Targeting Payments for Environmental Services

1. International Payments for Ecosystems (IPES) Publication Review Meeting UNEP, Geneva, 28-29 January 2008 Targeting Payments for Environmental Services Stefanie Engel ETH Zurich, Switzerland Email: stefanie.engel@env.ethz.ch Tobias Wünscher Center for Development Research (ZEF), Bonn, Germany Email: tobias.wuenscher@uni-bonn.de

2. Introduction Targeting of PES is a technique used to select among potential service providers, subject to their individual characteristics, those who contribute most effectively to the provision of desired ES. The necessity for targeting lies in the variability of provider characteristics. Water Services ES Carbon Services Biodiversity Services

3. Services Site 1 Site 2 Site 3 Site 4 Delivered Services Targeting Criteria 1. Environmental services 2. Risk of service loss (chance of service gain) in absence of payments 3. Costs of service provision

4. Services Site 1 Site 2 Site 3 Site 4 Delivered Services Targeting Criteria 1. Environmental services 2. Risk of service loss (chance of service gain) in absence of payments 3. Costs of service provision Additionality Risk x 0.4 Site 1 x 0.1 Site 2 x 1.0 Site 3 x 0.0 Site 4

5. Targeting Criteria 1. Environmental services 2. Risk of service loss (chance of service gain) in absence of payments 3. Costs of service provision Benefit Cost

6. Targeting Criteria 1. Environmental services 2. Risk of service loss (chance of service gain) in absence of payments 3. Costs of service provision Fixed payments give high production rent to those with low opportunity costs and those with higher opportunity costs cannot be incorporated.  Budget buys less benefits Opportunity Costs Site 2 64$ Site 4 Site 1 Site 5 Site 3

7. Targeting Criteria 1. Environmental services 2. Risk of service loss (chance of service gain) in absence of payments 3. Costs of service provision Opportunity Costs / ES Value (€) Opportunity Costs Environmental Service Value Site 4 Site 1 Site 2 64 € Site 4 Site 1 Site 3 Site 5 Site 2 Site 5 Site 3

8. Results from own targeting tool in Costa Rica (percentages in brackets)

9. Measurement of Environmental Services Interactions Parcel Parcel Desired land use Desired land use Slope Frontage Slope Frontage Interactions Interactions Intensity Intensity (Thresholds) Trade-offs ? ? Sub-Objective (reduce sediments) Sub-Objective (reduce chemicals) (Thresholds) ? Main Objective (good water quality)

10. Results from own targeting tool in Costa Rica (percentages in brackets)

11. Measurement of Environmental Services • Indexing approaches (Scores) • Weighted linear functions: Score = α(slope) + β (size) + γ (frontage) + etc. • Normalization of attributes: 1. Interval, 2. Ratio, 3. Z-normalization, etc. • Distance function approach • Non-parametric production function with $ as inputs and biophysical attributes as outputs • Iterative selection approach • Considers interactions between parcels by recalculating a parcel’s score after every selected parcel

12. Measurement of Risk • Analytical models • High level of theoretical soundness • Lacking an empirical data base their relevance for baseline determination is limited • Regression models • By far the most common approach to determine deforestation • Based on empirical data • Direction of causality? • Simulation (programming) models • Well suited for the dynamic analysis of relatively large time horizons • Endogenous variables, consequences of choices fed back into model

13. Measurement of Costs • Land values • Sale price • Rent • Farm budgets • Revenue minus costs • Inferring from proxy variables • Such as type of soil, distance to road, slope, climate • Screening contracts • Induce providers to reveal their type by offering a contract for each of the different “types” of providers believed to exist • Auctions • Competitive Inverse auctions to assess real WTA

14. GIS as Data Facilitating Framework 7 1 2 2 5 3 9 3 4 3 3 2 7 1 2 2 5 3 Carbon 4 8 5 5 2 3 9 3 4 3 3 2 5 4 4 2 4 3 1 8 5 5 2 3 6 8 8 4 6 9 1 8 3 7 6 6 2 7 6 6 1 7 Water 4 6 8 7 4 5 3 5 4 6 8 5 7 1 2 2 5 3 Biodiversity 9 3 4 3 3 2 4 8 5 5 2 3 5 4 2 4 3 1 Landscape 1 8 7 3 7 6 6 1 2 2 5 3 4 6 8 5 7 4 9 3 4 3 3 2 4 8 5 5 2 3 5 4 2 4 3 1 1 8 3 7 6 6 4 6 8 5 7 4 0.7 0.1 0.2 0.2 0.5 0.3 Threat 0.9 0.3 0.4 0.3 0.3 0.2 0.4 0.8 0.5 0.5 0.2 0.3 0.5 0.4 0.2 0.4 0.3 0.1 0.1 0.8 0.3 0.7 0.6 0.6 0.4 0.6 0.8 0.5 0.7 0.4 Opportunity Cost 17$ 13$ 20$ 12$ 25$ 33$ 94$ 34$ 40$ 32$ 32$ 20$ 24$ 38$ 57$ 15$ 24$ 30$ 53$ 45$ 22$ 42$ 23$ 10$ 221$ 81$ 33$ 70$ 62$ 6$ 43$ 16$ 88$ 55$ 75$ 14$ 7 1 5 Selected Sites 4 3 4 8 6 2 6 7 3 8 5

15. Biodiversity Score

16. Z - Normalization The z-value normalization for data sets with higher values preferred to lower values has the following formula: xi - mean z = ————— S.D. For data sets with lower values preferred to higher values the z-normalization has the following formula: mean - xi z = ————— S.D.

17. Total Additionality

18. Auction Systems an Alternative? • Make land-owner reveal his/her real Willingness to Accept (WTA) • Many years of experience in developed countries (e.g. USA, Australia) • Auction Systems do not always bring expected results (strategic bidding) • Require sufficient competition for program entry •  should be given in Costa Rica • Require sufficiently developed market understanding •  new concept for Costa Ricans • Should be easily integrated into current system •  should be given in Costa Rica PES Application Name: Alfonso Herrera Position: Hojaancha, Nicoya Hectares: 24 Minimum payment: 35$ / ha