Literature Context for Light-Duty Vehicles

To contextualize the Transportation Annual Technology Baseline (ATB) vehicle data, this page presents Base Year and projections of light-duty vehicle cost and fuel economy from recent, publicly available literature sources. More information about the reviewed literature and methods for processing estimates for alignment with ATB data is included in the Reviewed Literature and Methodology section next.

Projection Literature Comparison

Reviewed Literature and Methodology

Base Year Overview

Base Year vehicle cost and fuel economy are collected from the U.S. Environmental Protection Agency's (EPA's) fueleconomy.gov. This website provides historical and current vehicle label MSRP and fuel economy based on EPA's real-world testing for national-average drivers and conditions (US EPA, 2024). Vehicle MSRP and fuel economy data are merged with historical sales (Experian, 2023) to indicate top-selling vehicles (size of bubbles in chart) and to calculate a national sales-weighted average value across all makes-models-trims for each powertrain and vehicle class to compare to ATB values from (Islam et al., 2023).

Note market average values reflective of earlier markets (e.g., battery electric vehicles) may vary from ATB values because of the higher sparsity of available makes and models for vehicle classes and powertrains. ATB estimates are meant to reflect a representative average vehicle, using consistent vehicle assumptions across powertrains and vehicle classes where applicable. Some nascent markets may be dominated by a limited number of makes and models, which skews market averages compared to values for a representative vehicle. As a result, in some cases market average values exhibit unintuitive, nonmonotonic behavior when looking across vehicle size classes or electric ranges: For example, market average values of cost for battery electric compact vehicle (200-mile range) are higher than for midsize and small SUVs and average fuel economy is lower because of the dominance of high-performance luxury vehicles in that category; in addition, the small SUV battery electric vehicle (200-mile range) has a higher cost than the battery electric vehicle (300-mile range). ATB values are intended to reflect greater consistency in relative modeled vehicle prices or fuel economy across vehicle categories.

Projection Literature Overview

Annual Energy Outlook (AEO) (EIA, 2023): Produced and monitored by the U.S. Energy Information Administration (EIA), AEO is a yearly report that integrates relevant laws and potential regulations to project the long-term progress of the U.S. energy sector. The source used was the most recent iteration of AEO, which uses the underlying National Energy Modeling System (NEMS) to estimate vehicle attributes based on fuel economy regulations, technology cost-effectiveness, and consumer preference.

Automotive Deployment Options Projection Tool (ADOPT) (Brooker et al., 2015): ADOPT is a National Renewable Energy Laboratory (NREL) model that integrates the Future Automotive Systems Technology Simulator to estimate light-duty vehicle technology attributes based on consumer choice data and technology improvements. Vehicle price projections are based on multinomial logit and mixed logit model methods to infer consumer choice and estimate vehicle characteristics of ideal on-market vehicles incorporating Corporate Average Fuel Economy (CAFE) regulations. Values presented in this literature review are based on runs performed for the U.S. Department of Energy-funded Transportation Decarbonization Analysis.

Assessment of Light-Duty Vehicles (LDV) EV Costs and Consumer Benefits in the U.S. Analysis from the International Council on Clean Transportation (ICCT) 2022 (Slowik et al., 2022): Assumptions used in the ICCT report are based on vehicle component data and manufacturing costs to estimate modeled vehicle prices. The analysis projects prices from 2022 to 2035. The vehicle cost analysis is generally based on similar analyses completed by ICCT, based on inputs from the National Highway Traffic Safety Administration (NHTSA) and other data sources.

Regulations for Emissions from Light-Duty Vehicles and Emissions from the Environmental Protection Agency 2024 (U.S. Environmental Protection Agency (EPA), 2024): To inform greenhouse gas (GHG) emission regulations for light-duty vehicles, EPA produced the Optimization Model for reducing Emissions of Greenhouse Gases from Automobiles (OMEGA), which analyzes vehicle costs and other benefits in relation to controlling vehicle emissions. The model outputs consist of aggregate vehicle makes and models that reflect policy, production assumptions, and consumer choice preferences. The dataset included in this literature review reflects modeling results to support cost-benefit analysis for the "Final Rule: Multi-Pollutant Emissions Standards for Model Years 2027 and Later Light-Duty and Medium-Duty Vehicles."

Mapping Projection Literature to ATB Vehicles

To make direct comparisons across vehicle classes, powertrains, and fuel economy information in the literature to ATB data, the following processing was required for data mapping:

The 2023 ATB includes only base/standard vehicles. Therefore, if a source includes luxury/premium vehicle estimates, those were excluded from the literature review.
Vehicles classes from the literature were mapped to ATB vehicle classes, as documented in the Data Download. Vehicle classes outside the ATB scope (e.g., two-seaters, mini compact, crossover vehicles) were excluded from the literature review dataset.
To map ranges of plug-in electric vehicles, literature estimates of battery electric vehicle (BEV) ranges and plug-in hybrid electric vehicle (PHEV) ranges were assigned to the closest BEV/PHEV range among the ATB vehicles.
EPA GHG rulemaking (U.S. Environmental Protection Agency (EPA), 2024) processing: Aggregate vehicles attributes were calculated based on the sales-weighted average values from the OMEGA model results. Fuel economy values for non-plug-in electric vehicles were estimated based on the reported on-road CO₂ emissions per mile, converted to miles per gallon gasoline equivalent using the EPA assumptions of grams of CO₂ per unit gasoline (8,887 grams of CO₂ per gallon gasoline). Charge-depleting fuel economy values for plug-in electric vehicles were adjusted to reflect EPA assumptions on charging efficiency.

References

The following references are specific to this page; for all references in this ATB, see References.

US EPA. “Fuel Economy Label Updates.” Data and Tools, 2024. https://www.epa.gov/recalls/fuel-economy-label-updates.

Experian. “Automotive Data & Auto Industry Trends | Experian Automotive,” 2023. https://www.experian.com/automotive/auto-data.

Islam, Ehsan Sabri, Daniela Nieto Prada, Ram Vijayagopal, Charbel Mansour, Paul Phillips, Namdoo Kim, Michel Alhajjar, and Aymeric Rousseau. “Detailed Simulation Study to Evaluate Future Transportation Decarbonization Potential.” Report to the US Department of Energy, Contract ANL/TAPS-23/3. Argonne National Laboratory (ANL), Argonne, IL (United States), October 2023. https://anl.app.box.com/s/hv4kufocq3leoijt6v0wht2uddjuiff4.

EIA. “Annual Energy Outlook 2023.” Washington D.C.: U.S. Energy Information Administration, March 16, 2023. https://www.eia.gov/outlooks/aeo/.

Brooker, Aaron, Jeffrey Gonder, Sean Lopp, and Jacob Ward. “ADOPT: A Historically Validated Light Duty Vehicle Consumer Choice Model,” 2015-01–0974, 2015. https://doi.org/10.4271/2015-01-0974.

Slowik, Peter, Aaron Isenstadt, Logan Pierce, and Stephanie Searle. “Assessment of Light-Duty Electric Vehicle Costs and Consumer Benefits in the United States in the 2022–2035 Time Frame.” International Council on Clean Transportation, October 2022. https://theicct.org/publication/ev-cost-benefits-2035-oct22/.

U.S. Environmental Protection Agency (EPA). “Optimization Model for Reducing Emissions of Greenhouse Gases from Automobiles (OMEGA).” Data and Tools. Regulations for Emissions from Vehicles and Engines, March 2024. https://www.epa.gov/regulations-emissions-vehicles-and-engines/optimization-model-reducing-emissions-greenhouse-gases.