IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v18y2026i8p3854-d1919356.html

Optimizing Sustainable Agricultural Development via Evolutionary and Stackelberg Games

Author

Listed:
  • Dandan Qi

    (Institute of Business Economics, Harbin University of Commerce, Harbin 150028, China)

  • Linlin Zhao

    (Faculty of Economics, Harbin University of Commerce, Harbin 150028, China)

Abstract

The study explores the relatively underexamined role of artificial intelligence policies in sustainable agricultural development by investigating how governments, enterprises, and farmers interact under different policy incentives. A combination of tripartite evolutionary and Stackelberg game models is employed to examine how artificial intelligence can support more effective policy design, improve the speed of response, and foster greater collaboration among stakeholders. The analysis primarily draws on simulated data, reflecting the impact of policy incentives across various contexts. Findings suggest that artificial intelligence policies can meaningfully enhance cooperation, thereby promoting sustainable agricultural development. Higher levels of government incentives appear to encourage participation from both enterprises and farmers, while artificial intelligence contributes to faster and more precise policy adjustments. Theoretically, the study offers a framework for understanding artificial intelligence policy in agriculture and elucidates the mechanisms governing stakeholder interactions. From a practical perspective, the results provide cautious guidance for the design of artificial intelligence policies aimed at fostering sustainability.

Suggested Citation

  • Dandan Qi & Linlin Zhao, 2026. "Optimizing Sustainable Agricultural Development via Evolutionary and Stackelberg Games," Sustainability, MDPI, vol. 18(8), pages 1-34, April.
    • Handle: RePEc:gam:jsusta:v:18:y:2026:i:8:p:3854-:d:1919356
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/18/8/3854/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/18/8/3854/
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:jsusta:v:18:y:2026:i:8:p:3854-:d:1919356. 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.