Connecting CalEnviroScreen and Opportunity Zones to A ssess M aterial E nvironmental and S ocial F actors for R esponsible I nvesting 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 ( CalEnviroSc reen; 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 pictur e of pressures in large , urban California regions. All of the C ES 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, P articulate 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 change s 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, 201 9a ). N on - 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, 2019 b ). Therefore, OZ represent communities his torically 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 medi an family income of more than 80% of the statewide median family income, w hereas m etropolitan 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 ( Fundris e, 201 9 ) 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 q ualified 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 zo nes ( 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 O Z, 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 (Lowre y, 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 gentri fication or lack of real change. Furthermore, i ncorporating 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 C ES data will allow for informed decision - making (Greenfield et al., 2017) and more impact ful 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 environm ental factors, invest ors 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 C ES data was downloaded from the California Office of Environmental Health Hazard Assessment (OEHHA) in November 2018 . The data was compiled into an Exc el 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 C ES data, analysis could be done on the environmental and social landscape of the tracts For each C ES 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 C ES 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 C ES , 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 statistic al 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 Variance s Samples Compared All samples OZ to SF BA OZ to SF BA - OZ SF BA to SF BA - 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 SF BA to SF BA - 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 Polluti on 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 statist ical 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 T he 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 (O 3 ) 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 photochem ical 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 O zone ANOVA F - test Two Sample for Variances Samples Compared All samples OZ to SF BA OZ to SF BA - OZ SF BA to SF BA - 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 C ES represents the mean PM 2.5 concentrations in the census tract. PM 2.5 is particulate matter that is 2.5 m icrons 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 statis tical 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 d iesel PM score measures emissions from on - road and non - road sources; over 90% of d iesel 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 polluti on increases cancer risk and can lead to cardiovascular and respiratory health complications. The highest concentrations are found near ports, railways, and freeways (OEHHA, 2018 a ). Diesel PM may disproportionally affect low income, minority, and populatio n 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 d iesel 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 neighbo rhoods (Fruin et al., 2008) . However, future research could be done to determine if the d iesel 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 d iesel 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 SF BA SF BA to SF BA - OZ OZ to SF BA - 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 - value s from the F - tests were not significant. Therefore, there is not a significant difference between the three groups. 2d. Drinking Water C ES 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). L ow - income and minority communities may have lower quality water and face more related health impacts than higher - income , Wh ite commu nities (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, s tate, 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 contro lled at the c ounty level in LA, it is unsurprising that there are no s tatistical differences. However, there were differences in the SFBA data (Table 9). Table 9 : results from the statistical analysis for d rinking w ater of the three SFBA test groups Summary Table – SFBA Drinking Water ANOVA F - test Two Sample for Variances Samples Compared All samples OZ to SF BA OZ to SF BA - OZ SF BA to SF BA - 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 C ES measures p esticide 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 s pecies; 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, a round th e 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 enviro nmental 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 statisti cally 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 s tatistically 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 c ounty 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 a nd if they have varying health or environmental impacts. 2f. Toxic Release CES measures t oxic r elease based upon the toxicity - weighted concentrations of modeled chemical releases to air from facility emissions. LA County is exposed to a variety of pollut ants for a total of 8,230,308 pounds annually of on - site and off - site disposals and other releases ; i t is the 62 nd most polluted c ounty in the US ( EPA, 2017 a ) . 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 withi n 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 differen t based on the ANOVA; the F value (1.07) < F critical value (3.00) and the p - value > 0.05 (0.34). 2g. Traffic C ES measures traffic via its density, in vehicle - kilometers per hour per road length, within 150 meters of the census tract boundary. In 2016, L A had the worst traffic congestion in the world ( Ohnsman , 2017 ), with LA drivers spending about 12.7% o f 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 ac counted 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 e xperience 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 sign ificantly 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 s ite 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, 2018 b ). Additionally, toxic me tals and pesticides have been found in blood samples of residents near the sites. There are several communities affected by c leanup s ites in LA County including the Cities of Bell, Bell Gardens, Commerce, Compton, Cudahy, Huntington Park, Maywood, Paramoun t, South Gate, and Vernon, as well as the San Fernando and San Gabriel Valleys (EPA, 2017 b ). 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 c leanup s ites 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 c leanup s ites 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 (C ES 3.0, 2018). Pollution of groundwater can come from gasoline, oil, road salts, pesticides, and chemicals ( Groundwater Foundation, 2018). Polluted groundwater is difficult to rec tify d ue 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 C ES defines h azardous w aste 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 electro nic 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 h azardous w aste 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, h azardous w aste 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 C ES 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 recreationa l use (OEHHA, 2018 c ). 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 coppe r 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 differenc es, 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 C ES measures the sum of weigh ted solid waste sites and facilitie s 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 sto red (OEHHA, 2018 d ). Living in pr oximity 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 (Gre en Choices, 2018) and contaminate water or soil (Gorilla Bins, 2015). There are 18 landfills in LA County (County of LA Public Health, 2019 a ) , as well as hazardous waste disposal sites in every SFBA county ( Save the Bay, 2019 ). Results of analysis are be low (LA County, Table 16; SFBA Table 17). Table 16 : results from the statistical analysis for s olid w aste 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 trac ts. 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 Variances t - Test: Two - Sample Assuming Equal 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. Table s 18 (LA County) and 19 (SFBA) display the results of the data analysis for this compila tion. 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 Char acteristics 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 Equal Variances t - Test: Two - Sample Assuming Unequal 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 C ES 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 , 2019 b ), 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 a dults (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 a sthma of the three LA test gr oups 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. T hus , it cannot be said that OZ census tracts carry a greater asthma burden. Table 21 : results from the statistical analysis for asthm a 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 b irth w eight 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 l ow b irth w eight. Additionally, low - income families ( Martinson & Reichman, 2016 ) and minority communities (Sims et al., 2016) may face high er low birth weight rates. Results are below (LA County, Table 22; SFBA, Table 23). Table 22