Xianru Han

Xianru Han

Ph.D. Candidate in Agricultural and Resource Economics

University of Maryland

Biography

I am a fifth-year Ph.D. candidate in the Department of Agricultural and Resource Economics at the University of Maryland. I am an environmental and energy economist with a major focus on distributional effect of environmental policy and heterogeneous responses to environmental risk.

Interests

  • Environmental Economics
  • Energy Economics

Education

  • Ph.D. in Agricultural and Resource Economics, current

    University of Maryland

  • M.A. in Statistics, 2020

    Columbia University

  • B.S. in Agricultural and Resource Economics, Statistics, 2018

    University of Maryland

Publications

A Burning Issue: Wildfire Smoke Exposure, Retail Sales, and Demand for Adaptation in Healthcare

Wildfire events have increased in frequency and severity across the United States in recent decades. While a growing literature has documented the effects of wildfire smoke exposure on a wide range of health and socioeconomic outcomes, little is known about its impact on consumer behavior and household demand for adaptation in healthcare. We combine a newly developed and digitized dataset on daily wildfire smoke PM2.5 concentrations across the contiguous United States during 2006-2019 with weekly Nielsen retail scanner data to quantify how wildfire smoke exposure affects retail sales of air purifiers, bottled water, cold remedies, nasal products, cough products, and nutritional products. We find a positive and statistically significant impact of wildfire smoke exposure on the retail sales of these products. Dynamic effects are evident as wildfire smoke exposure in previous weeks also increases current sales. Nonlinear effects arising from the varying intensity of wildfire smoke exposure also reveal distinct patterns of demand for adaptation. We further explore how the effects of wildfire smoke exposure vary with socio-demographic characteristics, focusing on social vulnerability and highlighting the implications of environmental justice. Our results underscore the need for proactive policies to address the increased demand for medical and healthcare products as household adaptive measures during the wildfire season, particularly targeting socioeconomically vulnerable populations who may be prone to limited access to other preventive measures against wildfire.

Navigate through the haze: Wildfire smoke exposure and Metrorail ridership

Adverse weather events significantly impact the operations of urban transportation systems and change human travel behaviors. Over the decades, wildfires have emerged as a pressing concern due to their increased frequency and intensity, yet the relationship between wildfire smoke and public transportation usage remains largely unexplored. Leveraging high-resolution daily wildfire-driven PM2.5 concentration estimates and station-level Metrorail ridership data in the Washington Metropolitan Area spanning 2012–2019, we examine the effects of wildfire smoke exposure on Metrorail usage. We find that wildfire smoke exposure results in a 0.8% increase in Metrorail ridership on weekdays and a more pronounced 3.7% rise on weekends. Additionally, we show a stronger response in Metrorail ridership to wildfire smoke during off-peak hours compared to peak hours, with the most substantial increase observed during the winter. Our heterogeneity analysis further suggests that a lack of vehicle ownership and higher reliance on walking and public transportation are key factors leading to increased Metrorail ridership. Collectively, these results highlight the need for proactive service adjustments and effective communication strategies to accommodate the potential shifts in human travel behaviors and Metrorail ridership on days exposed to wildfire smoke.

Teaching Assistant

AREC 610: Microeconomic Applications in Agricultural and Resource Markets (Graduate)

University of Maryland, Spring 2022, Spring 2024

AREC 260: The Science of Gender in Economics and Development (Undergraduate)

University of Maryland, Fall 2021

GR5293: Topics in Data Science: Applied Machine Learning for Financial Modeling (Graduate)

Columbia University, Spring 2020

Contact