REDD+ in landscapes: drivers of deforestation, institutions and jurisdictions

Transcription

1 REDD+ Implementation and SFM Tokyo, 6-7 February 2014 REDD+ in landscapes: drivers of deforestation, institutions and jurisdictions L. Verchot,. Angelsen, M. Brockhaus, N. De Sy, M. Herold, N. Hosonuma, M. Kanninen, K. Korhonen-Kurki, A. Larson, A. Ravikumar, A. Wijaya

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3 REDD Examples of transformational change Changes in economic, regulatory and governance frameworks, including the devolution of rights to local users; Removals of perverse incentives, such as subsidies and concessions that serve selective economic interests and stimulate deforestation and forest degradation; and deforestation and forest degradation; and Reforms of forest industry policies and regulations that effectively reduce unsustainable extraction

4 REDD Both institutional and agency factors affect the direction of REDD+ policies Institutions: the formal and informal regulations, rules and norms that are established over time and that are not easily changed or transformed Policy arena: framed by institutions and shaped by the actions of the actors. It is characterized by hierarchical or inclusive processes, involving a range of powerful actors, which can foster or prevent certain policies and influence policy formulation

5 Analysis: Two-step QCA Outcome variable: Establishment of comprehensive policies targeting transformational change in the REDD+ policy domain (phase II) Successes: Indonesia, Vietnam, Brazil Six factors divided into two categories to explain outcome Institutional setting: pressure from forest-resource resource shortage effective forest legislation, policy and governance previously initiated policy change The policy arena: national ownership transformational coalitions inclusiveness of the policy process

6 QCA Step 1: Institutional setting results Pressure on forests Effective forest legislation Previously initiated change

7 Results I: Institutional setting The results reveal path dependencies and institutional stickiness in all the study countries: Only countries already undertaking institutional change (CHA) have been able to establish REDD+ policies in a relatively short period but only in the presence of either high h pressure from forest resource shortages (PRES: Brazil and Indonesia) or key features of effective forest legislation, policy and governance (EFF: Vietnam).

8 QCA Step 2: Policy arena results National ownership of process Inclusive process Coalitions for change Indonesia Note: Indonesia has the alternative configuration for enabling environment(pres*eff*cha) eff and the policy arena configuration is *OWN*COAL*incl THINKING beyond the canopy

9 Results II: Policy arena Where an enabling institutional setting is in place (EFF*CHA or PRES*eff*CHA), two conditions of the policy arena proved to be crucial for all three successful countries (Brazil, Vietnam and Indonesia): National ownership (OWN) Transformational coalitions (COAL) Countries that have these two conditions of the policy arena, but not the enabling institutional setting (e.g. Peru and Mozambique), were not successful in establishing REDD+ yet. The country with enabling policy conditions, but neither national ownership nor coalitions for transformation (Bolivia) was unsuccessful

10 Factors affecting national REDD+ policies Case Institutional setting Policy arena Outcome PRES EFF CHA OWN COAL INCL REDD Bolivia i Brazil Burkina Faso Cameroon DRC Indonesia Mozambique Nepal Peru PNG Tanzania Vietnam

11 Key Findings Measuring progress: Some reflections Context matters: previously initiated institutional change allows for faster REDD+ design, but is not sufficient. i There must either be pressure on forests or effective forest legislation, policy and governance in place. Actor-related factors of national ownership and transformational coalitions are crucial: but can only be effective in an enabling institutional setting

12 REDD Implementation How do we translate this to the realities on the ground Understanding drivers for effective implementation Land-use planning Multiple jurisdictions in landscapes

13 Apparent deforestation/degradation drivers for each continent LATIN AMERICA AFRICA ASIA 2% 4% 1% 2% 11% 10% 7% Deforestation 36% 57% 13% 39% 7% 41% 35% 37% 17% 4% 4% 8% 26% 6% 7% Degradation 9% 20% 70% 67% 62% Deforestation driver Forest degradation driver (Subsistence) THINKING beyond the canopy

14 Apparent drivers of deforestation Preliminary results South America Def. due to water r eservoirs 100% Clouds or resolution too low > > need of higher resolution data 90% 80% 70% 60% Built up (roads, urban,...) Mining 50% Water (river, reservoir,...) Other 40% Agriculture 30% Other 20% 10% 0% Venezuela Venezuela Paraguay Paraguay Bolivia Bolivia Argentina Argentina Other = deforestation without clear cause or follow up land use THINKING beyond the canopy

15 REDD Implementation Most drivers of DD are not forest related In seeking to address the drivers of deforestation and forest degradation, d REDD+ necessarily challenges multiple l established institutions and policies, and hence is likely to encounter resistance from existing institutional logics and actors. To do this, we must move beyond apparent drivers of DD and address the political economy of the status quo

16 Projecting deforestation in landscapes Carbon data for different land uses Scenarios of land use change Carbon outcomes in different land use scenarios Past Present Future 1 Future 2

17 Land-use change matrix Land use in 2000 Oak forest Pasture Scrub land Pine forest Pine oak forest Lakes Agriculture Fruit crops Agriculture irrigated No vegetation Dry forest Oak forest 5, ,014 1, , ,988 Pasture 1 9, , , ,691 Scrub land , , ,264 Urban Spruce forest Planted forest Total use in Pine forest 1, ,454 9, ,580 5, , ,982 Pine oak forest 1, ,912 62, ,334 2, , ,745 Lakes , ,050 Agriculture 616 6,908 3,577 2,161 3, ,263 10,133 2, ,669 1, ,566 Fruit crops , , , ,149 irrigated , , , , ,866 No veg. LandAgriculture , ,978 Dry forest 569 6,103 2,732 14,640 6, ,647 9,566 1, , ,722 Urban , ,840 Spruce for , ,725 Planted for Total 9,609 23,748 10,484 88,165 86,527 11, ,663 51,413 16,300 3, ,940 9,500 7, ,147 x 1000 Ha

18 Simulation of deforestation Forest Non-forest Deforestation Protected areas No data Purépecha, Michoacán, Mexico

19 Source: Adapted from Google Maps Inc., 2013 approx ha.

20 Simple REL for 4 countries using FAO FRA data Cameroon Indonesia For rest C stock (Mt) 3,500 3,000 2,500 2,000 1,500 1, Year For rest C stock (Mt) 18,000 16,000 14,000 12,000 10,000 8,000 6,000 4,000 2, Year Forest C stock (Mt) 1,500 Vietnam 1, Year Fores st C stock (Mt t) Brazil 80,000 70, ,000 50,000 40,000 30, ,000 10, Year

21 Step 2: Brazil Predict deforestation rates for legal Amazon Category Regression coefficient Deforestation rate ( ) Trend variable Deforestation dummy Forest stock Forest stock squared Log per capita GDP Agric GDP (%GDP) Population density Road denisty R N

22 Step 2: Category Regression coefficient Vietnam Deforestation rate ( ) Trend variable Deforestation dummy Predict Forest stock deforestation rates Forest stock squared Population density Road denisty R N

23 We have tools: CO 2 FIX model structure Decomposition Increment (yield tables) Competition (between or within cohorts) Cohort 3 Cohort 2 Cohort 1 Tree biomass stemwood Tree biomass foliage stemwood Tree biomass stemwood branches foliage foliage roots branches branches roots roots Litter Litter fall Humification Intermediate humus Humification Timber harvesting Harvest residues and mortality due to management Carbon in the atmosphere Raw material Primary Processing Production line: sawnwood boards paper firewood Burning of by-products Biofuels for energy Burning of disposed-off products to generate energy. recycling Products in use use disposal Products in landfill Fossil Fuels for energy decay Stable humus 23

24 CO 2 FIX REDD+ case: 2 Baseline data from Costa Rica 24

25 Disturbance (fire) and clearing modeled with the CO 2 FIX model

26 We have the tools for land use planning and comparative analysis Conservation Baseline 26

27 AGRICULTURE SECTOR Regional (Titling) National (Regulation) FORESTRY SECTOR National (Regulation) Regional (Permitting) AGRICULTURE SECTOR Regional (Titling) Regional (Projects) ENVIRONMENT SECTOR National (Regulation) STATE LANDS OIL PALM PLANTATION SMALLHOLDER COMMUNITIES REDD+ CONSERVA TION PROJECT NATIVE COMMUNITIES TIMBER CONCESSI ON CULTURE SECTOR Regional (Regulation) AGRICULTURE SECTOR Regional (Titling) FORESTRY SECTOR National (Regulation) Regional (Permitting)

28 Further reading: Masera, O.R., et al Modeling carbon sequestration in afforestation, agroforestry and forest management projects. Ecological Modelling 164, Brown, S., et al Baselines for land use change in the tropics: application to avoided deforestation projects. MITI 12, Hosonuma, N., et al An assessment of deforestation and forest degradation drivers in developing countries. Environ. Res. Lett. 7 p.12 Herold, M., et al A step wise frameworkfor for setting REDD+ forest reference emission levels and forest reference levels. CIFOR Info Brief No. 52. CIFOR. Romijn, E., et al Different forest definitions and their impact on developing REDD+ reference emission levels: a case study for Indonesia. Environ.Sci. &Policy33:246 33: Angelsen, A., et al Testing methodologies for REDD+: Deforestation drivers, costs and reference levels. Technical Report, UK Department of Energy and Climate Change, London, UK. pp Korhonen Kurki, K, et al Enabling factors for establishing REDD+ in a context of weak governance. Climate Policy. In press, available online.