The U.S. EJ movement emerged in the 1980s over race and class inequities in the distribution of environmental health hazards, environmental enforcement, and exclusionary environmental movements, science, and policy-making. Struggles to address these environmental inequalities continue to this day alongside important opportunities to advance the movement for environmental health equity. This especially true in California, where prominent EJ activism, precedent-setting EJ policies, and innovative EJ researchers have made the state a home for the development of cumulative impact mapping techniques that advance our understanding of the relationship between population vulnerability and the spatial concentration of multiple environmental health hazards [23
CalEnviroScreen emerged from this context as a novel cumulative impact mapping tool with broad implications for environmental and public health research and policy. However, it excludes racial status as an indicator of population vulnerability of exposure to multiple environmental health hazards in an effort to make it more widely applicable throughout California state government [49
]. As such, it departs from its historical foundations and comparable cumulative impact mapping tools in California [25
], as well as from a broad cross-section of scholarly literature that emphases the salience of racial factors, particularly racial segregation, in concentrating socioeconomic, health, and environmental disadvantages in the United States and California [1
]. In addition, CalEnviroScreen is limited in that its cumulative pollution burden indicator precludes researchers and policy makers from assessing the extent to which traffic patterns and/or industrial zoning—well documented pollution sources [5
]—contribute to the spatial concentration of multiple environmental health hazards in California.
These limitations are manifest in the only other multivariate analysis of CalEnviroScreen to date by Greenfield, Rajan and McKone [45
]. That study analyzed zip code-level associations between the CalEnviroScreen 3.0 cumulative impact score and its component environmental and socioeconomic variables. PCA was used in that analysis to uncover multivariate relationships in CalEnviroScreen while reducing the environmental and socioeconomic variables into composite factors and facilitating a spatial regression analysis of their effects on zip code-level disease burden (i.e., the hospitalization rate for at least one of 14 diagnostic categories that have potential environmental etiology). Similar to the present study, that analysis of CalEnviroScreen 3.0 found that industrial activity and air pollution contribute significantly to cumulative impacts in California, and that the SJV and the South Coast region were hot spots for cumulative impacts in the state. It also found that the socioeconomic factor produced from PCA of the CalEnviroScreen 3.0 data explained more of the variance in disease burden than the environmental factor variables. While insightful, such line of inquiry is dependent primarily on CalEnviroScreen data and the course spatial resolution of zip codes. It is further limited by not assessing the effect of racial segregation on cumulative pollution burdens in California, net of non-racial population vulnerability factors, industrial zoning, traffic density, and the spatial clustering of cumulative pollution burden in the state.
The present study “retools” CalEnviroScreen and begins to address these important limitations by incorporating conceptual frameworks and analytical techniques from the environmental health and inequality literature. Such scholarship illuminates the effect of race-based environmental health vulnerabilities on cumulative pollution burden, net of other factors accounted for—and unaccounted for—in CalEnviroScreen.
The resulting analytical framework advanced in this article is analogous to previous sociological efforts to reformulate cross-national analyses of environmental impacts using the “IPAT” model. The IPAT accounting equation specified environmental impacts (“I”) as the product of population (“P”), affluence (“A,” measured as per capita consumption or production levels), and technological factors (“T”). An alternative stochastic approach used in the “STIRPAT” model [75
] reformulated IPAT and facilitated multivariate regression analyses, which test sociological theories specifying how population, affluence, and technology contribute to environment impacts. Impacts analyzed in that line of inquiry include, for example, national emissions of CO2
] and ecological footprints (i.e., the amount of biologically productive land needed to sustainably support each individual in a nation) [78
Similarly, this study retooled CalEnviroScreen by moving away from the multiplicative framework wherein cumulative impacts are the product of cumulative pollution burden (environmental exposures and effects) and population vulnerability (sensitive populations and socioeconomic factors) (see Figure 1
). As shown in Figure 3
, the present study modeled the adjusted cumulative pollution burden percentile rankings (that excludes traffic density) as a multivariate function of racial and non-racial population vulnerability factors, traffic density and industrial zoning, and local and regional spatial dynamics. Bivariate correlations between CalEnviroScreen’s and this study’s cumulative pollution burden measures, and the individual pollution burden variables that comprise them, show both cumulative pollution burden indicators are substantively similar and heavily influenced by ambient air pollution levels. The primary difference between the cumulative pollution burden indicators results from excluding traffic density in the adjusted measure: traffic density has a moderate correlation with the CalEnviroScreen 2.0 cumulative pollution burden and a lower correlation with adjusted cumulative pollution burden.
This study demonstrates how CalEnviroScreen can be effectively reformulated to address long-lasting EJ concerns [5
] and related scholarship on environmental inequality, environmental health disparities, and cumulative impacts regarding the effects of race-based vulnerabilities on the unequal distribution of environmental health hazards [10
]. As found in previous cumulative impact mapping studies of California’s SJV [40
], the extrinsic vulnerability measures of socioeconomic status in CalEnviroScreen and the additional racial composition measures incorporated in this study were more strongly correlated with both cumulative pollution burden indicators than the intrinsic vulnerability measures of health status and susceptible populations. In addition, the percent of non-Latina/o black and Latina/o population in a tract were positively correlated with both cumulative pollution burden indicators. The extent of Latina/o presence in a tract, however, had the strongest bivariate correlation with cumulative pollution burden indicators. These patterns provided initial evidence of the continuing significance of race-based vulnerabilities in effecting cumulative pollution burden in California—regardless of whether traffic density is included in the cumulative pollution burden indicator.
As found in previous related research [45
], principal component analyses helped to reduce highly correlated variables into three dimensions of population vulnerability that were incorporated into the spatial error regression analyses of adjusted cumulative pollution burden. These principal component factors were economic disadvantage and separate cumulative disadvantage factors for black and Latina/o neighborhoods. Black cumulative disadvantage represented the extent of health vulnerability, economic disadvantage, and black populations in a tract. Latina/o cumulative disadvantage represented the extent of health vulnerability, economic disadvantage, limited English-speaking ability, and Latina/o populations in a tract.
The findings from the spatial error regression analyses have important scholarly and practical implications. First, economic disadvantage was the primary population vulnerability determinant of adjusted cumulative pollution burden rankings in California when racial factors were not considered in the regression analysis. However, when a tract’s black and Latina/o composition were considered alongside CalEnviroScreen 2.0 socioeconomic factors of economic disadvantage and linguistic isolation in the regression analysis, the percent of Latina/o population in a tract was the only population vulnerability indicator to have a significant effect on adjusted cumulative pollution burden. In addition, black and Latina/o cumulative disadvantage both had significant positive bivariate correlations with adjusted cumulative pollution burden. However, Latina/o cumulative disadvantage was the only cumulative disadvantage factor to maintain a statistically significant and positive effect on adjusted cumulative pollution burden percentile ranking in the spatial error regression analysis.
These racial patterns compare and contrast with previous cumulative impact mapping studies using CalEnviroScreen. In particular, zip code-level research on CalEnviroScreen 1.1 found that Latinas/os, followed by blacks, were significantly more likely than whites to live in areas with higher cumulative pollution burden, more susceptible populations, and lower socioeconomic status, net of population density and different cumulative impact scoring procedures [42
]. Similar race-based environmental health disparities for Latinas/os and blacks were found in bivariate analyses of CalEnviroScreen 2.0 [50
]. Racial factors were not considered in the multivariate analysis of CalEnviroScreen 3.0 by Greenfield, Rajan, and McKone [45
]. Thus, the present study offers a novel multivariate framework for integrating and analyzing CalEnviroScreen data and data external to the screening tool to advance scholarly and policy-relevant environmental and public health research on race-based environmental health vulnerabilities and cumulative pollution burden in California.
This article illustrates that Latinas/os in California are, on average, more likely to be exposed to social, health, and environmental burdens than other racial groups. This pattern was found even when controlling for a broader set of factors that generally contribute to environmental health disparities in California and throughout the United States [11
], but have yet to be accounted for in previous cumulative impact mapping studies using CalEnviroScreen. In particular, tract containment in the South Coast and SJV air basins, followed by the extent of industrial-zoned land and traffic density in a tract, were the strongest determinants of adjusted cumulative pollution burden rankings among all variables considered in this analysis. These findings support previous regional cumulative impact mapping studies that suggest living in the South Coast and SJV air basins is a significant and independent place-based vulnerability of exposure to cumulative pollution burdens [25
However, previous research merely focused on the vulnerability of nonwhite populations in the South Coast and SJV. This study builds environmental inequality research that is attentive to the distinct vulnerabilities of different racial groups [52
]. Specifically, it demonstrates the need to differentiate relative cumulative pollution burdens across nonwhite residential settlements to more fully assess the nature and extent of race-based environmental health vulnerabilities in California and its South Coast and SJV regions.
Despite the contributions of this study, future research should address its important limitations. First, data in CalEnviroScreen 2.0 and the additional sources used in this study inform our understanding of racial disparities in cumulative pollution burden in California between 2004 and 2013. Environmental health researchers have begun to analyze more recent CalEnviroScreen 3.0 data, and they claim similar associations evident between CalEnviroScreen 2.0 component indicators are found in CalEnviroScreen 3.0 [45
]. Future research should examine the extent to which the conceptual framework and findings from this study apply to CalEnviroScreen 3.0 data and more recent land use and racial composition data.
Second, future research with CalEnviroScreen should follow the lead of studies of environmental health vulnerabilities in Texas [80
] and Florida [81
], which demonstrate the need for environmental health and inequality researchers to untangle the broad “Latina/o” category. Doing so enables one to more fully understand how Latina/o environmental health vulnerabilities are affected by, for example, country of origin, English-speaking ability, income, and education. Data limitations of American Community Survey, such as the limited reliability of some of the average estimates [43
], will preclude researchers from fully pursuing such lines of inquiry in the future. However, there are opportunities available to reliably examine differential and spatialized race-based environmental health vulnerabilities if CalEnviroScreen data were merged with U.S. Decennial Census data on, for example, family structure, home ownership, and specific origin for Latinas/os (and other racial groups).
Third, this cross-sectional study did not attend to the important historical processes and events that concentrate cumulative pollution burdens in California neighborhoods. Future research could use urban sociological and environmental inequality literature as a guide [15
] and examine how segregationist ideologies, state policies, real estate industry actions, and various dynamics of neighborhood change spatially concentrated industrial land uses, major transportation corridors, cumulative pollution burdens, and vulnerable populations in California.
Fourth, this study did not attend to the fine-grained spatial scales, such as individual-level residential mobility patterns throughout a diverse set of spatial contexts overtime [78
], which may contribute to the environmental health disparities uncovered in the present study. What are the diverse pathways by which individuals are exposed to multiple environmental health hazards over space and time in California?
Lastly, this study may have limited generalizability to other contexts outside of California. Specifically, traffic density may be an important proxy for near-road air pollution in areas outside of California with less densely developed air monitoring networks. In those contexts, cumulative pollution burden may need to incorporate traffic density as a proxy environmental pollution indicator. In addition, this study emphasized the significance of racial segregation, especially of Latinas/os, as a determinant of concentrated cumulative pollution burden, net of other factors. Latina/o cumulative environmental health disparities evident in California are salient, on average, throughout the United States [52
]. However, the extent to which the residential segregation of Latinas/os, or blacks or other racial minorities for that matter, best represents population vulnerability and the social determinants of cumulative pollution burden may vary by region throughout the United States and likely throughout various international contexts. A key insight from this study and other cumulative impact mapping studies of California [25
], however, is that cumulative pollution burdens tend to be concentrated amongst the most vulnerable and segregated populations in a given locality. Researchers and policy-makers must recognize that tendency and direct their research and policy accordingly or they will miss the key social determinants of cumulative pollution burden in their respective area of study and regulatory authority.