IDEAS home Printed from https://ideas.repec.org/a/eee/finlet/v95y2026ics1544612326002667.html

Does artificial intelligence advance the coordinated reduction in pollution and carbon emissions? New micro evidence from China

Author

Listed:
  • Du, Sixuan
  • Meng, Zuhan
  • Feng, Haiyue
  • Liu, Siyuan

Abstract

Artificial intelligence (AI) is a transformative force in corporate governance, opening new pathways to environmental sustainability. Employing a unique micro-level dataset from 2013 to 2022, this study provides robust evidence that AI facilitates a coordinated reduction (CR) in pollution and carbon emissions, with each unit increase associated with a 0.115 and a 0.105 decrease in CO2 and SO2 emission intensities, respectively. AI achieves this by optimizing labor structures, fostering green innovation, and enhancing management efficiency. Additionally, the effect of AI on CR is stronger in state-owned, capital-intensive, human-capital-intensive, and mature firms and is reinforced by regional fintech initiatives. These findings confirm that AI is a strategic enabler of corporate environmental governance, providing both a theoretical and an empirical basis for its sustainable application.

Suggested Citation

  • Du, Sixuan & Meng, Zuhan & Feng, Haiyue & Liu, Siyuan, 2026. "Does artificial intelligence advance the coordinated reduction in pollution and carbon emissions? New micro evidence from China," Finance Research Letters, Elsevier, vol. 95(C).
    • Handle: RePEc:eee:finlet:v:95:y:2026:i:c:s1544612326002667
    DOI: 10.1016/j.frl.2026.109735
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1544612326002667
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.frl.2026.109735?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:95:y:2026:i:c:s1544612326002667. 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.