The application of artificial intelligence (AI) and the improvement of environmental, social, and governance (ESG) performance have become important concerns for contemporary firms. Understanding whether AI can be effectively integrated into corporate ESG practices has significant implications for sustainable development. Using panel data from Chinese A-share listed firms from 2009 to 2025, this study empirically examines the effect of AI application on corporate ESG performance through a fixed-effects model. The results show that AI application significantly improves corporate ESG performance, indicating that firms with higher levels of AI adoption tend to achieve better ESG outcomes. The heterogeneity analysis further reveals that this effect varies according to firms’ technological intensity, industry pollution characteristics, and the strength of regional environmental regulation. Specifically, the positive effect of AI application is more pronounced among firms with lower technological intensity, firms operating in non-heavily polluting industries, and firms located in regions with stricter environmental regulation. The mediation analysis shows that AI application enhances ESG performance by promoting human capital upgrading and green technological innovation, thereby strengthening firms’ internal capabilities and technological foundations for sustainable development. This study contributes to the literature by integrating AI application and ESG-oriented sustainable development within a unified analytical framework.
In the context of the digital economy and sustainable development, artificial intelligence (AI) has emerged as an important technological approach for enterprises to enhance ESG governance. Using Shanghai and Shenzhen A-share listed enterprises from 2010 to 2024 as the research sample, this study empirically examines the impact of AI application on corporate ESG performance. The results show that AI application significantly improves corporate ESG performance, with stronger effects observed among state-owned enterprises and large enterprises. These findings remain robust after excluding high-tech enterprises and shortening the sample period. This study extends the literature on the economic consequences of AI by shifting attention from financial outcomes to non-financial ESG performance and provides empirical evidence for enterprises to develop differentiated AI-enabled ESG strategies.
Against the backdrop of a new wave of scientific and technological revolution and industrial transformation, artificial intelligence has emerged as a pivotal technology for fostering new quality productive forces and advancing high-quality development, and is profoundly reshaping firms’ production organization and governance structures. Using data on Chinese A-share listed companies from 2016 to 2024, this study empirically examines the impact of AI on corporate internal control quality and its underlying mechanisms. The results indicate that AI significantly improves corporate internal control quality, mainly by enhancing firms’ human capital and reducing agency costs. Further heterogeneity analysis shows that the positive effect of AI on internal control quality is more pronounced among manufacturing firms, firms with higher levels of digital infrastructure, and firms with greater information transparency. From the perspective of internal corporate governance, this study extends the literature on the economic consequences of AI and provides empirical evidence on how AI, as embedded in a complex socio-technical system, empowers high-quality corporate development through institutional governance mechanisms. The findings also offer useful implications for governments seeking to refine AI-related policies and for firms aiming to promote the coordinated upgrading of intelligent transformation and internal control systems.
Junming Yang, Jing-Bo Cai, Li He et al.· Journal of Risk and Financia...· 1 citation
Positive effect of QM on AI adoption is amplified by high innovation sustainability and chief executive officers (CEOs) with IT backgrounds, and is particularly pronounced in large, non-state-owned firms within highly competitive industries and the eastern regions of China.
Artificial intelligence (AI) is increasingly viewed as a key driver of sustainable development, yet evidence on its role in corporate sustainable green innovation remains limited. Drawing on Resource Orchestration Theory and Signaling Theory, this study examines the impact of AI capability on sustainable green innovation using panel data from Chinese A-share listed companies during 2014–2023. The results show that stronger AI capability significantly promotes sustainable green innovation. AI investment serves as a mediating mechanism. However, the estimated indirect effect is negative, suggesting that the process of AI implementation may involve resource reallocation and organizational adjustment costs before innovation benefits can be fully realized. Environmental investment strengthens the positive impact of AI capability, whereas digital transformation weakens it. Robustness tests confirm the reliability of the findings. Further analyses indicate that the positive effect of AI capability is more pronounced in non-state-owned enterprises, low-technology firms, and firms in the growth stage. By revealing the mechanisms and boundary conditions through which AI capability influences sustainable green innovation, this study enriches the literature on AI-enabled sustainability and offers practical insights for firms pursuing long-term sustainable development.
Shuohuan Yan, Sheng Jin, Ju Wang et al.· Sustainability· 0 citations
AI is depicted as a strategic enabler in managerial accounting, with important implications for organizations in emerging markets seeking to leverage AI use for sustainable competitive advantage.
Huthaifa Al-Hazaima, Mohammad Barakat, Mahmoud Mahmoud et al.· Corporate & Business Strateg...· 0 citations
Although corporate digital transformation has attracted increasing attention, limited research has examined how generative artificial intelligence (GAI) affects firms’ environmental, social, and governance (ESG) performance. Unlike discriminative AI (DAI), GAI is more closely associated with creativity, real-time feedback, and continuous interaction. To address this gap, we construct a firm-level GAI index using machine learning-based textual analysis and investigate its association with ESG performance among Chinese listed companies. Our empirical results reveal that GAI adoption is positively associated with ESG performance, whereas DAI does not exhibit a similar relationship. We further identify three mechanisms through which GAI exerts its influence: creativity stimulation, enhanced customer engagement, and improved operational risk management. Furthermore, the positive association between GAI and ESG performance is stronger among firms with higher intelligent investment, greater CEO digital literacy, and stronger internal controls, and is more pronounced in state-owned enterprises (SOEs) and firms operating in environmentally non-sensitive industries. These findings offer valuable insights for policymakers and regulators into the distinct roles of generative and discriminative AI in shaping corporate ESG outcomes.
Tian Wang, Dong Lu, Yide Liu· Humanities and Social Scienc...· 0 citations