摘要
聚焦我国退役动力电池及其关键金属资源的时空分布特征,运用LSTM神经网络模型预测新能源乘用车销量,结合Weibull分布模型估算退役动力电池及关键金属资源存量,并进一步分析其空间分布特征.研究发现,2012~2035年我国动力电池退役量及关键金属资源累计存量存在明显的区域性差异和空间聚集性,当前广东、浙江、山东、北京、上海、河南动力电池累计退役量及关键资源累计存量最多,西藏、青海、宁夏、黑龙江、新疆、内蒙古最少;至2035年,江苏将取代北京成为累计退役量最多的6省份之一,累计退役量为225.46万t,甘肃将取代内蒙古成为累计退役量最少的省份之一,累计退役量为15.36万t.空间聚集性上,当前高-高集聚区为江苏,低-低集聚区有新疆、青海、四川、甘肃、内蒙古、吉林,未来高-高集聚范围将进一步扩大(新增安徽、上海),而低-低集聚区范围将缩小(四川退出).从退役动力电池综合利用白名单企业处理能力分布上看,当前我国虽总体处理能力过剩但存在空间错位性,未来上海、江苏、河北、广西、天津、广东、浙江、云南、河南再生利用能力将不能满足自身需求,需进一步优化回收处理产能布局.
The study focused on the spatiotemporal distribution characteristics of retired power batteries and critical metal resources in China.It used the LSTM neural network model to predict the sales volume of electric vehicles.It also estimated the stock of retired power batteries and critical metal resources by applying the Weibull distribution model,and further analyzed their spatial distribution characteristics.The research found that from 2012 to 2035,there were significant regional differences and spatial agglomeration in the cumulative retirement volume of power batteries and the cumulative stock of critical metal resources in China.Currently,Guangdong,Zhejiang,Shandong,Beijing,Shanghai,and Henan had the highest cumulative retirement volume of power batteries and the highest cumulative stock of critical metal resources,while Xizang,Qinghai,Ningxia,Heilongjiang,Xinjiang,and Inner Mongolia had the lowest.By 2035,Jiangsu was projected to replace Beijing as one of the six provinces with the highest cumulative retirement volume,with a cumulative retirement volume of 2.2546 million tons.Gansu was projected to replace Inner Mongolia as one of the provinces with the lowest cumulative retirement volume,with a cumulative retirement volume of 153,600 tons.In terms of spatial agglomeration,the high-high agglomeration area was found to be Jiangsu,while the low-low agglomeration areas included Xinjiang,Qinghai,Sichuan,Gansu,Inner Mongolia,and Jilin.It was projected that the scope of high-high agglomeration would expand further in the future(with Anhui and Shanghai being added),while the scope of low-low agglomeration would contract(with Sichuan no longer being part of this category).Regarding the distribution of processing capacities of the white-listed enterprises for the comprehensive utilization of retired power batteries,it was found that although China had an overall surplus in processing capacity,there was a spatial misalignment.It was anticipated that in the future,the recycling and regeneration capacities in Shanghai,Jiangsu,Hebei,Guangxi,Tianjin,Guangdong,Zhejiang,Yunnan,and Henan would not be sufficient to meet their own demands,and it was necessary to further optimize the layout of recycling and processing capacities.
作者
侯思雨
庄绪宁
杨凡
宋小龙
吴雯杰
赵静
HOU Si-yu;ZHUANG Xu-ning;YANG Fan;SONG Xiao-long;WU Wen-jie;ZHAO Jing(School of Resources and Environmental Engineering,Shanghai Polytechnic University,Shanghai,201209,China)
出处
《中国环境科学》
北大核心
2025年第8期4727-4736,共10页
China Environmental Science
基金
上海市浦东新区科技民生项目(PKJ2023-C03)
国家自然科学基金面上项目(42471331)。
关键词
新能源乘用车
退役动力电池
关键金属资源
时空分布
回收处理
electric vehicles
retired power batteries
critical metal resources
temporal and spatial distribution
recovery and treatment
