Constructing Valid Geospatial Tools for Environmental Justice (2024) / Chapter Skim
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5 Selecting and Analyzing Indicators and Datasets and CEJST Indicators
5 Selecting and Analyzing Indicators and Datasets and CEJST Indicators
Pages 99-136
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From page 99... ...
. It then more specifically considers the burden categories and indicators used in the White House Council on Environmental Quality (CEQ)
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determine if and how cumulative impacts could be captured. CEJST includes eight categories of what CEQ labels "burdens" -- climate change, energy, health, housing, legacy pollution, transportation, water and wastewater, and workforce development.
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Burden Indicator 1 Indicator 2 Indicator 3 Dataset Dataset Dataset Dataset Dataset Dataset Dataset Dataset Dataset 1 2 3 1 2 3 1 2 3 FIGURE 5.1 Illustration of the relationship between burdens, indicators, and datasets. In CEJST, multiple indicators are used for each burden category, and each indicator is represented by a singular dataset (shown by the red circles)
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For example, many annual average ambient particulate matter (PM2.5) concentration datasets are used across the federal government and scientific community, and each was developed using different methods and data inputs (e.g., statistical modeling, geophysical modeling, satellite remote sensing)
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. Use of FAIR data and CARE principles may serve to enhance the transparency and acceptance of the data as representative of community lived experience.
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This is also referred to as community validation and buy-in. Involving community members and other interested and affected parties throughout the indicator selection process can be crucial for building credibility, not only to ensure that it represents people's lived experiences but also to engender trust.
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territories of Puerto Rico, American Samoa, the Northern Mariana Islands, Guam, and the U.S. Virgin Islands; and • The indicator data are available at the census-tract scale or finer.
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resulted in a reduction of specific climate or socioeconomic burdens or a change in the designation of disadvantage. It may also be useful to examine how spatial patterns and geographic locations of DACs are changing over time in a specific urban area, state, region, or nationwide.
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If the percentage of people liv ing at or below 200 percent of the federal poverty line (i.e., the definition of "low income" in CEJST) in two tracts are compared, the differences between the two may be similar but smaller than the corresponding MOEs.
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. Analyzing indicators and datasets can also ensure that, barring specific stakeholder concerns about representation, the indicators and datasets selected for inclusion are independent of each other, reflect the state of the science, and are trusted by technical experts and validated by lived-experience data of community members.
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14008 categories is unstated, the committee did not identify any obvious omissions, given the committee's understanding of the objectives of the tool. The rationale for the inclusion or exclusion of specific indicators within each burden category in CEJST is not provided in the CEJST technical documentation, either in terms of why certain indicators but not others were included or in terms of the categorization of individual indicators in specific burden categories (e.g., the inclusion of PM2.5, which has many emission sources, in the energy burden category)
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= percent of a census tract's population in households where household income is at or below 200 percent of the federal poverty level, not including students enrolled in higher education. SOURCE: Adapted from CEQ, 2022a.
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Potential changes to the burden categories themselves are not addressed; as previously mentioned, categorizations of the indicators only affect the identification of disadvantage to the extent that the number of indicators in each burden category differs, although these categorizations could become important in future iterations of the tool, particularly if an integration approach such as a composite indicator is used. Climate Change CEJST includes five indicators in the climate change burden category: expected agriculture loss rate, expected building loss rate, expected population loss rate, projected flood risk, and projected wildfire risk.
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. Future climate risks are another set of potential indicators for which there are datasets appropriate for CEJST.
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CEJST could take advantage of these extensive resources to provide a more comprehensive set of climaterelevant indicators that are also more transparent and consistent with datasets, tools, and reports from other parts of the federal government. For example, the USGCRP's CMRA tool includes several future climate burden categories that are not included in CEJST: extreme heat, drought, and coastal inundation.
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. 16 For more on National Ambient Air Quality Standards, see https://www.epa.gov/criteria-air-pollutants/ naaqs-table (accessed February 15, 2024)
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As stated previously, the committee does not focus on indicator placement under specific burden categories because the indicator groupings are irrelevant to the identification of DACs in the CEJST's current formulation. The rationale for including PM2.5 but not other major air pollutants (e.g., ozone, nitrogen dioxide)
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The energy burden index used within the DOE's Energy Justice Mapping Tool provides an opportunity to include additional aspects of disadvantage, including the percent of households not connected to gas or electric grids and the number and average duration of power outages. Other indicators that could be considered in the Energy burden category include those used by the Department of Energy (DOE)
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However, poor health makes communities more vulnerable to the health outcomes associated with climate change, pollution, and other CEJST burden categories. Currently, health data and exposure data are separate burden categories in CEJST and are not integrated.
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Including additional indicators, such as cancer and lack of health insurance, that are distinct from the four already included can capture additional communities who are experiencing disproportionate health burdens. Although no such data currently exist to the committee's knowledge, considering pediatric asthma onset 22 See CMS's Provider of Services File -- Hospital & Non-Hospital Facilities at https://data.cms.gov/pro vider-characteristics/hospitals-and-other-facilities/provider-of-services-file-hospital-non-hospital-facilities (accessed February 15, 2024)
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between 1935 and 1940.25 The boundaries in the HOLC maps were converted to census tracts by the National Community Reinvestment Coalition. Within CEJST, census tracts that have National Community Reinvestment Coalition scores of 3.25 or more out of 4 are considered to have experienced historic underinvestment.
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, there is relatively little data on leaded pipes at a national scale. EPA was mandated by America's Water Infrastructure Act of 2018 to evaluate and report on the cost of replacing lead service lines in its quadrennial Drinking Water Infrastructure Needs Sur vey and Assessment (DWINSA)
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. 30 See EPA Air Toxics Screening Assessment, 2017 Results: https://www.epa.gov/AirToxScreen/2017 airtoxscreen-assessment-results (accessed February 16, 2024)
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Furthermore, the spatial pattern of truck traffic and resulting diesel PM2.5 31 See DOT's ETCE and its user guide at https://experience.arcgis.com/experience/0920984aa80a4362b8 778d779b090723 (accessed February 12, 2024)
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. 34 See https://www.epa.gov/ust/ust-finder (accessed February 26, 2024)
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Common wastewater pollutants such as total 35 For more information on the DMR Loading Tool data, see https://echo.epa.gov/trends/loading-tool/ resources/about-the-data (accessed February 26, 2024)
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. SDWIS covers a large part of the population, but it is only consistently available at the county level, and it only applies to community drinking water systems that serve at least 25 people or have more than 15 connections.
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Bae, Kang, and Lynch (2023) examined the length of time that com munity water systems across the country were out of compliance with Safe Drinking Water Act (SDWA)
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One difference is in the threshold used to define low income -- the federal poverty level. For 41 See https://www.census.gov/programs-surveys/decennial-census/decade.html (accessed February 16, 2024)
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Unlike the measures of income relative to the federal poverty level, this income measure allows for some differentiation across regions, which could capture, for example, differences in the cost of living. Thus, a community with low educational attainment could qualify as disadvantaged based on the workforce development burden even with an income level that is high relative to the nation as a whole, as long as it is low relative to its area.
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as socioeconomic indicators -- including Centers for Disease Control and Prevention and Agency for Toxic Substances and Disease Registry Social Vulnerability Index, the FEMA NRI, the Department of Health and Human Services Environmental Justice Index, the DOE's Energy Justice Mapping Tool, the Census Community Resilience Estimates, and the DOT ETCE. Although lack of health insurance coverage from any source can be a significant stressor, especially among disadvantaged populations (Brown et al., 2000)
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are publicly available, it represents an alternative and potentially superior way to incorporate this type of vulnerability as an indicator in the workforce development burden category in CEJST. Socioeconomic As described relative to the burden categories above, CEJST combines two socioeconomic indicators with other indicators within these burden categories to determine whether a tract is identified as disadvantaged within CEJST: low income (seven of the eight burden categories)
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Changing the socioeconomic burden indicator in CEJST would affect which census tracts are designated as disadvantaged. a While this section focuses mostly on the low-income indictor, the high school educa tion indicator is discussed in more detail in the workforce development section above.
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. Advocates and scholars of environmental justice and health inequities argue that measures of racism are necessary to identify and understand inequity or disadvantage.
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. Unlike unidimensional measures of segregation, 47 Read more about HOLC redlining maps in the University of Richmond project "Mapping Inequality: Redlining in New Deal America" at https://dsl.richmond.edu/panorama/redlining (accessed February 28, 2024)
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and then used latent class modeling to reduce them into one multidimensional measure of structural racism to predict birth outcomes. Similar to measures of racialized economic segregation, latent variable approaches to the Index of Disproportionality have the benefit of operationalizing structural racism as a multidimensional phenomenon and avoiding problems of multicollinearity.
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Community engagement, validation, and transparency in selecting and including burden categories and indicators can help evaluate how well the tool captures burdens that align with the lived experience of communities. Indicator groupings could become important in future iterations of the tool, especially if integration approaches for assessing cumulative impacts are implemented (discussed further in Chapter 6)
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