This study delves into the energy and emissions impacts of Shared Autonomous and Electric Vehicles (SAEVs) on disadvantaged communities in California. It explores the intersection of evolving transportation technologies—electric, autonomous, and shared mobility—and their implications for equity, energy consumption, and emissions. Through high-resolution spatial and temporalanalyses, this research evaluates the distribution of benefits and costs of SAEVs across diverse populations, incorporatingenvironmental justice principles. Our quantitative findings reveal that electrification of the vehicle fleet leads to a 63% to 71% decrease in CO2 emissions even with the current grid mix, and up to 84%-87% under a decarbonized grid with regular charging. The introduction of smart charging further enhances these benefits, resulting in a 93.5% - 95% reduction in CO2 emissions. However, the distribution of these air quality benefits is uneven, with disadvantaged communities experiencing approximately 15% less benefits compared to more advantaged areas. The study emphasizes the critical role of vehicle electrification and grid decarbonization in emissions reduction, and highlights the need for policies ensuring equitable distribution of SAEV benefits to promote sustainable and inclusive mobility.
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The path to transportation decarbonization will rely heavily on electric vehicles (EVs) in the United States. EV diffusion forecasting tools are necessary to predict the impacts of EVs on local energy demand and environmental quality. Few EV adoption models operate at a fine spatial scale and those that do still rely on aggregated demographic information. This adoption model is one of the first attempts to employ a synthetic population to examine EV distribution at a fine spatial and demographic scale. Using a synthetic population at the Census-Tract-level, enriched with household fleet body types and home-charging access, the researchers consider the effect of vehicle body type on EV spatial distribution and home-charging access in California. The project examines two EV body type mixes in a high electrification scenario where 8 million EVs are distributed across 6 million households in California: a “Small Vehicles” scenario where 6 million EVs are passenger cars and 2 million EVs are trucks, sport utility vehicles (SUVs), or vans and a “Large Vehicles” scenario with 4 million of each category. The authors find that an electrification scenario with more electric trucks and SUVs serves to distribute electrified households more evenly throughout the state, shifting them from urban to rural counties, while there is little impact on home-charging access.
To maximize the greenhouse gas (GHG) emission reduction potential of Battery Electric Vehicles (BEVs), it is critical to develop EV dynamic charging management strategies. These strategies leverage the temporal variability in emissions associated with generated electricity to align EV charging with periods of low-carbon power generation. This study introduces a deep neural network tool to enable BEV drivers to make charging sessions align with the availability of cleaner energy resources. This study leverages a Long Short-Term Memory network to forecast individual BEV vehicle miles traveled (VMT) up to two days ahead, using a year-long dataset of driving and charging patterns from 66 California-based BEVs. Based on the predicted VMT, the model then estimates the vehicle's energy needs and the necessity of a charging session. This allows drivers to charge theirvehicles strategically, prioritizing low-carbon electricity periods without risking incomplete journeys. This framework empowers drivers to actively contribute to cleaner electricity consumption with minimal disruption to their daily routines. The tool developed in this project outperforms benchmark models such as recurrent neural networks and autoregressive integrated moving averages, demonstrating its predictive capabilities. To enhance the reliability of predictions, confidence intervals are integrated into the model, ensuring that the model does not disrupt drivers' daily routine trips when skipping non-critical charging events. The potential benefits of the tool are demonstrated by applying it to real-world EV data, finding that if drivers follow the tool’s predictive suggestion, they can reduce overall GHG emissions by 41% without changing their driving patterns. This study also found that even charging in regions with higher carbon-intensity electricity than California can achieve Californian emission levels for EV charging in the short term through strategic management of non-critical charging events. This findingreveals new possibilities for further emissions reduction from EV charging, even before the full transition to a carbon-neutral grid.
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.
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.
