doi: 10.18178/joebm.2026.14.1.912
How Artificial Intelligence Empowers Corporate Green Innovation: Mechanism and Evidence from China’s Manufacturing Industry
,
Yilin Zhang 1,*
,
- 1 School of Finance and Public Administration, Harbin University of Commerce, Harbin 150028, Heilongjiang, China
- 2 School of Finance, Harbin University of Commerce, Harbin 150028, Heilongjiang, China
- Manuscript received October 15, 2025
- accepted January 2, 2026
- published February 27, 2026
Abstract
Corporate Green Innovation (CGI) is essential for coping with environmental challenges and promoting economic growth. To explore whether and how the application of Artificial Intelligence (AI) can promote CGI, and which types of enterprises can benefit more from AI in enhancing CGI, this paper takes the establishment of China’s National New Generation AI Innovation and Development Pilot Zone (NAIPZ) as a quasi-natural experiment to examine the impact of AI on CGI. The results confirm that AI significantly promotes CGI, especially in the eastern region, non-SOEs, and industries with higher competition or lower financing constraints. Mechanism analysis emphasizes that corporate productivity and Internal Governance (IG) are key channels. Based on the research results, we suggest that promote the further development of CGI by reducing corporate productivity, formulating and implementing policy differently, helping disadvantaged enterprise to unite high competition and low-financing-constrained enterprises together. The research results provide a transferable implementation approach for governments and enterprise to promote CGI with AI.
Keywords
- difference-in-differences method
- double machine learning model
- artificial intelligence
- corporate green innovation
- corporate productivity
- internal governance
How to Cite
Ju Jin, Yilin Zhang, and Shen Zhong, "How Artificial Intelligence Empowers Corporate Green Innovation: Mechanism and Evidence from China’s Manufacturing Industry," Journal of Economics, Business and Management, vol. 14, no. 1, pp. 75-80, 2026. https://doi.org/10.18178/joebm.2026.14.1.912
Copyright & License
Copyright © 2026 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).