Environmental diversity in recreational choice modelling

Ecological Economics 68 (2009) 2743–2750

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Ecological Economics j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / e c o l e c o n

METHODS

Environmental diversity in recreational choice modelling Angel Bujosa Bestard, Antoni Riera Font ⁎,1,2 Centre de Recerca Econòmica (UIB · Sa Nostra), Ctra. de Valldemossa km. 7.5, 07122 Palma de Mallorca, Spain

a r t i c l e

i n f o

Article history: Received 22 October 2008 Received in revised form 29 May 2009 Accepted 30 May 2009 Available online 13 July 2009 Keywords: Travel Cost Method Random parameter logit Recreation demand Environmental diversity Forests

a b s t r a c t The 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 configuration 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.

1. Introduction Beyond the provision of forest products (timber, hunting, grazing, etc.) and ecological services (soil formation, climate regulation, water purification, 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 conflict 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 that ⁎ Corresponding author. Tel.: +34 971 171381; fax: +34 971 172389. E-mail addresses: [email protected], [email protected] (A. Riera Font). 1 The authors are affiliate researchers at the Centre de Recerca Econòmica (UIB · Sa Nostra) and professors at the Department of Applied Economics, University of the Balearic Islands. 2 The 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 (Contract No.1211). 0921-8009/$ – see front matter © 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.ecolecon.2009.05.016

© 2009 Elsevier B.V. All rights reserved.

influence 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 configuration of land use need to be evaluated. There is no doubt that improved information concerning the social preferences for specific 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 conventional attributes. More precisely, a set of GIS-based geographical indicators is proposed to measure the environmental diversity, defined 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 2 provides a review of the different site-specific attributes used in previous recreational demand studies, outlying the need for additional measures of environmental diversity. Section 3 presents the theoretical background of Random Utility Models (RUM) underlying the Random Parameter Logit (RPL) specification. The data-set of recreational trips used in this empirical application to forest areas in Mallorca (Spain) as well as the environmental diversity measures suggested in the paper are commented in Section 4. Next, two RPL specifications are compared to evaluate

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the implications of introducing environmental diversity into sitechoice modelling in terms of model fit, predictive accuracy and welfare measurement error. Finally, some concluding remarks are offered in Section 6. 2. The consideration of environmental diversity in recreational demand applications Over the last 30 years, RUM have become the predominant approach 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 Mendelsohn, 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 measurement of quality and other features associated with recreational sites has been a challenge for economists since the first 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 including the presence of parking (Hynes and Hanley, 2006), boardwalks (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 of fishing species (Johnstone and Markandya, 2006), catch rates and expected catch rates (Provencher and Bishop, 2004), arboreal cover (Termansen et al., 2004b) and forest fires (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 allowed 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 the 1990s (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 preferences. 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 environmental 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 primary 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 necessary 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 environmental 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 complement 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 indicators to measure environmental diversity in and around forest sites. Section 4 provides a detailed description of these measures and other site-specific data used in this application to forest recreation in Mallorca. 3. Model specification Although RUMs have been known for many years, some approaches 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 individuals

A. Bujosa Bestard, A. Riera Font / Ecological Economics 68 (2009) 2743–2750

rather 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 specification, the utility Uni 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 specification, can be expressed as: Uni = βnVxni + eni

ð1Þ

where β′nxni is the nonstochastic portion of the indirect utility received during choice occasion if site i is visited, xni are observed variables related to the alternatives faced by individuals and βn is the vector of estimated coefficients 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 ith alternative is:
 πni = Pr βnVxni + eni N βnVxnj + enj 8j ≠ i:

ð2Þ

Following McFadden's specification of the multinomial logit model, it is assumed that εni are independent and identically distributed extreme value type I. Then, the site-selection probability in Eq. (2) can be expressed as (McFadden, 1974; Train, 2003): πni =

β nVxni

e I P

:

ð3Þ

eβ nVxnj

j=1

As βn is not a fixed constant across the sample, a probability function for the coefficient vector has to be specified. Therefore, the researcher has to estimate the parameters of that distribution which, in most applications, has been specified 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: Z πni =

eβ nVxni f ðβÞdβ: I P eβ nVxnj

ð4Þ

j=1

Finally, the log-likelihood function for a given value of the parameter vector β takes the form: LLðβÞ =

N I X X n=1

yni lnðπni ðβÞÞ

ð5Þ

i=1

where N represents the number of individuals in the sample, πni(β) are the choice probabilities from Eq. (4) and yni equals one when the nth individual chooses alternative i and 0 otherwise. 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. Data The island of Mallorca (Spain), the largest one in the Balearic Islands archipelago, has been chosen as study area for this application. Its 364,596 hectares of land, divided among agricultural uses (53.23%), natural areas including forests and wetlands (42.73%) and artificial 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 the

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special topographical and hydrological characteristics present in the island, have contributed to the biological wealth of its forests.3 In 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 153,115 ha of forestland suitable for outdoor recreation including 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 data Environmental 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 identified, measured and assembled within ArcGIS 9.2 software. 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 photography 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 homogeneous 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).4 Secondly, 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 2 km) have been considered. A third indicator is suggested to capture the attractiveness of land use configuration 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 development through the consideration of the biotic (predominant species, forest composition, arboreal cover classifications, 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 (from 3 In 2005, the mean temperature in the island oscillated between 11.9 and 26 Celsius degrees. Concerning its topographical characteristics, the island has 623 kilometres of coastline and its highest peak has 1,364 meters of altitude. 4 The 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://flash.lakeheadu.ca/~rrempel/patch.

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A. Bujosa Bestard, A. Riera Font / Ecological Economics 68 (2009) 2743–2750 Table 2 Spatial data classification.

Table 1 Landscape ecology metrics.a Index

Description

Formula

ak pk TLA

Area of patch k Perimeter of patch k Total landscape area

TLA =

l P

ak

k=1

NU MPS PSSD

Number of patches Mean patch size Patch size standard deviation

NU = l

l P

ak

MPS = k=1 NU s ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi l
P PSSD = NU 1− 1 ak − MPSÞ2 k=1

PSCOV Coefficient of patches variation TE

Total edge

ED

Edge density (amount of edge relative to the area) Mean patch edge (average amount of edge per patch) Mean perimeter-area ratio (shape complexity) Mean shape index adjusted for circular or square standard (shape complexity)

MPE MPAR MSI

PSCOV = PSSD MPS l P TE = pk ED =

k=1 TE TLA

MPE =

TE NU l P pk ak

MPAR = k =1 NU l P p pkffiffiffiffiffi 2 πak k =1 MSI = NU

Source: own elaboration from landscape ecology tools developed by the Centre for Northen Forest Ecosystem Research at Lakehead University (Ontario). a l represents the number of patches in the area of concern.

1 to 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 1 and 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 specific site.5 Table 2 summarises all site-attributes considered in the paper which can be classified in three different information levels: ‘primary data’ including variables obtained from existing data-sets and fieldwork inventory representing one-dimensional environmental attributes, ‘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 survey In order to collect data on recreational trips undertaken by Mallorcan residents, a population-specific 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.6 Given the lack of an official list or register to sample Mallorcan residents, they were chosen instead using random survey routes. As a result, 759 in-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 concerning

5

Although 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. 6 Following data from the Spanish National Statistics Institute, the Mallorcan population over 18 years of age was about 619,917 inhabitants in 2005.

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 defined in Section 4.

site frequentation, not only number of trips to forest but also the sites chosen and the activities undertaken in each specific site. 611 residents stated validly that they had taken one or more trips to forests in the last 12 months (80.50% of the sample) and respondents who had visited forests took an average of 10 trips 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 3 paid 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.7 Last section focused on the ‘socio7 The ‘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 definition has been used. Anyway, not significant differences have been found between the pattern of trips when the ‘typical trip’ definition has been used instead of the ‘most recent’ one.

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economic characteristics’ of residents: place and year of birth, level of studies, occupation, household composition, income, etc. This socioeconomic data describing sampled individuals was compared with demographic data from the 2005 Spanish 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 was 42 and mean monthly income 940 euros. 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 and 29.46% 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 scale 1:25,000 and Teleatlas digital data have been used to calculate the travel time and distance for each trip origin to the 28 available 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).9

Total edge

5. Model estimation and results

Visibility index

Two 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 other hand, an ‘unrestricted’ model has been implemented by adding to the previous attributes a set of variables measuring environmental diversity as presented in Section 4, that is, the landscape ecology metrics and the visibility and landscape quality indices. A short description of all these variables is provided in Table 3. NLOGIT Econometric Software has been used to maximize the simulated log-likelihood function with 1000 replications per observation. A backward stepwise procedure has been followed to obtain the most parsimonious model that identifies the key determinants of choice overcoming collinearity issues.10 Table 4 reports the models with the highest goodness-of-fit where all coefficients for the included variables were statistically significant. Concerning the estimation 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.

Landscape quality

8 The mileage cost has been set to 0.19 per kilometre according to the official cost per kilometre dictated by the Spanish Government in 2005. 9 A 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 and housewives). 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. 10 Alternative 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.

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Table 3 Description of the variables included in the final models. Variable

Description

Travel cost Picnic site

Travel cost in euros = 1 if a picnic site is present at the area and the visitors is a picnicker; = 0 otherwise Kilometres of marked trails for hiking when the visitor is a hiker; =0 otherwise = 1 if the site has a climbing area and the visitor undertakes adventure sports; = 0 otherwise Kilometres of roads accessible to cars within the recreational area Distance in kilometres from the site to the nearest main road Number of isolated buildings in the area = 1 if a reservoir is present at the site; = 0 otherwise = 1 if a water treatment plant is present at the site; = 0 otherwise Size in squared kilometres of the dry-farming area Size in squared kilometres of the citrus-farming area Size in squared kilometres of the conifer forests Size in squared kilometres of the mixed forests Size in squared kilometres of the scrubland area Size in squared kilometres of the Juniperus phoenicia area Mean perimeter-area ratio (landscape fragmentation measure) Coefficient of patches variation (landscape fragmentation measure) Total perimeter of patches (landscape fragmentation measure) Size in squared kilometres of the visible area from the highest point of the site Landscape quality index capturing the attractiveness of land for recreation

Hiking trails Climbing area Kilometres of roads Distance to main roads Isolated buildings Reservoir Water treatment plant Dry-farming area Citrus-farming area Conifer forest Mixed forest Scrubland Juniperus area MPAR shape complexity Coef. of patches variation

Source: own elaboration.

Three interactions have been included to capture the preferences of individuals for specific facilities especially oriented to the recreational activity that they were undertaking.11 In 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 (‘kilometres 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

11 Non-significant coefficients were obtained for the same attributes when they were tested for all individuals or for those individuals undertaking other activities nonrelated 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. 12 The 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.

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Table 4 Coefficient estimates.

Table 6 Marginal willingness to pay. Unrestricted model

Site attribute

Restricted model

Unrestricted model

Variationa (%)

b/St.Er.

Coefficient

b/St.Er.

− 17.518 2.079 7.792 6.550 10.188 1.742 6.678 − 2.818 − 2.512 − 5.716 − 8.692 − 8.241 − 3.533 − 7.156 3.181

− 0.3231 0.0524 0.0728 0.0239 0.3708 0.0007 0.0231 − 0.0009 − 2.4629 − 0.0015 − 0.1277 − 0.7349 − 0.0023 − 0.0028 0.0015 0.1766(⁎) 0.5528 0.0003 0.2419 0.6679

− 15.551 3.331 7.996 4.175 6.700 2.658 7.285 − 2.741 − 6.144 − 7.569 − 10.748 − 7.412 − 5.124 − 9.888 4.269 2.571 5.671 2.604 5.982 8.003

Picnic site Hiking trails Climbing area Kilometres of roads Distance to main roads Isolated buildings Reservoir Water treatment plant Dry-farming area Citrus-farming area Conifer forest Mixed forest Scrubland Juniperus phoenicia area MPAR shape complexity Coef. of patches variation Total edge Visibility index Landscape quality index

0.1052 0.1931 0.1000 1.0916 0.0011 0.0509 − 0.0026 − 2.2045 − 0.0029 − 0.2789 − 1.9911 − 0.0033 − 0.0048 0.0037

0.1622 0.2253 0.0740 1.1502 0.0022 0.0715 − 0.0028 − 7.6227 − 0.0046 − 0.3952 − 2.2872 − 0.0073 − 0.0087 0.0046 0.5466 1.7109 0.0009 0.7487 2.0672

54.21 16.68 − 26.06 5.37 97.08 40.37 8.60 245.78 58.37 41.73 14.87 119.86 81.92 26.69

0.5789 0.0016 0.7010 − 1660.244 − 2035.977 0.1846 0.1834

6.512 4.746 7.120

Variable

Restricted model Coefficient

Travel cost Picnic site Hiking trails Climbing area Kilometres of roads Distance to main roads Isolated buildings Reservoir Water treatment plant Dry-farming area Citrus-farming area Conifer forest Mixed forest Scrubland Juniperus phoenicia area MPAR shape complexity Coef. of patches variation Total edge Visibility index Landscape quality index Standard deviations Conifer forest Mixed forest Kilometres of roads Log-likelihood function Restricted log-likelihood McFadden-R2 Adjusted McFadden-R2

− 0.2729 0.0287(⁎) 0.0527 0.0273 0.2979 0.0003(⁎⁎) 0.0139 − 0.0007 − 0.6016(⁎) − 0.0008 − 0.0761 − 0.5411 − 0.0009 − 0.0013 0.0010

0.3938 0.0009

6.997 3.036

− 1709.012 − 2035.977 0.1606 0.1597

All estimated coefficients are statistically significant at a 1% level except those denoted by (⁎) and (⁎⁎) which are significant at 5% and 10% level. Source: own elaboration.

Table 5 In-sample forecast of site-choice probabilities. Site

Restricted model Actual probability (%)

Predicted probability (%)

1. Lluc 15.88 13.40 2. Sant Salvador 9.00 6.14 3. Mondragó 7.20 3.43 4. Randa 5.89 7.53 5. Punta de n'Amer 4.58 4.61 6. Cala Santanyí 4.26 3.50 7. Bellver 4.26 5.16 8. Betlem 3.93 5.19 9. Formentor 3.27 3.14 10. Castell dAlaró 3.27 3.49 11. St. Magadalena 3.27 4.84 12. La Trapa 3.11 3.91 13. Bunyola 3.11 2.26 14. Torrent de Pareis 2.62 3.30 15. La Victòria 2.62 2.24 16. Camí des Fangar 2.45 1.97 17. Can Picafort 2.45 3.21 18. Biniaraix 2.29 2.39 19. Albarca 2.13 2.31 20. Gorg Blau 1.96 2.45 21. Port de Sóller 1.80 1.79 22. Galatzó 1.64 1.41 23. Cala Deià 1.64 1.83 24. Bonany 1.64 2.71 25. Es Tossals 1.64 0.82 26. Sant Martí 1.47 1.70 27. Biniamar 1.47 4.36 28. Cúber 1.15 0.92 Total deviation from visitation rates (28 sites) Source: own elaboration.

Unrestricted model

Average absolute deviation

Predicted probability (%)

Average absolute deviation

0.0248 0.0287 0.0378 0.0164 0.0003 0.0076 0.0091 0.0126 0.0013 0.0021 0.0157 0.0081 0.0085 0.0068 0.0038 0.0049 0.0076 0.0009 0.0018 0.0049 0.0001 0.0023 0.0020 0.0107 0.0082 0.0022 0.0289 0.0023 0.2603

15.65 8.46 7.66 4.70 4.64 4.78 4.43 4.17 4.08 3.18 2.96 3.24 3.22 3.22 2.58 2.91 2.13 2.22 1.58 1.94 1.88 1.39 1.38 1.34 1.28 2.20 1.83 0.94

0.0022 0.0054 0.0046 0.0120 0.0006 0.0053 0.0017 0.0024 0.0080 0.0010 0.0031 0.0014 0.0011 0.0060 0.0004 0.0045 0.0032 0.0007 0.0055 0.0002 0.0008 0.0025 0.0025 0.0030 0.0036 0.0073 0.0035 0.0020 0.0947

Source: own elaboration. a The variation has been calculated as the percent change between the marginal WTP of both models. The ‘restricted model’ has been used as the base measure.

and unusual species (Juniperus phoenicia) rather than the most common ‘conifer’ and ‘mixed’ forests.13 Five variables measuring environmental diversity have remained statistically significant in the final specification of the ‘unrestricted’ model. Therefore, it can be concluded that environmental diversity represented by attributes such as visibility, landscape quality or level of landscape fragmentation is an important determinant of site-choice and, hence, is a reasonable proxy measure of recreationists' preferences. For instance, the significant parameter of the ‘coefficient of patches variation’ variable can be interpreted not only as an evidence of the preference of recreationists for site with more variation in patch size, but also as a proxy capturing the interest of individuals for those sites with higher patch type diversity. Overall, the results show the interest of individuals towards more heterogeneous sites with higher landscape fragmentation, uneven land uses, higher visibility and greater landscape quality. With regards to the goodness-of-fit of the models, McFadden-R2 improves from 16.06% in the ‘restricted’ model to 18.46% in the ‘unrestricted’ one when environmental diversity data is included. In the same line, if the adjusted McFadden-R2 is used in order to compensate the effect of the higher number of parameters considered in the ‘unrestricted’ model, the improvement in model specification is also evident.14 Two additional tests Wald and log-likelihood ratio tests have been performed in order to provide further evidence of the improvement in model specification achieved by environmental diversity data. Both tests evaluate the null hypothesis that all coefficients of additional variables are zero. Anyway, the null hypothesis of both tests is rejected showing the relevance of the environmental diversity data included in the ‘unrestricted’ model.15

13 Beyond the more common coniferous and mixed forests, other species such as Holm or evergreen oaks (‘quercus ilex’), carob tress (‘ceratonia siliqua’) or ‘juniperus phoenicia’ can be found in Mallorcan forests. In this case, only the last one has become a relevant factor explaining the choice of recreationists. 14 The so-called McFadden-R2 or Pseudo-R2 was defined by McFadden (1974) and the adjusted McFadden-R2 was suggested by Ben-Akiva and Lerman (1985). Although models with a larger value of both measures are preferred (Long, 1997), literature does not provide any standard or critical level to compare with these goodness-of-fit measures. In any case, the values obtained in this study are similar of those find in recent applications by Provencher and Bishop (2004), Birol et al. (2006) and Cutter et al. (2007). 15 The values for the Wald and Log-likelihood Ratio Test statistics were 61.37 and 97.53 respectively.

A. Bujosa Bestard, A. Riera Font / Ecological Economics 68 (2009) 2743–2750

However, following Greene and Hensher (2003), beyond the comparison of different models in terms of coefficient estimates and goodness-of-fit, it is more informative for policy makers to evaluate the ability to predict site-choice probabilities and calculate Willingness To Pay (WTP) measures. On the one hand, site-choice probability forecasts are an important policy metric very useful to assess, for instance, the impact of a new picnic facility on trip allocation. For this reason, individual site-choice probabilities to the 28 recreational areas have been calculated following Eq. (4). Table 5 lists actual and predicted mean probabilities as well as the average absolute deviation between actual and predicted values. As the ‘restricted’ model has a greater deviation from the observed share of visitation than the ‘unrestricted’ model, it can be concluded that the incorporation of new environmental diversity data reduces the error related to probability in-sample forecasts. On the other hand, the marginal WTP for site attributes has been calculated as the ratio between the coefficients of site attributes and travel cost (Haab and McConnell, 2002). Furthermore, the percent change between the WTP of both models has been calculated and presented in Table 6. With the exception of the ‘climbing area’ attribute, marginal WTP in the ‘unrestricted model’ is systematically higher than the marginal WTP in the ‘restricted’ one, suggesting that the ‘restricted model’ typically underestimates their true welfare measures. 6. Conclusion This paper has used GIS tools to characterize the physical background in which recreational choices are made as a way to provide (1) better proxy measurement of individuals' preferences overcoming potential missing variables bias and (2) a direct route to forest managers to better evaluate land use policies that may affect environmental diversity. A set of GIS-based indicators has been used to measure important factors of landscape heterogeneity such as land fragmentation, visibility and landscape quality. The results prove that people undertaking recreational activities show their preference for forests with greater visibility, landscape quality and uneven composition in terms of tree species and land uses. Overall, the consideration of such variables has improved the goodness-of-fit of the model and its predictive power to estimate site-choice probabilities. In addition, it has been proved that in the absence of environmental diversity measures, welfare estimates are systematically underestimated leading to considerable biases with important implications for environmental policy decision-making. Certainly, all these results have important implications for the future development of land policy. On the one hand, forest managers can analyse how recreational site-choices depend on environmental diversity in terms of land use configuration, analysing the impact of the location and proximity of agricultural and urban areas, the transitions between uses, the potential conflicts along borders of different uses, etc. on the recreational demand. On the other hand, the model can be used to determine the share of visits that a new recreational forest is likely to attract from surrounding residential areas at the time that predicts the impact on the existing ones. However, in spite of the potential of these models to become a useful instrument for forest managers, further research is needed to provide policy makers with all the necessary information. References Balaguer, P., Bauzà, A., Gómez-Valero, L., Clar, B., Colom, A., Mateu, J., 2002. Los usos recreativos de la comarca de Lluc: caracterización socioespacial de los usuarios de los espacios formales de ocio al aire libre. In: Blàzquez, M., Cors, M., González, J.M., Seguí, M. (Eds.), Geografía y territorio: el papel del geógrafo en la escala local. Universitat de les Illes Balears, Palma de Mallorca.

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