Artificial Intelligence Embedding and Corporate Carbon Productivity: Mechanisms of Technological Progress and Efficiency Optimization

ABSTRACT The driving mechanism of artificial intelligence (AI) embedding on manufacturing firms' carbon productivity has emerged as a critical pathway for promoting industrial green transformation and high‐quality development. Drawing upon absorptive capacity theory and the resource‐based view (RBV), this study constructs a dual‐mediator model driven by technology and efficiency. Grounded in the technology‐organization‐environment (TOE) framework, we integrate multidimensional indicators and employ the entropy method to establish an AI embedding evaluation index system that effectively distinguishes substantive AI embedding from symbolic AI embedding. Using a sample of A‐share listed manufacturing firms on the Shanghai and Shenzhen stock exchanges from 2008 to 2023, we systematically investigate the effect of AI embedding on corporate carbon productivity and its underlying transmission mechanisms. The results indicate that AI embedding drives the improvement of corporate carbon productivity primarily through two pathways: the enabling path of green technological innovation and the integrating path of energy utilization efficiency. The former creates incremental low‐carbon resources through technological breakthroughs, whereas the latter optimizes the allocation of existing resources through factor reorganization. Heterogeneity analysis reveals that the positive association between AI embedding and carbon productivity is more pronounced in firms with high carbon emissions, in industries with low competition, and in regions with less stringent environmental regulations. This study extends the explanatory boundary of the RBV regarding the construction of corporate green capabilities in the digital context and aims to provide both theoretical support and practical implications for achieving the synergistic goals of carbon reduction and efficiency improvement in the manufacturing sector.