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Smart Cities and Greener Futures: Evidence from a Quasi-Natural Experiment in China’s Smart City Construction

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  • Chengfeng Yu

    (School of Statistics and Mathematics, Zhongnan University of Economics and Law, Wuhan 430073, China)

  • Jiyu Yu

    (School of Finance, Hubei University of Economics, Wuhan 430205, China
    Collaborative Innovation Center for Emissions Trading System Co-Constructed by the Province and Ministry, Hubei University of Economics, Wuhan 430205, China)

  • Da Gao

    (School of Law and Business, Wuhan Institute of Technology, Wuhan 430205, China)

Abstract

As the digital economy becomes the new engine of economic growth, China has introduced a series of smart city policies aimed at promoting high-quality and sustainable urban development. This paper aims to evaluate the green development effects of China’s “Smart City Pilot” policy and to explore the heterogeneity of policy effects across different types of cities. Using panel data from 283 prefecture-level cities in China from 2006 to 2020, this study examines the relationship between smart city construction policy and urban green development efficiency using the green total factor productivity (GTFP). We employ the Causal Forest and mediation effect models to estimate the impact of smart city pilot policy on GTFP and explore the underlying mechanisms. The main results are: (1) The smart city pilot policy significantly enhances urban GTFP, a finding consistent across diverse policy evaluation approaches. (2) The influence of the policy on green development varies among cities, and such heterogeneity is effectively captured by the Causal Forest. (3) This varied impact primarily stems from urban location factors and inherent characteristics. Notably, the policy effect in Eastern China outpaces that in other regions. The policy yields greater green benefits with financial development and medical capital rises, but excessive government public expenditure curtails its positive influence. (4) The mediation mechanisms through which the smart city pilot policy promotes green development exhibit certain differences between the “high-effect group” and the “low-effect group”. The former predominantly leverages innovation-driven and agglomeration effects, while the latter chiefly relies on industrial structural advancement and rationalization.

Suggested Citation

  • Chengfeng Yu & Jiyu Yu & Da Gao, 2024. "Smart Cities and Greener Futures: Evidence from a Quasi-Natural Experiment in China’s Smart City Construction," Sustainability, MDPI, vol. 16(2), pages 1-28, January.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:2:p:929-:d:1324031
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    1. Burnside, A. Craig & Eichenbaum, Martin S. & Rebelo, Sergio T., 1996. "Sectoral Solow residuals," European Economic Review, Elsevier, vol. 40(3-5), pages 861-869, April.
    2. Stefan Wager & Susan Athey, 2018. "Estimation and Inference of Heterogeneous Treatment Effects using Random Forests," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(523), pages 1228-1242, July.
    3. Richard K. Crump & V. Joseph Hotz & Guido W. Imbens & Oscar A. Mitnik, 2008. "Nonparametric Tests for Treatment Effect Heterogeneity," The Review of Economics and Statistics, MIT Press, vol. 90(3), pages 389-405, August.
    4. Crafts, Nicholas, 2004. "Productivity Growth in the Industrial Revolution: A New Growth Accounting Perspective," The Journal of Economic History, Cambridge University Press, vol. 64(2), pages 521-535, June.
    5. Li, Ge & Wen, Huwei, 2023. "The low-carbon effect of pursuing the honor of civilization? A quasi-experiment in Chinese cities," Economic Analysis and Policy, Elsevier, vol. 78(C), pages 343-357.
    6. Rachel Ngai & Roberto Samaniego, 2011. "Accounting for Research and Productivity Growth Across Industries," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 14(3), pages 475-495, July.
    7. Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
    8. Jiban Khuntia & Terence J. V. Saldanha & Sunil Mithas & V. Sambamurthy, 2018. "Information Technology and Sustainability: Evidence from an Emerging Economy," Production and Operations Management, Production and Operations Management Society, vol. 27(4), pages 756-773, April.
    9. Tone, Kaoru & Tsutsui, Miki, 2010. "An epsilon-based measure of efficiency in DEA - A third pole of technical efficiency," European Journal of Operational Research, Elsevier, vol. 207(3), pages 1554-1563, December.
    10. Yan, Zheming & Sun, Zao & Shi, Rui & Zhao, Minjuan, 2023. "Smart city and green development: Empirical evidence from the perspective of green technological innovation," Technological Forecasting and Social Change, Elsevier, vol. 191(C).
    11. Caiming Wang & Jian Li, 2020. "The Evaluation and Promotion Path of Green Innovation Performance in Chinese Pollution-Intensive Industry," Sustainability, MDPI, vol. 12(10), pages 1-22, May.
    12. Yang, Qiuyue & Gao, Da & Song, Deyong & Li, Yi, 2021. "Environmental regulation, pollution reduction and green innovation: The case of the Chinese Water Ecological Civilization City Pilot policy," Economic Systems, Elsevier, vol. 45(4).
    13. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881, January.
    14. Tone, Kaoru, 2001. "A slacks-based measure of efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 130(3), pages 498-509, May.
    15. Lange, Steffen & Pohl, Johanna & Santarius, Tilman, 2020. "Digitalization and energy consumption. Does ICT reduce energy demand?," Ecological Economics, Elsevier, vol. 176(C).
    16. Chu, Zhen & Cheng, Mingwang & Yu, Ning Neil, 2021. "A smart city is a less polluted city," Technological Forecasting and Social Change, Elsevier, vol. 172(C).
    17. 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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    Cited by:

    1. Patrick Rehill, 2024. "How do applied researchers use the Causal Forest? A methodological review of a method," Papers 2404.13356, arXiv.org.

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