Pathways for AI-Driven Green and Low-Carbon Transition of Supply Chains

Abstract

Global supply chains account for approximately 60% of total carbon emissions worldwide, yet fragmented information across multiple actors and divergent interest objectives render systemic emission reductions unattainable through traditional management approaches. This paper delineates four pathways through which artificial intelligence (AI) drives the green and low-carbon transition of supply chains: intelligent demand sensing enhances supply-demand matching, curbing superfluous emissions at the source; intelligent logistics scheduling optimizes the trade-off among cost, delivery time, and energy consumption; chain-wide carbon footprint traceability addresses Scope 3 emissions; and supplier collaboration and empowerment promote green and low-carbon transformation. It also identifies structural limitations inherent in these pathways, including fragmented data governance, the opacity of algorithmic decision-making rationales, organizational incentive misalignments, and environmental rebound risks arising from the computing power consumption of AI itself. Accordingly, the paper proposes optimization directions such as constructing a cross-organizational carbon data governance framework, developing explainable carbon decision intelligence models, designing collaborative carbon-reduction incentive mechanisms that balance equity and efficiency, and establishing a full-life-cycle carbon performance evaluation system. Embedding institutional design into technical processes enables AI to evolve from an efficiency tool into an institutional infrastructure for the green governance of supply chains.