Assessing the recreational value of world heritage site inscription: A longitudinal travel cost analysis of Mount Fuji climbers

Tourism Management 60 (2017) 67e78

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Assessing the recreational value of world heritage site inscription: A longitudinal travel cost analysis of Mount Fuji climbers Thomas E. Jones a, *, Yang Yang b, Kiyotatsu Yamamoto c a

Graduate School of Governance Studies, Meiji University, TA Jimbocho Building, Research Laboratory 501, 1-1 Kanda-Surugadai, Chiyoda-ku, Tokyo, 1010064, Japan b School of Tourism and Hospitality Management, Temple University, Philadelphia, U.S.A c Environmental Sciences for Sustainability, Faculty of Agriculture, Iwate University, Morioka, Japan

h i g h l i g h t s
Investigated the longitudinal impact of WHS inscription on recreational value.
Domestic climber CS (2008e2013) calculated at Mount Fuji using ZTCM.
Results show post-inscription recreational value did not fluctuate significantly.
GIS spatial-temporal analysis found significant change in 2 zonal clusters.

a r t i c l e i n f o

a b s t r a c t

Article history: Received 12 October 2016 Received in revised form 5 November 2016 Accepted 6 November 2016

This study evaluates the impact of listing on the recreational value of one world heritage site (WHS) using a zonal travel cost method (ZTCM) and investigates the spatial and temporal pattern of consumer surplus (CS) in each calibrated zone. Climber demand is estimated at Mount Fuji during consecutive summer seasons (2008e2013). Per capita visit rates from 21 origin zones are used to calibrate the longitudinal ZTCM with panel count data generating CS estimates in the seasons before and immediately after inscription. Findings suggest that the value did not fluctuate significantly after WHS inscription. Furthermore, we use spatial analysis tools in GIS to investigate the spatial distribution of zonal CS estimates. Two clusters revealed significant change: central Japan and the peripheral regions. This study's originality derives from its hybrid, revealed preference approach to monitoring recreational value of cultural heritage, combining panel data with field surveys collected from Fuji climbers over six seasons. © 2016 Elsevier Ltd. All rights reserved.

Keywords: Longitudinal zonal travel cost method (ZTCM) World heritage site (WHS) inscription Consumer surplus (CS) Panel count data estimation

1. Introduction Inscription on UNESCO's World Heritage Site (WHS) list is said to provide a “magnet for visitors” (Fyall & Rakic, 2006). This “tourist enhancing effect” of WHS status (Yang, Lin, & Han, 2010) has been quantitatively supported by numerous studies including metaanalytical evidence of a positive relationship between visitor numbers and the presence of WHSs in 66 countries from 2000 to 2009 (Su & Lin, 2014). However, a considerable body of research disputes the correlation with visitor numbers, with contradicting empirical evidence from as far afield as Macau and Barcelona

* Corresponding author. E-mail addresses: [email protected] (T.E. Jones), [email protected] (Y. Yang), [email protected] (K. Yamamoto). http://dx.doi.org/10.1016/j.tourman.2016.11.009 0261-5177/© 2016 Elsevier Ltd. All rights reserved.

showing no significant effect of WHS inscription on visitation
nchez-García, & (Huang, Tsaur, & Yang, 2012; Palau-Saumell, Sa , 2012; Poria, Reichel, & Cohen, 2010). This debate Prats-Planaguma over visitation is part of a larger one that surrounds the cost-benefit implications of WHS listing. Monitoring the pre- and postinscription change in recreational value is thus fundamental to effective site management and marketing, since host organizations and residents could reject tourism-driven conservation if they feel that negative impacts outweigh the benefits (Lindberg, 2001). UNESCO already mandates newly-listed sites to create a management plan to counter environmental impacts such as trash and trail erosion, or social ones such as congestion and commercialization (Jimura, 2011). However, as economic analysis is not stipulated, WHS inscription often invites criticism due to the perceived -vis the perennial paradox of intensification of such impacts vis-a conservation and tourism development (Oates, 1999). In response,

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UNESCO has placed 38 WHSs on the endangered list in accordance with Article 11 (4) of the Convention. Yet such measures struggle to fend off claims of tokenism, for although a lack of compliance could theoretically result in sites being struck off the list, to date such a fate has befallen only two sites worldwide. A more pragmatic counterstrategy would be to establish rigorous monitoring of economic impacts at WHSs to track the benefits of inscription, which are often depicted as intangible apart from a relatively modest fund set aside for sites located in the least developed countries (Frey & Steiner, 2011). Documenting the tangible benefits of a WHS listing is thus vital in order to justify the impacts and conservation costs, yet until recently few valuation studies existed, and those consist primarily of stated preference techniques such as Contingent Valuation (CV) and Choice Model (CM) studies (Tuan & Navrud, 2007). Such crosssectional studies were unable to capture the effects of the inscription, so they have since been complemented by time-series research (Lee & Chang, 2008) that uses panel data (Yang et al., 2010) to econometrically assess the “tourist-enhancing effect” of the WHL, particularly in relation to cultural heritage sites (Yang & Lin, 2011). However, to date there have been no longitudinal revealed preference studies. This study proposes to fill that gap by employing panel data combined with a visitor intercept survey conducted every summer climbing season from 2008 to 13. The research field is Mount Fuji, one of Asia's premier mountain tourism destinations selected as a readily accessible, nontechnical peak that attracted increasing numbers of climbers in the run up to listing as a cultural WHS in 2013. Asia is the world's most rapidly growing tourism market and already accounts for 20% of all listed WHSs, the second largest number of sites after Europe. Primary data was collected from domestic climbers, a visitor segment with important economic impacts. The current crop of climbers also symbolize the continuation of centuries of worship-ascent, a pilgrim legacy that underpinned Fuji's 2013 inscription as a cultural WHS. Climbers' recreational value over six concurrent seasons from 2008 to 2013 was measured using a longitudinal zonal travel cost method (ZTCM) with panel count data estimation. Per capita visit rates from each zone were used to estimate a per-capita travel demand curve and calculate the consumer surplus (CS) in the years before and after inscription, offering a longitudinal revealed preference comparison of pre-and post-WHS economic benefits. Fyall and Rakic (2006) highlighted the lack of WHS research “at the micro site level,” so this paper's originality derives from its longitudinal yet micro approach to investigating the recreational value of a single, cultural WHS site before and after inscription (Yang et al., 2010). Using this hybrid method which combined primary and panel data, the aim was to isolate the recreational value of listing via revealed preference estimates of CS in the seasons before and immediately after inscription. Lastly, we conduct a spatialtemporal analysis on zonal estimates of CS, adding the spatial perspective to further scrutinize the longitudinal change of recreational values. 2. Literature review 2.1. Impacts of WHS inscription Conservation objectives lie at the heart of the World Heritage (WH) program. Founded in 1972 with a convention adopted by UNESCO's General Conference, the program's bellwether had been a cultural heritage relocation project triggered by the 1954 construction of the Aswan Dam along the Nile River (Frey & Steiner, 2011). However, although the contemporary objectives of the program remain focused on mobilizing resources for conservation

(WHC, 2016), the de facto image is that of a “top brand” that confers a competitive advantage over rival global tourism destinations (Buckley, 2004). Inscription is thus perceived as a reliable means of inflating visitor numbers (Shackley, 1998) that provides a “magnet for visitors” (Fyall & Rakic, 2006). The rapid growth in the number of applications to UNESCO's Tentative List is consequently portrayed as indirect evidence of this “tourist enhancing effect,” as national secretariats aggressively seek out listed status to increase the number of WHSs and thereby drive visitation (Ryan & Silvanto, 2014). The fierce competition that surrounds the inscription process is touted as further proof of the list's attractiveness that underpins its promotional and marketing significance as a brand (Ryan & Silvanto, 2009), especially in the tourism sector (Evans, 2001; Li, Wu, & Cai, 2008; Prideaux, 2002). In short, the underlying assumption of a positive correlation between inscription and visitation is fundamental to the list's success. This truism is used by proponents to legitimize the application process, with the forecasted increase in added value used to justify additional costs incurred via promotion and site management. However, in the race for listed status, we may be overlooking the considerable costs inherent in the inscription process, such as those resulting from heightened visitor demand and service standards (Kim, Wong, & Cho, 2007). Beyond fiscal burdens, host communities are also subject to a range of negative impacts including congestion and commercialization (Jimura, 2011). At cultural sites in particular, the transformation in modes and volume of visitor use, and the deterioration of local customs that often accompany inscription can amplify the socio-environmental impacts inherent in tourism development, threatening the authenticity and paradoxically eroding the very same intangible heritage which is the target of conservation (Kolar & Zabkar, 2010; Suntikula & Jachna, 2013). Aside from such direct impacts, listing also invites cumulative ones, such as additional layers of bureaucracy due to shifting spheres of political or administrative power. Territorial jurisdictions can become entangled anew, straining intra- or inter-agency coordination and even undermining national sovereignty (Hazen, 2008). Despite the magnitude and heterogeneity of these management issues at stake, the underlying logic in favour of inscription hinges on the aforementioned ability of WHS status to stimulate visitation that in turn drives added value. Taking first the assumed increase in visitation, UNESCO itself (2014) acknowledges that WHS listing can often accompany increased tourist numbers. However, inscription does not carry a universal mandate to monitor changes in either visitation or recreational value (Frey & Steiner, 2011). In the absence of a systematic UNESCO database, various academic studies have sought to uncover evidence of a positive correlation between visitor numbers and listed status. Several of these have noted a positive but comparatively small effect on visitation at listed sites (Kayahan & VanBlarcom, 2012). For example, American national parks with WHS status had 5.2% greater visitation than non-listed sites from 1990 to 1995 (Galvin, 1997). Likewise in Australia, overall WHS visitation was higher than at comparable control sites both pre-and post-listing (Buckley, 2002), and metaanalysis from 17 of the 24 WHSs in the UK also identified a zero to three percent increase in visitation after inscription (PWC, 2007). At the global level, a positive relationship was demonstrated between tourist numbers and the presence of WHSs in 66 countries between 2000 and 2009 (Su & Lin, 2014). This evidence supports findings from China, where a positive correlation emerged between WHS status and international tourist numbers, cited as evidence of the “greater tourist enhancing effect,” (Yang et al., 2010). However, other researchers have refuted the causal nature of the relationship between WHS inscription and visitation (Rodwell, 2002). An extensive literature review by Poria et al. (2010) found visitor

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awareness of WHS status to be moderate, barely affecting behavior. Nor did empirical results from Macau find that WHS listing had any significant effect on tourism promotion (Huang et al., 2012). In Barcelona, Palau-Saumell et al. (2012) also concluded that WHS status per se is not a significant visitor drawcard. Controversy even exists among equivalent studies at overlapping sites; for example, Patuelli, Mussoni and Candela's (2013) calculation in Italy that “an increase of one WHS in a region's endowment implies a 4% increase in [domestic] tourist inflows” contravenes Cellini’s (2011) comment that the presence of WHSs does not affect the growth rate of tourist overnights per resident. Even local tourism operatorsdpotentially the most direct beneficiaries of increased visitor numbersddoubted WHS's ability to alter the behavior of potential customers (Hall & Piggin, 2002). Conclusive evidence of the ‘WHS effect’ thus remains elusive, partly due to the difficulty in separating uncontrolled surges in visitation from long-term changes in recreational value. In fact, the rapid peaks in visitation often associated with listing have a number of side effects, including inefficient resource use, loss of profit potential, and a strain on social and ecological carrying capacities (Manning & Powers, 1984). Documenting the number and expenditure of visitors is thus necessary for efficient planning and the proper provision of services, particularly given that user expectations may be heightened due to a WHS inscription (Kim et al., 2007). Economic evaluation studies are also needed to justify the long-term conservation costs, for host organizations and residents are likely to reject the concept of tourism-driven conservation if they feel that the negative impacts outweigh the benefits (Lindberg, 2001). Such studies could in turn pave the way for more egalitarian selection criteria during the listing process, which remains susceptible to claims of favouritism due to the opaque nature of inscription decision-making (Frey & Steiner, 2011). Building on studies into the correlation between WHS status and visitation, a growing body of papers has therefore begun to focus on the economic value of WHSs (Choi, Ritchie, Papandrea, & Bennett, 2010; Kim et al., 2007). Such economic analysis of WHS inscription is in keeping with neo-liberal policy-making that uses market mechanisms as a tool for conservation (Pascual & Perrings, 2007), but until recently only a few economic valuation studies existed to offer direction to fiscal policy-makers. These consisted primarily of stated preference methods, such as Choice Model (CM) and Contingent Valuation (CV) studies, employed to assess the “use and non-use values” of public goods (Tuan & Navrud, 2007). However, such studies tend to focus on residents' willingness to pay via taxes or donations without investigating the tourists’ perspectives (Kim et al., 2007). Moreover, the existing literature suffers from two significant limitations related to the data sources and cross-sectional nature of prior studies. First, the study period is of vital importance to evaluating the impact of WHS listing as visitation fluctuates greatly over time, masking the pre- and postinscription effects among broader push and pull factors. The domination of one-off, cross-sectional studies raises doubts over the reliability of the time-dimensional effect of the list based on a fixed-effects model (Huang et al., 2012). Attempts have been made to work around this methodological shortfall by using time-series data to capture the effect of WHS inscription over time, as in the global study conducted by Lee and Chang (2008). Panel data from 2000 to 2005 was also utilized to identify key determinants such as relative income, population in the origin country, cost of travel, and tourism infrastructure for international tourists to China (Yang et al., 2010). Meanwhile at two Canadian sites, the increased visitation at the former was used to extrapolate a range of net present values over a 25 year period at the latter, which was then awaiting inscription (VanBlarcom & Kayahan, 2011). However, monitoring the “tourist enhancing effect” of WHS

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listing also requires economic analysis “at the micro site level” (Fyall & Rakic, 2006), but few existing studies employ data accurate enough to drill down into the socio-economic factors that affect individual consumer behavior (Fredman, 2008; Wang & Davidson, 2010). The evidence from the aforementioned studies is thus inconclusive as they have been conducted at multiple (and often incompatible) sites using imprecise, aggregated data such as total arrivals, tourist receipts or overnight stays. Instead, micro-data evaluation at the individual or household level is preferable for accuracy (Brida, Disegna, & Osti, 2013). In short, micro-level monitoring is required pre- and post-inscription to benchmark social welfare and plan for long-term WHS management. 2.2. Travel cost method Various quantitative methods have been applied to evaluate and assess the economic value of recreation and tourism resources as a particular type of non-marketed goods. There are two methodological categories that can be utilized to price such tourism resources: stated preference methods and revealed preference methods. Examples of the former, such as the contingent valuation methodology (CVM), elicit a willingness to pay (WTP) from potential visitors by directly surveying respondents to rank, rate, or select among alternatives at various levels of attributes (Noonan, 2003). Revealed preference methods, on the other hand, rely on observed behavior and survey data that indirectly tracks the total travel cost as a price surrogate. These two different categories of methods often yield different value estimates for the same resource, sometimes to a substantial extent, with estimates of CS computed using TCM tending to be higher than CVM equivalents (Armbrecht, 2014; Herath & Kennedy, 2004). This finding was further corroborated by a meta-regression analysis synchronizing previous WTP estimates which found that stated preference valuation usually presents a significantly lower estimate than the revealed preference approach (Shrestha & Loomis, 2003). The result can be explained by a set of inherent differences embedded in these two different methods, including, for example, the difficulty for the revealed preference methods to incorporate multi-site visits (Armbrecht, 2014) and differences in theoretical surplus definitions (Herath & Kennedy, 2004). In some cases, a hybrid stated preference and revealed preference method can be used to internalize the advantages of both methods (Boxall, Englin, & Adamowicz, 2003). Amongst revealed preference approaches, the travel cost method (TCM) has been widely used in assessing the economic value of various tourism and recreation resources. While the individual travel cost method (ITCM) examines the individual demand function as a function of travel cost, the zonal travel cost method (ZTCM) establishes the relationship between the frequency of visits from a given zone and the average travel costs of a visit. The former method is more appropriate for local sites that are visited frequently, whereas the latter suits sites whose visitors are less frequent but cover a broad geographic range (Fleming & Cook, 2008). ZTCM is thus utilized to estimate the tourism value of the Changbai Mountain Biosphere Reserve in China; after set-up of 37 residential zones, a two-step approach was used to calculate the CS under the demand curve (Xue, Cook, & Tisdell, 2000). Mayer (2014) also used a ZTCM as part of a cost-benefit analysis of the Bavarian Forest National Park in Germany. Moreover, ZTCM's practical implications for policy-makers have been extensively explored. For example, Chen et al. (2004) assessed the recreation value of Xiamen Island based on a ZTCM of 34 origin zones, proposing some managerial implications based on the estimated per-visit CS. Likewise Herath and Kennedy (2004) used the findings of a 13 origin zone model to estimate the economic value of Mount Buffalo

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National Park, supporting suggestions that the current entry fee system was not fully leveraging the potential economic value. Segment-based analysis calibrated the economic value of bird watching in the Kus¸cenneti National Park, Turkey by a ZTCM of 14 zones, with estimated value found to be much higher than annual investment and cost (Gürlük & Rehber, 2008). In Fleming and Cook (2008)’s ZTCM study of Lake McKenzie, Fraser Island, CS estimates were lowered after adjusting for multiple-site visits. Armbrecht (2014) assessed the value of two rural cultural institutions in Sweden, arguing that ZTCM was preferable to CVM because of simplicity and cost efficiency if visitors are not seeking multiple experiences. Apart from generating the economic value of non-market resources, TCM is flexible enough to investigate how endogenous factors could alter the resource value. Englin and Mendelsohn (1991) proposed a hedonic TCM that accommodates a set of site attributes, and the model results presented the revealed preference estimate of the recreational value of different attributes. Maddison (2001) and Lise and Tol (2002) investigated the influence of climate change on tourism by incorporating weather variables, such as temperature and precipitation, into their TCMs. The estimated coefficients of these weather variables enable better understanding of how climate change would influence the value of various tourism resources. Siderelis, Moore, and Lee (2000) considered perceptions of trail quality as one determinant of recreationists’ demand in a TCM, and they underlined a significant per trip increase in CS if social and environmental conditions were improved. Douglas and Johnson (2004) applied a TCM to value the foregone wages for leisure activities in the U.S.A. Hailu, Boxall, and McFarlane (2005) examined the impact of place attachment from previous trips on tourism demand, arguing that this social psychology influenced the welfare estimates over time. Longitudinal TCMs based on inter-temporal data are important to understand the change of value and in testing for stability of model results (Cooper & Loomis, 1990; Hellerstein, 1993). Due to the unobservable nature of CS estimates, these estimates cannot be externally validated, and therefore, to certain degree, they are only ordinally measurable (Stoeckl & Mules, 2006). The longitudinal analysis of these ordinal estimates is expected to provide a reliable picture of the stability of economic values. Cooper and Loomis (1990) pooled cross-sectional TCM regressions over five years and found that recreational demand was not stable over time. Compared to cross-sectional modeling over years (Poor & Smith, 2004), the longitudinal TCM owns several advantages, such as being able to control for unobserved factors (Hellerstein, 1993). Loomis (1999) used a fixed-effect ZTCM to understand the impact of wilderness use at U.S. national forests and parks. A set of statespecific dummies were included to capture the net impact of longitudinal change, and the serial dependence was also rectified. Weber, Mozumder, and Berrens (2012) applied a similar ZTCM to control for time-varying factors at a single site. By incorporating time-specific dummies, they also demonstrated the demand change over time. Over the past decade, several TCM specification and estimation issues have attracted ongoing research efforts, including specification of the opportunity cost (Fleming & Cook, 2008), zoning of origins (Prayaga, Rolfe, & Sinden, 2006; Stoeckl & Mules, 2006), function form of empirical model (Maddison, 2001), multi-site visits (Hill, Loomis, Thilmany, & Sullins, 2014) and heteroskedasticity problems in regression (Prayaga et al., 2006). In terms of travel cost measurement, based on a set of Monte-Carlo simulations, Stoeckl (2003) found that simply using a ‘scaling’ approach based on travel distance to represent travel cost is time-saving and still generates reasonably accurate welfare estimates. Several more sophisticated econometric models have also recently been

introduced. For example, to remedy the potential biases of on-site sampling associated with endogenous stratification and truncation, researchers utilized two econometric models, including zerotruncated model (Czajkowski, Giergiczny, Kronenberg, & Tryjanowski, 2014) and a two-stage hurdle model (Hill et al., 2014). Owing to a large number of zero trips from some zones, the Zero-Inflated Poisson (ZIP) model was introduced to estimate a ZTCM (Weber et al., 2012). To fit the data that are not equidispersed, a negative binomial model was used in TCM studies (Pascoe, Doshi, Dell, Tonks, & Kenyon, 2014). 3. Research methods and data 3.1. Study site Mount Fuji, Japan's highest peak at 3776 m, is located some 120 km southwest of Tokyo between Yamanashi and Shizuoka Prefectures. Five strata that accumulated during various volcanic eruptions have created an almost symmetrical cone that curves upward from the Pacific Ocean (UNESCO, 2012). Revered from afar since ancient times, a climbing culture emerged gradually as worshippers sought out sacred sites on and around the stratovolcano, and in the 14 t h century practitioners established a trail leading pilgrims to the summit (Polidor, 2007). Together with Fuji's art heritage, this legacy as the “object of worship-ascent,” formed the twin pillars of the WHS application accepted by the UNESCO General Conference in June 2013. Fuji's listing thus reflects Criterion (iii), representing “a living cultural tradition.” Only at a handful of other mountainous WHSs is the act of climbing itself viewed as having religious significance. This acknowledgement of the pilgrimage's value is unique within Japan, although other intangible heritage such as Japanese cuisine do feature amongst the country's tourism inventory. WHSs have grown rapidly from four in 1993, when Japan ratified the UNESCO treaty, to 17 in 2013 when Fuji was inscribed. Tourism to WHSs has attracted considerable attention in Japan, which could reflect a preference for “culturally-approved” destinations (Graburn, 1995 cited in; Jimura, 2011). Aside from the afore-mentioned methodological advantages, the ZTCM approach can be appropriately applied to Fuji's climbers for two reasons. First, the summit is an independent destination that attracts few of the multi-stop trips known to be a structural weakness of ZTCM approaches (Armbrecht, 2014). Fuji also has no close substitute sites, and length of stay does not vary substantially among climbers (Xue et al., 2000). Although great diversity of visitor motivation and behavior can exist even within single site destinations, the selection of Fuji climbers as a study sample has the advantage of considerable homogeneity in visitor motivation and behavior, with the majority aiming to reach the summit at dawn to see sunrise (Yamamoto, 2011). Geographically, Fuji is also a viable subject for ZTCM analysis due to its central location on the main island of Honshu, on the border of East and West Japan, and easily accessible location from both Kanto and Kansai. Domestic climbers are attracted from all across the Japanese archipelago, so the zonal origin can be said to cover a broad geographic range (Fleming & Cook, 2008). Due to the logistical requirements of a summit attempt, climbers are unlikely to make a repeat visit, and highly unlikely to return within the same season, which also strengthens the validity of the sample since “demand for experience is truncated” (Xue et al., 2000). Another methodological justification for site selection is that almost all contemporary climbers start out from the fifth station which forms the terminus of one of a twinlane, paved toll road known as the Subaru line (Fig. 1.). Completed in 1964 during Japan's post-war period of extended economic growth, such roads symbolized the transformation in access infrastructure coupled with rising living standards that

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Fig. 1. The Yoshida trail on Fuji's north face from 5th station trailhead to summit.

resulted in a rapid increase in climbers (Oyadomari, 1989). The 30 km Subaru line extends to an altitude of approximately 2300 m above sea level (Fig. 2), from where the round-trip climbing distance of 14 km makes this a readily accessible, nontechnical peak. The fifth station trailhead is located near the timber line and from here on the trail ascends through a barren landscape of volcanic scoria. This ensures few outstanding issues related to biodiversity or endangered species that could entail significant alternate nonuse values for the site, complicating the calculations (Armbrecht, 2014).

3.2. Data collection This section explains the survey site selection and exclusion criteria employed to ensure a targeted sample of seasonal, adult, and domestic climbers aiming for the summit. Primary data was collected from a study site adjacent to the Izumigataki Junction, approximately 1 km from the 5th station trailhead on the Yoshida route (Fig. 1). Although all of the four trails to Fuji's summit have 5th station trailheads, the Yoshida route in Yamanashi Prefecture, on Fuji's northern flank, is located at the second highest altitude and is the closest to the Kanto plain. As this trail offers the shortest round-trip access from Tokyo, it accounted for 58% of the total 1,841,085 Fuji climbers recorded in the 2008e2013 seasons by infrared beam counters at the 8th station (MOE, 2014). A median of 532 valid sheets were collected per season, representing 2.08% of the total climber population recorded by the counters during the survey period (Table S1.) The questionnaires targeted only descending climbers who had aimed to reach the summit, eliminating casual tourists with no intention of making a summit attempt that could have skewed the sample. Off-season climbers from September to June were also eliminated. Although Fuji is summited year-round, off-season attempts are discouraged by management organizations, and facilities such as trails and mountain huts are mostly closed. Also the presence of snow fields and ice in the off-season deters casual climbers, resulting in distinct profiles and climbing styles. Respondents were at least 18 years old and were selected randomly as they descended from the summit. One in three passing climbers was approached between the hours of 09:00e13:00 in order to capture the peak flow of climbers descending from the

sunrise summit attempt. The study site location and interception method remained constant from 2008 to 13, but the survey dates were varied over 4e5 day periods in the peak JulyeAugust climbing season to include a mix of weekdays and weekends. The questionnaire sheets in Japanese were completed on the spot to reduce recall bias and eliminate the non-response bias often associated with mail-back surveys (Baas, Ewert, & Chavez, 1993). A series of open and closed questions identified respondents’ zonal origins, along with their demographic profile and climbing behaviour (Jones & Yamamoto, 2016). 3.3. Econometric specification A panel count data specification was used to evaluate the recreational value of Mount Fuji for domestic climbers, and to investigate the impact of WHS inscription on this value under the conventional TCM framework. By incorporating the longitudinal information, the panel data model is able to include unobserved zone-specific factors (Hellerstein, 1993), which are difficult to account for in the cross-section model. ZTCM also reflects the number of visits from different zones, and it is reasonable to assume a specific discrete distribution of this variable as a count number. A conventional ZTCM model establishes the statistical relationship between visit rate and a set of explanatory variables as follows:

lnVRit ¼ Xit b

(1)

where VRit is the visit rate to Mount Fuji from zone i at time t. Xit contains the independent variables explaining the rate, and b is a vector of coefficients. To operationalize the model in the count data model, we use VRit¼yit/popit, where yit is the number of visits from zone i at time t, and popit is the total population of zone i at time t. Moreover, after writing out the specific independent variables included in Xit, the longitudinal ZTCM becomes:

lnyit ¼ lnpopit þ b0 þ b1  lngdp pcit þ b2  lncostit þ mi

(2)

where b0 is a constant, lngdp_pcit is the GDP per capita (in log) for zone i at time t; lncostit is the average travel cost (in log) from zone i at time t; mi represents the zone-specific time-invariant factor of zone i that is not captured in any other explanatory variables. For example, this factor could include the cultural tradition of climbing

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Fig. 2. Maps of zonal estimates of CS per capita. (Note: * indicates the use of alternative cost measure in 2013, *** indicates p < 0.01.)

Fuji for each single travel zone. Lastly, to probe the plausible structural change in 2013 after WHS inscription, we incorporate a dummy variable of yr 2013 (yr 2013 ¼ 1 if t  2013, and 0, otherwise) and its interactions with other explanatory variables. Therefore, the econometric model becomes:

lnyit ¼ lnpopit þ b0 þ b1  lngdp pcit þ b2  lncostit þ b3  yr2013it þ b4  yr2013it  lngdp pcit þ b5  yr2013it  lncostit þ mi (3) By testing the significance of b3, b4 and b5 either separately or jointly, we can evaluate the impact of WHS inscription on the travel demand function. To estimate the proposed econometric models, we can utilize either the Poisson model or the negative binomial model depending on the assumption of the dependent variable's distribution. The Poisson model specifies the probability function of dependent variable as:

elit litit yit ! y

f ðyit Þ ¼

(4)

where lit¼E(yitjxit). Note that the negative binomial model extends the conventional Poisson model by incorporating unobserved individual heterogeneity, and it is therefore able to capture overdispersion of data. In this model, the probability function becomes




f ðyit Þ ¼



G yit þ a1 a1
 * 1 Gðyit þ 1ÞG a lit þ a1

!a1

l*it * lit þ a1

!yit (5)

where G($) denotes the gamma function, and a is the variance parameter of the gamma distribution capturing the heterogeneity component. In the negative binomial model, we introduce the overdispersion parameter, a, reflecting the degree of over-dispersion, and the Poisson model corresponds to a ¼ 0. We estimate both Poisson and negative binomial models by directly maximizing the full log likelihood function, including the group specific constants, mi. In order to estimate the consumer surplus (CS) as an economic value, we assume that the travel cost increases until the visits from the zone are depressed to zero. This maximum cost is called the choke cost. Based on economic principles and the specification of our ZTCM, the estimated CS for travel zone i at time t can be calibrated as follows (Chotikapanich & Griffiths, 1998):

c ¼ CS it

1 b b2 þ 1

  b þm bi þ b b 1  lngdppcit  popit  exp b 0

b b þ1  costit 2

(6)

b should be less than 1. where b 2 According to ZTCM specification, it is important to propose a proper zoning policy to define the travel zones. Japan is frequently divided into nine regions, although these are not official administrative units (Akita, 1993). Regions can be further sub-divided into

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prefectures, representing the top tier of local government administration. Japan has a total of 47 prefectures, including 43 regular and two urban prefectures, one territory, and one metropolis. To ensure an even distribution of visit numbers across specified travel zones, we preserved the 16 individual prefectures in the Kanto and Chubu regions around Mount Fuji as individual travel zones. In the other seven regions, the most distant regions of Tohoku-Hokkaido and Kyushu-Okinawa were grouped into two respective travel zones. Finally, the intermediate regions of Kinki, Chugoku, and Shikoku were treated as individual travel zones, creating a total of 21 zones. Primary data was collected from 2008 to 2013. The data of population and GDP per capita of each zone were obtained from the Japanese statistical yearbook published by the Statistics Bureau of Japan. Transport and opportunity costs were employed to construct the travel cost variable, and accommodation and dining costs were excluded as per prior studies (Prayaga et al., 2006). In order to estimate the average transport cost for each zone, we regressed the transport expense (in log) collected from the climber survey against the geographic distance from the zone's central point to Fuji's summit (in log). The predicted value from the regression model is used as the average transport cost from each zone. Meanwhile, the opportunity cost has attracted considerable attention in prior research, with a 30e50% range proposed by Chavas, Stoll, and Seller (1989). One third of the wage has thus become a widely-used proxy, and it was applied here to moderate the hourly wage per zone, which was in turn generated from official statistics (MHLW, 2014). In our sample, 97% of Fuji climbers spent two days on their entire trip (Yamamoto, 2011). Therefore, the wage loss was calculated as the hourly zonal wage multiplied by two working days (16 h). Following inscription on the UNESCO list in 2013, a longdebated cost recovery mechanism was implemented at Mount Fuji in the form of a voluntary 1000 JPY (approximately 10 USD) donation collected from climbers at the trailhead (Jones, Yamamoto & Kobayashi, 2016). The donation e to be used for various conservation measures e could be considered as a portion of the travel cost, assuming that domestic climbers had prior knowledge of the new system, highly probable given the intense media coverage and awareness-building campaigns conducted by site managers. Therefore, this paper employed dual cost measures, the former without the donation contribution in 2013, and the latter with it for a robustness check. Table 1 presents the descriptive statistics of variables used in the proposed econometric model. 3.4. Geo-spatial analysis of clustering To unveil the spatial pattern of zonal CS estimates over year, we employ the Moran's I statistic as a global spatial autocorrelation measure to measure the level of global clustering patterns across different zones. The statistic at year t is specified as:

It ¼

n z0t Wzt $ So z0t zt

(7)

where zt is the vector of centered zonal CS estimates at year t, n is the total number of zones, and So is a scaling factor. W is a spatial Table 1 Descriptive statistics of dependent and independent variables. Variable

Obs

Mean

Std. Dev.

Min

Max

y lnpop lngdp_pc lncost

126 126 126 126

14,611.77 8.264 7.932 9.666

21,494.85 0.981 0.122 0.116

0 6.681 7.768 9.392

99,609 10.033 8.473 9.941

73

weighting matrix: the elements wii on the diagonal are set to zero; wij ¼ 1 if zone i is one of the four nearest neighbours of zone j, and wij ¼ 0, otherwise. The Moran's I statistic ranges from 1 (perfect negative autocorrelation with neighbours) to 1 (perfect positive autocorrelation with neighbours), with 0 indicating no spatial autocorrelation.

4. Results 4.1. Estimation results Table 2 presents the estimated results from the eight ZTCM panel count data models. In Model 1, only two independent variables are included (lngdp_pc and lncost). Both of these variables are statistically significant and consistent with expectations. A positive coefficient of lngdp_pc suggests that zones with a higher level of GDP per capita, and thus those zones with a higher disposable income, are associated with a higher demand to visit Mount Fuji. Conversely a negative coefficient of lncost indicates that higher costs e including both transport and opportunity costs e lead to a lower demand, which is consistent with demand theory. When the dummy variable yr2013 and associated interaction terms are included in Model 2, they are found to be statistically insignificant, suggesting that the WHS inscription exerts little impact on the travel demand of domestic climbers. Unlike Models 1 and 2 using the Poisson model, Models 3 and 4 were fitted based on the negative binomial model. Model 3 includes only the two major independent variables, while Model 4 also considers the structurebreak effect in 2013. They provide very similar estimated coefficients to their counterparts, Models 1 and 2. To gauge the existence of over-dispersion, the parameter a is found to be significant in Model 3, showing the importance of incorporating zonal heterogeneity into the panel count data model. However, by comparing the AIC and BIC values from Models 1 to 4, we find that Model 1 has the best goodness-of-fit with the lowest values of AIC and BIC. Models 5e8 use an alternative measure of cost, which includes the voluntary donation introduced in 2013. When compared to their counterparts in Models 1 to 4, the estimated coefficients vary little in terms of magnitude and significance, indicating results to be robust regarding the choice of cost measure. Also, the findings show that there are no significant structural breaks in the climber demand model after the 2013 WHS listing. Among those four models, Model 5, containing the Poisson specification, is selected to be the best-fit model based on lowest AIC and BIC values. Furthermore, Model 1 is seen to outperform Model 5 among the two best-fit models within the category of different cost measures. These results suggest that the over-dispersion in the dependent variable, zonal travel demand, is not significant, and a conventional Poisson model can adequately represent the data generation process. Also, from a statistical point of view, the cost measure without the 2013 voluntary donation yields a better fit model than the one incorporating the donation information. The estimated coefficients in Model 1 are plugged in to obtain the CS estimates, and Table 3 presents the estimates from 2008 to 2013, with and without the donation fee. CS can be reasonably understood as one means of estimating the market size (Hill et al., 2014). Table 3 reveals that Tokyo contributed a median share of 33% of total recreational value from 2008 to 2013. When combined with the surrounding prefectures of Chiba (8%), Kanagawa (15%) and Saitama (10%), the Kanto region accounts for almost two-thirds (65%) of the value. However, the region's proportion of estimated CS over the six year time period shows a slight decline compared to the 2008 figures (Tokyo e 35%; Kanto 67%).

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Table 2 Estimation results from longitudinal ZTCMs.

Constant lngdp_pc lncost

Model 1

Model 2

Model 3

Model 4

Model 5a

Model 6a

Model 7a

Model 8a

Poisson

Poisson

Negative binomial

Negative binomial

Poisson

Poisson

Negative binomial

Negative binomial

1.789 (6.397) 1.793** (0.705) 1.671** (0.737)

4.036 (7.079) 2.037*** (0.751) 2.092** (0.886) 3.173 (5.024) 0.0352 (0.297) 0.294 (0.584)

2.744 (7.425) 1.818** (0.795) 1.787** (0.825)

4.580 (7.806) 2.062** (0.818) 2.168** (0.963) 3.048 (5.236) 0.0331 (0.315) 0.283 (0.596) 6.341 (4.383) 126 0.291 741.2 817.8

1.811 (6.777) 1.765** (0.720) 1.650** (0.819)

3.521 (7.058) 2.000*** (0.750) 2.010** (0.882) 2.084 (5.268) 0.0356 (0.297) 0.192 (0.610)

2.387 (7.424) 1.798** (0.804) 1.734** (0.881)

3.952 (7.727) 2.019** (0.808) 2.070** (0.954) 1.926 (5.488) 0.0339 (0.313) 0.177 (0.623) 6.518 (5.479) 126 0.290 741.6 818.2

yr 2013 yr 2013* lngdp_pc yr2013*lncost lna N pseudo R-sq AIC BIC

126 0.799 734.1 799.4

126 0.799 739.2 813.0

5.877** (2.581) 126 0.290 736.0 804.1

126 0.799 735.2 800.4

126 0.799 739.6 813.4

6.066** (2.982) 126 0.289 737.1 805.2

(Notes: * indicates p < 0.10, ** indicates p < 0.05, *** indicates p < 0.01. Robust standard errors are presented in parentheses. a indicates the cost measure incorporating the 2013 voluntary donation).

4.2. Zonal analysis of welfare change In order to better understand the spatial pattern of CS estimates across different zones in Japan, the estimated CS for each zone from 2008 to 2013 is mapped out in Fig. 2. A single cluster is evident around Tokyo and the surrounding prefectures on the Kanto plain, a region characterized by the highest per capita CS. To statistically test the pattern of clustering, we further applied Moran's I statistical test (Yang & Wong, 2013), which is used to evaluate the statistical association across neighbouring units. The results suggest that this spatial clustering pattern is statistically significant from 2008 to 2013. The maps' findings offer visual confirmation that the spatial pattern varied little over the six-year research period. This reiterates the findings of all eight models, wherein higher levels of disposable income in Tokyo and the Kanto region are associated with a higher demand to visit Mount Fuji. Additionally, a negative

coefficient of lncost indicates that higher transport and opportunity costs result in reduced demand. A converse example of the importance of geographic proximity is evident in the case of Yamanashi Prefecture, due west of Tokyo, which enjoys a distinct ‘hometown advantage’ as the location of Fuji's most popular climbing trail and the study site of our survey. However, a different picture emerged when CS per capita change from 2008 to 2013 was analyzed. The time-series dimension depicted in the bottom, righthand map reveals a proportional increase in two clusters: the first being the central Japan region of Tochigi, Gunma, Nagano and Fukui, along with the host prefecture Yamanashi, and the second comprised of the outlying regions of Shikoku together with peripheries Kyushu, to the south, and Tohoku and Hokkaido to the north. A slight proportional decrease was noted in Tokyo as described above.

Table 3 Estimates of consumer surplus from 2008 to 2013 (in 100,000,000JPY). Zone

CS2008

CS2009

CS2010

CS2011

CS2012

CS2013

CS 2013y

Tohoku/Hokkaido Ibaraki Tochigi Gunma Saitama Chiba Tokyo Kanagawa Niigata Toyama Ishikawa Fukui Yamanashi Nagano Gifu Shizuoka Aichi Kinki Chugoku Shikoku Kyushu Total (nominal value) Total (constant value)

2.015 1.366 1.023 1.503 7.035 6.090 27.59 11.26 0.6940 0.3642 0.3188 0.2102 1.569 0.8865 0.7360 1.246 4.825 6.122 1.298 0.5258 1.263 77.94 79.57

2.106 1.282 1.007 1.474 6.955 6.025 24.28 10.46 0.6884 0.3120 0.2969 0.2110 1.392 0.8644 0.6983 1.090 4.708 5.845 1.247 0.5556 1.338 72.84 73.35

2.184 1.420 1.092 1.611 7.065 6.109 24.05 10.73 0.7182 0.3552 0.3035 0.2269 1.662 0.9342 0.7219 1.174 4.629 6.013 1.289 0.5781 1.436 74.30 75.86

1.982 1.338 1.034 1.519 6.728 5.796 22.96 10.22 0.6737 0.3339 0.2840 0.2130 1.567 0.8805 0.6783 1.109 4.409 5.652 1.184 0.5303 1.300 70.40 71.88

1.971 1.386 1.068 1.588 7.067 6.038 24.02 10.73 0.6943 0.3460 0.2950 0.2200 1.647 0.9229 0.7074 1.166 4.586 5.809 1.199 0.5370 1.296 73.30 73.01

1.964 1.369 1.063 1.554 6.956 5.958 23.82 10.57 0.6859 0.3409 0.2891 0.2169 1.607 0.9017 0.6925 1.139 4.567 5.773 1.184 0.5298 1.284 72.47 72.18

2.105 1.487 1.142 1.709 7.586 6.479 25.75 11.54 0.7442 0.3711 0.3168 0.2358 1.774 0.9926 0.7607 1.256 4.907 6.216 1.283 0.5754 1.386 78.62 78.30

(Note: y indicates the cost measure incorporating the 2013 voluntary donation of JPY 1000).

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5. Discussion 5.1. Discussion Despite multiple prior studies investigating the impact of WHS listing on recreational value, a fierce debate still surrounds the costbenefit implications of inscription due to a lack of economic evaluation and the failure of previous studies to isolate the impact of inscription as the independent variable. This paper employed a longitudinal ZTCM to offer a longer-term understanding of the change in value and also to test for stability of the model results (Cooper & Loomis, 1990; Hellerstein, 1993). The longitudinal approach had the added advantage of providing visual data that enabled simultaneous display of both cross-sectional and timeseries dimensions (Yang & Lin, 2011). In terms of answering the research question, these findings suggest that the WHS inscription in 2013 exerted little direct impact on the travel demand of domestic climbers. Our findings thus corroborate previous studies refuting the tourism-inducing effect of WHS listing (Huang et al., 2012; Palau-Saumell et al., 2012; Poria et al., 2010), and contravene exponents (Su & Lin, 2014; Yang et al., 2010). Moreover, the results do not support previous suggestions that WHS listing represents a highly “appealing brand” for Japanese visitors (Jimura, 2011) who prefer “culturally-approved” destinations (Graburn, 1995). Unlike the findings of Cooper and Loomis (1990), the crosssectional results revealed consistently stable recreational demand over the six-year period, as evidenced by the eight models shown in Table 2. Inter-model scoping lead to selection of Model 1, which was in turn used to calculate cross-sectional zonal estimates of per capita CS for the years 2008e13. The comparatively stable crosssectional results can partly be attributed to certain aforementioned site-specific factors, including Fuji's small proportion of multi-stop and repeat visitors and the homogeneity of travel motivation (Armbrecht, 2014), with the vast majority climbing to see the sunrise (Yamamoto, 2011). The selection of ZTCM is further justified by the results which confirmed that the Fuji climber market covers a broad geographic cross-section of the Japanese archipelago (Fleming & Cook, 2008). The inclusion of the two independent variables results in zones with a higher level of disposable income being associated with a higher visit demand, thereby accurately reflecting the reality that the median annual recreational value of Tokyo (33%) combined with the surrounding Kanto region (65%) is contributing a dominant market share (Table 3). The robustness of these findings can be externally validated via other field studies that have captured Fuji climber demographics (Yamamoto, 2011). Although such variables as population were controlled for, these results reflect the demographic imbalance of the Kanto plain which contains a larger proportion of younger residents than the national average due to favourable education and employment conditions. Convenient access from Tokyo to Fuji's north face is also a significant factor, since direct bus services are frequently available from Shinjuku, the Tokyo bus terminal, to the trailhead in Yamanashi. Conversely, higher transport and opportunity costs lead to comparatively lower demand amongst more distant regions, or vice-versa as in the case of Yamanashi's ‘hometown advantage.’ Time-series data analysis also provided a longitudinal depiction, with CS per capita change from 2008 to 2013 showing a proportional increase in two regional clusters: the central Japan region and the outlying regions at the southern and northern extremes of the archipelago. The concurrent decline noted in Tokyo is supported by the CS estimates (Fig. 3) which reveal an absolute decline from 2008 to 9 and 2010e11. The 2009 data could represent a ‘normalization’ process following heightened public awareness

75

linked to media coverage that surrounded Fuji's tentative WHS listing in 2008. Although outside the parameters of our research, a 132% annualized increase in climber numbers was observed in the 2008 season (MOE, 2016), supporting Ryan and Silvanto’s (2014) suggested tourism-enhancing effect of tentative listing. Further research is needed to verify the link between visitation and tentative listing, and compare the effect of this stage with the impact of inscription. The decline in 2011 is likely due to the aftereffects of the 3/11 disaster and subsequent sense of national mourning that stymied various tourism-related sectors across the country (Chew & Jahari, 2014). From a WHS management perspective, Fuji's cultural (not natural or mixed) status is important for two reasons. First, a stronger correlation between listing and visitation has been identified at natural rather than cultural WHSs (Su & Lin, 2014), although Yang et al. (2010) found the reverse effect in the case of international tourist arrivals in China. Our results support the former study, and the relative scarcity of natural WHSs could also help explain their extra appeal. The WHS program was originally established with a predominantly cultural agenda, and although it was later broadened to include a nature category, the current list reflects the continued dominance of cultural heritage e of 962 sites listed by 2012, 745 were cultural, 188 natural and 29 mixed property sites. Secondly, Fuji's case could have implications for other potential WHSs that are already world-famous attractions prior to listing. Our results lend support to the case for a greater increase in visitation and higher impact on the number of foreign visitors at lessestablished sights (Frey & Steiner, 2011; Van der Aa, 2005). Equally, adding one more WHS will have less impact on international arrivals to countries which already have multiple listed WHSs (Su & Lin, 2014). This could be true of Japan which had 20 properties listed as of 2016 although more research is needed to compare contexts such as the Japanese one with the prevailing westerncentric model (Bryce, Curran, O'Gorman, & Taheri, 2015). 5.2. Implications The findings have several implications for research, management and policy. The impact of listing on recreational value depends on such variables as the destination's WHS status (natural or cultural) and prior fame, and likely differs amongst international or domestic segments. Nonetheless, our hybrid, revealed preference approach offers a simple but effective methodology that combines ZTCM and GIS by incorporating cross-sectional and time-series analyses into an easily comprehensible visual aid. GIS has already been demonstrated as an effective tool for facilitating decision
lez-Caba
n, making in resource management (Baerenklau, Gonza Paez, & Chavez, 2010). Brainard (1999) used GIS to calculate and predict CS for a set of attractions based on TCM coefficients estimated elsewhere. Moreover, Cullinan, Hynes, and O'Donoghue (2011) conducted a geo-simulation based on TCM formula and estimated the value of potential sites with the aid of GIS. This paper's hybrid methodology could be a prototype for a simple addition to the resource managers' toolkit that would enable enhanced monitoring of recreational value to facilitate the decision-making process before, during and after WHS listing. Creation of such a platform to discuss visitor management issues in a transparent fashion is an important step since residents and host organizations could reject the core WHS concept of tourism-driven conservation if negative impacts appear to outweigh the benefits (Lindberg, 2001). The ZTCM also enables managers to predict climber sensitivity to changes in travel costs such as the new donation system introduced immediately after listing. These findings suggest that the current amount of 1000 JPY ($10US) will not make a significant difference in terms of reducing demand.

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T.E. Jones et al. / Tourism Management 60 (2017) 67e78

Fig. 3. Climber numbers and CS estimates in constant value 2008e13 (CPI adjusted).

The results also have implications for such international organizations as UNESCO, the inter-governmental agency that administers the WHS program. Market mechanisms are increasingly encouraged as a tool for conservation (Pascual & Perrings, 2007), and as evidenced by Fuji's new donation system, one increasingly common strategy seeks to maximize revenue for conservation funds. Innovative cost recovery schemes could include technical solutions such as advanced reservations via email or online booking (Schwartz, Stewart, & Backlund, 2012). However, UNESCO currently seeks to maintain a neutral stance by delegating the debate over development decisions to WHS managers, generating uncertainty over whether iconic destinations such as Fuji should be rationed, in keeping with sustainable tourism doctrines that seek to conserve quality of environment and visitor experience over quantity, or actively promoted, to leverage maximum recreational value for domestic and international audiences. UNESCO could do more to steer such decisions without interfering directly. A first step would be to openly acknowledge the costs and benefits of the WHS program in a more systematic and transparent manner, for example by redesigning the requirements of each site's management plan to ensure that it clearly identifies the recreational value of inscription from the perspective of a range of stakeholders. This would in turn encourage broader stakeholder participation in a comprehensive cost-benefit analysis which could empower certain destinations to question or ultimately veto WHS listing, while focusing collaborative efforts on other tentative sites that have more to gain from inscription.

2012; Yang et al., 2010), the domestic market was selected here due to the ZTCM approach employed. The exclusion of the small but growing sub-segment of international climbers helped to ensure the sample's demographic homogeneity and thus maintain its validity, but it is acknowledged that they have increasing symbolic importance in light of Fuji's listing, and remain a priority for future research. However, capturing the recreational value of the international climber segment using a ZTCM approach is problematic, and our longitudinal methodology could face additional challenges given that the seasonal composition of international climbers varies significantly. Further studies would thus be needed to control for macro-economic factors such as exchange rates, weather conditions, visa requirements and concern over nuclear radiation. This paper also faced three internal methodological limitations. First, data was only collected on the Yoshida route, one out of a total four climbing trails. However, this trail was selected because it has the largest market share, accounting for over half of all climbers recorded by infrared trail counters. The second shortcoming is also related to sample size, since questionnaires were only collected from 2008 onwards, preventing an evaluation of Fuji's listing on the tentative list. In order to capture the longitudinal impact more precisely, extending the post-listing sample would have been preferable but was not feasible due to changes in access arrangements and a typhoon that affected the 2014 sample. Finally, climber awareness of the listing was assumed, although a significant proportion of visitors have no prior knowledge about the destination's WHS status, and little understanding of the list itself (Hazen, 2009; King & Prideaux, 2010).

5.3. Limitations 6. Conclusion This paper also recognizes certain methodological limitations. For one thing, Fuji's iconic status prior to listing could be a limitation, since sites that were already prestigious international destinations prior to WHS status, such as the Pyramids of Giza or the Great Wall of China, tend to benefit less from additional visitation (Van der Aa, 2005). Frey and Steiner (2011) see the listing of international “super-sites” such as the Taj Mahal or Stonehenge as superfluous, and Fuji's fame prior to listing could raise similar questions. Unlike previous research into the tourism-inducing effect of WHS status that focused on international visitors (Huang et al.,

This paper investigated the underlying expectation of correlation between WHS listing and an increase in added recreational value by testing the pre- and post-inscription structural change in ZTCM function. Longitudinal, time-invariant, and zone-specific factors were accounted for to capture the pre-and post-inscription change in social welfare. Per capita visit rates from 21 zones, comprising 16 prefectures and five regions in Japan, were used to estimate a demand curve and calculate the CS in the years before and after inscription. No significant change in the domestic ZTCM model was observed before and after inscription, and further

T.E. Jones et al. / Tourism Management 60 (2017) 67e78

spatial-temporal analysis of zonal CS estimates suggested a generally stable pattern over time. However, GIS mapping revealed two clusters witnessing significant change of zonal CS estimates: central Japan and the peripheral regions. Acknowledgements This research was the recipient of JSPS KAKENHI Grant Number 26760023. Thanks to Dr. Shigeo Aramaki (Mount Fuji Research Institute) and all the respondents for help with the survey, also to Meiji University's International Collaboration Office for their International Guest Scholars Program. Appendix A. Supplementary data Supplementary data related to this article can be found at http:// dx.doi.org/10.1016/j.tourman.2016.11.009. References Akita, T. (1993). Interregional interdependence and regional economic growth in Japan: An input-output analysts. International Regional Science Review, 16, 231e248. Armbrecht, J. (2014). Use value of cultural experiences: A comparison of contingent valuation and travel cost. Tourism Management, 42, 141e148. Baas, J. B., Ewert, A., & Chavez, D. J. (1993). Influence of ethnicity on recreation and natural environment use patterns: Managing recreation sites for ethnic and racial diversity. Environmental Management, 17(4), 523e529.
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Kiyotatsu Yamamoto PhD in agriculture from the University of Tokyo where he was assistant professor (1999e2011). Now an associate professor in Iwate University's Faculty of Agriculture. Studies park planning and behavioral psychology of tourists in national parks and protected areas. Since the Great East Japan Earthquake and Tsunami Disaster in 2011, he has been tackling reconstruction to create community, conserve regional resources and promote tourism along the Iwate coastline.

Yang Yang (PhD) is an assistant professor in the School of Tourism and Hospitality Management at Temple University (School of Tourism & Hospitality Management, 1810 N. 13 t h Street, Speakman Hall 111, Philadelphia, PA 19,122. ). His areas of research interest include tourist flow analysis and financial analysis in the hotel industry.

Thomas Edward Jones (PhD) is associate professor at Meiji University's Graduate School of Governance Studies in Tokyo. His research revolves around social science solutions to visitor management issues and his publications focus on national parks, natural and cultural heritage, and nature-based tourism.