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