Uncovering Seasonal Heterogeneity in Forest Ecosystem Valuation: Evidence from a Meta-Analysis with Experimental Insights
Abstract
1. Introduction
- To what extent does willingness to pay (WTP) for forest ecosystem services vary across different seasons (spring, summer, autumn, and winter)?
- How robust are these seasonal differences in WTP once socioeconomic, methodological, and regional moderators are controlled for in a meta-regression framework?
- What are the implications of seasonal heterogeneity in WTP for policy design, benefit transfer, and sustainable forest management strategies?
2. Literature Review on Seasonal Preferences and the Application of Mixed-Effects Models
2.1. Seasonal Preferences
2.2. Meta-Analysis Framework and the Role of Mixed-Effects Modeling
3. Empirical Analysis
3.1. Systematic Review
3.2. Database Compilation
4. Meta-Analysis Specification and Estimation Results
4.1. Empirical Specification
4.2. Econometric Specification for Testing Seasonal Preferences and Their Effects
- is the constant term;
- represents the vector of coefficients for the continuous or categorical explanatory variables;
- through are coefficients for the seasonal dummy variables, capturing the seasonal heterogeneity in WTP estimates;
- is the error term, assumed to be independently and identically distributed;
- denotes the study identified, and refers to the time dimension (if applicable).
5. Results
5.1. Descriptive Results
5.2. Meta-Regression Estimation and Results
5.3. Testing Seasonal Variation Using the F-Test
5.4. Seasonal WTP Distributions
5.5. Results of Experimental Estimation Using Robust Mixed-Effects Model
6. Conclusions and Discussion
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Appendix A
Category | Value | Descriptions | |||
---|---|---|---|---|---|
WTP | WTP Estimated | KRW | WTP Estimated (KRW (Korean Won)) | ||
Type of natural resource | Mountain (forests) | 0, 1 | 1 if mountain and 0 otherwise | ||
Sea | 0, 1 | 1 if sea and 0 otherwise | |||
River (freshwater wetland) | 0, 1 | 1 if river/freshwater wetland and 0 otherwise | |||
Stream | 0, 1 | 1 if stream and 0 otherwise | |||
Park | National Park | 0, 1 | 1 if national park and 0 otherwise | ||
Provincial Park | 0, 1 | 1 if provincial park and 0 otherwise | |||
Local facilities | 0, 1 | 1 if local level and 0 otherwise | |||
Others (local park) | 0, 1 | 1 if others and 0 otherwise | |||
Tidal flat | 0, 1 | 1 if tidal flat and 0 otherwise | |||
Animal | 0, 1 | 1 if animal and 0 otherwise | |||
Other a | 0, 1 | 1 if other and 0 otherwise | |||
Type of economic value (value type) | Use value | 0, 1 | 1 if the use value is included and 0 otherwise | ||
Non-use value | 0, 1 | 1 if non-use value is included and 0 otherwise | |||
Use and non-use value | 0, 1 | 1 if both use and non-use value are included and 0 otherwise | |||
Payment unit of WTP | Unit on people | 0, 1 | 1 if payment unit is per person and 0 otherwise | ||
Unit on household | 0, 1 | 1 if payment unit is per household and 0 otherwise | |||
Others | 0, 1 | 1 if payment unit is ‘other’ and 0 otherwise | |||
Stated or Revealed preference | Stated preference b | 0, 1 | 1 if SP and 0 otherwise (RP) | ||
Contingent valuation | 0, 1 | 1 if contingent valuation and 0 otherwise | |||
Choice experiment | 0, 1 | 1 if choice experiment and 0 otherwise | |||
Others | 0, 1 | 1 if other methods were applied and 0 otherwise | |||
Elicitation formats | Single-bounded | 0, 1 | 1 if single-bounded and 0 otherwise | ||
Double-bounded | 0, 1 | 1 if double-bounded and 0 otherwise | |||
Others | 0, 1 | 1 if other formats were applied and 0 otherwise | |||
Scope of nature | National vs. local | 0, 1 | 1 if national and 0 otherwise | ||
Research site location | Seoul | 0, 1 | 1 if the site is Seoul and 0 otherwise | ||
Gyeonggi | 0, 1 | 1 if the site is Gyeonggi and 0 otherwise | |||
Gangwon | 0, 1 | 1 if the site is Gangwon and 0 otherwise | |||
Chungcheong | 0, 1 | 1 if the site is Chungcheong and 0 otherwise | |||
Jeonra | 0, 1 | 1 if the site is Jeonra and 0 otherwise | |||
Kyungsang | 0, 1 | 1 if the site is Kyungsang and 0 otherwise | |||
JeJu | 0, 1 | 1 if the site is JeJu and 0 otherwise | |||
Total sample | Number of samples | Number | The sample size | ||
Year | Year estimated | Number | The year in which the survey was conducted | ||
Year published | Number | The year in which the paper was published | |||
Quality of articles (studies) and data characteristics | Ph.D. thesis report | 0, 1 | 1 if WTP is from a Ph.D. thesis and 0 otherwise | ||
Natural science journal | 0, 1 | 1 if WTP is from a natural science journal and 0 otherwise | |||
Economic journal | 0, 1 | 1 if WTP is from an economic journal and 0 otherwise | |||
Academic paper | 0, 1 | 1 if WTP is from an academic paper and 0 otherwise | |||
Number of authors | 0, 1 | 1 if the author is in the paper is one and 0 otherwise | |||
Replications | 0, 1 | 1 if WTP is replicated and 0 otherwise | |||
Payment vehicle | Tax | 0, 1 | 1 if tax is applied and 0 otherwise | ||
Survey methods | Face to face | 0, 1 | 1 if face-to-face is applied and 0 otherwise | ||
Four seasons | Spring | 0, 1 | 1 if survey was conducted in spring and 0 otherwise | ||
Summer | 0, 1 | 1 if survey was conducted in summer and 0 otherwise | |||
Fall | 0, 1 | 1 if survey was conducted in fall and 0 otherwise | |||
Winter | 0, 1 | 1 if survey was conducted in winter and 0 otherwise | |||
Socioeconomic | Household monthly income | Number | (a) under KRW 1 million; (b) more than KRW 1 million~less than KRW 2 million; (c) more than KRW 2 million~less than KRW 3 million; (d) more than KRW 3 million~less than KRW 4 million; (e) more than KRW 4 million~less than KRW 5 million; (f) more than KRW 5 million~less than KRW 6 million; (g) more than KRW 7 million | ||
Education | Number | (a) under middle school; (b) high-school level; (c) undergraduate level; (d) postgraduate level | |||
Age | Number | (a) under 20; (b) 30s; (c) 40s; (d) 50s; (e) 60s; (f) over 70 | |||
Gender | Male | Number | Percentage of male respondents | ||
Female | Number | Percentage of female respondents |
Variables | Mean | Std. Dev. | Min. | Max. | Expected Sign | ||
---|---|---|---|---|---|---|---|
Willingness to pay | WTP (KRW Korean Won) | 26,951.9 | 48,679.5 | 0.1 | 316,248.0 | ||
Region | Id | 4.34 | 2.380 | 1 | 8 | N | |
Year | Time | 2006.71 | 5.743 | 1991 | 2020 | + or N | |
Type of natural resources | River | 0.00 | 0.046 | 0 | 1 | + | |
Sea | 0.03 | 0.169 | 0 | 1 | + | ||
Stream | 0.00 | 0.046 | 0 | 1 | + | ||
Parks | Park | 0.38 | 0.486 | 0 | 1 | + | |
National park | 0.42 | 0.495 | 0 | 1 | + or N | ||
Provincial park | 0.04 | 0.206 | 0 | 1 | + or N | ||
Local park | 0.25 | 0.435 | 0 | 1 | + or N | ||
Local facilities | 0.03 | 0.175 | 0 | 1 | + | ||
Tidal flats | 0.00 | 0.046 | 0 | 1 | + | ||
Animals | 0.00 | 0.065 | 0 | 1 | + | ||
Other | 0.01 | 0.120 | 0 | 1 | + | ||
Type of values | Use value | 0.49 | 0.500 | 0 | 1 | + | |
Non-use value | 0.51 | 0.500 | 0 | 1 | + | ||
Use and non-use value | 0.00 | 0.046 | 0 | 1 | + | ||
Payment unit of WTP | Unit: per person | 0.61 | 0.489 | 0 | 1 | − | |
Unit: per household | 0.33 | 0.471 | 0 | 1 | + | ||
Unit: other | 0.06 | 0.239 | 0 | 1 | + | ||
Methodology | Stated preference | 0.77 | 0.419 | 0 | 1 | − | |
Stated preference or revealed preference | Contingent valuation | 0.62 | 0.485 | 0 | 1 | − | |
Choice experiment | 0.15 | 0.359 | 0 | 1 | − | ||
Other methods | 0.23 | 0.419 | 0 | 1 | + | ||
Elicitation formats | Closed | 0.47 | 0.500 | 0 | 1 | − | |
Single-bounded | 0.18 | 0.364 | 0.0 | 1.0 | + | ||
Double-bounded | 0.12 | 0.302 | 0.0 | 1.0 | − | ||
Others | 0.20 | 0.386 | 0.0 | 1.0 | − | ||
Scope of nature | National vs. local | 0.22 | 0.412 | 0 | 1 | + | |
Total sample | Number of samples | 749.13 | 999.266 | 26 | 8457 | − | |
Years | Year published | 2008.43 | 5.728 | 1999 | 2022 | − or N | |
Quality of articles | Ph.D. thesis report | 0.04 | 0.186 | 0 | 1 | − | |
Natural science journal | 0.27 | 0.443 | 0 | 1 | + | ||
Economics journal | 0.22 | 0.416 | 0 | 1 | − | ||
Academic journal | 0.85 | 0.357 | 0 | 1 | − | ||
Number of authors | 0.31 | 0.462 | 0 | 1 | N | ||
Replications | 0.59 | 0.493 | 0 | 1 | − or N | ||
Payment vehicle | Tax | 0.42 | 0.4829 | 0.0 | 1.0 | − | |
Survey method | Face to face | 0.91 | 0.284 | 0 | 1 | − | |
Four seasons | Spring | 0.32 | 0.466 | 0 | 1 | N | |
Summer | 0.42 | 0.494 | 0 | 1 | N | ||
Autumn | 0.45 | 0.498 | 0 | 1 | N | ||
Winter | 0.13 | 0.337 | 0 | 1 | N | ||
Socioeconomic variables | Income | 3.33 | 2.006 | 1.00 | 23.00 | + or N | |
Education | 2.78 | 0.200 | 2.00 | 4.00 | + or N | ||
Age | 3.07 | 0.477 | 1.00 | 5.00 | N | ||
Gender | Male | 50.69 | 5.734 | 28.00 | 88.10 | N | |
Female | 49.71 | 5.775 | 11.90 | 72.00 | − or N |
Random Effects | Groups | Name | Variance | Std. Dev. | |
Time | Intercept | 1.00 | 1.00 | ||
Id | Intercept | 0.15 | 0.38 | ||
Residual | 1.7077 | 1.31 | |||
Number of observations | 476 | Groups: | Time (28) | id | 8 |
Fixed effects | |||||
Estimate | Std. error | df | t-value | Pr (>|t|) | |
Intercept | 6.18 *** | 0.50 | 127.80 | 12.40 | 0.00 |
River | −1.24 | 2.07 | 450.40 | −0.60 | 0.55 |
Sea | −0.71. | 0.42 | 445.00 | −1.71 | 0.09 |
Animals | 1.77 | 1.35 | 432.10 | 1.32 | 0.19 |
Provincial parks | 0.95 * | 0.38 | 428.60 | 2.48 | 0.01 |
Use and non-use value | 1.11 | 1.53 | 421.90 | 0.72 | 0.47 |
Unit: per household | 3.15 *** | 0.47 | 451.40 | 6.72 | 0.00 |
Unit: per person | 2.68 *** | 0.44 | 456.30 | 6.08 | 0.00 |
CVM | −0.99 *** | 0.26 | 449.70 | −3.89 | 0.00 |
CE | −1.43 *** | 0.32 | 450.60 | −4.45 | 0.00 |
Single-bounded | 0.50 * | 0.25 | 455.60 | 1.97 | 0.05 |
Double-bounded | 0.20 | 0.26 | 453.00 | 0.77 | 0.44 |
Nation vs. local | 1.35 * | 0.48 | 5.18 | 2.82 | 0.04 |
Number of samples | 0.00 | 0.00 | 438.20 | 0.96 | 0.34 |
Academic journals | 0.14 | 0.20 | 438.70 | 0.69 | 0.49 |
Payment of tax | −0.55 ** | 0.20 | 456.80 | −2.74 | 0.01 |
Face to face | 0.63 | 0.38 | 433.90 | 1.66 | 0.10 |
National parks | −0.25 | 0.30 | 451.50 | −0.84 | 0.40 |
References
- Brander, L.M.; Groot, R.; Schägner, J.P.; Guisado-Goñi, V.; Hoff, V.V.; Solomonides, S.; McVittie, A.; Eppink, F.; Sposato, M.; Do, L.; et al. Economic values for ecosystem services: A global synthesis and way forward. Ecosyst. Serv. 2024, 66, 101606. [Google Scholar] [CrossRef]
- Groot, R.D.; Brander, L.; Solomonides, S. Update of Global Ecosystem Service Valuation Database (ESVD); Report; Department for Environment, Food and Rural Affairs: Defra, UK, 2020. Available online: https://www.esvd.org/ (accessed on 20 March 2023).
- Liu, P.; Bi, X.; Luo, Q.; Whitehead, J.C. Effects of policy consequentiality and payment vehicle on contingent valuation: Structural estimates on preference misrepresentation. J. Environ. Econ. Manag. 2020, 103, 102350. [Google Scholar] [CrossRef]
- Millennium Ecosystem Assessment (MA). Ecosystems and Human Wellbeing Biodiversity Synthesis; World Resources Institute: Washington, DC, USA, 2005. [Google Scholar]
- Pisani, D.; Pettinelli, E.; Mariani, M.; Leone, A. Forest biomass estimates and global carbon stock distribution. Environ. Res. Lett. 2022, 17, 045001. [Google Scholar]
- TEEB. The Economics of Ecosystems and Biodiversity (TEEB) Initiative. United Nations Environment Programme. 2023. Available online: https://www.cbd.int/incentives/teeb?utm_source=chatgpt.com (accessed on 19 November 2023).
- Dominati, E.; Mackay, A.; Dodd, M. Seasonal variation of ecosystem services: A framework for measurement. Ecolo. Indic. 2021, 120, 106895. [Google Scholar]
- IPBES. Global Assessment Report on Biodiversity and Ecosystem Services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services; Brondizio, E.S., Settele, J., Díaz, S., Ngo, H.T., Eds.; IPBES Secretariat: Bonn, Germany, 2019; 1148p. [Google Scholar] [CrossRef]
- Soga, M.; Gaston, K.J. Towards a unified understanding of human–nature interactions. Nat. Sustain. 2022, 5, 374–383. [Google Scholar] [CrossRef]
- Buckley, R. Nature tourism and mental health: Parks, happiness, and causation. J. Sustain. Tour. 2020, 28, 1409–1424. [Google Scholar] [CrossRef]
- Fuller, R.A.; Irvine, K.N.; Devine-Wright, P.; Warren, P.H.; Gaston, K.J. Psychological benefits of greenspace increase with biodiversity. Biol Lett. 2007, 3, 390–394. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
- Gregory, N.; Bratman, C.B.; Anderson, M.G.; Berman, B.C.; Sjerp, D.V.; Jon, F.; Carl, F.; Howard, F.; James, J.G.; Gretchen, C.D. Nature and mental health: An ecosystem service perspective. Sci. Adv. 2019, 5, eaax0903. [Google Scholar] [CrossRef]
- Buckley, R.; Brough, P.; Hague, L.; Chauvenet, A.; Fleming, C.; Roche, E.; Sofija, E.; Harris, N. Economic values of protected areas via visitor mental health. Nat. Commun. 2019, 10, 5706. [Google Scholar] [CrossRef]
- Johnston, R.; Besedin, E.Y.; Stapler, R. Enhanced geospatial validity for meta-analysis and environmental benefit transfer: An application to water quality improvements. Environ. Resour. Econ. 2017, 68, 343–375. [Google Scholar] [CrossRef]
- Korea Forest Services (KFS). Statistical Yearbook of Forestry 2023; KFS: Daejeon, Republic of Korea, 2023; ISBN 11-1400000-000001-10. [Google Scholar]
- Yoo, S.H.; Kwak, S.J.; Lee, J.S. Using a choice experiment to measure the environmental effects of urban parks in Seoul. Lands. Urban Plan. 2014, 123, 134–143. [Google Scholar] [CrossRef]
- Yu, Z.; Ning, Z.; Chang, W.; Chang, S.; Yang, H. Optimal harvest decisions for the management of carbon sequestration forests under price uncertainty and risk preferences. For. Policy. Econ. 2023, 151, 102957. [Google Scholar] [CrossRef]
- Yu, Z.; Ning, Z.; Zhang, H.; Yang, H.; Chang, S. A generalized Faustmann model with multiple carbon pools. For. Policy. Econ. 2024, 169, 103363. [Google Scholar] [CrossRef]
- Sohn, J.; Lee, W.K.; Kim, S.; Kwak, D.A. Economic valuation of forest ecosystem services in South Korea. For. Sci. Technol. 2016, 12, 183–191. [Google Scholar]
- Kim, H.N.; Lee, S.Y. Economic value of forest recreation across seasons: A comparison using CVM and TCM. Korean J. Agric. Econ. 2018, 59, 45–67. [Google Scholar]
- Oh, H.; Lee, J. Seasonal variations in forest visitation and WTP for forest healing programs. For. Policy Econ. 2020, 118, 102252. [Google Scholar]
- Korea Tourism Organization (KTO). Annual Domestic Tourism Statistics Report 2022; KTO: Seoul, Republic of Korea, 2022; Available online: https://kto.visitkorea.or.kr (accessed on 18 August 2023).
- Lee, H.J.; Han, K.S. Four-season tourism patterns in national parks: Empirical evidence from Korea. J. Tour. Sci. 2019, 43, 95–112. [Google Scholar] [CrossRef]
- Stanley, T.D.; Doucouliagos, H. Meta-Regression Analysis in Economics and Business (Routledge Advances in Research Methods); Routledge: London, UK; New York, NY, USA, 2012. [Google Scholar]
- Havranek, T.; Stanley, T.D.; Doucouliagos, H.; Bom, P.; Geyer-Llingeberg, J.; Iwasaki, I.; Reed, W.R.; Rost, W. Reporting guidelines for meta-analysis in economics. J. Econ. Surv. 2020, 34, 469–475. [Google Scholar] [CrossRef]
- Taye, F.A.; Folkersen, M.V.; Fleming, C.M.; Buckwell, A.; Mackey, B.; Diwakar, K.C.; Le, D.; Hasan, S.; Ange, C.S. The economic values of global forest ecosystem services: A meta-analysis. Ecolo. Econ. 2021, 189, 107145. [Google Scholar] [CrossRef]
- Newbold, S.C.; Johnston, R.J. Benefits transfer with limited temporal data: Causes and corrections for bias. Environ. Resour. Econ. 2017, 67, 317–338. [Google Scholar]
- Kim, H.N.; Ahn, S.E. Economic Valuation for Improving Water Quality Using Meta-Regression Analysis; The Environment Forum, Korea Environment Institute: Sejong, Republic of Korea, 2018; Volume 22, ISSN 2287-3414. [Google Scholar]
- Kim, J.; Park, H. Valuing recreational ecosystem services with seasonal variation: A meta-analysis. For. Policy Econ. 2023, 152, 102957. [Google Scholar]
- Ali, A.; Shedayi, A.A. Willingness to pay as an economic instrument for managing nature-based tourism in Gilgit-Baltistan, Pakistan. Environ. Dev. Sustain. 2023, 27, 1–20. [Google Scholar] [CrossRef]
- Ansari, A.; Mahmood, S.; Khan, K.I.; Asghar, F. Fostering green creativity through environmental values: The role of intrinsic motivation, environmental identity and green HR practices. Pak. J. Commer. Soc. Sci. 2023, 17, 370–389. [Google Scholar] [CrossRef]
- Bartczak, A.; Lindhjem, H.; Navrud, S.; Zandersen, M.; Żylicz, T. Valuing forest recreation on the national level in a transition economy: The case of Poland. For. Policy. Econ. 2012, 20, 81–89. [Google Scholar] [CrossRef]
- Shrestha, R.K.; Loomis, J.B. Testing a meta-analysis model for benefit transfer in international outdoor recreation. Eco. Econ. 2001, 39, 195–206. [Google Scholar] [CrossRef]
- Park, C.H. Estimating the Value of Natural Tourism Resources in Considering the Seasonal. Master’s Thesis, Dong-Myung University, Busan, Republic of Korea, 2010. [Google Scholar]
- Bergh, V.B.; Button, K.J. Meta-analysis of environmental issues in regional, urban and transport economics. Urban. Stud. 1997, 34, 927–944. [Google Scholar] [CrossRef]
- Boutwell, J.L.; Westra, J.V. Benefit transfer: A review of methodologies and challenges. Resources 2013, 2, 517–527. [Google Scholar] [CrossRef]
- Carson, R.T.; Louviere, J.J.; Rose, J.; Swait, J. Frontiers in Modeling Discrete Choice Experiments: A Benefit Transfer Perspective. In Benefit Transfer of Environmental and Resource Values: A Guide for Researchers and Practitioners; Johnston, R.J., Rolfe, J., Rosenberger, R., Brouwer, R., Eds.; Springer: Dordrecht, The Netherlands, 2015. [Google Scholar]
- Rosenberger, R.S.; Loomis, J.B. Benefits Transfer of Outdoor Recreation Use Values; General Technical Report RMRS-GTR-72; Rocky Mountain Research Station, U.S.D.A. Forest Service: Fort Collins, CO, USA, 2001.
- Lee, D.; Choi, S. Temporal heterogeneity in ecosystem service valuation: A mixed-effects approach. For. Policy Econ. 2024, 153, 103363. [Google Scholar]
- Grammatikopoulou, I.; Vačkářová, D. The value of forest ecosystem services: A meta-analysis at the European scale and application to national ecosystem accounting. Ecosyst. Serv. 2021, 48, 101262. [Google Scholar] [CrossRef]
- Newbold, S.C.; Johnston, R.J. Valuing non-market valuation studies using meta-analysis: A demonstration using estimates of willingness to pay for water quality improvements. J. Environ. Econ. Manag. 2020, 104, 102379. [Google Scholar] [CrossRef]
- Roldan, D.; Sarmiento, J.P.; Rldan-arauz, F. Economic valuation meta-analysis of freshwater improvement in developed and developing countries. Are they different? J. Wat. Health 2021, 19, 736–749. [Google Scholar] [CrossRef]
- Filho, L.M.; Roebeling, P.; Bastos, M.I.; Rodrigues, W.; Ometto, G. A global meta-analysis for estimating local environmental service value functions. Environments 2021, 8, 76. [Google Scholar] [CrossRef]
- Giergiczny, M.; Valasiuk, S.; Salvo, M.D.; Signorello, G. Value of forest recreation. Meta-analyses of the European valuation studies. Econ. Environ. 2014, 4, 76–83. [Google Scholar]
- Zandersen, M.; Tol, R.S.J. A meta-analysis of forest recreation values in Europe. J. For. Econ. 2009, 15, 109–130. [Google Scholar] [CrossRef]
- Greene, W.H. Econometric Analysis, 7th ed.; Pearson: Boston, MA, USA, 2012. [Google Scholar]
- Nelson, P.N.; Kennedy, P.E. The use (and Abuse) of meta-analysis in environmental and natural resource economics: An assessment. Environ. Resour. Econ. 2009, 42, 345–377. [Google Scholar] [CrossRef]
- EFTEC. Valuing Environmental Impacts: Practical Guidelines for the Use of Value Transfer in Policy and Project Appraisal; Department of Environment, Food and Rural Affairs: London, UK, 2009.
- Boyle, K.J.; Wooldridge, J.M. Understanding error structures and exploiting panel data in meta-analytic benefit transfers. Environ. Resour. Econ. 2018, 69, 609–635. [Google Scholar] [CrossRef]
- Chen, D.G.; Peace, K.E. Applied Meta-Analysis with R; CRC Press: Boca Raton, FL, USA, 2013. [Google Scholar]
- Chen, M.H.; Ibrahim, J.G.; Sinha, D. Bayesian Survival Analysis; Springer: New York, NY, USA, 2013. [Google Scholar]
- Nelson, J.P.; Boyle, K.J.; Johnston, R.J. The economics of non-market valuation: Looking back and looking ahead. Annu. Rev. Resour. Econ. 2019, 1, 547–566. [Google Scholar]
- Aizaki, H.; Nakatani, T.; Sato, K. Stated Preference Methods Using R; CRC Press: Boca Raton, FL, USA, 2015. [Google Scholar]
- Ayala, A.D.; Mariel, P.; Meyerhoff, J. Transferring landscape values using discrete choice experiments: Is meta-analysis an option? Econ. Agrar. Recur. Nat. 2014, 14, 103–128. [Google Scholar]
- Buckley, R.; Brough, P.; Westaway, D. Forest recreation and nature tourism benefits vary strongly across seasons. Nat. Sustain. 2022, 5, 15–23. [Google Scholar]
- Tietenberg, T.; Lewis, L. Environmental and Natural Resource Economics, 11th ed.; Taylor and Francis: New York, NY, USA; London, UK, 2018. [Google Scholar]
- Boyle, K.J.; Kotchen, M.J.; Smith, V.K. The role of economics in ecosystem valuation. Ann. Rev. Resour. Econ. 2018, 10, 147–165. [Google Scholar]
- Hjerpe, E.; Hussain, A.; Phillips, S. Valuing type and scope of ecosystem conservation: A meta-analysis. J. For. Econ. 2015, 21, 32–50. [Google Scholar] [CrossRef]
- Koller, M. Robustlmm: An R package for robust estimation of linear mixed-effects models. J. Statis. Softw. 2016, 75, 1–24. [Google Scholar] [CrossRef]
- Brown, V.A. An introduction to linear mixed-effects modelling in R. Adv. Methods Pract. Psychol. Sci. 2021, 4, 1–19. [Google Scholar]
- Johnston, R.J.; Bauer, D.M. Using Meta-analysis for large-scale environmental service valuation: Progress, prospects, and challenges. Agric. Resour. Econ. Rev. 2020, 49, 23–63. [Google Scholar] [CrossRef]
- Stanley, T.D.; Jarrell, S.B. Meta-regression analysis: A quantitative method of literature surveys. J. Econ. Surv. 1989, 3, 161–170. [Google Scholar] [CrossRef]
- Smetana, K.; Mesltrom, R.T.; Malone, T. A meta-regression analysis of consumer willingness to pay for aquaculture products. J. Agric. Appl. Econ. 2022, 54, 480–495. [Google Scholar] [CrossRef]
- Wooldridge, J.M. Introductory Econometrics: A Modern Approach, 5th ed.; Cengage Learning: Boston, MA, USA, 2012. [Google Scholar]
- Bateman, I.J.; Harwood, A.R.; Mace, G.M.; Watson, R.T.; Abson, D.J.; Termansen, M. Economic analysis for ecosystem service assessments. Environ. Resour. Econ. 2011, 48, 177–218. [Google Scholar] [CrossRef]
- Hensher, D.A.; Rose, J.M.; Greene, W.H. Applied Choice Analysis: A Primer; Cambridge University Press: Cambridge, UK, 2005. [Google Scholar]
- Boyle, K.J.; Kotchen, M.J.; Smith, V.K. The role of benefit-cost analysis in environmental policy debates. Annu. Rev. Resour. Econ. 2018, 10, 77–92. [Google Scholar]
- Glass, G.V. Primary, secondary, and meta-analysis of research. Am. Educ. Res. Assoc. 1976, 5, 3–8. [Google Scholar]
- Johnston, R.J.; Rolfe, J.; Rosenberger, R.; Brouwer, R. Benefit Transfer of Environmental and Resource Values: A Guide for Researchers and Practitioners; Springer: Dordrecht, The Netherlands, 2020. [Google Scholar]
- Krieger, D.J. The Economic Values of Forest Ecosystem Services: A Review; The Wilderness Society: Washington, DC, USA, 2001. [Google Scholar]
- Korea Environment Institute (KEI). Environmental Valuation Database in Korea (EVIS). 2022. Available online: http://evis.kei.re.kr (accessed on 25 August 2022).
- McFadden, D.; Train, K. Contingent Valuation of Environmental Goods: A Comprehensive Critique; McFadden, D., Train, K., Eds.; Edward Elgar Pub: Northampton, MA, USA, 2017. [Google Scholar]
- Nelson, J.P. Fixed-effect versus random-effects meta-analysis in economics: A study of pass-through rates for alcohol beverage excise taxes. Adv. Econ. Bus. 2020, 9, 23–41. [Google Scholar] [CrossRef]
- Hafele, J.; Adamowicz, W.; Boxall, P. Compensating for loss in forest access: A choice experiment study of recreation in Alberta. For. Policy Econ. 2016, 71, 79–89. [Google Scholar]
- Bateman, I.J.; Cole, M.A.; Georgiou, S.; Hadley, D.J. Comparing contingent valuation and contingent ranking: A case study considering the benefits of urban river water quality improvements. J. Environ. Manag. 2006, 79, 221–231. [Google Scholar] [CrossRef]
- Pearce, D.W. Economic Values and the Natural World; The MIT Press: Cambridge, MA, USA, 1993. [Google Scholar]
- RISS. Research Information Sharing Service (RISS) Database. 2023. Available online: https://www.riss.kr (accessed on 17 May 2023).
- Newbold, T.; Johnston, E.A. Estimating the value of water quality improvements: A meta-analysis of contingent valuation and choice experiments. J. Environ. Econ. Manag. 2020, 103, 102350. [Google Scholar]
- Filho, L.; Bi, X.; Luo, Q.; Whitehead, J.C. Estimation of local environmental services across 12 biomes. Ecol. Indic. 2021, 120, 106895. [Google Scholar]
- Ali, K.; Amir, M.; Malik, M.S. Strategic motives, proactive environmental strategies and corporate performance: Role of business model innovation and competitive intensity. Pakistan. J. Commer. Soc. Sci. 2023, 17, 348–369. [Google Scholar] [CrossRef]
- Bank of Korea. Economic Statistics System (ECOS). 2024. Available online: https://ecos.bok.or.kr (accessed on 12 March 2024).
Authors | Ecosystem Services | Scale (Country, Area) | Value Type | Survey Method (Valuation Methods) | Observations (No. of Estimates) |
---|---|---|---|---|---|
Kim et al. (2018) [28] | Water quality (mainly rivers) | Korea | Indirect use | CVM CE | 51 studies, 139 observations |
Grammatikopoulou et al. (2021) [40] | Forest ecosystem services | Europe | Indirect use | Stated preference | 30 studies, 71 observations |
Newbold and Johnston (2020) [41] b | Water quality improvement | US | Total (use + non-use) | Stated preference | 51 studies, 140 observations |
Roldan et al. (2021) [42] | Drinking water quality | 30 countries | Indirect use | Face-to-face survey | 30 studies, 30 observations |
Filho et al. (2021) [43] | Ecosystem services a | Global scale | Total (use + non-use) | General valuation method | 636 observations (ESVD) |
Giergiczny et al. (2014) [44] | Forest recreation | Europe | Direct use | CVM TCM | 53 studies, 253 observations |
Content | |
---|---|
Main databases | EVIS (http://riss.or.kr/index.do; accessed on 25 August 2022), major academic sites (riss.or.kr; DBpia), relevant journal websites, major university library sites, National assembly libraries, and relevant research institute reports |
Main relevant keywords | Main natural resources: forests, mountains, wetlands, parks (national, provincial, local, etc.), and forest-dependent animals Non-market valuation: willingness to pay, willingness to accept, stated preference, revealed preference, contingent valuation, choice experiment/modeling, travel cost, hedonic price, and conjoint analysis Meta-topics: environment, economic value, non-market values, use value, non-use value, preservation value, environmental services, recreation, tours, visits, satisfaction, healing, education, and biodiversity |
Types of prominent journals and research institutes | Environmental economics, agricultural economics, economics, tourism and applied fields, marine economics, forest economics, forest management, and relevant natural science and public organizations |
Number of studies | 93 (initial) → 90 (after screening) |
Number of estimates | 485 (initial) → 476 (after screening) |
Timespan | 1999~2022 |
Ordinary Least Squares (OLS) | Mixed Effects (MEs) | |||||||
---|---|---|---|---|---|---|---|---|
Pooled OLS (POLS) | Weighted OLS (WOLS) | Robust MEs (RMEs) | Weighted RMEs (WRMEs) | |||||
Coefficients | Estimate | t-Value | Estimate | t-Value | Estimate | t-Value | Estimate | t-Value |
Intercept | 5.90 | 15.20 *** | 5.87 | 15.55 *** | 6.44 | 15.86 *** | 6.63 | 15.25 *** |
A. Types of items | ||||||||
River | 0.37 | 0.16 | 0.53 | 0.21 | −0.53 | −0.32 | −0.11 | −0.07 |
Sea | −0.17 | −0.39 | 0.01 | 0.02 | −0.68 | −2.05 * | −0.67 | −2.16 * |
Animals | −0.26 | −0.17 | −0.43 | −0.25 | 1.35 | 1.27 | 1.05 | 0.93 |
National Parks | 0.28 | 1.44 | 0.28 | 1.31 | −0.33 | −1.33 | −0.41 | −1.68 |
Provincial Parks | 1.22 | 3.24 ** | 1.23 | 2.88 ** | 0.79 | 2.56 * | 1.01 | 3.01 ** |
B. Instruments | ||||||||
Use and Non-Use Value | 2.96 | 1.87 * | 3.45 | 2.53 * | 1.81 | 1.47 | 2.58 | 2.41 * |
Unit: per Person | 2.13 | 4.63 *** | 1.46 | 2.93 ** | 2.13 | 6.02 *** | 1.59 | 4.17 *** |
Unit: per Household | 2.66 | 5.50 *** | 2.42 | 4.63 *** | 2.71 | 7.09 *** | 2.44 | 5.95 *** |
CVM | −0.70 | −2.82 ** | −0.59 | −2.11 * | −0.88 | −4.33 *** | −0.85 | −3.90 *** |
CE | −0.60 | −2.00 * | −0.66 | −2.03 * | −0.94 | −3.65 *** | −0.95 | −3.63 *** |
Single-Bounded | 0.55 | 2.17 * | 0.30 | 1.14 | 0.44 | 2.23 * | 0.36 | 1.82 * |
Double-Bounded | 0.28 | 1.06 | 0.19 | 0.76 | 0.49 | 2.30 * | 0.68 | 3.39 ** |
National vs. Local | 2.62 | 9.54 *** | 2.66 | 9.82 *** | 1.51 | 4.43 *** | 1.21 | 2.77 ** |
Number of Samples | 0.00 | −1.29 | −0.00 | −2.05 * | −0.00 | −0.95 | −0.00 | −0.20 |
Payment of Tax | −0.56 | −2.67 ** | −0.76 | −3.56 *** | −0.66 | −4.11 *** | −0.85 | −5.24 *** |
Face to Face | 0.92 | 2.57 * | 1.45 | 4.08 *** | 0.94 | 2.99 ** | 1.00 | 3.23 ** |
Academic Journals | 0.07 | 0.32 | 0.42 | 1.85 * | 0.06 | 0.34 | 0.15 | 0.87 |
C. Seasons | ||||||||
Summer | 0.00 | 0.02 | −0.24 | −1.33 | −0.18 | −1.16 | −0.10 | −0.65 |
Fall | 0.46 | 2.81 ** | 0.35 | 2.05 * | 0.67 | 5.17 *** | 0.71 | 5.38 *** |
Winter | −1.44 | −5.66 *** | −1.66 | −6.26 *** | −0.65 | −2.69 * | −0.64 | −2.60 * |
Model Fit Criteria | Multiple R2: 0.34 Adjusted R2: 0.31 | Multiple R2: 0.43 Adjusted R2: 0.39 | Conditional R2: 0.59 Marginal R2: 0.29 | Conditional R2: 0.08 Marginal R2: 0.03 |
Experimental Test 1 | Experimental Test 2 | Experimental Test 3 | ||||||
---|---|---|---|---|---|---|---|---|
Variables | Estimate | t-Value | Variables | Estimate | t-Value | Variables | Estimate | t-Value |
Intercept | 6.45 | 16.15 *** | Intercept | 6.44 | 15.86 *** | Intercept | 6.55 | 15.20 *** |
River | −0.57 | −0.35 | River | −0.53 | −0.32 | River | −0.55 | −0.34 |
Sea | −0.67 | −2.05 ** | Sea | −0.68 | −2.05 ** | Sea | −0.68 | −2.08 ** |
Animals | 1.36 | 1.28 | Animal | 1.35 | 1.27 | Animals | 1.37 | 1.29 |
Provincial Parks | 0.80 | 2.61 *** | Provincial Parks | 0.79 | 2.56 ** | Provincial Parks | 0.79 | 2.56 *** |
Use and Non-Use Value | 1.83 | 1.49 | Use and Non-Use Value | 1.81 | 1.47 | Use and Non-Use Value | 1.80 | 1.46 |
Unit: per Household | 2.72 | 7.13 *** | Unit: per Household | 2.71 | 7.09 *** | Unit: per Household | 2.66 | 6.93 *** |
Unit: per Person | 2.15 | 6.08 *** | Unit: per Person | 2.13 | 6.02 *** | Unit: per Person | 2.08 | 5.82 *** |
Single-Bounded | 0.46 | 2.36 ** | CE | −0.94 | −3.65 *** | CE | −0.98 | −3.66 *** |
Double-Bounded | 0.50 | 2.42 ** | CVM | −0.88 | −4.33 *** | CVM | −0.93 | −4.22 *** |
Payment of Tax | −0.65 | −4.08 *** | Single bound | 0.29 | 1.56 ** | Single-Bounded | 0.45 | 2.24 ** |
National vs. Local | 1.51 | 4.48 *** | Double bound | 0.37 | 1.80 * | Double-Bounded | 0.48 | 2.29 ** |
Number of Samples | −0.00 | −1.12 | Payment of Tax | −0.66 | −4.11 *** | Payment of Tax | −1.00 | −2.04 ** |
Academic Journals | 0.05 | 0.32 | National vs. Local | 1.51 | 4.43 *** | National vs. Local | 1.51 | 4.36 *** |
Face to Face | 0.92 | 2.96 ** | Number of Samples | −0.00 | −0.95 | Number of Samples | −0.00 | −0.99 |
Summer | −0.18 | −1.17 | Academic Journals | 0.06 | 0.34 | Academic Journals | 0.07 | 0.43 |
Fall | 0.67 | 5.18 *** | Face to Face | 0.94 | 2.99 *** | Face to Face | 0.92 | 2.92 *** |
Winter | −0.67 | −2.92 *** | Summer | −0.18 | −1.16 | Summer | −0.20 | −1.28 |
National Parks | −0.32 | −1.31 | Fall | 0.67 | 5.17 *** | Fall | 0.65 | 4.69 *** |
- | - | - | Winter | −0.65 | −2.69 *** | Winter | −0.66 | −2.73 *** |
National Parks | −0.33 | −1.33 | National Parks | −0.31 | −1.26 | |||
Experimental and interaction variables | ||||||||
CVCE | −0.89 | −4.52 *** | Summer: Use Value | −0.96 | −4.84 *** | CVCETAX | −0.76 | 4.89 *** |
Face to Face: Fall | 4.05 | 8.29 *** | ||||||
Unit for People: Summer | 1.15 | 4.36 *** | ||||||
Face to Face: Summer | 1.77 | 2.12 ** | ||||||
National vs. Local: Fall | 1.36 | 3.31 *** | ||||||
Model Fit Criteria | Conditional R2: 0.59 Marginal R2: 0.29 | Conditional R2: 0.65 Marginal R2: 0.53 | Conditional R2: 0.59 Marginal R2: 0.28 |
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Jeon, C.; Campbell, D. Uncovering Seasonal Heterogeneity in Forest Ecosystem Valuation: Evidence from a Meta-Analysis with Experimental Insights. Forests 2025, 16, 1508. https://doi.org/10.3390/f16101508
Jeon C, Campbell D. Uncovering Seasonal Heterogeneity in Forest Ecosystem Valuation: Evidence from a Meta-Analysis with Experimental Insights. Forests. 2025; 16(10):1508. https://doi.org/10.3390/f16101508
Chicago/Turabian StyleJeon, Chulhyun, and Danny Campbell. 2025. "Uncovering Seasonal Heterogeneity in Forest Ecosystem Valuation: Evidence from a Meta-Analysis with Experimental Insights" Forests 16, no. 10: 1508. https://doi.org/10.3390/f16101508
APA StyleJeon, C., & Campbell, D. (2025). Uncovering Seasonal Heterogeneity in Forest Ecosystem Valuation: Evidence from a Meta-Analysis with Experimental Insights. Forests, 16(10), 1508. https://doi.org/10.3390/f16101508