AI-driven carbon total factor productivity: Strategic lens on industrial enterprises

While artificial intelligence (AI) is profoundly reshaping global industry and holds immense potential for green transformation, significant debate and cognitive gaps persist regarding its net effect on firm-level carbon total factor productivity (CTFP) and the underlying operational pathways, necessitating systematic elucidation. This study develops a theoretical framework centered on strategic adaptation, stock optimization, and incremental innovation to empirically investigate the mechanisms and contingency conditions of AI's impact on CTFP in Chinese industrial enterprises. Findings reveal that AI significantly enhances CTFP, an effect positively moderated by an organizational resilience threshold and mediated through improved capacity utilization and incentivized low-carbon technology innovation. These positive effects are more pronounced in low-energy-intensity, high-market-competition, and technology-intensive firms. Moreover, these effects are broadly applicable across varying organizational slack and state ownership. The results underscore the importance of fostering a policy environment that supports the synergistic development of AI empowerment and organizational resilience to fully leverage AI's potential in advancing industrial CTFP. At the enterprise level, AI integration should be prioritized strategically. Furthermore, at the industry level, efforts should focus on shaping market and technological infrastructures that incentivize innovation and promote technological inclusivity.