Shadow-price-guided capacity governance in electric-vehicle battery reverse logistics: a hypergraph optimal transport study

We study planning and governance of large-scale reverse logistics networks for end-of-life electric vehicle batteries, an area with growing economic and environmental significance. The proposed approach models differentiated battery supply by state-of-health tiers, dismantling (recycling), and second-life utilization within a unified, three-layer network optimization framework structured via hypergraph links. We position this hypergraph optimal-transport model as a medium- to long-term planning instrument at the urban-agglomeration scale for the 2025–2035 horizon, rather than as a real-time operational dispatch tool for individual firms. Methodologically, the framework is, to our knowledge, the first to jointly capture continuous SOH-differentiated flows, coupled dismantling–reuse–recycling decisions, and dual-price-based policy instruments within a unified hypergraph optimal-transport formulation. Dual values from the model provide interpretable node-level congestion and scarcity signals, guiding targeted subsidies, capacity-voucher auctions, and inter-city quota trading. Leveraging operational data from 14 cities in China’s Yangtze River Delta (2025–2035), we identify a pronounced inter-city cost gradient and heterogeneous congestion patterns across dismantling and utilization layers. Bootstrap analyses across six structural–economic parameters confirm robust city-scale congestion and scarcity rankings, highlighting three high-leverage policy interventions: extending battery life, introducing step-change subsidies for remote dismantling facilities, and linking carbon charges to downstream capacity expansion. These insights are synthesized into a streamlined governance protocol comprising real-time congestion alerts, periodic capacity-voucher auctions, and dispersion audits, facilitating scalable, data-driven oversight of evolving electric vehicle battery recovery systems.