Mapping CalEnviroScreen to Opportunity Zones to assess material ESG factors.pdf - Microsoft Office User

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Connecting CalEnviroScreen and Opportunity Zones to Assess Material Environmental and Social Factors for Responsible Investing Emily Simso Boston Area Sustainable Investment Consortium December 15, 2019 Connecting CalEnviroScreen and Opportunity Zones 2 Abstract Opportunity Zones (OZ) provide a unique framework for responsible investors to make a significant impact in an economically distressed census tract. By aligning OZ areas with California Communities Environmental Health Screening Tool (CalEnviroScreen; CES), a dataset that measures environmental and social stressors, investors can better understand what factors most affect communities. In this report, Los Angeles (LA) and the San Francisco Bay Area (SFBA) were studied to gain a more holistic picture of pressures in large, urban California regions. All of the CES variables for the two data sets were analyzed using a single-factor ANOVA and subsequent two-sample F-tests for variance along with the appropriate t-test. Based upon these calculations, OZ in the two study sites are disproportionately impacted by cleanup sites and solid waste. LA sites are also affected by total CES 3.0 score, Particulate Matter 2.5, hazardous waste, educational attainment, and housing burden. SFBA OZ were affected by drinking water, groundwater threats, impaired water bodies, CV disease, and linguistic isolation. Therefore, investors have a range of possibilities to make positive changes in LA and the SFBA through investing strategies related to these factors. Connecting CalEnviroScreen and Opportunity Zones 3 Opportunity Zones (OZ) are economically distressed census tracts, in which certain investments may qualify for tax breaks (IRS, 2018), established by Congress via the Tax Cuts and Jobs Act of 2017 (Economic Innovation Group, 2019a). Non-white minorities are disproportionately represented in OZ (56% compared to 39% in United States) and more likely to be living in poverty (29% versus 15% in United States average; Economic Innovation Group, 2019b). Therefore, OZ represent communities historically neglected in investing conversations. Governors of each state may choose up to 25% of that state’s eligible low-income areas to be designated as OZ (Local Initiatives Support Coalition, 2018). Non-metropolitan census tracts must not have a median family income of more than 80% of the statewide median family income, whereas metropolitan areas may not have a median family income of more than 80% of the greater statewide median family income or the overall metropolitan median family income (Fundrise, 2019) depending on the location. Both groups must have a poverty level of at least 20%. Currently, there are over 8,700 OZ in the US of various sizes. Investments must be made via qualified Opportunity Funds (OF) to receive the incentives outlined in the program (Local Initiatives Support Coalition, 2018). OF must hold at least 90% of their assets in Qualified OZ Property and include stock, partnership, business property, equity, real estate, and/or business assets in the Qualified OZ. Additionally, investors receive greater benefits the longer they hold their investments within the OZ (Local Initiatives Support Coalition, 2018). It is estimated that $6.1 trillion in capital (primarily private) could potentially be invested into these zones (Bertoni, 2018). Therefore, there is high potential for revitalizing underserved neighborhoods and stimulating domestic economies in the long-term. However, there is simultaneous concern that the program will gentrify these census tracts. For example, while activity may shift into the OZ, it may not alter net activity or the flow of capital (Riquier, 2018). Investing in these neighborhoods also may not stimulate long-term change in programs like job development or education. Additionally, investing in OZ, but not the census tracts adjacent, may harm the surrounding neighborhoods (Weaver, 2018). There are also concerns that OZ are already gentrifying and receiving disproportionate investment increases, therefore not requiring the extra tax benefits (Lowrey, 2018). As there is not a mandate that investments be responsible, private investors interested in OZ may not be aware of the most impactful use of their funds to stimulate long- term, sustainable growth. Using an Environmental, Social & Governance (ESG) investment approach in OZ could allow for higher returns while promoting community development. ESG aims to achieve superior financial returns while addressing mission-based concerns (Caplan et al., 2013). ESG strategies can outperform their peers (Eccles et al., 2011; Caplan et al., 2013; Cortese et al., 2017), reduce risk (Clark et al., 2014), and lower volatility (Ashwin et al., 2016). Using an ESG approach may also lessen some concerns surrounding gentrification or lack of real change. Furthermore, incorporating a long-term ESG data source with OZ information could provide insights on more impactful investment opportunities. One appropriate data source is the California Communities Environmental Health Screening Tool (CalEnviroScreen; CES), which identifies census tracts that are vulnerable to Connecting CalEnviroScreen and Opportunity Zones 4 multiple pollution sources, as well as related health impacts (OEHHA, 2018). Combining OZ and CES data will allow for informed decision-making (Greenfield et al., 2017) and more impactful investing. This study focuses on LA County and counties in the San Francisco Bay Area (SFBA), both of which are highly relevant locations for environmental and economic concerns. The counties included in the SFBA group are: San Francisco, San Mateo, Marin, Sonoma, Napa, Solano, Contra Costa, Alameda, and Santa Clara. Counties beyond San Francisco County were included in the SFBA dataset to increase the amount of data points, allowing conclusions to be drawn from the statistical results. Additionally, the enlarged dataset is more comparable to LA’s data. The goal of the project is to determine if OZ in LA County and the SFBA have greater environmental burdens than non-OZ census tracts. By looking at specific environmental factors, investors can target the highest potential impact for OZ communities, promoting long-term health and development. Responsible investing strategies will allow investors to have greater impact on specific needs of the OZ communities. Methods The OZ data was downloaded from the Community Development Financial Institutions Fund website and the CES data was downloaded from the California Office of Environmental Health Hazard Assessment (OEHHA) in November 2018. The data was compiled into an Excel file and divided into three categories per urban area: OZ, non-OZ (ex. LA-OZ), and all census tracts (ex. LA). By matching the census tract numbers of the OZ data and the CES data, analysis could be done on the environmental and social landscape of the tracts. For each CES factor, a series of statistical tests were performed. First, a single-factor ANOVA was run to ascertain if a significant difference existed. If there was a difference, a two- sample F-test for variance was run comparing each factor to one another to determine whether the variance was equal or unequal, and finally the appropriate t-test for two-samples and their before-calculated variance was performed. If the ANOVA was insignificant, the two following tests were not performed. While the aggregate CES score was analyzed, the individual factors were also studied to gain a more comprehensive understanding of the specific burdens that disproportionately affect OZ census tracts. Results 1. CES 3.0 Score The CES 3.0 Score (LA County, Table 1; SFBA, Table 2) is a compilation of the individual factors measured in CES, specifically the Pollution Score multiplied by the Population Score. Higher scores indicate greater environmental burden. Connecting CalEnviroScreen and Opportunity Zones 5 Table 1: results from the statistical analysis for the CES 3.0 Score of the three LA test groups Summary Table – LA CES 3.0 Score ANOVA F-test Two Sample for Variances Samples Compared All samples OZ to LA OZ to LA-OZ LA to LA-OZ F 137.64 2.06 1.93 1.06 F crit 3 1.17 1.17 1.07 p-value 0 3.31E-13 2.10E-11 0.079 t-Test: Two-Sample Assuming Unequal Variances Samples Compared OZ to LA OZ to LA-OZ LA to LA-OZ t Stat 19.4 5 21.93 4. 12 t Critical two-tail 1.97 1.97 1.96 p-value two-tail 2.41E-60 8.05E-72 3.89E-05 There is not a significant difference (p>0.05) between LA County and LA-OZ but there is between OZ and the other two groups. Therefore, OZ carry greater overall environmental burdens than non-OZ. Table 2: results from the statistical analysis for the CES 3.0 Score of the three SFBA test groups Summary Table – SFBA CES 3.0 Score ANOVA F-test Two Sample for Variances Samples Compared All samples OZ to SFBA OZ to SFBA-OZ SFBA to SFBA-OZ F 127.03 0.86 1.01 1.18 F crit 3 0.78 1.25 1.09 p-value 0 1.60E-01 4.60E-01 0.0008 t-Test: Two-Sample Assuming Unequal Variances Samples Compared SFBA to SFBA-OZ t Stat 3 t Critical two-tail 1.96 p-value two-tail 2.70E-03 There was only a significant difference between SFBA to SFBA-OZ based on the results of the F-tests. Therefore, a deeper examination of the individual variables that comprise the score are warranted. The factors that make up the CES 3.0 Score were further analyzed to better understand these differences, first for the indicators that compile the Pollution Score and then for the Population Characteristics. 2. Pollution Score The Pollution Score (LA County, Table 3; SFBA Table 4) is calculated from 12 environmental factors, analyzed individually in subsequent sections. Connecting CalEnviroScreen and Opportunity Zones 6 Table 3: results from the statistical analysis for the Pollution Score of the three LA test groups Summary Table – LA Pollution Burden ANOVA F-test Two Sample for Variances Samples Compared All samples OZ to LA OZ to LA-OZ LA to LA-OZ F 25.48 1.1 1.1 1 F crit 3 1.17 1.17 1.07 p-value 9.88E-12 0.15 0.16 0.46 The p-values from the F-tests were not significant; therefore, there is not a statistical difference between the three LA groups. Table 4: results from the statistical analysis for the Pollution Score of the three SFBA test groups Summary Table - SFBA Pollution Score ANOVA F-test Two Sample for Variances Samples Compared All samples OZ to SFBA OZ to SFBA-OZ SFBA to SFBA-OZ F 19.16 1.11 1.15 1.03 F crit 3 1.25 1.25 1.09 p-value 5.37E-09 2.10E-01 0.15 2.60E-01 The p-values from the F-tests were not significant; therefore, there is not a statistical difference between the three SFBA groups. 2a. Ozone Ozone (O3) is naturally occurring in the earth’s atmosphere but can be problematic for human health at the ground level (US EPA, 2018). It is primarily a respiratory irritant, potentially leading to decreases in lung function or associated cardiopulmonary impacts (Krupnick et al., 1990; Chen et al., 2007). Ground level ozone forms due to photochemical reactions between organic vapors (generally stemming from anthropogenic activities), nitrogen oxides, and radiation (Lippmann, 1989). Methane can also lead to ozone formation; its main sources are wetlands, paddy rice fields, emissions from livestock, biomass burning, anaerobic decomposition of organic waste in landfills, and fossil methane emission (Heilig, 1994). CES measures the amount of daily maximum eight-hour ozone concentration. LA County received an F ozone score (the lowest possible), from See California due to its poor air quality, while the SFBA is more varied: San Francisco, San Mateo County, Marin, and Sonoma received an A; Napa a C; and Solano, Contra Costa, Alameda, and Santa Clara an F. The ANOVA run to compare ozone levels between the LA groups was not significant, as the F value (1.47) < F crit (3.00) and the p-value > 0.05 (0.23). Therefore, there are not differences between the three data sets. This may be because ozone levels in a specific area are subject to local, regional, and global influences (Jenkin, 2008). However, there was some variation between the SFBA groups (Table 5). Connecting CalEnviroScreen and Opportunity Zones 7 Table 5: results from the statistical analysis of the ozone levels of the three SFBA test groups Summary Table – SFBA Ozone ANOVA F-test Two Sample for Variances Samples Compared All samples OZ to SFBA OZ to SFBA-OZ SFBA to SFBA-OZ F 3.012 0.99 1 1 F crit 3 0.78 0.78 1.09 p-value 0.049 4.90E-01 5.00E-01 0.48 While the ANOVA showed a significant difference between the SFBA groups, the p- values from the F-tests were not significant. Therefore, there is not a significant difference in ozone levels for the groups. However, given the wide range of ozone scores for the SFBA, more research could be done to identify how these variations affect OZ. 2b. Particulate Matter (PM) 2.5 The PM 2.5 score calculated by CES represents the mean PM 2.5 concentrations in the census tract. PM 2.5 is particulate matter that is 2.5 microns or less in size, coming primarily from combustion including car engines, coal or natural gas fired power plants, or fireplaces (NRDC, 2014). High levels of PM 2.5 can lead to cardiovascular and respiratory health impacts (Bell, 2012) and an overall higher rate of mortality (Fann et al., 2011). Additionally, lower socioeconomic groups may experience higher rates of PM 2.5 than higher socioeconomic groups (Bell et al., 2013). The results are below (LA County, Table 6). Table 6: results from the statistical analysis for PM 2.5 of the three LA test groups Summary Table – LA PM 2.5 ANOVA F-test Two Sample for Variances Samples Compared All samples OZ to LA OZ to LA-OZ LA to LA-OZ F 8.76 1.16 1.18 1.01 F Crit 3 1.15 1.16 1.07 p-value 0.00016 0.041 0.029 0.37 t-Test: Two-Sample Assuming Unequal Variances Samples Compared OZ to LA OZ to LA-OZ t Stat 3.43 3.92 t Critical two-tail 1.97 1.97 p-value two-tail 0.00069 0.00011 There is not a significant difference between LA County and LA-OZ but there is between OZ and the other two groups. Therefore, PM 2.5 affects OZ more than non-OZ census tracts. This may be because lower socioeconomic groups are more exposed to PM 2.5 than affluent groups (Baxter et al., 2007). The ANOVA run to compare ozone levels between the SFBA groups was not significant, as the F value (0.95) < F crit (3.00) and the p-value > 0.05 (0.39). Therefore, there are not differences between the three SFBA data sets. Connecting CalEnviroScreen and Opportunity Zones 8 2c. Diesel PM The diesel PM score measures emissions from on-road and non-road sources; over 90% of diesel PM is less than 1 micron in diameter and typically composed of carbon (soot), organic compounds, and gaseous pollutants (California Air Resources Board, 2018). Diesel PM pollution increases cancer risk and can lead to cardiovascular and respiratory health complications. The highest concentrations are found near ports, railways, and freeways (OEHHA, 2018a). Diesel PM may disproportionally affect low income, minority, and population dense communities (Houston et al., 2008; Marshall, 2008), as their neighborhoods are more frequently clustered around the pollution sources. The results are below (LA County, Table 7; SFBA Table 8). Table 7: results from the statistical analysis for diesel PM of the three LA test groups Summary Table – LA Diesel PM ANOVA F-test Two Sample for Variances Samples Compared All samples OZ to LA OZ to LA-OZ LA to LA-OZ F 44.53 2.39 3.09 1.3 F crit 3 1.15 1.16 1.07 p-value 0 1.34E-27 2.63E-46 1.31E-09 t-Test: Two-Sample Assuming Unequal Variances Samples Compared OZ to LA OZ to LA-OZ LA to LA-OZ t Stat 5.54 6.3 2.51 t Critical two-tail 1.97 1.97 1.96 p-value two-tail 6.69E-08 1.07E-09 0.012 There are significant differences between each of the groups; therefore, OZ are not significantly different than non-OZ communities. This may be because LA is highly impacted by road sources of pollution that affect a range of neighborhoods (Fruin et al., 2008). However, future research could be done to determine if the diesel PM is coming from different sources for each of these groups. Investments into green transportation could decrease this environmental and health impact. Table 8: results from the statistical analysis for diesel PM of the three SFBA test groups Summary Table – SFBA Diesel PM ANOVA F-test Two Sample for Variances Samples Compared All samples OZ to SFBA SFBA to SFBA-OZ OZ to SFBA-OZ F 11.09 0.94 1.01 0.95 F crit 3 0.78 1.09 0.78 p-value 1.60E-05 3.50E-01 0.42 3.80E-01 While the ANOVA showed a significant difference between the three SFBA test groups, the p-values from the F-tests were not significant. Therefore, there is not a significant difference between the three groups. 2d. Drinking Water CES measures drinking water through its contaminants index. The Los Angeles Department of Water and Power currently tests drinking water for over 200 contaminants (County of Los Angeles Public Health, 2018). Some common contaminants for LA County are Connecting CalEnviroScreen and Opportunity Zones 9 arsenic, bromate, chromium (hexavalent), radiological contaminants, and total trihalomethanes (Environmental Working Group, 2018). Low-income and minority communities may have lower quality water and face more related health impacts than higher-income, White communities (Prevention Institute, 2018); in LA, this includes cities in South and Southeast LA and parts of the San Gabriel Valley. There are also variations in perceptions of water quality; 30% of Hispanic households and 25% of Black households in the LA-Long Beach Metro Area report that they have unsafe water, compared to only 12% of Non-Hispanic White households (United States Census Bureau, 2015). The San Francisco Public Utilities Commission tests its water quality 100,000 times a year to ensure local, state, and national standards are met (SFPUC, 2019). Some common contaminants in SFBA water are boron, chlorate, copper, and dissolved solids, such as soils (SFPUC, 2018). In the LA ANOVA, the groups were not statistically different from one another, as the F value (0.0066) < F critical value (3.00) and the p-value > 0.05 (0.99). Given that drinking water is controlled at the county level in LA, it is unsurprising that there are no statistical differences. However, there were differences in the SFBA data (Table 9). Table 9: results from the statistical analysis for drinking water of the three SFBA test groups Summary Table – SFBA Drinking Water ANOVA F-test Two Sample for Variances Samples Compared All samples OZ to SFBA OZ to SFBA-OZ SFBA to SFBA-OZ F 13.65 0.59 0.99 0.58 F crit 3 0.78 0.92 0.78 p-value 1.25E-06 2.80E-04 4.10E-01 0.00022 t-Test: Two-Sample Assuming Equal Variances Samples Compared OZ to SFBA OZ to SFBA-OZ t Stat -4.93 -5.24 t Critical two-tail 1.96 1.96 p-value two-tail 9.16E-07 1.75E-07 There was a significant difference between OZ to SFBA and OZ to SFBA-OZ but not between SFBA to SF-OZ; therefore, OZ in the SFBA experience a statistically relevant lowered quality of drinking water. 2e. Pesticides CES measures pesticide exposure based upon the total pounds of selected active pesticide ingredients (filtered for hazard and volatility) used in production-agriculture per square mile in the census tract. LA agencies primarily use pesticides to control various insect species; however, they soon may be illegal to use in public spaces (Mohan, 2018) due to their environmental and health impacts. In the SFBA, pesticide use has been decreased in the city (SF Environment, 2019) due to various concerns. Additionally, around the Bay Area, pesticide use is regulated to protect wildlife (US EPA, 2017e). Pesticides can increase cancer risk, chronic illnesses, and asthma, and lower neurological functioning (Beard et al., 2003; Alavanja et al., 2004). Pesticides Connecting CalEnviroScreen and Opportunity Zones 10 can also cause environmental risks, including groundwater pollution (Barrett, 1996), air pollution (Skinner et al., 1996), decreased biodiversity (Geiger et al., 2009), and diminished soil fertility (Troeh & Thompson, 2005). Based on the LA ANOVA, the groups are not statistically different from one another, as the F value (0.27) < F critical value (3.00) and the p-value > 0.05 (0.77). Similarly, the SFBA groups were not statistically different based on the ANOVA; the F value (0.97) < F critical value (3.00) and the p-value > 0.05 (0.38). Widespread pesticide use is also controlled at the county level, making it reasonable that there are not significant differences across census tracts. Future research could be done to see if specific pesticides are used at different levels and if they have varying health or environmental impacts. 2f. Toxic Release CES measures toxic release based upon the toxicity-weighted concentrations of modeled chemical releases to air from facility emissions. LA County is exposed to a variety of pollutants for a total of 8,230,308 pounds annually of on-site and off-site disposals and other releases; it is the 62nd most polluted county in the US (EPA, 2017a). While minority communities are more likely to live near polluting facilities in LA County, the industrial sites may have existed prior to residential move-in (Boone et al., 1999). However, minority move-in does play a role due to racial transitions within LA (Pastor et al., 2002). The SFBA also faces toxic release from a range of polluting sources, including old Silicon Valley companies (NBC Bay Area, 2019) and large factories (Fimrite, 2013). In the SFBA, Latinx communities are more likely to be affected by toxic release than other groups (Pastor et al., 2004). In the LA ANOVA, the groups were not statistically different from one another, as the F value (0.0042) < F critical value (3.00) and the p-value > 0.05 (1.00). Similarly, the SFBA test groups were not significantly different based on the ANOVA; the F value (1.07) < F critical value (3.00) and the p-value > 0.05 (0.34). 2g. Traffic CES measures traffic via its density, in vehicle-kilometers per hour per road length, within 150 meters of the census tract boundary. In 2016, LA had the worst traffic congestion in the world (Ohnsman, 2017), with LA drivers spending about 12.7% of their drive time in traffic. Given LA’s already expansive nature, adding additional roadways is impractical; instead, planners should focus on reducing peak usage and pricing strategies (Sorenson et al., 2008). Additionally, in 2006, mass transit only accounted for approximately one out of fifty trips taken in the region (Davis, 2006). In 2017, the metro saw its lowest usage rate in more than a decade (Nelson, 2018). These low usage rates also contribute to heavy traffic. Similarly, SFBA commuters now experience the second-longest traffic delays in the country (Richards, 2019); in 2016, the average time spent in traffic rose 9% compared to the Connecting CalEnviroScreen and Opportunity Zones 11 previous year (Metropolitan Transport Commission, 2017a). From 1991 to 2016, public transit use in the Bay Area fell 11% (Metropolitan Transport Commission, 2017b). Based upon the LA ANOVA results, the three groups are not statistically different; F value (1.34) < F critical value (3.00) and the p-value > 0.05 (0.26). Similarly, the SFBA tests groups were not significantly different based on the ANOVA; the F value (0.31) < F critical value (3.00) and the p-value > 0.05 (0.97). Given that both areas experience high traffic rates, it is understandable that there are no significant differences between the groups for either location. 2h. Cleanup Sites Cleanup site scores are determined based upon the sum of weighted cleanup sites (an area with harmful chemicals that need to be removed) within buffered distances to populated blocks of census tracts (CES, 2018). Chemicals from these sites can leech into buildings, soil, and water sources and cause airborne pollution (OEHHA, 2018b). Additionally, toxic metals and pesticides have been found in blood samples of residents near the sites. There are several communities affected by cleanup sites in LA County including the Cities of Bell, Bell Gardens, Commerce, Compton, Cudahy, Huntington Park, Maywood, Paramount, South Gate, and Vernon, as well as the San Fernando and San Gabriel Valleys (EPA, 2017b). In the SFBA, there are superfund sites in Sonoma, Contra Costa, Alameda, San Mateo, and Santa Clara (EPA, 2019), many of which are located close to water sources. The statistical results are below (LA County, Table 10; SFBA Table 11). Table 10: results from the statistical analysis for cleanup sites of the three LA test groups Summary Table – LA Cleanup Sites ANOVA F-test Two Sample for Variances Samples Compared All samples OZ to LA OZ to LA-OZ LA to LA-OZ F 21.95 2.63 3.43 1.31 F crit 3 1.15 1.16 1.07 p-value 3.25E-10 4.29E-34 2.33E-56 1.79E-10 t-Test: Two-Sample Assuming Unequal Variances Samples Compared OZ to LA OZ to LA-OZ LA to LA-OZ t Stat 3.74 4.25 1.76 t Critical two-tail 1.97 1.97 1.96 p-value two-tail 0.00022 2.88E-05 0.077 There is not a significant difference between LA County and LA-OZ, but there is between OZ and the other two groups. Therefore, Cleanup Sites affect OZ more than non-OZ census tracts. Connecting CalEnviroScreen and Opportunity Zones 12 Table 11: results from the statistical analysis for cleanup sites of the three SFBA test groups Summary Table - SFBA Cleanup Sites ANOVA F-test Two Sample for Variances Samples Compared All samples OZ to SFBA OZ to SFBA-OZ SFBA to SFBA-OZ F 26.14 3.42 4.32 1.26 F crit 3 1.25 1.25 1.09 p-value 5.53E-12 7.86E-26 1.08E-37 2.72E-06 t-Test: Two-Sample Assuming Unequal Variances Samples Compared OZ to SFBA OZ to SFBA-OZ SFBA to SFBA-OZ t Stat 3.71 3.98 1.42 t Critical two-tail 1.98 1.98 1.96 p-value two-tail 3.30E-04 1.20E-04 1.60E-01 There is a significant difference between OZ to SFBA and OZ to SFBA-OZ, but not between SF to SFBA-OZ. Therefore, OZ in the SFBA are more impacted by cleanup sites than non-OZ census tracts. 2i. Groundwater Threats Groundwater threats are calculated by the sum of weighted GeoTracker leaking underground storage tank sites within buffered distances to populated blocks of census tracts (CES 3.0, 2018). Pollution of groundwater can come from gasoline, oil, road salts, pesticides, and chemicals (Groundwater Foundation, 2018). Polluted groundwater is difficult to rectify due to its remoteness, its inability to change rapidly, and the slow rate of pollutant breakdown underground (Worldwatch Institute, 2000). Based upon the ANOVA results, the three LA groups are not statistically different; F value (1.34) < F critical value (3.00) and the p-value > 0.05 (0.26). However, there were differences in the SFBA data (Table 12). Table 12: results from the statistical analysis for groundwater threats of the three SFBA test groups Summary Table - SFBA Groundwater Threats ANOVA F-test Two Sample for Variances Samples Compared All samples OZ to SFBA OZ to SFBA-OZ SFBA to SFBA-OZ F 33.17 2.78 3.35 1.21 F crit 3 1.25 1.09 1.09 p-value 5.55E-15 1.85E-17 1.30E-04 1.30E-04 t-Test: Two-Sample Assuming Unequal Variances Samples Compared OZ to SFBA OZ to SFBA-OZ SFBA to SFBA-OZ t Stat 4.61 4.96 1.58 t Critical two-tail 1.98 1.98 1.96 p-value two-tail 1.06E-05 2.60E-06 1.10E-01 Connecting CalEnviroScreen and Opportunity Zones 13 There is a significant difference between OZ to SFBA and OZ to SFBA-OZ, but not between SF to SFBA-OZ. Therefore, OZ in the SFBA are more impacted by groundwater threats than non-OZ census tracts. 2j. Hazardous Waste CES defines hazardous waste as the sum of weighted hazardous waste facilities and large quantity generators within buffered distances to populated blocks of census tracts. Household hazardous waste includes paint products, cleaners and solvents, used oil or polishes, and electronic equipment (LA Sanitation, 2018). However, this waste can also come from larger scale facilities, such as laboratories or hospitals (EPA, 2018). The results are below (LA County, Table 13; SFBA Table 14). Table 13: results from the statistical analysis for hazardous waste of the three LA test groups Summary Table – LA Hazardous Waste ANOVA F-test Two Sample for Variances Samples Compared All samples OZ to LA OZ to LA-OZ LA to LA-OZ F 9.45 2.8 3.71 1.33 F crit 3 1.15 1.16 1.07 p-value 8.04E-05 5.25E-39 2.02E-64 2.05E-11 t-Test: Two-Sample Assuming Unequal Variances Samples Compared OZ to LA OZ to LA-OZ LA to LA-OZ t Stat 2.39 2.71 1.17 t Critical two-tail 1.97 1.97 1.96 p-value two-tail 0.018 0.0071 0.24 There is not a significant difference between LA County and LA-OZ but there is between OZ and the other two groups. Therefore, hazardous waste affects OZ more than non-OZ census tracts, which is expected given the higher concentration of related facilities in low socioeconomic areas. Table 14: results from the statistical analysis for hazardous waste of the three SFBA test groups Summary Table - SFBA Hazardous Waste ANOVA F-test Two Sample for Variances Samples Compared All samples OZ to SFBA OZ to SFBA-OZ SFBA to SFBA-OZ F 4.7 2.27 2.51 1.11 F crit 3 1.25 1.25 1.09 p-value 9.20E-03 2.92E-11 2.30E-02 2.30E-02 t-Test: Two-Sample Assuming Unequal Variances Samples Compared OZ to SFBA OZ to SFBA-OZ SFBA to SFBA-OZ t Stat 1.94 2.08 0.59 t Critical two-tail 1.98 1.98 1.96 p-value two-tail 5.60E-02 4.00E-02 5.60E-01 Connecting CalEnviroScreen and Opportunity Zones 14 There was only a significant difference between OZ to SFBA-OZ; therefore, OZ census tracts may experience higher rates of hazardous waste exposure than non-OZ tracts, but it cannot be definitively concluded. 2k. Impaired Water Bodies CES accounts for the sum of the number of pollutants across all impaired water bodies within buffered distances to populated blocks of census tracts. When bodies of water are contaminated, this can affect food sources, wildlife habitats and biodiversity, and limit recreational use (OEHHA, 2018c). Pollution sources are either point sources receiving waste load allocation or nonpoint receiving a load allocation (EPA, 2018). Common water pollutants in LA County come from industrial facilities and human activities, including copper leaking, fracking, plastic pellets, and septic facilities (LA Waterkeeper, 2018). Some of the main pollutants of concern for the SFBA are heavy metals, pesticides, mercury, and polychlorinated biphenyls (San Francisco Bay Keeper, 2013). Based upon the ANOVA results, the three groups are not statistically different; F value (0.0018) < F critical value (3.00) and the p-value > 0.05 (1.00). Given that there are few bodies of water in LA County, it is reasonable that there would not be significant differences, as the three groups share common water sources. However, there were differences among the SFBA test groups (Table 15). Table 15: results from the statistical analysis for impaired water bodies of the three SFBA test groups Summary Table - SFBA Impaired Water Bodies ANOVA F-test Two Sample for Variances Samples Compared All samples OZ to SFBA OZ to SFBA-OZ SFBA to SFBA-OZ F 7.41 1.45 1.51 1.04 F crit 3 1.25 1.25 1.09 p-value 6.20E-04 2.60E-03 9.00E-04 2.00E-01 t-Test: Two-Sample Assuming Unequal Variances Samples Compared OZ to SFBA OZ to SFBA-OZ t Stat 3.01 3.23 t Critical two-tail 1.98 1.98 p-value two-tail 3.20E-03 1.60E-03 There was a significant difference between OZ to SFBA and OZ to SFBA-OZ, but not between SFBA to SFBA-OZ. Therefore, OZ census tracts in the SFBA are more impacted by impaired water bodies than non-OZ tracts. 2l. Solid Waste CES measures the sum of weighted solid waste sites and facilities within buffered distances to populated blocks of census tracts. This includes facilities where household, industry, or commercial garbage and other wastes are collected, processed, or stored (OEHHA, 2018d). Living in proximity to a waste treatment site may have negative health outcomes, including gastrointestinal complications (Giusti, 2009), congenital abnormalities, or low-birth weight Connecting CalEnviroScreen and Opportunity Zones 15 (Forastiere et al., 2011). Landfills can contribute to greenhouse gas emissions (Green Choices, 2018) and contaminate water or soil (Gorilla Bins, 2015). There are 18 landfills in LA County (County of LA Public Health, 2019a), as well as hazardous waste disposal sites in every SFBA county (Save the Bay, 2019). Results of analysis are below (LA County, Table 16; SFBA Table 17). Table 16: results from the statistical analysis for solid waste of the three LA test groups Summary Table – LA Solid Waste ANOVA F-test Two Sample for Variances Samples Compared All samples OZ to LA OZ to LA-OZ LA to LA-OZ F 11.2 2.61 3.37 1.29 F crit 3 1.15 1.16 1.07 p-value 1.41E-05 8.45E-34 1.80E-54 1.61E-09 t-Test: Two-Sample Assuming Unequal Variances Samples Compared OZ to LA OZ to LA-OZ LA to LA-OZ t Stat 2.68 3.05 1.27 t Critical two-tail 1.97 1.97 1.96 p-value two-tail 0.0078 0.0025 0.21 There is not a significant difference between LA County and LA-OZ but there is between OZ and the other two groups. Therefore, OZ are more impacted by Solid Waste than other census tracts. Table 17: results from the statistical analysis for solid waste of the three SFBA test groups Summary Table - SFBA Solid Waste ANOVA F-test Two Sample for Variances Samples Compared All samples OZ to SFBA OZ to SFBA-OZ SFBA to SFBA-OZ F 6.49 1.63 1.72 1.06 F crit 3 1.25 1.25 1.09 p-value 1.50E-03 9.58E-05 1.56E-05 1.40E-01 t-Test: Two-Sample Assuming Unequal t-Test: Two-Sample Assuming Equal Variances Variances Samples Compared OZ to SFBA OZ to SFBA-OZ SFBA to SFBA-OZ t Stat 2.67 2.86 0.68 t Critical two-tail 1.98 1.98 1.96 p-value two-tail 8.80E-03 5.10E-03 5.00E-01 There was a significant difference between OZ to SFBA and OZ to SFBA-OZ, but not between SFBA and SFBA-OZ. Thus, OZ census tracts in the SFBA are more impacted by solid waste than non-OZ tracts. Connecting CalEnviroScreen and Opportunity Zones 16 3. Population Characteristics The Population Characteristics score is composed of eight variables, analyzed below. Tables 18 (LA County) and 19 (SFBA) display the results of the data analysis for this compilation. Table 18: results from the statistical analysis for Population Characteristics of the three LA test groups Summary Table - SFBA Population Characteristics ANOVA F-test Two Sample for Variances Samples Compared All samples OZ to LA LA to LA-OZ OZ to LA-OZ F 135.61 0.28 1.03 0.29 F crit 3 0.86 1.07 0.86 p-value 0.00E+00 0 0.25 0 t-Test: Two-Sample Assuming Equal Variances Samples Compared OZ to LA OZ to LA-OZ t Stat 14.81 16.97 t Critical two-tail 1.96 1.96 p-value two-tail 1.04E-47 6.18E-61 The comparisons between OZ to LA and OZ to LA-OZ were statistically different, while LA to LA-OZ was not; therefore, OZ in LA are more impacted by overall population factors than non-OZ. Table 19: results from the statistical analysis for Population Characteristics of the three SFBA test groups Summary Table - SFBA Population Characteristics ANOVA F-test Two Sample for Variances Samples Compared All samples OZ to SFBA OZ to SFBA-OZ SFBA to SFBA-OZ F 121.22 0.39 2.3 0.89 F crit 3 0.78 1.28 0.92 p-value 0.00E+00 3.29E-09 1.23E-07 0.012 t-Test: Two-Sample Assuming Unequal t-Test: Two-Sample Assuming Equal Variances Variances Samples Compared OZ to SFBA SFBA to SFBA-OZ OZ to SFBA-OZ t Stat 14.29 -2.9 23.08 t Critical two-tail 1.96 1.96 1.98 p-value two-tail 8.46E-44 3.80E-03 3.00E-50 All three groups were statistically different from one another; therefore, it cannot be said that OZ in the SFBA are holistically more impacted by population factors than non-OZ. A closer look into the components included in the score is warranted. 3a. Asthma CES measures age-adjusted rates of asthma related emergency department visits. As of 2016, 1.2 million children and adults had asthma in LA County, with Hispanics making up the largest affected demographic group (California Breathing, 2016). This accounts for 1 in 11 children and 1 in 14 adults (County of LA Public Health, 2019b), with an overall prevalence of Connecting CalEnviroScreen and Opportunity Zones 17 12.7, lower than the California average of 14.8% (California Department of Public Health, 2018). While LA has the fourth worst air quality in the United States, it ranks number 92 as a metropolitan area where it is difficult to live with asthma (AAFA, 2018). In LA, race and socioeconomic status may be linked to asthma rates (Simon et al., 2003). Pollution, particularly traffic related pollutants including carbon monoxide and nitrogen dioxide, and particulate matter can increase asthma risk (Delamater et al., 2012). Comparatively, 28% of children (ages 5-17), 11.4% of adults (18-64), and 13.4% of older people (65 years or older) have lifetime asthma in San Francisco (California Breathing, 2017). Additionally, communities that were historically redlined in the SFBA may be at a higher risk for asthma (Klivans & Green, 2019). Therefore, it is possible that some of these redlined communities that are also OZ may have higher asthma rates. Results are below (LA County, Table 20; SFBA, Table 21). Table 20: results from the statistical analysis for asthma of the three LA test groups Summary Table – LA Asthma ANOVA F-test Two Sample for Variances Samples Compared All samples OZ to LA OZ to LA-OZ LA to LA-OZ F 85.98 1.05 1.02 1.075 F crit 3 1.17 1.16 1.073 p-value 0 0.29 0.40 0.05 t-Test: Two-Sample Assuming Unequal Variances Samples Compared LA to LA-OZ t Stat 3.21 t Critical two-tail 1.96 p-value two-tail 1.34E-03 The p-values from the F-tests for OZ to LA and OZ to LA-OZ were not significant; however, the results of the t-test from LA to LA-OZ was significant. Thus, it cannot be said that OZ census tracts carry a greater asthma burden. Table 21: results from the statistical analysis for asthma of the three SFBA test groups Summary Table - SFBA Asthma ANOVA F-test Two Sample for Variances Samples Compared All samples OZ to SFBA OZ to SFBA-OZ SFBA to SFBA-OZ F 103.67 1.5 1.81 1.2 F crit 3 1.25 1.25 1.09 p-value 0.00E+00 1.00E-04 2.41E-06 1.70E-04 t-Test: Two-Sample Assuming Unequal Variances Samples Compared OZ to SFBA OZ to SFBA-OZ SFBA to SFBA-OZ t Stat 10.75 11.55 2.73 t Critical two-tail 1.98 1.98 1.96 p-value two-tail 4.09E-19 5.79E-21 0.0063 Connecting CalEnviroScreen and Opportunity Zones 18 All three tests were significant; therefore, it cannot be said that OZ census tracts carry a greater asthma burden than non-OZ tracts. 3a. Low Birth Weight Low birth weight is defined as a baby weighing less than five pounds eight ounces, usually due to premature birth or fetal growth restrictions (March of Dimes, 2018). Exposure to air pollutants or lead can lead to low birth weight. Additionally, low-income families (Martinson & Reichman, 2016) and minority communities (Sims et al., 2016) may face higher low birth weight rates. Results are below (LA County, Table 22; SFBA, Table 23). Table 22: results from the statistical analysis for low birth weight of the three LA test groups Summary Table – LA Low Birth Weight ANOVA F-test Two Sample for Variances Samples Compared All samples OZ to LA OZ to LA-OZ LA to LA-OZ F 31.1 1.14 1.12 1.01 F crit 3 1.17 1.17 1.07 p-value 3.76E-14 9.10E-02 1.20E-01 3.70E-01 The p-values for all of the F-tests were not significant; therefore, it cannot be said that LA OZ census tracts are more affected by low birth weight than non-OZ tracts. Table 23: results from the statistical analysis for low birth weight of the three SFBA test groups Summary Table - SFBA Low Birth Weight ANOVA F-test Two Sample for Variances Samples Compared All samples OZ to SFBA OZ to SFBA-OZ SFBA to SFBA-OZ F 26.75 0.94 0.97 1.04 F crit 3 0.78 0.78 1.09 p-value 4.24E-13 3.40E-01 4.40E-01 2.50E-01 The p-values for all of the F-tests were not significant; therefore, it cannot be said that SFBA OZ census tracts are more affected by low birth weight than non-OZ tracts. 3b. Cardiovascular Disease (CVD) CES measures the age-adjusted rate of emergency department visits for heart attacks per 10,000 people. CVD includes heart disease, failures, or attacks; strokes; arrhythmias; and heart valve complications (American Heart Association, 2017). In the United States, higher income volatility and more income drops is associated with greater CVD risk (Elfassy et al., 2019). Similarly, young people from low-income families are more likely to face CV disease due to environmental and societal risk factors (Ali et al., 2011). Ethnic and racial groups experience different rates of CVD – African Americans typically have higher rates – but environmental factors play a significant role (Kurian et al., 2007). The results are below (LA County, Table 24; SFBA, Table 25). Connecting CalEnviroScreen and Opportunity Zones 19 Table 24: results from the statistical analysis for CVD of the three LA test groups Summary Table – LA CVD ANOVA F-test Two Sample for Variances Samples Compared All samples OZ to LA OZ to LA-OZ LA to LA-OZ F 17.35 1.01 1.02 1.01 F crit 3 1.15 1.16 1.07 p-value 3.12E-08 4.60E-01 4.00E-01 3.60E-01 The p-values for all of the F-tests were not significant and resulted in equal variance for all of the groups. Therefore, LA OZ census tracts are not disproportionately impacted by CVD compared to non-OZ tracts. Table 25: results from the statistical analysis for CVD of the three SFBA test groups Summary Table - SFBA CVD ANOVA F-test Two Sample for Variances Samples Compared All samples OZ to SFBA OZ to SFBA-OZ SFBA to SFBA-OZ F 29.57 1.34 1.43 1.07 F crit 3 1.25 1.25 1.09 p-value 1.90E-13 1.40E-02 3.70E-03 1.00E-01 t-Test: Two-Sample Assuming Unequal Variances Samples Compared OZ to SFBA OZ to SFBA-OZ t Stat 6.2 6.65 t Critical two-tail 1.98 1.98 p-value two-tail 8.55E-09 9.62E-10 There is a significant difference between OZ to SFBA and OZ to SFBA-OZ, but not between SFBA and SFBA-OZ. Therefore, SFBA OZ census tracts are more impacted by CVD than non-OZ tracts. 3c. Educational Attainment Educational attainment refers to the highest level of education completed by an individual; 19% of Californian adults do not have a high school degree, compared to the 14% US average (OEHHA, 2018e). In compared places across California, East LA has the highest rate of individuals without a high school diploma (Statistical Atlas, 2018a). The SFBA is not as impacted by low educational attainment as neighborhoods in LA. Educated individuals have better health and live longer, and highly educated communities are less polluted overall (OEHHA, 2018e). While the attainment gap between White students and non-White students has narrowed in recent years, minority groups are still less likely to attain higher education opportunities (National Center for Education Statistics, 2017). The results are below (LA County, Table 26; SFBA, Table 27). Connecting CalEnviroScreen and Opportunity Zones 20 Table 26: results from the statistical analysis for educational attainment of the three LA test groups Summary Table – LA Educational Attainment ANOVA F-test Two Sample for Variances Samples Compared All samples OZ to LA OZ to LA-OZ LA to LA-OZ F 120.19 1.55 1.45 1.07 F crit 3 1.17 1.17 1.074 p-value 0.00E+00 3.15E-06 6.47E-05 5.80E-02 t-Test: Two-Sample Assuming Unequal Variances Samples Compared OZ to LA OZ to LA-OZ t Stat -16.27 -18.34 t Critical two-tail 1.97 1.97 p-value two-tail 2.21E-45 2.62E-54 The F-tests for the OZ to LA and OZ to LA-OZ were significant, but the LA to LA-OZ was not. Therefore, it can be said that OZ census tracts carry a greater educational attainment burden. Table 27: results from the statistical analysis for educational attainment of the three SFBA test groups Summary Table - SFBA Educational Attainment ANOVA F-test Two Sample for Variances Samples Compared All samples OZ to SFBA OZ to SFBA-OZ SFBA to SFBA-OZ F 108.93 1.16 1.37 1.18 F crit 3 1.25 1.25 1.09 p-value 0.00E+00 1.30E-01 9.20E-03 7.60E-04 t-Test: Two-Sample Assuming Unequal Variances Samples Compared OZ to SFBA-OZ SFBA to SFBA-OZ t Stat 13.36 2.8 t Critical two-tail 1.98 1.96 p-value two-tail 2.38E-25 5.20E-03 There was a significant difference between OZ to SFBA-OZ and SFBA to SFBA-OZ, but not between OZ to SFBA. Therefore, it cannot be concluded that OZ census tracts carry a greater educational attainment burden than non-OZ tracts. 3d. Linguistic Isolation The OEHHA measures linguistic isolation based upon the level of English spoken at home; 40% of Californians live in households speaking a language other than English (2018f). Linguistic isolation can limit societal integration, opportunities for educational or vocational advancement, social capital, and cause other barriers to success (Proximity One, 2019; Nawyn et al., 2012). Additionally, children raised in isolated households may experience higher rates of poverty and lowered cognitive or academic scores (Glick et al., 2012; Drake, 2014). Approximately 13.5% of LA County households experience linguistic isolation, though the trend is decreasing over time (Huntington Hospital & Pasadena Public Health Department, Connecting CalEnviroScreen and Opportunity Zones 21 2016). In the SFBA, roughly 9.2% of the population is linguistically isolated (Bay Area Equity Atlas, 2019). The results are below (LA County, Table 28; SFBA, Table 29). Table 28: results from the statistical analysis for linguistic isolation of the three LA test groups Summary Table – LA Linguistic Isolation ANOVA F-test Two Sample for Variances Samples Compared All samples OZ to LA OZ to LA-OZ LA to LA-OZ F 96.61 1.11 1.23 1.12 F crit 3 1.16 1.16 1.07 p-value 0.00E+00 1.30E-01 8.20E-03 4.70E-03 t-Test: Two-Sample Assuming Unequal Variances Samples Compared OZ to LA-OZ LA to LA-OZ t Stat 13.09 3.5 t Critical two-tail 1.97 1.96 p-value two-tail 1.01E-31 4.70E-04 The F-tests for the OZ to LA-OZ and the LA to LA-OZ were significant, but the OZ to LA was not. Therefore, it cannot be said that LA OZ census tracts have significantly different linguistic isolation than non-OZ census tracts. Table 29: results from the statistical analysis for Linguistic Isolation of the three SFBA test groups Summary Table - SFBA Linguistic Isolation ANOVA F-test Two Sample for Variances Samples Compared All samples OZ to SFBA OZ to SFBA-OZ SFBA to SFBA-OZ F 49.48 1.41 1.56 1.11 F crit 3 1.25 1.25 1.09 p-value 0.00E+00 5.50E-03 4.70E-04 2.70E-01 t-Test: Two-Sample Assuming Unequal Variances Samples Compared OZ to SFBA OZ to SFBA-OZ SFBA to SFBA-OZ t Stat 7.79 8.35 1.88 t Critical two-tail 1.98 1.98 1.96 p-value two-tail 3.59E-12 1.88E-13 0.061 There was a significant difference between OZ to SFBA and OZ to SFBA-OZ, but not between SFBA to SFBA-OZ. Therefore, SFBA OZ census tracts have significantly different linguistic isolation compared to non-OZ tracts. 3e. Poverty The federal poverty level is calculated based upon family size and household income; for a family of four, it is set at $25,100 (Families USA, 2018). Approximately 20.4% of LA residents live at or under the poverty level (US Census, 2018a), which is higher than the California average of 14.3% (PPIC, 2018). Minority groups and less educated individuals tend to face higher poverty rates (City Data, 2017; PPIC, 2018). Connecting CalEnviroScreen and Opportunity Zones 22 Poverty is also highly concentrated in LA in a corridor from downtown neighborhoods to South LA (Matsunaga, 2008). These neighborhoods are also more likely to face housing insecurity, poor maternal health outcomes, and lowered educational attainment, public safety, and school outcomes. In 2018, roughly 10% of SFBA residents were living in poverty (City and County of San Francisco, 2019); older residents (65 years and older), African Americans, and women were more likely to experience poverty. The highest poverty rates are in the suburban communities of San Pablo, East Palo Alto, Richmond, Oakland, and Calistoga (Vital Signs, 2019). The results are below (LA County, Table 30; SFBA, Table 31). Table 30: results from the statistical analysis for poverty of the three LA test groups Summary Table – LA Poverty ANOVA F-test Two Sample for Variances Samples Compared All samples OZ to LA OZ to LA-OZ LA to LA-OZ F 208.75 2.84 2.55 1.11 F crit 3 1.17 1.17 1.07 p-value 0.00E+00 6.12E-24 5.19E-20 6.96E-03 t-Test: Two-Sample Assuming Unequal Variances Samples Compared OZ to LA OZ to LA-OZ LA to LA-OZ t Stat -26.84 -30.32 4.99 t Critical two-tail 1.96 1.96 1.96 p-value two-tail 2.48E-97 1.30E-113 6.27E-07 The F-tests for all of the groups were significant, as were the results of the t-Tests. Therefore, there is not a significant different in regard to poverty between OZ and the other census blocks of LA. Table 31: results from the statistical analysis for poverty of the three SFBA test groups Summary Table - SFBA Poverty ANOVA F-test Two Sample for Variances Samples Compared All samples OZ to SFBA OZ to SFBA-OZ SFBA to SFBA-OZ F 175.45 0.68 0.84 1.24 F crit 3 0.78 0.8 1.09 p-value 5.94E-73 5.10E-03 1.20E-01 1.79E-05 t-Test: Two-Sample Assuming t-Test: Two-Sample Assuming Equal Unequal Variances Variances Samples Compared SFBA to SFBA-OZ OZ to SFBA t Stat 3.52 16.77 t Critical two-tail 1.96 1.96 p-value two-tail 4.40E-04 1.89E-58 Connecting CalEnviroScreen and Opportunity Zones 23 There was a significant difference between OZ to SFBA and SFBA to SFBA-OZ, but not between OZ to SFBA-OZ. Therefore, it cannot be definitively said that there is a statistical difference in poverty rates between OZ census tracts and non-OZ tracts. 3f. Unemployment The US Census Bureau measures unemployment for persons over 16 years old who are neither working nor looking for work (2018b). California’s unemployment rate stood at 3.9% in December 2018, a significant decrease from recent years (State of California Employment Development Department, 2018). LA County has a slightly higher rate, at 4.6%. The SFBA Counties experience unemployment at the following rates: San Francisco (2.0%), San Mateo (1.8%), Marin (2.0%), Sonoma (2.3%), Napa (2.3%), Solano (3.3%), Contra Costa (2.7%), Alameda (2.6%), and Santa Clara (2.2%). In California, minority groups tend to face higher levels of unemployment compared to Whites (State of California Labor Market Information Division, 2019). The results are below (LA County, Table 32; SFBA, Table 33). Table 32: results from the statistical analysis for unemployment of the three LA test groups Summary Table – LA Unemployment ANOVA F-test Two Sample for Variances Samples Compared All samples OZ to LA OZ to LA-OZ LA to LA-OZ F 82.19 2.65 3.74 1.41 F crit 3 1.16 1.16 1.07 p-value 0.00E+00 2.41E-34 1.19E-64 6.16E-16 t-Test: Two-Sample Assuming Unequal Variances Samples Compared OZ to LA OZ to LA-OZ LA to LA-OZ t Stat 7.14 8.11 3.37 t Critical two-tail 1.97 1.97 1.96 p-value two-tail 7.05E-12 1.38E-14 7.60E-04 The F-tests for all of the groups were significant, as were the results of the t-tests. Therefore, there is not a significant different in regard to unemployment between OZ and the other census blocks of LA. Connecting CalEnviroScreen and Opportunity Zones 24 Table 33: results from the statistical analysis for unemployment of the three SFBA test groups Summary Table - SFBA Unemployment ANOVA F-test Two Sample for Variances Samples Compared All samples OZ to SFBA OZ to SFBA-OZ SFBA to SFBA-OZ F 93.79 1.62 1.94 1.2 F crit 3 1.25 1.25 1.09 p-value 0.00E+00 1.20E-04 1.20E-07 2.20E-04 t-Test: Two-Sample Assuming Unequal Variances Samples Compared OZ to SFBA OZ to SFBA-OZ SFBA to SFBA-OZ t Stat 9.89 10.64 2.63 t Critical two-tail 1.98 1.98 1.96 p-value two-tail 4.63E-17 8.74E-19 0.0087 There was significant difference between all three tests groups; therefore, OZ census tracts are not disproportionately affected by unemployment compared to non-OZ tracts. 3g. Housing Burden The OEHHA measures housing burden based upon a household’s low income and high housing costs (2018g). When factoring in housing costs to the poverty level, California has the highest rate of poverty in the US (California Budget & Policy Center, 2017). Approximately two-thirds of Californians pay rent for a two-bedroom apartment at $1,500 or greater per month. More than two-thirds of individuals with unaffordable housing are people of color. In LA, 46.7% of households have housing burden; these households are also more likely to be low-income (Stebbins & Comen, 2018). Similarly, 36.5% of households in Santa Clara and 37.4% of households in San Francisco are housing burdened. The results are below (LA County, Table 34; SFBA, Table 35). Table 34: results from the statistical analysis for housing burden of the three LA test groups Summary Table – Housing Burden ANOVA F-test Two Sample for Variances Samples Compared All samples OZ to LA OZ to LA-OZ LA to LA-OZ F 128.74 1.67 1.55 1.07 F crit 3 1.17 1.17 1.07 p-value 0.00E+00 1.05E-07 3.43E-06 5.50E-02 t-Test: Two-Sample Assuming Unequal Variances Samples Compared OZ to LA OZ to LA-OZ t Stat -17.33 -19.52 t Critical two-tail 1.97 1.97 p-value two-tail 5.18E-50 1.49E-59 The F-tests for the OZ to LA and OZ to LA-OZ were significant, but the LA to LA-OZ was not. Thus, OZ census tracts more impacted by housing burden than non-OZ tracts. Connecting CalEnviroScreen and Opportunity Zones 25 Table 35: results from the statistical analysis for housing burden of the three SFBA test groups Summary Table - SFBA Unemployment ANOVA F-test Two Sample for Variances Samples Compared All samples OZ to SFBA OZ to SFBA-OZ SFBA to SFBA-OZ F 90.17 0.88 0.99 1.12 F crit 3 0.78 0.78 1.09 p-value 0.00E+00 2.10E-01 5.00E-01 1.30E-02 t-Test: Two-Sample Assuming Unequal Variances Samples Compared SFBA to SFBA-OZ t Stat 2.52 t Critical two-tail 1.96 p-value two-tail 1.20E-02 Only the comparison between SFBA to SFBA-OZ was significant; therefore, it cannot be said that OZ census tracts have greater housing burden than non-OZ tracts. Discussion Based upon the results of the statistical analysis, several CES factors emerged as significant for OZ in LA and the SFBA. Additionally, both LA and the SFBA had factors that were uniquely relevant for OZ in their region. 1. OZ in LA and the SFBA OZ in LA and the SFBA were significantly impacted by cleanup sites and solid waste. 1a. Cleanup Sites Both LA County and the SFBA have cleanup sites within their boundaries (EPA, 2017b, EPA, 2019c). People of color and low-income communities are more likely to live near contaminated sites (Reichel, 2018); of the population that lives within three miles of a site, 49.7% is a minority (make up 38.4% of US population overall) and 16.7% are below the poverty line (14.7% of US population overall; EPA, 2017d). African Americans are particularly affected by toxic waste facilities (Kramar et al., 2018). While there are ongoing discussions if race or class is a greater determinant of proximity to a cleanup site (Maranville et al., 2009; Denq et al., 2000), these communities undoubtedly face greater impacts of environmental racism than White, affluent groups. Additionally, cleanup sites were placed in low-income, communities of color after those groups had already moved in (Mohai & Saha, 2015), indicating this was an intentional policy choice to limit neighborhood potential. Investors could fund projects that study and subsequently clean up toxic waste sites, employing local community members to encourage long-term growth. Cleaned-up land could then be sold to businesses, conservation groups, the government, or other organizations to recoup the capital. Connecting CalEnviroScreen and Opportunity Zones 26 1b. Solid Waste Similarly, LA County and the SFBA have multiple solid waste sites and facilities within their boundaries (County of LA Public Health, 2019a; Save the Bay, 2019). Research has found that waste facilities are more likely to be in Black neighborhoods (Bullard, 1983) and that conditions may have gotten worse over time (Schlanger, 2017, Bullard et al., 2007). Communities of color are also more likely to live near a recycling facility, which can release toxic chemicals as byproducts of incineration (Fernandez & Floyd, 2019). Investors could fund research in clean recycling programs (such as alternatives to incineration) or strategies to reduce the use of single-use products. Projects that are specifically targeted to take place in LA and/or the SFBA should be given higher priority. 2. OZ in LA OZ in LA were also impacted by their overall CES 3.0 score, Population Characteristics, PM 2.5, hazardous waste, educational attainment, and housing burden. 2a. CES 3.0 Score Responsible investors could theoretically positively affect the overall CES 3.0 score through general investments in OZ. For example, projects that create jobs, skills development, or ownership structures for community members could positively impact various metrics. However, more targeted investments in a specific factor would likely have greater long-term impact. 2b. Population Characteristics OZ in LA were statistically different than non-OZ zones for their overall Population Characteristics score. Therefore, it may be relevant to consider holistic social programs for these census tracts, such as housing programs that also provide job training. While there is value in these sorts of wide-view investments, it may not lead to the same level of impact as addressing one factor with all available resources. 2b. PM 2.5 Los Angeles ranked highest in the United States for deaths related to PM 2.5 air pollution (Gander, 2019); therefore, addressing its concentrations is highly relevant. The neighborhoods with the highest concentrations are Chinatown, Downtown, Lincoln Heights, and Echo Park (Mackovich, 2018). Looking into OZ investment in these four areas would have significant impact. Investing in research for clean car engines, renewable energy, or sustainable fireplaces could have positive results, as these are main sources of PM 2.5 (NRDC, 2014). Additional investment in green infrastructure that filter the air, such as trees, could have additional benefits (Nowak et al., 2013; Liang et al., 2016). Investors could also join climate change coalitions to have a greater national or global impact. Some relevant groups include the Climate Action 100+, the Task Force on Climate- related Financial Disclosures, the Investor Group on Climate Change, and others. 2c. Hazardous Waste Minority communities, including Black and Latinx neighborhoods, in LA are more likely to be affected by hazardous waste than other groups (Boer et al., 1997; Lejano et al., 2000; Connecting CalEnviroScreen and Opportunity Zones 27 Pastor et al., 2002). Hazardous waste facilities were intentionally placed in minority, low-income neighborhoods; the facilities were placed in these already-established communities (Pastor et al., 2001). Therefore, investments in hazardous waste reduction in LA has significant potential for environmental and social good. Cleanup of hazardous waste sites could be privatized by offering the contaminated land for purchase at an auction; while this does pose some risks, responsible investors could purchase these sites and re-sell them once they meet clean-up standards (DeLong, 1995). Investors could also work with communities affected by the hazardous waste sites to ensure their needs are addressed and the land is sold to a group that will continue to serve the community in the future. 2d. Educational Attainment Individuals aged 25 years or older are 34.4% more likely to not have a high school diploma compared to other California residents (Statistical Atlas, 2018b); Hispanics and people identifying as Other are particularly affected. The three neighborhoods with the highest percentage of residents without a diploma are Central City, Boyle Heights, and Southeast LA. Investments in education via infrastructure (i.e. school buildings), clean transportation (such as school buses), and technology (including lower cost computers, outreach programs, and technology equity) could positively impact attainment levels. However, investors should be cognizant of the social and cultural barriers that exist in communities and their connection to education. 2e. Housing Burden In LA and the South Coast, 23.3% of people have housing costs that exceed 50% of household income (California Budget & Policy Center, 2017). While LA is constructing new affordable housing units, many of which are in low-income neighborhoods, the rents are oftentimes still too high for many residents (Bachrach, 2017). Additionally, few affordable units are being built on the Westside, limiting where people are able to live. Furthermore, many currently rent-restricted units may become market rate in the next five years (Woocher, 2019). Investors need to find mechanisms to increase affordable housing units. One option is helping restructure the debt LA currently faces with its affordable housing, which could lead to more government spending in the future. Investing in green bonds that create sustainable housing is another potential avenue. 2. OZ in the SFBA OZ in the SFBA were additionally impacted by drinking water, groundwater threats, impaired water bodies, CV disease, and linguistic isolation. The first three factors were combined in the analysis below due to their close relationships to one another. 2a. Drinking Water, Groundwater Threats & Impaired Water Bodies While the SFBA has relatively clean drinking water sources (SFPUC, 2019), the analysis showed OZ being more negatively impacted for drinking water, groundwater threats, and impaired water bodies. These factors are clearly linked, as the SFBA receives its water from a range of natural sources. Connecting CalEnviroScreen and Opportunity Zones 28 Investors could fund companies retrofitting buildings or communities with clean pipes or other infrastructure. Another option would be funding a blue bond that includes wastewater treatment and/or the prevention of water pollution (Nordic Investment Bank, 2019). An innovative funding measure along this framework could address all three CES factors. Similarly, a green bond that funds forestry efforts could address impaired water bodies, as trees reduce runoff, protect against soil erosion, capture pollutants, and filter water flowing into water bodies (American Forests, 2016; eXtension, 2019). 2b. CVD In the SFBA, heart disease is the leading cause of death for Whites and African Americans, and more greatly impacts men (Live Stories, 2019). Due to the relationship between CVD and environmental factors, understanding which are most relevant to different OZ is necessary analysis and would require further work. Investors could positively impact CVD through capital investment in hospital infrastructure, organizations that provide educational programs on healthy lifestyles, or retrofitting high-pollution sources in urban areas. 2c. Linguistic Isolation While 13.5% of LA County households (Huntington Hospital & Pasadena Public Health Department, 2016) and 9.2% of the SFBA population (Bay Area Equity Atlas, 2019) experience linguistic isolation, this factor may be more challenging to address through responsible investing than other factors. Investors could look into viable education initiatives or infrastructure, though it is imperative that programs are multi-lingual and preserve communities’ cultural integrity. Conclusion OZ in LA County and the SFBA have unique challenges that could be addressed via targeted investments. ESG strategies should be regionally specific to and intentional for the relevant communities to reduce gentrification risks and ensure long-term sustainable development. A robust dataset, such as CES, provides valuable insights to investment professionals. Possible limitations to the completed study are the broad generalization of the OZ; investors cannot parse out specific census tracts using the given methodology. This could be improved by examining census tracts on an individual level to determine which are most impacted by various ESG factors. This level of in-depth analysis would ensure that investors are as informed as possible before entering into an OZ project. Future research could examine toxic release in LA and the SFBA; this factor was expected to be significant for OZ versus non-OZ due to the environmental justice implications, though based upon these results they were not different. Additionally, using another data source could be a strategy to identify possible risks not considered here. More work should also be done to research the gentrification concerns for responsible investing projects in OZ to ensure Connecting CalEnviroScreen and Opportunity Zones 29 communities are not displaced. Finally, other cities or regional areas could be examined using a similar methodology to inform responsible investing. Overall, combining OZ and CES census tract data allows investors to make relevant and significant investments in environmental and social projects for disadvantaged communities. This ESG framework will help ensure long-term growth and positive financial outcomes alongside social good. Acknowledgements I would like to thank the Boston Area Sustainable Investment Consortium for funding this project. I would also like to thank the U.S. Department of the Treasury Community Development Financial Institutions Fund and the California Office of Environmental Health Hazard Assessment for providing free downloadable datasets. Sources Alavanja, M. C., Hoppin, J. A., & Kamel, F. 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