IDEAS home Printed from https://ideas.repec.org/a/spr/jknowl/v16y2025i2d10.1007_s13132-024-02262-8.html
   My bibliography  Save this article

How Does the Digital Economy Affect Carbon Emissions? Evidence from Panel Smooth Transition Regression Model

Author

Listed:
  • Wei Jiang

    (Qingdao University)

  • Xiaoyong Wu

    (Qingdao University)

  • Qili Yu

    (Qingdao University)

  • Mingming Leng

    (Lingnan University)

Abstract

This essay uses the panel smooth transition regression (PSTR) model to study how China’s digital economy affects carbon emissions, selects data from 30 provinces in China during 2011–2021, and constructs a digital economy evaluation index system for empirical analysis. As a nonlinear model, the PSTR model can more accurately reveal the linkage between the digital economy and carbon emissions. Compared with other nonlinear models, this model can obtain the conversion rate of different influence intensities in the digital economy. According to findings, digital economic growth has a significant curbing influence on carbon emissions. But as it advances to a certain point, this inhibiting impact weakens. Furthermore, the digital economy’s expansion could boost technological advancement, foreign direct investment, urbanization, and industrial structure optimization to curb carbon emissions in the early stage. Similarly, with the development of the digital economy to a certain extent, this inhibitory effect will weaken or increase. With China promoting a low-carbon economy against the backdrop of the contemporary internet age, it is essential to comprehend the effects of the digital economy on carbon emissions.

Suggested Citation

  • Wei Jiang & Xiaoyong Wu & Qili Yu & Mingming Leng, 2025. "How Does the Digital Economy Affect Carbon Emissions? Evidence from Panel Smooth Transition Regression Model," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 16(2), pages 9219-9245, June.
  • Handle: RePEc:spr:jknowl:v:16:y:2025:i:2:d:10.1007_s13132-024-02262-8
    DOI: 10.1007/s13132-024-02262-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13132-024-02262-8
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s13132-024-02262-8?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:spr:jknowl:v:16:y:2025:i:2:d:10.1007_s13132-024-02262-8. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.