IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v244y2025ics0960148125004100.html
   My bibliography  Save this article

How does green investor entry affect corporate carbon performance? Evidence from China

Author

Listed:
  • Liu, Maotao
  • Fang, Xubing

Abstract

As a special kind of institutional investor, green investors are an important driving force in promoting green and low-carbon development of enterprises. Using data from 2008 to 2020 of Chinese A-share non-financial listed firms, this study investigates how the green investor entry affect corporate carbon performance. Results show that green investor entry significantly enhances corporate carbon performance. Mechanism analysis reveals that green investor entry improves corporate carbon performance by improving ESG performance and enhancing environmental information disclosure quality. The positive effects are more pronounced in regions with stringent environmental regulations, heavily polluting industries, and non-state-owned enterprises. In addition, we find that green investor entry significantly improves firms' economic performance while enhancing their carbon performance, simultaneously realizing both economic enhancement and green development. Our results remain robust after a series of robustness and endogenous tests. Overall, our study not only provides direct micro-level evidence from developing economies regarding green investors' role in improving corporate carbon performance, but also offers critical insights for establishing robust green financial mechanisms to facilitate sustainable transformation.

Suggested Citation

  • Liu, Maotao & Fang, Xubing, 2025. "How does green investor entry affect corporate carbon performance? Evidence from China," Renewable Energy, Elsevier, vol. 244(C).
  • Handle: RePEc:eee:renene:v:244:y:2025:i:c:s0960148125004100
    DOI: 10.1016/j.renene.2025.122748
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148125004100
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.renene.2025.122748?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 search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Guanyan Lu & Bingxiang Li, 2025. "Artificial Intelligence and Green Collaborative Innovation: An Empirical Investigation Based on a High-Dimensional Fixed Effects Model," Sustainability, MDPI, vol. 17(9), pages 1-41, May.

    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:renene:v:244:y:2025:i:c:s0960148125004100. 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.journals.elsevier.com/renewable-energy .

    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.