IDEAS home Printed from https://ideas.repec.org/a/eee/eneeco/v147y2025ics0140988325004025.html
   My bibliography  Save this article

Exploring the role of artificial intelligence as a catalyst for energy technology innovation

Author

Listed:
  • Shao, Mingxing
  • Wen, Lei
  • Li, Sifei
  • Huang, Binyue

Abstract

Artificial intelligence (AI) has a strong spillover effect and acts as an essential propellant for advancements in technology and development. Using A-share listed enterprises between 2007 and 2022 as the sample, we assess the influence of AI on energy technology innovation (ETI). Our findings highlight that AI can promote ETI primarily by improving the human capital structure and encouraging enterprises to increase research and development (R&D) expenditure. This effect is more pronounced in enterprises with low-carbon transition strategy, those located in regions with abundant resource endowments, and those situated in clean energy base areas. Moreover, the study reveals that AI can positively affect ETI and contribute to enhanced enterprise environmental performance. The findings provide more thorough understanding of the critical role of AI in energy and innovation, offering practical recommendations for enterprises to leverage AI in boosting energy efficiency and lowering pollutant emissions, thereby aligning with as well as encouraging the attainment of carbon neutrality and peaking targets set by China.

Suggested Citation

  • Shao, Mingxing & Wen, Lei & Li, Sifei & Huang, Binyue, 2025. "Exploring the role of artificial intelligence as a catalyst for energy technology innovation," Energy Economics, Elsevier, vol. 147(C).
    • Handle: RePEc:eee:eneeco:v:147:y:2025:i:c:s0140988325004025
    DOI: 10.1016/j.eneco.2025.108578
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0140988325004025
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.eneco.2025.108578?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:eneeco:v:147:y:2025:i:c:s0140988325004025. 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/eneco .

    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.