IDEAS home Printed from https://ideas.repec.org/a/spr/fininn/v11y2025i1d10.1186_s40854-025-00783-y.html
   My bibliography  Save this article

Modeling ESG-driven industrial value chain dynamics using directed graph neural networks

Author

Listed:
  • Zhizhong Tan

    (Macau University of Science and Technology)

  • Siyang Liu

    (Southwestern University of Finance and Economics)

  • Qiang Liu

    (Macau University of Science and Technology)

  • Min Hu

    (Southwestern University of Finance and Economics)

  • Xiang Zhang

    (Southwestern University of Finance and Economics)

  • Wenyong Wang

    (Macau University of Science and Technology)

  • Bin Liu

    (Southwestern University of Finance and Economics)

Abstract

This study explores the dynamics of industrial value chains from the perspective of directed graph neural networks (DGNNs). This study focuses on the effects of environmental, social, and governance (ESG) factors on industrial value extension. The industrial value chain, which encompasses a comprehensive network ranging from raw material procurement to final product distribution, is characterized by intricate interconnections between internal and external stakeholders. Traditional quantitative methods, such as input–output (I–O) analysis, often fail to capture the complexity of these relationships. We model the shock propagation across the industrial chain by employing DGNNs, defining two types of hidden representations: demanding (incoming) and supplying (outgoing) embeddings. This dual representation enables a nuanced understanding of how ESG shocks influence the extension of industrial value from downstream and upstream industrial sectors. Our findings highlight potential areas for optimization and value-added opportunities, offering insights that can support more informed decision-making by corporations and policymakers as they navigate the evolving industrial landscape.

Suggested Citation

  • Zhizhong Tan & Siyang Liu & Qiang Liu & Min Hu & Xiang Zhang & Wenyong Wang & Bin Liu, 2025. "Modeling ESG-driven industrial value chain dynamics using directed graph neural networks," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 11(1), pages 1-23, December.
    • Handle: RePEc:spr:fininn:v:11:y:2025:i:1:d:10.1186_s40854-025-00783-y
    DOI: 10.1186/s40854-025-00783-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1186/s40854-025-00783-y
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1186/s40854-025-00783-y?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
    ---><---

    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:spr:fininn:v:11:y:2025:i:1:d:10.1186_s40854-025-00783-y. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.