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Shrestha, Ram K.; Loomis, John B.

doi:10.1023/a:1023658501572

Cited by 74 publications

(7 citation statements)

References 42 publications

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“…We found that the TCM estimates were significantly higher than the CV estimates at the 5% level. Our result of value difference from TCM and CV was consistent with some of the existing literature that found that TCM tended to generate higher-value estimates than the CV [45][46][47][48][49][50][51]. A range of framing and methodological issues may potentially explain differences between value estimates from the TCM and the CV.…”

Section: Willingness To Contributesupporting

confidence: 87%

Valuing the Recreation Benefits of Natural Springs in Florida

Grogan

et al. 2018

Water

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Karst springs are scenic natural resources in karst areas of Florida, currently under threat from increasing groundwater withdrawal from the Floridan Aquifer and pollution resulting from a variety of sources. This paper estimates the current recreation benefits from visiting springs using the travel cost method and elicits residents’ willingness to contribute for springs restoration using the contingent valuation method. It further compares the performance of count data models correcting for endogenous stratification and truncation, and finds that the annual consumer surplus per person per trip is between $20 and $43, and the annual total recreational value for the four springs studied is about $25 million. Furthermore, visitors are willing to contribute $12 to $14 per person per trip for springs restoration without reducing trip demand.

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Section: Willingness To Contributesupporting

confidence: 87%

Valuing the Recreation Benefits of Natural Springs in Florida

Grogan

et al. 2018

Water

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“…The findings of this study were not consistent to those obtained by Chae, Wattage, & Pascoe (2012) and Pirikiya et al (2016), which is estimated to have differences in social-economic characteristics, and sample size. A strong correlation also was found between the number of visits and the income level, when such results were shown in findings by Shrestha & Loomis (2003); Chae et al (2012); and Twerefou & Ababio (2012). Travel cost and income are not the main factors for attracting people to come to the lake.…”

Section: The Model Estimationmentioning

confidence: 74%

Identifying the determining factors of recreation demand in Kongar Lake of South Sumatera: An individual travel cost approach

Suhel

Bashir

Yuliana

2019

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Assessing the economic value of natural resources is always challenging and depends on various perspectives. Specifically, this study seeks to identify the determining factors of recreation demand regarding the Kongar Lake of South Sumatera. The data was obtained from a survey of 150 individuals who visited the lake. The approach used descriptive statistics and an individual travel cost method (ITCM) to conduct the investigation of distributional effects across variables on the number of visitors by using a linear multiple regression approach with a natural logarithm model. The findings suggest that (i) joint recreation demand is influenced by travel costs, income, distance, education, age, and work hours per day; (ii) in part the results of this study show that determining factors of recreation demand are based on travel cost, distance, and education; (iii) the consumer surplus was IDR.16,912 per visit and the annual recreational value included 1,720 people who visited the lake annually over an area of 5,298,288 per ha. The results of this study reveal that Kongar Lake has a considerable recreational value that, from this point of view, can help policymakers to make the case for preservation planning and sustainable utilization of natural resources.

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“…Here, however, values within Y k(n) can be specified at levels associated with the omitted nth observation, because a primary study is in fact available (Shrestha and Loomis 2003). This specification of methodological covariates should be considered a hypothetical best-case scenario, in that it forecasts transfer error with methodological variable values chosen to exactly match the nth hold-out observation in each of the N test cases.…”

Section: Convergent Validity and Transfer Errormentioning

confidence: 99%

“…For example, followingShrestha and Loomis (2003), preliminary models were estimated in which explanatory variables not significant at p<0.20 and p<0.10 were excluded. Results from the model were similar for all specifications.17 This study is nearly identical to the "unweighted, unrestricted" model developed byJohnston et al (2006b), differing only in a single variable characterizing the targeted species of a single observation (musky) and the omission of a single observation.…”

mentioning

confidence: 99%