Sven Wunder CIFOR. Payments for environmental services (PES) conditions for success

Transcription

1 Sven Wunder CIFOR Payments for environmental services (PES) conditions for success

2 Structure I. PES: definition and concept II. III. IV. Cases of implementation Challenges of design Conclusions and perspectives

3 I. PES: definition and concept

4 PES definition: 1. Voluntary transactions. between service users. and service providers. that are conditional on agreed rules of natural resource management. for generating offsite services. => A narrow, prototype definition (Weber) Wunder (01) Ecological Economics

5 Spatial externality justifies PES

6 Not all externalities are offsite Ecotourism benefits ccc

7 Where have PES been used? Env. services: 1) carbon ) watersheds ) biodiversity ) landscape beauty Types: a) User- vs. b) gov t-financed PES many more of a), but $/ people in b) Continents: 1) Americas ) Asia, Europe ) Africa Latin America: Municipal water utilities, HEPs Europe: PES embedded into agro-environmental (public) schemes; few corporate pilots (Vittel)

8 Why pay the polluter, not vice versa? - Alternatives (regulation, taxation) may not be: a) Legal ( right to pollute ) b) premium over and above legal ES compliance c) efficient (Coase theorem) d) fair? e) politically implementable Arguments for PES: - Direct (=quid pro quo contract) - Legitimate (=voluntary, negotiated) - Adaptive (flexible design) Efficient & equitable PES outcomes a priori likely

9 - REDD+ = Global PES (conditional payments) What s REDD+? - Successfully avoided deforestation - Avoided degradation (esp. logging) less; Restoration?? - REDD+ field projects: mostly incentives (PES, ICDPs...) -...but also disincentives (improved C&C, PAs...).0.009, MMA, Brasília

10 PES, REDD & forest transition Tree cover Time/ development level

11 II. Cases of PES implementation

12 Case 1: Pimampiro (Ecuador) - Defor pressure: high - Service: Watershed protection - Buyer: Municipal water company Seller: Comunity in upper watershed, 0 ha protected Institution: municipal gov t, NGO, community

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14 Case : National PSA (Costa Rica) - Defor pressure: low on average (1997- ) - Services: biodiv, C, water, landscape - Buyer: State C Rica Seller: Forest owners, nationally Institutions: mixed model FONAFIFO public + private funds

15 Case : Bolsa Floresta (Amazonas, Brazil) Mix: PES + social inv. (health& educ)+ ICDP: add value, spare land - C&C preexists; Bolsa Floresta rules go only marginally beyond PA rules - Project staff collaborates with env police

16 Case : Transamazon REDD (Para, Brazil) - Services: Carbon+ - Buyer: Amazon Fund/ NORAD etc. IPAM Provider: smallholder farmers, ag reform Institutions: Multilevel set-up PES + climate-smart ag adoption ( ICDP) PES follows on old Proambiente (public PES-like programme)

17 III. Challenges in PES design

18 Design lessons for PES 1. Target threat/ leverage areas. Target high-service areas. Pay acc. to customized cost levels. Enforce conditionality credibly (Armsworth et al. 01, Moxey & White 01, Engel 01; Wunder et al. 01)

19 Locatelli, Imbach & Wunder (01) Biodiv 1 Costa Rica: ES hotspots overlap little. => Target high ES areas Biodiv Carbon Wtshd Carbon Watershed +0.7 n.s. 1 Landscape

20 0 Target high-service, high-threat, & low-cost areas Biodiversity Watershed Carbon Landscape $ $ 1$ 9$ $ 17$ 1$ $ 1$ $ $ 1$ $ $ $ 0$ 7$ 0$ $ $ 70$ $ 1$ 1$ 7$ $ $ $ $ $ 1$ 10$ $ 0$ 0$ $ Threat Provision Cost Selected Sites Problem Concept Results Data & Methodology PES in Costa Rica Conclusions

21 Opportunity costs Mitigation supply curve REDD opportunity costs in Brazilian Amazon Avoided deforestation (ha) Börner et al. (010)

22 Opportunity costs Remotest areas REDD Opportunity Costs Brazilian Amazon, Extensive cattle Annual crops Intensive cattle Avoided deforestation (ha) Oil palm Soy Precious timbers Börner et al. (010)

23 Costs with three-tier differentiated PES 0 Costos de oportunidad CCX temporario CCX permanente Deforestacion evitada (ha) Börner et al. (010)

24 High vs. low environmental additionality: Variable Binary logistic regression Mean Standard deviation Coefficient estimation Standard error Activity paid *** Dummy: 1 = Asset-building PES Payment differentiation * Dummy: 1 = Yes Spatial targeting ** Ordinal: =Threat & ES density Conditionality * Ordinal: Monitoring*Sanction Sector financed Dummy: 1=Public Constant -.** p

25 Examining key PES design features N = [ -70] Wunder et al. (01)

26 V. Conclusions

27 Diagnostics 1. PES is a conceptually simple tool, with attractive ex ante implications for efficiency and equity. yet it has not yet gone to huge scales -- probably for good reasons:. Key preconditions (secure land tenure, organized ES buyers/ env. conscious gov ts) seldom apply at lowest-income stages of institutional development. High-income: Rights to pollute? Willingness to pay?. Little best practice design/ implementation (pay diversification, spatial targeting, conditionality). and thus maybe under-performed on environmental additionality (we lack more IE studies).

28 Perspectives for PES 1. A more complex full world has to deal with more spatial externalities. A moderate growth, with less hype, better design customized to contextual preconditions (I hope!). More rigorous IE cases would make better case. Eliminating some myths (e.g. crowding out). Horizontal scaling up has often better prospects. Vertical upscaling needs to make sense for ES provision and will depend on $ in public coffers!

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