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date: 20 October 2018

Valuing the Benefits of Green Stormwater Infrastructure

Summary and Keywords

Green stormwater infrastructure (GSI), a decentralized approach for managing stormwater that uses natural systems or engineered systems mimicking the natural environment, is being adopted by cities around the world to manage stormwater runoff. The primary benefits of such systems include reduced flooding and improved water quality. GSI projects, such as green roofs, urban tree planting, rain gardens and bioswales, rain barrels, and green streets may also generate cobenefits such as aesthetic improvement, reduced net CO2 emissions, reduced air pollution, and habitat improvement. GSI adoption has been fueled by the promise of environmental benefits along with evidence that GSI is a cost-effective stormwater management strategy, and methods have been developed by economists to quantify those benefits to support GSI planning and policy efforts. A body of multidisciplinary research has quantified significant net benefits from GSI, with particularly robust evidence regarding green roofs, urban trees, and green streets. While many GSI projects generate positive benefits through ecosystem service provision, those benefits can vary with details of the location and the type and scale of GSI installation. Previous work reveals several pitfalls in estimating the benefits of GSI that scientists should avoid, such as double counting values, counting transfer payments as benefits, and using values for benefits like avoided carbon emissions that are biased. Important gaps remain in current knowledge regarding the benefits of GSI, including benefit estimates for some types of GSI elements and outcomes, understanding how GSI benefits last over time, and the distribution of GSI benefits among different groups in urban areas.

Keywords: green infrastructure, benefits, cost-benefit analysis, stormwater, nonmarket valuation, ecosystem services, flooding, water quality, aesthetic

The Need for Values of GSI Benefits

Green stormwater infrastructure (GSI), a decentralized approach for managing stormwater that uses natural systems or engineered systems that mimic the natural environment, is being adopted by cities around the world to manage stormwater runoff. Such systems yield many benefits to people through environmental improvements, including reduced flooding, air pollution, and net CO2 emissions, and improved water quality, habitat creation, and aesthetic character.

GSI systems are studied by scientists such as engineers, landscape architects, ecologists, urban planners, and hydrologists to understand the physical effects that GSI has on many dimensions of environmental quality. Economists also study GSI systems to quantify the benefits of those effects to the people who are affected by the environmental changes wrought by GSI.

Green Stormwater Infrastructure

Cities have evolved over the course of history in their relationships with water. The earliest designs were for providing adequate supplies of water (“water supply city”). Engineers have gone on in cities to provide sewers to separate waste from drinking water (“sewered city”), drain excess water away from developed areas (“drained city”), and manage water with a focus on water quality and the ecological integrity of urban rivers and lakes (“waterways city”) (Brown, Keath, & Wong, 2009).

Traditional tools used for urban stormwater management are often referred to as “conventional” or “gray” infrastructure, and include curb-and-gutter systems, storm sewers, detention ponds, and deep tunnel water storage facilities (ECONorthwest, 2007; U.S. EPA, 2013). Terms and acronyms have proliferated in the study and application of urban drainage systems that do not rely entirely on gray infrastructure (Lamond, Rose, & Booth, 2015; Liptan, 2017). The set of terms includes (but is not limited to) sustainable drainage systems (SuDS), surface water management measures (SWMMs), sustainable urban water management (SUWM), stormwater control measures (SCMs), source control (SC), best management practices (BMPs), low impact development (LID), landscape stormwater management (LSM), water-sensitive urban design (WSUD), and green infrastructure (GI). Fletcher et al. (2015) provided a comprehensive discussion of the intellectual and geographic origins of these terms and how their meanings have evolved over time. Terminology varies among countries and with the types of stormwater management solutions that are included in the discussion. For example, LID refers to a style of development planning that affects more elements of the environment than hydrology, and can include changes in development design that yield more communal open space than conventional development plans (Jaffe, 2010).

For purposes of this article, the term GSI will be used, though some of the cases surveyed may better fall under some of the other terms. This article surveys the benefits of nonconventional approaches to urban stormwater management including strategies and solutions such as green roofs, rain gardens, rain barrels and cisterns, bioswales, permeable surfaces, planted trees, and green streets. It does not survey benefits that result exclusively from large-scale increases in dedicated open space, such as those analyzed by Kousky et al. (2013).

The GSI Movement

The United States, Europe, and the United Kingdom have vigorously embraced the movement away from sole reliance on conventional curb-and-gutter stormwater management (Charlesworth & Booth, 2017; Ellis, 2013; Mell, 2016; Roy et al., 2008), but this movement is not limited to those areas. Indeed, sustainable stormwater management strategies have been studied and deployed in diverse places such as Australia, Brazil, South Africa, Spain, New Zealand, India, and China (Charlesworth & Booth, 2017; Jia et al., 2014; Mell, 2016). The institutional structures within which GSI is, or is not, implemented vary widely across countries (Sage, Berthier, & Gromaire, 2015).

What has driven the spread of GSI approaches to stormwater management? One early and continued driver of research on and adoption of GSI solutions was concerns about cost. Conventional stormwater management entails costly investment in municipal infrastructure ranging from simple storm sewer expansion to massive underground stormwater storage projects. If precipitation can be managed where it falls rather than being directed into storm sewer infrastructure, sewer expansion costs can be reduced. Andoh and Declerck (1997) found that the costs of flood alleviation can be cut by 25% to 80% if stormwater is mitigated with source control rather than conventional centralized approaches. Reviews of cost studies have found repeatedly that GSI adoption can reduce the costs of stormwater management relative to sole reliance on conventional infrastructure (Braden & Ando, 2011; Jaffe, 2010; Lamond, 2017; U.S. EPA, 2007). Other research has found that the most cost-effective combined strategy for stormwater management uses a mixture of conventional and spatially optimized GSI elements, and the U.S. EPA has developed a tool called SUSTAIN to help municipalities identify cost-effective networks of green and conventional stormwater infrastructure to accomplish a specified set of management objectives (Lee et al., 2012).

A second major driver of interest in GSI has been its ability to provide cobenefits in multiple dimensions of environmental quality. Conventional stormwater management focuses entirely on preventing local flooding. However, those flood prevention strategies cause problems with water quality and hydrological regime in local rivers and streams, and the combination of degraded water quality and flashy stream flow regimes yields poor aquatic habitat and reduced fish populations (National Research Council, 2009). GSI approaches reduce flows of pollutants into water bodies and reduce the volume of runoff immediately following storms, resulting in better water quality and aquatic habitat. Some types of GSI can also produce other benefits such as aesthetic value, air filtration, heat reduction, and net CO2 flow reduction (Lamond, 2017; Schäffler & Swilling, 2013). Future visions of widespread GSI in urban areas put forth even more ambitious ideas about the benefits GSI may produce, such as improved mental health (Schäffler & Swilling, 2013) and a transition to a “water cycle city” in which rainfall and wastewater are harnessed to stretch water supplies in times of growing scarcity (Brown, 2008; Steffen, Jensen, Pomeroy, & Burian, 2013). Because different tools for stormwater management produce different bundles of benefits (e.g., conventional infrastructure is most reliable for minimizing local flooding, while bioswales and rain gardens produce aesthetic and aquatic habitat benefits), there can be trade-offs between the benefits that are produced by a stormwater management strategy (Londoño Cadavid & Ando, 2013).

GSI has multiple potential benefits and can save costs relative to conventional infrastructure, but many challenges can limit the adoption and implementation of GSI. GSI is spatially decentralized and often located on private land; many cities do not have adequate resources, plans, or institutions to ensure that GSI receives ongoing maintenance sufficient to keep up its functionality (Liptan, 2017; U.S. EPA, 2000). Municipal water management institutions often have limited capacity for innovation and elements that are resistant to change (Brown, 2008). Peculiarities of municipal finance may mean that funds available for conventional stormwater infrastructure cannot be used flexibly for green infrastructure initiatives (Keeley et al., 2013). More substantively, GSI can have some actual drawbacks that should be considered along with the benefits when choosing optimal GSI adoption. Some types of GSI take land away from other human use (Thurston, 2006); that opportunity cost can be large in cities with high property values and scarce space for parking and other urban needs. GSI infrastructure can even have negative effects on vulnerable city residents if its hydrologically optimal placement conflicts with housing and food production needs, especially in poor communities (Douglas, 2016; Maryati, Humaira, & Adianti, 2016) or if GSI design poses a drowning risk to nearby residents (Mguni, Herslund, & Jensen, 2016).

The Need for Benefit Estimates

Jaffe (2010) argues that GSI can be supported on the grounds of cost-effectiveness alone without estimating the values of the benefits produced by GSI. However, there are three reasons that benefit estimates are needed to guide public policy and investments. First, because GSI has both costs and benefits relative to conventional infrastructure, benefit estimates are needed to determine the optimal degree to which cities should shift toward GSI. GSI can be implemented in part through policies other than direct municipal investment, including standards on new development (Carter & Fowler, 2008), tradable impervious surface permits (Thurston, Goddard, Szlag, & Lemberg, 2003), and fees on impervious surface fees (Valderrama et al., 2013). But how stringent should standards be? How many permits should be issued? How much of a city should be covered by impervious surface? The answers to these questions indirectly determine how much GSI will be used, and they cannot be answered without estimates of the benefits of GSI (Ando & Netusil, 2013).

Second, because details of GSI design influence the composition of the bundle of benefits society will receive from an investment, detailed benefit estimates are needed to help guide GSI design to maximize the total net benefit people get from the suite of benefits produced. Papers such as Martin et al. (2007) and Hung et al. (2016) attempted to develop multicriteria optimization algorithms to accomplish benefit maximization. However, such algorithms must have some way to weight the importance of the different benefits from stormwater management. Comprehensive monetized benefit values will yield optimization outcomes that actually maximize human well-being, while other common approaches, such as Likert-scale surveys or expert elicitation, will not.

Third, benefit valuation can facilitate socially valuable adoption of GSI. Political processes do not readily incorporate qualitative information about the benefits of a practice. Quantitative, and better yet, monetary valuations make the benefits of GSI clear and easy to include in municipal decision making (Schäffler & Swilling, 2013; Vandermeulen, Verspecht, Vermeire, Van Huylenbroeck, & Gellynck, 2011).

Approaches to Estimating the Values of Benefits

This section presents an overview of important concepts and methods in the economics of benefit valuation. The concepts herein are well-known to environmental economists, but they may be unfamiliar to engineers, urban planners, and economists who study other fields. Sources for more background on fundamentals of environmental economics and valuation methods are listed in “Suggested Readings”.

Benefits occur when a good or a change in condition makes people better off. A good is called a private good if its benefits accrue to the person who buys it. For example, green roofs can reduce the cost of heating and cooling a building (see “Green Roofs, Vegetative Walls, and Vegetative Planters”); that private cost-reduction benefit accrues to the person who owns the building.

Most GSI installed on private property is not a pure private good because it generates many positive externalities—benefits to people who did not purchase the good. For example, green roofs contribute to flood reduction, which has benefits to many people other than the owners of the buildings with the green roofs. When a good produces positive externalities, economic theory states that one will tend to have less of that good than would be socially optimal because the people who pay for it do not reap all the benefits. If we understand the magnitude of positive externalities, we can design policies to encourage private individuals to choose efficient levels of goods that produce external benefits.

Economists measure the value of a good with two closely related measures. First, consider a change for the better such that a person receives a good they did not have before; willingness to pay (WTP) measures the amount of money a person could give up in exchange for that improvement and have utility held just constant. Second, consider a change for the worse such that a person loses a good they had; willingness to accept (WTA) measures the amount of money one would have to give a person in exchange for that loss to make them whole. In the literature valuing the benefits of GSI, WTP is the most common valuation concept because scholars are often studying GSI retrofits to improve urban conditions.

The benefits created by GSI one might want to value fall into two categories that influence what methods can be used to estimate their values: use and nonuse values. Use values accrue to people through actual direct experience. A person gains use value if their house is less likely to flood, if the water in the stream behind their house smells better because it is less polluted, if they are healthier and more comfortable because of lower summer heat and less air pollution, and if they enjoy seeing more different kinds of butterflies in the neighborhood and prefer the view of rain gardens to concrete rain gutters.

Nonuse values can result from some kinds of GSI as well. A person may appreciate just knowing that local rivers support a wider range of fish and amphibians; they may value the continued existence of endangered species (like salmonids) who use those habitats; and they may want to reduce net CO2 emissions so their children do not suffer the costs of extreme climate change.

Goods like water quality and decreased flood reduction cannot be purchased, so a market price does not exist to help measure WTP. However, economists have developed a suite of methods to estimate the values of nonmarket goods. Revealed preference methods use market data on actions that are related to the nonmarket good to be valued; such approaches can estimate use values but not nonuse values. For example, hedonic housing price analysis can estimate how much the price of a house is affected by flood frequency at that location, water quality in streams nearby, and the presence of potentially aesthetic GSI, and those estimates can be used to estimate marginal household WTP for changes in those attributes. Stated preference methods, namely, contingent valuation (CV) and choice experiments (CE), use surveys to elicit WTP for a nonmarket good directly. CV estimates WTP for one hypothetical scenario and its associated bundle of nonmarket goods (Loomis, Kent, Strange, Fausch, & Covich, 2000), while CE asks respondents to express preferences between scenarios with explicitly varied levels of multiple potential benefits, and thus can reveal marginal WTP for each benefit (Brent, Gangadharan, Lassiter, Leroux, & Raschky, 2017). Stated preference tools can be used to estimate both use and nonuse values, and they can estimate WTP for hypothetical changes.

The literature on the benefits of GSI contains research with two general strategies for benefit estimation. One approach is mechanistic. First, the researcher uses findings from disciplines such as engineering, hydrology, and landscape architecture to estimate the physical changes that will result from GSI installation; if one installs a certain number of bioswales, what are the resulting changes in flood frequency, water quality, and so forth? Second, the researcher applies economic estimates of the values of those types of physical changes to monetize the benefits. Braden and Ando (2011) give an example of this strategy in their estimate of the water quality and flood reduction benefits of large-scale use of LID development approaches. This technique is explicit about exactly what changes are being valued.

Another approach estimates the holistic value people place on GSI. One can conduct a hedonic analysis to estimate how house prices are affected by proximity to GSI elements (Mazzotta, Besedin, & Speers, 2014; Netusil, Levin, Shandas, & Hart, 2014) or one can carry out a CV analysis of WTP for a hypothetical new network of GSI (Shackleton et al., 2017). The resulting values include a suite of individual benefits—all the goods that motivated homeowners to pay a specific amount for a house on a green street, or survey respondents to state their WTP for GSI.

The mechanistic approach to GSI valuation uses a process called benefit transfer—applying benefits estimated in one place to a valuation problem somewhere else. Primary source valuation research can be expensive and slow so benefit transfer is an important technique in benefit valuation. However, there are many potential problems in using this technique. Physical processes can vary across locations. Status quo conditions (like water quality and the prevalence of impervious surfaces) can vary. Individual monetary values depend on individual preferences and income levels, and aggregate monetary values depend on population sizes. Johnston and Rosenberger (2010) discussed best practices for benefit transfer in general; scholars engaged in valuing GSI should heed that discussion.

Finally, in order to use GSI benefit estimates in a cost-benefit analysis, one must pay attention to the stream of benefits and costs over time. The net present value (NPV) of a project estimates the net value (benefit B minus cost C) of the project in each year t from the beginning of the project (t = 0) until the end of the project’s lifespan, T, and uses those data with a discount rate to calculate the NPV as NPV=t=0T(BtCt)(1+r)t.

Benefits of GSI Strategies

As interest in GSI has grown, so too have the number of peer-reviewed articles that value the benefits of these strategies. This section summarizes articles that have evaluated the benefits of (1) green roofs, vegetative walls, and vegetative planters; (2) tree planting; (3) rain barrels, cisterns, and rain gardens; (4) bioswales, retention basins, green streets, and permeable surfaces; (5) low-impact development; and (6) suites of GSI outcomes.

Green Roofs, Vegetative Walls, and Vegetative Planters

Green roofs, which are also referred to as eco-roofs, living roofs, or vegetative roofs, use vegetation to absorb rainfall, which reduces the velocity and volume of stormwater runoff. Other benefits attributed to green roofs include improvements in air quality, provision of urban habitat, and lower roof temperatures that can save energy for property owners and decrease the urban heat island effect. Green roofs, if appropriately designed, can also provide recreation access for residents, a space for urban agriculture, and improved views for people who overlook these sites (Bianchini & Hewage, 2012; Carter & Keeler, 2008). Benefits vary based on green roof type, location, and weather conditions with intensive green roofs, which have more soil and higher maintenance requirements, generating relatively higher stormwater benefits than extensive green roofs, which require only a thin layer of soil (Bianchini & Hewage, 2012).

Most studies have focused on a single building site to estimate benefits (Clark, Adriaens, & Talbot, 2008; Claus & Rousseau, 2012; Macmullan, Reich, Puttman, & Rodgers, 2009), with some studies scaling up estimates to larger areas (Carter & Keeler, 2008; Niu, Clark, Zhou, & Adriaens, 2010; Peng & Jim, 2015). Carter and Keeler (2008) quantified benefits from an experimental green roof plot to estimate the net present value (NPV) of extensive green roofs for an entire watershed, while Peng and Jim (2015) evaluate a hypothetical large-scale green roof installation in Hong Kong with a focus on climate-related benefits such as CO2 sequestration.

The approach used in all of these studies is to estimate physical changes (e.g., the amount of stormwater reduced) and then apply a dollar value to those changes. Costs and benefits are uncertain over the expected 40-year life of a green roof, so Carter and Keeler (2008) included a sensitivity analysis, Claus and Rousseau (2012) included a best and worst case scenario, Niu et al. (2010) incorporated uncertainty parameters, Nurmi, Votsis, Perrels, and Lehvävirta (2016) evaluated a low-benefit, high-cost scenario and a high-benefit, low-cost scenario, and Bianchini and Hewage (2012) used a Monte Carlo simulation to estimate the NPV of green roofs over their life cycle.

Results of these studies are mixed, with Carter and Keeler (2008) estimating a NPV for conventional roofs that exceeded green roofs by 10% to 14%, although their analysis only included avoided stormwater costs, energy savings, and air quality benefits. Nurmi, Votsis, Perrels, and Lehvävirta (2016) estimate private cost-benefit ratios that are less than 1 for the scenarios examined. Their analysis of a hypothetical program where green roofs would be installed on 10% of Helsinki rooftops found a social benefit-cost ratio of 0.9 for the low-benefit, high-cost scenario and a social benefit-cost ratio of 2.5 for the high-benefit, low-cost scenario. Peng and Jim (2015) calculated a life cycle benefit-cost ratio of 3.84 for extensive green roofs and 1.63 for intensive green roofs in Hong Kong.

The public benefits of green roofs are an important factor in generating a positive NPV to society in several studies (Claus & Rousseau, 2012; Macmullan et al., 2009; Nurmi et al., 2016). As discussed in “Approaches to Estimating the Values of Benefits,” incentives for installing green roofs may increase the private NPV to a property owner, but should not be included when evaluating projects from society’s viewpoint because they represent a transfer payment and do not increase social welfare (Carter & Keeler, 2008; Nurmi et al., 2016).

Other stormwater management approaches that use vegetation to control the timing and volume of runoff include vegetative stormwater walls, also known as green or living walls, green façades, which use climbers that attach directly to a building or supportive structure (Perini & Rosasco, 2013), and vegetative planters. Vegetative stormwater walls manage stormwater and rainfall along a vertical surface while vegetative planters capture rainfall, which distinguishes them from stormwater planters (Liptan, 2017).

Perini and Rosasco’s (2013) benefit-cost analysis evaluates green façades and living wall systems for a hypothetical office building using best, worst, and middle case scenarios. The NPV was always positive for one of the six green façades, with other estimates depending on the scenario and façade type. Perini and Rosasco (2016) examined economies of scope from installing a green façade and green roof on the same building. Public benefits played a minor role in both studies because of the project’s small scale.

Peer-reviewed literature that uses revealed or stated choice methods to value green roofs, vegetative walls, or vegetative planters is limited. One hedonic study estimated a 16.2% increase in apartment rents for buildings with a green roof compared to buildings without that feature in the Battery Park City area of New York City (Ichihara & Cohen, 2011). Apartment transactions were also the basis for a study in Helsinki, Finland that used the estimated effect of small parks (less than 1 hectare) as a proxy for the effect of extensive green roofs (Nurmi et al., 2016). The authors recommend using values of 0% to 1.2% as the increase in apartment sale price attributable to views of green roofs.

Tree Planting

Tree planting includes trees planted on privately and publicly owned property, street trees, which are planted in the public right-of-way, trees planted in bioswales, and trees planted on green roofs. Mature trees intercept and slow the rate of stormwater runoff in addition to providing other benefits such as improving water quality, reducing the urban heat island effect, improving air quality, reducing energy usage, providing habitat, and improving aesthetics (U.S. EPA, 2016).

The benefits of tree planting have been valued using modeling programs such as iTree Tools and STRATUM (McPherson, Simpson, Peper, Maco, & Xiao, 2005; McPherson, Simpson, Xiao, & Wu, 2011; Soares et al., 2011; USFS, n.d.). In these programs, the benefit of reducing stormwater runoff is calculated using avoided costs while aesthetic benefits are estimated using a benefit transfer approach.

McPherson et al. (2005) estimated that stormwater benefits from trees accounted for 51% of total annual benefits from trees in one U.S. city and 8% to 19% in four other U.S. cities. A study of street trees in Lisbon, Portugal attributed 23% of the total benefits from street trees to stormwater runoff (Soares et al., 2011), while McPherson et al. (2011) calculated that 8% of the total benefits from a program to plant one million trees in Los Angeles, California would be from reductions in stormwater runoff.

The dominant benefit from tree planting in most studies is aesthetic benefits measured by the impacts of trees on property values. These accounted for 59% to 75% of total benefits for four of the five cities in McPherson et al.’s (2005) study, 81% of total benefits for McPherson et al.’s (2011) study in Los Angeles, California, and 71% of total benefits from planting street trees in Lisbon, Portugal (Soares et al., 2011). These papers all relied on estimates from a single hedonic study by Anderson and Cordell (1988) in Athens, Georgia, which estimated an increase in a property’s sale price of 3.5% to 4.5% from having five trees in its front yard.

Adding property value increases (even though they are labeled aesthetic benefits) to other benefits from tree planting may introduce double counting if property owners considered private benefits from tree canopy, such as energy savings and improved air quality, when determining how much they would be willing to pay for a property. The use of Anderson and Cordell’s (1988) estimate also assumes a linear relationship between the number of trees and property values. However, research has found evidence of a nonlinear relationship between tree canopy and property sale prices (Kadish & Netusil, 2011; Netusil, Chattopadhyay, & Kovacs, 2010), so estimates from modeling programs that assume a linear relationship between trees planted and aesthetic benefits may be overestimating these benefits.

Hedonic price models have been used to estimate the effect on property sale prices of street trees, street landscape planting, and tree canopy in urban areas. As discussed in “Approaches to Estimating the Values of Benefits,” this method only captures what private property owners are WTP (or WTA) for changes in the number of trees, so public benefits generated by trees, such as a reduction in stormwater runoff, will not be captured using this approach. The results of those studies are as follows.

Donovan and Butry (2010) studied home sale prices in Portland, Oregon and found that the presence of the average number of street trees and canopy cover within 100 feet of a property increased sale prices by 3%. Pandit, Polyakov, Tapsuwan, and Moran (2013) found that broad-leaved street trees could increase a property’s sale price by more than 4% in Perth, Western Australia, although prices were not affected by trees located on a property itself or on neighboring properties, and palm trees were not found to affect sale prices. Two studies in Finland estimated positive values for urban trees; Tyrvainen (1997) found that increasing the amount of forested areas in a housing district increased apartment prices, and Tyrvainen and Miettinen (2000) estimated that houses with a view of forests were estimated, on average, to sell for a premium of 4.9%.

Several hedonic studies have found that property values are first increasing but then decreasing in quantities of urban trees. Ishikawa and Fukushige’s (2012) analysis of street landscape planting for 12 cities in Japan found a nonlinear relationship between the street landscape planting rate and land values; most cities were below the rate of street landscape planting that has the maximum estimated effect on land prices. Kadish and Netusil (2011) estimated the amount of tree canopy on a property’s lot that maximizes its sale price in an urbanized area in Multnomah County, Oregon. While increasing canopy on a lot from the current study area average to the “optimal” amount was estimated to increase a property’s sale price, the increase in sale price was less than the expected cost of planting and caring for trees. Diminishing returns from expanding tree canopy was also found in Netusil et al.’s (2010) analysis in Portland, Oregon, which included results from a first-stage and second-stage hedonic model. Results from the second-stage model were used to estimate benefits from nonmarginal changes in tree canopy; average benefit estimates, based on the mean canopy cover within one-quarter mile of properties in the study area, were between 0.75% and 2.52% of the mean sale price. Finally, Sander, Polasky, and Haight (2010) used nonlinear hedonic models of house prices in Minnesota and found that increasing tree coverage in a home’s neighborhood increases that home’s price up to 40% tree coverage, after which the marginal effect of greater tree coverage on price is negative.

Stated preference studies on urban tree canopy include Tyrvainen and Vaananen’s (1998) contingent valuation study of residents’ WTP for forested recreation areas in Joensuu, Finland. Most respondents expressed a positive WTP a seasonal fee to use these areas. A second stated preference study used a choice experiment in Lodz, Poland to value street tree programs (Giergiczny & Kronenberg, 2014). Estimates using different models were all positive, with similar average WTP values across the proposed programs. A large number of respondents, however, expressed a negative WTP, which the authors describe as consistent with 18% of respondents describing the current amount of tree canopy coverage as adequate.

In sum, hedonic studies find mixed results about trees and property values, with results varying by tree type, location on the property or in a public right-of-way, and with the amount of tree canopy coverage (Kadish & Netusil, 2011; Pandit et al., 2013). Although average WTP for trees in stated preference studies has been, on average, positive, there exists evidence of heterogeneity in preferences for expanding tree canopy in urban areas (Giergiczny & Kronenberg, 2014). These findings do not seem to be reflected in modeling programs that assume a constant, linear relationship between tree planting and benefits and find, in most study areas, that the majority of total benefits from tree planting are from aesthetic and property values effects (McPherson et al., 2005, 2011; Soares et al., 2011).

Rain Barrels, Cisterns, and Rain Gardens

Rain barrels and cisterns are containers that collect stormwater runoff from rooftops for irrigation and other uses. A rain garden is a vegetated area that allows stormwater to be intercepted and infiltrated at a higher rate than the surrounding landscape (Thurston, Taylor, Shuster, Roy, & Morrison, 2010). While several modeling tools incorporate these strategies to estimate GSI benefits (Jayasooriya & Ng, 2014), the literature focused specifically on estimating the benefits of these strategies is limited.

Logan (2014) estimated the cost savings from using harvested water as a substitute for municipal water and Thurston et al. (2010) used a reverse auction to estimate property owners’ WTA to have a rain barrel installed, which they adjust for the potential environmental impact of installing a rain barrel on a specific property. Bowman, Tyndall, Thompson, Kliebenstein, and Colletti (2012) used a contingent valuation survey to assess residents’ WTP for two low-impact design features—rain gardens and pervious pavers—in Ames, Iowa. Most respondents expressed a WTP for rain gardens in their subdivision, with a mean WTP of $1,396.

Bioswales, Retention Basins, Green Streets, and Permeable Surfaces

GSI includes many different types of vegetated or permeable areas that are explicitly designed to intercept stormwater runoff and allow for infiltration: bioswales, stormwater retention basins, green streets, permeable surfaces, and green alleys. No studies were identified that explicitly value the benefits of green alleys, but research has estimated the benefits of the other types of vegetated ground-level GSI.

Bioswales

Bioswales, which are sometimes referred to as swales, are typically long, narrow, and shallow vegetated areas that are designed to capture stormwater runoff. A Seattle, Washington program that redesigned residential streets to minimize stormwater runoff by incorporating bioswales, increasing vegetation, and reducing pavement was estimated in a series of hedonic price models to increase property sale prices by 3.5% to 5.1% (Ward, MacMullan, & Reich, 2008). The program included other changes to streets, such as adding sidewalks, and generated additional benefits such as traffic calming and increasing wildlife habitat.

Stormwater Retention Basins

Stormwater retention basins, an early design for intercepting and infiltrating stormwater runoff, are projects that typically feature a deep pit to manage large amounts of water, with limited amenity or wildlife values (Graham, 2016). Irwin, Klaiber, and Irwin (2017) used a hedonic price model to examine sale price effects from proximity to stormwater basins in Baltimore County, Maryland. They estimated that the sale price of adjacent properties declined by 13% and 14%, depending on model specification, with no price effect for nonadjacent properties; they also found that price discounts increased with a basin’s age.

Basin design was investigated by Lee and Li (2009) for two College Station, Texas subdivisions. A property’s sale price was not affected by its street network distance from basins whose sole function was flood control, but properties with a view of these facilities had a significantly lower sale price. In contrast, street network distance from multiuse detention basins, which integrated recreation facilities and flood control, had a significantly positive effect on property sale prices.

Green Streets

Green streets include changes to the areas between curbs and sidewalks, but can also include curb extensions, street planters, and rain gardens (Netusil et al., 2014). Green alley programs focus on stormwater management and other ecosystem services as part of a program to revitalize urban alleys, with all green alleys incorporating permeable surfaces (Newell et al., 2013).

Green street facilities were quite small, on average 465 square feet, in Netusil et al.’s (2014) study in Portland, Oregon compared with an average size of 16,727 square feet for stormwater retention basins in Irwin, Klaiber, and Irwin’s study (2017). While facility type (sidewalk bioswale, grassy bioswale, curb extension, or corner curb extension) was not found to affect the sale price of properties in Netusil et al.’s (2014) hedonic study, property sale prices were found to increase as distance from a facility increased, although by a small but statistically significant amount. Property sale prices were positively affected by increases in a green-street facility’s size, age, percentage of the facility covered by tree canopy, increases in the abundance of green street facilities within the census tract, and design complexity.

Permeable Surfaces

Permeable surfaces, also referred to as porous pavement or pervious pavers, are surfaces that allow water to penetrate. Several modeling tools incorporate permeable surfaces to estimate GSI benefits (Jayasooriya & Ng, 2014), and permeable surfaces are often included in projects, such as green alleys and green streets, that include other GSI strategies (Newell et al., 2013; Ward et al., 2008). Bowman, Tyndall, Thompson, Kliebenstein, and Colletti (2012) used a contingent valuation survey to assess residents’ WTP for pervious pavers in Ames, Iowa. Most respondents expressed a positive WTP for pervious pavers in their subdivision, with a mean WTP of $1,424.

Low-Impact Development

Low-impact development (LID) uses development designs to enhance on-site stormwater retention. Braden and Johnston (2004) estimated average total benefits of 2% to 5% from improved water quality and reduced flooding using a benefit transfer approach. Their analysis included all properties in the floodplain, including properties located downstream from areas that improved stormwater management. Braden and Ando (2011) found that water quality benefits were the largest contributor to overall benefits from LID; other benefits included reduced flood losses, reduced infrastructure costs, and cost savings from mitigating combined sewer overflows.

Hedonic studies have found that some LID design features decrease property sale prices. Williams and Wise (2009) found that sale prices decreased for properties with a swale-based stormwater system in comparison to those with a curb-and-gutters system. Decreasing lot sizes to increase the amount of open space in a subdivision also reduced property sale prices. Boatwright, Stephenson, Boyle, and Nienow’s (2014) analysis of cul-de-sacs, curb-and-gutters, and wide streets found that property sale prices increased for properties in cul-de-sacs and with curb-and-gutter systems, but declined for properties on wider streets.

Suites of GSI Outcomes

Some research on the benefits of GSI focuses on the values of the outcomes that could result from installation of a suite of GSI elements. First, Londoño Cadavid and Ando (2013) carried out a choice experiment (CE) study of hypothetical GSI improvement in a small Midwestern city. They found that households have significant WTP for infiltration that improves stream health and water quality and reduces flooding. Marginal WTP per household to go from the worst hydrological conditions in the hypothetical scenarios (which affect stream health) to the best was $34 per year. Second, Brent et al. (2017) used a CE in Melbourne and Sydney, Australia. They found marginal household WTP for maximal stream health is A$234/year. Total mean WTP for the highest levels of stream health, flash-flood prevention, reduced restrictions on water use, and temperature reductions was A$799 per year.

Future Study of GSI Benefits

A rich body of research exists estimating the private and public benefits of using GSI as part of an urban stormwater management strategy. While research indicates that the total benefits of GSI can be large, benefits vary by GSI strategy, local biophysical conditions, over the life cycle of a GSI strategy, and spatially (ECONorthwest, 2007). Public benefits are often the dominant GSI benefit, so accurate benefit estimates are needed to inform policies and municipal stormwater management plans. However, more work is needed in several important facets of the benefits of GSI, and future scholars should avoid several pitfalls of benefit estimation.

Benefit Estimation Pitfalls to Avoid

It is easy to apply a benefit value from one study to a current analysis, but previous benefit studies range widely in quality, and the simple application of a single value from one setting to another can be inaccurate and misleading (Johnston & Rosenberger, 2010). Benefit estimates should be based on peer-reviewed studies that use state-of-the-art estimation techniques. If original research is not available in a study area or in a similar location, then meta-analysis papers, if available, should be viewed as the preferred source for benefit estimates. Benefit transfer works best if enough papers exist on a subject to use meta-analysis and benefit function transfer, strategies that allow the analyst to develop a value estimate that controls for specific features of the study area.

Benefits are uncertain, so total benefit estimates should include a range of values that reflect likely scenarios and discount rates. The possibility of a nonlinear relationship between GSI strategies and benefits should be acknowledged; for example, there may be diminishing returns to increasing GSI, as seen in the hedonic literature on trees, or there may be increasing returns once GSI strategies reach a critical mass.

Valuation of net CO2 reduction can inadvertently be politicized. Future analyses of the benefits of carbon reductions should value emission reductions using the social cost of carbon supported by ongoing rigorous scientific analysis. Values of CO2 permits in tradable permit markets may not capture the true social value of avoided CO2 if the total number of permits in the market is not set correctly. The current best-practice estimate of the value of avoided carbon emissions is $42/ton (Interagency Working Group on Social Cost of Greenhouse Gases, U.S. Government, 2016). That estimate may usefully adjust in response to processes recommended by a National Academy of Sciences panel (Engineering National Academies of Sciences, 2017). However, analysts should avoid using greatly reduced values of avoided carbon emissions put forth without good intellectual rationale (Mooney, 2017).

Studies of the benefits of GSI must take care to avoid double counting (Mekala, Jones, & MacDonald, 2015). This can happen easily if a study includes housing price effects as a benefit in addition to estimates of values like flood reduction and local water quality improvement, or if a study adds together WTP values from a stated preference study along with estimates of some of the individual values that might be included in that. The section “Tree Planting” provided examples of this problem in the context of valuing the benefits of trees. However, traditional monetized welfare measurements (WTP and WTA) may fail to capture welfare changes in settings such as developing countries where money payments are not used in many exchanges. In such settings, benefit estimates should incorporate willingness to work (Shackleton et al., 2017).

Finally, care must be taken not to count transfers as benefits. If governments pay subsidies (or offer reduced tax rates) when a property owner installs GSI, that payment is a benefit to the property owner and can be included in a personal analysis of the costs and benefits to the property owner of that GSI element. However, such incentives are pure transfers from taxpayers to property owners, and the transfer itself does not generate a net increase in social well-being; thus, the payments should not be included as benefits in a cost-benefit analysis conducted from society’s point of view.

Areas for Future Research

While much research has been done on the benefits of GSI, the literature is far from complete. Several areas that are important topics for future research in this field are as follows.

Missing Values

At least three actual types of benefits have not been estimated robustly by the extant literature. First, little is known about the benefits of GSI through improving aquatic habitat and biodiversity. Research is needed that carries out full studies of the physical effects of GSI on habitat and species that are integrated with valuation of those changes. At present, the literature has only a few papers that value habitat and species in the urban settings that are relevant to GSI (see “Suites of GSI Outcomes”), and those are not connected to physical estimates of what actual effects GSI are likely to have. Some papers have estimated values of aquatic habitat and health not related to GSI; for example, Loomis et al. (2000) did a contingent valuation study of WTP to improve fish habitat in the Platte River by nutrient reduction, and Collins, Rosenberger, and Fletcher (2005) studied the benefits of restoration of aquatic life on a stream in Appalachia that was damaged by acidification. However, those contexts are very different from habitat improvement in urban streams, so benefit transfer of those values to habitat improvements from GSI is even less likely than usual to be valid. As research proceeds in this area, it would be helpful for scholars to develop consistent metrics of habitat quality so that meta-analysis of the literature will eventually be possible.

Second, climate change is expected to increase the frequency and severity of urban stream flooding, but most research on the benefits of flood reduction focus on large storm events such as hurricanes and major river flooding (Beltrán, Maddison, & Elliott, 2018). GSI strategies may reduce small-scale street and basement flooding; future research would do well to conduct more estimates of such benefits.

Third, cities and countries are promulgating regulations to increase the number of green roofs, but little is known about the impact of green roofs on sale prices of properties that themselves have green roofs or that overlook a green roof (European Commission, 2018; Liptan, 2017). Additional research is needed to fill that gap.

Distribution and Durability

Even when good research has estimated the benefits in a given year of a GSI project, the overarching impact of that project to society depends on how those benefits persist over time and how the benefits are distributed among different people in an urban community. Little work has been done to study either of those issues.

Urban neighborhoods vary widely in their racial and ethnic composition and in their affluence. A few papers provide worrisome hints that the benefits of GSI may not be distributed equitably among neighborhoods. GSI may or may not be equitably sited among neighborhoods of a city; Shackleton et al. (2017) found that poorer neighborhoods of cities in South Africa had relatively low levels of trees and green areas, while Chan and Hopkins (2017) found that green streets in Portland, Oregon were located disproportionately in low-income neighborhoods. Furthermore, GSI can yield benefits for people far downstream from its installation (Braden & Johnston, 2004), and the local benefits of a given type of GSI may vary among neighborhood type. For example, Zhou (2017) found that green streets have heterogeneous effects on housing prices depending on the neighborhood in which they are located, and in some cases the effects can even be negative. Much more research must be done on such issues as decision makers seek to ensure that the benefits of GSI are equitably distributed in urban areas.

Finally, scholars of GSI have expressed concerns about whether GSI benefits will be durable. City financing for operations and maintenance (O&M) can be tenuous (Liptan, 2017). GSI is decentralized and often installed, at least in part, on private property, so long-term durability depends on public engagement (Ando & Netusil, 2013). However, little research has seriously tackled questions about maintenance requirements for GSI or taken a life cycle approach to estimating benefits of GSI that include maintenance costs (Center for Neighborhood Technology & American Rivers, 2010). Future work would do well to help policy makers understand whether the benefits of GSI can be sustained.

Acknowledgments

This work is supported in part by USDA-NIFA Hatch project number #ILLU-470-323. The authors are grateful to two anonymous referees for helpful comments.

Further Reading

Ando, A. W., & Netusil, N. R. (2013). A tale of many cities: Using low-impact development to reduce urban water pollution. Choices 28(3), 1–6.Find this resource:

Braden, J. B., & Ando, A. W. (2011). Economic costs, benefits, and achievability of low-impact development-based stormwater regulations. In H. W. Thurston (Ed.), Economic incentives for stormwater control. Boca Raton, FL: CRC Press.Find this resource:

Braden, J. B., & Johnston, D. M. (2004). Downstream economic benefits from storm-water management. Journal of Water Resources Planning and Management, 130(December), 498–505.Find this resource:

Champ, P. A., Boyle, K. J., & Brown, T. C. (Eds.). (2017). A primer on nonmarket valuation. The economics of non-market goods and resources (2nd ed., Vol. 13). Dordrecht, The Netherlands: Springer Nature.Find this resource:

Charlesworth, S. M., & Booth, C. A. (2017). Sustainable surface water management: A handbook for SUDS. Hoboken, NJ: Wiley.Find this resource:

Cropper, M. L., & Oates, W. E. (1992). Environmental economics: A survey. Journal of Economic Literature, 30(2), 675–740.Find this resource:

Douglas, I. (2016). The challenge of urban poverty for the use of green infrastructure on floodplains and wetlands to reduce flood impacts in intertropical Africa. Landscape and Urban Planning, November.Find this resource:

Fletcher, T. D., Shuster, W., Hunt, W. F., Ashley, R., Butler, D., Arthur, S., . . . Viklander, M. (2015). SUDS, LID, BMPs, WSUD and more—The evolution and application of terminology surrounding urban drainage. Urban Water Journal, 12(7), 525–542.Find this resource:

Liptan, T. (2017). Sustainable stormwater management: A landscape-driven approach to planning and design. Portland, OR: Timber Press.Find this resource:

Londoño, C. C., & Ando, A. W. (2013). Valuing preferences over stormwater management outcomes including improved hydrologic function. Water Resources Research, 49(7), 4114–4125.Find this resource:

Mell, I. (2016). Global green infrastructure: Lessons for successful policy-making, investment and management. New York, NY: Routledge.Find this resource:

National Research Council. (2009). Urban stormwater management in the United States. Washington, DC: National Academies Press.Find this resource:

Netusil, N. R., Levin, Z., Shandas, V., & Hart, Ted. (2014). Valuing green infrastructure in Portland, Oregon. Landscape and Urban Planning, 124(April), 14–21.Find this resource:

Sage, J., Berthier, E., & Gromaire, M. C. (2015). Stormwater management criteria for on-site pollution control: A comparative assessment of international practices. Environmental Management, 56(1), 66–80.Find this resource:

Schäffler, A., & Swilling, M. (2013). Valuing green infrastructure in an urban environment under pressure—The Johannesburg case. Ecological Economics, 86(February), 246–257.Find this resource:

Thurston, H. W. (2011). Economic incentives for stormwater control. Boca Raton, FL: CRC Press.Find this resource:

Tietenberg, T. H., & Lewis, L. (2015). Environmental & natural resource economics (10th ed). Boston, MA: Pearson.Find this resource:

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