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The impacts of smart city construction on carbon total factor productivity: empirical evidence from China

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Abstract

Today, low-carbon development and smart city pilots are prevalent. Against this backdrop, an urgent need exists to clarify the impact of smart city construction on low-carbon development. However, studies on the low-carbon development effects of smart city construction remain scarce. Therefore, this in-depth study focuses on China, the world’s largest developing country, to examine the role of smart city construction in promoting low-carbon development. First, we calculate the carbon total factor productivity of 182 prefecture-level cities in China using the slacks-based global Malmquist–Luenberger index. Second, to empirically examine the impact of smart city construction on carbon total factor productivity, we employ a multi-period difference-in-difference (DID) model and a machine learning-based propensity score matching DID (PSM-DID) model. The results reveal that smart city construction significantly enhances carbon total factor productivity and low-carbon technological efficiency, while its impact on low-carbon technological progress is nonsignificant. Mechanism tests indicate that smart city construction can improve carbon total factor productivity through the following three channels: green technological innovation, industrial structure upgrading, and resource allocation. Heterogeneity tests indicate that all three batches of smart city construction improve carbon total factor productivity, and that the positive effect of the third batch is greater than that of the first and second batches. Furthermore, the carbon total factor productivity promotion effect of smart city construction is stronger in megacities and cities in the central region. Finally, we propose relevant policy implications.

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Data availability

All the data and code used for analysis in this study are available from the corresponding author upon reasonable request.

Notes

  1. Data source: BP statistical review of world energy 2022.

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Acknowledgements

The authors acknowledge the financial support provided by Humanity and Social Science Youth Foundation of Ministry of Education of China (21YJC790051).

Funding

This work was supported by Humanity and Social Science Youth Foundation of Ministry of Education of China (21YJC790051), Social Science Foundation of Hubei Province, China (23ZD222) and the “Fundamental Research Funds for the Central Universities”, Zhongnan University of Economics and Law (2722024BQ012).

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Authors and Affiliations

  1. School of Finance, Zhongnan University of Economics and Law, Wuhan, 430073, People’s Republic of China

    Zhongqi Wu

  2. School of Economics, Zhongnan University of Economics and Law, 182# Nanhu Avenue, East Lake High-Tech Development Zone, Wuhan, 430073, People’s Republic of China

    Xuliang Wang

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  1. Zhongqi Wu
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Zhongqi Wu contributed to conceptualization, writing—original draft, methodology, software, resources, and data curation. Xuliang Wang contributed to conceptualization, writing—original draft, writing—review and editing, and funding acquisition.

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Correspondence to Xuliang Wang.

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Appendix

Appendix

See Tables 7, 8, 9, 10 and 11.

Table 7 Machine learning PSM-DID regression results: control variables
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Table 8 Mechanism test results:control variables
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Table 9 Regression results of different SCC batches: control variables
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Table 10 Heterogeneity regression results: control variables
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Table 11 Abbreviations
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Wu, Z., Wang, X. The impacts of smart city construction on carbon total factor productivity: empirical evidence from China. Clean Techn Environ Policy (2024). https://doi.org/10.1007/s10098-024-02865-4

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