Inequality in energy transitions: Subnational air pollution disparities resulting from national decarbonization strategies

Energy transitions and decarbonization require rapid changes to a nation’s generation mix. There are a host of possible decarbonization pathways, yet there is vast uncertainty about how different decarbonization pathways will advance or derail the nation’s energy equality goals. We present a framework for investigating how decarbonization pathways, driven by a least cost paradigm, will lead to air pollution inequality across different vulnerabilities (e.g., low-income, energy poverty). If an equitable energy transition is the goal (i.e., one that reaches total equality), using least cost optimization capacity expansion models without strict renewable energy technology mandates will not accomplish this. Thus, it is imperative that decisions regarding national regarding national decarbonization pathways have strict mandates for equality outcomes or be driven by an equality focused paradigm.


Introduction
As countries push for electricity system decarbonization, there is the possibility that these infrastructure investments can simultaneously combat climate change while also driving economic recovery. However, there is a risk that electricity transition investment can lead to outcomes that worsen social inequalities if marginalized groups are excluded from the bene ts due to explicit exclusion or implicit human biases 1,2 .
Thus, there is large uncertainty regarding the degree to which decarbonization policies will exacerbate or alleviate social inequalities, and how they will impact co-pollutants (NO x , SO 2 , PM emissions) of the electricity sector. Currently, most national electricity planning models investigate how the nation can decarbonize the electricity sector using least cost optimization 3 , without considering how different decarbonization strategies will impact distributional equality of greenhouse gas and co-pollutant emissions across a nation [4][5][6] . This paper adds to the literature by evaluating the environmental sustainability (i.e., national air pollution emissions) and equality (i.e., distribution of air pollution) of eight national decarbonization strategies. Our equality analysis is focused on the distribution of air pollution emissions, with total equality being de ned as each region having equal levels of air pollution emissions from the electricity sector. A key contribution of our work is highlighting how a myopic, single-objective view of energy transition decision making impacts distribution of air pollution across the US, and how the bene ts of decarbonization (i.e., reduced emissions) are spread across different demographics (i.e., income groups, types of poverty experienced). National and regional sustainability are often measured through four different dimensions: economic, environment, social, and technical [7][8][9] . Some studies look at different aspects of sustainability in the electricity sector to compare renewable and non-renewable options through multi-criteria decision analysis (MCDA) 7,8,10,11 , while others use life-cycle assessments (LCA) to measure the sustainability of implementing renewable energy into the electricity sector 12,13 . To evaluate the sustainability of electricity, system transitions a host of models have been proposed 3 , with the most prevalent being least cost optimization models. Often in these models environmental sustainability metrics are calculated after the optimization has solved, or integrated as one of the constraints 14,15 , when in reality environmental and social objectives lend themselves more to a multiple objective optimization problem. Thus, economic optimization drives the model decision making while environmental factors are considered as a constraint or post analysis, potentially missing how myopic decision-making impacts national copollutant emissions, and vulnerable groups.
While much of the literature addresses environmental sustainability, there is a need for a deeper understanding regarding how different decarbonization pathways will affect pollution distribution across vulnerable groups 15 . Often social dimensions (i.e. equality, equity, and justice) are nonexistent, considered as retroactive analyses, or analyzed separately from capacity expansion models 15,16 . Based on the review of energy transition literature, Kohler et al. (2019) 16 illuminates the need for exploration in how energy transitions may place undue burden on regions with high poverty rates or low-income populations. Turkson et. al (2020) 15 indicates that there is a gap in the holistic understanding of energy systems and transitions on the four dimensions of sustainability. Our analysis addresses these limitations by quantifying how national energy transition policies, designed using a least cost paradigm, impact regional equality.
In previous electricity equality studies, one paper investigated the social and environmental implications of expanding power systems in developing countries with little to no existing infrastructure 11 at a subnational level. In Nock et al. the primary goal was to investigate how different stakeholder preferences towards equality (i.e., distribution of electricity access) impacted power grid construction 11 . The authors did not investigate how the distribution of air population emissions would change under different electri cation strategies, nor did they determine the implications of using least-cost paradigms for vulnerable communities. Another researched the sustainability and equity impacts of reaching electricity sector targets across European countries 13 . While this paper looks at four different optimization objective scenarios (base case, cost, equality, and renewable generation), their focus is on intercountry equality considerations. We build on this work by investigating how least cost optimization (dominant decision paradigm) impacts regional equality objectives across eight unique decarbonization scenarios, some of which include 100% renewable penetration requirements. Another key contribution of our work is creating a framework for investigating and quantifying the social and environmental impact of energy transitions across different vulnerability indicators.
Equitable energy transitions and energy justice have social, technical, and economic aspects 1, 17-21 .
Stemming from this intersection, the energy sector experiences injustices at three scales: micro, meso, and macro 22 . Micro scale injustices relate to local impacts on the environment and community health. Meso scale injustices are more focused on national-scale issues such as unequal access to renewable technologies. Lastly, macro scale injustices include global issues such as waste disposal 22 . Multiple studies have reviewed current energy justice research 19,20 , while others have provided frameworks for evaluating social sustainability [23][24][25] . One key study highlights that energy transitions and policies must focus on equity and centering communities who are disproportionately affected by air pollution in the current energy system to correct historical injustices 26 .
In this paper, we investigate and quantify the distributional equity impacts at the meso scale (i.e. national) and incorporate equality metrics into our analysis. Speci cally, our social sustainability analysis is focused on investigating the way different national decarbonization policies will impact the distribution of air pollution emissions across vulnerable groups. Vulnerable groups included in our analysis are as follows: low income, those in a region with high poverty rates, those with a high percent of income spent of energy bills, and those residing in a high cost of living area. We accomplish this by tying an electricity optimization model with an equality and sustainability analysis (see Methods). Here the electricity planning model is the Regional Energy Deployment System (ReEDS) model which determines the mix of power plants, and their operation based on a least cost framework. This model disaggregates national energy planning into 134 regions across the contiguous United States, allowing for a sub national analysis. One limitation is the spatial granularity of national electricity planning models, where sometimes the smallest region resolution is at the state level. While we use the smallest subnational disaggregation in our analysis, we acknowledge that the intraregional differences in air pollution may vary across different demographic groups, based on where power plants are located, and wind ow patterns in that part of the country [27][28][29] (see Methods for more limitations).

Results
National energy transitions under decarbonization goals. We investigate the environmental impacts (i.e., total air pollution emissions) and equality (i.e., regional distribution of emissions) of different electricity generation investment strategies under eight decarbonization strategies over a 40-year time-period (see Table 1 in Methods). Our decarbonization scenarios include the base case with no additional carbon constraints (Scenario A), two carbon cap scenarios which meet either the US nationally determined contributions (NDC) from the Paris Agreement or a pathway to stay under 1.5°C warming (Scenarios B and C respectively), and ve technology speci c portfolios which deploy either renewable energy (Scenarios D -F) or low carbon (Scenarios G and H) generation (see Methods Table 1 for scenario descriptions).
The annual generation by technology for the decarbonization scenarios is shown in Figure 1. For Scenario A (the Base Case), that implements no additional carbon constraints or policies, generation in 2010 has the majority of the generation is supplied by coal, and natural gas and nuclear, but by 2050 we see coal generation decrease to 7.5% (0.41 PWh), natural gas generation slightly increase to 20.0% (1.08 PWh), onshore wind increase to 33.8% (1.83 PWh), and solar PV generation increase to 20.9% (1.14 PWh) of total generation. The carbon cap scenarios (B and C), which place a strict limit on CO 2 emissions from the electricity sector, achieve their carbon caps primarily through deploying solar PV and onshore wind. In both scenarios, wind and solar represent less than 3% of the generation Onshore wind generation increases the most in technology speci c scenarios with an implemented renewable portfolio standard (RPS) (D, E, and F): Scenario D averages a 25.8% annual increase, Scenario F averages a 27.3% annual increase, and Scenario E averages a 28.7% annual increase. These annual increases lead to onshore wind representing approximately 50% of generation by 2050 in all three scenarios. Scenarios D, E, and F also see large solar deployment, again due to the implemented RPS. The highest generation of solar PV, CSP, biopower, and battery storage are deployed to meet the 100% renewable requirement by 2035 in Scenario E, with solar PV technology representing 35.0% of total generation by 2040. In the other seven scenarios, solar PV is still a large contributor to generation, with solar PV supplying 15-20% of total generation in 2040.
Scenarios that require low carbon technology [renewable sources, nuclear, or natural gas carbon capture and storage (CCS)] by 2035 (Scenario G) and 2050 (Scenario H) strategies primarily rely on natural gas generation until their low carbon requirement year when it is completely retired. Upon reaching the technology mandate natural gas is primarily replaced with natural gas CCS. Thus, without a mandate this technology would most likely continue to provide 10-20% of the total generation needs. See Table S By 2035, operating emissions from Scenarios C, E, and G are under 100 Mt CO 2 eq. emissions. Coal has completely retired by 2035 in these scenarios, so it is not contributing to emissions, and natural gas or natural gas CCS contribute under 10% of generation. By phasing out coal and natural gas plants, emissions from co-pollutants like NO x , SO 2 , and PM also fall signi cantly, with NO x levels at or below 0.02 Mt, SO 2 levels below 0.003 Mt, and PM levels below 0.002 Mt.
Air pollution distributional regional equality. While national level emissions analyses are important for measuring progress across the energy system as a whole, there are multiple entities that make decisions in the electricity sector (e.g., utilties, states). Regional inequalities resulting from energy transitions can manifest themselves in the unequal distribution of air pollution emissions across regions. There will be differences between who is responsible for emissions (life cycle emissions), and which populations bear the brunt of those emissions due to where they live (operational emissions). Life cycle emissions are important for global CO 2 eq. emissions, due to greenhouse gases impacting people regardless of where emissions are generated 30 . However, the operational co-pollutants will largely impact the health of people within the region, so operating emissions reductions of these pollutants will result in regional health bene ts [31][32][33][34] . We present an analysis of both life cycle and operating greenhouse gas emissions (CO 2 eq.) and operating co-pollutant emissions (NO x , SO 2 , PM 2.5 ) to illuminate how different decarbonization scenarios could impact emissions globally and regionally. The regions that have more CO 2 eq. emissions in these scenarios (part of Montana, Arizona, Kansas are a few) will therefore be more responsible for global CO2eq. emissions than other regions.
The distribution of operating emissions for NO x , SO 2 , and PM is seen in Figure 4 (a, b, c) respectively.
Once the technology mandate is met in 2035 (Scenarios E and G) or 2050 (Scenarios F and H), the operating co-pollutant emissions reach their lowest values. However, in Scenario E, PM operating emissions are worse in some regions in 2050 than they were in 2035. The large PM emission increases results from biopower being deployed in the Eastern US to meet the 100% renewable technology requirement. We also nd that the low carbon mandate scenarios (G and H) have greater operating NOx and PM emissions in the Eastern US due to emissions from biopower and natural gas CCS investments Thus, this highlights the need to consider co-pollutants in energy transitions due to local health effects (e.g., asthma) that results from increases in these emissions 35,36 Emissions distribution across vulnerable groups. Beyond regional analyses that measure the magnitude of air pollution, it is useful to understand the distribution of operating emissions across different demographic and socioeconomic indicators (median income, poverty rate, cost of living, energy burden) across regions. This investigation shows the impact of different energy transitions on vulnerable regions.
See Methods for data information about the demographic groups, and methods for the analysis. Here, we focus on operating NO x , SO 2 , and PM emissions, due to the local health impacts of these co-pollutants.
Here we de ne inequality between regions as the difference in emissions per capita between the worst off (e.g., median income <$50k) and best off (e.g., median income >$70k) demographic groups. While there are multiple avenues for measuring vulnerability, we focus on two measures. The rst is an absolute measure, while the second is a relative measure. Absolute measures indicate vulnerability based on a threshold (poverty level or median income), while relative measures indicate vulnerability based on income spent on a given expense (i.e., energy, transportation, rent).
Absolute vulnerability indicators. Our absolute vulnerability analysis focuses on median income and poverty rate across regions. The distribution of life cycle and operating emissions across different median income ( Figure 5) illustrates the disparities between high-and low-income regions (see Methods for group de nitions and the aggregation of median income census tracts to the ReEDS regions). Operating emissions across median income groups will impact health of vulnerable groups within regions, since pollutants like NO x , SO 2 , and PM have more local health affects 1,37,38 . In 2010, regions with the lowest median income see the highest incidence of emissions, and our ndings suggest that these regions will continue to bear the largest burden of emissions from these pollutants (and associated health impacts) 35 . For scenarios that have large penetrations of renewable and low carbon technologies, the distribution of operating NO x and SO 2 emissions across median income groups become equal (0 kg/capita) the year the carbon or technology requirement is mandated (2035 or 2050), but PM emissions remain greater in low-income regions even past the mandate year of 2035 because of biopower and natural gas CCS investments. Furthermore, 5 years before the scenario's mandate, PM operating emissions in these technology mandate scenarios (E, F, G, and H) are 59-71% higher in the lowest income group than the highest income group, so reaching the mandate does help reach equality in NO x and SO 2 emissions, but before those are met, the worse-off groups are still the most impacted. However, PM emissions past the mandate year (2035) in Scenarios E and G are still the highest in the lowest income group, indicating that a single-objective approach could leave vulnerable groups worse off even as we reach a low-carbon or 100% renewable energy system. Similar trends are seen across NO x and SO 2 emissions in these scenarios ve years prior to their mandates: NO x emissions in the lowest income group are 50-74% higher than the highest income group, and SO 2 emissions are 18-74%. Low-income communities have been disproportionately affected by NO x emissions 39 , and our analysis shows that across different decarbonization scenarios, the lowest income regions continue to have the highest NO x operating emissions, unless a national mandate requires complete retirement of fossil fuels (as seen in Scenarios E and F).
In addition to median income, percent of poverty within a region can identify regional vulnerability as another absolute measurement. Poverty groups were identi ed by the percent of the population in a region that is experiencing poverty, which the US O ce of Management and Budget classi es as a family who is under a certain threshold given their family makeup 40 . The group of regions with the highest percentage of residents in poverty (>15%) consistently had the highest life cycle and operating emissions per capita across 2010 -2050 for all co-pollutants (SI Figure S-16).
Relative vulnerability indicators. Or relative vulnerability analysis focuses on energy burden or cost-ofliving. Energy burden is de ned as the percent of household income spent on satisfying energy needs. As nations deploy more technologies, there is a chance that grid costs, and subsequently energy bills, could rise 41 , which would affect the people who currently have trouble paying for their energy bills. Thus, we highlight how emissions will change across groups that will likely experience di culty paying for electricity should costs rise. For the aggregation of energy burden and cost-of-living metrics to the ReEDS regions, see Methods. Operating emissions ( Figure 6) across different energy burden groups show the highest energy burden group having the highest emissions per capita either across the entire timeline (Scenarios A, B, D) or until the year the carbon cap or technology mandate requirement is 100% low carbon or renewable energy (Scenarios C, E, F, G, H). Before reaching these years (2035 or 2050) the highest energy burden group is worse off. In 2030 in Scenario E, NO x , SO2, and PM operating emissions are 42%, 18%, and 38% lower respectively in the lowest energy burden (energy burden <2.75%) group than in the highest (energy burden >3.75%) before falling to a 0% difference in 2035. Likewise in 2045 in Scenario F, NO x and PM operating emissions are 28% and 40% respectively lower in the lowest energy burden group compared to the highest. Once 2050 is reached, these emissions fall to 0 kg/capita and a 0% difference between the groups. Therefore, without strict fossil fuel retirement mandates ties to decarbonization strategies the most vulnerable regions will continue to experience the highest emission burden.
Cost-of-living is another relative measurement used to understand disparities of air pollution distribution, which is classi ed as the percent of income spent on housing and transportation. We recognize that absolute income does not capture the relative cost of achieving a certain standard of living in different parts of the country. When accounting for different costs-of-living across regions we nd similar results: where the operating emissions are greater in the highest cost of living group (greater than 66% of income to housing and transportation needs) until the year of the technology mandate. The highest and lowest cost-of-living groups have decreasing inequality across all pollutants in all scenarios, meaning the emissions between the highest cost-of-living group is approaching the emissions in the lowest cost-ofliving group. (NO x : difference of 18.3 kg/capita in 2010 to an average difference across scenarios of 2.6 kg/capita, SO 2 : difference of 55.6 kg/capita in 2010 to an average difference of 7.1 kg/capita, PM: difference of 1.8 kg/capita in 2010 to an average difference of 0.32 kg/capita) (SI Figure S-15).

Discussion
Here we investigated how national level decarbonization policies translate to national emissions and the distribution of emissions at subnational regions. In our analysis, we nd that no decarbonization scenario reaches operating emission distributional equality until they meet their mandate year of 2035 or 2050 (Scenarios C, E, F, G, and H). However, there are clear trade-offs between national emissions reductions and distribution of emissions across regions: to meet a 100% renewable requirement by 2035, there is a larger deployment of biomass power plants, which emit SO 2 and PM. These emissions from biomass plants will therefore cause surrounding communities to be negatively affected by these emissions and greater inequality across distributional air pollution. We nd that the carbon cap scenario which aims to keep warming under 1.5°C has less national reduction in emissions but results in a more equal distribution of air pollution (0 emissions) by 2050 for CO 2 eq. and co-pollutants like NO x , SO 2 , and PM.
The 100% renewables by 2050 (Scenario F) and low carbon technology mandates (Scenario G and H) also see this trend. This further highlights the multiple objective, and often con icting nature, of energy transition planning.
When addressing the multi-faceted lens of decarbonization, it is important to weigh both the national emissions and the distribution of those emissions. For example, reaching 100% renewable energy by 2050 will help the US decarbonize its electricity sector completely by then, but there are air pollution inequities as the nation decarbonizes, with the low-income regions seeing the highest emissions until the goal is met in 2050. This may be a byproduct of the least cost paradigm being the primary objective guiding technology deployment and makes the case for more multi-objective modelling efforts. Without a multi-objective view to energy planning, vulnerable groups could be burdened with greater amounts of emissions while the US decarbonizes. The continued inequality of air pollution distribution from historical trends will exacerbate health impacts among the most vulnerable communities. Four scenarios reach zero (or close to zero) operating emissions by their mandate in either 2035 (E and G) or 2050 (F and H), but not beforehand. This result indicates that achieving 100% renewable energy or low carbon by a given year may ensure an equal future beyond those years, but beforehand, low-income and poor regions are burdened with the highest emissions.
All scenarios with carbon policies implemented see improvements from Scenario A, which implements no additional carbon policies after 2020: there is at least a 20% reduction in co-pollutant emissions in the lowest income group in 2050 in the other scenarios compared to Scenario A. However, a gap persists between the best off and worse off regions across all demographic variables and time periods we consider. If an equitable energy transition is the goal (i.e., one that reaches total equality), decarbonization policies in the absence of strict technology mandates, and those guided by least cost optimization capacity expansion models may fall short of environmental justice and equality goals. Thus, it is imperative that decisions regarding national regarding national decarbonization pathways have strict mandates for equality outcomes or be driven by an equality focused paradigm. Two opportunities for future analysis present themselves. The rst is to investigate how changing the decision-making paradigm (i.e., changing the optimization objective function) in uences the equality outcomes between regions. Second is to investigate the trade-offs of air pollution distribution with other equality (e.g., distribution of costs and electricity bill increases), equity (e.g., health impacts), environmental (e.g., water consumption and land-use), and cost objectives. Deeper analysis of health impacts from energy transitions could also necessitate greater quanti cation of the monetary damages of air pollution 27,42−44 .
Equitable energy transitions exist at the intersection of technical, economic, and social justice objectives 1,17−21 . Working to achieve this goal of an equitable energy transition requires a multidisciplinary lens to understand who wins and losses in energy transitions. Our work begins to do this by using a least-cost optimization model coupled with a sustainability and equality analysis that measures air pollution across regions and demographic groups. This is a rst step in investigating the progress of achieving an equitable energy transition, by performing an analysis of how national policies translate to subnational equality. We have shown that a single objective of minimizing cost does not result in an equitable transition and vulnerable groups are at risk of existing in regions with higher emissions. When crafting public policy for energy transitions, decision makers can use this work as a source for indicating the need for a holistic multiple objective approach to energy system planning if we are going to ensure an equitable and sustainable future.

Methods
Here we present the methods of our work. We start by discussing how the electricity system and decarbonization scenarios are modeled, followed by the sustainability and equality analysis. We conclude this section by discussing the limitations of our analysis. Our work investigates the equality of decarbonization scenarios at the national and subnational level across 134 regions in the US. We do this by tying a national capacity expansion model with an air pollution assessment and distributional equity analysis.
A. Electric power system model In our electricity system analysis, we use the Regional Energy Deployment System (ReEDS) from the National Renewable Energy Lab (NREL) to de ne the resulting electricity generation pro les under different decarbonization scenarios, like a carbon cap or national renewable portfolio standard to reach different energy transition goals. The model outputs capacity, generation, emission, cost, retirements, and transmission data for regions in the US de ned by ReEDS for the model years. This data was used to analyze the impact of different decarbonization scenarios and ultimately the overall cost, sustainability, and equality trade-offs.
To perform decarbonization scenarios, a carbon cap was implemented as an exogenous input to the model. The carbon cap speci es the number of allowed emissions in the national electric sector for each year of the model run (2010 to 2050). As the ReEDS model solves each year, the operating emissions generated from the system cannot surpass the speci ed carbon cap. The model will not continue to the next solve year until it can nd a solution that meets the carbon cap emissions.

B. Decarbonization scenarios
Eight decarbonization scenarios were created for this analysis and are summarized in Table 1. Each decarbonization scenario was then run in ReEDS to forecast the US electricity system 2010 to 2050. See SI Table S-1 for details by year of carbon caps (Scenarios B and C) and national technology mandates (Scenarios D -H). Table 1 Description of decarbonization scenarios and their implemented policies in ReEDS (See SI National technology mandate implemented beginning in 2020 at 20% and increasing linearly to 100% renewable energy, natural gas CCS, or nuclear in 2050.  Table S-3 for heat rates for each power plant. Life cycle and operating emission rates for powerplants were obtained from literature. We assumed that operating emissions from renewable and nuclear sources were zero. Note that the life cycle emissions rates obtained from literature are in g/kWh of electricity generated, whereas the operating emissions rates are in pounds/MMBtu because they are fuel emission rates (see Eq. 2).
n Table 2 Life cycle and operating emission rates used in environmental sustainability analysis. See Table S-2 in SI for sources, and Figure S- 3 Assumed NOx life cycle emissions rate for natural gas CCS is the same as natural gas CC. 4 Assumed NOx life cycle emissions rate for IGCC is the same as coal. 5 Assumed Bituminous coal and PM 2.5. These equality metrics can be compared to air pollution to investigate the inequalities across regions for each decarbonization scenario. Data for median income and percent poverty were obtained from US Census Bureau, cost of living from ACS, and energy burden from US Department of Energy. A limitation of these equality metrics is that they are estimations from 2018, so they may not accurately represent what median income, percent in poverty, or energy burden may look like in future time periods. Thus, one limitation is lack of projection regarding human migration patterns at the subnational level, which may be impacted by rising temperatures, and changing weather patterns.

E. Limitations & caveats
Our work presents a subnational analysis of the environmental sustainability and equality impacts of national decarbonization strategies. Here we present some limitations and caveats for the work presented here.
Equality at the subnational level will depend on location of power plants and the demographics of the population living in the region. Our subnational analysis for the US investigated disparities across 134 regions, with some of these regions being as large as a state. Some limitations stemming from the subnational resolution is that 1) there can be disparities within our regions, 2) aggregating the poverty metrics to the regional level can mute intrastate disparities, and 3) power plant operational emissions make impact communities outside of the region they are situated in 27 . The goal of our work was to highlight potential inequalities under different decarbonization strategies. Before this work is implemented in a region, we recommend a detailed subnational analysis involving potential power plant locations, and detailed demographic information to future highlight intraregional risk. In this analysis, we chose to look at emissions per capita across regions and equality metric groups. This approach may cause emissions to be weighted greater in rural areas, due to smaller populations. However, our initial analysis of total emissions across regions and equality groups showed the most populous regions with the highest emissions, as is expected, and with the spatial granularity of the regions to be sometimes even at the state level, we felt the emissions across regions would be best These cost decreases will vary by location, workforce, and labor costs, as well as scarcity or abundance of input materials overtime. Future demand will vary based on level of electri cation of transportation sector, industrial sector, and buildings. Steinberg et al. (2017) projects that under high electri cation scenarios, demand will double, with the transportation sector accounting for 50% of incremental load.
The goal of our analysis was not to perfectly forecast which technologies would be used in future generation mixes, but to highlight how different decarbonization pathways might impact vulnerable groups. See SI for model speci cations used in this analysis. Figure 1 Annual generation mix (PWh) 2010 -2050 by technology for each decarbonization scenario resulting from the ReEDS model. We highlight that the renewable and low carbon technology mandate scenarios accommodate additional energy needs primarily through expanded investments in wind and solar generation. Here we see that the base case, US NDC, and 80% renewable energy decarbonization pathways retain coal generation through 2050.   Regional operating emissions per capita (kg/capita) in 2035 and 2050 for (a) NOx, (b) SO2, and (c) PM.

Declarations
Regional emission inequalities are a by-product of different technology investments across the nation. The operating emissions indicate who bears the burden of the emissions once the power plant is operating (operational emissions). In 2035 we nd Scenarios F and H (RE and low carbon 2050 targets), we see high levels of operating co-pollutant emissions, indicating that rapid renewable and clean technology deployment will be required to drastically reduce regional emissions within a 15-year time period. In contrast, in 2050 Scenario E (RE 2035 target) we see SO2 and PM emissions increase from 2035 to 2050. This highlight two key risks of strict mandates: waiting until the deadline to rapidly deploy or increasing emissions levels once the target have been achieved.

Figure 5
Distribution of NOx (left column), SO2 (middle column), and PM (right column) operating emissions per capita across median income groups 2010 -2050. Median income is an absolute vulnerability metric.
We highlight operating emissions because the annual operating emissions will have a myriad of effects on different communities. We highlight co-pollutant emissions due to the health risks resulting from living near a high emitter of people within the region. Here we see that under all decarbonization scenarios the lowest median income groups see the highest CO2eq. and PM emissions under all scenarios. The policies with the great reduction are the strict technology mandates (Scenarios E, F, G, and H), where the income groups reach close to equality by the mandate year. However, if a strict mandate is not followed then the carbon cap scenario (Scenario C) is the next best option due to carbon cap requirements of under 850 Mt operating CO2eq. emissions in 2030 -and trending downwards.