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Electric vehicle charging equity and accessibility: A comprehensive United States policy analysis
... The prioritization of corridor sites has previously been critiqued. Carlton and Sultana [25] pointed out a grey area overlooked by the Justice40 framework, where the National Electric Vehicle Infrastructure (NEVI) program was fully, and the Charging and Fueling Infrastructure (CFI) Discretionary Grant Program was primarily, focused on alternative fuel corridors for charger installation, leading to issues of overconcentration. In the U.S., census tracts located near corridors are likely to have 2.7 times more overall public chargers and 5.3 times more fast chargers [25]. ...
... Carlton and Sultana [25] pointed out a grey area overlooked by the Justice40 framework, where the National Electric Vehicle Infrastructure (NEVI) program was fully, and the Charging and Fueling Infrastructure (CFI) Discretionary Grant Program was primarily, focused on alternative fuel corridors for charger installation, leading to issues of overconcentration. In the U.S., census tracts located near corridors are likely to have 2.7 times more overall public chargers and 5.3 times more fast chargers [25]. This gap is even more pronounced in rural areas than in urban ones [15], [25]. ...
... In the U.S., census tracts located near corridors are likely to have 2.7 times more overall public chargers and 5.3 times more fast chargers [25]. This gap is even more pronounced in rural areas than in urban ones [15], [25]. The limitations of state-centric programs underscore the shortcomings of a reformist orientation in equity analysis, emphasizing the need for community-led and justice-driven alternatives [25], [26]. ...
This study explores the integration of AI in transportation electrification planning in Austin, TX, focusing on the use of Geospatial AI (GeoAI), Generative AI (GenAI), and Large Language Models (LLMs). GeoAI enhances site selection, localized GenAI models support meta-level estimations, and LLMs enable scenario simulations. These AI applications require human oversight. GeoAI outputs must be evaluated with land use data, GenAI models are not always accurate, and LLMs are prone to hallucinations. To ensure accountable planning, human planners must work alongside AI agents. Establishing a community feedback loop is essential to audit automated decisions. Planners should place Community Experience (CX) at the center of Urban Planning AI.
... Similar to gas stations, public chargers are mainly operated by private vendors. However, expanding the charging infrastructure and installing additional public chargers often requires government funding (Carlton and Sultana 2024). This expansion typically involves a high level of coordination across multiple government agencies, such as transportation, energy, and environmental departments, along with public engagement efforts involving a range of stakeholders, including advocacy groups, nonprofit organizations, and private vendors (TxDOT 2023). ...
... Living in urban or suburban areas is also related to the issue of unequal access to public EV charging (Jiao et al. 2024b). In fact, Carlton and Sultana (2024) reported that 60-80% of the census tracts in the U.S. do not have adequate access to public EV chargers. This has sparked a normative debate among scholars about how to effectively address and mitigate the issue of distributional inequality. ...
... While measuring accessibility inequality has been of keen interest to scholars, this does not mean that other conceptual frameworks have been neglected. Sufficietarian philosophical approaches have also been applied in policy initiatives, setting thresholds or baselines that should be met, such as in federal policies like Justice40 in the U.S. (Carlton and Sultana 2024). Soltani Mandolakani and Singleton (2024) further explored the application of equity and justice theories, showing how different theoretical frameworks, when applied in practice, can lead to varying policy outcomes. ...
Public electric vehicle charging stations (EVCSs) are vital for boosting EV adoption. This study investigates Seoul’s public EV charging patterns, taking into account the surrounding urban built environment. We collected built-environment data from land-use maps, Point of Interest (POI) data, and panorama images near public EVCS. The computer-vision technique was used to extract scene features from panorama images. We conducted a spatiotemporal analysis of public EVCS usage. The built-environment factors underwent dimensionality reduction and were assessed for outliers. Descriptive analysis revealed afternoon peak charging times and variations between chargers. Additional peaks are observed in the weekday late evening for chargers located near mega-retail stores. Public EVCS in Seoul were utilized more on weekdays than on weekends. Public EVCS in central business districts saw the most significant usage, with potential cases of overuse. An analysis of the built environment around the chargers showed unique characteristics, with some forming identifiable clusters. The most used public EVCS had more parking areas than other POIs, matching visual observations. Computer visioning mainly recognized highways, parking lots, and crosswalks as common features near the chargers. Outlier test results generally defined fast chargers in the central business district area as outliers. The results also demonstrated that built-environment measures from POI data and computer vision can be used in a complementary manner. Our study offers empirical findings to enhance the understanding of public EV charging usage. We demonstrated the use of POI data and computer-vision techniques to quantify the built environment.
... It has been widely adopted in planning practice and policy-making, due to its interpretability and comparability. In the few existing studies on the accessibility of charging infrastructure in recent years, most have primarily focused on China [7,22,27], Europe [8,28], and the United States [29][30][31][32], which are the top three countries (regions) globally in terms of charging infrastructure distribution [2]. Based on the content of these studies, they can be divided into two aspects, according to scale differences. ...
... Based on the content of these studies, they can be divided into two aspects, according to scale differences. On one hand, some research measures the unequal distribution of charging infrastructure across regions, such as between countries or within domestic regional differences, as well as the urban-rural divide [14,28,30]. On the other hand, some studies focus on measuring facility inequality within cities, often using communities or census tracts as the basic unit of analysis [7,22,27,29]. ...
... Within cities, considering the competition between units and the supply capacity of facilities, population density and the number of chargers (or charging capacity) are used to represent the demand and supply levels of charging behavior. In terms of research methodologies, the two-step floating catchment area (2SFCA) model, the Gaussian-based two-step floating catchment area (G2SFCA) model, and the least-cost path algorithm have been utilized in recent years to examine spatial differences in EVCS accessibility [7,8,27,29,30,33]. Among these methods, the 2SFCA model and its improved variants account for regional competition and the supply capacity of service facilities, often using population density and service capacity metrics (e.g., number of charger, station charging capacity, and number of vehicle turnover) to represent the demand and supply levels of charging behavior [7,8,33]. ...
The deployment of electric vehicle charging stations (EVCSs) is crucial for the large-scale adoption of electric vehicles and the sustainable energy development of global cities. However, existing research on the spatial distribution of EVCSs has provided limited analysis of spatial equity from the perspective of supply–demand relationships. Furthermore, studies examining the influence of the built environment on EVCS accessibility are scarce, and often rely on single methods and perspectives. To explore the spatial characteristics of EVCS accessibility and its influencing factors, using multi-source urban spatial data, this study initially employs the Gaussian two-step floating catchment area (G2SFCA) method to measure and analyze the spatial distribution characteristics of EVCS accessibility in Guangzhou, China, with consideration of supply–demand relationships. Subsequently, it integrates the MGWR and random forest (RF) models to comprehensively investigate the impact mechanism of the built environment on EVCS accessibility from the perspectives of spatial heterogeneity and non-linear relationship. The results show that the EVCS accessibility exhibits a “ higher in the west and lower in the east, with extreme core concentration” distribution pattern, and has significant spatial autocorrelation. The built-environment variables exhibit different scale effects and spatial non-stationarity, with widespread non-linear effects. Among them, the auto service, distance to regional center, and distance to subway station play important roles in influencing EVCS accessibility. These findings offer important guidance for the efficient and equitable layout of EVCSs in high-density cities.
... Recent policies and technological advancements have not only reduced EV costs and extended their driving ranges (Onstad, 2024) but also expanded the charging infrastructure, significantly increasing adoption rates (The White House, 2021 Despite this progress, the placement of EV charging stations often favors affluent urban areas, neglecting lower-demand rural and inner-city regions (Khan et al., 2022). This mismatch hinders accessibility for about 60-80% of U.S. census tracts that lack public charging facilities, contradicting federal equity objectives (Carlton and Sultana, 2024). The Biden-Harris administration aims to install 500,000 EV charging stations, emphasizing the need for equitable distribution to support all communities, especially those in rural areas where there is a strong proenvironmental commitment but inadequate infrastructure (Huether, 2021;The White House, 2021). ...
... Studies show a strong pro-environmental commitment in rural areas, suggesting high potential for EV adoption (Berenguer, Corraliza and Martín, 2005). Despite this, the market-driven placement of EV charging stations (EVSE) frequently neglects lower-demand rural and inner-city areas, leading to inadequate infrastructure that doesn't support mobility needs (Mortimer et al., 2022;Carlton and Sultana, 2024). Additionally, even as charging corridors expand fast charging access, about 60-80% of U.S. census tracts still lack public charging facilities, indicating a disconnect with federal equity objectives (Carlton and Sultana, 2024). ...
... Despite this, the market-driven placement of EV charging stations (EVSE) frequently neglects lower-demand rural and inner-city areas, leading to inadequate infrastructure that doesn't support mobility needs (Mortimer et al., 2022;Carlton and Sultana, 2024). Additionally, even as charging corridors expand fast charging access, about 60-80% of U.S. census tracts still lack public charging facilities, indicating a disconnect with federal equity objectives (Carlton and Sultana, 2024). ...
1 The transition to electric vehicles (EVs) to reduce greenhouse gas (GHG) emissions has been a 2 large part of climate policy. EV adoption relies on the charging stations that provide EVs with 3 power. Where public charging stations are placed impacts their utility, and unadvised planning 4 could risk furthering the transportation infrastructure divide between rural and urban areas. This 5 study examines the travel patterns of urban and rural areas to establish the varied need for 6 charging stations and measures the equity of the existing public charging station sites. The use of 7 job access as a measure of rurality reveals both the longer daily mileage traveled by people in 8 rural areas and the influence of job accessibility on trip miles. Public charging stations, or 9 electric vehicle supply equipment (EVSE), however, are concentrated in urban areas and still 10 inaccessible for many consumers. Both the current lack of stations and the higher expected 11 demand for public stations based on travel patterns highlight that more charging stations are 12 especially needed in rural areas. These findings underline the need for policy decisions on the 13 funding and placement of charging stations to be made with care to provide more equal access to 14 EVSE and foster EV adoption everywhere. 15 16
... This creates unequal access and limits EV adoption in disadvantaged areas (Zhou et al. 2022). Approximately 60-80% of U.S. census tracts lack public charging stations, contradicting federal goals of equitable access (Carlton and Sultana 2024). The Pacific Northwest National Laboratory (PNNL) defines an equitable energy system as one that provides economic, health, and social benefits to all individuals, regardless of ability, race, or socio-economic status (Tarekegne et al. 2021). ...
... Tailored EVSE strategies are necessary to address these disparities and encourage broader adoption (Hennessy and Syal 2023;Hsu and Fingerman 2020;Jonas and Macht 2024;Reck 2020). While there is a strong commitment to environmental sustainability, market-driven approaches to EVSE infrastructure frequently overlook rural and underserved urban areas (Berenguer et al. 2005;Carlton and Sultana 2024;Mortimer et al. 2022). To democratize EV adoption and ensure all communities benefit, policy frameworks must incorporate equity and justice, prioritizing access for lower-income and underserved regions (Bauer 2021;Soltani Mandolakani and Singleton 2024). ...
The transition to electric vehicles (EVs) to reduce greenhouse gas (GHG) emissions has been a large part of climate policy. EV adoption relies on the charging stations that provide EVs with energy. The placement of public charging stations impacts their utility, and unadvised planning could risk furthering the transportation infrastructure divide between rural and urban areas. This study provides an unconventional perspective on the problem. It is argued that the provision of public charging stations, or electric vehicle supply equipment (EVSE), should be considered in the context of the differential ability to substitute private vehicle travel by other modes or land use reforms. We first examine the travel patterns of urban and rural areas, sequentially excluding non-private vehicle then shopping journeys replaceable by online purchases, to establish the varied need for charging stations and measure the equity of the existing public charging station sites. The use of job access as a measure of rurality reveals both the longer daily mileage traveled by people in rural areas and the influence of job accessibility as a proxy for amenity access. An equity framework is formulated based on the classic Lorenz curve, with an extension to jointly consider job and EVSE access via a Cobb-Douglas-style production function. We characterize the state of equity in this production function for the United States as of 2024.
... On the contrary, we consider it a conversion factor because users' ability to pay determines the degree to which they can leverage charging infrastructure, engage in desired activities, and effectively use electrical equipment. Income is the primary measure of financial capacity in EV charging, transport, and energy research (Carlton & Sultana, 2024;Pereira et al., 2017;Sovacool et al., 2018). Across these fields, higher income consistently correlates with better access to charging, activities, and energy. ...
... It also encompasses the influence of design elements and building layouts (microenvironment) in the ease of infrastructure installation and service use, and weather (natural environment) effects on charging access and use. While the spatial distribution of public charging infrastructure is widely studied in literature (Carlton & Sultana, 2024;Hsu & Fingerman, 2021;Khan et al., 2022), our review reveals that there are opportunities for future research to delve deeper into the microenvironment and natural environment effects on charging access. ...
... The pivotal role of EV charging infrastructure in supporting personal vehicle electrification has motivated a growing body of research on its accessibility. Numerous case studies have examined this issue across leading EV markets, including the United States [26,11,15], China [29], and Europe [14], as well as at more localized scales such as California [17,33], Texas [20], Washington [13], and New York City [21]. While these studies are unified in their goal of addressing charging access as a critical concern, they differ considerably in their quantitative frameworks for defining and evaluating accessibility, reflecting diverse methodological choices and regional contexts. ...
... Distance-based measures offer an alternative to count-based approaches by assessing accessibility in terms of the travel distance or travel time between individuals and the nearest EVCS. For example, Carlton et al. [11] calculated travel time to the nearest EVCS for each pixel across the contiguous United States and aggregated the results to the census tract level. Similarly, Lou et al. [26] leveraged a disaggregated dataset of 121 million U.S. household locations to compute the shortest distance to up to five nearby EVCSs for each household. ...
Electric vehicle (EV) charging infrastructure is crucial for advancing EV adoption, managing charging loads, and ensuring equitable transportation electrification. However, there remains a notable gap in comprehensive accessibility metrics that integrate the mobility of the users. This study introduces a novel accessibility metric, termed Trajectory-Integrated Public EVCS Accessibility (TI-acs), and uses it to assess public electric vehicle charging station (EVCS) accessibility for approximately 6 million residents in the San Francisco Bay Area based on detailed individual trajectory data in one week. Unlike conventional home-based metrics, TI-acs incorporates the accessibility of EVCS along individuals' travel trajectories, bringing insights on more public charging contexts, including public charging near workplaces and charging during grid off-peak periods. As of June 2024, given the current public EVCS network, Bay Area residents have, on average, 7.5 hours and 5.2 hours of access per day during which their stay locations are within 1 km (i.e. 10-12 min walking) of a public L2 and DCFC charging port, respectively. Over the past decade, TI-acs has steadily increased from the rapid expansion of the EV market and charging infrastructure. However, spatial disparities remain significant, as reflected in Gini indices of 0.38 (L2) and 0.44 (DCFC) across census tracts. Additionally, our analysis reveals racial disparities in TI-acs, driven not only by variations in charging infrastructure near residential areas but also by differences in their mobility patterns.
... However, multi-criteria decision-making process often requires substantial input from policymakers and experts, which can introduce subjective biases (Carlton and Sultana, 2024). While domain knowledge is crucial, it is susceptible to biases and subjective interpretations. ...
... However, this approach poses challenges, as decision-makers often struggle to determine binary thresholds. Since CEJST relies on hard thresholds (Carlton and Sultana, 2024) to standardize its analysis, its omission of critical indicators (e.g., race) and binary thresholds compromise the objectivity and inclusivity of its outputs. ...
This paper introduces a unified, data-driven multi-criteria equity metric designed to address inequities in Electric Vehicle Charging Station (EVCS) accessibility and Electric Vehicle (EV) adoption. Existing studies predominantly focus on macro-level factors and rely on single-criterion or context-specific approaches, limiting their applicability to dynamic, multi-dimensional challenges. Additionally, current equity evaluations often involve inherent subjectivity, complicating decision-making processes. Focusing on the Dallas-Fort Worth area, this study advances the field by transitioning from city-level to census tract-level analysis. The proposed framework utilizes dimension reduction techniques to construct a unified equity metric and high-resolution clustering analysis to identify EV-related socio-demographic typologies. These typologies-EV developed, EV developing, and EV underdeveloped/disadvantaged-are validated against disadvantaged communities (DACs) defined by the Climate and Economic Justice Screening Tool (CEJST), mapped EV/EVCS distributions, and their corresponding socio-demographic radar plots. The data-driven equity metric has advantages in unity, objectivity, adaptability, and robustness compared to existing metrics. By identifying micro-level inequities, the framework provides tailored policy recommendations for each typology, supporting equitable EV infrastructure planning.
... Similar findings from U.S. studies show that the availability of EV charging stations is not determined by population density [18,22], but is correlated with median household income [18,[22][23][24] age [22], percentage of white-identifying population [18], and the presence of highways within a zip code area [15,18]. Extended this analysis, where central residents have better access to medium and quick chargers, showed that higher education levels, income, and private housing are linked to more equitable EV charging access. ...
Electric vehicles (EVs) are rapidly gaining popularity due to their environmental benefits, such as reducing greenhouse gas emissions. Considering the sociodemographic factors that influence the adoption of EVs is essential when developing equitable and efficient transportation policies. This article leverages the National Household Travel Survey (NHTS) 2022 data to analyze the sociodemographic factors influencing the adoption of EVs in the U.S. A binary logistic regression model and three machine learning models were employed to predict EV ownership in the U.S. The results of the regression model suggested that the Pacific division leads in EV adoption, most likely due to legislation and improved infrastructure, while regions such as East South Central suffer from lower EV adoption. The findings indicate that higher household income and home ownership significantly correlate with increased EV adoption. In contrast, renters and rural households exhibit lower adoption rates suggesting an increase in charging facilities in these regions can promote EV adoption. The Random Forest model outperforms others with an accuracy of 82.72%, suggesting its robustness in handling complex relationships between variables. Policy implications include the need for financial incentives for low-income households and increased charging infrastructure in rural and underserved urban areas to promote equitable EV adoption.
... Providing electric mobility solutions to diverse communities can lead to a more inclusive environmental movement, with the advantages of reduced emissions and sustainable living shared by everyone (Pan et al. 2021). This underscores the significance of equitable planning for EV charging infrastructure (Li et al. 2022), especially as the United States embarks on a new era of EV planning bolstered by substantial governmental grant support (Carlton and Sultana 2024). ...
The transportation sector significantly contributes to greenhouse gas emissions, highlighting the need to transition to Electric Vehicles (EVs) to reduce fossil fuel dependence and combat climate change. The US government has set ambitious targets for 2030, aiming for half of all new vehicles sold to be zero-emissions. Expanding EV charging stations is crucial for this transition, but social equity presents a significant challenge. The Justice40 program mandates that at least 40% of benefits be allocated to disadvantaged communities, ensuring they benefit from federal investments. Given the current concentration of EV ownership in affluent areas, merely installing charging stations in disadvantaged neighborhoods may not suffice. This article explores crowdfunding as a novel method to finance EV charging infrastructure, engaging, and empowering underserved communities. The paper concludes with a hypothetical case showing financing benefits for disadvantaged communities, exploring crowdfunding variations, and scaling to develop equitable EV charging networks.
Achieving sustainable development goals requires transformative approaches, with transportation offering significant decarbonization potential. While electric vehicle (EV) research has grown rapidly, early studies focused on bibliometric trends or impact of policies on EV adoption, neglecting the reverse dynamic—how research shapes policy. This paper fills this gap by analyzing how EV research influences policy using a methodological framework. A dataset of 44,246 Scopus articles, cross-referenced with Overton database quantifies the policy impact of EV research, identifying key contributors such as influential journals (Transportation Research Part A & D, Energy Policy), authors, and countries. Our findings reveal that articles on cost comparisons, environmental metrics, and integration solutions hold significant policy influence despite low traditional citation counts. Topic modeling highlights policy resonance in studies on battery technology, emission reduction, and grid integration. This work advocates for multi-metric approaches, combining bibliometric and policy impact assessments, to advance science-policy studies and drive societal change.
This study investigates disparities in Electric Vehicle (EV) adoption among different socio-economic groups and geographic areas within Texas. We focused on the Texas Triangle, which includes Austin, Houston, San Antonio, and the Dallas-Fort Worth metropolitan area. Using EV registration data, our study applied unsupervised machine learning techniques, specifically hierarchical clustering analysis, to identify distinct patterns of EV adoption. We conducted a longitudinal analysis to investigate changes in carmakers’ market share and to understand EV adoption patterns over time. We used Anselin Local Moran’s I analysis to profile the characteristics of Tesla owners. Our analysis results revealed significant market segmentation in the EV market, particularly between different sale price ranges and brands. Notably, Tesla emerged as a market leader with strong brand appeal in the mid to high price tiers. EV adoption has been spreading from the Texas Triangle. Clusters with overall high EV registration, as identified in Anselin Local Moran’s I analysis, were more associated with high-income, highly educated communities, where residents own multiple vehicles and are predominantly White. This trend was also observed among Tesla owners in relative to low-tier EV adopters. The mismatch exists between the density of registered EVs and the availability of both public and Tesla charging stations, with a notable disparity between urban centers and outskirts. More importantly, we highlight the reluctance of disadvantaged groups to purchase EVs, regardless of price or brand, underscoring the need for policy interventions that address distributive fairness. Current federal incentives and local rebate programs should better serve disadvantaged communities to promote equitable EV adoption.
A suitable market structure is critical for promoting the healthy, stable, and sustainable development of the electric vehicle market. This paper studies which market structures can best contribute to the development of electric vehicles at various stages. The main findings show that competition is the optimal market structure at each stage if the charging infrastructure layout aims to maximize the number of electric vehicles. Moreover, when the charging infrastructure layout follows profit maximization in the early stages, monopoly is discovered as the optimal charging market structure, but competitive structures would be optimal after the market penetration of electric vehicles reaches some degree. Given these findings, we propose a public-private partnership (PPP) scheme to increase the social acceptance of electric vehicles. Next, apply these models in China and find that an optimal market structure could additionally drive about 30 % of electric vehicle penetration. Despite the PPP scheme's risk of delaying electric vehicle diffusion by 4 years and extending exclusive management rights by 3 years, it only reduces electric vehicle final adoption by 6.56 %. The findings can provide strong theoretical support and policy guidance for authorities in charging market planning and governance to accelerate electric vehicle diffusion and the industry's sustainable development.
In realizing seamless mass adoption of electric vehicles (EVs), the government plays a vital role by framing and implementing various EV related policies. These policies, if framed adequately, play a key role in achieving electrification of transportation in States. The literature review suggests that there is a pressing need to develop a unified method to analyze the EV policy of a State and determine its effectiveness as well as key interventions to strengthen EV policy. Therefore, to determine the quality and the effectiveness of EV policies, a quantitative analysis of EV policies by using the Hamming distance and the L1 norm (Manhattan distance) has been proposed in this paper. The policies are analyzed based on essential interventions to promote and strengthen electric mobility. This paper proposes an L1 norm-based EV policy analysis that assigns a remoteness score to indicate the status of the existing EV policy and its implementation when compared to the ideal EV policy. The paper proposes an approach that is based on three methods to improve the remoteness score: cost of intervention implementation, benefit of the intervention to the EV ecosystem, and the number of move/steps that are required to reduce the remoteness score. The proposed approach as applied to various Indian State EV policies has identified the States whose EV policies have lower remoteness score, thereby highlighting the States having relatively better EV policies. The analysis of these methods for optimal intervention selection when implemented for various State EV policies shows that the intervention implementation cost-based method is the most economical one. This paper formulates a linear optimization problem with the objective of intervention-implementation cost minimization to develop an economical roadmap to reduce the remoteness score. Recommendations for EV policy makers and stakeholders using an optimization based quantitative analysis of EV policies in India are also presented in this paper.
With the increasing demand for electric vehicle public charging infrastructure (EVPCI), optimizing the charging network to ensure equal access is crucial to promote the sustainable development of the electric vehicle market and clean energy. Due to limited urban land space and the large-scale expansion of charging infrastructure, determining where to begin optimization is the first step in improving its layout. This paper uses a multidimensional assessment framework to identify spatial disparities in the distribution of EVPCI in Nanjing Central Districts, China. We construct a scientific evaluation system of the public charging infrastructure (PCI) layout from four spatial indicators: accessibility, availability, convenience, and affordability. Through univariate and bivariate local indicators of spatial autocorrelation (LISA), the spatial agglomeration pattern of the EVPCI service level and its spatial correlation with social factors are revealed. The results of this study not only identify areas in Nanjing where the distribution of PCI is uneven and where there is a shortage but also identify areas down to the community level where there are signs of potential wastage of PCI resources. The results demonstrate that (1) urban planners and policymakers need to expand the focus of PCI construction from the main city to the three sub-cities; (2) it is necessary to increase the deployment of PCI in Nanjing’s old residential communities; and (3) the expansion of PCI in Nanjing must be incremental and optimized in terms of allocation, or else it should be reduced and recycled in areas where there are signs of resource wastage. This study provides targeted and implementable deployment strategies for the optimization of the spatial layout of EVPCI.
Transportation systems play a pivotal role in facilitating access to out-of-home activities, enabling participation in various aspects of social life. But because of budgetary and physical limitations, they cannot provide equal access to all locations; inevitably, some places will be better served than others. This realization gives rise to two fundamental concerns in transportation equity: 1) accessibility inequality and 2) accessibility poverty. Accessibility inequalities may rise to the level of injustice when some socioeconomic groups systematically have lower access to opportunities than others. Accessibility poverty occurs when people are unable to meet their daily needs and live a dignified, fulfilling life because of a lack of access to essential services and opportunities. In this paper, we review two of the most widely used approaches for evaluating transport justice concerns with accessibility inequality and accessibility poverty: Gini coefficients/Lorenz curves and needs-gap/transit desert approaches, respectively. We discuss how their theoretical underpinnings are inconsistent with egalitarian and sufficientarian concerns in transport justice, and show how the underlying assumptions of these methods and their applications found in the transportation equity literature embody many previously unacknowledged limitations that severely limit their utility. We substantiate these concerns by analysing the equity impacts of Covid-19-related service cuts undertaken in Washington, D.C. during 2020. The paper also discusses how alternative methods for measuring transportation equity both better comport with the known impacts of such changes and are consistent with underlying moral concerns.
Switching to electric vehicles (EVs) has increased rapidly over recent years. This paradigm change provides an important pillar in the United States transport sector to reach sustainability goals. EVs rely on a network of charging locations to operate. This study analyses the spatial distribution, accessibility and usage patterns of the public EV infrastructure in the US. First, using a negative binomial regression model, the influence of socio-economic and other factors on the abundance of EV charging locations in a state is investigated. Second, analysis of the network’s use and of service areas generated around charging locations provides insight into the accessibility of these stations to populations living in urban and rural areas. Third, the study compares publicly available datasets on the EV charging infrastructure provided by different companies in the Miami urbanized area, and lastly, it analyses real-time data from the SemaConnect charging network. Results indicate increased access of residents to the EV charging infrastructure over the years. Economic activity, highway density and political preference were statistically associated with the number of charging stations. Charging behaviour was found to follow the patterns of a regular workday, indicating that EV owners rely primarily on the public infrastructure as opposed to charging their vehicles only at home.
The world is running out of time to avoid cataclysmic climate impacts. Therefore, determining which decarbonisation strategies are more effective and inclusive in reducing anthropogenic dependency on fossil fuels is vital for governments’ decisions on investment. This research argues that the electrification of private automobility is neither effective nor equitable. Considering the current electricity mix of the grid, this electrification merely shifts the CO2 emissions and other pollutants from urban to rural areas. The strategy of private automobility electrification does not look beyond the problem of tailpipe emissions and hence cannot eliminate the deficiencies of the car-dependent system that require system-wide solutions, such as traffic congestion and road accidents. Prioritising this strategy not only maintains existing inequities but also increases social injustice and
delays the implementation of more effective interventions. We argue that using private EVs structurally violates the biosphere and human communities in three ways: (1) production of inequities, (2) pollution and waste, and (3) the space of the exception (the ‘Electric Vehicle Bubble’). Finally, we conclude that eradicating private automobility is necessary to realise climate and transport justice. Focusing on inclusive strategies, such as supporting public transportation, shared mobility, and active travel modes, instead of offering incentives for EVs, are the means of progressive redistribution of wealth and can satisfactorily meet people’s basic needs and governmental climate targets.
Background
Mapping geographical accessibility to health services is essential to improve access to public health in sub-Saharan Africa. Different methods exist to estimate geographical accessibility, but little is known about the ability of these methods to represent the experienced accessibility of the population, and about the added-value of sophisticated and data-demanding methods over simpler ones. Here we compare the most commonly used methods to survey-based perceived accessibility in different geographical settings.
Methods
Modelled accessibility maps are computed for 12 selected sub-Saharan African countries using four methods: Euclidean distance, cost-distance considering walking and motorized speed, and Kernel density. All methods are based on open and large-scale datasets to allow replication. Correlation coefficients are computed between the four modelled accessibility indexes and the perceived accessibility index extracted from Demographic and Health Surveys (DHS), and compared across different socio-geographical contexts (rural and urban, population with or without access to motorized transports, per country).
Results
Our analysis suggests that, at medium spatial resolution and using globally-consistent input datasets, the use of sophisticated and data-demanding methods is difficult to justify as their added value over a simple Euclidian distance method is not clear. We also highlight that all modelled accessibilities are better correlated with perceived accessibility in rural than urban contexts and for population who do not have access to motorized transportation.
Conclusions
This paper should guide researchers in the public health domain for knowing strengths and limits of different methods to evaluate disparities in health services accessibility. We suggest that using cost-distance accessibility maps over Euclidean distance is not always relevant, especially when based on low resolution and/or non-exhaustive geographical datasets, which is often the case in low- and middle-income countries.
Background
Hospital length of stay (LOS) is a key indicator of hospital care management efficiency, cost of care, and hospital planning. Hospital LOS is often used as a measure of a post-medical procedure outcome, as a guide to the benefit of a treatment of interest, or as an important risk factor for adverse events. Therefore, understanding hospital LOS variability is always an important healthcare focus. Hospital LOS data can be treated as count data, with discrete and non-negative values, typically right skewed, and often exhibiting excessive zeros. In this study, we compared the performance of the Poisson, negative binomial (NB), zero-inflated Poisson (ZIP), and zero-inflated negative binomial (ZINB) regression models using simulated and empirical data.
Methods
Data were generated under different simulation scenarios with varying sample sizes, proportions of zeros, and levels of overdispersion. Analysis of hospital LOS was conducted using empirical data from the Medical Information Mart for Intensive Care database.
Results
Results showed that Poisson and ZIP models performed poorly in overdispersed data. ZIP outperformed the rest of the regression models when the overdispersion is due to zero-inflation only. NB and ZINB regression models faced substantial convergence issues when incorrectly used to model equidispersed data. NB model provided the best fit in overdispersed data and outperformed the ZINB model in many simulation scenarios with combinations of zero-inflation and overdispersion, regardless of the sample size. In the empirical data analysis, we demonstrated that fitting incorrect models to overdispersed data leaded to incorrect regression coefficients estimates and overstated significance of some of the predictors.
Conclusions
Based on this study, we recommend to the researchers that they consider the ZIP models for count data with zero-inflation only and NB models for overdispersed data or data with combinations of zero-inflation and overdispersion. If the researcher believes there are two different data generating mechanisms producing zeros, then the ZINB regression model may provide greater flexibility when modeling the zero-inflation and overdispersion.
The current increase of electric vehicles in Germany requires an adequately developed charging infrastructure. Large numbers of public and semi-public charging stations are necessary to ensure sufficient coverage. To make the installation worthwhile for the mostly private operators as well as public ones, a sufficient utilization is decisive. An essential factor for the degree of utilization is the placement of a charging station. Therefore, the initial site selection plays a critical role in the planning process. This paper proposes a charging station placement procedure based on real-world data on charging station utilization and places of common interest. In the first step, we correlate utilization rates of existing charging infrastructure with places of common interest such as restaurants, shops, bars and sports facilities. This allows us to estimate the untapped potential of unexploited areas across Germany in a second step. In the last step, we employ the resulting geographical extrapolation to derive two optimized expansion strategies based on the attractiveness of locations for electric vehicle charging.
Land use mixing, balanced land uses, and transportation accessibility have previously been indicated as significant impactors of travel behavior, yet this relationship has not been examined in the EVSE accessibility literature. Using an application of the unsupervised machine learning (ML) clustering algorithm Density-based spatial clustering of applications with noise (DBSCAN), this research identifies 34 areas of spatially clustered level-1, level-2, and DC Fast EVSE charging infrastructure in the Chicago Metropolitan Area. Results indicate that charging access is imbalanced across suburban and urban communities and much of this disparity can be tied to EVSE clustering and its associated land use regimes in the metropolitan area. The majority of EVSE clusters are comprised primarily of level-2 charging and exist in isolated commercial developments to the affluent north and west of the city. Only 26% of clusters are associated with mixed land uses that occur in higher-income dense neighborhoods such as Evanston. Level-3 charging forms a smaller proportion of clustered charging across the region but is primarily unclustered. From a travel behavior perspective, this research highlights a need for a wider abundance of public fast charging options for lower socioeconomic communities and not merely utilitarian charging allocations that perpetuate accessibility for the wealthy.
Many governments have supported the introduction of electric vehicles (EVs) through purchase subsidies or waiving fuel taxes. The key findings of this paper are that the benefits of EVs may have been overstated, at least for some countries, as their energy savings and climate mitigation advantages depend on such factors as annual kilometres travelled per vehicle, electricity fuel mix, vehicle size and even local conditions. Because serious climate change has already arrived in the form of increasing frequency and severity of extreme events, we do not have the decades required for electricity production to be predominantly from non-carbon sources. Further, there are a variety of other challenges facing private transport—EVs just as much as conventionally powered vehicles. These include traffic casualties, non-engine air and noise pollution, light pollution, land requirements for roads and parking, and the intrusion of roads into natural habitats. With the promotion of EVs, these other transport problems run the risk of being downplayed. If all the environmental challenges facing road vehicles are to be effectively and quickly tackled, significant reductions in road vehicular travel are needed.
Electric vehicles (EV) are an emerging mode of transportation that has the potential to reshape thetransportation sector by significantly reducing carbon emissions thereby promoting a cleaner environment and pushing the boundaries of climate progress. Nevertheless, there remain significant hurdles to the widespread adoption of electric vehicles in the United States ranging from the high cost of EVs to the inequitable placement of EV charging stations (EVCS). A deeper understanding of the underlying complex interactions of social, economic, and demographic factors which may lead to such emerging disparities in EVCS placements is, therefore, necessary to mitigate accessibility issues and improve EV usage among people of all ages and abilities. In this study, we develop a machine learning framework to examine spatial disparities in EVCS placements by using a predictive approach. We first identify the essential socioeconomic factors that may contribute to spatial disparities in EVCS access. Second, using these factors along with ground truth data from existing EVCS placements we predict future ECVS density at multiple spatial scales using machine learning algorithms and compare their predictive accuracy to identify the most optimal spatial resolution for our predictions. Finally, we compare the most accurately predicted EVCS placement density with a spatial inequity indicator to quantify how equitably these placements would be for Orange County, California. Our method achieved the highest predictive accuracy (94.9%) of EVCS placement density at a spatial resolution of 3 km using Random Forests. Our results indicate that a total of 74.18% of predicted EVCS placements in Orange County will lie within a low spatial equity zone – indicating populations with the lowest accessibility may require the highest investments in EVCS placements. Within the low spatial equity areas, 14.86% of the area will have a low density of predicted EVCS placements, 50.32% will have a medium density of predicted EVCS placement, and only 9% tend to have high EVCS placements. The findings from this study highlight a generalizable framework to quantify inequities in EVCS placements that will enable policymakers to identify underserved communities and facilitate targeted infrastructure investments for widespread EV usage and adoption for all.
While U.K. authorities have attempted to tailor measures to boost sales of electric vehicles (EVs) and support citizens through different schemes, the size and geographic coverage of the existing charging network are insufficient, which undermines electromobility promotion. There are 15,853 public charging points installed in the U.K. as of 3 August 2021, and the demands for public EV charging are rising. For rural areas, there is little support from local authorities or private companies. To identify how a charging station can be installed and work, this study researches existing charging stations nationwide. Generally, most Public Charging Stations (PCS) in rural areas have unsatisfactory cost-effectiveness due to their long payback period. This paper presents how many rural PCS are able to afford the cost in the first eight years. Based on the ever-increasing demands of the market, EV producers are switching their business strategies. Meanwhile, the rural areas may become urban with the same definition. When it comes to the analysis of cost-effectiveness, it is possible for the PCS to bring more elements into the calculation. For Capital Expenditure (CAPEX) and Operation Expenditure (OPEX), the unnecessary cost leaves more profit space, like the possibility of unplanned maintenance costs.
If coupled with a low-carbon electricity mix, electric vehicles (EVs) can represent an important technology for transport decarbonization and local pollutants abatement. Yet, to ensure large-scale EVs adoption, an adequate charging stations network must be developed. This paper provides the first comprehensive bottom-up analysis of the EV charging network in Europe. Combining a crowd-sourced database of charging stations with accessibility data and algorithms, we produce maps of the travel time to the most accessible EV charging station across Europe, we evaluate the charging points density and the number of active operators in different areas. We find that although recent years have witnessed a notable expansion of the EV charging network, stark inequalities persist across and within countries, both in terms of accessibility and of the charging points available to users. Our results allow for a better understanding of some of the key challenges ahead for ensuring mass EVs adoption throughout Europe and thus potentially reducing the environmental impact of the transport sector.
By definition, equity is concerned with justice. On a societal level, equity is concerned with the just distribution of resources in society. Because a wide range of theories of just distribution exist, equity considerations are multifaceted and create a normative conceptual space in which theories can be considered, argued, and applied. In the past few decades, the concept of equity has received increasing attention within the transportation literature, both within academic journals and practice-oriented books and reports. These works present various theories of justice, either implicitly or explicitly, within the context of transportation financing, investments, and service allocations. While explicit normative reviews as well as arguments have been presented, implicit applications and imprecise definitions of equity theories have largely obfuscated and over-simplified this expansive topic. Within a predominantly western, US and euro-centric context, this article uses concepts and theories from the fields of social psychology, philosophy, and economics to understand and clarify the concept of equity within the field of transportation.
This analysis finds that cost reductions in new electric vehicles (EVs) will lead to decreased used EV prices and cost parity with used gasoline vehicles for low-income households in the 2025-2030 time period.
Higher rates of depreciation for first owners of EVs will lead to larger benefits for lower-income second owners. By 2029, EVs will reach upfront price parity with the average vehicle purchased by a low-income household, less than two years after the average vehicle purchased by a high-income household. Currently, once accounting for fuel and other operating savings, some households in all income groups could save money by replacing at least one vehicle with an EV; this increases to 45% of households by 2025 and 95% of households by 2030.
Savings from EVs relative to income are significantly higher for low-income households, non-White households, and households in areas with higher levels of pollution. For car owners in the lowest-income quintile, savings from switching to EVs amount to $1,000 per household annually, or 7% of income, by 2030.
Even with widespread EV affordability, additional policy action would ensure equal access to EVs. Previous studies have shown that low-income EV buyers are more responsive to incentives, and that purchase incentives have become more important over time. Combustion vehicle phase-out regulations can force manufacturers to serve diverse markets, and broader access to financing for EVs will be critical for low-income households. In addition, policymakers will need to ensure targeted deployment of home and public charging deployment to support vulnerable communities and renters with less charging access.
Widespread electric vehicle (EV) adoption is crucial for achieving California’s climate goals. The inclusion of marginalized populations in this process is important and will require that they have access to charging infrastructure. Public EV charging stations may help reduce the EV adoption barriers affecting these populations. This study combines public charging station location data with American Community Survey data at the census block group level in California, finding that public charger access is lower in block groups with below-median household incomes and in those with a Black and Hispanic majority populations. These public charger access disparities are more pronounced in areas with a higher proportion of multi-unit housing, where they are critical for EV operation due to a lower likelihood of residential charger access. Controlling for distance to the nearest highway or freeway, multi-unit housing unit rate, and median household income, we find that Black and Hispanic majority block groups are the only race and ethnicity group that is significantly less likely to have access to any public charger in their block groups compared to the rest of the state. The odds of having public charger access for the group is 0.7-times that of the no majority reference group. The access gap is even larger for the publicly-funded charging stations where Black and Hispanic majority block groups are approximately half as likely as the no-majority reference group to have access. Hence directing a larger portion of the funding to underserved communities and further government involvement in filling the public charger access gap can be crucial in achieving widespread and equitable EV adoption.
Access to healthcare is a requirement for human well-being that is constrained, in part, by the allocation of healthcare resources relative to the geographically dispersed human population1–3. Quantifying access to care globally is challenging due to the absence of a comprehensive database of healthcare facilities. We harness major data collection efforts underway by OpenStreetMap, Google Maps and academic researchers to compile the most complete collection of facility locations to date. Leveraging the geographically variable strengths of our facility datasets, we use an established methodology⁴ to characterize travel time to healthcare facilities in unprecedented detail. We produce maps of travel time with and without access to motorized transport, thus characterizing travel time to healthcare for populations distributed across the wealth spectrum. We find that just 8.9% of the global population (646 million people) cannot reach healthcare within one hour if they have access to motorized transport, and that 43.3% (3.16 billion people) cannot reach a healthcare facility by foot within one hour. Our maps highlight an additional vulnerability faced by poorer individuals in remote areas and can help to estimate whether individuals will seek healthcare when it is needed, as well as providing an evidence base for efficiently distributing limited healthcare and transportation resources to underserved populations both now and in the future.
Charging infrastructure development is vital for the adoption of electric vehicles (EVs). Yet, on the surface, there seems to be significant disagreement about when, how and which kind of charging infrastructure should be developed and most importantly, for what reasons. These reasons are concealed in the stakeholders’ perspective on the future. Differences in stakeholders’ perspectives regarding expectations on the future EV charging infrastructure may be expected, but should they prove irreconcilable they may stall the roll-out. However, to date, it remains unknown what these stakeholders’ perspectives are, how they are aligned across stakeholders, which topics are heavily debated and which are agreed upon. This study uses Q-methodology to identify different perspectives on the future of roll-out of EV charging infrastructure. The analysis shows that stakeholders mainly differ in the extent fast charging should play an important role, the degree smart charging should be the standard in charging and how much government should intervene with infrastructure roll-out. There is a consensus on the importance of interoperability of charging stations. The four different perspectives were supported across different stakeholders, which supports the idea that perspectives are not strongly linked to the stakeholders’ interests.
Transportation policies, plans, and projects all flow through state institutions because of the substantial cost of infrastructure and the need to assess transportation system performance, including equity implications. But environmental justice scholarship interrogates the state’s role in perpetuating injustice. Most research and planning practice related to transportation equity has relied upon state-sponsored analytical methods. Transportation planners and scholars can benefit from critical assessments of these approaches. We propose a shift in focus from transportation equity to a broader consideration of transportation justice that is more closely aligned with models of social change promulgated in the environmental justice literature and by related movements.
Within climate–energy–transport scholarship and professions there is a growing consensus that electric vehicles (EVs), which include personal cars, sport utility vehicles (SUVs), vans, and pickup trucks, are essential for decarbonizing mobility. This article urges caution and pause before an EV lock-in and calls on geographers and other scholars, professionals, and sustainability advocates to consider the multiscale environmental and social problems associated with EVs. The article begins by reviewing the mainstream assumptions about mass EV uptake, with particular emphasis on projections forecasting more, not fewer, cars in the future. Using a mobility justice framework, I ask who is making these assumptions and why and discuss the influence of liberal economic theory on future projections of EVs. I next consider assumptions about the environmental efficacy and decarbonization potential of mass EV uptake and review how EV production and consumption might escalate rather than reduce global resource and energy demand. I also scale down to cities and describe how EVs will lay claim to many of the same spaces designated for green mobility, such as cycle tracks, bus lanes, and compact, walkable spaces. The conclusion proposes research questions to consider with regard to EVs, future transportation, future geographies, and future carbon emissions.
The San Francisco Bay Area (Bay Area) leads the United States and California in the rate of electric vehicle (EV) adoption. However, EVs only represent 3% of vehicles driving on Bay Area roads. Widespread EV adoption requires that all Bay Area residents participate in the EV revolution regardless of demographics or geography. Equitable access to EVs will ensure that all Bay Area residents benefit from improved air quality, lower fuel and maintenance costs, and a better driving experience. Below, we delve into the unique EV market in the Bay Area and present information and insights from the Bay Area Air Quality Management District’s (Air District) EV programs.
Fast charging is seen as a means to facilitate long-distance driving for electric vehicles (EVs). As a result, roll-out planning generally takes a corridor approach. However, with higher penetration of electric vehicles in urban areas, cities contemplate whether inner-city fast chargers can be an alternative for the growing amount of slow public chargers. For this purpose, more knowledge is required in motives and preferences of users and actual usage patterns of fast chargers. Similarly, with increasing charging speeds of fast chargers and different modes (taxi, car sharing) also switching to electric vehicles, the effect of charging speed should be evaluated as well as preferences amongst different user groups. This research investigates the different intentions and motivations of EV drivers at fast charging stations to see how charging behaviour at such stations differs using both data analysis from charging stations as a survey among EV drivers. Additionally, it estimates the willingness of EV drivers to use fast charging as a substitute for on-street home charging given higher charging speeds. The paper concludes that limited charging speeds imply that EV drivers prefer parking and charging over fast charging but this could change if battery developments allow higher charging speeds.
Background:
The Malaria Atlas Project (MAP) has worked to assemble and maintain a global open-access database of spatial malariometric data for over a decade. This data spans various formats and topics, including: geo-located surveys of malaria parasite rate; global administrative boundary shapefiles; and global and regional rasters representing the distribution of malaria and associated illnesses, blood disorders, and intervention coverage. MAP has recently released malariaAtlas, an R package providing a direct interface to MAP's routinely-updated malariometric databases and research outputs.
Methods and results:
The current paper reviews the functionality available in malariaAtlas and highlights its utility for spatial epidemiological analysis of malaria. malariaAtlas enables users to freely download, visualise and analyse global malariometric data within R. Currently available data types include: malaria parasite rate and vector occurrence point data; subnational administrative boundary shapefiles; and a large suite of rasters covering a diverse range of metrics related to malaria research. malariaAtlas is here used in two mock analyses to illustrate how this data may be incorporated into a standard R workflow for spatial analysis.
Conclusions:
malariaAtlas is the first open-access R-interface to malariometric data, providing a new and reproducible means of accessing such data within a freely available and commonly used statistical software environment. In this way, the malariaAtlas package aims to contribute to the environment of data-sharing within the malaria research community.
Over the past decades, transport researchers and policy-makers have devoted increasing attention to questions about justice and equity. Nonetheless, there is still little engagement with theories in political philosophy to frame what justice means in the context of transport policies. This paper reviews key theories of justice (utilitarianism, libertarianism, intuitionism, Rawls’ egalitarianism, and Capability Approaches (CAs)) and critically evaluates the insights they generate when applied to transport. Based on a combination of Rawlsian and CAs, we propose that distributive justice concerns over transport disadvantage and social exclusion should focus primarily on accessibility as a human capability. This means that, in policy evaluation, a detailed analysis of the distributional effects of transport policies should take account of the setting of minimum standards of accessibility to key destinations and the extent to which these policies respect individuals’ rights and prioritise disadvantaged groups, reduce inequalities of opportunities, and mitigate transport externalities. A full account of justice in transportation requires a more complete understanding of accessibility than traditional approaches have been able to deliver to date.
The R package gdistance provides classes and functions to calculate various distance measures and routes in heterogeneous geographic spaces represented as grids. Least-cost distances as well as more complex distances based on (constrained) random walks can be calculated. Also the corresponding routes or probabilities of passing each cell can be determined. The package implements classes to store the data about the probability or cost of transitioning from one cell to another on a grid in a memory-efficient sparse format. These classes make it possible to manipulate the values of cell-to-cell movement directly, which offers flexibility and the possibility to use asymmetric values. The novel distances implemented in the package are used in geographical genetics (applying circuit theory), but also have applications in other fields of geospatial analysis.
Education researchers often study count variables, such as times a student reached a goal, discipline referrals, and absences. Most researchers that study these variables use typical regression methods (i.e., ordinary least-squares) either with or without transforming the count variables. In either case, using typical regression for count data can produce parameter estimates that are biased, thus diminishing any inferences made from such data. As count-variable regression models are seldom taught in training programs, we present a tutorial to help educational researchers use such methods in their own research. We demonstrate analyzing and interpreting count data using Poisson, negative binomial, zero-inflated Poisson, and zero-inflated negative binomial regression models. The count regression methods are introduced through an example using the number of times students skipped class. The data for this example are freely available and the R syntax used run the example analyses are included in the Appendix.
Available freely at: http://pareonline.net/getvn.asp?v=21&n=2
Socioeconomic inequalities among foreign-born populations in the United States in minor-emerging gateways are rarely discussed in existing inequality literature. This study helps to fill that gap by examining inequality and spatial integration among immigrant, US–born White, and Black populations in a minor gateway metropolitan area of Greensboro–High Point, NC. The 2020 Census five-year (2016–2020) estimate of American Community Survey (ACS) housing and demographic data, the location quotient (LQ), Lorenz curve, and Gini Index are utilized. The LQ values suggest immigrant groups are more concentrated with Black populations than White populations, but US–born Black and White populations are far more segregated than immigrant groups. Lorenz curve and Gini coefficients confirm that White populations experience the least inequality while the greatest income inequality exists among US–born Black populations, followed by immigrant populations. Further, disparities in income are greater for immigrants living in their underrepresented communities than those who live in overrepresented neighborhoods. The study concludes that immigrant populations living in segregated neighborhoods with a higher proportion of racial and ethnic minorities have lower education and English language competency than those living in neighborhoods with a higher percentage of US–born residents, and hence have lower income inequality among populations.
New tools can guide US policies to better target and reduce racial and socioeconomic disparities in air pollution exposure.
President Biden has committed to a “whole of government approach” to address environmental and climate justice, which includes directing resources to historically underserved and overburdened populations. The Justice40 program is one of the signature programs in these efforts, requiring that 40 percent of the benefits of designated programs be targeted to disadvantaged communities. Because many federal spending programs that are part of the Justice40 initiative involve the transfer of funds from federal agencies to state governments, the Biden Administration will need the assistance of state officials if the initiative is to achieve its stated objectives. In this article, we study early state implementation of Justice40 in the area of transportation, focusing on the federal highway program and the new National Electric Vehicle Infrastructure (NEVI) program. Our analysis of interviews with state officials and state NEVI plans reveals only modest differences between states in Justice40 implementation based on the partisanship of gubernatorial leadership, despite outspoken resistance to the initiative from many Republican governors. We also find that states that have made previous policy and institutional commitments to allocate resources in a manner similar to Justice40 are generally more receptive to this federal initiative.
Passenger vehicles are an essential form of transportation and contribute significantly to greenhouse gas emissions and criteria air pollution. The health and climate effects associated with their use disproportionately impact low-income communities and people of color. A shift from conventional vehicles to zero-emission vehicles is essential to meet climate targets and reduce inequities. The transition to clean transportation is an opportunity to uplift underserved and marginalized communities while building a sustainable transportation system. We assess justice in California’s transition to electric passenger vehicles by analyzing publicly available data on electric vehicle adoption and rebate use to measure justice in three areas: distribution of electric vehicles, allocation of state incentives, and the social and historical context of redlining. We find electric vehicle adoption and rebate use are lower in low-income and Latino-majority ZIP codes and in formerly redlined neighborhoods, indicating that California’s electric vehicle transition has not been just thus far.
The utilization of electric vehicle (EV) charging equipment is a key driver of charging station economics, but current trends and factors related to the utilization of public charging infrastructure in the United States are not well understood. This study analyzes EV charging data from 3,705 nationwide public Level 2 (L2) and direct current fast charging (DCFC) stations over 2.5 years (2019–2022), observing utilization patterns over time. Regression analysis is used to assess the relationships between station utilization and several contextual and environmental factors. We conclude that local EV adoption is a strong indicator of utilization; L2 station utilization decreases with the size of the local charging network, while DCFC stations are less affected; and increased charging power has a greater effect on utilization for DCFC stations than L2. This study fills a critical research gap by reporting updated public charging station utilization statistics and analysis for the U.S. market.
Many communities have been marginalised in the ongoing policy and planning debates surrounding transportation electrification, even though well allocated charging infrastructure is essential for the environmental and societal benefits of Electric Vehicles (EVs) to be realised. This scoping review aims to synthesise the current state of knowledge and gaps surrounding transportation equity in EV charging research. Following PRISMA-Scr protocols, a literature search is conducted to locate articles that explicitly or implicitly discuss EV charging equity. Our review finds that research on charging equity is nascent and lacking in clear normative evaluations of equity compared to the wider body of transportation equity literature. Only slightly more than one-in-four of an identified 37 articles discuss equity and justice explicitly. Equity perspectives in charging research are dominated by North American and European perspectives, with limited perspectives from the rest of the world. Charging incentivisation schemes and planning efforts may not be equity focused and may favour wealthier individuals, and there are differences in the charging needs and desires of high adoption groups compared to low adoption groups. These findings, however, often come from geographically and philosophically limited contexts and there are gaps in the literature for new methodological and topical contributions to this area. ARTICLE HISTORY
Supporting the adoption of zero-emission vehicle (ZEVs), including plug-in electric vehicles (EVs), has become a priority for governments due to their ability to reduce petroleum demand, improve air quality, and reduce carbon dioxide (CO2) emissions. Optimal strategies to accelerate EV adoption must weigh the relative value of alternative policy mechanisms to consumers, including public charging infrastructure and vehicle purchase subsidies. We use a historically validated light-duty vehicle consumer choice tool, the ADOPT model, to simulate personal light-duty vehicle adoption and related emissions in California. ADOPT is updated to incorporate a quantification of the tangible value of public charging infrastructure, allowing us to simulate the impact of investments in public charging infrastructure and vehicle purchase subsidies under different scenarios. We show that both policies result in increased EV adoption, with the most effective policy varying depending on vehicle technology assumptions. Under conservative technology improvement assumptions, infrastructure investments are most effective in promoting EV sales and reducing CO2 emissions, while under optimistic technology improvement assumptions a combination of infrastructure and subsidies best supports EV sales and CO2 emission reductions.
Background
In 2015, the Lancet Commission on Global Surgery proposed timely access to surgical care facilities as an indicator of preparedness in surgical systems. Since then, several African and South American countries have mapped the population coverage for their surgical facilities. However, these estimates are missing for India, despite its large population, geographical scope, and sociodemographic diversity. We conducted a nationwide analysis of timely access to surgical care in India and point to rural–urban disparities.
Methods
We extracted a nationwide dataset of 20 802 geolocated surgical care facilities from the IndoHealMap project. We obtained accessibility motorised friction surface raster data cropped for India from the Malaria Atlas Project Explorer. We calculated the travel times from each grid cell (equivalent to 1 km²) in India to its nearest surgical facility under the optimal speed estimation by means of the Djikstra least-cost algorithm. District-level and state-level rural and urban populations were estimated through raster-based analysis via data from WorldPop, Urban-Rural Catchment Areas, and GADM version 3.6. The primary endpoint of the analysis was the proportion of population within 2 h of their nearest surgical care facility at national, state, and district levels. Wilcoxon tests adjusted for multiple comparisons (Holm-Bonferroni correction) were used to investigate rural-urban differences at a 5% significance level.
Findings
The motorised travel-times distribution is highly right-skewed, depicting that a large number of areas in India were within 2 h of their nearest surgical facility. At the national level, 99·2% of the rural population had timely access to surgical care facilities, compared with 99·8% of the urban population. However, less than 80% of the rural population in the northern and northeast regions had timely access to care. The rural regions had a significantly smaller proportion of residents with timely access to surgical care compared with their and urban counterparts at the district level (n=1299, effect size=0·57; p<0·001) and state level (n=71, effect size=0·51; p<0·001) analyses.
Interpretation
Our findings should be considered upper-bound estimates for geographical access because they assume readily available motorised transport and optimal travel speeds, which might not always be the case. These first-ever estimates can inform India's National Surgical, Obstetric, and Anesthesia Plan and which locations should be focused on for upcoming surgical facilities.
Funding
No funding to declare.
Access to and affordability of electric vehicle (EV) charging infrastructure are the two prominent barriers for EV adopttion. While major efforts are underway in the United States to roll-out public EV charging infrastructure, persistent social disparities in EV adoption call for interventions. In this paper, we analyze the existing EV charging infrastructure across New York City (NYC) to identify such socio-demographic and transportation features that correlate with the current distribution of EV charging stations. Our results demonstrate that population density is not correlated with the density of EV chargers, hindering New York’s EV adoption and decarbonization goals. On the contrary, the distribution of EV charging stations is heavily skewed against low–income, Black–identifying, and disinvested neighborhoods in NYC, however, positively correlated to presence of highways in a zip code. The results underscore the need for policy frameworks that incorporate equity and justice in the roll-out of EV charging infrastructure.
Electric vehicles (EVs) are considered a substitute for fossil-fueled vehicles due to rising fossil fuel prices and accompanying environmental concerns, and their use is predicted to increase dramatically shortly. However, the widespread use of EVs and their large-scale integration into the energy system will present several operational and technological hurdles. In the energy industry, an innovative solution known as the EVs smart parking lot (SPL) is introduced to handle EV charging and discharging electricity and energy supply challenges. This paper investigates social equity access and mobile charging stations (MCSs) for EVs, where the owner of MCSs is the EV parking lot. Accordingly, a new self-scheduling model for SPLs is presented in this paper that incorporates scheduling of the MCSs as temporary charging infrastructures while considering social equity access and optimizes SPL energy generation and storage schedule. The main objectives of this research are to (i) develop MCSs accessibility measures and quantify the equity impacts of MCSs locations by modeling prioritized demand based on several indices; (ii) determine the optimal set-points of SPL components (i.e., combined heat and power (CHP), photovoltaic system, electrical and heat-energy storage, and MCSs) to manage electrical peak demand and to maximize the economic benefits of SPLs. Results indicate that the proposed demand prioritization function model can meet the required EV charging demands for prioritized events, and the self-scheduling model for SPLs satisfies the charging demand of the EVs in the SPL location. Also, the social equity access to the EV charging stations is satisfied by analyzing the operation of MCSs around the prioritized demand of the prioritized events and social equity access indices.
Vehicle electrification, automation, and shared mobility – also referred to as the transportation three revolutions (3Rs) – are the emerging trends in future mobility. This study performed a comprehensive integrated analysis to investigate the potential future development of passenger transportation in the United States. A technical-economic mobility model, a chemical transport model, and a health impact assessment tool were utilized. This study first adopted several assumptions for vehicle sales under the impact of the 3Rs, and made projections to 2050 for vehicle stocks, distance travel, energy use, and carbon dioxide (CO2) emissions. This study then quantified the impacts of changing emissions on concentrations of fine particulate matter and associated health benefits. Compared to a projected 2050 business-as-usual case, the wide use of electrification could lead to reductions of ~50% in petroleum consumption and ~75% in CO2 emissions, and obtain health benefits of 5500 prevented premature deaths, corresponding to $58 billion annually. The net energy impacts of automation are highly uncertain, and the improved efficiency from automation might be offset by an increase in travel. Sharing would bring additional benefits. The combination of the 3Rs could maximize the energy savings, carbon mitigations, and health benefits. The results of this study suggest that policies/incentives are needed to promote the transition from single-occupied conventional vehicles to shared electric vehicles.
This study introduces an integrated modeling framework to evaluate long-term national corridor charging infrastructure requirements in the United States to support the growing inter-city charging demand with the rapid growth in the battery electric vehicle (BEV) market. The core model is an optimization model that considers spatial and temporal dimensions and models heterogeneous behaviors between travelers. The model also introduces the travelers’ inconvenience cost function by linking travelers’ acceptance of the charging infrastructure with exogenous technology and social factors. The inconvenience cost function simulates mode choice between BEVs and alternative modes by heterogenous travelers. We applied the framework to assess the inter-regional charging infrastructure requirements for the entire U.S. mainland interstate highway network. We evaluated impacts on the infrastructure design and its public acceptance with changes in policy, technology, and demographic characteristics, and we also quantified the importance of modeling full-scale inter-regional charging infrastructure requirements compared to the conventional regional level analyses.
Transportation equity is a way to frame distributive justice concerns in relation to how social, economic, and government institutions shape the distribution of transportation benefits and burdens in society. It focuses on the evaluative standards used to judge the differential impacts of policies and plans, asking who benefits from and is burdened by them and to what extent. Questions of transportation equity involve both sufficientarian and egalitarian concerns with both absolute levels of wellbeing, transport-related poverty and social exclusion as well as with relative levels of transport-related inequalities. Ultimately, the study of transport equity explores the multiple channels through which transport and land use policies can create conditions for more inclusive cities and transport systems that allow different people to flourish, to satisfy their basic needs and lead a meaningful life. Transportation equity issues broadly encompass how policy decisions shape societal levels of environmental externalities and what groups are more or less exposed to them, as well as how those decisions affect the lives of different groups in terms of their ability to access life-enhancing opportunities such as employment, healthcare and education. Equity is a crucial part of a broader concern with transport and mobility justice. The call for transport justice goes beyond distributive concerns, and yet justice cannot be achieved without equity.
Access terminology is evolving since its inception by Hansen in 1959. Accounting accessibility is central to multiple disciplines such as geography, transportation, health, economics, social sciences, etc. Developing indicators to measure access is a common practice and usually favor specific dimensions of access based on application. Although measuring accessibility and developing related indicators is a common practice, there are missing links in the indicator development, planning process, its implementation and related policy-making. Due to many available indicators, each differing in context, the practicality of implementation and their transferability is generally lost. Current work focuses on extracting commonalities between indicators and understanding how the contextual focus of indicators’ have changed over time to measure access. Requirements for improved access related policies, developing realistic measures and future research directions based on gaps in the identified access measures are suggested.
The cost to charge an electric vehicle (EV) varies depending on the price of electricity at different charging sites (home, workplace, public), vehicle use, region, and time of day, and for different charging power levels and equipment and installation costs. This paper provides a detailed assessment of the current (2019) levelized cost of light-duty EV charging in the United States, considering the purchase and installation costs of charging equipment and electricity prices from real-world utility tariffs. We find national averages of 0.14/kWh for plug-in hybrid EVs in the United States. Costs, however, vary considerably (e.g., 0.27/kWh for battery EVs) for different charging behaviors and equipment costs, corresponding to a total projected fuel cost savings between 10,500 compared with gasoline vehicles (over a 15-year time horizon). Regional heterogeneities and uncertainty on lifetime vehicle use and future fuel prices produce even greater variations.
The successful market entry of plug-in electric vehicles (PEVs) is contingent on them being adopted by consumers, the first of which will be early adopters. The current understanding of these early adopters is based on small samples of PEV buyers gathered at one point in time. Here we present multi-year (2012–2017) questionnaire survey data on the socio-demographic profile of 11,037 PEV adopters in California. Latent class cluster analysis reveals four heterogeneous groups of PEV buyers. 49% are High income families, 26% Mid/high income old families, 20% Mid/high income young families, and about 5% are Middle income renters. Using the latent classes as input factors in Bass diffusion models we show that high income families may not continue to be the largest group of PEV adopters, while high income families are 49% of the PEV market today, they only represent 3.6% of California households. For market growth to continue the mid/high- and middle-income clusters need to begin adopting PEVs in greater numbers than they are doing today. Policymakers will need to consider the different needs these consumers have for infrastructure and incentives compared to high income families.
The collection of big data, as an alternative to traditional resource-intensive manual data collection approaches, has become significantly more feasible over the past decade. The availability of such data, coupled with more sophisticated predictive statistical techniques, has contributed to an increase in attention towards the application of these data, particularly for transportation analysis. Within the transportation literature, there is a growing emphasis on developing sources of commonly collected public transportation data into more powerful analytical tools. A commonly held belief is that application of big data to transportation problems will yield new insights previously unattainable through traditional transportation data sets. However, there exist many ambiguities related to what constitutes big data, the ethical implications of big data collection and application, and how to best utilize the emerging data sets. The existing literature exploring big data provides no clear and consistent definition. While the collection of big data has grown and its application in both research and practice continues to expand, there is a significant disparity between methods of analysis applied to such data. This paper summarizes the recent literature on sources of big data and commonly applied methods used in its application to public transportation problems. We assess predominant big data sources, most frequently studied topics, and methodologies employed. The literature suggests smart card and automated data are the two big data sources most frequently used by researchers to conduct public transit analyses. The studies reviewed indicate that big data has largely been used to understand transit users’ travel behavior and to assess public transit service quality. The techniques reported in the literature largely mirror those used with smaller data sets. The application of more advanced statistical methods, commonly associated with big data, has been limited to a small number of studies. In order to fully capture the value of big data, new approaches to analysis will be necessary.
This paper provides the first empirical analysis of the homeowner-renter gap for electric vehicles. Using newly-available U.S. nationally representative data, the analysis shows that homeowners are three times more likely than renters to own an electric vehicle. The gap is highly statistically significant, and remains even after controlling for income. For example, among households with annual income between 100,000, 1 in 130 homeowners owns an electric vehicle, compared to 1 in 370 renters. Additional controls do little to narrow the gap. The paper argues that this is a version of what economists have called the “landlord-tenant’’ problem, and briefly discusses potential policy implications.
For the first time in half a century, real transformative innovations are coming to our world of passenger transportation. The convergence of new shared mobility services with automated and electric vehicles promises to significantly reshape our lives and communities for the better—or for the worse.
The dream scenario could bring huge public and private benefits, including more transportation choices, greater affordability and accessibility, and healthier, more livable cities, along with reduced greenhouse gas emissions. The nightmare scenario could bring more urban sprawl, energy use, greenhouse gas emissions, and unhealthy cities and individuals.
In Three Revolutions, transportation expert Dan Sperling, along with seven other leaders in the field, share research–based insights on potential public benefits and impacts of the three transportation revolutions. They describe innovative ideas and partnerships, and explore the role government policy can play in steering the new transportation paradigm toward the public interest—toward our dream scenario of social equity, environmental sustainability, and urban livability.
Many factors will influence these revolutions—including the willingness of travelers to share rides and eschew car ownership; continuing reductions in battery, fuel cell, and automation costs; and the adaptiveness of companies. But one of the most important factors is policy.
Three Revolutions offers policy recommendations and provides insight and knowledge that could lead to wiser choices by all. With this book, Sperling and his collaborators hope to steer these revolutions toward the public interest and a better quality of life for everyone.
Electric vehicles are considered as one of the most effective technologies for reducing current greenhouse gas emissions from the transport sector. Although in many countries, local and national governments have introduced incentives and subsidies to facilitate the electric vehicle market penetration, in Sweden, such benefits have been limited. Results from a survey carried out among private owners of electric vehicles are presented in this paper, including the analysis of the respondents socio-demographic characteristics, reasons for choosing an electric vehicle, charging locations and driving preferences, among others. The main results characterize current electric vehicle drivers as male, well-educated, with medium-high income; electric vehicles are used mainly for private purposes and charged at home during night time. Furthermore, the paper presents an analysis of the impact of large-scale penetration of electric vehicles on existing power distribution systems. The findings presented in this paper provide important insights for assuring a sustainable large-scale penetration of electric vehicles by learning from the experiences of early adopters of the technology and by analyzing the impact of different EV penetration scenarios on the power distribution grid.