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Exploring the Impact of the Digital Economy on Green Total Factor Productivity—Evidence from Chinese Cities

Author

Listed:
  • Zuoyufan Sheng

    (School of Business, The University of New South Wales, Sydney 2052, Australia)

  • Chengpeng Zhu

    (School of Finance and Economics, Jiangsu University, Zhenjiang 212013, China)

  • Mo Chen

    (School of Finance and Economics, Jiangsu University, Zhenjiang 212013, China)

Abstract

The digital economy promotes economic development, industrial upgrading, and environmental protection. In this study, we calculated green total factor productivity (GTFP) based on the SBM-DDF model and used the entropy method and principal component analysis to calculate a digital economy index. We used panel data from 282 Chinese cities to measure the driving effect of the digital economy on green total factor productivity. The study results show that the digital economy significantly increases GTFP. We then assessed the heterogeneity of this impact. We also explored the mechanisms by which the digital economy promotes green development and found that the digital economy can indirectly increase industrial production efficiency by promoting innovation in green technologies.

Suggested Citation

  • Zuoyufan Sheng & Chengpeng Zhu & Mo Chen, 2024. "Exploring the Impact of the Digital Economy on Green Total Factor Productivity—Evidence from Chinese Cities," Sustainability, MDPI, vol. 16(7), pages 1-13, March.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:7:p:2734-:d:1364228
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    References listed on IDEAS

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    1. Jie Huang & Yixin Shen & Jianjun Chen & Ying Zhou, 2022. "Regional Digital Economy Development and Enterprise Productivity: A Study of the Chinese Yangtze River Delta," Regional Science Policy & Practice, Wiley Blackwell, vol. 14(S2), pages 118-137, November.
    2. Du, Minzhe & Wu, Fenger & Ye, Danfeng & Zhao, Yating & Liao, Liping, 2023. "Exploring the effects of energy quota trading policy on carbon emission efficiency: Quasi-experimental evidence from China," Energy Economics, Elsevier, vol. 124(C).
    3. Xie, Mengmeng & Ding, Lin & Xia, Yan & Guo, Jianfeng & Pan, Jiaofeng & Wang, Huijuan, 2021. "Does artificial intelligence affect the pattern of skill demand? Evidence from Chinese manufacturing firms," Economic Modelling, Elsevier, vol. 96(C), pages 295-309.
    4. Nambisan, Satish & Wright, Mike & Feldman, Maryann, 2019. "The digital transformation of innovation and entrepreneurship: Progress, challenges and key themes," Research Policy, Elsevier, vol. 48(8), pages 1-1.
    5. Jianfeng Guo & Kai Zhang & Kecheng Liu, 2022. "Exploring the Mechanism of the Impact of Green Finance and Digital Economy on China’s Green Total Factor Productivity," IJERPH, MDPI, vol. 19(23), pages 1-18, December.
    6. Canh, Nguyen Phuc & Thanh, Su Dinh, 2020. "Financial development and the shadow economy: A multi-dimensional analysis," Economic Analysis and Policy, Elsevier, vol. 67(C), pages 37-54.
    7. Fukuyama, Hirofumi & Weber, William L., 2009. "A directional slacks-based measure of technical inefficiency," Socio-Economic Planning Sciences, Elsevier, vol. 43(4), pages 274-287, December.
    8. Byung M. Jeon & Robin C. Sickles, 2004. "The role of environmental factors in growth accounting," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 19(5), pages 567-591.
    9. Ajay Agrawal & Joshua Gans & Avi Goldfarb, 2019. "The Economics of Artificial Intelligence: An Agenda," NBER Books, National Bureau of Economic Research, Inc, number agra-1, May.
    10. Frey, Carl Benedikt & Osborne, Michael A., 2017. "The future of employment: How susceptible are jobs to computerisation?," Technological Forecasting and Social Change, Elsevier, vol. 114(C), pages 254-280.
    11. 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.
    12. Xue, Yan & Tang, Chang & Wu, Haitao & Liu, Jianmin & Hao, Yu, 2022. "The emerging driving force of energy consumption in China: Does digital economy development matter?," Energy Policy, Elsevier, vol. 165(C).
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