The electric automotive transition is crucial for achieving carbon neutrality, especially in emerging economies like China. However, the scarcity of critical materials in lithium-ion batteries (LIBs) challenges electric vehicle (EV) deployment targets. Our work delivers a comprehensive framework of EV battery recycling, considering resource compensation, environmental performance, geospatial optimization, and cost feasibility of closed-loop LIB recycling under China's carbon neutrality. Our findings show that meeting EV deployment targets will widen the supply-demand gap, with cobalt and manganese demand exceeding 2022 production levels by 54-fold and 116-fold, respectively. Battery recycling is crucial for mitigating material scarcity, necessitating a minimum 84% collection rate to stabilize supply by 2060. Battery recycling remains economically viable in most scenarios, generating a net profit of US$58 billion in the optimal scenario. Here, our work underscores inherent trade-offs among integrated metrics, informing battery recycling strategies to strengthen supply chain resilience and advance automotive electrification under decarbonization goals.

Large-scale deployment of electric vehicles (EVs) to meet climate goals imposes dual pressures on power system reliability: increased electricity demand and intensified daily load variability arising from uncertain charging behavior. This study quantifies the magnitude and spatial distribution of behavior-induced load variability in China under alternative climate scenarios, using a scalable model calibrated with minute-level EV charging data. EV adoption consistent with carbon-neutrality goals is projected to raise electricity demand by 3.2% in 2050, requiring an additional annual investment of approximately 220 billion CNY (about 31 billion USD) in battery storage capacity. Accounting for behavioral uncertainty, potential load fluctuations could increase by up to 82.7%. Regional heterogeneity in charging patterns drives distinct spatial profiles of electricity variability, influencing the effectiveness of demand-side management strategies. These findings highlight the importance of targeted, behaviorally informed interventions to mitigate grid reliability risks under deep EV adoption.