Equity and Social Inclusion
A resident’s neighborhood and income level can shape the ways they experience their city’s environmental hazards and benefits. The UESI’s equity and social inclusion approach evaluates how environmental benefits and burdens vary across a city, and how this distribution relates to socioeconomic conditions.
Siqi Tina Huang
The UESI’s approach to equity and social inclusion measures the distribution of environmental harms and goods within a city and assesses the relation between environmental performance and income distribution.
We developed graphic and numeric representations of the distributive equity of selected environmental outcomes (EO) for each city. A city’s performance is displayed through a series of curves that illustrate the distribution of both income and environmental burdens throughout a city’s neighborhoods. To quantify the relation between the two distributions, we provide a metric – the Concentration Index – that numerically represents the distribution of the environmental outcome in relation to an scenario of perfect equity. In other words, this metric informs about the distribution of the environmental outcome and whether it is unequally allocated to the poorest or richest segments of the population. We have developed a typology that classifies cities based on (1) their overall income inequality and (2) the distribution of environmental outcomes.
One caveat to our analysis is that the primary demographic and economic data are calculated at the neighborhood level. We therefore calculate measures of these characteristics’ distribution across neighborhoods, weighted by population, rather than across individuals per se. This approach is equivalent to assuming that all individuals within a neighborhood are identical in terms of both environmental and economic characteristics. We recognize, however, that administrative boundaries often encompass highly heterogeneous populations; some of the wealthiest live juxtaposed to the poorest (see www.unequalscenes.com). Given the granularity of the data, our approach should be interpreted as a first attempt to quantify this issue in several global cities.
The built and natural environment shapes how citizens choose to live their lives to achieve their full potential, and can both enabling and hindering their efforts to reach this goal. As described in its own chapters, the presence – or absence – of certain environmental conditions have tangible effects on people’s health, social capacity, economic opportunities and overall well-being. Unfortunately, unlike other factors – such as income, access to education, health and other public services – the distribution of environmental conditions have been largely left out of many traditional economic and social tools, such as censuses and household surveys, commonly used by governments at every level to inform policies and interventions.
Over the last two decades, there has been an important increase in academic analyses of how environmental benefits and burdens are distributed across different populations. However, there are two important gaps in this research. There are scant consistent, global analyses of environmental distributional equity – while there are important and very relevant tools used for this purpose, they are constrained to specific geographic regions or countries. Additionally, the potential interactions between the distribution of economic and social conditions and the distribution of environmental characteristics has not been extensively explored in global cities.
The UESI aims to address these gaps in the academic literature, and to act as a tool to help inform policy, complementing local datasets with a broader view of global performance across a range of different cities.
The UESI’s equity analysis draws on population and economic — either income per capita and Gross Domestic Product (GDP) per capita —data from a wide range of national and municipal registries and censuses across the UESI cities (see Metadata), as well as from globally derived datasets. Considering the association between GDP per capita and income,1Diacon, P. E., & Maha, L. G. (2015). The relationship between income, consumption and GDP: A time series, cross-country analysis. Procedia Economics and Finance, 23, 1535-1543. if income data is not available, the UESI uses GDP per capita (Kummu et al., 2018)2Kummu, M., Taka, M., & Guillaume, J. H. (2018). Gridded global datasets for gross domestic product and Human Development Index over 1990–2015. Scientific data, 5, 180004. for the equity analysis, albeit with less accuracy (see Methods – Data Sources for more details).
Since this analysis requires the data to be spatially explicit, the equity indicators were only calculated for indicators with neighborhood-level data: air pollution, urban heat island, tree cover, and transportation access indicators (see these Issue Profiles and the Metadata for more information about these indicators’ data sources). The water indicator is calculated at the city-wide scale; therefore equity indicators could not be calculated for this issue.
Some cities (e.g., Bangalore, Casablanca, Ho Chi Minh City, Delhi, Lima and Tel Aviv) do not publish income information at the neighborhood level and were not considered in this calculation. Similarly, equity indicators were only calculated for indicators with spatially-explicit, neighborhood-level data: air pollution, urban heat island, tree cover, and transportation access indicators (see these Issue Profiles and the Metadata for more information about these indicators’ data sources). The water and climate change policy indicators are calculated at the city-wide scale; therefore equity indicators were not calculated for these issues.
This method develops a descriptive typology for cities’ performance, rather than setting specific targets.
The Sustainable Development Goals (SDGs) and Social and Environmental Equity
The United Nations’ Sustainable Development Goal 11 (SDG 11) articulates aspirations to make cities inclusive, resilient and sustainable. Specifically, it aims to ensure that people have access to ‘adequate, safe and affordable’ housing and basic services (Target 11.1) and to ‘accessible and sustainable transport systems’ (Target 11.2), while reducing adverse environmental impacts (Target 11.6). It also seeks to foster inclusion by enhancing citizens’ capacity to participate in urban planning and governance (Target 11.3). 3Accompanying indicators for goal 11 include the “proportion of urban population living in slums, informal settlements, or inadequate housing” (indicator 11.1.1) and the “percentage of cities with a direct participation structure of civil society in urban planning and management which operate regularly and democratically” (indicator 11.3.2). Global indicator framework for the Sustainable Development Goals and targets of the 2030 Agenda for Sustainable Development. Retrieved from: https://unstats.un.org/sdgs/indicators/Global%20Indicator%20Framework_A.RES.71.313%20Annex.pdf. Other goals, such as SDGs 5 and 10, seek to promote gender equality, and reduce inequalities among and within countries. While the definition of these goals might not explicitly refer to urban areas, cities have a great part to play in achieving them, due to their prominent political and economic role as well as the fact that over 50% of the global population now lives in cities. The SDG 17 and the SDG 11 Monitoring Framework emphasizes the need to disaggregate the SDG indicators (e.g., the proportion of people living in slums and the percentage of people with access to public transport) by income, sex, race, ethnicity, disability status and age, to help ensure overall progress does not leave particular groups behind.4Accompanying indicators for goal 11 include the “proportion of urban population living in slums, informal settlements, or inadequate housing” (indicator 11.1.1) and the “percentage of cities with a direct participation structure of civil society in urban planning and management which operate regularly and democratically” (indicator 11.3.2). Global indicator framework for the Sustainable Development Goals and targets of the 2030 Agenda for Sustainable Development. Retrieved from: https://unstats.un.org/sdgs/indicators/Global%20Indicator%20Framework_A.RES.71.313%20Annex.pdf.
Box 1. Landscape of Environmental Equity Tools
By drawing upon concepts from the environmental justice literature, the UESI framework aims to assist cities in tracking progress towards greater inclusion in SDG 11. Environmental justice is an overarching concept that applies social justice considerations in relation to environmental decision-making. 5Ikeme, J. (2003). Equity, environmental justice and sustainability: Incomplete approaches in climate change politics. Global Environmental Change, 13(3), 195-206.Recent literature has identified three primary dimensions of environmental justice: distributive equity, procedural justice, and justice as recognition. 6Schlosberg, D. (2004). Reconceiving Environmental Justice: Global Movements And Political Theories. Environmental Politics, 13(3), 517–540.Distributive equity emphasizes the distribution of social, economic and political goods, costs, and privileges between members of different genders, social groups, and districts. 7Schlosberg, D. (2007). Defining environmental justice: Theories, movements, and nature. Oxford: Oxford University Press. Procedural justice generally emphasizes the fair access to and democratic participation in environmental policy-making.8Schlosberg, D. 9Agyeman, J., & Evans, B. (2004). ‘Just Sustainability’: The emerging discourse of environmental justice in Britain? The Geographical Journal, 170(2), 155-164. The final dimension, justice as recognition, emphasizes that a key condition for justice is the recognition of diversity and the ways it shapes experiences in the social, political and cultural spheres, which then significantly determines the distribution of goods and harms.10Young, I. M. (1990). Justice and the politics of difference. Princeton, N.J: Princeton University Press. See Table 1 for a detailed overview of different elements and definitions around environmental justice.
To inform the development of the UESI’s environmental equity indicator, we reviewed 33 studies and tools attempting to measure social or environmental equity. These include environmental equity screening tools developed in the United States, social deprivation indices applied across Europe, as well as environmental justice and pollution assessments in relation to socioeconomic inequalities in Korea, Hong Kong, and Brazil.
These 33 analyses predominantly emphasize the distributive dimension of environmental equity. They allow stakeholders to identify the distribution of environmental hazards and exposures in relation to demographic considerations, which generally span income, minority/ethnicity status, employment status, age group, and education levels.
These tools seek to analyze specific measures of environmental inequalities, such as the level of access to green space and transportation infrastructure; identify key areas for improvement in public health; or more broadly survey a spectrum of environmental justice issues. Some of the broad spectrum analysis tools surveyed within the United States, such as EJSCREEN and CalEnviroScreen, primarily rely on environmental and sociodemographic indicators to quickly identify communities with potential environmental concerns. This approach also helps locate especially vulnerable populations, such as schools or hospitals.11US EPA. (2016, December 19). Overview of Environmental Indicators in EJSCREEN [Overviews and Factsheets]. Retrieved June 14, 2017, from: https://www.epa.gov/ejscreen/overview-environmental-indicators-ejscreen.12Rodriquez, M., & Zeise, L. (2017, January). CalEnvironScreen3.0. Retrieved from: https://oehha.ca.gov/media/downloads/calenviroscreen/report/ces3report.pdf
Across these tools, data unavailability, as well as uncertainty within accessible data, create persistent challenges. Environmental equity tools can only offer information that is as detailed as the primary sources they draw on to gather data about demographic indicators and environmental benefits and hazards. These data are often unavailable at finer scales, or over a wide geographic range. New York City, for instance, has income data at the census tract and block group scales, which are both much smaller than a district, while other cities have similar data only aggregated to the city level. There are critical challenges when establishing a computational approach to balancing data quality, consistency, scale and coverage. 13US EPA. (2016, December 19). Overview of Environmental Indicators in EJSCREEN [Overviews and Factsheets]. Retrieved June 14, 2017, from: https://www.epa.gov/ejscreen/overview-environmental-indicators-ejscreenThere also needs to be serious considerations around putting data identifying vulnerable populations at granular scales into formats that could be used against them, 14Zárate, L. (26 April 2016). They are Not “Informal Settlements”—They are Habitats Made by People. The Nature of Cities. Retrieved from: https://www.thenatureofcities.com/2016/04/26/they-are-not-informal-settlements-they-are-habitats-made-by-people/. either by political parties or special interest groups.
Even where data are available, these data may themselves be proxies, estimates of actual values, or projections that have inherent uncertainties. Some tools assume that levels of environmental hazards at places of residence are appropriate proxies for actual exposure. Data on the time spent at home or at work, however, are often unavailable. In addition, indices may use data sources that themselves aggregate multiple data sources when measuring exposures, toxicities or emissions. 15London, J., Huang, G., & Zagofsky, T. (2011). Land of risk/land of opportunity: Cumulative environmental vulnerability in California’s San Joaquin valley. Davis, CA: UC Davis Center for Regional Change.As a result, tools such as EJSCREEN and the EJSM can only serve as screening tools to identify potential EJ communities, and not as formal assessment tools.16US EPA. (2016, December 19). Overview of Environmental Indicators in EJSCREEN [Overviews and Factsheets]. Retrieved June 14, 2017, from: https://www.epa.gov/ejscreen/overview-environmental-indicators-ejscreen17London, J., Huang, G., & Zagofsky, T. (2011). Land of risk/land of opportunity: Cumulative environmental vulnerability in California’s San Joaquin valley. Davis, CA: UC Davis Center for Regional Change.
Finally, the tools surveyed differ in their notions of equity, and thus select different indicators to represent socio-economic status. The US-based EJSCREEN, for instance, includes low-income and minority populations in their calculations, while California-based Environmental Justice Screening Method (EJSM) prioritizes “sensitive land uses” of areas with high concentrations of elderly citizens, children and populations with illnesses. 18Morello-Frosch, R., Pastor, M., Sadd, J., & Wander, Madeline. (2015, January). Environmental Justice Screening Method (EJSM). Retrieved from: https://dornsife.usc.edu/pere/cumulative-impacts/This diversity reflects the importance of environmental equity, as well as the challenges of measuring such an expansive and far-reaching topic.
Equity & Environment Terms
|Term and definition||Measurement and policy implication||Example studies|
|Environmental Justice: a broad overarching concept that encompasses social justice considerations in relation to environmental decision-making (Ikeme 2003). 19Ikeme, J. (2003). Equity, environmental justice and sustainability: Incomplete approaches in climate change politics. Global Environmental Change, 13(3), 195-206.The concept takes on three primary dimensions as illuminated by scholars in recent literature: distributive justice, procedural justice and justice as recognition (Schlosberg, 2004)20Schlosberg, D. (2004). Reconceiving Environmental Justice: Global Movements And Political Theories. Environmental Politics, 13(3), 517–540. https://doi.org/10.1080/0964401042000229025||Please refer to distributive justice, procedural justice and justice as recognition dimensions below.||
Anguelovski et al. (2016)21Anguelovski, I., Shi, L., Chu, E. K., Gallagher, D., Goh, K., Lamb, Z., … Teicher, H. (2016). Equity Impacts of Urban Land Use Planning for Climate Adaptation. Journal of Planning Education and Research, 36(3), 333–348.
Warner (2002)22Warner, K. (2002). Linking Local Sustainability Initiatives with Environmental justice. Local Environment, 7(1), 35-47.
Pearsall & Pierce (2010)23Pearsall, H and Pierce, J. (2010). Urban sustainability and environmental justice: evaluating the linkages in public planning/policy discourse. Local Environment, 15(6), 569-580.
|Distributive Justice: emphasizes the distribution of social, economic and political goods, costs, and privileges between members of different genders, social groups and districts (e.g. exposure to hazards) (Schlosberg, 2007)24Schlosberg, D. (2007). Defining Environmental Justice: Theories, Movements, and Nature. New York: Oxford University Press. https://doi.org/10.1093/acprof:oso/9780199286294.001.0001||Measurement would involve identifying differences in distribution of environmental harms and benefits. A just policy outcome ensures the equal distribution of hazards (e.g. air pollution concentrations) across districts and individuals of varying socioeconomic status, or has policy benefits (e.g. transportation access) directed at particularly vulnerable groups (e.g. the urban poor).||
Rawls (1971)25Rawls, J. (1971). A theory of justice (Original ed.). Cambridge, Mass: Belknap Press.
Bullard (1990)26 Bullard, R. D. (1990). Dumping in Dixie: race, class, and environmental quality. Boulder: Westview Press.
Flyvbjerg et al. (2003)27Flyvbjerg, Bent, Nils Bruzelius, and Werner Rothengatter. 2003. Megaprojects and Risk: An Anatomy of Ambition. Cambridge: Cambridge University Press.
Boone et al. (2009)28Boone, C. G., Buckley, G. L., Grove, J. M., & Sister, C. (2009). Parks and people: An environmental justice inquiry in Baltimore, Maryland. Annals of the Association of American Geographers, 99(4), 767-787. 10.1080/00045600903102949
Boyce et al. (2016)29Boyce, J. K., Zwickl, K., & Ash, M. (2016). Measuring environmental inequality. Ecological Economics, 124, 114-123. 10.1016/j.ecolecon.2016.01.014
|Procedural Justice: emphasizes the fair access to and democratic participation in environmental policy-making (Agyeman & Evans, 2004; Schlosberg, 2007)30Agyeman, J., & Evans, B. (2004). “Just sustainability”: the emerging discourse of environmental justice in Britain? The Geographical Journal, 170(2), 155–164.31Schlosberg, D. (2007). Defining Environmental Justice: Theories, Movements, and Nature. New York: Oxford University Press. https://doi.org/10.1093/acprof:oso/9780199286294.001.0001||Measurement would involve identifying differences in the quality and quantity of participation within decision-making processes, among different groups of individuals. A just policy outcome ensures that individuals of diverse backgrounds and origins are represented in planning processes, and that marginalized groups are able to have their ideas considered.||
Kuhn (1999)32Kuhn, S. (1999). Expanding public participation is essential to environmental justice and the democratic decisionmaking process. Ecology Law Quarterly, 25(4), 647-658.
Deacon & Baxter (2013)33Deacon, L., & Baxter, J. (2013). No opportunity to say no: A case study of procedural environmental injustice in canada. Journal of Environmental Planning and Management, 56(5), 607-623.
Bell (2014)34Bell, K. (2014). Achieving environmental justice: A cross-national analysis. Bristol: The Policy Press.
Kasymova & Gaynor (2014)35Kasymova, J., & Gaynor, T. S. (2014). Effective citizen participation in environmental issues: What can local governments learn? State and Local Government Review, 46(2), 138-145.
|Justice as Recognition: emphasizes the need to engage with historically-rooted lack of recognition in the social, political and cultural spheres that determines the distribution of goods and harms (Fraser, 1995; Young, 1990)36Fraser, N. (1995). From Redistribution to Recognition? Dilemmas of Justice in a “Post-Socialist” Age. New Left Review, 1(212), 68–93.37Young, I. M. (1990). Justice and the Politics of Difference. Princeton, NJ: Princeton University Press.||The broad policy end goal would be to prioritize the distributive and procedural needs of the marginalized or the vulnerable. The term has been broadly applied to the need for decision-makers to engage with the underlying mechanisms and drivers that produce injustices. Efforts to measure justice as recognition, however, are nascent. See Box 3 for a case study on justice as recognition and bikesharing.||
Fraser (1995)38Fraser, N. (1995). From Redistribution to Recognition? Dilemmas of Justice in a “Post-Socialist” Age. New Left Review, 1(212), 68–93.
Young (1990)39Young, I. M. (1990). Justice and the Politics of Difference. Princeton, NJ: Princeton University Press.
|Equality of outcome: exposures to environmental hazards should be equal for all populations and subgroups||Measuring the equality of outcomes would involve measuring levels of exposure across different populations (e.g., ensuring all populations are not exposed to air pollution levels above World Health Organization limits). Applying a strictly mathematically equal approach can carry several pitfalls or caveats. Some communities may have vulnerabilities, resources, or exposures to other pollutants that others communities do not; so an equal level of exposure to environmental harms could still affect communities in disproportionate ways. Alternatively, for an environmental good like public transportation, an equal outcome may not achieve policy goals of encouraging some population subgroups to access the good more than others.||Marshall (2008)40Marshall, J. D. (2008). Environmental inequality: air pollution exposures in California’s South Coast Air Basin. Atmospheric Environment, 42(21), 5499-5503.|
|Horizontal Equity: the equal distribution of environmental benefits and resources across individuals in the city regardless of income, race or genders||Measurement may include the analysis of how households, regardless of income levels or race, have access to environmental resources (e.g. accessibility in terms of distance to transit services or green spaces). Horizontal equity assumes individuals or households should be treated equally as a policy end goal regardless of ability or need.||
Delbosc & Currie (2011)41Delbosc, A., & Currie, G. (2011). Using lorenz curves to assess public transport equity.Journal of Transport Geography, 19(6), 1252-1259. 10.1016/j.jtrangeo.2011.02.008
Welch & Mishra (2013)42Welch, T. F., & Mishra, S. (2013). A measure of equity for public transit connectivity. Journal of Transport Geography, 33, 29-41. 10.1016/j.jtrangeo.2013.09.007
|Vertical Equity: emphasizes the distribution of environmental benefits and resources between groups with different abilities (focuses on addressing the needs of the marginalized or urban poor)||Measurement may include the analysis of how specific members of various subgroups, in particular lower income or marginalized communities, gain access to environmental resources. The policy end goal would involve paying greater attention to these individuals (e.g. attention to public transport service improvement for low-income populations) as a policy end goal to address social inequalities.||
Delbosc & Currie (2011)43Delbosc, A., & Currie, G. (2011). Using lorenz curves to assess public transport equity.Journal of Transport Geography, 19(6), 1252-1259. 10.1016/j.jtrangeo.2011.02.008
Welch & Mishra (2013)44Welch, T. F., & Mishra, S. (2013). A measure of equity for public transit connectivity. Journal of Transport Geography, 33, 29-41. 10.1016/j.jtrangeo.2013.09.007
Table 1. A range of different terms related to equity that have different measurement implications.
Environmental Equity in the UESI
The UESI framework focuses on distributive equity, a key component of environmental justice. While procedural representation and recognition are important conceptual components for the environmental justice principle, data that measures or approximates these pillars of equity is scarce across all UESI cities. By focusing on a set of core issue areas – exposure to air pollution, urban heat island effect, distance to public transportation, and tree cover deficit – we show how environmental burdens vary across neighborhoods of different income levels within cities. This approach is in line with current environmental justice and equity assessment tools such as EJSCREEN45US EPA. (2016, December 19). Overview of Environmental Indicators in EJSCREEN [Overviews and Factsheets]. Retrieved June 14, 2017, from: https://www.epa.gov/ejscreen/overview-environmental-indicators-ejscreen. and the Environmental Justice Screening Method (EJSM), enabling stakeholders to identify the distributions of environmental hazards and exposures in relation to demographic considerations (see Box 1, The Landscape of Environmental Equity Tools). 46Morello-Frosch, R., Pastor, M., Sadd, J., & Wander, Madeline. (2015, January). Environmental Justice Screening Method (EJSM). Retrieved from: https://dornsife.usc.edu/pere/cumulative-impacts/.While such demographic considerations may include multiple socioeconomic variables to highlight cumulative social vulnerabilities and intersectionalities from education levels, minority/ethnicity statuses, or age compositions, data on these variables at the desired neighborhood scale is only available and complete for a few cities (See Box 2, Bringing an intersectional analysis to the UESI). Balancing data quality, availability and coverage, the UESI focuses primarily on understanding how urban residents with varying incomes or standards of living may be affected by environmental conditions.
Given the different sources of both population and economic data, to achieve consistency in the results, we transform the original data that includes different forms of income data (e.g. household vs. individual income, monthly vs. yearly income, number of people within an income bracket vs. mean/median income within a neighborhood) to per capita income while maintaining local currencies for each city.
Box 2. Bringing an intersectional analysis to the UESI
Intersectionality explores how different structures of oppression can reinforce one another to make certain identities more vulnerable than they often appear to be when we view structures of oppression in isolation. 47Crenshaw, K (1989) Demarginalizing the Intersection of Race and Sex: a black feminist critique of antidiscrimination doctrine, feminist theory and antiracist politics. University of Chicago Legal Forum: 139–167.48Crenshaw, K (1991) Mapping the Margins: intersectionality, identity politics, and violence against women of color. Stanford Law Review, 43 (6): 1241-1299. Legal scholar Kimberlé Crenshaw, who first coined the term intersectionality,Crenshaw points out that Black women face a set of challenges that include both sexism and racism and, therefore, that a feminist movement that confronts sexism without confronting racism fails to empower all women. Structures of oppression, such as racism and sexism, are societally pervasive structures of supremacy that accord privileges to certain identities by discriminating against others through ideological domination, institutional control, and targeted abuse. 49Just Conflict. Systems of Oppression. Retreived from: http://www.creativeconflictresolution.org/jc/maps-1/systems-of-oppression.html. An intersectional framework highlights how it is insufficient to merely address systems of oppression in isolated ways. They must be dealt with simultaneously, as these systems operate in ways that are larger than the sum of their parts.50Osborne, N. (2015). Intersectionality and kyriarchy: A framework for approaching power and social justice in planning and climate change adaptation. Planning Theory, 14(2): 130–151.
Similarly, it is well established that socioeconomic status is not the only determinant of vulnerability. An economic analysis of inequity, one that relies only on income as a variable, can fail to fully capture the complexity of the people impacted by distributional differences in environmental amenities and burdens. As indicated previously in this report, income level is used as the primary socioeconomic indicator because the data is available for standardization across all of the UESI’s cities. However, policymakers who rely solely on income as a way of understanding equity may miss several aspects of people’s lived experiences and thus risk developing incomplete policy solutions. “The urban poor” do not all look the same, nor do they experience environmental pollution or lack of environmental amenities in the same way.51Wratten, E. (1995). Conceptualizing Urban Poverty. Environmental and Urbanization. 7 (1).
A study conducted across the United States study found that households of similar economic statuses have dissimilar exposures to environmental hazards based on their racial composition. 52Downey, L and Hawkins, N. (2009). Race, Income, and Environmental Inequality in the United States. Sociological Perspectives, 51(4): 759–781.As mentioned in the Air Pollution Issue Profile’s Environmental Injustice and Exposure to Air Pollution box, the national U.S. rate of exposure to particulate matter is higher for Black populations even once income level and geographic scale has been accounted for. 53Bell, M. L., & Ebisu, K. (2012). Environmental inequality in exposures to airborne particulate matter components in the United States. Environmental health perspectives, 120(12), 1699.Women are systematically paid less than men (in the United States, a reality even more pronounced for women of color), and thus their reliance on environmental services like public transport can be more acute. 54Saraogi, V. (3 January 2018). How much do you earn? Gender pay gap around the world. Thomson Reuters Foundation. Retrieved from: http://news.trust.org/item/20180103143925-g183u/. At the same time, as highlighted in Box 3 of the Transportation Issue Profile, women experience harassment on public transportation at disproportionally higher rates than men. 55Hamilton, K and Jenkins, L (2000). A Gender Audit for Public Transport: a new policy tool in the tackling of social exclusion. Urban Studies. 37 (10): 1793–1800.56Bell, K (2016). Bread and Roses: A gender perspective on environmental justice and public Health. International Journal of Environmental Research and Public Health. 13 (1005).57FIA Foundation (2016). Safe and Sound: international research on women’s personal safety on public transport. Fia Foundation Research Series, paper 6. Similarly, in water-stressed areas, due to gendered divisions of labor, the onus to find water for their households often falls on women. 58Chant, S. (2013). Cities through a “gender lens”: a golden “urban age” for women in the global South? Environment & Urbanization. 25(1): 9–29. These are a few examples that highlight how environmental inequities operate in ways that are linked to, but more complex than, income situations. Policy solutions should be sensitive to these inequities. For instance, bringing more public transit options to a low income area may be beneficial to some, but could still exclude women or people living with disabilities if specific measures to include them are not put in place.
It is essential to remember that systems of oppression manifest differently depending on the context. In the United States, for example, white men have historically led the environmental movement, perpetuating systems that value heterosexuality, whiteness, and maleness at the expense of those who do not fit those categories. 59Cronon, W. (1996). Uncommon Ground: rethinking the human place in nature. Norton & Co.The consequence of this is the development of environmental “solutions” that disproportionately reflect the value sets of this limited group of policymakers and advocates. This can be seen, for instance, in the creation of national parks that conceptualize nature as a commodity and indigenous communities as lesser beings responsible for threatening the United States’ “pristine” landscapes. 60Oatman-Stanford, H. (26 January 2018). From Yosemite to Bears Ears, Erasing Native Americans From U.S. National Parks. Collectors Weekly. Retrieved from: https://www.collectorsweekly.com/articles/erasing-native-americans-from-national-parks/. However, understanding this array of social dynamics may do nothing to clarify the social dynamics in a different country. Intersectionality requires high-degrees of local contextualization, which is another reason the UESI, as a global index, relies on income to provide a first assessment of environmental equity. Generally speaking, communities who don’t have access to systems of power and policy decision-making are also those who experience poverty the most acutely.
Finally, understanding the connection between income and environmental amenity provisioning is essential to the development of equitable environmental improvement policies. In general, proximity to amenities like green spaces and subway station increases housing property values, which may threaten low-income renters. 61Chrysanthou, A. (2016). The effect of environmental amenities on house prices. Erasmus university of Rotterdam, thesis. Infrastructural upgrades, including improving water resources, sustainable transportation, and green spaces, often rely on these property value increases to fund their installation. Ensuring that a neighborhood improvement will not result in the displacement of these communities, or other unforeseen consequences, is a crucial element in interpreting UESI data in a way that will truly lead to equitable and sustainable urban development.
Box 3. Beyond Distributional Justice – Bike-sharing in American cities.
While quantitative spatial data in UESI can reveal cases of distributional (in)justice, there is an increasing need to also capture data that assesses how marginalized communities’ experiences are (mis)recognized. In addition to measuring distributive justice, it is important to highlight “justice as recognition,” a concept that calls for explicitly acknowledging the diversity of participants and range of experiences they have.62Young, I. M. (1990). Justice and the Politics of Difference. Princeton University Press. In the case of bike-sharing equity, it is important to look beyond the spatial distribution of stations, and highlight data on the demographics of bike-sharing members, as well as to gather qualitative data on barriers to accessing bike-sharing services for low-income people of color. So far, there has been insufficient guidance on measuring differences in recognition in different societies. Efforts to think creatively about measurements and indicators of recognition are vital to better capturing issues of social inclusion and environmental justice.
Bike-sharing is a form of communal and environmentally sustainable mobility. Biking reduces transport fuel consumption and vehicle emissions and solves the “last-mile” problem by serving as a connection between public transit systems and travelers’ final destinations. While it is encouraging that more people elect to use an environmentally friendly mode of transportation, many bike-sharing systems in the U.S have raised environmental justice concerns. 63Schneider, B. (2017, July 14). Bike Sharing Still Has a Stubborn Diversity Gap. Retrieved November 28, 2017, from: https://www.citylab.com/equity/2017/07/what-keeps-bike-share-white/533412/.Distributional injustice is the most obvious concern: are bike-sharing stations concentrated in wealthy neighborhoods? However, the recognition aspect of justice is important as well: Who rides the shared bikes? To what extent are diversity and experiences incorporated into the design and implementation of the bike-sharing programs?64Schlosberg, D. (2004). Reconceiving Environmental Justice: Global Movements And Political Theories. Environmental Politics, 13(3), 517–540. https://doi.org/10.1080/0964401042000229025
Mobike in Washington D.C (Image credit: Mobike)
Distributional Justice: Who is riding the bikes?
Studies suggest that low-income populations and/or people of color are underserved by bike-sharing programs. A 2015 study of Indego Bikesharing user data in Philadelphia revealed that Indego members tend to be high-income residents. 65Hoe, N. (2015). Bike Sharing in Low-Income Communities: Results from a Spring 2015 Baseline Survey. Philadelphia, PA: Temple University. Retrieved from: http://betterbikeshare.org/wp-content/uploads/2015/05/REPORT_Low-Income-Bike-Share-Baseline-Eval_FINAL-09-18-15.pdf.In addition, 60 percent of Indego members identified as white, while the proportions of African Americans and Hispanic or Latino were 10% and 12% accordingly. 66Hoe, N. (2015). Bike Sharing in Low-Income Communities: Results from a Spring 2015 Baseline Survey. Philadelphia, PA: Temple University. Retrieved from: http://betterbikeshare.org/wp-content/uploads/2015/05/REPORT_Low-Income-Bike-Share-Baseline-Eval_FINAL-09-18-15.pdf.A 2016 Capital Bikeshare Member Survey Report suggested that White/Caucasian commuters represented 80 percent of the Washington, D.C. bikeshare program’s users, while African American (7 percent), Hispanic/Latino (7 percent) and Asian (7 percent) populations were significantly underserved.67LDA Consulting. (2017). Capital Bikeshare 2016 Member Survey Report. Washington, D.C. Retrieved from: https://d21xlh2maitm24.cloudfront.net/wdc/Capital-Bikeshare_2016MemberSurvey_Final-Report.pdf?mtime=20170303165531.
Justice as Recognition: Barriers to Bicycling and Using Bike-sharing
The disparity in bikeshare use reflects the fact that many bike-sharing programs fall short in recognizing the additional barriers facing low-income residents of color. A study by the Transportation Research and Education Center (TERC) at Portland State University suggests that people of color and lower-income residents face more impediments to both bicycling and bike-sharing than higher-income white residents. 68McNeil, N., Dill, J., MacArthur, J., Broach, J., & Howland, S. (2017). Breaking Barriers to Bike Share: Insights on Equity. Transportation Research and Education Center at Portland State University. Retrieved from: http://betterbikeshare.org/wp-content/uploads/2017/07/TREC_BreakingBarriersSummaryReport_emQeiBA.pdf.For example, low-income respondents of color (48 percent) cited the high costs associated with membership and the financial liability of any damage to a shared bike as significant barriers to using bike-sharing services.69McNeil, N., Dill, J., MacArthur, J., Broach, J., & Howland, S. Personal safety was also a barrier to bicycling for people of color. 70McNeil, N., Dill, J., MacArthur, J., Broach, J., & Howland, S. Low-income people of color were more concerned about how biking could expose them to harassment or crime, compared with high-income people of color and high-income white residents. 71McNeil, N., Dill, J., MacArthur, J., Broach, J., & Howland, S. Furthermore, a Chicago Tribune review of police statistics found that the Chicago police wrote twice as many citations to African Americans cyclists than to White and Latino cyclists.72Wisniewski, M. (2017, March 17). “Biking while black”: Chicago minority areas see the most bike tickets. Chicagotribune.Com. Retrieved from: http://www.chicagotribune.com/news/local/breaking/ct-chicago-bike-tickets-minorities-0319-20170317-story.html
Addressing Bike-sharing Equity
A failure to recognize the unique challenges faced by different populations limits the potential benefits of bike-sharing programs. As these shortfalls have become apparent, many bike-sharing programs and community organizations have taken steps to address bikesharing equity. For example, Chicago’s Divvy launched Divvy for Everyone (D4E), a program that provides a one-time $5 Annual Membership to Chicago residents 16 and older with an annual household income at or below 30% percent of the Federal Poverty Level. 73Divvy Bikes. (n.d.). Divvy for Everyone(D4E). Retrieved January 12, 2018, from https://www.divvybikes.com/pricing/d4eThe program also incorporates a cash payment to accommodate those who do not have credit or debit cards to pay for a standard Divvy membership. 74Divvy Bikes. Capital Bikeshare in Washington D.C offers a similar program and has worked with 19 nonprofit organizations to get 800 membership sign-ups in underserved communities, as of the beginning of 2018. The majority of bike-sharing programs (including those previously mentioned) all use docked stations, where users return bikes to one of the many designated bike racks around the city. However, several venture-backed private companies, such as Mobike and Ofo, have started dockless bike-sharing programs in many American cities. Dockless bike-sharing may also have the potential to address distributional bike-sharing equity concerns. Anecdotal evidence from Washington D.C suggests that dockless bike-sharing appeals more to young black commuters than the traditional bike-sharing models (such as Capital Bikeshare) due to the flexibility of use, easier renting procedures, and lower initial barriers to entry. 75Sturdivant-Sani, C. (2018, January 9). In D.C., Dockless Bikeshare Earns a More Diverse Ridership. Retrieved January 13, 2018, from: https://www.citylab.com/transportation/2018/01/can-dockless-bikeshare-pump-up-cyclings-diversity/549629/Data from dockless bike-sharing companies also indicates that dockless bikes are gaining popularity in African American communities (e.g Ward 7 and 8).76Sturdivant-Sani, C.
Calculating Equity and Social Inclusion in the UESI
Building on the rich literature and tools for analyzing distributions of environmental outcomes, 77Maguire, K., & Sheriff, G. (2011). Comparing distributions of environmental outcomes for regulatory environmental justice analysis. International journal of environmental research and public health, 8(5), 1707-1726.78Sheriff, G., & Maguire, K. (2013). Ranking Distributions of Environmental Outcomes Across Population Groups.79Groot, L. (2010). Carbon Lorenz curves. Resource and Energy Economics, 32(1), 45-64.80Padilla, E., & Serrano, A. (2006). Inequality in CO2 emissions across countries and its relationship with income inequality: a distributive approach. Energy Policy, 34(14), 1762-1772.81Cantore, N., & Padilla, E. (2007). Equity and CO2 emissions distribution in climate change integrated assessment modelling (No. 7001). Alma Mater Studiorum University of Bologna, Department of Agricultural Economics and Engineering.82Maguire, K., & Sheriff, G. (2011). Quantifying the Distribution of Environmental Outcomes for Regulatory Environmental Justice Analysis (No. 201102). National Center for Environmental Economics, US Environmental Protection Agency. the UESI has developed an approach for analyzing environmental and socioeconomic conditions. The UESI approach draws heavily on the use of graphical representations, such as concentration and Lorenz Curves, to capture the distribution of environmental outcomes and income across a city. In addition, the UESI approach includes a numerical representation of the distribution of income and environmental outcomes. Together, these representations shed light on the relationship between the distribution of environmental outcomes and income within cities.
Graphical representations of income and environmental outcomes
We use Lorenz and concentration curves to analyze the distribution of income and environmental outcomes (Air Pollution, as measured through Average Exposure to PM2.5 and NO2; Urban Heat Island Intensity; Distance to Public Transit; and Tree Cover Per Capita) respectively, and for each city. Both the Income Lorenz Curve and Environmental Concentration Curve are ordered by per capita income. In the plots, the x axis refers to the cumulative proportion of a city’s population – ranked by income – and the y axis is the cumulative proportion of income or environmental outcome distributed throughout the city for the Income Lorenz Curve and the Environmental Concentration Curve respectively. A 45-degree line that represents perfect distributive equity is also included as a frame of reference. If income and environmental burdens were distributed equally across each fraction of a city’s total population, the Lorenz and concentration curves would look like the 45-degree line.
Due to its definition, the Lorenz income curve will never be above the 45 degree line (i.e., the line of perfect equity). The distance between the income Lorenz curve and the 45 degree line of perfect equity indicates the degree of income distribution inequality: a greater distance between the two lines indicates a more unequal distribution. Environmental Concentration curves can be either above or below the line of perfect equity, and positions suggest different interpretations. If the curve is fully located above the line of perfect equity it indicates that the environmental outcome is more heavily allocated to those with less income. On the contrary, if the curve is fully located below the line of perfect equity it indicates that the environmental outcome is more heavily allocated to those with more income 83It is important to consider that this interpretation is based primarily on the definition of the environmental indicators as measuring environmental burden (i.e. higher values indicate worse quality or more negative conditions). If this definition were to change, so would the interpretation of the relative position of the Concentration Curves.84It is also important to mention that this approach is not intended to compare income versus environmental distributions – in other words, it should not be used to judge whether the distribution of an environmental outcome is more or less equitable than that of income for the same city, or between the same curves for different cities.. However, it’s important to note that Environmental Concentration curves can have sections located at both sides of the 45 degree line; in this cases the interpretation will not be as straightforward as it was mentioned before, and the interpretation would really on additional metrics, that we will explore in the following sections. Figure 1 provides a graphical representation of the Environmental Concentration Curves and their interpretation..
Figure 1. Examples of Environmental concentration curve and their interpretation. The left panel shows an Environmental Concentration Curve that falls above the line, indicating the allocation is placed on the poorer population. The right panel shows a concentration curve that falls simultaneously on both sides of the line of equity, making it difficult to obtain a conclusion about the burden allocation based exclusively on the curve, which we address in the next section using a numeric representation.
The specific interpretations of the concentration curves for different indicators follow below:
- PM2.5 Equity: Cumulative proportion of total exposure to PM2.5 concentration for the entire population (Negative Environmental Outcome)
- NO2 Equity: Cumulative proportion of total exposure to NO2 concentration for the entire population (Negative Environmental Outcome)
- UHI Equity: Cumulative proportion of total exposure to UHI Intensity for the entire population (Negative Environmental Outcome)
- Tree Cover Equity: Cumulative proportion of the total Tree Cover per capita for the entire population (Positive Environmental Outcome)
- Distance to Public Transit Equity: Cumulative proportion of total distance to nearest public transportation station for the entire population (Negative Environmental Outcome)
An example of two sample Environmental Concentration Curves, and the Income Lorenz Curves for Johannesburg can be seen in Figure 2. It is important to note that the Concentration curves do not indicate whether the cumulative exposure to air pollutants, UHI intensity, tree cover per capita, or distance to public transportation for a given city is large or small. They simply indicate whether the distribution of these environmental burdens or benefits are more or less equally distributed relatively to income.
Figure 2. Results of the distribution of Income Lorenz Curve (Red), UHI Intensity and NO2 Exposure Concentration Curves (Blue) for the city of Johannesburg. The Environmental Concentration Curve results indicate that UHI Intensity is concentrated in the low-income populations in the city.85Chakraborty, T., Hsu, A., Manya, D., & Sheriff, G. (2019). Disproportionately higher exposure to urban heat in lower-income neighborhoods: a multi-city perspective. Environmental Research Letters, 14(10), 105003. By contrast the NO2 exposure is only slightly more concentrated in high-income populations, and still within a marginal distance from the line of perfect equity. Finally, the Income Curve indicates that there is an important inequality in the distribution of income, due to the distance of the curve to the 45 degree line of equity.
Numeric representations of inequality of income and environmental outcomes
While numeric metrics have been developed to analyze distributive equity in different fields, their use has not been as extensive as graphical representations in assessing environmental outcomes. Existing examples of the use of numeric metrics to environmental assessments include Padilla and Serrano’s (2006) use of the Kakwani Index,86Padilla, E., & Serrano, A. (2006). Inequality in CO2 emissions across countries and its relationship with income inequality: a distributive approach. Energy Policy, 34(14), 1762-1772. and inequality indices such as the Atkinson and Kolm-Pollack, as detailed by Maguire and Sheriff (2011). 87Maguire, K., & Sheriff, G. (2011). Comparing distributions of environmental outcomes for regulatory environmental justice analysis. International journal of environmental research and public health, 8(5), 1707-1726.Although methodologically robust, the construction and interpretation of these indices can be challenging from a decision-maker’s perspective, particularly due to the complex mathematical definition of the indices. Therefore, building on the literature around the use of concentration indices as an accepted measure of inequalities, particularly around health-related outcomes88Kakwani, N., Wagstaff, A., & Van Doorslaer, E. (1997). Socioeconomic inequalities in health: measurement, computation, and statistical inference. Journal of econometrics, 87-103.89Kaufman, J. S. (2017). Methods in social epidemiology (Vol. 16). John Wiley & Sons.90Elgar, F. J., McKinnon, B., Torsheim, T., Schnohr, C. W., Mazur, J., Cavallo, F., & Currie, C. (2016). Patterns of socioeconomic inequality in adolescent health differ according to the measure of socioeconomic position. Social indicators research, 127(3), 1169-1180.91Costa-Font, J., Hernandez-Quevedo, C., & Sato, A. (2018). A Health ‘Kuznets’ Curve’? Cross-Sectional and Longitudinal Evidence on Concentration Indices’. Social indicators research, 136(2), 439-452. , we use this approach to explore the relationship between income and environmental outcome distributions.
The UESI’s approach to numerically quantify inequality uses the concentration curves presented in the previous section, and calculates a summary measure called the Environmental Concentration Index (ECI) using the following formula:
ECI = 1- 2*AUCenv
Where ECI is the concentration index for the environmental outcome (env) and AUC is the area under its corresponding curve. 92The trapezoidal rule is a method for calculating the integral of a function based on the construction of trapezoids for sequential sections of an interval, for this case the sections are build by the pairs of sequential cartesian points that define the Concentration Curve and the Lorenz Curve. The areas of the trapezoids are added and the sum is equal to the integral of the function, which by definition is the area under the curve (AUC).A concentration index value can range from -1 (i.e, the environmental burden is allocated to the poorest individual) to 1 (i.e., the environmental burden is allocated to the wealthiest person). 93Maguire, K., & Sheriff, G. (2011). Quantifying the Distribution of Environmental Outcomes for Regulatory Environmental Justice Analysis (No. 201102). National Center for Environmental Economics, US Environmental Protection Agency.The ECI serves a summarizing feature, providing a numeric value of inequality in a city, which is particularly useful for when conclusions are difficult to obtain from the graphical representations alone. Given its definition,94By definition the Concentration Index includes the assumption that the inequality in the distribution of the outcomes is equivalent for income groups. As a result, if there are similar levels of inequality for the poorest and richest groups – represented by a curve that is above and below the 45 degree line – the results of the ECI will be closer to zero, because both inequalities would mathematically compensate each other. however, the absolute value of the ECI is the net inequality of a city, while the sign, positive or negative, indicates where that net inequality is allocated, whether to richer or poorer populations. As a result, while useful by themselves, the ECI values should be analyzed in conjunction with the curves to have a more accurate interpretation of the results around the presence of different pockets of inequality.
Complementary to the ECI, the UESI also calculates the Gini Coefficient for each city – a commonly used metric of income inequality – using the already defined Lorenz Curve for income, this calculation is done using the same formula as the ECI as both of them are based on the area under the curve (AUC) value. A graphic example of these calculations is shown in Figure 3.
Figure 3. Example of Environmental concentration for Tree Cover per Capita and the Lorenz Income Curve for the city of Los Angeles.
Typology of relationships between income and environmental outcomes
Even though the ECI provides an important metric to describe the relationship between income and the environmental outcome, the picture is still incomplete because each city’s income inequality is a relevant factor to consider. To contextualize these results in a more meaningful way and to understand the interplay between environmental and socioeconomic inequalities, we developed a typology to categorize where cities fall in relation to each other using both the ECI and the Gini coefficients.
The typology’s four quadrants are defined based on two axes. The x-axis denotes the Environmental Concentration Index of a variable and the y-axis denotes the income inequality, as expressed through the Gini coefficient.The UESI uses a Gini coefficient value of 0.39 95Average Gini coefficient values for all countries in the WB National Datasets for 2014.and an ECI value of 0 to separate the quadrants.
To better understand the potential interaction between both distributions it is important to consider that the income distribution of a city – represented in the Gini value – reflects the level of homogeneity in the allocation of economic resources obtained by a household, resources that are used to provide an adequate standard of living for its inhabitants. On the other hand, the distribution of environmental outcomes – represented by the ECI values – reflects the inequality in the allocation of positive or negative environmental conditions that affects a sector of the population, which can impact their economic conditions, positively or negatively, relative to other segments of the population.
For example, a group of people disproportionately burdened by air pollution (a negative environmental outcome), such as PM2.5, may be further economically disadvantaged through additional healthcare costs resulting from air pollution-related respiratory problems as opposed to other populations that may not be as exposed. In another example, a segment of the population that has less access to Tree Cover (a positive environmental outcome) would have to spend resources, such as time and money, to travel to another area with higher tree cover if they expect to enjoy the same benefits tree cover affords.
This impact of the environmental allocation on the income distribution, which we call environmental pressure, is then a potential source of inequality – unaccounted by most traditional analysis. When the environmental pressure is placed on the poorer segments of the population – either though an unequal allocation of negative environmental outcomes to the poorest, or positive environmental outcomes to the richest – it can exacerbate the inequalities between the poorer and richer residents of a city to different degrees, by increasing the resources that the poorest need to invest to compensate for the negative impacts, or for gaining access to the positive ones. These degrees to which environmental burdens exacerbate income inequality are described by the scenarios detailed in the quadrants in Figure 4. The interpretation of the ECI values related to the quadrants will depend on the type of environmental outcome (EO) analyzed, positive or negative.
Figure 4: The four-quadrant typology of the UESI and the scenarios it defines.
Before exploring the results of this novel approach for analyzing environmental and income distribution among our cities, there are a few considerations that are relevant for discussion. The most significant difference is in the UESI’s use of neighborhood-level information to make comparisons, unlike more traditional uses of the Lorenz and Concentration curves whose data points are generally at the individual level. While making our equity calculations on the neighborhood level may disregard heterogeneity within neighborhoods, the UESI reflects a more conservative reflection of inequalities than those that happen at the micro or individual scale.
The results of this analysis have highlighted important aspects of inequality and its relation to the environmental burdens. The overall results suggest that while some cities in the global South, such as Johannesburg or Bangalore, are remarkably unequal in their income distribution, income inequality occurs in both developing and developed world cities. For instance, cities in the U.S., such as Boston and Atlanta, have relatively high income inequality, while cities in the global South like Jakarta demonstrate lower income inequality.
In general, and for most environmental outcomes analyzed, we can see that cities are present in most of the typology quadrants. A small number of cities are consistently located in the top right quadrant (Q2), where there is low income inequality and the environmental burdens do not disproportionately affect districts with lower income. The location of many other cities in the other quadrants (Q1, Q3, and Q4) indicates that environmental inequality is indeed a pervasive problem in global cities, regardless of their geographical locations or their income level, and that there is almost always a sector that is in some way disproportionately burdened with negative environmental outcomes or benefiting with positive environmental outcomes.
For detailed analyses, results and interpretations of equity calculations, please refer to the corresponding chapters.
Box 4. Engagement Processes
Although UESI relies on the best openly available data, it is a tool created from a distance. The UESI’s emphasis on equity could benefit from the inclusion of active public participation and engagement with local communities in the future. By integrating additional participatory mapping tools such as OpenStreetMap (OSM), we see great opportunities for participatory mapping and data ground-truthing. For example, a citizen could actively participate in the UESI by updating a bus stop that has been decommissioned on OSM and contribute to data accuracy and timeliness.
A notable community engagement mechanism is Environmental Justice Screening Methods (EJSM)’s community-based participatory research method (CBPR) that trains community members in the ground-truthing efforts and work with community members to drive policy changes. For example, EJSM trains community members to collect data and the data is mapped and compared against the database. 96Sadd, J., Morello-Frosch, R., Pastor, M., Matsuoka, M., Prichard, M., & Carter, V. (2014). The Truth, the Whole Truth, and Nothing but the Ground-Truth: Methods to Advance Environmental Justice and Researcher–Community Partnerships (p. 288). Health education & behavior, 41(3), 281-290.EJSM’s CBPR has been found to be effective in driving policy changes. 97Sadd, J., Morello-Frosch, R., Pastor, M., Matsuoka, M., Prichard, M., & Carter, V. Specifically, after practicing CBPR in multiple sites in Los Angeles, the Los Angeles Environmental Health and Justice Collaborative created a new policy campaign called “Clean Up, Green Up” and successfully convinced the City of Los Angeles to offer hazard removal incentives in the Wilmington, Boyle Heights and Pacoima neighborhoods, as well as to assist existing businesses to transition into greener ones. 98Sadd, J., Morello-Frosch, R., Pastor, M., Matsuoka, M., Prichard, M., & Carter, V. Future iterations of the UESI could aim to engage communities with environmental equity struggles with participatory mapping and data ground-truthing process and to ultimately drive policy changes.
Additional community engagement mechanisms that UESI could employ in the future include: collecting and responding to public feedback, publicizing webinars (on data-literacy and citizen science), holding community-mapping workshops, and engaging activists as researchers. For instance, EJAtlas is an environmental justice screening tool with a highly bottom-up collaborative process that emphasizes knowledge co-creation between activists on the ground and scholars. We invite future collaboration with local communities to expand and localize our definition of equity and to drive policy changes.