IDEAS home Printed from https://ideas.repec.org/a/eee/soceps/v105y2026ics0038012126000972.html

Artificial intelligence and socio-economic transformation

Author

Listed:
  • Liu, Jun
  • Song, Shunfeng

Abstract

This special issue provides a systematic review and summary of 20 artificial intelligence (AI)-related articles. These studies span multiple dimensions, including micro-level corporate governance, meso-level industrial synergy, and macro-level economic development. Through a comprehensive synthesis, the special issue finds that AI not only serves as a core tool for enhancing total factor productivity, driving disruptive innovation, and mitigating operational risks but also demonstrates significant governance efficacy in sustainability issues such as curbing ESG greenwashing and improving energy efficiency. Furthermore, this special issue explores the profound impacts of AI on labor market structures, wage equity, and regional inclusive growth. The special issue aims to provide theoretical support and empirical evidence for understanding the multifaceted roles of intelligent transformation in economic and social development.

Suggested Citation

  • Liu, Jun & Song, Shunfeng, 2026. "Artificial intelligence and socio-economic transformation," Socio-Economic Planning Sciences, Elsevier, vol. 105(C).
    • Handle: RePEc:eee:soceps:v:105:y:2026:i:c:s0038012126000972
    DOI: 10.1016/j.seps.2026.102510
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0038012126000972
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.seps.2026.102510?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:soceps:v:105:y:2026:i:c:s0038012126000972. 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/seps .

    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.