Artificial Intelligence-Driven Improvement in Total-Factor Carbon Productivity: Evidence from China’s Heavily Polluting Enterprises

Against the backdrop of intensifying global low-carbon transition and digital technologies’ profound empowerment of the real economy, widespread adoption of artificial intelligence (AI) provides critical technical support that can help heavily polluting enterprises with high energy consumption and emissions overcome the bottlenecks in green development and improve total-factor carbon productivity (TFCP). Using panel data on Chinese listed heavily polluting enterprises from 2013 to 2024 as the research sample, this study investigates artificial intelligence’s impact on firms’ total-factor carbon productivity. The results show that AI has a significant positive impact on TFCP, with mitigating financing constraints, promoting green innovation, and strengthening corporate governance serving as underlying mechanisms. Heterogeneity analysis indicates that such a promotive effect is more pronounced for heavily polluting enterprises in Eastern China, firms with high R&D investment, state-owned enterprises, large-scale firms, and enterprises with low levels of environmental information disclosure. Furthermore, environmental regulation, digital infrastructure, and human capital can amplify artificial intelligence’s productivity-enhancing effect on total-factor carbon productivity. On the basis of these findings, we propose policy suggestions focusing on attaching importance to artificial intelligence technologies, strengthening financial support, and advancing digital infrastructure construction so as to address the low-carbon-transition predicament pertaining to heavily polluting industries and achieve the coordinated development of economic growth and carbon emission reduction.