IDEAS home Printed from https://ideas.repec.org/a/eee/eneeco/v128y2023ics0140988323006333.html
   My bibliography  Save this article

Effect of digital transformation on enterprises' green innovation: Empirical evidence from listed companies in China

Author

Listed:
  • Tang, Maogang
  • Liu, Yinlin
  • Hu, Fengxia
  • Wu, Baijun

Abstract

Digital transformation could potentially promote enterprises' green innovation through optimizing resource allocation, innovation, and network effects. However, few empirical studies explore the mechanism of the promotion effect of digital transformation on enterprises' green innovation. In this context, this study explores this mechanism based on technological innovation effects, the learning-by-doing effect, innovation cooperation networks, and financial constraint alleviation. Based on microdata from Chinese A-share listed companies for a time period ranging from 2011 to 2020, this study adopts a multidimensional fixed effects model to conduct empirical analyses. The benchmark results suggest that digital transformation significantly promotes enterprises' green innovation. The findings remain robust after a series of robustness tests and an endogeneity test. Mechanism analysis confirms that digital transformation can facilitate enterprises' green innovation by promoting innovation effects, the learning-by-doing mechanism and spillover effects, innovation cooperation network formation, and financial constraint alleviation. Finally, we suggest that the government should implement a series of support policies, establish collaborative innovation platforms and organizations, explore the application scenarios of digital technologies in green innovation, and increase the construction of and investment in digital infrastructure to promote enterprises' green innovation.

Suggested Citation

  • Tang, Maogang & Liu, Yinlin & Hu, Fengxia & Wu, Baijun, 2023. "Effect of digital transformation on enterprises' green innovation: Empirical evidence from listed companies in China," Energy Economics, Elsevier, vol. 128(C).
  • Handle: RePEc:eee:eneeco:v:128:y:2023:i:c:s0140988323006333
    DOI: 10.1016/j.eneco.2023.107135
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0140988323006333
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.eneco.2023.107135?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:eneeco:v:128:y:2023:i:c:s0140988323006333. 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/eneco .

    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.