Post on 19-Dec-2015
Targeting Payments for Environmental ServicesTargeting Payments for Environmental Services
Stefanie EngelStefanie Engel
ETH Zurich, SwitzerlandETH Zurich, Switzerland
Email: Email: stefanie.engel@env.ethz.chstefanie.engel@env.ethz.ch
Tobias WünscherTobias Wünscher
Center for Development Research (ZEF), Bonn, GermanyCenter for Development Research (ZEF), Bonn, Germany
Email: tobias.wuenscher@uni-bonn.deEmail: tobias.wuenscher@uni-bonn.de
International Payments for Ecosystems (IPES) Publication Review MeetingInternational Payments for Ecosystems (IPES) Publication Review MeetingUNEP, Geneva, 28-29 January 2008UNEP, Geneva, 28-29 January 2008
IntroductionIntroduction
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
Targeting CriteriaTargeting 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
Targeting CriteriaTargeting 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
Targeting CriteriaTargeting Criteria
1. Environmental services
3. Costs of service provision
2. Risk of service loss (chance of service gain) in absence of payments
Benefit
Cost
Targeting CriteriaTargeting 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$
Targeting CriteriaTargeting 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 €
Baseline FlexAdd FlexScore FlexWater Flex
Payment Fixed Flexible Flexible Flexible Flexible
Budget Limit No Yes Yes Yes Yes
Selection Criteria Priority Area Mean Additio-nality / Mean Cost
Mean Score / Mean Cost
Mean Water Score / Mean Cost
Mean Cost
Total Cost (US$) 30,028 (100.00) 30,014 (99.95) 29,997 (99.90) 30,016 (99.96) 30,000 (99.9)
No. of Sites 20 (100) 56 (280) 62 (310) 44 (220) 68 (340)
Area (ha) 750.7 (100) 1350.2 (179) 1423.3 (190) 1178.7 (157) 1441.7 (192)
Mean Site Size (ha) 37.5 (100) 24.1 (64) 23.0 (61) 26.8 (72) 21.2 (57)
Total WaterScore 6,900 (100) 10,301 (149) 11,194 (162) 15,931 (231) 10,952 (159)
Total Env. Service Score 52,148 (100) 94,829 (182) 98,259 (188) 82,289 (158) 96,421 (185)
Total Additionality 1,969 (100) 4,033 (205) 3,909 (199) 3,211 (163) 3,798 (193)
Additionality/ 1000$ 65.6 (100) 134.3 (205) 130.3 (199) 107.0 (163) 126.6 (193)
Results from own targeting tool in Costa RicaResults from own targeting tool in Costa Rica
(percentages in brackets)(percentages in brackets)
Measurement of Environmental ServicesMeasurement of Environmental Services
Main Objective (good water quality)
Trade-offs
Parcel
Desired land use
Slope
Intensity
FrontageInteractions
Parcel
Slope
Intensity
FrontageInteractions
Sub-Objective(reduce chemicals)
Sub-Objective(reduce sediments)
??
?
Desired land use
Interactions
(Thresholds)
(Thresholds)
Baseline FlexAdd FlexScore FlexWater Flex
Payment Fixed Flexible Flexible Flexible Flexible
Budget Limit No Yes Yes Yes Yes
Selection Criteria Priority Area Mean Additio-nality / Mean Cost
Mean Score / Mean Cost
Mean Water Score / Mean Cost
Mean Cost
Total Cost (US$) 30,028 (100.00) 30,014 (99.95) 29,997 (99.90) 30,016 (99.96) 30,000 (99.9)
No. of Sites 20 (100) 56 (280) 62 (310) 44 (220) 68 (340)
Area (ha) 750.7 (100) 1350.2 (179) 1423.3 (190) 1178.7 (157) 1441.7 (192)
Mean Site Size (ha) 37.5 (100) 24.1 (64) 23.0 (61) 26.8 (72) 21.2 (57)
Total WaterScore 6,900 (100) 10,301 (149) 11,194 (162) 15,931 (231) 10,952 (159)
Total Env. Service Score 52,148 (100) 94,829 (182) 98,259 (188) 82,289 (158) 96,421 (185)
Total Additionality 1,969 (100) 4,033 (205) 3,909 (199) 3,211 (163) 3,798 (193)
Additionality/ 1000$ 65.6 (100) 134.3 (205) 130.3 (199) 107.0 (163) 126.6 (193)
(percentages in brackets)(percentages in brackets)
Results from own targeting tool in Costa RicaResults from own targeting tool in Costa Rica
Measurement of Environmental ServicesMeasurement 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
Measurement of RiskMeasurement of RiskAnalytical 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
Measurement of CostsMeasurement of Costs
Land valuesLand values
• Sale priceSale price
• RentRent
Farm budgetsFarm budgets
• Revenue minus costsRevenue minus costs
Inferring from proxy variablesInferring 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 Induce providers to reveal their type by offering a contract
for each of the different “types” of providers believed to for each of the different “types” of providers believed to
existexist
AuctionsAuctions
• Competitive Inverse auctions to assess real WTACompetitive Inverse auctions to assess real WTA
GIS as Data Facilitating Framework
Biodiversity
Water
Carbon
Landscape
8
362
87
6
35
417
4
5
0.4
0.50.1
0.90.4
0.7
0.6
0.40.8
0.30.8
0.1
0.8
0.20.3
0.40.5
0.2
0.5
0.40.7
0.30.5
0.2
0.7
0.30.6
0.30.2
0.5
0.4
0.10.6
0.20.3
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
51
94
7
6
48
38
1
8
23
45
2
5
47
35
2
7
36
32
5
4
16
23
34
51
94
7
6
48
38
1
8
23
45
2
5
47
35
2
7
36
32
5
4
16
23
33
62
94
7
5
87
38
1
4
86
45
2
6
46
35
2
8
61
32
5
5
97
23
34
51
94
7
6
48
38
1
8
23
45
2
5
47
35
2
7
36
32
5
4
16
23
3
Selected Sites
Bio_z-5.928 - -5.086-5.086 - -4.244-4.244 - -3.402-3.402 - -2.559-2.559 - -1.717-1.717 - -0.875-0.875 - -0.033-0.033 - 0.30.31 - 0.60.61 - 0.90.91 - 1.21.21 - 1.51.51 - 1.8No Data
Biodiversity ScoreBiodiversity Score
xi - meanz = ————— S.D.
mean - xi
z = ————— S.D.
The z-value normalization for data sets with higher values The z-value normalization for data sets with higher values preferred to lower values has the following formula:preferred to lower values has the following formula:
Z - NormalizationZ - Normalization
For data sets with lower values preferred to higher values the z-For data sets with lower values preferred to higher values the z-normalization has the following formula:normalization has the following formula:
Add4es0 - 0.0290.029 - 0.0580.058 - 0.0870.087 - 0.1160.116 - 0.1450.145 - 0.1740.174 - 0.2020.202 - 0.2310.231 - 0.26No Data
Total AdditionalityTotal Additionality
Auction Systems an Alternative?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 Ricashould be given in Costa Rica
• Require sufficiently developed market understandingRequire sufficiently developed market understanding new concept for Costa Ricansnew concept for Costa Ricans
• Should be easily integrated into current systemShould be easily integrated into current system should be given in Costa Ricashould be given in Costa Rica
PES Application
Name:
Position:
Hectares:
Minimum payment:
Alfonso Herrera
Hojaancha, Nicoya24
35$ / ha