IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v17y2025i9p3867-d1642162.html
   My bibliography  Save this article

Artificial Intelligence Development and Carbon Emission Intensity: Evidence from Industrial Robot Application

Author

Listed:
  • Xinlin Yan

    (College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China)

  • Tao Sun

    (College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China)

Abstract

The development of artificial intelligence as the core driving force for industrial upgrading in the era of smart manufacturing and its large-scale application are reshaping the pattern of urban industrial production and energy consumption, with far-reaching impacts on the realization of regional carbon emission reduction targets. To effectively measure the impact of Artificial Intelligence Development Level (AIDL) on the Carbon Emission Intensity (ICE) of Chinese cities, this study empirically examines the influence of AI development level on carbon emission intensity using panel data from 275 Chinese cities during the period from 2007 to 2019. Employing a Spatial Durbin Model and a Mediation Effect Model to conduct empirical testing, the results reveal that AI development level has a negative impact on carbon emission intensity, thereby suppressing the increase in carbon emission intensity. AI development level mitigates carbon emission intensity through two pathways: enhancing the level of technological innovation and optimizing industrial structure, exhibiting a reverse mediation effect with impact coefficients of −0.6216 and −0.5682, respectively, both statistically significant at the 1% level. Based on the empirical findings and the mediation effect analysis, this paper proposes corresponding policy recommendations. This study highlights the critical role of advancements in artificial intelligence and the application strategies of smart industrial robots in fostering sustainable smart cities. The findings support further exploration of AI’s impact on the environment and offer new perspectives for achieving urban sustainability.

Suggested Citation

  • Xinlin Yan & Tao Sun, 2025. "Artificial Intelligence Development and Carbon Emission Intensity: Evidence from Industrial Robot Application," Sustainability, MDPI, vol. 17(9), pages 1-16, April.
  • Handle: RePEc:gam:jsusta:v:17:y:2025:i:9:p:3867-:d:1642162
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/17/9/3867/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/17/9/3867/
    Download Restriction: no
    ---><---

    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:gam:jsusta:v:17:y:2025:i:9:p:3867-:d:1642162. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.