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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
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    References listed on IDEAS

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    1. 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).
    2. 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.
    3. 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.
    4. 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.
    5. 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).
    6. 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.
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