Estimating the Value of Recreation and Ecotourism Using Meta-Regression Analysis
Estimating the economic value of recreation and ecotourism is essential for sustainable ecosystem management and informed environmental policymaking. However, values derived from individual studies often vary because of subjective preferences and contextual variability, making it challenging to obta...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-07-01
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| Series: | Land |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-445X/14/7/1504 |
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| Summary: | Estimating the economic value of recreation and ecotourism is essential for sustainable ecosystem management and informed environmental policymaking. However, values derived from individual studies often vary because of subjective preferences and contextual variability, making it challenging to obtain generalizable estimates. To address this issue, this study employed a meta-regression analysis synthesizing 179 willingness-to-pay (WTP) observations obtained from 48 individual valuation studies conducted across various recreational and ecotourism sites in the Republic of Korea. Focusing specifically on national parks, which are prominent providers of cultural ecosystem services, we examined how site characteristics, study design factors, and valuation methodologies influenced estimated WTP values. Outliers were systematically identified and treated using statistical methods, with the random-effects model utilizing studentized residuals yielding the most robust results. Our findings revealed that national parks and studies employing the travel cost method (TCM) were associated with significantly higher WTP values. By applying the developed meta-regression model, we estimated that the total value of recreational and ecotourism services provided by national parks in the Republic of Korea was approximately USD 865.0 million in 2020. These results highlight the effectiveness of meta-regression analysis in synthesizing heterogeneous valuation studies, facilitating more accurate benefit transfers, and offering empirical insights to guide ecosystem service policy and management decisions. |
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| ISSN: | 2073-445X |