Sustainable battery recycling through spatial and technological alignment

IF 32.1 1区环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES

Nature Sustainability Pub Date : 2026-06-08 DOI:10.1038/s41893-026-01851-6

Xi Tian (, ), Fei Peng (, ), Jon McKechnie, Amir F. N. Abdul-Manan, Yaobin Liu (, ), Fanran Meng (, )

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引用次数: 0

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. Battery recycling has a crucial role in enabling circular economy and sustainable energy transition. This study presents a multi-scale analytical framework to assist the design of low-carbon, high-efficiency battery recycling systems.

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通过空间和技术对齐实现可持续电池回收

报废（EOL）电池回收对于可持续能源转型至关重要，但现有系统往往受到空间、技术和政策不匹配的影响，限制了环境效益。本文提出了一个集成了机器学习、生命周期评估和空间集成场景建模的多尺度分析框架，以评估电池回收系统的环境足迹和资源回收潜力。该框架能够对电池退役趋势进行高分辨率预测，评估回收技术并优化供需策略。以中国为例，我们分析了364个城市和300多个涉及24种电池化学成分的回收项目的EOL电池流量。结果表明：2020-2030年，中国东部地区EOL电池总容量为16.67 ~ 19.99 Mt，总体呈现东北-西南-西北热点迁移格局；省际协调可使处理能力利用率提高67.12%，但不能消除EOL电池供应与可用处理能力之间的空间不匹配。我们的优化方案表明，供需规划可以减少高达44%的排放量，并将锂回收率提高53%。这项工作为设计低碳、高效的电池回收系统提供了一个可扩展的工具，有助于循环经济和能源转型。电池回收在实现循环经济和可持续能源转型中发挥着至关重要的作用。本研究提出了一个多尺度分析框架，以协助设计低碳、高效的电池回收系统。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Nature Sustainability Energy-Renewable Energy, Sustainability and the Environment

CiteScore

41.90

自引率

1.10%

发文量

159

期刊介绍： Nature Sustainability aims to facilitate cross-disciplinary dialogues and bring together research fields that contribute to understanding how we organize our lives in a finite world and the impacts of our actions. Nature Sustainability will not only publish fundamental research but also significant investigations into policies and solutions for ensuring human well-being now and in the future.Its ultimate goal is to address the greatest challenges of our time.