IDEAS home Printed from https://ideas.repec.org/a/eee/reveco/v102y2025ics1059056025005088.html
   My bibliography  Save this article

Green innovation perspective: Artificial intelligence and corporate green development

Author

Listed:
  • Liu, Qilu
  • Du, Shanshan
  • Li, Min

Abstract

In the context of growing global environmental pressures, corporate green transformation has become an essential path to achieving sustainable development goals. This study uses a quasi-natural experiment based on the establishment of China's National AI Innovation and Application Pilot Zones and employs a multi-period difference-in-differences model to investigate the causal impact of AI on corporate green development using panel data of A-share listed firms from 2011 to 2023. The results show that AI significantly promotes green development at the firm level, and this finding is robust across multiple tests. Mechanism analysis further reveals that AI drives green transformation through two key channels: human capital upgrading and productivity enhancement. Heterogeneity analysis indicates that the positive effect of AI is more pronounced in state-owned enterprises, high-tech industries, and large-scale firms. This study contributes to the literature by providing micro-level evidence on the relationship between AI and sustainability and offers practical insights for policy design that aims to integrate technological progress with green transformation.

Suggested Citation

  • Liu, Qilu & Du, Shanshan & Li, Min, 2025. "Green innovation perspective: Artificial intelligence and corporate green development," International Review of Economics & Finance, Elsevier, vol. 102(C).
    • Handle: RePEc:eee:reveco:v:102:y:2025:i:c:s1059056025005088
    DOI: 10.1016/j.iref.2025.104345
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1059056025005088
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.iref.2025.104345?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

    for a different version of it.

    More about this item

    Keywords

    ;
    ;
    ;

    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:reveco:v:102:y:2025:i:c:s1059056025005088. 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/inca/620165 .

    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.