Long-term evolution of preferences forconservation projects in the Seto InlandSea, Japan: a comprehensive analytic
framework
Takuro Uehara1, Takahiro Tsuge2 and Takahiro Ota3
1

College of Policy Science, Ritsumeikan University, Ibaraki, Osaka, Japan
Department of Economics, Konan University, Kobe City, Hyogo, Japan
3
Graduate School of Fisheries and Environmental Science, Nagasaki University, Nagasaki City,
Nagasaki, Japan
2

ABSTRACT

Submitted 21 May 2018
Accepted 13 July 2018
Published 24 July 2018
Corresponding author
Takuro Uehara,takuro@fc.ritsumei.ac.jp
Academic editor
Robert Costanza
Additional Information and
Declarations can be found on
page 17

Background: The long-term evolution of preferences for nature is crucial toconservation projects, given their targeted long-term horizons. Neglecting to accountfor this evolution could lead to undesirable human–nature relationships. This study
compares the willingness to pay (WTP) for three coastal conservation projects in
the Seto Inland Sea, Japan, at two distant time points (1998 and 2015), and tests for
temporal transferability. It also compares protest responses that are often overlooked
in WTP practices, regardless of their utility for conservation projects.
Methods: Given the lack of a unanimous protocol for protest response analyses andtheir use in estimating WTP, we propose a comprehensive analytic framework that
integrates the two.
Results: We show that, while preferences for coastal ecosystem services were overall
stable and temporarily transferable, the preferences for certain aspects ofconservation projects considerably changed.
Discussion: This suggests the need to reconsider the projects’ scheme, not theecosystem services themselves, along with the clariﬁcation of beneﬁciaries and those
responsible for past destruction. We conclude by suggesting further studies with afocus on regions experiencing signiﬁcant social-ecological changes, such as
developing countries, by exploiting the rich asset of existing valuations. This could
contribute to the database for more temporal-sensitive ecosystem service valuations
utilized for beneﬁt transfers.
Subjects Ecosystem Science, Coupled Natural and Human Systems
Keywords Coastal ecosystem services, Protest responses, Willingness to pay, Contingent valuation

method, Beneﬁt transfer

DOI 10.7717/peerj.5366

INTRODUCTION

Copyright
2018 Uehara et al.

Understanding people’s valuations of nature and how they change in the long run is of
crucial importance to establishing and sustaining the desired relationships with nature(Uehara et al., 2016; Uehara & Mineo, 2017). The potential evolution of preferences
for nature indicates that a conservation project assuming constant preferences could lead

Distributed under
Creative Commons CC-BY 4.0

How to cite this article Uehara et al. (2018), Long-term evolution of preferences for conservation projects in the Seto Inland Sea, Japan: acomprehensive analytic framework. PeerJ 6:e5366; DOI 10.7717/peerj.5366

us to an undesired state. Ideally, conservation projects should be adaptive to evolution(Skourtos, Kontogianni & Harrison, 2010).
A key approach to understanding how people value nature (i.e., welfare) is measuring
the willingness to pay (WTP) for ecosystem services, whose techniques have beenwell-developed in environmental economics (Gómez-Baggethun et al., 2010; Freeman,
Herriges & Kling, 2014). However, there is scope for improvement in WTP-based studies;in particular, two aspects could foster better contributions to the literature. First, WTP
studies generally conduct one-time estimates, relative to the time horizon for conservation
projects, the related research on the evolution of WTP is short termed (e.g., from few weeks
to a year) (Skourtos, Kontogianni & Harrison, 2010). These short-term studies tend to
focus on stability and equality rather than evolution or changes (Jakus, Stephens & Fly,
2005). Moreover, they often assume the temporal stability of WTP, rather than conducting
explicit tests (Brouwer & Bateman, 2005; Costanza et al., 1997, 2014) or using simple
variations in previous WTP estimates for future projections (Kubiszewski et al., 2017).
Second, WTP estimates commonly exclude protest respondents (Brouwer &Martín-Ortega, 2012), that is, respondents who reject certain aspects of a conservation
project presented in a survey by saying “no” to a proposed bid for the project, even though
they positively value the ecosystem services (Freeman, Herriges & Kling, 2014). Protest
responses provide non-negligible information for conservation projects in a real-worldcontext, not in a vacuum (García-Llorente, Martín-López & Montes, 2011). While WTP
reveals preferences for ecosystem services that beneﬁt from conservation projects, protest
response analyses highlight preferences for project design and implementation. Reviewing
past environmental valuation studies, Meyerhoff & Liebe (2010) found that, on average,
the rate of protest responses is 17.69%, indicating a simple disposal could result in asigniﬁcant loss of information. In addition, it could lead to a biased WTP estimate if people
who protest systematically differ from those who do not (Brouwer & Martín-Ortega, 2012;
Freeman, Herriges & Kling, 2014).
Our study aims to understand the long-term evolution of preferences for coastal
ecosystem services by addressing the abovementioned, underdeveloped yet crucial topics:
evolution of welfare measured in WTP and that of protest responses. Since common
WTP practices exclude protest responses and there is no unanimous protocol on how todeal with them (Meyerhoff & Liebe, 2010), we propose a comprehensive analytic
framework that integrates WTP estimation and protest response analyses and comprises
ﬁve research questions. We compare the coastal and non-coastal residents’ preferences
for three hypothetical projects that provide coastal ecosystem services in the Seto
Inland Sea (SIS), Japan, at two distant time points, 1998 and 2015. A 17-year differenceis sufﬁcient to include next generations that were not included in the 1998 survey.

MATERIALS AND METHODS
Figure 1 presents a comprehensive analytic framework with the ﬁve research questions.
While common practices focus on the temporal comparison of WTP at two time points(RQ 4) as well as the temporal transferability of WTP and functions of WTP estimates

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Figure 1 Comprehensive analytic framework for the evolution of preferences for conservation
projects. The red dashed lines indicate a procedure overlooked in common WTP practices and beneﬁt transfers.
Full-size  DOI: 10.7717/peerj.5366/ﬁg-1(RQ 5) (Downing & Ozuna, 1996; Brouwer & Spaninks, 1999; Brouwer & Bateman, 2005;
Zandersen, Termansen & Jensen, 2007; Rosenberger, 2015), the present framework adds
three research questions: how have the shares of protest responses changed (RQ 1); howhave the reasons for protest responses changed (RQ 2); and is there a systematicdifference between protesters and non-protesters (RQ 3)? RQ 3 could provide important
information about model speciﬁcations that could elicit unbiased WTP, as discussed later.

Three hypothetical projects in the Seto Inland Sea
The three hypothetical projects were designed to elicit WTP for coastal ecosystem services in
the SIS, an enclosed coastal sea in western Japan (Fig. 2). The SIS was rich in ecosystemservices; however, these ecosystems were destroyed or degraded with the rapid economic
progress since the mid-20th century, resulting in, for example, declining ﬁsh catches,
destruction of coastal zones for landﬁlls and other anthropocentric uses, and water pollution(The Association for the Environmental Conservation of the Seto Inland Sea, 2015).
The projects include the restoration of the natural beauty of coastlines (Project 1),
conservation of seagrass beds as cradles of the sea (Project 2), and protection of natural
coastlines through a national trust (Project 3) (see Supplementary Information S1 formore details). We consider the same three projects in both 1998 and 2015; however,owing to certain changes in the SIS, we present changes in the hypothetical projects in the2015 survey.

Data generating processes
An internet survey was conducted in 1998 and 2015, in which coastal and non-coastal
residents were asked to respond to a questionnaire on WTP estimates for the three
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Seto Inland Sea
Figure 2 Location of Seto Inland Sea, Japan.

Japan
Full-size  DOI: 10.7717/peerj.5366/ﬁg-2

projects. For the 1998 survey, we utilized raw data collected by Tsuge & Washida (2003).
The survey website was posted at the top page of a national newspaper website, Asahi
Shimbun (www.asahi.com). An average of 440,000 people visited the website per month.
The survey was conducted between December 1 and 12, 1998. The 2015 survey was also
posted at Asahi Shimbun, and we requested an online internet survey company to collectthe sample to ensure the sample was sufﬁciently comparable with the 1998 survey. The
survey was conducted between December 2 and 7, 2015.

Protest response analysis
For the protest response analysis, we used the statistical analysis software STATA (Version
14.2) by StataCorp LP (http://www.stata.com).
Coding protest responses
Information on protest responses was generated from the reasons for rejecting bids for
projects in the questionnaire. Respondents could either choose from the reasons
available or provide an independent answer. To conduct quantitative analysis, we coded
the open answers and created eight categories, as explained in the “Results” section. Somereasons for rejection were valid and, thus, not considered a protest response (e.g., “I
support these projects, but the contribution amount is too high.”). Since there is noclear-cut deﬁnition of protest responses (Brouwer & Martín-Ortega, 2012), we follow
discussions in well-established textbooks on the valuation of the environment (Freeman,
Herriges & Kling, 2014; Rosenberger & Loomis, 2017) to choose protest responses fromreasons coded in the survey.

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Detection of systematic difference
While we note the potential impact of systematic differences between protestors andnon-protestors on WTP estimates (Freeman, Herriges & Kling, 2014), there is no
unanimous protocol to address such differences (Meyerhoff & Liebe, 2010). Here, wechose a logit model for a binary response to detect the systematic difference. That is,Prðyi ¼ 1jxi Þ ¼

exi β;
1 þ ex i β(1)where yi is a binary response regarding whether respondent i is a protestor (“1”) or
non-protestor (“0”). xi is a vector of explanatory variables and β is a vector of coefﬁcients.
With the logit model, we can identify factors that determine whether a respondentis a protestor.

Welfare analysis
To analyze dichotomous choice-contingent valuation data (Version 0.0.15), we used apackage by Nakatani, Aizaki & Sato (2016) run on R (Version 3.3.2 for Windows (64 bit))(R Core Team, 2016).
WTP estimate
We used a single-bounded dichotomous choice format, which was used in the 1998 study(Tsuge & Washida, 2003). It is less susceptible to bias than open-ended or payment
card formats (Mitchell & Carson, 1989). As bids, each respondent was shown one amongsix randomly selected amounts: 500, 1,000, 3,000, 8,000, 15,000, and 30,000 JPY. The
respondents were then asked if they were willing to pay the amount toward theimplementation of each project. We assumed the payment would be made only once.
To ensure that the respondents recognized the payment burden, we explained that the
donation amount would be deducted from the money used for other household purposes.
Those who agreed to donate the amount were asked to specify the expenditures they forfeit
for the donation.
The response data were analyzed using the binary logit model derived from the random
utility model (Hanemann, 1984). In the model, the following is assumed as the utility
respondent k obtains from the alternative:
Uki ¼ Vki þ eki ;(2)where i takes the symbol y when respondent k answers “yes” to the bid and n when
respondent k answers “no.” Vki and εki represent the observable deterministic term andunobservable error term of utility. It is assumed that respondent k considers cost andenvironmental improvement realized by the conservation project and chooses analternative with higher utility. The probability Pky that the respondent k will answer yes isequal to the probability that the utility from the alternative Uky, is larger than the utility
from the alternatives n, Ukn, as described below:
Pky ¼ Pr Uky > Ukn ¼ Pr Vky þ eky > Vkn þ ekn :Uehara et al. (2018), PeerJ, DOI 10.7717/peerj.5366

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Assuming error term εki follows a type-I extreme value distribution (Gumbel
distribution), probability Pky is described by the following binary logit model:
Pky ¼

1
;
1 þ eV(4)

where V denotes the utility difference function and the following linear function isassumed: V = a + bTk. In the utility difference function, Tk represents the bid offered torespondent k and a and b indicate the utility obtained from environmental improvement
and utility obtained from the payment. By extending the utility difference function as
follows, it is possible to analyze the inﬂuence of other factors (e.g., household income) on
the respondents’ answers: V = a + bTk + γzk, where, zk is a vector of other factors,
possibly affecting respondent k’s answer and γ is a vector of parameters for those factors.
The parameters are estimated by the maximum likelihood method (Greene, 2012). The
log likelihood function can be written as follows:
X X
lnL ¼
d lnPki ;
(5)
k
i ki
where d ki is a dummy variable, such that d ki = 1 when respondent k answers “yes” to a bid,and d ki = 0 otherwise.
The mean WTP can be calculated using the estimated parameters, a and b (Hanemann,
1984). It is obtained by integrating the probability that the respondent will answer “yes” tothe bid. However, since it is not realistic to integrate an extremely high amount, themaximum bid is often used as the integration upper limit. In this case, the mean WTP iscalculated as follows:
Z TmaxMean WTPðtruncated at Tmax Þ ¼
Pky dT;(6)0

where Tmax is the maximum bid.
Confidence intervals
We calculated the conﬁdence intervals using Krinsky and Robb’s technique (Krinsky &Robb, 1986), which is often employed in stated preference methods, such as the contingent
valuation method (CVM) and conjoint analysis (Downing & Ozuna, 1996; Zandersen,
Termansen & Jensen, 2007; Lew & Wallmo, 2017; Matthews, Scarpa & Marsh, 2017). Using
the technique, we draw 10,000 random coefﬁcients and compute 10,000 mean WTP
measures. Then, we ordered the 10,000 mean WTP measures from the smallest to largest
and selected the 95% conﬁdence limits.
Transferability test
Since it is impossible for people who are not born yet to report their future WTP and
conﬁrm the extent to which a current project will be supported by future generations,
we need to extrapolate a future WTP value by exploiting value information currently
available. A method that has been widely used is beneﬁt transfers, which involve
transferring existing value information to a new context (Rosenberger & Loomis, 2017).
There are two primary types of beneﬁt transfers: value and function transfers.
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Value transfers are the direct application of summary statistics in existing research such as
per unit measure of WTP (Rosenberger & Loomis, 2017). It generally assumes constantpreferences over time (Costanza et al., 1997, 2014). Function transfers tailor value
estimates by reﬂecting differences in the characteristics of contexts in a model
estimating WTP.
In addition to a strong interest in the transferability of WTP estimates given the scarcity
of resources, the temporal stability of the estimates and its testing methods have been
extensively studied, although most studies are limited to the short term (from a week to
two years) (Skourtos, Kontogianni & Harrison, 2010). The key focus is the statisticalequality of WTP and coefﬁcient parameters of models using various statistical tests such as
the t-test, Wald test, likelihood ratio test, Mann–Whitney test, and Kolgorov–Smirnov test
(Brouwer & Spaninks, 1999).
However, we did not conduct these statistical tests for two reasons. First, the statisticaltests examine statistical equality and ignore acceptable levels of accuracy in a real-worldcontext (Rosenberger, 2015). A review by Rosenberger (2015) reveals that most studies
failed to pass these tests. Second, the coefﬁcients estimated by the logit model used inthis study are not purely parameters of the utility function but products of parametersof the utility function and scale parameter (Train, 2009). Therefore, testing the statisticalequality of the estimated coefﬁcients does not necessarily mean examining the statisticalequality of the parameters of the utility function and there is a possibility of erroneous
judgments on the latter. On the other hand, since WTP estimates are calculated fromthe ratio of estimated coefﬁcients, the scale parameters of the numerator and denominatorare canceled out and not affected by them. Therefore, it is more meaningful to test forthe statistical equality of WTP estimates.
Hence, we evaluated the performance of value and function transfers by conducting apercentage transfer error (PTE) test, which is a type of transfer error test (Rosenberger,2015) that measures the difference between the beneﬁt transfer value (estimated using 1998
values or functions) and true value (2015 estimates). While the abovementioned tests focus
on equality, this test estimates maximum transfer error. The percentage transfer test is
calculated asVT  VP
PTE ¼
 100;(7)
VP
where VT is the transfer estimate and VP the known or actual estimate for the policy site.
PTE then measures the degree of difference between the transferred and actual estimatesat the policy site. Typically, PTE requires both estimates to be available within thecontext of a primary study that has derived them (Rosenberger, 2015, p. 309).
For a function transfer, we used models that include income as an explanatory variable
and incorporate average income for 2015 in the 1998 models for estimation. There arethree reasons to include income as an explanatory variable. First, it is consistent with theeconomic theory underpinning this method (Brouwer & Bateman, 2005; Hanemann,
1984). Second, it is statistically signiﬁcant (Brouwer & Bateman, 2005) in many empiricalstudies, including the present analysis. Finally, long-term income projection has been
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Table 1 Shares of protest responses by year and geographical origin.
Project 1

Project 2

Project 3

1998

2015

1998

2015

1998

2015

Coastal residents

24.4%

37.3%

20.4%

32.6%

17.5%

33.7%

Non-coastal residents

25.8%

41.6%

22.4%

39.0%

18.9%

38.2%

Total

25.7%

41.3%

22.2%

38.6%

18.8%

37.9%

well-studied and available from various sources such as government agencies and theOrganization for Economic Co-operation and Development.

RESULTS
While the sample sizes have the same order of magnitude (5,632 respondents for 1998 and
7,264 respondents for 2015), there are signiﬁcant differences in the rates of internet
accessibility, which could affect the compositions of the samples (Supplementary
Information S2 for descriptive statistics) and the following analyses. In Japan, personal
accessibility to the Internet signiﬁcantly increased from 13.4% in 1998 to 83.0% in 2015
(Ministry of Internal Affairs and Communications, 2017). While company employees
account for the largest share of respondents in both 1998 and 2015, which is consistent
with population characteristics, there are certain differences between the years. For
instance, university students with internet access accounted for 14% of the respondents
in 1998 but only 6% in 2015. Part-time workers, the unemployed, and housewives withinternet access accounted for 3% of the respondents in 1998 and 36% in 2015.
We calculated conﬁdence intervals using the Krinsky–Robb technique in the following
analysis on welfare changes and temporal transferability.

Protest responses
RQ 1: share of protest responses
The share of protest responses is greater in 2015 than in 1998 and larger for non-coastal
residents compared with coastal residents for all three plans (Table 1). The analysis ofvariance shows that these differences in the shares by year and geographical origin are
statistically signiﬁcant at the 10% level for all projects.
RQ 2: reasons for protest responses
We coded the protest responses into six types on the basis of multiple choices and open
answers to reasons underpinning the rejection of a bid proposed for the projects. Here,
we show the categorization of reasons by year and geographical origin (Tables 2–4).
The patterns are similar across all projects, except for the change in Reason 1 fornon-coastal residents.
The share of coastal residents increased for all three projects and they accounted for thesecond highest number of respondents protesting contribution to a fund (Reason 1).
The respondents were asked to contribute to a newly established local fund (The SIS
Environment Conservation Fund) to implement the projects. In the open answers, someUehara et al. (2018), PeerJ, DOI 10.7717/peerj.5366

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Table 2 Composition of reasons for protest by year and geographical origin for Project 1.
Reason to oppose bid

Coastal residents
1998

Non-coastal residents

2015

t-test***2015

t-test

23%

27%

***

37%

54%

***

33%

18%

***

1998

1. I support these projects, but I am against
contributing to a fund.

19%

33%

2. I support these projects, but I don’t
think I need to personallytake responsibility for funding.

42%

40%

3. I am opposed to the program itself.

34%

25%

4. I do not trust the survey.

3%

1%

3%

0%

***

5. Information is insufﬁcient to make a judgment.

1%

2%

4%

1%

***

6. Did not understand the questionnaire.

1%

0%

1%

0%

***

Total

100%

100%

100%

100%

N

96

170

1,330

2,762

**

Notes:The t-tests examine the null hypothesis of no difference in numbers of times each reason was cited between 1998 and2015.
** p < 0.05.
*** p < 0.01.

Table 3 Composition of reasons for protest by year and geographical origin for project 2.
Reason to oppose bid

Coastal residents
1998

Non-coastal residents

2015

t-test

1998

2015

***

t-test

28%

29%

41%

60%

***

1. I support these projects, but I am against
contributing to a fund.

20%

38%

2. I support these projects, but I don’t think
I need to personally take responsibility forfunding.

46%

49%

3. I am opposed to the program itself.

26%

11%

***

23%

10%

***

**

4%

0%

***

4%

1%

***

1%

0%

***

4. I do not trust the survey.

4%1%

5. Information is insufﬁcient to make a judgment.

3%

1%
*

6. Did not understand the questionnaire.

1%

0%

Total

100%

100%

100%

100%

N

80

149

1,155

2,595

Notes:* p < 0.10.
** p < 0.05.
*** p < 0.01.

respondents stated that it should be funded from tax revenues because it is a public goodwhose cost should be incurred by everyone.
In both years, coastal and non-coastal residents accounted for the highest numbers
in terms of the belief that funding was not their personal responsibility (Reason 2).
In particular, the number of non-coastal residents signiﬁcantly increased for Reason 2 andaccounted for a larger share of protest responses for 2015. In the open answers, bothcoastal and non-coastal residents claimed the project(s) should be funded by people whoare responsible for the environmental destruction, such as private companies andUehara et al. (2018), PeerJ, DOI 10.7717/peerj.5366

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Table 4 Composition of reasons for protest by year and geographical origin for Project 3.
Reason to oppose the bid

Coastal residents
1998

Non-coastal residents

2015

t-test

1998

2015

***

30%

29%

44%

59%

***

18%

10%

***

3%

1%

***

3%

1%

***

1%

0%

***

1. I support these projects, but I am against
contributing to a fund.

22%

38%

2. I support these projects, but I don’t think
I need to personally take responsibility for funding.

51%

47%

3. I am opposed to the program itself.

20%

11%

4. I do not trust the survey.

3%2%

5. Information is insufﬁcient to make a judgment.

3%

2%

**

*

6. Did not understand the questionnaire.

1%

0%

Total

100%

100%

100%

100%

N

69

154

974

2,538

t-testNotes:* p < 0.10.
** p < 0.05.
*** p < 0.01.

municipalities. A characteristic unique to non-coastal residents is that while they valued
these projects, some preferred to conserve the environment closer to their place ofresidence.
The rate of respondents opposed to the program itself (Reason 3) was lower in 2015.
In the open answers, certain respondents who chose Reason 3 stated they were dubious
about the effectiveness of the project(s). For example, some pointed out that the scales ofthe projects are too small to realize the beneﬁts mentioned.
RQ 3: systematic difference between protestors and non-protestors
Before building a model to estimate WTP, we tested the systematic difference between
protestors and non-protestors. The WTP estimate could be biased if there is a systematicdifference (Freeman, Herriges & Kling, 2014). However, there is no unanimous protocol
for the treatment of protest responses (Tobarra-González, 2015). Here, we adopted alogit model to explore factors inﬂuencing a respondent’s choice to protest or not. We chose
place of residence, income, and year as explanatory variables. Because of the 17-year gap
between 1998 and 2015, the samples were considered to be drawn from different
populations. Given that respondents are geographically located in different areas,
differentiating WTP by place of residence could also be informative for conservation
projects (e.g., more targeted fundraising). Income is a key variable in economic theory(Hanemann, 1984; Brouwer & Bateman, 2005). The results revealed (Table 5) that all threevariables explain the respondents’ choice to protest at the statistically signiﬁcant levels,
indicating the possibility of a systematic difference between protestors and non-protestors
by place of residence, income, and year. Therefore, it would be desirable to estimate
WTP by constructing models on the basis of these three variables. However, since incomehas 15 categories and it is not realistic to model each category separately, we use income as
an explanatory variable. Accordingly, we constructed four models for each project,resulting in a total of 12 models.
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Table 5 Logit models for three projects.
Project 1

Project 2

Project 3

Coef.

t-statCoef.

t-statCoef.

t-statCoastal_dummy-0.164-1.95*-0.241-2.74***-0.173-1.93*

Income-0.014-2.36**-0.015-2.42**-0.020-3.03***

Year

0.035

13.92***

0.041

15.53***

0.051

18.62***

Constant-71.479-14.01***-82.404-15.63***-102.785-18.73***

N

10,933

10,938

10,937

Log-likelihood-6,808.4-6,537.5-6,244.7

Notes:* p < 0.10.
** p < 0.05.
*** p < 0.01.

Figure 3 Conﬁdence intervals of mean WTP.  Indicates income was excluded from the model because
it was not statistically signiﬁcant (Supplementary Informations S3 and S4).
Full-size  DOI: 10.7717/peerj.5366/ﬁg-3

WelfareRQ 4: confidence intervals
This research question addresses the extent to which preferences for coastal ecosystemservices (i.e., welfare obtained from the services) have evolved over the 17 years by measuringchanges in people’s WTP for the projects. On the basis of the protest response analysis,
we built models to estimate WTP by year and geographical origin with income as an
explanatory variable when it is statistically signiﬁcant (Supplementary Information S3).
Figure 3 shows changes in the mean WTP with 95% conﬁdence intervals across 17 years
by geographical origin. The sample sizes differ signiﬁcantly by geographical origins but
have the same order of magnitude across time: 278–308 respondents for coastal residents
and 3,146–3,772 respondents for non-coastal residents. The conﬁdence intervals wererelatively wider for coastal residents due to their smaller sample sizes. The conﬁdence

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Table 6 Absolute percentage transfer errors with ranges.
Residency

Project 1
Project 2
Project 3

Value transfer

Function transfer

Mean jPTEj

Range

Mean jPTEj

Range

Coastal

9.3

8.4–9.8

3.5

1.7–4.7

Non-coastal

0.4

0.3–0.4

4.3

3.8–4.6

Coastal

8.2

6.8–9.2

12.0

8.9–15.4

Non-coastal

14.8

14.3–1518.0

17.1–18.5

Coastal

12.8

10.3–14.4

6.8

6.3–6.8

Non-coastal

3.2

3–3.1

5.5

5.1–5.8

Notes:
Better transfers by project and residency are in bold. The range is from the minimum to maximum difference of WTP(mean, lower bound, and upper bound WTPs), between 1998 and 2015.

intervals among the same geographical origin are comparable because of the similar
sample sizes.
The conﬁdence intervals overlap for all models, except for non-coastal residents in thecase of Project 2 (conservation of seagrass beds as cradles of the sea), indicating that only
welfare obtained from Project 2 increased for non-coastal residents at the statisticallysigniﬁcant level. The mean WTP increased by 17.4%, from 14,870 JPY in 1998 to
17,452 JPY in 2015. This change is also the largest among the point estimates of the mean
WTP: -8.5% for coastal residents in Project 1, -0.4% for non-coastal residents in Project 1,
8.9% for coastal residents in Project 2, -11.3% for coastal residents in Project 3, and3.3% for non-coastal residents in Project 3 (Supplementary Information S4).
The non-coastal residents’ mean WTP shows a stark contrast with their WTPs for the
rest whose mean WTPs barely changed (-0.4% and 3.3%).
RQ 5: temporal transferability
The research question is based on the extent of transferability of WTP estimates andmodels in 1998 to those in 2015. Table 6 presents the absolute PTEs for value and function
transfers. PTE measures the difference in percentage between true values calculatedusing the 2015 data and model and transferred values are estimated using value
information for 1998. Of the six transfers, four transfers performed better for the valuetransfer.

DISCUSSION
Protest responses
Protest response analyses are generally beyond the scope of WTP practices and beneﬁttransfers. However, our study revealed that it provides non-negligible information on
successful conservation projects that are implemented in the real world, not in a vacuum.
Changes in the share and composition of protest responses demonstrated those in
preferences for other aspects of a conservation project rather than the value of ecosystemservices measured in WTP.
Overall, these shares are larger than the average share of protest responses in previous
studies (mean: 17.69%; standard deviation: 11.30; median: 16.13; min.: 0; max.: 59.28)
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(Meyerhoff & Liebe, 2010). The shares increase in 2015, which indicates the growing
importance of protest response analyses as a source of information for conservation
projects. In addition to the possibility of an actual increase in the protest responses in the2015 population, the drastic changes in internet accessibility resulted in biased samples
with varying population attributes. However, since there appears to be no study on
temporal changes in the share of protest responses, we are still unaware if an increase in theshare of protest responses is a general trend and of the factors inﬂuencing the increase.
We leave this to further study.
The analysis of the reasons for protest highlights the need for policymakers to beadaptive and rethink the manner in which projects are implemented. More speciﬁcally,
there is a growing dislike for payment methods (i.e., establishing a fund) (Reason 1).
In addition, respondents who do not want to personally take responsibility for funding(Reason 2) account for the highest number of protestors, suggesting the reconsideration of
the payment method along with a reﬂection of those responsible for the past destruction of
ecosystems and project beneﬁciaries. Failure to account for these reasons could lead topolicymakers facing unexpected oppositions at the time of actual project implementation,even if the WTP estimates that do not include protest responses indicate the projects asvaluable. The choice of payment method is an important aspect of a project scheme(Freeman, Herriges & Kling, 2014) and this choice should be sensitive to the social context(Fischhoff & Furby, 1988). In addition to the method of payment, the recipient of thesepayments is an important aspect warranting consideration given that, according toprotest responses, some non-coastal residents prefer spending money on a similar
project closer to their place of residence (Reason 2).
The protest response analysis was also informative in correcting systematic differences
caused by the exclusion of the protest responses from the WTP estimate. The logit models
identiﬁed place of residence, income, and year as a source of systematic differences.
While place of residence and year seem to be straightforward, further study isrecommended to explain why income can be a signiﬁcant explanatory factor for why
respondents cast protest votes to draw larger implications. To obtain an accurate WTP
estimate, we recommend the protest response analysis, especially when the share is not as
small as that in our case study.

WelfareThe conﬁdence intervals showed that the welfare obtained from the projects measured in
WTP was stable over the 17 years, except for non-coastal residents in the case of Project
2 (conservation of seagrass beds as cradles of the sea). Because studies of long-term
evolution of WTP for a speciﬁc site are lacking, it is difﬁcult to determine whether our
ﬁnding is unique or conforms to other sites as well. However, these changes have the sameorder of magnitude as global estimates by Costanza et al. (2014), who used the world
database for valuation studies on ecosystem services: the unit value of estuaries decreasedby 8.2% (from 31,509 USD/ha per year in 1997 to 28,916 USD/ha per year in 2011)
and the unit value of seagrass or algae beds increased by 10.1% (from 26,226 USD/ha peryear in 1997 to 28,916 2007 USD/ha per year in 2011). However, Pendleton et al. (2016)
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conducted a closer examination of the data compiled by Costanza et al. (2014) andhighlighted the lack of accuracy and comprehensiveness, especially for marine and coastal
areas. For example, the database includes estimates from more than 20 years ago byassuming the temporal stability (or non-changing) of a unit value. This supports theimportance of primary studies on the temporal valuation of marine and coastal ecosystemservices.
The changes in WTP can be explained by factors affecting demand or supply of theecosystem services (Skourtos, Kontogianni & Harrison, 2010). Factors affecting demand
could include income, prices of other goods, and socioeconomic proﬁle, while thoseinﬂuencing supply may be the amount and quality of ecosystem services.
There are three possible reasons for a higher WTP for Project 2 among non-coastal
residents. First, while Project 2 was the same in both 2015 and 1998, the context wasdifferent. The 1998 survey presented the possibility of the seagrass bed being destroyed:“Moreover, 50 hectares of the largest remaining seagrass bed are currently proposed
for reclamation as airport and harbor construction progresses.” However, since theseagrass bed was destroyed by 2015, the corresponding survey addressed it as actuallydestroyed. This can be considered a scarcity signal. Previous studies also show thesensitivity of WTP to changes in the supply of ecosystem services in the SIS (Tokiyoshi
et al., 2005). Second, the ﬁrst reason may attract attention from those who do not live
in coastal zones because they beneﬁt from the airport and harbor construction. Finally, therestoration of the seagrass bed became a national agenda, and the Fisheries Agency
launched an investigative committee for seagrass beds and mudﬂats around six months
prior to our survey. There is no signiﬁcant difference between Project 2 on one hand,and Project 1 and 3 on the other regarding the changes in protest responses probably
because other factors, such as income and the various temporal factors shown in thelogit models (Table 5) outweigh the three reasons mentioned above.
The PTEs were not large compared with those in previous studies: the mean of the mean
PTE was 140 for the value transfer and 65 for the function transfer (Rosenberger & Loomis,2017). The performance of a transfer is considered to depend on contextual similarity(Rosenberger & Loomis, 2017). Since these previous studies are about a spatial transfer(i.e., between spatially different sites) and not a temporal transfer (i.e., between temporally
different but spatially same sites), the contextual difference resulting from the 17-year
gap is smaller than the spatial differences in previous studies (Rosenberger & Loomis,2017). It is difﬁcult to judge whether these transfer errors are small among long-term
temporal transfer studies since such studies are limited. Zandersen, Termansen & Jensen(2007) conducted a study on forest ecosystem services in Denmark and reported a PTE of
25 for 52 forests across a 20-year period. Boman et al. (2011) estimate this value at 17
for Sweden.
In contrast to general tendency (Rosenberger, 2015; Rosenberger & Loomis, 2017),
in this study, the value transfer performs better than the function transfer: of the six
transfers, four are better as value transfers. There are two possible explanations:
temporal contextual similarity and insufﬁciency of the function transfer. First, as

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Bateman et al. (2011, p. 383) argued, “the choice of (value vs. function transfer)depends crucially upon the degree of similarity of the sites under consideration.” As thecomparison of previous studies on spatial transfer revealed, study sites during 1998 and2015 seemed similar. Second, our function transfer did not sufﬁciently capture changes
because in general, function transfers perform better than value transfers as the former
can increase transfer accuracy by reﬂecting site characteristics (Rosenberger & Loomis,2017). In technical terms, there are two types of changes that affect WTP estimates.
First are changes in the WTP estimate model’s arguments and the second are those in the
coefﬁcients of the model (Whitehead & Hoban, 1999). The function transfers in ourstudy adjusted only income, an argument, and assumed that the coefﬁcients areconstant over the 17 years.
In addition, it is notable that non-coastal residents’ mean PTE and its range for Project 2
are the highest for both value and function transfers. This is reasonable because neither
the value nor function transfer reﬂected the loss of the seagrass bed, a change in the
supply side. This indicates that, while welfare was not as sensitive to time even in thelong term, it was sensitive to changes in the supply of ecosystem services (i.e., loss of seagrassbeds for Project 2). This calls a further study on contextually relevant research with particularfocus on supply-side changes. However, since Japan has been relatively stable in thesocioeconomic sense, our ﬁndings do not rule out the importance of other contextual
changes that affect the supply and demand of ecosystem services, such as income,demographics, perceptions of nature, and the preference structure of individuals, throughlearning procedures or cultural transmissions (Skourtos, Kontogianni & Harrison, 2010).

Limitations and future research
Our study is subject to two major limitations in terms of its implications forconservation projects: a biased sample and context-dependent results. First, because of
the signiﬁcant changes in internet accessibility from 1998 (13.4%) to 2015 (83.0%), thesample attributes may differ enough to inﬂuence the results (see Supplementary
Information S2 for descriptive statistics). Second, the overall stability of WTP could be
attributed to the speciﬁc context of Japan, where there is little drastic social-ecologicalchange affecting the supply and demand of ecosystem services. Therefore, the stability level
found in this study might not be applicable to other areas characterized by drastic
social-ecological changes, such as developing countries.
To derive more general implications for conservation projects, further studies on theevolution of preferences and development of methodology for protest response analyses
are encouraged. Further, given the asset of previous one-time studies on WTP
estimates in various contexts and time periods across the world (e.g., Ecosystem
Services Valuation Database (ESVD); Van Der Ploeg & De Groot, 2010), a similar temporal
study should be conducted by exploiting the asset to elicit more general ﬁndings about
the long-term evolution of preferences for nature. The accumulation of such studies
would allow us to construct a more temporal-sensitive database for the valuation of
ecosystem services and conduct a better temporal and spatial beneﬁt transfer. A caveat,
however, is the availability of raw data used for past studies. In particular, data for protest
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responses may be limited in their availability. Priority should, thus, be given to cases inwhich drastic social-ecological changes and adaptive conservation projects are expected.
Furthermore, while our study used CVM, conjoint analysis, another stated preference
method to measure WTP (Louviere, Hensher & Swait, 2000) may be promising. A conjoint
analysis uses proﬁles of a conservation project with various attributes (e.g., degree ofconservation, development, and cost) and elicits respondents’ preferences for trade-offs
among these attributes. This could be particularly useful when a project faces serious
trade-offs such as conservation vs. development. In addition, a conjoint analysis cancapture marginal changes in WTP caused by changes in attributes such as quantity of
ecosystem services supplied, and thus, could better capture factors affecting supply anddemand for ecosystem services.
As our study showed, protest responses could provide non-negligible information for aconservation project in a real-world context. Protest responses are by no means residual
information. In fact, several attempts have been made to use them (García-Llorente,
Martín-López & Montes, 2011; Cunha-e-Sá et al., 2012), although such analyses remain
underdeveloped (Brouwer & Martín-Ortega, 2012; Tobarra-González, 2015).
Although beyond the scope of this paper, the readers should note that being basedon economic theory, our study has a narrow focus relative to the broader spectrum of
individuals’ preferences for nature and their approaches to it. What WTP can capture
is limited in scope and economics is not the only approach to capture the preferences
regarding nature. First, WTP captures assigned values, not held ones (Brown, 1984).
While held values represent certain types of behavior (e.g., loyalty), end states(e.g., freedom), and quality (e.g., beauty) that individuals value, assigned values arewhat individuals assign to a given change over alternative outcomes based on theirpreferences (Segerson, 2017). Second, economic theory is silent regarding the motivations
underlying individuals’ preferences (Flores, 2017). However, exploring the motivations
behind their preferences may be insightful for designing and managing conservation
projects. One promising approach is Schwartz’s (2012) human value theory (HVT),
which explains the motivational basis for both attitudes and behavior. Hicks et al. (2015)
applied HVT to explore the motivations behind ﬁshers’ preferences for marine ecosystemservices. Finally, while WTP captures individual preferences, assuming consumer
sovereignty with short-term focus, sustainable values assuming community sovereignty
with long-term focus should also be considered (Costanza, 2000; Norton, Costanza &Bishop, 1998). Since conservation projects typically involve long-term horizons, thepreference inconsistencies between short- and long-term horizons should be eliminated(Norton, Costanza & Bishop, 1998). For example, Norton, Costanza & Bishop (1998)
propose a two-tiered decision structure to eliminate such inconsistencies.

CONCLUSIONSThis study investigated the evolution of preferences for conservation projects (i.e., welfare
measured in WTP and other project aspects assessed by protest responses). We comparedpreferences for three conservation projects in the SIS, Japan, at two distant time
points, 1998 and 2015. Owing to the lack of a unanimous protocol for protest response
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analysis and its use for WTP estimate, we proposed a simple comprehensive analytic
framework that integrates protest response and WTP analyses.
Protest responses provide useful information to render a project adaptive to changes inthe social-ecological system, the SIS. For instance, the payment method should bereconsidered. The welfare obtained from the projects was stable over a 17-year period,
except for non-coastal residents in the case of Project 2. This possibly reﬂects the factors
inﬂuencing changes in the demand and supply of the ecosystem services. Since thePTEs for both value and functional transfers were smaller than those in previous studies,
they can be considered temporarily transferable. A function transfer performs less than avalue transfer because of the contextual similarity over time and the insufﬁcient
speciﬁcation of functions for the transfer.
Further temporal studies are highly encouraged, with focus on locations where
signiﬁcant social-ecological changes have occurred or are expected to occur, such as indeveloping countries. These studies can contribute to not only the primary study site but
also the accumulation and sophistication of ecosystem services database such as ESVD.
Doing so will enable a better beneﬁt transfer when time and budget are unavailable toconduct a primary study.

ADDITIONAL INFORMATION AND DECLARATIONS
Funding
This research was supported by the Environment Research and Technology Development
Fund (S-13) of the Ministry of the Environment, Japan. The funders had no role in study
design, data collection and analysis, decision to publish, or preparation of the manuscript.

Grant Disclosures
The following grant information was disclosed by the authors:
Environment Research and Technology Development Fund (S-13) of the Ministry of theEnvironment, Japan.

Competing Interests
The authors declare that they have no competing interests.

Author Contributions
 Takuro Uehara conceived and designed the experiments, analyzed the data, preparedﬁgures and/or tables, authored or reviewed drafts of the paper, approved the ﬁnal draft.
 Takahiro Tsuge conceived and designed the experiments, analyzed the data, contributed
reagents/materials/analysis tools, prepared ﬁgures and/or tables.
 Takahiro Ota conceived and designed the experiments, performed the experiments,
contributed reagents/materials/analysis tools.

Data Availability
The following information was supplied regarding data availability:
The raw data are provided in the Supplemental File.
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Supplemental Information
Supplemental information for this article can be found online at http://dx.doi.org/10.7717/
peerj.5366#supplemental-information.

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