Historical Redlining Is Associated with Disparities in Environmental Quality across California

Abstract

Historical policies have been shown to underpin environmental quality. In the 1930s, the federal Home Owners' Loan Corporation (HOLC) developed the most comprehensive archive of neighborhoods that would have been redlined by local lenders and the Federal Housing Administration, often applying racist criteria. Our study explored how redlining is associated with environmental quality across eight California cities. We integrated HOLC's graded maps [grades A (i.e., "best" and "greenlined"), B, C, and D (i.e., "hazardous" and "redlined")] with 10 environmental hazards using data from 2018 to 2021 to quantify the spatial overlap among redlined neighborhoods and environmental hazards. We found that formerly redlined neighborhoods have poorer environmental quality relative to those of other HOLC grades via higher pollution, more noise, less vegetation, and elevated temperatures. Additionally, we found that intraurban disparities were consistently worse for formerly redlined neighborhoods across environmental hazards, with redlined neighborhoods having higher pollution burdens (77% of redlined neighborhoods vs 18% of greenlined neighborhoods), more noise (72% vs 18%), less vegetation (86% vs 12%), and elevated temperature (72% vs 20%), than their respective city's average. Our findings highlight that redlining, a policy abolished in 1968, remains an environmental justice concern by shaping the environmental quality of Californian urban neighborhoods.

Historical redlining is associated with disparities in wildlife biodiversity in four California cities

“…Rather, our results suggest that an individual is more likely to encounter a greater diversity of species with less search effort in California’s greenlined neighborhoods than in nongreenlined neighborhoods. This may potentially be explained by greenlined neighborhoods customarily having increased environmental quality [i.e., higher vegetation cover and reduced ecological disturbances ( 24 , 30 , 45 , 47 )], which in turn improves the likelihood of unique species occupying the given area. This holds broad implications for human well-being in urban spaces, as equity in nature access and quality, as well as promoting positive human-environment relationships (which are more likely with increased access to biodiverse spaces) are increasingly being considered as central issues of environmental justice ( 48 , 49 ).…”

“…Conversely, there was slight variation in which grade had the highest richness, with greenlined neighborhoods in San Diego and San Francisco and B-grade neighborhoods in LA and Oakland having the highest species richness. Both A and B-graded neighborhoods in California have relatively high environmental quality ( 45 ), suggesting that there may be more viable wildlife habitat compared to C and D-graded neighborhoods. We found consistent disparities on the clade level, with redlined neighborhoods frequently exhibiting the lowest species richness.…”

“…We focused on California, the most biodiverse and populous state in the United States, with some of the largest cities co-located with biodiversity hotspots. In addition, our previous work has demonstrated that previously redlined neighborhoods in California have higher pollution burdens, less vegetation, elevated temperatures, and more noise ( 45 )—habitat conditions that likely structure neighborhood biodiversity via bottom–up processes ( 8 ). First, we predicted that greenlined neighborhoods would detect more unique species with less sampling effort than redlined neighborhoods due to differences in green space and vegetation as well as potential skews in participation ( 33 , 46 ).…”

“…Finally, we examined differences in species communities (i.e., beta diversity) by comparing species assemblages among HOLC grades. We predicted that greenlined neighborhoods would be more dissimilar to redlined neighborhoods due to strong differences in environmental quality ( 8 , 30 , 45 ). We expected that HOLC grades that were closely ranked (i.e., A vs. B or C vs. D) would not differ in species assemblages.…”

Socioeconomic and sociodemographic correlations to COVID-19 variability in the United States in 2020

“…Moran I test on residuals was employed to check if the residuals exhibit spatial autocorrelation ( 42 ). This test is significant when the data exhibits clustering or anticlustering patterns and is insignificant if the spatial distribution is random ( 43 ). Having confirmed the non-random clustered distribution of residuals, one can conclude that either some of the important explanatory variables were missed from the model or there is an influence of some spatial process so the model systematically over/underpredicts the dependent variable at specific places.…”