IDEAS home Printed from https://ideas.repec.org/a/eee/tefoso/v201y2024ics0040162524000556.html
   My bibliography  Save this article

The effects of enterprise digital transformation on low-carbon urban development: Empirical evidence from China

Author

Listed:
  • Long, Yan
  • Liu, Liangcan
  • Yang, Bing

Abstract

Global climate change poses an unprecedented and urgent challenge, driving the international community towards a consensus on low-carbon sustainable development. Enterprise digital transformation is a strategic response to the evolving demands of the Information Age, which reshapes business processes and reduces energy consumption. This paper empirically investigates the impact of enterprise digital transformation on low-carbon urban development, based on matched “firm-city” data covering the period 2006–2020. The SBM-DDF model is employed to assess low-carbon urban development, and the text mining approach is utilized to calculate the digital transformation index of enterprises. The results indicate that enterprise digital transformation significantly facilitates low-carbon urban development and the primary mechanisms involve fostering green technology innovation, increasing the scale of green investment, and driving the transformation of industrial structure. This promotional effect is positively reinforced by government environmental concerns, government environmental subsidies, and fintech development. Additionally, the positive impact of digital transformation on low-carbon development occurs more predominantly in regions with lower levels of industrialization and marketization, and this effect becomes more evident after the digital transformation wave. By establishing a micro-level empirical foundation, this paper aims to contribute to digital transformation strategic decision-making within enterprises, while also offering a tangible and feasible pathway for advancing low-carbon urban transformation initiatives.

Suggested Citation

  • Long, Yan & Liu, Liangcan & Yang, Bing, 2024. "The effects of enterprise digital transformation on low-carbon urban development: Empirical evidence from China," Technological Forecasting and Social Change, Elsevier, vol. 201(C).
    • Handle: RePEc:eee:tefoso:v:201:y:2024:i:c:s0040162524000556
    DOI: 10.1016/j.techfore.2024.123259
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0040162524000556
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.techfore.2024.123259?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. Zhilong Lou & Nan Gao & Min Lu, 2024. "The Impact of Enterprise Digital Transformation on Low-Carbon Supply Chains: Empirical Evidence from China," Sustainability, MDPI, vol. 16(18), pages 1-22, September.

    More about this item

    Keywords

    Enterprise digital transformation; Low-carbon urban development; SBM-DDF model; Text mining;
    All these keywords.

    JEL classification:

    • O13 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Agriculture; Natural Resources; Environment; Other Primary Products
    • Q55 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environmental Economics: Technological Innovation
    • Q56 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environment and Development; Environment and Trade; Sustainability; Environmental Accounts and Accounting; Environmental Equity; Population Growth

    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:tefoso:v:201:y:2024:i:c:s0040162524000556. 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.sciencedirect.com/science/journal/00401625 .

    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.