Against the dual backdrop of the global drive toward carbon peaking and carbon neutrality, a core pillar of the United Nations Sustainable Development Goals (SDGs), and the accelerated integration of new-generation digital technologies into sustainable production practices, this study employs a micro-level perspective to systematically explore how AI innovation optimizes organizational, production, and investment structures to enable corporate low-carbon development. The study sample comprises 21,428 firm-year observations from Chinese A-share listed manufacturing companies over the period of 2010–2022. The results show that AI innovation can significantly reduce corporate carbon emission intensity, specifically achieving corporate low-carbon development through three paths: optimizing low-carbon organizational governance, upgrading emission-reducing production processes, and reorienting investment toward green assets. Further analysis shows that executives’ green cognition and government environmental attention together constitute dual internal and external driving forces for corporate carbon emission reduction. Heterogeneity analysis reveals that the emission reduction effect of AI innovation is more significant for enterprises with a low supply chain concentration, those in high-environmental-sensitivity industries, and those located in regions with underdeveloped factor markets. From the micro-perspective of corporate sustainable low-carbon development, this study offers further theoretical support and empirical evidence for regulators aiming to optimize AI innovation incentives, improve sustainable environmental governance, and advance global sustainable industrial development.
Lu et al. (Thu,) studied this question.