Author
Listed:
- Yi Cao
(Institute for the History of Science and Technology, Inner Mongolia Normal University, Hohhot 010022, China
Key Laboratory of Cognitive Intelligence for Sci-Tech Cultural Heritage, Inner Mongolia Autonomous Region, Inner Mongolia Normal University, Hohhot 010022, China
These authors contributed equally to this work.)
- Zhou Lan
(School of Law, University of Santo Tomas, Manila 1008, Philippines
These authors contributed equally to this work.)
- Jie Dong
(Institute for the History of Science and Technology, Inner Mongolia Normal University, Hohhot 010022, China
Key Laboratory of Cognitive Intelligence for Sci-Tech Cultural Heritage, Inner Mongolia Autonomous Region, Inner Mongolia Normal University, Hohhot 010022, China)
- Ling Cao
(School of Management Science and Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China)
Abstract
Enhancing the ESG performance of manufacturing enterprises represents a critical pathway for promoting high-quality economic development and achieving sustainable development goals. Leveraging the establishment of National New-Generation Artificial Intelligence Innovation and Development Pilot Zones as a quasi-natural experiment, this study examined A-share listed manufacturing enterprises on the Shanghai and Shenzhen Stock Exchanges from 2010 to 2023, employing a multi-period difference-in-differences model to systematically evaluate the policy’s impact on enterprise ESG performance and its underlying mechanisms. The empirical results demonstrate that the Artificial Intelligence Innovation and Development Pilot Zone policy exerts a significant positive effect on manufacturing enterprises’ ESG performance, with the robustness of this conclusion validated through parallel trends tests, placebo tests, and multiple robustness checks. A mechanism analysis revealed that the policy primarily enhances manufacturing enterprises’ ESG performance through two transmission channels: intensifying the R&D expenditure intensity and strengthening environmental compliance pressures. Furthermore, the enterprise resource allocation and operational efficiencies significantly moderate the policy effect, amplifying the enabling effect of the policy on ESG performance. A heterogeneity analysis indicates that, from the perspectives of enterprise ownership and responsibility orientation, the policy demonstrates more pronounced enabling effects on non-state-owned enterprises and non-high-pollution enterprises; from the perspectives of technological endowment and factor structure, the policy effects are more evident among high-tech enterprises, non-capital-intensive enterprises, and non-labor-intensive enterprises. This study elucidates the multi-dimensional transmission mechanisms through which the Artificial Intelligence Innovation and Development Pilot Zone policy empowers ESG development in manufacturing enterprises, providing theoretical foundations and practical guidance for refining artificial intelligence policy frameworks and promoting manufacturing enterprise sustainable development. The research findings also contribute empirical evidence from emerging economies to comparative research on global AI governance.
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