IDEAS home Printed from https://ideas.repec.org/a/eee/finlet/v86y2025ipcs1544612325017982.html

Green paradox of Ai: short-term pain and long-term redemption—The two faces of Chinese enterprises’ sustainable development

Author

Listed:
  • Fang, Jing
  • Li, Jingshuo
  • Bi, Pengfei

Abstract

This study examines the impact of artificial intelligence (AI) adoption on corporate sustainability using a panel of Chinese A-share listed firms from 2011 to 2023. Focusing on operational sustainability, environmental information disclosure, and carbon emissions performance, the results reveal a dual effect of AI that incurs short-term operational pressure of adoption costs and integration challenges, followed by substantial long-term gains in environmental transparency and emissions reduction. AI remarkably improves the quality and quantity of environmental disclosure while lowering carbon intensity. These findings demonstrate the tradeoff between short-term costs and long-term benefits, enriching the literature on digital transformation and sustainability. We provide actionable policy insights, emphasizing the influence of AI in advancing corporate environmental responsibility and supporting China’s carbon neutrality agenda.

Suggested Citation

  • Fang, Jing & Li, Jingshuo & Bi, Pengfei, 2025. "Green paradox of Ai: short-term pain and long-term redemption—The two faces of Chinese enterprises’ sustainable development," Finance Research Letters, Elsevier, vol. 86(PC).
    • Handle: RePEc:eee:finlet:v:86:y:2025:i:pc:s1544612325017982
    DOI: 10.1016/j.frl.2025.108544
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1544612325017982
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.frl.2025.108544?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:finlet:v:86:y:2025:i:pc:s1544612325017982. 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/frl .

    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.