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

2. Forests and global warming
Forests and climate change
Forests and global warming

3. Share of GHG emission

5. It’s getting hot

6. Range of other estimates
How much hotter?
CO2 concentration
IPCC 2001
Range of other estimates
400
650
1000

7. Emissions reduction (ER) = Actual emissions - Reference levels
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Payment based on ER
Emissions reduction (ER)
= Actual emissions
- Reference levels

8. Why include Reducing Emissions from Deforestation and forest Degradation (REDD) in a global climate regime
BIG:
1/5 of GHG emissions, but
not included in global climate regime
CHEAP: (Stern report)
Negative - $5/ton
50 % red: USD 5-15 billion
But problems of implementation (transaction costs)
QUICK:
Stroke of pen reforms
No deep restructuring of economy or new technoloigy
A wooden bridge to a clean energy future
WIN-WIN
Large transfer
Good governance?
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9. Reducing Emissions from Deforestation and forest Degradation (REDD)
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10. Forest management types
Global readiness funds (e.g. FCPF, UN-REDD, bilateral initiatives)
Global funds (e.g. Global Facility, FIP)
International carbon markets (e.g. Kyoto AAU market, EU-ETS, …)
INCENTIVES
INSTITUTIONS
Regular budgets
(national or sub-national government)
REDD fund (national or sub-national)
Verification
Sub-national
projects
Policies and Measures (PAM)
Performance
payments
(e.g. PES)
Monitoring, Reporting
Forest management types
Carbon rights holder
Other stakeholders
State (production)
Concession holder
Energy users
INFORMATION
State (conservation)
National & sub-national government agencies
Environmental services users
Private
Land owner
Farmers
Community
Community
Consumers
Others
Others
Others

11. 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. 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. Finding the right scale? Credit to countries, projects or both?
Nested approach:
Sequential: first project, then national
Simultaneous: both coexist
The most flexible:
Harmonization issues
Credit sharing
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14. Finding the money Voluntary market Compliance carbon markets (offsets)
CSR
Individuals
Compliance carbon markets (offsets)
UNFCCC
ETS US
Global fund
Bilateral donors
Auctioning of emission quotas (AAU)
Taxes (carbon, air travel, …)
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15. Finance: Current carbon markets
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16. MRV
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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)
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18. 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. 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
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20. Reference levels Forest carbon stock
Time
Past emissions (historical baseline)
Realized path Crediting baseline BAU baseline
Commitment period
REDD credits
Forest
carbon
stock
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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?
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22. Costs and reference levels
Carbon price determines reductions
RL determines overall pay & participation
$/tC
Marginal costs of AD
Carbon price
REDD rent
REDD
costs
Change in tC
(forest cover)
BAU defore-station
Ref. level
Realized reduced defore-station

23. 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-2 20
M-3 10
M-4
M-1 w/ scaling 90
5, 10, 20 80 70 60 50

24. What are the implications?
USD 5 billions in a fund based approach
Large distributional impacts
Deviations from BAU reduce effectiveness (reduced participation)

25. …. …. 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. National REDD policies
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27. Appropriate deforestation?
Some deforestation ok: benefits > costs
But strong externalities....
... and policy failures $
Production net benefits after
policy distortions
Production net benefits
Environmental costs
Note that most of the env. costs are at the global level, so private or even communal property will not help.
Alos important whether the value of forest area is in the land or the timber and other forest products.
Markets rarely exist, to understand deforestation enough to look at the first curve
Property rights: why open access? Include transaction costs to enforse property rights, not worth it -> open access). Make figure. A B C
deforestation
market
failure
policy
failure
C: actual deforestation; A: optimal deforestation

28. 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
We focus on the microeconomic part, that’s where most significant conclusions are.
Basic microeconomic theory:
Preferences + constraints = choice
So, look at economic incentives and the constraints.

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

30. 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)
Dev. Agencies, NGOs,
ICDP

31. 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
Basic economic tehory
Von Thunen, 1826

32. Comparison Subsistence (pop.) approach: Market 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. Extension of market approach
Homesteading, land races
Constraints at the farm level
Several farming systems
Environmental effects
Costs of property rights

34. Introducing forest rent
Deforestation A
Global + local + private forest benefits
Local + private forest benefits
Agricultural rent
Value B D C
Private forest benefits

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

36. 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
High
Negative
Very negative
Very low
Creating off-farm opportunities
Medium/high
Neutral/positive
Uncertain
Support to intensive agric sector
Moderate/ high
Positive
Selective support to extensive agric
Uncertain/ moderate
Moderate
Ignore extensive road building
Low/
moderate
More secure property rights
Medium
Moderate/
high
2.Increase forest rent and its capture
Higher price of forest products
Low
Positive/uncertain
Small
CFM – capture local public goods
Low/medium
PES – capture global public goods
Potentially high
Uncertain/positive
3.Protected areas

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
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38. Main players Northern governments Southern governments NGOs (divided)
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
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39. 5. Norway: 15 billion kroner (perhaps)
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:
Can money buy forests?
Super-optimistic about what aid can do
Previous aid projects a mixed success
Beware the game played
Conditionality: performance-based support
Long term targets as the only feasible way