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Artificial Intelligence and Corporate ESG Performance: A Mechanism Analysis Based on Corporate Efficiency and External Environment

Author

Listed:
  • Xinyue Yu

    (Business School, University of International Business and Economics, Beijing 100105, China)

  • Libo Fan

    (Business School, University of International Business and Economics, Beijing 100105, China)

  • Yang Yu

    (Business School, University of International Business and Economics, Beijing 100105, China)

Abstract

The rapid advancement of artificial intelligence (AI) has become a key driver in shaping firms’ environmental, social, and governance (ESG) performance. This study investigates the impact of corporate AI capabilities on ESG outcomes and examines how external environmental factors moderate this relationship. Using panel data from all A-share listed firms on the Shanghai and Shenzhen Stock Exchanges between 2010 and 2023, we measure firms’ AI capabilities through text analysis of annual reports and apply fixed-effects regression models to test our hypotheses. The results show that higher AI capability significantly improves ESG performance. Mechanism analysis suggests that AI enhances ESG outcomes by optimizing resource allocation and increasing efficiency in production and supply chains. Further, the positive effect of AI on ESG performance is more pronounced in industries with intense competition, while it is weakened under high environmental uncertainty. These findings contribute to the growing literature on AI and corporate sustainability by revealing both the internal mechanisms and contextual contingencies that shape ESG performance. The study offers practical insights for corporate managers aiming to leverage AI for sustainable development and provides policy recommendations for fostering supportive external environments.

Suggested Citation

  • Xinyue Yu & Libo Fan & Yang Yu, 2025. "Artificial Intelligence and Corporate ESG Performance: A Mechanism Analysis Based on Corporate Efficiency and External Environment," Sustainability, MDPI, vol. 17(9), pages 1-22, April.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:9:p:3819-:d:1640994
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    References listed on IDEAS

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