IDEAS home Printed from https://ideas.repec.org/a/ids/ijgeni/v46y2024i6p678-692.html
   My bibliography  Save this article

A prediction method of regional carbon emission peak based on energy consumption elasticity coefficient

Author

Listed:
  • Yingjie Zhang
  • Dongyuan Zhao

Abstract

In order to solve the shortcomings of the traditional methods in prediction accuracy and prediction efficiency, this paper proposes a regional carbon emission peak prediction method based on the elastic coefficient of energy consumption. First, carbon emission information is extracted directionally. Then, the elastic coefficient of energy consumption is calculated, and the carbon emissions are preliminarily calculated. After obtaining the carbon emissions in different paths, Lasso regression analysis method is used to analyse the impact of the elastic coefficient of energy consumption on the prediction results. By adjusting the harmonic parameter values to optimise the calculation results, the peak prediction results of carbon emissions are obtained after obtaining significant variables. Experimental results show that the prediction accuracy of this method is high, and the maximum kappa coefficient can reach 0.973. During the experiment, the method can complete 12 predictions, which shows that its prediction efficiency is relatively high.

Suggested Citation

  • Yingjie Zhang & Dongyuan Zhao, 2024. "A prediction method of regional carbon emission peak based on energy consumption elasticity coefficient," International Journal of Global Energy Issues, Inderscience Enterprises Ltd, vol. 46(6), pages 678-692.
    • Handle: RePEc:ids:ijgeni:v:46:y:2024:i:6:p:678-692
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=141926
    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:ijgeni:v:46:y:2024:i:6:p:678-692. 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=13 .

    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.