Artificial intelligence and green transformation of manufacturing enterprises

Chaobo Zhou, Haikuo Zhang, Jinhuika Ying, Shouchao He, Chong Zhang and Jiale Yan

International Review of Financial Analysis, 2025, vol. 104, issue PA

Abstract: The rapid development and widespread application of artificial intelligence (AI) has had a profound impact on the economy and society. However, we need to be sure that the use of AI technology can inject vitality into the green transformation (GT) of enterprises. Based on panel data from Chinese listed manufacturing companies spanning 2013 to 2022, this study asks the question in the manufacturing sector, using the establishment of China's new-generation AI innovation and development pilot zones as a quasi-natural experiment. Employing a multiperiod difference-in-differences model, we find that AI adoption significantly promotes GT in manufacturing enterprises. This conclusion remains robust when validated through a generalized random forest (GRF) model. Mechanism testing shows that improvements in enterprise environmental, social, and governance performance and information transparency serve as key drivers of AI's positive influence on GT. Additionally, media attention and executives with research and development backgrounds further enhance AI's role in promoting GT. Heterogeneity analysis using the GRF model reveals an inverted U-shaped relationship between Tobin's Q, enterprise age, and the treatment effect. As such, we uncover the underlying mechanisms of AI's impact on GT and offer insights for policymakers to actively and prudently advance AI development, supporting the integration of digital and real economies.

Keywords: Artificial intelligence; Generalized random forest; Green transformation; Quasi-natural experiment (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:finana:v:104:y:2025:i:pa:s105752192500417x

DOI: 10.1016/j.irfa.2025.104330

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