Open access
Technical Papers
Jan 31, 2017

Willingness to Pay for Riparian Zones in an Ozark Watershed

Publication: Journal of Water Resources Planning and Management
Volume 143, Issue 5

Abstract

Clear Creek watershed, located in the Ozark Mountain Region of Northwest Arkansas, has experienced ecosystem degradation as a result of increased urbanization. Riparian zones are known to play an integral role in maintaining ecosystem integrity and are often used as management tools for protecting watershed ecosystem services, especially in urbanizing areas. To determine whether residents of the Clear Creek watershed would be in favor of the establishment and maintenance of riparian zones in their region, a mail survey was conducted using contingent valuation methods (CVMs) to elicit local willingness to pay (WTP) for riparian zones. The study found that residents were willing to pay an average of $80.07 in increased state income taxes for the ecological services provided by riparian zones. Bid amount, income, and attitude toward willingness to pay were shown to have the most significant influence on the residents’ overall WTP. Survey results were also used to assess residents’ abilities to perceive ecosystem quality as a factor of WTP but analysis found this perception to be insignificant in affecting respondent WTP.

Introduction

Increased population and land-use changes can create contention among stakeholders, making it challenging to manage ecosystem services (Sabatier et al. 2005; Mouchet et al. 2014; Castro et al. 2016). A thorough understanding of the ecosystem and the related socioecological dynamics can facilitate decision making in the management of ecosystems, especially in areas of growing population (Tentes and Damigos 2015). According to Wainger et al. (2010), an improved understanding of ecosystem services can help guide decisions regarding natural resource conservation and management. Researchers have established that decisions, which can be reflected in the willingness to pay (WTP) for ecosystem services, are greatly influenced by continuous changes in land use (Bockstael 1996). The physiographic boundaries of watersheds can serve as useful spatial boundaries for the selection of populations in studies that examine the factors affecting WTP for protecting watershed ecosystem services (Bockstael 1996). As such, this study considers stakeholder WTP for riparian zone conservation in an Ozark watershed. This research can help guide environmental managers and policymakers in making management decisions for watersheds.
Clear Creek watershed, located in Washington County, Arkansas, is a subbasin of the Illinois River watershed (Fig. 1). Over the past two decades, increased urbanization has resulted in extensive land-use change throughout the Clear Creek area. According to census block data available at the watershed scale (CAST 2006), between the year 1990 and 2000 the population of Clear Creek watershed experienced a 50.36% increase, growing from 40,103 to 60,299. A population density of 303.98  people/km2 (787.30  people/mi2) was reported for the year 2000; however, this value is estimated to be much greater because the population of Washington County, where Clear Creek is located, has seen a population increase of 39.03% between 2000 and 2014, with much of this growth occurring in the urbanizing areas of Clear Creek watershed (USCB 2015). This increased urbanization has inadvertently disrupted ecological function within the watershed, leading to various ecological degradations such as riparian zone depletion, habitat fragmentation, and increased impermeable surfaces (Barbour et al. 1999; Urban et al. 2006; Mazor et al. 2009). As a result, the Arkansas Department of Environmental Quality has listed Clear Creek as not achieving its designated uses for primary contact due to high levels of pathogen indicators (fecal coliform) from sources of urban runoff (USEPA 2009; ADEQ 2016).
Fig. 1. Bioassessment sites and 2006 land use for Clear Creek watershed (reprinted from CAST 2006, with permission)
To counteract the effects of urbanization on watersheds, environmental managers have considered the establishment and maintenance of riparian zone ecosystems along the banks of local waterways. Riparian zones play an integral role in maintaining ecosystem integrity (Barbour et al. 1999; Fielding et al. 2005; Urban et al. 2006) and are often used as management tools for protecting watershed ecosystem services (Jorgensen et al. 2002; Mayer et al. 2007). A study by DeWalle (2008) suggests that land uses such as urbanization, forestry, mining, and agriculture can deplete riparian vegetation and thus have negative impacts on aquatic ecosystem function. The problems associated with riparian zone vegetation loss are warmer stream water temperatures (DeWalle 2008), increased bank erosion (Barbour et al. 1999; Mayer et al. 2007), reduction and fragmentation of wildlife habitat (Urban et al. 2006; Mayer et al. 2007), reduced water quality as a result of increased sedimentation and organic pollutants (Barbour et al. 1999; Mayer et al. 2007), and the reduction of aesthetic beauty (Cunningham et al. 2009). Because of this, a better understanding of how people value riparian zones can provide a valuable tool for improving watershed management and, as a result, lead to improved watershed ecosystem quality (Cho et al. 2005; Fielding et al. 2005; Wainger et al. 2010).

Valuation of Ecological Services

The establishment and maintenance of healthy ecosystems can provide numerous benefits to households and businesses through the ecological services they provide; however, several factors make it difficult to assign a monetary value to these services (Costanza et al. 1997). To assess the benefits of local ecosystems, environmental managers need information about the quality of the ecosystem they are managing, the value associated with the services provided by the ecosystem (Costanza et al. 1997; Johnson and Baltodano 2004), and the factors that affect such valuation (Pendleton et al. 2001; Hoyos et al. 2009; Spash et al. 2009).
Commonly used approaches for monetizing the value of ecosystem services include benefit transfer (BT), travel cost, and contingent valuation method (CVM) (Deacon and Kolstad 2000; Loomis 2000). Benefit transfer is a relatively inexpensive and timely approach for estimating the value of ecosystem services. This approach involves the use of values obtained through previous studies in order to calculate an estimate for the area in question. In using BT, analysis may involve the value-transfer and/or function-transfer approach (Elsin et al. 2010). While BT is considered to be a useful valuation approach for studies in which data collection options are limited, there are several factors that may affect the accuracy of results (Rosenberger and Stanley 2006).
With the travel cost method (TCM), economists can estimate demand shifts for improved water resource quality using visitor travel behavior data collected for a particular recreation site. Although Clear Creek offers some recreation opportunities, this does not encapsulate all benefits received by those living within the watershed. In order to estimate a response that includes both recreational and existence value, an approach known as the CVM can be used (Loomis 2000).
The contingent value method is a commonly used tool for providing insight into an individual’s use value, bequest value, and existence value for a public good or service (Mitchell and Carson 1989) when there are no existing markets for the public good or service (Costanza et al. 1997; Loomis et al. 2000; Farber et al. 2006; Ryan and Spash 2011; Loomis 2014; Castro et al. 2016). Several studies have utilized CVM to determine the public’s willingness to pay for a wide variety of ecosystem services, such as drinking water (Vásquez et al. 2009), fisheries improvements (Loomis 2006), national parks (Adams et al. 2008), and coastal zone quality improvements (Halkos and Matsiori 2012; Loomis and Santiago 2013), while other studies have measured what people prefer as the level of environmental quality (Loomis et al. 1996; Asakawa et al. 2004).
Various factors have been shown to affect WTP, including bid amount (Loomis et al. 2000), an individual’s familiarity with an environmental resource (Carson et al. 2001; Hoyos et al. 2009), previous use of an environmental resource (Kniivilä 2006; Halkos and Matsiori 2012; Kaffashi et al. 2015), direct experience with an environmental resource (Turpie 2003; Kaffashi et al. 2015), and respondent knowledge of environmental processes (Turpie 2003). Demographic characteristics including age (Amigues et al. 2002; Halkos and Matsiori 2012; Legesse 2015), gender (Pumphrey et al. 2008; Solomon and Johnson 2009; Halkos and Matsiori 2012), education level (Amigues et al. 2002; Grazhdani 2015; Legesse 2015), membership in environmental groups (Loomis et al. 2000; Halkos and Matsiori 2012), race (Turpie 2003), income level (Halkos and Matsiori 2012; Kreye et al. 2014; Grazhdani 2015), and attitude (Kotchen and Reiling 2000; Ryan and Spash 2011; Castro et al. 2016) have all been shown to be factors in WTP.
Ryan and Spash (2011) define attitude as an evaluative tendency and they state that the more positive the attitude toward environmental change, the more favorable the responses are for WTP. Ryan and Spash (2011) also explain that contingent valuation studies are useful ways of assessing whether or not attitude is a true factor in WTP. Jorgensen and Syme (2000) also found that attitude was a larger factor in WTP variability than price, payment vehicle, or income. Kniivilä (2006) found that general attitude—defined as an evaluative response toward an object by Eagly and Chaiken (1993)—toward conservation, regardless of personal experience with the conservation area, played a key role in WTP. Finally, attitude toward paying for or protecting a public good differs based on one’s individual perspectives toward public goods, collective action, and individual gain. Ostrom (2000) states, for example, that there are those who refuse to participate in supporting a public good regardless of the participation of others, those who are willing to participate only when they estimate that others will also participate, and those who are always willing to be part of collective action for a public good. This study attempted to capture this range of perspectives in respondent attitude to better understand the factors influencing WTP.
The way a community perceives itself in relation to its natural surroundings can also affect the value placed on the natural environment and hence its treatment (Pendleton et al. 2001; Sabatier et al. 2005; Silvano et al. 2005; Paradise 2005; Hoyos et al. 2009; Adams et al. 2013; Lewis and Popp 2013). Consequently, perception has the potential to affect the community’s WTP for ecosystem services provided by the natural environment (Hanemann 1984). Therefore, understanding perception of ecosystem integrity and its role in influencing WTP for ecosystem services can be an important part of the environmental management and policy-making process.
While there are studies that have examined the factors of willingness to pay for ecosystem quality of streams, few have been conducted for streams or watersheds in the Ozarks—or even in the southern part of the country—and none have considered perception of ecosystem quality as a factor of willingness to pay. For instance, Holmes et al. (2004) estimated the benefits and costs of riparian restoration projects along the Little Tennessee River in western North Carolina using CVM and concluded that riparian restoration in this watershed is an economically feasible investment of public funds. Although consumer perception was not considered, they suggested that understanding the value consumers place on ecosystem services would make ecosystem valuation easier. This study, therefore, goes a step further than most studies in examining whether or not one’s ability to accurately perceive ecosystem quality is a factor of willingness to pay for riparian zones in an Ozark watershed. Environmental managers can use the results from this study to identify the factors that drive stakeholder WTP in this region and, as a result, develop environmental management programs and policies that are better understood by and acceptable to stakeholders.

Research Methodology

To determine the value of establishing and maintaining riparian zones in the Clear Creek watershed, a survey was mailed to residents living within the watershed area. Contingent valuation in the form of a dichotomous choice question was used within the survey to elicit WTP for the ecological services provided by riparian zones. Additional questions were included which served to determine the factors having the greatest effect on WTP for these services.

Survey Sampling and Design

The target population for the study was all households in the Clear Creek watershed, an area that covers almost 198.37km2 (77  mi2) in Northwest Arkansas (CAST 2006). Computer program ArcView GIS was used to obtain address data for 28,101 households within the watershed and to assign the household parcels to urban or rural land uses. A random sample of 856 households was selected from this population to receive a survey through the mail.
The survey design followed methods derived from Dillman et al. (2009) and contained 62 questions organized into seven categories thought to affect WTP: (1) awareness of and experience in the watershed, (2) perceptions of ecosystem quality, (3) perceptions of human impacts on watershed ecosystems, (4) ability to identify watershed ecosystem services, (5) ability to identify ecosystem quality indicators, (6) WTP for watershed ecosystem integrity, and (7) demographic information including age, gender, length and type of residency, income, education, ethnicity, and membership in sporting and environmental organizations. Definitions were also provided within the survey for the terms watershed, watershed ecosystem, riparian zone, and biodiversity.
In order to assess WTP for ecosystem integrity, the CVM scenario focused on the ecosystem services provided by riparian zones along Clear Creek. The scenario was based on studies by Costanza et al. (1998), Loomis et al. (2000), and Mayer et al. (2007). The CVM scenario included the following informative statement:
Clear Creek flows for 14 miles through Springdale, Fayetteville, and Savoy. There are over 50 miles of stream banks in the Clear Creek watershed. Clear Creek watershed is an area of rapid growth in Northwest Arkansas, which provides new housing and job opportunities. However, this growth is leading to a loss of trees and grasses along streams in Clear Creek watershed. A riparian zone is the zone of trees and grasses along the banks of a stream. A riparian zone of at least 100 feet wide can provide ecosystem services such as, natural water purification, erosion control, wildlife habitat, climate regulation, and recreational uses.
Respondents were also informed that riparian zones could be established and maintained through state income tax dollars. They were then offered a value and asked if they would be willing to pay that amount in increased income taxes to maintain these zones. If their response was negative, they were asked to provide a value, if any, that they would be willing to pay for this service.
A binary, dichotomous choice question format was chosen because this method has been shown to reduce hypothetical bias and more accurately reveal actual demand (Loomis 2014). The bid amounts ranged between $1 and $100 and were chosen based on previous studies (Mitchell and Carson 1989; Green et al. 1998; Loomis 1996; Loomis et al. 2000; Brox et al. 2003; Cho et al. 2005; Schläpfer 2009). In an effort to capture more information from respondents, an additional open response option was included after the dichotomous choice CVM question.
In following previous studies assessing respondent attitude toward paying for a public good, including Jorgensen and Syme (2000), Ostrom (2000), Fielding et al. (2005), Pumphrey et al. (2008), and Ryan and Spash (2011), a question was added following the CVM question to assess respondent attitude toward WTP. The question asked them to choose a statement describing how they felt about paying to preserve watershed ecosystem quality. Examples of these statements include “I am happy to pay whatever is necessary,” “I can’t afford to pay more taxes,” and “People polluting the streams should pay, not me.”
Once the survey was developed, it was extensively pretested and revised. Specifically, the survey was pretested by more than 100 individuals living in the watershed or within neighboring watersheds and then refined based on their comments. Prior to implementation, the survey was reviewed and approved by the University of Arkansas’s Institutional Review Board (IRB# 08-01-332).

Statistical Analysis of Willingness to Pay

A binomial logistic regression model was used to identify factors that affected WTP. As previously discussed, various factors have been shown to affect WTP. For this study, 38 factors in six categories were hypothesized to influence an individual’s willingness to pay for ecological services provided by riparian zones. These categories involve (1) bid amount; (2) attitudes toward payment; (3) demographic factors; (4) experience with Clear Creek; (5) knowledge of macroinvertebrates, streambank erosion, and ecosystem quality; and (6) perceptions of Clear Creek, human influences on the ecosystem, and quality of life impacts. The 38 variables are presented in detail in Table 1.
Table 1. Hypothesized Willingness-to-Pay Variables
CategoryWTP variableFull nameDescription of response optionsSupporting literature
AttitudeattAttitudes toward willingness to payFour-level variable: will not pay as compared with happy to pay, pay if everyone pays, and cannot afford to payKotchen and Reiling (2000) and Ryan and Spash (2011)
BidbidWillingness to pay bid amount$1 to $100Loomis et al. (2000)
DemographicsageAge of respondentCategorical responses ages 16 through 60+Amigues et al. (2002), Halkos and Matsiori (2012), and Legesse (2015)
citylimWatershed residence in city limitsYes, no
eduHighest level of educationCategorical variable with seven levels of education from less than high school diploma through graduate degreeAmigues et al. (2002), Grazhdani (2015), and Legesse (2015)
envmemMembership in environmental organizationYes, noLoomis et al. (2000) and Halkos and Matsiori (2012)
raceRaceCategorical variable with eight options including otherTurpie (2003)
genGenderMale or femalePumphrey et al. (2008), Solomon and Johnson (2009), and Halkos and Matsiori (2012)
incHousehold incomeLess than $10,000 through $200,000+Halkos and Matsiori (2012), Kreye et al. (2014), and Grazhdani (2015)
resloResident locationBinary variable regarding urban versus rural residency of respondentCarson et al. (2001) and Hoyos et al. (2009)
resownOwn residence in watershedYes, no
sportmemMembership in sporting organizationYes, noLoomis et al. (2000) and Halkos and Matsiori (2012)
waterresLiving in the watershedYes, noCarson et al. (2001) and Hoyos et al. (2009)
ExperiencefishcreekHaving used Clear Creek for fishingNumber of days fishing a year
seecreekrdHaving seen the creek from the roadYes, no, I do not know
seecreekbankHaving seen the creek from the stream bankYes, no, I do not know
KnowledgecadIDAbility to identify a picture of a caddis fly (Trichoptera) larvaeOpen responseTurpie (2003)
flyIDAbility to identify a picture of a common fly (Diptera) larvaeOpen response
mayIDAbility to identify a picture of a mayfly (Ephemeroptera) larvaeOpen response
stoneIDAbility to identify a picture of a stonefly (Plecoptera) larvaeOpen response
eroIDIdentification of stream bank erosionYes, no, I do not know
bioknowKnowledge of biodiversity influence on ecosystem qualityChoice of one of seven options regarding different mixes of macroinvertebrates in the stream
geoknowKnowledge of the relationship between riffles and pools and overall ecosystem qualityYes, no, I do not know
rivknowKnowledge that Clear Creek flows into the Illinois RiverYes, no, I do not know
sedknowKnowledge of appropriate sedimentation level in a streamChoice of one of four pictorial representations of sediment levels
wildknowKnowledge regarding how stream bank erosion affects wildlife habitatYes, no, I do not know
treeknowKnowledge that trees benefit ecosystem qualityYes, no, I do not know
PerceptionsbiopercepBiodiversity quality along Clear Creek as good or badGood, badPendleton et al. (2001), Turpie (2003), Sabatier et al. (2005), Silvano et al. (2005), Paradise (2005), and Hoyos et al. (2009)
ecopercepPerception of the overall quality of Clear CreekRanking of 1 (poor) through 5 (excellent)
fishresSteam as valuable fishing resourceYes, no, I do not know
negHumans as negative influence on ecosystemYes, no, I do not know
posHumans as positive influence on ecosystemYes, no, I do not know
propvalStream as increasing property valuesYes, no, I do not know
rippercepRiparian zone quality along Clear Creek as good or badGood, bad
bankpercepStream bank quality along Clear Creek as good or badGood, bad
waterpercepWater quality along Clear Creek as good or badGood, bad
streamqolStreams as improving quality of lifeYes, no, I do not know
treesqolTrees along stream bank as improving quality of lifeYes, no, I do not know
Because perception of ecosystem quality was suspected to play a role in willingness to pay, survey responses regarding perception were statistically compared with bioassessment results taken from six sites within the watershed. Using bioassessment according to Barbour et al. (1999), the quality of streams within the watershed was assessed and then compared with respondent perceptions of the same stream sites. It was found that respondent perception differed significantly from bioassessment results, suggesting a disconnection between this population and the ecosystem services provided by the watershed (Hoyos et al. 2009). An expanded explanation of the methods and results for this assessment can be found in Lewis and Popp (2013).
Given the response rate, total number of observations, and structure of the variables, there were not enough degrees of freedom to estimate a model with all hypothesized variables. Therefore, the analysis focused on those variables expected to hold the greatest theoretical relevance (e.g., those variables from Table 1 cited in the literature to influence WTP). Next, preliminary analyses (including chi-square tests and Wald tests) were conducted on those variables with the greatest theoretical relevance to help determine a variable’s relevance (probable significance) to the final model. Based on preliminary findings, eight variables were tested together in the final model
WTP=f(bid,att,reslo,treepercep,envmem,sportmem,edu,inc)
(1)
where bid = bid amount; att = respondent’s attitude toward paying for riparian zones along the stream; reslo = whether the respondent lived in an urban or rural location; treepercep = perception of whether or not tree areas along a streambank improve quality of life; envmem and sportmem = whether the respondent belongs to an environmental or sports organization, respectively; edu = level of education attained by the respondent; and inc = respondent’s household income. A stepwise approach was used to evaluate the change in likelihood with the elimination of any variable that was insignificant in the eight variable model.
Mean WTP was derived using Hanemann (1989)
MeanWTP=1Bi×ln(1+expB0+BiXi)
(2)
where Bi = coefficient estimate on the bid amount; B0 = estimated constant; and BiXi = sum of the product of the average values of the other independent variables and their slope coefficients. The probability that a respondent would state yes or no to the WTP question was further estimated using the Hanemann (1984) equation
Probability(WTP=Yes)=1{1+[B0B1($X)]}
(3)

Results

Characteristics of Respondents

Of those surveyed, 224 (or 26%) of returned surveys were usable in this study. Although similar studies have boasted rates as high as 82% (Bowker et al. 2003), the response rate obtained in this study falls within the adequate range suggested by the literature (Adams et al. 2011; Groves 2006) and is on par with rates shown in more recent studies (Cho et al. 2005; Nicosia et al. 2014). Although nonresponse bias could be a factor in the results of the survey, previous studies by Groves (2006) and Nelson et al. (2015) found no clear empirical relationship between nonresponse rates and nonresponse biases in similar surveys. Based on the watershed population (CAST 2006; USCB 2015), the data are representative with a 95% confidence level and a 7% margin of error. Demographic characteristics of survey respondents, as well as their responses to questions related to overall awareness of Clear Creek, knowledge of ecosystem processes, and perceived impacts on Clear Creek ecosystem quality, can be found in Table 2.
Table 2. Descriptive Characteristics of Survey Respondents
CategoryCharacteristicPercentage
GenderMale58.56
Female41.44
Age20–251.81
26–304.52
31–3919.00
40–5022.17
51–6027.15
61+27.15
RaceWhite92.83
American Indian1.79
Asian2.24
African American0.90
Hispanic1.35
Other race or ethnic group1.79
EducationGraduate degree35.02
Some postgraduate degree7.83
4-year degree22.12
2-year degree4.61
Some post-high-school education17.51
High school diploma12.44
Less than high school0.46
IncomeUnder $10,0000.99
$10,000–$24,9994.43
$25,000–$49,99918.23
$50,000–$99,99934.48
$100,000–$199,99925.12
$200,000 or more16.75
Residence locationUrban47.77
Rural52.23
Property ownershipOwn property in watershed100.00
Live in watershed90.61
Years in watershed0–5 years34.16
6–10 years18.30
11–20 years25.01
21–30 years12.20
31–40 years4.27
41–50 years3.05
51–60 years2.40
61–700.61
ExperienceHave seen creek from road72.94
Have seen from stream bank46.54
Know Clear Creek flows into Illinois River58.94
Fished in Clear Creek5.42
PerceptionsPerceived eroded stream bank as bad73.64
Forested trail along creek improves quality of life86.11
Household activities have negative impact80.82
Household activities have positive impact86.70
Streams improve quality of life97.29
Trees along streams are good for ecosystem96.83
Streams increase property values49.09
KnowledgeKnowledge of stream function and sedimentation55.96
Knowledge of biodiversity42.06
Knowledge of macroinvertebrates0.00
MembershipSports organization membership19.82
Environmental organization membership12.16

Willingness-to-Pay Outcomes

The mean willingness to pay was $80.07 in additional annual state income taxes to establish a fund for riparian zones. A willingness-to-pay model was estimated with the hypothesized variables discussed previously. Though preliminary tests had suggested significant relationships between WTP and each variable, three of the eight hypothesized variables were significant in the estimated model (Table 3). The model was estimated again and the form that best describes Clear Creek watershed residents’ WTP is (Table 4)
WTP=f(bid,inc,att)
(4)
Table 3. Preliminary Logit Model Regression Results
ParameterEstimateStandard errorChi-squarep-value
Intercept1.370.614.980.0257
bid: Bid amount0.040.0113.940.0002
inc: Income0.690.276.720.0095
att(1): Cannot afford to pay2.320.5219.76<0.0001
att(2): Happy to pay2.530.839.290.0023
att(3): Pay if everyone pays1.940.4716.74<0.0001
reslo: Residence is in a urban location0.430.272.550.1102
edu: Graduate degree0.540.431.580.2093
edu: High school degree0.540.630.740.3898
edu: Post-high-school degree0.430.720.360.5477
sportmem: Hold membership in sports organization0.250.30.700.404
envmem: Hold membership in environmental organization0.020.360.000.9552
treepercep: Know trees benefit ecosystem quality0.040.340.020.8961
Table 4. Final Logit Model Regression Results and Global Statistics
CategoryExamined topicEstimateStandard errorDegrees of freedomChi-squareWald chi-squarep-value
TestLikelihood ratio5107.8<0.0001
Score594.46<0.0001
Wald558.06<0.0001
Effectbid: Bid amount111.330.0008
inc: Income16.290.0122
att: Attitude toward payment356.14<0.0001
ParameterIntercept1.560.4312.980.0003
bid: Bid amount0.030.0111.330.0008
inc: Income0.600.246.290.0122
att(1): Cannot afford to pay2.360.4922.71<0.0001
att(2): Happy to pay2.440.809.200.0024
att(2): Pay if everyone pays1.600.4214.850.0001
Bid amount was negatively correlated with WTP; as bid amount increased, the probability of answering yes to the CVM question decreased. A contrast analysis revealed that income was most significant when the model was refined to income levels between <$100,000 and $100,000+. At the $100,000+ income level, income was positively correlated with WTP; as income level increased, the probability of responding yes to the CVM question increased. The attitude of not being able to pay was negatively correlated with WTP, which suggests that the more respondents felt this way toward WTP, the less probability there was of choosing yes as a response to the CVM question. The attitude of being happy to pay was positively correlated, as was paying if everyone else pays. Probability estimates revealed that attitude was a strong factor in WTP and as bid amount increased, attitude acted as a more significant factor than income level (Fig. 2).
Fig. 2. Probability estimates at increasing bid amounts
In the survey, participants were asked questions concerning their perceptions of ecosystem quality for six sites within the Clear Creek watershed. These perception responses were found to be significantly different than the measured bioassessment outcomes for those individual sites (Lewis and Popp 2013). Because of the divergence between respondent perception of watershed quality and bioassessment results, these variables were hypothesized to affect respondent WTP, but with p values falling well above 0.05 for all sites, it was found that these differences in perception did not significantly affect respondent willingness to pay for riparian zones in the Clear Creek watershed.

Discussion

The mean willingness-to-pay value of $80.07 was lower than in some studies (Loomis et al. 2000; Nicosia et al. 2014), higher than values found in others (Cho et al. 2005; Castro et al. 2016), and fell within ranges shown in recent meta-analyses of ecological valuation studies (Barrio and Loureiro 2010; Kreye et al. 2014). Determining an accurate value for ecological services can be a complex process (Costanza et al. 1997). Because of this, it is difficult to determine which factors may independently or cumulatively affect WTP values found between similar studies. For instance, Kreye et al. (2014) found that studies conducted in the southern United States tended to elicit lower WTP values than other regions. Like this study, other studies by Loomis et al. (2000) and Nicosia et al. (2014) each examine WTP using a dichotomous choice CVM scenario with bid amounts ranging from $1 to $100. However, as opposed to paying for conservation through an annual tax, as was assessed in this study, they measured respondent WTP through an increase in monthly water fees. Because their results were significantly higher than those found in this study, as well as other studies eliciting payment through an annual tax scenario, it might be worthwhile for managers to consider a monthly payment option, as opposed to soliciting resident payment through an annual tax because Kreye et al. (2016) have noted that the policy process used to target a particular outcome (such as conservation) can affect WTP levels. Additionally, because the Loomis et al. (2000) annual value was almost twice as high as the value found by Nicosia et al. (2014), ($252 versus $133, respectively), it was hypothesized that the use of in-person, as opposed to mail-out, surveying techniques could have resulted in higher WTP values caused by respondents feeling social pressure to pay a higher amount (Nicosia et al. 2014).
The fact that mean willingness to pay was most significantly impacted by bid amount, income, and attitude toward paying indicates that Clear Creek residents are strongly motivated by financial factors. This is similar to other studies that have also identified bid amount (Loomis et al. 2000; Loomis 2014), income (Adams et al. 2008; Kreye et al. 2014), and attitude toward paying (Jorgensen and Syme 2000) as factors in WTP. Some studies have found level of education to have a positive impact on WTP for ecological services (Amigues et al. 2002; Ivanova and Tranter 2004; Grazhdani 2015; Legesse 2015; Castro et al. 2016), but others, including this study, show the effect of general education levels to be insignificant (Nicosia et al. 2014).
Direct experience with the watershed, perception of ecosystem integrity, and knowledge of ecosystem processes were also hypothesized to play a role in WTP, but data from the study show that WTP was not significantly affected by these variables. Although this survey contained questions assessing Clear Creek residents’ general knowledge of ecosystem processes and briefly mentions the benefits of maintaining ecosystem services, the previously discussed study by Loomis et al. (2000) included additional background information, such as diagrams, aimed at explaining the specific ecosystem services being valued and showing how local stakeholders could be directly affected by the maintenance of such services. It is possible that this additional information may have played a role in eliciting the higher WTP values found in that study, implying a usefulness of watershed education efforts.
Hanemann (1984) suggested that if people perceive themselves to be receiving direct goods and services from a functional ecosystem, then the probability that they would be willing to pay increases. The fact that many in this group did not receive direct-use benefits from the ecosystem services of Clear Creek and consistently misperceived ecosystem quality, yet were still often willing to pay something suggests that residents placed indirect use value on the services provided by riparian zones in Clear Creek watershed and therefore were willing to pay some amount to protect the watershed (Hanemann 1984; Mitchell and Carson 1989; Turpie 2003; Adams et al. 2013).
Some watershed managers may be pleased to learn that there is a willingness by some to provide financial resources to protect riparian zones even with inadequate perceptions of ecosystem quality. However, the fact that many residents appear to lack certain knowledge necessary to perceive and assess ecosystem quality implies that environmental managers may also benefit from the implementation of programs aimed at educating the community about their local watershed (Adams et al. 2013; Borisova et al. 2013). A recent study by Boellstorff et al. (2013) showed that 51% of respondents living in cities with populations roughly the size of Clear Creek were interested in learning more about watershed management, with residents indicating that they would prefer obtaining water resource information through reading fact sheets, bulletins, or brochures (50%), watching television coverage (48%), reading newspaper articles (46%), or visiting a website (45%). Although implementing a watershed education program would require additional funding, not addressing the disconnection in perception of ecosystem quality may result in conflict between environmental managers and the general public as stakeholders may feel that funds for conservation efforts are being misallocated. Aligning stakeholder perception to the reality of the ecosystem quality could result in more accurate willingness-to-pay outcomes, thus allowing funding be allocated more efficiently (Adams et al. 2013; Castro et al. 2016).

Conclusions

The results of this study indicate a discrepancy between public perceptions of ecosystem quality, the scientific measurement of ecosystem quality, and willingness to pay for improved ecosystem quality. In environmental management, a clear understanding of the public’s perceptions of ecosystem quality can indicate their relationship to and experience with that ecosystem, thus facilitating the implementation and funding of ecosystem management programs. The results of this study also suggest that one way to connect the public with the watershed ecosystem is to use financial costs and benefits as a means of communicating with stakeholders about protecting and managing ecosystem quality. In other words, if the primary factors that determine public WTP are related to monetary parameters (price, income, attitude toward paying), then messages related to ecosystem management would be most effective if communicated through the public’s inherent tendency to establish value based on their economic relationships to the goods and services provided by the ecosystem. This type of communication with stakeholders is important to consider when developing ecosystem management plans, policies, and educational outreach programs, especially. This study, for example, demonstrates that the public does not receive direct-use benefits from the watershed ecosystem services, but as evidenced by their willingness to pay something, there does exist enough understanding of the benefits derived from the ecosystem to place an indirect-use value on it. This indirect-use value can be leveraged when communicating the benefits of establishing an ecosystem management program for a watershed such as Clear Creek. For example, based on the results of this study, those ecosystem management programs that clearly demonstrate such benefits as increased property values, increased revenue to the community through job creation or tourism, or increased cost savings for water treatment would more likely lead to willingness to pay for the program. If environmental managers do not identify and understand the public’s perceptions of ecosystems in addition to establishing the value the public places on ecosystem integrity, there could be resulting political and budgetary difficulties throughout the ecosystem management process. In other words, this could potentially lead to a perceived misuse of funds by environmental managers or a perceived mismanagement of the ecosystem and therefore result in a lack of support from the community for the program.
There is a need for more research on the public’s WTP for ecosystem services in the Ozark region. The region is continually ranked as one of the best places to live and do business, which will continue to lead to growth in population in an area with an ecosystem that is easily impacted by land-use change. Future research could explore not only the WTP for ecosystem services in watersheds across the region, but could also further evaluate the factors that influence WTP, including those factors that influence attitude toward paying. Future studies could also explore a variety of payment vehicles in establishing WTP, studies that evaluate the influence of watershed education programs on perception and WTP, and studies that explore the public’s willingness to engage in collective action efforts to protect ecosystem services. Studies such as these could all facilitate the ability of ecosystem managers to establish successful watershed management and conservation programs.

Acknowledgments

Financial support for the bioassessment and survey production and mailing costs was provided in part by the Illinois River Watershed Partnership.

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Information & Authors

Information

Published In

Go to Journal of Water Resources Planning and Management
Journal of Water Resources Planning and Management
Volume 143Issue 5May 2017

History

Received: Apr 4, 2016
Accepted: Sep 16, 2016
Published online: Jan 31, 2017
Published in print: May 1, 2017
Discussion open until: Jun 30, 2017

Authors

Affiliations

Sarah E. Lewis, Ph.D. [email protected]
Managing Director of Research and Integration, The Sustainability Consortium, Univ. of Arkansas, 534 Research Center Blvd. Enterprise Center, Fayetteville, AR 72701 (corresponding author). E-mail: [email protected]
Jennie S. Popp, Ph.D.
Professor, Dept. of Agricultural Economics and Agribusiness, Division of Agriculture, Univ. of Arkansas, Fayetteville, AR 72701.
Leah A. English
Research Associate, Division of Agriculture, Center for Agricultural and Rural Sustainability, Univ. of Arkansas, Fayetteville, AR 72701.
Tolulope O. Odetola
Visiting Scholar, Dept. of Agricultural Economics and Agribusiness, Division of Agriculture, Univ. of Arkansas, Fayetteville, AR 72701.

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