Sustainable battery recycling through spatial and technological alignment

Abstract End-of-life (EOL) battery recycling is crucial for sustainable energy transition, yet existing systems often suffer from spatial, technological and policy mismatches that limit environmental benefits. Here we present a multi-scale analytical framework that integrates machine learning, life-cycle assessment and spatial integrated scenario modelling to evaluate the environmental footprint and resource recovery potential of battery recycling systems. This framework enables high-resolution projections of battery retirement trends, assessment of recycling technologies and optimization of supply–demand strategies. Using China as a case study, we analysed EOL battery flows across 364 cities and over 300 recycling projects involving 24 battery chemistries. The results reveal that, during 2020–2030, total EOL battery volumes amount to 16.67–19.99 Mt, with a northeast–southwest–northwest hotspot migration pattern. Interprovincial coordination can increase treatment capacity utilization by 67.12% but cannot eliminate spatial mismatches between EOL battery supply and available treatment capacity. Our optimization scenarios show that supply–demand planning could reduce emissions by up to 44% and increase lithium recovery by 53%. This work provides a scalable tool for designing low-carbon, high-efficiency battery recycling systems, contributing to the circular economy and energy transitions.