How does the construction of new generation of national AI innovative development pilot zones drive enterprise ESG development? Empirical evidence from China

Yujie Huang, Shucheng Liu, Jiawu Gan, Baoliu Liu and Yuxi Wu

Energy Economics, 2024, vol. 140, issue C

Abstract: In the context of the rapid development of artificial intelligence (AI) technology and the growing global attention to the ESG performance of enterprises, this study takes the “National New Generation Artificial Intelligence Innovation and Development Pilot Zone” as a quasi-natural experiment. Based on the unbalanced panel data of Chinese Shanghai and Shenzhen listed companies from 2007 to 2022, it uses the multi-period difference-in-differences model (DID) and the propensity score matching-difference-in-differences (PSM-DID) method to explore the impact and mechanism of the AI pilot policy on the ESG performance of enterprises. The empirical results show that this policy significantly improves the ESG performance of enterprises, and the robustness of the conclusion is verified through parallel trend tests, placebo tests, PSM-DID tests, etc. The heterogeneity analysis shows that the policy has different effects in different regions and industries, and the response is more significant in the eastern and central regions, as well as non-state-owned enterprises and heavily polluting industries. The analysis of the impact mechanism confirms the key role of green technology innovation and the level of R&D expenditure. Finally, this paper puts forward policy suggestions such as formulating differentiated policies, building innovation platforms, enhancing R&D investment, and establishing monitoring and evaluation mechanisms to promote the effective implementation of AI technology application by enterprises in ESG performance.

Keywords: Construction of AI innovative development pilot zones; ESG performance; Green technology innovation; R&D expenditure; Heterogeneity analysis (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:140:y:2024:i:c:s0140988324007199

DOI: 10.1016/j.eneco.2024.108011

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