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The impact of carbon policy on corporate risk-taking with a double/debiased machine learning based difference-in-differences approach

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  • Xing, Lu
  • Han, DongHao
  • Hui, Xie

Abstract

This study adopts the more cutting-edge DMLDID (double/debiased machine learning based difference-in-differences) approach to demonstrate the impact of carbon policy on corporate risk-taking, and the strong conclusion suggests that carbon policy significantly reduces corporate risk-taking. The further analysis concludes that carbon policy negatively impacts corporate risk-taking by reducing investor attention and raising financing constraints. Also carbon policy significantly reduces the risk-taking of SOEs and heavy polluters. This study has been shown to have great significance for firms to resist external policy risks, mitigate internal business risks, develop emission reduction strategies, and achieve sustainable corporate development.

Suggested Citation

  • Xing, Lu & Han, DongHao & Hui, Xie, 2023. "The impact of carbon policy on corporate risk-taking with a double/debiased machine learning based difference-in-differences approach," Finance Research Letters, Elsevier, vol. 58(PC).
    • Handle: RePEc:eee:finlet:v:58:y:2023:i:pc:s1544612323008747
    DOI: 10.1016/j.frl.2023.104502
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    References listed on IDEAS

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    1. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2018. "Double/debiased machine learning for treatment and structural parameters," Econometrics Journal, Royal Economic Society, vol. 21(1), pages 1-68, February.
    2. Ren, Yi-Shuai & Boubaker, Sabri & Liu, Pei-Zhi & Weber, Olaf, 2023. "How does carbon regulatory policy affect debt financing costs? Empirical evidence from China," The Quarterly Review of Economics and Finance, Elsevier, vol. 90(C), pages 77-90.
    3. Magerakis, Efstathios & Pantzalis, Christos & Park, Jung Chul, 2023. "The effect of proximity to political power on corporate cash policy," Journal of Corporate Finance, Elsevier, vol. 82(C).
    4. Lin, Chen & Lin, Ping & Zou, Hong, 2012. "Does property rights protection affect corporate risk management strategy? Intra- and cross-country evidence," Journal of Corporate Finance, Elsevier, vol. 18(2), pages 311-330.
    5. R. David Mclean & Mengxin Zhao, 2014. "The Business Cycle, Investor Sentiment, and Costly External Finance," Journal of Finance, American Finance Association, vol. 69(3), pages 1377-1409, June.
    6. He, Feng & Ding, Cong & Yue, Wei & Liu, Guanchun, 2023. "ESG performance and corporate risk-taking: Evidence from China," International Review of Financial Analysis, Elsevier, vol. 87(C).
    7. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey, 2017. "Double/Debiased/Neyman Machine Learning of Treatment Effects," American Economic Review, American Economic Association, vol. 107(5), pages 261-265, May.
    8. Wadho, Waqar & Chaudhry, Azam, 2018. "Innovation and firm performance in developing countries: The case of Pakistani textile and apparel manufacturers," Research Policy, Elsevier, vol. 47(7), pages 1283-1294.
    9. Mara Faccio & Maria-Teresa Marchica & Roberto Mura, 2011. "Large Shareholder Diversification and Corporate Risk-Taking," The Review of Financial Studies, Society for Financial Studies, vol. 24(11), pages 3601-3641.
    10. Yang, Shengyi, 2023. "Carbon emission trading policy and firm's environmental investment," Finance Research Letters, Elsevier, vol. 54(C).
    11. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2016. "Double/Debiased Machine Learning for Treatment and Causal Parameters," Papers 1608.00060, arXiv.org, revised Dec 2017.
    12. Yang, Zhenbing & Shao, Shuai & Yang, Lili, 2021. "Unintended consequences of carbon regulation on the performance of SOEs in China: The role of technical efficiency," Energy Economics, Elsevier, vol. 94(C).
    13. Dong, Xiyong & Yoon, Seong-Min, 2023. "Effect of weather and environmental attentions on financial system risks: Evidence from Chinese high- and low-carbon assets," Energy Economics, Elsevier, vol. 121(C).
    14. Kose John & Lubomir Litov & Bernard Yeung, 2008. "Corporate Governance and Risk‐Taking," Journal of Finance, American Finance Association, vol. 63(4), pages 1679-1728, August.
    15. Shu, Hao & Tan, Weiqiang, 2023. "Does carbon control policy risk affect corporate ESG performance?," Economic Modelling, Elsevier, vol. 120(C).
    16. Wang, Jie & Wang, Wanwan & Yuan, Fang, 2023. "Air pollution and corporate risk-taking: Evidence from China," International Review of Economics & Finance, Elsevier, vol. 86(C), pages 570-586.
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    More about this item

    Keywords

    Corporate risk-taking; Carbon policy; DMLDID; LCCP;
    All these keywords.

    JEL classification:

    • G38 - Financial Economics - - Corporate Finance and Governance - - - Government Policy and Regulation
    • M21 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Economics - - - Business Economics
    • D21 - Microeconomics - - Production and Organizations - - - Firm Behavior: Theory

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