Air Quality and Health

Research · Lawrence Berkeley National Laboratory · Aug 2023 – May 2024

Building a modeling workflow to estimate how U.S. appliance efficiency standards change regional air quality — and what that means for the communities living near or downwind of emitting facilities.

Context

The U.S. Department of Energy sets mandatory minimum efficiency levels for more than 70 covered products. Standards analyses already monetize selected national health benefits from reduced SO2 and NOx, but benefit-per-ton estimates aggregate away the specific generators, regions, and populations affected.

My role

I joined the Energy Technologies Area’s Economics Subgroup as a graduate researcher for the 2023–24 academic year, conducting the analysis as my Master of Development Practice capstone. I was responsible for building the appliance-load and energy-savings inputs that connect efficiency-standard scenarios to LBNL’s grid and air-quality models.

Methods and work performed

Using room air conditioners as the test case, I worked with LBNL residential metering data from Pennsylvania and NREL ResStock simulated load profiles, imputed missing readings by regression, combined DOE shipment projections with Residential Energy Consumption Survey stock estimates, and formatted hour-by-hour savings as inputs to LBNL’s grid model and the InMap air-quality model. Analysis in R and Python.

Outputs and contribution

LBNL researchers ran the completed inputs through the grid and health models, producing mortality and social-cost estimates under multiple grid scenarios and confirming the workflow functions end-to-end. Results can be disaggregated by disadvantaged-community status and race-ethnicity. This project sits at the center of how I approach climate work: connecting technical analysis to the people who bear the costs.

Implications

National averages hide local burdens. Making regional health effects computable — even imperfectly — changes what policymakers can be asked to consider.