Research on air quality and human health "co-benefits" from climate mitigation strategies represents a growing area of policy-relevant scholarship. Compared to other aspects of climate and energy policy evaluation, however, there are still relatively few of these co-benefits analyses. This sparsity reflects a historical disconnect between research quantifying energy and climate, and research dealing with air quality and health. The air quality co-benefits of climate, clean energy, and transportation electrification policies are typically assessed with models spanning social, physical, chemical, and biological systems. This review article summarizes studies to date and presents methods used for these interdisciplinary analyses. Studies in the peer-reviewed literature (n = 26) have evaluated carbon pricing, renewable portfolio standards, energy efficiency, renewable energy deployment, and clean transportation. A number of major findings have emerged from these studies: [1] decarbonization strategies can reduce air pollution disproportionally on the most polluted days; [2] renewable energy deployment and climate policies offer the highest health and economic benefits in regions with greater reliance on coal generation; [3] monetized air quality health co-benefits can offset costs of climate policy implementation; [4] monetized co-benefits typically exceed the levelized cost of electricity (LCOE) of renewable energies; [5] Electric vehicle (EV) adoption generally improves air quality on peak pollution days, but can result in ozone dis-benefits in urban centers due to the titration of ozone with nitrogen oxides. Drawing from these published studies, we review the state of knowledge on climate co-benefits to air quality and health, identifying opportunities for policy action and further research.
As policy organizations consider strategies to mitigate climate change, decarbonization initiatives can also reduce health-impacting air pollutants and may affect the associated racial disparities of adverse effects. With the U.S. EPA CO-Benefits Risk Assessment Health Impacts Screening Tool (COBRA), we compare three decarbonization scenarios and their impacts at the regional and county scales. COBRA calculates changes in county-level ambient fine particulate matter (PM2.5), and associated mortality impacts, for each decarbonization scenario. We compare these patterns with demographic data to evaluate the relative exposure reduction benefit across race and ethnicity. Carbon-free electricity would reduce national average ambient PM2.5 concentrations by 0.21 μg/m3, compared with a 0.19 μg/m3 reduction associated with carbon-free industrial activity, and a 0.08 μg/m3 reduction associated with carbon-free light duty vehicle (LDV) transportation. Decarbonization strategies also vary in terms of the racial groups most benefitting from each scenario, due to regional and urban/rural patterns in emission sources and population demographics. Black populations are the only group to experience relative exposure reduction benefits compared to the total population in every scenario, with industrial decarbonization yielding 23% greater reductions in ambient PM2.5 concentrations for Black populations than for the total U.S. population. The largest relative reduction in PM2.5 exposure was found for Asian populations in the carbon-free LDV transportation scenario (53%). The magnitudes of total air quality improvements by scenario vary across regions of the U.S., and generally do not align with the decarbonization policy that achieves the largest equity goal. Only the transportation decarbonization scenario meets the criteria of the Justice40 Initiative nationwide, fulfilling the 2021 commitment by U.S. President Biden that federal investments in clean energy are designed to allocate at least 40% of benefits to disadvantaged communities.
Air quality models can support pollution mitigation design by simulating policy scenarios and conducting source contribution analyses. The Intervention Model for Air Pollution (InMAP) is a powerful tool for equitable policy design as its variable resolution grid enables intra‐urban analysis, the scale of which most environmental justice inquiries are levied. However, InMAP underestimates particulate sulfate and overestimates particulate ammonium formation, errors that limit the model's relevance to city‐scale decision‐making. To reduce InMAP's biases and increase its relevancy for urban‐scale analysis, we calculate and apply scaling factors (SFs) based on observational data and advanced models. We consider both satellite‐derived speciated PM 2.5 from Washington University and ground‐level monitor measurements from the U.S. Environmental Protection Agency, applied with different scaling methodologies. Relative to ground‐monitor data, the unscaled InMAP model fails to meet a normalized mean bias performance goal of <±10% for most of the PM 2.5 components it simulates ( p SO 4 : −48%, p NO 3 : 8%, p NH 4 : 69%), but with city‐specific SFs it achieves the goal benchmarks for every particulate species. Similarly, the normalized mean error performance goal of <35% is not met with the unscaled InMAP model ( p SO 4 : 53%, p NO 3 : 52%, p NH 4 : 80%) but is met with the city‐scaling approach (15%–27%). The city‐specific scaling method also improves the R 2 value from 0.11 to 0.59 (ranging across particulate species) to the range of 0.36–0.76. Scaling increases the percent pollution contribution of electric generating units (EGUs) (nationwide 4%) and non‐EGU point sources (nationwide 6%) and decreases the agriculture sector's contribution (nationwide −6%).
A common challenge in evolutionary swarm robotics is the transfer of simulated results into real-world applications. This difficulty can arise in a variety of real-world settings and problems such as sensory differences in robots and changes in the environment. We identify this reality gap at a simulation level by comparing the evolved behaviours of simulated Kilobots in two different models with different levels of abstraction. Our aim is to identify the reality gap that occurs at simulation level by increasing the task difficulty and noting differences in outcomes. Insights gained in this process may help rule out any further causes of reality gap when moving to experiments with physical robots.
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