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The Path to Urban Sustainability: Urban Intelligent Transformation and Green Development—Evidence from 286 Cities in China

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  • Yangyang Zhong

    (Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Shenzhen 518110, China
    School of Public Administration, University of Electronic Science and Technology of China, Chengdu 611731, China)

  • Yilin Zhong

    (Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Shenzhen 518110, China
    School of Public Administration, University of Electronic Science and Technology of China, Chengdu 611731, China)

  • Longpeng Zhang

    (Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Shenzhen 518110, China
    School of Public Administration, University of Electronic Science and Technology of China, Chengdu 611731, China)

  • Zhiwei Tang

    (Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Shenzhen 518110, China
    School of Public Administration, University of Electronic Science and Technology of China, Chengdu 611731, China)

Abstract

Urban intelligence is reshaping urban innovation patterns, accelerating urban transformation, and significantly influencing green and sustainable development. By applying the non-radial directional distance function and an improved entropy method, this study measures the green development efficiency and levels across 286 Chinese cities from 2006 to 2020. The objectives of this study are twofold: first, to examine the impact of urban intelligence transformation on green development, and second, to investigate how urban intelligence influences common prosperity. The analysis employs a double/debiased machine learning model, with the “Smart City Pilot” policy as the focal point. The findings indicate that (1) urban intelligence transformation enhances both the level and efficiency of green development in Chinese cities; (2) this transformation fosters green development by driving urban innovation, upgrading industrial structures, and promoting green finance; and (3) the impact of urban intelligence varies across cities with different sizes, resource endowments, and marketization levels. Furthermore, the study constructs a common prosperity index to assess how urban intelligence contributes to residents’ well-being and social equity. The results suggest that urban intelligence transformation not only advances green development but also contributes to improving residents’ quality of life, thereby promoting a more equitable and prosperous society. These insights offer crucial policy guidance for China and other countries facing environmental and economic challenges in the digital age.

Suggested Citation

  • Yangyang Zhong & Yilin Zhong & Longpeng Zhang & Zhiwei Tang, 2024. "The Path to Urban Sustainability: Urban Intelligent Transformation and Green Development—Evidence from 286 Cities in China," Sustainability, MDPI, vol. 16(23), pages 1-32, November.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:23:p:10394-:d:1531063
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    as
    1. Shuming Ren & Lianqing Li & Yueqi Han & Yu Hao & Haitao Wu, 2022. "The emerging driving force of inclusive green growth: Does digital economy agglomeration work?," Business Strategy and the Environment, Wiley Blackwell, vol. 31(4), pages 1656-1678, May.
    2. Zhang, Hua & Xu, Tiantian & Feng, Chao, 2022. "Does public participation promote environmental efficiency? Evidence from a quasi-natural experiment of environmental information disclosure in China," Energy Economics, Elsevier, vol. 108(C).
    3. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2018. "Double/debiased machine learning for treatment and structural parameters," Econometrics Journal, Royal Economic Society, vol. 21(1), pages 1-68, February.
    4. Zhu, Bangzhu & Zhang, Mengfan & Zhou, Yanhua & Wang, Ping & Sheng, Jichuan & He, Kaijian & Wei, Yi-Ming & Xie, Rui, 2019. "Exploring the effect of industrial structure adjustment on interprovincial green development efficiency in China: A novel integrated approach," Energy Policy, Elsevier, vol. 134(C).
    5. Helmut Farbmacher & Martin Huber & Lukáš Lafférs & Henrika Langen & Martin Spindler, 2022. "Causal mediation analysis with double machine learning [Mediation analysis via potential outcomes models]," The Econometrics Journal, Royal Economic Society, vol. 25(2), pages 277-300.
    6. Du, Kerui & Cheng, Yuanyuan & Yao, Xin, 2021. "Environmental regulation, green technology innovation, and industrial structure upgrading: The road to the green transformation of Chinese cities," Energy Economics, Elsevier, vol. 98(C).
    7. Cao, Yuqiang & Hu, Yong & Liu, Qian & Lu, Meiting & Shan, Yaowen, 2023. "Job creation or disruption? Unraveling the effects of smart city construction on corporate employment in China," Technological Forecasting and Social Change, Elsevier, vol. 195(C).
    8. Ding, Dan & Liu, Xiaoping & Xu, Xiaocong, 2024. "Projecting the future fine-resolution carbon dioxide emissions under the shared socioeconomic pathways for carbon peak evaluation," Applied Energy, Elsevier, vol. 365(C).
    9. Daron Acemoglu & Pascual Restrepo, 2020. "Robots and Jobs: Evidence from US Labor Markets," Journal of Political Economy, University of Chicago Press, vol. 128(6), pages 2188-2244.
    10. Zhou, Xiaoyong & Zhou, Dequn & Wang, Qunwei & Su, Bin, 2019. "How information and communication technology drives carbon emissions: A sector-level analysis for China," Energy Economics, Elsevier, vol. 81(C), pages 380-392.
    11. He, Lingyun & Liu, Rongyan & Zhong, Zhangqi & Wang, Deqing & Xia, Yufei, 2019. "Can green financial development promote renewable energy investment efficiency? A consideration of bank credit," Renewable Energy, Elsevier, vol. 143(C), pages 974-984.
    12. Qu, Chenyao & Shao, Jun & Shi, Zhenkai, 2020. "Does financial agglomeration promote the increase of energy efficiency in China?," Energy Policy, Elsevier, vol. 146(C).
    13. Camboim, Guilherme Freitas & Zawislak, Paulo Antônio & Pufal, Nathália Amarante, 2019. "Driving elements to make cities smarter: Evidences from European projects," Technological Forecasting and Social Change, Elsevier, vol. 142(C), pages 154-167.
    14. Zhengrong Liu & Jianping Wu, 2023. "A Review of the Theory and Practice of Smart City Construction in China," Sustainability, MDPI, vol. 15(9), pages 1-22, April.
    15. Patrick Schulte & Heinz Welsch & Sascha Rexhäuser, 2016. "ICT and the Demand for Energy: Evidence from OECD Countries," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 63(1), pages 119-146, January.
    16. Zhou, P. & Ang, B.W. & Wang, H., 2012. "Energy and CO2 emission performance in electricity generation: A non-radial directional distance function approach," European Journal of Operational Research, Elsevier, vol. 221(3), pages 625-635.
    17. Barba-Sánchez, Virginia & Arias-Antúnez, Enrique & Orozco-Barbosa, Luis, 2019. "Smart cities as a source for entrepreneurial opportunities: Evidence for Spain," Technological Forecasting and Social Change, Elsevier, vol. 148(C).
    18. Yang, Jui-Chung & Chuang, Hui-Ching & Kuan, Chung-Ming, 2020. "Double machine learning with gradient boosting and its application to the Big N audit quality effect," Journal of Econometrics, Elsevier, vol. 216(1), pages 268-283.
    19. Yi, Hong & Hao, Ling & Liu, Aoran & Zhang, Ziyu, 2023. "Green finance development and resource efficiency: A financial structure perspective," Resources Policy, Elsevier, vol. 85(PA).
    20. Luo, Jigang & Wang, Zhen & Wu, Maohua, 2021. "Effect of place-based policies on the digital economy: Evidence from the Smart City Program in China," Journal of Asian Economics, Elsevier, vol. 77(C).
    21. Zhao, Jun & Jiang, Qingzhe & Dong, Xiucheng & Dong, Kangyin, 2021. "Assessing energy poverty and its effect on CO2 emissions: The case of China," Energy Economics, Elsevier, vol. 97(C).
    22. Liu, Yang & Dong, Xiucheng & Dong, Kangyin, 2023. "Pathway to prosperity? The impact of low-carbon energy transition on China's common prosperity," Energy Economics, Elsevier, vol. 124(C).
    23. Dan He & Manxin Zheng & Wei Cheng & Yui-yip Lau & Qingmei Yin, 2019. "Interaction between Higher Education Outputs and Industrial Structure Evolution: Evidence from Hubei Province, China," Sustainability, MDPI, vol. 11(10), pages 1-19, May.
    24. Shan, Yuli & Liu, Jianghua & Liu, Zhu & Xu, Xinwanghao & Shao, Shuai & Wang, Peng & Guan, Dabo, 2016. "New provincial CO2 emission inventories in China based on apparent energy consumption data and updated emission factors," Applied Energy, Elsevier, vol. 184(C), pages 742-750.
    25. Hao, Xiaoli & Miao, Erxiang & Sun, Qingyu & Li, Ke & Wen, Shufang & Xue, Yan, 2024. "The impact of digital government on corporate green innovation: Evidence from China," Technological Forecasting and Social Change, Elsevier, vol. 206(C).
    26. Wanfang Shen & Yufei Liu & Xiaowen Liu & Jianing Shi & Wenbin Liu & Chengye Liu, 2023. "The Effect of Industrial Structure Upgrading and Human Capital Structure Upgrading on Green Development Efficiency—Based on China’s Resource-Based Cities," Sustainability, MDPI, vol. 15(5), pages 1-26, March.
    27. Wen, Huwei & Wen, Changyong & Lee, Chien-Chiang, 2022. "Impact of digitalization and environmental regulation on total factor productivity," Information Economics and Policy, Elsevier, vol. 61(C).
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