Abstract View
High-Resolution Social Cost Modeling Offers Insight into Efficiency-Equity Trade-Offs in Pollution Mitigation Strategies
BRIAN GENTRY, Allen Robinson, Peter Adams, Carnegie Mellon University
Abstract Number: 554
Working Group: Aerosol Exposure
Abstract
Air pollution causes upwards of 100,000 premature mortalities per year in the United States, with fine particulate matter (PM2.5) causing a bulk of these mortalities. Measures to control air pollution are therefore a high-priority policy target that can yield large social benefits. Additionally, recent studies have demonstrated a significant difference in PM2.5 exposure between races, making air pollution control a key environmental justice issue. However, accurate modeling of health impacts of emissions requires extensive software experience and significant computational time.
Reduced complexity models (RCMs) seek to simplify this process by offering location-specific marginal social cost estimates for primary PM2.5 and major PM2.5 precursors, allowing for rapid screening of potential pollution mitigation policies. We extend the capabilities of one such model, EASIUR, to capture intra-urban area variabilities in social cost damages of primary PM2.5 emissions. This model combines the chemical transport model core of EASIUR to simulate the long-range transport of air pollutants with a Gaussian plume model for local transport. We find that social cost damages can vary by an order of magnitude within a single county, while spatially aggregated cost estimates are in good agreement with EASIUR and other RCMs. Social costs of primary PM2.5 may be previously underestimated due to spatial variation in marginal social cost estimates and emissions. Analysis of a source-receptor version of the model indicates that emissions in most urban areas also contribute to higher PM2.5 exposure among people of color than among white people. We demonstrate use of the tool by evaluating the distribution of benefits and costs of high electric vehicle adoption in the U.S. Combined with spatially resolved emissions inventories, this tool can be used to identify specific emissions sectors that yield great social benefits and reductions in exposure disparities across social groups.