Exploring the nexus of artificial intelligence-enabled supply chain, low-carbon innovation capabilities and integration

Purpose Artificial intelligence (AI) is widely recognized for its role in enhancing supply chain efficiency and resilience. However, its specific impact on advancing low-carbon supply chain integration (LSCI) remains a developing field of study. This study examines how AI-driven supply chain management practices enhance low-carbon innovation capabilities (LIC), leading to low-carbon value co-creation (LVC) and ultimately facilitating LSCI. Additionally, we test the effects of carbon footprint tracking (CFT) on LIC and LSCI. Design/methodology/approach Grounded in the dynamic capabilities and informational advantage theories, the proposed model was validated using the data collected from 421 medium and large Chinese logistics firms. Partial least squares structural equation modelling was used to analyze the data. Findings The findings demonstrated significant positive effects of AI-powered data practices (data acquisition, integration, analysis and interpretation) on LIC. CFT produced significant effects not only on LSCI but also on LIC, which in turn positively influences LVC. Furthermore, LIC and LVC positively impacted LSCI. The indirect effects of LIC and LVC also provided important findings. Originality/value This study is a pioneering effort to empirically investigate how AI-driven supply chain integration contributes to enhancing LIC and promoting LSCI. Furthermore, it highlights the often-overlooked role of CFT as a crucial factor in low-carbon innovation in AI-powered supply chain literature.