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Smarter factories, greener futures: does intelligent manufacturing enhance ESG performance in Chinese firms?

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  • Shengming Hu
  • Jingting Chen
  • Jie Xu
  • Longyan Han

Abstract

As awareness of sustainable development increases, the enhancement of corporate environmental, social, and governance (ESG) performance is gaining traction worldwide. Simultaneously, the focus on the intelligent transformation of businesses has spurred investigation into its effects on ESG. This study analysed data from listed Chinese manufacturing companies from 2009 to 2023 and considered China’s intelligent manufacturing pilot demonstration projects as a quasi-natural experiment. We employed a staggered difference-in-differences model to investigate the effects and mechanisms of intelligent manufacturing on corporate ESG performance. Our results show that implementing intelligent manufacturing significantly boosts a company’s ESG and sub-dimensional performance. This conclusion remained robust after a series of tests. Additionally, intelligent manufacturing promotes joint, and especially substantive, innovation with other institutions. It also enhances corporate ESG performance by transforming the employee structure to promote greater knowledge and skills. Finally, heterogeneity analysis revealed that intelligent manufacturing substantially benefits ESG performance for non-state-owned firms, large-scale enterprises, firms operating in capital-intensive and heavily polluting industries, and enterprises based in economically underdeveloped and resource-dependent cities. This paper provides an in-depth analysis of the relationship between intelligent manufacturing and ESG performance, offering a new pathway for corporations to enhance ESG outcomes.

Suggested Citation

  • Shengming Hu & Jingting Chen & Jie Xu & Longyan Han, 2026. "Smarter factories, greener futures: does intelligent manufacturing enhance ESG performance in Chinese firms?," Applied Economics, Taylor & Francis Journals, vol. 58(34), pages 7148-7166, July.
  • Handle: RePEc:taf:applec:v:58:y:2026:i:34:p:7148-7166
    DOI: 10.1080/00036846.2025.2532063
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