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
Transcript
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
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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 man-
made 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 Mean Std. Deviation
Number of Projects 0 28.00 1.95 3.29
Projects per Acre 0 30.93E-6 2.93E-6 5.92
Median Year Structure Built 1939 2006 1970 1
Total Population 20 5464 1427 787
Median Age 19.2 63.8 37.4 9.3
Per Capita Income (In 2016 Inflation Adjusted Dollars) $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 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
Variable 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
Per Capita Income 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
