IDEAS home Printed from https://ideas.repec.org/a/eee/streco/v78y2026icp326-342.html

How can green credit reduce environmental costs? Analysis based on an extended economic growth model with environmental constraints

Author

Listed:
  • Zhang, Shengcheng
  • Zhang, Chonghui
  • Fang, Xia
  • Zhang, Dongcai
  • Baležentis, Tomas

Abstract

Green credit is a financial tool stimulating environmental protection by allocating credit funds for cleaner and energy-efficient activities. However, the mechanisms of credit allocation can affect the effectiveness of green credit. This study discusses the concepts of lending discrimination and endogenous clean technology based on the economic growth model with environmental constraints. This allows explaining the mechanism through which lending discrimination and green innovation influence the effect of green credit on (reduction of) environmental impact. Furthermore, empirical data on Chinese provinces from 2012–-2022 confirm that lending discrimination weakens the positive effect of green credit in reducing environmental impact. Additionally, green credit can reduce environmental impact by enhancing corporate green innovation. This study provides theoretical and empirical evidence for understanding the relationship between green credit and environmental impact and offers insights for optimizing green credit.

Suggested Citation

  • Zhang, Shengcheng & Zhang, Chonghui & Fang, Xia & Zhang, Dongcai & Baležentis, Tomas, 2026. "How can green credit reduce environmental costs? Analysis based on an extended economic growth model with environmental constraints," Structural Change and Economic Dynamics, Elsevier, vol. 78(C), pages 326-342.
    • Handle: RePEc:eee:streco:v:78:y:2026:i:c:p:326-342
    DOI: 10.1016/j.strueco.2026.04.001
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0954349X26000603
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.strueco.2026.04.001?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

    for a different version of it.

    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:eee:streco:v:78:y:2026:i:c:p:326-342. 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/locate/inca/525148 .

    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.