How To Estimate The Recreation Value Of A National Park In Rumania

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1 Bulletin UASVM Horticulture, 69(2)/202 Print ISSN ; Electronic ISSN The Recreation Value of Piatra Craiului National Park: A Combined Travel Cost and Contingent Valuation Survey Diana E. DUMITRAS ), Marioara ILEA ), Ionel M. JITEA ), Felix H. ARION ) ) Faculty of Horticulture, University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca, 3-5 Manastur St, Cluj-Napoca, Romania; ddumitras@usamvcluj.ro. Abstract. This paper describes the steps of estimating the recreation value of a national park by combining two non-market valuation methods: the Travel Cost Method and the Contingent Valuation Method. The questionnaire was designed according to both methods by formulating specific questions. The survey was implemented in Piatra Craiului National Park during the summer of 20, one of the most visited national parks from Romania. The recreation value in terms of consumer surplus per trip is derived from the Poisson model and in terms of willingness to pay per trip from the random effects probit model. It was found that tourists gain significant benefits from recreating in the park. The estimated recreation values are expressed in monetary units and provide justification for attracting funds for developing tourism opportunities in a sustainable way. Moreover, the findings show that tourists are aware of the value of the park and do appreciate the opportunities offered by the park administrations. However, the process of revealing the benefits people gain after visiting the park should receive more attention from the decision makers when developing the management plans. Keywords: travel cost method, contingent valuation method, national park, Romania INTRODUCTION The estimation of the recreation value of the protected areas has been a subject of many studies in the last years probably due to the increased demand for tourism and to the international recognition of the importance to adopt a sustainable tourism. More attention is given to the methodology of estimating the recreation value of protected areas, which are considered as being public goods (Mitchell and Carson, 993). The aim of most of the nonmarket valuation studies is to inform the park managers and the other stakeholders about the real economic value of the protected areas. Numerous studies were conducted on this subject, however, only a few are using two non-market valuation methods at a time. More interest was noticed on using the contingent valuation method along with the choice experiments method (Hanley et al., 998, Morgas et al., 2002, Mogas, 2009), and the contingent valuation method along with the travel cost method (Herath and Kennedy, 2004, Loomis, 2006). Herath and Kennedy (2004) uses the travel cost method and the contingent valuation method to estimate the economic value of a national park and to reveal its economic value because the entrance fee system does not capture it. This is the case of all protected areas, in which no such studies were conducted to determine their real economic value. Loomis (2006) compares the estimates obtained using the travel cost method and the contingent valuation method for single and multiple destination trips. On the other hand, sustainable tourism leads to maximizing the positive benefits and to minimizing or preventing the negative impacts on the environment; however, this can be achieved only where the management actions are well planned or where an adaptive management is applied (Eagles et al., 2002). The economic valuation is one of the tools of adaptive management as regard to tourism in protected areas (Eagles et al., 2002). 6

2 The aim of this study is to estimate the recreation value of Piatra Craiului National Park by combining two non-market valuation methods: the travel cost method and the contingent valuation method. This approach offers more information about the value of the park in terms of recreation than by using only one method. The travel cost method leads to the estimation of the consumer surplus for the actual trip, whereas the contingent valuation method leads to the estimation of the willingness to pay per trip under several hypothetical situations. Thus, this approach helps the managers to know and understand the current and future demand for recreation in the area. MATERIALS AND METHODS The data were collected in summer 20 using an in-person survey. Tourists visiting Piatra Craiului National Park were approach while recreating (Fig.). In total, 87 usable questionnaires were obtained. Piatra Craiului National Park (4,800 ha) lies in the Meridional Carpathians and has four main canyons: Zarnesti, Dambovicioara, Brusturel, Dambovita. It is well-known for the longest and tallest limestone ridge from Romania (Om Peak - 2,238 m). Tourists can choose among the 3 hiking trails of medium and high level of difficulty. Climbing, mountain biking, visiting caves, nature observation, skiing and research are other recreation opportunities offered by the park administration. Inside the park, tourists are allowed to camp or they can stay overnight in the mountain refugees. Tourists can find information at the visitor centre from Zarnesti, where is also the park administration office, and on the information boards placed at the entrances of the parks and on the hiking trails. Tourists can access the park with no fee, except for camping and guided tours. Fig.. Map of Piatra Craiului National Park, Romania Source: After GD 230/2003 The questionnaire was designed based on the characteristics of the two non-market valuation methods: the travel cost method and the contingent valuation method (Champ et al., 7

3 2003). The travel cost method is a revealed preference method used to determine the consumer surplus (CS) based on individual s actual travel costs (Champ et al., 2003). The demand is determined based on the quantity demanded (the number of trips taken to the park by an individual) and the price (the travel cost. Socio-economic characteristics about the individuals may also influence the demand for recreation trips. Respondents were asked to report the number of trips taken to the park during the period and the travel costs associated to the current trip. The demand for recreation trips can be expressed as (Champ et al., 2003): r f tc r, y, z () where r is the number of trips taken by an individual to the park in the period , t cr the travel costs of the current trip, y the income and z a vector of other socio-demographic variables that may influence the demand. The probability of observing that an individual takes r trips can be modeled as: r exp( ) P( r) r! (2) where represents the expected number of trips, determined as a function of travel costs and individuals characteristics (Champ et al., 2003). The maximum likelihood method is used to estimate the Poisson model (Greene, 2003). The consumer surplus is calculated based on the estimated coefficients as: CS i (3) where TC. is the estimated coefficient for the travel cost (Champ et al., 2003). The contingent valuation method is a stated preference method used to elicit the individuals willingness to pay (WTP) for qualitative and quantitative changes in public goods (Mitchell et al., 993, Champ et al., 2003). Respondents are asked to accept or refuse the offer made on hypothetical markets. In this study, the hypothetical good is represented by a recreational trip in Piatra Craiului National Park; the offers made being hypothetical increases in travel costs. The respondents were asked using three single bounded dichotomous choice questions if they would have visited the park under the condition of three different levels of increases in travel costs. The contingent valuation method is based on the random utility theory, which presents utility as being composed from one systematic component and one random component (Hanemann, 984). It is assumed that each individual knows which choice maximizes his utility. Therefore it is expected that the offer made will be accepted at an additional cost if the utility is maximized: V ( j, y P, V0 ( j, y, 0, j 0, (4) and refuse it otherwise, where V is the indirect utility, y the income, s a vector of other individual characteristics, P the offer made (the increase in travel cost, and ε the error terms. The probability of saying Yes is a random variable since the researcher does not observe everything and may be expressed as: Pr( ye Pr[ V (, y P, V0 (0, y, 0 ] F ( V ) (5) where 0 ; F ( ) is the cumulative density function of. The maximum WTP can be determined by setting V equal to zero and solving for P. The probability that an individual accepts the offer corresponds to the standard probit model, since the cumulative density function is assumed to be standard normal (Hanemann et al., 999). This study uses the random effects probit model to estimate the probability that TC i 8

4 individuals would be willing to pay more to visit the park, as suggested by Loomis (997) in case of multiple responses per individual. The maximum likelihood method is used for estimation (Greene, 2003). The willingness to pay is calculated for each observation i from the data using the data and the estimated coefficients (Rollins et al., 2008): N WTP i ( X i ) (6) TC i where α is the intercept, TC. the estimated coefficient for the travel costs and X the vector of cross products of remaining coefficients and independent variables. RESULTS AND DISCUSSIONS The variables included in the models are described in Tab.. Tab.. Descriptive statistics of variables used in regression models Variable Description Mean ± std. dev. Number of visits Visits taken previously to the park 2.93 ± 3.98 Travel costs (RON/trip) Travel costs from home to the park (two-way 82.8 ± 5.87 Increases in travel costs The level of increase in travel costs offered to tourists 6.44 ± 0.37 (RON/trip) with 20% 20% higher costs Increases in travel costs The level of increase in travel costs offered to tourists 4.09 ± (RON/trip) with 50% 50% higher costs Increases in travel costs The level of increase in travel costs offered to tourists 82.8 ± 5.87 (RON/trip) with 00% 00% higher costs Income (RON/household) Monthly income per household ± Age (year Age of the respondent ± 8.5 Gender = male, 0 = female 0.55 ± 0.50 Education = less than 9 classes, 2 = high school, 3 = 3.65 ±.6 college/university degree, 4 = post-graduate degree (MSc, PhD) Camping = went camping, 0 = otherwise 0.2 ± 0.4 Length < 3 days = took a trip up to 3 days long, 0 = otherwise 0.76 ± 0.43 Travel cost results Several models were estimated to investigate the influence of different potential factors on the trip (Tab. 2). Model is estimated considering only the influence of socioeconomic factors along with the travel costs. All estimated coefficients are statistically significant at 5% level or above. The negative sign of travel costs was expected indicating that tourists take fewer trips as the travel costs increase. The estimated coefficient for income is negative; however, it has a small influence on the decision because of its minor value. The negative sign for income was encountered in other studies as well (Shrestha et al., 2002), being explained by the preference of people with higher income towards other types of tourism. Older tourists tend to take more trips; the same holds for male and more educated tourists. Model 2 includes a dummy variable for people who went camping, its estimated coefficient having a positive influence on the demand for recreation trips. Income variable was not significant (t= -0.89, p>0.05) and excluded from the analyses. Model 3 includes a dummy variable for people who took shorter trips, up to 3 days. The corresponded estimated coefficient was positive and significant, meaning that there is a higher demand for short trips. 9

5 Estimation results of Poisson models Variable Model Model 2 - with Camping - Model 3 - with Short trip - Constant (0.3640) (0.3822) (0.6279)*** Travel costs (0.005)*** (0.006)*** (0.005)*** Income (0.0004)** Age (0.0073)** (0.0075)*** 0.04 (0.0070)*** Gender (0.539)*** (0.565)*** (0.58)*** Education (0.0733)*** 0.94 (0.0663)*** (0.0657)*** Camping (0.450)*** Length < 3 days (0.242)*** Log-likelihood No. of obs CS (RON) Standard errors in parenthesis; Levels of significance are indicated as: ***=%; **=5%, *=0% Tab. 2. Contingent valuation results Several models were estimated in the case of the contingent valuation method as well (Tab. 3). Model 3 includes socio-economic characteristics of the respondents. Age was not statistically significant (t=-0.49, p>0.05) and thus, excluded from the analysis. Tourists are willing to pay less if the travel costs increase. Male tourists, as well as higher educated respondents are willing to pay more to recreate in the park. Income coefficient is negative, meaning that the probability of saying Yes decreases as the income increases, but the influence of this factor on the decision is very small. Tab. 3. Estimation results of Random-effects probit models Variable Model 3 Model 4 - with Camping - Model 5 - with Short trip - Constant.5959 (0.7569)**.606 (0.7355) (0.6279) Travel costs (0.0067)*** (0.0074)*** (0.0048)*** Income (0.0004)** (0.0005)*** (0.0003)*** Gender.646 (0.490)***.2740 (0.4497)*** (0.3036)*** Education (0.986) (0.2307)* (0.760)** Camping.688 (0.742)** Length < 3 days.36 (0.3828)*** Log-likelihood No. of obs % correct predictions WTP (RON) (n=26) WTP camping (RON) (n=54) WTP non-camping (RON) (n=207) WTP short trip (RON) (n=98) WTP long trip (RON) (n=63) Standard errors in parenthesis; Levels of significance are indicated as: ***=%; **=5%, *=0% Model 4 includes a dummy variable for tourists who went camping. Its corresponded positive coefficient indicates those tourists are willing to pay more for camping trips. Model 3 includes a dummy variable for tourists who took short trips. The estimated coefficient for 20

6 short trips is positive and statistically significant. Tourists seem to be willing to pay almost twice more to take short trips than long ones. CONCLUSION This study highlights the importance of using more non-market valuation methods to estimate the economic value of a national park as regard the tourism activity. Moreover, using methods that are based on data that express the behavior of consumers from different perspectives may help in gathering more accurate and real information about the demand for recreation and the benefits of recreating in a national park. The travel cost method was based on data that describe the actual trip, whereas the contingent valuation method is based on data that help to understand the future behavior of tourists and their benefits. The willingness to pay is less than the consumer surplus due to several factors, among which the fact that the travel cost method leads to estimates of the Marshallian surplus, while the contingent valuation method leads to estimates of the Hicksian surplus (Champ et al., 2003, Herath and Kennedy, 2004). These results are consistent with previous studies, where the demand for trips was found to be influenced by the travel costs, the length of the trip, income, age and gender (Dumitras, 2008). The findings of the pervious study lead to a value of 32.6 RON for the consumer surplus and a value of RON for the willingness to pay. The probability of saying Yes to travel costs increases being influenced by the level of increases in travel costs, the length of the trip and age. These procedures offer a wider spectrum about the current and future demand for recreation, which has become an important subject in the management planning of protected areas (Eagles, 2004). The park administrations and other stakeholders may use this information in justifying the need of funds to develop tourism opportunities and services in a sustainable manner. Acknowledgments. Financial support for this work was provided by CNCSIS UEFISCSU, project PNII RU type PD code 27/200. REFERENCES. Champ, P., K.J. Boyle and T.C. Brown (2003). A primer on nonmarket valuation. Kluwer Academic Publishers. 2. Eagles, P.E.J. and S.F. McCool (2004). Tourism in national parks and protected areas: planning and management. CABI Publishing. UK. 3. Dumitraş, D.E. (2008). Estimarea indicatorilor de bunăstare în mediul rural (Estimation of welfare measures in the rural area). PhD thesis. USAMV Cluj-Napoca. 4. Eagles, P.F.J., S.F. McCool and C.D. Haynes. (2002). Sustainable tourism in protected areas: guidelines for planning and management. IUCN Gland. Switzerland and Cambridge. 5. Hanley, N., R.E. Wright and V. Adamowicz (998). Using choice experiments to value the environment. Environ Resource Econ. (3-4): Mitchell, R.C. and R.T. Carson (993). Using surveys to value public goods. The contingent valuation method. Washington DC. Resources for the Future. 7. Mogas, J., P. Riera and R. Brey (2009). Combining contingent valuation and choice experiments. A forestry application in Spain. Environ Resource Econ. 43: Romania. Government Decision No. 230 (2003). on the delimitation of the biosphere reserves, national parks and natural parks and constitution of their administrations. 9. Shrestha, R.K, A.F. Seidl and A.S. Moraes (2002). Value of Recreational Fishing in the Brazilian Pantanal: A Travel Cost Analysis Using Count Data Models. Ecological Economics. 42: