non-users’ trade-off between natural scenery, water quality, ecological functions and biodiversity...

Non-users’ trade-off between natural scenery, water quality,ecological functions and biodiversity conservation: a wayto preserve wetlands

Sara Kaffashi • Mad Nasir Shamsudin •

Alias Radam • Khalid Abdul Rahim •

Mohd Rusli Yacob

Published online: 6 February 2013

� Springer Science+Business Media New York 2013

Abstract The inclusion of both non-use values and val-

ues placed by non-users provide more reliable results about

the real values of wetlands. A choice experiment method

was conducted to estimate the willingness to pay for

environmental conservation in non-users’ communities

adjacent to the Shadegan International Wetland (SIW) in

Iran. A random parameter logit (RPL) model was devel-

oped to derive the marginal value and compensating sur-

plus of the respondents for five attributes of the non-market

values of SIW. The trade-off between five different wet-

land attributes showed that water quality improvement and

biodiversity conservation were the most highly valued

attributes. The results demonstrated that about 66 % of

non-users were willing to donate money for the contribu-

tion in SIW conservation, suggesting that non-users have

the potential to contribute to SIW conservation programs.

Keywords Non-users � Choice experiment � Random

parameter logit � Shadegan International Wetland �Compensating surplus

1 Introduction

Wetlands support people’s lives with various products and

functions. They are an important source of goods (e.g.,

food, fuel wood, fresh water and construction materials)

and services (e.g., pollution control, water treatment, and

nutrient deposition). However, in competition between

existence of wetland and polluted industries, wetlands are

often the losers because most industries are unfamiliar with

the wide range of values provided by wetlands. Shadegan

International Wetland (SIW), as the largest wetland in Iran,

is presumed to be one of the losers of this competition.

Poor management, disorganized and unplanned construc-

tion, accompanied by lack of information about the real

values of this wetland, have lead to the increasing rate of

destruction of this ecosystem (Kaffashi et al. 2011).

It is believed that evaluating the true value of natural

assets according to the comparable values with other eco-

nomic sectors of the country can improve long-term against

short-term economic benefits. In economics, values are tied

to human wants and costs imposed to them to satisfy

human wants (Badola et al. 2010). Since most of wetlands’

values are not included in market price, the economic value

of wetland can be threatened by uncertainty. Evaluating the

total economic value of preserving wetlands will increase

social benefits and in essence will maximize social welfare.

While researchers are mostly interested in estimating the

value of certain wetland area based on users’ perceptions,

total economic value of wetlands should include non-use

values and values placed by non-users as well. The prob-

lem is that the benefits from wetlands are often not real-

izable in conventional economic terms and are often

received by those who do not bear the costs because they

are distant from the wetland in space or time (Ambastha

et al. 2007; Wattage and Mardle 2007). Since the motives

of the non-users are mostly non-use values, estimating non-

users’ values could provide more reliable results about the

real values of wetlands and possibly lead to more plausible

alternative policies (Fonseca 2009). Generally, non-users

are located away from the wetland and value the preser-

vation or existence value of it. However, others of these

respondents may value the possibility of future use of

wetland (Bateman and Langford 1997). The aim of this

S. Kaffashi (&) � M. N. Shamsudin � A. Radam �K. A. Rahim � M. R. Yacob

Serdang, Malaysia

e-mail: [email protected]

123

Environ Syst Decis (2013) 33:251–260

DOI 10.1007/s10669-013-9436-7

study is hence to determine the economic value of different

functions and services of SIW for non-users of this eco-

system. A choice experiment (CE) method was applied for

the analysis.

Most natural resource economic value studies have tried

to capture non-users’ willingness to pay (WTP) by using

contingent valuation method (CVM). Bateman and Lang-

ford (1997) reported the non-users’ WTP for non-use val-

ues of the Norfolk Broads preservation. The results of their

study showed that non-users had positive WTP, although it

was 3.5 times smaller than users’ WTP. Kniivila’s (2006)

studied whether non-users lack of familiarity with the

valued resource caused invalid and unreliable responses in

CVM surveys. The study reported that there was no sig-

nificant difference in validity of responses of non-users of

the resource compared to those with previous experience of

the resource. Horton et al. (2003) assessed the non-users’

WTP for protected areas in the Amazon (Brazil). Their

study also found that non-users were significantly willing

to protect the Brazilian Amazon forest. Fonseca (2009)

studied the value of Fijian coral reefs for non-user house-

holds in the Metro Atlanta area. Using CVM, the results of

the study demonstrated an average value of about US$

13.9. Other studies such as by Klocek (2004), Rogers et al.

(2012), Jørgensen et al. (2012), and Martınez-Paz and Perni

(2011) also confirmed the significant and positive WTP of

non-users in natural resources conservation.

2 Methodology

2.1 Choice experiments

The CE method was applied in this study due to its capa-

bility in valuing the diverse attributes of the natural envi-

ronment (Hensher et al. 2005a, b; Louviere et al. 2000).

Like CVM, CE is used for measuring both use values in

practical applications and non-use values for theory (Ad-

amowicz et al. 1994, 1998). Bennett and Blamey (2001)

and Alpizar et al. (2001) argued that CE has grown in

popularity because of the quantity of information it can

provide, its ability to generate values for resource attri-

butes, its realism relative to other methods and because of

concerns regarding the validity of CVM.

Given the complexity of natural resource decisions, CE

method results can be used to investigate the importance of

attributes and to gain useful information about people’s

preferences over a number of decision alternatives (Bat-

eman et al. 2002). The data then can be used to estimate the

economic value of various combinations of attributes and

their levels. Hence, more information can be collected from

a single CE survey than from CVM.

2.1.1 Questionnaire design

Choice experiment is based on questionnaires to gather

information. Hence, respondents were presented with a

hypothetical market. The first step in developing a CE

questionnaire was to identify relevant attributes of the non-

market goods under valuation. Several meetings were con-

ducted with wetland managers and local authorities to select

appropriate attributes and related levels. Two series of focus

group studies were conducted (one with university lecturers

of Iran and another with staffs of the Natural environment

sector of Shadegan Environmental Conservation Office) to

aid in questionnaire design and identify relevant attributes

and levels (Kaffashi et al. 2012). In addition, review of the

attributes and attribute levels used in the previous studies of

Shadegan Wetland (e.g., Zare-Maivan 2004; PCE 2002;

Ansari and Mohammadi 2006; Internal reports of DOE of

Iran 1999 and 2005; DOE of Khuzestan Province 1995,

1996, 1997; and Fishery research Organization of Khuzestan

Province 1995) helped us to select those attributes which

were either policy relevant or expected to influence the

respondents’ choices (Alpizar et al. 2001). Based on the

focus group discussion and the literatures, the relevant

attributes and levels for economic valuation of SIW that were

selected included natural scenery (NS), water quality

(WAT), biodiversity (with emphasis on endangered bird

species) (BIO), ecological functions (EF) and conservation

value (price) (CV) (Table 1). As shown in the table, each

attribute was set at three levels based on historical quality

change of the attribute. In this way, the minimum level was

the ‘‘current condition’’ or ‘‘status quo,’’ while the maximum

level designated the best condition, historically. Since one

primary purpose of this study was to calculate the social

welfare measure, it was necessary to include a monetary

attribute. The price vector used in the design was based on

Iranian national parks’ entrance fees. The payment vehicle

was assumed to be a hypothetical donation to improve the

wetland conservation.

2.1.2 Experimental design

Based on the selected attributes and levels, the experi-

mental design technique and SPSS software were used to

obtain orthogonal design. While full factorial design

included 324 alternatives, using fractional factorial design

resulted in 16 alternatives. The final design then consisted

of just 10 alternatives in five choice sets, each choice set

including two purposed options, plus status quo.

2.1.3 Questionnaire development

The questionnaire was categorized into three subsections.

The first part included the CE questions. Three unique

252 Environ Syst Decis (2013) 33:251–260

123

options were presented, distinguished by their attributes

and associated cost. Option A and option B entailed various

combinations of conservation along with some yearly cost

to the households, while option C was always a weak

conservation scenario (the current situation), to which no

cost was attached. The respondents were asked to choose

one of the two options presented in each question,

according to their preference, or the ‘‘no change’’ or ‘‘status

quo’’ box if they like current condition to continue in

Shadegan wetland without extra cost to them but more

losses in naturalness, hydrological and ecological function

of wetland. The respondents were also asked to answer

each choice set independent than others. It was expected

that the respondents would value those levels of NS, BIO,

WAT and EF that might be expected to result in a higher

quality of life and offer greater utility of the wetlands’

natural resources (Kaffashi et al. 2012). The choice of

alternative A, B or the status quo to answer each question

yielded information about the value of each selected sce-

nario to any given respondent. The second part contained

questions to gauge respondents’ attitudes on different

aspects of SIW. In this section, series of statement were

Table 1 Attributes and their levels used for SIW valuation

Attribute Attribute levelLevel 1 level 2 level 3

Natural Scenery: Satisfaction level from knowing that natural scenery is as close as possible to its natural state and relatively unaffected by human activity.

Not satisfactory* Less Satisfactory SatisfactoryNot satisfactory*: several constructions and activities destroyed natural beautyLess Satisfactory: allowed some constructions in those parts of wetland which is closed to petrochemical and industrial zone Satisfactory: The natural scenery preserved beautifully with little damage and pollution, no threats to the scenery from industrial development

Water quality: physical, chemical and biological characteristics of Shadegan wetland with emphasis on nutrients, Electrical conductivity, biological and chemical oxygen demand, water color, odor and phytoplankton bloom.

Unacceptable* Moderately acceptable AcceptableUnacceptable*: polluted water, stink, unclear, algal bloom Moderately acceptable : unclear, moderately polluted water Acceptable; without odor, color and acceptable water quality standard view point of contaminant

Biodiversity: Refers to the number of endangered, vulnerable and rare bird species.

Low* Medium HighLow*: 50% of historic population with no strict management and continued decline of familiar, rare and endangered species. Medium: Improvement some essential condition of wetland and mend ecosystem health until we have 65% of historic population. High: Improvement wetland condition including restoration habitats, ecosystem health and process and prevention of pollution and diverse human activities until we have 80% of historic population. .

Ecological functionsRefers to those processes that wetlands perform independent of human intervention, such as nutrient cycling, flood flow alteration, sediment stabilization and pollution retention.

Weak* Moderate PerfectWeak*: allow further degradation of Shadegan wetland and losses in wetland functions until wetland has no more capacity for flood control, sediment and nutrient retention and other functions. Moderate: restoration just those functions that have direct Impact on human like e.g. flood control Perfect: Improvement all functions to original condition

Conservation value Rials 0*Rials 15000Rials 22500Rials 30000

* Status quo or current condition of SIW

Environ Syst Decis (2013) 33:251–260 253

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presented to gauge the perceptions about environmental

policy in general and wetlands conservation in particular

on a five-point scale ranging from ‘‘strongly agree’’ to

‘‘strongly disagree.’’ The questions related to visiting or

using SIW were also included in this section. These

questions later helped us to distinguish the non-users of the

wetland and consider them for further analysis. However,

the interviews were merely to elicit responses to the

questionnaire, but open-ended questions on respondents’

disagreement to participate in interview or always choos-

ing status quo were included as well. The last section of

the questionnaire contained questions regarding socio-

economic profile of the respondents. This section included

questions about age, gender, occupation, education level,

family size, income level and residential status.

2.2 Welfare measurement

The CE technique relies on both random utility theory

(Thurstone 1927; McFadden 1974; Manski 1977) and the

characteristics theory of value (Lancaster 1966). Thus, the

individual utility function (for individual i), where the

respondent is facing a set of K alternatives (j = 1,…, K),

can be specified as:

Uij ¼ Vij þ eij ð1Þ

where Uij is the utility that individual i obtains from an

alternative choice set j, Vij is a non-stochastic utility

function and eij is a random component. Consider an

individual was asked to choose between alternative goods

that are assumed to be differentiated by their attributes and

levels. In choosing between them, the respondent is

assumed to compare the preference relation represented

by utility that he or she could get with either choice before

selecting the alternative with the higher utility:

Pij ¼ ðVij þ eijÞ[ ðVik þ eikÞ¼ P½ Vij � Vik

� �[ ðeij � eikÞ�

ð2Þ

Assuming that the vector Vij is linear, the utility function of

the respondent’s components can be written as follows:

Vij ¼ b1Xij þ b2X2inj þ . . .þ bnXnij ð3Þ

where, Xs are variables in the utility function and bs are the

coefficients to be estimated. If a single vector of the

coefficients, bs, applies to the whole associated utility

function, then:

Pij ¼expðbVijÞ

Rij expðbVikÞð4Þ

where Pij = probability that respondent i will choose

alternative j; Xij and Xik = vectors of attributes i and j; and

b = vector of coefficients.

The above formula is expressed as the probability of

choosing alternative j over alternative k when the differ-

ences between the deterministic parts of their utility exceed

the differences in their error parts. The assumption of

independently and identically distributed error terms

implies independence of the irrelevant attributes (IIA),

meaning that the ratio of choice probabilities for any two

alternatives is unchanged by the addition or removal of

other, unselected alternatives (Blamey et al. 2001). This

assumption is one limitation of conditional logit models

(CLM), in which, if an IIA property is violated, the results

would be biased. Hence, some other model random

parameter logit (RPL) or Latent Class should be used that

does not need the IIA property (Kaffashi et al. 2012).

A RPL model, which is a generalization of the standard

multinomial logit model, was applied in this study for two

main reasons. First, it explicitly accounts for unobserved

preference heterogeneity across respondents, and second,

in the RPL model the alternatives are not independent

(Cameron and Trivedi 2005; Hensher and Greene 2003).

Thus, it has an advantage of relaxing the IIA assumption of

independence of irrelevant alternatives (IIA) (Birol and

Cox 2007). In this case, the random utility function is given

by the following expression:

Uij ¼ biXij þ eij ¼ b0Xj þ biXj þ eij ð5Þ

where Uij is the utility of alternative j for individual i, and bis the sum of the population mean (b0) and the individual

deviation from the mean (b). We assumed that all variables

other than CV (the conservation value, or cost variable) are

random and normally distributed. The specification of RPL

can be found in Hensher and Greene (2003) and Hensher

et al. (2005a, b).

For an estimation of willingness to pay (WTP), the price

or cost attribute must be included. For a marginal change in

an attribute, the WTP value is typically derived by dividing

the b value of each non-monetary attribute by the b value

of the price attribute (Kaffashi et al. 2012). Accordingly,

the average willingness to pay for changes in attributes can

be computed using the following formula (Holmes and

Adamowicz 2003):

$ Welfare ¼ 1=bcð Þ V0 � V1½ � ð6Þ

where bc is the value of the price attribute, and V0 and V1

represent indirect utility functions before and after the

change under consideration.

2.3 Data collection

2.3.1 Study site

With an area of 537,000 hectares, SIW is the largest wet-

land in Iran (Fig. 1). Following the demise of much of the


123

Mesopotamian marshlands (UNEP 2001), it has become

the largest wetland in the Middle East (PCE 2002). In

1975, SIW was designated as a Ramsar site and among the

top 50 Ramsar sites in the world. In addition, it has been

recognized as an important bird area (Evans 1994) and a

wetland of international importance (Scott 1995). SIW

contains outstanding plant and animal diversity and is

certainly one of the most diverse wetlands in Iran (National

Iranian Botanical Park 2000). According to the Interna-

tional Union for Conservation of Nature (IUCN) criteria,

thirteen species of globally threatened birds such as the

Marbled Teal (Marmaronetta angustirostris) have been

recorded in this wetland (Scott 2001). During the past

several decades, the SIW has suffered various ecological

assaults. The SIW confronts acute problems, where its

ecosystem is threatened by the conversion of natural hab-

itats and contamination from waste material and sewage

from adjacent factories. The major consequence of degra-

dation of this wetland ecosystem is that so many of the

previously recorded threatened bird species have not been

reported in recent years (DOE of Iran 2005; Behruzi Rad

2010). Because of its inadequate management, the SIW has

been included in the Ramsar Convention’s Montreux

Record (of wetlands at risk of ecological change) since

1993 (Ramsar Convention Bureau 2010).

2.3.2 Sampling and data collection

The populations targeted for this study were non-users of

SIW. Those respondents without direct or indirect use were

classified as non-users, even though they might decide to

visit the wetland in future or the existence value of the

wetland was important to them.

Based on the National Oceanic and Atmospheric

Administration (NOAA) panel guidelines (Arrow et al.

1993), in-person interviews were conducted in this study

for acquiring qualitative data from respondents. The deci-

sion was made to only include respondents who were at

least 18 years. A stratified random sampling design was

applied. The region of respondents’ homes was selected as

the stratification factor, assumed to represent standard of

living. These regions were selected based on land and

houses prices and people life style. Therefore, three strata

were selected. During the interviews, the respondents were

informed about the objectives of the study and were pre-

sented with information about SIW. This information was

Fig. 1 Shadegan International Wetland. Source PCE (2002)


123

supported by photos and the map of the study area. The

features of the wetland were highlighted and information

about its vital statistics, its valuable flora and fauna, and the

problems it was facing were provided.

Within the study area, 500 respondents were selected

from neighboring cities of SIW. From the total number of

respondents solicited, 270 were eliminated because of their

refusal to participate or due to being a user or visitor of

SIW. Therefore, the present study was carried out with 230

completed questionnaires, of which 150 of the respondents

were from Ahvaz city and 80 from Abadan city. Statistical

analysis and estimation of models were carried out using

Limdep 8 (Nlogit 3) and SPSS softwares.

3 Results and discussion

From overall (230 respondents) sample, 50 % of respon-

dents were male (Table 2). The mean age of respondents

was 35 years. The average household size was five people

per family. For the education level (years of schooling),

30.4 % of respondents had more than 14 years of school-

ing, 20.4 % had 14 years of schooling, 29.1 had 12 years

of schooling and 7.1 % had 5 years of schooling. Six

percent had membership in non-governmental organiza-

tions (NGOs). For occupation, 7.4 % of the respondents

were currently unemployed, 85.7 % classified as currently

employed and 6.9 % were retired. The average household

gross income was 558,194.85 Rials.

In the simple multinomial logit model, the parameters of

variables EF2 and EF3 were negative, which is the reverse

of prior expectation (Table 3). Constant term was also

positive and not significant, which makes the estimation of

welfare impossible. The pooled interaction model was

superior to the simple model in terms of model fit and

agreement with expected variables signs (Table 4). The

positive function of all the attributes means that a higher

quality of SIW is preferred than the base level, or status

quo. However, neither the parameter of EF was significant.

Both levels of the variable NS were positive with prior

expectation and were significant at the 5 % level. The

positive signs mean that better quality of NS resulted in

greater utility for the respondents. The variable BIO was

significant at the 10 % for level two, and 1 % for level

three. Both levels had the expected positive sign, indicating

that the respondents preferred greater biodiversity in the

wetland even if they would have to pay for that. The signs

of WAT were positive as with prior expectation and were

significant at the 5 % and 1 % level for level two and three,

respectively. The positive signs indicate that increasing the

level of water quality brings respondents more utility than

would the status quo. Level three of WAT had a higher

coefficient compared to other wetlands’ attributes. It indi-

cates that the respondents were specifically more con-

cerned about water quality. The coefficients of EF (both

levels) were positive but insignificant. The conservation

value (CV) was significant at 5 % level with the expected

negative sign. The negative sign indicates that the

respondents preferred those conservation programs (or

improvements in conservation) that cost them less. The

constant term was significant at 5 % level with expected

negative sign. In our analysis, the constant term took the

value of one (1) for baseline option and zero (0) for

improved options. Hence, a negative sign indicates that

respondents preferred improved options in wetland con-

servation than the base line.

The respondents’ demographic characteristics were

important intercept shifters in estimating the final model.

As shown in Table 4, gender, household size, age, income,

education and membership in NGOs were the most influ-

ential variables in the CE model. The interactions between

membership in environmental NGOs and level three of EF,

education and level two of EF and level 2 of NS, income

and EF (level three) were positive and significant. It shows

that those respondents with higher education, higher

income and membership in NGOs were more likely to pay

to conserve the wetland for better condition than the cur-

rent situation. The interaction between age and EF1 was

positive, indicating younger respondents were less con-

cerned about improvement in ecological functions of wet-

lands. The negative interactions between household size

and level three of EF and NS indicate that the large-sized

families were less concerned about improving the wetland

environment. The negative interaction between gender and

BIO shows that women were less willing to contribute in

improving biodiversity level of SIW than men.

3.1 Welfare measures

The marginal WTP was calculated by computing the

marginal rate of substitution between the attributes of

interest and the cost factor (in other words, taking the total

derivative of the utility index). This ‘‘value ratio’’ is also

identifiable between non-monetary elements of utility

(attribute tradeoffs), called the implicit price (IP) (Hanley

and Barbier 2009). The estimated IP results demonstrated

that water quality and biodiversity levels had the highest

marginal value (Table 4). Since the IP was estimated for

significant variables only, both levels of EF did not enter in

the equation.

In CE method, compensating surplus (CS) can be used

to estimate welfare for different management scenarios

associated with the value of multiple changes in attributes.

For example, in our study, wetland conservation could alter

the NS, BIO, WAT and the EF. The price for improvement


123

(paid in donations) was implemented as a conservation

value attribute. The CE method is consistent with utility

maximization and demand theory. Once the parameters are

estimated, CS welfare measures required to effect a desired

change in wetland conservation, CS, can be calculated by

using the following formula (Hanley and Barbier 2009):

CS ¼ ln R exp Vi1ð Þ � ln R exp Vi0ð Þ½ �=a¼ �1=bCV Vi1 � Vi0ð Þ ð7Þ

while

Vi0 ¼ aþ bNS0 þ bBIO0 þ bWAT0 þ bEF0 ð8Þ

where a is the marginal utility of income (represented by

the coefficient of the monetary attribute in the CE), and Vi0and Vi1 represent indirect utility functions before and after

the change under consideration, respectively.

The attribute levels were used to characterize different

management scenarios (Table 5). The estimated CS for

medium level of the SIW management was 25,847.45 Rilas

(US$ 2.75), and for perfect management scenario, this value

increased to 49,549.32 Rilas (US$ 5.28). However, there is a

probability of uncertainty in the results based on proposed

scenarios and definition of perfect or medium level man-

agement. Overall, these values show the utility that people

derive simply from improvement in the wetland condition

and a change in their condition from the status quo.

Table 2 Socio-economic profile of the respondents

Variable Description Number Frequency (%) Mean

Overall Ahvaz Abadan

Age (year) Continuous variable 34.5 35.5 35.13

Gender

Male Dummy: 1 = male, 0 = female 115 50 50 51

Female 115 50 49 49

Education Level Continuous variable; years of schooling 14.17

Elementary school 16 7 5 9.1

Secondary school 46 20 17.5 22.7

High school 67 29.1 32.5 22.5

College 31 13.5 12.5 14.5

Graduate or postgraduate 70 30.4 32.5 28.2

Number of household Continuous variable 5.4 5.04 5.23

Membership in NGOs Dummy: 0 = no membership, 1 = membership

Yes 14 7 11.7 0

No 216 93 88.3 100

Employment status Dummy: 0 = unemployed, 1 = currently employed

and retiredCurrently unemployed 17 7.4 14.5 8.2

Currently employed 197 85.7 80 88.8

Retired 16 6.9 5.5 3.0

Income (Rials) Annual household income after declining tax

and other costs

558,194.85

Low (\3,000,000) 32 13.9 18.3 11.8

Medium (3,100,000–6,400,000) 108 47.0 50 43.6

High ([6,500,000) 90 39.1 31.7 44.5

Table 3 Results of Simple multinomial logit model

Variable Coefficient T-ratio

NS2 2.859 3.871

NS3 0.777 1.306

BIO2 0.416 0.843

BIO3 2.916** 2.395

WAT2 3.065* 3.806

WAT3 6.090* 3.305

EF2 -0.403 -0.939

EF3 -2.087 -2.319

CV -0.0008** -2.006

Log likelihood function -915.780

R-sqrd 0.282

* significant at 1 %, ** 5 %


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4 Conclusion

This study was conducted to assess the value that was

placed by non-users on SIW. The inclusion of non-users’

value would substantially affect the total value of wetlands,

and exclusion of them could lead to underestimating the

overall value of the wetlands (Kniivila 2006). Non-users

could potentially find more ‘‘non-use’’ value in the wet-

lands than users have found, adding substantially to the

wetlands’ total overall value. However, due to the fact that

non-users do not bear the associated costs because of dis-

tance or time, the value placed by them is rarely included in

WTP estimates. Ignoring non- users’ value by neglecting a

potentially large source of revenue for wetland manage-

ment will subsequently result in failure of better imple-

mentation in wetland conservation. The findings of this

study indicated that non-users place high value in con-

serving the wetlands.

The application of the CE method gave the opportunity

of trading off between different non-market attributes. The

results demonstrated that in trading off between five dif-

ferent wetland attributes, conservation of biodiversity

(BIO) and water quality improvement (WAT) had higher

priority for respondents. This supports the argument that

the benefit accrued to society in conserving wetlands is

larger than the other purposed alternative uses. Manage-

ment and policies should reflect the values of the entire

country and even in the case of international wetlands like

SIW, the values of international societies as well. A deci-

sion-making system must be developed in a way that

realistically provides an opportunity for all people for

whom wetlands in general and SIW in particular are

Table 4 Results of RPL model

and IP

* significant at 1 %, ** 5 % and

*** 10 % level

Dummy coding was used where

current condition got value of 0

and alternative value of 1

Variable Coefficient T-ratio Marginal WTP (Rial)

Constant -2.0025** -2.087 -17899.75(-43460.84 to 7661.44)

NS2 2.0395** 2.192 18230.11 (8109.67 to 28350.55)

NS3 1.522*** 2.146 13611.86(5778.22 to 33001.94)

BIO2 0.924* 1.742 8265.36 (2569.246 to 19099.97)

BIO3 4.228* 2.850 37797.9(14017.86 to 61578.12)

WAT2 2.423** 2.331 21663.1(10034.08 to 33292.22)

WAT3 6.316* 2.735 56458.77(29609.32 to 83308.22)

EF2 0.735 1.161 6575.08 (-4297.82 to 17447.98)

EF3 0.392 0.256 3505.57 (-24316.5 to 31327.5)

CV -0.111** -2.223

EF3_HHSI -0.211 -3.727

EF3_NGO 0.982 1.704

EF2_EDU 0.730 3.962

NS3_HHS -0.120 -1.995

NS2_EDU 0.411 3.693

EF3_INCO 0.390 7.107

EF1_AGE 0.321 2.602

BIO1_GEN -0.931 -4.579

Log likelihood function -741.148

R-sqrd 0.413

RsqAdj 0.406

Table 5 Hypothetical future

scenarios and their

compensating surplus

Attributes Status quo Scenario 1 Scenario 2

NS Not satisfactory Satisfactory Satisfactory

BIO 50 % of historical level 65 % of historical level 80 % of historical level

WAT Not acceptable Moderately acceptable Acceptable

EF Weak Moderate Perfect

CV 0 25,847.45 Rilas (US$ 2.75) 49,549.32 Rilas (US$ 5.28)


123

important to participate in major decisions affecting wet-

land ecosystems.

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