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How Can the Development of Digital Economy Empower Green Transformation and Upgrading of the Manufacturing Industry?—A Quasi-Natural Experiment Based on the National Big Data Comprehensive Pilot Zone in China

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  • Qiansheng Gong

    (School of Economics and Management, Xi’an Shiyou University, Xi’an 710065, China
    Shaanxi (University) Oil and Gas Resources Economics and Management Research Center, Xi’an Shiyou University, Xi’an 710065, China)

  • Xiangyu Wang

    (School of Economics and Management, Xi’an Shiyou University, Xi’an 710065, China
    Shaanxi (University) Oil and Gas Resources Economics and Management Research Center, Xi’an Shiyou University, Xi’an 710065, China)

  • Xi Tang

    (School of Economics and Management, Xi’an Shiyou University, Xi’an 710065, China
    Shaanxi (University) Oil and Gas Resources Economics and Management Research Center, Xi’an Shiyou University, Xi’an 710065, China)

Abstract

Using the panel data of the manufacturing industry in 30 provinces of China from 2005 to 2021, this research takes the establishment of a Chinese national-level comprehensive big data pilot zone as a quasi-natural experiment, empirically analyzes the processing effect of digital economy development on the green transformation and upgrading of manufacturing industry by using a time-varying DID model. The results show that the development of the digital economy can significantly promote the green transformation and upgrading of the manufacturing industry. Further analysis reveals that the development of the digital economy has a significant effect on the green transformation and upgrading of the manufacturing industry in regions with low economic development levels and regions with high network development levels. The development of the digital economy can significantly stimulate the green technology innovation of enterprises and promote the upgrading of industrial structures so as to promote the green transformation and upgrading of the manufacturing industry. Manufacturing agglomeration and environmental regulation intensity have moderating effects and threshold effects, respectively, on the impact of digital economy development on the green transformation and upgrading of the manufacturing industry.

Suggested Citation

  • Qiansheng Gong & Xiangyu Wang & Xi Tang, 2023. "How Can the Development of Digital Economy Empower Green Transformation and Upgrading of the Manufacturing Industry?—A Quasi-Natural Experiment Based on the National Big Data Comprehensive Pilot Zone ," Sustainability, MDPI, vol. 15(11), pages 1-19, May.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:11:p:8577-:d:1155414
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    References listed on IDEAS

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    1. Meijuan Wang & Mingzhi Zhang & Haiqian Chen & Donghua Yu, 2023. "How Does Digital Economy Promote the Geographical Agglomeration of Manufacturing Industry?," Sustainability, MDPI, vol. 15(2), pages 1-19, January.
    2. Hal R. Varian, 2010. "Computer Mediated Transactions," American Economic Review, American Economic Association, vol. 100(2), pages 1-10, May.
    3. Daron Acemoglu & Pascual Restrepo, 2018. "The Race between Man and Machine: Implications of Technology for Growth, Factor Shares, and Employment," American Economic Review, American Economic Association, vol. 108(6), pages 1488-1542, June.
    4. M. Hashem Pesaran, 2006. "Estimation and Inference in Large Heterogeneous Panels with a Multifactor Error Structure," Econometrica, Econometric Society, vol. 74(4), pages 967-1012, July.
    5. Anderson, Dennis, 2001. "Technical progress and pollution abatement: an economic view of selected technologies and practices," Environment and Development Economics, Cambridge University Press, vol. 6(3), pages 283-311, July.
    6. Kevin J. Stiroh, 2002. "Information Technology and the U.S. Productivity Revival: What Do the Industry Data Say?," American Economic Review, American Economic Association, vol. 92(5), pages 1559-1576, December.
    7. Guy Michaels & Ferdinand Rauch & Stephen J. Redding, 2012. "Urbanization and Structural Transformation," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 127(2), pages 535-586.
    8. Chou, Yen-Chun & Hao-Chun Chuang, Howard & Shao, Benjamin B.M., 2014. "The impacts of information technology on total factor productivity: A look at externalities and innovations," International Journal of Production Economics, Elsevier, vol. 158(C), pages 290-299.
    9. Kangxian Ji & Xiaoting Liu & Jian Xu, 2023. "Digital Economy and the Sustainable Development of China’s Manufacturing Industry: From the Perspective of Industry Performance and Green Development," Sustainability, MDPI, vol. 15(6), pages 1-23, March.
    10. Sven M. Laudien & Robin Pesch, 2019. "Understanding the influence of digitalization on service firm business model design: a qualitative-empirical analysis," Review of Managerial Science, Springer, vol. 13(3), pages 575-587, June.
    11. Li, Pei & Lu, Yi & Wang, Jin, 2016. "Does flattening government improve economic performance? Evidence from China," Journal of Development Economics, Elsevier, vol. 123(C), pages 18-37.
    12. Dou, Yue & Zhao, Jun & Dong, Xiucheng & Dong, Kangyin, 2021. "Quantifying the impacts of energy inequality on carbon emissions in China: A household-level analysis," Energy Economics, Elsevier, vol. 102(C).
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