IDEAS home Printed from https://ideas.repec.org/a/eee/eneeco/v141y2025ics0140988324007977.html
   My bibliography  Save this article

Can artificial intelligence empower energy enterprises to cope with climate policy uncertainty?

Author

Listed:
  • Zhong, Qian
  • Zhang, Qun
  • Yang, Jingjing

Abstract

This study investigates the effect of climate policy uncertainty (CPU) on firm-level investment and through which artificial intelligence (AI) may act upon this relationship. Using panel data from listed energy enterprises in China from 2010 to 2019, we demonstrate that CPU significantly inhibits energy enterprises' investments, mainly by exacerbating their financing constraints. This effect is more pronounced in firms with strong environmental awareness, strong internal control, high environmental, social, and governance scores, or in the traditional energy industry. Furthermore, we find that AI adoption weakens the impact of CPU on firm-level investments, primarily through two potential mechanisms: mitigating the customer concentration risk and enhancing green patent commercialization. On average, a 1 % increase in the degree of AI adoption by energy firms can boost their investment expenditure by 0.0065 %. Furthermore, AI's role in mitigating the negative impact of CPU on energy firms' investments is more significant in non-resource-based cities, cities with high economic growth rates, and cities with advanced IT infrastructure. Our findings provide a deeper understanding of the forces driving sustainable energy transitions in the evolving climate policy landscape.

Suggested Citation

  • Zhong, Qian & Zhang, Qun & Yang, Jingjing, 2025. "Can artificial intelligence empower energy enterprises to cope with climate policy uncertainty?," Energy Economics, Elsevier, vol. 141(C).
  • Handle: RePEc:eee:eneeco:v:141:y:2025:i:c:s0140988324007977
    DOI: 10.1016/j.eneco.2024.108088
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0140988324007977
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.eneco.2024.108088?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    More about this item

    Keywords

    Climate policy uncertainty; Artificial intelligence; Corporate investment; Energy enterprises; Green patent commercialization;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G3 - Financial Economics - - Corporate Finance and Governance
    • G38 - Financial Economics - - Corporate Finance and Governance - - - Government Policy and Regulation
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
    • Q55 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environmental Economics: Technological Innovation

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:eneeco:v:141:y:2025:i:c:s0140988324007977. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eneco .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.