Equity implications of electric vehicles: A systematic review on the spatial distribution of emissions, air pollution and health impacts

Scaling up electric vehicles (EVs) provides an avenue to mitigate both carbon emissions and air pollution from road transport. The benefits of EV adoption for climate, air quality, and health have been widely documented. Yet, evidence on the distribution of these impacts has not been systematically reviewed, despite its central importance to ensure a just and equitable transition. Here, we perform a systematic review of recent EV studies that have examined the spatial distribution of the emissions, air pollution, and health impacts, as an important aspect of the equity implications. Using the Context-Interventions-Mechanisms-Outcome framework with a two-step search strategy, we narrowed down to 47 papers that met our inclusion criteria for detailed review and synthesis. We identified two key factors that have been found to influence spatial distributions. First, the cross-sectoral linkages may result in unintended impacts elsewhere. For instance, the generation of electricity to charge EVs, and the production of batteries and other materials to manufacture EVs could increase the emissions and pollution in locations other than where EVs are adopted. Second, since air pollution and health are local issues, additional location-specific factors may play a role in determining the spatial distribution, such as the wind transport of pollution, and the size and vulnerability of the exposed populations. Based on our synthesis of existing evidence, we highlight two important areas for further research: (1) fine-scale pollution and health impact assessment to better characterize exposure and health disparities across regions and population groups; and (2) a systematic representation of the EV value chain that captures the linkages between the transport, power and manufacturing sectors as well as the regionally-varying activities and impacts.


Introduction
Electric vehicles (EVs) have emerged as a central technology to decarbonize the transport sector and achieve climate stabilization [1]. Considering the recently announced decarbonization pledges, including the net-zero targets, the International Energy Agency projects that accelerated EV adoption can result in a net reduction of 580 Mt CO 2 -eq emissions in 2030, which is 25% more than the net reduction projected for current policy scenario (i.e. 460 Mt) [2]. As gasoline combustion for running internal combustion engine vehicles (ICEVs) also causes air pollution, electrifying road transport is expected to bring substantial co-benefits for air quality and human health [3][4][5][6]. For instance, an ambitious EV scenario is found to reduce air-pollution-related premature deaths in China by around 17, 000 in 2030, which is nearly 70% more than the premature deaths avoided in low-ambition EV scenario.
To ensure a just transition from ICEVs to EVs, it is crucially important to understand the distributional impacts of EVs and their equity implications. However, despite a growing consensus that EV adoption would generally reduce emissions and pollution [2][3][4][5][6], how these impacts might vary across different locations or population groups remains poorly understood. Academic literature on the equity implications of EVs and future modes of mobility is growing, which considers not only the environmental impacts but also the social impacts on jobs and other political economy dimensions [7,8,11]. Such questions have been explored using different justice frameworks such as mobility justice [9], environmental justice [10], and just energy transition more broadly [7].
In particular, one important aspect of EV's equity implications is the potential changes in the geographical distribution of the emissions and air quality impacts. The transition from ICEVs to EVs may reduce pollution from transport sector but increase pollution from power and industry sectors due to the increased demand for power generation and automobile parts such as batteries [2]. This will disproportionately affect the people living in regions of power production and automobile battery manufacturing.
The pollution exposure and health damages are already highly inequitable at present. Globally, nearly 50% of the air-pollution-related deaths occur in China and India [12]. The average concentration of fine particulate matter (PM 2.5 ) across the African continent is almost 4.5 times higher than in the United States [13]. Environmental injustice also exists within a country. For instance, across the United States, the average PM 2.5 exposure among black and Hispanic populations is nearly 30% higher than white population, which is largely driven by the different locations where these populations reside [14]; in China, the average concentration for PM 2.5 is 30% higher for urban populations than rural populations [13]. The transition from ICEVs to EVs can worsen this existing inequality by shifting air pollution from the richer urban areas where EVs are likely to get adopted to poorer rural areas where the power plants that generate electricity to run those EVs are located [10].
Given the large-scale transition to EVs is anticipated in the coming decades, the future distribution of the emissions and health impacts from the transportation sector will largely be shaped by EVs. Traditionally, transport emissions and health impacts have been dominated by gasoline combustion that is used to fuel the ICEVs. Looking to the future, EVs are projected to scale up rapidly. For instance, the share of EVs in annual car sales is expected to increase from less than 10% to more than 30% between now and 2030 globally [2]. As a result, the future impacts of the transportation sector will be influenced by, first and foremost, how quickly EVs penetrate the vehicle market, hence displacing the pollution impacts from gasoline and diesel consumption. In the meantime, new emissions and pollution may emerge in other sectors that support the EV transition, such as increased power sector emissions to support EV charging [15][16][17][18]. This shift from the transportation sector to other sectors results in new mechanisms through which multiple locations may experience differential impacts.
By systematically reviewing the existing literature, we focus on one important aspect of the equity implications regarding the spatial distributions: how would the transition to EVs for road transportation affect future emissions, air quality, and health impacts in different locations? To support this overarching question, we ask three specific questions when synthesizing the findings from the literature: (1) Which factors or mechanisms are found to be critical in determining the spatial distributions of the impacts? (2) What types of quantitative methods have been used to quantify the spatial distributions? (3) What are the gaps in the existing methods that need to be improved to better quantify these mechanisms? Here, we use the term region to indicate a county, province, or a country, depending on the unit of analysis in the study. This focus on location is important because different sectoral activities, such as EV operation, power generation and vehicle production, tend to be located in different geographical regions.
Methodologically, we first build a Context-Interventions-Mechanisms-Outcome (CIMO) framework to decide our search scope and relevant inclusion and exclusion criteria. We then undertake a two-step search strategy, first starting with a broad search and then an expanded search based on the search results from the first step. Finally, we perform a careful review of each paper included in our final list to organize their findings about the implications on spatial distribution and the underlying mechanisms.

Analytical framework
We use the Context-Interventions-Mechanisms-Outcome (CIMO) framework to identify papers relevant to this review [19]. CIMO is commonly used to search policy-focused literature. We select this strategy because it aligns well with our goal to find papers that examined the mechanisms that drive the emission and health impact of EVs.
• For context ('C'), our goal is to identify papers that examined the emission and health impacts of EVs in major world regions and countries. • For interventions ('I'), we include policy and investment decisions that encourage the development of EVs across its value chain. • For mechanisms ('M'), we focus on identifying the mechanisms that determine the sub-national or cross-regional distribution of emission and health impacts of EVs. • For outcomes ('O'), we are interested in three main outcomes: (i) emissions, including the emissions of CO 2 , SO 2 , NO x , primary PM 2.5 , and volatile organic compounds (VOC); (ii) pollution exposures, including ambient PM 2.5 and ozone concentration; and (iii) health impacts, especially premature deaths.
A detailed CIMO-based analytical framework is presented in supplementary note 1.

Search strategy
Based on the CIMO framework, we searched for peerreviewed papers published in English between January 2010 and August 2022. We focused on papers published in 2010 and onwards because EV is a rapidly evolving technology, and its uptake has increased significantly only in the past decade.
Specifically, we took two steps to identify papers included in our review.
Step 1: we first searched for relevant papers on ScienceDirect and Google Scholar using multiple relevant search strings. As our focus is not only on the emission and health impacts of EVs, but also on the spatial distribution of those impacts, we added distribution-related keywords in our search strings. For example, to search for papers that studied the distribution of impacts by location, we used the following search string-'emission' AND 'electric vehicle' AND 'regional variation' . We observed that using search strings with multiple keywords narrowed down the search considerably and provided a small number of results.
Step 2: we then expanded the search in two dimensions. First, we added additional keywords to search for papers on distributional impacts. We included keywords such as 'equity' , 'justice' , and 'disparity' in our search strings. Second, to search papers for the world regions that were underrepresented in the initial search (e.g. Europe, India, and Africa), we added specific geographic regions in the search strings. We did this because our initial search mostly yielded papers focused on China and the USA. The complete list of search strings for both steps is provided in supplementary note 2.
Regarding our inclusion criteria, we only included papers that provided a quantitative assessment of the distribution of at least one outcome of interest (e.g. emissions, pollution, and/or health). Since studies that examined the distributions across populations often also provide a geographic angle related to where people live, we included papers that studied the impacts across different population groups in addition to different locations. Regarding our exclusion criteria, we did not include papers that did not perform a quantitative assessment of the distribution of emission and/or health impacts.
To ensure that we did not miss out on key papers, we reviewed the papers identified in step 1. We then applied the same inclusion and exclusion criteria to the new papers identified in step 2, and arrived at the final list of papers for review.

Overview of the papers included in the review
As shown in figure 1, we found 9838 papers from our initial two-step search. After removing duplicates, we were down to 2334 papers. We then screened the titles and abstracts of these papers. We excluded the papers that were not specifically focused on EVs (e.g. papers that studied the transportation sector in general) or did not provide a quantitative assessment of the distribution of emissions or health impacts of EVs (e.g. those life-cycle analyses that only focused on processes but not the location of those processes). Given our research interest, we only included papers that examined the impacts of interventions focused on EVs. These interventions include policy or investment decisions to increase the adoption of EVs. These could also include future scenarios designed to project the emission and/or health impacts under varying levels of EV penetration. Based on these criteria, we arrived at a list of 79 papers. This initial list of 79 papers was then carefully screened by a group of eight people. The full paper was read to further assess whether the paper indeed provided relevant insights that are essential for this review, particularly on geographic variations.
After the initial screening, we narrowed down to 47 papers that offered insights to our core research questions [3-6, 10, 15-18, 20-57]. These 47 papers were again read and reviewed by the same group of eight people to sythesize the key insights. Then, a second round of review was conducted by three authors of this study. We divided these papers in such a manner that every single paper was read by at least two different people. In case there were discrepancies or disagreements about the coding for the paper, we discussed these papers in detail among all the four coauthors and arrived at a decision based on our consensus. The final list of papers included in this review is provided as a supplementary data file.
Specifically, for each paper, the reviewer was asked to summarize and synthesize the information for the following aspects (table 1): Here we summarize the research scope of the 47 papers included in our review and highlight four general patterns (figure 2).
First, the global south countries were understudied, despite their central importance in shaping the climate and environmental future. More than 50% of the papers included in our study were focused on developed countries, mainly the United States [

Metric Explanation
Year Timeframe of analysis Country/region of interest Country/Region focus of analysis Unit of analysis Spatial resolution for estimating emission and/or health impacts, such as: 1) Grid (e.g., 36 by 36 km spatial grids), ii) County, iii) Province, or iv) Region (e.g., one country or a set of countries) Sector of interest Sectoral focus of analysis, such as (i) Gasoline-production, (ii) Vehicle-manufacturing, (iii) Power, and/or (iv) Transport Metric of impact Eight emission and health variables, including CO2, NOx, SO2, Primary2.5, VOC, Ambient PM2.5, Ozone, and Health impacts. Refer to section 3.2 for discussions that link metrics of impact with concepts of equity. Additional detail on impacts and their equity implications Five questions on emission and health impacts of EVs, and their equity implications: • Does the paper estimate differences due to transport fuel, and transport emission standards? • Does the paper discuss long-term (e.g. premature deaths) and/or short-term health impacts (e.g. NO2 impacts)? • Does the paper estimate changes in impacts with time?
• Does the paper mention terms such as 'energy justice' , 'environmental justice' , or 'mobility justice'? • Does the paper estimate impact for variables other than spatial distribution of emission and/or health impacts?
About 25% of papers examined emerging economies like China [3,5,10,26,31,39,41,45,50] and India [42,53], with more focused on China (about 20%). The remaining 25% of the paper had a multi-region [  Here 'Multi-region' includes studies that focused on several countries in different regions of the world, such as comparative studies between China, the United States and Germany [24] or between four developing countries and four developed countries [55]. 'Grid-level' studies include the studies that use spatial grids finer or comparable to county/city level, which were often studies using air quality modeling.
requires future efforts. These regions are emerging new markets for EVs and are projected to be important contributors to future carbon emissions and airpollution-related deaths [58,59]. Understanding the equity implications of transport electrification in these global south regions will be critical for tackling both local and global sustainability challenges. Second, although many studies considered the impacts beyond the transportation sector, the sectoral coverage was still incomprehensive in most studies. The adoption of EVs, first and foremost, influences the emissions and pollution impacts of the transportation sector. Yet, cross-sectoral linkages are important to understand the life-cycle impacts of the whole EV value chain. For the papers included in our review, 90% of the studies covered both transportation sector and power sectors, as electricity generation activities have strong ties to support EV operations [3-6, 10, 15-18, 20-23, 25-36, 38-43, 45-49, 51, 53, 55-57]. About half of the studies further examined gasoline production and vehicle manufacturing activities: 20% of the studies considered the changes in gasoline production and electricity generation activities to support the EV transition [21,27,31,33,35,36,38,43,53], while 10% of the studies considered the emissions and pollution impacts associated from the vehicle manufacturing activities [15,17,20,26,46], including the production of batteries for EVs and the manufacturing of materials required for vehicles (e.g. steel). However, only 20% of the studies included all four key sectors (i.e. transportation, power, gasoline production, and manufacturing) [4, 23, 25, 28-30, 42, 48, 49]. This pattern indicates that although the research community has started to recognize the importance of cross-sectoral linkages, a lot more needs to be done to account for the multi-sector, economy-wide impacts of EVs.
Third, studies at fine spatial resolution were insufficient. More than 60% of the papers compared emissions and pollution impacts across different countries or regions, without demonstrating the potential differential impacts across states, provinces, or counties within one region [5, 6, 15, 16, 18, 20-23, 25, 27, 30, 35-40, 43, 44, 46-49, 51, 52, 54-56]. About 15% of the papers examined the differential impacts across provinces [3,26,29,31,41,42,50]. This spatial scale is helpful in informing real-world decisions since many energy and climate decisions are made by provincial/state governments and relevant decisionmakers. Only about 25% of the papers took a further step to study finer resolution impacts that vary across counties or spatial grids that are finer than states/provinces [4, 10, 17, 24, 28, 32-34, 45, 53, 57]. Most of these finer-scale studies used air quality modeling methods to simulate the geographic distributions of pollution concentrations. To estimate the gridded emissions as input to air quality models, these studies often treated power sector emissions as point sources (based on current locations of plants) and transportation sector emissions as non-point sources (based on information on road infrastructure and traffic volumes). Despite the efforts to assess fine-scale emission impacts, studies still often make simplified assumptions based on present-day spatial patterns, i.e. scaling down the baseline power/ transportation sector emissions across all the spatial grids within the same province/state. These simplifications, though justifiable in most cases [60], do not properly account for the nuance of EV impacts depending on which power plants and vehicle choices are being impacted [61,62].
Finally, the majority of the studies that examined the distribution of EV impacts were based on scenario analyses, e.g. designing counterfactual scenarios for the past or policy-relevant scenarios for the future. These scenario-based analyses allowed them to estimate and compare how sectoral activity could change with a higher share of EVs in the vehicle fleet, which in turn changes the emissions of both greenhouse gases and air pollutants. To account for the pollution and health impacts from these changes in emissions, some studies used air pollution models (e.g. WRF-CMAQ or WRF-Chem [3,5,33,45]) and health impact assessment methods (e.g. concentration-response relationships from epidemiological studies [5,[32][33][34]) to estimate the corresponding exposure and health damages.

Summary of the distributional impacts
We focus on spatial distributions as one key aspect of equity consideration. For the outcomes examined in each study (i.e. emissions, air pollution, and/or health), we classified the findings into three broad categories: (1) all winning, i.e. as a result of EV transition, the paper found all the locations being covered in the study would always benefit from a reduction in emissions, pollution or health damages; (2) winners and losers, i.e. in all scenarios examined in the study, it is always the case that some locations would benefit while others may suffer from the EV transition; and (3) scenario dependent, i.e. the paper found that losers may occur only in a specific setting and the distributional consequences would depend on the scenario designs.
To illustrate how we classified the studies, here we use one paper as an example, i.e. Ji et al 2015, 'Environmental Justice Aspects of Exposure to PM 2.5 Emissions from EV Use in China' [10]. This paper found that the primary PM 2.5 emissions would decrease in regions where EVs are adopted, making these regions 'winners' . In contrast, increased EV adoption would result in an increase in primary PM 2.5 emissions in regions where electricity for running EVs is generated, making them 'losers' . These patterns of winners and losers were found in all the scenarios that they examined. Thus, in our synthesis, we categorize this study as 'winners and losers' for their key outcome on 'primary PM 2.5 emissions' .
Based on our synthesis of all 47 papers, we highlight two key insights ( figure 3). First, while the majority of the studies did quantify the varying CO 2 emissions across regions or sub-national units, very few studies quantified the disparities in air pollution and health. As we were interested in examining both carbon and air pollution emissions, we included papers that only assessed the spatial distribution of CO 2 emissions and did not examine air quality or health impacts. We found that more than 90% of the papers provided evidence of spatial variations in CO 2 emissions as a result of EV transition [3, 4, 15-18, 20-49, 51-57]. In comparison, less than 35% of the papers provided information on the spatial variations in precursor air pollutant emissions (e.g. SO 2 , NO x , primary PM 2.5 , or VOC) [3-6, 10, 22, 31-33, 40-42, 45, 50, 53, 55], exposure to ambient air pollution (e.g. ambient PM 2.5 or O 3 ) [3-5, 22, 32-34, 41, 45, 53], or the health impacts (e.g. premature mortality) [3, 5, 22, 32-34, 41, 45]. Since air pollution and health impacts are highly local, this finding underscores the importance of analyzing the pollution and health disparities in order to understand the local effects of transport electrification.
Second, among the studies that examined the spatial variations in addition to the aggregate impacts, many identified potential unequal outcomes where the EV transition creates winners and losers across different locations. For some studies [4-6, 10, 15-18, 20-23, 26, 28, 30, 31, 36, 37, 40, 42-44, 49, 52, 55-57], such tradeoffs exist in all scenarios being examined, while other studies found tradeoffs only in a subset of the scenarios under certain conditions [3, 29, 33-35, 38, 41, 50, 54]. For instance, Peng et al 2018 found a net reduction in air pollutants and CO 2 emissions across all provinces in China when the penetration of low-carbon sources reached 40% of the electricity mix; otherwise, the deployment of EVs could exacerbate the emissions and pollution impacts in some provinces [3]. Our synthesis thus highlights the importance of going beyond the aggregate impacts that are often positive (e.g. a net reduction in emissions or health exposure/damages for the whole region). A detailed assessment of location-varying impacts is needed to understand the unequal local impacts of EV transition.

Key mechanisms for the unequal regional impacts
We further delve into the subset of the papers that examined the location-varying impacts (i.e. the nongrey segments in figure 3). The goal here is to synthesize key mechanisms that may contribute to inequitable distributions. We find that the drivers, processes and mechanisms that connect the EV transition with the resulting distributional impacts are quite complex  ( figure 4). In addition, the conclusions are often determined by which sectors and mechanisms are being considered; and only a small number of papers examined how these spatial patterns might change over time.
First, the adoption rate of EVs is affected by many location-specific factors, where multiple energy-related sectors are further influenced by the operation and manufacturing of EVs. EV adoption is determined by several factors such as supporting policies like government subsidies and financial incentives, as well as the consumers' preference and acceptance to EVs, their socio-economic conditions (e.g., income level), and their driving patterns and habits. The increase in EV adoption directly affects the transportation sector (e.g. lowering gasoline and Figure 5. Illustrative examples to demonstrate the mechanisms through which electric vehicles influence the spatial distribution of the impacts. Examples 1, 2, 3 are based on Wu and Zhang [55], Ji et al [10], and Hung et al [30], respectively. diesel consumption). In the meantime, the increase in EV adoption also brings changes to the power sector (e.g. increasing electricity generation) and the industrial sector (e.g. lowering gasoline and diesel production, and increasing battery and material production). In this step, the key determinants for the different geographic impacts are the locations of the relevant energy activities, shaped by the energy markets and EV supply chains [3-6, 10, 15-18, 20-57].
Second, the amount of CO 2 and air pollutant emissions vary across sectors and relevant energy activities, which in turn results in differential spatial distributions. The emission impacts also depend on whether gasoline or diesel ICEVs are being displaced by EVs. In terms of tank-to-wheel emissions (i.e. the emissions from driving vehicles), gasoline ICEVs have lower levels of NO x and SO 2 emissions, but higher levels of non-methane volatile organic compounds (NMVOC) and CO 2 emissions than diesel ICEVs. In terms of well-to-tank emissions (i.e. the emissions from production, processing, and delivery of transport fuels), gasoline ICEVs have higher levels of emissions for CO 2 and air pollutants (including NO x , SO 2 , PM 2.5 and NMVOC) than diesel ICEVs. Regarding upstream sectors, coal-based power generation emits substantial amounts of SO 2 and NO x , and the mining activities as part of the battery production process also emits SO 2 . The emissions from these upstream activities may lead to unintended impacts in locations different from where EVs are being deployed. In addition, we find the spatial differences in the emission factors (i.e. emissions per unit energy activities) also play an important role, especially for the air pollutant emissions. Such differences are driven not only by fuel type and efficiency of locally-used technologies, but also by the stringency of air pollution control policies for the transportation, power, and industrial sectors [3-6, 10, 15-18, 20-57].
Third, the precursor air pollutant emissions influence the concentrations of air pollution, such as fine particulate matter (PM 2.5 ) and ozone (O 3 ), affecting the pollution exposure level of residents. In this step, the spatially-varying impacts can be further influenced by the non-linear atmospheric processes, such as local meteorological conditions that would affect the wind transport of pollution, as well as the chemical formation of secondary pollutants, including secondary aerosols and O 3 [3-5, 32-34, 41, 45, 53].
Finally, the human health impacts from the exposure to ambient air pollution are also affected by the characteristics of the exposed population. Here, the additional factors that could widen the spatial variations include the size and vulnerability of the populations [3-5, 32-34, 41, 45]. In addition to the long-term exposure to PM 2.5 and ozone, NO 2 from transportation sector emissions has been found to have short-term health damages, such as increasing risks of asthma attacks, allergies, and other respiratory diseases [63][64][65]. However, none of our reviewed studies on EVs estimated these short-term health damages from NO 2 exposure, highlighting an important area for future research More broadly, most of the studies included in our review do not clearly state what definition of equity they adopted and what metrics were used to measure the disparities. To this end, we select three papers as examples to demonstrate what geographic distributions and relevant mechanisms they studied, and what could be the relevant equity definitions/metrics the authors adopted implicitly (figure 5): • Example 1: Wu and Zhang [55] compared the emissions of CO 2 and air pollutants between developed countries (Germany, France, the US, and Japan) and developing countries (China, Russia, India, and Brazil). Compared to conventional gasoline-based vehicles, they found that utilizing EVs reduces transport-related CO 2 emissions in all regions. However, the associated air pollutant emissions may increase in developing countries due to their dependence on thermal power generation combined with the high line loss rates in electricity transmission.
Here equity could be defined as countries emit their fair share of CO2 emissions and have similar levels of air pollutant emissions on per capita basis. The equity assessment could be operationalized by comparing the CO 2 emissions and air pollutant emissions across countries at different developmental stages and income levels.
• Example 2: Ji et al [10] examined the PM 2.5 -related health impacts due to the urban use of EVs in China. Although deploying EVs reduces emissions from the transportation sector in urban areas, it increases the air pollutant emissions from the power sector in rural areas where most power plants are located. This further leads to differential health impacts.
Here equity could be defined as rural and urban populations face similar levels of pollution exposure and health impacts. The equity assessment could be operationalized by comparing the air pollutant emissions and ambient PM 2.5 concentrations in rural versus urban areas.
• Example 3: Hung et al [30] studied the impacts of electricity trade on the carbon footprints across different regions in Europe. They found that the trade of electricity could relocate the electricity generation activities to other regions, thus influencing the distribution of CO 2 emissions across regions.
Here equity could be defined as countries are responsible for similar amount of consumptionbased CO2 emissions. The equity assessment could be operationalized by comparing the production versus consumption-based emissions across regions.

Discussion and conclusion
Based on the CIMO framework, we used a two-step search strategy to identify and review papers that assessed the impacts of EV transition on the spatial distribution of emissions, air quality and health. We find that the distributional impacts often vary across the outcomes of interest (e.g. CO 2 emission vs. air quality, vs. health) and across scenario assumptions (e.g. the cleanness of the electricity used to power the EVs). Many factors play a role in shaping the spatial distribution. For instance, while the locations of EV adoption often benefit from cleaner vehicles, other locations that support the production of electricity, battery, or other materials may suffer from increased emissions and impacts.
Our review also reveals that assessing the equity implications of EVs is analytically challenging. First, it involves complex drivers and cross-sectoral dynamics that are difficult to characterize. The adoption of EVs not only influences energy consumption in the transportation sector. It also drives changes in other sectors and locations, such as the power generation activities to supply the electricity, as well as the battery and manufacturing activities to produce the EVs. Besides emissions, the ambient pollution level is further influenced by atmospheric chemistry and transport processes, while the associated human health impacts are also shaped by the size and vulnerability of the exposed population. Methodologically, characterizing the complex cause-effect relationships from energy activities to emissions and health imposes new challenges to the quantitative representation of the coupled energy-atmosphere-health systems. It requires pushing the frontiers of both systems modeling and empirical evidence.
We also find that most studies still lack a clear definition of equity and how it is operationalized, which may limit the usefulness of the studies to inform real-world decisions. Policymakers and other stakeholders have increasingly highlighted the importance of highlighting winners and losers in designing and implementing equitable policies to support the transition towards EVs and other lowcarbon technologies. However, our review suggests that most papers did not explicitly explain what is meant by equity and how to measure it to consider different outcomes and equity principles. This underscores the importance of putting equity at the center of energy research, as proposed by the research community in recent years [66,67].
While existing research has provided useful methods and insights to capture the equity implications, we highlight two important areas where future research is urgently needed. First, we need more information about the pollution and health impacts at a fine spatial scale. Although many studies have quantified the impacts on emissions, taking further steps to assess the air pollution and health disparities is important to understand the impacts on human wellbeing. Second, a systematic representation of the linkages between the transportation, power and manufacturing sectors is needed to better understand the region-specific activities and impacts. Although many studies have started to consider upstream activities, limited research has provided a holistic view of the whole EV value chain with a solid representation of all the important cross-sectoral linkages.
Conceptually, a framework that provides a comprehensive assessment of the spatial distributions needs to include the following elements (figure 6): Figure 6. A conceptual framework to assess the regionally varying impacts of electric vehicles, with considerations of cross-sectoral and cross-regional linkages.
(a) for energy and emissions: representation of the whole value chain of EVs, including not only the cross-sectoral linkages (e.g. between transportation and power and industrial sectors) but also the relevant market linkages within each sector (e.g. electricity trade and EV supply chain), and (b) for exposure and health: representation of the atmospheric processes and sociodemographic factors that may further result in geographical differences in pollution formation and transport, as well as the associated disparities in population exposure and health impacts. Arguably, including all these system linkages is neither feasible nor desirable in one research project. Nevertheless, this conceptual framework summarizes all mechanisms that were found to be important in the papers we reviewed. It provides the big picture and a potential roadmap for future research to better quantify the spatially varying impacts of EV transition on emissions, air quality and health.
Finally, to provide a holistic assessment of the equity-related impact of the shift to EVs, it is crucial to also assess its impact on labor across the automobile value chain in addition to emissions and health. Strategies designed to develop the EV industry to spur local job growth in a country could lead to a worsening of pollution and health impacts at subnational levels [42,68]. However, with the increasing need for developing strategies for just energy transition, governments are increasingly facing the challenge of addressing economic, environmental, and climate concerns together. They may not always be able to identify win-win solutions, but it is crucial to examine these trade-offs.

Data availability statement
All data that support the findings of this study are included within the article (and any supplementary files).