Environmental Justice Aspects of Exposure to PM2.5 Emissions from Electric Vehicle Use in China

Shuguang Ji, Christopher R. Cherry, Wenjun Zhou, Rapinder Sawhney, Ye Wu, Siyi Cai, Shuxiao Wang, Julian Marshall

Research output: Contribution to journalArticlepeer-review

43 Scopus citations

Abstract

Plug-in electric vehicles (EVs) in China aim to improve sustainability and reduce environmental health impacts of transport emissions. Urban use of EVs rather than conventional vehicles shifts transportation's air pollutant emissions from urban areas (tailpipes) to predominantly rural areas (power plants), changing the geographic distribution of health impacts. We model PM2.5-related health impacts attributable to urban EV use for 34 major cities. Our investigation focuses on environmental justice (EJ) by comparing pollutant inhalation versus income among impacted counties. We find that EVs could increase EJ challenge in China: most (∼77%, range: 41-96%) emission inhalation attributable to urban EVs use is distributed to predominately rural communities whose incomes are on average lower than the cities where EVs are used. Results vary dramatically across cities depending on urban income and geography. Discriminant analysis reveals that counties with low income and high inhalation of urban EV emissions have comparatively higher agricultural employment rates, higher mortality rates, more children in the population, and lower education levels. We find that low-emission electricity sources such as renewable energy can help mitigate EJ issues raised here. Findings here are not unique to EVs, but instead are relevant for nearly all electricity-consuming technologies in urban areas.

Original languageEnglish (US)
Pages (from-to)13912-13920
Number of pages9
JournalEnvironmental Science and Technology
Volume49
Issue number24
DOIs
StatePublished - Dec 15 2015

Fingerprint

Dive into the research topics of 'Environmental Justice Aspects of Exposure to PM2.5 Emissions from Electric Vehicle Use in China'. Together they form a unique fingerprint.

Cite this