Air pollution disparities and equality assessments of US national decarbonization strategies

The transition to cleaner energy sources and decarbonization necessitates rapid changes in a nation's electricity generation mix. While various decarbonization pathways exist, uncertainty remains regarding their impact on energy equality goals. This research addresses this gap by presenting a framework for assessing how least-cost decarbonization pathways affect air pollution inequality among vulnerable populations (low-income, minority communities) in the US. The core research question is: How will different decarbonization strategies, optimized for least cost, impact the distribution of air pollution, specifically focusing on PM2.5, across various demographic groups in the US? The study's purpose is to inform policy decisions by quantifying the distributional effects of decarbonization policies on air quality and identifying strategies to mitigate environmental injustices. The importance of this study stems from the urgent need to address climate change while simultaneously ensuring equitable access to clean energy and minimizing negative health consequences for vulnerable communities. Ignoring the co-pollutant impacts of decarbonization could exacerbate existing health disparities, emphasizing the necessity of a just and equitable energy transition.

Existing literature highlights the risk of energy transition investments worsening social inequalities if marginalized groups are excluded from benefits. Most national electricity planning models focus on least-cost optimization without considering sub-national air pollution distribution. This study builds upon existing research by incorporating principles of distributional energy justice, which emphasizes the equitable distribution of environmental risks, harms, and benefits. While some studies have investigated air quality co-benefits of decarbonization at the system level, uncertainty remains concerning spatial distribution, particularly within vulnerable communities. Previous research has documented historical air pollution exposure disparities across racial and income groups, highlighting the need for a forward-looking framework to evaluate future disparities under different decarbonization plans. While some studies have integrated equality and distributional analyses into electricity system decision-making, they often lack the high spatial resolution or focus on specific demographics crucial for understanding local-level impacts. This study differentiates itself by quantifying the impact of national energy transition policies on sub-national equality using high spatial resolution and a least-cost paradigm for power plant investment decisions.

This research uses a combined modeling approach. First, the Regional Energy Deployment System (ReEDS) model, a least-cost optimization model, simulates US electricity sector operations from 2010 to 2050 under eight decarbonization scenarios. These scenarios include a base case (no new policies), carbon cap scenarios aligned with US Nationally Determined Contributions (NDC) and a 1.5°C pathway, and national technology mandates for renewable energy (80% and 100% by 2035 and 2050) and low-carbon technologies (100% by 2035 and 2050). ReEDS outputs electricity generation by technology, which are then input into an environmental sustainability model to calculate air pollution emissions (CO2, NOx, SO2, PM2.5) at regional and national levels. Second, a reduced complexity air pollution model, the Intervention Model for Air Pollution (InMAP), simulates air pollution transport, deposition, and ambient concentrations. InMAP uses ReEDS's emission outputs as inputs and provides high-resolution (census tract level) air pollution concentration data. Finally, a distributional equality analysis examines the distribution of air pollution concentrations across demographic groups (median income, poverty rate, and race/ethnicity) obtained from the American Community Survey (ACS). Population-weighted average concentrations are calculated to assess disparities. The analysis focuses on operating emissions, those directly from power plants, to understand local health impacts. The model's spatial resolution is a limitation, as emissions are distributed across ReEDS regions rather than at individual power plant locations. This limits the precision of emission allocation and the modeling process's spatial specificity. Emission rates for various technologies were obtained from the literature. Assumptions included zero operating emissions from renewable and nuclear sources. The study also incorporated area-weighting to allocate emissions from ReEDS regions to the more granular InMAP grid.

The analysis reveals significant disparities in air pollution exposure across different decarbonization scenarios and demographic groups. Without decarbonization policies, Black and high-poverty communities experienced considerably higher PM2.5 concentrations than the national average (0.19–0.22 µg/m³ higher). Scenarios with more than 80% renewable or low-carbon technology deployment achieved equality of air pollution concentrations across demographic groups. National operating emissions (CO2, NOx, SO2, PM2.5) decreased across all scenarios compared to the base case, with scenarios involving aggressive carbon caps or technology mandates showing the most significant reductions. The 1.5°C pathway and the 100% renewable energy deployment by 2035 scenario reduced inequalities between racial groups most rapidly. Black communities experienced the highest PM2.5 concentrations until high renewable or low-carbon technology penetration was achieved. Without decarbonization policies, Black communities faced up to 34% higher PM2.5 exposure than the national average. High-poverty census tracts also had higher PM2.5 concentrations until significant clean energy deployment was implemented. Income disparities in PM2.5 concentrations were less pronounced than racial or poverty disparities. The 100% renewable energy scenarios generally had the lowest PM2.5 emissions per megawatt-hour of generation by their mandate year. Interestingly, without a strict renewable energy mandate, the 1.5°C pathway often had the lowest or second-lowest ratio over the entire modeling horizon.

The findings underscore the importance of considering distributional equity in decarbonization strategies. While least-cost optimization alone may not achieve environmental justice, stringent technology mandates or aggressive carbon caps can effectively reduce air pollution disparities. The observed persistence of air pollution inequalities, even in scenarios with significant emissions reductions, highlights the influence of historical inequities and the need for targeted interventions. The study’s focus on the electricity sector necessitates further research into other emission sources to gain a comprehensive understanding of overall air pollution inequalities. The reliance on area-weighting in the InMAP model introduces a degree of uncertainty in emission allocation; power plant-level data would improve accuracy. The disparity between the best-off and worst-off regions across all demographics persisted even after significant emissions reductions. To achieve an equitable energy transition, policy must explicitly prioritize equality alongside cost minimization.

This study demonstrates that achieving equitable decarbonization requires policies that go beyond least-cost optimization. Stringent renewable or low-carbon technology mandates are crucial for achieving air pollution concentration equality across demographic groups, particularly for historically disadvantaged communities. Future research should explore alternative optimization paradigms that prioritize equity alongside cost effectiveness, investigate the interplay of air pollution with other social and environmental factors, and expand the analysis to incorporate other emission sectors. This work provides a valuable framework for evaluating the social and environmental impacts of decarbonization strategies and can inform the development of just and equitable energy transitions.

The analysis focuses solely on the electricity sector, neglecting other significant sources of air pollution (transportation, industry, residential). The use of 2010 population data and 2018 equality metrics may not perfectly reflect future demographic shifts. The area-weighting method used in InMAP introduces uncertainty in allocating emissions from ReEDS regions to the finer spatial resolution of InMAP. The ReEDS model's sequential nature and least-cost paradigm may not fully capture future technological advancements, changing demand, or policy interventions. Future research could address these limitations by incorporating more comprehensive data and refined modeling techniques.