IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i2p929-d1324031.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/2/929/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/16/2/929/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881, October.
    13. 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.
    14. Lange, Steffen & Pohl, Johanna & Santarius, Tilman, 2020. "Digitalization and energy consumption. Does ICT reduce energy demand?," Ecological Economics, Elsevier, vol. 176(C).
    15. 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).
    16. 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).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Julius Owusu, 2023. "Randomization Inference of Heterogeneous Treatment Effects under Network Interference," Papers 2308.00202, arXiv.org, revised Jan 2024.
    2. Michael Lechner, 2023. "Causal Machine Learning and its use for public policy," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 159(1), pages 1-15, December.
    3. Huifang E & Shuangjie Li & Liming Wang & Huidan Xue, 2023. "The Impact of ICT Capital Services on Economic Growth and Energy Efficiency in China," Energies, MDPI, vol. 16(9), pages 1-21, May.
    4. Nathan Kallus, 2023. "Treatment Effect Risk: Bounds and Inference," Management Science, INFORMS, vol. 69(8), pages 4579-4590, August.
    5. Ganesh Karapakula, 2023. "Stable Probability Weighting: Large-Sample and Finite-Sample Estimation and Inference Methods for Heterogeneous Causal Effects of Multivalued Treatments Under Limited Overlap," Papers 2301.05703, arXiv.org, revised Jan 2023.
    6. Nathan Kallus, 2022. "Treatment Effect Risk: Bounds and Inference," Papers 2201.05893, arXiv.org, revised Jul 2022.
    7. Lundberg, Ian & Brand, Jennie E. & Jeon, Nanum, 2022. "Researcher reasoning meets computational capacity: Machine learning for social science," SocArXiv s5zc8, Center for Open Science.
    8. Athey, Susan & Imbens, Guido W. & Metzger, Jonas & Munro, Evan, 2024. "Using Wasserstein Generative Adversarial Networks for the design of Monte Carlo simulations," Journal of Econometrics, Elsevier, vol. 240(2).
    9. Gabriel Okasa & Kenneth A. Younge, 2022. "Sample Fit Reliability," Papers 2209.06631, arXiv.org.
    10. Julius Owusu, 2024. "A Nonparametric Test of Heterogeneous Treatment Effects under Interference," Papers 2410.00733, arXiv.org.
    11. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Christian Hansen & Kengo Kato, 2018. "High-dimensional econometrics and regularized GMM," CeMMAP working papers CWP35/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    12. Dimitris Bertsimas & Agni Orfanoudaki & Rory B. Weiner, 2020. "Personalized treatment for coronary artery disease patients: a machine learning approach," Health Care Management Science, Springer, vol. 23(4), pages 482-506, December.
    13. Ruoxuan Xiong & Allison Koenecke & Michael Powell & Zhu Shen & Joshua T. Vogelstein & Susan Athey, 2021. "Federated Causal Inference in Heterogeneous Observational Data," Papers 2107.11732, arXiv.org, revised Apr 2023.
    14. Qin, Quande & Li, Xin & Li, Li & Zhen, Wei & Wei, Yi-Ming, 2017. "Air emissions perspective on energy efficiency: An empirical analysis of China’s coastal areas," Applied Energy, Elsevier, vol. 185(P1), pages 604-614.
    15. Davide Viviano & Jelena Bradic, 2019. "Synthetic learner: model-free inference on treatments over time," Papers 1904.01490, arXiv.org, revised Aug 2022.
    16. Naguib, Costanza, 2019. "Estimating the Heterogeneous Impact of the Free Movement of Persons on Relative Wage Mobility," Economics Working Paper Series 1903, University of St. Gallen, School of Economics and Political Science.
    17. Rina Friedberg & Julie Tibshirani & Susan Athey & Stefan Wager, 2018. "Local Linear Forests," Papers 1807.11408, arXiv.org, revised Sep 2020.
    18. Combes, Pierre-Philippe & Gobillon, Laurent & Zylberberg, Yanos, 2022. "Urban economics in a historical perspective: Recovering data with machine learning," Regional Science and Urban Economics, Elsevier, vol. 94(C).
    19. Koch, Bernard & Sainburg, Tim & Geraldo, Pablo & JIANG, SONG & Sun, Yizhou & Foster, Jacob G., 2021. "Deep Learning of Potential Outcomes," SocArXiv aeszf, Center for Open Science.
    20. Undral Byambadalai & Tatsushi Oka & Shota Yasui, 2024. "Estimating Distributional Treatment Effects in Randomized Experiments: Machine Learning for Variance Reduction," Papers 2407.16037, arXiv.org.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:16:y:2024:i:2:p:929-:d:1324031. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.