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Can Smart City Policy Facilitate the Low-Carbon Economy in China? A Quasi-Natural Experiment Based on Pilot City

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  • Shuangshuang Fan
  • Shengnan Peng
  • Xiaoxue Liu
  • Baogui Xin

Abstract

This paper studies the impact of the implementation of smart city policy (SCP) on the development of low-carbon economy (LCE) in China. For this purpose, we developed a nonconvex meta-frontier data envelopment analysis (DEA) approach to measure LCE and used the differences-in-difference (DID) analysis method in the econometric model to empirically analyze the impact of SCP on LCE, using the dataset of 230 cities from 2005 to 2018. The results show that the implementation of SCP can significantly improve the LCE of cities, and the dynamic effect test presents that the promotion of smart cities to low-carbon economy increases with time. In addition, SCP promotes the development of LCE by optimizing government functions and improving the efficiency of governance and the degree of implementation openness. But there is heterogeneity between different cities as follows: the implementation of SCP has a more significant effect on the promotion of LCE in central and western regions in China and large-scale cities and cities without strict environmental protection planning. Finally, the robustness test verifies the reliability of the experimental data again and puts forward conclusions and policy recommendations.

Suggested Citation

  • Shuangshuang Fan & Shengnan Peng & Xiaoxue Liu & Baogui Xin, 2021. "Can Smart City Policy Facilitate the Low-Carbon Economy in China? A Quasi-Natural Experiment Based on Pilot City," Complexity, Hindawi, vol. 2021, pages 1-15, July.
    • Handle: RePEc:hin:complx:9963404
    DOI: 10.1155/2021/9963404
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    Cited by:

    1. Xingwei Li & Yicheng Huang & Xiangxue Li & Xiang Liu, 2023. "Mechanism of smart city policy on the carbon emissions of construction enterprises in the Yangtze River Economic Belt: a perspective of the PESTEL model and the pollution halo hypothesis," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-12, December.
    2. Liu, Xiaoxi & Yuan, Xiaoling & Ye, Nan & Zhang, Rui, 2023. "An intelligent low carbon economy management scheme based on the genetic algorithm enabled replacement recommendation model," Technological Forecasting and Social Change, Elsevier, vol. 193(C).

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