The transportation sector accounts for a substantial portion of US greenhouse gas (GHG) emissions, with light-duty vehicles (LDVs) being the largest contributor. Decarbonizing the LDV sector is crucial for meeting national climate goals. This study addresses the critical need to quantify the combined effects of vehicle electrification and grid decarbonization on reducing LDV GHG emissions. The context is the ongoing transition towards electric vehicles (EVs) and the simultaneous efforts to decarbonize the electricity grid. The purpose is to provide a comprehensive assessment of the emission reductions achievable through these two major strategies, considering the complexities of state-level variations and potential material constraints. The importance stems from informing policy decisions and strategic planning for achieving ambitious emissions reduction targets. The study builds upon existing research on vehicle electrification and grid decarbonization but offers a more nuanced analysis by incorporating state-level variations in EV adoption rates and grid emissions intensities. This detailed approach provides a more realistic and accurate assessment of the potential emissions reductions achievable in the near and long term. Existing studies often focus on national averages, overlooking the significant heterogeneity across states in their EV adoption and grid carbon intensity profiles. This study acknowledges these variations, leading to a more precise understanding of the effectiveness of various decarbonization strategies. The findings will be vital for policymakers to effectively design and implement policies to optimize the transition to a lower-carbon transportation sector.

A significant body of literature exists on the potential for both vehicle electrification and grid decarbonization to reduce transportation emissions. Studies have independently modeled the effects of each strategy, but comprehensive analyses that integrate the two, accounting for regional variations, are comparatively scarce. Many studies focus on national averages, overlooking the substantial heterogeneity across US states in their energy mix and EV adoption rates. This lack of granular data affects the accuracy of predictions for emissions reduction potential. Previous research provides valuable insights into the lifecycle emissions of EVs versus internal combustion engine vehicles (ICEVs), the role of battery production, and the carbon intensity of electricity grids. However, a more holistic understanding is needed, integrating these aspects with state-level dynamics and considering the compounding effects of simultaneous advancements in both EV adoption and grid decarbonization. This review highlights the need for a state-level model that accounts for the regional variations in EV adoption and grid decarbonization. Such a model improves the accuracy of emission reduction estimations, providing a more realistic picture of the future emissions landscape. In addition, several studies have investigated the impacts of supplementary strategies, such as fleet turnover and vehicle miles traveled (VMT) reduction, these are considered in our analysis, extending beyond a singular focus on EV adoption and grid decarbonization.

This study employs a state-level model to assess the impact of vehicle electrification and grid decarbonization on US LDV GHG emissions. The model comprises two key components: a fleet model and an emissions model. The fleet model incorporates data on historical vehicle stock, projected vehicle sales, and vehicle scrappage rates to estimate the number of vehicles (by class, powertrain, and age) in each state over time. This model uses a logistic function to project future EV sales, calibrated to achieve a target of 50% EV sales nationally by 2030. Equation (1) describes the dynamics of vehicle stock in each state: N_y,p,c = N_y-1,p,c + Sales_y,p,c - Scrapped_y,p,c, where N represents the number of vehicles, y is the vehicle year, p represents the powertrain (ICEV or EV), and c represents the vehicle class (car or truck). The equations (2) through (8) describe how state-level initial conditions (percentages of electric cars and trucks in 2021) are estimated and used to calibrate the logistic growth model for future EV sales in each state. This methodology allows for a realistic representation of the spatial distribution of EVs and its impact on GHG emissions across the United States. The emissions model calculates use-phase emissions for each vehicle by multiplying the number of vehicles by the VMT (vehicle miles traveled) and the GHG intensity of travel. The GHG intensity of travel depends on vehicle powertrain (ICEV or EV) and is calculated using equations (10) and (11) taking into account fuel economy data from the VISION Model, grid emissions factors from NREL's Cambium models and vehicle production emissions from Woody et al. (2022). The model considers two grid decarbonization scenarios: business-as-usual and 95% decarbonization by 2035. The model incorporates data on fuel economy, grid emissions factors, and vehicle production emissions, allowing a comprehensive assessment of the life cycle emissions of both ICEVs and EVs. The model is calibrated to account for several factors, including variations in vehicle miles traveled based on vehicle age and class, differences in fuel economy between EV and ICEV models, and state-specific grid emissions intensities. Sensitivity analyses are conducted to assess the impact of changes in assumptions on the model's results. Specifically, a sensitivity analysis is performed to evaluate the impact of California’s Zero Emission Vehicle goal exceeding the federal target. Additionally, two early retirement policies (ER1 and ER2) are modeled to investigate the potential for accelerating the turnover of older vehicles and their associated emissions. Each scenario is tested with and without maintaining constant vehicle miles traveled.

The key findings of the study highlight the significant impact of both vehicle electrification and grid decarbonization on reducing LDV GHG emissions. Reaching a 50% EV sales target by 2030 would lead to a substantial decrease in emissions, with the magnitude of reduction depending on the rate of grid decarbonization. Under a business-as-usual grid scenario, emissions would decrease by 24% compared to 2005 levels, while a rapidly decarbonizing grid would result in a 26% decrease. The study demonstrates that the emissions reductions from these two factors are synergistic – their combined effect is greater than the sum of their individual impacts. In the 95% decarbonized grid scenario, a 50% reduction in LDV emissions from 2005 levels is achievable by 2035 with the 50% EV sales target. A large percentage of short-term emission reductions (80-87% in 2030, and 50-65% in 2035) result from fleet turnover, incorporating improvements in fuel economy and the replacement of older, less efficient vehicles with newer ones. While the model suggests slight underestimation of emissions savings from electrification in the short-term when using national average data instead of state-level data, this difference becomes negligible as the grid decarbonizes. The study also reveals that stricter state-level policies (as seen in states that adopted California’s ZEV standards) contribute marginally to national emission reductions. Analysis of early vehicle retirement policies indicates that accelerating the replacement of older vehicles could further enhance emission reduction, especially when combined with higher rates of EV adoption. However, the effectiveness of these early retirement strategies is dependent on the pathways implemented (constant vs. increased vehicle miles traveled). Battery material constraints are identified as a potential obstacle to rapid EV adoption; however, these constraints can be potentially alleviated through advancements in battery technology, increased recycling rates, and the use of alternative battery chemistries. The results underscore the need for integrated decarbonization strategies, rather than focusing on individual policies in isolation, to effectively mitigate the impact of the LDV sector on climate change.

The findings of this study offer crucial insights for policymakers seeking to effectively decarbonize the transportation sector. The synergistic effect of vehicle electrification and grid decarbonization highlights the importance of pursuing both strategies concurrently, maximizing their combined impact. The small difference observed between state-level and national modeling approaches suggests that while national-level models can provide useful estimations, state-level granular detail improves the accuracy of predictions, especially in the short-term. The relatively modest contribution of stricter state-level policies to national emission reductions emphasizes the significance of national-level regulations in driving widespread adoption of EVs. The study emphasizes the importance of integrated decarbonization strategies, involving a combination of vehicle electrification, grid decarbonization, and fleet turnover acceleration. This integrated approach can accelerate emissions reduction by capitalizing on the compounding effects of these different strategies. The identified battery material constraints highlight the need for research and development efforts aimed at improving battery technology, recycling, and exploration of alternative battery chemistries. The findings demonstrate the importance of considering various factors including vehicle miles traveled, early retirement strategies, and regional differences in policy implementation when designing and evaluating transportation decarbonization policies.

This study provides a comprehensive assessment of the combined effects of vehicle electrification and grid decarbonization on LDV GHG emissions. The findings underscore the synergistic benefits of these two strategies and highlight the importance of an integrated approach to achieve ambitious emissions reduction targets. While the analysis shows significant emission reductions are possible by 2035, challenges remain, particularly related to battery material constraints. Future research should focus on further refining state-level models to incorporate additional factors such as consumer behavior and the impacts of shared mobility services. Furthermore, investigation of the economic feasibility and social equity implications of different decarbonization pathways is warranted.

The model relies on several assumptions, including projections of future vehicle sales, grid decarbonization rates, and technological advancements in battery technology. Uncertainty in these projections could affect the accuracy of the emission reduction estimates. The model primarily focuses on use-phase emissions and does not fully capture the emissions associated with vehicle production and end-of-life management. While the study incorporates state-level variations, it does not account for all potential sources of regional heterogeneity, such as differences in driving patterns and consumer preferences. The analysis assumes a constant relationship between vehicle age and VMT, while in reality, this relationship could vary due to factors such as changes in infrastructure and transportation patterns. Finally, the model does not explicitly incorporate the potential impact of policy uncertainty and market volatility on EV adoption and grid decarbonization trajectories.