IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v383y2025ics0306261925001023.html
   My bibliography  Save this article

Implementing a provincial-level universal daily industrial carbon emissions prediction by fine-tuning the large language model

Author

Listed:
  • Feng, Zhengyuan
  • Sun, Yuheng
  • Ning, Jun
  • Tang, Shoujuan
  • Liu, Guangxin
  • Liu, Fangtao
  • Li, Yang
  • Shi, Lei

Abstract

Accurate daily predictions of industrial carbon emissions can improve our understanding of industrial activities. In this paper, we propose a novel approach to predict daily industrial carbon emissions by fine-tuning pre-trained language models. This method constructs a general carbon emissions model applicable to all provinces, providing accurate predictions in natural language format and outperforming traditional carbon emissions models. It achieves Mean Absolute Error (MAE) of 0.005757, Root Mean Squared Error (RMSE) of 0.008982, and Mean Absolute Percentage Error (MAPE) of 5.485714 %. The results suggest that fine-tuned T5 with prompt text significantly improves the predictive accuracy for daily industrial carbon emissions compared with typical multi-layer perception and long short-term memory numerical models. A single model can be adapted to the prediction of industrial carbon emissions in 31 provinces of China.

Suggested Citation

  • Feng, Zhengyuan & Sun, Yuheng & Ning, Jun & Tang, Shoujuan & Liu, Guangxin & Liu, Fangtao & Li, Yang & Shi, Lei, 2025. "Implementing a provincial-level universal daily industrial carbon emissions prediction by fine-tuning the large language model," Applied Energy, Elsevier, vol. 383(C).
    • Handle: RePEc:eee:appene:v:383:y:2025:i:c:s0306261925001023
    DOI: 10.1016/j.apenergy.2025.125372
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261925001023
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2025.125372?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:appene:v:383:y:2025:i:c:s0306261925001023. 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/wps/find/journaldescription.cws_home/405891/description#description .

    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.