IDEAS home Printed from https://ideas.repec.org/a/eee/finana/v96y2024ipbs1057521924006641.html
   My bibliography  Save this article

Data factor agglomeration and urban green finance: A quasi-natural experiment based on the National Big Data Comprehensive Pilot Zone

Author

Listed:
  • Wang, Huizong
  • Hao, Yulong
  • Fu, Qiang

Abstract

Using panel data from 246 cities in China from 2008 to 2021, we investigate the impact of data factor agglomeration on the development of urban green finance through a quasi-natural experiment based on the National Big Data Comprehensive Pilot Zone. Findings reveal that the effect of data agglomeration, characterized by the construction of big data comprehensive pilot zone, has significantly improved urban green finance development in the pilot zones. Further research shows that data factor agglomeration can expand the development scale of urban green finance by promoting industrial structure upgrading and improve the development efficiency of urban green finance by driving innovation in digital technology, both of which can promote urban green finance development. Furthermore, the impact of data factor agglomeration on urban green finance development is influenced by geographic location and urban administrative level, with greater significance for cities in the eastern region and those with high administrative levels. Meanwhile, the human capital level and environmental regulation strength positively moderate the efficacy of the data factors' agglomeration. Our study explores the effective and realistic approaches to promote the development of urban green finance from the perspective of data factor agglomeration, providing a reference for countries to accelerate the construction of good data factor ecosystems and promote green finance reform in depth.

Suggested Citation

  • Wang, Huizong & Hao, Yulong & Fu, Qiang, 2024. "Data factor agglomeration and urban green finance: A quasi-natural experiment based on the National Big Data Comprehensive Pilot Zone," International Review of Financial Analysis, Elsevier, vol. 96(PB).
  • Handle: RePEc:eee:finana:v:96:y:2024:i:pb:s1057521924006641
    DOI: 10.1016/j.irfa.2024.103732
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1057521924006641
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.irfa.2024.103732?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:finana:v:96:y:2024:i:pb:s1057521924006641. 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/620166 .

    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.