Environmental Justice Aspects of Exposure to PM2. 5 Emissions from

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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, and Julian D Marshall Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.5b04927 • Publication Date (Web): 28 Oct 2015 Downloaded from http://pubs.acs.org on October 28, 2015

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Environmental Science & Technology Environmental Science & Technology

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Environmental Justice Aspects of Exposure to PM2.5

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Emissions from Electric Vehicle Use in China

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Shuguang Ji,1* Christopher R. Cherry,2* Wenjun Zhou,3 Rapinder Sawhney,1 Ye Wu,4 Siyi

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Cai,4 Shuxiao Wang,4 and Julian D. Marshall5

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1

Department of Industrial and Systems Engineering, University of Tennessee, Knoxville, TN, USA

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Department of Civil and Environmental Engineering, University of Tennessee, Knoxville, TN, USA

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Department of Business Analytics and Statistics, University of Tennessee, Knoxville, TN, USA

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4

School of Environment, Tsinghua University, Beijing, 100084, P.R. China

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Department of Civil and Environmental Engineering, University of Minnesota, Minneapolis, MN, USA

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Word Count: 5,405 text words plus 1,500 word-equivalents for figures = 6,905 total word-

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equivalents. We designed figures 1, 2, 3, 4 and table 1 to be small (one column in print).

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AUTHOR INFORMATION Corresponding Authors *

[a] Christopher Cherry, phone: (865) 974-7710, fax: (865) 974-2669, e-mail: [email protected].

[b] Shuguang Ji, phone: (865) 951-8083, fax: (865) 974- 0588, e-mail: [email protected]. Notes The authors declare no competing financial interest. 1 ACS Paragon Plus Environment

Environmental Science & Technology

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ABSTRACT

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Plug-in electric vehicles (EVs) in China aim to improve sustainability and reduce environmental

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health impacts of transport emissions. Urban use of EVs rather than conventional vehicles shifts

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transportation’s air pollutant emissions from urban areas (tailpipes) to predominantly rural areas

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(power plants), changing the geographic distribution of health impacts. We model PM2.5-related

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health impacts attributable to urban EV use for 34 major cities. Our investigation focuses on

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environmental justice (EJ) by comparing pollutant inhalation versus income among impacted

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counties. We find that EVs could increase EJ challenge in China: most (~77%, range: 41-96%)

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emission inhalation attributable to urban EVs use is distributed to predominately rural

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communities whose incomes are on average lower than the cities where EVs are used. Results

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vary dramatically across cities depending on urban income and geography. Discriminant analysis

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reveals that counties with low income and high inhalation of urban EV emissions have

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comparatively higher agricultural employment rates, higher mortality rates, more children in the

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population, and lower education levels. We find that low-emission electricity sources such as

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renewable energy can help mitigate EJ issues raised here. Findings here are not unique to EVs,

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but instead are relevant for nearly all electricity-consuming technologies in urban areas.

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2 ACS Paragon Plus Environment

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INTRODUCTION

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Sustainable development aims to address economic development, social equity, and

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environmental protection.1 Plug-in electric vehicles (EVs) are often considered as a technology

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to support sustainable development in the transportation sector.2, 3 Several prior studies have

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focused on environmental sustainability of EVs, focusing on greenhouse gas (GHG) emissions4-8

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or local air pollution9 and public health.2,

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emissions occur where vehicles are used; for urban EVs, use-phase emissions instead occur at

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(for fossil fuels) the power plant where electricity is generated. This shift remedies some intra-

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urban environmental justice (EJ) challenges,13-17 but potentially creates new challenges by

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exporting pollution to populations far from urban centers.

10-12

For conventional vehicles (CVs), use-phase

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In China, EVs, including electric bikes (e-bikes) and electric cars (e-cars), are often

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considered an approach toward sustainable transportation, balancing mobility, energy security,

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GHG emissions, and air pollution. From 2011 to 2014, the annually estimated sales of e-bikes in

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China grew from 31.0 million units to 34.2 million units.18 In the meantime, the annual sales of

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full plug-in e-cars in China increased from 5,579 to 45,048 vehicles.19, 20 The Chinese central

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government also designed an ambitious plan to add 5 million pure e-cars and plug-in hybrid e-

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cars on the road by 2020.21 In the short and medium term, EVs may not reduce GHG emissions

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or local air pollution due to reliance on coal electricity generation.2, 4, 7, 22, 23 In the long term,

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EVs could reduce fossil fuel use and GHG emissions relative to CVs if electricity generation

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transitions to cleaner energy.4, 7 Especially, with the booming penetration of hydro, wind, solar,

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and other cleaner energy as well as supports from central and local governments in China,24, 25 a

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significant portion of future power may come from sustainable energy sources. At that time, EVs

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could improve relative to CVs.



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Despite significant research on environmental and economic sustainability aspects of EVs in

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China,26-30 to our knowledge, no research has evaluated EJ aspects of EV’s environmental health

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impacts across populations. This paper targets that gap. We focus on current distributional

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aspects of health impacts from fossil power plant emissions attributable to urban EVs (pure plug-

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in battery e-cars). The primary focus of the article is EVs, but the results are generalizable and

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would apply broadly to other examples of urban electricity consumption. In prior research,2 we

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calculated health impacts of PM2.5 from EVs and CVs using an intake fraction (iF) health

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assessment framework. Here, we extend the prior work to evaluate EJ. Specifically, using census

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data, we investigate demographic characteristics (e.g., income) of those who benefit from urban

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EVs (city dwellers) versus those who inhale pollution from electricity generation

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(predominantly, rural populations downwind of fossil power plants).

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Environmental justice is an ethical concept related to the distributional fairness of impacts:

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which groups are more exposed or less exposed to environmental risks,31-33 and are those risk-

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differences necessary, avoidable, or remediable.34 Prior research has investigated EJ aspects of

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electricity generation. For example, Levy et al.35 investigated particulate matter (PM) emission

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control strategies from power plants near Washington, D.C., USA. They found that half of the

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health benefits accrued among the 25% of the population with lower education level (below high

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school). Touché and Rogers reported that Texas power plants relying on more hazardous fossil

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fuels are located in lower socioeconomic communities.36 Levy et al.37,

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objective frameworks to include EJ into U.S. air pollution management (focusing on PM2.5

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mortality) for fossil power plants.

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developed multi-

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China is facing similar EJ challenges. Brajer et al.39 found that in China, between-city

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income inequity increased from 1990 to 2004 and that air pollution impacts are correlated with


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the inequity. Schoolman and Ma,40 analyzing locations of air and water emissions in Jiangsu,

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reported that communities with a higher proportion of rural migrants were likely to live near to

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industrial emission sources. They concluded that the environmental health impact of power plant

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emissions depends on their location, the locations of population centers, and the transport of

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emissions from source to receptor. Ji et al.2, 23 estimated emission intake in urban and rural areas

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in China from urban use of EVs. They found that, on average, non-urban populations inhale

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approximately 52% (range: 19%-64%) of emissions from urban use of EVs. An important

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context for those findings is the large and growing disparity between urban and rural incomes:

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the rural-urban difference in average income per person increased from 2.8× in 2000 (2,253

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RMB [US$338] rural versus 6,280 RMB [US$941] urban) to 3.1× in 2011 (6,977 RMB

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[US$1,046] rural versus 21,810 RMB [US$3,270] urban).41

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A shortcoming of the rural/urban comparison is that it does not distinguish impacts based on

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traditional indicators of social status (e.g., income, education). Here, we focus on EJ implications

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of urban EVs, using two methods: investigating disparities between income and inhalation

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among the exposed populations; and applying discriminant analysis on multiple population

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groups exposed to air pollution.

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MATERIALS AND METHODS

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We extend the methods employed in Ji et al.2, 23 to estimate PM2.5 pollution intake, focusing on

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locations of emissions (coal power plants) and where they are inhaled. In this paper, “inhalation”

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refers to total PM2.5 emission inhalation attributable to urban EVs use. We employ an iF

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framework to estimate inhalation of PM2.5 in China. Intake fraction (iF) is defined as the

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proportion of a pollutant that is inhaled relative to the amount emitted. Intake fraction can be



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calculated in multiple ways, based on data or models. It is an intuitive method to compare

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inhalation and health impacts across locations, technologies, fuels, and populations. Further

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information about estimating and using iF is available elsewhere.42 For conserved primary

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pollutants in global cities, the reported intra-urban iF range is 0.6-260 ppm (population-weighted

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values: median: 26 ppm, mean: 39 ppm, interquartile range: 14-52 ppm).43 Intake fractions are

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higher for Chinese cities than for the global average (population-weighted values for China:

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median: 34 ppm, mean: 45 ppm, interquartile range: 21-64 ppm).43, 44

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Concentration-based estimates of iF would require fine resolution pollutant emissions and

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concentrations. A simplified regression approach developed for China by Zhou et al.45 uses

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pollutant transport models from a sample of fossil power plants to estimate iF; then they regress

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iF against population data surrounding fossil power plants to obtain a predictive iF model

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(R2=0.86 - 0.95). We use that model to estimate iF from coal power plants emissions throughout

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China. The model inputs are population living in the radii of 100km, 500km, 1,000km, and

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farther than 1,000km from 2,640 coal power plants in China. County population data are from

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the 2010 census.46 The database of coal power plants in China applied here was previously

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derived by researchers at Tsinghua University47 based on plant-level information (name, address,

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capacity, generation) from the China Electricity Council (CEC)48 and emission factor

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information from Zhao et al.49 The emission factor for each coal power plant is averaged to

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reflect coal and non-coal generation in that region. Additional details on the emission dataset are

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in the SI. We assume an arbitrary vehicle-travel distance as the basis for our comparisons:

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specifically, we evaluate the impact if 109 vehicle kilometers traveled by CVs were substituted

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by equivalent EVs in each city. If average energy efficiency of an e-car is 180 Wh km-1 and

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electricity transmission and in-plant use loss in China is 14%,2 the energy demand from 109


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vehicle kilometers traveled by EVs in each city will be 205.2 GWh. This energy demand would

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represent 0.2% - 4.7% of the total annual urban electricity use in each city we investigated, a

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small but likely growing percentage of total urban electricity consumption. Because of little

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available data on vehicle kilometers traveled by EVs in China, the assumption of 109 vehicle-

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kilometers traveled is based on fleet size, percentage of EVs, and annual vehicle kilometers

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traveled by light duty vehicles in Beijing (e.g., Beijing had ~5 million CVs in 2010.50 If 1% were

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replaced by EVs, each traveling ~20,000 km y-1,51 that shift would be 109 vehicle-kilometers y-1

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by EVs.). Our results could easily be scaled up or down for alternative bases. We calculate PM2.5

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emissions from the corresponding power grid, then apply iF values to estimate county-level

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inhalation of PM2.5 emissions. This approach is most useful to evaluate EJ aspects of replacing

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CVs with EVs in an urban area where most urban CV emission are inhaled by those dwelling in

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the city.52 We investigate total inhalation attributable to EVs for the 34 cities analyzed in Ji et al.2

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We employ per capita gross regional product (GRP per capita) as our measure of county-level

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average income, and investigate whether exposures are greater for higher- or lower-income

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counties, relative to the city where the EV is used. We apply discriminant analysis to investigate

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demographic factors that are correlated with emission inhalations.

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Income and Inhalation Disparity Analysis. We conduct individual case studies for 34

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cities, covering all of China’s urbanized provinces. Using ArcGIS, each of China’s counties

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(n=2,872) are assigned by distance to each coal power plant (n=2,640). We use the iF methods

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described above to assess iF from each coal power plant to each county (i.e., each county

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represents a proportion of the total iF from a coal power plant, related to county population and

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distance from the coal power plant). Applying coal power plant emission factors for each grid to

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urban EV use, weighted in proportion to the relative capacity of the coal power plant, we



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estimate total inhalation of emissions at the county level from an arbitrary urban power demand.

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Our approach is described in equations (1) and (2).

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ℎ = ∑ ×

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(1)

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ℎ = ∑ ∑ ! × "∑&

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#$

%'( #%

(2)

)* + ,

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Total inhalation in county x (Eq. 1) is equal to the sum over all coal power plants (j=1 to n) of

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the county’s proportional iF ( ) from each coal power plant j times the total emissions from an

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individual coal power plant j ( ). Equation (1) can be decomposed (see Eq. 2) using the sum of

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the regression coefficients times a binary (1 or 0) indicator variable ( ) to determine which

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distance bin county x falls in from coal power plant j, times the population of the county ( ).

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The total emissions from an individual coal power plant is estimated by allocating the total

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electricity demands from coal power plants in power grid l ()* ) to the attributable coal power

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plants in proportion to their relative generation ∑&

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plant j and ∑/ . -. is the sum of electricity generations of all coal power plants in its power grid

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k. Total emissions from each coal power plant is estimated by multiplying the electricity

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demands allocated to each coal power plant by the average emission factor ( ) of each power

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plant. Inhalation values for each coal power plant are summed over total emissions from

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arbitrary energy demands in each power grid (EV use in this case), to estimate the impact per

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energy production. We include 14% generation and transmission losses, China’s average.41, 53

#$

%'( #%

, where - is the generation of coal power


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Total inhalation in county x is the sum of incremental inhalation from emissions from all coal

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power plants.

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Figure 1a illustrates GRP per capita at the county level in China based on 2010 census data,

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the most recent data available.46 Much of the wealth is on the coast or northern borders. Figure

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1b illustrates corresponding per capita inhalation of emissions from coal power plants for each

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county based on EV use in 34 Chinese cities throughout China. Using Huai River-Qin Mountain

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line as dividing line between northern and southern China, most of the inhalation per capita

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occurs in the northern portion of China; and less of the inhalation per capita occurs in the

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southern interior portion of China, with the exception of the band of power plants and population

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centers in the Chengdu and Chongqing regions. The higher per capita inhalation also corresponds

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to higher population densities (Figure 1c) resulting in much higher total inhalation.

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Figure 1. [a] Per capita gross regional product (in RMB) by county in China (darker color

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corresponds to higher values); [b] Per capita inhalation from coal power plants (g PM2.5

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from 109 vehicle kilometers traveled by EVs in each of 16 power grids); [c] Population

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density (people km-2).

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Emissions from electricity generation from EV recharging in a city could potentially be

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inhaled over a wide area, including in the city where the EVs are operated. For EV inhalation, we 9 ACS Paragon Plus Environment


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attribute inhalation of PM2.5 (from coal power plants serving urban EV charging) to counties

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with higher or lower income, relative to the city where the EV is operated. This contrasts CV

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emissions, where nearly all exposure and inhalation occurs in the city where the CV is operated.

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Inhalation of primary PM2.5 emissions from EVs is classified into four groups:

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•

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inhalation than the city where the urban EV is operated. •

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

•

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Group A – lower-income, lower-inhalation: the county has lower income and lower

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.

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Discriminant Analysis. Beyond income, other metrics can also be used to quantify EJ

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concerns. Discriminant analysis can extract information from large quantities of socioeconomic

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data. Before conducting discriminant analysis, we aggregate the inhalation for each county in

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China by assuming 109 vehicle kilometers traveled by EVs in each power grid. The per capita

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inhalation for all counties is compared with census data. Our dataset provides 167 attributes (e.g.,

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age, industry, and education levels) for all (n=2,872) counties in China. Discriminant analysis

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requires grouping of data. Based on income and total inhalation, we classify different counties

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into one of three mutually exclusive groups. (The prior section [“Income and Inhalation

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Disparity Analysis”] employed four groups [Groups A-D]. The three groups employed here for

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discriminant analysis are entirely distinct, and bear no direct relation, to those four groups.)


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Thresholds were chosen such that these three groups are mutually exclusive (inhalation per

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capita greater than or less than 3 ug per capita; GRP per capita greater than or less than 65,000

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RMB [US$10,484]). We employ the labels “advantaged” for the higher-income, lower-inhalation

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counties; “disadvantaged” for lower-income, higher-inhalation counties; and, “unclassified” to

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reflect all other counties. Here “advantaged” means a higher-income county that gains health

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outcomes (i.e., experiences a reduction in environmental risk) via a policy or a technology (in

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this case, increased use of EVs), potentially at the expense of others. A “disadvantaged” county

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is lower-income and bears comparatively larger incremental health costs from the

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policy/technology. The data, showing how we classified counties, are in Figure 2; this figure

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highlight the spread of data and differences between the advantaged and disadvantaged counties.

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The 34 cities we investigate (brown triangles) generally fall in the advantaged (38%) or

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unclassified (56%) groups; only two cities (6% of the 34 cities, representing 14% of the total

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population of the 34 cities) are in the disadvantaged group. After grouping, we compare the

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counties in the advantaged and disadvantaged group to investigate socioeconomic differences

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across multiple variables.

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Figure 2: Bivariate scatter plot of pollution inhalation and income. Green circles represent

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advantaged counties (130 counties; total population >86 million people). Red squares

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represent disadvantaged counties (70 counties; total population >45 million people). Brown

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triangles represent 34 major cities in the case study (total population >256 million people).

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Blue pluses represent unclassified counties (2,672 counties; total population >1.2 billion

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people). “Advantaged”, “disadvantaged”, and “unclassified” are defined in the text.

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RESULTS AND DISCUSSION

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Income and Inhalation Disparity Analysis. We evaluate conditions for 34 specific cities where

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EVs are likely to operate. In each case, we compare within-urban emissions and inhalations

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attributable to urban CV use, versus total emissions and inhalation (disaggregated by county)

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attributable to urban EV use. In both cases (EVs and CVs), we consider the income of each

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county where the inhalation occurs.

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Figure 3 presents, for all n=2,872 counties, inhalation and income distributions

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attributable to EV used in 12 representative cities. (Results for all 34 major cities are in


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Supporting Information (SI) Figure S1.) In these figures, each county is represented by a dot and

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the city where the EVs are operated is located at the intersection of the red lines. In high-income

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cities such as Beijing, Dalian, Shanghai, and Guangzhou, a large portion of primary PM2.5

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emissions are inhaled by populations (bottom/left) who have lower income compared to the

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populations in these cities. In contrast, lower-income cities (e.g., Chongqing, Shijiazhuang, and

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Harbin) have a substantially higher inhalation compared to some higher income counties.

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Supporting information Table S1 reports the detailed proportion of inhalation of primary PM2.5

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emissions from EV use in four groups: lower income with lower inhalation (bottom/left of SI

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Figure S1 quadrant), lower income with higher inhalation (top/left of the quadrant), higher

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income with lower inhalation (bottom/right of the quadrant), and higher income with higher

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inhalation (top/right of the quadrant) compared with the income and inhalation level of the city

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adopting EVs.

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Figure 3. Inhalation of primary PM2.5 emissions attributable to urban EV use, showing

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results for one city in each of the 16 regional electricity grids. Each scatterplot represents,

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for one urban area, the by-county inhalation of emissions attributable to EV use in that

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city. The red dash lines in each scatter-plot divide counties into four groups - lower-income

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with lower-inhalation (bottom/left), lower-income with higher-inhalation (top/left), higher-

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income with lower-inhalation (bottom/right), and higher-income with higher-inhalation

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(top/right). (The terms “lower” and “higher” refer to conditions in the county, relative to

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the city; for example, “lower-inhalation” means that a county’s PM2.5 inhalation

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attributable to urban EV use is lower than the PM2.5 inhalation in the city attributable to

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urban CV use.) Note that y-axis scales vary among scatterplots. The cross point of dash

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lines represents urban use of EVs for that electricity grid. The percentage value given on


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each of the scatterplots shows the percentage of primary PM2.5 emissions will be inhaled by

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populations who have lower income than residents in the urban area where EVs are used

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(range among cities shown here: 41% - 96%, population-weighted mean: 77%). In general,

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on average, urban use of EVs rather than CVs shifts inhalation of emissions to locations

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where income is lower than in the urban area.

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To illustrate, if 109 vehicle kilometers are traveled by EVs (e-cars) in Shanghai, Shanghai

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residents will cumulatively inhale 14.8 g primary PM2.5 emissions. Over 85% (i.e., 307 g, or 21×

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what Shanghai residents inhale) of primary PM2.5 emissions from this switch will be inhaled by

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populations in counties with average income lower than in Shanghai; and ~3% of those

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emissions are inhaled by populations in the poorest 10th of the China’s counties (i.e. in counties

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whose GRP per capita is in the bottom 10th percentile in China (

Environmental Justice Aspects of Exposure to PM2. 5 Emissions from

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