{"title":"Temperature and electric vehicle adoption: A ZIP code-level analysis in the US","authors":"Gaia Cervini, Jinha Jung, Konstantina Gkritza","doi":"10.1016/j.trd.2024.104435","DOIUrl":null,"url":null,"abstract":"<div><div>Electric vehicle (EV) users living in colder or warmer climates experience shorter traveling ranges, slower acceleration, and longer recharge times, which might discourage EV adoption. Using data from seven US states, we analyze the link between temperature and EV adoption at the ZIP code level. We collect land surface and air temperature data, along with sociodemographic, infrastructure, land cover, elevation, and electoral data. Using random forest regression, we predict battery electric (BEV) and plug-in hybrid electric (PHEV) vehicle population change rates and penetrations. Our findings reveal that temperature variation and temperature extremes are among the top predictors of BEV and PHEV adoption. Understanding this relationship is crucial for assessing EV feasibility in diverse climates and ensuring equitable access to the technology. This study underscores the importance of incorporating environmental factors in strategies to promote EV adoption.</div></div>","PeriodicalId":23277,"journal":{"name":"Transportation Research Part D-transport and Environment","volume":null,"pages":null},"PeriodicalIF":7.3000,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Transportation Research Part D-transport and Environment","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1361920924003924","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENVIRONMENTAL STUDIES","Score":null,"Total":0}
引用次数: 0
Abstract
Electric vehicle (EV) users living in colder or warmer climates experience shorter traveling ranges, slower acceleration, and longer recharge times, which might discourage EV adoption. Using data from seven US states, we analyze the link between temperature and EV adoption at the ZIP code level. We collect land surface and air temperature data, along with sociodemographic, infrastructure, land cover, elevation, and electoral data. Using random forest regression, we predict battery electric (BEV) and plug-in hybrid electric (PHEV) vehicle population change rates and penetrations. Our findings reveal that temperature variation and temperature extremes are among the top predictors of BEV and PHEV adoption. Understanding this relationship is crucial for assessing EV feasibility in diverse climates and ensuring equitable access to the technology. This study underscores the importance of incorporating environmental factors in strategies to promote EV adoption.
期刊介绍:
Transportation Research Part D: Transport and Environment focuses on original research exploring the environmental impacts of transportation, policy responses to these impacts, and their implications for transportation system design, planning, and management. The journal comprehensively covers the interaction between transportation and the environment, ranging from local effects on specific geographical areas to global implications such as natural resource depletion and atmospheric pollution.
We welcome research papers across all transportation modes, including maritime, air, and land transportation, assessing their environmental impacts broadly. Papers addressing both mobile aspects and transportation infrastructure are considered. The journal prioritizes empirical findings and policy responses of regulatory, planning, technical, or fiscal nature. Articles are policy-driven, accessible, and applicable to readers from diverse disciplines, emphasizing relevance and practicality. We encourage interdisciplinary submissions and welcome contributions from economically developing and advanced countries alike, reflecting our international orientation.