Evaluating Health Damages of Air Pollution from Power Generation: Impact of Spatial Resolution on Estimates of Racial and Income Disparities

The power sector is a leading source of ambient fine particulate matter (PM2.5), which has been identified as the primary contributor to detrimental health impacts caused by air pollution. This burden disproportionately impacts vulnerable ethnic and socioeconomic groups, exacerbating existing environmental inequities. Power system models (PSMs) are essential tools for optimizing electricity system planning and operations. However, they often fail to account for the social implications of energy production, resulting in decisions that may overlook energy justice priorities. Incorporating marginal health damage costs into PSMs offers an opportunity to address environmental justice in decision-making processes.

In this study, we assess how the spatial resolution of an air pollution modeling framework influences the assessment of marginal health damage cost estimates by racial-ethnic and income group from power plants in Texas. We utilize CALPUFF, an air pollution dispersion model, to simulate ambient concentrations of PM2.5 caused by coal and natural gas power plant emissions at different spatial resolutions. We then calculate marginal health damage costs based on mortality risk by race and income level, and the value of a statistical life during different time periods to represent winter and summer conditions. Here, we apply the modeling framework to a set of diverse power plants and show how variations in spatial resolution of modeled air pollution fields can influence estimates of marginal health damage costs by race and income. Moreover, our findings explore the implications of spatial resolution and model uncertainty for decision-making aiming to integrate socioeconomic disparities into power system planning.