Vehicle electrification has attracted strong policy support in California due to its air quality and climate benefits from adoption. However, it is unclear whether these benefits are equitable across the state’s sensitive populations and socioeconomic groups and whether disadvantaged communities are able to take advantage of the emission savings and associated health benefits of electric vehicle (EV) adoption. In this study, we analyze the statewide health impacts from the reduction of on-road emissions reduction (from reducing gasoline powered cars) and the increase in power plant emissions (from EV charging) across disadvantaged communities (DACs) detected by using the environmental justice screening tool CalEnviroScreen. The results indicate that EV adoption will reduce statewide primary PM2.5 emissions by 24.02-25.05 kilotonnes and CO2 emissions by 1,223-1,255 megatonnes through 2045, and the overall monetized emission-related health benefits from decreased mortality and morbidity can be 2.52-2.76 billion dollars overall. However, the average per capita per year air pollution benefit in DACs is about $1.60 lower than that in the least 10% vulnerable communities in 2020, and this disparity expands to over $31 per capita per year in 2045, indicating that the benefits overlook some of the state's most vulnerable population, and suggesting clear distributive and equity impacts of existing EV support policies. This study contributes to our growing understanding of environmental justice rising from vehicle electrification, underscoring the need for policy frameworks that create a more equitable transportation system.
View the NCST Project Webpage
This study solicited information directly from decision-makers in private businesses operating fleets of medium- and heavy-duty trucks in California via interviews and pre-interview questionnaires. Additional interviews were conducted with truck manufacturers, consultants and other businesses providing services to the freight industry including leasing and auction. All these data were collected in 2021 and 2022. Fleet decision-makers describe what determines when and why they acquire and retire trucks and how they use those determinants. The purpose is to better understand vehicle turnover in the trucking sector. Direct contact with fleet decision-makers was preceded by a review of relevant literatures. This review helped in the design of joint questionnaires and interview protocols. Results are presented as 1) a set of determinants (internal to each fleet, external, and linking internal to external), 2) a typology based on decision-making structure, adaptation, and complexity, 3) case studies of decision-making types, 4) generalizations across fleets, and 5) extension to fleet consideration of alternative fuel trucks. One overarching conclusion is drawn: fleet truck turnover behavior varies widely—our highest-level abstraction—the typology—results in more than 20 types among 90 fleets allowing that some types involve mixed types of structure, adaptation, and/or complexity. Few fleets’ decision-making conforms to the commonly assumed model of total cost of ownership; many more do not. This report describes the varied ways fleets acquire and retire trucks, extends this to understand how this variety is already affecting freight fleets’ consideration of alternative fuel trucks, and poses questions as to how understanding this variety aids in promotion of zero-emission trucks.
The increasing diversity of vehicle type holdings and growing demand for BEVs and PHEVs have serious policy implications for travel demand and air pollution. Consequently, it is important to accurately predict or estimate the preference for vehicle holdings of households as well as the vehicle miles traveled by vehicle body- and fuel-type to project future VMT changes and mobile source emission levels. Leveraging the 2019 California Vehicle Survey data, this report presents the application of a utility-based model for multiple discreteness that combines multiple vehicle types with usage in an integrated model, specifically the MDCEV model. The model results suggest the important effects of household demographics, residence location, and built environment factors on vehicle body type and powertrain choice and usage. Further the predictions associated with changes inbuilt environment factors like population density can inform the design of land-use and transportation policies to influence household vehicle holdings and usage that can in turn impact travel demand and air quality issues in California.
This research developed EV Explorer 2.0, an online vehicle cost calculator (VCC) to meet the requirements of transportation network company (TNC) drivers considering acquiring an electric vehicle (EV). The tool was built to also support the needs of other users considering an EV, including other types of gig economy drivers as well as the general population of non-professional drivers. EV Explorer 2.0 includes several important features and functionalities to support the TNC driver use case that are not found in any other available tool: (1) It allows users to estimate TCO for used vehicles as well as new (others only estimate TCO for new vehicles); (2) Outputs include ridehail-driving income estimates, accounting for EV trip bonuses offered by Uber, net driving costs; (3) Estimates of total cost of driving (TCD) include charging network membership fees and charging session fees (in addition to electricity prices). It also includes key features found in other leading tools, such as presenting and tailoring EV purchase/lease incentive estimates (based on a database we developed), and innovative features to benefit all users, such asanimations conveying the social and environmental impacts of vehicle choice. Design features were informed and validated inuser testing with TNC drivers who had expressed interest in EV adoption.
Using a sample of approximately 7,000 California PEV drivers recruited from California Clean Vehicle Rebate Program applicants, two logistic regression models are specified to analyze responses by PEV lessees and purchasers to the question of what they would do in the absence of the federal tax credit. Possible responses include: purchase/lease the same PEV, switch to a different PEV, switch to a conventional or hybrid (non-plug in) vehicle, or not acquire a vehicle at all. Several key insights are found: higher discounts from the tax credit increase the probability of lessees indicating they would not lease a PEV at all. For purchasers, in addition to not purchasing any vehicle at all, the probability of purchasing a conventional vehicle, or another PEV also increase. These findings could have implications for California’s ability to reach its ZEV milestones and are important to consider due to recent changes to the US federal tax credit. Our findings indicate that many PEV adopters would likely not adopt their PEV without the tax credit, potentially more so for leased compared to purchased vehicles. There are also unique results for lessees related to the impact of home ownership. Renters are more likely than homeowners to lease a conventional vehicle than a PEV in the absence of the tax credit. This finding contributes to the literature which finds homeowners to be more likely to adopt a PEV than renters, emphasizing the importance of access to at-home charging for PEV adoption. These results show how incentives may be more influential for adoption decisions in the PEV lease market point to factors associated with consumers’ PEV adoption behavior in the absence of the federal tax credit.
