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REDD Arild Angelsen Professor, IØR, UMB, Ås & Senior Associate, CIFOR, Indonesia.

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Presentation on theme: "REDD Arild Angelsen Professor, IØR, UMB, Ås & Senior Associate, CIFOR, Indonesia."— Presentation transcript:

1 REDD Arild Angelsen Professor, IØR, UMB, Ås & Senior Associate, CIFOR, Indonesia

2 UNIVERSITETET FOR MILJØ- OG BIOVITENSKAP Forests and global warming Forests and climate change

3 UNIVERSITETET FOR MILJØ- OG BIOVITENSKAP Share of GHG emission

4 UNIVERSITETET FOR MILJØ- OG BIOVITENSKAP

5 UNIVERSITETET FOR MILJØ- OG BIOVITENSKAP It’s getting hot

6 UNIVERSITETET FOR MILJØ- OG BIOVITENSKAP How much hotter? CO2 concentration IPCC 2001Range of other estimates

7 UNIVERSITETET FOR MILJØ- OG BIOVITENSKAP Institutt for økonomi og ressursforvaltning 7 Emissions reduction (ER) = Actual emissions - Reference levels Payment based on ER

8 UNIVERSITETET FOR MILJØ- OG BIOVITENSKAP Institutt for økonomi og ressursforvaltning 8 Why include Reducing Emissions from Deforestation and forest Degradation (REDD) in a global climate regime 1. BIG: –1/5 of GHG emissions, but –not included in global climate regime 2. CHEAP: (Stern report) –Negative - $5/ton –50 % red: USD 5-15 billion –But problems of implementation (transaction costs) 3. QUICK: –Stroke of pen reforms –No deep restructuring of economy or new technoloigy –A wooden bridge to a clean energy future 4. WIN-WIN –Large transfer –Good governance?

9 UNIVERSITETET FOR MILJØ- OG BIOVITENSKAP Institutt for økonomi og ressursforvaltning 9 Reducing Emissions from Deforestation and forest Degradation (REDD)

10 Others Community Private State (conservation) State (production) Forest management types Others Community Land owner National & sub-national government agencies Concession holder Carbon rights holder REDD fund (national or sub- national) Policies and Measures (PAM) Performance payments (e.g. PES) INFORMATION Regular budgets (national or sub- national government) Regular budgets (national or sub- national government) Others Consumers Farmers Environmental services users Energy users Other stakeholders INCENTIVES Sub-national projects Sub-national projects International carbon markets (e.g. Kyoto AAU market, EU- ETS, …) Global funds (e.g. Global Facility, FIP) Global readiness funds (e.g. FCPF, UN-REDD, bilateral initiatives) Verification Monitoring, Reporting INSTITUTIONS

11 UNIVERSITETET FOR MILJØ- OG BIOVITENSKAP REDD in a global climate agreement  What is REDD: –Aid –PES –CAT  Agreement on some broad principles in Copenhagen – maybe!  “Technical solutions exist, it’s a question of political will” ???? –Distribution –Hold-out game

12 UNIVERSITETET FOR MILJØ- OG BIOVITENSKAP 12 Scope of REDD Changes in:Reduced negative change Enhanced positive change Forest area (hectare) Avoided deforestation Aforestation & reforestation (A/R) Carbon density (carbon per hectare) Avoided degradation Forest regeneration & rehabilitation (carbon stock enhancement) Forest carbon (C) = forest area (ha) * carbon density (C/ha)

13 UNIVERSITETET FOR MILJØ- OG BIOVITENSKAP Institutt for økonomi og ressursforvaltning 13 Finding the right scale? Credit to countries, projects or both? Nested approach: 1.Sequential: first project, then national 2.Simultaneous: both coexist The most flexible: -Harmonization issues -Credit sharing

14 UNIVERSITETET FOR MILJØ- OG BIOVITENSKAP Finding the money  Voluntary market –CSR –Individuals  Compliance carbon markets (offsets) –UNFCCC –ETS –US  Global fund –Bilateral donors –Auctioning of emission quotas (AAU) –Taxes (carbon, air travel, …) Institutt for økonomi og ressursforvaltning 14

15 UNIVERSITETET FOR MILJØ- OG BIOVITENSKAP Institutt for økonomi og ressursforvaltning 15 Finance: Current carbon markets

16 UNIVERSITETET FOR MILJØ- OG BIOVITENSKAP Institutt for økonomi og ressursforvaltning 16 MRV

17 UNIVERSITETET FOR MILJØ- OG BIOVITENSKAP Institutt for økonomi og ressursforvaltning 17 MRV …  The technologies are (almost) there  But they come at a cost, sometimes a very high cost  MRV not an hindrance for moving ahead, but impose limitations for what we can do  IPCC guidelines fairly good for deforestation, less developed for degradation  Reward better MRV (e.g. the level of discounting)

18 UNIVERSITETET FOR MILJØ- OG BIOVITENSKAP Reference level  Should we pay for 100% of the emissions reduction, or a smaller percentage of them?  The two meanings of baseline: 1. Business as Usual (BAU) baseline –a technical prediction of what would happen without REDD –benchmark to measure the impact of REDD policies 2. Crediting baseline (= reference level) –benchmark for rewarding the country (or project) if emissions are below that level (or penalize if above, depending on liability) –like an emission quota in a CAT system

19 UNIVERSITETET FOR MILJØ- OG BIOVITENSKAP Institutt for økonomi og ressursforvaltning 19 How to predict (BAU) deforestation?  National historical deforestation  National circumstances: –Forest cover, reflecting stage in forest transition –GDP/capita –War, disasters, ….  Other factors? –Population –Commodity prices  Ex ante adjustment –Brazil being rewarded due to the economic crisis

20 UNIVERSITETET FOR MILJØ- OG BIOVITENSKAP Institutt for økonomi og ressursforvaltning 20 Reference levels Time Past emissions (historical baseline) Realized path Crediting baseline BAU baseline Commitment period REDD credits Forest carbon stock

21 UNIVERSITETET FOR MILJØ- OG BIOVITENSKAP Institutt for økonomi og ressursforvaltning 21 How to set reference levels  Main principle: BAU + common but differentiated responsibilities  Crediting baseline < BAU: A balance between:  The risk of ’tropical hot air’ and higher effectiveness  REDD participation and acceptability  “Positive incentives” (UNFCCC) –No-lose systems –No liability  REDD: move from IPES to CAT  Who owns the REDD rent?

22 UNIVERSITETET FOR MILJØ- OG BIOVITENSKAP Costs and reference levels Change in tC (forest cover) 0 BAU defore- station Marginal costs of AD Carbon price Realized reduced defore- station REDD rent REDD costs Ref. level $/tC Carbon price determines reductions RL determines overall pay & participation

23 UNIVERSITETET FOR MILJØ- OG BIOVITENSKAP A proposal (Meridian report, Norwegian UNFCCC submission)  Reference levels based on: –Historical natioanal deforestation –National forest cover –GDP/capita (or LDC) –Global additionality (scaling) factor (global RL < global BAU)  OSIRIS scenarios: Scenario National historical deforestation Forest cover GDP per capita/ LDC quota Global additionality scaling factor Funding (USD billions/year) Meridian 1 (M-1)100 5 M M M M-1 w/ scaling100905, 10, 20 M-1 w/ scaling100805, 10, 20 M-1 w/ scaling100705, 10, 20 M-1 w/ scaling100605, 10, 20 M-1 w/ scaling100505, 10, 20

24 UNIVERSITETET FOR MILJØ- OG BIOVITENSKAP What are the implications?  USD 5 billions in a fund based approach  Large distributional impacts  Deviations from BAU reduce effectiveness (reduced participation)

25 UNIVERSITETET FOR MILJØ- OG BIOVITENSKAP …. …. implications?  Global additionality scaling might increase effectiveness, particularly at high funding levels  Generous reference levels have a costs (e.g. 100 to 130 % scaling (option 4), with USD 5bn reduce global emission reductions from 42 to 29%)

26 UNIVERSITETET FOR MILJØ- OG BIOVITENSKAP Institutt for økonomi og ressursforvaltning 26 National REDD policies

27 UNIVERSITETET FOR MILJØ- OG BIOVITENSKAP Appropriate deforestation?  Some deforestation ok: benefits > costs  But strong externalities.... ... and policy failures deforestation Production net benefits Environmental costs market failure $ Production net benefits after policy distortions policy failure A BC C: actual deforestation; A: optimal deforestation

28 UNIVERSITETET FOR MILJØ- OG BIOVITENSKAP 3. A framework for an agent-based analysis of causes of deforestation  Distinguish variables at different levels(e.g. in regression analysis)  Think within a cause- effect framework  Avoid ’cheap’ political explanations  Focus on micro-level models (level 1-3) Microeconomic defor. models

29 UNIVERSITETET FOR MILJØ- OG BIOVITENSKAP Some key issues in economic modelling of deforestation 1. Smallholders or large actors: “the needy or the greedy” 2. Deforestation an investment or dis- investment; agent or national/global level modelling 3. Frontier deforestation (open access) vs. mgt. of land with secure property rights 4. Subsistence (population) or market approach

30 UNIVERSITETET FOR MILJØ- OG BIOVITENSKAP a. Subsistence approach Basic relationship (needs = production) sN = xH H =s N/x What drives deforestation (higher H)? poverty (reach subsistence level s) population growth (increase in N) low productivity (x)

31 UNIVERSITETET FOR MILJØ- OG BIOVITENSKAP b. Market approach (von Thünen) What drives deforestation? high prices (p) high productivity (x) low wages (alt.empl.) (w) small access costs (q) extension: forest clearing gives property rights

32 UNIVERSITETET FOR MILJØ- OG BIOVITENSKAP Comparison  Subsistence (pop.) approach: –Farmers clear forest to survive –Ignore consumption aspirations –Ignore migration effects –Development (aid) community  Market approach: –Farmers clear forest because it is profitable –Unconventional results: intensification, discount rates, land reforms, credit programmes  Different policy implications

33 UNIVERSITETET FOR MILJØ- OG BIOVITENSKAP Extension of market approach  Homesteading, land races  Constraints at the farm level  Several farming systems  Environmental effects  Costs of property rights

34 UNIVERSITETET FOR MILJØ- OG BIOVITENSKAP Introducing forest rent Deforestation A Global + local + private forest benefits Local + private forest benefits Agricultural rent Value BD C Private forest benefits

35 UNIVERSITETET FOR MILJØ- OG BIOVITENSKAP Capture forest rent  Much of forest rent is a public good.  Key question: how to “internalize externalities” 1. Institutional arrangements (CFM) 2. Markets (PES) - Assumes property rights allocated and secure

36 UNIVERSITETET FOR MILJØ- OG BIOVITENSKAP Policies for reduced deforestation Policy Effectiveness (forest conservation) Direct costs of policy (efficiency) Effect on inequality/ poverty Agricultural yield Political viability 1.Reduce (extensive) agriculture rent Depressing agric prices HighNegative Very negativeVery low Creating off-farm opportunities HighMedium/highNeutral/positiveUncertainHigh Support to intensive agric sector Moderate/ highHighUncertainPositiveHigh Selective support to extensive agric Uncertain/ moderate HighPositive Moderate Ignore extensive road building HighNegative Low/ moderate More secure property rights UncertainMediumUncertainPositive Moderate/ high 2.Increase forest rent and its capture Higher price of forest products ModerateLowPositive/uncertainSmallModerate CFM – capture local public goods ModerateLow/mediumPositiveSmallModerate PES – capture global public goods Potentially highMedium/highUncertain/positiveSmall Moderate/ high 3.Protected areasModerate/ highMediumUncertainSmallModerate

37 UNIVERSITETET FOR MILJØ- OG BIOVITENSKAP Institutt for økonomi og ressursforvaltning 37 In the end: REDD is a game  What game is it? –A collective action game –The development aid game Very high expectations  The very different perceptions and interests –“Squeeze the lemon” (developing countries): get as much $$$ for as little action as possible –Avoid massive $$$ transfers (developed countries): get as much action for as little $$$ as possible

38 UNIVERSITETET FOR MILJØ- OG BIOVITENSKAP Main players  Northern governments –Climate benefits –Use as offset  Southern governments –Dramatic impacts of CC –Quick and big money  NGOs (divided) –Concern about market flooding & buying out –Project funding  Indigenous & local people –Elite capture, buying forests –Opportunities for making money  Private sector –CSR –Cheap offsets Institutt for økonomi og ressursforvaltning 38

39 UNIVERSITETET FOR MILJØ- OG BIOVITENSKAP  It’s a good idea –Climate change is real –Important to stop deforestation –Money can make things move forward  Buy credibility and influence –Development aid as a political instrument  High risk of being naïve in two ways: 1. Can money buy forests? –Super-optimistic about what aid can do –Previous aid projects a mixed success 2. Beware the game played Conditionality: performance- based support Long term targets as the only feasible way 5. Norway: 15 billion kroner (perhaps)


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