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Digital transformation and corporate green total factor productivity: Based on double/debiased machine learning robustness estimation

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  • Wei, Rongrong
  • Xia, Yueming

Abstract

With the rapid development of the digital economy, China's digital technology and industry continue to integrate deeply into all areas of society. Digital transformation(DT) has become an important engine and key pedestal for economic and social transformations and upgrades. This paper takes 3525 listed companies from 2013 to 2021 as a sample to measure the green total factor productivity(GTFP) of listed enterprises based on the super-efficiency slacks-based measure with the Global Malmquist-Luenberger Index (SBM-GML) and the super-efficiency epsilon-based measure with the Global Malmquist-Luenberger Index (EBM-GML). It draws on the two existing DT indicators to explore the impact and mechanism of enterprise DT on GTFP. The results show that DT can significantly enhance GTFP, and this conclusion still holds after double/debiased machine learning and other robustness tests; the heterogeneity analysis shows that DT of high-tech enterprises, manufacturing industries and low-financialisation enterprises has a more obvious effect on the enhancement of GTFP; and the four-stage mediated impact mechanism suggests that the effect of DT on GTFP can be achieved by improving internal control ability and technological innovation ability. This paper will provide relevant policy insights on how to better drive enterprise DT and green low-carbon development under the “dual-carbon” goal.

Suggested Citation

  • Wei, Rongrong & Xia, Yueming, 2024. "Digital transformation and corporate green total factor productivity: Based on double/debiased machine learning robustness estimation," Economic Analysis and Policy, Elsevier, vol. 84(C), pages 808-827.
  • Handle: RePEc:eee:ecanpo:v:84:y:2024:i:c:p:808-827
    DOI: 10.1016/j.eap.2024.09.023
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    More about this item

    Keywords

    Digital transformation; Green total factor productivity; Internal control; Technological innovation; Double/debiased machine learning;
    All these keywords.

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
    • G38 - Financial Economics - - Corporate Finance and Governance - - - Government Policy and Regulation
    • O31 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Innovation and Invention: Processes and Incentives

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