IDEAS home Printed from https://ideas.repec.org/a/eee/eneeco/v136y2024ics0140988324004560.html
   My bibliography  Save this article

An inquiry into the nexus between artificial intelligence and energy poverty in the light of global evidence

Author

Listed:
  • Ding, Tao
  • Li, Hao
  • Liu, Li
  • Feng, Kui

Abstract

Energy poverty is a global challenge that constrains economic development, jeopardizes people's health, and impedes the improvement of people's lives. Artificial intelligence (AI) could be an important tool to reverse this dilemma. We utilize a panel data covering 64 countries during 2000–2019 to examine AI's impact on energy poverty. The findings reveal that the application of AI effectively alleviates energy poverty. After a series of robustness checks, this conclusion remains valid. Moreover, heterogeneity tests show that AI significantly alleviates energy poverty in high-income countries and lower-middle-income countries, but this positive influence is not found in upper-middle-income countries. Mechanism tests indicate that the application of AI can generate economic effects, driving technological progress and enhancing human capital, thereby easing energy poverty. Further discussion reveals AI's impact on energy poverty is long-lasting. In addition, its effects are more prominent in countries that do not participate in cooperative organizations. Our findings offer a fresh perspective and approach to improving the global state of energy poverty. Based on these conclusions, we provide relevant policy implications.

Suggested Citation

  • Ding, Tao & Li, Hao & Liu, Li & Feng, Kui, 2024. "An inquiry into the nexus between artificial intelligence and energy poverty in the light of global evidence," Energy Economics, Elsevier, vol. 136(C).
  • Handle: RePEc:eee:eneeco:v:136:y:2024:i:c:s0140988324004560
    DOI: 10.1016/j.eneco.2024.107748
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0140988324004560
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.eneco.2024.107748?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:136:y:2024:i:c:s0140988324004560. 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.