IDEAS home Printed from https://ideas.repec.org/a/eee/finana/v97y2025ics1057521924008044.html
   My bibliography  Save this article

Promoting or inhibiting: The impact of artificial intelligence application on corporate environmental performance

Author

Listed:
  • Liu, Yeshen
  • Wang, Beibei
  • Song, Zhe

Abstract

Existing research mainly focuses on the economic consequences of artificial intelligence (AI) applications for firms, while less attention is given to its non-economic effects. This study uses data from listed non-financial companies in China between 2008 and 2021, first comparing various methods to effectively measure firm-level AI application and then assessing its impact on corporate environmental performance. The findings indicate that AI application enhances the growth of environmental performance. This growth comes primarily through decreased operating costs, increased operational efficiency, and enhanced employee productivity. AI-powered growth is concentrated among manufacturing firms and is associated with larger fixed assets. Additionally, it is concentrated in firms with lower conventional low-skilled labor and is linked to higher unconventional high-skilled labor. We also find that AI application more effectively translates environmental performance into reputation and market value. Overall, this study offers valuable insights and implications for environmental governance in emerging market firms.

Suggested Citation

  • Liu, Yeshen & Wang, Beibei & Song, Zhe, 2025. "Promoting or inhibiting: The impact of artificial intelligence application on corporate environmental performance," International Review of Financial Analysis, Elsevier, vol. 97(C).
    • Handle: RePEc:eee:finana:v:97:y:2025:i:c:s1057521924008044
    DOI: 10.1016/j.irfa.2024.103872
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1057521924008044
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.irfa.2024.103872?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

    Artificial intelligence application; Environmental performance; Operating costs; Operational efficiency; Employee productivity;
    All these keywords.

    JEL classification:

    • Q56 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environment and Development; Environment and Trade; Sustainability; Environmental Accounts and Accounting; Environmental Equity; Population Growth
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • M15 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - IT Management

    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:finana:v:97:y:2025:i:c:s1057521924008044. 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/620166 .

    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.