IDEAS home Printed from https://ideas.repec.org/a/eee/tefoso/v224y2026ics0040162525005116.html

The dual environmental impact of AI technologies: Analyzing direct effects vs. economic growth dynamics

Author

Listed:
  • Ahmed, Zahoor

Abstract

Unlike past studies, this research makes a novel contribution by analyzing the dual environmental impact of artificial intelligence (AI), including both its direct effects and its indirect effects through economic growth, on the load capacity factor (LCF), a comprehensive proxy for environmental sustainability, controlling the influence of political globalization (POG), renewable energy (REN), and political corruption (PCR). Analyzing panel data from 1996 to 2021 for the 10 largest economies, the Method of Moments Quantile Regression (MM-QR) is employed to uncover heterogeneous impacts across the distribution. Furthermore, the Feasible Generalized Least Squares (FGLS), the Driscoll-Kraay (DK) standard errors, and the Panel Corrected Standard Errors (PCSEs) are employed for robustness. The empirical analysis reveals that AI directly reduces LCF across all quantiles, indicating that AI adoption hampers environmental sustainability. However, the moderating effects of AI on LCF through economic growth are positive, implying that AI, indirectly through economic growth, can contribute to environmental sustainability. Both PCR and POG contribute to environmental degradation, while the use of green energy augments the LCF with an increasing impact. Economic growth poses varying effects on the LCF at different percentiles. These findings provide critical insights for policymakers, offering a significant contribution to the sustainable development literature.

Suggested Citation

  • Ahmed, Zahoor, 2026. "The dual environmental impact of AI technologies: Analyzing direct effects vs. economic growth dynamics," Technological Forecasting and Social Change, Elsevier, vol. 224(C).
    • Handle: RePEc:eee:tefoso:v:224:y:2026:i:c:s0040162525005116
    DOI: 10.1016/j.techfore.2025.124480
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0040162525005116
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.techfore.2025.124480?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:tefoso:v:224:y:2026:i:c:s0040162525005116. 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.sciencedirect.com/science/journal/00401625 .

    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.