Assessing the tradeoffs in emissions, air quality and health benefits from excess power generation due to climate-related policies for the transportation sector

As the transportation sector continues to decarbonize through electrification, there is growing interest in quantifying potential tradeoffs in air pollution and health impacts due to potential excess emissions from the power sector. This study investigates air pollution and health impacts of policy-driven changes in the transportation sector and the associated power generation demand in the Northeast and Mid-Atlantic United States. Five illustrative scenarios were designed to capture the effects of different policies under the first mandatory market-based program to reduce greenhouse gases in the US power sector (Regional Greenhouse Gas Initiative—RGGI) and the Transportation and Climate Initiative (TCI). Considering future power generation with new renewable energy investments to meet demands from decarbonized transportation, the scenarios were framed using: 1. 2030 reference cases for both sectors and a hybrid TCI portfolio, 2. Departure from the reference cases defined by Pennsylvania included or not in RGGI, and 3. Power grid emissions estimated under clean energy standard (CES) policy and hybrid TCI portfolio. While the cross-sectoral policy effect on domain-wide concentrations is modest (max ΔPM2.5 ∼ 0.06 μg m3, ΔNO2 ∼ 0.3 ppbv, ΔO3 ∼ 0.15 ppbv), substantial increases in Ohio and West Virginia were attributed to Pennsylvania joining RGGI. With CES enacted and Pennsylvania in RGGI, significant reductions are seen in average concentrations (max ΔPM2.5 ∼ 1.2 μg m3, ΔNO2 ∼ 1.1 ppbv, ΔO3 ∼ 1.7 ppbv) except for Louisiana and Mississippi with corresponding disbenefits. When focusing exclusively on emissions reductions from transportation, the hybrid TCI portfolio had health benefits of 530 avoided adult deaths, and 46 000 avoided asthma exacerbations. With a ‘business as usual’ power grid, these benefits remain comparable and are mainly driven by NO2, followed by PM2.5 and O3. However, if Pennsylvania joins RGGI, total health benefits and spatial distribution change substantially, with a large portion of adverse health impacts moving from TCI states to Ohio and West Virginia. The overall monetized impact of a CES scenario can substantially exceed the estimated average range of 66–69 Billion US$, depending on the interaction with transportation decarbonization strategies and other drivers of exposure.


Introduction
Globally, electrification of the transportation sector with decarbonized electricity is at the core of current strategies for carbon mitigation, with added potential reductions in air pollutants from the involved supply and demand sectors and associated public health co-benefits (Driscoll et al 2015, Peng et al 2018, Grubert 2021, Zhang et al 2021).Emissions from fossil fuel combustion from mobile sources and energy generation units (EGUs) contribute to the degradation of air quality through the formation of air pollutants such as fine particulate matter (PM 2.5 ), nitrogen dioxide (NO 2 ) and ozone (O 3 ) (Penn et al 2017, Zawacki et al 2018).Exposure to these pollutants and their precursors has been associated with adverse health outcomes, such as cardiovascular and respiratory diseases, neurological impacts, lung cancer, and increased premature mortality (Pope andDockery 2006, Jerrett et al 2009).As previous studies have demonstrated, the evolution inherent to these two key sectors adds complexity to the already difficult task of deriving the respective impacts of energy markets, state-level energy and pollution rules, and Clean Air Act regulations on air quality (Sharabaroff et al 2009).
In the United States, steady improvements have followed various regulations under the Clean Air Act Amendments along with other state and local emission control measures on mobile sources (Thomson et al 2018).Despite recent attempts to roll back regulations on coal-fired EGUs, the energy mix in electricity generation has changed dramatically in the past decade, moving away from coal and towards renewables and natural gas, along with progress in reducing emissions from vehicles (Millstein et al 2017, Buonocore et al 2019, Grubert 2021).Previous research has shown that this shift in electricity and transportation has had major positive CO 2 and regional air quality impacts (Bistline et al 2022).However, little research has focused on the consequences of concurrent changes in transportation and energy sector on public health and air quality.This is important because vehicle electrification is a major strategy for reducing the emissions from transportation.Vehicle electrification will increase electrical generation; if this generation is not met with renewables, it will be met with fossil-fueled EGUs (Buonocore et al 2022).This could result in increased air pollution emissions, potentially eroding public health gains and producing new environmental justice (EJ) issues, or exacerbating old ones around existing EGUs.Previous research has demonstrated that inequitable energy transition can inadvertently sustain such exposure disparities (Tessum et al 2019, Colmer et al 2020), putting at higher risk low-income populations, racial and ethnic minorities (Jbaily et al 2022).
Using air quality modeling systems such as the Community Multiscale Air Quality model (CMAQ), Penn et al (2017) reinforce the importance of pollutant, location, and source-specific models of health impacts in the design of health-risk minimizing emissions control policies, estimating 21 000 premature mortalities per year from EGU emissions in the U.S., mainly driven by sulfur dioxide emissions forming PM 2.5 .Kerl et al (2015) demonstrated the potential to reduce health impacts by applying a reduced form modeling system based on CMAQ with the decoupled direct method to a power system operational model.Similarly, Luo et al (2021) developed a modeling system to study the effect of energy storage and redispatch on air pollution from the power sector in Texas, concluding that infrastructure updates and strategic shifts in the location and timing of pollutant releases can lead to 61%-97% reduction of adverse health impacts.In the same tightly woven Texas grid, Luo et al (2022) have investigated the inability of current decarbonization strategies to address long-standing exposure inequities, concluding that changes are unlikely without impactful decisions on infrastructure, additional investments in renewable energy and the aging transmission network.While there is a strong body of research focusing on the power generation and transportation sectors independently, coupling the sectors has been proven to be challenging in spatial scales that come with nonuniform policies and a mix of energy transmission grids, with the only example available developed for the localized case of Taiwan (Lin et al 2020).The novelty of this study lies in the importance of assessing cross-sectional effects at a regional level by leveraging air quality modeling system capabilities to integrate with power market models and drive health impacts and monetized estimates of targeted policies.

Materials and methods
In the United States, initiatives such as the Regional Greenhouse Gas Initiative (RGGI) (RGGI 2023) and Transportation and Climate Initiative (TCI) (TCI 2023) were the first market-based programs to regulate emissions from EGUs and transportation sectors respectively.Within the participating RGGI states, fossil-fuel-fired electric power generators with a capacity of 25 megawatts or greater ('regulated sources') are required to hold allowances equal to their CO 2 emissions over a three-year control period.On the other hand, TCI focuses on the transportation sector through a cap-and-invest program, with participating states taking action through working groups focused on regional priorities, such as clean vehicles and fuels.Both refer to a region that contains the same 12 Northeast and Mid-Atlantic states including Connecticut (CT), Delaware (DE), Maine (ME), Maryland (MD), Massachusetts (MA), New Hampshire (NH), New Jersey (NJ), New York (NY), Pennsylvania (PA), Rhode Island (RI), Vermont (VT), Virginia (VA), and the District of Columbia (DC), TCI is a cap-and-invest program that evolves around policies targeting changes in the transportation fuel through a cap on CO 2 emissions.This cap was to have been steadily lowered through 2032 until it aligned with each juristiction's emissions reduction goal.The effect of the transportation policies on human health was previously established by the Transportation, Equity, Climate, and Health Study (TRECH-I) (Arter et al 2021).This work presents the second phase of the study (TRECH-II), where the focus shifts towards coupling associated changes in energy demand and future generation to help understand and relations to the decarbonization of both sectors at a regional scale.

Scenario design
The design of the modeled scenarios is based on the simultaneous creation of emission inventories for the power and transportation sectors.To capture the effect of transportation-related emissions for the year 2030, we relied on definitions and estimates from TRECH-I, starting with the TCI reference ('businessas-usual' (BAU)) option.The second transportation option reflects the policy scenario with the largest estimated health benefits due the most ambitious CO 2 emissions reduction cap (25%), and a hybrid investment portfolio focused on public transit, walking, and biking infrastructure within the TCI states (hereafter referred to as 'TCI 25% hybrid').
For the power sector, the area of interest involves multiple independent system operators: Pennsylvania-New Jersey-Maryland (PJM), New York (NYISO), and New England (ISO-NE).To capture the complexity and dynamic nature of energy demand, production, and carbon allowance trade, we relied on definitions and emission estimates from the Integrated Planning Model (IPM) application (ICF 2023) to simulate the market-based energy mixture evolution in the PJM/NYISO/ISO-NE regions for the next decade under the following conditions: As a result, the first condition manifests into a 'BAU' energy scenario with PA acting outside the RGGI policies and one with PA part of RGGI.The resulting IPM estimates for all conditions rely on the latest ISO load projections, 2020 natural gas prices (AEO average of Reference and High Resource), and updated state energy policies, including offshore wind resources.
The table in figure 1 shows the resulting matrix of combined sector scenarios: (B1) BAU energy scenario and TCI reference transportation, (B2) BAU energy scenario and TCI 25% hybrid transportation, (B3) energy scenario with PA in RGGI and TCI reference transportation, (B4) energy scenario with PA in RGGI and TCI 25% hybrid transportation, and (B5) CESs policy implementation and the TCI 25% hybrid transportation (TCI reference transportation scenario is irrelevant with CES enacted).

Domain and models
The modeling domain covers the eastern half of the U.S. with 12 km × 12 km horizontal grid cell resolution (figure 1), which captures both the 12 states previously considering signing on to the TCI, and the electricity markets of the Eastern Interconnect.We used output from the IPM to account for changes in the emission of pollutants related to the implementation of policies in the form of scenarios that reflect potential changes in technologies, infrastructure, pricing, and additional drivers.IPM provides true integration of wholesale power, system reliability, environmental constraints, fuel choice, transmission, capacity expansion, and all key operational elements of generators on the power grid in a linear optimization framework.The model captures a detailed representation of every electric boiler and generator in the power market being modeled.Uncertainties related to changes in the rate of electrification were not studied explicitly as such would require developing, coupling, and independently evaluating IPM with a regional electrical dispatch model.Detailed estimates of capacity and generation for each modeled scenario from IPM are presented in figure S1.The core of the emissions processing methodology is based on the 2016 National Emissions Inventory (NEI) modeling platform and the Sparse Matrix Operator Kernel Emissions (SMOKE) Modeling System (Baek and Seppanen 2021) with a new interface that links the two models (IPM-SMOKE) and maintains consistent inventory assumptions between them (i.e.consistent active and retired plants with stack information; for details see supplementary information section 1.2 and figure S1) using the National Electric Energy Data System and other proprietary databases.
Besides the customized preprocessor for the emissions model, air quality was simulated using a comprehensive modeling framework centered around the CMAQ (Appel et al 2021) with a detailed model flowchart presented in figure 2. Meteorological inputs were prepared using the Weather Research Forecasting (Skamarock et al 2019) for a wintertime (January) and a summertime (July) monthly episode with a spin-up time of two weeks.Air quality modeling simulations were then performed for each case using CMAQ v5.2.1 (US EPA Office for Research and Development 2018) and annual concentration estimates were derived.Following the methodology previously described by Buonocore et al (2023), and Arter et al (2021), the health impact analysis relies on the application of the BenMAPR tool to quantify the results of such policy-driven changes in air quality.Further information on how each of these models was configured for this study along with details on the steps to update related portions of NEI inventories can be found in the supporting information.

Emissions
Emissions estimates obtained for a range of different transportation sector scenarios (including the two options involved in this work) have been presented and discussed in the work of Arter et al (2021).Looking at the differences between the baseline and the '25% TCI' datasets for 2030, the EV load adds 5.2 TWh, or roughly 0.5%, to the combined PJM/NYISO/ISO-NE region.Generation in the region increases by 3.1 TWh by 2030 in response to the higher load while the remaining 2 TWh is met by imports from the Midcontinent ISO and the Southeast ISO into PJM.
Emission inputs for the energy sector were first generated using annual inventories from the 2016v2 NEI platform and projected to 2030 using the standard processing methodology distributed with the official release (Case A_baseline and A_proj2030).Case B relies on an updated 2016v2 EGU inventory to account for differences between 2016 and 2020 EGU units at the stack level, as these are the base years for the NEI and IPM underlying databases, respectively.This ensures that all removals of decommissioned units and additions of new units were accounted for and differences between NEI and the IPM inventories were harmonized at the state and facility level.The above set of cases allows for a direct comparison of typical NEI-derived emission estimates with IPMconstrained illustrative scenarios, as shown in table 1. NEI projections for 2030 revealed a strong decline in NO x and SO 2 levels.However, the overall trend is mixed for PM 2.5 , as emissions for this pollutant are influenced by a different stage in the transition from coal-burning plants, that in a future with increased energy demand are responding with higher utilization at the operator and state level.Therefore, within the NEI projections, the TCI region emission totals for PM 2.5 , VOCs, and NH 3 continued to increase.The 2020 NEI case that builds on the IPM inventory changes, demonstrates a trend where old units are decommissioned and replaced by cleaner facilities that leads to lower emissions for all pollutants.
The impact of each scenario varied for each pollutant and between states (see tables S3 and S4).Table S4 demonstrates that transportation-related changes with a BAU electrical grid resulted in a mixed effect with mostly reduced SO 2 and to a lesser degree PM 2.5 levels, and small increases for NH 3 , VOCs, and NO x in the TCI region (scenario B2-B1).As expected, the effect outside the TCI states was rather small but nonetheless resulted in uniformly less emissions.If Pennsylvania becomes a part of RGGI, there is a significant overall reduction in emissions (4.5%-19.4%)within TCI, and a slight decrease in all pollutant levels besides NO x outside the TCI states (scenario B3-B1).This effect gets slightly reduced when at the same time a 25% hybrid portfolio scenario for the transportation sector is in place (4.4%-17.9%),which translates to slightly higher (up to 0.6%) overall emissions outside TCI (scenario B4-B2).Isolating the effect of the transportation policy in an electrical grid with PA in RGGI, reveals small increases in most pollutants within TCI (0.2%-1.4%) except SO2 (−2.2%).Outside TCI, the effect remains similar, with a small reduction observed for NO x levels (Scenario B4-B3).Moving to describe the effect of employing a CES policy, we note the highest emission reductions outside the TCI domain (20%-37%), Table 1.Annual total EGU emission estimates in tons from the standard NEI 2016v2 platform inventory for the TCI region (A_Baseline) and its 2030 projection (A_2030proj), along with modified NEI inventories for 2020 (B_Baseline), mapped at the state level using IPM-driven future year scenarios (B1 to B5). independent of the transportation sector policy in place and whether Pennsylvania is part of RGGI or not.Within TCI, the scenario differences also capture consistent emission reductions that are significantly higher when compared to a BAU electrical grid with PA not in RGGI (9%-27.4%)than the reductions observed when compared to an electrical grid with PA in RGGI and a reference transportation sector (3.5%-13.4%).It is worth noting that while the positive effects of the CES policy are demonstrated outside TCI, there are a few exceptions (Illinois, Louisiana, Mississippi, Nebraska, and South Dakota) where NO x emissions are elevated owing to a projected increase in generation demand that gets dispatched to existing and newly built gas fired power plants (table S5).

Air quality
Maps comparing annual differences between the different illustrative scenarios based on surface layer PM 2.5 , NO 2 , and 8 hr average O 3 concentration levels are shown in figures 2-4, respectively.In addition, the same CMAQ-estimated averages were spatially subset for each State, the TCI region, and the entire domain, and are provided in table S6.The comparison between figures 2(a) and (d) show decreasing PM 2.5 levels over the TCI region that carry over both scenario deltas with a similar magnitude but different spatial patterns.Evidently, the electrification of the transportation sector (figure 3 While the patterns compared to B3-B1 appear similar, we do note additional negative impacts on the PM 2.5 levels in most central states surrounding the Ohio River.The positive effect for NO 2 and O 3 observed over Louisiana is attributed to the shift in energy production from the rest of the domain to lower NO x emitting power plants.Figures 2-4(e) show the differences in pollutant levels from a shift caused by implementing a 25% hybrid TCI portfolio in the transportation sector, while the power grid includes Pennsylvania in RGGI (B4-B3).Compared to the analogous paradigm under a BAU power generation, we observed a strong increase in PM 2.5 over Ohio and the surrounding states, which reaches towards southern states (Tennessee, North Carolina, South Carolina, Mississippi, Alabama, Georgia).Similarly, an increase in O 3 is evident mostly over the Southern states, while NO 2 pattern and levels are comparable to the BAU power grid equivalent (B2-B1).
Implementing the CES policy under an electrified transportation sector (B5-B2) had a uniformly positive effect across the domain, with the highest impact around the Ohio River basin (figure 3(c)).The effect appears to be very similar for NO 2 , except for Louisiana power plants, which seem to be more heavily involved in the power generation shift, following the transmission pattern simulated by IPM.Ozone levels are also affected in a similar way, with a negative impact to be spilling out from Louisiana to the neighboring Mississippi.Figures 2-4(f) demonstrate the implementation of a CES, but this time the differences include the effects of implementing the 25% hybrid TCI portfolio on the emissions from the transportation sector (B5-B3).While patterns of the ambient concentration differences for all three pollutants are quite similar compared to the previous set (B5-B2), careful inspection reveals a deeper impact on the Northeast US, which is the main component of the TCI policy focus.However, this scenario comparison (B5-B3) vs. (B5-B2) is affected not only by transportation section policies, but Pennsylvania's decision to join RGGI, making direct comparisons difficult when looking at ambient concentration patterns alone.For this reason, results from a detailed health impact analysis are necessary to provide a quantifiable measure of combinatorial effect of policies and tradeoffs between the sectors.

Health impacts
The health benefits of each policy change vary according to changes in air quality.Table 2 reports pollutantspecific changes in specific health outcomes associated with the monetized estimate of the overall impact.The health benefits of a CES with TCI in place and Pennsylvania in RGGI were the highest.Moving to this scenario from one in which PA is not in RGGI but TCI is in place results in total health benefits of $66 billion (95% CI: $31 billion to $120 billion) annually, including 5300 (95% CI: 4500-6200) lives saved and 190 000 (95% CI: 1900-380 000) asthma exacerbations avoided annually (table 2, figure 6(c)).When moving to this scenario from one where TCI is not in effect, but PA is in RGGI, we estimated potential monetized benefits of $69 billion (95% CI: $32 billion to $120 billion) annually, largely driven by 6700 (95% CI: 5200-8500) avoided deaths, and 230 000 (2800-460 000) asthma exacerbations avoided annually (table 2, figure 6(f)).In both cases, benefits are much more widespread and cover almost the entirety of the Eastern U.S. and are much larger than any scenario without a CES.In both CES scenarios, the only states that experienced any health impacts were Louisiana and Mississippi, largely due to significant increases in the operation of natural gas power plants, that despite a noteworthy reduction of SO 2 was accompanied by a 99%-270% increase in NO x (figures 5(c) and (f)), with more details in table S6-S8).
In scenarios without a CES in place, the aggregate health benefits across the Eastern U.S. were still positive, but lower.TCI going into effect while PA is not in RGGI would result in $5.6 billion in health benefits ($2.1 billion to $11 billion) annually, largely driven by avoiding 550 deaths (95% CI: 340-780) and 47 000 (95% CI: 1100-93 000) asthma exacerbations annually (table 2, (B2-B1)).Benefits largely occur in the Northeast U.S., with the greatest benefits occurring in the coastal cities (figure 6(a)), corresponding to air pollution reductions in these areas.TCI going into effect while Pennsylvania is in RGGI would still have aggregate benefits across the region, but lower; there would be 370 (95% CI: 200-580) lives saved and 43 000 (1100-85 000) asthma exacerbations avoided annually, with a total value of health benefits at $3.9 billion (95% CI: $1.2 billion to $8.5 billion) annually (table 2, (B4-B3)).In Table 2. Pollutant-specific changes in health impacts between the different illustrated policy scenarios, reported for a series of health outcomes.We present both case counts (#) and monetized estimates ($) in 2016 USD.Values presented are central estimates with 95% confidence intervals (95% CIs) in parenthesis and rounded to two significant figures (negative numbers denote disbenefits).this scenario, Eastern Pennsylvania, New York, New England and Maryland generally experience health benefits, and these occur further west, including Ohio, Michigan, West Virginia, Indiana, and Illinois (figure 4(e)).

Discussion
We compared changes in emission reductions, air quality, and corresponding health impacts across five illustrative decarbonization scenarios in the transportation and energy sectors.The scenarios considered include strategies that rely on existing electricity generation facilities and can be achieved through operational changes and strategies that rely on new investments in generation achievable within a decade.When focusing exclusively on emissions reductions from transportation, the 25% hybrid TCI portfolio had health benefits comparable to those of a 'BAU' grid that resulted in 550 avoided adult deaths and 47 000 avoided asthma exacerbations.Most health benefits in cases where transportation scenarios are enabled are driven by lower overall exposure to NO 2 .However, if Pennsylvania joins RGGI, the total health benefits and the location of the benefits and impacts change substantially.The total number of deaths avoided is reduced by about 30%, avoided asthma exacerbations remain approximately the same, but a large portion of adverse health impacts moves from within the TCI states to Ohio and West Virginia, since coal-fired power plants in those states respond to the increased electricity demand.While the effect of transportation and energy sector policies on domainwide concentrations is modest, we do find that if Pennsylvania does join RGGI, there are substantial concentration changes in Ohio and West Virginia while the overall pollutant levels over the TCI region remain comparable.This was proportionally allocated to NO 2 and O 3 exposures, while PM 2.5 effects were slightly negative.If PA joins RGGI and a CES is enacted, there are significant reductions in average concentrations, except for Louisiana and Mississippi where disbenefits in the same range are observed.Most of the health benefits in CES cases were driven by substantially lower PM 2.5 exposures, followed by NO x and O 3 .
Our results demonstrate that replacing coal-fired generation with renewable energy yields major benefits to climate and human health, although there are limitations that manifest as elevated NO x emissions in regions where natural-gas EGUs contribute more to the grid.As other current analyses have demonstrated for different portions of the US energy grid, replacing coal with natural gas generation is unlikely to be an optimal long-term approach.Therefore, it is imperative to keep in mind the challenges associated with new infrastructure changes, particularly strategic deviations from current transmission lines that are in constant interaction with electricity production costs and the penetration of renewable energy to achieve deep decarbonization.At the same time, it is critical to realize that without such difficult decisions and infrastructure changes, existing disparities at the level of overburdened communities and subpopulations within the same grid interconnects will continue to follow patterns dictated by the power grid, perpetuating a major EJ issue.Despite not being this study's main objective, when looking at potential disparities at a national scale, the scenarios that are driven by Pennsylvania's decision result in different patterns and consequently exposures.This further supports the proposed framework through its uniqueness to deal with multiple interconnects and states and further be used to answer the very same important EJ questions at different scales.
Despite using different modeling frameworks, our conclusions with respect to Pennsylvania's future and the surrounding air quality are in accordance with recent research on RGGI.Previous work by Yang et al (2021) demonstrated that if PA joined RGGI, there would be 'leakage' of emissions from Pennsylvania into largely Ohio, Indiana, and Illinois.Our results show that on the current grid, this would be further enhanced by additional electrical load from the deployment of EVs in the Northeastern US.The scenarios in which a CES is in place show that this effect can be largely alleviated with large-scale decarbonization, with the exception of Louisiana and Mississippi.This indicates that additional policies on air pollution emissions may be necessary to prevent inadvertent increases in air pollution due to electrical grid dispatching decisions that increase emissions from heavy polluting EGUs.
It is worth mentioning that modeling frameworks like the one employed in this study have varying degrees of uncertainties, some of which we were able to incorporate.There are uncertainties in parameterizations of variables throughout the modeling chain-electricity demand, power plant dispatch and response of power plants to changes in demand, emissions factors, atmospheric chemistry and physics, concentration-response functions (CRFs) quantifying the relationship between increased exposure to air pollutants and health impacts, and the monetary valuation of those impacts.For CRFs and valuation of health impacts, uncertainties that are reasonably well-understood and linearly scale, we were able to quantify these uncertainties using 95% confidence intervals (CIs) or uncertainty ranges for the CRFs and for pricing.The real results of this increase in EV deployment are also dependent on deeper and more complex uncertainties that cannot be quantified using simple CIs.Many of these involve complex interactions with the electrical grid and economics and would require a number of sensitivity analyses to understand.Examples include, but are not limited to: retirement (both planned and unplanned) of existing power plants; construction and sitting of new power plants; changes in natural gas prices that could result from changes in U.S. demand and supply, along with the possibility of increasing export capacity due to construction of liquefied natural gas plants which may more closely tether the U.S. and European natural gas markets and prices; increasing uptake of residential heat pumps and the effect this has on increasing electricity demand in winter, but reducing natural gas demand in the summer; and construction of additional transmission capacity, which can alter electrical grid dynamics, pricing, and dispatch order of existing power plants.
Broadly, our results demonstrate the value and necessity of including changes in power generation when evaluating electrification policies.Even though TCI is not being adopted by the states that originally explored its implementation, this is an important dimension that states and regulators need to assess when developing transportation emissions reduction policies.The increased electricity needs that result from electrification transportation (Schnell et al 2019, Choma et al 2020, Waite and Modi 2020), and potentially buildings (Knobloch et al 2020, Buonocore et al 2022), are an important part of the full life cycle impacts of these policies.Including them in assessments and formally evaluating the effects of increased electricity demand is critical for detecting and preventing the inadvertent creation or exacerbations of areas with high air pollution around power plants.

Figure 1 .
Figure 1.Scenario matrix showing energy (vertical) and transportation (horizontal) policy-based transitions and the air quality modeling domain with the TCI region highlighted.