IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v18y2025i8p2041-d1635924.html
   My bibliography  Save this article

An Evolutionary Game Analysis of Decision-Making and Interaction Mechanisms of Chinese Energy Enterprises, the Public, and the Government in Low-Carbon Development Based on Prospect Theory

Author

Listed:
  • Xiao Liu

    (School of Business, Qingdao University, Qingdao 266071, China)

  • Qingjin Wang

    (School of Business, Qingdao University, Qingdao 266071, China)

  • Zhengrui Li

    (School of Business, Qingdao University, Qingdao 266071, China)

  • Shan Jiang

    (School of Foreign Language, Qingdao University, Qingdao 266071, China)

Abstract

The low-carbon development (LCD) of energy markets not only serves as a critical enabler in combating global climate change and advancing the green economy but also enhances global industrial competitiveness. Grounded in prospect theory, this study develops a tripartite evolutionary game model involving three core energy market stakeholders, i.e., energy enterprises, the public, and the government, to investigate the determinant factors and decision-making mechanisms underlying the LCD of energy enterprises, with subsequent simulation analyses conducted through MATLAB R2024a. The research findings indicate that loss aversion serves as the primary driver for energy enterprises’ adoption of LCD strategies. Public supervision demonstrates optimal effectiveness only under conditions of low risk and low loss, while risk sensitivity remains the dominant factor influencing the government’s strategic choices. Notably, government incentives combined with public supervision demonstrate significant synergistic effects in accelerating the corporate transition toward LCD. Accordingly, the government should actively promote LCD strategies to mitigate transformation risks for energy enterprises while concurrently optimizing regulatory frameworks to reduce public supervision costs and amplify incentive benefits, thereby fostering active public participation in LCD.

Suggested Citation

  • Xiao Liu & Qingjin Wang & Zhengrui Li & Shan Jiang, 2025. "An Evolutionary Game Analysis of Decision-Making and Interaction Mechanisms of Chinese Energy Enterprises, the Public, and the Government in Low-Carbon Development Based on Prospect Theory," Energies, MDPI, vol. 18(8), pages 1-20, April.
  • Handle: RePEc:gam:jeners:v:18:y:2025:i:8:p:2041-:d:1635924
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/18/8/2041/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/18/8/2041/
    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:jeners:v:18:y:2025:i:8:p:2041-:d:1635924. 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.