2025/01/28 by Xiaodan Zhou, Shu Yang, Zhou, Xiaodan +3
Economics, Econometrics and Finance · Energy · #Applications (stat.AP) #Climate Change Policy and Economics #Energy, Environment, Economic Growth #Energy, Environment, and Transportation Policies #FOS: Computer and information sciences
paper · pdf · doi:10.48550/arxiv.2501.16958
openalex publication_date 2025/01/28 · openalex created_date 2025/01/30 · openalex updated_date 2026/07/28
Recent studies have shown associations between redlining policies (1935-1974) and present-day fine particulate matter (PM2.5) and nitrogen dioxide (NO2) air pollution concentrations. In this paper, we reevaluate these associations using spatial causal inference. Redlining policies enacted in the 1930s, so there is very limited documentation of pre-treatment covariates. Consequently, traditional methods fails to sufficiently account for unmeasured confounders, potentially biasing causal interpretations. By integrating historical redlining data with 2010 PM2.5 and NO2 concentrations, our study aims to discern whether a causal link exists. Our study addresses challenges with a novel spatial and non-spatial latent factor framework, using the unemployment rate, house rent and percentage of Black population in 1940 U.S. Census as proxies to reconstruct pre-treatment latent socio-economic status. We establish identification of a causal effect under broad assumptions, and use Bayesian Markov Chain Monte Carlo to quantify uncertainty. Our analysis indicates that historically redlined neighborhoods are exposed to notably higher NO2 concentration. In contrast, the disparities in PM2.5 between these neighborhoods are less pronounced. Among the cities analyzed, Los Angeles, CA, and Atlanta, GA, demonstrate the most significant effects for both NO2 and PM2.5.