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 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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Yi Cao
Inner Mongolia Normal University
Zhou Lan
Universidad Santo Tomás
Jie Dong
Inner Mongolia Normal University
Sustainability
Nanjing University of Information Science and Technology
University of Santo Tomas
Inner Mongolia Normal University
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Cao et al. (Thu,) studied this question.
synapsesocial.com/papers/69ec5b2388ba6daa22daca94 — DOI: https://doi.org/10.3390/su18094190