In recent years, artificial intelligence (AI) technology has rapidly advanced and found widespread application in corporate management. Leveraging AI to enhance Environmental, Social, and Governance (ESG) performance and promote sustainable development has become a focal point for both academia and industry. This study aims to explore the impact of AI-driven ESG practices on the sustainable development performance of central state-owned enterprises in China. It analyzes the specific effects of AI technology in corporate governance, environmental protection, and social responsibility, and evaluates its contribution to the overall sustainable development of enterprises. The study employs a survey method, targeting 200 managers and employees from Central state-owned enterprises. The questionnaire comprises 15 questions covering three dimensions: corporate governance, environmental protection, and social responsibility. Descriptive statistics and correlation analysis are used to conduct an in-depth analysis of the collected data. The results indicate that respondents positively assess central state-owned enterprises in terms of corporate governance, environmental protection, and social responsibility, with particularly strong performance in social responsibility. Additionally, a regression analysis model is constructed. The results demonstrate that AI technology can enhance the practices and foster the sustainable development of central state-owned enterprises. Furthermore, ESG serves as a mediating factor between AI adoption and improvements in sustainable development performance. The findings provide practical insights for improving corporate management efficiency, enhancing environmental performance transparency, and boosting social image and brand value.
The rapid advancement of artificial intelligence (AI) technology has become a major catalyst for global technological progress. AI’s capability to analyze vast amounts of data allows for more accurate market forecasts and risk assessments, thereby aiding management in making better-informed strategic decisions. In the realm of Environmental, Social, and Governance (ESG), AI assists enterprises with environmental monitoring, fulfilling social responsibilities, and optimizing governance structures. By employing intelligent methods, AI enhances a company’s ESG performance and promotes sustainable development. Despite the growing integration of AI in ESG practices, its effectiveness and specific impacts on the sustainable development of central state-owned enterprises remain underexplored. In China, where central state-owned enterprises are fundamental to the national economy, these enterprises carry substantial responsibilities related to resource utilization, environmental protection, social responsibility, and governance structures. Therefore, investigating the influence of AI-driven ESG practices on the sustainable development performance of these enterprises presents significant academic value and offers practical guidance for implementation.
The significance of ESG metrics for China’s central state-owned enterprises is profound. These metrics are crucial not only for the sustainable development of the enterprises themselves but also for the broader economic and social development of the nation. As pillars of the national economy, central state-owned enterprises bear significant responsibility in resource utilization, environmental protection, social responsibility, and governance structures. ESG metrics not only help central state-owned enterprises achieve economic benefits but also enhance their social and environmental performance, boosting long-term competitiveness and contributing to the country’s high-quality development and comprehensive social progress. The application of AI in enhancing ESG performance and promoting corporate sustainable development is showing emerging trends. AI plays a vital role in environmental protection. For example, using sensors and data analysis technologies, AI can monitor air quality, water resources, and carbon emissions in real-time. This capability helps enterprises promptly identify and address environmental issues, thereby achieving more refined and scientific environmental management. In terms of social responsibility, AI can evaluate an enterprise’s performance in labor conditions, supply chain management, and social contributions through big data analysis. Regarding corporate governance, AI technologies are applied in risk management, compliance monitoring, and decision support systems, enhancing the transparency and efficiency of corporate governance. For instance, through natural language processing, AI can automatically review and analyze large volumes of legal and compliance documents, identify potential risks, and provide recommendations, ensuring that enterprises adhere to relevant regulations and standards18. Overall, these applications of AI not only improve an enterprise’s ESG performance but also drive companies toward more sustainable and responsible operations, demonstrating the substantial potential of technological innovation in corporate management.
The motivation for studying the relationship between AI-driven ESG practices and the sustainable development of central state-owned enterprises lies in the transformative potential of AI technology. AI excels in data processing, intelligent decision-making, and process optimization, making it a crucial tool for enhancing ESG performance. Therefore, exploring how AI drives ESG practices in central state-owned enterprises is of significant practical and academic value. The innovations of this paper include systematically analyzing the specific applications of AI technology across various ESG domains, thereby addressing the current research gap concerning the comprehensive impact of AI on environmental, social, and governance aspects. Additionally, this study constructs a model to evaluate the impact of AI-driven ESG practices on the sustainable development of central state-owned enterprises, providing detailed data support and a theoretical basis through quantitative analysis and empirical research.
This study aims to comprehensively investigate the application effects of AI in the ESG strategies of central state-owned enterprises and to assess its impact on the sustainable development performance of these enterprises. Specific objectives include analyzing the application and effects of AI in the ESG practices of central state-owned enterprises, exploring its impact on environmental protection, employee welfare, and corporate governance, and quantitatively evaluating the effectiveness of AI in the ESG strategies of central state-owned enterprises through empirical research. Additionally, the direct impact of AI technology on corporate sustainable development performance is evaluated, highlighting its potential to enhance resource utilization efficiency, improve social image, and foster long-term sustainability. Furthermore, the mediating role of ESG performance between AI application and corporate sustainable development performance is examined, elucidating how AI technology can drive corporate sustainability by optimizing ESG practices. By achieving these objectives, this study seeks to provide both theoretical foundations and practical guidance for central state-owned enterprises in developing and implementing AI-driven ESG strategies. This approach aims to enhance environmental performance, social value, and governance efficiency, ultimately laying the groundwork for the attainment of long-term sustainability goals.
The application of AI in ESG practices holds significant promise. The theory of AI encompasses fields such as machine learning, data mining, and natural language processing, focusing primarily on how computer systems can emulate human intelligence, learn, and adapt to their environments. This theoretical framework elucidates the methods and impacts of applying AI technologies within the ESG domain. For instance, machine learning techniques can assist companies in identifying potential environmental risks and social issues within large datasets, providing intelligent solutions. Natural language processing technologies facilitate real-time monitoring and analysis of employee feedback and societal sentiments, enabling companies to promptly adjust their corporate social responsibility strategies. Regarding the environment, Aljohani observed that AI technology aids companies in real-time environmental data monitoring, risk prediction, and intelligent environmental management through big data analysis and machine learning. Concerning society, Richey noted that AI optimizes employee management, enhances satisfaction, and monitors supply chain compliance, thereby improving corporate social responsibility fulfillment. In terms of governance, Bhima found that AI enhances internal audit efficiency, risk management accuracy, and decision-making support, bolstering corporate governance structure and transparency. These research findings underscore the pivotal role of AI in ESG practices, providing companies with innovative solutions to enhance their sustainable development performance and social impact.
This study, utilizing survey data, factor analysis, and regression modeling, examines the impact of AI-driven ESG practices on the sustainable development performance of central state-owned enterprises. The findings contribute to the existing literature in several important ways:
First, this study addresses a gap in the literature concerning the relationship between AI and ESG performance, emphasizing the role of AI technology in advancing the sustainable development of central state-owned enterprises. The study demonstrates that AI not only enhances corporate governance and social responsibility by optimizing resource management, improving production efficiency, and fostering innovation, but also promotes comprehensive improvements across ESG dimensions, particularly in environmental management. Although AI technology has yielded notable advancements in corporate governance and social responsibility, particularly in terms of management efficiency and social image, opportunities remain for further improvements in environmental performance. Specifically, areas such as emission reduction and resource optimization continue to present challenges. Future research could explore the full potential of AI technology in these critical areas.
A key contribution of this study is the identification of the mediating role of ESG in leveraging AI to enhance the sustainable development performance of central state-owned enterprises. The regression analysis demonstrates that the adoption of AI technology not only directly improves sustainable development performance but also indirectly facilitates the achievement of sustainability goals by optimizing ESG performance. This finding expands the scope of AI technology’s application in corporate sustainable development strategies and offers a novel perspective for academic research in related fields. Specifically, ESG performance serves as a mediating variable between AI technology and corporate sustainable development, highlighting that, while enterprises pursue economic benefits, they simultaneously enhance their social responsibility, environmental performance, and governance structure, thereby providing a solid foundation for long-term sustainability.
Furthermore, this study contributes new empirical evidence by emphasizing the heterogeneous impact of AI technology on different ESG dimensions. The results indicate that AI’s effect on the environmental dimension remains preliminary and localized, while its application in the social and governance dimensions is more developed and exhibits significant outcomes. By examining the variations in AI application across different enterprises, the study uncovers the complex relationship between AI usage and the enhancement of corporate sustainable development performance, offering valuable insights for both academic and practical understanding of how AI drives corporate sustainability.
This study offers specific practical guidance for policymakers and corporate managers. By thoroughly analyzing the role of AI technology in ESG practices, the study suggests that central state-owned enterprises should not only encourage the adoption of AI technology but also intensify their investment and efforts in environmental protection, particularly in areas such as resource optimization and emissions control. Corporate managers are encouraged to leverage AI technology to improve corporate governance and enhance their social responsibility, thereby achieving a balanced outcome that benefits both economic performance and societal contributions.
In conclusion, this study not only enriches the theoretical understanding of the relationship between AI technology and corporate sustainable development but also provides valuable empirical evidence derived from practical data analysis for central state-owned enterprises in implementing AI-driven ESG strategies. Future research could further investigate the heterogeneous effects of AI technology across various types of enterprises, particularly in different national, industrial, and market contexts, to offer more comprehensive theoretical and practical insights for advancing global sustainable development practices.
This study also acknowledges several challenges and limitations. Firstly, substantial variations exist in the ESG performance among different central state-owned enterprises, which could be attributed to differences in company size, industry characteristics, and geographical factors. Secondly, despite the potential of AI technology, its actual application effects require further validation and optimization. Moreover, the limited sample size of this study, comprising only 200 respondents, may not adequately represent the ESG landscape of all central state-owned enterprises. Future research endeavors should enlarge the sample size and employ a combination of quantitative and qualitative methodologies to delve deeper into the specific impact of AI on ESG performance across diverse contexts. Additionally, careful consideration should be given to the long-term implications and potential risks associated with AI technology in ESG practices to offer more comprehensive guidance and recommendations.
