Socially Vulnerable Communities and Green Stormwater Infrastructure planning in Washington D.C.

doi:10.21203/rs.3.rs-2977496/v2

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Socially Vulnerable Communities and Green Stormwater Infrastructure planning in Washington D.C.

https://doi.org/10.21203/rs.3.rs-2977496/v2

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In the face of natural or manmade disasters, marginalized communities often bear disproportionate vulnerabilities, shaped by complex interplays of political, economic, demographic, and historical factors. This study employs the Pressure and Release (PAR) model to elucidate the roots of unequal resource distribution and varying vulnerability levels among communities. Focusing on Washington DC, the research delves into the social vulnerability of its Southeast communities through Pareto ranking and an additive model. Results reveal the advantages of Pareto ranking, emphasizing the intertwined nature of vulnerability factors. Concurrently, the study examines Green Stormwater Infrastructure (GSI) programs, revealing their exclusionary practices. Drawing on environmental justice literature, the research connects socio-economic indicators to infrastructure disparities. The study hypothesizes that comprehensive social vulnerability contributes to the unequal distribution of both grey and green stormwater infrastructure. The findings underscore the urgency of addressing maintenance neglect and community exclusion within GSI practices.

Social vulnerability

Green stormwater infrastructure

Pressure and Release Model

When a natural or manmade disaster occurs, communities may be differentially affected based on a number of reasons. The classic Pressure and Release (PAR) model describes the political, economic, demographic and historical factors that influence unequal resource distribution and produce varying degrees of vulnerability in relation to disaster (Blaikie et al. 2014). The model aims to explain why economically or politically marginalized communities often have limited access to resources and why intervention in these communities is of low priority for the government, factors which are both linked to disinvestment or exclusion from market-based development approaches. By addressing root causes and the dynamic pressure, the PAR model provides a theoretical explanation of why economically or politically marginalized communities live in unsafe locations or buildings and is applied worldwide (Blaikie et al. 2014).

Disaster literature has also shown that discriminatory policies and development patterns have influenced the location and outcomes of marginalized communities and subsequently excluded these communities from pre-disaster protective measures or post-disaster investment (Hendricks and Van Zandt 2021; Bullard and Wright, 2009). Oliver-Smith (1999) argues that cumulative underdevelopment patterns, subsequent racial marginalization and associated poverty, and antidemocratic development influence the differential vulnerability of communities. In New Orleans (US), studies shows that segregative practices limiting location choice have led African American communities to settle in flood-prone areas that have incurred more damage during disasters and have been excluded from the protective measures that whiter and wealthier communities were granted (Bullard and Wright 2009). In Houston (US), neighborhoods located outside of floodplains with more black and Hispanic residents experienced greater degrees of flooding during 2017’s Hurricane Harvey, likely as a result of racial residential stratification (Smiley 2020). These theoretical and empirical descriptions imply that marginalized communities, as a result of discriminatory policies and development patterns, have access to less safe housing, infrastructure, or built environment.

Stormwater infrastructure, as a built environment component critical to preventing flood-related disasters (Peterson 1979; Tarr 1984), has also been shown to be unevenly distributed within cities. In this paper, we consider stormwater infrastructure as an infrastructure composed of grey infrastructure- pipes and other hardscape elements- and Green Stormwater Infrastructure (GSI)- interventions including bio-swales, ponds, and rain gardens designed to intercept and filter runoff by mimicking natural processes (Porse 2018). Similarly, a growing body of Environmental Justice literature focusing on the distribution of stormwater infrastructure, which delivers flooding mitigation and other benefits, empirically demonstrates that certain socio-economic factors are closely correlated with the distribution of stormwater infrastructure. This can be also seen in Porse (2018), which reveals that higher values of sociodemographic indicators in LA (US), such as the proportion of rental households, correlate with lower capacity of stormwater infrastructure. Another example is Mandrano and Meenar’s work (2017) that found a negative relationship between minority-populated areas in Philadelphia (US) and the location of GSI, with the authors tying the inequitable distribution of GSI to market forces. For Baker et al. (2019), the density of above-ground GSI in Portland (US) is negatively associated with the median income of census tracts. These studies collectively suggest that GSI is inequitably distributed from the perspective of Environmental Justice (EJ).

Several of these studies suggest that relying on market-based approaches, voluntary citizen participation, or redevelopment projects to implement GSI has led, and will continue to lead, to the spatial grouping of GSI in and around affluent communities (Mandrano and Meenar 2017; Lim 2018). This is due to the self-reinforcing nature of these approaches, which rely on existing access to political, financial, and other resources to construct GSI. These empirical findings correspond with Hendricks and Van Zandt’s (2021) argument that infrastructure plays a role as a mechanism that transmits unequal impacts across communities, and thus intensifies environmental injustice.

The existing GSI literature establishes a clear association between specific socio-economic or demographic indicators, such as race and income, and the unequal distribution of infrastructure (Mandrano and Meenar 2017; Porse 2018; Lim 2018). However, when examining stormwater infrastructure, it is crucial to consider its distribution from the perspective of disasters and hazards. This is because stormwater infrastructure plays a critical role in effectively managing and reducing flooding events. Recognizing that disasters are not solely natural occurrences but also social events (Perry 2007), it becomes imperative to analyze the distribution of stormwater infrastructure alongside the overall social vulnerability of communities. While natural hazards like earthquakes or hurricanes can trigger disasters, the impact of these events is shaped not only by their magnitude but also by the existing social structure, including socioeconomic disparities between different classes and regions (i.e., vulnerability). Fothergill (1998) argues that disasters bring underlying social processes into sharper focus as they compress these processes into a condensed and intense timeframe. Even when communities face similar natural disaster events in terms of magnitude and timing, the varying levels of vulnerability can lead to significantly different outcomes. Thus, when considering the distribution of stormwater infrastructure, it is crucial to take into account the socially vulnerable status of communities and its implications.

Although there are no consistent metrics set for the social determinants that affect disaster vulnerability, the social vulnerability index (SoVI) was invented and is widely utilized to represent the relative ability of a community or city to prevent human suffering in disaster events (Cutter 2003). Social vulnerability literature has identified some common dimensions of social vulnerability - such as income and poverty, race and ethnicity, and education level - that differentially affect a community’s impact from a given disaster context (Van Zandt et al. 2012). In addition to the commonly used factors of class and race, other root causes such as gender, age, or disabilities are also crucial to consider (Enarson 1998; Fothergill 1998; Aldrich and Benson 2008). Social vulnerability is more accurately described not by one or two factors, but a range of characteristics acting collectively. In this way, social vulnerability indices attempt to capture the cumulative effects of past discriminatory practices through surface level indicators of current conditions (Hendricks and Van Zandt 2021).

In this sense, even though previous environmental justice literature has linked the inequitable distribution of GSI to specific socio-economic factors (Mandrano and Meenar 2017; Porse 2018; Lim 2018), they do not look to link between GSI and broader social vulnerability. Until the writing of this paper, the authors of this article are not aware of previous studies that have shown whether marginalized communities with less access to protective GSI are socially vulnerable or whether socially vulnerable communities are relatively excluded from the flood mitigation benefits of stormwater infrastructure. In this context, this study will use SoVI to analyze the relationship between traditional grey infrastructure and social vulnerability from the perspective of disaster planning.

Based on the empirical findings from GSI literature from the EJ perspective and the theoretical description of social vulnerability, this study hypothesizes that the comprehensive social vulnerability leads to the unequal distribution of grey and green stormwater infrastructure. Therefore, the objective of this paper is to corroborate the previous social vulnerability theories saying that socially vulnerable communities have access to less safe infrastructure to cope with potential floods in both quantitative and qualitative ways.

This article is organized as follows. First, the case study of Washington (DC) is shown. Section 3 focuses on presenting the methodology adopted and specifics about the data used. Section 4 shows the main results obtained in the regression analysis, and section 5 present findings based on the correlations between GSI and SoVI indicators, including the influence on urbanization, stormwater infrastructure and planning implications. Thereafter, limitations, future research directions and main conclusions are discussed.

The District of Columbia (DC), the capital city of the United States, is adjacent to the northern edge of the Potomac River and located between Maryland and Virginia (Figure 1). DC is a demographically divided city - based on the 2017 American Community Survey (ACS) data, of the 450 census block groups that make up DC, the 126 census block groups with Black residents comprising over 80% of total residents are concentrated in the Southeast DC, while most of the 76 block groups with white residents comprising over 80% of total residents are located in the Northeast DC (Figure 5). The median household income of the 76 majority-white census block groups averaged $152,900, which is more than three times the average of the majority-Black census block groups. Over 15% of Black DC households have an education level below high school graduation, compared to only 2% of white households. Census data also reflects significant differences in family composition, property values, and poverty levels between the majority-Black and majority-white areas of DC.

DC’s grey stormwater infrastructure has both a combined sewer system (CSS) and a more recently constructed municipal separate storm sewer system (MS4) that now covers 70% of the DC area. Sewer system construction began around 1810, and the total pipe length between the CSS and MS4 systems is now more than 1,800 miles. Over 40% of the surface of DC is impervious, and the annual precipitation is about 40 inches, according to the 50-year precipitation archive (National Center for Environmental Information 2020). Parts of DC are increasingly vulnerable to various types of flooding (National Capital Planning Commission and Washington, D.C. Silver Jackets 2018; Department of Energy and Environment 2020). The National Capital Planning Commission (2008) said that the National Mall and the neighboring areas are susceptible to periodic flooding due to the geographical location at the confluence of the Potomac and Anacostia Rivers, the relatively flat slope and broad floodplains, and the urban development upslope in the watershed, that increases the stormwater runoff draining to the area. The report also noted that Downtown DC does not have enough storm sewer capacity, making the area particularly vulnerable to interior flooding (National Capital Planning Commission, 2008). The current floodplain map of DC reflects the flood risks associated with the Potomac and Anacostia Rivers but Nobel (2017, Sep 21) warned that greater flood damage beyond the mapped floodplain boundary could occur due to mismanagement of the Potomac Park Levee System.

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3.1. Indicators for Social Vulnerability

The literature on the social vulnerability index discusses various indicators that contribute to the index, but a consensus on specific indicators reflecting social vulnerability or serving as proxies remains elusive. The social structure determining individuals' actions and socioeconomic statuses differs among societies, leading to regional variations in vulnerability indicators based on institutional and cultural differences within a country. Nevertheless, common indicators have been identified in the literature.

Poverty is a pervasive indicator of social vulnerability. Impoverished individuals lack the financial means to prepare for disasters, purchase emergency supplies, or recover from their aftermath (Morrow, 1999; Clark et al., 1998). Poverty significantly influences vulnerabilities at individual, household, and community levels throughout all stages of a disaster. While the total economic loss for wealthier individuals may be greater, the impact is more devastating for the poor due to the vulnerability of their housing and settlements (Morrow, 1999; Clark et al., 1998).

Gender is a critical factor closely related to social vulnerability. Feminist arguments emphasize that gender relations often assign different family and work roles to men and women, affecting their vulnerability discriminately (Phillips & Morrow, 2007). Women and female-headed households, in particular, disproportionately suffer from the effects of disasters due to intersecting responsibilities as income earners and family caregivers (Enarson, 1998). Caregiving responsibilities may contribute to higher mortality rates for women due to restricted ability to seek safety (Fothergill, 1998). Additionally, female-headed households' connection to poverty exacerbates their vulnerability to disasters (Enarson & Morrow, 1997).

Age is a relevant factor in vulnerability, as both children and the elderly may face challenges in responding to disasters (Clark et al., 1998). Older individuals, lacking economic resources and physical abilities, may experience health-related outcomes and recovery hardships (Morrow, 1999). Preferences of the elderly to stay at home contribute to their vulnerability, with reluctance to evacuate and adapt to new living arrangements (Aldrich & Benson, 2008; Gladwin & Peacock, 1997). Children without adequate family support may struggle to respond effectively due to psychological and physical effects (Morrow, 1999).

Ethnic minority status is a vulnerability factor, primarily due to the higher likelihood of poverty among ethnic minorities (Bianchi & Spain, 1996). Racial discrimination and discriminatory institutions increase the social vulnerability of racial and ethnic minorities (Bullard, 2008; Fothergill, 1999). Racial discrimination in housing searches may concentrate minorities in hazard-prone areas, and race and language become significant factors in response and recovery, as seen in the Hurricane Andrew case (Phillips & Morrow, 2007; Gladwin & Peacock, 1997).

3.2. Calculating Social Vulnerability Index (SoVI)

Two methods are generally utilized in existing literature for calculating the SoVI and mapping it: one involves adding standardized or percentile scores of each indicator to create an integrated index, and the other uses the numerical value of individual indicators as an index. The latter method offers the advantage of easily identifying or focusing on specific vulnerabilities (Van Zandt et al., 2012). However, the purpose of organizing the SoVI, such as allocating emergency preparedness funding and identifying communities needing support, requires determining the most vulnerable communities.

To decide the most vulnerable communities, comparing the index through the sum or average of indicators is more straightforward than comparing individual indicators. However, using the arithmetic mean and sum presents two problems. First, assuming that each vulnerability indicator evenly contributes to vulnerability may distort the actual social vulnerability. Assigning equal weight to each indicator may not properly reflect vulnerabilities, as the contribution of each indicator may differ. The second problem is that the arithmetic sum or average may not properly reflect individual indicator scores. For example, if an elderly population in a community is large, their vulnerability may disproportionately influence the overall vulnerability score, potentially overlooking other vulnerabilities.

To address these issues, Rygel et al. (2006) proposed calculating the social vulnerability index of a community through Pareto ranking, a method that considers relative weights of each vulnerability component. This method offers a more nuanced and accurate assessment of social vulnerabilities, ensuring that decision-makers do not overlook communities requiring specific support.

Pareto ranking, also known as Pareto multi-criterion optimization, has been successfully applied in various fields, such as architecture design (Wright & Loosemore, 2001). In the context of social vulnerability, it provides a solution to the challenge of determining which communities are most vulnerable practically. Architects have used Pareto ranking's optimization principles to efficiently design buildings by considering multiple factors contributing to overall efficiency. Similarly, planners can apply Pareto ranking to identify and prioritize vulnerable communities based on a comprehensive evaluation of various indicators.

In conclusion, the discussion highlights the importance of considering multiple indicators in assessing social vulnerability and the challenges associated with traditional methods of calculating the Social Vulnerability Index. Pareto ranking emerges as a promising alternative, offering a more nuanced understanding of vulnerabilities and aiding decision-makers in allocating resources effectively.

The above figure shows the spatial distribution of social vulnerability by the index built based on the additive model that consists of indicators of the poverty level, gender, age, and race. Among the four quadrants, the Southeast quadrant has a higher level of social vulnerability compared to others. This reflects the social equity issues of race, income, and education that have been accrued historically. Given the overall DC, the Southeast show similar social vulnerability, except for only a few block groups. Among 106 census block groups, 68 block groups have 80% to 100% score, 35 block groups have 60 to 80% score. In other words, given the city-wide social vulnerability, the vulnerability gap between the Southeast communities looks inconsiderable.

By limiting the scope of the analysis to DC's Southeast, differences in vulnerabilities between 106 census block groups become apparent. The below figure shows the spatial distribution of social vulnerability in Southeast communities. The figure above maps the SoVI built by the additive model, and the map below displays the social vulnerability ranks by Pareto ranking. The two maps identify the social vulnerability of each census block group according to their quartile score; red color represents the top 10% of the most vulnerable census block groups, and green color represents relatively less vulnerable communities.

The social vulnerability maps of DC Southeast obtained through both methods are quite similar. The overall rank of the communities does not change, except for a few block groups. These results may provide two interpretations: (1) Pareto ranking is not as efficient in capturing communities in need of special attention as the additive model is, and (2) the ranks determined by comparing two indicators do not show a significant difference from the ranking by SoVI because the dataset of four indicators does not have extremely high or low values like outliers.

However, the research outcomes of Rygel et al. (2006) prove that some communities with very high values on only one indicator show clear differences between the additive model and Pareto ranking. Additionally, the communities with especially high values create considerably different vulnerability maps for Hampton Roads, Virginia (Rygel et al., 2006). In this dataset for the Southeast, a few block groups that have relatively high values in certain indicators also show more than a 10% score increase in the Pareto Ranking compared to the additive model’s SoVI. As a result, this proves that Pareto ranking captures unusually large or small values of indicators well and identifies communities requiring special attention in disaster planning due to their unique vulnerability stemming from only one factor.

4.1. Planning Mechanism for GSI

Some studies highlight that the implementation mechanism of Green Stormwater Infrastructure (GSI) could significantly impact its distribution. GSI programs employ various policy approaches, including public investment, regulation through building permits, and incentivizing the private sector for voluntary implementation (Dunn, 2010). However, each approach introduces its own challenges regarding the equitable distribution of GSI.

Firstly, public investment involves utilizing public land, specifically in the case of green stormwater infrastructure. This requires a site at the point source of stormwater runoff, often publicly owned areas like rights of ways, streets, and parks. However, public lands belonging to the governmental department driving GSI implementation may constitute only a small portion of the necessary sites. This necessitates a policy approach that fosters collaboration between stakeholders, including municipal departments, water authorities, and communities (Wendel et al., 2011). If associated communities lack the capacity to effectively partner, GSI implementation may be slowed or hindered. Environmental justice literature has well-documented the link between community capacity and environmental harm or benefits, indicating that poor communities may face challenges in participating in decision-making processes, potentially concentrating the success of GSI implementation in only affluent communities.

Other policy approaches, such as regulation and voluntary implementation using incentives, could also inadvertently exclude marginalized communities. GSI-related regulations often mandate implementation in redevelopment (Dunn, 2010). While this implementation does not discriminate based on affluence, the redevelopment process itself may lead to gentrification, disproportionately affecting communities of color (Essoka, 2010; Shin, 2009). Additionally, encouraging voluntary investment through incentives relies more on individual or community capacity than the collaborative public investment approach does. Heynen et al. (2006) theoretically demonstrate that these approaches result in the inequitable distribution of green infrastructure, utilizing a "Marxist urban political ecology framework."

Voluntary implementation reveals a clustering pattern across neighborhoods. Green et al. (2012) analyzed the adoption of voluntary rain garden programs and found spatial clustering near previous adopters. They argue that shared experiences influence neighbors' trust and program participation. However, unfamiliarity and limited public knowledge remain obstacles to voluntary GSI practice (Keeley et al., 2013). This clustering phenomenon based on shared experiences aligns with the diffusion of information on environmentally related behaviors between neighbors, as identified in various studies (Robe and Weber, 2016; Hopper and Nielsen, 1991). Nevertheless, this information diffusion process across communities is associated with socioeconomic characteristics, as impoverished neighborhoods may isolate residents from networks, depriving the broader community of their human capital (Office of Policy Development and Research, 2011). Poverty also hampers individuals from becoming part of wider social networks (Matthews & Besemer, 2014).

In summary, the current GSI implementation mechanisms—collaborative public investment, mandatory implementation through redevelopment, and inducing citizens’ voluntary participation—may result in the inequitable distribution of GSI, potentially excluding marginalized communities. Planners must carefully consider whether collaborative approaches and high community capacity are prerequisites for successful public investment. Similarly, in market-driven approaches, planners should ensure that sufficient incentives are provided for marginalized individuals to participate without financial burdens, and information about voluntary participation is widely disseminated without discriminating against poor and affluent communities.

4.2. GSI planning in DC

DC has led GI planning and policies and has implemented various policies and programs. The below table summarizes the current GI planning programs and policies. Except for the Stormwater Management Regulation, all the GI programs are driven by Department of Energy and Environment and DC water. (In the case of Green Alley program, the District Department of Transportation collaborates with DC water, but this program is one of sub programs of DC water.) Also, except for the DC clean river project, Green Infrastructure Design Challenge, Green Alley Partnership, and Stormwater Management Regulation, most of implementation approaches are voluntary implementation using incentives.

Table. DC GI planning summary
GI policy and planning	Agency	Implementation	Goal
Stormwater Management Regulation	District of Columbia Municipal Regulations	Mandatory regulation for new construction	Reducing Stormwater Pollution from properties
RiverSmart Homes	Department of Energy & Environment	Incentive for voluntary participation	Reducing Stormwater Pollution from properties
Stormwater Retention Credit Trading	Department of Energy & Environment	Mix of regulation and voluntary participation	Reducing Stormwater Pollution from properties and Storm runoff control
Green Area Ratio	Department of Energy & Environment	Zoning regulation for new construction or renovation	Reducing Storm runoff and heat island effect Improving Air Quality
DC Clean River Projects	DC Water	Public investment with collaborative approach and voluntary programs	Reducing Combined Sewer Overflows through integration of green and grey infrastructure Relieving floods in the Northeast Boundary Area
Green Infrastructure Design Challenge	DC Water	Public investment with collaborative approach (only within agencies)	Reducing Stormwater Pollution and Storm runoff control from properties
GI Certification	DC Water	Voluntary training program	Better GI construction and maintenance
Green Alley Partnership	District Department of Transportation and DC Water	Public investment with collaborative approach (only within agencies)	Reducing Stormwater Pollution from streets, Storm runoff control, and landscaping

4.2. SWOT analysis for DC GSI

Strength

The strengths of Green Infrastructure (GI) programs currently implemented in DC align with commonly mentioned GI benefits. Firstly, DC can mitigate flood risks through these programs. Vivattene & Ellis (2013) simulated a comprehensive GI network, including green roofs and pervious pavement, revealing a potential maximum reduction of storm runoff by 30%. Urban development, which reduces pervious surfaces, amplifies stormwater volume, peak discharges, and rainfall-runoff speed, compromising hydrological and sewerage system water storage capacity (Olechnowicz and Weinerowska-Bords 2014; Zahran et al., 2008). In this flooding context, GIs play a crucial role in reducing storm runoff amounts and delaying concentration, contributing to flood risk reduction. While further research is needed to determine the overall impact of GIs on future flood risk, dismissing the role of GI in flood risk reduction would be implausible.

The second strength lies in the potential of wide-ranging GI implementation to contribute to the water quality improvement of Potomac and Anacostia rivers in DC. Storm runoff transports pollutants from urban surfaces, and filtering storm runoffs through GI can help prevent river pollution. Moreover, stormwater management through GI may be more cost-effective than existing gray infrastructure, considering both fixed facility investment and operating costs (Ashley et al., 2011). This cost evaluation does not account for the environmental benefits that GIs may bring beyond rainwater management. Considering these additional benefits, GI emerges as a financially efficient approach to climate change (Copenhagen Climate Adaptation Plan, 2011, June).

Weakness

The first weakness lies in the difficulty of quantifying GI’s water quality improvement capacity at a city-wide or catchment area scale compared to its stormwater storage capacity (Ashley et al., 2011). Traditional pipe systems can measure water quality and quantity changes at pipeline endpoints, but quantifying quantity and flow rate in GI systems is scientifically challenging (Ashley et al., 2011).

The second weakness pertains to GI maintenance. Some GIs, like swales and dry ponds, require more frequent maintenance, leading to high costs. Depending on the city-wide GI network construction, operation, and maintenance costs vary greatly, posing challenges for poor communities or local governments that may struggle to afford it. Nurturing a workforce with new skills for the operation and maintenance of new facilities is also a challenge. While DC Water runs the GI Certification Program, it relies on voluntary participation, and the resulting maintenance effects are unknown. Additionally, for GIs installed through redevelopment, new construction, and property owners' applications, DC agencies do not provide incentives for maintenance, raising questions about property owners' commitment to GI maintenance.

Opportunity

Some GIs store rainwater to reduce storm runoff, offering potential benefits for property owners, such as water savings for gardening or cleaning. GIs on streets or rights of ways storing water can contribute to expanding tree cover or supporting a community garden, fostering community-level infrastructure upgrades. Initiatives like the Green Alley Partnership not only reduce and filter storm runoff through sustainable design and Low Impact Development (LID) techniques but also create additional green spaces. These green spaces help lower streets’ ambient temperature, reducing energy use for cooling and heating. Moreover, reducing stormwater runoff saves energy used in pumping and treating stormwater.

Threat

One significant threat to DC's green infrastructure planning is the reliance on voluntary participation from residents, potentially leading to an inequitable distribution of GI. Washington, DC exhibits significant socioeconomic gaps between communities, with substantial differences in median household income, wealth, education levels, and property values among various ethnic groups. These factors contribute to the inequitable distribution of GI, as socioeconomic disparities limit voluntary program participation. Furthermore, the absence of a city-wide perspective control tower for GI planning exacerbates the discriminatory distribution issue. While powerful agencies like the DOEE and DC Water focus on water quality improvement, they do not consider the city-wide distribution of GIs. The lack of relevant departments overseeing GI distribution from a city-wide perspective may worsen ongoing discriminatory distribution issues.

5.1 Social Vulnerability in Washington DC

From a practical planning perspective, the question of which communities are most vulnerable in a disaster is crucial for determining support. To address this question, this study analyzed the social vulnerability of DC Southeast communities using both Pareto ranking and an additive model. While the results did not significantly differ between the additive model and Pareto ranking, the comparison revealed clear advantages of the Pareto ranking. It frees researchers from the need to assign relative weights to vulnerability indicators and effectively reflects vulnerabilities in communities that require special attention in the planning process.

On the other hand, the similarity in results suggests close relationships among social vulnerability factors such as poverty level, gender, age, and race in DC Southeast. This similarity arises because Southeast's communities exhibit similar values for all indicators. However, the overall vulnerability in the Southeast is generally higher than in other quadrants of DC. In other words, the heightened social vulnerability in the Southeast is not attributable to any particular factor. Considering the common arguments in the literature that social vulnerability factors are closely related, the overall high social vulnerability in DC Southeast may also result from the interaction of vulnerability factors. Therefore, for a deeper understanding and more detailed assessment of social vulnerability in the Southeast, further studies are necessary to identify the relationships between each vulnerability factor.

On this other hand, we manually calculated ranks by creating six scatter plots of the four indicators. In other words, all the indicators were not considered simultaneously. We compared pairs of indicators six times and averaged each rank calculated from the pairing. This manual process may inevitably introduce errors in the social vulnerability ranks, potentially slightly affecting the reliability of the analysis results. This limitation remains a consideration in this study.

5.2 Green Stormwater infrastructure in Washington DC

In DC, various GI programs have been implemented by many agencies. The implementation of a wide range of programs has helped increase large and small GI facilities. As of 2019, the total number of GIs installed on DC reached 21,190. This volume increase would certainly have contributed to reducing the risk of flooding in DC and improving water quality in the Potomac and Anacostia Rivers. Moreover, these vast numbers of GIs also contribute to water and energy savings throughout the city. However, the current programs do not pay attention to the maintenance of GI. If the involved agencies do not oversee or support the maintenance of existing GIs that is built by voluntary participants, it can be difficult for the GIs to function properly. On the one hand, the biggest problem above all is that GI practices may be excluding marginalized and impoverished communities, and this issue was actually verified through an empirical study. However, there are no relevant agencies to deal with this issue. If the local gap in infrastructure or built environment increases, certain vulnerable communities become more vulnerable. DC must verify the distribution of GI and find ways to resolve the inequitable distribution.

In practical disaster planning, identifying the most vulnerable communities is paramount for effective support allocation. This study delved into the social vulnerability of DC Southeast communities using both Pareto ranking and an additive model. While results showed similarities between the models, the Pareto ranking emerged with clear advantages, avoiding the need for assigning relative weights to vulnerability indicators and effectively highlighting communities demanding special attention in planning.

The results pointed to close relationships among social vulnerability factors—poverty, gender, age, and race—in DC Southeast. Although these factors exhibited similarity, the overall vulnerability in the Southeast surpassed that of other quadrants, hinting at a complex interplay among vulnerability factors. Further studies are essential for a nuanced understanding, emphasizing the need to explore the intricate relationships between each vulnerability factor.

Additionally, the study assessed Green Stormwater Infrastructure (GSI) programs in DC, highlighting the impact of a multitude of voluntary implemented programs, leading to a significant increase in GSI facilities. However, concerns arise regarding the neglect of GSI maintenance within existing programs. The absence of oversight or support for maintenance, particularly in voluntary participation-driven projects, jeopardizes the proper functioning of GSIs. Crucially, the study suggested the exclusion of marginalized and impoverished communities in GSI practices, underscoring the urgency for addressing this issue.

To contextualize the study's findings, it's essential to revisit the Pressure and Release (PAR) model introduced in the initial sections. The PAR model elucidates the political, economic, demographic, and historical factors influencing unequal resource distribution and varying degrees of vulnerability in disasters (Blaikie et al. 2014). This model provides a theoretical framework explaining why marginalized communities often face limited access to resources and low government intervention, factors linked to disinvestment and exclusion from market-based development approaches. Applying the PAR model to the study's focus on stormwater infrastructure distribution underscores the socio-economic factors influencing the equitable provision of infrastructure. The discriminatory policies and development patterns highlighted in the PAR model resonate with the empirical findings of the study, especially in the context of Green Stormwater Infrastructure (GSI) and its uneven distribution.

As DC experiences a surge in GSI installations, the lack of attention to maintenance and the exclusion of vulnerable communities pose challenges. Bridging the gap between social vulnerability and GSI distribution is essential. DC must undertake a comprehensive assessment of GSI distribution, ensuring equitable access and addressing the unique challenges faced by marginalized communities. In doing so, the city can harness the full potential of GSI programs to enhance resilience, mitigate disaster risks, and promote environmental justice across its diverse communities.

Disclosure of Potential Conflicts of Interest:

The authors declare that they have no conflicts of interest, financial or otherwise, that could influence the objectivity or integrity of this research.

Research Involving Human Participants and/or Animals:

No human participants or animals were involved in this study.

Informed Consent:

As this study did not involve human participants, informed consent was not applicable.

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Socially Vulnerable Communities and Green Stormwater Infrastructure planning in Washington D.C.

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1. Introduction

2. Study Area

3. Social vulnerability Index

4. Green Stormwater Infrastructure (GSI)

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