Are EVs Cleaner Than We Think? Evaluating Consequential Greenhouse Gas Emissions from EV Charging

TL;DR

The paper addresses the problem of quantifying consequential GHG emissions from large-scale EV adoption by accounting for induced changes in generation, storage, and transmission—captured via a capacity-expansion analysis of the WECC grid circa 2030. It contrasts long-run marginal emissions (LR-MER) with short-run marginal emissions (SR-MER) and average emission rates (AER), showing that SR-MER substantially overestimates emissions impacts while AER can misestimate depending on region; LR-MER is typically far lower due to capacity additions of low-carbon resources. The study also demonstrates that EV charging flexibility (8- and 24-hour) can unlock greater emission reductions, particularly under 24-hour scheduling that aligns with solar and storage deployments, and that optimizing charging signals around SR-MER may actually worsen long-term emissions. The findings highlight the value of capacity-expansion-aware metrics for evaluating EV climate benefits and suggest emissions-focused charging signals and workplace or daytime charging strategies to maximize reductions, rather than relying on crude price or SR-based cues.

Abstract

While electrifying transportation eliminates tailpipe greenhouse gas (GHG) emissions, electric vehicle (EV) adoption can create additional electricity sector emissions. To quantify this emissions impact, prior work typically employs short-run marginal emissions or average emissions rates calculated from historical data or power systems models that do not consider changes in installed capacity. In this work, we use an electricity system capacity expansion model to consider the full consequential GHG emissions impact from large-scale EV adoption in the western United States, accounting for induced changes in generation and storage capacity. We find that the metrics described above do not accurately reflect the true emissions impact of EV adoption-average emissions rates can either under- or over-estimate emission impacts, and short-run marginal emissions rates can significantly underestimate emission reductions, especially when charging timing is flexible. Our results also show that using short-run marginal emission rates as signals to coordinate EV charging could increase emissions relative to price-based charging signals, indicating the need for alternative control strategies to minimize consequential emissions.

Figures (8)

• Figure 1: Annual long-run marginal, short-run marginal, and average power system emission rates in WECC. For each zone, the marginal emission rates are calculated for WECC as a whole and based on a 5% EV adoption rate increase in each zone; the average emission rates are calculated for each zone based on the zonal emissions and demand. Results are based on the "Base" EV stock numbers (15.6 million EVs).
• Figure 2: Short-run ("SR") and long-run ("LR") marginal generation profiles for different technologies due to 5% EV demand perturbation under various charging flexibility levels for mid-electrification baseline case (15.6 million EVs). Positive (red) values indicate that the generation increased in that hour when demand was added, while negative (blue) values indicate that the generation was decreased in that hour when demand was added.
• Figure 3: WECC system-level marginal emission rates (a), marginal installed capacity (b), and marginal annual generation (c) of energy resources due to 5% EV demand increase (780,000EVs) in WECC under different EV charging flexibility levels .
• Figure 4: EV charging demand profiles (a and b) and the charging demand differences between the short-run marginal emission minimization ("Minimize SRME1&2") and long-run cost minimization cases ("Minimize LR-Costs") in WECC under 8- and 24-hour flexibility levels.
• Figure 5: Marginal emission rates and generation at the WECC level due to 3% demand increase in South California (S.CA) under the 24-hour flexibility scenario. The marginal emission rates and generation are after minimizing SRME1. (a) shows the distributions of short-run marginal emission rates and (b) shows marginal generation by technology.