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Environmental Justice

Paper Session

Monday, Jan. 4, 2027 2:30 PM - 4:30 PM (EST)

Grand Hyatt Washington
Hosted By: Association of Environmental and Resource Economists
  • Chair: Tihitina Andarge, University of Massachusetts-Amherst

Picking Where to Measure: Sorting vs. Siting in Water Quality Monitoring

Laura Grant
,
Claremont McKenna College
Danae Hernandez-Cortes
,
Arizona State University
Christian Langpap
,
Oregon State University

Abstract

Across sectors from education to health, spatial patterns in resource allocation and risk exposure reflect both household sorting and policy-driven siting. In environmental justice, sorting occurs when disadvantaged groups settle in lower-amenity areas due to housing costs, while siting refers to differential monitoring and regulatory responses.

The U.S. Clean Water Act (CWA) mandates water quality monitoring but leaves placement to state discretion, creating potential inequality through demographic differences in monitoring and regulatory oversight. We provide the first comparison of randomly and strategically placed monitors, isolating policy-driven disparities from population traits.

Our analysis combines high-resolution demographic and water quality data across the U.S. Demographics come from the Census Bureau’s Gridded Environmental Impacts Frame, providing race, ethnicity, income, and population at a 0.01-degree resolution. We classify grid cells as near random monitors, strategic monitors, or unmonitored streams. Monitoring data come from two sources: the National Rivers and Streams Assessment (NRSA), with 2,000 randomly placed sites sampled every five years, and the Water Quality Portal (WQP), with roughly 20,000 agency-placed sites from 1989–2019 reflecting regulatory priorities. NRSA captures sorting alone; WQP reflects both sorting and siting.

We estimate census-tract-level models of monitor placement and density as functions of demographic composition for both monitor types. Random sites identify sorting, while differences between random and strategic models identify siting. Controls include river miles, stream order, population, tract area, and county and year fixed effects.

We find demographic differences in both random and strategic placement, with monitors less likely in tracts with higher shares of Hispanic or Black residents and more likely in predominantly White tracts. Effects are larger for strategic monitors, with 60–96\% of the total effect attributable to siting, indicating under-monitoring in disadvantaged communities. This has implications for water quality regulation and CWA implementation, making monitoring bias a key concern for equitable environmental governance.

Regulating Noise Pollution

Takanao Tanaka
,
University of California-Berkeley
Takeru Sugasawa
,
Housing Research and Advancement Foundation of Japan
Shinsaku Takikawa
,
London School of Economics

Abstract

Millions of households are exposed to harmful traffic noise, a widespread and costly environmental externality, yet evidence on the benefits of noise mitigation policies remains scarce because long-run, large-scale noise measurements are rarely available. We study Japan’s Noise Prevention Act, which combines vehicle noise-emission standards with road infrastructure policies such as low-noise pavement. Using measured noise data for roughly 17,000 major road segments from 2002 to 2022, a dataset rarely used in economics, newly collected information on pavement upgrades obtained through information-disclosure requests, and residential land-price data, we provide three sets of evidence. First, average roadside noise declined by about 3 dB over two decades, corresponding to a 50% reduction in noise energy and roughly 20% lower perceived loudness, with larger declines along initially louder road segments. Second, an engineering-based decomposition indicates that noise mitigation policies explain most of this decline: low-noise pavement and tighter vehicle noise-emission standards each account for roughly 40%. Third, exploiting staggered pavement upgrades, we estimate that low-noise pavement reduces local noise by about 3 dB and raises nearby residential land prices by 7% within 100 meters and 3% within 100–200 meters. These estimates imply a capitalization effect of approximately 2% per 1 dB noise reduction near treated roads. Taken together, the results show that noise regulation can generate economically meaningful improvements in environmental quality and local property values. Jurisdictions with weaker or inactive noise regulation, such as the United States, where federal noise regulation has been largely inactive for more than half a century, may leave substantial welfare gains unrealized.

Pollution, Population, and Production: A Structural Analysis of Wildfire Smoke and Spatial Sorting

Jadeep Mandia
,
Indian Institute of Management Ahmedabad
Christopher Knittel
,
Massachusetts Institute of Technology

Abstract

Wildfires in the United States are becoming more frequent and severe, with California bearing the greatest burden. While prior research emphasizes direct damages and health effects, this study examines how wildfire incidents and their smoke influence household migration and firm performance. Exploiting temporal variation in wildfire and smoke exposure from 2011–2023, we combine household-level migration and demographic data with firm-level information on employment, revenue, and survival. Both hazards cause persistent population losses, with smoke having the larger effect: a one–standard-deviation increase in smoke exposure reduces household counts by 0.3–0.4% each year following exposure, whereas comparable wildfire exposure lowers them by about 0.2%. By contrast, smoke exposure has particularly strong effects on older households: those aged 50 and above experience declines exceeding 0.5% in several lags, while younger groups show small, short-run inflows, reflecting differences in preferences toward smoke exposure and likelihood of homeownership. These heterogeneous responses translate into meaningful shifts in neighborhood composition, altering the spatial sorting of households across affected areas. Smoke exposure also leads to sizable and lasting contractions in business activity, reducing firm counts by about one percent per standard deviation, with smaller firms disproportionately affected. To capture the general equilibrium implications of these shifts, we estimate a residential sorting model in which local amenities are endogenously supplied by firms operating within neighborhoods. We find that, on average, households are willing to pay 3.2% of property value to avoid such smoke exposure, rising to 4% among those aged 65 and above. The model highlights that older households are key drivers of local service demand—particularly in health and retail—while education services are least responsive to changes in the older population.

Zoning and Neighborhood Air Quality: Evidence from HOLC Boundaries

Mingxuan Fan
,
National University of Singapore
Corbett Grainger
,
University of Wisconsin-Madison
Siyuan Hu
,
University of Wisconsin-Madison

Abstract

Does exclusionary zoning cause environmental inequality? We estimate the causal effect of municipal residential zoning on local air pollution exposure across 39 major U.S. cities. As zoning is endogenous to neighborhood amenities, we instrument for present-day zoning status using historical Home Owners' Loan Corporation redlining boundaries. To separate the regulatory legacy of redlining from pre-existing neighborhood characteristics captured by the HOLC maps, we control extensively for pre-period manufacturing activity, transportation networks, and other historical proxies for local pollution sources. The first stage is strong and monotonic: areas assigned lower historical grades are more likely to permit multi-family housing today. Our preferred 2SLS estimate implies that multi-family zoning increases residents’ annual PM2.5 exposure by about 1.2 ug/m^3 (13% of the sample mean). These results indicate that land-use regulation plays a quantitatively important role in shaping disparities in personal exposure to air pollution in U.S. cities.

Discussant(s)
Corbett Grainger
,
University of Wisconsin-Madison
Simon Greenhill
,
University of California-Berkeley
Lala Ma
,
University of Kentucky
Randy Walsh
,
University of Pittsburgh
JEL Classifications
  • Q5 - Environmental Economics
  • D6 - Welfare Economics