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Open AccessArticle
Artificial Intelligence Empowerment and Carbon Emission Performance: A Systems Perspective on Sustainable Cleaner Production
by
Shun Li
Shun Li
Shun Li was born in Ankang, Shaanxi Province, China, in 1999. He earned his bachelor’s degree in a [...]
Shun Li was born in Ankang, Shaanxi Province, China, in 1999. He earned his bachelor’s degree in economics from Hebei GEO University in 2022 and his master’s degree in business administration from Semyung University, South Korea, in 2024. He is currently pursuing a PhD in business administration at Wonkwang University, South Korea. His research interests include corporate production management and sustainability, business economics, environmental economics and policy, corporate innovation management, and artificial intelligence.
,
Ruijie Song
Ruijie Song
Sanggyun Na
Sanggyun Na
Professor Dr. Sanggyun Na is a professor at the College of Business Administration, Wonkwang South a [...]
Professor Dr. Sanggyun Na is a professor at the College of Business Administration, Wonkwang University, South Korea, and currently serves as the 39th President of the Korean Association of Business Education. He received his PhD in business administration with a focus on production and operations management. His research primarily focuses on production and operations management, technology, and innovation management, corporate innovation, and sustainable business development. Professor Na has published extensively in South Korean and international journals, contributing to the scholarly discourse on innovation-driven growth, sustainable operations, and business education reform. His academic work bridges theory and practice, offering managerial implications for firms adapting to digital transformation and environmental challenges. In addition to his research, he has been actively engaged in academic service, promoting interdisciplinary collaboration and the globalization of South Korean business education. Through his teaching, publications, and professional leadership, Professor Na continues to advance the fields of operations management and sustainable business strategies while mentoring the next generation of scholars and business leaders.
Tingxian Yan
Tingxian Yan
College of Business Administration, Wonkwang University, No. 460, Iksandae-ro, Iksan 54538, Republic of Korea
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Author to whom correspondence should be addressed.
Systems 2025, 13(10), 916; https://doi.org/10.3390/systems13100916 (registering DOI)
Submission received: 21 August 2025
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Revised: 4 October 2025
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Accepted: 15 October 2025
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Published: 18 October 2025
Abstract
Amid China’s pursuit of its “dual carbon” goals, systematic theoretical and empirical research remains limited to the potential role of artificial intelligence (AI) in enhancing firms’ carbon emission performance (CEP). From a systems perspective, this study developed a dynamic learning game model that integrates a constant elasticity of substitution (CES) production function, an AI-enabled abatement function, and institutional constraints to analyze firms’ cleaner production and technology adoption under simultaneous budgetary and emission constraints. Empirically, we drew on panel data of 3404 Chinese A-share listed firms from 2013 to 2023 and employ a two-way fixed-effect model to examine the effect of AI empowerment on CEP. The results showed that AI significantly improves CEP overall, though its effect is potentially constrained by energy rebound effects. Robustness checks using alternative measures and specifications confirmed the reliability of the findings and further indicated that AI’s abatement effect became stronger after 2018, consistent with technological maturity and institutional improvement. Mechanism analysis suggests two plausible pathways: (1) improving ESG performance and strengthening environmental governance; and (2) stimulating green innovation to support low-carbon technology development and application. Heterogeneity analysis indicates that AI’s effects are more evident in regions with higher marketization, in private firms, and in non-pollution-intensive industries. By contrast, firms led by executives with overseas experience tend to exhibit weaker effects, a pattern consistent with institutional fit and localization considerations. This study contributes to cleaner production theory by highlighting firm-level mechanisms of AI-enabled carbon governance while offering practical insights for low-carbon transitions and digital decarbonization strategies in developing economies.
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MDPI and ACS Style
Li, S.; Song, R.; Na, S.; Yan, T.
Artificial Intelligence Empowerment and Carbon Emission Performance: A Systems Perspective on Sustainable Cleaner Production. Systems 2025, 13, 916.
https://doi.org/10.3390/systems13100916
AMA Style
Li S, Song R, Na S, Yan T.
Artificial Intelligence Empowerment and Carbon Emission Performance: A Systems Perspective on Sustainable Cleaner Production. Systems. 2025; 13(10):916.
https://doi.org/10.3390/systems13100916
Chicago/Turabian Style
Li, Shun, Ruijie Song, Sanggyun Na, and Tingxian Yan.
2025. "Artificial Intelligence Empowerment and Carbon Emission Performance: A Systems Perspective on Sustainable Cleaner Production" Systems 13, no. 10: 916.
https://doi.org/10.3390/systems13100916
APA Style
Li, S., Song, R., Na, S., & Yan, T.
(2025). Artificial Intelligence Empowerment and Carbon Emission Performance: A Systems Perspective on Sustainable Cleaner Production. Systems, 13(10), 916.
https://doi.org/10.3390/systems13100916
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