Author
Listed:
- Zhen-Er Jiang
(School of Business, Nanjing University, Nanjing 210093, China)
- Fu Huang
(Department of Economics and Management, Jiangsu College of Administration, Nanjing 210009, China)
- Qiang Wu
(Yangtze River Delta Economic and Social Development Research Center, Nanjing University, Nanjing 210093, China)
Abstract
With the rapid advancement of artificial intelligence (AI), its deep integration into corporate operations has become the key driver for firms to reconfigure factor resources, boost green total factor productivity, and achieve green transformation. This analysis empirically investigates the influence of AI on corporate green transformation using panel data of China’s listed companies from 2015 to 2022. This research employs a multidimensional fixed effects linear model to analyze the relationship, finding that AI significantly enhances corporate green transformation. Mechanism analysis reveals that AI promotes green transformation by enhancing firm research and development (R&D) and firm green innovation capabilities. Heterogeneity analysis shows that the positive impact of AI on corporate green transformation is more significant in the eastern region, post-COVID−19, and in low-pollution industries. The impact is also significantly and positively moderated by the development of the non-state-owned economy and the development degree of product markets. These findings suggest that AI is a critical tool for promoting sustainable economic growth and green transformation in businesses.
Suggested Citation
Zhen-Er Jiang & Fu Huang & Qiang Wu, 2025.
"The Impact of AI on Corporate Green Transformation: Empirical Evidence from China,"
Sustainability, MDPI, vol. 17(17), pages 1-20, August.
Handle:
RePEc:gam:jsusta:v:17:y:2025:i:17:p:7782-:d:1737396
Download full text from publisher
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:gam:jsusta:v:17:y:2025:i:17:p:7782-:d:1737396. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.