Artificial Intelligence Empowerment and Carbon Emission Performance: A Systems Perspective on Sustainable Cleaner Production

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.