IDEAS home Printed from https://ideas.repec.org/a/ids/ijetma/v28y2025i1-2-3p76-90.html
   My bibliography  Save this article

Peak carbon emission prediction of expressway toll stations using GRA-LSTM under the dual carbon background

Author

Listed:
  • Yali Liang
  • Zengli Fang
  • Fang Wang
  • Gaoling Li
  • Yongjian Guo

Abstract

In order to reduce the error of carbon emission peak prediction and shorten the prediction time, an expressway toll station carbon emission peak prediction method based on the GRA-LSTM model is proposed in the background of dual carbon. Firstly, analyse the dual carbon goals and the characteristics of sustainable development. Secondly, convert the energy consumption generated during the vehicle's payment process into the vehicle's carbon emissions data. Finally, use the grey correlation analysis (GRA) method based on the collected carbon emission data, to calculate the correlation degree between the factors affecting carbon emissions. Using the long short-term memory (LSTM) model to construct a carbon emission peak prediction model, and the output result is the carbon emission peak prediction result. The experimental results show that the proposed method can shorten the prediction time while reducing the prediction RSME.

Suggested Citation

  • Yali Liang & Zengli Fang & Fang Wang & Gaoling Li & Yongjian Guo, 2025. "Peak carbon emission prediction of expressway toll stations using GRA-LSTM under the dual carbon background," International Journal of Environmental Technology and Management, Inderscience Enterprises Ltd, vol. 28(1/2/3), pages 76-90.
    • Handle: RePEc:ids:ijetma:v:28:y:2025:i:1/2/3:p:76-90
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=144501
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    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:ids:ijetma:v:28:y:2025:i:1/2/3:p:76-90. 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: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=11 .

    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.