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<p>Artificial intelligence has become an important enabler of corporates’ environmental, social, and governance performance. Using data on Chinese A-share listed companies from 2010 to 2022, this study constructs a firm-level AI application index with machine learning methods. Within a vector-space framework, we compare the explanatory power of mainstream text-based AI indicators by means of cosine similarity and Euclidean distance. We then systematically examine the impact of AI application on corporate ESG performance and its underlying mechanisms. The empirical results show that AI application significantly improves corporates’ ESG performance, and this effect is partially mediated by enhanced green innovation. At the same time, AI-related costs generate a cost squeeze effect that offsets part of the positive impact, while green opinion pressure from stakeholders with environmental preferences prompts the company’s green governance and positively moderates the ESG-enhancing effect of AI. Heterogeneity analyses indicate that the positive impact of AI on ESG is more pronounced for non-high-tech industry, manufacturing and heavily polluting industry, and firms located in central and western China. In addition, state-owned enterprises and whose top managers have only domestic backgrounds benefit more from AI application. Accordingly, this study suggests that governments should improve the AI + ESG regulatory framework, strengthen financial policy support, and implement differentiated and targeted industrial policies. At the firm level, companies should embed AI more deeply into ESG strategic planning, build intelligent ESG data governance systems, promote green-innovation-oriented ESG improvement, and optimize capital structure to mitigate cost squeeze. Overall, the paper not only advances the methodology for constructing and validating corporate AI indicators in corporate finance research, but also offers a practical pathway for governments and enterprises to achieve sustainable development goals and a green–digital “twin transition.”</p>

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Mechanisms and multidimensional heterogeneity of artificial intelligence technology in enhancing corporate ESG performance: evidence from China

  • 期刊论文

  • 发表日期：

    2026年2月7日

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  • Nantong Peng,
  • Jiahao Chen,
  • Chuandi Fang,
  • Hang Yu,
  • Bailing Yuan

摘要

Artificial intelligence has become an important enabler of corporates’ environmental, social, and governance performance. Using data on Chinese A-share listed companies from 2010 to 2022, this study constructs a firm-level AI application index with machine learning methods. Within a vector-space framework, we compare the explanatory power of mainstream text-based AI indicators by means of cosine similarity and Euclidean distance. We then systematically examine the impact of AI application on corporate ESG performance and its underlying mechanisms. The empirical results show that AI application significantly improves corporates’ ESG performance, and this effect is partially mediated by enhanced green innovation. At the same time, AI-related costs generate a cost squeeze effect that offsets part of the positive impact, while green opinion pressure from stakeholders with environmental preferences prompts the company’s green governance and positively moderates the ESG-enhancing effect of AI. Heterogeneity analyses indicate that the positive impact of AI on ESG is more pronounced for non-high-tech industry, manufacturing and heavily polluting industry, and firms located in central and western China. In addition, state-owned enterprises and whose top managers have only domestic backgrounds benefit more from AI application. Accordingly, this study suggests that governments should improve the AI + ESG regulatory framework, strengthen financial policy support, and implement differentiated and targeted industrial policies. At the firm level, companies should embed AI more deeply into ESG strategic planning, build intelligent ESG data governance systems, promote green-innovation-oriented ESG improvement, and optimize capital structure to mitigate cost squeeze. Overall, the paper not only advances the methodology for constructing and validating corporate AI indicators in corporate finance research, but also offers a practical pathway for governments and enterprises to achieve sustainable development goals and a green–digital “twin transition.”