IDEAS home Printed from https://ideas.repec.org/a/eee/reveco/v101y2025ics1059056025003612.html
   My bibliography  Save this article

Supply chain decarbonisation effects of artificial Intelligence: Evidence from China

Author

Listed:
  • Ren, Baoping
  • Qiu, Zhaoxuan
  • Liu, Bei

Abstract

Existing studies have not yet discussed the synergetic decarbonisation effects among supply chain firms driven by artificial intelligence (AI) applied in leading focal firms, nor have they explored the corresponding mechanisms in depth. Focusing on the supply chain, this paper constructs a dataset with “focal firm - associated firm - year” observations, to examine the external spillover effects of AI on supply chain decarbonisation. Our findings reveal that AI employed by focal enterprises significantly contribute to curbing carbon emissions among associated enterprises within the supply chain, primarily through green technological innovation (GTI). The role of substantive GTI is predominant, whereas symbolic GTI appears to have an insignificant impact. Meanwhile, from the perspective of synergy, the vertical interaction effect of the supply chain and the horizontal peer effect of industry can strengthen the supply chain decarbonisation effect of AI technology. This paper provides a new synergetic insight for fully releasing the decarbonisation potential of AI based on supply chain interaction, providing empirical evidence and policy references to promote industrial green inclusive development and realize the global zero-carbon goal.

Suggested Citation

  • Ren, Baoping & Qiu, Zhaoxuan & Liu, Bei, 2025. "Supply chain decarbonisation effects of artificial Intelligence: Evidence from China," International Review of Economics & Finance, Elsevier, vol. 101(C).
    • Handle: RePEc:eee:reveco:v:101:y:2025:i:c:s1059056025003612
    DOI: 10.1016/j.iref.2025.104198
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1059056025003612
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.iref.2025.104198?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.

    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:101:y:2025:i:c:s1059056025003612. 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.