangalore

164

Russia

0.458

3.100

Yaroslavl

173

Brazil

0.631

6.138

Brasilia

98

Germany

0.877

13.777

Stuttgart

1

Italy

0.741

15.721

Padua City

53

Indonesia

0.514

6.351

Samarinda

290

Turkey

0.460

5.207

Bursa

431

Britain

0.766

13.124

Belfast

26

Japan

0.721

9.319

Sapporo

76

Canada

0.753

13.120

Vancouver

73

South Korea

0.731

8.661

Weishan

31

France

0.759

11.699

Lille

28

Argentina

0.627

4.593

Rosario

130

Australia

0.776

23.901

Melbourne

34

South Africa

0.593

7.259

Durban

172

Saudi Arabia

0.322

2.857

Jeddah

500

G20 countries

0.562

3.563

Stuttgart

1

Non G20 countries

0.494

2.957

Singapore

5

Global whole

0.544

3.337

Stuttgart

1

Source CCC of CASS

From the perspective of traffic congestion, Britain, the United States and Australia are in the leading position, while emerging economies such as China, India and South Africa perform poorly. Australia and the UK are in a leading position in terms of power abundance, but there is a large gap between their cities. From the perspective of ecological diversity, the United States and the United Kingdom are in the leading position, while emerging economies such as Brazil, South Africa and India perform poorly. In terms of climate comfort, Australia and Japan have obvious urban advantages, but there is a large gap between them. From the perspective of environmental pollution degree, cities in various countries generally have low scores, which indicates that environmental pollution is a common problem in urban development, especially in China and India. From the perspective of natural disasters, cities in various countries generally score higher (Table 7.14).

Source CCC of CASS

Environmental toughness as a whole

Natural disaster

Environmental pollution degree

Climate comfort

Ecological diversity

Power abundance

Traffic congestion

Coefficient of variation

Mean value

Coefficient of variation

Mean value

Coefficient of variation

Mean value

Coefficient of variation

23.901

0.776

10.914

0.790

16.406

0.382

13.650

0.792

2.472

Coefficient of variation

Mean value

0.598

10.544

Mean value

Coefficient of variation

0.947

6.069

Coefficient of variation

Mean value

0.623

Mean value

Australia

5.881

0.491

8.322

0.814

5.239

0.266

5.319

0.709

4.775

0.526

2.787

0.510

5.988

0.539

China

7.259

0.593

4.855

0.744

6.431

0.317

16.245

0.741

9.623

0.441

9.169

0.826

4.519

0.482

South Africa

Table 7.14 Statistical analysis of sub-index of environmental toughness of representative countries

3.654

0.442

6.363

0.838

2.605

0.227

2.754

0.526

4.846

0.416

4.320

0.629

5.249

0.555

India

6.138

0.631

10.607

0.897

14.685

0.355

5.459

0.717

4.176

0.478

4.275

0.779

5.846

0.520

Brazil

9.319

0.721

2.656

0.581

4.626

0.447

20.843

0.799

3.777

0.652

6.071

0.825

36.432

0.606

Japan

6.878

0.732

7.122

0.843

9.817

0.373

8.939

0.752

3.706

0.706

4.629

0.795

5.535

0.628

U.S.A

13.124

0.766

14.626

0.821

6.390

0.410

4.823

0.652

5.642

0.648

19.234

0.973

5.057

0.641

Britain

7.2 Environmental Thoroughness 343

344

7 Explanatory Indicators of Global Urban Sustainable Competitiveness

Table 7.15 The top 20 world cities by social inclusion indicators Region

Country

City

Index

World rank

Asia

Japan

Tokyo

1.0000

1

Asia

Korea

Seoul

0.9785

2

Europe

Czech Republic

Prague

0.9609

3

Asia

Japan

Osaka

0.9564

4

Asia

Taiwan, China

Taipei

0.9547

5

Asia

Japan

Shizuoka-Hamamatsu M.M.A.

0.9323

6

Asia

Japan

Kitakyushu-Fukuoka

0.9218

7

Asia

Japan

Sapporo

0.9200

8

Asia

Taiwan, China

Tainan

0.9199

9

Asia

Japan

Niigata

0.9086

10

Asia

Korea

Busan

0.9043

11

Asia

Japan

Nagoya

0.8954

12

Asia

Taiwan, China

Kaohsiung

0.8928

13

Asia

Japan

Kumamoto

0.8917

14

Asia

Taiwan, China

Taichung

0.8911

15

Europe

Poland

Warsaw

0.8817

16

Europe

Romania

Bucuresti

0.8756

17

Asia

Saudi Arabia

Riyadh

0.8729

18

Asia

Korea

Daejeon

0.8706

19

Asia

Thailand

Bangkok

0.8704

20

Source CCC of CASS

7.3 Social Inclusion 7.3.1 General Landscape of Social Inclusion 7.3.1.1

Overview of the Head City

Asia and Europe lead the world, and Asian cities dominate absolutely. The top 20 cities of global social inclusion show obvious polarization in global distribution, with 17 Asian cities and 3 European cities, indicating that Asian cities are ahead of other cities in terms of social inclusion. In terms of national dimension, Japan has 8 seats, followed by Taiwan, China, with 4 cities (Table 7.15). The distribution of social inclusion of top 100 cities worldwide is uneven across continents, and the mean and fluctuation range of social inclusion on each continents are different. On the one hand, from the perspective of the world’s top 100 cities, the global intercontinental distribution of social inclusion shows a pattern of “leading in Asia, following in Europe and North America, lagging behind

7.3 Social Inclusion

345

Table 7.16 Intercontinental distribution of social inclusion of top 100 cities worldwide Region

Sample

Mean

CV

Optimal city

Index

World rank

N. America

10

0.8040

0.0234

New York

0.8469

37

Europe

27

0.8122

0.0564

Bragg

0.9609

3

Asia

60

0.8432

0.0682

Tokyo

1.0000

1

S. America

2

0.8061

0.0149

Sao Jose dos Campos

0.8146

46

Oceania

1

0.7966

–

Auckland

0.7966

66

Africa

0

–

–

Cairo

0.6518

288

Global

1006

0.5526

0.3025

Tokyo

1.0000

1

Note CV is the abbreviation of coefficient of variation Source CCC of CASS

in South America, Oceania and Africa”. Asian cities account for 60% of the top 100 cities in the world, while European and North American cities account for 27% and 10% respectively. From the perspective of the best cities, the global cities lead the continents, of which Tokyo, Japan’s social inclusion is in an absolute dominant position, 36 higher than New York. On the other hand, except for Africa, the mean of social inclusion of the top 100 cities in the world is relatively small. Among them, the social inclusion of African cities is lower than that of other continents, and Cairo, as the best city, is lower than the average of other continents. In addition, the fluctuation of social inclusion in each continent is relatively small, and the coefficient of variation in Asia and Europe is relatively large, which indicates that there are certain differences between the cities in the two continents (Table 7.16).

7.3.1.2

Global Urban Spatial Landscape of Social Inclusion

The intercontinental distribution of global urban social inclusion shows a hierarchical distribution, with significant differences between intercontinental and intracontinental cities. The intercontinental distribution of global urban social inclusion is shown in Table 7.17, Figs. 7.7 and 7.8. According to the proportion of the top 100 cities, it can be judged that the overall global social inclusion shows a gradual decreasing pattern with higher levels in Europe and Oceania, followed by Asia and North America, and lower levels in South America and Africa. In comparison, the mean of social inclusion index of cities in Europe, Oceania and North America is relatively high, and the difference between cities in the continent is relatively small, and the coefficient of variation is lower than the global average. Although Asian cities are absolutely ahead of the top 100 in terms of social inclusion, the social inclusion index of the remaining cities is relatively low, with an average of 0.0018 higher than the global mean and 0.1345 lower than the highest European mean, indicating that the overall advantage is not obvious. At the same time, the coefficient of variation of urban social inclusion in Asia is relatively large, which indicates that there are

346

7 Explanatory Indicators of Global Urban Sustainable Competitiveness

some differences in urban social inclusion within Asia. Finally, the social inclusion of African cities is lower than the global average, and there are large internal differences. The mean and fluctuation range of the sub-indicators of global urban social inclusion are quite different. On average, the cost of living is high and the cultural inclusion is low. The former has the lowest coefficient of variation and the latter has the highest coefficient of variation, which indicates that the difference in cost of living is relatively small, but there is a large difference in cultural inclusion. In addition, the coefficient of variation of medical health is relatively large, indicating that there is a large gap between cities in the world. From the perspective of the best cities, Tokyo and Shizuoka-Binsong metropolitan area in Japan have three leading Table 7.17 Intercontinental distribution of social inclusion of cities worldwide Region

Sample

N. America

131

Oceania

7

Africa S. America

Proportion of top 100 cities (%)

Mean

CV

7.63

0.6200

0.1660

14.29

0.6414

0.1358

102

0.00

0.3243

0.4212

75

2.67

0.4944

0.2834

Europe

126

21.43

0.6889

0.1357

Asia

565

10.62

0.5544

0.2808

Global

1006

9.94

0.5526

0.3025

Source CCC of CASS 90

60

30

0 -180

-120

-60

0

60

120

180

-30

-60

-90

Fig. 7.7 Spatial distribution of social inclusion indicators of 1006 cities worldwide. Data Source CCC of CASS

7.3 Social Inclusion

347 90

60

30

0 -180

-120

-60

0

60

120

180

-30

-60

-90

Fig. 7.8 Spatial distribution of social inclusion indicators of the top 100 cities worldwide. Data Source CCC of CASS

Table 7.18 Statistical description of sub-indicators of global urban social inclusion

Social inclusion

Mean

CV

Optimal city

Heritage protection

0.6224

0.2947

Tokyo

Social security

0.5545

0.2982

Shizuoka-Hamamatsu M.M.A.

Social equity 0.5139

0.2616

Bragg

Cost of living

0.9097

0.1938

Konya

Cultural inclusion

0.3085

0.8007

Sao Jose dos Campos

Medical health

0.4117

0.4077

Tokyo

Source CCC of CASS

indicators in the world, and their social inclusion is relatively high (Table 7.18 and Fig. 7.9).

348

7 Explanatory Indicators of Global Urban Sustainable Competitiveness

Fig. 7.9 Kernel density of global urban social inclusion indicators

7.3.2 National Pattern of Social Inclusion 7.3.2.1

Overview of G20 Countries

The social inclusion of cities in Japan, the European Union and the United States is relatively high, while that in China is relatively strong. It can be seen from Table 7.33 that Japan’s urban social inclusion ranks first in the world, with 10 cities ranking within 100 (actually within 30 in the world); the mean of Japan’s urban social inclusion is 0.9151, 39.6% higher than the global average, and the coefficient of variation is 84.9% lower than the global average, indicating that there is little difference in social inclusion among Japanese cities. European cities and the United States are the second most socially inclusive cities, ranking in the top 500 cities in the world. Among them, 22 cities in the European Union rank in the top 100 in the world, with an average of 0.15 points higher than the global average. Urban social inclusion in emerging countries is relatively low, and urban social inclusion in China is relatively strong as a whole. There are 27 cities in the top 100, but more than 2/3 of them are still behind the global 500. The average urban social inclusion is 0.5735, only 0.02 points higher than the global average; and the coefficient of variation is 0.2025, which is at a high level among countries in the world. In Brazil, South Africa and other countries, urban social inclusion is relatively low, especially in South Africa, where only one city has entered the global 500; its average social inclusion is lower than the global average (Table 7.19).

7.3 Social Inclusion

349

Table 7.19 Distribution of social inclusion indicators of sample cities in G20 countries Country

Proportion of top 20 cities (%)

Proportion of top 100 cities (%)

Proportion of 101–200 cities (%)

Proportion of 201–500 cities (%)

Proportion of top 500 cities (%)

China

20.00

27.00

18.00

31.33

27.80

U.S.A

0.00

5.00

15.00

13.33

12.00

15.00

22.00

24.00

4.20

13.20

Mexico

0.00

0.00

4.00

6.67

4.80

India

0.00

1.00

4.00

5.67

4.40

Germany

0.00

3.00

8.00

0.67

2.60

Italy

0.00

2.00

5.00

2.00

2.60

U.K.

0.00

2.00

5.00

1.67

2.40

Brazil

0.00

1.00

0.00

1.67

1.20

Japan

40.00

10.00

0.00

0.00

2.00

France

0.00

0.00

1.00

2.33

1.60

Canada

0.00

5.00

2.00

0.67

1.80

Russia

0.00

2.00

5.00

8.00

6.00

Korea

15.00

7.00

1.00

0.00

1.60

Indonesia

0.00

0.00

4.00

3.00

2.60

Turkey

0.00

4.00

3.00

2.33

2.80

Australia

0.00

0.00

0.00

1.67

1.00

Argentina

0.00

1.00

2.00

1.00

1.20

S. Africa

0.00

0.00

0.00

0.33

0.20

Saudi Arabia

5.00

4.00

2.00

1.00

1.80

95.00

90.00

88.00

85.33

86.80

5.00

10.00

12.00

14.67

13.20

100.00

100.00

100.00

100.00

100.00

European Union

G20 Non-G20 Global whole

Source CCC of CASS

Urban social inclusion in G20 countries is higher than that in non G20 countries, and the internal differentiation is relatively small. From the distribution of urban social inclusion in G20 and non-G20 countries in each ranking segment, urban social inclusion in G20 countries is completely superior to that in non-G20 countries, with absolute advantages. Among the top 100 cities in the world, 90% are from G20 countries, and 86.8% are from the top 500 countries. Specifically, the average urban social inclusion of G20 countries is 0.1682 points higher than that of non-G20 countries, and the former’s coefficient of variation is lower than the latter’s

350

7 Explanatory Indicators of Global Urban Sustainable Competitiveness

Table 7.20 Statistical description of social inclusion indicators of G20 sample cities Country

Mean

CV

Optimal city

World rank

China

0.5735

0.2025

Taipei

5

U.S.A

0.6349

0.1460

New York

37

European Union

0.7288

0.1146

Czech Republic

3

Mexico

0.5869

0.1467

Mexico city

113

India

0.4716

0.2643

Bangalore

96

Germany

0.7360

0.0769

Berlin

21

Italy

0.7151

0.0630

Milan

74

U.K.

0.7055

0.0677

West Yorkshire

75

Brazil

0.5051

0.1818

Sao Jose dos Campos

46

Japan

0.9151

0.0458

Tokyo

1

France

0.6130

0.1303

Paris

168

Canada

0.7521

0.0958

Toronto

44

Russia

0.6373

0.1178

Moscow

36

Korea

0.8456

0.0874

Seoul

2

Indonesia

0.6094

0.1554

Jakarta

105

Turkey

0.6836

0.1561

Istanbul

38

Australia

0.6155

0.0958

Adelaide

218

Argentina

0.6098

0.1652

Buenos Aires

65

S. Africa

0.4875

0.2416

Cape Down

304

Saudi Arabia

0.7358

0.1247

Riyadh

18

G20

0.5969

0.2279

Tokyo

1

Non-G20

0.4287

0.4276

Bangkok

20

Global whole

0.5526

0.3025

Tokyo

1

Source CCC of CASS

0.1997, which indicates that the urban social inclusion of non-G20 countries is low, and the internal differentiation is relatively significant (Table 7.20).

7.3.2.2

Overview of Representative Countries

The sub-indicators of social inclusion of representative countries are quite different, and Japan’s social inclusion is relatively strong. In addition to the cost of living, the biggest difference of other indicators of social inclusion among the representative countries is more than 2 times. Japan (0.6741) with the largest cultural inclusion is 4.9 times that of India (0.1371) with the lowest one. The difference between the representative countries is more significant. The sub indicators of social inclusion in Japan are relatively strong, with the other five indicators ranking first

7.4 Scientific and Technological Innovation

351

Table 7.21 Statistical analysis of sub-indicators of social inclusion in representative countries Australia Brazil

U.S.A

S. Africa

Japan

India

U.K.

China

Heritage Mean 0.6381 protection CV 0.1369

0.6587 0.6712 0.6017 0.8252 0.4842 0.6699 0.6534

Social security

Mean 0.6064

0.3080 0.5064 0.2057 0.8556 0.5786 0.5395 0.6439

CV

0.0993

0.3374 0.2616 0.2049 0.1156 0.2141 0.1166 0.1476

Social equity

Mean 0.4685

0.4264 0.4202 0.3355 0.7255 0.4617 0.6230 0.5476

CV

0.0659

0.0537 0.2286 0.0508 0.0774 0.1540 0.1346 0.1725

Living cost

Mean 0.9766

0.8995 0.9790 0.9806 0.9642 0.9338 0.9645 0.9563

CV

0.0219

0.0404 0.0098 0.0074 0.0258 0.0846 0.0510 0.1168

Cultural inclusion

Mean 0.4410

0.4481 0.5832 0.5286 0.6741 0.1371 0.5808 0.2406

CV

0.2297

0.3748 0.2239 0.2825 0.0978 1.1765 0.1624 0.8847

Medical health

Mean 0.4950

0.3911 0.5803 0.5211 0.8426 0.4716 0.6463 0.3459

CV

0.2660

0.3046 0.2270 0.3240 0.1040 0.2906 0.1402 0.3720

Social inclusion

Mean 0.6155

0.5051 0.6349 0.4875 0.9151 0.4716 0.7055 0.5735

CV

0.1818 0.1460 0.2416 0.0458 0.2643 0.0677 0.2025

0.0958

0.1839 0.1860 0.3113 0.1022 0.4635 0.1608 0.1838

Source CCC of CASS

in the representative countries except for the cost of living. China’s social inclusion is above the global average level, but there is a certain gap between China and Japan, the world’s leading country. Among them, medical health ranks the lowest among the representative countries, with a gap of nearly 0.5 points between China and Japan. India and South Africa are relatively low in social inclusion, with the lowest representative countries in both sub rankings (Table 7.21).

7.4 Scientific and Technological Innovation 7.4.1 Overall Pattern of Scientific and Technological Innovation 7.4.1.1

Head City Overview

The top cities of global scientific and technological innovation are unevenly distributed across continents. The top cities of global scientific and technological innovation are unevenly distributed across continents. From the distribution of the top 20 cities of global scientific and technological innovation on all continents, they are all concentrated in North America, Asia and Europe, among which, the number of top 20 cities of North America scientific and technological innovation is the largest,

352

7 Explanatory Indicators of Global Urban Sustainable Competitiveness

9 are shortlisted cities, followed by Asia, 7 are shortlisted cities, and the number of European shortlisted cities is the smallest, 4 in total (Table 7.22). Among the top 100 cities of global scientific and technological innovation, the average value and fluctuation range of cities in Asia and North America are relatively close. Among them, the average value of urban scientific and technological innovation in North America and Asia is the highest, the average value of urban scientific and technological innovation in South America is the lowest, and the average value of urban scientific and technological innovation in Europe and Oceania is in the middle. From the perspective of the best cities of science and technology innovation in all continents, the corresponding best cities in Asia, North America, Europe, Oceania and South America are Tokyo, New York, London, Sydney and Sao Paulo, respectively, ranking first, second, fourth, 44th and 59th in the world (Table 7.23). Table 7.22 Top 20 cities in the world in science and technology innovation indicators Regional

National

City

Index

Ranking

Asia

Japan

Tokyo

1

1

N. America

U.S.A

New York

0.96

2

Asia

China

Beijing

0.955

3

Europe

U.K

London

0.928

4

N. America

U.S.A

San Francisco

0.888

5

Europe

France

Paris

0.883

6

Asia

Korea

Seoul

0.875

7

N. America

U.S.A

Boston

0.871

8

Asia

China

Shanghai

0.853

9

N. America

U.S.A

Chicago

0.841

10

Asia

Japan

Osaka

0.832

11

N. America

U.S.A

Seattle

0.829

12

N. America

U.S.A

Los Angeles

0.825

13

Asia

Singapore

Singapore

0.8

14

N. America

Canada

Toronto

0.796

15

Asia

China

Taipei

0.78

16

N. America

U.S.A

Philadelphia

0.775

17

Europe

Germany

Munich

0.769

18

N. America

U.S.A

Austin

0.767

19

Europe

Russia

Moscow

0.766

20

Source CCC of CASS

7.4 Scientific and Technological Innovation

353

Table 7.23 Intercontinental distribution of the top 100 cities in the world Regional

Sample

Mean value

Coefficient variation

Optimal city

Index

Ranking

Asia

27

0.713

0.156

Tokyo

1

1

Europe

31

0.696

0.104

London

0.928

4

N. America

35

0.716

0.125

New York

0.96

2

S. America

3

0.648

0.019

Sao Paulo

0.665

59

Oceania

4

0.674

0.049

Sydney

0.705

44

Global

1006

0.311

0.602

Tokyo

1

1

Source CCC of CASS

7.4.1.2

Overall Spatial Pattern

Cities with advanced level of scientific and technological innovation are mainly concentrated in Europe, North America and Asia. According to the average distribution of global urban science and technology innovation, urban science and technology innovation is relatively developed in Oceania, Europe and North America, relatively weak in Africa, and moderate in Asia and South America. According to the coefficient of variation of the intercontinental distribution of global urban scientific and technological innovation, the fluctuation range of urban scientific and technological innovation in Oceania is relatively small, that in Asia and Africa is relatively large, and that in Europe, North America and South America is in the middle. In terms of the proportion of the top 100 global cities in all continents, no city in Africa has entered the top 100 in science and technology innovation. The proportion of the top 100 cities in science and technology innovation in South America and Asia is relatively low. The proportion of the top 100 cities in science and technology innovation in Oceania is relatively high. The proportion of the top 100 cities in science and technology innovation in Europe and North America is in the middle (Table 7.24, Figs. 7.10 and 7.11). Table 7.24 Intercontinental distribution of global urban science and technology innovation indicators Regional

Sample

Mean value

Coefficient variation

Asia

565

Europe

126

4.78

0.262

0.599

24.60

0.477

0.352

N. America

131

26.72

S. America

75

4.00

0.449

0.460

0.3

Oceania

7

0.461

57.14

0.591

0.212

Africa Global

102

0.00

0.194

0.628

1006

9.94

0.311

0.602

Source CCC of CASS

Proportion of top 100 cities (%)

354

7 Explanatory Indicators of Global Urban Sustainable Competitiveness 90

60

30

0 -180

-120

-60

0

60

120

180

-30

-60

-90

Fig. 7.10 Spatial distribution of scientific and technological innovation indicators in 1006 cities around the world. Source CCC of CASS

The average value of the sub indicators of global urban science and technology innovation is generally low, and the fluctuation range is quite different. In terms of the average value of the sub indicators of scientific and technological innovation, except that the paper index of global cities is greater than 0.5, the average value of other sub indicators of scientific and technological innovation is less than 0.5, and the University index and scientific and technological enterprise index are relatively low, and the performance of the Knowledge density and patent index of global cities is in the middle. From the variation coefficient of the sub indicators of scientific and technological innovation, the fluctuation range of the scientific and technological enterprise index and University index is relatively large, the fluctuation range of the paper index is relatively small, and the fluctuation range of the patent index and Knowledge density is in the middle. Among the five sub indicators, Tokyo ranks first in the world in three indicators, New York, Moscow and Tokyo in one indicator (Table 7.25 and Fig. 7.12).

7.4 Scientific and Technological Innovation

355 90

60

30

0 -180

-120

-60

0

60

120

180

-30

-60

-90

Fig. 7.11 Spatial distribution of scientific and technological innovation indicators of the top 100 cities in the world. Source CCC of CASS

Table 7.25 Statistical description of sub indicators of global urban science and technology innovation

Technological innovation

Mean value

Coefficient variation

Optimal city

Patent index

0.276

0.829

Tokyo

Paper index

0.547

0.318

Beijing

Technology enterprise index

0.125

1.5

Tokyo

University index

0.19

1.154

New York

Cultural facilities

0.347

0.637

Moscow

Technological innovation

0.311

0.602

Tokyo

Source CCC of CASS

356

7 Explanatory Indicators of Global Urban Sustainable Competitiveness

Fig. 7.12 Core density map of global urban science and technology innovation indicators. Source CCC of CASS

7.4.2 National Pattern of Scientific and Technological Innovation 7.4.2.1

G20 Country Profile

The number of emerging economies in the top cities of global technological innovation is still lower than that of developed economies, and China and the United States occupy an absolute advantage in the number of top cities in the G20 countries’ technological innovation rankings. By comparing the ranking of urban science and technology innovation in G20 countries, it is found that among the top 20 cities in the world, 3 cities in China, 8 cities in the United States, 2 cities in Japan, 1 city in Germany, the United Kingdom, France, Canada, Russia and South Korea, and no cities in other countries are in the top 20. Among the top 100 cities in terms of scientific and technological innovation in the world, the United States has the highest proportion, up to 20%, followed by China, accounting for 10%. Indonesia, South Africa and Saudi Arabia do not have any cities in the top 100 cities in the world. Among the top 101–200 cities in terms of scientific and technological innovation in the world, China and the United States have no significant difference. China accounts for 13% of the top 101–200 cities. No city in Mexico or Argentina has entered the top 101–200 cities in the world. The number of Chinese cities accounts for 24.67% of the top 201–500 cities in global scientific and technological innovation, far higher than 9% of the United States. Neither the United Kingdom nor Canada has any cities in the top 201–500 cities in the world. Among the top 500 cities in terms of scientific and

7.4 Scientific and Technological Innovation

357

technological innovation in the world, China has the highest proportion, accounting for 20.8%, followed by the United States, accounting for 14.2%. South Africa and Saudi Arabia have the lowest proportion, accounting for no more than 1% (Table 7.26). Overall, the average value of urban technological innovation in emerging economies is still lower than that in developed economies, and the degree of internal differentiation is higher than that in developed economies. By comparing the urban science and technology innovation in G20 countries, it is found that in terms of the average value of science and technology innovation, cities in Canada, Japan, Britain, Germany and Australia have obvious advantages, while cities in India, Indonesia, Saudi Arabia and Mexico have relatively poor performance. In terms of the coefficient of variation of scientific and technological innovation, the fluctuation range of cities in Canada, Germany and Italy is relatively small, while that in Saudi Arabia, China and India is relatively large. From the perspective of the best cities of Table 7.26 Ranking and distribution of scientific and technological innovation indicators of G20 sample cities National

Top 20 proportion (%)

Top 100 Proportion (%)

Proportion of 101–200 (%)

Proportion of 201–500 (%)

Top 500 proportion (%)

China

15.00

17.00

13.00

24.67

20.80

U.S.A

40.00

28.00

16.00

9.00

14.20

Mexico

0.00

1.00

0.00

5.00

3.20

India

0.00

1.00

6.00

8.33

6.40

Germany

5.00

5.00

7.00

0.33

2.60

Italy

0.00

4.00

4.00

1.67

2.60

U.K

5.00

4.00

8.00

0.00

2.60

Brazil

0.00

2.00

1.00

4.67

3.40

Japan

10.00

3.00

5.00

0.67

2.00

France

5.00

1.00

3.00

1.67

1.80

Canada

5.00

6.00

3.00

0.00

1.80

Russia

5.00

1.00

2.00

7.00

4.80

Korea

5.00

2.00

4.00

0.67

1.60

Indonesia

0.00

0.00

1.00

1.67

1.20

Turkey

0.00

1.00

1.00

3.67

2.60

Australia

0.00

3.00

2.00

0.33

1.20

Argentina

0.00

1.00

0.00

2.00

1.40

South Africa

0.00

0.00

2.00

1.00

1.00

Saudi Arabia 总计

0.00

0.00

1.00

0.67

0.60

95.00

80.00

79.00

73.00

75.80

Source CCC of CASS

358

7 Explanatory Indicators of Global Urban Sustainable Competitiveness

Table 7.27 Statistical description of scientific and technological innovation indicators of G20 sample cities National

Mean value

Coefficient variation

Optimal city

Ranking

France

0.492

0.339

Paris

6

U.S.A

0.542

0.325

New York

2

U.K

0.594

0.204

London

4

China

0.263

0.591

Beijing

3

Turkey

0.324

0.396

Istanbul

57

Italy

0.537

0.202

Rome

35

Russia

0.32

0.344

Moscow

20

Japan

0.6

0.306

Tokyo

1

Canada

0.642

0.140

Toronto

15

Australia

0.574

0.223

Sydney

44

Germany

0.582

0.165

Munich

18

Korea

0.552

0.268

Seoul

7

India

0.216

0.560

Bombay

80

Indonesia

0.222

0.438

Jakarta

186

Argentina

0.329

0.381

Buenos Aires

72

Mexico

0.249

0.438

Mexico city

78

Brazil

0.309

0.422

Sao Paulo

59

Saudi Arabia

0.244

0.595

Riyadh

102

South Africa

0.392

0.458

Cape Down

120

Global

0.327

0.582

Tokyo

1

Source CCC of CASS

science and technology innovation in the G20 countries, 9 cities rank in the top 20 of the world’s science and technology innovation, 16 cities rank in the top 100 of the world’s science and technology innovation, and 19 cities rank in the top 200 of the world’s science and technology innovation (Table 7.27).

7.4.2.2

Overview of Representative Countries

According to the intercontinental division, China, Japan and India in Asia, the United Kingdom in Europe, the United States in North America, Brazil in South America, South Africa in Africa and Australia in Oceania are mainly selected for comparative study. In general, developed economies have more advantages in the sub indicators of scientific and technological innovation, and the sub indicators of emerging economies have greater internal differentiation. From the average value of patent index, Japan, the United States and the United Kingdom have developed urban science and technology innovation, while India and

7.5 External Contacts

359

Brazil have relatively poor urban science and technology innovation. According to the coefficient of variation of sci-tech innovation index, the fluctuation range of urban sci-tech innovation in Britain, Japan and Australia is relatively small, while that in India and Brazil is relatively large. n terms of the average paper index, the cities of Australia, Britain and Japan have higher paper index, while the cities of India and China have lower paper index. According to the coefficient of variation of paper index, the fluctuation range of urban paper index in Britain, Japan and Australia is relatively small, while that in South Africa, China and India is relatively large. From the average value of technology enterprise index, Japan and the United States have obvious urban advantages, while India and Brazil have low technology enterprise index; from the coefficient of variation of technology enterprise index, the fluctuation range of cities in the United States and Australia is relatively small, while that in India and Brazil is relatively large. From the average of University index, the City University index of India and China is lower, while that of Australia, Britain and the United States is higher. According to the coefficient of variation of University index, the fluctuation range of cities in Australia and Britain is relatively small, while that in China and India is relatively large. From the average of the Knowledge density index, the urban advantages of Britain and Japan are obvious, while that of China and India are not good. From the coefficient of variation of Knowledge density index, the fluctuation range of cities in Britain and Japan is relatively small, while that in China and India is the largest (Table 7.28).

7.5 External Contacts 7.5.1 Overall Pattern of External Contacts 7.5.1.1

Overview of the Head City

The head city of global external contact continent distribution is uneven. In terms of the distribution of the top 20 cities in the world in each continent, they are all concentrated in North America, Europe and Asia. Among them, the number of the top 20 cities in North America is the highest, with 8 cities in the list, followed by 7 cities in Europe, and the number of Cities in Asia is the lowest, with 5 cities in total (Table 7.29). Among the top 100 cities in the world, the average and fluctuation range of cities in each continent are close. The mean values of North America and Europe were the highest, South America the lowest, and Asia and Oceania the middle. From the perspective of the best cities with external connections on all continents, Europe, North America, Asia, Oceania and South America are Paris, New York, Beijing,

Source CCC of CASS

Technological innovation

Cultural facilities

University index

Technology enterprise index

Paper index

Patent index

0.263 0.591

Coefficient variation

0.768

Coefficient variation

Mean value

0.224

1.34

Coefficient variation

Mean value

0.121

2.083

Mean value

0.07

Coefficient variation

0.318

Coefficient variation

Mean value

0.509

0.598

Coefficient variation

Mean value

0.296

Mean value

China

0.325

0.542

0.329

0.515

0.663

0.455

0.698

0.297

0.206

0.685

0.264

0.589

U.S.A

0.56

0.216

0.734

0.25

0.904

0.089

2.587

0.045

0.313

0.509

1.203

0.143

India

0.306

0.6

0.263

0.642

0.523

0.416

0.94

0.344

0.103

0.742

0.218

0.702

Japan

0.204

0.594

0.2

0.629

0.236

0.638

1.03

0.22

0.089

0.754

0.156

0.575

U.K

Table 7.28 Statistical analysis of representative national scientific and technological innovation sub indicators

0.458

0.392

0.514

0.496

0.642

0.303

1.568

0.109

0.36

0.588

0.314

0.404

South Africa

0.422

0.309

0.511

0.347

0.799

0.232

2.172

0.064

0.166

0.611

0.737

0.22

Brazil

0.223

0.574

0.422

0.301

0.158

0.68

0.803

0.267

0.112

0.771

0.225

0.546

Australia

360 7 Explanatory Indicators of Global Urban Sustainable Competitiveness

7.5 External Contacts

361

Table 7.29 Global contact indicators top 20 cities in the world Region

Country

City

Index

Rank

European

French

Paris

1.000

1

North America

United States

New York

0.978

2

European

U.K.

London

0.961

3

Asia

China

Beijing

0.932

4

European

Netherlands,

Amsterdam

0.928

5

Asia

Turkey

Istanbul

0.914

6

Asia

China

Shanghai

0.890

7

European

Spain

Barcelona

0.890

8

European

Italy

Milan

0.889

9

North America

United States

Chicago

0.885

10

North America

United States

Atlanta,

0.883

11

European

Spain

Madrid

0.882

12

North America

United States

Dallas-Fort Worth

0.881

13

North America

United States

Los Angeles

0.873

14

North America

United States

In Houston

0.871

15

European

Russia

Moscow

0.865

16

North America

United States

Washington, D.C.

0.863

17

Asia

Singapore

Singapore

0.862

18

Asia

Japan

Tokyo

0.859

19

North America

Canada

Toronto

0.852

20

Source CCC of CASS

Sydney and Bogota respectively, ranking first, second, fourth, 21st and 88th in the world (Table 7.30). Table 7.30 Intercontinental distribution of external connections among the top 100 global cities Region

Sample

Mean

CV

Best city

Index

Rank

Asia

25

0.781

0.089

Beijing

0.932

4

Europe

44

0.787

0.089

Paris

1.000

1

North America

25

0.806

0.085

New York

0.978

2

South America

2

0.712

–

Bogota

0.713

88

Oceania

4

0.769

0.083

Sydney

0.847

21

Global

1006

0.444

0.411

Paris

1.000

1

Source CCC of CASS

362

7.5.1.2

7 Explanatory Indicators of Global Urban Sustainable Competitiveness

Overall Spatial Pattern

The most developed cities in the world are in Europe, North America and Asia. From the mean characteristics of the intercontinental distribution of global cities, the cities in Oceania, Europe and North America have relatively developed external connections, while those in Africa are relatively weak, and those in Asia and South America are in the middle. In terms of the variation coefficient of global cities’ external contact distribution, Oceania cities’ external contact fluctuation range is small, Africa cities’ external contact fluctuation range is large, and North America, Europe, Asia and South America cities’ external contact fluctuation range is in the middle. In terms of the proportion of the global top 100 cities in each continent, no African city ranks in the top 100 in terms of external connections. The proportion of the top 100 cities in South America and Asia is relatively low, the proportion of the top 100 cities in Oceania and Europe is relatively high, and the proportion of the top 100 cities in North America is in the middle (Table 7.31, Figs. 7.13 and 7.14). The mean value and fluctuation range of the sub-index of external links of global cities are quite different. According to the mean value of the sub-index of external contact, the shipping convenience of global cities is relatively high, the aviation convenience of global cities is relatively low, and the enterprise connection, information connection, information access convenience and scientific research connection of global cities are in the middle. In terms of the coefficient of variation of the sub-index of external contact, the fluctuation range of global urban aviation convenience is relatively large, the fluctuation range of global urban shipping convenience is relatively small, and the fluctuation range of global urban enterprise connection degree, information connection degree, information access connection degree and scientific research connection degree is in the middle. New York leads the world in three of the six sub-indices, while Paris, London and Sydney each have one (Table 7.32 and Fig. 7.15). Table 7.31 Intercontinental distribution of indicators of global city external linkages Region

Sample

Asia

565

Europe

126

North America

131

South America

75

Oceania

7

Africa Global Source CCC of CASS

Proportion of top 100 cities (%)

Mean

CV

4.40

0.406

0.370

34.90

0.582

0.371

19.10

0.571

0.294

2.70

0.406

0.360

57.10

0.680

0.190

102

0.00

0.331

0.471

1006

9.94

0.444

0.411

7.5 External Contacts

363

90

60

30

0 -180

-120

-60

0

60

120

180

-30

-60

-90 Fig. 7.13 Spatial distribution of external contact indicators for 1006 cities in the world. Source CCC of CASS 90

60

30

0 -180

-120

-60

0

60

120

180

-30

-60

-90

Fig. 7.14 Spatial distribution of external linkages indicators of the top 100 cities in the world. Source CCC of CASS

364

7 Explanatory Indicators of Global Urban Sustainable Competitiveness

Table 7.32 A statistical description of the sub-index of global city external contact Global connectivity

Mean

CV

Best city

Degree of enterprise connection

0.586

0.550

New York

Degree of information contact

0.347

0.635

New York

Information accessibility

0.408

0.543

Sydney

Shipping convenience

0.838

0.186

New York

Aviation convenience

0.130

1.248

Paris

Degree of scientific research connection

0.428

0.626

London

External contact degree

0.444

0.411

Paris

Source CCC of CASS

Fig. 7.15 SA nuclear density map of global city external linkages indicators. Source CCC of CASS

7.5.2 The Pattern of Countries with Foreign Contacts 7.5.2.1

Overview of the Head City

The number of cities in the global head of external contact of emerging economies is still lower than that of developed economies, and China and the United States have an absolute advantage in the number of cities in the top ranking of external contact of G20 countries. Rankings of the G20 countries city outside contact is found, in front of the global city outside contact number 20 in the proportion of China and the United States are two cities, Italy, Britain, Turkey, Japan, France, Canada, Russia, each has a city, no other countries into the global top 20 cities. Among the top 100 global cities in terms of external connections, the United States has the highest proportion, accounting for 20%, followed by China, accounting for 10%. Argentina, South Africa and Saudi Arabia do not have a single city in the top 100. Among the global cities in the top 101–200, the United States has the highest number of cities, accounting for 25%, followed by China, accounting for

7.5 External Contacts

365

17%. Mexico, South Korea and Indonesia do not have any cities in the top 101–200. Among the top 201–500 global cities in terms of the number of foreign contacts, Chinese cities account for the highest proportion (37.7%), followed by the United States (8.7%), and no German city ranks in the top 201–500. Among the top 500 global cities with external connections, Chinese cities account for the highest proportion, reaching 28%, followed by the US with 14.2%, and South Africa and Saudi Arabia with the lowest proportion, accounting for less than 1% (Table 7.33). On the whole, the average of external connections of cities in emerging economies is still lower than that of developed economies, and the degree of internal differentiation is higher than that of developed economies. A comparison of external linkages among G20 countries found that cities in France, Germany, Italy and the UK had significant advantages over cities in India, Brazil and Russia in terms of average external linkages. In terms of the coefficient of variation of external links, cities in Germany, Italy and Canada have a small fluctuation range, while cities Table 7.33 The ranking distribution of external contact indicators of Cities in G20 countries Country

>20 (%)

>100 (%)

101–200 (%)

201–500 (%)

>500 (%)

China

10.0

10.0

17.0

37.7

28.0

United States

10.0

20.0

25.0

8.7

14.2

European Union

25.0

33.0

26.0

3.0

13.6

Mexico

0.0

1.0

0.0

5.0

3.2

India

0.0

5.0

2.0

2.7

3.0

Russia

0.0

7.0

6.0

0.0

2.6

Brazil

5.0

4.0

7.0

0.7

2.6

Germany

5.0

7.0

3.0

0.7

2.4

Italy

0.0

0.0

1.0

3.0

2.0

Indonesia

5.0

1.0

2.0

2.3

2.0

Turkey

5.0

4.0

4.0

0.3

1.8

U.K.

5.0

4.0

3.0

0.7

1.8

Japan

5.0

1.0

1.0

2.3

1.8

Canada

0.0

1.0

0.0

2.3

1.6

South Korea

0.0

1.0

0.0

2.0

1.4

The French

5.0

2.0

1.0

1.3

1.4

Argentina

0.0

3.0

1.0

0.7

1.2

Australia

0.0

0.0

1.0

1.3

1.0

South Africa

0.0

0.0

1.0

1.0

0.8

Saudi Arabia

0.0

0.0

1.0

1.0

0.8

G20 countries

80.0

87.0

82.0

76.7

79.8

Non G20 countries

20.0

13.0

18.0

23.30

20.2

100.0

100.0

100.0

World as a whole Source CCC of CASS

100.0

100.0

366

7 Explanatory Indicators of Global Urban Sustainable Competitiveness

Table 7.34 Statistical description of external contact indicators of G20 sample cities Country

Mean

CV

Best city

Rank

France

0.7134

0.1825

Paris

1

United States

0.6404

0.2192

New York

2

U.K.

0.6914

0.2130

London

3

China

0.4455

0.2445

Beijing

4

Turkey

0.4495

0.3955

Istanbul

6

Italy

0.6900

0.1443

Milan

9

Russia

0.3276

0.5071

Moscow

16

Japan

0.5659

0.2236

Tokyo

19

Canada

0.6958

0.1405

Toronto

20

Australia

0.6717

0.2070

Sydney

21

Germany

0.7131

0.0996

Berlin

24

South Korea

0.5479

0.2296

Seoul

27

India

0.3013

0.4952

Mumbai

54

Indonesia

0.3848

0.2964

Jakarta

71

Argentina

0.4522

0.3294

Buenos Aires

91

Mexico

0.4133

0.3209

Mexico City

99

Brazil

0.3548

0.4274

Sao Paulo

124

Saudi Arabia

0.3807

0.3870

Riyadh

128

South Africa

0.5239

0.2457

Johannesburg

141

European Union

0.7068

0.1401

Paris

1

G20 countries

0.4691

0.3818

Paris

1

Non G20 countries

0.3746

0.4659

Singapore

18

Global

0.4440

0.4113

Paris

1

Source CCC of CASS

in Russia, Brazil and India have a large fluctuation range. In terms of the best cities in G2O countries, 9 cities are in the top 20, 16 cities are in the top 100, and 19 cities are in the top 200 (Table 7.34).

7.5.2.2

Overview of Representative Countries

According to the continental division, the focus here is to choose China, Japan and India in Asia, the United Kingdom in Europe, the United States in North America, Brazil in South America, South Africa and Australia in Oceania for comparative study. In general, cities in developed economies have more obvious advantages in the sub-index of external relations, while cities in emerging economies have greater internal differentiation in each sub-index.

7.6 Ranking of Explanatory Indicators of Global Urban Sustainable …

367

According to the mean value of the index, cities in Britain, Australia and the United States are highly connected to the outside world, while those in India and Brazil are relatively poor. In terms of the coefficient of variation of the index, cities in Australia, The United Kingdom and the United States have a small fluctuation range of external contact, while cities in India and Brazil have a large fluctuation range of external contact. In terms of the mean value of corporate connectivity, Australia, Japan and the United States have a higher degree of corporate connectivity, while India and Brazil have a lower degree of corporate connectivity. In terms of the coefficient of variation of enterprise connection, the urban enterprise connection degree of China, Japan and Australia fluctuates slightly, while that of India and Brazil fluctuates greatly. From the mean of the degree of information contact, cities in India and Japan have a low degree of information contact, while cities in Australia, the United States and the United Kingdom have a high degree of information contact. From the variation coefficient of information connection degree, cities in Japan and Australia have a small fluctuation range, while cities in India and South Africa have a large fluctuation range. From the mean value of information access convenience, American and U.K. cities have obvious advantages. According to the coefficient of variation of information access convenience, the fluctuation range of American and U.K. cities is small, while that of Chinese cities is the largest. From the average value of shipping convenience, Japanese and U.K. cities have obvious advantages. In terms of the variation coefficient of shipping convenience, cities in Japan and Britain fluctuate less, while cities in Brazil fluctuate more. From the mean value of aviation convenience, the city advantages of THE UK and the US are obvious, the city advantages of the UK are obvious, and the city performance of Brazil is relatively poor. From the coefficient of variation of aviation convenience, cities in Britain and Australia fluctuate less. From the mean of scientific research linkages, the cities of Britain and Australia have obvious advantages. From the coefficient of variation of scientific association degree, cities in Japan and Australia fluctuate less (Table 7.35).

7.6 Ranking of Explanatory Indicators of Global Urban Sustainable Competitiveness

Source CCC of CASS

External contact

Enterprise connection

Degree of scientific research connection

Aviation convenience

Shipping convenience

Information accessibility

Degree of information contact

0.446 0.245

CV

0.102

CV Mean

0.686

0.771

Mean

0.272

CV

1.070

CV Mean

0.140

0.116

CV Mean

0.882

0.602

Mean

0.324

CV

0.313

CV Mean

0.448

Mean

China

0.219

0.640

0.204

0.774

0.300

0.680

1.059

0.206

0.157

0.865

0.099

0.770

0.356

0.554

U.S.A

0.495

0.301

1.068

0.283

0.720

0.319

1.423

0.053

0.093

0.872

0.456

0.352

0.743

0.249

India

Table 7.35 Statistical analysis of sub-index of external contact of representative countries

0.224

0.566

0.108

0.796

0.202

0.650

1.197

0.135

0.064

0.946

0.214

0.543

1.427

0.152

Japan

0.213

0.691

0.325

0.770

0.337

0.752

0.496

0.318

0.042

0.937

0.088

0.702

0.471

0.547

U.K.

0.246

0.524

0.496

0.724

0.254

0.629

0.760

0.154

0.116

0.860

0.254

0.343

0.523

0.315

South Africa

0.427

0.355

0.645

0.567

0.444

0.502

1.054

0.073

0.358

0.473

0.197

0.385

0.476

0.356

Brazil

0.207

0.672

0.124

0.868

0.166

0.815

0.537

0.188

0.302

0.806

0.264

0.622

0.230

0.661

Australia

368 7 Explanatory Indicators of Global Urban Sustainable Competitiveness

Country

Singapore

Japan

U.S.A.

U.K.

U.S.A.

France

China

Japan

U.S.A.

U.S.A.

Spain

Russian

Sweden

Republic of Korea

Germany

Germany

U.S.A.

Spain

China

Germany

City

Singapore

Tokyo

New York-Newark

London

San Francisco-Oakland

Paris

Hong Kong

Osaka

Los Angeles-Long Beach-Santa Ana

Chicago

Barcelona

Moscow

Stockholm

Seoul

Munich

Stuttgart

Boston

Madrid

Shenzhen

Frankfurt am Main

20

19

18

17

16

15

14

13

12

11

10

9

8

7

6

5

4

3

2

1

Sustainable competitiveness

42

24

324

18

83

19

213

17

272

118

84

122

224

98

192

8

20

29

183

1

Economic vitality

11

85

230

157

1

4

259

48

387

83

356

417

141

9

75

255

184

278

106

5

Environmental thoroughness

188

98

53

81

157

67

2

272

36

115

192

55

4

151

168

72

83

37

1

41

Social inclusion

92

33

29

8

69

18

7

24

20

50

10

13

11

22

6

5

4

2

1

14

Scientific and technological innovation

(continued)

158

56

12

26

67

34

27

52

16

8

10

14

103

31

1

48

3

2

19

18

External contacts

7.6 Ranking of Explanatory Indicators of Global Urban Sustainable … 369

Country

U.S.A.

Canada

China

U.S.A.

U.S.A.

Germany

Australia

Italy

China

U.S.A.

U.K.

U.S.A.

U.S.A.

U.S.A.

Australia

Japan

U.K.

China

Italy

Canada

U.S.A.

City

Philadelphia

Toronto

Taipei

Houston

Miami

Berlin

Melbourne

Rome

Shanghai

Seattle

Manchester

Atlanta

San Jose

Cleveland

Sydney

Hiroshima

Birmingham

Beijing

Milan

Montreal

Dallas-Fort Worth

(continued)

41

40

39

38

37

36

35

34

33

32

31

30

29

28

27

26

25

24

23

22

21

Sustainable competitiveness

75

134

385

223

55

146

88

7

3

97

60

31

201

388

119

153

61

49

140

112

50

Economic vitality

115

162

137

716

139

90

126

112

198

47

108

295

458

114

34

17

256

272

68

190

49

Environmental thoroughness

144

56

74

50

255

26

291

467

110

334

186

123

47

100

302

21

223

176

5

44

231

Social inclusion

27

47

46

3

104

181

44

61

90

21

53

12

9

35

52

38

77

23

16

15

17

Scientific and technological innovation

(continued)

13

38

9

4

50

280

21

122

154

11

36

32

7

23

42

24

33

15

57

20

35

External contacts

370 7 Explanatory Indicators of Global Urban Sustainable Competitiveness

Country

Argentina

Austria

Israel

U.S.A.

Germany

Switzerland

Japan

Japan

U.S.A.

Denmark

Germany

U.S.A.

U.S.A.

Australia

U.S.A.

Republic of Korea

China

U.S.A.

Malaysia

Canada

City

Buenos Aires

Vienna

Tel Aviv-Yafo

Denver-Aurora

Hamburg

Zurich

Nagoya

Kitakyushu-Fukuoka

Baltimore

Copenhagen

Hannover

Salt Lake City

San Diego

Perth

Washington, D.C.

Incheon

Suzhou

Raleigh

Kuala Lumpur

Vancouver

(continued)

61

60

59

58

57

56

55

54

53

52

51

50

49

48

47

46

45

44

43

42

Sustainable competitiveness

92

96

16

99

179

113

26

53

10

79

72

13

52

128

45

116

41

123

57

617

Economic vitality

73

171

148

532

279

432

133

238

134

3

24

19

102

225

14

43

146

29

2

405

Environmental thoroughness

69

175

196

23

35

158

442

80

204

236

122

358

7

12

107

78

139

367

32

65

Social inclusion

28

97

75

148

165

39

169

26

67

116

42

36

149

62

40

51

159

260

45

72

Scientific and technological innovation

(continued)

51

66

168

182

453

17

283

108

153

86

41

30

313

183

45

43

47

102

37

91

External contacts

7.6 Ranking of Explanatory Indicators of Global Urban Sustainable … 371

Country

Netherlands

Kazakhstan

Switzerland

Belgium

U.S.A.

China

U.S.A.

U.S.A.

U.K.

Germany

Finland

Republic of Korea

Turkey

Republic of Korea

U.S.A.

Spain

Israel

U.S.A.

Brazil

U.S.A.

City

Amsterdam

Astana

Geneva

Brussels

Detroit

Guangzhou

Austin

Orlando

West Yorkshire

Cologne

Helsinki

Daejeon

Istanbul

Ulsan

Richmond

Valencia

Jerusalem

Columbus

Sao Paulo

Bridgeport-Stamford

(continued)

81

80

79

78

77

76

75

74

73

72

71

70

69

68

67

66

65

64

63

62

Sustainable competitiveness

2

527

63

163

293

12

62

281

178

82

85

102

37

68

138

71

149

5

291

95

Economic vitality

25

252

109

104

200

72

31

505

282

211

69

81

60

323

293

80

66

7

159

40

Environmental thoroughness

195

824

279

425

147

390

93

38

19

58

135

75

339

134

57

632

97

322

225

61

Social inclusion

269

59

70

135

208

100

225

57

95

41

151

111

126

19

37

60

63

87

426

34

Scientific and technological innovation

(continued)

360

124

107

145

69

136

363

6

284

53

105

423

63

59

40

79

22

64

782

5

External contacts

372 7 Explanatory Indicators of Global Urban Sustainable Competitiveness

Country

U.S.A.

China

Qatar

Israel

Mexico

Belgium

U.S.A.

Saudi Arabia

Japan

Republic of Korea

Republic of Korea

Italy

China

U.S.A.

U.K.

Australia

United Arab Emirates

Republic of Korea

City

Phoenix-Mesa

Nanjing

Doha

Haifa

Mexico City

Antwerp

Hartford

Riyadh

Sapporo

Gwangju

Busan

Naples

Xiamen

Milwaukee

Glasgow

Adelaide

Dubai

Daegu

(continued)

99

98

97

96

95

94

93

92

91

90

89

88

87

86

85

84

83

82

Sustainable competitiveness

225

23

94

111

15

139

595

177

181

174

286

9

131

436

130

304

136

145

Economic vitality

124

865

125

101

51

303

95

87

41

76

1000

33

55

512

27

610

510

805

Environmental thoroughness

34

33

218

131

407

140

203

11

86

8

18

556

130

113

389

88

29

265

Social inclusion

139

230

125

89

137

121

108

146

182

166

102

223

207

78

133

238

43

177

Scientific and technological innovation

(continued)

263

25

148

94

176

117

72

203

386

330

128

240

162

99

187

81

55

76

External contacts

7.6 Ranking of Explanatory Indicators of Global Urban Sustainable … 373

Country

Chile

Spain

Greece

China

Germany

U.S.A.

South Africa

Germany

China

China

China

Australia

New Zealand

Germany

Russian

U.S.A.

Canada

U.S.A.

Colombia

Costa Rica

Saudi Arabia

City

Santiago de Chile

Malaga

Athens

Wuxi

Dortmund

Louisville

Pretoria

Essen

Tianjin

Foshan

Taichung

Brisbane

Auckland

Dresden

Saint Petersburg

Virginia Beach

Calgary

Las Vegas

Bogota

San Jose

Medina

(continued)

120

119

118

117

116

115

114

113

112

111

110

109

108

107

106

105

104

103

102

101

100

Sustainable competitiveness

195

263

321

135

33

51

473

147

43

125

203

158

155

106

159

22

54

172

360

331

249

Economic vitality

973

292

832

350

297

45

689

23

214

136

314

239

549

21

653

54

63

373

189

178

678

Environmental thoroughness

76

283

243

235

52

396

77

101

66

357

15

126

59

170

544

344

287

24

370

116

198

Social inclusion

391

306

124

221

64

387

170

134

48

84

205

271

88

224

203

144

187

246

66

289

129

Scientific and technological innovation

(continued)

476

253

88

111

129

340

120

174

74

96

265

338

87

68

235

225

151

279

44

146

241

External contacts

374 7 Explanatory Indicators of Global Urban Sustainable Competitiveness

Country

China

China

Peru

China

Germany

U.S.A.

U.K.

Saudi Arabia

U.S.A.

China

France

U.S.A.

Germany

Ireland

Canada

Netherlands

U.S.A.

U.S.A.

Belgium

Spain

Italy

City

Dongguan

Wuhan

Lima

Kaohsiung

Dusseldorf

Tampa-St. Petersburg

Belfast

Jedda

Worcester

Hangzhou

Lyon

New Haven

Leipzig

Dublin

Hamilton

Hague

Buffalo

Charlotte

Liege

Zaragoza

Torino

(continued)

141

140

139

138

137

136

135

134

133

132

131

130

129

128

127

126

125

124

123

122

121

Sustainable competitiveness

575

268

202

48

78

89

21

6

144

46

216

230

56

253

65

115

32

186

392

226

101

Economic vitality

175

210

150

339

375

74

96

64

37

116

135

551

111

500

26

71

8

244

651

904

249

Environmental thoroughness

119

160

326

249

427

85

229

62

143

714

269

40

768

54

152

508

165

13

247

28

137

Social inclusion

82

229

302

142

243

210

143

31

167

79

154

25

213

708

152

199

172

193

155

32

257

Scientific and technological innovation

(continued)

289

248

227

39

179

214

170

29

164

198

49

78

370

355

169

121

77

230

160

113

181

External contacts

7.6 Ranking of Explanatory Indicators of Global Urban Sustainable … 375

Country

U.S.A.

China

China

U.S.A.

China

Brazil

U.S.A.

China

U.S.A.

Japan

Portugal

Norway

China

France

U.K.

U.S.A.

China

China

Jordan

Italy

Saudi Arabia

City

Colorado Springs

Chengdu

Qingdao

Nashville-Davidson

Macao

Rio de Janeiro

San Antonio

Zhongshan

Minneapolis-Saint Paul

Sendai

Lisbon

Oslo

Ningbo

Lille

Liverpool

Provo-Orem

Changzhou

Zhengzhou

Amman

Venice

Dammam

(continued)

162

161

160

159

158

157

156

155

154

153

152

151

150

149

148

147

146

145

144

143

142

Sustainable competitiveness

356

353

652

271

234

28

86

212

214

4

90

167

81

133

151

581

11

27

218

193

35

Economic vitality

998

155

880

453

327

160

110

28

447

32

13

179

84

245

88

557

91

50

444

521

232

Environmental thoroughness

202

124

125

90

172

816

141

435

84

132

89

27

185

48

187

258

649

264

43

49

193

Social inclusion

572

285

251

145

278

593

180

228

204

54

106

130

101

282

114

76

344

258

99

65

259

Scientific and technological innovation

(continued)

427

232

217

192

366

368

84

147

200

60

62

349

112

229

133

233

215

109

166

89

341

External contacts

376 7 Explanatory Indicators of Global Urban Sustainable Competitiveness

Country

Netherlands

China

China

U.K.

Iran

Puerto Rico

U.S.A.

Japan

Italy

South Africa

U.S.A.

Thailand

U.S.A.

Australia

Canada

Italy

Mexico

Bulgaria

U.S.A.

China

City

Rotterdam

Tainan

Changsha

Leicester

Tehran

San Juan

Providence

Shizuoka-Hamamatsu M.M.A.

Verona

Johannesburg

Baton Rouge

Bangkok

New Orleans

Gold Coast

Ottawa-Gatineau

Bologna

Leon

Sofia

Indianapolis

Shenyang

(continued)

182

181

180

179

178

177

176

175

174

173

172

171

170

169

168

167

166

165

164

163

Sustainable competitiveness

476

77

565

346

424

69

25

105

298

14

264

395

173

76

573

596

100

198

205

91

Economic vitality

485

145

400

522

161

86

97

347

543

16

316

92

215

61

6

929

58

468

284

70

Environmental thoroughness

42

240

156

403

181

369

594

452

20

456

575

103

6

461

684

399

292

68

9

94

Social inclusion

140

136

163

290

85

93

329

220

81

156

127

176

164

103

216

150

188

138

162

171

Scientific and technological innovation

(continued)

167

180

80

286

46

93

358

173

28

238

141

177

390

201

252

310

119

157

323

98

External contacts

7.6 Ranking of Explanatory Indicators of Global Urban Sustainable … 377

U.S.A.

Italy

U.S.A.

Hungary

Uruguay

China

U.S.A.

Venezuela

U.S.A.

China

Belarus

Portugal

Saudi Arabia

China

Iran

China

France

Thailand

Venezuela

Czech Republic 203

Ogden

Florence

Kansas City

Budapest

Montevideo

Zhuhai

Honolulu

Barcelona-Puerto La Cruz

Oklahoma City

Dalian

Minsk

Porto

Mecca

Xi’an

Ahvaz

Hefei

Marseille-Aix-en-Provence

Samut Prakan

Valencia

Prague

202

201

200

199

198

197

196

195

194

193

192

191

190

189

188

187

186

185

184

183

U.S.A.

Pittsburgh

Sustainable competitiveness

Country

City

(continued)

267

964

284

227

156

330

261

219

366

426

364

66

944

47

80

401

171

73

450

74

103

Economic vitality

123

363

182

143

631

996

576

957

65

319

467

77

370

144

291

118

131

12

120

209

142

Environmental thoroughness

3

975

159

790

102

783

39

70

238

104

31

277

996

245

162

297

22

211

120

384

233

Social inclusion

73

215

891

173

86

584

56

477

112

206

115

226

315

227

372

256

83

141

160

446

30

Scientific and technological innovation

(continued)

58

401

508

73

135

893

165

830

82

186

139

210

333

569

308

246

65

175

178

657

101

External contacts

378 7 Explanatory Indicators of Global Urban Sustainable Competitiveness

Country

France

U.S.A.

China

U.S.A.

Japan

Malaysia

Italy

Venezuela

Romania

Brazil

U.S.A.

U.S.A.

Dominican Republic

China

U.S.A.

U.K.

South Africa

Iran

China

U.S.A.

City

Toulouse

Charleston-North Charleston

jinan

Cape Coral

Niigata

Johor Bahru

Catania

Caracas

Bucuresti

Brasilia

Columbia

Riverside-San Bernardino

Santo Domingo

Zhenjiang

Memphis

Bristol

Cape Town

Karaj

Jiaxing

Rochester

(continued)

223

222

221

220

219

218

217

216

215

214

213

212

211

210

209

208

207

206

205

204

Sustainable competitiveness

107

251

344

274

30

104

148

790

175

58

488

244

963

576

250

188

70

379

34

245

Economic vitality

57

369

812

317

62

52

361

237

857

208

98

117

243

218

99

174

22

703

180

105

Environmental thoroughness

266

92

796

304

222

454

224

432

315

325

342

17

846

257

318

10

755

161

666

311

Social inclusion

236

346

528

120

74

174

300

366

537

49

252

214

276

268

336

250

888

96

309

194

Scientific and technological innovation

(continued)

140

264

833

213

70

218

314

487

306

115

417

104

260

126

393

376

861

212

205

131

External contacts

7.6 Ranking of Explanatory Indicators of Global Urban Sustainable … 379

Country

United Arab Emirates

Turkey

Argentina

Argentina

Brazil

U.S.A.

Angola

Indonesia

Kuwait

China

U.S.A.

Italy

United Arab Emirates

Turkey

China

Turkey

Brazil

Sweden

Poland

Panama

City

Abu Dhabi

Ankara

Mendoza

Rosario

Greater Vitória

Dayton

Luanda

Jakarta

Kuwait City

Nantong

Cincinnati

Genoa

Sharjah

Bursa

Nanchang

Izmir

Porto Alegre

Gothenburg

Poznan

Panama City

(continued)

243

242

241

240

239

238

237

236

235

234

233

232

231

230

229

228

227

226

225

224

Sustainable competitiveness

452

243

141

416

262

184

320

114

459

93

185

655

512

903

44

433

467

551

464

39

Economic vitality

385

107

10

488

434

492

431

961

158

36

386

748

795

357

38

119

130

861

473

872

Environmental thoroughness

212

79

228

535

64

274

108

155

270

284

169

142

105

861

656

915

481

356

82

30

Social inclusion

295

209

113

242

284

168

378

425

248

55

280

599

186

602

217

716

404

454

118

281

Scientific and technological innovation

(continued)

114

257

191

239

195

261

299

402

142

161

381

116

71

776

328

946

276

420

100

125

External contacts

380 7 Explanatory Indicators of Global Urban Sustainable Competitiveness

Country

Italy

Oman

China

U.K.

Mexico

U.K.

Japan

China

Mexico

China

U.K.

U.S.A.

Canada

U.S.A.

U.S.A.

Cuba

Venezuela

U.S.A.

India

China

City

Bari

Muscat

Fuzhou(FJ)

Nottingham

Tijuana

Newcastle upon Tyne

Kumamoto

Zibo

Juarez

Yantai

Sheffield

Akron

Quebec

Grand Rapids

Knoxville

Havana

Maracaibo

Birmingham

Delhi

Yangzhou

(continued)

263

262

261

260

259

258

257

256

255

254

253

252

251

250

249

248

247

246

245

244

Sustainable competitiveness

229

255

59

956

362

64

67

132

36

110

176

422

204

189

157

368

109

246

297

397

Economic vitality

440

855

46

353

181

20

122

93

35

149

438

443

509

491

309

329

79

424

183

147

Environmental thoroughness

128

127

509

952

592

359

338

163

626

232

145

566

184

14

316

414

189

138

71

267

Social inclusion

325

123

219

766

480

128

270

122

264

161

314

476

320

265

189

470

105

239

362

255

Scientific and technological innovation

(continued)

351

75

281

604

591

224

302

236

359

61

311

510

413

403

545

441

155

242

292

130

External contacts

7.6 Ranking of Explanatory Indicators of Global Urban Sustainable … 381

Country

Republic of Korea

Canada

China

Venezuela

China

Mexico

China

Colombia

China

Iraq

Indonesia

Spain

Dominican Republic

Canada

China

Argentina

China

U.S.A.

Italy

China

City

Changwon

Edmonton

Shaoxing

Maracay

Quanzhou

Monterrey

Changchun

Medellin

Taiyuan

Erbil

Surabaya

Seville

Santiago de Los Caballeros

Winnipeg

Xuzhou

Cordoba

Urumqi

Portland

Palermo

Tangshan

(continued)

283

282

281

280

279

278

277

276

275

274

273

272

271

270

269

268

267

266

265

264

Sustainable competitiveness

211

738

127

499

623

279

126

517

606

444

763

164

381

513

315

182

947

288

121

143

Economic vitality

563

286

78

669

212

470

100

380

229

498

917

528

201

881

187

381

368

449

82

202

Environmental thoroughness

252

208

114

458

199

307

129

700

153

117

500

273

327

99

194

173

961

216

60

106

Social inclusion

352

244

91

299

349

263

158

545

201

335

710

245

231

117

279

374

796

423

71

316

Scientific and technological innovation

(continued)

322

150

97

448

268

343

243

362

197

407

348

228

267

220

255

301

583

275

144

439

External contacts

382 7 Explanatory Indicators of Global Urban Sustainable Competitiveness

Country

China

China

Iraq

China

France

China

Turkey

Poland

Mexico

Brazil

China

Paraguay

France

Italy

U.S.A.

Poland

Ecuador

China

Turkey

Nigeria

Israel

City

Chongqing

Taizhou(JS)

Baghdad

Huizhou

Nantes

Weihai

Gebze

Krakow

Guadalajara

Campinas

Wenzhou

Asuncion

Nice

Padova

Allentown

Warsaw

Quito

Shijiazhuang

Adana

Lagos

Be’er Sheva

(continued)

304

303

302

301

300

299

298

297

296

295

294

293

292

291

290

289

288

287

286

285

284

Sustainable competitiveness

162

892

405

335

636

328

38

547

217

618

524

469

396

345

180

241

231

194

886

154

470

Economic vitality

360

310

559

727

574

154

18

53

262

127

446

205

302

170

448

454

129

320

635

408

836

Environmental thoroughness

690

728

299

217

352

16

761

230

261

397

301

462

308

63

807

280

392

215

782

256

87

Social inclusion

748

283

382

211

274

58

399

107

273

568

292

192

310

153

469

400

233

348

393

450

94

Scientific and technological innovation

(continued)

317

204

543

196

226

83

494

138

85

551

277

300

202

132

761

400

137

498

307

361

134

External contacts

7.6 Ranking of Explanatory Indicators of Global Urban Sustainable … 383

Country

U.S.A.

Greece

Mexico

China

China

Russian

Chile

Azerbaijan

China

U.S.A.

South Africa

Algeria

China

Kazakhstan

India

U.S.A.

Malaysia

China

Algeria

China

Russian

City

Sacramento

Thessaloniki

San Luis Potosi

Dongying

Hsinchu

Tyumen

Valparaiso

Baku

Guiyang

El Paso

Durban

Algiers

Taizhou(ZJ)

Almaty

Bangalore

Bakersfield

Ipoh

Weifang

Oran

Kunming

Ufa

(continued)

325

324

323

322

321

320

319

318

317

316

315

314

313

312

311

310

309

308

307

306

305

Sustainable competitiveness

519

361

711

458

108

137

296

406

314

906

310

165

228

348

258

257

117

221

312

736

161

Economic vitality

342

407

289

658

217

367

222

934

480

44

172

325

389

280

442

758

224

450

263

15

94

Environmental thoroughness

375

112

726

241

498

534

96

281

282

804

722

207

436

121

729

310

25

298

378

331

221

Social inclusion

373

202

570

308

671

443

110

319

421

390

313

328

272

293

377

402

98

574

321

232

222

Scientific and technological innovation

(continued)

426

199

327

473

437

559

90

627

256

254

251

464

237

316

325

824

118

723

249

184

194

External contacts

384 7 Explanatory Indicators of Global Urban Sustainable Competitiveness

Country

Brazil

Bolivia

China

Mexico

U.S.A.

China

China

Poland

Brazil

Brazil

Serbia

Colombia

Russian

China

Mexico

China

France

Croatia

China

China

Argentina

City

Ribeirao Preto

Santa Cruz

Haikou

Merida

Omaha

Nanning

Jinhua

Lodz

Sao Jose dos Campos

Belo Horizonte

Belgrade

Cali

Samara

Harbin

Queretaro

Shantou

Bordeaux

Zagreb

Jiaozuo

Hohhot

Santa Fe

(continued)

346

345

344

343

342

341

340

339

338

337

336

335

334

333

332

331

330

329

328

327

326

Sustainable competitiveness

497

265

336

340

233

248

384

548

569

357

383

674

333

299

313

495

87

350

316

731

378

Economic vitality

383

531

441

30

39

270

165

982

572

336

193

268

231

390

344

644

59

207

445

1003

226

Environmental thoroughness

182

248

507

45

271

386

201

174

167

395

91

475

46

150

268

148

246

177

364

580

885

Social inclusion

341

343

546

147

237

380

481

132

340

342

183

253

414

266

354

241

198

416

331

109

644

Scientific and technological innovation

(continued)

309

354

607

143

127

380

262

189

661

290

149

397

588

295

431

244

258

347

288

270

415

External contacts

7.6 Ranking of Explanatory Indicators of Global Urban Sustainable … 385

Country

France

Mexico

China

Guatemala

Nigeria

China

China

China

Lebanon

China

India

China

Indonesia

China

Mexico

China

Ukraine

Indonesia

U.S.A.

Mexico

China

City

Toulon

Aguascalientes

Zhoushan

Guatemala City

Ikorodu

Taian

Huzhou

Langfang

Beirut

Luoyang

Mumbai

Yancheng

Batam

Lanzhou

Torreon

Xiangtan

Kiev

Samarinda

Sarasota-Bradenton

Puebla

Zaozhuang

(continued)

367

366

365

364

363

362

361

360

359

358

357

356

355

354

353

352

351

350

349

348

347

Sustainable competitiveness

498

683

129

240

791

309

349

292

374

371

332

554

754

222

200

386

808

863

169

427

208

Economic vitality

394

343

67

290

349

423

611

786

341

597

789

663

191

465

321

616

429

667

234

264

227

Environmental thoroughness

449

253

149

572

219

438

343

381

443

286

227

242

394

366

285

361

958

324

540

234

474

Social inclusion

683

296

364

775

184

355

857

185

912

411

80

317

247

498

398

471

993

407

500

581

493

Scientific and technological innovation

(continued)

526

216

382

878

172

247

647

297

790

399

54

304

188

223

416

616

710

484

450

388

320

External contacts

386 7 Explanatory Indicators of Global Urban Sustainable Competitiveness

Country

U.S.A.

U.S.A.

China

U.S.A.

Tunisia

China

China

Brazil

China

Colombia

Brazil

U.S.A.

Philippines

Mexico

Brazil

El Salvador

Mexico

China

China

Indonesia

Turkey

City

Tucson

McAllen

Zhuzhou

Tulsa

Tunis

Wuhu

Jining

Manaus

Yinchuan

Cartagena

Recife

Fresno

Manila

Villahermosa

Curitiba

San Salvador

Matamoros

Xuchang

Daqing

Pekanbaru

Gaziantep

(continued)

388

387

386

385

384

383

382

381

380

379

378

377

376

375

374

373

372

371

370

369

368

Sustainable competitiveness

631

277

160

238

307

492

642

282

510

168

645

372

306

714

447

170

583

124

393

210

196

Economic vitality

760

745

901

452

430

299

138

307

743

240

418

275

680

959

628

396

257

56

556

199

529

Environmental thoroughness

250

571

385

573

650

574

372

423

95

480

595

537

587

695

329

303

387

368

421

810

214

Social inclusion

420

631

460

542

923

449

307

738

262

339

298

474

322

327

403

358

218

254

337

788

68

Scientific and technological innovation

(continued)

452

807

558

504

911

472

335

729

106

332

547

274

378

590

375

324

234

222

374

357

190

External contacts

7.6 Ranking of Explanatory Indicators of Global Urban Sustainable … 387

Country

Russian

China

Brazil

China

Iran

Brazil

Libya

Russian

China

China

China

China

China

China

Nigeria

Mexico

Germany

China

Kenya

China

China

City

Kazan

Huaian

Jundiai

Baotou

Shiraz

Fortaleza

Tripoli

Perm

Zhangzhou

Yichang

Liaocheng

Lianyungang

Ordoss

Anshan

Abuja

Toluca

Bremen

Jiangmen

Nairobi

Dezhou

Putian

(continued)

409

408

407

406

405

404

403

402

401

400

399

398

397

396

395

394

393

392

391

390

389

Sustainable competitiveness

259

563

283

377

206

504

889

577

317

237

474

252

319

572

807

780

713

287

242

359

750

Economic vitality

382

755

856

365

42

251

788

493

741

594

609

701

481

599

89

333

930

864

132

539

843

Environmental thoroughness

553

419

724

312

146

453

873

376

398

354

582

251

319

406

597

642

671

306

539

263

183

Social inclusion

684

559

197

502

178

350

497

408

914

397

507

412

475

365

473

323

441

447

801

516

261

Scientific and technological innovation

(continued)

500

553

345

428

185

862

312

537

315

536

466

389

470

404

497

614

815

585

792

319

465

External contacts

388 7 Explanatory Indicators of Global Urban Sustainable Competitiveness

Country

China

China

China

China

Turkey

Mexico

China

China

China

China

Ecuador

China

Argentina

Brazil

China

Brazil

China

Indonesia

Brazil

Russian

Iraq

City

Jilin

Handan

Xianyang

Cangzhou

Antalya

Saltillo

Rizhao

Xiangyang

Yueyang

Ezhou

Guayaquil

Panjin

Mar Del Plata

Sorocaba

Binzhou

Salvador

Linyi

Bandung

Belem

Saratov

Kirkuk

(continued)

430

429

428

427

426

425

424

423

422

421

420

419

418

417

416

415

414

413

412

411

410

Sustainable competitiveness

910

651

727

653

660

819

500

438

611

400

801

142

478

266

273

390

352

338

285

671

403

Economic vitality

825

724

242

524

763

194

573

266

167

235

326

328

613

725

404

719

807

632

598

655

922

Environmental thoroughness

802

382

715

191

405

633

486

578

603

430

490

676

408

349

472

377

51

422

402

416

164

Social inclusion

910

369

510

330

486

311

540

557

455

767

375

856

538

536

729

472

405

624

633

386

179

Scientific and technological innovation

(continued)

818

918

663

337

344

430

479

728

811

481

336

492

592

617

562

725

326

572

408

471

419

External contacts

7.6 Ranking of Explanatory Indicators of Global Urban Sustainable … 389

Country

China

China

Mexico

Brazil

U.S.A.

China

Iran

South Africa

China

Saudi Arabia

Indonesia

China

Bangladesh

China

Mexico

Peru

China

Bolivia

China

China

Iran

City

Nanyang

Xinxiang

Hermosillo

Goiania

Albuquerque

Anyang

Mashhad

Port Elizabeth

Maoming

Buraydah

Balikpapan

Kaifeng

Dhaka

Zhanjiang

Culiacan

Arequipa

Deyang

Cochabamba

Beihai

Xining

Tabriz

(continued)

451

450

449

448

447

446

445

444

443

442

441

440

439

438

437

436

435

434

433

432

431

Sustainable competitiveness

737

347

260

678

295

209

442

355

926

456

199

232

318

280

845

429

166

716

419

334

703

Economic vitality

808

661

308

828

511

995

351

371

947

657

694

925

463

472

907

571

219

196

752

639

768

Environmental thoroughness

551

568

801

865

483

601

466

309

546

465

374

133

401

786

736

697

380

562

345

492

410

Social inclusion

422

435

707

790

640

726

679

515

234

438

881

800

675

448

429

353

304

535

417

389

332

Scientific and technological innovation

(continued)

902

598

499

835

747

595

456

392

207

371

797

904

564

502

825

435

250

516

538

273

271

External contacts

390 7 Explanatory Indicators of Global Urban Sustainable Competitiveness

Country

China

India

China

Latvia

China

Malaysia

China

Iraq

China

Kazakhstan

China

China

Brazil

China

China

China

China

Mexico

Indonesia

China

China

City

Huangshi

Chennai

Jieyang

Riga

Changde

Kuching

Hengyang

Sulaymaniyah

Suqian

Shymkent

Liuzhou

Yingtan

Joinville

Ma’anshan

Xinyu

Zhaoqing

Qinhuangdao

Reynosa

Malang

Puyang

Jinzhou

(continued)

472

471

470

469

468

467

466

465

464

463

462

461

460

459

458

457

456

455

454

453

452

Sustainable competitiveness

686

483

514

477

278

308

239

236

439

190

294

735

303

854

493

150

608

410

235

370

152

Economic vitality

466

544

783

469

530

474

420

411

271

397

564

585

525

685

707

152

751

267

332

306

504

Environmental thoroughness

520

447

180

867

365

333

607

441

615

640

409

835

426

350

478

276

411

111

489

166

721

Social inclusion

437

778

556

873

345

514

691

465

357

889

532

886

670

803

482

490

456

190

818

119

575

Scientific and technological innovation

(continued)

398

716

733

342

445

523

638

318

721

567

599

987

455

817

462

488

609

193

461

92

512

External contacts

7.6 Ranking of Explanatory Indicators of Global Urban Sustainable … 391

Country

Turkmenistan

Egypt

China

China

Mexico

India

Georgia

Indonesia

Ghana

China

China

China

China

Poland

Brazil

Brazil

Mexico

Russian

Nigeria

India

Peru

City

Ashgabat

Cairo

Luohe

Sanya

Cancun

Kochi

Tbilisi

Palembang

Accra

Baoding

Chaozhou

Pingdingshan

Guilin

Wroclaw

Grande Sao Luis

Uberlandia

Pachuca de Soto

Yaroslavl

Port Harcourt

Pune

Trujillo

(continued)

493

492

491

490

489

488

487

486

485

484

483

482

481

480

479

478

477

476

475

474

473

Sustainable competitiveness

460

441

839

546

533

526

676

431

521

564

329

729

557

461

269

256

337

197

394

799

455

Economic vitality

717

671

579

173

188

428

277

168

732

422

337

791

197

315

439

261

652

374

414

793

273

Environmental thoroughness

673

210

883

320

515

739

767

73

313

433

586

323

884

707

154

244

213

552

789

288

785

Social inclusion

672

191

763

451

562

511

794

200

359

547

669

318

301

643

294

277

954

700

830

175

919

Scientific and technological innovation

(continued)

391

95

580

718

715

703

964

209

305

582

446

449

321

683

221

396

331

519

434

123

772

External contacts

392 7 Explanatory Indicators of Global Urban Sustainable Competitiveness

Country

China

Russian

Russian

China

China

India

China

China

Russian

China

China

Colombia

Colombia

Honduras

Indonesia

China

China

China

China

Mexico

Brazil

City

Bengbu

Tomsk

Novosibirsk

Jiujiang

Yulin(SX)

Coimbatore

Baoji

Hebi

Barnaul

Heze

Pingxiang

Pereira

Barranquilla

Tegucigalpa

Makassar

Chenzhou

Ziyang

Yangjiang

Zigong

Chihuahua

Londrina

(continued)

514

513

512

511

510

509

508

507

506

505

504

503

502

501

500

499

498

497

496

495

494

Sustainable competitiveness

555

486

409

365

587

322

463

624

632

454

247

692

640

389

300

289

207

435

835

599

423

Economic vitality

345

744

708

399

656

570

359

364

506

269

568

676

304

456

690

455

527

620

988

955

552

Environmental thoroughness

679

340

675

482

770

661

305

645

637

764

600

685

412

735

434

400

469

363

278

388

491

Social inclusion

428

525

569

858

817

727

494

505

445

467

828

603

522

799

596

288

809

567

196

297

534

Scientific and technological innovation

(continued)

560

557

637

412

534

496

620

597

377

880

770

660

973

463

742

750

571

518

490

968

495

External contacts

7.6 Ranking of Explanatory Indicators of Global Urban Sustainable … 393

Country

China

China

China

Indonesia

Egypt

Morocco

China

Indonesia

China

China

China

China

Argentina

China

Turkey

China

China

China

China

China

Indonesia

City

Liaoyang

Shangrao

Panzhihua

Padang

Alexandria

Casablanca

Huaibei

Bogor

Luzhou

Benxi

Fushun

Ganzhou

La Plata

Ningde

Samsun

Longyan

Mianyang

Yuxi

Anqing

Siping

Medan

(continued)

535

534

533

532

531

530

529

528

527

526

525

524

523

522

521

520

519

518

517

516

515

Sustainable competitiveness

677

847

829

376

590

529

380

387

813

668

421

679

490

743

375

545

793

479

373

699

449

Economic vitality

416

496

554

526

730

561

490

660

151

769

569

412

790

638

471

128

479

548

281

567

372

Environmental thoroughness

330

691

446

517

360

543

190

524

588

493

506

477

591

220

542

533

428

314

678

604

579

Social inclusion

419

523

610

664

360

654

573

544

361

487

464

663

561

430

583

275

195

560

699

734

807

Scientific and technological innovation

(continued)

411

524

535

485

440

690

563

365

544

505

478

561

635

373

578

159

206

795

820

552

421

External contacts

394 7 Explanatory Indicators of Global Urban Sustainable Competitiveness

Country

Brazil

China

China

Pakistan

China

Mexico

China

Russian

China

China

China

China

China

Brazil

China

Angola

China

China

China

Brazil

China

City

Joao Pessoa

Zunyi

Songyuan

Karachi

Loudi

Mexicali

Yichun(JX)

Tolyatti

Shangqiu

Xiaogan

Jingdezhen

Jingmen

Quzhou

Teresina

Sanmenxia

Huambo

Zhoukou

Liupanshui

Xingtai

Juiz De Fora

Jincheng

(continued)

556

555

554

553

552

551

550

549

548

547

546

545

544

543

542

541

540

539

538

537

536

Sustainable competitiveness

531

633

766

369

708

977

411

733

432

425

391

650

786

598

712

597

367

858

562

453

719

Economic vitality

566

260

784

503

546

936

623

384

497

728

523

944

608

203

691

993

590

520

750

746

246

Environmental thoroughness

644

746

503

855

530

950

672

854

545

518

641

692

669

391

445

531

569

413

598

547

800

Social inclusion

667

439

609

947

744

987

909

687

594

686

632

709

841

618

808

774

722

291

811

512

665

Scientific and technological innovation

(continued)

676

865

615

602

612

857

640

1000

568

641

418

570

671

685

540

298

579

171

642

528

994

External contacts

7.6 Ranking of Explanatory Indicators of Global Urban Sustainable … 395

Country

India

China

China

Libya

Russian

Nigeria

India

China

Russian

China

China

China

China

Russian

China

China

Jamaica

China

China

U.S.A.

China

City

Bhiwandi

Qinzhou

Sanming

Misratah

Krasnodar

Kano

Kozhikode

Hengshui

Astrakhan’

Jingzhou

Yiyang

Zhumadian

Yulin(GX)

Irkutsk

Wuzhou

Shiyan

Kingston

Tongling

Yibin

Albany

Mudanjiang

(continued)

577

576

575

574

573

572

571

570

569

568

567

566

565

564

563

562

561

560

559

558

557

Sustainable competitiveness

794

120

446

40

399

468

552

669

666

525

404

619

777

664

276

900

661

935

402

440

323

Economic vitality

641

331

867

348

221

595

662

989

712

575

711

798

875

753

346

870

849

819

537

602

415

Environmental thoroughness

590

557

702

457

701

532

513

630

732

611

570

444

317

760

620

928

136

906

510

612

974

Social inclusion

645

212

648

518

131

434

757

379

656

835

668

488

621

724

636

548

367

1002

703

571

998

Scientific and technological innovation

(continued)

677

156

789

458

163

491

700

785

632

717

680

486

908

506

764

601

432

866

514

629

698

External contacts

396 7 Explanatory Indicators of Global Urban Sustainable Competitiveness

Country

India

China

Cameroon

Turkey

Brazil

China

China

China

China

India

Russian

Bangladesh

Cambodia

China

Bolivia

China

China

Cote d’ivoire

China

China

India

City

Kolkata

Weinan

Douala

Sanliurfa

Campo Grande

Xianning

Huainan

Changzhi

Xinyang

Kannur

Ryazan

Chittagong

Phnom Penh

Meishan

La Paz

Karamay

Shuozhou

Abidjan

Leshan

Suining

Dehra Dun

(continued)

598

597

596

595

594

593

592

591

590

589

588

587

586

585

584

583

582

581

580

579

578

Sustainable competitiveness

270

734

491

765

187

191

848

644

578

945

658

301

620

343

418

351

726

752

904

739

607

Economic vitality

413

545

852

355

311

674

984

670

820

772

683

378

718

486

553

962

781

967

392

534

902

Environmental thoroughness

464

559

495

938

737

705

567

693

448

845

328

815

564

521

561

617

619

459

945

653

289

Social inclusion

746

694

787

521

930

743

392

682

468

623

508

823

458

549

520

529

513

782

635

777

235

Scientific and technological innovation

(continued)

822

744

649

379

613

888

438

531

352

515

957

732

668

689

387

530

659

843

475

353

110

External contacts

7.6 Ranking of Explanatory Indicators of Global Urban Sustainable … 397

Country

Argentina

China

Mongolia

China

Russian

China

Venezuela

Brazil

China

Nigeria

Indonesia

Bangladesh

China

Mexico

Philippines

China

India

China

Saudi Arabia

Honduras

China

City

San Miguel de Tucuman

Chuzhou

Ulan Bator

Nanchong

Kemerovo

Qingyuan

Maturín

Feira De Santana

Datong

Zaria

Semarang

Rajshahi

Fangchenggang

Morelia

Cebu

Shanwei

Hyderabad

Guangan

Ta’if

San Pedro Sula

Lishui

(continued)

619

618

617

616

615

614

613

612

611

610

609

608

607

606

605

604

603

602

601

600

599

Sustainable competitiveness

434

612

358

516

549

305

682

630

327

917

445

830

496

675

985

567

662

591

649

520

742

Economic vitality

645

276

840

607

340

362

502

258

398

726

659

759

722

216

377

516

906

734

903

709

140

Environmental thoroughness

522

830

178

725

118

751

239

371

759

870

205

953

699

806

990

259

417

415

609

647

636

Social inclusion

503

896

905

926

157

872

586

466

929

661

527

626

565

732

992

598

501

530

820

677

649

Scientific and technological innovation

(continued)

459

346

769

621

152

503

350

329

521

914

424

821

611

995

809

245

989

593

673

395

854

External contacts

398 7 Explanatory Indicators of Global Urban Sustainable Competitiveness

Country

China

China

China

Mexico

Brazil

Russian

China

China

China

China

Sudan

China

Pakistan

China

China

Russian

Venezuela

State of Palestine

India

China

City

Yangquan

Yingkou

Nanping

Tampico

Cuiaba

Chelyabinsk

Tonghua

Jinzhong

Zhangjiakou

Dandong

Khartoum

Qujing

Lahore

Shaoguan

Linfen

Orenburg

Barquisimeto

Gaza

Durg-Bhilai Nagar

Yan’an

(continued)

639

638

637

636

635

634

633

632

631

630

629

628

627

626

625

624

623

622

621

620

Sustainable competitiveness

290

448

341

994

697

817

588

809

382

961

722

559

412

725

857

684

667

789

505

215

Economic vitality

642

542

274

501

535

684

699

756

666

775

558

749

596

507

873

508

233

648

220

550

Environmental thoroughness

589

949

899

973

254

511

418

300

585

548

379

473

555

687

373

703

608

527

440

730

Social inclusion

620

755

761

908

555

798

637

305

771

384

712

582

614

837

356

730

768

563

739

911

Scientific and technological innovation

(continued)

566

891

749

724

988

694

394

266

594

874

467

619

653

670

779

847

832

554

269

482

External contacts

7.6 Ranking of Explanatory Indicators of Global Urban Sustainable … 399

Country

China

Iran

Mexico

China

Turkey

Russian

China

China

Haiti

China

Gabon

Iraq

China

China

South Africa

Morocco

India

Iran

Nicaragua

China

Uganda

City

Yuncheng

Hamadan

Celaya

Ji’an

Kayseri

Rostov-on-Don

Liaoyuan

Huanggang

Port-au-Prince

Suzhou (AH)

Libreville

Basra

Tongliao

Xuancheng

Vereeniging

Marrakech

Malappuram

Esfahan

Managua

Chengde

Kampala

(continued)

660

659

658

657

656

655

654

653

652

651

650

649

648

647

646

645

644

643

642

641

640

Sustainable competitiveness

489

462

864

890

326

511

414

616

609

920

988

646

990

580

407

821

691

539

544

745

553

Economic vitality

195

799

879

974

457

402

391

584

739

910

484

779

483

757

482

283

731

767

166

915

737

Environmental thoroughness

882

424

528

526

921

341

932

599

576

864

818

674

918

455

718

290

226

550

837

752

558

Social inclusion

410

566

580

478

878

492

940

765

825

797

920

741

863

650

942

436

395

855

689

676

655

Scientific and technological innovation

(continued)

451

422

444

855

871

293

773

414

565

845

581

454

285

517

384

541

589

691

735

886

684

External contacts

400 7 Explanatory Indicators of Global Urban Sustainable Competitiveness

Country

China

China

Mexico

Turkey

Libya

India

India

India

Indonesia

Turkey

Sri Lanka

Iran

China

China

Russian

China

China

China

China

China

Russian

City

Dazhou

Yunfu

Cuernavaca

Mersin

Benghazi

Ahmedabad

Kota

Guwahati

Tasikmalaya

Eskisehir

Colombo

Orumiyeh

Heyuan

Neijiang

Voronezh

Huludao

Luliang

Suihua

Hanzhong

Huaihua

Omsk

(continued)

681

680

679

678

677

676

675

674

673

672

671

670

669

668

667

666

665

664

663

662

661

Sustainable competitiveness

843

812

635

884

537

759

861

413

475

827

899

718

707

487

428

566

922

518

574

601

760

Economic vitality

970

845

773

935

517

475

640

577

665

618

338

894

614

884

990

723

619

713

204

519

591

Environmental thoroughness

347

720

614

734

618

716

295

662

762

712

383

209

741

748

625

171

880

351

596

485

750

Social inclusion

409

836

666

945

941

697

333

731

802

628

240

413

925

415

484

406

762

396

499

860

753

Scientific and technological innovation

(continued)

996

678

675

682

713

643

356

693

708

919

219

600

477

549

758

425

945

727

507

751

665

External contacts

7.6 Ranking of Explanatory Indicators of Global Urban Sustainable … 401

Country

India

China

Nigeria

China

India

Russian

Saudi Arabia

China

China

Nigeria

China

Mexico

China

Colombia

China

China

China

Viet Nam

Pakistan

Ukraine

Pakistan

City

Puducherry

Fuzhou(JX)

Benin City

Fuyang

Kollam

Krasnoyarsk

Hufuf-Mubarraz

Tieling

Chifeng

Enugu

Yongzhou

Veracruz

Meizhou

Ibague

Shizuishan

Guigang

Shaoyang

Haiphong

Islamabad

Krivoi Rog

Bahawalpur

(continued)

702

701

700

699

698

697

696

695

694

693

692

691

690

689

688

687

686

685

684

683

682

Sustainable competitiveness

823

850

865

542

681

787

254

538

592

508

723

875

776

841

311

773

275

853

779

756

339

Economic vitality

874

582

742

241

869

625

499

330

565

634

824

977

809

514

854

945

636

617

892

782

305

Environmental thoroughness

778

638

336

681

659

811

828

754

512

494

664

934

639

765

262

337

713

581

935

631

660

Social inclusion

728

936

286

844

785

970

784

831

653

533

804

783

662

832

887

324

776

587

833

870

651

Scientific and technological innovation

(continued)

804

605

272

936

704

709

644

954

539

511

639

894

626

429

748

759

956

603

879

587

706

External contacts

402 7 Explanatory Indicators of Global Urban Sustainable Competitiveness

Country

China

Russian

China

China

China

China

Brazil

Mexico

China

India

Nigeria

Brazil

Iraq

Philippines

China

Egypt

China

Russian

Viet Nam

India

Morocco

City

Anshun

Novokuznetsk

Suizhou

Chongzuo

Chizhou

Bozhou

Maceio

Acapulco

Liuan

Thiruvananthapuram

Akure

Natal

Karbala

Davao

Jiamusi

Port Said

Baise

Yekaterinburg

Can Tho

Ludhiana

Meknes

(continued)

723

722

721

720

719

718

717

716

715

714

713

712

711

710

709

708

707

706

705

704

703

Sustainable competitiveness

482

466

485

880

602

615

781

695

915

871

856

363

894

605

844

772

437

540

430

758

614

Economic vitality

393

586

248

905

776

688

896

589

814

334

740

324

747

733

296

675

515

580

646

437

592

Environmental thoroughness

844

667

862

275

744

897

745

355

817

773

942

346

484

479

766

628

682

836

719

504

784

Social inclusion

629

617

805

444

895

795

698

884

838

312

754

440

931

871

742

900

875

839

918

611

745

Scientific and technological innovation

(continued)

907

856

844

577

586

863

739

734

903

892

852

787

480

745

846

622

522

574

532

826

714

External contacts

7.6 Ranking of Explanatory Indicators of Global Urban Sustainable … 403

Country

China

China

Peru

Nigeria

Viet Nam

Nigeria

China

Colombia

India

Zimbabwe

Russian

Mexico

China

India

Nigeria

China

Iran

Tunisia

Indonesia

Viet Nam

Mexico

City

Qiqihar

Huangshan

Chiclayo

Jos

Da Nang

Aba

Fuxin

Cucuta

Mangalore

Harare

Nizhny Novgorod

Oaxaca

Ankang

Salem

Ibadan

Baicheng

Rasht

Safaqis

Denpasar

Ho Chi Minh City

Tuxtla Gutierrez

(continued)

744

743

742

741

740

739

738

737

736

735

734

733

732

731

730

729

728

727

726

725

724

Sustainable competitiveness

751

824

626

715

833

586

918

535

836

680

840

826

354

639

907

870

528

893

698

522

837

Economic vitality

163

186

421

358

729

971

401

604

787

643

228

287

164

871

693

841

153

846

715

754

991

Environmental thoroughness

613

260

237

840

772

652

823

814

658

197

109

839

541

902

708

900

348

916

827

431

584

Social inclusion

824

267

789

705

613

814

554

287

821

576

303

541

553

892

589

696

693

692

956

588

496

Scientific and technological innovation

(continued)

877

211

838

801

993

679

372

231

707

771

687

555

870

848

730

837

469

697

760

339

654

External contacts

404 7 Explanatory Indicators of Global Urban Sustainable Competitiveness

Country

China

China

Brazil

China

Viet Nam

India

Chile

Kenya

India

China

Mexico

Iraq

India

Iran

Russian

China

China

India

Morocco

Congo

India

City

Laibin

Chaoyang

Aracaju

Baishan

Hanoi

Jodhpur

Concepcion

Mombasa

Nagpur

Xinzhou

Poza Rica

Nasiriyah

Surat

Qom

Izhevsk

Baoshan

Jiayuguan

Bhubaneswar

Rabat

Pointe-Noire

Thrissur

(continued)

765

764

763

762

761

760

759

758

757

756

755

754

753

752

751

750

749

748

747

746

745

Sustainable competitiveness

550

936

600

593

325

822

795

908

704

914

641

659

556

451

302

543

816

603

814

891

622

Economic vitality

494

478

121

850

555

603

536

958

601

908

785

562

965

253

206

983

192

897

250

647

581

Environmental thoroughness

362

984

439

468

797

769

393

710

538

925

853

763

519

907

523

501

200

404

727

698

829

Social inclusion

792

975

334

433

877

509

483

605

431

906

967

558

432

749

418

638

249

948

720

850

915

Scientific and technological innovation

(continued)

695

662

303

666

740

652

979

872

406

991

829

756

765

850

442

686

259

405

961

655

777

External contacts

7.6 Ranking of Explanatory Indicators of Global Urban Sustainable … 405

Country

Zambia

Turkey

Nepal

Cameroon

Nigeria

Ghana

Brazil

Colombia

Mexico

Zambia

Philippines

Togo

Nigeria

Russian

China

India

Senegal

India

China

Morocco

Tanzania

City

Lusaka

Diyarbakir

Kathmandu

Yaounde

Owerri

Kumasi

Florianopolis

Bucaramanga

Tlaxcala

Kitwe

Cagayan de Oro

Lome

Uyo

Khabarovsk

Shangluo

Asansol

Dakar

Visakhapatnam

Ya’an

Tangier

Dar es Salaam

(continued)

786

785

784

783

782

781

780

779

778

777

776

775

774

773

772

771

770

769

768

767

766

Sustainable competitiveness

934

503

571

536

811

481

796

700

802

690

515

494

705

532

746

902

764

987

862

706

849

Economic vitality

312

288

780

883

406

322

818

999

627

318

366

721

254

113

156

213

900

265

682

803

960

Environmental thoroughness

893

704

776

335

937

863

821

565

924

838

602

983

353

668

516

869

904

849

471

525

963

Social inclusion

394

772

681

595

401

885

816

615

848

723

962

951

601

491

368

750

864

718

600

706

517

Scientific and technological innovation

(continued)

474

849

468

881

606

958

460

722

934

457

754

628

966

364

369

869

922

527

410

806

443

External contacts

406 7 Explanatory Indicators of Global Urban Sustainable Competitiveness

Country

Turkey

China

China

India

India

India

China

China

Congo

Colombia

India

Venezuela

China

Colombia

Philippines

Bangladesh

China

Republic of Moldova

Mexico

India

City

Denizli

Zhangjiajie

Ulanqab

Aurangabad

Amritsar

Patna

Hezhou

Tianshui

Brazzaville

Villavicencio

Jalandhar

Ciudad Guayana

Hulunbuir

Santa Marta

General Santos City

Sylhet

Qingyang

Chisinau

Xalapa

Lucknow

(continued)

806

805

804

803

802

801

800

799

798

797

796

795

794

793

792

791

790

789

788

787

Sustainable competitiveness

757

530

579

408

949

523

720

480

997

502

398

951

701

828

628

560

694

770

769

484

Economic vitality

994

169

103

736

891

294

622

972

938

738

313

797

817

705

822

858

987

762

853

821

Environmental thoroughness

463

549

450

749

805

677

606

651

993

799

933

951

680

852

529

476

696

670

616

206

Social inclusion

371

564

463

619

717

972

652

939

989

690

903

876

592

725

577

695

453

958

917

479

Scientific and technological innovation

(continued)

433

938

367

762

778

805

860

596

436

975

766

720

803

752

631

951

905

618

542

901

External contacts

7.6 Ranking of Explanatory Indicators of Global Urban Sustainable … 407

Country

China

India

Ukraine

China

China

Iran

Nigeria

Bangladesh

Russian

Pakistan

India

India

Congo

China

Indonesia

Nigeria

India

Pakistan

Iran

China

Eritrea

City

Zhaotong

Jaipur

Kharkov

Jixi

Guangyuan

Kerman

Oshogbo

Khulna

Volgograd

Hyderabad

Erode

Tirupati

Kinshasa

WuZhong

Bandar Lampung

Warri

Jamshedpur

Faisalabad

Kermanshah

Shuangyashan

Asmara

(continued)

827

826

825

824

823

822

821

820

819

818

817

816

815

814

813

812

811

810

809

808

807

Sustainable competitiveness

1000

873

912

897

621

806

702

771

993

443

417

874

838

972

895

860

610

885

973

741

810

Economic vitality

461

895

924

654

714

583

489

801

946

697

668

887

837

839

593

949

765

560

706

695

804

Environmental thoroughness

896

775

771

723

898

981

321

868

976

605

833

877

294

876

927

563

648

753

502

293

826

Social inclusion

974

988

524

543

607

966

827

862

747

678

819

338

424

713

991

427

773

826

388

326

933

Scientific and technological innovation

(continued)

970

814

898

608

783

897

726

489

667

784

969

208

705

920

851

816

731

696

788

294

651

External contacts

408 7 Explanatory Indicators of Global Urban Sustainable Competitiveness

Country

India

Morocco

China

China

India

Mauritania

China

Pakistan

India

Saudi Arabia

India

Armenia

China

India

Pakistan

India

China

Philippines

Afghanistan

India

China

City

Mysore

Fes

Bazhong

Wuhai

Tiruchirappalli

Nouakchott

Bayannur

Sialkot

Kolhapur

Tabuk

Vellore

Yerevan

Lincang

Varanasi

Rawalpindi

Ranchi

Tongchuan

Bacolod

Kabul

Tiruppur

Baiyin

(continued)

848

847

846

845

844

843

842

841

840

839

838

837

836

835

834

833

832

831

830

829

828

Sustainable competitiveness

665

457

940

627

342

693

911

761

688

834

415

744

465

866

749

887

558

220

855

638

561

Economic vitality

898

513

978

876

540

842

868

859

673

433

451

851

588

650

878

810

621

477

792

403

301

Environmental thoroughness

643

859

941

683

891

792

794

536

875

420

832

470

627

879

717

908

665

780

694

842

621

Social inclusion

952

984

719

934

960

769

660

591

916

370

461

622

608

874

969

928

579

963

982

531

442

Scientific and technological innovation

(continued)

664

798

576

674

282

794

799

887

646

334

719

859

921

633

688

819

823

737

533

753

755

External contacts

7.6 Ranking of Explanatory Indicators of Global Urban Sustainable … 409

Country

India

India

Indonesia

Russian

Nigeria

Nigeria

India

China

India

India

India

India

China

Iraq

Indonesia

India

Russian

China

India

Iran

Mexico

City

Meerut

Gwalior

Jambi

Makhachkala

Onitsha

Ilorin

Siliguri

Hechi

Kurnool

Madurai

Rajkot

Bokaro Steel City

Qitaihe

Najaf

Banjarmasin

Nashik

Vladivostok

Zhongwei

Cuttack

Ardabil

Durango

(continued)

869

868

867

866

865

864

863

862

861

860

859

858

857

856

855

854

853

852

851

850

849

Sustainable competitiveness

788

883

728

589

797

657

818

932

798

568

582

507

501

896

570

923

928

905

724

656

643

Economic vitality

811

933

770

833

354

950

298

1004

388

704

686

547

981

778

587

860

863

672

912

866

916

Environmental thoroughness

429

706

819

858

497

437

686

943

892

957

655

743

757

711

788

923

946

505

583

756

881

Social inclusion

452

616

840

659

383

578

764

781

977

986

495

625

946

924

868

702

997

758

829

751

812

Scientific and technological innovation

(continued)

483

972

699

623

493

873

746

947

701

952

763

884

931

573

982

895

867

941

520

983

610

External contacts

410 7 Explanatory Indicators of Global Urban Sustainable Competitiveness

Country

India

India

China

India

China

Indonesia

Turkey

India

India

China

India

Nigeria

China

India

Pakistan

Uzbekistan

India

India

India

India

India

City

Jamnagar

Indore

Pu’er

Bhopal

Lijiang

Pontianak

Konya

Vadodara

Raurkela

Wuwei

Jammu

Sokoto

Pingliang

Cherthala

Peshawar

Tashkent

Dhanbad

Guntur

Aligarh

Allahabad

Vijayawada

(continued)

890

889

888

887

886

885

884

883

882

881

880

879

878

877

876

875

874

873

872

871

870

Sustainable competitiveness

637

815

768

629

753

931

909

472

748

954

709

721

509

762

710

792

506

783

767

778

471

Economic vitality

698

877

975

761

696

800

664

427

720

1005

605

914

838

679

862

710

777

626

615

649

624

Environmental thoroughness

560

740

813

803

808

624

809

940

774

965

488

738

956

296

179

742

610

514

822

747

577

Social inclusion

641

526

627

760

721

376

550

957

865

880

590

907

996

462

381

867

815

539

937

504

950

Scientific and technological innovation

(continued)

624

940

885

930

810

681

648

883

630

913

584

839

971

656

385

712

447

509

658

409

900

External contacts

7.6 Ranking of Explanatory Indicators of Global Urban Sustainable … 411

Country

China

India

Philippines

Russian

India

Nigeria

China

Lao

Ukraine

India

India

China

India

Pakistan

India

India

India

Ukraine

Tanzania

Congo

Syrian

City

Jinchang

Kanpur

Zamboanga

Ulyanovsk

Saharanpur

Kaduna

Zhangye

Vientiane

Dnipropetrovs’k

Muzaffarnagar

Srinagar

Hegang

Sangali

Quetta

Hubli-Dharwad

Chandigarh

Bareilly

Lvov

Zanzibar

Lubumbashi

Damascus

(continued)

911

910

909

908

907

906

905

904

903

902

901

900

899

898

897

896

895

894

893

892

891

Sustainable competitiveness

916

979

868

950

782

594

604

933

541

803

673

648

879

831

785

943

670

876

747

846

717

Economic vitality

435

766

538

606

943

426

352

921

436

913

630

885

379

969

847

997

931

285

612

976

834

Environmental thoroughness

988

980

992

499

890

332

622

820

866

787

872

895

635

688

733

913

920

487

634

731

793

Social inclusion

457

902

922

485

842

351

658

714

1000

953

634

971

904

704

786

869

879

489

961

347

791

Scientific and technological innovation

(continued)

791

793

1004

546

977

278

925

802

909

625

868

960

556

550

501

876

827

939

985

896

634

External contacts

412 7 Explanatory Indicators of Global Urban Sustainable Competitiveness

Country

Ukraine

Yemen

China

Iran

Pakistan

China

India

Pakistan

Rwanda

India

Pakistan

India

India

Viet Nam

Myanmar

Uzbekistan

Ukraine

Argentina

Ghana

India

India

City

Zaporizhzhya

Sana’a’

Heihe

Yazd

Multan

Guyuan

Agra

Sukkur

Kigali

Imphal

Gujranwala

Jabalpur

Moradabad

Bien Hoa

Rangoon

Namangan

Odessa

Salta

Sekondi

Durgapur

Mathura

(continued)

932

931

930

929

928

927

926

925

924

923

922

921

920

919

918

917

916

915

914

913

912

Sustainable competitiveness

800

755

685

952

968

852

957

654

775

804

913

625

585

859

851

867

924

888

939

991

948

Economic vitality

886

600

300

702

806

764

954

177

953

968

844

518

223

956

826

794

927

979

942

911

578

Environmental thoroughness

903

825

926

460

496

912

663

798

967

777

917

887

922

889

709

856

874

646

623

969

593

Social inclusion

604

639

994

519

385

990

657

847

834

813

894

806

851

893

701

854

646

506

921

551

752

Scientific and technological innovation

(continued)

796

935

853

738

841

981

296

813

917

842

929

831

548

974

736

775

650

889

781

767

927

External contacts

7.6 Ranking of Explanatory Indicators of Global Urban Sustainable … 413

Country

China

India

Ethiopia

India

India

Bangladesh

India

India

India

China

Tanzania

India

China

Liberia

Iran

India

Sierra Leone

Iraq

Niger

India

Ukraine

City

Dingxi

Belgaum

Addis Ababa

Bhavnagar

Gulbarga

Bogra

Ajmer

Solapur

Amravati

Longnan

Mwanza

Ujjain

Jiuquan

Monrovia

Zahedan

Nanded Waghala

Freetown

Mosul

Niamey

Firozabad

Donetsk

(continued)

953

952

951

950

949

948

947

946

945

944

943

942

941

940

939

938

937

936

935

934

933

Sustainable competitiveness

919

784

842

941

869

740

937

927

825

730

901

820

672

696

732

967

689

634

925

647

805

Economic vitality

236

889

923

637

541

940

926

462

941

827

918

831

774

771

848

909

629

948

185

410

835

Environmental thoroughness

657

964

966

919

911

781

841

989

451

812

860

871

894

886

758

909

851

848

850

791

654

Social inclusion

459

980

849

759

647

1003

674

552

899

927

897

861

715

793

853

955

846

597

363

810

680

Scientific and technological innovation

(continued)

962

928

636

959

780

963

786

529

645

899

998

757

932

937

890

950

906

923

287

967

672

External contacts

414 7 Explanatory Indicators of Global Urban Sustainable Competitiveness

Country

Sudan

China

India

India

Benin

Egypt

Zimbabwe

India

Mozambique

India

Pakistan

India

Benin

India

Syrian

India

Malawi

India

Djibouti

Syrian

Mali

City

Nyala

Yichun(HLJ)

Jhansi

Gorakhpur

Abomey-Calavi

Suez

Bulawayo

Tirunelveli

Matola

Nellore

Sargodha

Kayamkulam

Cotonou

Malegaon

Hamah

Bikaner

Blantyre-Limbe

Warangal

Djibouti

Latakia

Bamako

(continued)

974

973

972

971

970

969

968

967

966

965

964

963

962

961

960

959

958

957

956

955

954

Sustainable competitiveness

962

938

965

584

877

832

970

663

921

534

966

687

958

613

929

882

971

881

774

942

978

Economic vitality

376

419

986

692

487

951

893

964

247

409

687

823

464

495

460

459

633

920

1001

952

899

Environmental thoroughness

960

978

968

689

944

857

991

779

878

936

947

831

970

795

931

843

910

955

929

901

905

Social inclusion

733

882

976

673

612

845

999

983

642

981

740

901

1004

756

883

685

780

898

822

932

770

Scientific and technological innovation

(continued)

743

912

990

948

575

980

976

864

383

978

965

882

836

933

834

774

525

924

926

808

942

External contacts

7.6 Ranking of Explanatory Indicators of Global Urban Sustainable … 415

Country

Kyrgyzstan

Burkina Faso

India

Nigeria

Syrian

Morocco

Guinea

Nigeria

Myanmar

Cote d’ivoire

Madagascar

Syrian

Somalia

Myanmar

Somalia

Malawi

Burundi

Congo

Burkina Faso

Mozambique

Syrian

City

Bishkek

Ouagadougou

Raipur

Maiduguri

Aleppo

Agadir

Conakry

Nnewi

Nay Pyi Taw

Bouake

Antananarivo

Al-Raqqa

Mogadishu

Mandalay

Hargeysa

Lilongwe

Bujumbura

Tshikapa

Bobo Dioulasso

Maputo

homs

(continued)

995

994

993

992

991

990

989

988

987

986

985

984

983

982

981

980

979

978

977

976

975

Sustainable competitiveness

984

981

930

986

974

878

1005

969

989

959

992

872

960

983

980

420

995

953

898

946

976

Economic vitality

939

802

830

928

1006

963

985

890

882

1002

888

816

335

937

425

176

796

700

681

735

813

Environmental thoroughness

1000

888

982

1004

1002

959

977

834

1005

998

939

987

930

994

948

554

999

954

629

979

847

Social inclusion

913

606

890

1006

959

859

985

737

978

1005

711

943

949

965

944

630

843

866

736

688

585

Scientific and technological innovation

(continued)

949

291

986

1001

999

741

984

943

812

953

702

992

916

875

711

915

828

944

800

669

513

External contacts

416 7 Explanatory Indicators of Global Urban Sustainable Competitiveness

Yemen

Tajikistan

Mozambique

Congo

Congo

Yemen

Yemen

Chad

Central African 1005

Congo

Aden

Dushanbe

Nampula

Kananga

Bukavu

Taiz

Hodeidah

N’Djamena

Bangui

Kisangani

1006

1004

1003

1002

1001

1000

999

998

997

996

Congo

Mbuji-Mayi

Sustainable competitiveness

Country

City

(continued)

1004

1002

1006

1003

1001

955

999

982

975

996

998

Economic vitality

919

533

980

829

815

395

966

992

476

932

677

Environmental thoroughness

1003

971

962

1001

986

995

997

985

914

972

1006

Social inclusion

968

964

935

973

852

938

1001

979

735

779

995

Scientific and technological innovation

1003

840

692

997

955

1006

1002

858

768

910

1005

External contacts

7.6 Ranking of Explanatory Indicators of Global Urban Sustainable … 417

Chapter 8

A New Set of Standards for Global City Classification

8.1 Introduction 8.1.1 Global City Classification is an Important Theoretical and Practical Problem The process of globalization makes cities in different countries and regions form a global urban system with increasingly close ties. However, there are still significant differences in the global urban system. With the development of science and technology, the upgrading of globalization and the increasing complexity of global industrial divisions, the relationship between different cities in the global urban system is increasingly flattened and networked. The argument proposed by Friedmann (1986) in last century that some cities have the “command and control” function for other cities in the global economic system still has a strong insight into the deep understanding of the internal hierarchical structure of current global urban system. Therefore, the study of global urban classification is still necessary for cities, countries and the whole world. It is of great theoretical significance to study the global urban classification. In the global urban system, the flows of products and factors between cities have broken the limits of national boundaries, which makes the study of global urban functional system more complex. Especially with the divisions of global value chain, the development of global production network and the advent of the intelligent era, the internal hierarchical structure of global cities has undergone significant changes. The research of global urban classification involves the theory of urban functional system, the theory of urban spatial interaction, etc., which has been the hot topics of urban economics, spatial economics and economic geography. At the same time, how to understand and explain the new trend in the development of global urban system is also an important and developing problem. It is also of great practical significance to study the global urban classification. First, with the arrival of the urban world, the global urban system is the skeleton and © China Social Sciences Press 2023 P. Ni et al., The World: 300 Years of Urbanization Expansion, https://doi.org/10.1007/978-981-99-3553-6_8

419

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8 A New Set of Standards for Global City Classification

blood of the global system. It is very important to understand the global urban system and its development trend. Second, the analysis of the differences and inequalities within the global urban system from a more comprehensive perspective has important reference value for us to understand the challenges in the global urbanization development and promote the sustainable development of global cities. Third, in the urban world, cities have become an important space carrier of a country’s competitiveness. Therefore, the study of global city classification has a direct reference role for a country to recognize its position in the global urban system, and then to reveal the country’s global position. Finally, for a specific city, to understand the specific status of a city in the global urban system from the functional perspective can provide decision-making reference and theoretical basis for relevant decision makers to make more competitive and sustainable policies based on the city level. In a word, the problem of global city classification is an important problem that all countries, cities and other relevant decision-makers are concerned about.

8.1.2 There Are New Changes in the Development of Global Urban System As human beings gradually enter into the global and intelligent urban society, the connotation, function, form, pattern and impacts of cities have changed or are undergoing profound changes, which makes it necessary for us to analyze and understand the global urban system from a new framework. Economic globalization is still advancing in twists and turns, and is constantly reshaping the relationship between cities. At present, the connotation of globalization has changed from the globalization of commodity trade, production and service trade to the globalization of scientific and technological innovation. According to Friedman’s book, the world is flat, globalization has entered the stage of 3.0. In the new stage of globalization, in addition to the traditional finance, scientific and technological innovation has become more important in determining the role of cities in the division of global value chain and global production network. In this process, the internal structure of global urban system will be reconstructed, and some new cities will rise, so it is necessary to study the new trends of global urban system. Cities will dominant the future world. After the world’s urbanization rate is over 50% in 2008 for the first time, the global urbanization continues to accelerate. According to the world urbanization prospects released by the UN in 2018, by 2050 the global urbanization rate is expected to reach 68%, and the world will soon enter a mature urban world. Therefore, the study of global urban system is closely related to the overall welfares of human society. With the rapid development of information, network and intelligence technology, human beings have stepped into the intelligent urban society, and the functions and forms of cities are undergoing a turning change. Driven by the new technology, cities will no longer be limited to the production, exchange and consumption of

8.1 Introduction

421

“hard materials” represented by tangible products, but the role of “soft materials” represented by intangible products such as information, knowledge and ideas will become more important in urban development. This change in urban functions is causing changes in urban morphology and the internal functional structure of the global urban system. The hierarchical relationship in the global urban system is weakening, while the network relationship is strengthening. It brings new challenges for us to classify the global cities.

8.1.3 The Theory of Global City and Its System Needs New Further Development The research on global urban system has a long history and includes rich literature. There are roughly two stages: Researches in the first stage mainly study global cities from the perspective of agglomeration degree. In essence, global cities are regarded as a spatial agglomeration economy. Therefore, the more population, industry and other factors a city agglomerates, the higher its rank in the global urban system. The early urban and regional economics mainly studies cities from the perspective of agglomeration, such as the central place theory proposed by Christaller and Lösch. Regional economics, new urban economics and new economic geography under the neoclassical framework all emphasize the importance of urban agglomeration (Henderson, 1974; Fujita and Ogawa, 1982; Fujita and Krugman, 1995). Therefore, cities can be classified by their economic agglomeration (Commendatore et al., 2017) and population agglomeration (Zhong et al., 2017). In the classic study of global city, Friedmann’s “world city” theory emphasizes the “command and control” function of high-rank global cities in the global economic system and measure a city’s rank using the agglomeration of multinational companies and their branches. Sassen’s “global city” theory mainly uses the agglomeration degree of advanced productive services to classify global cities. In essence, the above research mainly studies global cities and their systems from the perspective of agglomeration. The second stage of researches is mainly based on the new phenomenon that the global urban system increasingly form a more closely connected network system with the development of globalization and classifies the global cities from the perspective of connection degree. Its core is to regard the global urban system as a network system. Therefore, if a city is more closely connected with other cities, its position in the global urban system will be more important. Taylor (2001) based on his “interlocking model”, uses the distribution of “advanced productive services” such as banking, insurance, law, consulting management, advertising and accounting firms to measure a city’s global connections. Based on the above idea, the GaWC has conducted a lot of researches on global city classification. Another representative theory of global city classification based on connection degree is the theory of “flow space” proposed by Castells (1996), which emphasizes the role of information flow. Derudder et al.

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8 A New Set of Standards for Global City Classification

(2003) studies the classification of 234 cities in the world under this idea. It should be noted that the research of global city classification based on connection needs to use relational data. The relational data selected by scholars are often different. For example, Esparza and Krmenec (2000) uses trade flow data, Jung et al. (2008) uses traffic flow, Derudder and Witlox (2008) use aviation flow to measure the connection between cities. Although the above research provides a theoretical basis for the classification of global cities, there are still following problems. First, there is a lack of strict logic in the classification framework. Global cities have rich connotation, including a wide range of dimensions, and the existing research on global city classification based on the degree of agglomeration and connection emphasizes the different aspects of global cities. In order to classify global cities accurately, we must establish a more general analysis framework. Second, when we grade global cities, it is one-sided to only consider the degree of connection or the degree of agglomeration. Only using the degree of agglomeration to grade global cities will overestimate the impact of scale factors on urban grades. Especially in the current global production network, cities in developed countries tend to transfer the low-end and labor-intensive sectors in the industrial chain to the cities in developing countries. At this time, the employment and industrial scale of cities in developing countries may be larger. However, due to their inferior position in the division of global value chain, the rank of cities in developing countries may not be greatly improved. Only considering the degree of connection may overestimate the position of some hub type or special functional cities in the global city system. For example, although the cities that are aviation hubs have a high flow of people and logistics, their local economic scale may not be increased in the same proportion. Only considering the degree of connection will overestimate their city rank. Tourism cities tend to have a high flow of people, but their overall industrial development level is not necessarily high. Third, it did not absorb new developments in practices, especially considering the new connotation of cities under new technology. With the increase of human knowledge, the progress of technology and the change of production mode, the factors that determine the degree of urban agglomeration and connection are no longer limited to the traditional tangible products and factors, and the role played by intangible factors and products such as knowledge, information and service is increasingly obvious. Therefore, it is necessary to design a more scientific and comprehensive global city classification framework. In view of this, we decided to release the classification of 1006 cities with a population of more than 500,000 in the world. These 1006 cities cover 6 continents and 136 countries or regions, including 565 Asian cities, 126 European cities, 131 North American cities, 102 African cities, 75 South American cities and 7 Oceanian cities, which basically include the main cities in different regions of the world today. Based on the existing researches, the innovation and improvement of the global urban classification framework in this report are as follows. First, from the perspective of elasticity of substitution, we propose a more general theoretical framework for global urban classification based on the degree of agglomeration and connection. Second, we design a new empirical framework and index system for global urban

8.2 Theory and Method

423

classification considering both the agglomeration and connection. Third, because of the significant changes in the connotation of cities in the intelligent era, we simultaneously consider the intangible “soft” factors and the tangible “hard” factors in the global urban classification framework. Fourth, when selecting the indicators to measure the agglomeration and connection degree, we emphasize not only the traditional financial factors but also the scientific and technological innovation factors.

8.2 Theory and Method 8.2.1 The Theoretical Framework of Global City Classification: An Analysis Based on Elasticity of Substitution The rank of a city in the global urban system is closely related to its position in the global urban industrial system. With the development of global value chain and global production network, the industrial relationship between global cities is increasingly close. Therefore, global cities will also form a functional system due to the industrial divisions. Hence, the more difficult a city is to be replaced by other cities in the global urban functional system, the higher its rank in the global urban system. Specifically, the elasticity of substitution of a city is determined by the factors it agglomerates and the products it produces. First, the more scarce the factors agglomerated in a city are, the lower the elasticity of substitution of the factors is. Hence, the city’s functions are more difficult to be replaced in the urban system. The factors here include natural environment, geographical location, labor force, land, capital, technology and so on. For example, if a city has a good geographical location to make it a key node in the transportation network, then the city’s elasticity of substitution is lower, and it is easier to be at a higher rank in the global urban system. For example, large cities often have higher efficiency because they can agglomerate more skilled labor (Davis and Dingel, 2014; Behrens et al., 2014), and the skilled labor they agglomerate cannot be replaced by the low skilled labor of other cities, which will lead to the high rank of large cities in the urban system. Second, cities that produce products with low elasticity of substitution tend to have higher rank. In the urban system, there will be a hierarchy formed by heterogeneous products (Duranton and Puga, 2001, 2005). When the substitution elasticity of products produced by one city is higher, it means that its market share is more easily replaced by other cities. Especially when there exists transportation cost, the market competitiveness of products with high elasticity of substitution will decline faster with the increase of transportation distance, so the economic hinterland of cities producing products with high elasticity of substitution will be smaller. Hence, their rank in the global urban system will be lower. The theoretical analysis of Fujita

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8 A New Set of Standards for Global City Classification

et al. (1999) shows that, given the location of the central city, the products with low demand elasticity are more difficult to be replaced by other products, so the cities producing the products with low demand elasticity are more likely to appear in the location closer to the central city. In reality, cities agglomerate different industries and factors, so the rank of a city in the global urban system depends on the total substitution elasticity of all industries and factors in the global production system. Cities with lower substitution elasticity have a higher rank in the global urban system. We define the elasticity of substitution of a city as a function of the following variables: Si = f ( A, R)

(8.1)

In the above formula, Si is the substitution elasticity of the city, A and R is the agglomeration and connection degree of a city respectively. In the global urban system, the so-called agglomeration degree refers to the number of global highend factors agglomerated by the city. Because the substitution elasticity of high-end factors is often lower, so if a city agglomerates more global high-end factors, then the city is more difficult to be replaced by other cities, and its rank is higher. The so-called connection degree is to measure one city’s elasticity of substitution from the perspective of network topology. When a city has a high centrality in the global production network, it shows that the city is a key node in the global production network and has an irreplaceable function in maintaining the stability of the whole global network. Therefore, cities with high degree of connection have higher rank. Based on above analysis, in order to measure the degree of agglomeration and connection more comprehensively, we further divide the degree of agglomeration into two dimensions: hard agglomeration and soft agglomeration, and the degree of connection into two dimensions: hard connection and soft connection. Among them, all “hard” factors refer to tangible products or factors, such as population, enterprises, etc.; all “soft” factors refer to intangible products or factors, such as knowledge, information, etc. This new classification framework for global cities can solve the problems when only considering the degree of agglomeration or connection. Figure 8.1 reports the conceptual framework of global city classification.

8.2.2 Index System and Data Source According to Fig. 8.1, we design a global city classification index system, as shown in Table 8.1. The global city classification index system designed in this report is composed of three levels of indexes. The synthesis method of the indexes is as follows:

8.2 Theory and Method

425

hard agglomeration agglomeration soft agglomeration Rank of Cities hard connection connection soft connection Fig. 8.1 Conceptual framework of global city classification. Source Compiled by the Author

Table 8.1 Index system of global city classification Level 1 index

Level 2 index

Level 3 index

Agglomeration

Hard agglomeration

High end industry agglomeration (enterprise) High income population agglomeration (population)

Soft agglomeration

Patent agglomeration Paper agglomeration

Connection

Hard connection

Aviation connection (population) Multinational corporations’ connection (enterprises)

Soft connection

Information connection Knowledge connection

Source compiled by the Author

Si = Ai + Ri Ai = Ah i + Asi

(8.2)

Ri = Rh i + Rsi In the above formula, Si is the global city grade score of city i, Ai is the agglomeration degree of city i, Ri is the connection degree of city i, Asi is the soft agglomeration degree, Ah i is the hard agglomeration degree, Rh i is the hard connection degree and Rsi is the soft connection degree. See Table 8.1 for details. We use the simple arithmetic average to calculate each index. We normalized each indicator in Table 8.1 as 0–1, and the calculation method is as follows: S=

X − Min(X ) Max(X ) − Min(X )

(8.3)

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8 A New Set of Standards for Global City Classification

In the above formula, X is the original score of each indicator and S is the score after normalization. The specific calculation methods and data sources of each indicator are as follows: (1) High-end industry agglomeration. This index is calculated based on the distribution of global top corporate headquarters in banking, technology and other industries. Specifically, we use the number of global top 1000 bank headquarters, global top 1000 technology headquarters, Forbes 2000 corporate headquarters (excluding technology and financial enterprises), the number of global top 75 financial multinational company headquarters (excluding those overlapping with the front), and the number of global top 25 financial multinational companies (excluding those overlapping with the front) headquarters in each city. (2) High income population agglomeration. We measure it using the number of people whose annual income is more than $20,000, we get the data from the EIU database. (3) Patent agglomeration. We measure it using the number of patent applications in each city. The data is from the database of world intellectual property organization. (4) Paper agglomeration. We measure this index using the total amount of papers published in each city and collect the data from Web of Science. (5) Aviation connection. We measure this index using the city’s number of international flight. The data comes from each city’s airport websites, Wikipedia and IAA websites. (6) Multinational corporations’ connection. According to the distribution of the headquarters and branches of 175 advanced productive service enterprises in the world, including law, management consulting, accounting, finance and advertising, we calculate it using the GaWC method. (7) Information connection. Measured by a city’s search heat on Google, the data comes from google trends. (8) Knowledge connection. Measured by the number of papers published by cities in cooperation with other cities. We use the top 100,000 literatures with the highest citation in 2017 from the web of science website. The sample is 1006 cities in the world. If there is no special statement, all indicators are annual data in 2017.

8.2.3 City Classification Method At present, the clustering method is the most commonly used method in the study of city classification. Because the clustering method can identify different sub-group in the whole sample relatively accurately. The clustering method can be further divided into hierarchical clustering and non-hierarchical clustering. Compared with non-hierarchical clustering, the advantage of hierarchical clustering method is that it

8.3 Empirical Analysis

427

does not need to specify the number of clusters in advance. The hierarchical clustering method can use the dendrogram to display the hierarchical structure in samples, so the conclusion is relatively objective. Therefore, after using the indicator system in Table 8.1 to get scores of each city in the sample, we then use the hierarchical clustering method to classify the whole sample. Taking the bottom-up method as an example, hierarchical clustering method first calculate the linkages between sample points and then merge the nearest points into the same class each time, and then calculate the linkages between the classes, and again merge the nearest classes into a large class. On this basis, we constantly merges until only one class is left.

8.3 Empirical Analysis 8.3.1 Global City Centrality Classification The number of global cities is the smallest, and the number of regional gateway cities is the largest. According to the results of hierarchical clustering, the global city system is a multi-level nested structure, As can be seen from Fig. 8.2, 1006 cities worldwide according to city level can be divided into 3 layers, 2 category (Strong international cities and weak international cities), 5 grades (A, B, C, D, E) 10 levels in total (A+, A, B+, B, C+, C, D+, D, E+, E). The first category is strong international cities, which can be divided into: firstclass global cities (A), second-class international hub cities (B); third-class international gateway cities (C); second category is weak international cities, which can be divided into: the fourth is the regional hub city (D); the fifth is the regional gateway city (E). The number of A+ cities is 3; the number of A cities is 2; the number of B+ cities is 3; the number of B cities is 26; and the number of C+ cities and C cities are 29 and 96 respectively. Similarly, the number of D+ cities is 122; the number of D cities is 266; the number of E+ cities and E cities is 389 and 70 respectively.

Fig. 8.2 Global city classification. Source Compiled by Author

428

8 A New Set of Standards for Global City Classification

8.3.2 An Analysis of the Characteristics of the Overall Classification of Global Cities The higher the degree of agglomeration and connection, the stronger the hardness and softness, the higher the city level. From Table 8.2, it can be seen that the total number of all cities is 1006, and the mean value of the city grade score is 0.2565, the standard variance is 0.1327, and the coefficient of variation is 0.5172. The mean value of D+ and above cities is higher than that of all cities, and A+ city is the highest, which is 0.9635. Similarly, the coefficient of variation of E+ and above cities is smaller than that of all cities, and the coefficient of variation of A level cities is the smallest, indicating that the internal difference of this type of cities is small; the coefficient of variation of E level cities is the largest, which is 0.5172, indicating that the internal development difference of this type of cities is large. As shown in Fig. 8.3, the higher the level, the darker the color, and the higher level cities are mainly distributed in the northern hemisphere. The number of high level cities in Europe, North America and Asia is much higher than that in other continents. See the following for specific analysis.

8.3.2.1

Composition and Characteristics of a Cities

Cities in Asia, Europe and North America scored the highest. It can be seen from Table 8.3 that there are only 3 A+ cities, New York-Newark, London and Tokyo, which belong to United States of America, United Kingdom and Japan respectively. From the continental perspective, North America, Europe and Asia occupy 1 seat Table 8.2 Global city classification City level

Number of cities

Mean value

Standard deviation

Coefficient of variation

A+

3

0.9635

0.0320

0.0332

A

2

0.9052

0.0006

0.0006

International hub city (B)

B+

3

0.7585

0.0178

0.0234

B

26

0.6423

0.0464

0.0723

International gateway city (C)

C+

29

0.5322

0.0251

0.0471

C

96

0.4185

0.0354

0.0845

Regional hub city (D)

D+

122

0.3269

0.0181

0.0553

D

266

0.2429

0.0244

0.1003

Regional gateway city (E)

E+

389

0.1769

0.1900

0.1072

E

70

0.0776

0.0404

0.5208

1006

0.2565

0.1327

0.5172

Global city (A)

All cities Source CCC of CASS

8.3 Empirical Analysis

429

90

60

30

0 -180

-120

-60

0

60

120

180

-30

-60

-90 Fig. 8.3 Distribution of global cities. Source Compiled by Author

Table 8.3 A+ cities

City level

City

Country

Continent

A+ Cities

New York-Newark

United States of America

North America

London

United Kingdom Europe

Tokyo

Japan

Asia

Source CCC of CASS

respectively, which shows that the comprehensive strength of North America, Europe and Asia is strong. From the perspective of characteristics, New York-Newark and London belong to high agglomeration and high connection cities, and Tokyo belongs to high agglomeration and middle connection cities; from the perspective of softness and hardness, all of them belong to strong hardness and strong softness cities. It shows that the higher the degree of agglomeration and connection, the stronger the hardness and softness, the stronger the comprehensive strength of the city. From Table 8.4, it can be seen that there are only 2 cities of A, Beijing and Paris, which belong to China and France respectively. From the continental perspective, Asia and Europe occupy 1 seat respectively, which shows that the overall comprehensive strength of Asian and European cities is constantly improving. From the perspective of characteristics, Beijing and Paris belong to high agglomeration and high connection cities; from the perspective of softness and hardness, both belong to strong hardness and strong softness cities. It shows that the higher the

430

8 A New Set of Standards for Global City Classification

Table 8.4 A cities

City level

City

Country

Continent

A cities

Beijing

China

Asia

Paris

France

Europe

Source CCC of CASS

degree of agglomeration and connection, the stronger the hardness and softness, the stronger the overall strength of the city.

8.3.2.2

Composition and Characteristics of B Cities

The comprehensive strength of Asian cities is constantly improving. The number of B+ cities is 3. As can be seen from Table 8.5, B+ cities include Seoul, Shanghai and Chicago, which are respectively subordinate to Republic of Korea, China and United States of America. From the continental perspective, Asian cities occupy 2 seats and North American cities occupy 1 seat, indicating that the comprehensive strength of Asian cities is constantly improving. From the perspective of characteristics, Seoul is a high agglomeration middle connection city; Shanghai and Chicago are middle agglomeration middle connection cities; from the perspective of softness and hardness, all of them are middle hardness and strong softness cities. It shows that the degree of connection and hardness of the three cities need to be strengthened. B city is 28. From Table 8.6, it can be seen that B cities mainly include Sydney, Dublin, Vienna, Sao Paulo, etc., and their countries are Australia, Ireland, Austria, Brazil, etc.; from the perspective of the continent of representative cities, European cities occupy 9 seats, Asian cities and North American cities occupy 4 seats and 2 seats respectively, and Oceania and South American cities occupy 1 seat respectively. The overall comprehensive strength of cities in Oceania and South America needs to be strengthened. From the perspective of characteristics, B cities are generally the middle agglomeration and middle connection city; from the perspective of softness and hardness, most of them are middle hardness and strong softness cities. It shows that the degree of agglomeration, connection and hardness of grade B cities need to be strengthened. Table 8.5 B+ cities

City level

City

Country

Continent

B+ cities

Seoul

Republic of Korea

Asia

Shanghai

China

Asia

Chicago

United States of America

North America

Source CCC of CASS

8.3 Empirical Analysis Table 8.6 B cities

431

City level

City

Country

Continent

B cities

Sydney

Australia

Oceania

Dublin

Ireland

Europe

Vienna

Austria

Europe

Sao Paulo

Brazil

South America

Brussels

Belgium

Europe

Munich

Germany

Europe

Moscow

Russian Federation

Europe

Amsterdam

Netherlands

Europe

Toronto

Canada

North America

Boston

United States of America

North America

Osaka

Japan

Asia

Stockholm

Sweden

Europe

Istanbul

Turkey

Asia

Madrid

Spain

Europe

Singapore

Singapore

Asia

Milan

Italy

Europe

Hong Kong

China

Asia

……

……

……

Note Due to the large Number of B cities, one representative city in each country is selected, …… represents a city without listing in B city. The following tables are the same Source CCC of CASS

8.3.2.3

Composition and Characteristics of C Cities

C grade cities need to improve connection and hardness. The number of C+ cities is 29. As can be seen from Table 8.7, C+ cities mainly include Melbourne, Buenos Aires, Dubai, Warsaw, etc., the countries are Australia, Argentina, United Arab Emirates, Poland, etc.; from the perspective of the continent of representative cities, European cities occupy 9 seats, Asian cities occupy 5 seats, and North American, Oceania and South American cities occupy 2 seats respectively, indicating that European and Asian cities have obvious advantages in C+ cities. From the aspect of characteristics, C+ cities belong to middle agglomeration and middle connection city; from the perspective of softness and hardness, most of them belong to the weak hardness and middle softness cities. It is indicated that C+ cities need to be strengthened in terms of agglomeration, connection and hardness. The number of C cities is 96. As can be seen from Table 8.8, C cities mainly include Brisbane, Abu Dhabi, Cairo, Rio de Janeiro, etc., the countries are Australia, United Arab Emirates, Egypt, Brazil, etc.; from the perspective of the continent of

432 Table 8.7 C+ city

8 A New Set of Standards for Global City Classification

City level

City

Country

Continent

C+ cities

Melbourne

Australia

Oceania

Buenos Aires Argentina

South America

Dubai

United Arab Emirates

Asia

Warsaw

Poland

Europe

Copenhagen

Denmark

Europe

Frankfurt am Main

Germany

Europe

Helsinki

Finland

Europe

Bogota

Colombia

South America

Montreal

Canada

North America

Prague

Czech Republic

Europe

Kuala Lumpur

Malaysia

Asia

Seattle

United States of America

North America

Oslo

Norway

Europe

Zurich

Switzerland

Europe

Bangkok

Thailand

Asia

Athens

Greece

Europe

Auckland

New Zealand

Oceania

Budapest

Hungary

Europe

Mumbai

India

Asia

Guangzhou

China

Asia

……

……

……

Source CCC of CASS

representative cities, European cities occupy 15 seats, Asian cities occupy 9 seats, African cities occupy 4 seats, North America and Oceania occupy 3 seats and 1 seat respectively, indicating that the comprehensive strength of African cities in C cities is gradually increasing. From the aspect of characteristics, C cities generally belong to the middle agglomeration and low connection types; from the perspective of softness and hardness, most of them belong to the weak hardness and middle softness type. It is indicated that C cities need to improve connection and hardness.

8.3 Empirical Analysis Table 8.8 C cities

433

City level

City

Country

Continent

C cities

Brisbane

Australia

Oceania

Abu Dhabi

United Arab Emirates

Asia

Cairo

Egypt

Africa

Rio de Janeiro Brazil

South America

Sofia

Bulgaria

Europe

Krakow

Poland

Europe

Stuttgart

Germany

Europe

Lyon

France

Europe

Incheon

Republic of Korea

Asia

Rotterdam

Netherlands

Europe

Vancouver

Canada

North America

Doha

Qatar

Asia

Zagreb

Croatia

Europe

Nairobi

Kenya

Africa

Riga

Latvia

Europe

Bucharest

Romania

Europe

Charlotte

United States of America

North America

Lima

Peru

South America

Casablanca

Morocco

Africa

Mexico City

Mexico

North America

Johannesburg

South Africa

Africa

Lisbon

Portugal

Europe

Nagoya

Japan

Asia

Geneva

Switzerland

Europe

Belgrade

Serbia

Europe

Riyadh

Saudi Arabia

Asia

Ankara

Turkey

Asia

Kiev

Ukraine

Europe (continued)

434

8 A New Set of Standards for Global City Classification

Table 8.8 (continued)

City level

City

Country

Continent

Valencia

Spain

Europe

Bologna

Italy

Europe

Delhi

India

Asia

Jakarta

Indonesia

Asia

Manchester

United Kingdom Europe

Santiago de Chile

United States of America

North America

Hangzhou

China

Asia

……

……

……

Source CCC of CASS

8.3.2.4

Composition and Characteristics of D Cities

D grade cities need to improve their connection and hardness. The number of D+ cities is 122. As can be seen from Table 8.9, D+ cities mainly include Baku, Alexandria, Addis Ababa, Karachi, etc., and the countries are Azerbaijan, Egypt, Ethiopia, Pakistan, etc.; from the perspective of the continent of representative cities, Asian cities occupy 19 seats, European cities occupy 11 seats, and African, North American and South American cities occupy 4 seats, 7 seats and 6 seats respectively, indicating that African and South American cities are gradually increasing in D+ cities. From the aspect of characteristics, like the C level cities, D+ cities generally belong to the middle agglomeration and low connection types; from the perspective of softness and hardness, most of them belong to the weak hardness and middle softness types. It is indicated that D+ cities need to improve connection and hardness. The number of D cities is 266. From Table 8.10, it can be seen that the D cities mainly include Gold Coast, Algiers, Cordoba, Muscat, etc., and the countries are Australia, Algeria, Argentina, Oman, etc.; from the perspective of the continent of representative cities, Africa has the most cities, accounting for 23 seats; followed by Asia, accounting for 22 seats; South America, Europe and North America Cities occupy 9, 8 and 7 seats respectively, which shows that African and Asian cities have a large number of D cities, and their comprehensive strength needs to be improved. From the perspective of characteristics, most of D cities belong to the type of middle agglomeration and low connection; from the perspective of softness and hardness, most of them belong to the type of weak hardness and weak softness. It shows that the degree of connection, hardness and softness need to be improved in D cities.

8.3 Empirical Analysis

435

Table 8.9 D+ cities City level

City

Country

Continent

D+ cities

Baku

Azerbaijan

Asia

Alexandria

Egypt

Africa

Addis Ababa

Ethiopia

Africa

Karachi

Pakistan

Asia

Panama City

Panama

North America

Campinas

Brazil

South America

Minsk

Belarus

Asia

Antwerp

Belgium

Europe

San Juan

Puerto Rico

North America

Wroclaw

Poland

Europe

Santa Cruz

Bolivia

South America

Cologne

Germany

Europe

Novosibirsk

Russian Federation

Europe

Quito

Ecuador

South America

Nantes

France

Europe

Manila

Philippines

Asia

Medellin

Colombia

South America

San Jose

United States of America

North America

Tbilisi

Georgia

Asia

Almaty

Kazakhstan

Asia

Busan

Republic of Korea

Asia

Hague

Netherlands

Europe

Hamilton

Canada

North America

Beirut

Lebanon

Asia

Milwaukee

United States of America

North America

Dhaka

Bangladesh

Asia

Guadalajara

Mexico

North America

Lagos

Nigeria

Africa

Hiroshima

Japan

Asia

Gothenburg

Sweden

Europe

Colombo

Sri Lanka

Asia

Tunis

Tunisia

Africa

Izmir

Turkey

Asia

Barcelona-Puerto La Cruz

Venezuela

South America (continued)

436

8 A New Set of Standards for Global City Classification

Table 8.9 (continued) City level

City

Country

Continent

Montevideo

Uruguay

South America

Malaga

Spain

Europe

Tehran

Iran (Islamic Republic of)

Asia

Kingston

Jamaica

North America

Thessaloniki

Greece

Europe

Tel Aviv-Yafo

Israel

Asia

Florence

Italy

Europe

Kolkata

India

Asia

Belfast

United Kingdom

Europe

Amman

Jordan

Asia

Ho Chi Minh City

Viet Nam

Asia

Dongguan

China

Asia

……

……

……

Source CCC of CASS

8.3.2.5

Composition and Characteristics of E Cities

E grade cities need to improve the degree of agglomeration, connection, hardness and softness. The number of E+ cities is 389. From Table 8.11, it can be seen that E+ cities mainly include Kabul, Mar del Plata, Suez, Faisalabad, etc., and the countries are including Afghanistan, Argentina, Egypt, Pakistan, etc.; from the perspective of the continent of representative cities, Africa has the largest number of cities, accounting for 27 seats; followed by Asian cities, accounting for 24 seats; Cities in South America, North America and Europe occupy 6, 4 and 3 seats respectively. It can be seen that, similar to D level cities, African and Asian cities have a large number of E+ cities. From the aspect of characteristics, most of the E+ cities mainly belong to the type of low agglomeration and low connection; from the aspect of softness and hardness, they belong to the type of weak hardness and weak softness. It shows that E+ cities need to improve their agglomeration and connection, hardness and softness. The number of E cities is 70. From Table 8.12, it can be seen that E cities mainly include Huambo, Victoria, Tyumen, and Zamboanga, etc., and the countries are including Angola, Brazil, Russian Federation, Philippines, etc.; from the perspective of the continent of representative cities, Asian cities account for the most, accounting for 15 seats; African cities account for 8 seats; South America, Europe and North America cities account for 2,2,1 seats respectively. It shows that the internal differentiation of Asian cities is serious, including the top cities with A+ level and the relatively underdeveloped cities with E level. The internal differentiation problem deserves attention.

8.3 Empirical Analysis

437

Table 8.10 D cities City level

City

Country

Continent

D cities

Gold Coast

Australia

Oceania

Algiers

Algeria

Africa

Cordoba

Argentina

South America

Muscat

Oman

Asia

Sharjah

United Arab Emirates

Asia

Luanda

Angola

Africa

Lahore

Pakistan

Asia

Asuncion

Paraguay

South America

Curitiba

Brazil

South America

Liege

Belgium

Europe

Cotonou

Benin

Africa

Poznan

Poland

Europe

La Paz

Bolivia

South America

Lomé

Togo

Africa

Santo Domingo

Dominican Republic

North America

Kazan

Russian Federation

Europe

Guayaquil

Ecuador

South America

Toulon

France

Europe

Cebu

Philippines

Asia

Kinshasa

Congo

Africa

Cartagena

Colombia

South America

Astana

Kazakhstan

Asia

Havana

Cuba

North America

Ulsan

Republic of Korea

Asia

Accra

Ghana

Africa

Phnom Penh

Cambodia

Asia

Harare

Zimbabwe

Africa

Bishkek

Kyrgyzstan

Asia

Tegucigalpa

Honduras

North America

Douala

Cameroon

Africa

Kuwait City

Kuwait

Asia

Abidjan

The Republic of Cote d’ivoire

Africa

Kigali

Rwanda

Africa

Monrovia

Liberia

Africa

Johor Bahru

Malaysia

Asia

Blantyre-Limbe

Malawi

Africa (continued)

438

8 A New Set of Standards for Global City Classification

Table 8.10 (continued) City level

City

Country

Continent

Vientiane

Lao People’s Democratic Republic

Asia

Tripoli

Libya

Africa

Antananarivo

Madagascar

Africa

Tulsa

United States of America

North America

Chisinau

Republic of Moldova

Europe

Rabat

Morocco

Africa

Maputo

Mozambique

Africa

Puebla

Mexico

North America

Pretoria

South Africa

Africa

Managua

Nicaragua

South America

Kathmandu

Nepal

Asia

Abuja

Nigeria

Africa

Kitakyushu-Fukuoka

Japan

Asia

Dakar

Senegal

Africa

San Salvador

El Salvador

North America

Medina

Saudi Arabia

Asia

Dar es Salaam

United Republic of Tanzania

Africa

Khartoum

Sudan

Africa

Bursa

Turkey

Asia

Zaragoza

Spain

Europe

Valencia

Spain

Europe

Guatemala City

Guatemala

North America

Yerevan

Armenia

Asia

Kampala

Uganda

Africa

Baghdad

Iraq

Asia

Tashkent

Uzbekistan

Asia

Odessa

Ukraine

Europe

Mashhad

Iran (Islamic Republic of)

Asia

Kochi

India

Asia

Surabaya

Indonesia

Asia

West Yorkshire

United Kingdom

Europe

Lusaka

Zambia

Africa

Valparaiso

Chile

South America

Hsinchu

China

Asia

……

……

……

Source CCC of CASS

8.3 Empirical Analysis Table 8.11 E+ cities

439

City level

City

Country

Continent

E+ Cities

Kabul

Afghanistan

Asia

Mar Del Plata

Argentina

South America

Suez

Egypt

Africa

Faisalabad

Pakistan

Asia

Gaza

State of Palestine

Asia

Uberlandia

Brazil

South America

Abomey-Calavi

Benin

Africa

Cochabamba

Bolivia

South America

Ouagadougou

Burkina Faso

Africa

Bujumbura

Burundi

Africa

Tomsk

Russian Federation

Europe

Asmara

Eritrea

Africa

Davao

Philippines

Asia

Brazzaville

Congo

Africa

Pereira

Colombia

South America

Port-au-Prince

Haiti

North America

Libreville

Gabon

Africa

Lilongwe

Malawi

Africa

San Pedro Sula

Honduras

North America

Conakry

Guinea

Africa

Kumasi

Ghana

Africa

Mombasa

Kenya

Africa

Shymkent

Kazakhstan

Asia

Benghazi

Libya

Africa

Changwon

Republic of Korea

Asia

Djibouti

Djibouti

Africa

Bulawayo

Zimbabwe

Africa

Bouake

The Republic of Cote d’ivoire

Africa

Bamako

Mali

Africa

Nouakchott

Mauritania

Africa

Ipoh

Malaysia

Asia (continued)

440 Table 8.11 (continued)

8 A New Set of Standards for Global City Classification

City level

City

Country

Continent

Provo-Orem

United States of America

North America

Ulan Bator

Mongolia

Asia

Chittagong

Bangladesh

Asia

Arequipa

Peru

South America

Mandalay

Myanmar

Asia

Fes

Morocco

Africa

Nampula

Mozambique

Africa

Toluca

Mexico

North America

Niamey

Niger

Africa

Ibadan

Nigeria

Africa

Niigata

Japan

Asia

Freetown

Sierra Leone

Africa

Nyala

Sudan

Africa

Mogadishu

Somalia

Asia

Dushanbe

Tajikistan

Asia

Mecca

Saudi Arabia

Asia

Mwanza

United Republic of Tanzania

Africa

Denizli

Turkey

Asia

Ashgabat

Turkmenistan

Asia

Maracaibo

Venezuela

South America

Kharkov

Ukraine

Europe

Damascus

Syrian Arab Republic

Asia

Sana’a’

Yemen

Asia

Erbil

Iraq

Asia

Kerman

Iran (Islamic Republic Asia of)

Varanasi

India

Asia

Padang

Indonesia

Asia

Newcastle upon Tyne

United Kingdom

Europe

Da Nang

Viet Nam

Asia

N’Djamena

Chad

Africa (continued)

8.3 Empirical Analysis Table 8.11 (continued)

441

City level

City

Country

Continent

Bangui

Central African Republic

Africa

Kitwe

Zambia

Africa

Baoji

China

Asia

……

……

……

Source CCC of CASS

From the perspective of characteristics, almost all E cities belong to the type of low agglomeration and low connection; from the perspective of softness and hardness, they belong to the type of weak hardness and weak softness. It shows that E cities need to strengthen their agglomeration and connection, hardness and softness.

8.3.3 Global City Types with Characteristics of Agglomeration and Connection Agglomeration is the premise of connection, and it is difficult to achieve a higher level of connection at a low level of agglomeration. Cluster method is used to divide the types of agglomeration and connection into three categories (high, middle and low) respectively. By combining 1006 cities in the world, according to the types of agglomeration and connection, they can be divided into nine categories (high agglomeration and high connection, high agglomeration and middle connection, high agglomeration and low connection, middle agglomeration and high connection, middle agglomeration and middle connection, middle agglomeration low connection, low agglomeration and high connection, low agglomeration and middle connection, low agglomeration and low connection). Among them, the number of cities with high agglomeration and high connection is 4; the number of cities with high agglomeration and middle connection is 3; the number of cities with high agglomeration and low connection is 1; the number of cities with middle agglomeration and middle connection, middle agglomeration and low connection and low agglomeration and low connection are 91, 329 and 578 respectively. The number of other cities is 0. It can be seen from Table 8.13 that the number of cities with low agglomeration and low connection type is the largest, indicating that agglomeration and connection are positively related, and that cities with low agglomeration and high connection; low agglomeration and middle connection types are all 0, indicating that agglomeration is the premise of connection, and it is difficult to achieve high-level connection with a low level of agglomeration. The mean value of agglomeration degree of all cities is 0.2026, the standard deviation is 0.1160, and the coefficient of variation is 0.5725; the mean value of connection degree is 0.2703, the standard deviation is 0.1433, and the coefficient of

442 Table 8.12 E cities

8 A New Set of Standards for Global City Classification

City level

City

Country

Continent

E cities

Huambo

Angola

Africa

Greater Vitória Brazil

South America

Tyumen

Russian Federation

Europe

Zamboanga

Philippines

Asia

Kisangani

Congo

Africa

Bogra

Bangladesh

Asia

Vereeniging

South Africa

Africa

Matola

Mozambique

Africa

Pachuca de Soto

Mexico

North America

Nay Pyi Taw

Myanmar

Asia

Sekondi

Ghana

Africa

Misrata

Libya

Africa

Warri

Nigeria

Africa

Ta’if

Saudi Arabia

Asia

Hargeysa

Somalia

Asia

Safaqis

Tunisia

Africa

Sanliurfa

Turkey

Asia

Samut Prakan

Thailand

Asia

Ciudad Guayana

Venezuela

South America

Lvov

Ukraine

Europe

Hodeidah

Yemen

Asia

Namangan

Uzbekistan

Asia

Nasiriyah

Iraq

Asia

Homs

Syrian Arab Republic

Asia

Zahedan

Iran (Islamic Republic of)

Asia

Be’er Sheva

Israel

Asia

Kayamkulam

India

Asia

Hegang

China

Asia

……

……

……

Source CCC of CASS

0

91

329

Middle agglomeration and high connection

Middle agglomeration and middle connection

Middle agglomeration and low connection

Low agglomeration 0 and high connection

1

High agglomeration and low connection

–

0.2726

0.3897

–

0.6648

0.7757

3

High agglomeration and middle connection

Mean value of agglomeration degree

0.7545

City quantity

High agglomeration 4 and high connection

City type

–

0.0599

0.0879

–

–

0.1958

0.0863

Standard deviation of agglomeration degree

Table 8.13 City types by agglomeration and connection degree

–

0.2197

0.2256

–

–

0.2524

0.1144

Coefficient of variation of agglomeration degree

–

0.2902

0.5864

–

0.4277

0.7335

0.9788

Mean value of connection degree

–

0.0814

0.1061

–

–

0.0141

0.0151

–

0.2804

0.1809

–

–

0.0193

0.0155

(continued)

Standard deviation Coefficient of of connection degree variation of degree of connection

8.3 Empirical Analysis 443

0

578

1006

Low agglomeration and middle connection

Low agglomeration and low connection

All cities

Note – Means none Source CCC of CASS

City quantity

City type

Table 8.13 (continued)

0.2026

0.1257

–

Mean value of agglomeration degree

0.1160

0.0399

–

Standard deviation of agglomeration degree

0.5725

0.3174

–

Coefficient of variation of agglomeration degree

0.2703

0.2016

–

Mean value of connection degree

0.1433

0.0701

–

0.5302

0.3475

–

Standard deviation Coefficient of of connection degree variation of degree of connection

444 8 A New Set of Standards for Global City Classification

8.3 Empirical Analysis

445

variation is 0.5302. It can be seen that the coefficient of variation (agglomeration degree and connection degree) of all types of cities does not exceed the coefficient of variation of all cities. From the perspective of agglomeration degree, the mean value of high agglomeration and high connection and high agglomeration and middle connection cities are higher than that of other types of cities, indicating that these cities have obvious advantages in agglomeration economy; the mean value of low agglomeration and low connection cities is the smallest, and the coefficient of variation is the largest, indicating that the agglomeration economy is at a relative disadvantage, and the internal urban development differences are large. From the aspect of connection degree, the mean value of high agglomeration and high connection cities is much higher than that of other types of cities, which indicates that such cities have high connection degree with the outside world and strong economic development ability. In the same way, the mean value of low agglomeration and low connection cities is the smallest, and the coefficient of variation is the largest, which indicates that the economic connection degree is in a relative disadvantage, and the internal urban development difference is large.

8.3.3.1

Cities with High Agglomeration and High Connection and Their Distribution

European cities have strong comprehensive strength in high agglomeration and high connection. The number of high agglomeration and high connection cities is 4. From Table 8.14, it can be seen that high concentration and high connection cities include New York-Newark, London, Beijing and Paris, which are respectively subordinate to United States of America, United Kingdom, China and France; the population of New York-Newark and Beijing is more than 20 million, and that of London and Paris is more than 12 million. The per capita GDP of these cities is generally high, especially New York-Newark is as high as $89,135.75, while Beijing is as low as $18,748.91; from the perspective of the continent, European cities occupy 2 seats, North American and Asian cities occupy 1 respectively, which shows that European cities have strong comprehensive strength in high agglomeration and high connection. Table 8.14 High agglomeration and high connection cities City type

City

Country

Continent

High agglomeration and high New York-Newark United States of America North America connection

Source CCC of CASS

London

United Kingdom

Europe

Beijing

China

Asia

Paris

France

Europe

446

8 A New Set of Standards for Global City Classification

Table 8.15 High agglomeration and middle connection cities City type

City

Country

Continent

High agglomeration and middle connection

Tokyo

Japan

Asia

Seoul

Republic of Korea

Asia

Hong Kong

China

Asia

Source CCC of CASS

8.3.3.2

Cities with High Agglomeration and Middle Connection and Their Distribution

Asian cities have strong comprehensive strength in high agglomeration and middle connection. It can be seen from Table 8.15 that the cities in high agglomeration and middle connection include Tokyo, Seoul and Hong Kong, which are respectively subordinate to Japan, Republic of Korea and China; the population of Tokyo and Seoul are all over 25 million, and Hong Kong is 7.4 million. The per capita GDP of these cities is generally high, especially Hong Kong is as high as $47,264.37, while Seoul is low as $29,417.38; from the perspective of the continent, all three cities belong to In Asia, it shows that Asian cities have a strong comprehensive strength in the aspect of degree in high agglomeration and middle connection, and also emphasizes that Asian cities should enhance the ability of connection degree.

8.3.3.3

Cities with High Agglomeration and Low Connection and Their Distribution

Osaka needs to improve its soft and hard connection. From Table 8.16, it can be seen that high concentration and low connection cities only include Osaka, which belongs to Japan; Osaka has a population of more than 20 million and a per capita GDP of $46,538.61, which belongs to Asia, indicating that Osaka needs to improve its soft and hard connection. Table 8.16 High concentration and low connection cities City type

City

Country

Continent

High agglomeration and low connection

Osaka

Japan

Asia

Source CCC of CASS

8.3 Empirical Analysis

8.3.3.4

447

Cities with Middle Agglomeration and Middle Connection and Their Distribution

European and Asian cities need to improve the degree of agglomeration and connection. There are 91 cities of middle agglomeration and middle connection. As can be seen from Table 8.17, the cities of middle agglomeration and middle connection mainly include Sydney, Dublin, Vienna, Dubai etc., and the countries are including Australia, Ireland, Austria, the United Arab Emirates, etc.; the average population size of these cities is 6.837 million, and the average GDP per capita is $42,924.92. From the perspective of the continent of representative cities, Europe has the largest number of cities, occupy 25 seats; next are Asian cities, occupy 10 seats; third are North America, South America and Africa cities, occupy 4 seats respectively, Oceania occupy 2 seats, which shows that European cities have strong comprehensive strength in middle agglomeration and middle connection, but also need to improve the degree of agglomeration and connection.

8.3.3.5

Cities with Middle Agglomeration and Low Connection and Their Distribution

Asian cities have a large number of cities with middle agglomeration and low connection. The number of cities with middle agglomeration and low connection is 329. From the observation of Table 8.18, it can be seen that the cities of middle agglomeration and low connection mainly include Adelaide, Cordoba, Abu Dhabi, Alexandria etc., and the countries are including Australia, Argentina, United Arab Emirates, Egypt, etc.; the average population size of such cities is 3.407 million, and the average GDP per capita is $25,423.55; from the perspective of the continent of representative cities, Asia has the largest number of cities with 18 seats, followed by Europe with 12 seats, South America with 8 seats, North America, Africa and Oceania with 6, 5 and 1 seats respectively, indicating that Asian cities have a large number of middle agglomeration and low connection, and need to improve their soft hard connection.

8.3.3.6

Cities with Low Agglomeration and Low Connection and Their Distribution

Asian cities need to pay attention to urban differentiation while enhancing their agglomeration and connection. The number of cities with low agglomeration and low connection is the largest, 578. From the observation of Table 8.19, it can be seen that the low concentration and low connection cities mainly include Gold Coast, Algiers, Baku, Muscat, etc., and the countries are belong to are Australia, Algeria, Azerbaijan, Oman, etc. the per capita GDP of these cities is generally low, with an average of $7112.58 and an average population of 1.945 million. In terms of the continent to which the representative cities belong, African cities are the most, accounting

448

8 A New Set of Standards for Global City Classification

Table 8.17 Middle agglomeration and middle connection cities City type

City

Country

Continent

Middle agglomeration and middle connection

Sydney

Australia

Oceania

Dublin

Ireland

Europe

Vienna

Austria

Europe

Dubai

United Arab Emirates

Asia

Buenos Aires

Argentina

South America

Cairo

Egypt

Africa

Panama City

Panama

North America

Sao Paulo

Brazil

South America

Brussels

Belgium

Europe

Copenhagen

Denmark

Europe

Warsaw

Poland

Europe

Sofia

Bulgaria

Europe

Munich

Germany

Europe

Moscow

Russian Federation

Europe

Amsterdam

Netherlands

Europe

Helsinki

Finland

Europe

Bogota

Colombia

South America

Lyon

France

Europe

Toronto

Canada

North America

Prague

Czech Republic

Europe

Kuala Lumpur

Malaysia

Asia

Doha

Qatar

Asia

Zagreb

Croatia

Europe

Bucuresti

Romania

Europe

Nairobi

Kenya

Africa

Chicago

United States of America

North America

Oslo

Norway

Europe

Mexico City

Mexico

North America

Johannesburg

South Africa

Africa

Lima

Peru

South America

Casablanca

Morocco

Africa

Lisbon

Portugal

Europe

Stockholm

Sweden

Europe

Zurich

Switzerland

Europe

Istanbul

Turkey

Asia (continued)

8.3 Empirical Analysis

449

Table 8.17 (continued) City type

City

Country

Continent

Bangkok

Thailand

Asia

Riyadh

Saudi Arabia

Asia

Belgrade

Serbia

Europe

Kiev

Ukraine

Europe

Madrid

Spain

Europe

Singapore

Singapore

Asia

Athens

Greece

Europe

Auckland

New Zealand

Oceania

Budapest

Hungary

Europe

Milan

Italy

Europe

Mumbai

India

Asia

Jakarta

Indonesia

Asia

Manchester

United Kingdom

Europe

Shanghai

China

Asia

……

……

……

Note Due to the large number of middle agglomeration and middle connection, one representative city in each country is selected. The following tables are the same Source CCC of CASS

for 39 seats; Asian cities are the second, accounting for 33 seats; South American and North American cities are the third, accounting for 10 and 9 seats respectively; European and Oceanian cities are the 3 and 1 respectively, indicating that African and Asian cities have a large number of low agglomeration and low connection cities, and the degree of agglomeration and connection need to be strengthened. At the same time, it also shows that Asian cities are seriously divided, including the relatively developed cities in high agglomeration and high connection and the underdeveloped cities in low agglomeration and low connection.

8.3.4 Differences Types in Global City from the Perspective of “Hard” and “Soft” The development gap between softness and hardness cities is large, and the softness can change the hardness to a certain extent. Using clustering method, the types of hardness (hard agglomeration + hard connection) and softness (soft agglomeration + soft connection) are divided into three categories (strong, middle and weak)respectively. By combining 1006 cities in the world, according to the types of hardness and softness, they can be divided into nine categories (strong hardness and strong softness, strong hardness and middle softness, strong hardness and weak

450

8 A New Set of Standards for Global City Classification

Table 8.18 Middle agglomeration and low connection cities City type

City

Country

Continent

middle agglomeration and low connection

Adelaide

Australia

Oceania

Cordoba

Argentina

South America

Abu Dhabi

United Arab Emirates

Asia

Alexandria

Egypt

Africa

Karachi

Pakistan

Asia

Campinas

Brazil

South America

Minsk

Belarus

Asia

Antwerp

Belgium

Europe

San Juan

Puerto Rico

North America

Krakow

Poland

Europe

Santa Cruz

Bolivia

South America

Hannover

Germany

Europe

Santiago de Chile

United States of North America America

Novosibirsk

Russian Federation

Europe

Quito

Ecuador

South America

Manila

Philippines

Asia

Marseille-Aix-en-Provence

France

Europe

Medellin

Colombia

South America

Almaty

Kazakhstan

Asia

Incheon

Republic of Korea

Asia

Rotterdam

Netherlands

Europe

Calgary

Canada

North America

Riga

Latvia

Europe

Beirut

Lebanon

Asia

Dhaka

Bangladesh

Asia

Rabat

Morocco

Africa

Austin

United States of North America America

Guadalajara

Mexico

North America

Cape Town

South Africa

Africa

Lagos

Nigeria

Africa

Tunis

Tunisia

Africa

Colombo

Sri Lanka

Asia (continued)

8.3 Empirical Analysis

451

Table 8.18 (continued) City type

City

Country

Continent

Gothenburg

Sweden

Europe

Medina

Saudi Arabia

Asia

Nagoya

Japan

Asia

Ankara

Turkey

Asia

Barcelona-Puerto La Cruz

Venezuela

South America

Montevideo

Uruguay

South America

Valencia

Spain

Europe

Tehran

Iran (Islamic Republic of)

Asia

Kingston

Jamaica

North America

Thessaloniki

Greece

Europe

Yerevan

Armenia

Asia

Jerusalem

Israel

Asia

Florence

Italy

Europe

Chennai

India

Asia

Bristol

United Kingdom

Europe

Ho Chi Minh City

Viet Nam

Asia

Santiago de Chile

United States of North America America

Wuhan

China

Asia

……

……

……

Source CCC of CASS

softness, middle hardness and strong softness, middle hardness and middle softness, middle hardness and weak softness, weak hardness and strong softness, weak hardness and middle softness, weak hardness and weak softness). It can be seen from Table 8.20 that the number of strong hardness and strong softness cities is 5; the number of middle hardness and strong softness cities is 16; the number of middle hardness and middle softness cities is 11; the number of weak hardness and strong softness, weak hardness and middle softness and weak hardness and weak softness cities are 16, 331 and 627, respectively. The number of other cities (strong hardness and middle softness, strong hardness and weak softness, middle hardness and weak softness) is 0. It shows that the hardness is not completely related to the softness. The softness can be strong if it hardness is weak, but hardness is difficult to be strong if it softness is weak. To some extent, the softness can change the hardness. The mean value hardness of all cities is 0.0782, the standard deviation is 0.1272, and the coefficient of variation is 1.6255; it can be seen that the coefficient of variation of weak hardness and weak softness cities (hardness) exceeds the coefficient of variation of all cities, indicating that there is a large difference in their internal

452

8 A New Set of Standards for Global City Classification

Table 8.19 Low agglomeration and low connection cities City type

City

Country

Continent

Low agglomeration and low connection

Gold Coast

Australia

Oceania

Algiers

Algeria

Africa

Baku

Azerbaijan

Asia

Muscat

Oman

Asia

Kabul

Afghanistan

Asia

Mendoza

Argentina

South America

Suez

Egypt

Africa

Addis Ababa

Ethiopia

Africa

Luanda

Angola

Africa

Faisalabad

Pakistan

Asia

Asuncion

Paraguay

South America

Gaza

State of Palestine

Asia

Natal

Brazil

South America

Cotonou

Benin

Africa

Cochabamba

Bolivia

South America

Ouagadougou

Burkina Faso

Africa

Santo Domingo

Dominican Republic

North America

Lome

Togo

Africa

Bujumbura

Burundi

Africa

Ufa

Russian Federation

Europe

Guayaquil

Ecuador

South America

Asmara

Eritrea

Africa

Cebu

Philippines

Asia

Kinshasa

Congo

Africa

Cali

Colombia

South America

Tbilisi

Georgia

Asia

San Jose

United States of America

North America

Havana

Cuba

North America

Port-au-Prince

Haiti

North America

Shymkent

Kazakhstan

Asia

Tegucigalpa

Honduras

North America

Bishkek

Kyrgyzstan

Asia

Conakry

Guinea

Africa

Djibouti

Djibouti

Africa

Accra

Ghana

Africa (continued)

8.3 Empirical Analysis

453

Table 8.19 (continued) City type

City

Country

Continent

Phnom Penh

Cambodia

Asia

Libreville

Gabon

Africa

Harare

Zimbabwe

Africa

Douala

Cameroon

Africa

Abidjan

The Republic of Cote d’ivoire

Africa

Kuwait City

Kuwait

Asia

Monrovia

Liberia

Africa

Vientiane

Lao People’s Democratic Republic

Asia

Mombasa

Kenya

Africa

Tripoli

Libya

Africa

Kigali

Rwanda

Africa

Antananarivo

Madagascar

Africa

Blantyre-Limbe

Malawi

Africa

Johor Bahru

Malaysia

Asia

Bamako

Mali

Africa

Nouakchott

Mauritania

Africa

El Paso

United States of America

North America

Ulan Bator

Mongolia

Asia

Chittagong

Bangladesh

Asia

Arequipa

Peru

South America

Mandalay

Myanmar

Asia

Chisinau

Republic of Moldova

Europe

Marrakech

Morocco

Africa

Maputo

Mozambique

Africa

Tijuana

Mexico

North America

Port Elizabeth

South Africa

Africa

Managua

Nicaragua

South America

Kathmandu

Nepal

Asia

Niamey

Niger

Africa

Abuja

Nigeria

Africa

Dakar

Senegal

Africa

San Salvador

El Salvador

North America

Freetown

Sierra Leone

Africa (continued)

454

8 A New Set of Standards for Global City Classification

Table 8.19 (continued) City type

City

Country

Continent

Dammam

Saudi Arabia

Asia

Khartoum

Sudan

Africa

Mogadishu

Somalia

Asia

Dushanbe

Tajikistan

Asia

Samut Prakan

Thailand

Asia

Dar es Salaam

United Republic of Tanzania

Africa

Safaqis

Tunisia

Africa

Antalya

Turkey

Asia

Guatemala City

Guatemala

North America

Ashgabat

Turkmenistan

Asia

Kampala

Uganda

Africa

Maracaibo

Venezuela

South America

Odessa

Ukraine

Europe

Tashkent

Uzbekistan

Asia

Damascus

Syrian Arab Republic

Asia

Sana’a’

Yemen

Asia

Baghdad

Iraq

Asia

Mashhad

Iran (Islamic Republic of)

Asia

Tel Aviv-Yafo

Israel

Asia

Jaipur

India

Asia

Surabaya

Indonesia

Asia

Amman

Jordan

Asia

Lusaka

Zambia

Africa

N’Djamena

Chad

Africa

Valparaiso

Chile

South America

Bangui

Central African Republic

Africa

Huainan

China

Asia

……

……

……

Source CCC of CASS

development; the mean value softness of all cities is 0.4157, the standard deviation is 0.1500, and the coefficient of variation is 0.3607, and it can be seen that the coefficient of variation of all types of cities (softness) is not exceeded the coefficient of variation of all cities. It can be observed from Table 8.20 that, in terms of hardness, the mean value of strong hardness and strong softness cities is 0.8652, which is higher than that of other types of cities, indicating that such cities have absolute advantages in hard

8.3 Empirical Analysis

455

Table 8.20 City types by hard and soft degree City type City quantity

Mean value of hardness degree

Standard deviation of hardness degree

Coefficient of variation of hardness degree

Mean value of softness degree

Standard deviation of softness degree

Coefficient of variation of softness degree

Strong hardness and strong softness

5

0.8652

0.1102

0.1273

0.9493

0.0565

0.0595

Strong hardness and middle softness

0

–

–

–

–

–

–

Strong 0 hardness and weak softness

–

–

–

–

–

–

Middle hardness and strong softness

16

0.5599

0.0785

0.1402

0.7649

0.0331

0.0432

Middle hardness and middle softness

11

0.4742

0.0280

0.0590

0.6425

0.0682

0.1061

Middle 0 hardness and weak softness

–

–

–

–

–

–

Weak hardness and strong softness

0.3147

0.1096

0.3484

0.7765

0.0560

0.0722

16

(continued)

456

8 A New Set of Standards for Global City Classification

Table 8.20 (continued) City type City quantity

Mean value of hardness degree

Standard deviation of hardness degree

Coefficient of variation of hardness degree

Mean value of softness degree

Standard deviation of softness degree

Coefficient of variation of softness degree

Weak hardness and middle softness

331

0.1176

0.0975

0.8289

0.5395

0.0776

0.1439

Weak 627 hardness and weak softness

0.0259

0.0425

1.6432

0.3241

0.0812

0.2507

All cities 1006

0.0782

0.1272

1.6255

0.4157

0.1500

0.3607

Note – Means none Source CCC of CASS

agglomeration and hard connection; the minimum mean value of weak hardness and weak softness cities is 0.0259, and the maximum coefficient of variation is 1.6432, indicating that such cities are relatively inferior in hard agglomeration and hard connection, and there are great differences in internal urban development. From the aspect of softness degree, the mean value of strong hardness and strong softness cities is 0.9493, which is much higher than other types of cities, indicating that such cities have absolute advantages in soft agglomeration and soft connection. Similarly, the mean value of weak hardness and weak softness cities is the smallest, 0.3241; the coefficient of variation is the largest, 0.2507, which shows that such cities are in a relative disadvantage in soft agglomeration and soft connection, and there are great differences in internal urban development.

8.3.4.1

Strong Hardness and Strong Softness Cities and Their Distribution

European and Asian cities have strong strength in terms of strong hardness and strong softness. The number of strong hardness and strong softness cities is 5. From Table 8.21, it can be seen that strong hardness and strong softness cities include New York-Newark, London, Tokyo, Beijing and Paris, which are respectively subordinate to United States of America, United Kingdom, Japan, China and France; the per capita GDP of such cities is generally high, especially New York-Newark is as high as $89,135.75; the lowest is Beijing, which is $18,748.91. In terms of population scale, Tokyo has a maximum of 42 million people, while London and Paris have a relatively smaller population scale, with an average of 12.2733 million people. From the continental perspective, European and Asian cities occupy 2 seats respectively,

8.3 Empirical Analysis

457

Table 8.21 Strong hardness and strong softness cities City type

City

Country

Continent

Strong hardness and strong softness

New York-Newark

United States of America

North America

London

United Kingdom

Europe

Tokyo

Japan

Asia

Beijing

China

Asia

Paris

France

Europe

Source CCC of CASS

and North American cities occupy 1, indicating that European and Asian cities have strong comprehensive strength in terms of strong hardness and strong softness.

8.3.4.2

Middle Hardness and Strong Softness Cities and Their Distribution

European and Asian cities have strong comprehensive strength in middle hardness and strong softness type. The number of middle hardness and strong softness cities is 16. From the observation of Table 8.22, it can be seen that the middle hardness and strong softness cities mainly include Sydney, Seoul, Moscow, Toronto etc., and the countries are including Australia, Republic of Korea, Russian Federation, Canada, etc.; the per capita GDP of such cities is relatively high, with an average of $48,568.57 and an average population of 10.8055 million. From the perspective of the continent to which the representative cities belong, the cities of Asia and Europe occupy 4 seats respectively; the cities of North America occupy 2 seats; the cities of South America and Oceania occupy 1 seat respectively, indicating that the comprehensive strength of the cities of Europe and Asia is strong in the middle hardness and strong softness cities.

8.3.4.3

Middle Hardness and Middle Softness Cities and Their Distribution

European cities have strong comprehensive strength in middle hardness and middle softness types. The number of middle hardness and middle softness cities is 11. From the observation of Table 8.23, it can be seen that the middle hardness and middle softness cities mainly include Brussels, Dublin, Vienna, Dubai, etc. the countries they belong to are Belgium, Ireland, Austria, United Arab Emirates, etc. the average per capita GDP of such cities is $61,986.53, the highest is $93,831.45 of Switzerland; the population scale is small, the average is 4.5551 million; from

458

8 A New Set of Standards for Global City Classification

Table 8.22 Middle hardness and strong softness cities City type

City

Country

Continent

Middle hardness and strong softness

Sydney

Australia

Oceania

Seoul

Republic of Korea

Asia

Moscow

Russian Federation

Europe

Toronto

Canada

North America

Amsterdam

Netherlands

Europe

Sao Paulo

Brazil

South America

Chicago

United States of America

North America

Singapore

Singapore

Asia

Madrid

Spain

Europe

Istanbul

Turkey

Asia

Milan

Italy

Europe

Hong Kong

China

Asia

……

……

……

Note Due to the large number of middle hardness and strong softness cities, one representative city in each country is selected. The following tables are the same Source CCC of CASS

Table 8.23 Middle hardness and middle softness cities City type

City

Country

Continent

Middle hardness and middle softness

Brussels

Belgium

Europe

Dublin

Ireland

Europe

Vienna

Austria

Europe

Dubai

United Arab Emirates

Asia

Munich

Germany

Europe

Dallas-Fort Worth

United States of America

North America

Taipei

China

Asia

Zurich

Switzerland

Europe

Bangkok

Thailand

Asia

……

……

……

Source CCC of CASS

the perspective of representative cities of continent, European cities occupy 5 seats; Asian cities occupy 3 seats; North America occupies 1 seat, which indicates that European cities have strong comprehensive strength in middle hardness and middle softness cities.

8.3 Empirical Analysis

459

Table 8.24 Weak hardness and strong softness cities City type

City

Country

Continent

Weak hardness and strong softness

Melbourne

Australia

Oceania

Berlin

Germany

Europe

Boston

United States of America

North America

Stockholm

Sweden

Europe

Rome

Italy

Europe

Barcelona

Spain

Europe

Osaka

Japan

Asia

Guangzhou

China

Asia

……

……

……

Source CCC of CASS

8.3.4.4

Weak Hardness and Strong Softness Cities and Their Distribution

European cities have strong comprehensive strength in weak hard and strong soft cities. The number of weak hardness and strong softness cities is 16. From Table 8.24, it can be seen that the weak hardness and strong softness cities mainly include Melbourne, Berlin, Boston, Stockholm, etc. the countries they belong to are Australia, Germany, United States of America, Sweden, etc. the per capita GDP of these cities is relatively high, with an average of $55,408.17, and the population scale is relatively small, with an average of 6.7884 million. From the perspective of the continent of representative cities, European cities occupy 4 seats; Asian cities occupy 2 seats; North America and Oceania occupy 1 seat respectively, which shows that European cities have strong comprehensive strength in weak hardness and strong softness cities, but also need to improve their hard agglomeration and hard connection.

8.3.4.5

Weak Hardness and Middle Softness Cities and Their Distribution

European cities have a large number of cities of weak hardness and middle softness types. The number of weak hardness and middle softness cities is 331. From Table 8.25, it can be seen that the weak hardness and middle softness cities mainly include Brisbane, Buenos Aires, Abu Dhabi, Cairo, etc. the countries they belong to are Australia, Argentina, the United Arab Emirates, Egypt and so on. The average per capita GDP of such cities is $27,886.25, and the population scale is small, with an average of 3.8277 million people. From the perspective of the continent of representative cities, European cities are the largest, accounting for 24 seats, followed by Asian cities, accounting for 18 seats, South America, accounting for 9 seats, North America, Africa and Oceania respectively accounting for 5, 5 and

460

8 A New Set of Standards for Global City Classification

2 seats, which shows that European cities have a large number of weak hardness and middle softness cities, so it is urgent to improve the degree of hard agglomeration and hard connection. Table 8.25 Weak hardness and middle softness cities City type

City

Country

Continent

Weak hardness and middle softness

Brisbane

Australia

Oceania

Buenos Aires

Argentina

South America

Abu Dhabi

United Arab Emirates

Asia

Cairo

Egypt

Africa

Karachi

Pakistan

Asia

Rio de Janeiro

Brazil

South America

Sofia

Bulgaria

Europe

Minsk

Belarus

Asia

Antwerp

Belgium

Europe

San Juan

Puerto Rico

North America

Warsaw

Poland

Europe

Copenhagen

Denmark

Europe

Santa Cruz

Bolivia

South America

Stuttgart

Germany

Europe

Quito

Ecuador

South America

Novosibirsk

Russian Federation

Europe

Lyon

France

Europe

Helsinki

Finland

Europe

Manila

Philippines

Asia

Bogota

Colombia

South America

Tbilisi

Georgia

Asia

Incheon

Republic of Korea

Asia

Rotterdam

Netherlands

Europe

Montreal

Canada

North America

Prague

Czech Republic

Europe

Kuala Lumpur

Malaysia

Asia

Doha

Qatar

Asia

Zagreb

Croatia

Europe (continued)

8.3 Empirical Analysis

461

Table 8.25 (continued) City type

Source CCC of CASS

City

Country

Continent

Bucuresti

Romania

Europe

Riga

Latvia

Europe

Nairobi

Kenya

Africa

Beirut

Lebanon

Asia

Denver-Aurora

United States of America North America

Lima

Peru

South America

Casablanca

Morocco

Africa

Mexico City

Mexico

North America

Oslo

Norway

Europe

Johannesburg

South Africa

Africa

Lisbon

Portugal

Europe

Nagoya

Japan

Asia

Geneva

Switzerland

Europe

Belgrade

Serbia

Europe

Gothenburg

Sweden

Europe

Riyadh

Saudi Arabia

Asia

Tunis

Tunisia

Africa

Colombo

Sri Lanka

Asia

Ankara

Turkey

Asia

Barcelona-Puerto La Cruz

Venezuela

South America

Kiev

Ukraine

Europe

Montevideo

Uruguay

South America

Valencia

Spain

Europe

Athens

Greece

Europe

Auckland

New Zealand

Oceania

Budapest

Hungary

Europe

Tehran

Iran (Islamic Republic of) Asia

Kingston

Jamaica

North America

Jerusalem

Israel

Asia

Bologna

Italy

Europe

Mumbai

India

Asia

Jakarta

Indonesia

Asia

Manchester

United Kingdom

Europe

Santiago de Chile

United States of America North America

Chengdu

China

Asia

……

……

……

462

8.3.4.6

8 A New Set of Standards for Global City Classification

Weak Hardness and Weak Softness Cities and Their Distribution

Asian cities have a large number of weak hardness and weak softness cities, with serious internal differentiation. The number of weak hardness and weak softness cities is 627. From the observation of Table 8.26, it can be seen that weak hardness and weak softness cities mainly include Gold Coast, Algiers, Kabul, Mendoza, etc., and their countries are Australia, Algeria, Afghanistan, Argentina, etc. The per capita GDP of such cities is generally low, with an average of $7861.22 and a small population of 2.1165 million. In terms of the continent to which the representative cities belong, African cities are the most, accounting for 39 seats; Asian cities are the second, accounting for 37 seats; South American and North American cities are the third, accounting for 10 seats respectively; European and Oceanian cities are 5 and 1 seats respectively, indicating that African and Asian cities have a large number of weak hardness and weak softness cities, which need to be strengthened; meanwhile, the internal differences is serious Asian cities, including the relatively developed cities with strong hardness and strong softness, and the underdeveloped cities with weak hardness and weak softness.

8.3.5 Classification of Chinese Cities There are no global cities in Chinese cities, with the largest number of regional gateway cities. The situation of Chinese cities in the global city classification is shown in Table 8.27, where the number of A+ cities is 0; the number of A cities is 1; the number of B+ cities is 1; the number of B cities is 2; the number of C+ cities and C cities are 4 and 18, respectively. Similarly, the number of D+ cities is 22; the number of D cities is 100; the number of E+ cities and E cities are 141 and 2, respectively. The total number of Chinese cities is 291, and the mean value score of city rank is 0.2379, the standard variance is 0.0992, and the coefficient of variation is 0.4170. The mean value of D and above cities is higher than that of Chinese cities, and A+ city is the highest, which is 0.9056. Similarly, the coefficient of variation of all types of cities is smaller than that of Chinese cities, and the coefficient of variation of E cities is the smallest, which indicates that the internal differences of these cities are small; the coefficient of variation of B cities is the largest, which is 0.1611, which indicates that the internal development differences of these cities are large.

8.3.5.1

Composition and Characteristics of a Cities

Beijing has the highest score of city level. There is no A+ level city in China. As can be seen from Table 8.28, there is only 1 A City, Beijing. From the perspective of characteristics, Beijing is a high agglomeration and high connection city; from the

8.3 Empirical Analysis

463

Table 8.26 Weak hardness weak softness cities City type

City

Country

Continent

Weak hardness and middle softness

Gold Coast

Australia

Oceania

Algiers

Algeria

Africa

Kabul

Afghanistan

Asia

Mendoza

Argentina

South America

Baku

Azerbaijan

Asia

Muscat

Oman

Asia

Sharjah

United Arab Emirates

Asia

Suez

Egypt

Africa

Addis Ababa

Ethiopia

Africa

Luanda

Angola

Africa

Faisalabad

Pakistan

Asia

Panama City

Panama

North America

Asuncion

Paraguay

South America

Gaza

State of Palestine

Asia

Goiania

Brazil

South America

Cotonou

Benin

Africa

Cochabamba

Bolivia

South America

Ouagadougou

Burkina Faso

Africa

Lome

Togo

Africa

Bujumbura

Burundi

Africa

Santo Domingo

Dominican Republic

North America

Saint Petersburg

Russian Federation

Europe

Guayaquil

Ecuador

South America

Toulon

France

Europe

Asmara

Eritrea

Africa

Cebu

Philippines

Asia

Kinshasa

Congo

Africa

Cali

Colombia

South America

San Jose

United States of America

North America (continued)

464

8 A New Set of Standards for Global City Classification

Table 8.26 (continued) City type

City

Country

Continent

Havana

Cuba

North America

Almaty

Kazakhstan

Asia

Port-au-Prince

Haiti

North America

Changwon

Republic of Korea

Asia

Tegucigalpa

Honduras

North America

Bishkek

Kyrgyzstan

Asia

Conakry

Guinea

Africa

Djibouti

Djibouti

Africa

Accra

Ghana

Africa

Phnom Penh

Cambodia

Asia

Libreville

Gabon

Africa

Harare

Zimbabwe

Africa

Douala

Cameroon

Africa

Abidjan

The Republic of Cote d’ivoire

Africa

Kuwait City

Kuwait

Asia

Monrovia

Liberia

Africa

Vientiane

Lao People’s Democratic Republic

Asia

Mombasa

Kenya

Africa

Tripoli

Libya

Africa

Kigali

Rwanda

Africa

Antananarivo

Madagascar

Africa

Blantyre-Limbe

Malawi

Africa

Johor Bahru

Malaysia

Asia

Bamako

Mali

Africa

Nouakchott

Mauritania

Africa

Birmingham

United States of America

North America

Ulan Bator

Mongolia

Asia

Dhaka

Bangladesh

Asia

Arequipa

Peru

South America

Mandalay

Myanmar

Asia

Chisinau

Republic of Moldova

Europe (continued)

8.3 Empirical Analysis

465

Table 8.26 (continued) City type

City

Country

Continent

Fes

Morocco

Africa

Maputo

Mozambique

Africa

Tijuana

Mexico

North America

Port Elizabeth

South Africa

Africa

Managua

Nicaragua

South America

Kathmandu

Nepal

Asia

Niamey

Niger

Africa

Lagos

Nigeria

Africa

Kitakyushu-Fukuoka

Japan

Asia

Dakar

Senegal

Africa

San Salvador

El Salvador

North America

Freetown

Sierra Leone

Africa

Dammam

Saudi Arabia

Asia

Khartoum

Sudan

Africa

Mogadishu

Somalia

Asia

Dushanbe

Tajikistan

Asia

Samut Prakan

Thailand

Asia

Dar es Salaam

United Republic of Tanzania

Africa

Safaqis

Tunisia

Africa

Antalya

Turkey

Asia

Guatemala City

Guatemala

North America

Ashgabat

Turkmenistan

Asia

Caracas

Venezuela

South America

Kampala

Uganda

Africa

Odessa

Ukraine

Europe

Tashkent

Uzbekistan

Asia

Damascus

Syrian Arab Republic

Asia

Yerevan

Armenia

Asia

Sana’a’

Yemen

Asia

Baghdad

Iraq

Asia

Mashhad

Iran (Islamic Republic of)

Asia

Tel Aviv-Yafo

Israel

Asia (continued)

466

8 A New Set of Standards for Global City Classification

Table 8.26 (continued) City type

City

Country

Continent

Ahmedabad

India

Asia

Surabaya

Indonesia

Asia

Amman

Jordan

Asia

Newcastle upon Tyne

United Kingdom

Europe

Ho Chi Minh City

Viet Nam

Asia

Lusaka

Zambia

Africa

N’Djamena

Chad

Africa

Concepcion

Chile

South America

Bangui

Central African Republic

Africa

Hohhot

China

Asia

Source CCC of CASS

Table 8.27 Global city classification of Chinese cities City level Global city (A)

Number of cities

Mean value

Standard deviation

Coefficient of variation

A+

0

–

–

–

A

1

0.9056

–

–

International hub city (B)

B+

1

0.7527

–

–

B

2

0.6616

0.1066

0.1611

International gateway city (C)

C+

4

0.5297

0.0294

0.0556

C

18

0.4183

0.0391

0.0934

Regional hub city (D)

D+

22

0.3235

0.0136

0.0421

D

100

0.2421

0.0241

0.0995

Regional gateway city (E)

E+

141

0.1772

0.0183

0.1031

E

2

0.1305

0.0011

0.0088

291

0.2379

0.0992

0.4170

Chinese city Source CCC of CASS

perspective of softness and hardness, it is a strong hardness and strong softness city. It shows that the higher the degree of agglomeration and connection, the stronger the hardness and softness, the stronger the overall strength of the city. Table 8.28 A cities

City level

City

Country

Continent

A cities

Beijing

China

Asia

Source CCC of CASS

8.3 Empirical Analysis Table 8.29 B+ cities

467

City level

City

Country

Continent

B+ cities

Shanghai

China

Asia

Source CCC of CASS

Table 8.30 B cities

City level

City

Country

Continent

B cities

Hong Kong

China

Asia

Taipei

China

Asia

Source CCC of CASS

8.3.5.2

Composition and Characteristics of B Cities

B level cities have obvious differences in agglomeration and softness. It can be seen from Table 8.29 that the number of B+ cities is 1, Shanghai. From the aspect of characteristics, Shanghai belongs to the cities with middle agglomeration and middle connection, and from the aspect of softness and hardness, it belongs to the cities with middle hardness and strong softness. It shows that the degree of agglomeration, connection and the hardness of Shanghai need to be strengthened. As can be seen from Table 8.30, the number of B cities is 2, Hong Kong and Taipei. From the perspective of characteristics, Hong Kong belongs to the high agglomeration and middle connected city; Taipei belongs to the middle agglomeration and middle connected city; from the perspective of softness and hardness, Hong Kong belongs to the middle hardness and strong softness city; Taipei belongs to the middle hardness and middle softness city. It shows that there are obvious differences in the degree of agglomeration and the degree of softness in B cities.

8.3.5.3

Composition and Characteristics of C Cities

C grade cities need to improve the hardness. The number of C+ cities is 4. As can be seen from Table 8.31, the C+ cities include Guangzhou, Shenzhen, Chengdu and Nanjing. From the perspective of characteristics, C+ cities all belong to middle agglomeration and middle connected; from the perspective of softness and hardness, only Chengdu is a weak hardness and middle softness city, and the other three cities are weak hardness and strong softness cities. It shows that the hardness of C+ cities needs to be strengthened. The number of C cities is 18. From Table 8.32, it can be seen that C cities mainly include Hangzhou, Wuhan, Tianjin, Chongqing, etc. In terms of characteristics, C cities generally belong to the type of middle agglomeration and low connection; in terms of softness and hardness, most of them belong to the type of weak hardness and middle softness. It shows that C cities needs to improve the degree of connection and hardness.

468

8 A New Set of Standards for Global City Classification

Table 8.31 C+ cities

City level

City

Country

Continent

C+ cities

Guangzhou

China

Asia

Shenzhen

China

Asia

Chengdu

China

Asia

Nanjing

China

Asia

Source CCC of CASS

Table 8.32 C cities

City level

City

Country

Continent

C cities

Hangzhou

China

Asia

Wuhan

China

Asia

Tianjin

China

Asia

Chongqing

China

Asia

Xi’an

China

Asia

Qingdao

China

Asia

Changsha

China

Asia

Xiamen

China

Asia

Hefei

China

Asia

Dalian

China

Asia

Shenyang

China

Asia

Jinan

China

Asia

Zhengzhou

China

Asia

Kunming

China

Asia

Suzhou

China

Asia

Harbin

China

Asia

Fuzhou(FJ)

China

Asia

Ningbo

China

Asia

Source CCC of CASS

8.3.5.4

Composition and Characteristics of D Cities

D grade cities need to improve their connection and hardness. The number of D+ cities is 22. From Table 8.33, it can be seen that the D+ cities mainly include Changchun, Wuxi, Shijiazhuang, Taiyuan, etc. In terms of characteristics, D+ cities belong to the type of middle agglomeration and low connection; in terms of softness and hardness, they belong to the type of weak hardness and middle softness. It shows that D+ cities need to improve the connection and hardness. The number of D cities is 100. From Table 8.34, it can be seen that D cities mainly include Xinzhu, Guilin, Taizhong, Luoyang, etc. From the perspective of characteristics, most of the cities of D belong to the type of middle agglomeration

8.3 Empirical Analysis Table 8.33 D+ cities

469

City level

City

Country

Continent

D+ cities

Changchun

China

Asia

Wuxi

China

Asia

Shijiazhuang

China

Asia

Taiyuan

China

Asia

Nanchang

China

Asia

Guiyang

China

Asia

Nanning

China

Asia

Lanzhou

China

Asia

Zhuhai

China

Asia

Urumqi

China

Asia

Dongguan

China

Asia

Kaohsiung

China

Asia

Wenzhou

China

Asia

Haikou

China

Asia

Nanyang

China

Asia

Xuzhou

China

Asia

Nantong

China

Asia

Changzhou

China

Asia

Foshan

China

Asia

Macao

China

Asia

Yantai

China

Asia

Zhongshan

China

Asia

Source CCC of CASS

and low connection; from the perspective of softness and hardness, most of them belong to the type of weak hardness and middle softness. It shows that the city of D needs to improve its connection and hardness.

8.3.5.5

Composition and Characteristics of E Cities

E grade cities need to improve the degree of agglomeration, connection, hardness and softness. The number of E+ cities is 141. As can be seen from Table 8.35, E+ cities mainly include Baoji, Suqian, Huangshi, Chifeng, etc. From the perspective of characteristics, E+ cities belong to the type of low agglomeration and low connection; from the perspective of softness and hardness, they belong to the type of weak hardness and weak softness. It shows that E+ cities need to improve their agglomeration and connection, hardness and softness. It can be seen from Table 8.36 that the number of E cities is 2, Hegang and Liaoyuan. From the aspect of characteristics, all E cities belong to the type of low

470 Table 8.34 D cities

8 A New Set of Standards for Global City Classification

City level

City

Country

Continent

D cities

Hsinchu

China

Asia

Guilin

China

Asia

Taichung

China

Asia

Luoyang

China

Asia

Yinchuan

China

Asia

Yangzhou

China

Asia

Hohhot

China

Asia

Jilin

China

Asia

Zhenjiang

China

Asia

Lianyungang

China

Asia

Weifang

China

Asia

Huizhou

China

Asia

Mianyang

China

Asia

Anyang

China

Asia

Yancheng

China

Asia

Shantou

China

Asia

Tangshan

China

Asia

Quanzhou

China

Asia

Xining

China

Asia

Jiaxing

China

Asia

Zibo

China

Asia

Weihai

China

Asia

Handan

China

Asia

Tainan

China

Asia

Xiangyang

China

Asia

Note There are many cities of D level, so only 25 cities are selected as representatives. The following table is the same Source CCC of CASS

agglomeration and low connection; from the aspect of softness and hardness, they belong to the type of weak hardness and weak softness. It shows that E cities need to strengthen their agglomeration and connection, hardness and softness.

References Table 8.35 E+ cities

Table 8.36 E cities

471

City level

City

Country

Continent

e+ cities

Baoji

China

Asia

Suqian

China

Asia

Huangshi

China

Asia

Chifeng

China

Asia

Huanggang

China

Asia

Jingzhou

China

Asia

Qingyuan

China

Asia

Qinzhou

China

Asia

Taian

China

Asia

Lijiang

China

Asia

Suining

China

Asia

Loudi

China

Asia

Putian

China

Asia

Chengde

China

Asia

Rizhao

China

Asia

Longyan

China

Asia

Hanzhong

China

Asia

Karamay

China

Asia

Ordoss

China

Asia

Baoji

China

Asia

Zaozhuang

China

Asia

Jinzhong

China

Asia

Dandong

China

Asia

Zhangjiajie

China

Asia

Meizhou

China

Asia

Meishan

China

Asia

City level

City

Country

Continent

E cities

Hegang

China

Asia

Liaoyuan

China

Asia

Source CCC of CASS

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Commendatore, P., Kubin, I., Mossay, P., & Sushko, I. (2017). The role of centrality and market size in a four-region asymmetric new economic geography model. Journal of Evolutionary Economics, 27(5), 1095–1131. Davis, D. R., & Dingel, J. I. (2014). The comparative advantage of cities (No. w20602). National Bureau of Economic Research. Derudder, B., & Witlox, F. (2008). Mapping world city networks through airline flows: Context, relevance, and problems. Journal of Transport Geography, 16(5), 305–312. Derudder, B., Witlox, F., & Catalano, G. (2003). Hierarchical tendencies and regional patterns in the world city network: a global urban analysis of 234 cities. Regional Studies, 37(9), 875–886. Duranton, G., & Puga, D. (2001). Nursery cities: Urban diversity, process innovation, and the life cycle of products. American Economic Review, 91(5), 1454–1477. Duranton, G., & Puga, D. (2005). From sectoral to functional urban specialisation. Journal of Urban Economics, 57(2), 343–370. Esparza, A. X., & Krmenec, A. J. (2000). Large city interaction in the US urban system. Urban Studies, 37(4), 691–709. Friedmann, J. (1986). The world city hypothesis. Development and Change, 17(1), 69–83. Fujita, M., & Krugman, P. (1995). When is the economy monocentric? von Thünen and Chamberlin unified. Regional Science and Urban Economics, 25(4), 505–528. Fujita, M., & Ogawa, H. (1982). Multiple equilibria and structural transition of non-monocentric urban configurations. Regional Science and Urban Economics, 12(2), 161–196. Fujita, M., Krugman, P., & Mori, T. (1999). On the evolution of hierarchical urban systems1. European Economic Review, 43(2), 209–251. Henderson, J. V. (1974). The sizes and types of cities. American Economic Review, 64(4), 640–656. Jung, W.-S., Wang, F., & Stanley, H. E. (2008). Gravity model in the Korean highway. EPL (Europhysics Letters), 81(4), 48005. Sassen, S. (1991). The Global City: New York, London, Tokyo. Princeton, NJ: Princeton University Press. Taylor, P. J. (2001). Specification of the world city network. Geographical Analysis, 33(2), 181–194. Zhong, C., Schläpfer, M., Müller Arisona, S., Batty, M., Ratti, C., & Schmitt, G. (2017). Revealing centrality in the spatial structure of cities from human activity patterns. Urban Studies, 54(2), 437–455.

Appendix

Theory and Method of Urban Competitiveness Evaluation City, in the process of development, relaying on the internal organization efficiency and the external economic advantage which are formed by its factor endowments and space foundation, 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, known as the competitive level of the city. In terms of time and level, urban competitiveness can be divided into short-term competitiveness and long-term competitiveness. Short-term competitiveness is the ability to use direct factors and environment to create current wealth, while longterm competitiveness is the ability to use basic factors and environment to create wealth and provide utility sustainably. In the short term, the composition of economic competitiveness is the urban business environment; In the long run, the component of sustainable competitiveness is urban living environment. The relationship between the two is shown in Fig. A.1. On this basis, this report intends to build the following urban competitiveness model: urban sustainable competitiveness determines urban economic competitiveness through explanatory variables of economic competitiveness, and urban economic competitiveness further influences urban sustainable competitiveness through explanatory variables of economic competitiveness.

© China Social Sciences Press 2023 P. Ni et al., The World: 300 Years of Urbanization Expansion, https://doi.org/10.1007/978-981-99-3553-6

473

474

Appendix

Urban utility: performance of sustainable competitiveness

Urban value: performance of economic competitiveness

Direct factors and environment: composition of economic competitiveness

Basic factors and environment: composition of sustainable competitiveness Fig. A.1 The relationship between urban sustainable competitiveness urban economic competitiveness. Source CCC of CASS

Urban Economic Competitiveness Determining Mechanism and Definition of Urban Economic Competitiveness The city is an informal open organization composed of people, private sector, quasipublic sector, and public sector. In cities, enterprises organize their employees to create and provide private products and services to local and external markets, and public sectors organize employees to create and provide localized public goods and services, which together constitute a relatively independent complex in urban space. The determination of the competitiveness of a single city: In fact, the business choice of a company depends on the environmental conditions of its location, and the business choice of the enterprise also determines the level of added value of the enterprise. In a city, the local elemental environment and the external environment that can be effectively utilized determine the size, structure, and efficiency of the city’s industrial system (including industry and industry links), and the industrial system determines the urban value creation. The combination of the factors which influence the operation of enterprise group determines the industry choice of the enterprise group and determines the level of added value of the enterprise group. Comparison of global urban competitiveness: under the background of global integration, there are a large number of urban areas around the world with different endowments and costs in terms of subject quality and factor environment, as well as different distances and costs to approach and utilize factor environment outside cities. Under the open economic system, the difference of comparative advantages between cities caused by different factor environments leads to the industrial differences and division of labor among urban regions, which determines the scale, level,

Appendix

475

structure and efficiency of the corresponding urban industrial system, and thus the value created by cities is also very different. From the perspective of enterprises, a globalized company may layout its global industrial chain according to the factors and environmental conditions of different cities around the world, so as to form its global value chain. From the perspective of cities, under the global urban system, the system composed of environmental systems of various global urban elements determines the industrial network system of global cities, and the industrial network system among global cities determines the global value chain system. Global competition and change of patterns: as population, enterprises and some important factors of production between cities are fluid, differences in urban factor environments lead to differences in potential benefits. Therefore, there is not only division of labor, cooperation and trade among relevant cities, but also complex and diverse competition. Urban competition leads to the flow and allocation of resources and elements between cities following the principle of maximizing the interests of the subject, and the economic system tends to the general equilibrium including the balance of urban space. However, due to the change of the elements and environment between cities and the subjective quality, the reconfiguration of resources, elements and industries in space will be caused, and the process of the original general equilibrium is often interrupted and tends to a new equilibrium. Figure A.2 simplifies the process: A city forms its open factor environment system, cultivates its open industrial system and creates its value system by attracting the element, industry and wealth of B and C, using the factor environment of B and C and cooperating with their industries. The value system and industrial system of A city, in global competition, are also element systems which in turn affect the city itself which also applies to city B and C. In the cooperation and competition between cities in factor environment, industrial system and value gains, through the decision and reaction of factor environment, industrial system and value gains, the competitiveness of many cities is decided simultaneously and the pattern is changing constantly. According to the mechanism of city competition and development, the global competitiveness of a city can be understood as the ability of a city, in the process of cooperation, competition and development and compared with other cities, to attract, scramble for, control, and transform resources, occupy market and create value in a Fig. A.2 The determination mechanism of urban economic competitiveness. Source compiled by the author

A Value

C

Industry

B

476

Appendix

Table A.1 The revealed index system of global urban economic competitiveness Content

Data source

Economic increment

1.1 Increment of GDP

The average increment of GDP in five consecutive years 2012–2017, base year 2018, data from EIU

Economic density

1.2 GDP per square kilometers

The total GDP of urban administrative district in 2017 divided by the area, and adjusted by GDP per capita, data from EIU and CCC of CASS

Source CCC of CASS

faster, more efficient and more sustainable way to provide benefits for its residents (Ni PengFei, 2013). According to mechanism and definition, urban economic competitiveness can be divided into revealed (performance or output) competitiveness and composite (interpretative or input) competitiveness. For the economic competitiveness of cities, on the one hand, from the perspective of input, the elements and environment of each city are very different; on the other hand, from the perspective of output, the output of each city, namely the value created, can be compared with the unified standard.

The Revealed Framework and Index System of Economic Competitiveness According to the above definition, competitiveness, from the revealed or output aspect, is mainly expressed as the scale, level and growth of a city in terms of creating value and capturing economic rents within its space scope. According to the index minimization principle, economic density (GDP per capita) is an appropriate indicator of the efficiency and level of value creation, while economic increment (the difference between the current year’s GDP and the previous year’s GDP, considering the stability of the data, taking the average of the increment of the past 5 year’s GDP) is an appropriate indicator of the scale and growth rate of value creation. These two indexes can be used to synthesize an indicator reflecting the explained variables of economic competitiveness (Table A.1).

The Interpretive Framework and Index System of Economic Competitiveness Based on the perspective of the comprehensive environment of the elements and the theoretical model of the national economic cycle, this paper establishes a model of urban competitiveness including six potential variables: GUECJ = βLFJ + γLEJ + δSEJ + εHEJ + GCJ + αIQJ

(1)

Appendix

477

In the above equation, LF refers to local factors, including talents, science and technology and financial factors, is the main force of urban competition and development and the driving force for determining competitiveness. LE stands for living environment, refers to the local demand, reflecting the size of local demand and consumption capacity which is also a driving force of urban competitiveness. SE, soft environment, includes system, culture and social security, which affects the cost of urban production and transaction. HE, hard environment, includes infrastructure and ecological environment, which is the basic condition of doing business and determines the convenience of doing business. GC, global connection, encompasses the soft and hard connections between the city and the external environment, which determines the city’s ability to use external factors and demands. IQ refers to the quality of enterprises, which is the overall status of enterprises and industries of the city, including the status of large enterprises and industrial clusters. Enterprises and industries are the mainstays of urban development and competition. Those 6 variables have different contributions and functions to urban competitiveness, but each is indispensable. This model is centered on the enterprise (industry), taking the internal and external connection as the main line, communication system as the foundation, supply and demand as the content, and integrated the main body and the environment, the supply and demand, the stock and the increment, the software and the hardware, the internal and external and other factors that affect the competitiveness. The above six latent variables refer to six aspects, each of which accommodates many specific urban competitiveness factors. In accordance with the principle of grasping key factors and data availability, this article selects 35 indicators in 6 aspects to construct an interpretive indicator system for urban competitiveness (Table A.2).

Urban Sustainable Competitiveness Determining Mechanism and Definition of Urban Sustainable Competitiveness The decision mechanism of sustainable competitiveness of cities is basically the same as that of economic competitiveness. The difference is that sustainable competitiveness is a long-term mechanism and economic competitiveness is short-term. Therefore, the factors and environment that determine sustainable competitiveness are more fundamental and indirect, while the results of demonstrating urban sustainable competitiveness are more top-level and direct (Fig. A.3). Urban sustainable competitiveness refers to the ability of a city to enhance its advantages in economy, society, ecology, innovation, global connection and other aspects, and to seek systematic optimization to continuously meet the complex and advanced welfare utility of citizens. According to the mechanism and definition, urban sustainable competitiveness can be divided into revealed (performance or output) competitiveness and composition (interpretative or input) competitiveness. For the sustainable competitiveness

Quantity of museums

Medical & Health-care organizations per capita

Calculated by days of comfort temperature, annual precipitation, days of disastrous weather and days of good visibility

Calculated by PM2.5, CO2 emissions per capita and SO2 emissions per capita

The combined proportion of 10 types of land-forms such as forests, lakes, grasslands, rivers, farmland, glaciers and tundra, etc

House price/ income

Quality of Golf course

2.1 Heritage protection

2.2 Medical & Health-care

2.3 Climate comfort

2.4 Environmental pollution

2.5 Ecological diversity

2.6 Cost of living

2.7 Golf Course

Labor force (15–59)

(continued)

Crawler data from Google map

Data from Numbeo

Extraction data

Extraction data

Extraction data

Crawler data from Google map

Crawler data from Google map

Calculated by EIU data

Website search and calculation

Website search and calculation

1.6 Labor force

Quality of patents

1.4 Patent index

2. Living environment

Number of published papers

1.3 Paper index

Website search and calculation

Calculated by EIU data

Calculated by exchange transaction amount data

1.2 Stock exchange index

World Bank

Source

1.5 Youth population Percentage of Youth (20–29) ratio

Credit availability index of Doing Business report revised by city

1.1 Financing convenience

1. Local factors

Indicator content

Secondary indicator

Primary indicator

Table A.2 Global urban economic competitiveness interpretive index system

478 Appendix

Network speed

Distance to Top100 big port

Comprehensive evaluation of infrastructure of airport

Calculated according to the historical data of 6 kinds of natural disasters

4.3 Information accessibility

4.4 Shipping convenience

4.5 Airport index

4.6 Natural Disaster index

Results of Doing Business report adjusted by crawler data of public opinions on city’s business convenience

3.6 Business convenience

Calculated by night light data

Quality of library / city area

3.5 Knowledge density

4.2 Power adequacy

World Intellectual Property Report revised by city

3.4 Property Protection

Numbeo traffic data adjusted by crawler data of public opinions on traffic topic

Calculated by number of Starbucks, McDonald and Walmart

3.3 Cultural inclusion

4.1 Traffic congestion

Index of Economic Freedom revised by city

3.2 Economic freedom

4. Hardware environment

Crime rate

3.1 Social security

3. Software environment

Indicator content

Secondary indicator

Primary indicator

Table A.2 (continued)

(continued)

Columbia University World Bank

Extraction data

Website search and calculation

Crawler data

Extraction data

Numbeo.com Crawler data

World Bank Crawler data

Crawler data from Google map

World Intellectual Property Report

Crawler data from Google map

Index of Economic Freedom report

Data from Numbeo

Source

Appendix 479

Aggregate calculation of HQ distribution data of Forbes’ 2000 enterprises

GDP/labor force (15–59)

Grading of the best university in each city and adjusted by the quality of universities and colleges in each city

6.3 Enterprise connection

6.4 Labor productivity

6.5 University index

Source CCC of CASS

Aggregate calculation of HQ distribution data of top 1000 technology enterprises

6.2 Multinational Technology enterprises

connection degree calculation of distribution data of 25 technology multinational companies

5.5 Technology enterprise connections

Aggregate calculation of HQ distribution data of top 1000 banks

connection degree calculation of distribution data of 75 financial multinational companies Website search and calculation

5.4 Financial enterprise connections

6.1 Multinational Banks

Connection degree calculation of number of cooperative papers published

5.3 Research connections

6. Enterprise quality

Google trends & Baidu trends

5.2 Information connection

Website search and crawler data from Google map

Calculated by EIU data

Website search and calculation

Website search and calculation

Website search and calculation

Website search and calculation

Crawler data

Crawler data

Website search and calculation

Number of air lines

5.1 Airline index

Source

5. Global connections

Indicator content

Secondary indicator

Primary indicator

Table A.2 (continued)

480 Appendix

Appendix

481

Fig. A.3 The determination mechanism of urban sustainable competitiveness. Source compiled by the author

A Populat -oin utility

C

B

Elements

Fundamental environment

of cities, on one hand, from the perspective of input, it is the fundamental environment, with more long-term significance, that determines future development and competition; on the other hand, from the perspective of output, it is the population situation, with more long-term significance, that indicates future competition and development.

The Revealed Framework and Index System of Sustainable Competitiveness According to the definition of sustainability, from the perspective of performance or output, sustainable competitiveness is mainly manifested as the scale, level and growth of a city’s residents’ welfare utility within its space scope. According to the index minimization principle, high-income population density (the average highincome population size) is an appropriate indicator of high welfare utility, while highincome population growth (or the growth rate and size of high-income population, considering the stability of the data, taking the average of the past 5 year’s data) is an appropriate indicator of the scale and growth rate of utility creation. These two indexes can be used to synthesize an indicator reflecting the explained variables of sustainable competitiveness (Table A.3). Table A.3 The revealed indicator system of global urban sustainable competitiveness Content

Data source

High-income 1.1 Annual The average annual growth rate of high-income population from population growth of 2012 to 2017, base year 2018, data from EIU growth high-income population High-income 1.2 The high-income population in the urban administrative region in population High-income 2017 divided by the administrative area, and adjusted by GDP per density population capita. data from EIU and CCC of CASS per squared kilometers Source CCC of CASS

482

Appendix

The Interpretive Framework and Index System of Sustainable Competitiveness According to the above mechanisms and definitions, a sustainable competitive city should be: a vibrant business city; an innovation-driven knowledge city; a socially inclusive harmonious city; an environmentally friendly eco-city; and a globally connected international city. Based on this, a model of urban sustainable competitiveness including five explanatory variables is constructed: GUSCJ = αEVJ + γERJ + δSCJ + βTIJ + ECJ

(2)

From which, GUSCJ , EVJ , ERJ , SCJ , TIJ , ECJ , represent global urban sustainable competitiveness, economic vitality, environmental resilience, social inclusion, technological innovation, and external connections. Economic vitality mainly refers to the entrepreneurial environment and entrepreneurial performance. Economic vitality mainly refers to entrepreneurial environment and entrepreneurial performance, which is the foundation of sustainable competitiveness. Environmental resilience, including ecological environment and infrastructure, is the hardware basis for urban sustainable development. Social inclusion includes a variety of soft environments such as security, integrity, inclusiveness and social order. It reflects the capacity of social mobilization and social integration and is the software foundation for urban sustainable development. Scientific and technological innovation mainly refers to the innovation atmosphere and conditions, which is the ultimate driving force and inexhaustible driving force of urban development. External connections determine the extent to which cities are involved and influenced globally (Table A.4).

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 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.

Calculated according to the historical data of 6 kinds of natural disasters

(continued)

Columbia University World Bank

Extraction data

2.6 Natural Disaster index

Calculated by days of comfort temperature, annual precipitation, days of disastrous weather and days of good visibility

2.4 Climate comfort

Extraction data

Extraction data

The combined proportion of 10 types of land-forms such as forests, lakes, grasslands, rivers, farmland, glaciers and tundra, etc.

2.3 Ecological diversity

Extraction data

Numbeo.com Crawler data

Calculated by EIU data

Calculated by EIU data

Calculated by EIU data

World Intellectual Property Report

World Bank Crawler data

Source

2.5 Environmental Calculated by PM2.5, CO2 emissions per capita and SO2 emissions per capita pollution

Calculated by night light data

GDP/labor force (15–59)

1.6 Labor productivity

2.2 Power abundance

Economic density (GDP/area) change in 5 years

1.4 Economic density growth

Numbeo traffic data adjusted by crawler data of public opinions on traffic topic

Percentage of Youth (20–29)

1.3 Youth population ratio

2.1 Traffic congestion

World Intellectual Property Report revised by city

1.2 Property Protection

2. Environment resilience

Results of Doing Business report adjusted by crawler data of public opinions on city’s business convenience

1.1 Business convenience

1. Economic vitality

Indicator content

Secondary ndicator

Primary indicator

Table A.4 Global urban sustainable competitiveness interpretive index system

Appendix 483

4. Sci & Tech innovation

Number of published papers

HQ distributions of sic & tech enterprises

Grading of the best university in each city and adjusted by the quality of universities and colleges in each city

Quantity of libraries / city area

4.3 Tech enterprises index

4.4 University index

4.5Knowledge density

Medical & Health-care organizations per capita

3.6 Medical & Health-care

4.2 Paper index

Calculated by number of Starbucks, McDonald and Walmart

3.5 Cultural inclusion

Quantity of patents

House price/ income

3.4 Cost of living

4.1 Patent index

Crawler data from Twitter & Micro-blog

(continued)

Crawler data from Google map

Website search and crawler data from Google map

Website search and calculation

Website search and calculation

Website search and calculation

Crawler data from Google map

Crawler data from Google map

Data from Numbeo

Data from Numbeo

Crawler data from Google map

Gini coefficient

Quantity of museums

Source

3.3 Social equity

3.1Heritage protection

3. Social Inclusion

Indicator content

3.2 Social security Crime rate

Secondary ndicator

Primary indicator

Table A.4 (continued)

484 Appendix

Google trends & Baidu trends

Network speed

Distance to Top100 big port

Number of air lines

Connection degree calculation of number of cooperative papers published

5.2 Information connection

5.3 Info accessibility

5.4 Shipping convenience

5.5Airline index

5.6 Research connections

Source CCC of CASS

Multinational corporations’ connection

5.1 Enterprise connection

5. Global connections

Indicator content

Secondary ndicator

Primary indicator

Table A.4 (continued)

Crawler data

Website search and calculation

Website search and calculation

Crawler data

Crawler data

Website search and calculation

Source

Appendix 485

486

Appendix

Sample Cities The selection of sample cities is the basis for conducting research on urban sustainable competitiveness. In order to ensure the breadth and typicality of the research, the sample cities studied in this project are based on the “World Urbanization Outlook” issued by the United Nations Department of Economics and Affairs in 2015. Excluding samples of urban population less than 500,000, and combined with the specific circumstances of China and some other countries. In the end, 1006 cities were selected. In terms of spatial distribution, samples of this report involve cities in 135 countries and regions from 6 continents. These 1006 cities basically cover cities in different fields and different stages of development in the world today. For specific list of cities and countries, please refer to Chapter 1.

Data Collection The Global Urban Competitiveness Report is a research project that requires high quality and quantity of data. A data collection group and an AI & big data group were set up and started working from April 2019. After nearly half a year of search and collation, it achieved an ideal indicator coverage. The data used in the report mainly comes from four sources, including governmental statistic agencies; international statistic agencies; reports and survey data from international research institutions and companies; big data from web crawlers. For specific information, please refer to Tables A.1, A.2, A.3, and A.4 of the Appendix.

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 nondimensionalized. 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: X i = (xiQ−2x) , X i is the converted value of xi , xi is the original data, x is the average value, Q 2 is the variance, and X i is the standardized data. The calculation formula of the exponential method is: X i = xx0ii , X i is the converted value of xi , xi is the original data, x0i is the maximum value, and X i is the exponent.

Appendix

487

i −x Min ) The calculation formula of the threshold value is: X i = (x(xMax− , X i is the x Min ) converted value of xi , xi is the original data, x Max is the largest sample value, and x Min is the smallest sample value。 ni The calculation formula of the percentage level method is: X i = (ni +N , X i is i) the converted value of xi , xi is the original data„ n i is the number of samples with values less then xi , and Ni is the number of sample values greater than or equal to xi except xi . The nondimensionalization 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.

Calculation Method of Variable of Urban Competitiveness Calculation Method of Revealed Variable of Economic and Sustainable Competitiveness In terms of economic density: taking into account the deviation of GDP per squared kilometers, the annual GDP per capita of the current year is used as the correction factor to carry out the non-linear weighted comprehensive correction. The so-called nonlinear weighted synthesis method (or "multiplication" synthesis method) refers  wj x j for comprehensive evaluation. to the application of nonlinear models g = Therein w j is the weight coefficient and x j represents the relevant indicator. Regarding the economic increment: considering the volatility of economic growth, the index was expressed as the average of the past five years GDP increment. In terms of high-income population density: taking into account the deviation of the average number of high-income population, the annual GDP per capita of the current year is used as the correction factor to carry out the non-linear weighted comprehensive correction. The so-called nonlinear weighted synthesis method (or “multiplication” synthesis method) refers to the application of nonlinear models  w g = x j j for comprehensive evaluation. Therein w j is the weight coefficient and x j represents the relevant indicator. Regarding the high-income population growth: considering the fluctuation of population growth and negative population growth, the index is composited with standardized population size of sample cities in the base year and the standardized population growth rate in the past 5 consecutive years.

Calculation Method of Interpretative Variable of Economic and Sustainable Competitiveness Although the interpretative indexes of urban competitiveness designed in the report are secondary index, in fact, including the original index, the system of interpretative urban competitiveness index has 3 levels. When the tertiary index synthesizes the secondary index and the secondary index synthesizes the primary index, the method

488

Appendix

of standardization and equal weight addition is adopted. The standardization method is described above. Its formula is:  z il = z il j j

where, z il represents each of the secondary indexes, and z il j represents each of the tertiary indexes. Zi =



z il

l

where, Z i represents each of the primary indexes, and z il represents each of the secondary indexes.

Special Statements The global urban competitiveness assessment system is based on the research model of Chinese urban competitiveness report by Dr. Ni Pengfei and combined with the latest trend of urban development in the world. However, the global competitiveness assessment system and measurement methods are different from the China urban competitiveness report.

Afterwords

The Global Urban Competitiveness Report 2019–2020 (GUCR) is a joint work by nearly 100 experts from Chinese and foreign universities, authoritative statistical agencies and R&D departments of companies. The year-long project was initiated by Dr. Ni Pengfei from National Academy of Economic Strategy, CASS and Marco Kamiya of UN-HABITAT. The theoretical framework, indicator system, research framework and conclusions of the Global Urban Competitiveness Report 2019–2020 were decided by Dr. Ni Pengfei and Marco Kamiya. Mr. Guo Jing, Deputy Editor-inChief from School of Government Management (Shenzhen University) was responsible for project coordination, data collection and processing and summary. He also written a part of the report. As for urban competitiveness, this report divides into two parts: economic competitiveness and sustainable competitiveness, and designs different index systems to measure the economic competitiveness and sustainable competitiveness of 1,006 cities around the world. Based on the relationship between global city competitiveness and municipal finance, this report has prepared the theme report of global municipal finance experience and methods. The draft of the report is written by the author after refining the theory, collecting data, measuring and drawing conclusions. The contributors of each chapter are: Chapter 1: Ranking of Global Urban Competitiveness 2019, collectively organized by the research group; Chapter 2: The World: 300 Years of Urbanization Expansion, Ni Pengfei, Li bo (Tianjin University of Technology), Ma Hongfu (Tianjin University of Finance and Economics), Xu Haidong (National Academy of Economic Strategy, CASS); Chapter 3: Experience and Methods of Global Municipal Financing, Marko Kamia (UN-Habitat), Liz Paterson Guntner (UN-habitat), Serge Allou (UCLG World Secretariat), Luc Aldon (UCLG World Secretariat), Huáscar Eguino (IDB), Axel Radics (IDB), Mosha.A.C (University of Botswana), Martim O. Smolka (Senior fellow of Lincoln Institute of Land Policy); Chapter 4: Global Urban Economic Competitiveness Performance, Xu haidong (National Academy of Economic Strategy, CASS); Chapter 5: Explanatory indicators of Global Urban Economic Competitiveness, Gong Weijin (National

© China Social Sciences Press 2023 P. Ni et al., The World: 300 Years of Urbanization Expansion, https://doi.org/10.1007/978-981-99-3553-6

489

490

Afterwords

Academy of Economic Strategy, CASS); Chapter 6: Global Urban Sustainable Competitiveness Performance Guo Jinhong (School of Economics, Nankai University); Chapter 7: Explanatory indicators of Global Urban Sustainable Competitiveness, Li qihang (Shandong University of Finance and Economics); Chapter 8: A New Set of Standards for Global City Classification, Cao qingfeng (Tianjin University of Finance and Economics), Guo Jinhong (School of Economics, Nankai University); Appendix: Ni pengfei, Guo Jing (School of Government Management, Shenzhen University). The measurement data of the whole report was completed by the research group under the leadership of Ni Pengfei. Last but not least, we would like to express our gratitude to all entities and individuals involved in the support for their support and contribution. Marco Kamiya, Ni Pengfei Nonmember 3, 2019