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Valuation of species and nature conservation in Asia and Oceania: a meta-analysis

Environmental Economics and Policy Studies volume 14, pages 1–22 (2012)Cite this article

Abstract

We conduct a meta-analysis (MA) of around 100 studies valuing species and nature conservation in Asia and Oceania, using both revealed and stated preferences methods. Dividing our dataset into two levels of heterogeneity in terms of good characteristics (species vs. nature conservation more generally) and valuation methods, we show that the degree of regularity and conformity with theory and empirical expectations is higher for the more homogenous dataset of contingent valuation of species. For example, we find that willingness to pay (WTP) for preservation of mammals tends to be higher than other species and that WTP for species preservation increases with income (elasticity below one). For the full dataset we find that marine habitats are valued significantly higher than other habitat types in the region. Despite some encouraging results, more research is required to answer the question of how homogenous is homogenous enough in MA, especially when moving towards using MA for benefit transfer and policy use.

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Notes

  1. Originally quoted in Stanley and Jarrel (2005).

  2. http://www.teebweb.org/.

  3. An alternative approach to dealing with classical MA challenges, not pursued here, is to use Bayesian techniques (e.g. Moeltner et al. (2007).

  4. For simplicity and brevity we do not elaborate the details of how nature conservation may increase utility e.g. related to market goods and household production, e.g. as done by Van Houtven et al. (2007) for water quality.

  5. The ecosystem services and functions and total economic value from nature and biodiversity conservation are discussed in depth elsewhere, and not elaborated in detail here (see e.g. Fromm 2000).

  6. We did not include Master theses for practical reasons (hard to find and/or to get hold of) and because many are written in the native language.

  7. Since the Australian database ENVALUE is no longer updated, has been (partly) integrated with EVRI and includes limited study information, our main search used the EVRI database.

  8. We do not claim to have collected an exhaustive database of all studies in Asia and Oceania, but we are confident that we cover the majority of such studies in the region until 2009. Further, it is unlikely that our search has been biased in any way.

  9. Studies that reported results with per unit of an area were excluded, as the total size typically was not given.

  10. See the tables in the supplementary Appendix for classifications of the studies along some of these main dimensions.

  11. Which also tend to be reflected in actual conservation policies (see e.g. Metrick and Weitzman 1996).

  12. We also considered using population density of the country of study as a variable, for example as done by Brander et al. (2006) for wetlands. However, we think link between nature conservation and population density may be overly tenuous, and excluded this variable in our analysis.

  13. A small number of studies survey foreign populations, e.g. Bandara and Tisdell (2005) study OECD citizens’ WTP for the preservation of the Giant Panda in China.

  14. We also tested two other stratifications of the data: by survey and by author. Results (available from the authors) show that in many model specifications of the two stratifications equal effects (and random effects) cannot be rejected.

  15. Standard error of WTP estimates was generally not reported, making it impossible to weigh estimates by level of precision in the meta-regressions, a procedure recommended in the MA literature (e.g. USE PA (2006)). Using the sample sizes as proxy would also loose to many observations.

  16. A comprehensive test would have included other explanatory variables with different model specifications, but for sake of simplicity and brevity, we only present the model with the income variable here.

  17. We also tried other groupings or specifications of types of species, such as size, degree of”charisma” across types of species etc., but found that using the biological classification”mammal” worked best in our models. Adding dummies for each species is not feasible due to the limited number of observations for each.

  18. The variable mandatory is excluded here as it is collinear with the valuation method variables.

  19. The TCM variable is the”hidden” category in Model 3, now that other non-SP methods are excluded. In Models 1-2 the TCM variable is excluded as it is not significant across models.

  20. Since R 2 obtained from random-effects models is not directly comparable to standard R 2 OLS, the comparison should be interpreted with caution.

  21. The values of methodological variables would typically be set at some best practice level, at the average sample value (Stapler and Johnston 2009) or drawn from the MA sample distribution (Johnston et al. 2006), since there is no such information for an unstudied policy site.

  22. \( {\hbox{TE}}=\frac{{|{\hbox{WTP}}_{T}-{\hbox{WTP}}_{B} |}}{{{\hbox{WTP}}_{B}}} \), where T = transferred (predicted) value from study site(s), B = estimated (observed) true value (“benchmark”) at policy site.

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Acknowledgments

We would like to thank Vic Adamowicz, Ståle Navrud and Randall Rosenberger for constructive comments. Funding from the Environment and Economy Programme for Southeast Asia (EEPSEA) is greatly appreciated.

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Authors and Affiliations

  1. Norwegian Institute for Nature Research (NINA), Gaustadalleen 21, 0349, Oslo, Norway

    Henrik Lindhjem

  2. College of Economics, Hue University, 100 Phung Hung Street, Hue, Vietnam

    Tran Huu Tuan

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  1. Henrik Lindhjem
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  2. Tran Huu Tuan
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Correspondence to Henrik Lindhjem.

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Lindhjem, H., Tuan, T.H. Valuation of species and nature conservation in Asia and Oceania: a meta-analysis. Environ Econ Policy Stud 14, 1–22 (2012). https://doi.org/10.1007/s10018-011-0019-x

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  • DOI: https://doi.org/10.1007/s10018-011-0019-x

Keywords

  • Asia
  • Biodiversity
  • Meta-analysis
  • Oceania
  • Valuation

JEL Classification

  • Q26
  • Q51
  • Q57
  • H41