Research

Publications 

1.  "Air Pollution Exposure and Donation to Its Victims: Evidence From Online Charitable Giving" (with Peng Shen, Xincheng Wang, Yinxiao Wang, Yucheng Wang and Shuhuai Zhang) Journal of Environmental Economics and Management (2025): 103245.

2.  "Modeling Behavioral Response to Infectious Diseases Under Information Delay” (with Haosen He and Frederick Chen) Review of Economic Design (2025): 1-22.

3. "Observational Studies Generate Misleading Results About the Health Effects of Air Pollution: Evidence From Chronic Air Pollution and COVID-19 Outcomes." (with Marc N. Conte, Matthew Gordon,Nicole A. Swartwood,and Rachel Wilwerding) PloS One 19.1 (2024): e0296154.

4.  "An Agent-based Model of Elephant Crop Consumption Walks Using Combinatorial Optimization" (with Haosen He, Erin Buchholtz, Frederick Chen, and Susanne Vogel) Ecological Modelling  (2022): 464, 109852.  

5. "COVID-19's U.S. Temperature Response Profile"  (with Richard Carson, Samuel Carson, Thayne Dye, Samuel Mayfield, and Daniel Moyer) Environmental and Resource Economics (2021): 1-30. 

Working papers

"Mismeasured and Misunderstood: Unmasking the Temperature Proxy Problem in Climate Impact Estimates" with Richard T. Carson. R&R at Journal of the Association of Environmental and Resource Economists.

Thousands of studies estimate prospective climate change impacts by examining how temperature affects a wide range of outcomes of interest. Potential measurement error in temperature variables is routinely ignored, and when acknowledged, typically assumed to be small in magnitude with a classical (i.i.d. normal) distribution, leading estimates to be attenuated toward zero. We formally document the sources of errors introduced in the construction of temperature proxy variables and show this widely held assumption is wrong using a best-case approach that treats readings at 1,120 U.S. weather stations as truth. Overall, the average downward bias in estimated impacts is substantial. However, due to the non-classical nature of the measurement errors, overestimates are not rare. Estimation problems are concentrated where attention focuses—the tails of the temperature distribution.

"Exposure, Vulnerability and Environmental Injustice: Air Pollution and Emergency Medical Care in NYC" with Marc N. Conte and Rachel Wilwerding. Under review.

Air pollution creates inequitable environmental burdens through both hazard exposure and vulnerability. Using Emergency Medical Service records in NYC, we find that air pollution increases dispatches significantly across diverse health and behavioral outcomes, with disproportionate impacts on disadvantaged communities. EPA monitoring networks systematically underestimate pollution in disadvantaged areas, biasing causal estimates downward by up to 28\%. Under everyday pollution levels, disadvantaged communities appear to manage certain respiratory conditions through accessible defensive technologies without relying on EMS. However, during the 2023 Canadian wildfires, an extreme pollution event, structural constraints limited the effectiveness of defensive behaviors, revealing increased vulnerability in these communities.

"Smoggy Instruments: A Breath of Bias in Air Pollution Impact Studies" with Zhenxuan Wang. [slides]

Instrumental variable (IV) methods dominate empirical studies of air pollution’s impacts, yet their validity often rests on untested exclusion restrictions. Using comprehensive Chinese pollution and weather data, we identify three systematic threats to these instruments. First, simulations demonstrate co-pollutant bias, where instruments influence multiple pollutants. Second, measurement-error tests reveal non-classical correlations between satellite-based pollution proxies and meteorological instruments. Third, a difference-in-differences analysis shows that infrastructure expansion alters local thermal inversions, confounding exogeneity. We document these structural violations and offer practical recommendations for empirical researchers using IV strategies in environmental applications.

Work in progress

"Unintended Digital Precaution in Extreme Weather" with Tin Cheuk Leung [slides]

Resting papers

"Estimating the Impact of Climate Change: An Exploration of the Bin Regression Model" with Richard T. Carson and Dalia Ghanem

The “bin” regression model has been put forward as a flexible semi-parametric method for representing a climate variable and it has emerged as the workhorse approach for empirical work (e.g., Deschênes and Greenstone, 2011). Our paper is the first to formally explore econometric properties of the bin regression approach. We show that, although the bin regression approach often produces reasonable results, that the approach produces consistent parameter estimates only under very stringent and highly unlikely assumptions about the true data generating procedure. Problems with the bin regression approach are likely to be most severe in the tail bin categories, where most policy interest with respect to climate change impacts lies. We propose alternatives to bin model for the climate change impacts that produce consistent estimates and generally have better efficiency properties.