1. 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 International Payments for Ecosystems (IPES) Publication Review Meeting UNEP, Geneva, 28-29 January 2008

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. ES Water Services Carbon Services Biodiversity Services

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

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

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

6. Targeting Criteria 1. Environmental services 3. Costs of service provision 2. Risk of service loss (chance of service gain) in absence of payments 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 1 Site 2 Site 3 Site 4 Site 5 64$

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

8. BaselineFlexAddFlexScoreFlexWaterFlex PaymentFixedFlexible Budget LimitNoYes Selection CriteriaPriority AreaMean Additio- nality / Mean Cost Mean Score / Mean Cost Mean Water Score / Mean Cost Mean Cost Total Cost (US$)30,028 (100.00)30,014 (99.95)29,997 (99.90)30,016 (99.96)30,000 (99.9) No. of Sites20 (100)56 (280)62 (310)44 (220)68 (340) Area (ha)750.7 (100)1350.2 (179)1423.3 (190)1178.7 (157)1441.7 (192) Mean Site Size (ha)37.5 (100)24.1 (64)23.0 (61)26.8 (72)21.2 (57) Total WaterScore6,900 (100)10,301 (149)11,194 (162)15,931 (231)10,952 (159) Total Env. Service Score52,148 (100)94,829 (182)98,259 (188)82,289 (158)96,421 (185) Total Additionality1,969 (100)4,033 (205)3,909 (199)3,211 (163)3,798 (193) Additionality/ 1000$65.6 (100)134.3 (205)130.3 (199)107.0 (163)126.6 (193) Results from own targeting tool in Costa Rica (percentages in brackets)

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

10. BaselineFlexAddFlexScoreFlexWaterFlex PaymentFixedFlexible Budget LimitNoYes Selection CriteriaPriority AreaMean Additio- nality / Mean Cost Mean Score / Mean Cost Mean Water Score / Mean Cost Mean Cost Total Cost (US$)30,028 (100.00)30,014 (99.95)29,997 (99.90)30,016 (99.96)30,000 (99.9) No. of Sites20 (100)56 (280)62 (310)44 (220)68 (340) Area (ha)750.7 (100)1350.2 (179)1423.3 (190)1178.7 (157)1441.7 (192) Mean Site Size (ha)37.5 (100)24.1 (64)23.0 (61)26.8 (72)21.2 (57) Total WaterScore6,900 (100)10,301 (149)11,194 (162)15,931 (231)10,952 (159) Total Env. Service Score52,148 (100)94,829 (182)98,259 (188)82,289 (158)96,421 (185) Total Additionality1,969 (100)4,033 (205)3,909 (199)3,211 (163)3,798 (193) Additionality/ 1000$65.6 (100)134.3 (205)130.3 (199)107.0 (163)126.6 (193) (percentages in brackets) Results from own targeting tool in Costa Rica

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 Sale price Rent Rent Farm budgets Revenue minus costs 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 Induce providers to reveal their type by offering a contract for each of the different “types” of providers believed to existAuctions Competitive Inverse auctions to assess real WTA Competitive Inverse auctions to assess real WTA

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

15. Biodiversity Score

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

17. Total Additionality

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