CLOSUP Student Working Paper Series Number 38 April 2018
Disparities in Green Infrastructure Implementation in Washtenaw County, Michigan Aleah Rogalski, University of Michigan
This paper is available online at http://closup.umich.edu Papers in the CLOSUP Student Working Paper Series are written by students at the University of Michigan. This paper was submitted as part of the Winter 2018 course PubPol 495 Energy and Environmental Policy Research, that is part of the CLOSUP in the Classroom Initiative. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the view of the Center for Local, State, and Urban Policy or any sponsoring agency
Center for Local, State, and Urban Policy Gerald R. Ford School of Public Policy University of Michigan
Disparities in Green Infrastructure Implementation in Washtenaw County, Michigan
Aleah Rogalski Public Policy 495: Energy and Environmental Policy Research April 25th, 2018
Abstract: Many Michigan cities’ water infrastructure are unable to handle excessive stormwater which can result in flooding or combined sewer overflows. Green infrastructure has been used as a solution to allow water to naturally infiltrate into the ground, thus preventing the effects from heavy precipitation in urban areas. However, environmental injustice may influence the distribution of green infrastructure projects. Using statistical analysis, this research explores how socioeconomic variables impact green infrastructure placement within Washtenaw County, Michigan. Analysis revealed that income and educational attainment, as well as home age, were statistically significant factors that influenced the placement of green infrastructure projects. Neighborhoods with higher median income and more residents with at least a bachelor’s degree were more likely to have green infrastructure projects. Additionally, neighborhoods with older homes increased the likelihood of green infrastructure projects. As policymakers and urban planners create plans for more climate-resilient communities, it will be important to account for disparities in education and income. By doing so, policymakers can ensure that disadvantaged neighborhoods receive equal benefit from green infrastructure placement, so that all communities are positively impacted.
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Introduction: As climate change progresses over the next century, communities around the Midwest will need to adapt to the consequences of changing weather patterns. The Union of Concerned Scientists’ projections for future Midwestern climate are alarming: over the next decades, temperatures in the Midwest will likely increase 2.4-2.8°F, and by the end of the century could increase by up to 10°F (Hayhoe, VanDorn, Naik & Wuebbles, 2009). The frequency of heavy precipitation events is projected to increase by 20-30% in the winter and spring, particularly around the Great Lakes region, which would increase the chance of flash flooding (Hayhoe, VanDorn, Naik & Wuebbles, 2009). Though most attention to climate change is given to changes in temperature, it is actually precipitation events that may be the most damaging. As Michigan experiences more extreme catastrophic precipitation events, larger cities such as Ann Arbor, Detroit, Lansing, Kalamazoo, and Grand Rapids will need to think critically about how to handle excessive rain. With so much infrastructure and pavement in urban areas, rainfall may become a problem for cities. Impervious surfaces like concrete prevent water from naturally infiltrating into soil and recharging groundwater, so instead, water pools on the top of concrete. In order to prevent flooding, rain water is diverted into ‘gray’ infrastructure, or manmade water systems consisting of sewage drains and pipes. Quite simply, the state’s aging water infrastructure and sewage systems may not be able to manage these heavy precipitation events. In February 2018, Michigan faced extreme flooding, particularly in urban areas like Lansing, Kalamazoo, and Grand Rapids. The flooding caused $2.5 million in damages in Kalamazoo County, and a state of emergency was declared in Lansing and Lansing Township (Barrett, 2018). Across the state, highways were closed, and citizens were temporarily displaced from their homes, some requiring emergency evacuation with help from local first responders. One
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record-breaking storm in 2014 caused more than $1 billion in property damage across Southeast Michigan (Kuban, 2016). The wide variety of problems due to increased flooding include combined sewer overflows (CSOs), particularly in Detroit, where sewer systems overflow with storm water allowing untreated sewage to spill into nearby waterways (Edelson & Messner, 2016). Many cities are implementing green infrastructure to combat these flooding issues. Green infrastructure, also known as low-impact development, is a method of urban development that includes natural landscapes to improve water filtration, flood control, and quality of life (Foster, Lowe & Winkelman, 2011). Examples of green infrastructure can include downspout disconnection, permeable pavement, green alleys and streets, bioswales, rain gardens, parks and wetlands, and urban forestry. These projects often compliment traditional gray infrastructure, and can be implemented at a macro level by city planners or incorporated at a micro level on private property. While thinking about environmental issues and urban planning, architects, urban planners, and community leaders should be aware of potential inequalities and inequities that may arise. These environmental inequities can be referred to as environmental injustice or environmental racism, and Michigan has had a history with this issue. The Flint water crisis in 2015 became one of the most infamous examples of environmental injustice, where a majority black, poor community was poisoned from lead-tainted water because the government refused to utilize corrosion control to save money. Besides Flint, many poor and minority communities have faced health disparities. In Southwest Detroit, citizens are fighting the decision to allow Marathon Petroleum refinery to emit additional sulfur pollution into a poor community that already suffers from poor air quality (Turner-Handy & Whyte, 2016). Additionally, data shows
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that minority communities tend to have higher rates of asthma and increased exposure to lead poisoning in Michigan (Roelofs, 2016). Many cities in Michigan have implemented green infrastructure projects as a step to prevent the effects from heavy precipitation in urban areas. However, considering Michigan’s track record of environmental injustice, does income affect the placement of green infrastructure factors? Do wealthier neighborhoods in Michigan see a disproportionate number of green infrastructure projects? Literature Review: Much of the literature on green infrastructure are case studies that pertain to general barriers to implementation. There has been little research on the impact on low-income communities, or the different barriers to implementation for low-income communities. General Barriers to Implementation: In one article which used four case studies in London, Glasgow, Washington DC, and Philadelphia, researchers analyzed the different combinations of green and gray infrastructure, as well as the coordinating laws and social context presented in each case study (Dolowitz, Bell & Keeley, 2017). In London, urban planners and policy makers decided to use only gray infrastructure while Philadelphia only used green infrastructure. Glasgow and Washington D.C. used a mixture of the two strategies. They found that the choice of green or gray infrastructure was “highly dependent on diverse factors such as access to capital investment, institutional flexibility, local leadership regulatory frameworks, and urban social context” (p.90). They also found that grey and green infrastructure choice was dependent on technical constraints. Glasgow, Washington DC, and Philadelphia used green infrastructure solutions because drainage infrastructure is publicly owned and a wider range of policy instruments could be used.
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A separate case study in European cities measured the effectiveness of urban sustainability policies. Researchers studied why so few tools for urban sustainability are used, especially when so many are available (Jensen & Elle, 2007).
The research analyzes the
motivations, benefits, and barriers to use urban development tools to manage projects, calculate environmental outcomes, and monitor sustainability. Examples of tools include environmental impact assessment (EIA), life-cycle analysis (LCA), and green accounting. The researchers studied 60 case studies on urban sustainable development in eight countries, covering sectors such as energy, sewage, water, waste, transportation, green and blue structures, land use, buildings, and more. Case studies have shown that tools can lead to a more sustainable urban infrastructure. The research noted that those who did use tools were often obligated to because of legislative demands. However, the voluntary use of tools is limited for several reasons. First, decision makers often don’t have knowledge of the tools in their field. Second, data is often difficult to obtain. Third, tools are used incorrectly. Thus, research shows the need for simple, reliable tools within urban sustainability. Similarly, a case study in the Energy Corridor District (ECD) in Houston, Texas noted the barriers that exist specifically within the business community (Kim, Kim & Demaire, 2017). Using survey data, researchers found that barriers to implementation include lack of incentives, lack of knowledge of the clientele, and lack of knowledge about the development teams; however, education, financial incentives, and innovations within policy systems can overcome these barriers. Education, in particular, needs to be focused on the clients and the development and construction teams, as planners have already gathered this information. In a case study in Cleveland, researchers applied adaptive management techniques (or experimental policy implementation) to green infrastructure projects such as rain gardens.
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Researchers then analyzed the social, economic, and environmental aspects of these processes, and found that adaptive management projects can fail if there is not active buy-in and support from stakeholders. Stakeholders also need to be flexible and able to set initial goals on the project. Researchers also found that a lack of single agency controlling stormwater runoff was a barrier to implementation. The lack of a plan to repurpose the vacant land in this manner, as well as public disengagement and lack of knowledge also were problematic (Chaffin et al., 2007). In short, green infrastructure can be difficult to implement without knowledge of the tools available, and flexibility and support from the community or stakeholders. Further, different community factors, such as local laws, local leadership, and existing infrastructure may further inhibit green infrastructure. Barriers to Implementation in Low-Income Communities: Though there has been policy research on green infrastructure in general terms, little of this research focuses on lower-income communities. Lower-income communities may face different barriers to implementation than middle- or high-income communities, but this subject has not been well researched. Studies have shown the benefits of green infrastructure in low-income communities. Not only does green infrastructure reduce volume to sewer and stormwater systems, it also can have substantial impacts on urban livability. In a report on policy solutions to alleviate urban poverty, Dunn (2010) stated that green infrastructure can improve urban water quality and air pollution, improve public health, reduce crime, generate “green collar� jobs, improve food security, and beautify the community. By increasing vegetation in the community, green infrastructure can reduce air pollution by filtering airborne pollutants and offset urban heat island effects. Green roofs can make buildings more energy efficient as well, reducing the demand for heating and
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cooling, thus lowering the cost of energy bills. Additionally, green spaces can enhance community aesthetics, thereby increasing property values and giving new life to blighted neighborhoods. Green infrastructure and green space provide free recreation, and reduce crime by drawing more people into the community. Further, green infrastructure can provide the need for both skilled and unskilled labor for the engineering and designing aspects of green infrastructure, as well as the implementation and maintenance. While the benefits of green infrastructure for vulnerable communities are clear, there is still an unequal distribution of green infrastructure projects within municipalities. Evidence shows that there is an inequitable distribution of public and private investments into green infrastructure between low- and high-income communities: A case study focused on environmental justice in Philadelphia shows that disadvantaged communities are less likely to receive green infrastructure, that higher levels of community capacity may lead to an increased number of green infrastructure projects, and that vacant land can be an opportunity for green infrastructure investment due to regulations on development (Mandarano & Meenar, 2017). The researchers used geographic information systems (GIS) data from the Philadelphia Water Department and organized data into three categories: public green infrastructure implementation, legally enforced private green infrastructure implementation, and voluntary private implementation. The analysis focuses on the characteristics of disadvantaged communities, as well as the capacity of the community to operate effectively as partners in implementation, which contribute to the distribution of public and private investments into green infrastructure. The research found that communities with a higher percentage of Black and Hispanic residents, single-parent households, and generally impoverished communities were less likely to have private regulatory and private voluntary green infrastructure. However, areas with more vacant
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lots had more green infrastructure projects, because they are an opportunity for new development, for which regulations require green infrastructure. The authors explain these disparities in part by the community’s capacity to implement green infrastructure. In particular, education above a bachelor’s degree meant there was more likely to be private voluntary and private regulatory green infrastructure projects. Overall, little of the existing literature studies green infrastructure implementation in vulnerable communities. Furthermore, the research that has included low-income communities shows the inequitable distribution of green infrastructure, and does not explicitly research the barriers that differently resourced communities face in green infrastructure implementation. More research is needed to discover how green infrastructure implementation differs between communities, so that cities can better anticipate possible problems when creating green infrastructure policy. Methods: Broadly, this research will answer whether income affects the placement of green infrastructure projects, and thus leads to a disproportionate number of green infrastructure installments in wealthier neighborhoods. Factors such as median age, race, educational attainment, unemployment, and median home age will also be included in this analysis. Washtenaw County is a prime location for analyzing disparities in green infrastructure based on economic income. A research study by the Martin Prosperity Institute studied economic segregation in U.S. metro areas (2015). By studying income, education, and occupation --three factors that produce socioeconomic class standing-- research concluded that Ann Arbor, Michigan had the 8th highest level of overall economic segregation in the United States (Mellander & Florida, 2015). Due to the level of economic segregation in neighborhoods across
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Washtenaw county, this region provides an opportunity to test whether there are economic disparities in placement of green infrastructure. Studying this area in particular allows for more accurate analysis of how income affects green infrastructure implementation, since there is less economic or social mixing which could skew the data (i.e. A high-income household situated in a low-income neighborhood). Data Sources: This research conducts two linear regressions on two dependent variables. These dependent variables are the amount of green infrastructure projects per census block group, as well as the green infrastructure projects per acre. Separate analysis of these dependent variables is needed in order to account for differences between urban and rural areas. The City of Ann Arbor, Washtenaw County Water Resources, the University of Michigan, and the Huron River Watershed Council developed a map of green infrastructure projects in Washtenaw county (Rain Gardens in Washtenaw County, 2018). The map includes all the green infrastructure projects managed by these groups, and the map is updated quarterly with new public and private rain gardens. Using GIS Mapping technology, this green infrastructure map is overlaid with census information. The map is divided into Census block groups, the smallest geographical unit in the U.S. Census. The number of green infrastructure projects within each block group is the dependent variable. The projects per acre variable is included to account for differences between urban and rural areas. In Image 1, the number of green infrastructure projects in Washtenaw County is shown within each census block tract, while Image 2 shows a close-up view of green infrastructure placement within the census block tracts of Ann Arbor, Michigan. These images were created in ArcMaps, a GIS mapping tool. Variables:
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The independent variables for this research are income, median age, race, educational attainment, unemployment, and median home age. This data comes from the 2012-2016 American Community Survey (ACS), located on Social Explorer. On the ACS, these variables are referred to as Per Capita Income (In 2016 Inflation Adjusted Dollars), Median Age by Sex, Race, Educational Attainment for Population 25 Years and Over, Unemployment Rate for Civilian Population in Labor Force 16 years and Over, and Median Year Structure Built. Table 1 shows the minimum, maximum, mean, and standard deviation for each variable included in this analysis. Based on the previous literature, income may be one of the most influential factors in green infrastructure. In Dolowitz, Bell and Keeley’s (2017) research, capital investment was considered to be an important factor. Resource-poor communities are disadvantaged in their ability to successfully advocate for public green infrastructure projects. Additionally, poor residents may not be able to afford to maintain or invest in their own private green infrastructure projects, thus relying on public support and investment (Mandarano & Meenar, 2017). Furthermore, within the literature, community buy-in and support was considered key to implementing green infrastructure (Chaffin et al., 2007). Within poorer areas, community members may be less able to give their time to green infrastructure projects, as they may be working longer hours or odd shifts. Similarly, if a community member becomes unemployed, they may be forced to readjust their personal finances and possibly limit their engagement in such projects. Median age is particularly important for a community like Ann Arbor. Ann Arbor is a college town, and thus has small neighborhoods of only college-aged students. These students do not live in these housing units permanently, so they are less likely to invest time or money into
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the property or the surrounding community. Meanwhile, neighborhoods with an older median age are more likely to have permanent homeowners who are willing to invest time into their property. Different stages of life can significantly alter an individual’s willingness and ability to invest in green infrastructure. Young parents and elderly homeowners may be unlikely to invest in green infrastructure, while recently retired adults may be more compelled to do home and outdoor renovations that they have long put off. Based on Mandarano and Meenar’s (2017) case study, race may have an effect on green infrastructure. Minority communities are often underserved by environmental amenities, like parks and green spaces. This may be due to lower income, a lack of representation in municipal governments, or due to a lack of community support from city leadership. Thus, they may not be able to engage in local decision-making concerning green infrastructure projects. As seen throughout much of the literature, educational attainment could also be a significant factor. The literature demonstrated that, for urban planners, businesses, and business clientele, an increased knowledge of urban planning tools and green infrastructure increased the likelihood of implementation (Kim, Kim & Demaire, 2017; Jensen & Elle, 2007). In addition, education was an important factor in terms of civic engagement, as residents with higher education levels had the knowledge and skills to become problem-solvers and leaders within their communities (Mandarano & Meenar 2017). Additionally, homeowners and community members may be more likely to be aware of and implement green infrastructure if they have more formal education and have had more opportunities to be exposed to the concept. As seen in Mandarano & Meenar’s (2017) analysis of green infrastructure placement in vacant lots, new regulations may require green infrastructure projects in new developments which would make median home age an important factor. Newer neighborhoods may already
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include green infrastructure in their designs, making it easier to implement than in older neighborhoods that need to be retrofitted. Table #1: Descriptive Statistics Variable
Minimum
Maximum
Std. Deviation
Mean
Number of Projects
0
28.00
1.95
3.29
Projects per Acre
0
30.93E-6
2.93E-6
5.92
1939
2006
1970
1
20
5464
1427
787
19.2
63.8
37.4
9.3
$2,986
$99,625
$35,405
$16,296
Percentage of Total Population: White Alone
12.1
100.0
74.3
19.4
Percentage of Population 25 Years and Over: Some College
0.0
73.8
26.2
13.4
Unemployment Rate
0.0
43.1
6.4
6.3
Median Year Structure Built Total Population Median Age Per Capita Income (In 2016 Inflation Adjusted Dollars)
Image #1: Green Infrastructure Map of Washtenaw County, Michigan
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Image #2: Green Infrastructure Map of Ann Arbor, Michigan
Results: Two linear regression models were conducted based on the number of projects per census block tract and the projects per acre. For the number of projects per census block tract, two variables were statistically significant. Per Capita Income (in 2016 inflation adjusted dollars) was statistically significant (p-value less than 0.001) and positively correlated, showing that wealthier communities are more likely to have green infrastructure projects in place. Additionally, Median Year Structure Built was statistically significant (p-value less than 0.001) and negatively correlated, meaning that as the median home age increased, there are more green infrastructure projects. The R2 value is 0.161. Using the R2 value, 16.1% of all green infrastructure projects can be accounted for by this model. This is a relatively low value for social science research and may demonstrate that other independent variables are missing. All other variables, such as unemployment, percentage of total population with a Bachelor’s degree, percentage of white
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population, and median age came back with non-significant results. These results are shown in Table 2. For the number of projects per acre, three variables were statistically significant. Per Capita Income was positively correlated and statistically significant, showing that as income increased, there are more green infrastructure projects. Median Year Structure Built was negatively correlated and statistically significant (less than 0.001) meaning that as home age increased, there are more green infrastructure projects. Finally, Population with Bachelor Degree was also statistically significant and positively correlated, meaning that with more formal education, there are more green infrastructure projects. This variable was not statistically significant when analyzing the number of projects per census block tract. Using the R2 value, 20.9% of all green infrastructure projects can be accounted for by this model, which is also relatively low. Other variables like unemployment, percentage of total white population, and median age came back with non-significant results. These results are shown in Table 2. Both models show similar patterns when accounting for urban and rural differences, which suggests that this factor does not determine green infrastructure implementation. The only exception is the percentage of total population with a Bachelor’s degree, which was found to be significant in the projects per acre regression model but not in the number of projects model. However, in the projects per acre regression model, the education variable had a higher p-value of 0.044, which demonstrates that it is not as significant of a variable as Median Year Structure Built or Per Capita Income. Percentage of total white population was not a statistically significant variable, and further statistical analysis of racial data for the percentage of black and Asian populations was
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not statistically significant either. However, the percentage of total white population has a negative coefficient, while the percentages of total black and total Asian population have positive coefficients, as seen in Table 3. This means that neighborhoods with a higher proportion of white residents have slightly fewer green infrastructure projects, while neighborhoods with a higher proportion of black and Asian neighborhoods have slightly more green infrastructure projects. This analysis on racial data had similar R2 values. For the number of projects regression model, the percentage of total white population had a slightly higher R2 value of 0.161 than the percentage of total black or Asian population with 0.160 as seen in Table 3 below. In the projects per acre regression model, all three race populations had the same R2 value of 0.209. Because the percentage of total white population had a slightly higher R2 value in the number of projects regression model, this variable was used as the reference group.
Table # 2: Results of Linear Regression Model for Green Infrastructure Projects. Number of Projects
Projects per Acre
Coefficient
Standard Error
Significance
Coefficient
Standard Error
Significance
Median Year Structure Built
-0.053
0.012
>0.001
-1.38E-07
2.06E-08
>0.001
Median Age
-0.002
0.028
0.949
-5.25E-08
4.85E-08
0.280
6.35E-05
1.58E-05
>0.001
8.93E-11
2.75E-11
0.001
Total Population: White
-0.008
0.012
0.520
-3.78E-09
2.17E-08
0.862
Population with Bachelor’s Degree
0.027
0.020
0.175
7.12E-08
3.52E-08
0.044
Unemployment Rate
-0.012
0.037
0.749
5.06E-08
6.55E-08
0.440
Variable
Per Capita Income
R-Squared Value
R-Squared = .161 (Adjusted R Squared = .140)
R-Squared = .209 (Adjusted R Squared = .189)
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Table #3: Results of Linear Regression for Race Number of Projects Variable
Coefficient
Projects per Acre
Standard R-Squared Error Significance Value Coefficient
Standard Error
Significance
R-Squared Value
Total Population: White (percentage)
-0.008
0.012
0.52
0.161
-3.78E-09
2.17E-08
0.862
0.209
Total Population: Black (percentage)
0.002
0.015
0.884
0.160
-4.42E-09
2.56E-08
0.863
0.209
Total Population: Asian (percentage)
0.001
0.021
0.972
0.160
8.53E-11
3.62E-08
0.998
0.209
Analysis: The three variables that were statistically significant were Per Capita Income, Median Year Structure Built, and Population with Bachelor’s Degree (for projects per acre only). Per Capita Income and Median Year Structure Built were the most significant, with p-values of less than 0.001. This research suggests that Per Capita Income does influence green infrastructure placement. In Washtenaw County, there were more green infrastructure projects in neighborhoods with a higher per capita income, and less green infrastructure projects in areas with lower levels of income. This shows that lower-income communities are being underserved, which supports findings from previous literature (e.g. Mandarano and Meenar’s (2017) spatial models). There may be several reasons for this. First, current policy and financial incentives, such as Ann Arbor’s residential stormwater credits which discount water bills for implementing rain gardens and rain barrels, may not be enough to incentivize private implementation for lowincome residents. Additionally, in terms of public implementation of green infrastructure, city planners in Washtenaw County may be looking for communities that are most able to act as
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effective partners, both in terms of civic engagement and financial investment (Chaffin et al., 2007; Dolowitz, Bell & Keeley, 2017). Median Year Structure Built was statistically significant and negatively correlated, meaning that there are more green infrastructure projects in neighborhoods with older homes. This may be because newer homes and neighborhoods have already incorporated green infrastructure in their initial development. Within the Ann Arbor city code, chapter 63 outlines requirements for stormwater management; It requires all new site plans to include stormwater management designs, and outlines what stormwater management techniques are needed for different sized establishments (City of Ann Arbor Code of Ordinances, 2013). Thus, green infrastructure projects do not need to be implemented in new homes and neighborhoods, and instead need to be retrofitted into neighborhoods with older homes. The population with a Bachelor’s degree also was statistically significant, but this variable had a far larger p-value, and thus was less significant than Per Capita Income or Median Year Structure Built. This research supports Mandarano and Meenar’s (2017) research results that a greater number of green infrastructure projects occur in neighborhoods where more residents have a bachelor’s degree or more. In particular, Mandarano and Meenar (2017) noted that residents with higher levels of education were more capable of being leaders and problemsolvers in their communities. Median age, unemployment, and race were not statistically significant variables. Median age was analyzed because Ann Arbor is a college town with many students who do not have the time,
ability,
or
incentive
to
invest
in
their
temporary
housing.
While
students may not have the incentive to implement green infrastructure, their landlords may receive some benefit for doing so. Additionally, unemployment was not statistically significant.
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This may be because Washtenaw County, and Ann Arbor in particular, have a lower than average unemployment rate (Bureau of Labor Statistics 2018). The regression models on race were not statistically significant. This did not support Mandarano and Meenar’s (2017) research, which showed that minority communities had fewer green infrastructure projects. The regression models showed that white residents have slightly fewer green infrastructure projects, while neighborhoods with a higher proportion of black and asian residents had more green infrastructure projects. While these variables are not statistically significant, it suggests that there may be some over-compensation on the part of the city to include green infrastructure in areas with a higher proportion of minority residents. Future Research These findings have implications for future research on green infrastructure implementation. Primarily, more research is needed for areas like Detroit which is currently implementing an extensive green infrastructure plan. Research that is more specifically tailored to this region could help assess how green infrastructure is being implemented based on income, education, race, and home age, and ensure that there is equal distribution across these demographics. Detroit may also be a good place to study how green infrastructure programs that are targeting low-income communities are working. Detroit’s green infrastructure plan seems to be prioritizing low- and middle-income communities by allocating block grants specifically to these communities (Detroit Water and Sewerage Department 2014). However, research on green infrastructure in Detroit may be difficult to produce since data from the city of Detroit is limited, and public officials may be disinclined to share data for outside research. More research is also needed to assess how certain tools can help overcome disparities. In particular, financial incentives, such as green infrastructure tax-credits, can help overcome
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disparities based on wealth. Researchers may want to assess specific policy tools rather than broad case studies, so that policy makers and community leaders have a better assessment of how economic tools and incentives can contribute to private-property implementation. Additionally, research is necessary to analyze how home age influences green infrastructure placement. Currently, there is little research on this subject. Older homes may have more green infrastructure projects publicly and privately implemented in order to retrofit these homes to fit new regulations, but more research is necessary to verify this hypothesis. Finally, although the results showed that race was not a statistically significant variable in Washtenaw County, further research should be conducted in areas that are more racially segregated, or where racial disparities are more prevalent. Detroit in particular may be an area for this kind of study because it is so ethnically and racially diverse. Studying race variables could help advance knowledge on environmental injustice and environmental racism. Policy makers and community leaders may need to further consider how income, median home age, and education play a role in green infrastructure planning. Finding new ways to make green infrastructure education and resources accessible to low-income communities will be an important step towards creating an equitable distribution of green infrastructure and other environmental amenities. In addition, policy makers may need to adopt new strategies to incentivize and prioritize green infrastructure in disadvantaged communities. Additionally, when creating green infrastructure plans, it’s important to remember these disparities in order to directly combat them during the planning phase. Municipal governments and NGOs may want to use GIS information to help prioritize investments to lower-income parts of the community, and to ensure that all communities members are receiving a benefit from new green infrastructure implementation.
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Citations: Ann Arbor Code of Ordinances. (2013). Retrieved April 23, 2018, from https://www.a2gov.org/departments/systems-planning/planning-areas/waterresources/Pages/Post-Construction-Stormwater-Management.aspx Barrett, M. (2018, March 9). $2.5M in damage caused by Kalamazoo record-breaking floods. MLive Media Group. Retrieved March 18, 2018, from http://www.mlive.com/news/kalamazoo/index.ssf/2018/03/flood_victims_form_coalition _t.html Bureau of Labor Statistics. (2018). Local Area Unemployment Statistics--Ann Arbor, MI Metropolitan Statistical Area, 2012-2018 [Data set]. Retrieved from https://data.bls.gov/pdq/SurveyOutputServlet
Chaffin, B. C., Shuster, W. D., Garmestani, A. S., Furio, B., Albro, S. L., Gardiner, M., . . . Green, O. O. (2016). A tale of two rain gardens: Barriers and bridges to adaptive management of urban stormwater in Cleveland, Ohio. Journal of Environmental Management,183(2), 1st ser., 431-441. Retrieved February 3, 2018, fromhttps://wwwsciencedirectcom.proxy.lib.umich.edu/science/article/pii/S0301479716303644?_rdoc=1&_fmt=high& _origin=gateway&_docanchor=&md5=b8429449ccfc9c30159a5f9aeaa92ffb. Detroit Water and Sewerage Department. (2014, August 1). Green Infrastructure Plan for the Upper Rouge Tunnel Area. Retrieved April 19, 2017, from
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