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The optimal management of wetlands: quantifying trade-offs between flood risks, recreation and
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biodiversity conservation
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Ekin Birol1, Nick Hanley2, Phoebe Koundouri3, Yiannis Kountouris3
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Washington, DC 20006-1002, USA, & Department of Land Economy, University of Cambridge.
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Email:e.birol@cgiar.org; Fax: +1 202 467 4439
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2
Economics Department, University of Stirling, Stirling FK9 4LA, Scotland
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3
DIEES, Athens University of Economics and Business 76, Patission Street, Athens 104 34,
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Corresponding author. International Food Policy Research Institute, 2033 K Street, NW
Greece
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The optimal management of wetlands: quantifying trade-offs between flood risks, recreation and
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biodiversity conservation
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Abstract:
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This paper employs a simple choice experiment to estimate the value of management options for
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the Bobrek wetland in Poland. The local public’s valuation of several wetland management
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attributes, including flood risk reduction, biodiversity conservation and improvement of
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recreational access, are investigated. A latent class model is estimated to account for
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heterogeneity in the preferences of the local public. The results reveal that there is considerable
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preference heterogeneity among the local public; however on average they derive the highest
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values from reductions in flooding risk. The results of this study are expected to assist policy
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makers in undertaking effective flood risk reduction measures and formulating efficient,
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equitable and sustainable wetland management policies in accordance with the European
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Union’s Water Framework Directive (2000/60/EC).
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Index terms: 6314 Demand estimation, 6324 Legislation and regulations (6615), 0497 Wetlands
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(1890), 1821 Floods, 0410 Biodiversity
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Keywords: choice experiment, latent class model, flood risk, biodiversity conservation,
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recreation
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1. Introduction
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Flood risks and the effects of flooding episodes have re-emerged as an important natural hazard
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concern in central and northern Europe since the 1990’s. These concerns have also been
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exacerbated as a result of widespread and increasing awareness in global climate change, and
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significant wetland loss. Global climate change and wetland loss are expected to increase the
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frequency and extent of floods in the future (Nichols et al., 1999). These floods are expected to
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cause significant changes in current land use and population patterns, and impose significant
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economic damages. Contrary to flooding episodes in the past, recent floods in Europe have
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milder effects in terms of loss of human life. Economic costs of flooding, however, are rapidly
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increasing as a result of high costs of damages to infrastructure and production, and disruptions
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to transport.
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The estimated costs of the damages of the floods of 1997 and 2001 are in the region of
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one billion USD for Poland, and 250 million USD for the surrounding countries (Brakenridge et
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al, 1997; 2001). As a consequence of the increasing economic and social costs of floods,
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European governments have taken a more active approach to flood risk reduction. The Bobrek
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catchment in the Upper Silesia region of Poland is susceptible to flooding as a result of centuries-
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long mining activities in this area, which has also had significant effects on its landscape. The
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projected social, economic and environmental costs of predicted future flooding in this region are
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extremely high. Paradoxically, land deformation caused by the mining industry and consequent
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floods have had a beneficial consequence ecologically. Unique ecological habitats have been
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formed in the flooded areas, harbouring important biodiversity riches that attract increasing
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scientific interest in terms of their conservation. In addition to the various economic values that
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these biodiversity-rich habitats generate (see for example Pearce and Moran, 1994; Christie et al,
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2006), the wetland is also of high recreational value to local residents.
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These habitats are, however, threatened by the current flood control policies, which are
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based on the construction of artificial barriers and spoil heaps. Under current trends, levels of
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biodiversity in these habitats will decline. Local authorities and the national government,
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motivated by EU directives, are attempting to identify a balance between flood risk reduction
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and biodiversity conservation in this wetland area. The EU is committed to conserving the
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ecological status and especially the biodiversity riches in the wetlands under the Water
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Framework Directive (WFD, 2000/60/EC). Article 1(a) of the WFD in particular calls for the
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prevention of further deterioration of European wetlands, their protection and the enhancement
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of their status. Wetland conservation is also provided for under the Birds Directive
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(1979/409/EC) and the Habitats Directive (1992/43/EC).
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This paper aims to contribute to the EU-wide debate over wetland conservation and flood
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risk reduction policy, by using a simple choice experiment. The aim of the study is to quantify
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the tradeoffs between flood risk reduction, biodiversity conservation and improvements in access
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to the wetland for recreational purposes. The study explicitly accounts for local public’s
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preference heterogeneity by analyzing choices using a Latent Class Model. To this end, stated
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preferences over constructed alternatives; data on environmental attitudes, household level
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social, economic and demographic characteristics, as well as data on households’ past
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recreational activities in the wetland and flood damages suffered in the past are collected from
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192 households located in the wetland catchment. Our main conclusion is that households derive
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the highest benefits from a reduction in flood risks to a ‘low’ level. In terms of the value of
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changes in attributes, flood risk reduction is followed by either improvements in access to the
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wetland for recreational purposes, or by conservation of high levels of biodiversity, depending
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on the latent class. These results have important repercussions for the design of efficient,
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effective and equitable wetland management projects and policies.
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The following section presents a brief review of the relevant literature. The theoretical
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underpinnings of the choice experiment method and the econometric model estimated are
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introduced in Section 3. Section 4 describes the case study and Section 5 explains the survey
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design and administration. The results of the econometric estimation are reported in Section 6,
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and the implications of the results are discussed. Section 7 concludes the paper with implications
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for wetland management policy in the region.
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2. Previous literature
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A number of non-market valuation techniques have been employed to estimate the value of flood
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risk reductions in Europe. Brouwer and van Ek (2004) employed an integrated impact
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assessment method to estimate the benefits of flood risk reduction in the Netherlands. Ragkos et
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al. (2006) carried out a contingent valuation study to estimate the value of flood control in the
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Zazari-Cheimaditida Wetland in Greece. To our knowledge, however, no study has so far
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employed the choice experiment method to estimate the benefits of flood risk reduction.
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Previous choice experiment studies implemented in Europe have investigated the value of
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conserving biodiversity, either in terms of its components (e.g., a certain species) (see for
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example, Hanley et al., 2003; Horne and Petajisto, 2003; Bennett and Willis, 2007) or in terms of
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conserving biodiversity as a part of a wider ecosystem (e.g., wetland or forests) (see for example,
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Carlsson et al., 2003; Birol et al., 2006a; 2006b; Birol and Cox, 2007). In addition, Christie et al.
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(2006) have used the method to estimate the relative importance of different aspects of
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biodiversity to the general public. The choice experiment method has also been applied to
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estimate recreational demand in Europe. For example, Hanley et al. (2002), employed a choice
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experiment to model the demand for rock-climbing in Scotland, whilst Horne et al. (2005) used
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this method to inform forest management at recreational sites in Finland (see Birol et al. (2008)
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for a thorough review of the choice experiment studies undertaken in Europe).
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3. The choice experiment method
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The choice experiment method is a survey based non-market valuation method that has become
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increasingly popular over the past decade for valuation of public goods and environmental
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policies. The method involves creation of a hypothetical market and asks survey respondents to
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perform sequential choices among different bundles, described in terms of the good’s
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characteristics, or of policy characteristics. The theoretical basis for the choice experiment
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method is therefore provided by Lancaster’s characteristics theory of value, according to which
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demand is defined over the characteristics of the good rather than on the good itself (Lancaster,
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1966). The econometric specification is derived under the assumption of random utility as a
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theory of how people choose between discrete alternatives (Manski, 1977).
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The conditional logit model has been the primary model for analyzing stated preference
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data from choice experiments. However it has been recognised to have a major shortcoming
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namely the fact that it imposes homogenous preferences for all respondents (unless these can be
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adequately represented using interactions with observable socio-economic characteristics). To
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take into account possible preference heterogeneity among respondents a number of models
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alternative to the standard conditional logit model have been proposed. In this paper we employ
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the Latent Class Model (LCM) to capture underlying preference heterogeneity within responses
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to the choice questions.
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In the LCM approach, preference heterogeneity is accounted for by a discrete distribution
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over unobservable endogenous (latent) classes of respondents (Wedel and Kamakura, 2000).
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Preferences are assumed to be homogeneous within each class but are allowed to differ across
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classes. The population is thus represented as consisting of a finite number of segments, or
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classes. Respondents are allocated to segments simultaneously with the analysis of choices. The
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number of the segments is endogenously determined by the data while membership to a segment
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depends probabilistically on the respondent’s observable socio-economic or attitudinal and
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behavioural characteristics.
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In the random utility framework for the LCM the utility a respondent i who belongs to
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segment s derives from option j is given by
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U ij s   s X ij   ij s
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where  s is the segment specific vector of coefficients, X ij is the vector of attributes associated
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with each alternative and  ij s is the random component of utility for each segment. Since the
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vectors of coefficients differ between segments, preference heterogeneity across segments is
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captured. Under the assumption of independently and identically distributed (iid) error terms that
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follow a Type 1 extreme value distribution, the probability option j is selected by a respondent i
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belonging to segment s is given by:
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Prij s 
exp(  s X ij )
 exp( 
s
X ih )
(1)
(2)
h
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Membership to a specific segment is determined by a likelihood function M that classifies
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respondents to one of the segments with probability Pis . The membership likelihood function is
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given by M is  a s Z i   is where Zi is a vector of socio-economic and other observed
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characteristics of the respondent and ξis is an error term. Assuming that this error term is also iid
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and follows a type 1 extreme value distribution, the probability that a respondent i belongs to
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segment s is given by
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Pris 
exp( a s Z i )
 exp( a s Z i )
(3)
s
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The joint probability that individual i belongs to segment s and chooses alternative j is given by
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Pijs  ( Pij / s ) * ( Pis )  [
exp(  s X ij )
 exp(  s X ih )
h
] [
exp( a s Z i )
]
 exp( as Z i )
(4)
s
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4. The case study
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The choice experiment study reported in this paper was implemented in the city of Sosnowiec,
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located in the Bobrek catchment, in the Upper Silesia Region of Poland. The region is an
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important industrial center located within the Upper Silesian Coal Basin. Five rivers run through
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the wider area, including the Biala, Brynica, Jaworznik, Wielonka and Rawa, making the region
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susceptible to flooding episodes (Figures 1 and 2).
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Insert Figure 1 here
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Insert Figure 2 here
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The main economic activities in the area include heavy industry and mining with some
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of the world’s largest bituminous coalmines located in the region. The mines are concentrated
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close to the region’s rivers, and have ongoing impacts on their morphology. Mining activities
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have been taking place in this area for over two centuries. Scientific evidence from Central
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Mining Institute, Silesian University, AGH University of Science and Technology, and Krakow
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University of Technology shows that the mining industry has significantly deformed the local
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landscape and the riverbeds, thereby rendering the region extremely vulnerable to floods even
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after light rainfall. Given the size of the local communities, it is estimated that approximately
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50,000 individuals are currently at risk from flooding.
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In 1992 the Polish government facilitated the construction of concrete barriers on the
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rivers’ banks in order to minimize the risk of flooding in the region. Mining industries were
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deemed responsible for protecting their mines by constructing spoil heaps on the rivers’ banks.
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This plan, however, was not successful since it increased the speed of water flow, thereby
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generating negative externalities for downstream communities. Moreover, recreational activities
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in the catchment were limited as a result of the blocking of the river access by concrete barriers.
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Furthermore, this policy was not successful in providing flood control as the extensive floods of
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1997 and 2001 can attest.
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The high economic and social costs of flooding episodes are borne mainly by the local
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residents, but also by the overall national economy and the nearby countries. Despite these costs,
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past floods have also brought about benefits: unique ecological wetland habitats have been
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formed on those lands that have been flooded by the rivers. New species of both flora and fauna
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have colonised these habitats. Ecologists recognise these biodiversity riches and assert that they
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should be conserved. In addition, these habitats are now of high recreational value, with the
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potential to serve as an attractive tourism destination. The ecological health of these habitats is
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currently under threat from pollution caused by the spoil heaps created by the mining industry.
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5. Survey design and administration
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A choice experiment crucially relies on the definition of the good to be valued in terms of its
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attributes and levels. The attributes need to be those that the public considers important regarding
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the proposed policy change or resource, whilst attribute levels should be achievable with and
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without a proposed policy change (Bateman et al., 2003). The good valued in this choice
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experiment study is a “wetland management plan”. Following discussions with scientists from
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the Central Mining Institute, the Silesian University, the AGH University of Science and
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Technology and the Krakow University of Technology, and drawing on the results of focus
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group discussions with randomly selected members of the local population, a simple
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experimental design to include three wetland management plan attributes was chosen. These
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were surface and underground flooding risks; biodiversity found in the wetland; and access to the
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river bank for recreational purposes. All three of these attributes were specified to have one of
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two levels.
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The flood risk attribute refers to the predicted risk of flooding in the area in the next 10
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years. At present, the risk of flooding is high. If, however, both underground and surface barriers
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are improved, the flow of water will slow down and the risks of flooding will be reduced. The
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materials proposed for this improvement are wood for surface barriers, and concrete for
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underground ones. This choice of materials would not spoil the aesthetic beauty of the wetland.
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The river access attribute refers to the public’s access to the riverbank for recreational purposes
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(e.g., walking, cycling, fishing). At the moment access to the riverbank is difficult, as a result of
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the concrete vertical walls that were built a few years earlier as an (unsuccessful) flood risk
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reduction measure. If however, these concrete walls are demolished and the river is re-canalised
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similarly to its natural state by using natural materials, such as wood rather than concrete, the
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riverbank could easily be accessed for recreational purposes. Finally, the biodiversity attribute
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refers to the number of different species of flora and fauna, their population levels, number of
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different habitats and their size in the wetland ecosystem in the next ten years. As a result of past
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flooding biodiversity levels have increased to higher levels than before. Present regulations,
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however do not protect this biodiversity, as they allow mining companies to create spoil heaps
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and discharge mine drainage water into the river. This practice poses a considerable threat to the
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newly formed habitats, and it is expected to decrease the biodiversity levels to a low level within
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the next ten years. If, however, mining companies are prohibited from creating spoil heaps and if
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in addition reclamation activities, such as afforestation take place, biodiversity levels can reach a
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higher level in the next ten years.
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The attributes described above represent an adequate simplification of the aspects of
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wetland management in this particular case study. Wetland management will be affected by the
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permitted extent of flooding. In other words the area and quality of the wetland habitats are the
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immediate effects of the extent of flooding. Furthermore the design of the flood defences will
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have impacts on the level of biodiversity that the wetland habitat will be able to support. The
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same flood defences will define the nature of the recreational activities and the degree of access
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to the wetland.
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The payment vehicle used was a percentage change in the local taxes paid by the
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households in the next ten years. A percentage change in the household’s present tax level was
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preferred over fixed changes in the tax levels, since the former allows for a continuous monetary
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variable. Furthermore, allowing for higher and lower tax levels compared to the status quo level
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enables understanding of whether the households are willing to pay to have higher/lower levels
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of these attributes or willing to accept compensation to let go higher/lower levels of these.
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Finally, taxation was preferred as a payment vehicle over voluntary donations since households
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may have the incentives to free-ride with the latter (Whitehead, 2006). Table 1 summarizes the
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definition of the attributes and their levels.
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Insert Table 1 here
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Using experimental design techniques (Louviere et al., 2000) an orthogonalization
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procedure was used. This procedure resulted in a simple choice experiment design which
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consisted of 32 pair-wise comparisons of wetland management plans. These were randomly
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blocked into four versions, each containing eight choice sets consisting of two wetland
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management plans and an opt-out alternative, which represented the status quo, in which case no
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management actions would be undertaken and tax rates would not change. Inclusion of the status
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quo or another baseline scenario is important for the welfare interpretation of choice experiment
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estimates and for their consistency with demand theory (Louviere et al., 2000; Bennett and
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Blamey, 2001; Bateman et al., 2003).
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Insert Figure 3 here
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The choice experiment survey was implemented in March and April 2007 in the city of
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Sosnowiec, using in-house face-to-face interviews. Time and budget constraints allowed for a
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sample of 200 households from the local population. A quota sample was collected and the
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survey was administered to be representative of the local population in terms of income and
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geographical distribution (i.e., distance from the river). Those household members who took
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part in the survey were by and large those who were the main household decision makers. In
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total 96 percent of those approached, i.e., 192 households agreed to be interviewed.
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The choice experiment survey started with the enumerators reading a statement
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identifying the current issues in the area regarding flood risks, biodiversity conservation and use
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of the riverbank for recreational activities. Subsequently the households were presented with a
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description of the attributes used in the choice experiment and they were asked to state their
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preferred wetland management plan among three such plans in eight choice sets. Overall a total
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of 1536 choices were elicited from 192 households.
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Data were also collected on the households’ social, demographic and economic
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characteristics, as well as information on whether the household uses the riverbank for
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recreational activities, and on flooding episodes that have affected the household in the past
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decade. Descriptive statistics for the key variables are presented in Table 2.
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Insert Table 2 here
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Even though on average households are located almost half a kilometre (462 meters)
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away from the river, almost 13% have been flooded an average of 2.5 times in the past decade.
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The total average damages suffered by flooded households in the past ten years is 7115.8 zloty
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(€1871), and for some as high as 25000 zloty (€6574). Less than a third of these flooded
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households have been compensated, most of whom (49%) by the mining industry, some (28%)
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by the government and a minority (13%) by an insurance company. Over a half of the sample are
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regular visitors of the riverbanks and rivers. They stated that they engage in a wide array of
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recreational activities ranging from walking and sailing to appreciating its aesthetic beauty and
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bird watching, as well as for educational purposes. 66.7% of the respondents stated that they
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would like to visit the wetland in the future to engage in recreational activities.
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In addition to these social, economic, demographic, flood related and present and future
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recreational wetland use data, several attitudinal and behavioural questions were asked in order
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to understand respondents’ behaviour and consciousness with regards to environmental issues.
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Households’ actual environmental behaviour was assessed via questions eliciting their purchase
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of organic produce, environmental publications and fair-trade products; their donations to
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environmental organisations, and shopping at environmentally friendly shops. These were
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measured on a five point scale ranging from “never” to “always” and an environmental
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behaviour index (EBI), ranging from 5 to 20, was calculated. The level of the respondents’
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environmental consciousness was measured through questions eliciting how concerned they are
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with regards to several environmental issues such as the scarcity of environmental resources,
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ecological crisis, human interference with the environment and the current situation of wetlands
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in Poland. An environmental consciousness index (ECI) was calculated by using a Likert scale
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which ranged from 5 to 30. The sample averages for ECI and EBI are also reported in Table 2.
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6. Results
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6.1. Latent class model
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In addition to the Latent Class Model (LCM) the data were also analysed by random parameter
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logit (RPL) model, which accounts for unobserved, unconditional heterogeneity at the individual
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level, and also by Covariance Heterogeneity (Cov Het) model, which investigates heterogeneity
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in the random component of utility. Ben-Akiva and Swait (1986) test for comparing non-nested
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probabilistic choice models is employed to investigate whether RPL model, Cov Het model or
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the LCM is the true specification for this data set. The idea behind this test is to examine whether
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the systematic preference heterogeneity in this particular data set can be better explained at the
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individual level or at the segment level. The Ben-Akiva and Swait test results rejects the null
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hypothesis that the RPL and Cov Het models are the true specification. Similar to Birol et al.
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(2006b) and Colombo and Hanley (2007), preference heterogeneity in this specific data set is
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better explained at the segment level that at the individual level.
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In the best fitting Latent Class Model (LCM) the heterogeneity among segments is
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captured by social and economic variables indicating whether the household’s property has been
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flooded in the past; whether the household visits the rivers for recreational purposes; whether the
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household has a child living in the household and whether the respondent has a university
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degree. The LCM was estimated with 2, 3, 4 and 5 segments. The log likelihood, 2, Bozdogan
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Akaike Information Criterion (AIC3) and Bayesian Information Criterion (BIC) statistics were
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computed for each of the models and are reported in Table 3.
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Insert Table 3 here
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Determination of the optimal numbers of segments requires a balanced assessment of the
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statistics reported in Table 3 (Louviere et al. 2000; Wedel and Kamakura 2000; Andrews and
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Currim 2003). The log likelihood decreases and 2 increases as more segments are added,
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indicating the presence of multiple segments in the sample. The BIC and AIC3 statistics decrease
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monotonically as the number of segments increases, but for all four statistics, the marginal effect
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becomes very small after the three-segment model. The BIC statistic is minimized at three
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segments, indicating that a model with three segments is the optimal solution in this empirical
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application. AIC3, on the other hand is minimized at the five segments. Andrews and Currim
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(2003) have demonstrated that the BIC and AIC3 statistics never under-fit the number of
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segments but may sometimes over-fit, and that over-fitting the true number of segments produces
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larger parameter bias (Andrews and Currim 2003). Moreover, when respondents where assigned
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to each segment, none was allocated to the third segment. Therefore, we chose the two-segment
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model, as shown in Table 4.
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Insert Table 4 here
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The first part of Table 4 displays the utility coefficients associated with the wetland
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management plan attributes, while the second part reflects the coefficients for membership in the
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various segments. The membership coefficients for the second segment are normalized to zero,
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allowing us to identify the remaining coefficients of the model. All other coefficients are
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interpreted relative to this normalized segment (Boxall and Adamowicz 2002).
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For segment one, the utility coefficients reveal that all of the wetland management plan
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attributes are significant determinants of choice. Respondents in this segment prefer those
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wetland management plans which provide lower levels of flood risk, and easy riverbank access.
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The coefficient on flood risk reduction is twice that of easier access to the riverbank for
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recreational purposes (although recall that these attribute levels are qualitatively described). The
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coefficient on ASC is insignificant, whereas the coefficient on tax is negative and significant as
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expected a priori. The segment membership coefficients for this segment reveal that having been
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flooded at least once in the past, having visited the wetland for recreational activities and having
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a university degree decreases the probability of a given respondent belonging to this first
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segment. Interestingly, these respondents seem to prefer lower levels of biodiversity to higher
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levels.
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For the second segment the ranking of the attributes and the sign of some of their effects
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on utility change. All the estimated coefficients for the second segment are statistically
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significant. The positive and significant ASC points to the possible existence of status quo bias
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(Adamowicz et al., 1998). Respondents in this segment are more likely to choose wetland
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management plans which reduce flood risk, and generate high levels of biodiversity conservation
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and easier riverbank access. The coefficient on the flood risk attribute is the largest, indicating
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that this attribute is the most important determinant of wetland management plan choice. This
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attribute is followed by biodiversity and riverbank access in order of magnitude (again, with the
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caveat that these level changes are qualitative). The segment membership coefficients of this
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segment can be implicitly interpreted in relation to the signs of the estimated statistically
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significant parameters for segment one. Consequently, households who have been flooded in the
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past, as well as those with a university degree and those who visit the wetland for recreational
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purposes are more likely to belong to this segment.
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The relative size of each segment is estimated by inserting the estimated coefficients into
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equation (3), which generates a series of probabilities that a given household belongs to each of
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the two segments. Households are then assigned to a segment based on the largest probability
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score among the three segments. Using this procedure, we find that 62.5% of the sample belongs
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to the first segment and 37.5% to the second. Descriptive statistics for the characteristics of each
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segment are given in Table 5.
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Insert Table 5 here
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Respondents in the first segment have a significantly higher environmental behaviour
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index (EBI), although the environmental consciousness index (ECI) is not statistically
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significantly different across the two segments. Households in segment one have significantly
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lower income levels and pay significantly less local taxes than those in the second segment. In
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segment two, respondents, who are the main household decision makers, are more likely to have
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a university degree, which might explain their higher household income levels. In addition,
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households in segment one are less likely to own their own home and cars, which might explain
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the lower levels of local taxes they pay. Households in the second segment are larger, though the
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percentage of households with at least one child does not significantly differ across the two
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segments. The two segments’ average distances to the rivers do not statistically significantly
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differ, however those households in segment two are more likely to visit the wetland for
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recreational purposes (both at present and in the future), and also more likely to have had their
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property flooded at least once in the past.
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6.2. Welfare estimates for wetland management plan attributes
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The choice experiment method is consistent with utility maximization and demand theory
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(Bateman et al. 2003). Compensating surplus (CS) welfare measure for changes in the wetland
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management plan attributes can be derived from the estimated parameters by using the following
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formula (Hanemann, 1984; Bateman et al., 2003).:
ln  exp(Vi1 )  ln  exp(Vi 0 )
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CS 
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where ,  localtax, the coefficient on the monetary attribute, gives the marginal utility of income and
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Vi 0 and Vi1 represent indirect utility functions before and after policy change under
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consideration.
i
i
(8)
 localtax
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The marginal value of change in a single wetland management plan attribute can be
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estimated as a ratio of coefficients. The ratio represents the marginal rate of substitution between
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local tax and the wetland management plan attribute in question, or the marginal welfare
387
measure (willingness to pay (WTP)) for a change in any of the attributes:
388
 2 x attribute 

WTP  

 localtax 
389
where  attribute is the coefficient on the river management attributes (level of flood risk,
390
biodiversity and river access). This coefficient on the wetland management plan attributes are
391
multiplied by two since their binary levels were effects coded (see, Hu et al., 2004).
(9)
18
392
Table 6 reports the implicit prices, or marginal WTP values, for each of the wetland
393
management plan attributes estimated using the Wald procedure (Delta method) in LIMDEP 8.0
394
NLOGIT 3.0. For comparison purposes, WTP estimates are reported for the two segments of the
395
LCM, as well as for the weighted average of these two segments. Poe et al. (1994) convolutions
396
process reveals that WTP estimates for the two segments are different at the 95% confidence
397
level.. The ranking of attributes also differs across the two segments. Therefore the distributional
398
impacts of benefits generated by wetland management plan attributes should be taken into
399
consideration.
400
Table 6 here
401
The implicit prices reported in Table 6 do not provide estimates of CS for the alternative
402
management scenarios. In order to estimate the households’ CS for improvements in wetland
403
management over the status quo, three possible management scenarios are created:
404
405
406
407
408
409
Scenario 0- Status quo, this is the baseline situation where the flood risk level is high,
biodiversity level is low and the access to the river for recreational purposes is difficult.
Scenario 1- In this scenario flood risk is reduced to a low level and the access to the
riverbank for recreational purposes is improved.
Scenario 2- In this scenario conservation actions are undertaken to generate a high level
of biodiversity, while access to the riverbank for recreational purposes is improved.
410
Scenario 3- In this scenario flood risk is reduced to a low level, conservation actions are
411
undertaken to generate a high level of biodiversity and the access to the riverbank for
412
recreational purposes is improved.
413
CS estimates for these scenarios are reported in Table 7. These estimates reveal local
414
households’ average WTP in terms of higher local taxes to move from the status quo (Scenario
19
415
0) to Scenarios 1, 2 and 3. Similarly to the single attribute estimates reported above, the CS
416
estimates for the two segments, as well as for the weighted average of the two segments differ
417
significantly at the 95% confidence level. These findings highlight the necessity to take social
418
equity issues into account while designing wetland management plans.
419
Insert Table 7 here
420
As indicated in Table 7, the highest welfare improvement, i.e. CS, for respondents in the
421
first segment is generated by the first management scenario, followed by the third and second
422
management scenarios, respectively. Specifically the welfarem improvements for households in
423
the first segment is 9.4 zloty per month for the first scenario, 6.9 zloty per month for the third
424
scenario and 0.7 zloty per month for the second scenario. This indicates that the welfare of
425
households in the first segment is improved the most under reduced flood risk and easier access
426
to the wetland’s recreational amenities, while the lowest improvement in welfare results from
427
improving biodiversity level and wetland access. For respondents in the second segment the
428
improvement in welfare is the highest for the third scenario (161.6 zloty per month), which
429
proposes to reduce flood risk while improving biodiversity. On the other hand, at 73.4 zloty per
430
month, the second scenario which proposes to improve biodiversity and wetland access while
431
maintaining high flood risk, generates the lowest impact on welfare.
432
The welfare estimates reported in Table 7 indicate that the primary policy
433
recommendation resulting from this particular case study is the reduction of flood risk to a low
434
level. If the target is to maximize aggregate welfare, then flood risk reduction should be coupled
435
with improving biodiversity. The implementation of this scenario (scenario 3) requires a
436
compromise between the protection of human settlements and livelihoods from potential floods,
437
and the conservation of the biodiversity rich wetland habitats.
20
438
439
7. Conclusions and policy implications
440
This paper reports the results of a choice experiment study undertaken in the Bobrek wetland,
441
located in the Upper Silesia region of Poland. The aim of this study was to investigate the local
442
public’s preferences for alternative wetland management scenarios, defined by their impacts on
443
flood risk, biodiversity conservation and riverbank access for recreational purposes. The data are
444
analysed with the Latent Class Model (LCM) which enabled capturing of heterogeneity in the
445
local public’s preferences at the segment level.
446
The results reveal that the population consists of two different segments. Overall the
447
local public derives the highest values (that is economic benefits) from flood risk reduction. This
448
result is not surprising considering that the catchment area is susceptible to flooding episodes, as
449
extensive floods of 1997 and 2001 and the social and economic damages these have caused can
450
attest.
451
There is however considerable heterogeneity with regards to the local public’s valuation
452
of biodiversity conservation and improvements in access to the riverbank for recreational
453
purposes. The ranking of these two attributes changes depending on the segment. The first
454
segment derives negative values from biodiversity attribute, i.e. this segment would need to be
455
compensated if the wetland management plan were to generate higher levels of biodiversity.
456
There are therefore important distributional equity issues that need to be taken into consideration
457
while designing wetland conservation and flood risk reduction plans.
458
Compensating surplus (CS) measures for different wetland management scenarios are
459
also calculated for the two segments and for the weighted average of the two segments. The
460
results reveal that according to the weighted average, the local public are WTP the most for a
21
461
wetland management scenario that reduces flooding risk, generates higher levels of biodiversity
462
conservation as well as easy access to the riverbank for recreational purposes. The estimated CS
463
for this scenario is as much as 161.6 zloty per household per month, which is almost 88% of the
464
current monthly local taxes paid by an average household in the area. Overall the main finding
465
of this study is that the primary policy goal should be the reduction of flood risk, however in
466
order to maximise total social welfare, a compromise should be reached between reducing flood
467
risk and conserving biodiversity rich wetland habitats.
468
The European Union's Water Framework Directive (WFD) requires regulators to
469
compare benefits and costs in terms of whether waters should be designated as “heavily
470
modified”, and thus qualify for lower environmental targets than “Good Ecological Status”. The
471
estimated benefits generated by the wetland management plan attributes should be compared to
472
their costs, in order to investigate which one of the wetland management plans introduced in this
473
study would improve total net welfare in this region of Poland. With the use of the benefits
474
transfer method, this study could also provide policy-makers with useful information for the
475
management of other similar wetlands in Poland, as well as in Europe, given the current mandate
476
under the WFD.
477
22
478
479
8. Acknowledgements
480
We gratefully acknowledge the European Union's financial support through the AQUASTRESS
481
project under the 6th Framework Programme. We would like to thank Elzbieta Druzynska and
482
Andrzej Potocki for valuable comments, suggestions and fruitful discussions. We would also like
483
to thank Haris Gianniakidis and Nickolaos Syrigos for their assistance in data collection.
484
23
485
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578
28
579
580
9. Tables
581
582
Table 1: Selected wetland management plan attributes, their definitions and levels
Attribute
Definition
Levels
Flood Risk
Risk of flooding in the area in the next 10 years
Low, High*
Riverbank Access
Public’s access to the riverbank for recreational Easy, Difficult
purposes in the next 10 years
Biodiversity
Number of different species of plants and animals, Low, High
their population levels, number of different habitats
and their size in the wetland ecosystem in the next
10 years.
Local Tax
Percentage change in the monthly local tax paid by -10%, -5%, 0, +5%
every household in the area in the next 10 years
583
+10%
*Status quo attribute levels are underlined
584
29
585
586
Table 2. Descriptive statistics of respondents and their households, Sample Size=192
Household and respondent level characteristics
Mean (s.d.)
Age of the respondent (in years)
45.6 (16.2)
Household size
Monthly gross household income (in zloty)
2.8 (1.1)
2478.1 (1253)
Monthly household local tax (in zloty)
183.9 (11.8)
Household’s distance from the river in meters
462 (249.8)
Number of flood episodes suffered in the last decade
2.52 (2.99)
Total damages to the household from floods in the last decade (in
7115.8 (6611)
zloty)
Percentage
Respondent with a University degree and above
26
Household with at least one child
70.8
Household uses the riverbank for recreational purposes
54.6
Household would like to use the riverbank in the future for
66.7
recreational purposes
587
Household’s property flooded
13
Flooded households compensated
28
Source: Upper Silesia River Management Choice Experiment, 2007
588
30
589
590
Table 3: Selection of Number of Segments
Number of
Number of
Log
Segments
Parameters
Likelihood
ρ2
AIC3
BIC
(LL)
1
5
-1498.71
0.11186
3012.414
1517.049
2
14
-1359.11
0.19458
2760.226
1410.472
3
23
-1274.58
0.24468
2618.156
1358.953
4
32
-1246.46
0.26134
2588.918
1363.850
5
41
-1227.49
0.27258
2577.982
1377.898
591
The sample size is 1536 choices from 192 households (N); ρ2 is calculated as 1-(LL)/LL(0);
592
AIC3 (Bozdogan AIC) is (-2LL+3P); BIC (Bayesian Information Criterion) is –LL+(P/2)*ln(N).
593
31
594
595
Table 4. Two-segment LCM estimates for wetland management plan attributes
Segment 1
Segment 2
Utility function: Wetland management plan attributes
Coeff. (s.e.)
ASC
-0.150 (0.105)
1.267***(0.130)
Flood Risk
0.288***(0.057)
0.550***(0.039)
Biodiversity
-0.116**(0.054)
0.255***(0.037)
Riverbank Access
0.149***(0.058)
0.192***(0.040)
Local Tax
-0.093***(0.005)
-0.008***(0.003)
Segment membership function: Household and respondent
characteristics
Constant
0.660* (0.380)
-
-2.310*** (0.874)
-
Recreationalist
-0.616*(0.356)
-
University degree
-0.709* (0.393)
-
Child
-0.073 (0.378)
-
Property Flooded
Log Likelihood
-1359.113
ρ2
0.195
Observations
1536
596
Source: Upper Silesia River Management Choice Experiment, 2007.
597
Two-tailed tests showed 10% (*), 5% (**) and 1% (***) significance levels.
598
599
32
600
Table 5. Characteristics of the households belonging to the two segments
Respondent Characteristics
Segment 1
Segment 2
Mean. (s.d.)
ECI
15.392
(2.511)
13.694 (3.045)
EBI
16.289
(2.424)
16.551 (2.131)
Age
46.483 (17.057)
44.222 (14.516)
Local tax *
172.660 (112.762)
202.539 (109.504)
Income***
2189.46 (1006.764)
Household size*
2.683
(1.0956)
Distance from the
466.75
(244.396)
2959.062
3
(1451.881)
(1.163)
454.167 (256.815)
catchment
Percentage
University degree***
0
69.4
Child
68.3
75
Own Home***
50.8
83.3
Own Car**
59.2
73.6
Property Flooded***
0
34.7
Recreationalists***
47.5
66.7
Future visit***
63.3
72.2
Sample size
120
72
601
Source: Upper Silesia River Management Choice Experiment, 2007
602
T-tests and Pearson Chi square tests show significant differences among the segments at (*)10%
603
significance level; (**) at 5% significance level, and (***) at 1% significance level.
604
33
605
Table 6. Marginal willingness to pay (implicit prices) and 95% confidence intervals for river
606
management attributes (all figures are in zloty per household per month).
Attribute
Flood Risk
Biodiversity
Riverbank
access
607
LCM
608
Segment 1
Segment 2
Weighted
6.194
137.5
88.26
(4.714-7.04)
(92.9-235.6)
-2.495
63.75
42.17
(-3.864- -1.265)
(39.864-116.8)
612
(23.466-72.526)
3.204
48
31.202
(2.469-4.705)
(27.636-92.8)
614
(18.199-59.764)
609
610
(59.83-149.86)
611
613
615
616
Source: Upper Silesia River Management Choice Experiment, 2007.
617
Welfare measures are calculated with the Delta method of the Wald
618
procedure contained within LIMDEP 8.0 NLOGIT 3.0.
619
620
34
621
Table 7. Compensating surplus and 95% confidence intervals for three river management
622
scenarios (all figures are in zloty per household per month).
Attribute
Scenario 1
Scenario 2
Scenario 3
623
LCM
Segment 1
Segment 2
Weighted
9.4
185.5
119.5
(7.2- 11.8)
(120.5- 328.4)
0.7
111.8
(-1.4- 3.4)
(67.5-209.6)
6.9
249.3
(3.3- 10.5)
(160.4- 445.2)
624
625
626
(78- 209.6)
73.4
627
628
(41.7-132.3)
161.6
629
630
(101.5- 282.2)
631
632
Source: Upper Silesia River Management Choice Experiment, 2007.
633
634
635
636
35
637
10. Figures
638
639
Figure 1. Location of the study site in Poland
640
641
642
643
644
645
646
Upper Silesia
647
648
Sosnowiec
649
650
651
652
36
653
Figure 2. Map of the Bobrek catchment
654
655
Source: Krakow University of Technology, (Elzbieta Druzynska, Personal
656
communication, 2007)
657
37
658
Figure 3. Example choice set
Assuming that the following three wetland management plans were the only choices you
had, which one would you prefer?
Management plan
Characteristics
Management plan
Management plan
Neither
A
B
Management plan:
Status Quo
Flood risk
Low
Low
High
Biodiversity
Low
High
Low
River access
Difficult
Easy
Difficult
5% decrease
5% decrease
Same as now
Management plan
Management plan
Neither management
A
B
plan
Monthly local tax
I prefer
(Please tick as appropriate)
659
660
38