Environmental diversity in recreational choice modelling

METHODSEnvironmental diversity in recreational choice modellingAngel Bujosa Bestard,Antoni Riera Font ⁎,1,2Centre de Recerca Econòmica (UIB ·Sa Nostra),Ctra.de Valldemossa km.7.5,07122Palma de Mallorca,Spaina b s t r a c ta r t i c l e i n f o Article history:Received 22October 2008Received in revised form 29May 2009Accepted 30May 2009Available online 13July 2009Keywords:Travel Cost MethodRandom parameter logit Recreation demandEnvironmental diversity ForestsThe accuracy of environmental valuation studies relies,to a great extent,on the suitability of the proxy measures used to capture individuals'preferences.While important advances have been achieved in the last years concerning the characterization of the physical background in which recreational choices are made,Travel Cost Method applications have failed to consider the heterogeneity of landscape and the spatial con figuration of land use.This paper presents an empirical application to forest recreation in Mallorca (Spain),implementing a random parameter logit model to evaluate in terms of goodness-of-fit,model predictions and welfare measurements the effects of environmental diversity on the recreational site-choice process.©2009Elsevier B.V.All rights reserved.1.IntroductionBeyond the provision of forest products (timber,hunting,grazing,etc.)and ecological services (soil formation,climate regulation,water puri fication,etc.),the social function of forests is becoming more and more prominent in recent years (Coles and Bussey,2000;Ward et al.,2005).Unfortunately,outdoor recreation has been proven to be incompatible with other land uses (Lacaze,2000),especially when the demand for such leisure activities surpasses the available supply of recreational sites causing an unacceptably high stress on forest ecosystems.This is the case of some Mallorcan forests with visitation rates exceeding their site carrying capacity by four to eight times and causing a con flict of interests between different land uses (Balaguer et al.,2002).In these circumstances,the reallocation of visitors from crowded areas across the remaining sites and the provision of additional forests intended for outdoor recreation constitute two legitimate policies,not only for reducing the anthropogenic impact on forests,but also for satisfying the needs of society in terms of recreational facilities.However,to achieve a successful implementation of both strategies,policy makers require a previous understanding of the factors thatin fluence the choices made by the public when deciding where to visit for their recreational activities.For this reason,in addition to those factors traditionally considered,such as accessibility,recreational facilities and environmental attributes (e.g.arboreal cover,water bodies,scenic vista,forest fires,etc.),landscape heterogeneity and the spatial con figuration of land use need to be evaluated.There is no doubt that improved information concerning the social preferences for speci fic facilities and environmental attributes will allow forest managers to target new infrastructures at appropriate non-crowded sites increasing their recreational attractiveness and,hence,displacing recreators from congested areas.At the same time,it will make possible to identify the most suitable locations to establish new recreational forests,avoiding the presence of unwelcome interferences from disturbing land uses such as industry,agriculture and urban areas.This paper suggest the use of Geographical Information Systems (GIS)to improve the characterization of the physical context in which recreational choices are made beyond the consideration of conven-tional attributes.More precisely,a set of GIS-based geographical indicators is proposed to measure the environmental diversity,de fined as the environment's property of being diverse or having components differing in quantity and/or in quality.The remainder of the paper is organized as follows.Section 2provides a review of the different site-speci fic attributes used in previous recreational demand studies,outlying the need for additional measures of environmental diversity.Section 3presents the theoretical background of Random Utility Models (RUM)underlying the Random Parameter Logit (RPL)speci fication.The data-set of recreational trips used in this empirical application to forest areas in Mallorca (Spain)as well as the en-vironmental diversity measures suggested in the paper are commen-ted in Section 4.Next,two RPL speci fications are compared to evaluateEcological Economics 68(2009)2743–2750⁎Corresponding author.Tel.:+34971171381;fax:+34971172389.E-mail addresses:angel.bujosa@uib.es ,antoni.riera@uib.es (A.Riera Font).1The authors are af filiate researchers at the Centre de Recerca Econòmica (UIB ·Sa Nostra)and professors at the Department of Applied Economics,University of the Balearic Islands.2The authors gratefully acknowledge Maurici Ruiz,Professor at the University of the Balearic Islands,and Robert L.Hicks,Associate Professor at The College of William and Mary of Williamsburg,for their comments and suggestions on this paper as well as the funding support from the Department of the Environment of the Balearic Islands Government (ContractNo.1211).0921-8009/$–see front matter ©2009Elsevier B.V.All rights reserved.doi:10.1016/j.ecolecon.2009.05.016Contents lists available at ScienceDirectEcological Economicsj o u r n a l h om e p a g e :w w w.e l sev i e r.c o m /l oc a t e /e c ol e c o nthe implications of introducing environmental diversity into site-choice modelling in terms of modelfit,predictive accuracy and welfare measurement error.Finally,some concluding remarks are offered in Section6.2.The consideration of environmental diversity in recreational demand applicationsOver the last30years,RUM have become the predominant ap-proach of the Travel Cost Method(TCM)(Greene et al.,1997;Phaneuf and Smith,2005).This model provides a convenient way to explain the choice among mutually exclusive alternatives incorporating relevant substitution and site quality effects and,consequently, overcomes the recreational demand representation of previous TCM approaches such as the varying parameter model(Vaughan and Russell,1982),the hedonic Travel Cost Method(Brown and Mendel-sohn,1984)and Morey's model(Morey,1981).From a policy perspective,the study of individuals'choices of recreational sites with varying levels of access costs and quality characteristics turns out to be an ideal vehicle,not only for modelling the allocation of visits among alternative sites,but also for attaching values to recreational areas as well as to single attributes related to non-market commodities(e.g.,water quality).However,as the results of this analysis depend to a great extent on the suitability of the proxy measures used to capture the preferences of individuals,the mea-surement of quality and other features associated with recreational sites has been a challenge for economists since thefirst applications of RUMs to recreational demand(Hanemann,1982;Bockstael et al., 1986).As a result,a wide range of site attributes describing the availability of public services and facilities has been considered in-cluding the presence of parking(Hynes and Hanley,2006),board-walks(Parsons and Massey,2003),ramps and boat launch (Provencher and Bishop,2004),view points(Termansen et al., 2004a),playground and sports facilities(Kinnell et al.,2006),trails (Scarpa et al.,2004)and picnic areas(Cutter et al.,2007).Similarly,a large amount of environmental quality measures comprising the scenic vista(Hynes and Hanley,2006),water bodies(Zandersen et al., 2007),water quality(Cutter et al.,2007),biological quality and number offishing species(Johnstone and Markandya,2006),catch rates and expected catch rates(Provencher and Bishop,2004), arboreal cover(Termansen et al.,2004b)and forestfires(Haener et al.,2004)have been used.Finally,other variables involving general aspects of outdoor activities like congestion(Timmins and Murdock, 2007)and accessibility(Knoche and Lupi,2007)have also been considered.At the same time that RUMs were incorporated in TCM, remarkable developments of computer hardware and software al-lowed an improvement and a generalization of GIS in the recreational demand framework.The use of GIS has transformed many aspects of valuation practice providing a means of relaxing some of the restrictive assumptions implicit in TCM applications until the1990s (Lovett and Bateman,2001;Bateman et al.,2003).On the one hand, the modelization of road networks through GIS has allowed an improvement of the measurement of travel distance,which had usually been undertaken through the use of straight lines(Loomis et al.,1995;Bhat and Bergstrom,1997),and a better identification of origins and zones(Bateman et al.,1999).On the other hand,the use of higher resolution data has lead to more precise definitions of substitute recreation opportunities(Termansen et al.,2004b) through an enhanced measurement of variables that had previously been treated in a rather simplistic way(Lovett and Bateman,2001) and the incorporation of new environmental attributes,such as, elevation(Moeltner and Shonkwiler,2005),slope(Zandersen et al., 2007)and the length of boundaries between the recreational site and adjacent regions classified as natural areas(Termansen et al., 2004b).However,valuation studies have underutilized the capacity of GIS to enhance the spatial representation of the environment and,in this way,to provide more reasonable proxy measures to capture pre-ferences.More precisely,a review of recreational demand literature has given evidence of the presence of major gaps in the set of attributes needed to evaluate environmental policies(Cropper,2000) related to the heterogeneity of landscape(Eade and Moran,1996;Troy and Wilson,2006)and the spatial configuration of land use(Lewis and Plantinga,2007)in and around the recreational areas.In this sense,and given the important effects of land uses on the ecosystems' functions as well as their implications on a large variety of en-vironmental policy issues(Turner,1990),appropriate measures of environmental diversity are needed to(1)improve the specification of current recreational choice models overcoming missing variables bias and to(2)increase the representation of natural capital in policy decision-making(Eade and Moran,1996;Troy and Wilson,2006).Following Bockstael(1996),it is not just the total forested land,but its size,shape and conflicting land uses,among other factors,which determine the diversity of a landscape.Therefore,the measurement of environmental diversity,given its complexity,requires an exhaustive geographical characterization concerning a wide set of territorial pri-mary data regarding the biotic(vegetation,flora,fauna,etc.),abiotic (topography,climatology,geology,hydrology,etc.)and anthropogenic elements(land use,infrastructures,equipment,etc.)of the territory of concern.As shown above,previous studies on recreational demand have already included many of these attributes one by one.Nevertheless, such a one-dimensional perspective measuring single attributes is probably an imprecise proxy for representing landscape heterogeneity and the spatial configuration of land use.In fact,following Naveh (2000)and Ortega et al.(2008),a landscape holistic approach rather than the consideration of individual elements in isolation is neces-sary for studying environmental diversity of forests in depth.In this context,GIS geoprocessing tools can provide not only a careful geographical characterization consisting of a large set of single en-vironmental attributes,but also an integrated approach composed by different multicriteria indicators which are intended to reveal,from a holistic view,the uneven composition and pattern of natural areas, that is,their environmental diversity.At this point,although hundreds of landscape indices have been proposed by ecologists to quantify various aspects of landscape heterogeneity(Gustafson,1998;O'Neill et al.,1999),there is not consensus in their application to the recreational demand framework. While some authors have criticized the use of objective measures arguing that individuals do not respond to scientific indicators of environmental quality(McDaniels et al.,1998;Whitehead et al., 2000),the main body of literature has supported their use to com-plement other measures based on qualitative ladders and rankings (Bockstael et al.,1987;Smith et al.,1997;Phaneuf and Smith,2005). This paper attempts to incorporate a set of objective GIS-based in-dicators to measure environmental diversity in and around forest sites.Section4provides a detailed description of these measures and other site-specific data used in this application to forest recreation in Mallorca.3.Model specificationAlthough RUMs have been known for many years,some ap-proaches have recently become applicable since the development of simulation methods(e.g.simulated maximum likelihood estimation). The mixed or RPL model,which consider random taste variation, unrestricted substitution patterns and correlation in unobserved factors over time(Train,2003),has become very common in the recreational choice literature(Herriges and Phaneuf,2002).The RPL generalizes the standard logit model by allowing the coefficients associated with observed variables to vary randomly over individuals2744 A.Bujosa Bestard,A.Riera Font/Ecological Economics68(2009)2743–2750rather than being fixed for everyone (Train,1998;Mistiaen and Strand,2000)providing a convenient way to consider the heterogeneous preferences of consumers in recreational choice modelling.Following RUMs speci fication,the utility U ni that an individual n receives from choosing to visit site i on a given choice occasion,when a choice set of i =1,…,I exists,is assumed to take the form of the conditional indirect utility function which,following a linear speci-fication,can be expressed as:U ni =βV n x ni +e nið1Þwhere β′n x ni is the nonstochastic portion of the indirect utility received during choice occasion if site i is visited,x ni are observed variables related to the alternatives faced by individuals and βn is the vector of estimated coef ficients for individual n representing that individual's tastes.The error term εni captures the variation in preferences among individuals in the population.As the individual is assumed to visit the recreation site that yields him the greatest utility,the probability πni of choosing the i th alternative is:πni =Pr βV nx ni +e ni N βV n x nj +e nj 8j ≠i :ð2ÞFollowing McFadden's speci fication of the multinomial logit model,it is assumed that εni are independent and identically dis-tributed extreme value type I.Then,the site-selection probability in Eq.(2)can be expressed as (McFadden,1974;Train,2003):πni =e βV n x ni PI j =1e βV nx nj:ð3ÞAs βn is not a fixed constant across the sample,a probability function for the coef ficient vector has to be speci fied.Therefore,the researcher has to estimate the parameters of that distribution which,in most applications,has been speci fied asa normal β~N (b ,W )distribution with parameters b and W (Revelt and Train,1998;Train,1998;McFadden and Train,2000).In this way,the choice probability for individual n visiting site i become the integral of expression (3),consequently:πni =Ze βV nx ni I j =1e βV n x njf βðÞd β:ð4ÞFinally,the log-likelihood function for a given value of the pa-rameter vector βtakes the form:LL βðÞ=X N n =1X I i =1y ni ln πni βðÞðÞð5Þwhere N represents the number of individuals in the sample,πni (β)arethe choice probabilities from Eq.(4)and y ni equals one when the n th individual chooses alternative i and 0otherwise.As the solution to expression (5)involves the evaluation of a multiple-dimensional integral which does not have a closed-form,the estimation of such model requires the use of simulation methods (Revelt and Train,1998).4.DataThe island of Mallorca (Spain),the largest one in the Balearic Islands archipelago,has been chosen as study area for this application.Its 364,596hectares of land,divided among agricultural uses (53.23%),natural areas including forests and wetlands (42.73%)and arti ficial uses such as urban areas and infrastructures (4.04%),constitute an ideal environment for investigating the effects of landscape diversity on recreational site-choices.The Mediterranean climate,as well as thespecial topographical and hydrological characteristics present in theisland,have contributed to the biological wealth of its forests.3In this sense,although conifer pines are predominant,especially Pinus halepensis ,other species can also be found alone or in mixed forest compositions (e.g.Quercus ilex,Olea europea ,Ceratonia siliqua,Juniperus phoenicea ,etc.).The 153,115ha of forestland suitable for outdoor recreation in-cluding a wide range of activities such as hiking,picnicking,going for a walk,camping,observing the flora and fauna and adventure sports (biking,climbing,etc.),have been considered in this application.However,only those sites with visitation rates over 1%have been included in the final 28-site choice set.GIS-based data concerning environmental diversity of such areas and information on recreational trips undertaken by Mallorcan residents have been jointly used to estimate the RPL model presented in the section above.4.1.GIS-based dataEnvironmental diversity of Mallorcan forests has been represented using a GIS data model.The biotic and abiotic elements of landscape and the anthropogenic transformations experimented have been identi fied,measured and assembled within ArcGIS 9.2software.Data from three different sources (the Balearic Islands topographic map at scale 1:25,000,the National Institute of Meteorology and the land use map from the National Forest Inventory at scale 1:50,000)has been used and complemented with analogical cartography,aerial photo-graphy and fieldwork inventory.As explained above,beyond the consideration of one-dimensional attributes already included in previous recreational demand studies (e.g.the presence of recreational facilities,forest composition,etc.),a set of objective indicators has been implemented to measure environmental diversity in and around forest sites and capture its effects on recreational choices.Firstly,the territory has been divided into patches of homo-geneous regions differing from their surroundings (Forman,1995)and the area,edge and perimeter (borders of adjacent patches)of each region has been measured and used to calculate a set of landscape ecology metrics (see Table 1).Although some of these indices are simple statistical indicators (e.g.number of patches,mean patch size,patch size standard deviation,etc.)more complex measures are also proposed to model edge density,the variation of patches and shape complexity (Turner,1990;Haines-Young and Chopping,1996).4Secondly,a visibility index has been developed to measure the visible area from the highest point of each recreational site.This measure has been obtained from the combination of a digital elevation model and geoprocessing operators.Therefore,a 360-degree buffer has been constructed and the visible portion of the circle has been calculated using GIS tools.For this purpose,all factors involved in seeing a scenic vista,namely the abiotic characteristics of that place (altitude,slope,aspect,distance to the coast,presence of elevations such as mountains or hills,streams,etc.)as well as the distance that the human eye can visualize (approximately 2km)have been considered.A third indicator is suggested to capture the attractiveness of land use con figuration to undertake recreational activities.More precisely,a landscape quality index is developed to analyse the transitions between forest and agricultural uses in addition to urban develop-ment through the consideration of the biotic (predominant species,forest composition,arboreal cover classi fications,burned forest areas,etc.)and the anthropogenic elements (infrastructures,urban areas,farms,etc.)present in the site and its surroundings.The landscape quality index has been calculated by assigning different weights (from3In 2005,the mean temperature in the island oscillated between 11.9and 26Celsius degrees.Concerning its topographical characteristics,the island has 623kilometres of coastline and its highest peak has 1,364meters of altitude.4The landscape ecology tools developed by the Centre for Northen Forest Ecosystem Research at Lakehead University (Ontario)have been used in this application.Details on these tools are available at http://flkeheadu.ca/~rrempel/patch .2745A.Bujosa Bestard,A.Riera Font /Ecological Economics 68(2009)2743–27501to 5)to those attributes present in and around forest areas evaluating their impact on outdoor recreation.In this way,while those attributes negatively affecting the recreational experience (e.g.urban areas,water treatment plants,power lines,burned forest areas,etc.)have been assigned a low weight,between 1and 2,attributes enhancing outdoor recreation (special forest compositions,protected areas,recreational facilities,etc.)have been assigned a higher one.Finally,the extension (in hectares)of each attribute has been used to calculate the weighted mean representing the landscape quality measure of each speci fic site.5Table 2summarises all site-attributes considered in the paper which can be classi fied in three different information levels:‘primary data ’including variables obtained from existing data-sets and field-work inventory representing one-dimensional environmental attri-butes,‘secondary data ’derived from primary variables using GIS tools and ‘integrated territorial data ’that requires the combination of primary variables and GIS-based geoprocessing operators.4.2.Outdoor recreation surveyIn order to collect data on recreational trips undertaken by Mallorcan residents,a population-speci fic sampling scheme was used to avoid on-site sampling problems.Sampled individuals were drawn from the whole population and not only from those participating in outdoor recreation.6Given the lack of an of ficial list or register to sample Mallorcan residents,they were chosen instead using random survey routes.As a result,759in-person individual home interviews were carried out by trained interviewers.The questionnaire was developed and tested in a pilot survey and the final version was administered from April to July 2006.It was divided in different sections.The first one,‘knowledge of the forest environment ’,focused in analysing the information that residents had about forest concerning dimensions,legal protection,ecological and recreational services provided by forests,etc.The second part of the questionnaire,‘forest land frequentation ’,collected data concerningsite frequentation,not only number of trips to forest but also the sites chosen and the activities undertaken in each speci fic site.611residents stated validly that they had taken one or more trips to forests in the last 12months (80.50%of the sample)and respondents who had visited forests took an average of 10trips each.With regards to the purpose of the trip,the survey indicated that going for a walk was the most popular activity in forests (43.70%),followed by picnicking (23.57%),hiking (21.28%),adventure sports (6.22%)and other activities (5.23%).Section 3paid attention to the ‘typical trip ’,gathering data on the means of transport,size of the group,time spent in the site,costs associated with the visit,etc.7Last section focused on the ‘socio-5Although some of these attributes have been included in the model as presence/absence variables (e.g.recreational facilities and water treatment plants),detailed data regarding their extension has also been available to calculate the landscape quality weighted mean.6Following data from the Spanish National Statistics Institute,the Mallorcan population over 18years of age was about 619,917inhabitants in 2005.Table 1Landscape ecology metrics.a Index DescriptionFormulaa k Area of patch kp k Perimeter of patch k TLA Total landscape area TLA =P l k =1a kNU Number of patches NU=lMPS Mean patch sizeMPS =Pl k =1a kNU PSSDPatch size standard deviationPSSD =ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi1NU −1Pl k =1a k −MPS Þ2 s PSCOV Coef ficient of patches variation PSCOV =PSSD MPSTE Total edgeTE =Pl k =1p k ED Edge density (amount of edge relativeto the area)ED =TE TLA MPE Mean patch edge (average amount of edge per patch)MPE =TE NUMPAR Mean perimeter-area ratio (shape complexity)MPAR =P l k =1p ka kNU MSIMean shape index adjusted for circular or square standard (shape complexity)MSI =Pl k =1pk 2ffiffiffiffiffiπa kp NUSource:own elaboration from landscape ecology tools developed by the Centre for Northen Forest Ecosystem Research at Lakehead University (Ontario).al represents the number of patches in the area of concern.7The ‘typical trip ’refers to the most visited site over all the year.In Mallorca it is quite common to observe recreationists repeating the same trip many times,especially when they are involved in activities such as picnic or walking.For this reason,and given the problems shown by some individuals in the pilot survey ofremembering the details of their ‘most recent ’trip,the ‘typical ’trip de finition has been used.Anyway,not signi ficant differences have been found between the pattern of trips when the ‘typical trip ’de finition has been used instead of the ‘most recent ’one.Table 2Spatial data classi fication.Data sources:(1)Balearic Islands Topographic Map 1:25,000.Balearic Island Government.(2)National Institute of Meteorology.Ministry of Environment.(3)National Forest Inventory.Ministry of Environment.(4)Own elaboration from fieldwork inventory.(5)Own elaboration by means of GIS-based tools.(6)Environmental diversity measures as de fined in Section 4.2746A.Bujosa Bestard,A.Riera Font /Ecological Economics 68(2009)2743–2750economic characteristics’of residents:place and year of birth,level of studies,occupation,household composition,income,etc.This socio-economic data describing sampled individuals was compared with demographic data from the2005Spanish Census.In general terms,no significant differences were found and,hence,it was concluded that the sample was representative of the whole population.The mean age in the sample was42and mean monthly income940euros.48.77%of respondents were male and the main nationality among sampled individuals was Spanish(91.33%)followed by Argentinean(2.13%), Italian(1.15%),German(0.82%)and British(0.82%)among others. Concerning the education level of the sample,33.22%had completed primary studies,37.32%secondary studies and29.46%tertiary studies. Regarding the occupation,65.30%were employed, 4.09%were unemployed,10.64%were househusbands or housewives,11.95% were retired and,lastly,8.02%were students.The Mallorcan road map at scale1:25,000and Teleatlas digital data have been used to calculate the travel time and distance for each trip origin to the28available recreational sites.When more than one route was available for a specific individual,it has been assumed that the shortest one was chosen.The mileage cost and the opportunity cost of driving time have been jointly considered to estimate the travel cost.8 Concerning the opportunity cost of driving time,it has been conservatively calculated by multiplying the round-trip travel time by the one-third of the individual's wage,as proposed by recreational demand literature(Englin and Shonkwiler,1995;Phaneuf and Smith, 2005).95.Model estimation and resultsTwo RPL models have been estimated to evaluate the effects of environmental diversity on recreational choice modelling.On the one hand,a‘restricted’model has been specified using conventional approaches for characterizing site quality as well as one-dimensional indicators representing specific environmental aspects.On the otherhand,an‘unrestricted’model has been implemented by adding to the previous attributes a set of variables measuring environmental diversity as presented in Section4,that is,the landscape ecology metrics and the visibility and landscape quality indices.A short description of all these variables is provided in Table3.NLOGIT Econometric Software has been used to maximize the simulated log-likelihood function with1000replications per observa-tion.A backward stepwise procedure has been followed to obtain the most parsimonious model that identifies the key determinants of choice overcoming collinearity issues.10Table4reports the models with the highest goodness-of-fit where all coefficients for the in-cluded variables were statistically significant.Concerning the estima-tion search for variables accounting for random effects,the evidence suggests that,in the present data-set,only the consideration of the ‘conifer forests’,the‘mixed forests’and the‘kilometres of roads’attributes as random parameter has significantly improved the model fit,indicating that there is random variation in tastes with respect to forest composition and accessibility.Three interactions have been included to capture the preferences of individuals for specific facilities especially oriented to the recrea-tional activity that they were undertaking.11In this sense,the variable ‘picnic site’highlights the interest of picnickers for recreational areas providing tables,grills,WC,drinkable water,etc.At the same time,the variable‘hiking trails’shows the preference of hikers for areas with marked trails.Finally,‘climbing area’equals one when this facility is available on the site and the individual takes on adventure sports.For the statistically significant variables,the signs and magnitudes conform to expectations and the results show that,in the current empirical context,all variables related to recreational facilities(‘picnic site’,‘hiking trails’and‘climbing area’)and accessibility in(‘kilo-metres of roads’and‘isolated buildings’12)and around the site (‘distance to main roads’)have a positive coefficient.Consequently, the presence of any of these attributes in a recreational site increases its probability of being chosen among all available alternatives.In contrast,other variables capturing less desirable attributes(‘water treatment plant’and‘reservoir’),disturbing land uses(‘dry-farming’and‘citrus-farming’)and travel cost have a negative impact on visitation probabilities.Concerning forest composition,individuals try to avoid‘scrubland’at the same time that they prefer more peculiar Table3Description of the variables included in thefinal models.Variable DescriptionTravel cost Travel cost in eurosPicnic site=1if a picnic site is present at the area and thevisitors is a picnicker;=0otherwiseHiking trails Kilometres of marked trails for hiking when thevisitor is a hiker;=0otherwiseClimbing area=1if the site has a climbing area and the visitorundertakes adventure sports;=0otherwise Kilometres of roads Kilometres of roads accessible to cars within therecreational areaDistance to main roads Distance in kilometres from the site to thenearest main roadIsolated buildings Number of isolated buildings in the areaReservoir=1if a reservoir is present at the site;=0otherwiseWater treatment plant=1if a water treatment plant is present atthe site;=0otherwiseDry-farming area Size in squared kilometres of the dry-farming area Citrus-farming area Size in squared kilometres of the citrus-farming area Conifer forest Size in squared kilometres of the conifer forests Mixed forest Size in squared kilometres of the mixed forests Scrubland Size in squared kilometres of the scrubland area Juniperus area Size in squared kilometres of the Juniperusphoenicia areaMPAR shape complexity Mean perimeter-area ratio(landscapefragmentation measure)Coef.of patches variation Coefficient of patches variation(landscapefragmentation measure)Total edge Total perimeter of patches(landscapefragmentation measure)Visibility index Size in squared kilometres of the visible areafrom the highest point of the siteLandscape quality Landscape quality index capturing theattractiveness of land for recreationSource:own elaboration.8The mileage cost has been set to0.19per kilometre according to the official cost per kilometre dictated by the Spanish Government in2005.9A zero opportunity cost of travel time has been assigned to those individuals that have stated that they do not earn any salary(mainly students,househusbands andhousewives).As noted by a reviewer,this assumption could lead to biased coefficient estimates if a correlation between their socioeconomic characteristics(e.g.age or gender)and some environmental attributes exists.However,not all students and housewives have reported a zero income.Indeed,no association between the opportunity cost of travel time and other socioeconomic characteristics has been found in the data.10Alternative specifications with different sets of variables and different stepwise procedures have been implemented.The stability of the coefficients estimates and the significant variables across steps suggest that the stepwise method used to drop insignificant variables has not influenced the significance of remaining variables.11Non-significant coefficients were obtained for the same attributes when they were tested for all individuals or for those individuals undertaking other activities non-related to that specific facility.The rationale behind such result is based on the idea that individuals only take into account those facilities enhancing their recreational experience,ignoring other equipment non-related to the recreational activity in which they are involved.12The variable‘isolated buildings’captures the presence of remote constructions within forests usually linked to old agricultural uses or new infrastructures.However, all these buildings share a common characteristic,the presence of footpaths and trails that increase the accessibility of the area.2747A.Bujosa Bestard,A.Riera Font/Ecological Economics68(2009)2743–2750。