IDEAS home Printed from https://ideas.repec.org/a/eee/glofin/v58y2023ics1044028323000807.html
   My bibliography  Save this article

Can the green credit policy reduce carbon emission intensity of “high-polluting and high-energy-consuming” enterprises? Insight from a quasi-natural experiment in China

Author

Listed:
  • Wang, Yufeng

Abstract

Previous studies have examined the effect of green credit policy (GCP) on innovation, the environment, and corporate performance. However, few studies have focused on GCP's impact on carbon reduction of high-polluting and high-energy-consuming (“two high”) enterprises. Based on millions of unbalanced panel data from the China Taxation Survey database from 2009 to 2016, this study considers the most acclaimed GCP (“Green Credit Guideline” in 2012) as a quasi-natural experiment and adopts a difference-in-difference (DID) method along with interactive fixed effects to study the impact of GCP on the carbon emission intensity of “two high” enterprises. In general, we find that GCP significantly reduces the carbon emission intensity of “two high” enterprises. This effect is achieved by optimizing the energy structure, promoting technology transformation, and increasing the intensity of innovation input. A heterogeneous analysis shows that the GCP has a significant suppression effect on “two high” enterprises in eastern and western regions, although its impact is less evident in central areas. Moreover, it shows that non-state-owned, unsubsidized, medium-scale, and large-scale “two high” enterprises are more significantly negatively impacted by the GCP. Finally, to further address the endogeneity problem, the propensity score matching–difference-in-difference (PSM-DID) estimation is conducted and pragmatic policy implications are proposed to improve the development and effectiveness of the GCP.

Suggested Citation

  • Wang, Yufeng, 2023. "Can the green credit policy reduce carbon emission intensity of “high-polluting and high-energy-consuming” enterprises? Insight from a quasi-natural experiment in China," Global Finance Journal, Elsevier, vol. 58(C).
  • Handle: RePEc:eee:glofin:v:58:y:2023:i:c:s1044028323000807
    DOI: 10.1016/j.gfj.2023.100885
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1044028323000807
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.gfj.2023.100885?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.

    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:glofin:v:58:y:2023:i:c:s1044028323000807. 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/620162 .

    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.