Conference Agenda

We show that interactions between overlapping environmental regulations—particularly when one involves a threshold-based standard—can substantially attenuate the benefits predicted when policies are evaluated in isolation. This attenuation arises because conventional models and analyses typically assume local emissions are exogenous to upwind regulatory shocks. To study the impacts of this assumption, we develop a model in which threshold-based air-quality standards overlap with regulations targeting major point sources. We then empirically test the model’s predictions and document evidence consistent with regulatory rebound: when upwind power-plant emissions fall (e.g., due to point-source rules), local emissions in NAAQS-constrained areas rise. We find that regulatory rebound offsets 50–70 percent of expected PM₂.₅ improvements. To quantify rebound’s impact, we develop a high-resolution modeling framework that combines advanced particle-trajectory modeling (NOAA’s HYSPLIT) with machine learning to map upwind power-plant emissions to downwind air quality with state-of-the-art accuracy. We use these predictions to quantify how sustained reductions in upwind emissions of PM₂.₅ precursors affect downwind local PM₂.₅ concentrations. The results are consistent with local pollution rebounding in response to upwind emission reductions—particularly in NAAQS-constrained areas. Our findings underscore the importance of accounting for behavioral and regulatory interactions when designing and evaluating environmental policy—critical for efficiency and equity.