IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i3p1814-d742578.html
   My bibliography  Save this article

Smart City Projects Boost Urban Energy Efficiency in China

Author

Listed:
  • Zhengge Tu

    (School of Economics and Business Administration, Central China Normal University, Wuhan 430079, China
    Research Center of Low-carbon Economy and Environmental Policies, Central China Norma University, Wuhan 430079, China)

  • Jiayang Kong

    (School of Economics and Business Administration, Central China Normal University, Wuhan 430079, China
    Research Center of Low-carbon Economy and Environmental Policies, Central China Norma University, Wuhan 430079, China)

  • Renjun Shen

    (School of Economics and Business Administration, Central China Normal University, Wuhan 430079, China
    Research Center of Low-carbon Economy and Environmental Policies, Central China Norma University, Wuhan 430079, China)

Abstract

Policy makers around the world are turning to smart city projects in an effort to address the challenges of population growth, energy efficiency, and environmental sustainability. Previous studies have evaluated the effect of smart city projects on air quality. However, evidence on the impact of the projects on energy efficiency remains unclear. This study gathered prefecture-level city panel data in China, and used three strategies, namely a difference-in-differences estimator, a matching difference-in-differences estimator, and a counterfactual model using a machine learning algorithm, to assess the impact of smart city projects on energy efficiency. This study reported similar results across these strategies above. That is, after the introduction of a smart city project, energy efficiency had a remarkable and sizeable increase, ranging from 4 to 7 per cent. Moreover, this study shows that the effects of smart city projects increased over time. In addition, this study found that the effects varied according to the characteristics of the cities.

Suggested Citation

  • Zhengge Tu & Jiayang Kong & Renjun Shen, 2022. "Smart City Projects Boost Urban Energy Efficiency in China," Sustainability, MDPI, vol. 14(3), pages 1-16, February.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:3:p:1814-:d:742578
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/3/1814/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/3/1814/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Laurent Gobillon & Thierry Magnac, 2016. "Regional Policy Evaluation: Interactive Fixed Effects and Synthetic Controls," The Review of Economics and Statistics, MIT Press, vol. 98(3), pages 535-551, July.
    2. Zheng, Wei & Walsh, Patrick Paul, 2019. "Economic growth, urbanization and energy consumption — A provincial level analysis of China," Energy Economics, Elsevier, vol. 80(C), pages 153-162.
    3. Cao, Yanni & Kong, Lingqiao & Zhang, Lufeng & Ouyang, Zhiyun, 2021. "The balance between economic development and ecosystem service value in the process of land urbanization: A case study of China’s land urbanization from 2000 to 2015," Land Use Policy, Elsevier, vol. 108(C).
    4. Susan Athey & Mohsen Bayati & Nikolay Doudchenko & Guido Imbens & Khashayar Khosravi, 2021. "Matrix Completion Methods for Causal Panel Data Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(536), pages 1716-1730, October.
    5. Xu, Yiqing, 2017. "Generalized Synthetic Control Method: Causal Inference with Interactive Fixed Effects Models," Political Analysis, Cambridge University Press, vol. 25(1), pages 57-76, January.
    6. 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).
    Full references (including those not matched with items on IDEAS)

    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. Irene Botosaru & Bruno Ferman, 2019. "On the role of covariates in the synthetic control method," The Econometrics Journal, Royal Economic Society, vol. 22(2), pages 117-130.
    2. Xiong, Ruoxuan & Pelger, Markus, 2023. "Large dimensional latent factor modeling with missing observations and applications to causal inference," Journal of Econometrics, Elsevier, vol. 233(1), pages 271-301.
    3. Tomasz Serwach, 2023. "The European Union and within‐country income inequalities. The case of the new member states," The World Economy, Wiley Blackwell, vol. 46(7), pages 1890-1939, July.
    4. Michał Marcin Kobierecki & Michał Pierzgalski, 2022. "Sports Mega-Events and Economic Growth: A Synthetic Control Approach," Journal of Sports Economics, , vol. 23(5), pages 567-597, June.
    5. Stefano, Roberta di & Mellace, Giovanni, 2020. "The inclusive synthetic control method," Discussion Papers on Economics 14/2020, University of Southern Denmark, Department of Economics.
    6. Tomasz Serwach, 2022. "The European Union and within-country income inequalities. The case of the New Member States," Working Papers hal-03548416, HAL.
    7. Victor Chernozhukov & Kaspar Wüthrich & Yinchu Zhu, 2021. "An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic Controls," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(536), pages 1849-1864, October.
    8. Giulio Grossi & Marco Mariani & Alessandra Mattei & Patrizia Lattarulo & Ozge Oner, 2020. "Direct and spillover effects of a new tramway line on the commercial vitality of peripheral streets. A synthetic-control approach," Papers 2004.05027, arXiv.org, revised Nov 2023.
    9. David Gilchrist & Thomas Emery & Nuno Garoupa & Rok Spruk, 2023. "Synthetic Control Method: A tool for comparative case studies in economic history," Journal of Economic Surveys, Wiley Blackwell, vol. 37(2), pages 409-445, April.
    10. Rafaty, R. & Dolphin, G. & Pretis, F., 2020. "Carbon pricing and the elasticity of CO2 emissions," Cambridge Working Papers in Economics 20116, Faculty of Economics, University of Cambridge.
    11. Dallas Dotter & Duncan Chaplin & Maria Bartlett, "undated". "Impacts of School Reforms in Washington, DC on Student Achievement," Mathematica Policy Research Reports 44e95d7566434a21b8d57f951, Mathematica Policy Research.
    12. Hugo Freeman & Martin Weidner, 2021. "Low-rank approximations of nonseparable panel models," The Econometrics Journal, Royal Economic Society, vol. 24(2), pages 40-77.
    13. Dmitry Arkhangelsky & Guido Imbens, 2023. "Causal Models for Longitudinal and Panel Data: A Survey," Papers 2311.15458, arXiv.org, revised Jun 2024.
    14. Bennato, Anna Rita & Davies, Stephen & Mariuzzo, Franco & Ormosi, Peter, 2021. "Mergers and innovation: Evidence from the hard disk drive market," International Journal of Industrial Organization, Elsevier, vol. 77(C).
    15. Boese-Schlosser, Vanessa A. & Eberhardt, Markus, 2023. "How Does Democracy Cause Growth?," Discussion Papers, Research Unit: Transformations of Democracy SP V 2023-501, WZB Berlin Social Science Center.
    16. Eberhardt, Markus, 2022. "Democracy, growth, heterogeneity, and robustness," European Economic Review, Elsevier, vol. 147(C).
    17. Cong Wang, 2024. "Counterfactual and Synthetic Control Method: Causal Inference with Instrumented Principal Component Analysis," Papers 2408.09271, arXiv.org, revised Sep 2024.
    18. Callaway, Brantly & Karami, Sonia, 2023. "Treatment effects in interactive fixed effects models with a small number of time periods," Journal of Econometrics, Elsevier, vol. 233(1), pages 184-208.
    19. Marc K. Chan & Simon S. Kwok, 2022. "The PCDID Approach: Difference-in-Differences When Trends Are Potentially Unparallel and Stochastic," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(3), pages 1216-1233, June.
    20. Guido W. Imbens & Davide Viviano, 2023. "Identification and Inference for Synthetic Controls with Confounding," Papers 2312.00955, 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:14:y:2022:i:3:p:1814-:d:742578. 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.