Credit: ACS, Ji et al. Click to enlarge.

Despite significant research on environmental and economic sustainability aspects of EVs in China, to our knowledge, no research has evaluated EJ aspects of EV’s environmental health impacts across populations. This paper targets that gap. We focus on current distributional aspects of health impacts from fossil power plant emissions attributable to urban EVs (pure plug-in battery e-cars). The primary focus of the article is EVs, but the results are generalizable and would apply broadly to other examples of urban electricity consumption. In prior research, we calculated health impacts of PM_2.5 from EVs and CVs using an intake fraction (iF) health assessment framework. Here, we extend the prior work to evaluate EJ.

… Environmental justice is an ethical concept related to the distributional fairness of impacts: which groups are more exposed or less exposed to environmental risks, and are those risk-differences necessary, avoidable, or remediable. Prior research has investigated EJ aspects of electricity generation. … China is facing similar EJ challenges. … Here, we focus on EJ implications of urban EVs, using two methods: investigating disparities between income and inhalation among the exposed populations; and applying discriminant analysis on multiple population groups exposed to air pollution.

—Ji et al.

Using census data, the researchers investigated the demographic characteristics (e.g., income) of those who benefit from urban EVs (city dwellers) versus those who inhale pollution from electricity generation (predominantly, rural populations downwind of fossil power plants). They modeled PM^2.5-related health impacts attributable to urban EV use for 34 major cities.

The team classified the inhalation of primary PM_2.5 emissions from EVs into four groups:

• Group A, lower-income, lower-inhalation: the county has lower income and lower inhalation than the city where the urban EV is operated.

• Group B, lower-income, higher-inhalation: the county has lower income and higher inhalation than the city where the urban EV is operated.

• Group C, higher-income, lower-inhalation: the county has higher income and lower inhalation than the city where the urban EV is operated.

• Group D, higher-income, higher-inhalation: the county has higher income and higher inhalation than the city where the urban EV is operated.

Group B especially reflects a potential EJ concern.

For a separate, discriminant analysis (discriminant analysis can extract information from large quantities of socioeconomic data), they classified different counties into one of three mutually exclusive groups, which bear no direct relationship to the first four groups: “advantaged” for higher-income, lower-inhalation counties; “disadvantaged” for lower-income, higher-inhalation counties; and, “unclassified” to reflect all other counties.

Of the total increase in PM_2.5-inhalation caused by a shift to EVs in China, the poorest counties (the bottom 10^th percentile counties representing 7.4% of the population) will inhale 8.7% while the richest counties (the top 10^th percentile counties representing 12.5% of the population) will inhale 10.5%. Thus, we estimate that the average increase in exposure burden from EVs in China would be 40% greater for the poorest counties than for the richest counties. Low-income rural communities likely will not directly benefit from urban EV use. EVs, like with other examples of increased urban electricity consumption or rural electricity production, could represent new exposures for non-urban poor counties.

The disadvantaged counties are primarily located in less development areas in China−areas that are primary agricultural. A policy implication of our research is the need to consider ways to avoid or remedy impacts to these lower-income communities; future policy relevant to EVs (and to urban electricity consumption in general) should aim to investigate and tackle this EJ challenge head on.

—Ji et al.

The researchers also observed that future improvements in cleaner power generation can have immediate impacts across the transportation sector—an effect that is impossible to achieve with a large fleet of aging conventional vehicles. Thus, the government has more regulatory and economic control over transportation emissions that could result in reductions in total pollution and greenhouse gas emissions.

As of 2014, China emerged as one of the world’s largest producers and users of renewable energy. From 2010 to 2014, the percentage of total electricity generated by non-fossil sources increased from 17% to 20% for wind and hydro power and from 1.8% to 2.4% for nuclear power. These increases in renewables and nuclear power can positively impact PM_2.5-related EJ in China.

—Ji et al.

Resources

• Shuguang Ji, Christopher R. Cherry, Wenjun Zhou, Rapinder Sawhney, Ye Wu, Siyi Cai, Shuxiao Wang, and Julian D. Marshall (2015) “Environmental Justice Aspects of Exposure to PM2.5 Emissions from Electric Vehicle Use in China” Environmental Science & Technology doi: 10.1021/acs.est.5b04927