1. Co-authors: Ingo Brauer, Holger Gerdes, Andrea Ghermandi, Onno Kuik, Anil Markandya, Stale Navrud, Paulo Nunes, Marije Schaafsma, Hans Vos, Alfred Wagtendonk Scaling up ecosystem service values: methodology and applications. I – Valuing European Wetlands Luke Brander Institute for Environmental Studies (IVM), VU University Amsterdam Division of Environment, Hong Kong University of Science and Technology Email: lukebrander@gmail.com

2. Outline Introduction Value transfer and scaling-up values Methodology: meta-analysis and GIS Case study: European wetlands under climate change Conclusions and discussion

3. Introduction Ecosystem services are generally public goods without market prices. ES are generally valued on a limited spatial scale, e.g. individual ecosystems. Need for value information at larger spatial scales, e.g. river basin, national, regional, global (e.g. TEEB). What are the possibilities for scaling-up value information for large numbers of ecosystems?

4. Value transfer and scaling up Value transfer: estimating the value of a ‘policy site’ using existing value information for a ‘study site’. Scaling-up is value transfer to a larger geographic scale, e.g. to the entire stock of an ecosystem at a regional scale. Value transfer is already complex: Need to account for differences in study and policy site characteristics.

5. Non-constant marginal values Small changes in ecosystem size will not affect values from the rest of the ecosystem stock. Larger scale changes may result in increasing marginal ecosystem service values. Multiplying a constant unit value by total quantity will under- estimate total value. Need to adjust marginal values to account for large-scale changes

6. Non-constant marginal values Marginal value Area of ecosystem / Supply of services € / ha A B Critical threshold PBPB PAPA Under-estimate change in value

7. Proposed method for scaling up values 1. Construct database of primary value estimates 2. Estimate value function (including ecosystem abundance variable) 3. Construct database of ecosystem sites using GIS 4. Estimate site-specific values (pre- and post-change) 5. Multiply site-specific values by change in site area 6. Aggregate to policy relevant spatial level Meta- analysis Spatial Data (GIS) Estimate values

8. Limitations to this method: Critical ecological thresholds –Point at which an ecosystem ceases to function –High uncertainty Limited value information for large scale changes –Available primary value estimates generally relate to current levels of overall service provision –The value of large changes in service provision are unknown Method is limited to measuring ‘small’ non-marginal changes in ecosystem extent.

9. Case study: Value of wetland loss under climate change Meta-analytic value function for wetlands Wetland change due to climate change in Europe GIS data on wetland sites Scaling-up value results

10. Meta-analysis of wetland values Meta-analysis: statistical summary of results from existing studies Wetland data from Brander et al. (2006); Ghermandi et al. (2007) Temperate climate zone wetlands 222 value observations Focus on changes in area Values for almost all ecosystem services Standardised values to USD/ha/year 2005

12. Meta-analytic value function Dependent variable y: Annual value of wetland service(s) per hectare (USD 2005) Study characteristics Xsi: – Valuation method Wetland characteristics Xwi: – Size – Services provided – Wetland type Context characteristics Xci: – GDP per capita (EU NUTS2; US State) – Population within 50km radius – Wetland abundance within 50km radius

13. Meta-analytic value function Variable Coefficientp-value (constant) -0.9700.709 Study variablesContingent valuation method 0.3170.625 Hedonic pricing -2.328**0.043 Marginal valuation 0.828*0.053 Wetland variablesInland marshes -0.2110.726 Peatbogs -2.266***0.004 Salt marshes 0.073*0.901 Intertidal mudflats -0.2390.672 Wetland size before change, ha (ln) -0.218***0.000 Recreational hunting -1.355***0.002 Natural habitat and biodiversity 1.211**0.012 Context variablesReal GDP per capita, USD (ln) 0.430***0.004 Population in 50km radius (ln) 0.503***0.000 Wetland area in 50km radius, ha (ln) -0.1250.118 Adj R 2 = 0.37; N = 222

14. European wetland change due to climate change Numerous and complex impacts of CC on wetlands Negative and positive impacts Spatially explicit projections of wetland change are not available Assume 8% loss in area 2000 - 2050 (Nicholls, 2004)

15. GIS – Spatial data on wetlands Corine land cover data (EEA) 50,533 wetlands in EU –Wetland size (ha) –Wetland type (5 categories) –Wetland abundance (within 50 km radius) –Population (within 50 km radius) –Income per capita (NUTS2 level)

16. EU wetlands

17. Wetland abundance

18. Scaling up values Substitute site specific variable values for 2000 and 2050 into value function. Calculate average of 2000 and 2050 annual per ha values for each wetland. Multiply by change in area to give value change per wetland.

19. Annual value of wetland loss CountryNumber of wetlands Change in wetland area (ha) Change in value of ecosystem services (millions €) Czech Rep 105-719 -1.371 Denmark 729-13,197 -19.107 Finland 14,140-157,757 -13.923 France 1,419-28,653 -64.289 Germany 1,391-33,516 -43.329 Greece 302-5,181 -10.760 Italy 344-5,511 -22.372 Netherlands 273-21,580 -34.404 Sweden 20,242-218,330 -19.690 United Kingdom 2,119-60,295 -58.998 Total 50,533 -739,662 -417.676

20. Discussion and conclusions Value transfer on a large scale –GIS based –Scale, substitutes, population, and income effects Common problems of value transfer and meta-analysis hold: –Limited number of studies for some ES –Reliability of primary valuation estimates –Accounting for ecosystem quality Limitations: –Does not produce service specific values –Data may not support service specific meta-analyses –Population and income effects are the same across all ES –Assume that supply of ES is proportional to ecosystem size