{"title":"中国充电桩时空演变特征研究。","authors":"Haixia Feng, Meng Guo, Lei Yu, Jianchang Huang, Qiuxia Li, Zhixin Xu","doi":"10.1016/j.scitotenv.2024.177239","DOIUrl":null,"url":null,"abstract":"<p><p>New energy vehicles (NEVs) are pivotal for reducing emissions in the transportation sector, and charging facilities are fundamental to the sustainable growth of the NEV industry. To scientifically plan and optimize the development and layout of China's electric vehicle charging infrastructure, this paper analyzed the growth, spatial distribution and the spatiotemporal evolution characteristics of EV charging station in China from 2009 to 2023 using the geographically weighted regression (GWR) model, geographically and temporally weighted regression (GTWR) model and the STIRPAT model. The main research conclusions are as follows: The growth trend of charging station is generally consistent with the growth trend of NEV, albeit with a certain time lag. The evolution of charging stations in China exhibits clear spatio-temporal differentiation patterns: In terms of spatial distribution, charging stations are concentrated in eastern coastal provinces; Regarding influencing factors, GDP has a much greater impact on charging stations than population and temperature. GDP has a larger influence in southern provinces compared to those in northern provinces, while temperature has a significantly higher impact on northern provinces than on southern provinces; Population has a greater influence in southwestern and northeastern provinces; However, from a spatiotemporal evolution perspective, population is the main driving force behind the annual growth of charging station. The spatiotemporal evolution of charging station in China have gradually shifted from being primarily policy-driven to being market-driven This study provides support for the optimization of the layout of China's charging infrastructure and the stable power grid load, which is great significance for promoting the development of green and low-carbon transportation in China.</p>","PeriodicalId":422,"journal":{"name":"Science of the Total Environment","volume":" ","pages":"177239"},"PeriodicalIF":8.0000,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Research on the spatiotemporal evolution characteristics of China's charging stations.\",\"authors\":\"Haixia Feng, Meng Guo, Lei Yu, Jianchang Huang, Qiuxia Li, Zhixin Xu\",\"doi\":\"10.1016/j.scitotenv.2024.177239\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>New energy vehicles (NEVs) are pivotal for reducing emissions in the transportation sector, and charging facilities are fundamental to the sustainable growth of the NEV industry. To scientifically plan and optimize the development and layout of China's electric vehicle charging infrastructure, this paper analyzed the growth, spatial distribution and the spatiotemporal evolution characteristics of EV charging station in China from 2009 to 2023 using the geographically weighted regression (GWR) model, geographically and temporally weighted regression (GTWR) model and the STIRPAT model. The main research conclusions are as follows: The growth trend of charging station is generally consistent with the growth trend of NEV, albeit with a certain time lag. The evolution of charging stations in China exhibits clear spatio-temporal differentiation patterns: In terms of spatial distribution, charging stations are concentrated in eastern coastal provinces; Regarding influencing factors, GDP has a much greater impact on charging stations than population and temperature. GDP has a larger influence in southern provinces compared to those in northern provinces, while temperature has a significantly higher impact on northern provinces than on southern provinces; Population has a greater influence in southwestern and northeastern provinces; However, from a spatiotemporal evolution perspective, population is the main driving force behind the annual growth of charging station. The spatiotemporal evolution of charging station in China have gradually shifted from being primarily policy-driven to being market-driven This study provides support for the optimization of the layout of China's charging infrastructure and the stable power grid load, which is great significance for promoting the development of green and low-carbon transportation in China.</p>\",\"PeriodicalId\":422,\"journal\":{\"name\":\"Science of the Total Environment\",\"volume\":\" \",\"pages\":\"177239\"},\"PeriodicalIF\":8.0000,\"publicationDate\":\"2024-12-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Science of the Total Environment\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://doi.org/10.1016/j.scitotenv.2024.177239\",\"RegionNum\":1,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/11/2 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q1\",\"JCRName\":\"ENVIRONMENTAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Science of the Total Environment","FirstCategoryId":"93","ListUrlMain":"https://doi.org/10.1016/j.scitotenv.2024.177239","RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/11/2 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
Research on the spatiotemporal evolution characteristics of China's charging stations.
New energy vehicles (NEVs) are pivotal for reducing emissions in the transportation sector, and charging facilities are fundamental to the sustainable growth of the NEV industry. To scientifically plan and optimize the development and layout of China's electric vehicle charging infrastructure, this paper analyzed the growth, spatial distribution and the spatiotemporal evolution characteristics of EV charging station in China from 2009 to 2023 using the geographically weighted regression (GWR) model, geographically and temporally weighted regression (GTWR) model and the STIRPAT model. The main research conclusions are as follows: The growth trend of charging station is generally consistent with the growth trend of NEV, albeit with a certain time lag. The evolution of charging stations in China exhibits clear spatio-temporal differentiation patterns: In terms of spatial distribution, charging stations are concentrated in eastern coastal provinces; Regarding influencing factors, GDP has a much greater impact on charging stations than population and temperature. GDP has a larger influence in southern provinces compared to those in northern provinces, while temperature has a significantly higher impact on northern provinces than on southern provinces; Population has a greater influence in southwestern and northeastern provinces; However, from a spatiotemporal evolution perspective, population is the main driving force behind the annual growth of charging station. The spatiotemporal evolution of charging station in China have gradually shifted from being primarily policy-driven to being market-driven This study provides support for the optimization of the layout of China's charging infrastructure and the stable power grid load, which is great significance for promoting the development of green and low-carbon transportation in China.
期刊介绍:
The Science of the Total Environment is an international journal dedicated to scientific research on the environment and its interaction with humanity. It covers a wide range of disciplines and seeks to publish innovative, hypothesis-driven, and impactful research that explores the entire environment, including the atmosphere, lithosphere, hydrosphere, biosphere, and anthroposphere.
The journal's updated Aims & Scope emphasizes the importance of interdisciplinary environmental research with broad impact. Priority is given to studies that advance fundamental understanding and explore the interconnectedness of multiple environmental spheres. Field studies are preferred, while laboratory experiments must demonstrate significant methodological advancements or mechanistic insights with direct relevance to the environment.