This document discusses developing forest reference emission levels and reference levels (FRELs/FRLs) for REDD+ and their implications for economic incentives. It proposes a stepwise approach for countries to establish FRELs/FRLs based on available data and capacity. Case studies from Brazil, Indonesia, Peru and Vietnam show how to model drivers of deforestation and estimate FRELs/FRLs. Comparing FRELs to actual emissions helps determine a country's performance in REDD+ and potential payments. Establishing credible FRELs/FRLs is important for REDD+ financial incentives but must balance climate and development goals.

1. Developing FRELs/FRLs and
Potential Implications of Economic Incentives
Series of modules for GHG National Inventory from
LULUCF with REDD+ Focus
MINAM MRV REDD+ Workshop
Lima, Peru, 28-30 April 2014
Contributors:
Arief Wijaya, Lou Verchot, Daniel Murdiyarso, Martin Herold,
Arild Angelsen, Erika Romijn, Sabastine Ugbaje and
Kalkidan Mulatu

2. Outline
 Forest reference emission level/reference level definition
 FRELs/FRLs estimation approaches
 Stepwise approach proposal
 Case study in setting up REL in four tropical countries
 Impacts to REDD+ financial incentives

3. CIFOR Global Comparative Study on REDD+
 GCS Module 3: REDD+ MRV and Carbon Emissions
measurement
– Assessment of major deforestation drivers
– Setting national reference emission levels (RELs)
– Monitoring, reporting, verification (MRV) for REDD+
– Six case study countries: Brazil, Peru, Indonesia,
Vietnam, Tanzania and Cameroon

4. Setting a forest reference emissions
levels/forest reference levels (FRELs/FRLs)
 Forest reference levels (FRLs) and forest reference
emission levels (FRELs) are a business as usual (BAU)
baseline of carbon emission projection to assess a country’s
performance in implementing REDD+ (UNFCCC, 2011)
 The uses of FRLs:
– to establish a reference point or benchmark against which actual
emissions (and removals) are compared
– to serve a benchmark for payments in a results-based REDD+
mechanism
 FRELs/FRLs development is data-driven approach (need
exhaustive and reliable activity data and emission factors)

5. Proposals for estimating FRELs/FRLs
 REL using historical emission (Santilli, et al, 2005 and
Mollicone, et al, 2007)
 REL using adjusted historical emission. This approach
assumed that future deforestation is estimated from
historical data with some adjustments (e.g. population
density, agriculture land demand, GDP, etc) (Amano, et al,
2008)
 REL with forward looking (may or may not consider
historical emission) (e.g. Strassburg, et al, 2009)

6. Capacity gaps of 99 non-annexes I countries
Consideration of factors for capacity assessment:
1.Requirements for monitoring forest carbon on national level (IPCC GPG)
2.Existing national capacities for national forest monitoring
3.Progress in national GHG inventory and engagement in REDD
4.REDD particular characteristics: importance of forest fires, soil carbon, deforestation rate
5.Specific technical challenges (remote sensing)

7. Proposing stepwise approach for setting up
FRELs/FRLs
 Guidance suggests to use historical data; adjusted for
national circumstances
 Data driven approach: the less data a country has the more
it should rely on the data – need to manage the
uncertainties
 Why a stepwise approach?
• Data availability and quality varies
• IPCC GPG LULUCF (2003) use Tiers as a mechanism to deal with
uncertain & incomplete data for estimation on national level
• Match data availability and uncertainty and allow for broad country
participation
• Motivation to reduce uncertainties over time

8. Proposing a Tier-ed approach – Tier 1
 Simple extrapolation using historical forest area estimates –
assumes no change in trend
 May use IPCC approach 1 data (FAO FRA)
 No consideration of drivers information
 Importance of consistency and transparency
 Uncertainty: ± 75% of prediction and accuracy is based on
available trend data (bias?) – corridor approach
 Suitable for exploration and international comparison
 Can be applied to most of developing countries

9. Simple REL for 4 countries using FAO FRA data

10. Higher tier approaches for REL development
 Retain predictive power of historical trend data and move to
more driver-based assessment and predictions
 Include data-driver reasoning for deviations from historical
trend (i.e. national circumstances)
 Higher tiers use national data:
– Deforestation and emissions and understanding of historical processes
using data on drivers and activities causing forest carbon stocks
change
– Establish relationship with underlying causes (proxies)
– Justification why and how deforestation varies from historical trend on
the level of drivers and activities

11. Example of spatial analysis of national
deforestation drivers

12. Stepwise approach for FRELs/FRLs
(Herold, et.al, 2011)

13. Modeling deforestation drivers
Variables M1 M2 M3 M4
Historical annual deforestation (2000-2005) ✓ ✓ ✓
Other land use/socio-economic factors (2005) ✓ ✓
Forest cover (share of land area) (2005) ✓ ✓ ✓
Square of forest cover (share of land area) (2005) ✓

14. Identification of deforestation drivers
Countries Drivers
Vietnam - Drivers vary from region to region
- Conversion to agriculture & shrimp Farms
- unsustainable extraction of woods/land clear
- Land conversion to infrastructure
- Unsustainable logging (legal, illegal)
- Forest fires
Indonesia - Oil palm
- Mining
- Other: Roads, Settlement, aqua culture dev’t
- Forest fires
- Degradation and logging (Sumatra, Kalimantan)
- Swidden (slash-and-burn) Agriculture
Brazil - Agriculture and ranching, pasture lands
- Human settlement
- Logging
- Soy bean/sugarcane plantation
- Infrastructure
Peru - Mining sector
- Fast economic growth
- Expansion of Agriculture
- Logging for domestic uses and trade
- Unplanned human settlement and infrastructure

15. Deforestation drivers modeling
Available Variables
(Mean Values)
Countries
Vietnam Indonesia Brazil Peru
Annual Deforestation (2005-2010) (ha) 518,192 769,162 1,275,148 163,335
Annual Deforestation (2000-2005) (ha) 420,712 527,024 3,266,150 91,096
Forest cover (2005) (ha) 10,257,429 96,475,685 316,285,800 63,045,790
Planted forest (2005) (ha) 26133.25
Agriculture and similar land uses (2005) 180771.7 141179.6
Bare land (2005) (ha) 119386
Residence (2005) (ha) 10448.22
Population Density 2005 (km2) 404 20.46 20.53
GDP (current prices) 228406.9
Mining (2005) (ha) 1520.80
Palm oil (2005) (ha) 33445.16
Commercial plantation (ha) 37812.31
Timber Concession (ha) 131610.2
Soy bean (ha) 16427.64
GDP cattle ranching (2000) 14294.23
Concession area (ha) 151130
Protected Area (ha) 390798.7
Number of Observation 64 226 719 63

16. Step 2:
Brazil
Predict
deforestation
rates for legal
Amazon
2005- 2009
Category Regression coefficient
Historical deforestation
(2000-2005)
0.628***
(0.02)
Forest stock (share of land) 0.009***
(0.00)
0.026***
(0.00)
Soybean plantation 0.026
(0.02)
0.089***
(0.03)
Rubber plantation -0.043***
(0.01)
0.044*
(0.02)
Population density 0.036
(0.03)
0.104**
(0.05)
Agriculture GDP (%) 0.161***
(0.03)
0.480***
(0.05)
Per Capita GDP -0.313***
(0.07)
0.076
(0.11)
GDP based on current price
(2005)
-0.112***
(0.02)
-0.405***
(0.04)
R2 0.914 0.767
N 719 719
***p<0.01, **P<0.05, *p < 0.1

17. Step 2:
Vietnam
Predict
deforestation
rates
2005- 2010
Category Regression coefficient
Historical deforestation
(2000 - 2005)
0.520***
(0.12)
Forest stock (share of land) 0.029
(0.02)
0.711***
(0.02)
Planted forest area -0.102
(0.12)
-0.002
(0.13)
Agricultural land 0.284***
(0.07)
0.320***
(0.08)
Settlement -0.087
(0.08)
-0.056
(0.09)
Total bare land 0.210**
(0.12)
0.401***
(0.12)
R2 0.961 0.947
N 64 64
***p<0.01, **P<0.05, *p < 0.1

18. Step 2:
Peru
Predict
deforestation
rates
2005- 2010
***p<0.01, **P<0.05, *p < 0.1
Category Regression coefficient
Historical deforestation
(2000-2005)
.955***
(0.03)
Forest stock (share of
land)
.001
(0.00)
0.069***
(0.01)
Forest concession -0.017
(0.02)
0.033
(0.08)
Protected area 0.054***
(0.02)
0.274***
(0.06)
Population density 1.378***
(0.44)
8.789***
(1.48)
R2 0.994 0.907
N 63 63

19. Projection of deforestation 2010 - 2015
Countries Deforestation Forecast 2010-2015 (thousand ha)
M1 M2 M3 M4 M5 (BAU1) M6 (BAU 2)
Vietnam 547
(2.1)
654
(0.35)
661
(1.86)
514
(0.54)
518 469
Indonesia 924
(2.19)
888
(1.67)
605
(1.98)
755
(0.49)
769 623
Brazil 285
(0.74)
339
(0.66)
410
(0.93)
1,318
(0.29) 939 2,006
Peru 347
(1.11)
295
(0.99)
279
(6.91)
302
(0.29)
163 123

20. Forest carbon emissions equation

21. Carbon biomass estimation approach
 Field data (e.g. National forest inventory)
 Direct remote sensing measurement
– Empirical models where RS data is calibrate to field estimates
(Baccini et al. 2004, 2008, Saatchi et al. 2007, Blackard et al.
2008)
 Stratify and Multiply (SM) method
– Assign an average biomass value to land cover/vegetation type
map (Asner et al. 2010)
 Combine and Assign approach
– Extension of SM, GIS and multi-layers information (Gibbs et al.
2007)
 Ecological Models approach
– RS data to parameterize the biomass model (Hurtt et al. 2004)

22. Available carbon stocks maps and its
uncertainty
Source: Avitabile, et.al. University of Wageningen (in preparation)

23. Implications to national carbon emissions
estimate (Indonesia)
Source
Cumulative Emission from
LUCF 2000 -2009
(in Gt CO2e)*
Methods Remarks
FAOStat 3.140
FRA country report
(EF = 138 ton C/ha)
Net forest conversion
MoE - Second National
Communication to UNFCCC
7.443 IPCC Guidelines 2006 Net forest conversion
Winrock International
(Harris, 2012)
3.468
Carbon Bookkeping model
(RS + Field)
Gross deforestation
MOF (official) 1.760
Approach 1 + NFI
(Tier 1 or 2)
Net forest conversion
(peat?) - carbon
emissions potential
MOF + Saatchi (CIFOR) 1.811
Approach 1 + Global EF
(Tier 1 or 2)
Net forest conversion
* does not include peat emissions and peat fire

24. Lesson learnt from CIFOR Global Comparative
Study on REDD+ on setting up FRELs/FRLs
 Countries should start as soon as possible for estimating
their national REL/RL based on the best available data (tier
1 estimate)
– If we have less data the more we depend on those data
 Countries should invest for collecting national level data (i.e.
time series forest cover change and local emissions factor
data)

25. Comparison of global forest cover loss and
national prediction (Brazil)

26. Lesson learnt from CIFOR Global Comparative
Study on REDD+ on setting up FRELs/FRLs
 Countries should start as soon as possible for estimating
their national REL/RL based on the best available data (tier
1 estimate)
– If we have less data the more we depend on those data
 Countries should invest for collecting national datasets (i.e.
time series forest cover change and local emissions factor
data)
 Estimate on carbon stocks change at national/sub-
national/project scale should comply with the convention
(i.e. IPCC guidelines)

27. Indicative national REL estimate to 2020
(Indonesia)
Source: Indonesian REDD+ Agency, MRV working group

28. Lesson learnt from CIFOR Global Comparative
Study on REDD+ on setting up FRELs/FRLs
 Participation of local people is important to significantly
reduce risks of REDD+ and to improve credibility of national
climate change mitigation programs
Source: Indonesian REDD+
Agency

29. Impacts to REDD+ incentives
 Emissions reductions that can claim for REDD+ financial incentives
should be additional of those resulted from national climate change
mitigation program (i.e. based on self financed/national efforts)
– Differentiate between REL as business as usual (BAU) and crediting baseline
(or financial incentive benchmark)
Source: redd-net.org

30. Impacts to REDD+ incentives
 Emissions reduction that can claim REDD+ incentives
should be additional of those of national climate change
mitigation program (based on self financed/national efforts)
 Carbon emissions reduction should be estimated following
Tier 2 approach
 Trade off between climate change mitigation and national
economic development sectors is the main challenge to
engage in phase 3 of REDD+ implementation (i.e. results-
based payment at national scale)
– Phase 1 and 2  funding of REDD+ project established at
subnational/project levels can be financed through non-market
mechanism (e.g. international donors, project based, etc)
– Phase 3  REDD+ project should be ideally financed through market
based mechanism